all india coordinated research project on agrometeorology

ALL INDIA COORDINATED RESEARCH PROJECT ON AGROMETEOROLOGY

Annual Report 2006-2007

Central Research Institute for Dryland Agriculture Santoshnagar, Hyderabad – 500 059

Andhra Pradesh

Coordinating Cell Staff

Scientific

Dr. G.G.S.N. Rao, Project Coordinator (Agrometeorology)

Dr. V.U.M. Rao, Principal Scientist (Agrometeorology)

Shri A.V.M. Subba Rao, Scientist, Senior Scale (Agrometeorology)

Technical

Mr. I.R. Khandgonda, T-5

Secretarial

Mrs. Y. Padmini

Supporting Mr. A. Mallesh Yadav

Prepared & Edited by

GGSN Rao, VUM Rao, AVMS Rao and US Saikia

Printed at

Balaji Scan Private Limited A.C. Guards, Hyderabad-4. Tel : 23303424 / 25

CONTENTS

Preface

Acknowledgement

1. Introduction .. 1

2. Weather during 2007 .. 8

Rabi 2006-07

3. Crop Weather Relationships .. 13

4. Crop Growth Modelling .. 49

5. Weather Effect on Pests and Diseases .. 51

6. Agroclimatic Characterization .. 60

Kharif 2007

7. Crop Weather Relationships .. 73

8. Crop Growth Modelling .. 94

9. Weather Effects on Pests and Diseases .. 101

10. Economic Impact of Agromet Advisory Services .. 110

11. Climate Change Studies .. 116

Research Publications .. i

Staff position .. vii

Budget sanctioned to AICRPAM centers for the years 2004-05 and 2005-06

.. viii

��

Preface

Despite the development of many best-bet practices in agriculture, weather elements still cause lot of uncertainty in production. The recent extreme weather events and its impact on Indian agriculture are evident from steep decline in food grain production during 2002 due to drought and reasonable harvest during 2007-08 due to even distribution of rainfall. Recent trends indicate that many key climatic parameters are likely to show more extremes in near future with continued emission of green house gases. All these factors make weather one of the key factors influencing the success of Indian agriculture.

In this endavour, the role of All India Coordinated Research Project on Agrometeorology (AICRPAM) is significant in finding regions vulnerable to climate change and to provide the necessary agro advisories to mitigate its affects. Towards achieving this goal, the project focuses on research programs such as finding out the impacts of higher temperature and change in rainfall etc. on crops through modeling work out the probabilities of extreme weather events, prepare contingency crop plans for different climate change scenarios, to find out the optimum weather requirements in preparation of weather insurance products, developing suitable decision support systems and forewarning of pests and diseases. Modern technologies like remote sensing, GIS and Information Communication Technology (ICT) in expanding the horizon and information of research dissemination activities of AICRPAM will play increasing role in coming years.

The efforts of the Cooperating Centres of AICRPAM in pursuing the above research programs are appreciable, but more needs to be done in the areas of simulation modelling and climate change research. There is a need for strong linkages between AICRPDA and AICRPAM to improve the production and minimize risks in dryland agriculture. The Annual Progress Report of 2006-07 contains results of research carried out during Rabi 2006-07 and Kharif 2007 across 25 centres in the country. I take this opportunity to congratulate the effects made by the agrometeorologists of all the centres and the Project Coordinator, Dr. GGSN Rao and his staff at the Coordinating Unit in compilation of this valuable report.

(B. Venkateswarlu) Director

Acknowledgement

I wish to place deep sense of gratitude to Indian Council of Agricultural

Research for its continuous and generous help during the period under study. The

encouragement and guidance from Hon’ble Director General and Secretary, DARE,

Dr. Mangla Rai; Deputy Director General (NRM), Dr. A.K. Singh and Assistant

Director General (Agronomy), Dr. A.K. Gogoi, is gratefully acknowledged. The

encouragement and guidance received from the Director, Dr. B. Venkateswarlu,

CRIDA in running the Project and preparing this Annual Report is acknowledged

with sincere thanks.

The sincere efforts of the Agrometeorologists of all 25 Cooperating Centres

in brining useful results and made it possible to compile a comprehensive report.

Help rendered by my colleagues, Drs. VUM Rao and AVM Subba Rao in compiling

the results of the reports are highly appreciated. Thanks are also due to

Shri I.R. Khandgonda and Smt. Y. Padmini in preparing necessary diagrams and

typing the manuscript. Also the continuous support from the Shri Satyanarayana,

N. Manikandan, Ms. Harini, Ms. Pallavi, Mr. Mallesh Yadav and Mr. Mahesh is

acknowledged.

(G.G.S.N. Rao) Project Coordinator (Ag. Met.)


1

1. INTRODUCTION

The All India Coordinated Research Project on Agrometeorology was initiated by ICAR in May 1983 with the establishment of Coordinating Cell at the Central Research Institute for Dryland Agriculture, Hyderabad and 12 Cooperating Centers at various State Agricultural Universities. After evaluating the progress made by the project and realizing the importance of agrometeorological research support for enhancing food production, ICAR had extended the Cooperating Centers to the remaining 13 Agricultural Universities of the country w.e.f. April 1995. The network of 25 Agrometeorological Cooperating Centers are Akola, Anantapur, Anand, Bangalore, Bhubaneswar, Bijapur, Dapoli, Faizabad, Hisar, Jabalpur, Jorhat, Kanpur, Kovilpatti, Ludhiana, Mohanpur, Palampur, Parbhani, Raipur, Rakh Dhiansar, Ranchi, Ranichauri, Samastipur, Solapur, Thrissur and Udaipur. The Quinquinnial Review Team has reviewed the research progress of the project in 1992, 1998-99 and recently in 2006.

1.1 OBJECTIVES

� To study the agricultural climate in relation to crop planning and assessment of crop production potentials in different agroclimatic regions

� To establish crop-weather relationships for all the major rainfed and irrigated crops in different agroclimatic regions

� To evaluate the different techniques of modification of crop micro-climate for improving the water use efficiency and productivity of the crops

� To study the influence of weather on the incidence and spread of pests and diseases of field crops

1.2 TECHNICAL PROGRAMME

The Technical Program for the years 2006-07 for different centers of the Project and a common core program decided for all the centers are as given below with emphasis on location-specific research needs.

AICRP Agrometeorology

2

1) Agroclimatic Characterization (All centers)

� Development of database on climate and crop statistics

� Daily weather data from IMD, SAU and other agencies for the past 30 years of the respective State

� Block / Tehsil / Mandal level area and production of major crops for the past 30 years

� Classification of crop growing environment – Agro-Climatic Zone-wise (NARP)

� Delineation of areas with high and low spread and productivity for predominant crops of the region

� Identification of climate and soil constraints – assets and constraints – to crop production

� Assessment of production potentials and yield gaps

� Opportunities for increasing production

� Agroclimatic analysis

� Rainfall probability analysis

� Dry and wet spells

� Water balance studies and harvestable rain water for every week

� Climatic and agricultural drought analysis

� Length of growing season and its variability

� Optimum sowing windows for crops and cultivars

� Preparation of crop-weather calendars for those crops for which agro advisories are rendered in your area

� Climatic variability and change

� Analysis of rainfall and temperature trends

� Variability in the onset and cessation of rainy season

� Climatic variability and its influence on crop productivity

� Consolidation of agroclimatic analysis in the form of Technical Report and initiation of work on Atlas of the State


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2) Crop-Weather Relationships (All Centers)

