annals of bangladesh agriculture

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706, Bangladesh

ISSN 1025-482X (Print)2521-5477 (Online)

ANNALS OF BANGLADESH AGRICULTURE

Vol. 23 December 2019 No. 2

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ANNALS OF BANGLADESH AGRICULTURE(Ann. Bangladesh Agric.)


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M. R. Karim, S. M. M. R. Sumon, S. H. Soad, S. H. M. F. Siddiki, A. R. Dey and M. A. Ehsan - Prevalence and Factors Affecting the Parasitic Infections in Calves at Selected Areas of Bangladesh

M. M. Rahman and M. M. H. Oliver - Detection and Contouring of BAU-Kul Using Image Processing Techniques

S. Arofi, M. M. Rahman, H. K. Shiragi, M. A. Alam, M. M. Islam and J. C. Biswas - Aggregate Stability in Soils of Twelve Agro-Ecological Zones of Bangladesh Based on Organic Carbon and Basic Cations

M. A. Hoque and S. Mahmud - Morphological Characterization and Evaluation of Nineteen Gladiolus Germplasm

J. Hosen, M. M. Rahman, J. Alam, Z. C. Das, M. A. H. N. A. Khan and M. G. Haider - Pathology of Fowl Typhoid and Molecular Detection of its Pathogen

D. Parvin, J. U. Ahmed, M. M. Hossain and M. Mohi-Ud-Din - Quest for Suitable Storage Condition for Sustainable Processing Quality of Potato Tubers

Z. A. Riyadh, M. A. Rahman, M. G. Miah, S. R. Saha, M. A. Hoque, S. Saha and M. M. Rahman - Performance of Aroid Under Jackfruit-Based Agroforestry System in Terrace Ecosystem of Bangladesh

M. Sharkar, J.U. Ahmed, M.A. Hoque and M. Mohi-Ud-Din - Influence of Harvesting Date on Chemical Maturity for Processing Quality of Potatoes

S. Mashiat, N. A. Ivy, M. G. Rasul, M. M. Haque and M. S. Raihan - Phenotypic Characterization of Zinc and Iron Rich Cytoplasmic Male Sterile Lines of Rice

ReviewT. Ane and S. Yasmin - Agriculture in the Fourth Industrial Revolution

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Vol. 23 December 2019 No. 2 ISSN 1025-482X (Print) 2521-5477 (Online)

ANNALS OF BANGLADESH AGRICULTURE A Journal of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU)


ANNALS OF BANGLADESH AGRICULTURE A Journal of Bangabandhu Sheikh Mujibur Rahman Agricultural University


BOARD OF EDITORS

Editor-in-Chief Prof. Dr. Md. Giashuddin Miah

Vice-Chancellor

Executive Editor Prof. Dr. A. K. M. Aminul Islam

Director (Research)

Associate Executive EditorDr. Md. Mohammad Golam Mostofa

Associate Director (Publication)

Members

Prof. Dr. Md. Khurshed Alam BhuiyanDepartment of Plant Pathology

Prof. Dr. Jalal Uddin AhmedDepartment of Crop Botany

Prof. Dr. G. K. M. Mustafizur RahmanDepartment of Soil Science

Prof. Dr. Md. Jahangir AlamDept. of Fisheries Biology & Aquatic Environment

Prof. Dr. Md. Ruhul AminDepartment of Entomology

Prof. Dr. Satya Ranjan SahaDepartment of Agroforestry & Environment

Prof. Dr. Md. Abdul KarimDepartment of Agronomy

Prof. Dr. Md. Golam RasulDepartment of Genetics & Plant Breeding

Prof. Dr. Md. Tofazzal IslamInstitute of Biotechnology and Genetic Engineering

Prof. Dr. M. KamruzzamanDepartment of Agricultural Economics

Prof. Dr. Md. Morshedur RahmanDepartment of Dairy & Poultry Science


This biannual periodical Annals of Bangladesh Agriculture is indexed in CAB International Abstracting Service and is visible in the website: www.bsmrau.edu.bd

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For correspondence

Executive EditorAnnals of Bangladesh AgricultureBangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706, BangladeshE-mail: [email protected]

Printed at : Sowrov Media Products 314, Elephant Road, Dhanmondi, Dhaka-1205. Mobile : 01718 419 001 E-mail: [email protected], [email protected]

Published by : Bangabandhu Sheikh Mujibur Rahman Agricultural University Gazipur 1706, Bangladesh



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M. R. Karim, S. M. M. R. Sumon, S. H. Soad, S. H. M. F. Siddiki, A. R. Dey and M. A. Ehsan 1

PREVALENCE AND FACTORS AFFECTING THE PARASITIC INFECTIONS IN CALVES AT SELECTED AREAS OF BANGLADESH

M. R. Karim1*, S. M. M. R. Sumon1, S. H. Soad2, S. H. M. F. Siddiki1

A. R. Dey3 and M. A. Ehsan2

Abstract

Gastrointestinal parasitic infections are considered as one of the major impediments in profitable livestock farming in subtropical and tropical countries. The present study was carried out to investigate the prevalence of gastrointestinal (GI) parasites and to determine the effects of different factors in the occurrences of GI parasitic infections in calves. A cross-sectional study, including 413 fecal samples from calves, was conducted in Pabna, Sirajgonj and Gazipur districts of Bangladesh. The samples were examined using standard coprological techniques like sedimentation and floatation techniques, and lugol’s iodine and modified Ziehl-Neelson staining. The overall prevalence of gastrointestinal parasitism was 45.3% and commonly identified parasites were Toxocara spp. (20.3%), Strongyloides spp. (3.9%), Fasciola sp. (1.0%), Moniezia spp. (1.5%), Giardia sp. (10.4%) and Cryptosporidium spp. (7.0%). Among the factors, the age, sex and health status had significant effects on the GI parasitic infections in calves. Therefore, special care such as routine fecal examination for parasitism and proper deworming program should be taken to maintain good health and husbandry of calves for profitable livestock production.

Keywords: Gastrointestinal parasites, prevalence, calves, coprological techniques.

1Department of Medicine, Faculty of Veterinary Medicine and Animal Science, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Department of Medicine, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, 3Department of Parasitology, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh. *Corresponding author: [email protected]

Ann. Bangladesh Agric. (2019) 23 (2) : 1-13 ISSN 1025-482X (Print) 2521-5477 (Online)

IntroductionGastrointestinal (GI) parasitism is a disease caused by different genera of parasites that inhabit the digestive tract of animals, causing inappetence, anemia, diarrhea, poor growth, and economic losses in the herds. Basically, GI parasitism in livestock is caused by helminths and protozoa (Pinilla León et al., 2019). These infections are rarely associated with high mortality and estimated that about 10% animals die annually due to parasitic diseases in the world (Chavhan et al., 2008). However,

their effects are usually characterized by reduced livestock productivity as indicated by a slower growth rate, low milk production, low body condition score (BCS) as well as additional therapeutic cost (Charlier et al., 2015). The productivity losses through reduced feed intake and decreased efficiency in feed utilization due to subclinical or chronic infections are also hindering profitable livestock industry (Akanda et al., 2014). In addition, these infections enhance susceptibility to secondary infections and

2 Prevalence and Factors Affecting the Parasitic Infections in Calves at Selected Areas of Bangladesh

losses resulting from condemnation of carcasses and organs (Hendawy, 2018; Gunathilaka et al., 2018).

One of the major constraints in livestock production is parasitic infection (Jabber and Green, 1983). The prevalence of parasitic infection depends on ecology, geographical and climatic condition prevailing in Bangladesh (Hossain et al., 2004). The farmers usually rear their cattle under traditional husbandry practices. Nutritional status of the animals in general is not satisfactory as they are over-worked but under-fed or half-fed, which makes the animal susceptible to diseases including different parasitic diseases. About 50% calves until 1-year of age die due to GI parasitism (Sardar et al., 2006). Rahman and Ahmed (1991) reported that calves gained body weight by 400 gm/day when treated for parasitic diseases compared to 200 gm/day in non-treated calves. It was also reported that anthelmintic treated calves reached to sexual maturity in 24 months compared to 36-40 months by non-treated calves. Afazuddin (1985) estimated an annual loss of 0.1 million Bangladesh Taka due to parasitic diseases in Military Farm, Savar, Dhaka. Unfortunately, in Bangladesh the parasitic diseases are neglected or overlooked sometimes since the infected animals show little or no clinical signs (Alim et al., 2012).

In this study, we selected three districts namely, Pabna, Sirajgonj and Gazipur. The geo-climatic conditions of these three districts and the water logging and low lying areas of Pabna and Sirajgonj districts are expected to favors the growth, development and survival of various parasites or their hosts. Besides, there are several factors, such as breed, age,

sex, nutritional and immune status which may influence the occurrences of GI parasitic infections. Although, previous studies in some selected areas of Bangladesh revealed wide prevalence of GI parasitism in livestock (Paul et al., 2016; Ahmed et al., 2015; Islam et al., 2014), no precise report on the infections is available in calves of these areas. Considering this, the present research work was undertaken to determine the prevalence of GI parasitic infections of calves in Pabna, Sirajgonj and Gazipur districts and to evaluate the effect of geographic location, breed, age, sex, nutritional status and fecal consistency on occurrence of GI parasitism.

Materials and MethodsStudy areaThe study was conducted in different locations of Sirajgonj, Pabna and Gazipur districts. The milk pocket areas of Sirajgonj and Pabna were chosen, because the farmers over there are mostly dependent on dairy farming and parasitic infection is more common in these areas. On the other hand, the industrial zone Gazipur was selected to compare the prevalence and diversity of GI parasitic infections with that found in another two districts. Among the 24 Upazilas of these three districts, six Upazilas were selected for this study, such as Sadar and Santhia Upazilas of Pabna district; Sadar, Ullapara and Shahjadpur Upazilas of Sirajgonj district; and Sadar Upazila of Gazipur district (Fig. 1).

Study design and data collectionA cross-sectional study was conducted in randomly selected dairy farms of study areas during the period from January 2018 to June 2018. The minimum sample size


(n=368.64) was calculated using the formula, n= Z2P(1-P)/d2 based on a prevalence of 60% with desired precision of 5% at 95% confidence level (Thrusfield, 2007). A total of 413 calves were selected randomly for the collection of fecal samples ageing up to 8 months. During fecal sample collection, a pretested questionnaire was used to record the age, sex, health condition, breed, and level of consistency of fecal materials of calves. The calves were divided into three age groups viz. ≤ 3 months, >3 to ≤ 6 months and > 6 to ≤ 8 months; different sex viz. male and female; different health status group viz. poor and normal; different breeds viz. Non-descriptive indigenous and crossbred. The ages of the calves were determined by interviewing the farmers or by examining the teeth. The health status of the calves was determined by visual observation. Well fleshed calves having no

bony prominence and glistening hair coat was considered as calves with normal health. Calves with externally visible ribs and other bony prominence and rough hair coat were considered as poor health conditioned (Pinilla et al., 2018).

Fecal sample collection and preservationSingle fresh fecal sample from each consenting calf was collected in labeled and sterile stool containers containing SAAF solution (Sodium Acetic Acid Formalin). The fecal samples was taken either directly from the rectum of the animals or from the ground immediately after defecation using disposable gloves. Before collection, the animals were restrained properly and all possible hygienic measures including wearing apron, hand gloves and gumboot were taken to avoid contamination.

Fig. 1. Sampling locations of Pabna, Sirajgonj and Gazipur districts. The three districts have 24 Upazilas, of which this study included Sadar and Santhia Upazilas of Pabna; Sadar, Ullapara and Shahjadpur Upazilas of Sirajgonj; and Sadar Upazila of Gazipur.


About 20-25 gram of feces was collected from each calf and transported to the laboratory in ice box, and examined as early as possible.

Examination of fecal samplesThe fecal samples were analyzed using standard parasitological screening techniques namely, sedimentation followed by floatation technique to detect the eggs, cysts and oocysts of parasites (Taylor et al., 2016). For identification of Giardia cysts and Cryptosporidium oocysts, lugol’s iodine and modified Ziehl-Neelson staining were performed, respectively. The eggs, cysts and oocysts of parasites were identified from their morphological characters, using a light optical microscope with a magnification of 10 x and 40 x.

Simple Sedimentation TechniqueAbout 10 grams of faces and 100 ml of saline solution were taken in a glass cylinder. The mixture was thoroughly stirred to make a uniform suspension of the fecal particles. The suspension was then allowed to pass through a sieve (30-50 meshes to the inch) into another glass cylinder and then allowed to stand for half an hour. The supernatant fluid was carefully poured off and a small amount of sediment was taken out with the help of a medicinal dropper and was placed on a glass slide. A coverslip was placed on it and care was taken to avoid bubble formation between the glass slide and the coverslip. The slide was then placed under a compound microscope and examined with low power objective 10x.

Flotation techniqueFlotation procedure was performed using Sheather’s Sucrose solution. The fecal pellet

was resuspended in 10 ml of Sheather’s solution (specific gravity 1.27 g/ml) and mixed thoroughly. The mixture was increased up to the brim of the centrifuge tube and centrifuged for 10 minutes at 4000 rpm. The downward force created by the centrifugal spinning enhanced the buoyancy of the eggs in the viscous solution and drove them to the surface meniscus where they were concentrated and resulted in greater parasite recovery. Examination of a few drops of the fluid from the topmost layer revealed the eggs and oocysts/cysts (Dryden et al., 2005).

Lugol’s iodine stainingDirect smear from the sediment of each concentrated fecal sample was prepared on a clean glass slide, diluted with a drop of Lugol’s iodine, covered with coverslip and finally examined under light microscope at 40X magnification to observe the Giardia cysts (Hendrix, 2002).

Modified Ziehl-Neelson stainingThin smears were prepared from sediments of concentrated fecal samples and air-dried. The smears were fixed with absolute methanol for 5 minutes, air dried and stained with carbol-fuchsin (0.34% fuchsin and 4% w/v phenol) for 30 minutes. Smears were washed with tap water and decolorized with 1% acid-alcohol (1 ml hydrocloric acid and 99 ml of 96% ethanol) for 2 minutes; washed with tap water and counterstained with 1% methylene blue for another 2 minutes, rinsed again in tap water and air-dried. The stained smears were examined by microscope using oil immersion objective to screen oocysts of Cryptosporidium (Tahvildar-Biderouni and Salehi, 2014).


Statistical analysisThe data generated from the questionnaire and parasite identification were recorded in the Statistical Package for the Social Sciences (SPSS 20.0). Descriptive statistics and the Chi-square test were done to determine the significant effects of different explanatory variables on percentage values of parasitism.

Results and DiscussionOverall prevalence of GI parasitic infections in calves

Out of the 413 calves examined through fecal examination, 187 were found infected with one or more species of GI parasites indicating an overall prevalence of 45.3% (Table 1). The identified helminths were the snail-borne trematode, namely, Fasciola sp. (1.0%), two species of protozoa, namely, Giardia sp. (10.4%) and Cryptosporidium spp. (7.0%), two species of nematodes, namely, Toxocara spp. (20.3%) and Strongyloides spp. (3.9%) and one cestode Moniezia spp. (1.5%) (Fig. 2). However, mixed infection was found only in case of nematodes (Toxocara spp. and

Strongyloides spp.) with the prevalence of 1.2%.

From this study, it was observed that the prevalence of Toxocara spp. (20.3%) was the highest whereas, Fasciola sp. (1.0%) infection was the lowest among the parasitic infections in calves (Table 1). The overall prevalence of GI parasitism was lower than the findings of Paul et al. (2016) and Aktaruzzaman et al. (2013) who reported that 72.65% and 76.9% cattle were infected with various helminths at Sylhet and Sirajgonj, respectively in Bangladesh. Similarly, Bhattacharyya and Ahmed (2005) and Singh et al. (2008) recorded 65.2% and 80.0% incidence of gastrointestinal helminthes, respectively in cattle in India. The variation between the present and earlier results might be due to the differences among the geographical locations and climatic conditions of the study areas, feeding, management and genetic variation in host resistance as well as a gradual increase in awareness of farmers about routine deworming in study areas. Similar to this study, calves were mostly infected with Toxocara spp. and

Table 1. Overall prevalence of GI parasitic infection in calves

Types of Parasites Name of Parasites No. Infected(N=413) Prevalence (%)

NematodeToxocara spp. 84 20.3Strongyloides spp. 16 3.9Mixed (Toxocara spp. and Strongyloides spp.) 5 1.2

Trematode Fasciola sp. 4 1.0Cestode Moniezia spp. 6 1.5

ProtozoaGiardia sp. 43 10.4Cryptosporidium spp. 29 7

Overall 187 45.3


high prevalence of infection might be related to prenatal infection with 3rd larval stage, and poor hygienic condition during post-natal period (Miller et al., 2013). However, similar types of parasites as reported in this study were detected by different scientists in different areas with variable rate of infections (Ahmed et al., 2015; Islam et al., 2014; Fayer, 2010; Xiao, 2010). In Bangladesh, most of the calves in rural areas are reared in scavenging or semi scavenging system, where they graze on the fields. This type of practice may favor the parasitic infestations in calves.

Diversity of GI parasitic infection according to study locationThe prevalence of different GI parasitic infections in three study districts varied from 44.5 to 46.3%. The lowest parasitic infection (44.5%) was recorded in Pabna district though infection by different species of parasites in study areas was statistically not significant

(Table 2). The slight difference in the prevalence of GI parasitic infections might be due to variation in geo-climatic conditions of these areas of study.

Prevalence of GI parasitic infections in relation to ageThe prevalence of GI parasitic infections varied significantly (p= 0.037) among different age groups of calves and the highest infection was recorded in calves of >3 to ≤ 6 months (54.4%) and lowest in calves of ≤ 3 months (36.6%) of age (Table 3).

The prevalence of Toxocara spp. infection was the highest in calves of >3 to ≤6 months (29.4%) and the lowest in the calves of >6 to ≤8 months (8.2%). The protozoan infection by Giardia sp. (16.4%) and Cryptosporidium spp. (8.2%) were the highest in the calves of >6 months to ≤8 months. Prevalence of snail-borne trematode infection was found

Fig. 2. Identified GI parasites in this study.


Table 2. Diversity of GI parasitism in calves in different study areas

Type of ParasitesStudy area

p-valueSirajgonj (%)(n= 163)

Pabna (%)(n= 209)

Gazipur (%)(n= 41)

Toxocara spp. 31 (19.0) 42 (20.1) 11 (26.8) 0.54Strongyloides spp. 7 (4.3) 9 (4.3) 0 (0.0) 0.40Mixed (Toxocara spp. and Strongyloides spp.)

4 (2.5) 0 (0.0) 1 (2.4) 0.08

Fasciola sp. 1 (0.6) 2 (1.0) 1 (2.4) 0.57Moniezia spp. 3 (1.8) 3 (1.4) 0 (0.0) 0.68Giardia sp. 18 (11.0) 20 (9.6) 5 (12.2) 0.83Cryptosporidium spp. 11 (6.7) 17 (8.1) 1 (2.4) 0.42Total 75 (46.0) 93 (44.5) 19 (46.3)

Table 3. Prevalence of GI parasitic infections in calves in relation to Age

Type of ParasitesAge category

p-value≤ 3 months (%)(n=172)

>3 to ≤ 6 months (%)(n= 180)

> 6 to ≤ 8 months (%) (n= 61)

Toxocara spp. 26 (15.1) 53 (29.4) 5 (8.2)

0.037

Strongyloides spp. 7 (4.1) 7 (3.9) 2 (3.3)Mixed (Toxocara spp. and Strongyloides spp.)

2 (1.2) 3 (1.7) 0 (0.0)

Fasciola sp. 0 (0.0) 1 (0.6) 3 (4.9)Moniezia spp. 0 (0.0) 5 (2.8) 1 (1.6)Giardia sp. 16 (9.3) 17 (9.4) 10 (16.4)Cryptosporidium spp. 12 (7.0) 12 (6.7) 5 (8.2)Total 63 (36.6) 98 (54.4) 26 (42.6)

to increase with the increase of age and was highest at age of >6 to ≤8 months (4.9%). Fasciola sp. was not recorded in calves less than 3 months of age. Susceptibility of calves to Moniezia spp. was highest in calves of >3 to ≤6 months (2.8%) and absent in calves under 3 months (Table 3). This result is inclined with the results by Raza et al. (2010) and Samad et al. (2004) who reported that these parasites are mostly prevalent in young age.

The cause of this high prevalence in young cattle might be due to sudden exposure to grassland containing huge number of eggs of parasites, and possibly due to lack of necessary protective immunity of the calves. Fasciola sp. and Moniezia spp. were not observed in calves under 3 months of age. This might be the consequences of feeding habit of calves and time requirement for completing the life cycle of these parasites. However, Rahman


and Mondal (1983) found heavy infection of Fasciola sp. in cattle of 2-3 years of age than in the young cattle.

Sex-wise distribution of GI parasites in calves

In the present study, prevalence of GI parasitic infection was observed significantly (p= 0.029) higher in male (48.4%) than in female calves (42.5%) (Table 4). In males, the highest prevalent parasite was Toxocara spp. (18.8%) followed by Giardia sp. (12.0%), Cryptosporidium spp. (9.9%), Strongyloides spp. (4.7%), Fasciola sp. (1.6%), and Moniezia spp. (1.0%).

In female calves, the highest prevalence was recorded for Toxocara spp. (21.7%) and lowest for Fasciola sp. (0.5%) (Table 4). Similar to this finding, higher prevalence was reported in male animals than in females by some other studies in Bangladesh, Pakistan and Ethiopia (Paul et al., 2016; Hailu et al., 2011; Ibrahim et al., 2008).On the other hand, higher rate of parasite infection in female

animals than in male was also reported (Das et. al., 2010; Islam and Taimur, 2008). However, Siddiki et al. (2010) observed that both male and female Red Chittagong Cattle breed and crossbred animals were equally susceptible to parasitic infections. The higher percentage of infection in the male cannot be explained exactly, but it might be due to the neglected attitude of the farmers toward the management of male animals since many of the farms target milk production thereby focusing more on the health of females. In addition, higher feed and water intake might make the male individual more susceptible to any infection (Paul et al., 2016).

Variation in GI parasitism according to breed

In this study, prevalence of parasitic infection was more common in cross breed calves (48.0%) than in indigenous calves (34.5%). But the difference was not significant (p= 0.816) (Table 5). In both breeds, the highest prevalence of GI parasitic infection was recorded for Toxocara spp. (21.6, 15.5%)

Table 4. Prevalence of GI parasitic infections in calves of different sexes

Type of ParasitesSex category

p-valueMale (%)(n= 192)

Female (%)N= 221

Toxocara spp. 36 (18.8) 48 (21.7)

0.029

Strongyloides spp. 9 (4.7) 7 (3.2)Mixed (Toxocara spp. and Strongyloides spp.) 1 (0.5) 4 (1.8)Fasciola sp. 3 (1.6) 1 (0.5)Moniezia spp. 2 (1.0) 4 (1.8)Giardia sp. 23 (12.0) 20 (9.0)Cryptosporidium spp. 19 (9.9) 10 (4.5)Total 93 (48.4) 94 (42.5)


and the lowest for Fasciola sp. (0.9%, 1.2%). Similar findings had been described by Gadre (2007) who reported that the infection rates with GI parasitic infections in cross-bred cattle were relatively higher than in local dairy animals. This study revealed that the infection rate with Toxocara spp. was comparatively higher in cross-breed (21.6%) than in the indigenous calves (15.5%) which are almost similar to the earlier report by Roy (2010).

Holstein Frisian and Jersey are usually adapted in countries having relatively low temperature with minimal chances of parasitic exposure. The parasitic ecology and reproduction are closely related to an optimal environmental condition, which is not normally common in these countries. But, Bangladesh is a tropical country with hot-humid environment which is favorable for parasite reproduction. For this reason, crossbred animals in Bangladesh become readily infected by parasites and different predisposing factors including managing of these animals in parasitic load environment further worsen the condition.

Effect of health status on GI parasitic infections

Health status of calves had significant (p= 0.004) effect on the occurrence of GI parasitism and infections were higher in poor health conditioned calves (76.5%) than that of normal conditioned (39.1%). In calves with poor health, the highest prevalence was recorded in case of Toxocara spp. (44.1%) followed by Giardia sp. (11.8%), Strongyloides spp. (7.4%), Fasciola sp. (4.4%), Cryptosporidium spp. (4.4%), and Moniezia spp. (1.5%). In healthy calves, the highest prevalence was found for Toxocara spp. (15.7%) and the lowest for Fasciola sp. (0.3%) (Table 6).

This finding coincides with the result reported by Ilyas et al. (2016) and Alim et al. (2012). Malnourished animals are more susceptible to any infection as they are immune compromised. It appears that malnutrition in animals increases their susceptibility to the parasitic infection (Biswas et al., 2014). It may also happen due to the fact that the poor

Table 5. Prevalence of GI parasitism in calves according to breed

Type of ParasitesBreed

p-valueIndigenous (%)(n= 84)

Crossbred (%)(n= 329)

Toxocara spp. 13 (15.5) 71 (21.6)

0.816



and weak animals, as a result of any other causes, are not able to resist the challenge of parasitic infection and become easily infected.

Relationship between fecal consistency and GI parasitic infections in calves

Calves with loose feces had more parasitic infection (56.5%) than the calves with formed (21.7%) and soft (18.7%) feces, however the difference was not significant (Table 7). Among the parasites, Toxocara

spp. was more common in calves with each type of feces (loose = 21.7%, formed=18.7% and soft=21.0%) whereas Fasciola sp. and Moniezia spp. were found in calves with soft (0.6% and 2.9%, respectively) and loose feces (4.8% and 1.6%, respectively). Loose feces is a common clinical finding in many parasitic as well as bacterial and viral diseases that make animal immune-compromised and vulnerable. For this reason, calves with loose feces might have more parasitic infection than others.

Table 6. Health status related prevalence of GI parasitism in calves

Type of ParasitesHealth status

p-valueNormal (%) (n= 345) Poor (%) (n= 68)

Toxocara spp. 54 (15.7) 30 (44.1)

0.004


Table 7. Prevalence of GI parasitic infections in calves based on fecal consistency

Type of ParasitesFecal Consistency

p-valueFormed (%)(n=180)

Soft (%)(n= 171)

Loose (%)(n= 62)

Toxocara spp. 39 (21.7) 32 (18.7) 13 (21.0)

0.424

Strongyloides spp. 7 (3.9) 7 (4.1) 2 (3.2)Mixed (Toxocara spp. and Strongyloides spp.)

2 (1.1) 2 (1.2) 1 (1.6)

Fasciola sp. 0 (0.0) 1 (0.6) 3 (4.8)Moniezia spp. 0 (0.0) 5 (2.9) 1 (1.6)Giardia sp. 16 (8.9) 17 (9.9) 10 (16.1)Cryptosporidium spp. 12 (6.7) 12 (7.0) 5 (8.1)Total 76 (42.2) 76 (44.4) 35 (56.5)


ConclusionsGI parasitic infections are common among calves in Pabna, Sirajgonj and Gazipur districts. The highest prevalence was found for Toxocara spp. and the lowest for Fasciola sp. It was also found that age, sex and health status had significant effects on the prevalence of GI parasitism in calves. Parasitic diseases pose great effects on health and production of animals. Economic losses due to mortality and morbidity in calves per year in Bangladesh may be determined by further studies. More extensive research including molecular epidemiology may be conducted to develop cost effective sustainable control strategies against GI parasitism.

Acknowledgements

The authors acknowledge the financial support from the Research Management Wing (RMW) of Bangabandhu Sheikh Mujibur Rahaman Agricultural University (BSMRAU) and University Grants Commission of Bangladesh. We are grateful to Professor Dr. Abu Sadeque Md. Selim, Department of Animal Science & Nutrition, BSMRAU for making the laboratory space and equipment available for this study.

ReferencesAfazuddin, M. 1985. General incidence and

therapeutic measures of parasitic diseases in cattle of Savar Military Dairy Farm. MSc Thesis, Department of Medicine, Bangladesh Agricultural University, Mymensingh.

Ahmed, R., P. K. Biswas, M. Barua, M. A. Alim, K. Islam and M. Z. Islam. 2015. Prevalence of gastrointestinal parasitism

of cattle in Banskhali upazilla, Chittagong, Bangladesh. J. Adv. Vet. Anim. Res. 2(4): 484-488.

Akanda, M. R., M. M. I. Hasan, S. A. Belal and A. C. Roy. 2014. A survey on prevalence of gastrointestinal parasitic infection in cattle of Sylhet division in Bangladesh. American J. Phyto. Clin. Therap.2: 855–860.

Aktaruzzaman, M., S. A. Rony, M. A. Islam, M. G. Yasin and A. K. M. A. Rahman. 2013. Concurrent infection and seasonal distribution of gastrointestinal parasites in cross-bred cattle of Sirajganj district in Bangladesh. Vet. World. 6(10): 720-724.

Alim, M. A., S. Das, K. Roy, S. Sikder, Mohiuddin, M. Masuduzzaman and M. A. Hossain. 2012. Prevalence of gastrointestinal parasites in cattle of Chittagong division, Bangladesh. Wayamba J. Animal Sci. 4: 1-8.

Bhattacharyya, D. K. and K. Ahmed. 2005. Prevalence of helminthic infection in cattle and buffaloes. Indian Vet. J. 82: 900-901.

Biswas, H., A. R. Dey, N. Begum and P. M. Das. 2014. Epidemiological aspects of gastrointestinal parasites in buffaloes in Bhola, Bangladesh. Indian J Anim Sci. 84: 245–250.

Charlier, J., F. V. Velde, M. Van Der Voort, J. V. Meensel, L. Lauwers, V. Cauberghe, J. Vercruysse and E. Claerebout. 2015. Econohealth: Placing helminth infections of livestock in an economic and social context. Vet. Parasitol. 212: 62–67.

Chavhan, P. B., L. A. Khan, P. A. Raut, D. K. Maske, S. Rahman, K. S. Podchalwar, and M. F. Siddiqui. 2008. Prevalence of nematode parasites of ruminants at Nagpur. Vet. World. 1: 140.

Das, S., A. K. F. H. Bhuiyan, N. Begum, M. A. Habib and T. Arefin. 2010. Fertility and


parasitic infestation of Red Chittagong cattle. Bangladesh Vet. 27(2): 74-81.

Dryden, M. W., P. A. Payne, R. Ridley and V. Smith. 20115. Comparison of common fecal flotation techniques for the recovery of parasite eggs and oocysts.Vet. Therapeut. 6 (1): 15-28.

Fayer, R., M. Santin and D. Macarisin. 2010. Cryptosporidium ubiquitum n. sp. in animals and humans. Vet. Parasitol. 172: 23-32.

Gadre, S.K. and H. Harada. 2007. Surface water pollution in three urban territories of Nepal, India, and Bangladesh. Environ. Manage. 28: 483-496.

Gunathilaka, N., D.Niroshana, D. Amarasinghe and L. Udayanga. 2018. Prevalence of gastrointestinal parasitic infections and assessment of deworming program among cattle and buffaloes in gampaha district, Sri Lanka. BioMed Res. Int. https://doi.org/10.1155/2018/3048373.

Hailu, D., A. Cherenet, Y. Moti and T. Tadele. 2011. Gastrointestinal helminth infections in small-scale dairy cattle farms of jimma town, Ethiopia. Ethiop. J. Sci. Technol. 2(1): 31-37.

Hendawy, S. H. M. 2018. Immunity to gastrointestinal nematodes in ruminants: effector cell mechanisms and cytokines. J. Parasit. Dis. 42: 471-482

Hendrix, C. M. 2002. Laboratory procedures for veterinary technicians. Mosby, Philadelphia.

Hossain, M. J., M. Amin, M. Mostofa, M. Sharif and S. M. A. Khalid. 2004. Efficacy of levanid against natural gastrointestinal nematodiasis and paramphistomiasis in sheep. Bangladesh Vet. J. 21 (2): 70-73.

Ibrahim, M. M., M. A. A. Ghamdi and M. S. A. Gahmdi. 2008. Helminths Community

of Veterinary Importance of Livestock in Relation to Some Ecological and Biological Factors. Turkiye Parazitol Derg. 32: 42-47.

Ilyas, N., M. M. Hossain, M. J. U. Bhuyan and M. M. H. Khan. 2016. Prevalence of Gastro-intestinal Nematodes Infection of Cattle in Bangladesh. American J. Phyto. Clin. Therap. 4(3): 091-097.

Islam, M. R., M. G. Yasin, M. A. Al Noman, N. Begum, M. M. H. Mondal. 2014. Prevalence of gastrointestinal parasites in cattle at Vangura upazila in Pabna district of Bangladesh. Int. J. Nat. Soc. Sci. 1: 45-52.

Islam, K. B. M. S. and M. J. F. A. Taimur. 2008. Helminthic and protozoan internal parasitic infections in free ranging small ruminants of Bangladesh. Slov. Vet. Res. 45: 67-72.

Jabber, M. and D. A. G. Green. 1983. The status and potential of livestock within the context of agricultural development policy in Bangladesh. The University of Wales. Aberystwyth, United Kingdom. 113 P.

Miller, R. S., M. L. Farnsworth and J. L. Malmberg. 2013. Diseases at the livestock-wildlife interface: status, challenges, and opportunities in the United States. Prev. Vet. Med.110: 119-132.

Paul, A., P. C. Baishnab, H. Kobir, S. Akhter, T. J. Chowdhury, B. Jha and M. M. Rahman. 2016. Status of internal parasitism of cattle at Sylhet Government Dairy Farm, Bangladesh. Int J Nat Sci. 6(2): 54-34.

Pinilla, J. C., P. Florez, M. T. Sierra, E. Morales, R. Sierra, M. C. Vasquez and D. Ortiz. 2018. Point prevalence of gastrointestinal parasites in double purpose cattle of Rio de Oro and Aguachica municipalities, Cesar state, Colombia. Vet Parasitol Reg Stud Reports. 12: 26-30.

Pinilla León, J. C., N. U. Delgado and A. A. Florez. 2019. Prevalence of gastrointestinal parasites in cattle and sheep in three municipalities in


the Colombian Northeastern Mountain. Vet. World. 12(1): 48-54.

Rahman, M. F. and Z. Ahmed. 1991. Final report of “Pilot project for the control of parasitic disease of animals in Bangladesh”, Bangladesh Livestock Research Institute, Dhaka.

Rahman, M. H. and M. M. H. Mondol. 1983. Helminth parasites of cattle (Bosindicus) in Bangladesh. Indian J. Parasitol. 7(2): 173-174.

Raza, A. M., S. Murtaza, H. A. Bachaya, A. Qayyum and M. A. Zaman. 2010. Point prevalence of Toxocara vitulorum in Large Ruminants Slaughtered at Multan Abattoir. Pak Vet J. 30: 242-244.

Roy, M. M. Alam, A. K. M. A. Rahman, M. Shahiduzzaman, M. S. Parvez and E. H. Chowdhury. 2010. Prevalence of cryptosporidiosis in crossbred calves in two selected areas of Bangladesh. Bangladesh J. Vet. Med.12: 185-190.

Samad, M. A., K. M. M. Hossain, M. A. Islam and S. Saha. 2004. Concurrent infection of gastro-intestinal parasites and bacteria associated with diarrhea in calves. Bang. J. Vet. Med. 2(1): 49-54.

Sardar, S. A., M. A. Ehsan, A. K. M. M. Anower, M. M. Rahman and M. A. Islam. 2006.

Incidence of liver flukes and gastro-intestinal parasites in cattle. Bang. J. Vet. Med. 4 (1): 39-42.

Siddiki, A. Z., M.B. Uddin, M. B. Hasan, M. F. Hossain, M. M. Rahman, B. C. Das, M. S. Sarkerand M. A. Hossain. 2010. Coproscopic and haematological approaches to determine the prevalence of helminthiasis and protozoan diseases of Red Chittagong Cattle (RCC) breed in Bangladesh. Pak. Vet. J. 30(1): 1-6.

Singh, A., A. K. Gangwar, N. K. Shinde and S. Srivastava. 2008. Gastrointestinal parasitism in bovines of Faizabad. J. Vet.Parasitol. 22(1): 31-33.