Center Crop(s) Akola Cotton, Soybean

Anand Groundnut, Mustard, Wheat, Aonla

Anantapur Groundnut + Pigeon Pea, Chick Pea

Bangalore Fingermillet, Pigeon Pea, Groundnut, Mango

Bijapur Rabi Sorghum, Pearlmillet, Sunflower

Bhubaneswar Rice, Greengram, Sesamum, Rabi Groundnut

Dapoli Rice, Field bean, Mango, Fingermillet, Cashew

Faizabad Rice-wheat system, Chickpea

Hisar Pearlmillet, Mustard, Soybean, Castor

Jabalpur Soybean, Chickpea, Rice

Jorhat Maize, Potato

Kanpur Rice-Wheat system, Mustard

Kovilpatti Blackgram, Greengram, Senna, Fodder crop

Ludhiana Rice-Wheat system, Mustard, Soybean

Mohanpur Rice-Mustard, Potato

Palampur Rice, Wheat, Mustard, Tea

Parbhani Cotton, Sorghum, Soybean

Raipur Rice-wheat, Soybean, Linseed

Rakh Dhiansar Maize, Wheat, Mustard/Potato

Ranchi Rice, Maize

Ranichauri Amaranthus, Apple, Bean-Radish-Pea rotation

Samastipur Rice-wheat, winter Maize

SolapurThrissur Udaipur

Soybean, Sorghum, Safflower Coconut, Cashew, Cocoa, Cardamom Maize, Mustard, Wheat


4

3) Crop Growth Modeling

� Compilation of phenology for every crop species

Crop Lead Center Associated Centers

Wheat Ludhiana Palampur, Ranichauri, Anand, Faizabad, Kanpur, Raipur

Rice Raipur

Ranchi

Ludhiana, Palampur, Faizabad, Kanpur, Mohanpur, Jorhat, Samastipur, Bhubaneswar, Dapoli, Thrissur, Hyderabad (ANGRAU, VCP)

Soybean Jabalpur Solapur, Parbhani, Raipur, Akola, Ludhiana, Hisar

Groundnut Anand Anantapur, Bangalore, Bhubaneswar

Mustard Hisar Rakh Dhiansar, Udaipur, Mohanpur, Jorhat, Anand, Ludhiana

Sorghum CRIDA Bijapur, Parbhani, Solapur, Palem (VCP)

Pearlmillet CRIDA Hisar, Solapur, Bijapur

4) Weather Effects on Pests and Diseases

Center Crop(s) Pests/diseases

Anand Groundnut Mustard, Potato Okra

Late leaf spot Aphids, Blight Fruit borer

Anantapur Groundnut Ber

Leaf webber, RHC, PSND Powdery mildew

Akola Safflower Cotton

AphidsBLB, H. Armigera

Bangalore Groundnut Redgram

Early leaf spot, Late leaf spot Aphids, Heliothis


5


Bijapur Sunflower Grapes

Pomegranate

NecrosisPowdery mildew, Downy mildew, AnthracnoseBLB

Bhubaneswar Greengram RiceGroundnutSesamum

YMV, Powdery mildew Blight, Stem borer Leaf spot Leaf webber

Faizabad Rice WheatChickpea

Blight, BPH BlightPodborer

Jabalpur Soybean Chickpea

Girdle beetle Heliothis

Kovilpatti Cotton Blackgram

Heliothis, BLB, Alternaria leaf spot Powdery mildew, Alternaria leaf spot

Ludhiana Maize RiceCotton

Stem borer Stem borer Sucking pests

Mohanpur Mustard RicePotato

Aphids, Alternaria blight Stem borer, Leaf folder, Rice hispa Late blight

Palampur Rice Mustard

BlastAphids

Parbhani Cotton Sorghum

H.Armigera, Boll worm Shootfly (K), Sugary secretion (R)

Ranchi Mung and Soybean Bihar Red Hairy Caterpillar


6


Ranichauri Apple Amaranthus

Apple scab Leaf webber

Solapur Safflower Soybean

AphidsWilt

Raipur Rice GM, Stemborer, Leaf blast

Kanpur Rice WheatMustard

BlightRustAlternaria blight, Aphids

Thrissur Cashew Tea mosquito bug

Udaipur Mustard Maize

Aphids, PM Downy mildew, Stem borer

Hisar Cotton Wheat

HA Karnal bunt

Rakh Dhiansar Mustard Tomato

AphidsFruit borer

ANGRAU,Hyderabad

Rice

MangoGrape

Groundnut

Stemborer, Jassids, Hispa, Sheath blight Hopper, Stone weevil, Leaf webber Powdery mildew, Downy mildew, Anthracnose, RustJassids, Thrips, Rust, Leaf spot


7

5) Agromet Advisory Services (All Centers)

� Agricultural Planning based on seasonal climate forecasts

� Monitoring of crop and weather situation, twice in a week

� Development of contingency plans for aberrant weather situation

� Assessment of soil moisture status through

� Water balance models

� Actual field measurements

� Generation of agro-advisories through Expert Committee chaired by the Director of Research of the SAU

� Monitoring of extreme weather events and their impacts on farming systems on near real-time basis

� Utilization of models for value-addition to agro-advisories

� Monitoring effective dissemination of advisories and feedback from farmers

� Regular transmission of weather advisories to the Agromet Coordinating Unit, CRIDA

� Development of regional websites linked to crop weather outlook website


8

2. WEATHER CONDITIONS DURING THE YEAR 2007 Onset of Southwest Monsoon (June – September)

Southwest monsoon advanced over the south Andaman Sea, Nicobar Islands and parts of southeast Bay of Bengal on 10th May about 5 days ahead of its normal date. However, the subsequent advance was delayed by the formation of the cyclonic storm ‘Akash’ (13th –15th May) over the east central Bay, which had an unconventional origin in the mid-latitude westerlies. The system moved northeastward and crossed Bangladesh coast. It disrupted the monsoon flow over the region. The monsoon revived gradually and arrived over Kerala on 28th May, four days prior to the normal date. Once again, the monsoon flow pattern was disrupted due to the formation of the Super Cyclonic Storm ‘Gonu’ over the east central Arabian Sea (1st – 7th June), which crossed Oman coast and subsequently the Makaran coast. Further advance of monsoon took place on 8th June, after a hiatus of 9 days. It covered the northeastern states by 10th June, Peninsular and Central India by 25th June and subsequently the entire country on 4th July, nearly 11 days ahead of normal date.

Flood situation

The uneven distribution of rainfall in space and time caused flood situation in many states viz. Assam, Meghalaya, Arunachal Pradesh, Manipur, Tripura, Andhra Pradesh, Kerala, Karnataka, Maharashtra, Orissa, Chattisgarh, Gujarat, Rajasthan, Madhya Pradesh, West Bengal, Jharkhand, Bihar, Uttar Pradesh, Himachal Pradesh, Uttarakhand, Jammu & Kashmir, Punjab and Haryana during various parts of the season.

Withdrawal of Monsoon

This year, there was an unusual delay in the withdrawal of monsoon from extreme west Rajasthan, due to the prevalence of cyclonic circulations, availability of moisture and sporadic rainfall over the region. However, the southwest monsoon withdrew from western parts of Rajasthan and some parts of Punjab and Haryana on 30th September. The normal date of withdrawal from west Rajasthan is 15th

September.


9

During the 2007 monsoon season, the cumulative rainfall from 1st June to 30th September 2007 (Table 1.1) was excess in 13, normal in 17 and deficient in 6 meteorological sub-divisions. Five sub-divisions (West Uttar Pradesh, Haryana, Chandigarh and Delhi, Punjab, Himachal Pradesh and East Madhya Pradesh) experienced moderate drought conditions (rainfall deficiency of 26% to 50%) at the end of the season. Arunachal Pradesh received deficient rainfall (20% below its LPA).

Table 1.1. IMD Seasonal Rainfall (June – September) – 2007

S.No.Center Actual Normal Excess or

deficit Deviation

1 Andaman & Nicobar Islands

1702 1756 -53 -3

2 Arunachal Pradesh 1462 1835 -373 -20

3 Assam & Meghalaya 1703 1885 -183 -10

4 Naga, Mani, Mizo, Tripura 1286 1241 45 4

5 Sub-Hima. West Bengal 2063 1955 108 6

6 Gangetic West Bengal 1649 1136 512 45

7 Orissa 1442 1165 277 24

8 Bihar Plateau (Jharkhand) 1218 1093 125 11

9 Bihar Plains 1360 1039 320 31

10 East Uttar Pradesh 748 914 -166 -18

11 Plains of West Uttar Pradesh

473 773 -300 -39

12 Uttaranchal 1519 1223 296 24

13 Haryana, Chandig & Delhi 313 470 -158 -34

14 Punjab 355 502 -146 -29

15 Himachal Pradesh 498 774 -276 -36

16 Jammu & Kashmir 498 514 -16 -3


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S.No.Center Actual Normal Excess or

deficit Deviation

17 West Rajasthan 231 263 -31 -12

18 East Rajasthan 528 624 -96 -15

19 West Madhya Pradesh 861 904 -43 -5

20 East Madhya Pradesh 765 1097 -332 -30

21 Gujarat (Daman Dadar & N. Haveli)

889 486 404 83

22 Saurashtra & Kutch 1164 934 231 25

23 Konkan & Goa 3317 2802 515 18

24 Madhya Maharashtra 905 700 205 29

25 Marathwada 655 704 -49 -7

26 Vidarbha 1075 976 99 10

27 Chhattisgarh 1099 1206 -107 -9

28 Coastal Andhra Pradesh 747 575 172 30

29 Telangana 798 767 31 4

30 Rayalaseema 742 381 361 95

31 Tamil Nadu & Pondicherry 339 316 23 7

32 Coastal Karnataka 3588 3174 414 13

33 North int. Karnataka 686 491 195 40

34 South int. Karnataka 917 659 258 39

35 Kerala 2784 2143 641 30

36 Lakshadweep 1467 985 482 49


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Rainfall distribution during monsoon season

The southwest monsoon rainfall (June to September) for the period 1st June to 30th September 2007 for the country as a whole and the four broad homogeneous regions are as follows:

Region Actual (mm) Normal (mm) Percentage Departure

All-India 936.9 892.2 5%

Northwest (NW) India 520.8 611.6 -15%

Central India 1073.8 993.9 8%

South peninsula 907.3 722.6 26%

Northeast (NE) India 1485.9 1427.3 4%

In 2007, the southwest monsoon seasonal (June to September) rainfall over the country as a whole was 105% of its LPA. Seasonal rainfall over NW India was below its LPA by 15%. However, over south Peninsula, seasonal rainfall was above its LPA by 26%. Similarly, Central India and NE India also experienced above average seasonal rainfall (8% and 4% above LPA respectively). The above average performance of the monsoon rainfall over the country was mainly due to the excess rainfall observed over South Peninsula and Central India.

Out of 513 meteorological districts for which data were available, 144 districts (28%) received deficient rainfall (rainfall deficiency more than 19%) during the season, out of which 77 districts (15%) experienced moderate drought conditions (rainfall deficiency 26% to 50%) and 30 districts (6%) experienced severe drought conditions (rainfall deficiency 51% and more). The rainfall was excess (actual rainfall higher than LPA by 20% or more) over 164 districts (32%) during the season.

Post Monsoon (October – December) 2007

Post Monsoon (October – December) rainfall was excess in 2 sub-divisions, Normal in 5 sub-divisions, deficit in 11 sub-divisions and Scanty in 18 sub-divisions, out of 36 sub-divisions.


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During the year, 5 out of 25 centers of the All India Coordinated Research Project on Agrometeorology, viz., Faizabad, Kanpur, Ranchi, Solapur and Udaipur (Arjia) received deficient rainfall and rest of the centers received normal rainfall (Table 1.2).

Table 1.2. Annual Rainfall from AICRPAM centers during 2007

S.No. Center Actual Normal % Departure 1. Akola 780 813 -4 2. Anand 1143 853 34 3. Anantapur 990 646 53 4. Bangalore 969 932 4 5. Bhubaneswar 1590 1498 6 6. Bijapur 716 594 21 7. Dapoli 4304 3500 23 8. Faizabad 634 1139 -44 9. Hisar 506 451 12 10. Jabalpur 1086 1209 -10 11. Jorhat 1933 2148 -10 12. Kanpur 590 879 -33 13. Kovilpatti 656 752 -13 14. Ludhiana 619 733 -16 15. Mohanpur 2023 1665 22 16. Palampur 1849 1498 23 17. Parbhani 854 962 -11 18. Ranchi 754 1458 -48 19. Ranichauri 1345 1239 9 20. Raipur 1507 1138 32 21. Rakh Dhiansar 1311 1115 18 22. Samastipur 2467 1235 100 23. Solapur 523 723 -28 24. 25.

Thrissur Udaipur

3990524

2822658

41-20


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RABI 2006-07 3. CROP-WEATHER RELATIONSHIPS

WHEAT Udaipur

To study the impact of weather on the growth and development of wheat crop, the crop was sown on four dates, viz., 4th November, 17th November, 3rd

December and 16th December 2006 with five varieties viz., Lok-, Raj-3765, Raj-4037, GW-273 and GW-173. Sensitiveness of the wheat cultivars were measured in terms of thermal sensitivity index (TSI), which is defined as the ratio of difference in days taken for maturity under different thermal environments to average duration under normal sown conditions. The variety, GW-273, is a thermo sensitive variety whereas GW-173 is found to be a moderately thermo tolerant variety Table 3.1.

Table 3.1. TSI values of different wheat varieties at Udaipur

Variety Averageduration TSI values Thermal tolerance

Lok-1 119 12.6 Moderately sensitive

Raj-3765 118 15.0 Moderately sensitive

Raj-4037 118 14.4 Moderately sensitive

GW-273 121 15.7 Sensitive

GW-173 116 9.9 Moderately sensitive

If TSI value is <5 = Tolerant; 5.1-10 = Moderately tolerant; 10.1 to 15 = Moderately sensitive; >15 = Sensitive

Sowing dates and varieties exhibited marked variation in HUE and GDD accumulation. The highest GDD (1678.0) and heat use efficiency for total dry matter and grain yield (8.20 kg/ha/�C day) was recorded in 4th November sowing. GDD and HUE decreased with delayed sowing (Table 3.2).


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Table 3.2. Accumulated GDD and Heat Use Efficiency of wheat at Udaipur

Treatment GDD HUE for total dry matter (kg/ha/� days)

HUE for grain yield (kg/ha/� days)

Sowing date

04th Nov 1678 8.20 3.47

17th Nov 1647 7.91 3.44

03rd Dec 1626 7.54 3.13

16th Dec 1618 6.71 2.72

Variety Lok-1 1644 7.18 3.21

Raj-3765 1650 7.39 3.12

Raj-4037 1653 7.82 3.36

GW-273 1660 7.66 3.00

GW-173 1653 7.80 3.26

Effect of mean temperature on yield of wheat Two years of experimentation (2005-06 and 2006-07) revealed that the mean temperature of 20.5 and 21.5 °C at sowing found conducive for obtaining grain yield of 54.88 and 56.56 q/ha, respectively (Fig. 3.1). Similarly effect of mean temperature during milking to maturity on grain yield was also studied and it was found that mean temperature of 22°C during milking to maturity found conducive for optimum yield. Higher mean temperature (above 24°C) during milking to maturity caused reduction is grain yield by about 9 to 19 percent. The relationship between mean temperature during emergence stage and grain yield was developed and given below:

The predictive regression model was as under

Y = 22.28 + 1.59 X R2 = 0.867

Where, Y = grain yield (q/ha)

X1 = mean temperature during emergence


15

0.00

10.00

20.00

30.00

40.00

50.00

60.00

4th November 17th November 3rd December 16th December

Sowing date

Gra

in y

ield

(q/h

a)

0

5

10

15

20

25

Mea

n te

mpe

ratu

re

Yield (q/ha) Mean Temperature (oC)

Fig.3.1. Effect of mean temperature on grain yield in different dates of sowing at Udaipur

The validity of regression model was tested for actual yield obtained during this year and data in this respect are presented in Table 3.3. It was observed that predicted yield was deviated by 1.8 to 2.1 percent from actual in timely sown crop. However, the predicted yield was deviated by -11.9 percent from actual yield in late sown crop (16th December).