Tahvildar-Biderouni, F. and N. Salehi. 2014. Detection of Cryptosporidium infection by modified ziehl-neelsen and PCR methods in children with diarrheal samples in pediatric hospitals in Tehran. Gastroenterol Hepatol Bed Bench. 7(2): 125-30.

Taylor, M. A., R. L. Coop, and R. L. Wall. 2016. Veterinary Parasitology: 4th ed. Blackwell publishing, Oxford.

Thrusfield, M. 2007. Veterinary epidemiology. 3rd Edition, Blackwell science, Oxford, UK.

Xiao, L. 2010. Molecular epidemiology of cryptosporidiosis: an update. Exp Parasitol. 124(1): 80-89.

M. M. Rahman and M. M. H. Oliver 15

Detection anD contouring of Bau-Kul using image Processing techniques

m. m. rahman1* and m. m. h. oliver1

abstract

Automated grading and sorting of fruits during harvesting period are needed for securing better market prices. In order to introduce such automation facilities in Bangladesh, edging and contouring information of the locally grown fruits is important. This study reports the first endeavor towards the use of image processing techniques for a popular jujube variety (BAU-Kul) in Bangladesh. Image processing techniques were used for segmentation, and contouring on the basis of color Thresholding, edge detection and contour detection in Python-OpenCV software. Six random samples of BAU-Kul fruit were used for the research. Perimeter lengths obtained from the image analysis of the six samples ranged from 17.9 cm to 20.20 cm with an average of 19.29 (±1.02) cm. The measured lengths on the other hand, varied from 16.2 cm to 19.1 cm with an average of 17.75 (±1.3) cm. Consequently, the average error in calculation was limited to only 7.98%. This indicates the fact that images captured through mobile devices can be used for detection and contouring of BAU-Kul samples with fairly high accuracy (92.02%). These information provides a foreground basis of automation for the grading and sorting systems of BAU-Kul fruits in Bangladesh.

Keywords: BAU-Kul, contour detection, edge detection, image segmentation.

1Department of Agricultural Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


introductionJujube (baroi) is one of the popular fruits containing vitamin A and C. It is used in different food preparations such as Jam, Jelly, Chatney, Pickles and Juice. There are many jujube varieties that reportedly contains 85.9% water, 0.8% protein, 0.1% fat, 12.8% carbohydrate, 0.03% calcium, 0.03% phosphorus and 0.8% iron (Uddin and Hussain, 2012). In Bangladesh, popular varieties include Apple kul, BAU kul, BARI kul, Narkeli, and Sabzi. This study particularly deals with BAU-Kul developed by the Germplasm Center at Bangladesh Agricultural University. This

variety is most popular in the country because of its attractive size and texture. It is widely grown in Bangladesh ranging from sandy to saline and hilly and char land areas (Rahman and Islam, 2013; KGF, 2014). As with the other fresh produces, the market value of BAU-Kul depends on its sizes and color features. Although graders are available to separate products based on their sizes, no grading system based on the shape and colorimetric automation is available in Bangladesh. In particular, scientific information relating to separation and grading of BAU-Kul is not available in the literature.

16 Detection and Contouring of BAU-Kul Using Image Processing Techniques

In order to develop a real time, nondestructive, and automated grading-sorting system for any fruit, the factorial combination of the size (detection, contouring and edging), and color (segmentation) is important. This technology requires the help of advance image processing techniques that include color space model, color Thresholding, edge detection and contour detection. Color is a property of an individual object which comes from the visible light reflecting off the object surface. In combination with color, Hue Saturation Value (HSV) space model are often used to locate the defects on fruits’ surface in agricultural fields (Phakade et al., 2014). There has been reports (Blasco et al., 2009; Lin et al., 2011) that color Thresholding can be used for the segmentation process of foreground images. Recent developments in automation has also experienced the use of Canny Edge detection method for the detection of edges in an image (Choudhary et al., 2017; Rajani and Veena, 2019).

In such case, contour detection techniques are increasingly being used for analyzing noisy (Abubakar, 2013) and medical images (Senthilkumaran and Vaithegi, 2016). Similar approaches for computer vision technology for fruits (Feng and Qixin, 2004; Mahendran et al., 2012; Nandi et al., 2016; Sahu and Potdar, 2017) and vegetables (Deng et al.,

2017; Deulkar and Barve, 2018) have also been reported in the literature. More recently, a detailed contour based approach has been described by Septiarini et al. (2019). This emerging science of imaging has a potential application in the agricultural sector particularly, in the automatic grading (Banot and Mahajan, 2016; Nandi et al., 2016; Deulkar and Barve, 2018) and sorting of agricultural products. In order for automation of this sector in Bangladesh, computer vision and related researches are required. Despite being a promising technology, very few scientific studies of this kind have been carried out in Bangladesh. In particular, many uniquely shaped and colored fruits of Bangladesh (for instance, BAU-Kul) have not been studied using advanced techniques. This study is going to shed some light on this area by employing color segmentation and contour detection approaches for BAU-Kul images. In order to achieve this, necessary algorithms will be generated using Python-OpenCV as recommended by Devi et al. (2017), Koirala et al. (2019) and Yonekura et al. (2019). The outcome of this research will provide a substantial basis in the development of an automated grading-sorting system for BAU-Kul products in Bangladesh.

fig. 1. overall architecture behind Bau Kul detection and contouring.


materials and methodsThe overall process of the BAU-Kul detection system has been illustrated in the following (Fig. 1) flowchart. Theoretically, the process of BAU-Kul detection system involves low-level processing and high level processing. In low-level processing, the digital color images were captured using a portable mobile devices. The captured RGB images were converted to HSV, and later to grayscale images in order to extract the Thresholding images.

In high level processing, the image was then processed through the Canny Edge function in order to obtain the parametric shapes of BAU-Kul samples. At the final step, the Thresholding and Canny Edge images were used to obtain the desired fits of contours around the BAU-Kul samples (Table 1).

The implementation of algorithm for contour fitting of BAU-Kul therefore, comprises of

the following consecutive steps i.e., (a) image acquisition (b) pre-processing (c) thresholding followed by canny edge detection, and (d) contouring. These steps and their mathematical models have been described as follows:

(a) image acquisition

Samples of BAU-Kul were collected from the local market in Dhaka city in the month of February, 2019. Six randomly collected BAU-Kul samples were pictured using mobile devices. A description of the experimental set up has been summarized in Table 2.

The samples were laid out on a non-reflective surface, and naturally diffused sunlight (2-5 W/m2) was used for capturing these images. The device was set a fixed height so as to keep the focal length within (26-33 mm) for all the images so that the shadow effects could be minimized. The captured images were then saved as a .jpg/.jpeg format for further processing.

table 1. Bau-Kul detection and contouring algorithmalgorithm actionsStart

Step-1: Read BAU-Kul image into the Python-OpenCV Integrated Development Environment (IDE) from the particular folder.

Step-2: Convert RGB image into the HSV color and Gray color.Step-3: Thresholding images by converting graysacle image into binary image and

Canny edge detector uses for edge detection of BAU-KulStep-4: Contour fitting and determine the contour perimeter

Stop

table 2. experimental set up for image acquisition Properties Value Properties ValueExposure value 0 Shutter speed (sec) 1/33

Color regime RGB Focal length (mm) 26-33 (equivalent to 35 mm focal length film)

White balance AWB ISO 400-500Sample image size (pixel) 3456 × 4608 F-stop f/2.2


(b) image Pre-processing

The captured images were processed in two consecutive phases. In the first phase, Python-OpenCV computer languages were used to process the BAU-Kul color (RGB) images for detection and contouring. Normally, RGB (Red Green Blue) defines color in terms of a combination of primary colors,

where color information of the image is not separated from luminance. In contrast to RGB, HSV is used to separate image luminance from color information. That why, the RGB color images of the products were read into the Python-OpenCV Integrated Development Environment (IDE) and converted into the HSV color. The HSV model describes the colors similar to how human eyes tend to perceive color (Dash et al., 2017) and is often preferred over the RGB model. Use of HSV model is particularly chosen in situations where color description plays an integral role. In this model, ‘Hue’ represents the color, ‘Saturation’ represents the amount to which that respective color is mixed with white, and ‘Value’ represents the amount to which that respective color is mixed with black (Gray level).

BAU Kul Sample-1 BAU Kul Sample-2

fig. 2. Bau-Kul samples for image processing.

# Image reading from files image = cv2.imread ('E:/BAU-Kul sample-1.jpg')

# Getting green HSV color representation hsv_img = cv2.c v t C o l o r ( i m a g e , cv2.COLOR_BGR2HSV) cv2.imshow('HSV_Image_1', hsv_img)

# Converting the HSV image to Grayscale imagesRGB_again = cv2.c v t C o l o r ( h s v _img, cv2.COLOR_HSV2RGB) gray = cv2.cvtColor(RGB_again, cv2.COLOR_RGB2GRAY) cv2.imshow('Gray_Image', gray)


(c) thresholding and canny edge detection

The BAU-Kul sample images were segmented using Canny Edge and Thresholding .

The action of Thresholding image segmented the image and produced binary images from a grayscale image. This is most effective in images with high levels of contrast (Shapiro George, 2002). The thresholding is used as an operation which involves tests against a function of threshold T of the form:

# Thresholding function ret, threshold = cv2.threshold(gray,90, 255, 0)cv2.imshow(‘Threshold Image’, threshold)

T=T[x,y, p(x,y), f(x,y)]………...............……………………………………………..………. (1)

where,

f(x,y) is the gray level point of (x,y), and

p(x,y) indicates some local properties of this point.

# Canny edge detection function(ret, thresh) = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)edge = cv2.Canny(thresh, 100, 200)cv2.imshow(‘Canny Edge_Image’, edge)

The threshold image is defined as, g(x,y)=1 if f(x,y)>T, and g(x,y)=0 if f(x,y)≤T. Usually, if the image intensity, f(x,y) is less than a set threshold value of T, it is assumed to be a black pixel (Shapiro and George, 2002). Any higher value of f(x,y) otherwise yields a white dot.

In addition, Canny Edge detection is also an image segmentation technique, which extracts useful structural information (Muthukrishnan and Radha, 2011) from different vision objects, and reduce dramatically the amount of data to be processed. This method is quite complex and have five-step processes (Canny, 1986) that

(i) de-noises the image with a 5×5 Gaussian filter:

, , * , , expg m n G m n f m n where Gm n

21

22 2

2 2

rv v= = - +v v^ ^ ^ bh h h l ...................... (2)

(ii) calculates edge gradients and direction for each pixel:

, , , , , [,,]tanG m n g m n g m n and m n

g m ng m n

m nm

n2 2 1i= + = -^ ^ ^ ^ ^

^h h h h hh

........................... (3)

(iii) applies non-maximum suppression (NMS) on edges obtained to thin out the edge ridges, and

(iv) sets a double threshold on all the detected edges to eliminate false positives.


It also analyzes all the edges and their connection to each other to keep the real edges and discard weaker ones (Minichino and Howse, 2015). In OpenCV, a single function is used to complete the whole process which is called c v 2 . C a n n y ( ) . During the edge detection, the points where the intensity of colors changes significantly are found, and turned on, while turning the rest of the pixels off. The edge pixels are in an image, and there is no particular requirement that the pixels representing an edge are all contiguous.

(d) contour and contour perimeter

Finally, cv2. find Contours() and cv2.draw Contours() and functions were used to locate and visualize the contours in BAU-Kul images. Contour perimeter or curve length were also computed using the cv2.arcLength() function (Koirala et al., 2019) which will evaluate the accuracy of manually determined perimeter with computed one.

results and DiscussionHSV images produced from RGB originals of BAU-Kul sample image 1 and 2 have been shown in Fig. 3. This image is best for using the colors interactively. The HSV values are (143O, 8.2 %, 77%) for BAU-Kul sample 1 and (143O, 8.6%, 73%) for BAU-Kul sample 2 while the RGB color values of original images 1 and 2 were counted to be (179, 195, 185) and (169, 185, 175), respectively.

# Contour detection function and Contour approximation function(contours, _) = cv2.findContours(edge.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)total = 0

# for contour in contours:

epsilon = 0.001 *cv2.arcLength(contour, True)approx = cv2.approxPolyDP(contour, epsilon, True)cv2.drawContours(image, [approx], -1, (0, 0, 255), 3)total += 1print (“I found {0} RET in that image”, format(total))cv2.imshow(‘Contour_Image’, image)

# Calculation of contour perimeterperimeter = cv2.arcLength(cnt, True)print(“Perimeter of contour:”, perimeter)

HSV Image for Bau-Kul sample-1

HSV Image for BAU-Kul sample-2

fig. 3. hsV images for Bau Kul samples.


Usually, HSV is much easier for a user to obtain a desired color as compared to using RGB (Poorani et al., 2013). Thresholding technique of BAU-Kul images isolates objects by converting grayscale images into binary images are shown in Fig. 4.

Threshold Image for Bau-Kul sample-1

Threshold Image for Bau-Kul sample-2

fig. 4. Thresholding images for Bau Kul samples.

This results showed that BAU-Kul image transformed into partially black and white, and the background of that images is transformed into completely white. In such kinds of thresholding techniques, the darker region (black and white) usually indicates the foreground (Efford, 2000) while the brighter (white) region is noted as the background. This however, would be useful for edge detection of the products as shown in Fig. 5.

Canny Edge for Bau-Kul sample-1

Canny Edge for Bau-Kul sample-2

fig. 5. Canny Edge images for Bau Kul samples.


The results of Canny Edging of the input BAU-Kul samples (Fig. 5) are in fact, binary images in which the white pixels closely approximate the true edges of the original BAU-Kul product. However, some white dots were also identified in the middle of the image which apparently occurred due to the natural lights reflecting from the surface of the BAU-Kul sample when images were captured using mobile devices. The red color contours are obtained on the original BAU-Kul images (Fig. 6), which provides the line segments corresponding to the shapes of the objects in the images. If the lightings during images capturing are properly made, the shadow of the fruits on the surface could be minimized, and the contour of BAU-Kul will be more accurate. This manuscript however, present a simple technique in which images captured from normal mobile devices can be used for such kinds of operation. In case of commercial operations however, proper lighting arrangements can significantly improve the accuracy of edge detection.

Contour for Bau-Kul sample-1

Contour for Bau-Kul sample-2

fig. 6. contour images for Bau Kul samples.

Perimeter QuantificationThe algorithm was also employed in computing the perimeter of all the six BAU-Kul samples as shown in Table 3. The obtained pixelated values were transformed into linear lengths in centimeter. In order for accurate calculation, a standard square of known dimensions (4 cm × 4 cm) were used for calibration purpose in each case. In addition, the actual lengths of the samples around their exact photographed edges were measured using a graduated scale with ±0.1 cm accuracy.

The difference between the measured and the calculated values were termed and expressed as % errors. As can be seen from Table 3, the algorithm employed under this manuscript was able to predict the perimeters of the BAU-Kul samples with considerable accuracy with errors ranging from 6.33 to 10.13%. The calculated lengths of the six samples ranged from 17.9 cm to 20.20 cm with an average


(±SD) of 19.29(±1.02) cm. The measured lengths on the other hand, varied from 16.2 cm to 19.1 cm with an average (±SD) of 17.75(±1.3) cm. Consequently, the average error was limited to only 7.98% (±1.02). This indicates the fact that the algorithms employed

in this manuscript can be used for determining the BAU-Kul samples (Table 3) with considerable accuracy (92.02%) using images from mobile devices. If image acquisition can be done in a properly designed chamber, the accuracy can be substantially increased.

table 3. evaluation of contour perimeter for selected Bau-Kul samplessamples calculated Perimeter (cm) measured Perimeter (cm) % error

S1

17.90 16.20 9.49

S2

20.20 18.90 6.46

S3

19.36 17.40 10.13

S4

20.39 19.10 6.33

S5

19.67 18.00 8.49

S6

18.22 16.90 7.22


conclusionsImage processing techniques are used to measure the qualities of fruits based on color space model, Thresholding, edge and contour detection methods. In this research, Thresholding segmentation, edge and contour detection were obtained from the color images of BAU-Kul by using Python-OpenCV platform. The edge and contour of BAU-Kul images were found to be good enough to be used for operational purposes. The contours will however, be more accurate if the suitable lighting conditions are employed in order to minimize the shadow of images on the background surfaces. Nonetheless, the algorithms employed in this manuscript can be used for determining the BAU-Kul samples with considerable accuracy (92.02%). The information generated from this research will be helpful for commercial grading and sorting facilities thriving for automation. This would ultimately help the growers get better price according to the shape and sizes of BAU-Kul.

referencesAbubakar, F. M. 2013. Study of Image segmentation

using thresholding technique on a noisy image. Int. J. Sci. Res. 2(1): 49-51.

Banot, S. and P. M. Mahajan. 2016. A fruit detecting and grading system based on image processing-review. Int. J. Innov. Res. Electric. Electron. Instr. Contr. Eng. 4(1): 47-52.

Blasco, J., N. Aleixos, S. Cubero, J. Gómez-Sanchís and E. Moltó. 2009. Automatic sorting of satsuma (citrusunshiu) segments using computer vision and morphological features. Comput. Electron. Agric. 66(1): 1-8.

Canny, J. 1986. A computational approach to edge detection. IEEE Trans. Pattern Analys. Mach. Intel. 8(6): 679-698.

Choudhary, P., R. Khandekar, A. Borkar and P. Chotaliya. 2107. Image processing algorithm for fruit identification. Int. Res. J. Eng. Technol. 4(3): 2741-2743.

Dash, S. S., P. C. B. Naidu, R. Bayindir and S. Das. 2017. Artificial intelligence and evolutionary computations in engineering systems. Proc. ICAIECES. Springer Nature Singapore, 468 P.

Deng, L., H. Du and Z. Han. 2017. A carrot sorting system using machine vision technique. Appl. Eng. Agric. 33(2): 149-156.

Deulkar, S. S. and S. S. Barve. 2018. An automated tomato quality grading using clustering based support vector machine. 3rd Int. Conf. Communic. Electron. Syst. Pp. 1128-1133.

Devi, T. G., P. Neelamegam and S. Sudha. 2017. Image processing system for automatic segmention and yield prediction of fruits using open CV. Int. Conf. Curr. Trend. in Comp. Electric. Electron. Communic. Pp. 758-762.

Efford, N. 2000. Segmentation. Pp. 250-270. Digital Image Processing: A Practical Introduction Using Java. Pearson education, England.

Feng, G. R. and C. Qixin. 2004. Study on color image processing based intelligent fruit sorting system, Fifth World Congress on Intel. Contr. Auto. 6 (6): 4802-4805.

Kanimozhi, B. and R. Malliga. 2017. Classification of ripe or unripe orange fruits using the color coding technique. Asian J. Appl. Sci. Technol. 1(3): 43-47.

Koirala, A., K. B. Walsh, Z. Wang, and C. McCarthy. 2019. Deep learning for real-time fruit detection and orchard fruit load


estimation: Benchmarking of mango YOLO. Precis. Agric. 20(6): 1107-1135.

KGF. 2014. Annual Report (Jan-Dec, 2014), Krishi Gobeshona Foundation, 26 P.

Lin, C., C. H. Su, H. S. Huang and K. C. Fan. 2011. Colour image segmentation using relative values of RGB in various illumination circumstances. Int. J. Com. 5(2): 252-261.

Mahendran, R., G. C. Jayashree and K. Alagusundaram. 2012. Application of computer vision technique on sorting and grading of fruits and vegetables. J. Food Process Technol. S1-001.

Minichino, J. and J. Howse. 2015. Learning OpenCV 3 computer vision with python. 2nd Edition. Packt publishing ltd. livery place, Birmingham, UK. 55 P.

Muthukrishnan, R. and M. Radha. 2011. Edge detection techniques for image segmentation. Int. J. Comp. Sci. Info. Technol. 3(6): 259-267.

Nandi, C. S., B. Tudu and C. Koley. 2016. A machine vision technique for grading of harvested mangoes based on maturity and quality. IEEE Sensors J. 16(16): 6387-6396.

Phakade, S. V., D. Flora, H. Malashree and J. Rashmi. 2014. Automatic fruit defect detection using HSV and RGB color space model. Int. J. Innov. Res. Comp. Sci. Technol. 2(3): 67-73.

Poorani, M., T. Prathiba and G. Ravindran. 2013. Integrated feature extraction for image retrieval. Int. J. Comp. Sci. Mob. Comp. 2(2): 28-35.

Rahman, M. M., and A. Islam. 2013. Adaptation technologies in practice and future potentials in Bangladesh. Climate Change Adaptation Actions in Bangladesh. Springer, Tokyo. Pp. 305-330.

Rajani, S. and M. N. Veena. 2019. Medicinal plants segmentation using thresholding and edge based techniques. Int. J. Innov. Technol. Explor. Eng. 8(6S4): 71-76.

Sahu, D. and R. M. Potdar. 2017. Defect identification and maturity detection of mango fruits using image analysis. Americ. J. Art. Intel. 1(1): 5-14.

Senthilkumaran, N. and S. Vaithegi. 2016. Image segmentation by using thresholding techniques for medical images. Comp. Sci. Eng.: An Int. J. 6(1): 1-13.

Septiarini, A., H. Hamdani, H. R. Hatta and K. Anwar. 2019. Automatic image segmentation of oil palm fruits by applying the contour-based approach. Scientia Hort. 261: 1-7.

Shapiro, L. G. and C. S. George. 2002. Computer Vision. New jersey, Prentice hall. ISBN 0-13-030796-3.

Uddin, M. B. and I. Hussain. 2012. Development of diversified technology for jujube (Ziziphus jujuba L) processing and preservation. World J. Dairy Food Sci. 7(1): 74-78.

Yonekura, T., A. Iwamoto, H. Fujita, and M. Sugiyama. 2019. Mathematical model studies of the comprehensive generation of major and minor phyllotactic patterns in plants with a predominant focus on orixate phyllotaxis. PLoS Comp. Biol. 15(6), e1007044.

S. Arofi, M. M. Rahman, H. K. Shiragi, M. A. Alam, M. M. Islam, and J. C. Biswas 27

AggregAte stAbility in soils of twelve Agro-ecologicAl zones of bAnglAdesh bAsed on orgAnic

cArbon And bAsic cAtions

S. Arofi1, M. M. rahman1*, h. K. shiragi2, M. A. Alam1, M. M. islam3, and J. c. biswas4

Abstract

Soil aggregate is one of the vital indicators of soil health that depends on organic carbon (OC), texture and basic cations. A total of 206 soil samples were collected from 12 agro-ecological zones (AEZs) of Bangladesh to study the effects of organic carbon (OC), basic cations (Ca, Mg, K and Na) and different sized soil particles on soil aggregate stability. Soil samples were analyzed for bulk density, pH, OC, texture, basic cations and water stable soil aggregates (WSA) following standard protocols. Data revealed that OC positively increased WSA, while monovalent basic cations Na+ and K+ showed negative effects. Water stable soil aggregates and C stock of 0.25 mm sized soil fraction were found higher than that of larger sized soil fractions of 0.5, 1.0 and 2.0 mm. The roles of divalent basic cations Ca2+ and Mg2+ on WSA were found to be indistinct and need to be studied further.

Keywords: Soil aggregates, carbon stock, soil fraction, soil health.

1Department of Soil Science, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Soil Resource Development Institute, Dhaka 1215, 3Department of Agronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 4Krishi Gobeshona Foundation, Dhaka 1215, Bangladesh. *Corresponding author: [email protected]


introductionSoil carbon (C) contents and aggregates are very important for sustaining soil health and crop productivity. Conservation and retention of terrestrial C in soil are very important in mitigating negative effects of global warming and climate change as well as in improving soil quality. Soil aggregation is a continuous process where organic matter plays a dominant role which depends on organic matter supply through addition of crop residues during harvest, application of different manures and composts, biochar, basic cations in soils, microbial abundance and soil and crop management practices. Soil aggregate is a basic factor influencing the functions of

soils which has the ability to support plant and animal life. Aggregate stability is used as an indicator of soil structure (Six et al., 2000). Aggregation in soil results from the rearrangement of particles, flocculation and cementation (Duiker et al., 2003). Aggregate stability is important for enhancing porosity, improving drainage, improving soil fertility, increasing agronomic productivity and decreasing erosion.

Aggregation is mediated by biota, ionic bridging, clay, soil organic carbon (SOC) and carbonates. SOC acts as a binding agent and as a nucleus in the formation of aggregates. Depletion of SOC through continuous deep tillage and intensive cropping are major

28 Aggregate Stability in Soils of Twelve Agro-Ecological Zones of Bangladesh

causes of reduction of stable soil aggregates (Six et al., 2002; Rahman, 2013). If soil disturbance through tillage reduces, the rate of SOC mineralization decreases and consequently SOC storage increases (Paustian et al., 1997; Putte et al., 2010; Schimel and Schaeffer, 2012). SOC storage in agricultural soils has the greatest potential to improve soil aggregates as well as soil health. Since the clay particles and organic matter act as glues to create aggregates, soils having high organic matter and clay can accumulate more C through sequestration and exhibit stronger soil aggregation. The OC content in soils of Bangladesh is low because of faster microbial decomposition of soil organic matter (SOM) mediated by high temperature and moisture and also as a consequence of application of little or no organic fertilizer to soils (BARC, 2018; Rahman, 2013). Moreover, crop production in Bangladesh solely depends on inorganic fertilizers where nitrogenous fertilizers especially urea alone contributes 55% of the total inorganic fertilizers, which also results in faster microbial decomposition of SOM (Alam et al., 2019). Soil aggregate stability in the coastal saline zone is low since monovalent cations sodium (Na+) and potassium (K+) have dispersive effects, while in the calcareous region soil aggregates stability is high due to divalent cations calcium (Ca2+) and magnesium (Mg2+) which have flocculation effects in soils (Rengasamy and Marchuk, 2011). Moreover, the coastal saline soils are high in divalent cation Mg2+, which acts as a monovalent cation and has dispersing effect. In a study Roy et al. (2019) found that soil C positively related to the formation of soil aggregates. However, in general, studies on soil aggregates and

factors affecting soil aggregate formation in Bangladesh are rather scanty. Therefore, the present study was conducted to evaluate the effects of organic C, basic cations and different sized soil particles on the stability of soil aggregates in different agro-ecological zones (AEZs) of Bangladesh.

Materials and Methods

study sites and soil sample collectionThe study was conducted using soil samples collected from 12 agro-ecological zones (AEZs) of Bangladesh viz., Old Himalayan Piedmont Plain (AEZ 1), Tista Meander Floodplain (AEZ 3), Karatoya- Bangali Floodplain (AEZ 4), Ganges Tidal Floodplain (AEZ 13), Old Meghna Estuarine Floodplain (AEZ 19), Northern and Eastern Piedmont Plain (AEZ 22), Chittagong Coastal Plain (AEZ 23), Level Barind Tract (AEZ 25), North-eastern Barind Tract (AEZ 27), Madhupur Tract (AEZ 28), Northern and Eastern Hills (AEZ 29) and Akhaura Terrace (AEZ 30). The numbers of collected soil samples of the mentioned AEZs were 10, 10, 12, 20, 7, 14, 15, 12, 10, 73, 16 and 7, respectively with a total of 206 soil samples. Soil samples (0–15cm) were collected from arable land during November to December in both 2017 and 2018 after harvesting of transplanted aman rice using core samplers to determine soil bulk density. A bulk sample from each sampling point was also collected to analyze other soil parameters. The soil samples were air dried, ground and sieved (2mm) and analyzed in the Laboratory of Soil Science Department, BSMRAU, Gazipur.


soil sample analysisThe soil samples were analyzed for bulk density, texture, pH, organic C, Na, K, Ca, Mg and water stable soil aggregates. For aggregates, soil was sieved by four different sized sieves (2, 1, 0.5 and 0.25 mm) and water stable soil aggregates were determined by the wet sieving method. To determine aggregates, 10 g air dried soil sample was taken in 50 ml conical flask and the sample was kept overnight under water. The soaked soil samples were then transferred to sieves of respective sizes. Sieves with soil were taken to agitation rack placed under water in plastic box containing 15 L water and agitated 20 times per 40 seconds. Then sieves were kept in oven at 105 °C for 2 hours. After complete drying, weight of sieves with soil was recorded and again agitated in NaOH solution (1.6 g/L). Again sieves were dried in oven and weight of dried sample with sieve was recorded as mentioned above. Empty sieve weight was also recorded and calculation was done following (Castellanos-Navarrete et al., 2013). Soil texture was determined by the Bouyoucos hydrometer method as described by Gee and Baunder (1986). The collected core samples were oven dried at 105°C for 24 hours and bulk density was calculated (Rowell, 1994). Soil pH was measured by a glass electrode pH meter (Horiba model No. M-8L) using a soil: water ratio of 1:2.5 (Jackson, 1973). The basic cations (Na, K, Ca, and Mg) were determined by the ammonium acetate extraction method (Thomas, 1982) using an atomic absorption spectrophotometer and reading was taken at a 285.2 nm wavelength. Organic C was determined by the wet oxidation method (Walkley and Black, 1934). Sodium adsorption ratio (SAR), monovalent

cation adsorption ratio (MCAR) and cations ratio of soil structural stability (CROSS) were determined using procedures proposed by Rengasamy and Marchuk (2011). Soil C stock was calculated using the following equation (Rahman et al., 2016).

Soil C stock (t ha-1) = Soil C % × soil bulk density (g/cc) × depth of soil (cm)

statistical data analysis The data collected on different parameters were subjected to statistical analysis (Gomez and Gomez, 1984). The Microsoft Excel and SPSS 20 software programs were used wherever appropriate to perform statistical analysis. Relationships among the parameters were established through correlation and regression analysis. Mean differences among the treatments were interpreted by using the least significant difference (LSD) test at 5% level of significance.

results and discussion

Soil physical properties in different AEZsBulk density (BD), texture and aggregate stability of soils of different AEZs are presented in Table 1. Soil BD insignificantly differed among different AEZs (p>0.05). The highest BD (1.45 g/cc) was recorded in soils of AEZ 27 and 29, while the lowest density (1.36 g/cc) was recorded in soils of AEZ 13. The data on soil BD of the 12 AEZs were found moderate (1.36-1.45 g/cc) which revealed that the soils are neither clayey nor sandy. Sand particle (2.00-0.05 mm) in the soil samples varied from 13.50 to 38.43%, where the highest sand content (38.43%) was recorded for soils of AEZ 29 and the lowest (13.50%) for soils of


AEZ 4. Silt particles (0.05-0.002 mm) in the soil samples varied from 34.06-59.50%, where the highest (59.50%) was recorded for AEZ 4 and the lowest (34.06%) for soils of AEZ 28. Clay particles (<0.002 mm) in the soil samples varied from 21-38.20%, where the highest clay content (38.20%) was recorded for AEZ 28 and the lowest clay content (21%) was for AEZ 27. The Soils of AEZ 1, 3, 23, 29 and 30 were of loamy texture, those of AEZ 4, 19 and 27 were silt loam and those of AEZ 22 and 28 were clay loam. Silty clay texture was found in AEZ 13 and silty clay loam texture in AEZ 25. Soil textures are good in the AEZ studied, because these were neither heavy nor light. So, it could be concluded that soils of the selective AEZs were good for crop production.

Total soil aggregates (TSA) varied from 26.48 to 53.95%, where the highest and lowest TSA were recorded in soils of AEZ 30 and 13, respectively. Aggregate stability in different AEZs followed the order AEZ 30>22> 25>27>29>23>19>1>28>3>4>13, where the values were 53.95, 50.47, 49.90, 48.99, 48.42, 46.16, 43.85, 42.54, 41.74, 41.47, 38.44 and 26.48%, respectively. The low TSA in the AEZ is due to high Na content in the Ganges Tidal Floodplain soil. To increase and maintain aggregate stability of soils, regular application of different organic materials (manures, compost, crop resides etc.) to crop fields are recommended which can enhance SOC as well as aggregate stability of soils. Organic C content in soils is low in Bangladesh because of improper land management and use of high

Table 1. Physical characteristics of soil samples collected from different AEZs of Bangladesh

AEZ No.Soil physical properties (Mean ± S.E.)

BD ( g/cc) Sand (%) Silt (%) Clay (%) TSA (%)

1 1.41±0.00 33.50±3.92 40.00±4.83 26.50±2.17 42.54±6.31

3 1.42±0.01 29.16±2.99 44.66±4.67 26.33±6.04 41.47±4.26

4 1.39±0.01 13.50±3.50 59.50±2.50 27.00±1.00 38.44±2.36

13 1.36±0.01 26.50±2.33 36.40±2.87 37.09±4.10 26.48±1.57

19 1.39±0.02 20.57±1.87 54.42±1.52 25.00±1.43 43.85±2.88

22 1.40±0.01 32.14±5.40 37.64±3.45 30.28±4.28 50.47±0.87

23 1.41±0.01 24.20±4.80 49.00±1.51 26.80±5.53 46.16±2.86

25 1.38±0.00 14.00±1.00 53.50±1.50 32.50±2.50 49.90±0.04

27 1.45±0.00 33.00±2.00 46.00±2.00 21.00±0.00 48.99±1.77

28 1.39±0.00 27.84±1.17 34.06±0.77 38.20±1.10 41.74±1.36

29 1.45±0.01 38.43±3.96 39.18±3.51 22.43±3.48 48.42±1.60

30 1.41±0.01 35.66±1.20 38.33±0.88 26.00±2.00 53.95±1.92

CV % 3.25 38.02 30.24 47.64 19.57BD = bulk density, TSA = total soil aggregates


rates of inorganic fertilizers, especially the N fertilizer urea, which stimulates microbial decomposition of organic matter the resultant effect being low soil aggregates.

soils chemical properties Soil pH, organic C (OC), C stock (CS) and total N (TN) varied among different AEZs (Table 2). Soil pH value varied 4.96-7.65, which was strongly acidic to slightly alkaline. The highest pH (7.65) was recorded in soils of the AEZ 13 and the lowest (4.96) in AEZ 27. BARC (2018) ranked soils of AEZ 3, 22, 23, 27, 28 and 29 as strongly acidic, soils of AEZ 1, 19, 25 and 30 as slightly acidic and soils of AEZ 4 and 13 as slightly alkaline.

Soil pH governs most physical, chemical and biological processes in soils which in turn may affect formation of soil aggregates. Among the soil organic matter decomposer microbial communities, bacteria and fungi are dominant and bacteria prefer a neutral soil reaction, while fungi like acidic soils for their functions (Rousk et al., 2009). Bacteria and fungi greatly influence soil aggregate formation. Six et al. (2002) reported several biological processes resulting in the formation of soil biological macro-aggregates which include the release of exudates from both bacteria and fungi which facilitate strong bonding among soil particles and help formation of aggregates. Also, fungal hyphae may encapsulate fine soil particles into aggregates.

Table 2. Soil pH, organic C, C stock and N contents in different AEZs of Bangladesh

AEZ No.Chemical characteristics of soils (mean ± S.E.)

pH OC (%) CS (t/ha) TN (%)

1 5.70±0.28 0.93±0.11 19.75±2.44 0.07±0.02

3 5.44±0.24 0.81±0.10 17.25±2.13 0.07±0.02

4 7.44±0.38 1.22±0.04 25.46±0.76 0.11±0.00

13 7.65±0.14 0.81±0.05 16.73±1.04 0.07±0.02

19 5.82±0.22 1.29±0.45 25.95±8.26 0.11±0.11

22 5.18±0.17 1.01±0.10 21.27±2.04 0.09±0.03

23 5.47±0.26 0.92±0.18 19.60±3.79 0.08±0.03

25 6.29±0.38 0.98±0.05 20.48±1.13 0.08±0.00

27 4.96±0.38 0.88±0.06 19.22±1.41 0.08±0.00

28 5.33±0.04 0.94±0.02 19.54±0.43 0.08±0.02

29 5.22±0.16 1.04±0.10 22.76±2.13 0.09±0.03

30 6.15±0.32 1.05±0.23 22.31±4.86 0.09±0.03

CV % 8.44 43.67 39.35 40.81OC = organic carbon, CS = carbon stock, TN = total nitrogen


Soil OC varied from 0.81-1.29% across AEZs (Table 2) studied. The highest OC was recorded for AEZ 19 and the lowest for AEZ 3 and 13. Soil OC content was found to be very low in all the AEZs. The lower OC content is the result of continuous intensive crop cultivation solely depending on inorganic fertilizers with little or no application of organic fertilizers (Rahman, 2013). Furthermore, high temperature and moisture in the tropical and subtropical countries like Bangladesh stimulate microbial decomposition of inherent and applied crop residues or organic fertilizers. Such conditions result in low OC as well as low N contents in soils. Organic C content in soil is one of the vital components that may largely regulate soil physical, chemical and biological properties and play a tremendous role in soil aggregate formation. Kumar et al. (2013) reported that plants and soil organic matter played a vital role in the formation of soil aggregates where the quantity of OM application and their quality especially C content govern the formation and stabilization of soil aggregates.