Table 3.3. Predicted and actual grain yield of wheat based on sowing date and mean temperature at Udaipur

Sowing date Grain yield (q/ha) Deviation (%) Predicted Actual (2006-07)

4th Nov 56.9 58.1 2.0 17th Nov 53.4 56.6 2.1 3rd Dec 51.0 51.9 1.8 16th Dec 49.8 43.9 -11.9

Faizabad To study the impact of weather on wheat crop, three varieties of wheat viz.,HUW-234, NW-2036, NW-1014 were sown on 25th Nov, 10th Dec, and 25th Dec of 2006. From the weather parameters recorded at different growth stages, relationships have been worked out between the temperature and the yield. The effect of average temperature recorded during the flowering stage on grain yield is shown in Fig.3.2.


16

y = -0.0435x3 + 0.6401x2 - 1.6088x + 39.732R2 = 0.8508

35

37

39

41

43

45

47

49

14.7 15 15.2 15.8 16.1 16.4 16.9 17.3 17.4 17.8 18

Temp.(oC)

Yiel

d(q/

ha)

Fig. 3.2. Effect of average temperature at flowering stage on grain yield of wheat at Faizabad

The heat and radiation use efficiency at different growth stages have been computed and are given in the following Table 3.4 and 3.5. It was observed that the heat use efficiency has increased till the flowering stage and gradually declined thereafter. The early sown crop was relatively more efficient in heat utilization compared to the other dates of sowing. The radiation use efficiency between 75-90 DAS was highest which coincides with the reproductive phase of the crop. The radiation use efficiency did not vary among the genotypes as well as dates of sowing. Table 3.4. Heat use efficiency of wheat (g/m2/°C day) as affected by various phenophases at Faizabad

Treatments Latetillering

Latejointing

Earemergence

50% Flowering Milking Dough Maturity

Sowing dates: Nov.25 0.26 0.86 1.17 1.18 0.96 0.89 0.75

Dec.10 0.24 0.72 0.97 1.06 0.87 0.79 0.72

Dec.25 0.16 0.61 0.92 1.00 0.81 0.77 0.66

Genotypes: HUW-234 0.26 0.78 1.09 1.37 1.06 0.92 0.79

NW-2036 0.22 0.60 0.86 0.94 0.79 0.76 0.66

NW-1014 0.26 0.79 1.07 1.19 0.95 0.86 0.74


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Table 3.5. Radiation use efficiency (g/MJ) of wheat as affected by various treatments at Faizabad

Treatments DAS

15 30 45 60 75 90 105 120

Sowing dates

Nov.25 0.7 0.8 0.8 2.1 2.9 2.7 2.5 2.5

Dec.10 0.7 0.7 0.8 1.9 2.7 2.7 2.4 2.4

Dec.25 0.7 0.7 0.8 1.8 2.7 2.4 2.3 2.2

Genotypes

HUW-234 0.7 0.7 0.8 2.1 2.8 2.6 2.4 2.4

NW-2036 0.6 0.7 0.8 2.0 2.7 2.4 2.3 2.2

NW-1014 0.6 0.7 0.8 2.1 2.8 2.6 2.3 2.3

Palampur Three varieties of wheat viz., HS240, HPW-42, HPW-147 were sown under three dates of sowing viz., 6th Nov, 20th Nov and 5th Dec 2006. The phenology and the corresponding accumulated growing degree-days are given in the Table 3.6. The total crop duration under different treatments varied from 151 (late sown conditions) to 172 (early sown conditions) days and the corresponding growing degree-days vary from 1655 to 1762. The correlation coefficients between biomass and accumulated growing degree-days, accumulated evaporation and accumulated rainfall were computed and presented in the Table 3.7. All the values were highly significant at one percent level. The pooled analysis of 8 years data on effect of sowing dates on grain and straw yield indicated that the crop sown by 20th Nov recorded highest yields in the mid hills of Himachal Pradesh as the chances of receiving 20 percent more rainfall through western disturbances is high as compared to late sown crops.


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Table 3.6. Number of days and accumulated growing degree-days (AGDD) taken for completion of different phenophases at Palampur

Treatment Emergence Tillering Heading 50% Grain filling

Physiological maturity

V1D1 9(70) 33(248) 133(1075) 144(1276) 168(1736)

V1D2 11(85) 36(230) 128(1090) 143(1375) 160(1703)

V1D3 8(77) 38(222) 121(1080) 135(1350) 151(1655)

V2D1 9(70) 35(260) 135(1107) 146(1310) 170(1773)

V2D2 12(95) 38(239) 129(1108) 144(1396) 161(1723)

V2D3 7(70) 39(230) 121(1080) 134(1328) 152(1675)

V3D1 10(78) 34(255) 137(1141) 148(1346) 172(1812)

V3D2 11(85) 37(235) 130(1128) 147(1461) 163(1762)

V3D3 9(85) 40(237) 122(1100) 135(1350) 154(1714)

Figures in parenthesis are accumulated growing degree-days D1 = 6th Nov, D2 =20th Nov and D3 = 5th Dec 2006

Table 3.7. Correlation coefficient between biomass production, AGDD, AEVP and ARF at Palampur

Variety Date of sowing Indices Correlation coefficient

HS 240 6th Nov AGDD 0.9705

20th Nov AGDD 0.9708

5th Dec AGDD 0.9752

HPW 42 6th Nov AGDD 0.9770


5th Dec AGDD 0.9741

HPW 147 6th Nov AGDD 0.9749


5th Dec AGDD 0.9748

Cont…


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HS 240 6th Nov AEVP 0.9659

20th Nov AEVP 0.9694

5th Dec AEVP 0.9769

HPW 42 6th Nov AEVP 0.9717


5th Dec AEVP 0.9761

HPW 147 6th Nov AEVP 0.9695


5th Dec AEVP 0.9765

HS 240 6th Nov ARF 0.7277

20th Nov ARF 0.7368

5th Dec ARF 0.7340

HPW 42 6th Nov ARF 0.7366

20th Nov ARF 0.7332

5th Dec ARF 0.7349

HPW 147 6th Nov ARF 0.7416

20th Nov ARF 0.7285

5th Dec ARF 0.7338

AGDD, Accumulated growing degree days (o days); AEVP, Cumulative evaporation (mm) and ARF, Cumulative rainfall (mm); All correlation values are significant at 1 % level

Ranichauri To study the impact of derived weather parameters on two varieties of wheat crop viz., UP-1109 and Sonalika were sown in three dates viz., 30th Oct (D1), 20th

Nov (D2) and 10th Dec 2006 (D3). The number of growing degree days for different crop growth stages under different dates of sowing and the corresponding actual evapotranspiration were given in the Table 3.8.


20

Table 3.8. Cumulative growing degree-days and actual evapotranspiration (AET) at different phenological stages at Ranichauri

Phenological Stage Date of occurrence Cumulative GDD Cumulative AET

(mm)

Germination D1 10.11.06 108 14.9 D2 28.11.06 41 7.8 D3 20.12.06 46 11.1 CRI Stage D1 19.11.06 175 25.5 D2 27.12.06 158 35.3 D3 27.01.07 71 16.4 Tillering D1 25.12.06 326 59.3 D2 28.02.07 338 101.4 D3 28.02.07 251 82.5 Jointing D1 05.01.07 360 68.0 D2 12.03.07 387 125.2 D3 12.03.07 300 106.3 Anthesis D1 20.03.07 597 167.8 D2 17.04.07 744 225.3 D3 17.04.07 657 206.4 Milking D1 30.03.07 691 197.0 D2 27.04.07 860 257.5 D3 27.04.07 773 238.6 Harvesting D1 22.05.07 1383 359.8 D2 04.06.07 1405 367.0 D3 04.06.07 1318 348.1

D1 = 6th Nov, D2 =20th Nov and D3 = 5th Dec 2006

The total dry matter accumulated and the corresponding growing degree-days were correlated and found to be significant. The following liner regression was fitted between total dry matter and GDD.

TDM = -19.463 + 0.0821 * GDD R2 = 0.899


21

HisarIn order to study the impact of weather parameters on the growth and the

yield potential of wheat crop in the climatic conditions of Hisar, two varieties viz., C-306 and WH-1025 were sown under three dates of sowing, i.e., 28th Oct, 30th Oct and 10th Nov 2006. The wheat variety C-306 and WH-1025 expressed good inter-relation of its seed yield with different meteorological parameters. Regression models were developed separately for vegetative and reproductive phases for the above two varieties are given below (Table 3.9).

Table 3.9. Multiple correlations between seed yield and weather parameters in wheat cv. C 306 and WH-1025 at Hisar

Growth Phases

Multiple regression equation R2

C 306

Vegetative phase (sowing to heading)

Y= -63672.5 +1961.7X1 -8950.2 X2 +821.6 X3 -7545.4X4

+34061.6X5

0.76**

Reproductive phase (heading to physiological maturity)

Y= 449997.8 -1124.9 – 9701.2 X2 -3753.9X3 -12647.8X4

+6174.6X5

0.58**

WH-1025

Vegetative phase (sowing to heading)

Y= -664434-1307.8X1 -1252.3X2 -3303.4 X3-51346.9 X4+10759.2X5

0.68

Reproductive phase (heading to physiological maturity)

Y = 81976-158.6 X1+745.6X2+459.4 X3-719.9X4+1427.7X5

0.55

X1= Tmax, X2= Tmin, X3= RH mean, X4= BSSH, X5= Ep;

** Significant at (P = 0.01) level of significance


22

LudhianaCrop weather relationship were studied in wheat crop cultivar PBW-502 that

was grown under flat and bed method of sowing under three dates of sowing viz., 3rd Nov, 17th Nov and 7th Dec 2006. The phenological calendar for the two methods of sowing did not show any variability in respective of growth stages. Delayed sowing reduced the crop duration considerably by about 30 days from the early sown date. The diurnal distribution of short wave radiation and its absorption, its interception in different sowing methods were recorded at grain filling stage and presented in the Table 3.10.

Table 3.10. Distribution of short wave radiation (SWR), photo-synthetically active radiation (PAR) and net-radiation in wheat crop under first date of sowing (3rd Nov 06) at Ludhiana Time(Hour)

IncomingSWR

(W/m2)

SWR (%) Incoming PAR

(W/m2)

PAR (%) Net radiation (W/m2)Interception Albedo Interception Albedo

S1-Flat 0930 552.5 78.9 12.6 140.4 91.1 4.4 441

1030 703.7 76.0 14.0 199.6 92.2 3.1 538

1130 825.9 77.5 14.1 230.8 83.8 2.7 649

1230 866.6 65.8 13.4 252.6 67.9 2.5 620

1330 785.2 74.1 15.6 243.3 91.0 2.6 624

1430 738.6 74.0 16.5 193.4 93.5 3.2 611

1530 622.3 76.6 14.0 156.0 88.0 4.0 501

1630 389.7 79.1 14.9 115.4 94.6 2.7 111

S2-Bed 0930 552.5 78.9 12.6 140.4 77.8 4.4 441

1030 703.7 61.2 19.0 199.6 85.9 3.1 517

1130 825.9 70.4 18.3 230.8 64.9 8.1 678

1230 866.6 57.7 15.4 252.6 66.7 3.7 640

1330 785.2 65.9 19.3 243.3 84.6 3.8 592

1430 738.6 68.5 19.7 193.4 90.3 3.2 606

1530 622.3 74.8 16.8 156.0 88.0 6.0 498

1630 389.7 73.1 16.4 115.4 91.9 2.7 122


23

The interception of short wave radiation was higher in the flat sowing method compared to bed sowing method in the first date of sowing. The diurnal variation of the net radiation availability in the crop for both the methods of sowing was almost the same, whereas under the delayed conditions the difference between the sowing methods on the interception of short wave radiation was not significant.

RaipurTo asses the effect of different thermal environment on growth and

development of wheat varieties under Chhattisgarh, six varieties of wheat viz., Sujata, Kanchan, GW-273, Lok-1, Ratan and Arpa were sown under four dates of sowing viz., 26th Nov, 6th Dec, 16th Dec and 26th Dec 2006. In general the delayed sown crop recorded low yields compared to earlier and normal sown conditions as the reproductive and maturity stages of the crop are subjected to higher air temperatures. Among the varieties lowest was recorded by Sujata and highest by Arpa and GW-273. The data pertaining to crop duration under different phenophases, its corresponding degree-days, photothermal units and heliothermal units are given in the following Table 3.11.

Table 3.11. Effect of different thermal regimes on phenology of wheat varieties

Varieties/ Sowing dates

Seedling Vegetative Reproductive Maturity

Phenology

26 Nov. 21 32 (53) 28 (81) 25 (106)

06 Dec. 21 31 (52) 26 (78) 23 (101)

16 Dec. 20 30 (50) 26 (76) 21 (97)

26 Dec. 19 29 (48) 22 (70) 19(89)

Sujata 20 32 (52) 28(80) 25 (105)

Kanchan 20 30 (50) 26 (76) 23 (99)

GW 273 20 32 (52) 26 (78) 24 (102)

Lok 1 20 27 (47) 25 (72) 23 (95)

Ratan 20 28 (48) 25 (73) 21 (94)

Arpa 20 26 (46) 25 (71) 22 (93)

Cont…


24

Varieties/ Sowing dates


Growing Degree-Days

26 Nov. 372 459 (831) 476 (1307) 450 (1757)

06 Dec. 372 443 (815) 440 (1255) 403 (1658)

16 Dec. 357 426 (783) 433 (1216) 369 (1585)

26 Dec. 343 407 (750) 354 (1104) 327 (1431)

Sujata 357 458 (815) 477 (1292) 443 (1735)

Kanchan 357 425 (782) 434 (1216) 403 (1621)

GW 273 357 458 (815) 440 (1255) 420 (1675)

Lok 1 357 378 (735) 407 (1142) 421 (1563)

Ratan 357 393 (750) 409 (1159) 364 (1523)

Arpa 357 364 (721) 407 (1128) 377 (1505)

Photothermal Units 26 Nov. 4056 5016 (9072) 5363 (14435) 5034 (19469)

06 Dec. 4056 4835 (8891) 4947 (13838) 4695 (18533)

16 Dec. 3904 4633 (8537) 4847 (13384) 4282 (17666)

26 Dec. 3748 4437 (8185) 3902 (12087) 3780 (15867)

Sujata 3904 4487 (8391) 5868 (14259) 5210 (19469)

Kanchan 3904 4633 (8537) 4847 (13384) 4708 (18092)

GW 273 3904 4937 (88410 4997 (13838) 4906 (18744)

Lok 1 3904 4114 (8018) 4508 (12526) 4629 (17155)

Ratan 3904 4281 (8185) 4558 (12733) 4200 (16933)

Arpa 3904 3957 (7861) 4453 (12314) 4410 (16724)

Heliothermal Units

26 Nov. 2956 3683 (6639) 3701 (10340) 4030 (14370)

06 Dec. 2956 3571 (6527) 3463 (9990) 3799 (13789)

16 Dec. 2823 3506 (6329) 3410 (9739) 3333 (13072)

26 Dec. 2697 3397 (6094) 2721 (8815) 2750 (11565)

Cont…


25

Varieties/

Sowing dates


Sujata 2823 3704 (6527) 3763 (10290) 4080 (14370)

Kanchan 2823 3506 (6329) 3410 (9739) 3679 (13418)

GW 273 2823 3704 (6527) 3463 (9990) 3971 (13961)

Lok 1 2823 3143 (5966) 3148 (9114) 3553 (12667)

Ratan 2823 3271 (6094) 3174 (9268) 3209 (12477)

Arpa 2823 3021 (5844) 3086 (8930) 3378 (12308)

Figures in parentheses are cumulative values

It is observed that the duration of seedling, vegetative, reproductive and maturity stage decreased considerably when the sowing was delayed from 26th Nov. to 6th, 16th and 26th Dec. Similarly the other derivatives also decreased considerably. Different wheat varieties exhibited differential response to the above derivatives.