Roy et al. (2019) found that high organic C enhanced soil aggregate formation. Organic matter acts as a glue in binding soil particles together and makes soil aggregates stronger. Improvement in soil structure contributes to nutrient retention and as well as erosion control. As the OC content in soils of the selected AEZs of Bangladesh is low, the potential of aggregate formation is also low. Hence, regular application of organic materials whatever the sources is recommended to maintain soil health and productivity.

Four basic cations (Na, K, Ca, Mg) found in the soils of different AEZs under study

are presented in Table 3. The exchangeable Na+, K+, Ca+ and Mg+ contents in soils varied widely (Na+ 0.05-1.54, K+ 0.09-0.29, Ca+ 1.98-21.21 and Mg+ 0.51-2.64 cmol/kg). The highest exchangeable Na+, K+, Ca+ and Mg+ contents were found at AEZ 13, while the lowest amount of exchangeable Na+ and K+ were found in AEZ 1, and Ca+ and Mg+ in AEZ 30. The exchangeable Na+ contents in all the AEZs were moderate, whereas K+ contents were very low. Islam (2008) reported that most of the soils of Bangladesh (75%) contained K below critical level and 17% below the optimum level. The Ca+ contents were found to be optimum to high and Mg+ were found to be at the optimum level in all the AEZs. Research findings revealed that monovalent cations like Na+ and K+ may have a dispersive effect in soils, while divalent cations Ca2+ and Mg2+ show flocculation effects (Rengasamy and Marchuk, 2011).

Aggregate stability in different soil particle fractionsIt was observed that the stability of soil aggregates increased with the decrease soil particle size with some exception (Fig. 1). Higher amounts of aggregates were found in 0.25 mm sized soil samples. The trend of aggregate stability of different sized soil samples under different AEZs followed the order 0.25> 0.50> 1>2mm. Soil aggregates in 0.25 mm sized soil samples varied from 115 to 200 g/kg, with the lowest in AEZ 13 and the highest in AEZ 4. Aggregates stability in 2 mm sized soil sampled varied from 46 to 143 g/kg, with the lowest in AEZ 13 and the highest in AEZ 25. Roy et al. (2019) found that smaller sized soil particles contributed to a higher degree of aggregate stability. Soil


Table 3. Basic cations in soils of different AEZs of Bangladesh

AEZ No.Basic cations in soils (c-mol/kg) (Mean ±S.E.)

Na+ K+ Ca2+ Mg2+

1 0.05±0.01 0.09±0.00 3.07±0.81 1.34±0.06

3 0.09±0.01 0.11±0.02 2.91±0.30 1.15±0.10

4 0.11±0.00 0.13±0.01 6.36±1.20 1.55±0.10

13 1.54±0.16 0.29±0.01 21.21±1.60 2.64±0.20

19 0.75±0.26 0.11±0.01 5.12±0.37 1.48±0.37

22 0.21±0.02 0.16±0.01 3.74±0.57 1.18±0.19

23 0.54±0.12 0.14±0.01 4.87±0.76 1.43±0.35

25 0.20±0.01 0.12±0.02 3.05±0.62 1.00±0.19

27 0.19±0.01 0.10±0.02 2.76±0.11 1.90±0.30

28 0.29±0.02 0.13±0.00 6.50±0.51 1.64±0.07

29 0.21±0.05 0.18±0.04 2.80±0.44 0.91±0.14

30 0.07±0.01 0.22±0.03 1.98±0.52 0.51±0.07

CV % 59.39 44.11 33.24 40.95

Fig. 1. Water stable soil aggregates of different soil particle size fractions in different AEZs of Bangladesh (As = aggregate stability).

0255075

100125150175200225

1 3 4 13 19 22 23 25 27 28 29 30AEZ

AS (2 mm) AS (1 mm)AS (0.5 mm) AS (0.25 mm)

Agg

rega

tes d

offr

emt s

ozed

spo;

(g/k

g)


and crop management practices may affect and breakdown larger aggregates, while the smaller aggregates may exist in high percentage (Simansky, 2013). Aggregate size distribution and its stability can change considerably because of tillage method (Singh et al., 2014; Beare et al., 1994). Lands usage and management (cropping systems) are important factors influencing soil aggregates size distribution and stability (Lebron et al., 2002).

Among the 12 AEZs in the present study, the lowest amount of water stable soil aggregates was found in AEZ 13 i.e. in the Ganges Tidal Floodplain. Because of higher concentration of Na+ in the soils of AEZ 13, soil aggregates dispersed and hence the lower soil aggregate content.

Effects of monovalent and divalent basic cations on soil aggregates stabilityThe monovalent basic cations Na+ and K+ showed negative relation with soil aggregates (Fig. 2). Decreasing the amount of Na+ in soil was found to be increasing soil aggregates. Similar results were also found in case of K+ content and aggregate stability of soils. Since Na+ and K+ disperse clay particles, they hinder aggregation

of soil and negatively affect soil structure. Sodium and K+ can cause clay dispersion and swelling which result in the degradation of soil aggregates (Rengasamy et al., 2016).

The divalent basic cations Ca+ and Mg+ also showed negative relation with soil aggregates (Fig. 3). Data indicated that Ca2+ and Mg2+ increased soil aggregates. Correlation matrices of different ratios of basic cations like SAR, MCAR and CROSS with aggregates also showed negative relations (Table 4.4). Although, divalent cations Ca2+ and Mg2+ are known as flocculating agents (Rengasamy and Marchuk, 2011), they showed dispersion effect in the current study. Contradictions exist in case of Ca2+ and Mg2+ on their capability either as flocculating and / or dispersing agents in soil systems. Curtin et al. (1994) and Keren (1991) reported that Mg2+ enhances dispersion of soil aggregates in many cases; however, the efficacy of Ca+ as a flocculating agent is documented in many cases. The diameter of a hydrated Ca2+ ion is slightly less than that of Mg2+ ion and therefore, Ca2+ adsorbs more strongly on clay surface (Gardner, 2003). Furthermore, higher mobility and abundance of Mg2+ ion in

fig. 2. relationship between na+ and K+ with total soil aggregates (tsA).


soils attributes weaker formation of organo-mineral bonding and thus contributes to less aggregated soils (Gransee and Fuhrs, 2013).

conclusions Soil organic C content was found to have positive and monovalent basic cations have negative effects on aggregate stability of soil, while divalent basic cations played indistinct roles which need to be studied further. Aggregate stability and C stock in the 0.25 mm sized soil fraction were higher than those in larger sized soil fractions.

AcknowledgementsThe study was funded by Research Management Wing (RMW) of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.

referencesAlam, M. A., M. M. Rahman, J. C. Biswas,

S. Akhter, M. Maniruzzaman, A. K. Choudhury, A. B. M. S Jahan, M. M. U. Miah, R. Sen, M. Z. U. Kamal, M. A. Mannan, M. H. K. Shiragi, W. Kabir and N. Kalra. 2019. Nitrogen transformation and carbon sequestration in wetland paddy

field of Bangladesh. Paddy Water Environ. 17(4): 677-688.

BARC. 2018. Fertilizer Recommendation Guide. Bangladesh Agricultural Research Council, Farmgate, Dhaka 1215, Bangladesh.

Beare, M. H., P. F. Hendrix and D. C. Coleman. 1994. Water-stable aggregates and organic matter fractions in conventional-and no-tillage soils. Soil Sci. Soc. Am. J. 58(3): 777-786.

Castellanos-Navarrete, A., A. Chocobar, R. A. Cox, S. Fonteyne, B. Govaerts, N. Jespers and N. Verhulst. 2013. Soil aggregate stability by wet sieving: A practical guide for comparing crop management practices. International Maize and Wheat Improvement Center, Spain, 1-4.

Curtin, D., H. Steppuhn and F. Selles. 1994. Clay dispersion in relation to sodicity, electrolyte concentration, and mechanical effects. Soil Sci. Soc. Am. J. 58(3): 955-962.

Duiker, S. W., F. E. Rhoton, J. Torrent, N. E. Smeck and R. Lal. 2003. Iron (hydro) oxide crystallinity effects on soil aggregation. Soil Sci. Soc. Am. J. 67(2): 606-611.

Gardner, R. C. 2003. Genes for magnesium transport. Curr. Opin. Plant Biol. 6: 263-267.

Gransee, A. and H. Fuhrs. 2013. Magnesium mobility in soils as a challenge for soil and plant analysis, magnesium fertilization and root uptake under adverse growth conditions. Plant Soil. 368: 5-21.

fig. 3. relationship between ca2+ and Mg2+with total soil aggregates (tsA).


Gee, G. W., and J. W. Baunder. 1986. Particle-size analysis 1. Methods of Soil Analysis: Part 1-Physical and mineralogical methods, 383-411. American Society of Agronomy, Agronomy Monographs 9(1), Madison, Wisconsin.

Gomez, K. A. and A. A. Gomez. 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons.

Islam, M. S. 2008. Soil fertility history, present status and future scenario in Bangladesh. Bangladesh J. Agric. Environ. 4: 129-151.

Jackson, M. L. 1973. Estimation of phosphorus content. Soil Chemical Analysis, Printer Hall, New Delhi (India).

Keren, R. 1991. Specific effect of magnesium on soil erosion and water infiltration. Soil Sci. Soc. Am. J. 55(3): 783-787.

Kumar, R., K. S. Rawat, J. Singh, A. Singh and A. Rai. 2013. Soil aggregation dynamics and carbon sequestration. J. Appl. Nat. Sci. 5(1): 250-267.

Lebron, I., D. L. Suarez and T. Yoshida. 2002. Gypsum effect on the aggregate size and geometry of three sodic soils under reclamation. Soil Sci. Soc. Am. J. 66(1): 92-98.

Paustian, K. A. O. J. H., O. Andren, H. H. Janzen, R. Lal, P. Smith, G. Tian and P. L. Woomer. 1997. Agricultural soils as a sink to mitigate CO2 emissions. Soil Use Manage. 13: 230-244.

Putte, A. van den, G., Goversa, J. K. DielsaGillijns and M. Demuzerea. 2010. Assessing the effect of soil tillage on crop growth: A meta-regression analysis on European crop yields under conservation agriculture. Eur. J. Agron. 33: 231-241.

Rahman, M. M. 2013. Nutrient-use and carbon sequestration efficiencies in soils from different organic wastes in rice and tomato cultivation. Commun. Soil Sci. Plant Anal. 44(9): 1457-1471.

Rahman, F., M. M. Rahman, G. K. M. Rahman, M. A., Saleque, A. S., Hossain and M. G. Miah. 2016. Effect of organic and inorganic fertilizers and rice straw on carbon sequestration and soil fertility under a rice–rice cropping pattern. Carbon Manage. 7(1-2): 41-53.

Roy, S., M. M. Rahman, G. K. M. M. Rahman, M. G. Miah and M. Z. U. Kamal, 2019.

Structural stability under different organic fertilizers management in paddy soil. Ann. Bangladesh Agric. 23(1): 15-24.

Rengasamy, P. and A. Marchuk. 2011. Cation ratio of soil structural stability. Soil Res. 49: 280-285.

Rengasamy, P., E. Tavakkoli and G. K. McDonald. 2016. Exchangeable cations and clay dispersion: net dispersive charge, a new concept for dispersive soil. Eur. J. Soil Sci. 67(5): 659-665.

Rousk, J., P. C. Brookes and E. Baath. 2009. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl. Environ. Microbiol. 75(6): 1589-1596.

Rowell, D. L. 1994. Chapter 14. The preparation of saturation extracts and the analysis of soil salinity and sodicity. Soil science: methods and applications. Ed. Rowell, DL, 277-302.

Schimel, J. P. and S. M. Schaeffer. 2012. Microbial control over carbon cycling in soil. Front. Microbiol. 3: 1–11.

Simansky, V. 2013. Soil organic matter in water-stable aggregates under different soil management practices in a productive vineyard. Arch. Agron. Soil Sci. 59(9): 1207-1214.

Singh, R. C., L. Sangeeta and C. D. Singh. 2014. Conservation tillage and manure effect on soil aggregation, yield and energy requirement for wheat (Triticum aestivum) in vertisols. Indian J. Agricul. Sci. 84(2): 267-271.

Six, J., E. T. Elliott and K. Paustian. 2000. Soil structure and soil organic matter II. A normalized stability index and the effect of mineralogy. Soil Sci. Soc. Am. J. 64(3): 1042-1049.

Six, J., R. T. Conant, E. A. Paul and K. Paustian. 2002. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil. 241(2): 155-176.

Thomas, G. W. 1982. Exchangeable cations. Methods of soil analysis. Part 2. Chemical and microbiological properties, (methodsofsoilan2), 159-165.

Walkley, A. and C. A. Black. 1934. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37(1): 29-38.

MORPHOLOGICAL CHARACTERIZATION AND EVALUATION OF NINETEEN GLADIOLUS GERMPLASM

M. A. Hoque1* and S. Mahmud1

Abstract

Nineteen gladiolus germplasms were characterized and evaluated in the Research Field of the Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, during November 2017 to May 2018 for identifying suitable line(s) to release as a variety for commercial cultivation. The accession G8 produced the highest number of shoots (3.3) and effective shoots (3.0) per hill. The accessions that produced flower stalk within 75 days of planting included BARI gladiolus- 3 (G1, 58.7 days), G2 (62.3 days), G3 (65.7 days), BARI gladiolus- 5 (G4, 61.3 days), G5 (55.7 days), BARI gladiolus- 6 (G7, 71.3 days), G10 (74.0 days), G14 (73.3 days), G15 (75.0 days) and G19

(67.3 days). The highest rachis length was recorded in BARI gladiolus- 3 (53.7 cm) which was statistically similar with the rachis length of G6 (46.0 cm), G10 (46.3 cm), G11 (47.0 cm) and BARI gladiolus- 1 (G12, 46.3 cm) but significantly differed with other accessions. Most of the accessions in general, produced more than 10 florets per spike. Vase life of the accessions varied and G11 had the highest vase life of 9-11 days and this was close to 9-10 days in G9 and 8-9 days in BARI gladiolus- 3 (G1), G8, G10, G17, G18 and G19. The highest number of corm per hill was recorded in G8 (10.3) followed by G5 (8.7), G16 (8.3), G17 (7.7) and BARI gladiolus- 3 (6.7). Number of cormels per hill ranged from 9.0-941.7 with an average of 237.0. Based on various plant, flower colour, corm and cormel production characters, the gladiolus accessions G3, G5, G8, G9, G10, G11, G14, G16, G17, G18 and G19 may be considered for further study.

Keywords: Gladiolus, cormel, commercial cultivation, variety development.

1Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


IntroductionOnce, only the well-off bought flowers to colour festivals. Now, even the low to mid income groups love to present flowers on beautiful moments. So, the present position of floriculture in our country is more or less uprising. About 10,000 hectares of land is now devoted to flower cultivation in Bangladesh (Rakibuzzaman et al., 2018). As days go, demand for flower is increasing very rapidly. In valentines’ day of 2009, flower of TK 2 crore were sold in Dhaka in 2 days (Khan, 2013).

In Bangladesh, commercial floriculture is expanding very rapidly. Today, floriculture has emerged as a lucrative profession in Bangladesh with a much higher potential for returns than most other fields and horticultural crops (Sultana, 2003). Bangladesh is well suited for cut flower and ornamental production due to the favorable climatic and other conditions like cheap land, low labour cost, relatively low capital investment and high value addition (Dadlani, 2004). Bangladesh has very good potentialities to become an important supplier of flower and ornamental

38 Morphological Characterization and Evaluation of Nineteen Gladiolus Germplasm

plants for Asia, the Middle East and Europe (Momin, 2006).

Gladiolus (Gladiolus sp) belongs to the family Iridaceae is an important cut flower grown worldwide including Bangladesh. It is sold at almost every corner of city areas by the retailers. In our country, its demand is increasing because of its elegant spike, rich varied colours and long vase life. Now-a-days, the farmers are commercially cultivating this crop in Bangladesh (Islam and Haque, 2011). Gladiolus coming into play in recent times occupied the top position with a percent market share of 31.11 (Rakibuzzaman et al., 2018). Presently, the Floriculture Division of BARI is conducting research on gladiolus along with other flower crops. They have already released 6 (six) varieties of gladiolus which is not sufficient and more varieties need to be released. By this time, 280 numbers of flower and ornamental germplasm including gladiolus are being collected from different sources (Ara et al., 2010); which is not sufficient. So, to enrich the genetic resources of this crop, more germplasm from home and abroad are needed to be collected for further evaluation with a view to improvement and development of the crops. Some progressive farmers, nurserymen and private entrepreneurs have already been collected different gladiolus germplasm from abroad and other sources. Those genotypes need to be collected for evaluation and conservation properly to develop and enrich germplasm pool. Selection of better plant type from the collected germplasm can be of immense value for further improvement of this crop. It is, therefore considered indispensable to collect, characterize, evaluate and finally, to select the promising one(s) among the

selected materials. Hence, the present study on gladiolus was undertaken.

Materials and MethodsThe experiment was conducted at the research field of the Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur from November 2017 to May 2018. Nineteen gladiolus germplasm were previously collected from farmer’s field and nurseries of Godkhali, Benapole, Jessore, Bogra, Savar areas through direct visit and were included in the experiment. Cut flower stick of all collected germplasm are presented in Plate 1 and 2. The germplasm were numbered as G1, G2, G3, G4, G5, G6, G7, G8, G9, G10, G11, G12, G13, G14, G15, G16, G17, G18 and G19. Out of these germplasm, G1, G4, G7 and G12 has already been released by the Bangladesh Agricultural Research Institute as BARI gladiolus- 3, BARI gladiolus- 5, BARI gladiolus- 6 and BARI gladiolus- 1, respectively. The unit plot size was 1.2 m X 1.2 m. Medium sized (3.5-4.5 cm) corms of different gladiolus germplasm were planted at about 6-9 cm depth in the plot maintaining a spacing of 30 cm X 15 cm. The experiment was set up on November 12, 2017 following Randomized Complete Block Design (RCBD) with 3 replications. Manures and fertilizers were applied at the rate of cowdung- 10 t/ha, Urea- 300 kg/ha, TSP- 375 kg/ha, MoP- 300 kg/ha, Boric acid- 12.0 kg/ha and Zinc sulphate- 8.0 kg/ha (Azad, 2017). Entire quantity of manures and fertilizers except urea were applied during final land preparation and mixed with soil. Half of urea was top dressed after 25 days of planting and rest half was applied during spike initiation stage. Different intercultural operations like irrigation,

M. A. Hoque and S. Mahmud 39

weeding, earthing up, stacking, pesticide and fungicide application were performed as needed. The spikes were cut when lower one or two florets showed color but still in tight bud stage. The cut spikes were kept into water to study the vase life. Corms and cormels were harvested only when the leaves turned into brown colour (Mukhopadhyay, 1995). The collected data were statistically analyzed using computer MSTAT-C program. Mean separation was done by Duncan’s Multiple Range Test (DMRT).

Results and DiscussionPlant characteristics of 19 germplasm have been presented in Table 1. Results revealed that base colour of the germplasm were either green or tan (pale brown) or light tan or light pink. Number of leaf was found to be varied significantly. The highest number of leaves per plant was recorded in G15 (11.7) which was statistically similar to G3 (11.3). The lowest number of leaves per plant (8.0) was recorded in G5, G8, G10 and G13. This result was in agreement with the findings of Hossain et al. (2011), who found it ranged from 8.50-12.25. Number of shoot per hill varied significantly, which ranged from 1.0-3.3 with an average of 2.1. Likewise, significant variation was found in number of effective shoot per hill which varied from 1.0-3.0 with an average of 1.5. The accession G8 produced the highest number of shoot (3.3) and effective shoots (3.0) per hill. It indicated that all the shoots of gladiolus did not effectively produce inflorescence. Variation was observed as to the days to flower spike initiation. The accession G16 took the highest time (81.3 days) for spike initiation; whereas, G5 took the lowest (55.7 days). Days to spike initiation is very

important as it determines the earliness or lateness of the flower crop. The accessions that produced flower stalk within 75 days of planting included BARI Gladiolus- 3 (58.7 days), G2 (62.3 days), G3 (65.7 days), BARI Gladiolus- 5 (61.3 days), G5 (55.7 days), BARI Gladiolus- 6 ( 71.3 days), G10 (74.0 days), G14 ( 73.3 days), G15 (75.0 days) and G19 (67.3 days). Tirkey et al. (2018) reported to have spikes of gladiolus after 53.4-67.0 days of planting. Variation in days to spike initiation seem to be genetically controlled as reported by Pragya et al. (2010) in gladiolus. Regarding diseases, leaf blight was found to infect in BARI Gladiolus- 3 (G1), G6, G13 and G15; whereas, cut worm infestation was found in BARI Gladiolus- 3 (G1), G2, BARI Gladiolus- 5 (G4) and G15 (Table 1).

Plant height in gladiolus is important as it determines lodging of the crop. Longer plant tends to be lodged and needs to be staked. Plant height in different accessions found to be varied (Fig. 1). The longest plant was observed in the accession BARI gladiolus- 5

(92.3 cm), which was statistically different from all other accessions. The accession G2 had a plant height of 76.3 cm and it was similar to the plant height of BARI gladiolus- 3 (65.0 cm), G3 (74.3 cm) and G16 (72.7 cm) but significantly differed from rest of the accessions. The shortest plant (42.0 cm) was observed in G13. The accessions G5 (51.7 cm), G6 (54.0 cm), BARI gladiolus- 6 (60.0 cm), G8 (53.7 cm), G9 (55.3 cm), G10 (59.0 cm), G11 (54.3 cm), BARI gladiolus- 1 (52.7 cm), G14 (59.7 cm), G17 (59.3 cm), G18 (58.7 cm) and G19 (56.7 cm) produced medium sized plants, where the plant height ranged from 50-60 cm (Fig. 1). Hossain et al. (2011) recorded more or less similar range of 46.52-58.65 cm plant


height in the studied genotypes of gladiolus; whereas, Singh et al. (2017) recorded a range from 80.3-134.7 cm plant height with a mean of 112.0 cm in ten hybrids of gladiolus. As the genotypes of present study has been collected from farmers field and they are hopefully open pollinated; so, the variation in plant height of different gladiolus genotypes might be due to the genetic difference as well as growing environment and management practices.

The spike length, rachis length, number of side spike and number of floret per spike varied significantly among the germplasm. The longest spike was found in BARI gladiolus- 3 (72.0 cm) which was statistically similar to G10 (69.7 cm) but differed from rest of the accessions. In majority of the accessions, spike length was recorded as more than 50.0 cm. Rachis length differed from 19.0 cm to 53.7 cm with an average of 36.4

Table 1. Plant characteristics of nineteen gladiolus germplasm

Accession Base colour of plant

Number of leaves /

PlantNumber of shoot/hill

Number of effective shoot/hill

Days to 1st spike initiation

Disease Insect

G1 Green 10.0 cd 1.0 d 1.0 d 58.7 hi LB Cut wormG2 Tan 10.0 cd 1.3 cd 1.0 d 62.3 fgh None Cut wormG3 Tan 11.3 ab 1.3 cd 1.0 d 65.7 fg None NoneG4 Light tan 9.3 cde 1.7 bcd 1.7 bcd 61.3 gh None Cut wormG5 Light pink 8.0 f 3.0 ab 1.7 bcd 55.7 i None NoneG6 Tan 10.0 cd 3.0 ab 1.7 bcd 78.3 abc LB NoneG7 Tan 8.3 ef 2.3 a-d 1.7 bcd 71.3 de None NoneG8 Light tan 8.0 f 3.3 a 3.0 a 76.0 a-d None NoneG9 Tan 8.7 ef 2.0 a-d 1.3 cd 78.0 abc None NoneG10 Light tan 8.0 f 2.3 a-d 1.0 d 74.0 bcd None NoneG11 Green 8.3 ef 1.3 cd 1.0 d 78.7 abc None NoneG12 Green 10.3 bc 1.0 d 1.0 d 80.0 a None NoneG13 Tan 8.0 f 2.7 abc 2.3 abc 78.0 abc LB NoneG14 Green 10.0 cd 1.0 d 1.0 d 73.3 cd None NoneG15 Tan 11.7 a 2.3 a-d 1.0 d 75.0 b-d LB Cut wormG16 Light tan 9.0 def 3.3 a 1.7 bcd 81.3 a None NoneG17 Light tan 9.0 def 3.3 a 2.7 ab 78.0 abc None NoneG18 Tan 10.3 bc 1.3 cd 1.0 d 79.3 ab None NoneG19 Light tan 8.7 ef 2.3 a-d 1.3 cd 67.3 ef None NoneMean

CV (%)

-

-

9.3

7.06

2.1

17.43

1.5

13.48

72.3

4.20

-

-

-

-Tan= Pale brown and LB= Leaf blight

G1= BARI gladiolus- 3, G4= BARI gladiolus- 5, G7= BARI gladiolus- 6 and G12= BARI gladiolus- 1.



Fig. 1. Plant height (cm) at spike initiation stage influenced by gladiolus germplasm.

100.0

80.0

60.0

40.0

20.0

0.0

Plan

t hei

ght (

cm)

Accession

cm. The highest rachis length was recorded in BARI gladiolus- 3 (53.7 cm) which was statistically similar to the rachis length of G6 (46.0 cm), G10 (46.3 cm), G11 (47.0 cm) and BARI gladiolus- 1 (46.3 cm) but significantly differed from other accessions. Tirkey et al. (2018) recorded a variable rachis length in six gladiolus genotypes that ranged from 37.3 cm to 62.7 cm. The variation in different characters among varieties might be due to variation of genetic traits and the effect of prevailing environmental conditions (Kumar, 2015). Regarding number of side spike, only 5 accessions produced side spikes and majority of the accessions had no side spikes.

Significant variation was found regarding number of floret per spike, which ranged from 7.0-15.7. The highest number of florets per spike was recorded in G11 (15.7) followed by G10 (14.8), BARI gladiolus- 3 (14.0) and G2 (13.7). The accession G13 (7.0) had the

lowest number of florets per spike (Fig. 2). In general, most of the accession produced more than 10 florets per spike. Hossain et al. (2011) reported a range of 8.4-14.3 florets per spike, while studied with five different genotypes of gladiolus. Whereas, Tirkey et al. (2018) recorded to have 11.2- 15.0 florets per spike, while working with six genotypes of gladiolus and Rashmi (2006) obtained 14.1- 16.7 florets per spike while working with 11 genotypes of gladiolus.

Variable flower characters were observed and recorded in the studied accessions (Table 3). Nineteen accessions had different floret colour and marking (Plate 1 and Plate 2). Although, BARI gladiolus- 3 (G1) and G6 produced white coloured floret but they differed regarding floret marking. The florets of BARI gladiolus- 3 (G1) had pink scar inside; while, the florets of G6 had red butterfly scar inside. The accession BARI gladiolus- 5 (G4) and


G8 had also produced similar colour (yellow) of florets but they also differed in floret marking. The floret colour of the accessions G3, G5, G8, G9, G10, G11, G14, G16, G17, G18 and G19 was very attractive. Floret breadth and length in the studied accessions also varied. The highest breadth (12.0 cm) and length (12.2 cm) were recorded in G15, indicated the largest flower size. The accession G11 produced the smallest sized florets among

the accessions with a breadth and length of 6.5 cm and 6.2cm, respectively (Table 3). Bhat et al. (2017) recorded a breadth range of 6.0-12.0 cm in fifty studied genotypes of gladiolus, which supported the present study. Vase life in the accessions varied and G11 had the highest vase life (9-11 days) followed by G9 (9-10 days), BARI gladiolus- 3 (8-9 days), G8 (8-9 days), G10 (8-9 days), G17 (8-9 days), G18 (8-9 days) and G19 (8-9 days).

Table 2. Flowering characteristics of nineteen gladiolus germplasmAccession Spike length (cm) Rachis length (cm) Number of side spikeG1 72.0 a 53.7 a 1.0 b G2 55.3 cd 39.0 b-e 0.0 cG3 66.0 b 43.0 bc 0.0 cG4 61.7 bc 41.7 bc 0.0 cG5 53.7 cd 40.7 bcd 1.0 bG6 53.3 cd 46.0 ab 0.0 cG7 42.3 ef 36.7 c-f 0.0 cG8 40.6 ef 27.8 g 0.0 cG9 51.0 d 38.7 b-e 0.0 cG10 69.7 ab 46.3 ab 2.0 bG11 57.0 cd 47.0 ab 0.0 cG12 61.7 bc 46.3 ab 0.7 bcG13 37.7 f 25.3 gh 0.7 bcG14 54.3 cd 31.0 efg 0.0 cG15 41.0 ef 28.3 fg 0.0 cG16 47.7 de 29.0 fg 0.0 cG17 52.7 cd 33.0 d-g 0.0 cG18 38.7 ef 19.3 h 0.0 cG19 38.7 ef 19.0 h 0.0 cMean

CV (%)

52.4

9.40

36.4

12.61

0.3

15.80G1= BARI gladiolus- 3, G4= BARI gladiolus- 5, G7= BARI gladiolus- 6 and G12= BARI gladiolus- 1.



Fig. 2. Number of florets/spike in different gladiolus germplasm.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19Accession

Num

ber o

f flo

rets

/spi

ke

abc abcc-f c-f

ef fg

cdebcd bcd

h

fg

gh gh

fg

c-fdef

aba

b-e

Corms and cormels characteristics of nineteen gladiolus accessions are presented in Table 4. The highest number of corms was recorded in G8 (10.3), which differed statistically from other accessions. The accessions G9, G11 and G14 produced the lowest number of corms per hill (1.3). Number of corms per hill is very important in gladiolus as the corms are the sources of seed for the following years. Weight of individual corm was also found to be varied significantly among the accessions. The highest corm weight was

obtained from in BARI gladiolus- 5 (75.7 g) and the lowest in G18 (12.3 g). Likewise, the diameter of large corm was observed the highest (6.6 cm) in BARI gladiolus- 5 (G4) and the lowest (3.6 cm) in G10 and G18 (Table 4). Number of cormels per hill was also varied widely among the accessions and ranged from 9.0-941.7 with an average of 237.0. Individual weight of cormels in the accessions was also varied and ranged from 0.1 -1.1 g. The accession G6 produced the largest cormel (Table 4).


Plate 1. Flower spikes of different gladiolus germplasm (G1-G10).


Plate 2. Flower spikes of different gladiolus germplasm (G11-G19).


Table 3. Floret characteristics of nineteen gladiolus germplasm

Accession Colour of florets Floret marking Floret length (cm)

Floretbreadth (cm)

Vase life (days)

G1 White Pink scar inside 11.3 ab 11.7 a 8-9

G2 Light red White scar inside 10.7 bc 10.0 b 6-7

G3 Deep red White scar inside 10.7 bc 10.1 b 7-8

G4 Yellow None 10.8 bc 10.1 b 6-8

G5 Light pink Deep red butterfly scar inside 10.2 c 10.0 b 7-9

G6 White Red butterfly scar inside 8.3 efg 8.2 d 5-7

G7 Violet White scar inside 7.8 fg 8.3 d 7-8

G8 Yellow Orange scar on side of petal 7.5 gh 6.8 gh 8-9

G9 Tan Velvety, white scar inside 8.5 ef 8.0 de 9-10

G10 Light violet White scar inside 8.0 fg 7.3 efg 8-9

G11 Light yellow Deep red butterfly scar inside 6.2 i 6.5 h 9-11

G12 Brick Red Yellow butterfly scar inside 8.5 ef 8.0 de 7-8

G13 Light orange None 6.8 hi 7.2 fgh 6-7

G14 Cream Yellow scar inside 8.7 ef 8.5 cd 7-8

G15 Red White scar inside 12.2 a 12.0 a 6-7

G16Light yellow (Biscuit color) None 9.9 cd 9.2 c 7-8

G17 Light pink Inside white with red butterfly scar 6.8 hi 6.8 gh 8-9

G18 Pink White scar inside 9.1 de 8.5 cd 8-9

G19 Orange White scar inside 8.0 fg 7.8 def 8-9

Mean

CV (%)

-

-

-

-

9.0

5.88

8.7

5.03

-

-G1= BARI gladiolus- 3, G4= BARI gladiolus- 5, G7= BARI gladiolus- 6 and G12= BARI gladiolus- 1.


Table 4. Corms and cormels characteristics of nineteen gladiolus germplasm

Accession Number of corms/hill

Weight of individual corm (g)

Diameter of the largest corm (cm)

Number of cormels/hill

Weight of cormel/hill (g)

Weight of individual cormel (g)

G1 6.7 cd 46.5 cd 5.8 bc 941.7 a 266.9 a 0.3G2 5.7 de 20.4 ij 4.5 f-i 451.3 b 117.2 bc 0.3G3 5.3 def 55.0 b 6.2 ab 213.3 cde 90.5 cd 0.4G4 5.3 def 75.7 a 6.6 a 146.7 d-g 85.2 d 0.6G5 8.7 b 34.8 ef 5.3 cde 398.3 b 131.5 b 0.3G6 3.3 hi 24.9 ghi 3.9 ij 9.0 g 9.5 g 1.1G7 3.7 ghi 20.3 ij 4.1 g-j 38.7 fg 17.4 g 0.4G8 10.3 a 40.5 de 5.1 def 316.7 bc 94.9 cd 0.3G9 1.3 j 20.1 ij 4.7 efg 43.7 fg 22.0 fg 0.5G10 4.3 e-h 12.4 k 3.6 j 337.7 bc 51.7 e 0.2G11 5.0 efg 22.0 hi 4.6 fgh 70.7 efg 16.3 g 0.2G12 2.3 ij 14.1 jk 3.9 j 38.7 fg 86.5 d 2.2G13 5.7 de 28.5 fgh 5.1 def 418.3 b 109.5 bcd 0.3G14 1.3 j 22.3 hi 4.0 hij 27.0 fg 8.1 g 0.3G15 1.3 j 21.5 hi 4.7 fg 311.3 bc 116.8 bc 0.4G16 8.3 b 47.6 c 5.8 bcd 229.0 cd 57.5 e 0.3G17 7.7 bc 31.1 fg 4.9 ef 185.0 c-f 48.2 ef 0.3G18 3.3 hi 12.3 k 3.6 j 226.3 cd 33.8 efg 0.1G19 4.0 fgh 38.9 e 5.6 bcd 100.0 d-g 18.8 g 0.2Mean

CV (%)

4.9

15.53

31.0

12.25

4.9

7.35

237.0

14.96

72.8

20.83

0.3

-G1= BARI gladiolus- 3, G4= BARI gladiolus- 5, G7= BARI gladiolus- 6 and G12= BARI gladiolus- 1.

ConclusionsThe collected gladiolus germplasm varied in different characters with released varieties. Based on flower colour and other characters, the gladiolus accessions G3, G5, G8, G9, G10, G11, G14, G16, G17, G18 and G19 may be considered for further study.

AcknowledgementsThe authors acknowledge the Research Management wing (RMW) of BSMRAU to offer fund for conducting the experiment

and successful implementation of the project activities.

ReferencesAra, K. A., S. M. Sharifuzzaman and M. Rafiuddin.

2010. Collection and evaluation of chrysanthemum genotypes. Annual Research Report on Flower and Ornamentals, HRC, BARI. Pp. 11-15.

Azad, A. K , B. K. Goshami, M. L. Rahman, P. K. Malaker, M. S. Hasan and M. H. H. Rahman. 2017. Krishi Projukti Hatboi (Handbook on Agro-technology), 7th Edition, Bangladesh


Agricultural Research Institute, Gazipur 1701, Bangladesh. Pp. 321-324.

Bhat, Z. A., I. T. Nazki, Nelofar and B. Hamid. 2017. Evaluation of gladiolus cultivars for growth, flowering, spike yield and corm yield under temperate regions of Kashmir. Indian Hort. J. 7(3/4): 203-207.

Dadlani, N. K. 2004. Prospects of floriculture in Bangladesh. A Consultancy Report. FAO/UNDP (IHNDP/BGD/97/04).

Hossain, M. D., K. H. Talukder, M. Asaduzzaman, F. Mahmud, N. Amin and M. A. Sayed. 2011. Study on morphological characteristics of different genotypes of gladiolus flower. J. Sci. Foundation. 9(1 & 2): 01-08.

Islam, M. S. and A. F. M. E. Haque. 2011. Performance of gladiolus under protected cultivation in the rainy season. Bangladesh J. Agril. Res. 36(2): 285-290.

Khan, R. A. 2013. Flower market development in Bangladesh. Paper presented in the national seminar on floriculture development in Bangladesh held on 18 May, 2013 at the BARC Conference Room-1, Farmgate, Dhaka.

Kumar, M. 2015. Morphological characterization of gladiolus (Gladiolus hybridus Hort.) germplasm. J. Plant Dev. Sci. 7(4) : 359-362.

Momin, M. A. 2006. Floriculture survey in Bangladesh. A Consultancy Report. FAO/ UNDP (IHNDP/ BGD/ 97/06).

Mukhopadhyay, A. 1995. Gladiolus. Publications and information division, ICAR, New Delhi. India. 35 P.

Pragya, J. K. Ranjan, B. L. Attri, B. Das, H. Krishna and N. Ahmed. 2010. Performance of gladiolus genotypes for cut flower and corm production under high altitude of Uttarakhand. Indian J. Hort. 67: 389-90.

Rakibuzzaman, M., S. Rahul, M. R. Jahan, M. I. Ifaz and A. F. M. J. Uddin. 2018. Flower industry in Bangladesh: Exploring floriculture potential. Int. J. Bus. Soc. Sci. Res. 7(1): 50-56. [Cited from http://www.ijbssr.com/currentissueview/14013304]

Rashmi, L. 2006. Evaluation of promising hybrids of gladiolus. An unpublished MS thesis. Department of horticulture, college of agriculture, Dharwad university of agricultural sciences, Dharwad, Karnataka, India.

Singh, N., S. Tamta, A. K. Pal, T. S. Rana, R. K. Roy and S. K. Tewari. 2017. Characterization of gladiolus germplasm using morphological, physiological and molecular markers. Biochem. Genet. Springer Science. [https://doi.org/10.1007/s 10528017-9835-4].

Sultana, N. 2003. Floriculture exports from Bangladesh. A paper presented in international floriculture conference on 6th November, 2003, BARC, Farmgate, Dhaka.

Tirkey, T., S. Tamrakar, G. Sharma and M. Sahu. 2018. Effect of planting dates and cultivars on floral characters of gladiolus (Gladiolus grandiflorus) under Chhattisgarh Plains. Int. J. Curr. Microbiol. App. Sci. 7(06): 1964-1976 [doi: https://doi.org/10.20546/ijcmas.2018.706.233].

J. Hosen, M. M. Rahman, J. Alam, Z. C. Das, M. A. H. N. A. Khan, M. G. Haider 49

PATHOLOGY OF FOWL TYPHOID AND MOLECULAR DETECTION OF ITS PATHOGEN

J. Hosen1, M. M. Rahman1, J. Alam1, Z. C. Das2, M. A. H. N. A. Khan3 and M. G. Haider1*

Abstract

Salmonellae are important group of pathogens responsible for human and animal diseases. The present study was undertaken with the aim to study pathology of fowl typhoid caused by Salmonella entarica subsp. enteric serovar Gallinarum and to identify Salmonella serovars by polymerase chain reaction (PCR) based molecular method isolated from commercial layer, broiler and sonali chickens of Gazipur district, Bangladesh. A total of 150 cloacal, intestinal and liver swab samples were collected in sterie nutrient and tetrathionate broth from apparently healthy, sick and dead chicken the necropsy. Organ samples were collected in 10% buffered neutral formalin. The collected tissues were fixed, processed, sectioned, stained with hematoxylin and eosin (H&E) and examined at low and high power microscopic fields. Grossly, the liver appeared larger and hemorrhagic with focal necrosis. Catarrhal inflammation on intestinal mucosa was seen. The ova were deformed, discolored and cystic. Microscopically, focal necroses with the infiltration of mononuclear cells were seen with congestion of the central vein. Spleen showed severe depletion of lymphoid cells in white pulp along with reticuloendothelial cell hyperplasia. The section of ovary showed deformed ova with hemorrhages. Samples were subjected to various cultural, biochemical, and molecular examinations and the prevalence was identified 28% cases. Isolated bacteria appeared gram (-)ve, and arranged in short chain. PCR was performed targeting invA gene of Salmonella Gallinarum and amplified 184-bp fragment of the isolates confirmed specific infectivity.

Keywords: Fowl typhoid, chickens, Salmonella, pathology, molecular detection.

1Department of Pathobiology, 2Department of Gynecology, Obstetrics & Reproductive Health Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 3Department of Pathology, Bangladesh Agricultural University, Mymensingh, Bangladesh. *Corresponding author: [email protected]


Introduction

Poultry is an important component in livestock sector in Bangladesh. In many countries poultry is the principal source of economics and high quality human foods. However, it is a highly sensitive and risk oriented venture. It plays a significant role in poverty alleviation and economic development of Bangladesh. Approximately 70.4% of total animal protein supplied in the country is contributed by

poultry meat (Rahman et al., 2017). To satisfy market demands for poultry meat and eggs by the mostly urban, municipal and rural populations, commercial layer farming with high yielding strains of chickens has expanded rapidly in different areas in Bangladesh. However due to this disease, the producers are facing numerous problems in farm operations and management (Arbelot et al., 1997).

50 Pathology of fowl typhoid and molecular detection of its pathogen

Poultry is essential not only for the national economy of Bangladesh but also the welfare of human beings. Several constraints such as the diseases, poor husbandry, low productivity and high price of feed affect the optimal performance of this industry in Bangladesh (Haque et al., 1991; Rashid et al.,2013).

Fowl typhoid (FT) is an acute infectious enteritis (Okwori et al., 2007) causing heavy mortality in growers or adult chickens although chicks can be affected (Jordan and Pattison, 1992). It is caused by the bacterium Salmonella enteric serovars Gallinarum (Jordan and Pattison, 1992 and Aiello, 1998), a member of the family enterobacteriaceae which is widely distributed throughout the world (Roa, 2000). Salmonella enteric serovars Gallinarium is highly adapted and seldom causes significant problems in hosts other than chickens, turkeys and pheasants (Jordan and Pattison, 1992; Aiello, 1998; Mdegela et al., 2000; Okwori et al., 2007).

The disease occurs sporadically or enzootically in most countries in the world including Bangladesh. FT losses often begin at hatching time and losses continue to laying age (Shivaprasad, 1997). Salmonella. Gallinarum is very important in poultry health because they are responsible for massive destruction of poultry (Gast, 1997). FT seriously threatened the poultry industry in the early 1900s due to widespread outbreaks accompanied by high mortality (Shivaprasad, 2000). Fowl typhoid is one of the major constraints of poultry industry in Bangladesh (Das et al., 2005). The disease is considered as OIE, list B disease (Calnek et al., 1997). Considering the above facts, the present study was designed to study

the clinical signs, gross and histopathological lesions of fowl typhoid along with isolation and identification of Salmonella organism from commercial chickens by biochemical and PCR-based methods.

Materials and MethodsIn this study a total of 50 cloacal swab samples from commercial broiler/layer chickens, 50 liver swab samples and 50 intestinal samples from apparently sick or dead chickens were subjected to bacteriological isolation, identification, histopathological study and molecular diagnosis of Salmonella Gallinarum. The study was performed at the Department of Pathobiology at Bangabadhu Sheikh Mujibur Rahman Agricultural University (BSMRAU) and National Institute of Biotechnology (NIB), Savar, Dhaka.

Collection of samplesDead and diseased chickens suspected to bacterial infection were collected from different commercial layer and broiler farms of Gazipur district. Samples were collected from the same flock along with the necropsy study. Using septic cotton swabs, all the cloacal samples were collected in test tubes containing10 ml tetrathionate broth (TTB) according to methods described elsewhere (Haider et al., 2008).

Cultural and biochemical tests

All the bacteriological samples were incubated for 24 hours in TTB. Then, all the samples were primarily cultured in Nutrient agar and then subcultured on the Salmonella-Shigella (SS) agar, Triple sugar iron (TSI) agar, Brilliant green agar (BGA), Eosine


methylene blue (EMB) agar (Haider et al., 2008). The presumptive colonies of different bacteria in various media were characterized microscopically using Gram’s stain. Five basic sugars such as glucose, sucrose, lactose, mannitol and maltose were used for sugar fermentation test (Haider et al., 2008). Motility test were performed forth separation of motile and non-motile bacteria according to the method described elsewhere (Haider et al., 2008).

Histological study of tissue samplesTissue samples collected from intestine, liver, lungs, heart, ovary and different other organs were collected and fixed in10% neutral buffered formalin and further processed for histopathological examination (Mashkoor et al., 2013).

PhotomicrographyPhotomicrography was taken using photo micrographic camera (ZEISS AxioCamERc5s) facilitated by Department of Gynecology, Obstetrics and Reproductive Health, BSMRAU.

Detection of Salmonella Gallinarum by Polymerase chain reaction (PCR)

From liver, lungs and other tissue samples the genomic DNA of Salmonella Gallinarum were extracted using DNA extracting kits (Promega Corp. Madison, WI, USA). (Haider et al., 2009). Extracted DNA amplification was carried out using primers specific for the gene invA byusing commercial PCR kits in a thermocycler (Gene amplification PCR system 9600, eppendorf, Germany). PCR reaction mixture and PCR conditions were

same as described elsewhere (Haider et al., 2009). Amplified products were separated by gel electrophoreses on 1.5% agarose gel containing 5µg per ml ethedium bromide with a100bp ladder as molecular weight marker

Results and DiscussionPrevalence of isolated and identified organismsA total of 150 samples were collected during the study period. The bacterial flora isolated from the cloaca, liver and intestine of healthy and sick/dead poultry are shown in the Table 1. The cloacal samples were taken from both live and dead chickens whereas internal organ samples were taken only from dead/sick chickens. The prevalence of Salmonella Gallinarum was found in 28% cases, which was confirmed by different sugar fermentation and biochemical tests. This result is lower than the reports of other authors (Jordan and Pattison, 1996; Jones et al., 2001)

In case of commercial layer, among 117 samples tested, 39(33.33%) were positive for Salmonella and in case of sonali chickens 3 (16.67%) were positive for Salmonella from 18 samples where as all samples (15) collected from broiler chicken were negative for Salmonella (Table 2).

Clinical signsThe affected chickens exhibited drowsiness, weakness, loss of appetite, ruffling feathers, poor growth, labored breathing, attendant cytoseparate from healthy chikens and sometimes greenish-yellow diarrhea were observed. The adult affected chickens showed depression, anorexia, diarrhea and dehydration. There was found the drop of feed consumption


was seen in all the infeceted chickens and appeared similar with the findings of previous authors (Watkins et al., 2003; Haider et al., 2008; Saha et al., 2012; Ogie et al., 2013; Dutta et al., 2015 and Unze et al., 2017).

Gross lesions

Grossly, the liver appeared swollen, congested along with bronze discoloration and in few cases liver revealed hemorrhages and focal necrosis (Fig. 1a) found similar with the previous findings of Calnek et al. (1997); Madhuri and Sadana (2005) and Saha et al. (2012). The cardiac lesions consisted of mild to moderate congestion and petechial hemorrhage in the base of the heart. In few cases, multiple white nodules with distorted shapes were observed (Fig. 1b). Ogie et al. (2013) and

Uneze et al. (2017) also reported similar findings. Spleenomegaly along with multiple necrotic foci on the surface of the spleen was found. Pinpoint hemorrhages were seen in the spleen (Fig. 1c) and kidney. Lungs were highly congested with pneumonic lesions. The ova were deformed, discolored and cystic (Fig. 1d). The observation of this study was supported by the findings of Madhuri and Sadana (2005); Beyaz et al. (2010); Garcia et al. (2010); Nazir et al. (2012); Saha et al. (2012) and Uneze et al. (2017). Catarrhal inflammation is a frequent was also evident in some cases characterized by thick slimy mucus exudates on mucosal surfaces in the lumen of intestine (Fig. 1e). The findings were in agreement with the findings of Saha et al. (2012) who found 70.6% intestine hemorrhagic to catarrhal enteritis while 29.4%

Table 1. Salmonella Gallinarum isolated from different organs (n=150) of chickens

Swabs# Total number of samplesNo. of positive cases

% of positive casesTotal

Cloacal Healthy 75 942

28Sick/Dead 75 33

Intestinal Healthy - -33

44Sick/Dead 75 33

Liver Healthy - -33

44Sick/Dead 75 33

(#Cloacal swabs were taken from both live and dead chickens where as intestine and liver swabs were taken only from dead/sick chickens)

Table 2. Prevalence of Salmonella Gallinarum isolated from broiler, sonali and layer chickens (n=150)

Type of poultry No. of case No. of positive cases % of positive casesBroiler 15 0 0Cock (Sonali) 18 3 16.67Layer 117 39 33.33

Lane 1: Negetive control (NC), Lane 2-6: Showing 284bp band specific for invA gene of Salmonella.


only hemorrhagic and congested which also supported by other authors Madhuri and Sadana (2005); Ogie et al. (2013) and Uneze et al. (2017).

Histopathology

Microscopically, the hepatitis was characterized by leukocytic infiltration at perivascular areas along with hydropic vacuolation in hepatocytes, multiple necrotic foci noticed with Kupffer cell hyperplasia. The section of livers showed congestion, hemorrhages, focal degeneration, focal necrosis with infiltration of mononuclear cells and round cells and congestion of the central vein (Fig. 2a). Such histopathological study of liver were supported by Calnek et al. (1997); Hossain et al. (2006); Nwiyi and Omadamiro (2012) and Joshua et al. (2015).

Section of the hearts showed severe degeneration and fragmentation of muscle fibers (non suppurative myocarditis) with leukocytic infiltration (Fig. 2b). Fibrinous pericarditis with infiltration of heterophils, lymphocytes and macrophages was also observed in some cases. Spleen showed necrosis of lymphoid follicles leading to severe depletion of lymphocytes in white pulp along with reticuloendothelial cell hyperplasia (Fig. 2c). Lungs showed diffuse congestion and hemorrhages. Haemorrhage was also seen inthe alveoli (red hepatization). Depletion of sero-fibrinous exudate was seen in lung alveoli and inter-lobular septa (Fig. 2d). The section of ovary showed different shaped ova with hemorrhages (Fig. 2e). These histopathological findings were in agreement

Fig. 1. Gross pathological changes in visceral organs of chickens caused by Salmonella Gallinarum (a) bronze discoloration with enlargement of liver, (b) necrotic nodule on heart, (c) splenomegaly and pin point hemorrhages were seen in the spleen, (d) catarrhal inflammation and thick slimy mucusexudates on mucosal surfaces in the lumen of intestine, (e) ova were deformed, discolored and cystic.


with Garcia et al. (2010); Ogie et al. (2013); Joshua et al. (2015) and Uneze et al. (2017).

The section of the intestine showed desquamation of mucosal epithelium resulting in denuded villi and lumen was filled with necrotic mass, congestion, hemorrhage sand infiltration of inflammatory cells (Fig. 2f). These findings were supported by the result of Saha et al. (2012) who reported that 47.1% infiltration of heterophils and lymphocytes in the mucosa. The predominating cells in inflamed intestinal mucosa due to FT are heterophils and lymphocytes (Hossain et al., 2006; Nwiyi and Omadamiro, 2012).

Colony morphology On Salmonella-Shigella (SS) agar, all the isolates produced translucent, black, smooth, small round colonies which are positive for Salmonella Gallinarum. All the suspected Salmonella isolates produced pink color colony with black centre (Fig. 3a). Black colonies were produced on Xylose Lysine Deoxycholate (XLD) Agar medium from isolated Salmonella organism (Fig. 3b). White colonies were produced on Nutrient Agar (NA) medium from isolated Salmonella organism (Fig. 3c). Black colonies were produced on TSI medium from isolated Salmonella

Fig. 2. Hispathological changes in different organs of chickens caused by Salmonella Gallinarum, (a) section of liver showing vacular degeneration and necrosis of hepatocytes, (H & E, X 100), (b) section of heart showing scatteredly distribution of more acidophilic muscle fibers with few heterophils and round mononuclear cells among the muscle fibers, (H & E, X 100), (c) section of spleen showing focal degeneration and necrosis of lymphocytes (H & E, X 40), (d) section of intestine showing hemorrhage and infiltration of inflammatory cells, (H & E, X 40), (e) section of ovary showing different shaped ova with hemorrhage, (H & E, X 40), (f) section of lungs showing severe congestion, infiltration of heterophils and pink color exudate in alveoli, (H & E, X 40).


organisms (Fig. 3d). White colonies were produced on Brilliant Green Agar (BGA) medium (Fig. 3e). All these were suggestive for bacterial isolate belonging to Salmonella spp. (Sujatha et al., 2003; Muktaruzzaman et al., 2010; Hyeon et al., 2012).

In microscopic examination smears stained Gram’s stain revealed gram-negative, non motile, pink colored, short rod shaped bacteria, arranged in single and paired (Fig.4). These findings support the report of Ambily and Mini (2014). Hanging drop preparation of bacterial growth in nutrient broth showed the bacteria non motile belonging to similar others (Grimont et al., 2000).

Results of biochemical testsSix of the isolates fermented glucose, maltose, lactose, mannitol and dulcitol with producing acid, which are typical characteristics for S. Gallinarum. There was no fermentation of lactose, sucrose and arabinose. Acid production was marked by the color change from reddish to yellow. Gas production was marked by accumulation of gas in the Durham’s tube. These results were supported by Hossain (2006) who stated that among five basic sugars the Salmonella ferment dextrose, maltose and mannitol with production of acid and gas but no fermentation was observed in lactose and sucrose. Proux et al. (2002) reported that the biovar Salmonella Pullorum and Salmonella Gallinarum were differentiated by the use of sugars such as maltose, dulcitol and glucose.

Fig. 3. Colony characters of Salmonella Gallinarumin different agar medium, (a) production of black colonies on Salmonella-Shigella (SS) Agar medium, (b) production of black colonies on Xylose Lysine Deoxycholate (XLD) Agar medium, (c) production of white colonies on Nutrient Agar (NA) medium, (d) production of black colonies on TSI medium, (e) production of white colonies on Brilliant Green Agar (BGA) medium.


Molecular detection of S. Gallinarum by PCR

Polymerase chain reaction with invA primer, 5 isolates showed positive band at 284bp i.e. the five (5) isolates were found to be Salmonella Gallinarum. All the Salmonella suspected culture subjected to PCR amplification, generated a product of approximate molecular size 284bp fragment of invA gene (100 specific for Salmonella spp.) 100bp DNA marker was used as a molecular weight marker. The band size detected in all the Salmonella isolates and

analyzed by agarose gel electrophoresis (Fig. 5). All conditions and results found in the PCR are supported by the findings of the several authors such as Simone et al. (2009); McClelland et al. (2001); Kisiela et al. (2005); Jawad and Hamadani (2011). Maximum investigators (Guo et al., 1999; Ferretti et al., 2001; Schneder et al., 2002) try to establish a method, which can reduce the periods of Salmonella identification procedures from various samples and they recommended this invA gene for detection of Salmonella.

Fig. 4. Isolated Salmonella Gallinarum showing rod shape, short chain forming bacteria, (Modified Gram’s stain, X 100).


ConclusionsSalmonella Gallinarum is a causal agent of fowl typhoid. It is a major concern of poultry industry in recent days in Bangladesh. As such, researchers, veterinarians, farmers and the government are working in tandem to search for new method of testing and characterization for Salmonella Gallinarum. Clinical signs, gross lesions, biochemical tests and histopathological lesions study are helpful for the detection of fowl typhoid though there have some contradictions. Howerver, PCR based methods for identifying pathogens provide more advantageous options for this purpose than conventional testing. The

PCR protocol adapted targeting invA gene is specific for Salmonella spp. Therefore PCR methods described here might be used in combination with biochemical study to identify fowl typhoid.

ReferencesAbdu, P. A. 2007. Manual of Important Poultry

disease in Nigeria. 2nd Edition. MacChin

Multimedia Designers, Zaria Pp. 42-47.Agbaje, M. R. Davies, M. A. Oyekunle, O. E.

Ojo, F. O. Fasina, and P. A. Akinduti, 2010. Observation on the occurrence and transmission pattern of Salmonella Gallinarum in commercial poultry farms in Ogun State, South Western Nigeria. Afr. J.

Fig. 5. Agarose gel electrophoresis for visualization of invA gene (284 bp) of Salmonella.


Microbiol. Res. 4(9): 796-800.Aiello, S. E. (Ed) 1998. The Merck Veterinary

Manual, 8th Edition, Merck & Co., Inc, USA. Pp. 1995-1996.

Ambily, R. and M. Mini. 2014. Salmonellosis in Japanese Qiuails-AReport from central Kerala, India. Int. J. Sci. Res. Vol. 3. Pp 361-362.

Arbelot, B., J. F. Dayon, D. Mamis, J. C. Gueye, F. Tall, H. Samb, 1997. Seroprevalence survey of dominant avian diseases in Senegal: mycoplasmosis, fowl typhoid and pullorum disease, Newcastle, infectious bursal and infectious bronchitis diseases. Rev. Elev. Med. Vet. Pay. 50: 197-203.

Beyaz, L., A. Atasever, F. Aydin, K. S. Gumusoy and S. Abay. 2010. Pathological and clinical findings and tissue distribution of Salmonella Gallinarum infection in turkey pools. Turk. J. Vet. Ani. Sci. 34: 101-110.

Calnek, B. W., H. J. Barnes, C. W. Beard, L. R. McDougald, and Y. M. Saif. 1997. Diseases of Poultry. 10th edition. Iowa State University Press, Ames, Iowa, USA. Pp. 131- 140.

Das, P. M, D. M. M, Rajib, M. Noor, M. R. Islam. 2005. Retrospective analysis on the proportional incidence of poultry diseases in greater Mymensingh district of Bangladesh. In: proceeding of 4th international poultry show and seminar, from February 28 to March 2, 2003, held in Bangladesh-Chaina Friendship Conference Centre, Agargoan, Dhaka. Pp. 35-39.

Dutta, P., M. K. Borah, R. Gangil, and R. Singathia, 2015. Gross/ Histopathological impact of Salmonella Gallinarum isolated from layer chickens in Jaipur and their antibiogram assay. Int. J. Advanced. Vet. Sci. and tech. 4(1): 153-159.

Ferretti, R., L. Mannaazzu, , L. Cocolin, G. Comi, and F. Clementi, 2001. Twelve-hour PCR-based method for detection of Salmonella spp. in food. Appl. Envir. Microbiol. 74: 977-978.

Garcia, K. O., A. M. Santana, O. C. Freitas Neto, Jr. A. Berchieri and J. J. Fagliari, 2010. Experimental infection of commercial layers using a Salmonella enteric serovar Gallinarum strain: blood serum component and histopathological changes. Brazilian. J. Vet. Pathol. 3(2): 111-117.

Gast, R. K. 1997. Salmonella infection. In B. W. Calnek, H. J. Barnes, C. W. Beard, L. R. McDougald, Y. M Saif, editors. Disease of Poultry. 10th edn. Iowa State University Press. Iowa, USA.

Grimont, P. A. D., F. Grimont, and P. Bouvet. 2000. Taxonomy of the genus Salmoenlla. In C. Wray and A. Wray, eds. Salmonella in Domestic Animals, CABI Publishing, Oxon, U.K. Pp. 1–17.

Guo, L., P. B. Killfer and J. D. A. Morris, 1999.Use of arbitrarily primed Polymerase chain reaction to study Salmonella ecology in a turkey production. Poult. Sci. 78: 24-31.

Haider, M. G., E. H. Chowdhury, M. A. H. N. A. Khan, M. T. Hossain and M. S. Rahman. 2008. Experimental pathogenesis of Pullorum disease with the local isolate of Salmonella enterica serovar. Enterica subspecies Pullorum in pullets in Bangladesh. Korean J. Poult. Sci. 35: 341-350.

Haque, M. E., M. A. Hamid, M. A. R. Howleder and Q. M. E. Haque. 1991. Performance of native chicks and hens reared together or separately under rural condition in Bangladesh. Bangladesh Vet. 8: 11-13.

Hossain, M. S., E. H. Chowdhury, M. M. Islam, M. G. Haider, and M. M. Hossain. 2006. Avian Salmonella infection: isolation and identification of organisms and histopathological study. Bangladesh J. Vet. Med. 4: 7-12.

Hyeon, J. Y., J. W. Chon, I. G. Hwang, H. S. Kwak, M. S. Kim, S. K. Kim, I. S. Choi, C. S. Song, C. Park and K. H. Seo. 2012. Prevalence, antibiotic resistance and molecular characterization of Salmonella


serovars in retail meat products. J. Food. Protec. 74: 161-166.

Jawad, A. A. K. and A. H. A. Hamadani. 2011. Detection of fimA and fimC genes of Salmonella isolates by using polymerase chain reaction. J. Basrah. Res. 37(4): 1817-2695.

Jones, M. A., P. Wigley, K. L. Page, S. D. Hulme and P. A. Barrow. 2001. Salmonella pathogenicity island 2 type III secretion system but not the Salmonella pathogenicity island 1 type III secretion system for virulence in chickens. Infect. Immune. 69: 5471-5476.

Jordan, F. T. W. and M. Pattison, 1992. Poultry Disease 4th ed. W. B. Sauder Company Ltd London. Pp. 169-171.

Jordan, T. W. and M. Pattison. 1996. Poultry diseases, (4th edn). Iowa State Press, Ames.

Joshua, B. I., B. J. O. Olabode, O. S. Blessing, F. M. Yakassai, K. P. Rinle, C. D. Chukwu DorisIsioma, R. A. Gambo, A. B. Olatunde, M. G. Davou, A. J. Saidu, O. P. Ademola. 2015. Gross and Histo-Pathological changes in Japanese Quail (Coturnix Coturnix Japonica) experimentally infected with Salmonella Enterica Serovar Gallinarum. Ani. Vet. Sci. 3(3): 84-88.

Khan, M. A. H. N. A., A. S. M. Bari, M. R. Islam, P. M. Das and M. Y. Ali. 1998. Pullorum disease in semi nature chicks and its experimental pathology. Bangladesh Vet. J. 32:124-128.

Kisiela, D., M. Kuczkowski, L. Kiczak, A. Wieliczko and M. Ugorski. 2005. Differentiation of Salmonella Gallinarum biovar Gallinarum from Salmonella Gallinarum biovar Pullorum by PCR-RFLP of the fimH gene. J. Vet. Med. B. 52(5): 214-218.

Madhuri, D. and J. R. Sadana. 2005. Sequential pathological changes of fowl typhoid in plasmid cured vaccinated and unvaccinated chickens following challenge with Salmonella Gallinarum var Duisburg. Indian J. Vet. Pathol. 29(2): 82-84.

Mashkoor, J. A. K., M. Z. Khan, R. Z. Abbas, M. K. Saleemi and F. Mahmood. 2013. Arsenic induced clinico-hemato-pathological alterations in broilers and its attenuation by vitamin E and selenium. Pak. J. Agric. Sci. 50: 131-138.

McClelland, M., K. E. Sanderson, J. Spieth, S. W. Clifton, P. Latreille, L. Courtney, S. Porwollik, J. Ali, M. Dante, F. Du, S. Hou, D. Layman, S. Leonard, C. Nguyen, K. Scott, A. Holmes, N. Grewal, E. Mulvaney, E. Ryan, H. Sim, L. Florea, W. Miller, T. Stoneking, M. Nhan, R. Waterston and R. K. Wilson. 2001. Complete genome sequence of Salmonella enteric serovar Typhimurium LT2. Nature. 25, 413(6858): 852-856.

Mdegela, R. H., M. G. S. Yongolo, U. M. Minga and J. E. Olsen. 2000. Molecular epidemiology of Salmonella Gallinarum in chickens in Tanzania. Avian Pathol. 29(5): 457-463.

Muktaruzzaman, M., M. G. Haider, A. K. M. Ahmed, K. J. Alam, M. M. Rahman, M. B. Khatun, M. H. Rahman. and M. M. Hossain. 2010. Validation and refinement of Salmonella Pullorum (SP) colored antigen for diagnosis of Salmonella infections in the field. Int. J. Poult. Sci. 9 (8): 801-808.

Nazir, S., K. S. Ahmad, D. M. Maqbool, M. M. Saleem, K. F. Ahmad and A. Amare 2012. Pathology of spontaneously occurring salmonellosis in commercial broiler chickens of Kashmir Valley. J. World’s Poult. Res. 2(4): 63-69.

Nwiyi, P. and O. Omadamiro. 2012. Sero-prevalence and isolation of chicken infected with Salmonella: Haematological and Pathological Evaluation. J. Ani. Feed Sci. 2(6): 483-487.

Ogie, A. J., A. E. Salako, B. O. Emikpe, E. A. Amosun, S. A. Adeyemo, and P. O. Akinoluwa. 2013. The comparative susceptibility of commercial and Nigerian indigenous chicken ecotypes to Salmonella


Gallinarum infection. Sokoto. J. Vet. Sci. 11(2): 49-56.

Okwori, A. E., G. A. Hasimil, J. A. Adetunji, I. O. Akaka and S. A. Junards. 2007. Serological survey of Salmonella Gallinarum antibodies in chicken around Jos, Plateau Sate. Nigerian Onl. J. Health Allied. 6: 2.

Parmer, D. and R. Davies. 2007. Fowl typhoid in small back yard laying flock.Vet. Rec. 160: 348

Popoff, M. Y., J. Bockemuhl and L. L. Gheeshing. 2003. Antigenic formulas of the Salmonella Serovars, 8th ed. WHO collaborating centre for reference and research on Salmonella. supplement 2001 (No:45) to the Kauffmann- White Scheme Research in Microbiology, 154: 173-174.

Proux, K., F. Humbert, E. Jouy, C. Houdayer, F. Lalande, A. Oger and G. Salvat, 2002. Improvements required for the detection of Salmonella Pullorum and Gallinarum.The Canadian. J. Vet. Res. 66: 151-157..

Rahman, M. M., K. S. Huque, N. G. Das and M. Y. A. Khan. 2017. Comparative market supply of protein from livestock and fish in the selected Urban areas of Rajshahi district in Bangladesh. Research on Agriculture, Livestock and Fisheries. Vol. 4, No. 1, April 2017: 29-36.

Rashid, M. H., C. Xue, M. R. Islam, T. Islam, Y. Cao. 2013. Alongitudinal study on the incidence of infectious diseases of commercial layer chickens in Bangladesh. Prev. Vet. Med. 109: 354-358.

Reynolds, D. L. 2003. Multi causal enteric diseases. In: Diseases of poultry (ed. Y. M. Saif), Iowa State University Press, Ames. Pp. 1169-1171.

Roa, G. 2000. A Comprehensive Textbook on PoultryPathology.Medical publisher ltd. Pp. 7-10.

Saha, A. K., M. A. Sufian, M. I. Hossain and M. M. Hossain. 2012. Salmonellosis in layer chickens: pathological features and isolation of bacteria from ovaries and inner content of laid eggs. J. Bangladesh Agric. Univ. 10(1): 61–67.

Schneder, A., C. Gronewald, M. Fandke, B. Kurth, S. Barkowski and K. Berghof. 2002. Real-time detection of the genus Salmonella with the light cycler system. Biochemistry. 4: 19-21.

Shivaprasad, H. L. 1997. Pullorum disease and fowl typhoid. In Calnek, B. W, Barnes, H. J, Beard C. W, McDougald, L R, Saif Y M, editors. Disease of Poultry, 10th ed. Iowa State University Press. Iowa, USA.

Shivaprasad, H. L. 2000. Fowl typhoid and pullorum disease. Review of Scientific Technology, Off. Int. Epiz. 19(2): 405-424.

Simone, A., R. Mendes, B. P. Jaqueline, Z. Fábio, V. F. L. Manoel and B. J. Ângelo. 2009. Molecular differentiation between salmonella enteric subsp. enteric Serovar pullorum and salmonella enteric subsp. enteric serovar Gallinarum. Brazilian J. Microbiol. 40: 184-188.

Sujatha, K., K. Dhanalakshmi and A. S. Rao. 2003. Isolation and characterization of Salmonella Gallinarum from chicken. Indian Vet. J. 80(5): 473-474.

Uneze, B. C., I. H. Iheukwumere, C. E. Okeke. 2017. Sequential pathological study of fowl typhoidin experimentally infected chicks. J. Health, Med. and Nursing. Vol. 35. Pp. 118-121.

Watkins, J. R., A. I. Flower and L. C. Grumbles. 2003. “Salmonella organism in animal feed products used in poultry feed”. J. Sci. 3: 290–301.

Quest for suitable storage Condition for sustainable ProCessing Quality of Potato tubers

d. Parvin1, J. u. ahmed1, M. M. Hossain2 and M. Mohi-ud-din1*

abstract

Processing quality of potato tubers depend on the physico-chemical properties which changes within the time in long-term storage period. The present study was conducted to find out a suitable storage condition that could be able to maintain the processing quality of potatoes. Three potato varieties namely Asterix (BARI Alu-25), Courage (BARI Alu-29) and Lady Rosetta (BARI Alu-28) and three different storage conditions viz. Bamboo chamber (BC), earthen chamber with evaporative cooler (EC) and refrigerator (RF) were used in this study. Data were recorded monthly-basis on the physico-chemical processing qualities of the potato tubers. Potato tubers stored in RF was able to maintain higher dry matter content and lower weight loss, shrinkage and energy content than BC and EC. Though the physical qualities of the refrigerated tubers were well-maintained, but produced considerably higher amount of mean reducing sugars (2.69 mg/g FW) which was 11.6 and 17.9% higher than the mean of BC (2.41 mg/g FW) and EC (2.28 mg/g FW), respectively; and higher mean sucrose contents (2.46 mg/g FW) which was 6.5 and 18.8% higher than the mean of BC (2.31 mg/g FW) and EC (2.07 mg/g FW), respectively. EC maintained significantly lower amount of mean glucose (0.17 mg/g FW), fructose (1.96 mg/g FW) and total soluble sugar (4.20 mg/g FW) contents than BC and RF. Compared to pre-storage, mean reducing sugar content was increased by 1.5, 1.7 and 2.0 times in EC, BC and RF, respectively until 90 days of storage and the increase in mean sucrose content was 1.7-, 2.1- and 2.3-fold in EC, BC and RF, respectively. Among the varieties, Courage and Lady Rosetta were suitable for long-term storage for processing than Asterix. Chips produced from the potatoes stored in the EC acquired significantly higher scores for sensory attributes than that of BC and RF. Results clearly depicted that potatoes stored in EC were more suitable for processing due to moderate retention of dry matter content (22.13%) and lower accumulation of different sugars and were able to retain processing quality up to 90 days of storage than that of BC and RF.

Keywords: Evaporative cooler, earthen chamber, bamboo chamber, reducing sugar, sucrose.

1Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


introduction

Potato (Solanum tuberosum L.) is the most important food crop in the world after wheat, rice and maize. In Bangladesh, potato is a

prominent crop in consideration of production and its internal demand. The potato area and yield rate has significantly increased, which contributed 7.83% increase in total volume of production (BBS, 2018). But the storage

62 Quest for Suitable Storage Condition for Sustainable Processing Quality of Potato Tubers

facilities of the country are not sufficient for this increased produce. Due to inadequate cold storage facilities to hold the produce for longer periods, large quantities are spoiled before they could be consumed (Hossain and Mia, 2009). There are about 390 cold storages in Bangladesh with a capacity of about 5.3 million tons that can store only 53% of produced potatoes including seeds (BBS, 2018).