SamastipurThe growth and yield attributes of four wheat varieties viz., HD-2824, HP-

1761, HD-2733, HUW-468 grown under five dates of sowing viz., 15th Nov, 25th Nov, 05th Dec, 15th Dec and 25th Dec 2006 were studied. The phenology and thermal heat units required for different growth stages are given in the Table 3.12. The crop duration under different phenological stages and the corresponding accumulated heat units were significantly influenced by the sowing dates. Varietal difference in respect of thermal and heat unit accumulation was found to be significant. The variety HD-2733 took significantly higher thermal time for seedling emergence than the rest of the varieties. The effect of accumulated heat units up to maturity on the grain yield of wheat is presented in Fig.3.3. The variation in the yield could be explained upto 66 percent through this relationship.


26

Table 3.12. Effect of sowing dates on phenophases and heat unit accumulation of wheat varieties at Samastipur

Treatment

Growth stages (Days after sowing)

Seedling

Emergence

Teller

initiation

Boot

stage

50 % ear

head

emergence

Milk

stage

Dough

stage Maturity

Sowing dates

15-Nov-06 6.4(95) 31.6(441) 73.3(860) 84.3(1004) 89.0(107) 129.5(1644) 136.9(1804)

25-Nov-06 7.2(90) 36.0(453) 69.7(776) 81.3(932) 85.9(983) 122.9(1533) 129.0(1658)

05-Dec-06 8.3(104) 34.0(399) 69.3(779) 78.8(894) 83.9(963) 117.3(1502) 125.7(1671)

15-Dec-06 8.7(99) 29.2(298) 64.8(716) 74.9(853) 80.9(936) 110.3(1349) 107.8(1453)

25-Dec-06 10.0(92) 31.8(282) 61.8(663) 73.3(818) 78.1(886) 103.0(1349) 107.8(1453)

CD (0.05) 0.4(5.09) 0.8(6) 0.9(6.9) 1.2(9.7) 1.0(8.7) 1.0(14.7) 0.99(28.2)

Varieties

HD 2824 8.0(93) 32.0(369) 69.3(776) 80.8(925) 85.8(993) 117.8(1520) 123.3(1631)

HP 1761 7.9(91) 32.8(379) 66.9(747) 77.6(889) 82.8(956) 116.1(1479) 122.8(1627)

HD 2733 8.6(100) 31.9(370) 69.3(776) 80.7(932) 85.5(994) 118.3(1529) 124.0(1651)

HUW 468 7.9(92) 33.3(380) 65.6(736) 74.8(855) 80.2(929) 114.1(1454) 122.3(1614)

CD (0.05) 0.3(4.6) 0.7(5.8) 0.8(6.3) 1.0(8.7) 0.9(7.8) 0.9(13.1) 0.89(25.2)

Figures in parenthesis are heat units


27

Fig. 3.3. Relation between heat unit and grain yield of wheat at Samastipur

Kanpur The heat unit requirements of wheat crop sown under three dates of sowing for three different genotypes are given in the Table 3.13.

Table 3.13. Heat units at different phenophases of wheat crop as affected by dates of sowing and genotypes at Kanpur

Treatment Emergence CRI Tillering Jointing Pani.Ini. Flowering Anthesis Milking Dough Maturity

Sowing dates

Nov. 30 127 286 464 603 830 901 1039 1181 1396 1620

Dec. 15 112 237 396 610 738 875 1001 1187 1367 1637

Dec. 30 95 206 475 644 813 931 1076 1236 1403 1590

Genotypes

HD-2285 104 237 433 594 755 867 985 1144 1339 1551

K-8804 115 244 446 620 791 899 1040 1204 1388 1623

K-9107 115 248 457 643 835 940 1091 1255 1439 1673


28

It is observed that the heat unit requirement varied considerably from emergence to maturity in all the dates of sowing. Among the varieties HD-2285 recorded less number of heat units when compared to other varieties at all the growth stages.

RanchiThe occurrence of different phenological stages in wheat crop was

monitored and the number of days and the accumulated heat units required to attain different stages were worked out and illustrated in the Fig. 3.4.

Fig.3.4. Heat Unit requirement of wheat crop (Rabi, 2006-07) at Ranchi

It was observed that the heat unit requirement of wheat crop irrespective of date of sowing remains almost same at all the stage of the crop growth. The heat use efficiency for the timely sown wheat crop was worked out to be 2.39 kg/ha per day and the light use efficiency was about 3.26 kg/ha per hour of BSS (Table 3.14.).

0

200

400

600

800

1000

1200

1400

1600

1800

Ger

min

atio

n

CR

I

Max

.Till

erin

g

Late

join

ting

Boo

t

Flow

erin

g

Milk

ing

Mat

urity

GD

D

Normal Moderate Late Very Late


29

0 500 1000 1500 2000 2500

Laxmi

Deoki

Shaktiman-1

Suwan

Varie

ties

Heat units

MaturityCob initationtasseling

Table 3.14. HUE and LUE of wheat crop in rabi at Ranchi

Treatments

(Sowings)

BSS (hrs) GDD (deg

days)

Grain yield

(Kg/ha)

HUE (Kg/ha/deg

day)

LUE

(Kg/ha/hr of BSS)

Normal 1154 1572 3768 2.39 3.26

Mod. Late 1108 1578 3139 1.98 2.83

Late 1049 1593 3078 1.93 2.90

Very late 966 1609 2909 1.80 3.01

Average 1069.25 1588 3223.5 2.03 3.00

MAIZESamastipur

The accumulation of heat units in different maize varieties viz., Laxmi, Deoki, Shakhtiman-1, Suwan under different crop growth stages is given in Fig.3.5. The period required and the corresponding heat accumulation for seedling emergence did not differ significantly among the cultivars. However, it differed significantly for all the other crop stages. The variety Deoki accumulated higher heat units at tasseling, cob initiation and silking stage than other varieties. At maturity this variety accumulated higher heat units, which was at par with Laxmi.

Fig.3.5. Accumulation of heat units by different maize varieties at Samastipur


30

MUSTARDUdaipur

Three years (2004-2005 to 2006-07) pooled data of mustard crop (Table 3.15) showed that the maximum seed yield of 19.42 q/ha was recorded when mean sowing temperature was around 26.2°C. However, the seed yield significantly decreased by 1.14 and 5.69 q/ha when the sowing was delayed and the temperature were lower by 2.2 and 4.9°C respectively as compared to normal sowing date (5th October). Perhaps, the differential temperatures occurred during reproductive stage might be responsible in recording lower yields in late season crops. It was further noticed that the crop sown on 5th October escaped from aphid infestation as the lower air temperature during peak infestation stage and did not favour its growth.

Table 3.15. Effect of sowing dates and irrigation levels on seed yield (q/ha) of mustard at Udaipur

Treatment 2004-05 2005-06 2006-07 Mean Mean Sowing

Temp. (oC)

Sowing date

5th Oct 19.39 17.12 21.76 19.42 26.2

20th Oct 18.26 15.48 21.09 18.28 24.0

4th Nov 15.71 12.28 13.21 13.73 21.3

C.D. (0.05) 1.498 0.758 1.162 0.862

Irrigation levels

I0 12.03 9.75 11.84 11.21

I2 17.51 15.05 14.29 15.62

I3 20.09 17.19 24.10 20.46

I4 21.51 17.86 24.52 21.29

C.D. (0.05) 1.730 0.875 1.342 0.849

Among the irrigation levels, the maximum seed yield of 21.29 q/ha was obtained with the application of three irrigations but it was on par with two irrigations (20.46 q/ha) treatment. The seed yield increased significantly up to two irrigations over control and one irrigation by 82.5 and 30.9 %, respectively.