Potato can either be cooked and consumed directly or processed to a variety of commercial products (Lisinski and Leszczynski, 1989). As potato has a wide consumption so it is required to store for long and short time when the potato is not available. The chemical traits of potato tubers can change within storage time as it can respire during storage. As a result, dry matter breaks down and weight loss is occurred (Gottschalk and Ezhekiel, 2006). Processing quality of potato tubers is determined by high dry matter and low reducing sugar and phenol contents (Kadam et al., 1991). High dry matter content increases chip yield, crispy-consistency and reduces oil absorption during cooking (Rommens et al., 2010). For better chipping 1.5 mg/g sucrose, 20 to 27% dry matter content and 1.07 to 1.10 specific gravity should be maintained in processing potato (Work et al., 1981). Kabira and Berga (2003) reported that potato tubers had 20-24 % DM content indicating that they are ideal for processing chips.

Reducing sugars accumulation in potato tubers during low temperature storage is of prime industrial concern due to its participation as substrate in Millard reaction at elevated temperature. The high level of reducing sugars gives rise to commercially

unacceptable brown crisp color (Blenkinsop et al., 2002). Low temperature (4°C) storage helps to reduce problem of sprout growth and losses due to diseases and rotting. However, the resultant low temperature sweetening of the tubers reduces the chip quality within a short time period (Isherwood, 1973). Wiltshire and Cobb (1996) reported that higher temperature increase metabolism, respiration and physiological aging of potato tubers, resulting in the observed earlier sprouting and starch breakdown, ultimately lower DM content. Optimum reducing sugar content for processing potatoes is 1.0 mg/g and it should not exceed 3.30 mg/g (Davies and Viola, 1992).

To keep the dry matter content at desirable level low temperature is prerequisite (Work et al., 1981). The evaporative cooled storage structure has proved to be useful for short term and on-farm storage of fruits and vegetables in hot and dry regions (Jha and Chopra, 2006). The high cost involved in developing cold storage or controlled atmosphere storage is a pressing problem in several developing countries including Bangladesh. But evaporative cool chamber is able to maintain temperature at 10-15°C below ambient as well as at a relative humidity of 90% depending on season (Basediya et al., 2013). Evaporative cooling is an environment friendly air conditioning system that operates using induced process of heat and mass transfer where water and air are working fluids. It is very cheap and fulfills all the requirements to the small farmers in rural area (Dadhich et al., 2008).

Due to warm weather, it is a great challenge to maintain the processing quality of the stored potatoes in Bangladesh. Different types of pits,

D. Parvin, J.U. Ahmed, M.M. Hossain and M. Mohi-Ud-Din 63

earthen house, bamboo house and cold storage have been using to store potatoes in the sub-continent including Bangladesh. Bangladesh Agricultural Research Institute (BARI) and other research partners prescribed an ambient type potato storage (bamboo house) and a coolbot for storing potatoes after harvest (CIP, 2013 and BAMD, 2018). But none of the techniques has been proved to be efficient to maintain processing quality of potato varieties during the storage period of this country.

Considering the above facts, the aim of this experiment was to find out a suitable storage condition to maintain the processing quality of potatoes at a desirable level by means of economical friendly method.

Materials and MethodsThe research was carried out in the Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Salna, Gazipur. Seed potatoes of three processing varieties viz. Asterix (BARI Alu-25), Courage (BARI Alu-29) and Lady Rosseta (BARI Alu-28) were collected from Bangladesh Agricultural Development Corporation (BADC) and Tuber Crops Research Center (TCRC) of Bangladesh Agricultural Research Institute (BARI) and then cultivated at the field laboratory of the Department of Crop Botany (Block 14) by following standard cultivation and management practices for potato prescribed by BADC and TCRC, BARI. After harvesting, potatoes were subjected to curing for 10 days and sent to the different storage conditions. Potato tubers were stored in 3 conditions: a low cost earthen chamber with evaporative cooler (EC), bamboo chamber (BC) [as recommended by TCRC, BARI] and refrigerator (RF).

design and fabrication of storage chambersBC was made by bamboo and bamboo sheets and its size was 5ft×4ft×3ft (L×W×H). The size of EC was 5ft×4ft×3ft (L×W×H) and it was made by clay soil. Three holes were made on earthen wall among those two for ventilation purpose and one was used for the entrance of airflow with vapor by evaporative cooler (Model- WRA-S99, Walton, Bangladesh). An electricity power supply was connected to the evaporative cooler as well as narrow tube was jointed to the cooler for continuous water supply. A giant refrigerator (Model- ER-202F, General Electronics. Japan) was used as the refrigerated storage. A thermo-hygrometer was kept inside each chamber as well as in the external environment.

data collection Data were collected at the day before storage and then 1-month interval basis up to 3 months on dry matter (%), weight loss (%), shrinkage (%), glucose, fructose, reducing sugar, sucrose, and total soluble sugar and starch contents. After the termination of 90 days storage, sensory evaluation of chips prepared from stored potatoes was conducted.

Dry matter content (%), weight loss (%) and shrinkage (%) were determined as the procedures described by Abano et al. (2011). Energy content of potato tubers was calculated according to Bradbury (1986) by using the equation E = - 17.38M + 1699, where, E = Energy in kJ per unit weight (100 gm) and M = Moisture content (%). Cooling efficiency was determined according to Abano et al. (2011) using the maximum and minimum temperatures of external environment and the maximum temperature inside storage.


extraction and determination of soluble sugars Sugar content of potato flesh was extracted by following the procedure of Xue (1985) with slight modification. Briefly, 0.5 g of fresh potato flesh was extracted thrice with 5 ml of 80% (v/v) ethanol at 80°C for 30 min and the extracts were centrifuged at 5000 rpm for 10 min with a refrigerated centrifuge (Model- BCBHR-201, Bio LAB, Canada). The supernatants were combined in a 50 ml beaker and placed in a water bath at 80-85°C until the volume is reduced to about 1 ml. The sugar extract was transferred to a 10 ml volumetric flask by 3-4 wash with distilled water and used for assaying total soluble sugars, reducing sugar, sucrose, glucose and fructose contents.

Glucose content was estimated spectro-photometrically by glucose enzymatic assay kit (Linear Chemical, Spain) following the procedure attached with the kit pack. Fructose and sucrose content of potato flesh was measured by the anthrone colorimetric method following the procedure of Kang et al. (2009). Reducing sugar content was measured by DNS colorimetric method following the procedure of Miller (1959) with some modifications. Total soluble sugar was calculated by the summation of glucose, fructose and sucrose contents as done by the Adu-Kwarteng et al. (2014). A series of standard solution was made for glucose, fructose, sucrose and reducing sugar to prepare standard curves for the quantification of the sugar components.

extraction and determination of starch Starch content was extracted following the procedure described by Kang et al. (2009). Then 1 ml of aliquot was mixed with 1 ml of distilled water and 5 ml of anthrone reagent, and boiled for 15 min. After cooling for 10

min in the dark, the absorbance was read at 620 nm using spectrophotometer. A series of standard solution was made using starch for the preparation of standard curve.

Preparation of chips and sensory evaluationAfter the termination of 90 days’ storage, potato chips were produced according to Kita et al. (2014) with slight modification. After washing potatoes were cut into slices of 2 ± 0.1 mm thickness with a potato slicer, washed in cold saline water (NaCl @ of 20 g/L) and superficially dried by paper towels. The chips were deep fried about 3 min in refined rice bran oil heated to 180°C. After discharging of the oil and cooling, chips were taken for sensory evaluation. Sensory evaluation was performed by an untrained panel (n=10, 21-30 yrs, 4 males and 6 females) who were the regular consumer of potato chips. Samples were randomly coded before being served to the panel. Five sensory quality parameters (color, texture, taste, crispiness and the overall acceptability) were individually evaluated based on a 9-point hedonic scale (1: dislike extremely and 9: like extremely) as described by Meilgaard et al. (2007).

statistical analysisStatistical analysis was performed using Statistix 10 data analysis software. Monthly data collected throughout the storage duration were averaged and subjected to two-way analysis of variance for mean comparison, and significant differences were calculated according to Tukey’s HSD test. Data were reported as mean ± standard error (SE). Differences at p≤ 0.05 were considered to be statistically significant. Periodical data were presented in the graphs as the mean ± SE of 3


varieties with 3 replications for each storage conditions.

results and discussionstorage temperature and relative humidityDaily mean temperature (°C) and mean relative humidity (RH) during the storage period (April 2016 to July 2016) were presented in Fig. 1. The temperature in the bamboo

chamber (BC) ranged from 27.0-32.0°C (mean 29.99°C) during the storage period which was 3.0-6.0°C lower than the external environmental temperature (30.5-35.0°C). Temperature inside the earthen chamber (EC) was ranged from 25-30°C (mean 27.93°C) which was 5.5-10.0°C lower than the external temperature during the period of storage. The moist air by evaporative cooler reduces the inside temperature of the EC. On the

fig. 1. daily mean temperature (a) and daily mean relative humidity (b) of the external environment, bamboo chamber, earthen chamber and refrigerator of the entire storage duration.


other hand, temperature in the refrigerator (RF) ranged from 10-12°C (mean 10.99°C) which was much lower than external air, BC and EC temperature. The relative humidity (RH) of the external environment ranged from 57.0-82.0% (mean 70.1%) was lower than the inside humidity of EC which ranged from 75.0-94.0% (mean 86.7%). The RH of EC was 12.0-16.0% higher than the external humidity as moist air was pumped inside the EC by evaporative cooler. The RH of BC was 68.0-89.0% (mean 78.8%) and that was 7.0-11.0% lower than the external humidity. Besides, in the RF, the relative humidity ranged from 13.5-26.5% (mean 18.8%) that was much lower compared to other two storage conditions. The cooling efficiency of the BC, EC and RF was 44.4, 105.6 and 488.9%, respectively (Fig 2).

The ANOVA showed that the main effects of the varieties were significant for all the processing qualities, but the main effect of storage conditions were significant except dry matter content (DM) and total soluble sugars (TSS) (Table 1). Two-way interaction between the varieties and storage conditions was insignificant for all processing qualities apart from fructose content.

dry matter content (%)

The highest mean DM content was found in the RF (22.44%) compared to BC (21.84%) and EC (22.13%) but the differences between them were insignificant (Table 2). Among the varieties, DM content of Asterix was significantly lower than those of Courage and Lady Rosetta in all storage conditions. DM content of potato varieties was decreased with the increased storage duration in all three storage condition (Fig 3a). But the degree of declining DM content was varied within the storage duration as well as among the potato varieties. Results revealed that the DM content was decreased slowly up to 30 days but at the later stages with the progression of storage duration, the DM content declined rapidly in all three potato varieties. This decline in DM content was happened probably due to the loss of moisture from the tubers and maintenance respiration during storage (Addisu et al., 2014). The decrease in DM content with storage time in the present study corroborate the results of de Freitas et al. (2012) and Addisu et al. (2014) who reported a significant decreasing trend in the specific gravity and DM content of the potato tubers stored in different storage conditions. The decrease in the DM with

Table 1. Mean squares of variance and their effect on dry matter content (DM), weight loss (Wl), shrinkage (sr), energy content (eng), reducing sugar (rs), sucrose (suC), glucose (glu), fructose (fru), total soluble sugar (tss) and starch content (sC) of potato tubers stored in different storage conditions

Source of variation DF

Mean SquaresDM WL SR ENG RS SUC GLU FRU TSS SC

Variety(V) 2 42.77** 1.09** 4.75** 0.64** 0.97** 0.73** 0.23** 6.07** 103.99** 14.45**

Storage(S) 2 0.81 2.76** 3.57* 0.27** 0.39** 0.35** 0.28** 2.23** 3.33 6.35**

V×S 4 0.09 0.04 0.27 0.014 0.01 0.05 9.52 0.26* 0.83 0.53Error 16 0.56 0.16 0.90 0.018 0.033 0.02 18.50 0.06 1.34 0.09

*indicates significant at p ≤ 0.05; **indicates significant at p ≤ 0.01


storage time can be attributed to the gradual respiratory biochemical starch breakdown to sugars that is used up to maintain life of the tuber with concurrent production of carbon dioxide and water vapor (Senkumba et al., 2017; Addisu et al., 2014).

Results of the study clearly showed that, the mean DM content was higher in the RF compared to EC and BC in all sampling days (Fig 3a). This might be due to the low temperature in RF compared to other two conditions that prevented evaporation of moisture from tissues and less respiratory loss. Result of the present study agreed with the findings of de Freitas et al. (2012) who reported that lower storage temperature (4 and 8°C) tended to be more effective in maintaining DM content. Potatoes retained comparatively low DM content in BC and EC was due to early sprouting & higher carbohydrate breakdown in comparatively high temperature in these storage then refrigerator. Wiltshire and Cobb (1996) reported that higher temperature increase metabolism, respiration and physiological aging of potato tubers, resulting in the observed earlier sprouting and starch breakdown, ultimately lower DM content. Kabira and Berga (2003) reported that potato

tubers had 20-24% DM content indicating that they are ideal for processing chips. The result of the current study clearly showed that EC and RF could be able to retain desirable DM content (above 20%) for processing chips up to the 90 days than that of BC.

tuber weight lossMean percent weight loss (WL) of potato tubers in the BC (3.96%) was significantly higher than EC (3.40%) and RF (2.85%) (Table 2). Though the varieties Courage and Lady Rosetta were able to maintain lower WL than that of Asterix but the varietal differences were insignificant for all storage conditions. WL of potato tubers increased with the advancement of storage period and at the later stage of storage, there was a tremendous increase of WL in all storage conditions (Fig 3b). The highest WL was found in BC in all sampling dates as the high temperature enhances the respiration and evaporation of water from potato. The gradual increase in WL was due to maintenance respiration which converts the valuable starch in presence of oxygen to carbon dioxide, water and heat (Tester et al., 2005). Ezekiel et al. (2007) reported that the gradual WL increased due to respiration and evaporation, sprouting and sprout growth. Besides, high weight loss at room temperature is due to prevailing high temperature and low relative humidity which are reported to increase respiration rate (Burton, 1966), evaporation (Schippers, 1971) and sprouting (Burton, 1973). In this study, WL was less in EC compared to BC in all three potato varieties. Prevalence of high humidity and low vapor pressure deficit in evaporative cooled storage proved effective in reducing the WL as compared to room temperature

fig. 2. Cooling efficiency of the storage conditions used in this study.


storage as already reported by Burton (1966). RF showed minimum WL compared to other storage conditions (Fig 3b). Similar results were reported by Perumal et al. (1980), i.e., maximum WL of potato tuber at room temperature and minimum WL at refrigerated condition during the storage period.

shrinkageMean percent shrinkage (SR) of potato tubers in BC (4.91%) was markedly higher than EC (3.44%) and RF (2.93%) (Table 2).

Varietal differences within and among the storage conditions were not significant. SR of potato tubers was increased with the storage duration in all storage conditions (Fig 3c). But the degree of increment varied within the storage conditions, storage duration and potato varieties. The SR was higher in BC than that of EC and RF for all potato varieties, because high temperature (Sonnewald and Sonnewald, 2014) and low humidity (Abano et al., 2011) were responsible for the loss of water from potato tuber causes higher SR in

fig. 3. dry matter content (a) weight loss% (b) shrinkage (c) and energy content (d) of three processing potato varieties in three storage conditions recorded at 30-day interval up to 90 days of storage. Means were calculated from three varieties with three replicates. Vertical bars represent the ±SE values for the data point.


the BC. In the EC, the SR was low (5.92% at 90 days) compared to BC and this might be due to comparatively low temperature with high relative humidity created by evaporative cooler inside the EC (Abano et al., 2011). The potato tubers of RF showed the minimum SR (4.65% at 90 days) due to the very low temperature.

energy contentPotato tubers stored in BC had significantly higher mean energy content (4.24 kJ/g) than that of EC (3.97 kJ/g) and RF (3.91 kJ/g) (Table 2). Lady Rosetta maintained significantly higher mean energy content than that of Asterix and Courage in all storage conditions. Energy content of the potato

tubers increased with the storage duration in all storage conditions (Fig. 3d). Results revealed that with the advancement of the storage duration, energy content of the potato tubers increased up to the end of the storage. Similar results were found by Abano et al. (2011) and Zhitian et al. (2002). The highest energy content was found in potato tubers of BC (4.68 kJ/g at 90 days) and the lowest was found in the RF. Higher energy content apparently indicates the lower starch content available in the tubers.

reducing sugar contentSignificantly higher mean reducing sugar content (RS) was found in the potato tubers

Table 2. Summary statistics showing the values and means of different physico-chemical qualities of three processing potato varieties stored in different storage conditions

Storage condition Variety DM WL SR ENG RS SUC GLU FRU TSS SC

Bamboo chamber

Asterix 19.10b 4.29a 4.66a 3.83c-e 2.80ab 2.49ab 0.41b 3.90a 6.82b 94.86c

Courage 23.07a 3.82ab 3.90a 4.41ab 2.22c 2.22b-d 0.16e 2.33bc 4.72c 99.68ab

Lady Rosetta 23.33a 3.75ab 3.99a 4.46a 2.20c 2.21bc 0.18de 2.10bc 4.50cd 101.52a

Mean 21.84A 3.96A 4.19A 4.24A 2.41B 2.31A 0.27B 2.78A 5.35B 98.69A

Earthen chamber

Asterix 19.65b 3.74ab 4.53a 3.71de 2.59bc 2.40b 0.24d 2.51b 5.16c 96.72c

Courage 23.25a 3.15a-c 2.76a 4.07a-d 2.13c 1.85d 0.12e 1.74c 3.72d 100.11ab

Lady Rosetta 23.49a 3.30a-c 3.03a 4.14a-c 2.12c 1.93cd 0.12e 1.62c 3.68d 102.86a

Mean 22.13A 3.40B 3.44AB 3.97B 2.28B 2.07B 0.17C 1.96B 4.20C 99.91A

Refrigerator

Asterix 20.12b 3.37a-c 3.88a 3.65e 3.13a 2.91a 0.82a 4.01a 7.76a 94.99c

Courage 23.45a 2.48c 2.55a 4.02b-e 2.50bc 2.33bc 0.33c 2.34bc 5.01c 100.4a

Lady Rosetta 23.74a 2.69bc 2.35a 4.06a-d 2.44bc 2.13b-d 0.36bc 2.21bc 4.70c 102.16a

Mean 22.44A 2.85C 2.93B 3.91B 2.69A 2.46A 0.51A 2.86A 5.83A 99.19A

CV (%) 3.41 11.95 13.04 3.42 7.39 6.79 7.50 10.17 5.95 3.17

Values and means in a column followed by same lowercase and uppercase letter(s), respectively are not statistically different at p < 0.05 by Tukey’s HSD test. Values are the average of 4 sampling dates with 3 replicates.

DM= dry matter content (%), WL= weight loss (%), SR= shrinkage (%), ENG= energy content (kJ/g FW), RS= reducing sugar (mg/g FW), SUC= sucrose (mg/g FW), GLU= glucose (mg/g FW), FRU= fructose (mg/g FW), TSS= total soluble sugar (mg/g FW) and SC= starch content (mg/g FW).


stored in RF (2.69 mg/g FW) than that of EC (2.28 mg/g FW) and BC (2.41mg/g FW), but there was no statistically significant difference between mean RS content of EC and BC (Table 2). Courage and Lady Rosetta showed significantly lower RS content than Asterix both in BC and RF, but the difference among these varieties in the EC was not significant. RS content of potato tubers increased progressively with the increased storage duration in all storage conditions (Fig 4a). Similar increase in the RS content of ambient stored potatoes was reported by Pandey et al. (2017). The increase of RS content was rapid in RF compared to BC and EC. At the 90 days of storage period, potato varieties retained 3.71 mg/g RS content in the RF while that was 2.89 and 3.20 mg/g in EC and BC, respectively (Fig 4a). It indicated that very low temperature enhanced the increase of RS in potatoes and it might be due to the increased invertase enzyme activity at low temperature condition. Huang et al. (1999) found the similar result in low temperature storage condition. RS content of potatoes was remarkably low in tubers stored at the room temperature and under evaporative cooled storage as compared to the tubers stored in refrigerated storage (Mehta and Kaul, 1988). Reducing sugars such as fructose and glucose in reaction with α-amino groups form dark color and give a bitter taste to fried potatoes (Davies and Viola, 1992). Sweeter taste and soft texture in a fried potato product probably due to the low content of starch and increased content of RS (Adams, 2004). Optimum content of RS for processing potatoes is 1.00 mg/g and it should not exceed 3.30 mg/g (Davies and Viola, 1992). In our study, potato tubers retained RS content below 3.30 mg/g

up to 90 days in EC (2.89 mg/g) and BC (3.20 mg/g), but RF (3.71 mg/g) failed to do so. Therefore, the EC was considerably suitable for storing potatoes to maintain desirable RS content for processing in the storage.

sucrose contentSignificantly lower mean sucrose content (SUC) was found in the potato tubers stored in EC (2.07 mg/g FW) than that of RF (2.46 mg/g FW) and BC (2.31 mg/g FW), but there was no statistically significant difference between mean SUC content of BC and RF (Table 2). Variety Asterix showed significantly higher SUC content than that of Courage and Lady Rosetta both in EC and RF, but the difference among these varieties in BC was not significant. SUC content of potato tubers was increasing with the storage period in every storage condition (Fig 4b). The increase in SUC content was rapid in RF compared to EC and BC. Potato tubers retained highest SUC content (3.60 mg/g FW) at the last day of storage period in RF than that of EC (2.68 mg/g FW) and BC (3.21 mg/g FW) (Fig 4b). It might be due to the breakdown of starch molecule and its conversion into sucrose in the low temperature condition (Edward et al. 2002). Comparatively lower SUC content in the EC was due to the intermediate storage temperatures prevent accumulation sucrose, glucose and fructose contents in potato (Sowokinos, 1990). But, the potato tubers of BC contained more SUC content than EC because high temperature was attributed to the increase in SUC concentration in potato tubers (Timm et al., 1968). Potato tubers with high SUC level tend to accumulate more RS and are therefore not suitable for processing; and potatoes with SUC content greater than


1.5 mg/g FW is good for chips (Sowokinos et al., 1987) and it can be maximized up to 2.80 mg/g FW (Work et al., 1981) processing. The potato of the EC showed below 2.80 mg/g FW SUC up to the 90 days. On the contrary, the potato tubers of RF and BC lost processing quality after 60 days of storage period as the SUC content of potatoes of these two conditions were 3.60 and 3.21 mg/g FW, respectively. So, potatoes retained processing quality for long time in the EC than RF and BC.

glucose content

Potato tubers stored in the EC retained significantly lower mean glucose (GLU) content (0.17 mg/g FW) than that of BC (0.27 mg/g FW) and RF (0.51 mg/g FW) (Table 2). Among the potato varieties, Asterix showed significantly higher GLU content than that of Courage and Lady Rosetta in all three storage conditions. GLU content of potato tubers increased with the advancement of storage duration in all storage conditions (Fig 4c). The increase of GLU content was rapid

fig. 4. reducing sugars (a) sucrose, (b) glucose, (c) and fructose, (d) of three processing potato varieties in three storage conditions recorded at 30-day interval up to 90 days of storage. Means were calculated from three varieties with three replicates. Vertical bars represent the ±SE values for the data point.


in low temperature storage condition (RF) for all varieties. GLU is one kind of RS is produced by the breakdown of starch in very low temperature (Dogras et al., 1991) and therefore, GLU content of potato tubers was very high in RF (1.01 mg/g at 90 days) (Fig 4c). Besides, the GLU content of potato tubers in BC was high compared to EC, because the amount of GLU increased in potato tubers after prolonged storage at high temperature (Claassen et al., 1991). When glucose content of potato tubers exceeded 2.5% (above 0.25 mg/g FW), the potato slices became colored and processing quality will be deteriorated (Gould and Plimpton, 1985). The processing quality of the tubers stored in BC and RF diminished before the 60 days’ storage due to accumulation of more than 0.25 mg/g FW

GLU content (Fig 4c). On the contrary, potato tubers of EC retained the GLU content below 0.25 mg/g FW up to the 60 days. So, EC was more suitable for storage of processing potato compared to other storages.

fructose contentPotato tubers stored in the EC retained significantly lower mean fructose (FRU) content (1.96 mg/g FW) than that of BC (2.78 mg/g FW) and RF (2.86 mg/g FW) (Table 2). Among the potato varieties, Asterix showed significantly higher FRU content than those of Courage and Lady Rosetta in all three storage conditions. FRU content of potato tubers increased with storage time in all storage conditions (Fig 4d). The potato tubers of EC retained the lowest mean FRU content (4.11 mg/g FW) followed by BC (6.11 mg/g FW) and RF (6.64 mg/g FW) at the 90 days of the storage period (Fig 4d). FRU being one kind of RS, will increase in long storage

period both in very low temperature and high temperature (Dogras et al., 1991 and Claassen et al., 1991). Being a RS, FRU can participate in the Maillard reaction causing unacceptable browning of food products.

total soluble sugar contentThe mean total soluble sugars (TSS) of potato tubers was higher in RF (5.83 mg/g FW) compared to BC (5.35 mg/g FW) and EC (4.20 mg/g FW) (Table 2). Among the potato varieties, Asterix showed significantly higher TSS content than that of Courage and Lady Rosetta in all three storage conditions. TSS content found to be increased in processing potato varieties with the advancement of storage period in all storage conditions (Fig 5a). Potato tubers stored in RF retained higher TSS content compared to BC and EC at all sampling dates because, the low temperature was responsible for the starch degradation into SUC due to inactivation of glycolytic enzymes and SUC is further hydrolyzed into GLU and FRU by the activity of enzyme invertase (Sonnewald, 2001). TSS content was higher in the BC compared to EC due to comparatively high temperature inside the chamber enhanced the solubility of the starch (Kaur et al., 2009). Therefore, EC was the suitable storage condition for the potatoes of processing purposes.

starch contentHighest mean starch content (SC) was found in EC (99.91 mg/g FW) followed by RF (99.19 mg/g FW) and BC (98.69 mg/g FW) though the differences among them were not statistically significant (Table 2). Variety Asterix retained significantly lower mean SC content than Courage and Lady Rosetta


in all storage conditions. SC of potato tubers declined in all storage conditions with the advancement of storage duration (Fig 5b). Similar decrease in the SC of ambient stored potatoes was reported by Pandey et al. (2017) and Bhattacharjee et al. (2014). The decrease in SC might be due to the increasing hydrolysis of SC by starch degrading enzyme (Cochrane et al., 1991) and its conversion into sugar (Smith, 1987) as storage duration was proceeded. Besides, the potato being an underground stem, the photosynthate, largely sucrose was stored in tubers as starch; some sucrose was also used for respiration and since this was a reversible process starch could also be converted back into sucrose at the storage (Olsen et al., 2005). During storage starch degradation occurs primarily through the action of starch phosphorylase and reducing sugars accumulate through various enzymatic reaction (Sowokinos, 1990). After 60 days of storage, the SC of potato tubers decreased rapidly in BC (92.81 mg/g FW at 90 days) compared to EC (96.35 mg/g FW) and RF (94.24 mg/g FW) (Fig 5b). Potatoes stored in

the BC lost 10.5% SC at 90 days of storage in comparison with pre-storage SC, whereas EC and RF lost 7.1 and 9.1% SC, respectively. Thus, EC and RF were tended to be more efficient to maintain SC than BC. de Freitas et al. (2012) also found that lower storage temperatures (4 and 8ºC) were more effective to maintain high SC.

sensory evaluationSensory evaluation revealed that, chips produced from the potato tubers stored in the EC acquired significantly higher scores of sensory attributes compared to that of BC and RF (Table 3). The sensory characteristics like color, texture, taste, crispiness and overall acceptability are very good indicator for the preparation of good quality chips (Elfnesh et al., 2011). The poor color and taste scores in the chips produced from BC and RF were likely to be due to the reaction between the high RS and a free amino acid or amino group in the Maillard reaction (Fennema, 1996) and the formation of melanoidin pigments (Laerke and Christiansen, 2005). Among the varieties,

fig. 5. total soluble sugar (tss) content (a) and starch content (b) of three processing potato varieties in three storage conditions recorded at 30-day interval up to 90 days of storage. Means were calculated from three varieties with three replicates. Vertical bars represent the ±SE values for the data point.


chips produced from Courage and Lady Rosetta made comparatively higher scores in sensory attributes than Asterix in all storage conditions.

ConclusionsIn the RF, the physical qualities (DM content, weight loss, shrinkage and energy content) of potato tubers remained optimum for the processing purposes. But in the refrigerated storage (10-12ºC), sugar contents of potato tubers increased markedly with the advancement of storage time and contributed to the dark chip color and lower acceptance in the sensory evaluation. Besides, this system is very high energy consuming and costly compared to other storage system. In the EC, the DM content (≥20%), RS (≤ 3.30 mg/g FW) and SUC (≤2.80 mg/g FW) of

potato tubers were within the desirable limit for processing up to the 90 days, whereas the potato tubers lost the processing quality after 60 days of storage in BC. Therefore, EC was more suitable for long-term storage of processing potatoes regarding tuber attributes and sensory evaluation. Besides, this system was economically feasible as it consumes low energy compared to RF. Variety Courage and Lady Rosetta were suitable for long-term storage for processing compared to Asterix as these varieties were able to maintain the desirable limit of physico-chemical processing qualities throughout the storage duration.

acknowledgements The authors sincerely acknowledge the Research Management Wing (RMW) of Bangabandhu Sheikh Mujibur Rahman

Table 3. Sensory evaluation of potato chips prepared after 90 days of storage from different potato varieties stored in different storage conditions

Storage condition Variety Color Texture Taste Crispiness Overall

acceptanceBamboo chamber

Asterix 3.8±0.13d 3.4±0.16e 4.3±0.15g 3.2±0.13d 3.2±0.13c

Courage 5.0±0.21b 5.2± 0.20c 4.9±0.23ef 4.8±0.20bc 4.8±0.20b

Lady Rosetta 4.9±0.28bc 4.8±0.13cd 5.0±0.21ef 4.6±0.16c 4.7±0.15b

Mean 4.6±0.38B 4.5±0.55C 4.7±0.22C 4.2±0.50C 4.2±0.52B

Earthen chamber

Asterix 4.8±0.20bc 4.6±0.16d 5.2±0.13de 3.7±0.15d 3.6±0.16c

Courage 7.3±0.21a 7.5±0.17a 6.3±0.26ab 7.5±0.22a 7.7±0.15a

Lady Rosetta 7.1±0.18a 7.3±0.15a 6.5±0.22a 7.3±0.21a 7.4±0.16a

Mean 6.4±0.80A 6.5±0.94A 6.0±0.40A 6.2±1.23A 6.2±1.31A

Refrigerator Asterix 3.2±0.25e 3.5±0.17e 4.5±0.22fg 3.6±0.22d 3.3±0.15c

Courage 4.7±0.15bc 6.5±0.17b 5.8±0.20bc 5.2±0.20b 4.9±0.28b

Lady Rosetta 4.4±0.22c 6.3±0.15b 5.7±0.15cd 5.1±0.23bc 4.6±0.16b

Mean 4.1±0.46C 5.4±0.97B 5.3±0.42B 4.6±0.52B 4.3±0.49B

CV (%) 12.25 8.83 12.30 12.83 11.28Values and means in a column followed by same lowercase and uppercase letter(s), respectively are not statistically different at p<0.05 by Tukey’s HSD test. Values are the average of 10 replicates.


Agricultural University, Bangladesh for the financial support to conduct the research. The authors also acknowledge the contribution of the Tuber Crops Research Center (TCRC) of Bangladesh Agricultural Research Institute (BARI) and Bangladesh Agricultural Development Corporation (BADC) for providing seed tubers for this research.

references:Abano, E. E., E. Teye, R. S. Amoah and J. P. Tetteh.

2011. Design, construction and testing of evaporative cooling bran for storing sweets potatoes in the tropics. Asian Journal of Agricultural Research. 5(2): 115-126.

Adams, J. B. 2004. Raw materials quality and the texture of processed vegetables. Texture in Foods. Vol. 2, Pp. 342-363. D. Kilcast (ed) Solid Foods. Woodhead Publ. Ltd., Cambridge.

Addisu, S., G. Bultson and N. Dechassa. 2014. Effect of variety and storage on the tuber quality of potatoes cultivated in the eastern highlands of Ethiopia. Sci. Technol. Arts Res. J. 3(1): 84-89.

Adu-Kwarteng, E. E., O. Sakyi-Dawson, G. S. Ayernor, V. Truong, F. F. Shih and K. Daigle. 2014. Variability of sugars in staple-type Sweet Potato (Ipome batatus) cultivars: the effects of harvest time and storage. International Journal of Food Properties. 17: 410-420.

BAMD. 2018. Rangpur farmers storing potato in natural cold storages. Bangladesh Agricultural Marketing Department. Available via https://thefinancialexpress.com.bd /na t iona l / coun t ry / r angpur -farmers-storing-potato-in-natural-cold-storages-1527694886. Accessed on 27 December 2019.

Basediya, A. L., D. V. K. Samuel, and V. Beera. 2013. Evaporative cooling system for

storage of fruits and vegetables - a review. J. Food Sci. Technol. 50(3): 429–442.

BBS. 2018. Yearbook of Agricultural Statistics of Bangladesh. 2018. Bangladesh Bureau of Statistics. Government of the People’s Republic of Bangladesh.

Bhattacharjee, A., T. S. Roy, M. N. Haque, M. A. I. Pulok and M. M. Rahman. 2014. Changes of sugar and starch levels in ambient stored potato derived from TPS. International Journal of Scientific and Research Publications. 4(11): 1-5.

Blenkinsop, R. W., L. J. Copp, R. Y. Yada and A. G. Marangoni. 2002. Changes in compositional parameters of potato (Solanum tuberosum L.) during low temperature storage and their relationship to chip processing quality. J. Agric. Food Chem. 50(16): 4545-4553.

Bradbury, J. H. 1986. Determination of energy from moisture content in foods containing small amount of fat and dietary fiber. J. Agric. Food Chem. 34: 358-361.

Burton, W. G. 1966. The Potato: A survey of its history and factors influencing its yield, nutritive value, quality and storage. 382 P. Zonen, Wageningen. Holland.

Burton, W. G. 1973. Physiological and biochemical changes in the tuber as affected by storage conditions. Proceedings 5th Triennial Conference of European Association of the Potato Research, Norwich. 63 P.

CIP. 2013. Innovative Potato Storage for Smallholder Farmer Households in Bangladesh. International Potato Center. Available via https://cipotato.org/potato/innovative-potato-storage-for-smallholder-farmer-households- in-bangladesh/ . Accessed on 27 December 2019.

Claassen, P. A. M., M. A. W. Budde, H. J. de Ruyter, M. H. van Calker and A. van Es.


1991. Potential role of pyrophosphate: fructose 6-phosphate phospho-transferase in carbohydrate metabolism of cold stored tubers of Solanum tuberosum Bintje. Plant Physiology. 95: 1243-1249.

Cochrane, M. P., C. M. Duffus, M. J. Allison and G. R. Mackay. 1991. The effect of low temperature storage on the activities of alpha and beta amylase and alpha glycosidase in potato tubers. J. Potato Res. 34 (4): 333-341.

Dadhich, S. M., H. Dadhich and R. C. Verma. 2008. Comparative study on storage of fruits and vegetables in evaporative cool chamber and in ambient. Int. J. Food Eng. 4(1): 1–11.

Davies, H. V., R. Viola. 1992. Regulation of sugar accumulation in stored potato tubers. Postharvest News Inf. 3(5): 97-100.

de Freitas, S. T., E. I. P. Pereira, A. C. S. Gomez, A. Blackmann, F. Nicolson and D. A. Bisognin. 2012. Processing quality of potato tubers produced during autumn and spring and stored at different temperatures. Horticultura Brasileira. 30(1): 91-98.

Dogras, C., A. Siomos and C. Psomakells. 1991. Sugar and dry matter changes in potatoes stored in a clamp in mountainous region of northern Greece. Potato Research. 34: 211-214.

Edwards, C. G., J. W. Engle, C. R. Brown, J. C. Peterson and E. J. Sorensen. 2002. Changes in colour and sugar content of yellow-fleshed potatoes stored at three different temperatures. American Journal of Potato Research. 79: 49-53.

Elfnesh, F., T. Tekalign and W. Solomon. 2011. Processing quality of improved potato (Solanum tuberosum L.) cultivars as influenced by growing environment and blanching. African J. Food Sci. 5: 324-332.

Ezekiel, R., B. Singh, D. Kumar and A. Mehta. 2007. Processing quality of two potato varieties grown at two locations and stored at 4, 10 and 12 ºC. Potato Journal. 34: 164-173.

Fennema, O. R. 1996. Food Chemistry. Third Edition. Marcel Dekker Inc. New York.

Gottschalk, K. and R. Ezekiel. 2006. Storage. Pp. 489-522. In Handbook of Potato Production, Improvement, and Postharvest Management. Food Products Press, New York-London-Oxford.

Gould, W. A. and S. Plimpton. 1985. Quality evaluation of potato cultivars for processing. Research publication 305, The Ohio State University.

Hossain, M. A. and M. A. M. Miah. 2009. Post-harvest losses and technical efficiency of potato storage systems in Bangladesh. Final Report, National Food Policy Capacity Strengthening Programme (NFPCSP). 1 P.

Huang, Y. H., D. H. Picha, A. W. Kilili and C. E. Johnson. 1999. Changes in invertase activities and reducing sugar content in sweet potato stored at different temperatures. J. Agric. Food Chem. 47: 4927-4931.

Isherwood, F. A. 1973. Mechanism of Starch-Sugar inter-conversion in Solanum tuberosum. Phytochemistry. 12: 2579-2591.

Jha, S. N. and S. Chopra. 2006. Selection of bricks and cooling pad for construction of evaporatively cooled storage structure. Inst. Engineers. (I) (AG) 87: 25–28.

Kabira, J. and L. Berga. 2003. Potato processing quality evaluation procedure for research food industry applications in east and central Africa. Kenya Agric. Res. Insti., Nairobi.

Kadam, S. S., S. S. Dhumal and N. D. Jambhale. 1991. Structure, nutritional composition,


and quality. Pp. 9-36. In: D. K. Salunkhe, et al. (ed.) Potato: Production, Processing, and Products. CRC Press. Boca Raton.

Kang, M. R., M. M. Hanafi, S. M. Shahidullah, A. H. M. A. Rahman, A. M. Akanda and A. Khair. 2009. Virus free seed potato production through sprout cutting technique under net-house. African Journal of Biotechnology. 9(36): 5852-5858.

Kaur, A., N. Singh, R. Ezekiel and N. S. Sodhi. 2009. Properties of starches separated from potatoes stored under different conditions. Food Chemistry. 114: 1396-1404.

Kita, A., A. Bąkowska-Barczak, G. Lisińska, K. Hamouz, and K. Kułakowska. 2014. Antioxidant activity and quality of red and purple flesh potato chips. Food Science and Technology. 62(1): 525-531.

Laerke, P. E. and J. Christiansen. 2005. Identification of pre-harvest biomarkers in potato (Solanum tuberosum L.) for improvement of the post-harvest management of fry-colour. Acta Horticulturae. 682: 493-499.

Lisinski, G. and W. Leszczynski. 1989. Potato tubers as a raw material for processing and nutrition. In G. Lisinski and W. Leszczynski (ed) Potato Science and Technology. Department of Storage and Food Technology. Agricultural Academy, Wroclaw, Poland.

Mehta, H. and H. N. Kaul. 1988. High temperature storage of potato (Solanum tuberosum L.) for processing – a feasibility study. Plant Foods for Human Nutrition. 38: 263-268.

Meilgaard, M., G. V. Civile, and B. T. Carr. 2007. Sensory Evaluation Techniques. 4th Edition. CRC Press. Florida, USA.

Miller, G. L. 1959. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Annals of Chemistry. 31 (3): 426-428.

Olsen, N., G. Kleinkopf, L. Woodell and T. Brandt. 2005. Processing quality: Cultivars, vine kill and storage. Presented at the Idaho Potato Conference.

Pandey, V., V. A. Kumar and A. Brar. 2017. Biochemical Behaviour of Potato Tubers during Storage. Chem. Sci. Rev. Lett. 6(23): 1818-1822.

Perumal, N. K., K. R. Dhiamn, and T. A. Sahota. 1980. Dormancy and sprouting behavior of some Indian cultivars and cultures of potato. Bangladesh Hort. 8: 1-7.

Rommens, C. M., R. Shakya, M. Heapand and K. Fessenden. 2010. Tastier and healthier alternatives to French Fries. Journal of Food Science. 75: 109-115.

Schippers, P. A. 1971. The relation between storage conditions and changes in weight and specific gravity of potatoes. American Potato Journal. 48: 313-319.

Senkumba, J., A. Kaaya, A. Atukwase, and A. Wasukira. 2017. Technical Report: Effect of storage conditions on the processing quality of different potato varieties grown in Eastern Uganda. CRP, RTB. 22 P.

Smith, O. 1987. Effect of cultural and environmental conditions on potato processing. Pp.73-74. In W.F. Talburt and O. Smith (ed) Potato Processing. Van Reinhold Company. New York.

Sonnewald, S. and Sonnewald, U. 2014. Regulation of potato tuber sprouting. Planta. 239: 27-38.

Sonnewald, U. 2001. Control of potato tuber sprouting. Trends Plant Sci. 6: 333-335.

Sowokinos, J. R. 1990. Stress induced alteration in carbohydrate metabolism. Pp. 137- 158. In The Molecular and Cellular Biology of the Potato. Wallingford, UK.


Sowokinos, J. R., P. H. Orr, J. A. Knoper and J. L. Varns, 1987. Influence of potato storage and handling stress on sugars, chip quality and integrity of the starch (amyloplast) membrane. American Potato Journal. 64: 213–226.

Tester, R. F., R. Ansell, C. E. Snape and M. Yusuph. 2005. Effect of storage temperatures and annealing conditions on the structure and properties of potato (Solanum tuberosum L.) starch. Intl. J. Biol. Macromol. 36: 1-8.

Timm, H., M. Yamaguchi, M. D. Clegg and J. C. Bishop. 1968. Influence of high temperature exposure on sugar content and chipping quality of potatoes. America Potato Journal. 45: 359-365.

Wiltshire, J. J. and A. H. Cobb. 1996. A review of the physiology of potato tuber dormancy. Annals Appl. Biol. 129: 553-569.

Work, T. M., A. S. Kezis and R. H. True. 1981. Factors determining potato chipping quality. Technical Bulletin 103, Life sciences and agriculture experiment station, University of Maine at Orono.

Xue, L. 1985. A handbook of Experiments for plant physiology. Shanghai Science and Technology, Shanghai.

Zhitain, Z., C. C. Wheatley and H. Corke. 2002. Biochemical changes during storage of sweet potato roots differing in dry matter content. J. Postharvest Biol. Technol. 24: 317-325.

Z. A. Riyadh, M. A. Rahman, M. G. Miah, S. R. Saha, M. A. Hoque, S. Saha and M. M. Rahman 79

PERFORMANCE OF AROID UNDER JACKFRUIT-BASED AGROFORESTRY SYSTEM IN TERRACE ECOSYSTEM OF BANGLADESH

Z. A. Riyadh1, M. A. Rahman1*, M. G. Miah, S. R. Saha1, M. A. Hoque2 S. Saha1 and M. M. Rahman1

Abstract

The terrace ecosystem is considered as hotspot of jackfruit tree (Artocarpus heterophyllus Lam) in Bangladesh having potential for understory cropping. However, most of the jackfruit orchards are often found utilized or underutilized. A field experiment was conducted under the jackfruit orchard to study the performance of aroids (Colocasia esculenta L.) from April to October, 2017 in Belabo upazila of Narsingdi district. Four distances (1, 2, 3 and 4 m) from the base of jackfruit tree were considered for aroid planting to evaluate its performance as agroforestry crop in comparison to sole aroid. Land use and economic performances of agroforestry and sole systems were also evaluated. The results indicated that the production of jackfruit increased by 62.73%, while the yield of aroid reduced by 33.48% in agroforestry systems as compared to the yields of sole (non-agroforestry) systems. In agroforestry system, the photosynthetically active radiation (PAR) was severely reduced by 85-77% on aroid crop that caused yield reduction. It was also observed that soil temperature was lower in agroforestry system as compared to sole cropping of aroid, while soil moisture showed inverse trend in sole jackfruit. Economic analysis in terms of benefit cost ratio (BCR) was 2.60 in agroforestry, while the BCR of sole aroid was only 1.83. The land equivalent ratio (LER) was 2.31 in agroforestry system. The present results indicate that aroid cultivation in jackfruit-based agroforestry system under terrace ecosystem can ensure overall higher production and improve economic return.

Keywords: Benefit cost ratio, land equivalent ratio, active radiation, sole crop, understory.

1Department of Agroforestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


IntroductionBangladesh has a population of about 163 million making it one of the most densely populated countries of the world and struggling hard to feed the increasing population (BBS, 2018). Bangladesh has increased national capacity for securing food access of large population (GoB, 2013). Though the grain food production increased, food and nutrition

security remain under challenge due to insufficient vegetable and fruit production against huge demand. The country has only 7.76 million ha of arable land and per capita arable land availability decreasing at an alarming rate, from 0.174 ha in 1961 to 0.048 ha in 2016 (World Bank, 2018). Furthermore, the soil fertility of arable land is decreasing day by day due to intensive cropping with

80 Performance of aroid under Jackfruit-Based Agroforestry System in Terrace Ecosystem

improper management. Under these scenarios, it is necessary to develop alternate systems that could increase crop production and land use efficiency wherever possible. Because of increasing demand for food, fruits, timber, fodder, fuel wood, and poles, production of multiple products from the same land management unit are indispensable. So, combined production system integrating field crops with perennial trees, which is called agroforestry, may be a viable option to overcome the future challenges. Such systems would increase production per unit area and per unit time, and at the same time would maximize the utilization efficiency of natural resources (Bhuiyan et al., 2012). Moreover, such cropping systems are highly productive and sustainable that provides the opportunity for year-round production. Worldwide, fruit tree-based agroforestry systems are highly popular among resource limited producers (Bellow, 2004) and are capable in providing higher economic return even under stressed growing conditions prevailing under the upland situations than the other annual crops (Bikash et al., 2008). Jackfruit based agroforestry systems are widely found in terrace ecosystem of Bangladesh with various vegetable and spice crops. Though most of the jackfruit trees are not planted in a systematic manner, there is enough space to grow suitable understorey crops and reported to increase the overall production (Miah et al., 2018).

Under jackfruit-based agroforestry system, aroid (Colocasia esculenta L) is a compatible crop due to its shade tolerant nature and is extensively grown in terrace ecosystem. Aroid is a popular vegetable, which is widely grown in Kharif season and contributes a considerable amount of total supply of vegetables during

lean period (August to October). Aroids are rich sources of carbohydrate and contains enough protein (Verma and Singh, 1996). Despite huge demand of aroids, its production is low in Bangladesh. Thus, there is a scope of cultivating aroid in jackfruit orchards to increase the system productivity and economic benefits, and augment the supply of aroids in domestic market. There is not enough information on the performance of aroid as a component of jackfruit-based agroforestry system. In this context, the present experiment was undertaken with the aim to evaluate the performance and economic return of aroid and micro-environmental changes in jackfruit-based agroforestry system in the terrace ecosystem of Bangladesh.

Materials and Methods

The experiment was conducted in the existing jackfruit orchards at Belabo upazila of Narsingdi district during April 2017 to October 2017. Geographically, the study site is located at 24°05´ North latitude and 90°50´ East longitude at an elevation of 9m above the sea level and characterized by a sub-tropical climate with mild-summer and winter, heavy rainfall during the months from April to September and scanty rainfall during the rest of the year. The soil of the experimental area is clay-loam in texture belonging to Madhupur Tract (AEZ-28) and classified as shallow red-brown under Inceptisol soil category according to USDA Soil Taxonomy (Brammer, 1971; Shaheed, 1984). The land topography is characterized by upland and closely associated narrow-valleys, popularly alluded to as Chala and Baid, respectively.


The soil of the study area is red-brown in color and strongly acidic in nature with low-organic matter content and poor fertility levels.

The field experiment was conducted in randomized complete block design (RCBD) with three replications. Each jackfruit tree was considered as unit plot for a single replication. Plot size of each replication were not similar due to various age, canopy size and shape of jackfruit trees. There were five treatments for aroid viz. 1-meter distance from tree base (1m), 2-meter distance from tree base (2m), 3-meter distance from tree base (3m), 4-meter distance from tree base (4m) and open field (non-agroforestry/control). There were two treatments for jackfruit tree, open field (non-agroforestry) and agroforestry. Heterogeneous jackfruit trees with the age between 26 and 30 years were selected for this study. The seed tubers of aroid (local var. Gaitta) were planted on 3rd April 2017 at a depth of 6-8 cm by maintaining the spacing at 50 cm × 30 cm. Rainfed aroid plots were fertilized with cowdung, Urea, TSP and MoP at the rates of 5000, 150, 100 and 120 kg ha-1, respectively (FRG, 2012). The full doses of cowdung, TSP and MoP were applied at the time of final land preparation. Urea was applied in two equal splits at 60 and 90 days after planting (DAP) of aroid. Weeding was done twice at 30 and 45 DAP. Earthing up of aroid was done by taking up the soil from the space between the rows of aroid field at 60 and 90 DAP.

Aroid was harvested on 4th October when the leaves become pale yellow after 180 DAP. Data on plant height and leaves per plant were measured when it reached peak at 130 DAP; whereas, number of sucker per hill, cormel number per hill, corm weight

per plant, total cormel weight per hill and yield were recorded from the average of ten plants at harvesting. The yield performance of jackfruit tree in both the agroforestry and non-agroforestry systems were measured. Photosynthetically active radiation (PAR) above aroid in agroforestry and open field were measured by sunfluxceptometer in terms of µmolm-2s-1. Soil moisture percentage was recorded by DSMM500 soil moisture meter and the soil temperature (ºC) was measured by Temp 4/5/6 Thermistor Thermometer. All microclimatic data were taken at noon. Benefit cost ratio (BCR) and land equivalent ratio (LER) were calculated according to the procedure Mead and Willey (1980). The recorded data were statistically analyzed to find out the significance of the results by the “Analysis of Variance” (ANOVA) technique using computer package “Statistix 10”. The mean differences of treatments were compared by Least Significant Difference (LSD) at P < 0.05. Finally, relevant tables and figures were prepared according to the objectives of the study.

Results and DiscussionPerformance of aroidPlant height: The plant height of aroid varied significantly in agroforestry system in comparison to that of non-agroforestry system (Table 1). The tallest plant (116.23 cm) was found at 1m distance from jackfruit tree base in agroforestry system and the shortest (95.42 cm) was found in sole (open) aroid field. However, aroid plant height increased by 21.81, 19.34, 12.69 and 5.64%, respectively, at 1, 2, 3 and 4m distance from the tree base, when equated with sole aroid field. This result might be due to variations of light percentage


in different distances from tree base. Crops grown in low light levels usually exhibit apical dominancy due to high auxin production in shaded condition (Hillman, 1984). Similar results have also been reported for aroid in agroforestry systems under reduced of light levels (Bhuiyan et al., 2013).

Leaves per plant: Though agroforestry system had a little effect on the number of leaves per plant of aroid, it increased with increasing of planting distances from tree base (Table 1). Nevertheless, the number of leaves decreased by 11.35, 11.14, 10.48 and 4.80%, respectively, at 1, 2, 3 and 4m distance from the jackfruit tree base in comparison with that of non-agroforestry condition. This result might be due to lower production of photosynthate under low light conditions that might have reduced the number of leaves per plant in shaded condition. Similar results were reported in agroforestry system in the previous study for onion (Allium cepa) by Miah et al. (1999) and aroid (Colocasia esculenta) by Bhuiyan et al. (2013).

Number of suckers per hill: Significantly the highest number of sucker (5.56) was found in

open field condition and the lowest (3.63) at 1m distance from tree base in agroforestry system (Table 1). In comparison to non-agroforestry (open) field, the number of suckers per hill was significantly attenuated by 34.71, 31.65, 27.52 and 22.12%, respectively at 1, 2, 3 and 4m distances from the tree base. This might be due to shade effect under agroforestry system. Rahman et al. (2010) also reported that the number of branches per plant of tomato decreased gradually with the increase of shade levels.

Yield contributing characters of aroid: Yield contributing features of aroid also significantly reduced in agroforestry systems as compared to sole (open) aroid field (Table 1). However, distance regimes of 1, 2, 3 and 4m from jackfruit tree base caused notable reduction in corm weight (26.34, 18.27, 11.96 and 9.25%, respectively), number of cormel per hill (43.30, 35.77, 29.23 and 18.66%, respectively) and total cormel weight per hill (43.06, 40.73, 31.33 and 27.02%, respectively) relative to that of sole aroid field (Table 1). These findings might be due to increasing competition for available resources (light, water and nutrients) sharing between jackfruit tree and aroid plant with decreasing of planting distances towards tree base.

Table 1. Growth and yield contributing characters of aroid in jackfruit-based agroforestry- and sole aroid-system

Treatments Plant height (cm)

No. of leaves per plant

No. of suckers per hill

Individual corm wt. (g)

No. of cormels per hill

Total cormel weight per hill (g)

1m 116.23 (±1.55)a 4.06 (±0.03)b 3.63 (±0.14)c 73.48 (±2.12)d 11.00 (±0.62)d 160.58 (±3.57)c2m 113.87 (±1.69)a 4.07 (±0.04)b 3.80 (±0.17)c 81.53 (±2.19)c 12.46 (±0.70)cd 167.15 (±4.92)c3m 107.53 (±2.23)b 4.10 (±0.05)b 4.03 (±0.08)bc 87.83 (±1.79)b 13.73 (±0.61)c 193.67 (±9.04)b4m 100.80 (±1.30)c 4.36 (±0.12)ab 4.33 (±0.14)b 90.53 (±1.59)b 15.78 (±0.59)b 205.80 (±5.03)bSole Aroid 95.42 (±1.43)c 4.58 (±0.13)a 5.56 (±0.12)a 99.76 (±3.01)a 19.40 (±0.70)a 282.01 (±3.99)a

Values are means (± standard errors) (n = 3). Different alphabetical letters within the same column indicate significant differences among the treatments according to a least significant difference (LSD) test (p < 0.05).


Yield of aroid: Significant difference was observed in the yield of aroid between agroforestry and open field (Fig. 1). Both the maximum cormel yield (16.92 ton ha-1) and corm yield (5.98 ton ha-1) were recorded in sole aroid field, while the minimum cormel (9.63 ton ha-1) and corm (4.40 ton ha-1) yield were recorded at 1m distance from tree base in agroforestry system. These findings may be due to the competition for available growth resources like light, water and nutrients between jackfruit trees and aroids (Fig. 1). Competition for available light was a major factor in reducing maize yields in plants

grown closest to the trees (Everson et al., 2004) as the shade of the tree induces stress conditions to the understory crop (Dufour et al., 2013). Competition for nutrients and water are another negative influence of trees on the yield of associated crops (Ong et al., 2002). From two different previous studies on ber and aroid based agroforestry system and Colocasia esculenta under 6 years old aonla (Emblica officinalis) tree recorded that the yield reduction of 28.3% and 20.5%, respectively, (Wadud, 1999; Das et al., 2011).

Performance of jackfruitAlthough the present study was conducted in unmethodically established jackfruit orchard, it was evident that agroforestry system had a positive influence on the yield performance of jackfruit (Table 2). Proper management practices of agroforestry system notably increased the number of fruits per tree (113.75%), fruit length (3.78%) and yield per tree (62.73%). However, the single fruit weight reduced by 13.54% and fruit diameter by 17.62%, as compared with that of the non-agroforestry jackfruit trees (Table 2). Though the fruit size and weight vary with genetic variability, on average the individual fruit

Cormel Corm

4.4 d

9.63 c 10.02 c11.62 b

12.34 b

4.89 c

Treatments1m 2m 3m 4m Sole Aroid

5.27 b 5.43 b5.98 a

16.92 a

Cor

m y

ield

(to

n ha

-1)

Cor

mel

yie

ld (

ton

ha-1)

7

6

5

4

3

2

1

0

20

18

16

14

12

10

8

6

4

2

0

Fig. 1. Yield of aroid in jackfruit-based agroforestry system and sole aroid field.

Table 2. Yield and yield contributing characters of jackfruit in agroforestry and non-agroforestry system

ParametersSystems

Variation (%)Agroforestry Non-agroforestry

Number of fruits per tree* 20.67(±6.23) 9.66(±.88) +113.75Single fruit weight of jackfruit (kg) 9.70(±3.55) 11.22(±1.43) -13.54Fruit length (cm) 42.20(±2.65) 40.67(±3.38) +3.78Fruit diameter (cm) 30.33(±3.63) 36.82(±6.88) -17.62Yield (kg tree-1) * 173.25(±37.57) 106.46(±9.34) +62.73

Values are means (± standard errors) (n = 3). * Significant at the 0.05 level of probability.


weight was lower in agroforestry, which may be due to profuse bearing of fruits. Moreover, the yield increment of jackfruit in agroforestry system might be due to external application of manures and fertilizers to the crop component as well as decomposition of crop residues that would have been taken up by the tree. Similar observation was also reported by Miah et al. (2018) where eggplant and papaya were evaluated in jackfruit orchard.

Economic and land use performanceThe economic and land use analyses revealed that higher benefits were obtained in agroforestry system in comparison to sole cropping system (Table 3). The highest net return (BDT 241860/ha) and BCR (2.60) was estimated in jackfruit-aroid agroforestry system as against their respective sole cropping systems (Table 3). The LER of jackfruit-aroid agroforestry system was 2.31, indicating that 2.31 times higher land would be required to produce similar amount of production in sole cropping as compared to agroforestry system (Table 3). This result might be due to excellent yield from jackfruit without additional management practices and satisfactory yield of aroid in agroforestry system. The higher economic benefits from jackfruit-based agroforestry system have also

been reported in other studies such as eggplant (Miah et al., 2018; Rahman et al., 2018) and pineapple (Hasan et al., 2008).

Microclimatic modification in the agroforestry system This study demonstrated that during the whole experimental period, on an average, the photosynthetically active radiation (PAR), soil temperature and moisture significantly varied in jackfruit-based agroforestry as compared to open field (Fig. 2). In comparison to open field, agroforestry resulted in the reduction of PAR by 85.54, 84.02, 80.11 and 77.85%, and soil temperature by 8.09, 8.03, 6.77 and 5.28% at 1, 2, 3 and 4m distances from tree base, respectively (Fig. 2). While it impressively raised the soil moisture content by 9.64, 23.81, 24.90 and 13%, respectively at the distances of 1, 2, 3 and 4m from the jackfruit tree base (Fig. 2). Though, light transmission was greatly influenced by canopy volume and ordination, PAR availability significantly decreased with reducing distance towards the tree base. To find out the relationship between PAR and crop yield, regression analysis was carried out. Polynomial relationships of aroid yields with PAR represented strong association as evident by R2= 0.96 (Fig. 3). The R2 value indicates that 96% of contribution to the yield of aroid

Table 3. Economic and land use performances of jackfruit based agroforestry system in compared to sole cropping system

Systems Total Cost (a) (BDT/ha)*

Return (BDT/ha) Gross income (b+c)

Net return(b+c-a) BCR LER

Jackfruit (b) Aroid (c)Agroforestry 150930 173200 219590 392790 241860

(135360)#2.60 2.31

Sole aroid 173430 - 317540 317540 144110 1.83 -Sole jackfruit - 106500 - 106500 106500 - -

* 1$= 83 BDT. #Additional income over sole jackfruit orcharding.


can be explained by the above polynomial regression equation. The regression line showed that if the aroid plants received 820 µmol m-2 s-1 PAR level, yield could be reached to 20-ton ha-1, and beyond this PAR level, the yield would be decreased by 0.00002-ton ha-1

with respect to per unit increment of PAR (Fig. 3). It was evident that the yield of aroid was greatly reduced by severe reduction of light grown under jackfruit tree. The results of the present study corroborated by the results of Rivest et al. (2009). The lower temperature in agroforestry might be due to shade casting by the canopy of the jackfruit trees. Overall higher moisture content in agroforestry may be attributed to the lower rate of evaporation of water from the soil surface and due to continuous shedding of litter from jackfruit trees which increased water retention capacity. Several studies in agroforestry systems in different region of the world have shown that soil temperature was substantially lower and soil moisture content was higher than open field (Rahman et al., 2018; Lin et al., 2015; Singh et al., 2012).

ConclusionsConsidering the present findings, it may be concluded that aroid cultivation under jackfruit orchard may be effective in improving system productivity and profitability. Jackfruit yield was increased dramatically by 62.73%, whereas the aroid yield was reduced by 33.48% in agroforestry systems. Though the yield of aroid was reduced in agroforestry due to heavy shade provided by jackfruit tree, it can be successfully cultivated in jackfruit orchards to generate substantial additional income and ensure better microclimate. Hence, cultivation of aroid under jackfruit-based agroforestry system might be encouraged.

AcknowledgmentsThe authors are grateful to the authority of Bangladesh Agricultural Research Council (BARC) for providing financial support

25

20

15

10

50 200 400 600 800 1000 1200 1400

PAR (µmol m-2 s-1)

1M 2M 3M -4M OPEN

y= -2E-05x2 + 0.0393x + 3.4262R2 = 0.9596

Yie

ld (

ton h

a-1)

Fig. 2. PAR, Soil temperature and moisture percentage in agroforestry system as well as sole cropping system at different distant regimes amid the experimental period. (Vertical bars indicate standard error and different alphabetical letters indicate significant differences among various treatments).

Fig. 3. Relationship between Photosynthetically Active Radiation (PAR) and the yield of aroid in jackfruit-based agroforestry system.


to conduct the experiment under National Agricultural Technology Project-2 (Grant No. 595). We are also thankful to the farmers of Belabo for their kind support during field experimentation.

ReferencesBBS. 2018. Statistical pocket book

Bangladesh-2018, Bangladesh Bureau of Statistics, Ministry of Planning, Government of Peoples’ Republic of Bangladesh, Dhaka.

Bellow, J. G. 2004. Fruit-tree-based agroforestry in the western highlands of Guatemala: an evaluation of tree-crop interactions and socioeconomic characteristics, Doctoral dissertation, University of Florida.

Bhuiyan, M. M. R., S. Roy, P. Bala, A. K. Azad, M. N. Nasir and N. C. Barmon. 2013. Performance of aroids grown under multilayer agroforestry system. Eco-friendly Agril. J. 6(01): 01-05.

Bhuiyan, M. M., S. Roy, P. C. Sharma, M. H. Rashid and P. Bala. 2012. Impact of multistoried agro-forestry systems on growth and yield of turmeric and ginger at Mymensingh, Bangladesh. Crop Production. 1(1): 19-23.

Bikash, D., S. Ranvir and S. Kumar. 2008. Fruit based agroforestry system for uplands. Horticulture and agroforestry research programme. ICAR Research Complex for Eastern Region Ranchi, Jharkhand, India. 341-345.

Brammer, H. 1971. Soil resources. Soil Survey Project Bangladesh. AGL: SF/Pak. 6 Technical Report 3, Bangladesh.

Das, D. K., O. P. Chaturvedi, R. K. Jha and R. Kumar. 2011. Yield, soil health and economics of aonla (Emblica officinalis Gaertn.) based agri-horticultural systems in eastern India. Curr. Sci. 101: 786-790.

Dufour, L., A. Metay, G. Talbot and C. Dupraz. 2013. Assessing light competition for cereal production in temperate agroforestry systems using experimentation and crop modeling. J. Agro. Crop. Sci. 199:217–227.

Everson, T. M., C. Everson and W. Van Niekirk. 2004. Agroforestry in rural farming systems: a case study from the Drakensberg mountains in KwaZulu-Natal. Indigenous forests and woodlands in South Africa: policy, people and practice. University of KwaZulu-Natal Press, Scottsville, South Africa. Pp. 650-658.

FRG. 2012. Fertilizer Recommendation Guide, Bangladesh Agricultural Research Council (BARC), Farmgate, Dhaka 1215.

GoB. 2013. Millennium Development Goals: Bangladesh Progress Report 2012. Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh.

Hasan, M. K., M. M Ahmed and M. G. Miah. 2008. Agro economic performance of jackfruit-pineapple agroforestry System in Madhupur Tract. J. Agric. Rural Dev. 6: 147-156.

Hillman, J. R. 1984. Apical dominance. In: Wilking, M.b. (ed.). Advanced plant Physiology. Pitman, London. Pp. 127-148.

Lin, B. B., A. J. Burgess and E. H. Murchie. 2015. Ten adaptation for climate-sensitive crops using agroforestry: Case studies for coffee and rice. Tree-Crop Interactions: Agroforestry in a Changing Climate. 278 P.

Mead, R. and R. W. Willey. 1980. The concept of ‘‘Land Equivalent Ratio’’ and advantages in yields from intercropping. Exp. Agric. 16:217–228.

Miah, M. G., M. A. Rahman and M. M. Haque. 1999. Performance of onion under different reduced light levels for agroforestry and intercropping systems. Bull. Inst. Trop. Agric. Kyushu University. 22: 31–38.


Miah, M. G., M. M. Islam, M. A. Rahman, T. Ahamed, M. R. Islam and S. Jose. 2018. Transformation of jackfruit (Artocarpus heterophyllus Lam.) orchard into multistory agroforestry increases system productivity. Agrofo. Syst. 92(6): 1687-1697.

Ong, C. K., J. Wilson, J. D. Deans, J. Mulayta, T. Raussen and N. Wajja-Musukwe. 2002. Tree–crop interactions: manipulation of water use and root function. Agril. Water Management. 53(1-3): 171-186.

Rahman, A., M. A. Rahman, M. G. Miah, M. A. Hoque and M. M. Rahman. 2018. Productivity and profitability of jackfruit-eggplant agroforestry system in the terrace ecosystem of Bangladesh. Turkish J. Agril. Food Sci. Tech. 6(2): 124-129.

Rahman, K. M., M. A. Mondol, G. M. M. Rahman, M. Harun-or-Rashid and M. Shamsunnahar. 2010. Performance of tomato under multistoried agroforestry system. J. Agrofor. Environ. 4(2): 109-111.

Rivest, D., A. Cogliastro, A. Vanasse and A. Olivier. 2009. Production of soybean associated with different hybrid poplar clones in a tree-based intercropping system

in southwestern Québec, Canada. Agric. Ecosyst. Env. 131(1-2): 51-60.

Shaheed, S. M. 1984. Soils of Bangladesh: general soil types. Soil Resources Development Resources Institute (SRDI), Farmgate, Dhaka, Bangladesh.

Singh, A. K., K. Pravesh, S. Renu and R. Nidhi. 2012. Dynamics of tree-crop interface in relation to their influence on microclimatic changes-a review. HortFlora Rese. Spectrum. 1(3): 193-198.

Verma, R. B. and P. k. Singh. 1996. Effect of nitrogen and potassium levels on growth, yield and nutrient uptake of Colocasia. J. Root Crops, 22: 139-143.

Wadud, M. A. 1999. Performance of four summer vegetables under reduced-light conditions for Agroforestry systems, MS Thesis, Dept. of Agroforestry and Environment, BSMRAU, Gazipur, Bangladesh.

World Bank. 2018. Collection of development indicators of the World Bank-2018. Available via https://data.worldbank.org/indicator/AG.LND.ARBL.HA.PC?locations=BD. Accessed on 10 Mar 2019.

Influence of HarvestIng Date on cHemIcal maturIty for ProcessIng QualIty of Potatoes

m. sharkar1, J. u. ahmed1, m. a. Hoque2 and m. mohi-ud-Din1*

abstract

The present study was conducted to find out a suitable harvesting date of processing potato varieties (Asterix, Courage and Lady Rosetta) from three different harvest dates [80, 90, and 100 days after planting (DAP) harvest] by chemical maturity monitoring. Eighty DAP harvest resulted the lowest mean total soluble sugar (TSS) (3.77 mg/g FW), reducing sugar (RS) (1.57 mg/g FW), sucrose (2.40 mg g-1 FW), fructose (0.77 mg/g FW) and polyphenol (238.94 µg/g FW) contents in all the varieties and at the same DAP harvest, dry matter (DM) content (21.71%) and chip color index (CCI) (0.67) remained at the lowest. Tubers harvested at 80 DAP produced good quality and acceptable colored processed products as it meets up the required processing quality, but lesser DM content might increase the cost of the product. Optimum DM content (24.07%) with moderate level of different sugar contents and acceptable CCI (1.13 to 1.85, <2.00) was found at 90 DAP harvest. Therefore, 90 DAP harvest could be considered as suitable harvesting date for processing by compromising some quality parameters (TSS, RS, sucrose, fructose and polyphenol contents). Among the varieties, Lady Rosetta and Courage were preferable for producing quality potato products. Highly significant and positive correlation existed between CCI and different chemical parameters. A strong correlation coefficient (r = 0.822**) and good fit (R2 = 0.6755) of the regression equation (CCI = 0.9341RS – 0.4969) between CCI and RS indicated that RS content played the vital role in the browning of the processed potato products.

Keywords: Reducing sugar, sucrose, chip color index, processing potatoes.

1Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


IntroductionPotato (Solanum tuberosum L.) is one of the most important vegetable crops and it is a part of daily food utilization of almost all the world population (Mathur, 2003). It is a staple diet in European countries and its utilization both as processed and fresh forms are increasing considerably in Asian countries (Brown, 2005). It is consumed in different forms like boiled or fried and many different processed products like chips,

French fries, powder, potato papad etc. which are enjoyed across the generations and continents. Processing quality of potato tubers is determined by high dry matter and low reducing sugar and phenol contents (Kadam et al., 1991). High dry matter content increases chip yield, crispy-consistency and reduces oil absorption during cooking (Pedreschi et al., 2005; Rommens et al., 2010) and high polyphenols are responsible for an enzymatic darkening of potato products (Wang-Pruski

90 Influence of Harvesting Date on Chemical Maturity for Processing Quality of Potatoes

and Nowak, 2004). Low reducing sugars and phenol contents are required to avoid dark color and bitter taste of processed products, which negatively affect consumer acceptance (Wang-Pruski and Nowak, 2004). For making good quality French fry, the reducing sugar content of potatoes is required to be low (Marquez and Anon, 1986). The principal reason for the critical role of hexose reducing sugars in potato quality is the fact that frying at high temperature results in a typical Maillard reaction between these sugars and the amino acid groups of nitrogenous compounds, resulting in a dark colored, bitter testing product (Shallenberger et al., 1959).

Sugar contents in potato are complex, and the concentration levels vary according to the maturity of the harvested crop as well as the environment where the crop is grown. Apart from cultivar differences, maturity may be one of the principal factors affecting sugar content in potato tuber (Burton and Wilson, 1978). There is considerable variation among cultivars with regards to their susceptibility to the degree of maturation and related to physiological process (Es and Hartmans, 1987). However, tuber may be physically mature without being chemical maturation (low sugar concentration). Optimal processing quality for potato tuber is defined currently in terms of a chemical maturity when sucrose and glucose contents are minimal (Sowokinos and Preston, 1988). The levels of sugar in processing potato should be 1.5 mg/g fresh weight or less at harvest to minimize accumulation of reducing sugars.

Chemical Maturity Monitoring (CMM) involves analysis of tuber sugar contents, is a highly useful approach for tuber

management during pre- and post-harvest periods (Sowokinos and Preston, 1988). Processing quality during storage is adversely affected by field treatments that enhanced tuber maturity at harvest. By minimizing the harvest of immature or stressed tubers and carefully assessing their progress in storage, product quality in chips and French fries can be controlled with minimal loss of raw source material (Mazza, 1983). CMM is one of the commercially feasible methods to rapidly measure the chemical maturity of potatoes from the field up to the time of processing on the basis of sugar content. Potatoes reach chemical maturity during a normal growth process when free sugars drop to minimum level for processing (Preston, 2003). Monitoring the chemical maturity of potatoes during development, at harvest, and in storage can help to minimize losses from immaturity and/or stress effects that influence processing product color i.e. chips and fries (Preston, 2003).