31

Intercepted PAR in mustard Intercepted PAR increased with the increasing levels of irrigation in all

sowing dates. Under no irrigation treatment intercepted least PAR at each crop stage was less as compared to one, two and three irrigations. The maximum incepted PAR was observed during 75 to 90 DAS in all treatments. The IPAR decreased after 90 DAS in all treatments.

RaipurTo minimize the adverse effect of delayed sowing in mustard, which was

mostly grown under rice-based cropping systems, three varieties of mustard crop, viz., Varuna, Kranti and Vardan were grown in three dates of sowing viz., 30th Nov, 10th Dec and 20th Dec 2006 to optimize the dates of sowing with suitable cultivars. The accumulated growing degree-days and their derivatives under different thermal environments are given in the Table 3.16.

It was observed that these variables vary considerably from sowing to maturity. The accumulated GDD required for emergence were higher at first and third date of sowing as compared to second date of sowing in all the varieties.

Table 3.16. Accumulated growing degree days, photo-thermal units and helio-thermal units in different growth stages at Raipur

Varieties (V) Sowing

Dates (D)

P1 P2 P3 P4 P5 P6

Varuna D1-30. Nov. 5 42 48 64 89 111

D2-10 Dec. 5 43 49 67 90 108

D3-20 Dec. 6 43 57 67 87 105

Kranti D1-30 Nov. 5 43 49 65 91 109

D2-10 Dec. 5 45 50 69 90 108

D3-20 Dec. 6 46 52 66 89 106

Cont…


32


Dates (D)

P1 P2 P3 P4 P5 P6

Vardan D1-30 Nov. 5 43 51 63 89 110

D2-10 Dec. 5 44 52 64 89 107

D3-20 Dec. 6 45 54 62 87 105

Growing Degree Days (Cumulative)

Varuna D1-30 Nov. 89 635 731 986 1425 1867

D2-10 Dec. 79 625 715 1042 1457 1856

D3-20 Dec. 87 644 792 1062 1454 1884

Kranti D1-30 Nov. 89 651 744 1004 1465 1820

D2-10 Dec. 79 655 733 1076 1457 1856

D3-20 Dec. 87 700 813 1043 1496 1908

Vardan D1-30 Nov. 89 651 771 968 1424 1843

D2-10 Dec. 79 640 771 995 1436 1834

D3-20 Dec. 87 680 849 975 1454 1884

Photo-thermal Units (Cumulative)

Varuna D1-30 Nov. 960 6909 7962 10769 15824 21135

D2-10 Dec. 860 6820 7800 11556 16425 21212

D3-20 Dec. 939 7048 8761 11877 16557 21779

Kranti D1-30 Nov. 960 7076 8100 10978 16314 20575

D2-10 Dec. 860 7140 7997 11938 16425 21212

D3-20 Dec. 939 7692 8999 11650 17062 22084

Vardan D1-30 Nov. 960 7076 8394 10561 15824 20853

D2-10 Dec. 860 6985 8413 10999 16171 20949

D3-20 Dec. 939 7465 9420 10873 16557 21779

Cont…


33


Dates (D)

P1 P2 P3 P4 P5 P6

Helio-thermal Units (Cumulative)

Varuna D1-30 Nov. 619 5173 5846 7871 11684 15516

D2-10 Dec. 650 5077 5892 8305 12197 15676

D3-20 Dec. 681 5118 6324 8703 12014 15958

Kranti D1-30 Nov. 619 5301 5925 8022 12103 15061

D2-10 Dec. 650 5344 6039 8610 12197 15676

D3-20 Dec. 681 5574 6472 8505 12439 16195

Vardan D1-30 Nov. 619 5301 6164 7740 11684 15283

D2-10 Dec. 650 5214 6195 7963 12055 15448

D3-20 Dec. 681 5400 6720 7816 12014 15958

P1 – Emergence, P2 - First flower appearance, P3 - 50% flowering, P4 - Start of seed filling, P5 - End of seed filling and P6 - Physiological maturity.

Hisar Mustard cultivar ‘Laxmi’ when sown on three different dates 08th Oct (S1), 20th Oct (S2) and 05th Nov (S3) 2006 with growth manipulation treatments differed significantly for accumulated heat units to attain different phenological stages upto physiological maturity except at emergence where all sowing dates were at par. Early sown crop (8th October) accumulated more heat units (2863) as compared to 20th October sown crop (1653) and 5th November sown crop (1452) to attain physiological maturity (Table 3.17).


34

Table 3.17. Effect of different treatments on accumulated heat units (°day) taken to different phenophases in mustard at Hisar

Treatments Emergence 5th true

leafFBV FFO 50% F 100% F SSF ESF PM

Sowing Dates

S1 80 364 594 717 951 1039 1062 1656 2863

S2 83 300 535 665 822 874 1369 1438 1653

S3 91 349 529 621 713 758 803 1340 1452

CD at 5% NS 13.4 17.2 28.7 6.19 4.84 15.2 25.3 28.8

Growth Manipulationss

L1 86 338 551 668 794 855 879 1453 1633

L2 85 337 551 665 933 986 1640 1546 3071

L3 84 336 556 668 793 860 906 1461 1624

L4 84 339 554 669 795 861 887 1451 1629

CD at 5% NS NS NS NS 5.54 4.77 12.7 20.3 24.4

FBV= flower bud visible, FFO= first flower opened, 50% F= 50 per cent flowering, 100% F= 100 per cent flowering, SSF= start of seed filling, ESF= end of seed filling, PM= physiological maturity

Seed yield of mustard did not differ significantly with sowing dates. However, biological yield recorded difference with each other, where with delay in sowing the value of biological yield decreased significantly (Table 3.18).

Table 3.18. Effect different treatments on seed yield, biological yield and harvest index in mustard at Hisar

Treatment Seed yield (q ha-1) Biological yield (q ha-1) Harvest index (%)

Sowing Dates

S1 22.4 77.3 28.98

S2 23.7 76.6 30.78

S3 20.4 66.7 30.36

Cont…


35

CD at 5% NS 7.5 NS

Growth Manipulations

L1 23.1 76.8 30.23

L2 18.4 62.8 29.11

L3 23.4 75.9 30.77

L4 23.6 78.8 30.06

CD at 5% 2.2 5.4 NS

The relationship between the individual weather parameters such as maximum temperature and the seed yield was worked out and given in the Fig.3.6.

S e

e d

y i

e l d

(kg

ha-1

)

Fig.3.6. Relationship of seed yield (kg ha-1) with maximum temperature during reproductive phase in mustard

There is a decrease of 3.14 q/ha of seed yield with an increase of maximum temperature 1°C during reproductive phase.

y = -3139.2x + 78059R2 = 0.6491

1000

1500

2000

2500

3000

24.1 24.1 24.2 24.2 24.3 24.3 24.4

Maxmium Temp. O C


36

MohanpurMustard crop variety YS-B9 was grown under eight dates of sowing during

22nd Oct to 10th Dec 2006. The number of days required to attain different phenological stages and the corresponding accumulated growing degree days under various dates of sowing are given in the Table 3.19.