Different research institutions in Bangladesh prescribed the suitable harvesting dates of their developed processing potato varieties. However, the indication is based on yield and physical maturity, although chemical maturity is more precise indication used to determine the quality of processing potato varieties. Evidently, the chemical maturity is considered to be the key factor that affects the processing quality of potato. The approach of this study is to check the effect of harvesting dates on the chemical maturity and polyphenol contents of potato tubers; and to determine their relationship with chip color of the processing varieties cultivated in Bangladesh.

M. Sharkar, J. U. Ahmed, M. A. Hoque and M. Mohi-Ud-Din 91

materials and methodslocation, plant materials and experimental designThe experiment was conducted at the research farm of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh. Three processing potato varieties (viz. Asterix, Courage and Lady Rosetta) cultivated in Bangladesh were collected from the Tuber Crops Research Center (TCRC) of the Bangladesh Agricultural Research Institute (BARI) and Bangladesh Agricultural Development Corporation (BADC). The experiment was laid out in a 2-factored Randomized Complete Block Design with 4 replications. Three processing potato varieties represented Factor A and harvesting dates of 80, 90, and 100 days after planting (DAP) represented Factor B.

land preparation, fertilizer application and tuber plantingThe experimental field was mechanically ploughed, laddered and leveled until fine tilth. Irrigation channels were prepared around the plots as per design. A fertilizer dose of N, P, K and S were applied as per fertilizer recommendation guide in the form of Urea, Triple Super Phosphate (TSP), Muriate of Potash (MP), Gypsum, respectively along with cow dung. Full doses of cow dung (10 t/ha), TSP (150 kg/ha), MP (250 kg/ha), Gypsum (120 kg/ha), and half doses of Urea (125 kg/ha) were applied at the final land preparation. The rest half doses of Urea (125 kg/ha) were applied as top dressings at 30 DAP followed by earthing up and light irrigation. Fertilizers were placed in row

which was deeper than the tuber row. Unit plot size was 4m × 3m and the tubers were planted following 60 cm × 25 cm spacing. Sixteen seed tubers were placed in every row.

Intercultural operations and tuber harvesting

Intercultural operations such as weeding, irrigation, earthing up etc. were done manually. First earthing up was done at 30 DAP when the plant attained a height of about 15-20 cm while the last one was done after 20 days of first earthing up. First irrigation was applied just after planting while the rest irrigations were done at 20, 30, 40, and 50 days after planting. Ridomil Gold was sprayed at the rate of 5 g/L once to prevent the late blight disease of potato. Hand picking was done to prevent the cutworm attack. The haulms were cut at 70, 80, and 90 DAP. Tubers were harvested 10 days after haulm cutting. After harvesting potato tubers were classified into four grades according to Karim et al. (2010), estimating diameter at the middle of the potato tubers. These grades were under grade (<28 mm), grade A (28-40mm), Grade B (41-55 mm) and over grade (>55 mm). Among these grades, grade A and grade B together were considered as processable grade tuber.

Determination of tuber dry matter content

Five processable grade tubers were chopped in thin pieces and about 50 g fresh sample were oven dried at 80°C till constant weight was achieved. Then the moisture percentage was determined and this was deducted from the total 100% moisture to get the dry matter (%).


extraction and determination of soluble sugars Sugar content of potato was extracted by following the procedure of Xue (1985) with slight modification. Briefly, 500 mg of fresh potato flesh was extracted thrice with 5 ml of 80% (v/v) ethanol at 80°C for 30 min and the extracts were centrifuged at 5000 rpm for 10 min. The supernatants were combined in a 50 ml beaker and placed in a water bath at 80-85°C until the volume is reduced to about 1 ml. The sugar extract was transferred to a 10 ml volumetric flask by 3-4 wash with distilled water and used for assaying total soluble sugars, reducing sugar, sucrose, glucose and fructose contents.

glucose content: Glucose content was estimated by glucose enzymatic assay kit (Linear Chemical, Spain) following the procedure attached with the kit pack. Briefly, 500 µl of sugar extract was taken in a test tube and 5 ml of enzyme reagent was added to convert glucose to a colored product. Then the tubes were kept for 10 min in room temperature and the absorbance reading was taken at 500 nm. A standard curve was made from a series of standard solution of glucose and the amount of glucose was estimated using the standard curve.

fructose content: Fructose content of potato flesh was measured by the anthrone colorimetric method following the procedure of Kang et al. (2009). Briefly, 1 ml of sugar extract was taken in a test tube and 5 ml of anthrone reagent (conc. H2SO4: anthrone powder = 1 Lit: 2 gm) was mixed in it and was incubated at 40°C for 10 min. Then it was allowed to cool down and absorbance was read at 510 nm. A standard curve was

prepared from a series of standard solution of fructose and the amount of fructose was estimated using the standard curve.

reducing sugar content: Reducing sugar content was measured by DNS colorimetric method following the procedure of Miller (1959) with some modifications. Initially, 3 ml of the sugar extract was taken in the test tube and 3 ml of DNS reagent was added. The content was heated in a boiling water bath for 5 min and then 1 ml of 40% Rochelle salt solution was mixed when the contents of the tubes were still warm. Then the mixture was kept for cooling and the intensity of dark red color was taken at 510 nm. A series of standards were prepared using glucose. Then the amount of reducing sugar present in the samples were calculated using the standard curve.

sucrose content: Sucrose content was measured through the procedure of Kang et al. (2009). Initially, 0.75 ml of extract was taken in a test tube and 0.25 ml of 2M KOH was mixed and boiled for 10 min. Then it was allowed to cool down at room temperature and 5 ml anthrone reagent was added to the mixture and incubated at 40°C for 15 min. The absorbance was taken at 510 nm after cooling the mixture. A series of standard solution of sucrose were prepared to make a standard curve to calculate the amount of sucrose present in the sample.

total soluble sugar content: Total soluble sugar was calculated by the summation of glucose, fructose and sucrose contents as done following the procedure described by Adu-Kwarteng et al. (2014).


extraction and determination of total polyphenol contentThe extraction was done by following the procedure of Nayak et al. (2011). In short, 1 g of potato flesh was peeled, chopped and homogenized with 10 ml of HPLC grade methanol to a uniform consistency by mortar and pestle. The samples were centrifuged at 15,000g at 4°C for 20 min and the supernatants were stored at -20°C for further analysis. Total polyphenol content was determined spectrophotometrically according to the Folin-Ciocalteu method (Singleton et al., 1999, Ainsworth and Gillespie, 2007) with slight modification. The absorbance of the reaction solutions was measured at 765 nm against a blank. The measurements were compared to a standard curve of gallic acid solutions and expressed as µg of gallic acid equivalents per g fresh weight ± standard error (µg GAE/g FW) ± SE.

Potato chips production and determination of chips color indexPotato chips from processable grade were produced according to Kita et al. (2014) with slight modification. After washing potatoes were cut into slices of 2 ± 0.1 mm thickness with a potato slicer, washed in cold saline water (NaCl @ of 20 g/L) and superficially dried by paper towels. The chips were deep fried about 3 min in refined rice bran oil heated to 180°C. After discharging of the oil and cooling, chips were packed in aluminum foil packages and taken for further use.

Chips Color Index was determined by using “USDA Color Standards for Frozen French Fried Potatoes” according to Pedreschi et al. (2012) with some modifications. Each

sample consisted of 10 chips were underwent to 10 assessors for sensory assessment. Each assessor compared each sample chip with the pictures shown in the standard color chart presented in the “USDA Color Standards for Frozen French Fried Potatoes” and a code was assigned based on the color tone of the analyzed sample. The color assessment was done under an open light condition similar to a day light overcast sky. The chart was designed to evaluate the color of French fries, but it has been used successfully as well to evaluate the color of potato chips (Pedreschi et al., 2005).

statistical analysis Statistical analysis was performed using Statistix 10 software. Data were subjected to two-way analysis of variance (ANOVA) for mean comparison, and significant differences were calculated according to Duncan’s multiple range test (DMRT). Data were reported as mean ± standard error (SE). Differences at p<0.05 were considered to be statistically significant. Correlation analysis was performed by Minitab 16 software.

results and DiscussionThe ANOVA for processing qualities revealed that the main effects of the varieties and harvest dates were significant for all the quality parameters (Table 1). Two-way interaction between the varieties and harvest dates were significant apart from reducing sugar and chip color index.

Dry matter content

Dry matter (DM) content was increased up to 90 DAP harvest and thereafter decreased in all three potato varieties (Fig. 1a). The


increment was significant in variety Lady Rosetta and Asterix but in case of courage it was non-significant; and the decrease in DM content after 90 DAP harvest was significant only in Asterix. In all 3 varieties, the highest DM content was attained at 90 DAP harvest compared to 80 and 100 DAP.

In case of harvest dates, 90 DAP harvest (24.07%) yielded significantly higher DM than 80 DAP (21.71%) and 100 DAP (22.27%) DAP harvest. A slight decrease in dry matter accumulation at later stage of maturity was also observed earlier (Söğüt and Öztürk, 2011) that resembles the present findings. This reduction in dry matter accumulation at the late harvest was due to the imbalance between maintenance respiration and slow growth of the tubers at high soil temperatures (Söğüt and Öztürk, 2011).

Mean DM content of the varieties showed statistically significant difference among them. Lady Rosetta had significantly higher DM content (24.87%) as compared to Asterix (20.02%) and Courage (23.14%). The result indicated that variety Lady Rosetta could be preferred as suitable variety and 90 DAP harvest could be optimum to get higher DM content. Sharkar et al. (2019) also suggested that 90 DAP harvest was suitable for processing considering optimum dry matter

content, specific gravity, starch content, processable tuber yield and total tuber yield in the similar growing conditions.

Dry matter is an index of better processing quality, as it results in lesser oil absorption and less frying time (Pavlista and Ojala, 1997). Particularly with crisps, lower dry matter contents require more energy to drive out the moisture and more oil to replace it (Lulai and Orr, 1979). If too much oil is required, i.e. dry matters are low; the resulting crisps are soggy and oily with a reduced shelf-life and increased production costs. Higher dry matter results in better textured products, crisps are crunchier and french fries are mealier and firmer (Genet, 1992).

total soluble sugar content

Total soluble sugar (TSS) was found to be increased with crop maturity in all the three varieties, but the increment was significant only in Asterix (Fig. 1b). In all 3 varieties, the lowest TSS was attained at 80 DAP harvest.

In case of harvest dates, 80 DAP harvest (3.77 mg/g) gave significantly the lowest TSS than 90 DAP (4.70 mg/g) and 100 DAP (7.01 mg/g) harvest. Taking varietals means of TSS, variety Lady Rosetta had significantly the lowest TSS (2.67 mg/g FW) as compared

Table 1. Mean squares of variance and their effect on processing quality of potato tubers Source of variation DF Mean Squares

DM TSS Glucose Fructose RS Sucrose Polyphenol CCIVariety (V) 2 72.27** 177.96** 0.51** 0.77** 2.57** 138.90** 22193.40** 2.03**

Harvest date (HD) 2 18.28** 33.56** 0.91** 0.09* 2.01** 35.42** 15074.80** 4.93**

V×HD 4 5.26* 24.29** 0.10** 0.15** 0.11 27.12** 3341.50** 0.03Error 24 1.92 0.22 0.01 0.02 0.06 0.21 228.40 0.03*indicates significant at p ≤ 0.05; **indicates significant at p ≤ 0.01. Here, DF= degrees of freedom, DM= dry matter, TSS= total soluble sugars, RS= reducing sugars, CCI= chip color index.


to Courage (3.22 mg/g FW) and Asterix (9.60 mg/g FW). The result revealed that 80 DAP harvest may be suitable harvesting time to get lower level of TSS and variety Lady Rosetta can be preferred for processing potato products.

Lower TSS in potatoes can be desirable because lower TSS apparently indicate lower reducing sugar and sucrose content in these potatoes varieties.

glucose and fructose contents

In all varieties, there were significant decline in glucose content with the increasing harvest dates (Fig. 2a). Hundred DAP harvest had the lowest glucose content (0.07 mg/g FW) compared to 80 DAP (0.60 mg/g FW) and 90 DAP (0.45 mg/g FW) harvest. Among the varieties, significantly the lowest mean glucose content was achieved in Courage (0.25 mg/g FW) and Lady Rosetta (0.25 mg/g FW) compared with Asterix (0.61 mg/g FW).

Fructose content was increased with crop maturity in all varieties, but the increment was significant in Asterix (Fig. 2b). At all harvesting dates, Asterix contained significantly higher amount of fructose than Courage and Lady Rosetta.

From the harvest dates, it was found that 100 DAP harvest yielded significantly higher fructose content (1.08 mg/g FW) than that of 80 DAP (0.77 mg/g FW) and 90 DAP (0.90 mg/g FW) harvest. Among the varieties, significantly the highest mean fructose content was achieved in Asterix (1.19 mg/g FW) compared with Courage (0.87 mg/g FW) and Lady Rosetta (0.69 mg/g FW).

In the present study, the declining tendency in the glucose content with crop maturity apparently due to the rapid conversion of glucose molecule to starch. Increasing fructose content with the crop maturity might be due to the accumulation of unused fructose molecules in the potato tissue and comparatively slower conversion of fructose molecules to starch.

fig. 1. effect of harvest date, variety and their interaction on the (a) dry matter content and (b) total soluble sugar content of potatoes. mean (±se) was calculated from four replicates. Vertical bars represent ±SE value for the mean. Different uppercase letters, prime-marked uppercase letters and lowercase letters on the columns are significantly different within the groups at p ≤ 0.05 by DMRT.


total reducing sugar content The reducing sugar (RS) content markedly increased with increasing the harvest dates in all potato varieties (Fig. 2c). Lowest RS contents were found at 80 DAP harvest in all three varieties. Variety Asterix resulted in comparatively higher RS content than that of Lady Rosetta at all harvest dates.

In case of harvest dates, 80 DAP (1.57 mg/g FW) harvest gave significantly the lowest mean RS content than 90 DAP (1.94 mg/g FW) and 100 DAP (2.39 mg/g FW) harvest. Taking the varietal means, variety Courage (1.70 mg/g FW) and Lady Rosetta (1.70 mg/g FW) had significantly lower mean RS content than that of Asterix (2.5 mg/g FW). That is, variety Asterix yielded 1.4 times higher RS content than that of Courage and Lady Rosetta.

The result also indicated that 80 DAP harvest could be most suitable harvest date to obtain the lowest reducing sugar content and variety Courage and Lady Rosetta were comparatively preferable than Asterix. Because, Marwaha (1998) suggested that, minimum reducing sugar content should be taken as the basis for chemical maturity of potatoes under short day conditions. Low reducing sugar and phenol contents are required to avoid dark color and bitter taste of processed products, which negatively affect consumer acceptance (Wang-Pruski and Nowak, 2004). Lisinska and Leszczynski (1989) reported that a low reducing sugar content is required (<2.5–3 mg of reducing sugar per gram of potato) to minimize color development during frying of potato chips, which is generated by the non-enzymatic Maillard browning reaction. The upper acceptable limit of reducing sugar content is 150 mg/100g fresh weight (Pandey

et al., 2005). For making good quality French fry, the content of reducing sugars in potatoes is required to be low (Marquez and Anon, 1986). Because the presence of high levels of reducing sugars not only causes browning, but also generates acrylic amide by the Maillard reaction which is a highly undesirable attribute for consumers (Chen et. al., 2010).

sucrose contentSucrose content was increased with the crop maturity in all varieties, but the increment was significant only in Asterix (Fig. 2d). Variety Asterix resulted in significantly higher sucrose content at all harvest dates in comparison with those of Courage and Lady Rosetta.

Regarding the harvest dates, 80 DAP (2.40 mg/g FW) harvest gave significantly the lowest mean sucrose content than 90 DAP (3.36 mg/g FW) and 100 DAP (5.87 mg/g FW) harvest. Hundred DAP harvest resulted in significantly the highest mean sucrose content (5.87 mg/g FW) which was about 2-fold higher than those of 80 and 90 DAP harvest. Considering the varieties, the lowest mean sucrose content was found in Lady Rosetta. Variety Asterix (7.80 mg/g FW) yielded the highest mean sucrose content which was 4-fold higher than that of Courage (2.10 mg/g FW) and Lady Rosetta (1.73 mg/g FW).

The amount of sucrose found in potatoes at harvest is influenced by many factors. These factors may include variety, planting date, growing location, soil fertility, water availability, and any stress-inducing event. The results of the present study indicated that 80 DAP harvest could be better to maintain the low sucrose level. Although sucrose does not participate in non-enzymatic browning


of processed product directly, it serves as a substrate for reducing sugar production via the storage activated enzyme invertase (Pressey, 1969). So the level of sucrose in processing potato tubers should be 1.5 mg/g or less at harvest to minimize accumulation of reducing sugar over long-term storage (Burton, 1966; Sowokinos and Preston, 1988).

total polyphenol content Total polyphenol content increased with increasing the harvest dates and the increment was significant up to 90 DAP harvest in all varieties (Fig. 3a). The lowest polyphenol

content was found at 80 DAP harvest in all three varieties.

In case of harvest dates, 80 DAP harvest (238.94 µg/g FW) gave significantly lower mean polyphenol content followed by 90 DAP (296.99 µg/g FW) and 100 DAP (303.67 µg/g FW) harvest. Among the varieties, significantly lower mean polyphenol content was estimated in Lady Rosetta (233.66 µg/g FW) compared with Courage (287.21 µg/g FW) and Aserix (318.73 µg/g FW).

The results of the study showed that Lady Rosetta gave the lowest mean polyphenol

fig. 2. effect of harvest date, variety and their interaction on the, (a) glucose content, (b) fructose content, (c) reducing sugar content and (d) sucrose content of potatoes. Mean (±SE) was calculated from four replicates. Vertical bars represent ±SE value for the mean. Different uppercase letters, prime-marked uppercase letters and lowercase letters on the columns are significantly different within the groups at p ≤ 0.05 by DMRT. *indicates the interaction effect is not significant by ANOVA.


content at 80 DAP harvest which is required for good colored and quality processed product. Higher polyphenol contents are responsible for an enzymatic darkening (Wang-Pruski and Nowak, 2004). It was reported that minimum polyphenol content is taken as the basis for chemical maturity of potatoes for processing because low reducing sugar and polyphenol contents are required to avoid dark color and bitter taste of processed products, which negatively affect consumer acceptance (Wang-Pruski and Nowak, 2004).

chip color index The Chip color index (CCI) was found to be increased with crop maturity and the increment was significant for all the 3 varieties up to 100 DAP harvest (Fig. 3b). The lowest CCI was found at 80 DAP harvest in all the three varieties. Variety Courage yielded markedly lower CCI than those of Lady Rosetta and Asterix at all dates to harvest.

Considering the harvest dates, significantly lower CCI was achieved in 80 DAP harvest

(0.67) followed by 90 DAP (1.42) and 100 (1.94) DAP harvest. Among varieties, lower mean CCI was obtained in Courage (1.09) followed by Lady Rosetta (1.12) and Asterix (1.82).

The result revealed that 80 DAP harvest is most suitable if CCI score of 2.0 is taken as upper limit of acceptability (Pedreschi et al., 2012). Processing varieties Courage and Lady Rosetta yielded desirable CCI at all 3 DAP harvests. The chips produced from Asterix were caramelized at all dates of harvest and degree of caramelization increased at later harvest dates due to higher contents of sugar and polyphenol than Courage and Lady Rosetta (Plate 1). More recent studies refute the opinion that potato chip color is a result of a caramelization of sugars (Gould, 1988) and production of acrylamide (Mottram et al., 2002) as a result of the browning reaction, also known as Maillard reaction. A non-enzymatic browning develops during the frying process, which has been attributed to a reaction among reducing sugars (glucose and

fig. 3. effect of harvest date, variety and their interaction on the (a) total polyphenol content and (b) chip color index of potatoes. Mean (±SE) was calculated from four replicates. Vertical bars represent ±SE value for the mean. Different uppercase letters, prime-marked uppercase letters and lowercase letters on the columns are significantly different within the groups at p ≤ 0.05 by DMRT. *indicates the interaction effect is not significant by ANOVA.


fructose), the amino acid lysine, and proteins; as the frying process is extended, the product may be burned (caramelization) resulting in a bitter off flavor (Gould, 1988). Glucose and fructose cause darkening of potato chips, even if present in a very small amount. Hoover and Xander (1961) indicated the discoloration

of chips affected most markedly by glucose content (r2= ± 0.88) and also by fructose and polyphenol.

correlationsCorrelation studies revealed significant positive correlations between CCI and

Plate 1. color of chips produced from the potato varieties harvested at different days after planting. The “USDA Color Standards for Frozen French Fried Potatoes” was used for the indexing of chip color (Pedreschi et al., 2012). Values in the photographs indicate corresponding CCI.


different chemical parameters [TSS (r = 0.692**), RS (r = 0.822**), sucrose (r = 0.680**), and polyphenol content (r = 0.653**)] of the tested potato varieties (Fig. 4) and these co-efficients indicated that these parameters were responsible for the browning and darkening of processed potato products. However, highly significant positive correlation co-efficient (r = 0.822**) between CCI and RS content indicated that RS could be the key sugar responsible for the browning and darkening of the chips and around 82% variation in chip color could be accounted by RS. The regression equation CCI = 0.9341RS - 0.4969 revealed that if RS increases 0.9341 mg/g FW then chip color index (CCI) will increase by 1 unit. A good fit (R2 = 0.6755) to the regression equation between CCI and RS apparently indicated that RS content was the

vital sugar that could be responsible for the discoloration of processed potato products. This result is in agreement with the findings of Roe et al. (1990) and Ezekiel et al. (2003) who also found that the maximum amount of variation in the chip color was accounted by the reducing sugar alone.

conclusionsAs per the monitoring process, all processing varieties become chemically mature at 80 DAP harvest. Although potato tubers harvested at 80 DAP produce quality and good colored processing products, but lesser DM content may increase the cost of the product. As optimum DM content with moderate level of different sugar contents and acceptable CCI (<2.00) achieved at 90 DAP harvest, therefore, 90 DAP harvest could be mentioned

fig. 4. relationship of chip color index (ccI) with total soluble sugar (tss), sucrose, reducing sugar (rs) and total polyphenol contents of three processing potato varieties. **indicates the correlation coefficient is significant at p ≤ 0.01.


as suitable harvest date by compromising some quality parameters (TSS, RS, sucrose, fructose and polyphenol contents). Variety Courage and Lady Rosetta were found more suitable for processing purposes than Asterix. Highly significant and positive correlation (r = 0.822**) between CCI and RS and the good fit of the regression line between them indicated that reducing sugar content played the vital role in the browning of the processed potato products.

acknowledgements The authors sincerely acknowledge the Research Management Wing (RMW) of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh for the financial support to conduct the research. The authors also acknowledge the contribution of Tuber Crops Research Center (TCRC) of the Bangladesh Agricultural Research Institute (BARI) and Bangladesh Agricultural Development Corporation (BADC) for providing seed tubers for this research.

referencesAdu-Kwarteng, E. E., O. Sakyi-Dawson, G. S.

Ayernor, V. Truong, F. F. Shih and K. Daigle. 2014. Variability of sugars in staple-type sweet Potato (Ipomea batatus) cultivars: The effects of harvest time and storage. International Journal of Food Properties. 17: 410-420.

Ainsworth, E. A. and K. M. Gillespie. 2007. Estimation of total phenolic content and other oxidation substrates in plant tissues using folin-ciocalteu reagent. Nature Protocols. 2: 875-877.

Brown, C. R. 2005. Antioxidant in potato. American Journal of Potato Research. 82: 163-72.

Burton, W. G. 1966. The Potato: A Survey of its History and of the Factors Influencing its Yield, Nutritive Value, Quality and Storage. Veenmand and Sonen, Wageningen, The Netherlands.

Burton, W. G. and A. R. Wilson. 1978. The sugar content and sprout growth of tubers of potato cultivar record, grown in different localities, when stored at 10, 2, and -2°C. Potato Research. 21: 145-162.

Chen, J. Y., Z. Han, M. Yelian and A. Mitsunaka. 2010. Nondestructive determination of sugar content in potato tubers using visible and near infrared spectroscopy. Japan Journal of Food Engineering. 11: 59 - 64.

Ezekiel, R., B. Singh and D. Kumar. 2003. A reference chart for potato chip colour for use in India. Journal of the Indian Potato Association. 30: 259-265.

Genet, R. A. 1992. Potatoes- the Quest for Processing Quality. New Zealand Institute for Crop & Food Research Ltd., Private Bag 4704, Christchurch.

Gould. W.A., 1988. Quality of potatoes for chipping manufacture. Pp. 10-20. Supplement to: American Potato Journal vol. 95, The Potato Association of America, Symposium on Potato Quality Industry Needs for Growth. Fort Collins, Colorado, USA.

Hoover, E. F. and P. A. Xander. 1961. Potato composition and chipping quality. American Potato Journal. 38(5): 163-170.

Kadam, S. S., B. N. Wankier and N. R. Adsule. 1991. Potato Production, Processing, and Products. Boca Raton: CRCs Press, USA.

Kang, Y. Y., S. R. Guo, J. Li and J. J. Duan. 2009. Effect of root applied 2-4-epibrassinolide on carbohydrate status and fermentative enzyme activities in cucumber (Cucumis sativus L.) seedlings under hypoxia. Plant Growth Regulator. 57: 259-269.


Karim, M. R., M. M. Hanafi, S. M. Shahidullah, A. H. M. A. Rahman, A. M. Akanda and A. Khair. 2010. Virus free seed potato production through sprout cutting technique under net-house. African Journal of Biotechnology. 9 (36): 5852-5858.

Kita, A., A. Bąkowska-Barczak, G. Lisińska, K. Hamouz, and K. Kułakowska. 2014. Antioxidant activity and quality of red and purple flesh potato chips. Food Science and Technology. 62(1): 525-531.

Lisinska, G. and W. Leszczynski. 1989. Potato Science and Technology. Elsevier Applied Science, London, UK.

Lulai, E. C. and P. H. Orr. 1979. Influence of potato specific gravity on yield and oil content of chips. American Potato Journal. 56: 379 - 390.

Marquez, G. and M. C. Anon. 1986. Influence of reducing sugars and amino acids in the color development of fried potatoes. Journal of Food Science. 51: 157-160.

Marwaha, R. S. 1998. Factors determining processing quality and optimum processing maturity of potato cultivars grown under short days. Journal of Indian Potato Association. 25: 95–102.

Mathur, A. 2003. Studies on phosphorylation status of starch in potato tubers (Solanum tuberosum L.). MSc. Thesis, Department of Biotechnology and Environmental Sciences. Thapar Institute of Engineering and Technology, Patiala, India. Pp. 10-14.

Mazza, G. 1983. Processing/Nutritional quality changes in potato tubers during growth and long term storage. Canadian Institute of Food Science and Technology Journal. 16 (1): 39- 44.

Miller, G. L. 1959. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Annals of Chemistry. 31 (3): 426-428.

Mottram, D. S., B. L. Wedzicha and A. T. Dodson. 2002. Acrylamide is formed in the Maillard reaction. Nature. 419: 448-449.

Nayak, B., J. D. J. Berrios, J. R. Powers, J. Tang and Y. Ji. 2011. Colored potatoes (Solanum tuberosum L.) dried for antioxidant-rich value-added food. Journal of Food Processing and Preservation. 35: 571-580.

Pandey, S. K., S. M. P. Khurana, S. V. Singh, D. Kumar and P. Kumar. 2005. Evaluation of Indian and exotic potato varieties for sustaining processing industries in North Western plains of India. Indian Journal of Horticulture. 62:155-159.

Pavlista, A. D. and J. C. Ojala. 1997. Potatoes: Chips and French Fry Processing. Pp. 284-287. In: O. Smith et al. (ed.) Processing Vegetables- Science and Technology. Technomac Publishing Co., Lancaster. USA.

Pedreschi, F., A Bunger, O. Skurtys, P. Allen and X. Rojas. 2012. Grading of potato chips according to their sensory quality determined by color. Food Bioprocess Technology. 5: 2401–2408.

Pedreschi, F., P. C. Moyano, K. Kaack, and K. Granby. 2005. Color changes and acrylamide formation in fried potato slices. Food Research International. 38: 1-9.

Pressey, R. 1969. Role of invertase in the accumulation of sugars in cold stored potatoes. American Potato Journal. 14: 269- 290.

Preston, A. D. 2003. Maintinance of Potato Processing: Chemical Maturity Monitoring. Presented at the Idaho Potato Conference on January. 22, 2003.

Roe, M. A., R. M. Faulks and J. L. Belsten. 1990. Role of reducing sugars and amino acids in fry colour of chips from potatoes grown under different nitrogen regimes. Journal of the Science of Food and Agriculture. 52: 207-214.


Rommens, C. M., R. Shakira, M. Heap and K. Fessenden. 2010. Tastier and healthier alternatives to French fries. Journal of Food Science. 75: 109-115.

Shallenberger, R. S., O. Smith and R. H. Treadway. 1959. Food Color Changes: Role of the sugar in the browning reaction in potato chips. Journal of Agricultural Food Chemistry. 7: 274- 277.

Sharkar, M., J. U. Ahmed, S. F. Ahmed, S. M. Z. Al-Meraj and M. Mohi-Ud-Din. 2019. Effect of harvesting dates on the yield and tuber quality of processing potatoes. Bangladesh Journal of Agricultural Research. 44(1): 179-193.

Singleton, V. L., R. Orthofer and R. M. Lamuela-Raventos. 1999. Analysis of total phenols and other oxidation substrates and antioxidants by means of Folin-Ciocalteu reagent. Methods in Enzymology. 299: 152-178.

Söğüt, T. and F. Öztürk. 2011. Effects of harvesting time on some yield and quality traits of

different maturing potato cultivars. African Journal of Biotechnology. 10(38): 7349-7355.

Sowokinos, J. R. and D. A. Preston. 1988. Maintenance of Potato Processing Quality by Chemical Maturity Monitoring. Station Bulletin 586-1988 (Item no. Ad-SB-3441) Minnesota Agricultural Experiment Station.

Van Es, A. and A. J. Hartmans. 1987. Starch and sugars during tuberization, storage and sprouting in storage of potatoes. Pp. 79-113. In A. Rastovski et al. (Ed.) Storage of Potatoes: Post-harvest Behavior, Store Design, Storage Practice, Handling. Pudoc, Wageningen. The Netherlands.

Wang-Pruski, G. and J. Nowak. 2004. Potato after cooking darkening. American Journal of Potato Research. 81: 7-16.

Xue, L. 1985. A Handbook of Experiments for Plant Physiology. Shanghai Science and Technology, Shanghai, China.

PHENOTYPIC CHARACTERIZATION OF ZINC AND IRON RICH CYTOPLASMIC MALE STERILE LINES OF RICE

S. Mashiat1, N. A. Ivy1*, M. G. Rasul1, M. M. Haque2 and M. S. Raihan1

Abstract

A total of 23 cytoplasmic male sterile (A) lines with their corresponding maintainer (B) lines of rice were used for the experiment. The experiment was carried out in completely randomized block design with two replications in the experimental field of the Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur during Aman season 2017. A total of 13 characters were studied. BRRI 1A, IR 62 A and IR 68888 A had shown both the earliness and dwarfness character. The higher yield per plant was obtained from Gan 46 A, Straw A, Sugundhi dhan 2A, IR 68888 A and BRRI 1A. Zinc and Iron content of phenotypically best performed rice genotypes were determined in brown condition by using X-Ray Fluorescence technique (XRF). Small differences between Genotypic and Phenotypic coefficient of variation were recorded for all the characters studied which indicated less influence of environment in expression of the characters. Correlation study revealed that selection based on weight of filled grain per panicle, thousand grain weight and number of grains per panicle would be effective for increasing grain yield per plant. Path co-efficient study showed that direct selection based on weight of filled grain per panicle and number of effective tiller per plant would be indicator for yield improvement. Considering yield and studied characters IR 58 A, IR 62 A, IR 68888 A, Gan 46 A and BRRI 1A could be used as cytoplasmic male sterile line which is enriched with zinc and iron.

Keywords: Male sterility, maintainer, zinc, iron, grain yield.

1Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, 2Department of Agronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


IntroductionRice occupies the enviable prime place among cereals as this unique grain helps to sustain two thirds of the world’s population. It is the second most widely consumed food grain in the world next to wheat (FAOSTAT, 2018). But beyond easing hunger pains and providing carbohydrates for energy, cultivated rice has little nutritional value. It is a poor source of essential micronutrients such as Fe (0.80 mg) and Zn (1.09 mg) per 100 g of rice (USDA Nutrient Database, 2016). It

means many people who depend on rice as a staple food are effectively being starved of essential micronutrients. Nutritionists call it “hidden hunger”. Iron deficiency is the most common nutritional disorder in the globe affecting between 2 to 5 billion people. In Bangladesh 49% of pregnant woman and 53% of preschool children are anemic due to iron deficiency (Jahan et al., 2013). It impairs immunity, reduces the physical growth and cognitive development. One-third of the human population, particularly children and women suffer from Zn deficiency related

106 Phenotypic Characterization of Zinc and Iron Rich Cytoplasmic Male Sterile Lines of Rice

health problems such as growth retardation, loss of appetite, impaired immune function, hair loss, diarrhea, eye and skin lesions, weight loss, delayed healing of wounds, and mental lethargy (Swamy et al., 2016).

In different countries, the hybrid rice is produced by using cytoplasmic male sterility (CMS) system. CMS is a condition under which a plant is unable to produce functional pollen. Male sterile (A) line is used as female line in commercial hybrid production plot. It is also characterized by agronomical superiority, stable sterility, wide regeneration spectrum, abortive anther and highly synchronized. Plant breeding programs in biofortification of staple food crops such as rice requires screening of germplasm having Fe and Zn dense grains to be used as donor parents. Ideally, once rice is biofortified with vital nutrients, the farmer can grow indefinitely without any additional input to produce nutrient packed rice grains in a sustainable way. By using zinc and iron rich male sterile line we can easily develop hybrid rice with high zinc and iron content. Study of genotypic and phenotypic coefficient of variation help to find out the apparent variation is due to genetic or growing environment in the expression of the traits. By correlation and path analysis study, the extent of the influence of the characters on yield would be known. Keeping in view the above perspectives, the present investigation is carried out to study genetic variability, character association and characterization of the selected genotypes for choosing a desired cytoplasmic male sterile line for development of hybrid rice.

Materials and MethodsThis experiment was conducted at the experimental field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur during Aman season 2017. Twenty three (23) male sterile (A) line with their corresponding maintainer (B) line were used for this research (Table 1) which were collected from the Department of Genetics and Plant Breeding of BSMRAU, Gazipur. The experiment was laid out in completely randomized block design (RCBD) with two replications. The length of the plot is 30m and width is 5m. Each entry was planted with 20 X 20 cm spacing. The date of seed sowing in the seed bed was 22 July 2017. The seedlings of 21 days ages of A line were transplanted with their corresponding B line at 12 August 2017. Data on days to 50% heading, days to maturity were recorded at respective stage of crop while, plant height, effective tillers per plant, panicle length were recorded at the time of harvest and number of grains per panicle, weight of filled grain per panicle, thousand grain weight, grain length and width and grain yield per plant were recorded after harvest. The amount of zinc and iron in the grain were measured in brown condition by using X-ray Fluorescence Spectrophotometer in Laboratory of Harvest Plus, Bogura. All data obtained for the characters were statistically analyzed. Means values were separated by least significant difference (LSD). Genotypic and phenotypic coefficient of variation were calculated following the formula given by Burton (1952). For calculating the genotypic correlation coefficient for all possible combinations the formula suggested by Miller et al. (1958), Hanson et al. (1956) and Johnson et al. (1955) were adopted. Correlation

S. Mashiat, N. A. Ivy, M. G. Rasul, M. M. Haque and M. S. Raihan 107

coefficients were further partitioned into components of direct and indirect effects by path coefficient analysis which was originally developed by Wright (1921 and 1923) and later described by Dewey and Lu (1956).

Results and DiscussionMean performanceThe mean values of all genotypes for each character are described in Table 2. The mean performance ranges from 55.3-75.3 days for 50% heading. The maximum days by Dubsail A and the minimum days was required by IR 58 A, IR 62A and IR 68888 A. The minimum days required for maturity was observed in the genotype IR 62 A (75.3 days) and the maximum days was for Begun bitchi A (102.3 days). The genotype BRRI 1A produced the shortest plant (72.7cm). IR 58 A, IR 62 A, IR 68888A and Gan 46 A also produced shorter plant which are non- significant with the shortest one. Straw A produced the tallest plant (157 cm). The highest number of effective tillers per plant were produced by BRRI 1A (23.3) and the

lowest number of effective tillers (5.33) per plant were produced by Khazar A. The highest panicle length was observed in Dakshahi A (30.07cm) and the lowest panicle length was observed in genotype BRRI 1A (24.27cm). The highest number of grains (260.3) per panicle was found in Sugundhi dhan 2A and the lowest one was found in IR 68888 A (69). The maximum weight of filled grains per panicle was found in Straw A (5.2g) and the minimum weight observed in Malsire A (1.03g). The maximum thousand grain weight was found in Straw A (26.49g) and the minimum thousand grain weight was found in Malsire A (0.89g). The lowest grain length was observed in Begun bitchi A (5.3mm) and the highest grain length was observed in Elai A (11.7mm). The lowest grain width was recorded in Begun bitchi A (1.1mm) and the highest grain width was observed in IR 62 A (2.03mm). The highest grain yield per plant was observed in the genotype Gan 46 A (38.43g) followed by Straw A (35.7g) and Sugundhi dhan 2A (33.64g). The lowest grain yield per plant was observed in Khazar A (7.64g).

Table 1. List of 23 cytoplasmic male sterile lines (A line) of riceA1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11A12

IR 58 AIR 62 AIR 68888 AGan 46 A BRRI 1ADakshahi A Kalijira 12A Kalijira 2A Dubsail A Sugundhi dhan 2ABashmoti-IR 3643ATilkapur A

A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23

Khazar A Khutichikon A Chinirri A Straw A Begun bitchi A Rajbut A Nayon moni A Lalsoru A Malsire ARaduni pagol A Elai A

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23 g

11.6

1 k

5.4

kl1.

53 f-

h16

.37

hiA

1574

.7 a

b10

1.67

a12

7.33

cd

12.3

d-f

28.7

6 ab

186.

3 b

2.3

c-e

12.3

1 j

5.7

jk1.

66 c

-f27

.62

cdA

1665

.33

f91

.33

f15

7 a

11 fg

29.6

a19

7.6

b5.

2 a

26.4

9 a

9.6

b1.

63 d

-g35

.7 b

A17

74.6

7 ab

102.

3 a

142.

67 a

b15

b-f

28.7

7 ab

158.

7 b-

d2.

02 d

-f12

.22

j5.

3 l

1.1

j26

.77

c-f

A18

67.7

de

93.7

e12

5 cd

19.6

7 a-

c27

.16

a-c

83.7

e1.

14 g

12.6

j7.

4 f

1.3

i18

.28

g-i

A19

61.3

3 h

84.6

7 h

149.

83 a

b15

.3 b

-f29

.67

a11

0 de

1.16

g10

.47

m7.

2 f

1.46

g-i

16.7

6 hi

A20

71.3

c96

.33

c14

3.13

ab

13 c

-f26

.17

a-c

141

c-e

1.54

fg10

.95

l6.

8 g

1.36

hi

18.6

4 f-

iA

2168

.7 d

93.6

7 e

150.

83 a

b19

a-d

27.2

a-c

106.

3 de

0.89

g8.

33 o

6.4

h1.

37 h

i16

.79

hiA

2265

.3 f

90.7

f14

3.67

ab

15 b

-f28

.8 a

b10

7.67

de

1.44

fg13

.43

hi8.

36 d

e1.

9 ab

18.9

3 e-

iA

2371

.33

c94

.67

de14

9.23

ab

14.7

b-f

28.7

3 ab

128

c-e

2.74

b-d

21.4

2 c

11.7

a1.

57 e

-g25

.78

c-g

LSD

(1%

)1.

259

1.43

813

.64

5.75

83.

969

43.3

40.

7357

0.38

070.

3743

0.17

037.

406

Not

e: M

eans

follo

wed

by

com

mon

lette

r are

not

sign

ifica

ntly

diff

eren

t by

LSD

50%

H=

days

to fi

fty p

erce

nt h

eadi

ng, D

M=

days

to m

atur

ity, P

H=

plan

t hei

ght (

cm),

NET

= nu

mbe

r of e

ffect

ive t

iller

s per

hill

, PL=

pan

icle

leng

th (c

m),

NG

= nu

mbe

r of g

rain

per

pan

icle

, WFG

P= w

eigh

t of fi

lled

grai

ns p

er p

anic

le(g

), TW

= te

st w

eigh

t(g),

GL=

gra

in le

ngth

(mm

), G

W=

grai

n w

idth

(mm

), G

YP=

gra

in

yiel

d pe

r pla

nt (g

), LS

D=

leas

t sig

nific

ant d

iffer

ence

.


By studying the all characters, IR 58 A, IR 62 A, IR 68888 A, Gan 46 A and BRRI 1A genotypes were the best to select as male sterile line for hybrid production of rice as they have good yield and dwarf in nature. Though Straw A, Sugundhi dhan 2A, Chinirri A and Dubsail A having good yield but due to their long plant height, they were not selected as best male sterile line, as dwarfness is preferable for female parent. The zinc and iron content of the best selected male sterile line was measured and presented in Figure 1. Zinc content was found between 39.50 ppm to 48.60 ppm and iron content was found between 16.10 ppm to 52.90 ppm among the best selected varieties. Exceptionally high iron content was observed in BRRI 1A which should be studied further.

Genetic variability

Genotypic, phenotypic and environmental variance (σ 2g, σ 2p and σ 2e), coefficient of variation (GCV and PCV), heritability in

broad sense (h2b), genetic advance (GA) and GA in percent of mean are presented in Table 3. The high degree of genotypic variation indicated preponderance of additive gene effects. Genotypic and phenotypic variances were much pronounced for 50% heading, days to maturity, plant height, number of grains per panicle and thousand grain weight indicating greater scope of selection for the improvement of these traits. Minimum genotypic and phenotypic variances were observed for rest of the traits. The differences between GCV and PCV were higher for number of effective tillers per plant, panicle length, number of grains per panicle and weight of filled grain per panicle indicating environmental influence of these traits. Minimum differences between GCV and PCV were observed for rest of the traits. Close value of GCV and PCV indicated that majority portion of the phenotypic variances was genetic in nature i. e., less environmental influence for these traits. Heritability in broad sense (h2b) was high for

Fig. 1. Graphical representation of zinc and iron content of five cytoplasmic male sterile lines of rice.

60

50

40

30

20

10

0

IR 58 A IR 62 A IR 68888 A Gan 46 A BRRI 1A

Zinc content (ppm) Iron content (ppm)


able

3. G

enet

ic v

aria

bilit

y pa

ram

eter

s of c

ytop

lasm

ic m

ale

ster

ile li

ne (A

line

) of r

ice

Trai

tsG

rand

M

ean

SEC

V (%

)σ

2 pσ

2 gσ

2 eG

CV

PCV

h2 bG

AG

A (%

of

mea

n)

50%

H67

.20.

330.

8540

.46

40.1

30.

339.

439.

4699

.19

12.9

919

.34

DM

91.0

60.

380.

7261

.89

61.4

60.

438.

618.

6499

.31

16.0

917

.67

PH (c

m)

127.

013.

584.

8872

8.99

690.

5138

.48

20.6

921

.26

94.7

252

.68

41.4

8

NET

14.9

71.

5117

.49

19.1

612

.29

6.87

23.4

229

.24

64.1

95.

7938

.66

PL (c

m)

27.3

21.

046.

615.

672.

403.

275.

678.

7142

.37

2.08

7.60

NG

146.

0311

.38

13.5

022

04.3

618

15.7

112

9.55

29.1

832

.15

82.3

779

.67

54.5

6

WFG

P (g

)2.

240.

1914

.97

1.36

1.25

0.11

49.8

552

.05

91.7

32.

2098

.35

TW (g

)15

.05

0.10

1.15

22.2

822

.25

0.03

31.3

331

.35

99.8

79.

7164

.50

GL

(mm

)7.

600.

092.

242.

842.

810.

0322

.06

22.1

798

.98

3.44

45.2

0

GW

(mm

)1.

580.

044.

750.

050.

040.

0112

.64

13.5

087

.61

0.39

24.3

7

GY

P (g

)26

.55

1.95

12.6

916

6.69

155.

3411

.35

46.9

448

.62

93.1

924

.79

93.3

4

50%

H=

days

to fi

fty p

erce

nt h

eadi

ng, D

M=

days

to m

atur

ity, P

H=

plan

t hei

ght (

cm),

NET

= nu

mbe

r of e

ffect

ive

tille

rs p

er h

ill, P

L= p

anic

le le

ngth

(cm

), N

G=

num

ber o

f gra

in p

er p

anic

le, W

FGP=

wei

ght o

f fille

d gr

ains

per

pan

icle

(g),

TW=

test

wei

ght (

g), G

L= g

rain

leng

th(m

m),

GW

= gr

ain

wid

th (m

m),

GY

P= g

rain

yie

ld p

er p

lant

(g),

SE=

stan

dard

err

or, σ

2 p =

phe

noty

pic

varia

nce,

σ 2 g

= g

enot

ypic

var

ianc

e, σ

2 e =

env

ironm

enta

l var

ianc

e, G

CV

= ge

noty

pic

coeffi

cien

t of v

aria

tion,

PC

V=

phen

otyp

ic c

oeffi

cien

t of v

aria

tion,

h2 b

= he

ritab

ility

in b

road

sens

e (%

) and

GA

= ge

netic

adv

ance

.


all the traits except number of productive tillers per plant and panicle length. High GA and GA in percent of mean were observed for number of grains per panicle indicated that the trait could be improved through selection and had additive gene action.

Character associationGenotypic correlation coefficients (rg) for all the traits are shown in Table 4. Fifty percent heading showed the highest significant positive genotypic correlation with days to maturity. Days to maturity along with fifty percent heading showed the highest significant negative genotypic correlation with number of effective tillers per hill. Plant height showed the highest significant positive genotypic correlation with panicle length and the highest significant negative genotypic correlation with grain yield per plant. Number of effective tillers per plant showed significant positive genotypic correlation with grain yield per plant and significant negative genotypic correlation with panicle length. Number of grain per panicle showed highest significant positive genotypic correlation with weight of filled grain per panicle and grain yield per plant. Weight of filled grains per panicle showed highest significant positive genotypic correlation with thousand grain weight and grain yield per plant. Thousand grain weight showed significant positive genotypic correlation with grain yield per plant. Grain length showed significant positive genotypic correlation with grain width.

Path co-efficient analysisIn the present investigation grain yield per plant was considered as a resultant variable and

50% heading, days to maturity, plant height, number of effective tillers per hill, panicle length, number of grains per panicle, weight of filled grain per panicle, thousand grain weight were causal (independent) variables (Table 5). Weight of filled grains per panicle exhibited the highest positive direct effects. Days to maturity exhibited the lowest positive direct effects. Thousand grain weight exhibited the highest negative direct effects but positive indirect effects via most of the traits led them to significant positive correlation with grain yield per plant. Number of grain per panicle exhibited the lowest negative direct effects but positive indirect effects via most of the traits led them to significant positive correlation with grain yield.

The residual effect was 0.07 indicated that only 93% of the variability observed for grain yield was represented by 11 traits studied. Therefore, 7% variability represented by other traits that was not included in this experiment which might be also have major role in determination of grain yield of the rice.

ConclusionsThe highest mean performance for yield in male sterile lines was found in genotype Gan 46 A followed by Straw A and Sugundhi dhan 2A. For earliness and dwarfness, BRRI 1A, IR 62 A and IR 68888 A can be selected. Regarding yield and studied characters IR 58 A, IR 62 A, IR 68888 A, Gan 46 A and BRRI 1A could be used as cytoplasmic male sterile line which is enriched with zinc and iron. Grain yield per plant was highly significantly and positively correlated with weight of filled grains per panicle at genotypic levels. Highly significant and positive inter


able

4.

Gen

otyp

ic c

orre

latio

n co

effici

ent (

r g) o

f cyt

opla

smic

mal

e st

erile

line

(A li

ne) o

f Ric

e

Para

met

ers

DM

PH (cm

)N

ETPL (cm

)N

GW

FGP

(g)

TW (g)

GL

(mm

)G

W(m

m)

GY

P(g

)

50%

H0.

9660

**0.

6651

**-0

.642

3**

0.34

61**

0.09

71N

S-0

.090

7 N

S-0

.245

2*-0

.400

4**

-0.3

715*

*-0

.328

5**

DM

0.69

92**

-0.6

223*

*0.

3432

**0.

0886

NS

-0.1

085

NS

-0.2

643*

-0.3

461*

*-0

.424

7**

-0.3

514*

*

PH (c

m)

-0.5

539*

*0.

7141

**-0

.073

8 N

S-0

.241

6*-0

.358

2 N

S-0

.224

1 N

S-0

.420

4**

-0.5

268*

*

NET

-0.6

404*

*-0

.181

1 N

S-0

.059

9 N

S0.

0635

NS

-0.0

155

NS

0.00

89 N

S0.

3961

**

PL (c

m)

-0.1

036

NS

-0.1

134

NS

-0.1

140

NS

-0.0

669

NS

-0.2

674*

-0.4

073*

*

NG

0.71

41**

0.21

33**

-0.3

267*

*-0

.026

5 N

S0.

5780

**

WFG

P(g)

0.82

16**

0.17

61 N

S0.

2779

*0.

8755

**

TW(g

)0.

5750

**0.

4271

**0.

7574

**

GL

(mm

)0.

4444

**0.

0639

NS

GW

(mm

)0.

2857

*

*and

** in

dica

te si

gnifi

canc

e at

5%

and

1%

leve

ls, r

espe

ctiv

ely.

50

% H

= da

ys to

fifty

per

cent

hea

ding

, DM

= da

ys to

mat

urity

, PH

= pl

ant h

eigh

t (cm

), N

ET=

num

ber o

f effe

ctiv

e till

ers p

er h

ill, P

L= p

anic

le le

ngth

(cm

), N

G=

num

ber o

f gra

in p

er p

anic

le, W

FGP=

wei

ght o

f fille

d gr

ains

per

pan

icle

(g),

TW=

test

wei

ght (

g), G

L= g

rain

leng

th (m

m),

GW

= gr

ain

wid

th (m

m),

GY

P=

grai

n yi

eld

per p

lant

(g).

S. Mashiat, N. A. Ivy, M. G. Rasul, M. M. Haque and M. S. Raihan 113T

able

5.

Part

ition

ing

of g

enot

ypic

corr

elat

ion

into

dir

ect (

bold

pha

se) a

nd in

dire

ct co

mpo

nent

of c

ytop

lasm

ic m

ale s

teri

le

line

(A li

ne) o

f ric

e

Para

met

ers

50 H

DM

PH (cm

)N

ETPL (cm

)N

GW

FGP

(g)

TW (g)

GL

(mm

)G

W(m

m)

GY

P(g

)

50%

H0.

3398

0.12

25-0

.311

1-0

.500

40.

1368

-0.0

12-0

.122

10.

1269

-0.0

517

-0.0

573

-0.3

285*

*

DM

0.32

820.

1268

-0.3

27-0

.484

80.

1356

-0.0

109

-0.1

460.

1368

-0.0

446

-0.0

655

-0.3

514*

*

PH (c

m)

0.22

60.

0887

-0.4

677

-0.4

315

0.28

230.

0091

-0.3

252

0.18

53-0

.028

9-0

.064

8-0

.526

8**

NET

-0.2

183

-0.0

789

0.25

910.

7791

-0.2

531

0.02

23-0

.080

6-0

.032

8-0

.002

0.00

140.

3961

**

PL (c

m)

0.11

760.

0435

-0.3

34-0

.498

90.

3952

0.01

28-0

.152

70.

059

-0.0

086

-0.0

412

-0.4

073*

*

NG

0.03

30.

0112

0.03

45-0

.141

1-0

.040

9-0

.123

40.

9612

-0.1

103

-0.0

421

-0.0

041

0.57

80**

WFG

P (g

)-0

.030

8-0

.013

80.

253

-0.0

466

-0.0

448

-0.0

881

0.99

61-0

.425

0.17

720.

0929

0.87

55**

TW (g

)-0

.083

3-0

.033

50.

1675

0.06

94-0

.045

1-0

.026

30.

9759

-0.5

173

0.17

420.

0659

0.75

74**

GL

(mm

)-0

.136

-0.0

439

0.10

48-0

.012

1-0

.026

40.

0403

0.23

71-0

.297

50.

129

0.06

850.

0639

NS

GW

(mm

)-0

.126

2-0

.053

90.

1966

0.00

69-0

.105

70.

0033

0.37

41-0

.221

0.05

730.

1542

0.28

57*

*and

** in

dica

te si

gnifi

canc

e at

5%

and

1%

leve

ls, r

espe

ctiv

ely;

Res

idua

l Effe

ct (R

) = 0

.073

3.

50%

H=

days

to fi

fty p

erce

nt H

eadi

ng, D

M=

days

to m

atur

ity, P

H=

plan

t hei

ght (

cm),

NET

= nu

mbe

r of e

ffect

ive

tille

rs p

er h

ill, P

L= p

anic

le le

ngth

(cm

), N

G=

num

ber o

f gra

in p

er p

anic

le, W

FGP=

wei

ght o

f fille

d gr

ains

per

pan

icle

(g),

TW=

test

wei

ght (

g), G

L= g

rain

leng

th (m

m),

GW

= gr

ain

wid

th (m

m) a

nd

GY

P= g

rain

yie

ld p

er p

lant

(g).

114 Phenotypic Characterization of Zinc and Iron Rich Cytoplasmic Male Sterile Lines of Rice

character association at genotypic levels were obtained between days to maturity vs. fifty percent heading. Correlation study revealed that selection based on weight of filled grain per panicle, thousand grain weight and number of grains per panicle would be effective for increasing grain yield per plant. The results of path coefficient analysis revealed that weight of filled grain per panicle had the highest positive direct effect on grain yield per plant followed by number of effective tiller per plant.

Acknowledgements

The project was funded by the Research Management wing (RMW), BSMRAU, Gazipur. The authors are grateful to Mohammed Harun or Rashid, Senior Scientific Officer, Oilseed Breeding, Bangladesh Agricultural Research Institute, Gazipur for his kind assistance and valuable advice for conducting research work.

ReferencesBurton, G. W. 1952. Quantitaive inhereitance in

grasses. Proc. 6th Intercropping. Grassland Cong. 1: Chowdhury, N. H. 1991. Studies on quality of rice in Bangladesh. In: Proceeding of the workshop on chemical aspects of rice grain quality, IRRI, Philippines. Pp. 23-127.

Dewey, D. R. and K .H. Lu. 1959. A correlation and path coefficient analysis of components of crested wheat grass production. Agronomy Journal, 52: 515-518.

Hanson, C. H., H. F. Robinson and R. E. Comstock. 1956. Biometrical studies of yield in segregating populations of Korean Lespedza. Agronomy Journal 48: 268-272.

Jahan, G. S., L. Hassan, S. N. Begum and S. N. Islam, 2013. Identification of iron rich rice genotypes in Bangladesh. Journal of Bangladesh Agricultural University, 11(1): 73-78.

Johnson, H. W., H. F. Robinson and R. E. Comstock. 1955. Estimates of genetic and environmental variability in soybean. Agronomy Journal, 47: 314-318.

Swamy, B. P. M., M. A. Rahman, M. A. Inabangan-Asilo, A. Amparado, C. Manito, P. Chadha- Mohanty, R. Reinke and I. H. Slamet-Loedin. 2016. Advances in breeding for high grain Zinc in Rice. 9(1): 49.

United States Department of Agriculture. 2016. Nutrient data laboratory.

United Nations Food and agriculture organization, corporate statistical database (FAOSTAT). (2018). Crops/Regions/World list/Production Quantity (pick lists), Rice (paddy).

Wright, S. 1921. Correlation and Causation. Journal of Agricultural Research, 20: 557-587. Wright, S. 1923. Theory of path coefficient. Genetics. 8: 239-255.

ReviewAgRicultuRe in the FouRth industRiAl Revolution

t. Ane1* and s. Yasmin1

Abstract

Agriculture and industry are tied up and both are complementary to each other. The fourth industrial revolution is an advanced digital technology, it focuses an opportunity that could change the environment in the way human think and work. The farms and factories must implement smart technology to move very fast and it should be an innovative applications to embrace the fourth industrial revolution robustly for Bangladesh. The fourth industrial revolution concept combines artificial intelligence and big data that have achieved significant attention and popularity in precision farming like in monitoring, diagnosing insect pests, measuring soil moisture, diagnosing harvest time and monitoring crop health status and reducing complicated monitoring by human. Industry that extend precision agriculture using artificial intelligence with robotic technology in fourth industrial revolution and its application is embedding into smart observation that retrieve real-time information from field level data with minor human interference. The fourth industrial revolution builds a smart farming technology which brings advanced and sustainable changes for both production and agroprocessing. The fourth industrial revolution extends farms production and also increase their value. This paper reviewed the past effects of industrial revolution, discussed expanded benefit into smart farming and predicted impacts of fourth industrial revolution in Bangladesh agriculture.

Keywords: Smart farming, entrepreneurship, internet of things, 4IR.

1Department of Computer Science & Information Technology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. *Corresponding author: [email protected]


introductionInnovative technology offers benefits such as maximize production volume and minimize the risk of failure. In industry, the first industrial revolution was started in 1780 which represented the manufacturing movement of textiles processes with the introduction of mechanical production plants used by liquid water or steam. It automated manufacturing of textiles and movement of production from homes to factories. Steam power and the cotton gin are an important objects in this period. Thirty years later, the second industrial revolution was initiated which opened the era

of mass production. Late 1960s third industrial revolution was introduced. From that moment on, it was possible to automate production using electronics and information technology (IT). The fourth industrial revolution had opened in 2011 in Germany which focused computerization and an innovative production concept (Sung, 2014).

The fourth industrial revolution could not only suggest how to overcome the previous problems but also furnish the foundation for the production development adopting new forms in working applications frame for farming 4.0. Industry 4.0 brings a concept

116 Agriculture in the Fourth Industrial Revolution

“collective” and “value chain” (Toanalyz, 2018). To make autonomously connection of computer based algorithm for open field agriculture, industry 4.0 builds intelligent networks among machines, work and systems in general through creating the entire value chain with the help of Internet of Things (IoT) which can control themselves and each other (Lasi et al., 2015; Xu, 2012; Hofmann, 2017; Ferracane, 2015). Industry revolution is shown in Fig 1.

The IoT leads 4IR in agricultural sector while device status can change dynamically under the environmental condition and devices can operate the next function based on the input parameter is instructed by the farmers (Zambon et al., 2019).A survey report states

that, food demand is continuously growing, we will have to produce 70 percent more food grain by next 30 years (Clercq et al., 2018). Hence we should use new advanced technology to grow more food grain.

The world changes by the advanced applications of technology (Maddox, 2018). It described the applications of agriculture 4.0 which changes the business of farming. A digital farming is more productive and sustainable than the output of past. To grow more production the smart agricultural technology has no alternative. This paper illustrates the 4IR effects in short on agriculture and analyzes its impact on agricultural productivity, profitable application through large-scale farming concepts, robotic applications in agricultural farms.

Fig. 1. From industry 1.0 to 4.0 (shows the industry revolution : 1st industrial revolution used water and steam power for mechanize production, 2nd industrial revolution for mass production using used electric power, 3rd industrial revolution for automate production using electronics and information technology and 4iR is digital revolution that was emphasized by a fusion of technologies) [https://www.seekmomentum.com/blog/manufacturing/the-evolution-of-industry-from-1-to-4]

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Effects of 4IR on agricultureTraditional agricultural system cannot satisfy the growing demand of food needs without the productive support of innovative technological efficiency. Promoting 4IR effects in agriculture, first of all a convenient environment is needed to build up which is safe in rural life and environment that provides a space for cyber technology and cloud infrastructures. A smart farming culture is specified through 4IR and it allows agricultural site to enhance productivity in competitive way. 4IR applies agricultural robotics technology that has beneficial outcome for promoting advanced agricultural system reducing labor costs and increasing quality (Duckett, 2018). 4IR in agriculture suppose to include artificial intelligence, robotics technology and human workers then the smart farming outcome will be exponentially improved.

The robotis used for advanced agricultural applications such as pruning, weeding, spraying and monitoring. At the harvesting phase “harvesting robots” are used in order to expand food harvest efficiency. The development of robotic innovations is more achievable. There is a “vegetable-picking robot” which can be used for harvesting vegetables specifically. Robotic technology will operate in every area of the agricultural process such as weed control, planting seeds, harvesting, environmental monitoring and soil analysis. Therefore, robotic application has influential impact in advanced aricultural production.

Farmers will not be required to work on physically to perform any kind of actions. A robot can do with the given human commands and programs embedded on it. Interest in

precision agriculture is now demanding criteria on future agricultural development which minimize environmental pollution by using harmful pesticides, fertilizers and maximize the agricultural production. Field Robots used in agriculture applications is shown in Fig 2, that demonstrates an image while tomatoes are collected by the farmers that’s followed the traditional way. However, by applying robotic arms farmers can pick up fruits quick and efficient way without damaging any tomato. Programming could be set by instructions which accepts and makes rapid action based on real time information.

Influence of 4IR on the rural environment The 4IR makes a transition period for developing countries where small farmers have to be concerned in a particularly producing food and raw materials for the urban economy. But in rural farmers most of them gets a drawbacks when come into contacting with buyers, suppliers of inputs and bankers. The 4IR has brought a vast change in manufacturing, distributing and consuming for the rural environment and life. Existing technology with technical support acts as skilled part to overcome difficult problems. Internet, web and online sites are free access in through the farmers are getting benefits in their field application, the farmers get all the update open fields information using mobile phone and the devices are connected IoT devices and cloud computing. IoT-based smart farming systems provide the rural farmers to get instant access of monitoring the light, temperature, humidity and soil moisture at fields using connected sensors with no time interval.


Agricultural consumption and product distributionProduct distribution has a set of rules involved in making a product or service available for use or consumption so that it makes ensured directly and indirectly goods are available to clients. Bangladeshi farmers living in rural area may be far from direct touching from customers. In majority cases the farmers are doing farm business and there is intermediate stakeholder in between customers and farmers.That causes a gap in agricultural business. The all sectors participation is going to be helpful for better production and consumption both. 4IR can cut off the gap by creating a

knowledge bridge between the farmers and make the production services available to the field workers.

It provides a clear information details in front of the clients who are directly involved in consumption and production. Real time data is demanded to choose the best one from huge production.

First to recent revolution has shown a policy while self-sufficiency is considered as first industrial revolution, second industrial revolution is for development in processing and storage technologies and third industrial revolution reviewed quantity to quality. To

Fig. 2. comparison view of traditional and modern farming sites: (a) tomato planting by man labor, (b) fields robots in agriculture, (c) spray drones, (d) agricultural drone, (e) robots harvesting plants, (f) vertical farming technology [https://www.shutterstock.com/search/agriculture+robotics]

a b c

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understand and manage the demand based agricultural production, crops distribution information is fetched and analyzed in computerized way by applying 4IR technology. There is a great possibility for customized production through optimizing supply and demand chain that can adjust time and output in order to keep stabilized prices normal in market (Imam et al., 2014).

increase agricultural productivityTo increase agricultural productivity the modern technology skills can play very essential role in few ways such astransportation facilities, irrigation facilities, institutional credit, proper marketing facilities, supply of quality inputs,consolidation of holdings, agricultural education,land reforms, co-operative farming, reduction of population on land, provision of better manure seeds etc. Villagers are linked with marketing which in turns achieve the farmer’s interest with better farm technology and sufficient profit.Quality of input, control field measurements and better irrigation facilities helps to increase productivity.

Marketing infrastructure and proper arrangements scope for better selling should be strengthened prices. Supplying quality inputs, controlling market prices should be check out at right time. To save the farmers from the clutches of moneylenders, adequate credit facilities, rural credit scheme and loan facility at low interest must be available to the small farmers.

To enhance crop productivity, the farmers need for agricultural education that guides the rural farmers about newly invented agricultural tools and its associated technical

support. Proper crop caring may take another role for better crop production. A large part of our population is dependent on land and causes fragmentation, it creates a harmful effect for crop productivity. It immediately is needed population reduction on land. For better productivity insecticides and pesticides should be supplied properly at the low rates all over the country side. Modern technology leads to big farming which is called as co-operative farming. Now agriculture will become profitable area through large-scale farming concepts.

In this paper, we propose an approachesthat can be taken asmedium ofbetter agricultural production in Bangladesh. Many factors associated tobetter and technical way for production, 4IR can make a farmer concerned about better crop production and provides an easy access to information,product selling in actual price market and enhance the production in co-operative farming. Afterwards farmers would be connected to direct market and keep them involved inbig agribusiness.

Response of 4iR in BangladeshIn Bangladesh, since history of industrialization using 2nd or 1st IR technologies established few SME clusters. These old fashioned SME clusters performing very poor and it makes unable to cope with the modern technologies. An example can be picked up like importing coconut oil gets uncompetitive market in our country (Aninias, 2018). The local entrepreneurs get them inability to produce coconut oil as much as imported oil because they don’t know the appropriate refining technology and its application so hence these poor technology used in local production farm is supposed to be exterminated.


Industrial revolution will destroy job opprotunity in many sectors as a predition which has reported in survey but open thousand jobs in different skilled sectors. In Bangladesh top demanding technology are designing like smart phones, biometric sensors, GPS system, wifi and social media. By using applications, the human power is shifting technological based algorithm performance. Industries are growing automated. In Bangladesh development planning for sustainable economic cycle while fourth industrial revolution undoubtly is necessary. As the consequences of industrial revolution poor skilled manpower will be disappered still there, human have accessibility in choice for designing technology to make more productive system in technology oriented environment.

Digitizing agriculture farm management in collaboration with 4IR will bring a vast progressive change that expands product traceability and sustainability of smallholder farmers in Bangladesh. Knowledge development policy for new entrepreneurship is not expectedly progressing in our country. The new and potential entrepreneurs should have appropriate information about production as well as relevant mechanism to produce selected products. The number of entrepreneurs should be increased for potential entrepreneurship functional output. Government and scholars who are talking about 4IR and holding seminars, conferences, discussion forum in the media may play a vital and effective role at entrepreneurship enrollment in business that may provide knowledge for entrepreneur to apply appropriate technologies in respective fields. We should come forward to make a rapidly change in transition of 1st IR technologies

into 2nd or 3rd IR technology and then it will make knowledgeable and expert system for achievement of 4IR outcomes in agriculture.

importance of robotics in agricultureGraphical views are presented to compare and analysis the importance of using the robotic application in agriculture. Results depicts the sales rating of robots from previous to following years and later how gradually the number of agricultural robots have been increased through the Fig. 3 and Fig. 4. From the graph (Fig. 3) illustrates worldwide industrial robot shipments increased by percent from 97,000 in 2004 to around 384,000 in 2018 (Source: Global agricultural robot revenue) and the graph (Fig. 4) shows the agricultural robot revenues between 2015 to 2024 (www.statista.com).The figures delineate the number of selling robots and the revenues from agricultural robots within years to years.

There is the period between 2004 to 2009 experienced a steady fluctuation from 97 to 60 (in 1,000 units)shipments of industrial robots in worldwide. For the period 2010-2018 it was surged from 166 to 384 (in 1,000 units).While in year 2017 to 2018 the robotic shipments was accumulated from 384 to around 382(in 1,000 units).It is obvious that sales of robot in industry has increased considerably for the following years and the revenues of the agricultural robot market worldwide is seen in following fig.4 from years 2015 to 2024 in while the period between 2015 to 2016 experienced a steady increase the earnings from robotics in agriculture. For the period 2023-24 it was surged from 53 to 74.5. As it can be said from above two graphs, to accommodate more production oriented


agricultural system there is no any little scope that we can think without accepting robotic technology for agriculture in Bangladesh.

conclusionsProductive agriculture leads the industry to be progressive. Both agriculture and industry are

interdependent. Agriculture has embraced the 4IR support to increase productivity, cut the downtime significantly and hence agriculture is becoming more technologically intelligent. It has resulted sustainable ways of farming where it allows using smart applications of internet of things and artificial intelligence for farm

Fig. 4. Agricultural robot revenue (in billion u.s. dollars) worldwide from 2015 to 2024 in statistic result in graph (a) the agricultural robot market worldwide from 2015 to 2018, (b) the agricultural robot market worldwide from 2019 to 2024) [https://www.statista.com/statistics/938833/agricultural-robot-revenue-worldwide/]

Fig. 3. Worldwide sales of industrial robots from 2004 to 2018 (in 1,000 units). Worldwide industrial robot shipments increased by one percent from about 382,000 in 2017 to around 384,000 in 2018. [Source: https://www.statista.com/statistics/264084/worldwide-sales-of-industrial-robots/ ]

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management systems. This may strengthen the farmer’s ability to take field action based on real time data parameter, improve animal welfare and production quality as well as minimize harmful outputs on the environment. To achieve the goals 4IR on our agricultural ecosystem is highly required. The goal of 4IR is to provide technology-based advanced agriculture in order to meet the present demand along with updated technology. 4IR technological progress can be implemented at all sector of existing agriculture to make a huge productive changes in many intelligent ways rather than few and limited services.

References:Aninias, R. 2018. How (and why) entrepreneurs

should cash in on the coconut craze, October 11, 2018. Retrived via https://www.bworldonline.com/sparkup-trends-world-coconut-congress/

Clercq, De. M., A. Vats and A. Bie. 2018. Agriculture 4.0-The Future of Farming Technology, February 2018.

Duckett, T., S. Pearson, S. S. Blackmore, B. Grieve. 2018. Agricultural Robotics: The future of robotic agriculture, UK-RAS (Robotic and Autonomous System), 2018, ISSN 2398-4414

Ferracane, M. 2015. Europe. The Europian Centre for International Political Economy.

Hofmann, E. and M. Rüsch. 2017. Industry 4.0 and the current status as well as future prospects on logistics. J. Com. in Indy. 89: 23-34.

Imam, Y., B. Chibok and Y. Gamama. 2014. Channels of Distribution of Agricultural Produce in Nigeria. Journal of Biology, Agriculture and Healthcare. 4(22): 62-66

Industry 4.0 Revolution Technique, from first to fourth industrial version. Retrieved from: https://www.seekmomentum.com/blog/manufacturing/the-evolution-of-industry-from-1-4

Lasi, H., P. Fettke, T. Feld and M. Hoffmann. 2014. Industry 4.0. Business & Information Systems Engineering. Journal of Service Science and Management. 6, 239-242

Maddox, T. 2018. Agriculture 4.0: How digital farming is revolutionizing the future of food. Available via: https://www.feednourishthrive.com/agr icu l ture-4-0-d ig i ta l - fa rming-revolutionizing-future-food/

Agriculture robotics images. https://www.shutterstock.com/search/agriculture+robotics

Industrial robots sales data collected from worldwide statista.com in 2004-2018. Retrieved from https://www.statista.com/statistics/264084/worldwide-sales-of-industrial-robots/

Global agricultural robot revenue. https://www.statista.com/statistics/938833/agricultural-robot-revenue-worldwide/

Sung, J. 2014. The 4IR and Precision Agriculture.DOI:10.5772/intechopen.71582.

Toanalyz, O., E. Colangelo and T. Steckel. 2018. Farming in the Era of Industry 4.0. 51st CIRP Conference on Manufacturing Systems. DOI: 10.1016/j.procir.2018.03.176

Xu, X. 2012. From cloud computing to cloud manufacturing. New Zealand. https://doi.org/10.1016/j.rcim.2011.07.002

Zambon, I., M. Cecchini, G. Egidi, M. Saporito and A. Colantoni. 2019. Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Multidisciplinary Digital Publish Institute: processes. 7(1): 36. DOI: 10.3390/pr7010036





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