Table 3.19. Days required to achieve different phenological stages and accumulated growing degree days (AGDD) for different dates of sowing in mustard at Mohanpur

Stages D1 D2 D3 D4 D5 D6 D7 D8

Emergence to

1st leaf 4(86) 4(105) 3(77) 4(95) 4(93) 5(102) 2(50) 4(78)

1st flower bud 30(623) 26(473) 26(442) 28(493) 32(93) 23(357) 22(342) 21(324)

50% flowering 37(735) 39(7279) 37(617) 40(675) 36(595) 35(536) 37(527) 37(519)

1st siliqua 37(735) 39(709) 37(617) 39(649) 33(538) 35(536) 38(538) 39(544)

End of

flowering55(1015) 55(966) 52(843) 63(969) 59(873) 60(885) 58(877) 56(835)

1st siliqua

maturity73(1261) 75(1204) 73(1107) 78(1217) 73(1139) 78(1174) 74(1067) 70(1059)

100% siliqua

maturity92(1524) 92(1478) 84(1333) 89(1375) 78(1224) 82(1238) 82(1210) 76(1154)

Figures in parenthesis are AGDD values.

The total number of days from emergence to physiological maturity varies between 76-92 days. The late sown crop matured in less number of days compared to the early sown crop. The accumulated growing degree days vary from the 1155 to 1524 degree under various treatments. The heat use efficiency of the crop decreased considerably with delayed sowing. It varied from 0.048 to 1.234 kg/ha/°day.


37

CHICKPEAJabalpur

The performance of different chickpea cultivars viz., JGK-3, JGG-1 and JG-

322 sown under three dates of sowing viz., 8th Nov, 28th Nov and 18th Dec 2006 was

studied. The phenological observations and growth parameters were recorded in all

the treatments. Growing Degree days requirement for different growth phases under

different dates of sowing of the varieties showed that the early sown crop took more

number of days to reach physiological maturity compared to late sown dates. The

heat unit requirements in early sown crop were higher compared to December sown

crop (Fig. 3.7). The relationship between temperature recorded during the period

50% flowering to physiological maturity and the dry matter and the seed yield is

given in Fig.3.8. It was observed that increase in temperature decreases the

biomass production of chickpea. Higher seed yield of chickpea was recorded in

crop planted in early November (8th Nov) as compared to crop planted in late

November (28th Nov) and mid-December (18th Dec). Highest seed yield of 19.3 q/ha

was recorded in Desi type under 8th November sowing and lowest yield recorded by

Kabuli type in 18th December sowing (Table 3.20).

Table 3.20. Seed yield (q/ha) as influenced by date of sowing and chickpea varieties at Jabalpur

Date of sowing JGK- 3 (Kabuli) JGG-1 (Gulabi) JG-322 (Desi)

D1 (8thNov.06) 16.0 16.7 19.3

D2 (28th Nov.06) 16.7 15.0 14.6

D3 (18th Dec.06) 6.7 10.7 13.7


38

Fig.3.7 Relationship between temperatures (50% Flowering to Physiological Maturity) and dry matter yield at physiological maturity of chickpea at Jabalpur

Figreq

Fai

of cviz.30th

phe

.3.8. Effect ouired for differ

zabadTo study

chickpea unde, Awarodhi (V

h Oct, 10th Nenological stag

of sowing datrent phenopha

the effect of der different cro

V1), Radhey (VNov, 20th Novges are given

39

tes and chickases at Jabalp

different sowinop growing en

V2), Uday (V3) wv of 2006. Thin Table 3.21.

kpea varietiespur

g dates on thenvironments, twere grown uhe heat unit .

Annual R

s in growing

e growth and three varietiesnder three datrequirements

Report 2006-07

degree-days

development s of chickpea tes of sowing

s at different


40

Table 3.21. Heat units for various phenological stages of chickpea in different dates of sowing at Faizabad

Treatments Sowing -

Emergence

Emergence-

Vegetative

Vegetative- 50%

flowering

50% flowering

- Podding Maturity Total

Sowing date

Oct. 30 126 1294 241 253 486 2399

Nov. 10 137 1099 217 237 399 2088

Nov. 20 117 954 206 234 337 1847

Genotype

Awarodhi 133 1102 209 255 424 2123

Radhey 133 1106 226 235 475 2174

Uday 126 1100 211 248 434 2118

The effect of maximum temperature during reproductive stage on the yield of chickpea is given in the Fig.3.9. It was observed that with increase in temperature from 27-33°C yield decrease approximately from 26-23 q/ha.

Fig.3.9. Effect of maximum temperature during reproductive stage on the yield of chickpea at Faizabad

The variability in seed yield due to dates of sowing and the varieties showed that productivity of Desi variety under all the dates of sowing was highest (Table 3.22)

Table 3.22.Seed yield (q/ha) as influenced by date of sowing and variety at Jabalpur Date of sowing JGK (Kabuli) JGG-1 (Gulabi) JG-322 (Desi)

8th Nov 16.0 16.7 19.3

28th Nov 16.7 15.0 14.6

18th Dec 6.7 10.7 13.7

y = -0.1821x + 25.817R2 = 0.4974

20

2122

2324

2526

27

27.2 27.4 27.8 28.3 28.7 29.1 29.4 29.6 30.5 31.2 32.3 32.5 32.7 32.8 32.9

Max. temp.( oC)

Yiel

d(q/

ha)


41

POTATOLudhiana

Two varieties of potato viz. Kufri Chandramukhi, Kufri Jyothi were sown on 18th Oct, 01st Nov, 16th Nov 2006 with three irrigation treatments viz., CPE 25mm, CPE 50mm and CPE 75mm. The growing degree-days availed by cultivars at different phenological stages under three dates of sowing and irrigation treatments are given in Table 3.23.

Table 3.23. Growing degree-days (GDD) availed by potato cultivars at different phenological stages at Ludhiana

Phenological stages D1 = 18th Oct.06 D2 = 01st Nov.06 D3 = 16th Nov.06

I1 I2 I3 I1 I2 I3 I1 I2 I3

Kufri Chandramukhi

Sowing to start emergence 216 216 216 126 126 126 185 185 185

216 216 216 126 126 126 185 185 185

Start to 50% emergence 18 18 18 37 37 37 21 21 21

234 234 234 163 163 163 206 206 206

50% to complete emergence 36 36 36 52 52 52 23 23 23

271 271 271 215 215 215 229 229 229

Complete emergence to Tuber initiation 259 259 259 231 231 231 181 181 181

529 529 529 446 446 446 410 410 410

Tuber initiation to physiological maturity 551 534 520 611 611 611 513 513 513

1080 1063 1049 1057 1057 1057 1022 1022 1022

Kufri Jyothi

Sowing to start emergence 216 216 216 215 215 215 185 185 185

216 216 216 215 215 215 185 185 185

Start to 50% emergence 18 18 18 31 31 31 21 21 21

234 234 234 246 246 246 206 206 206

50% to complete emergence 36 36 36 77 77 77 23 23 23

271 271 271 323 323 323 229 229 229

Complete emergence to Tuber initiation 290 290 290 133 133 133 202 202 202

560 560 560 455 455 455 430 430 430

Tuber initiation to physiological maturity

555 534 520 639 639 639 628 628 628

1115 1094 1049 1049 1049 1049 1058 1058 1058

I1 = CPE 25mm, I2 = CPE 50mm, I3 = CPE 75mm


42

It was observed that Kufri Chandramukhi took less number of days to complete its phenological stages as compared to other cultivars in first date of sowing. Both the cultivars matured earlier under third irrigation treatment followed by second and the first. In general, Kufri Chandramukhi took more number of days to complete the phenological stages under first and second date of sowing as compared to Kufri Jyothi. However, under third date of sowing the cultivar Kufri Jyothi took slightly higher number of degree-days compared to Chandramukhi.

MohanpurThe effect of date of planting on different phenological stages of Kufri Jyothi

variety revealed that the number of days required to achieve different phenological stages is more in early planted crop compared to the delayed sown crop (Table 3.24).

Table 3.24. Days required and accumulated growing degree days (GDD) for different phenological stages at Mohanpur

Phenophases Dates of planting

06th Nov 13th Nov 20th Nov 27th Nov 04th Dec

Planting to 100% Emergence 23 (465) 21 (378) 21 (355) 19 (295) 18(287)

Planting to stolonisation 28 (520) 26 (4601) 26 (436) 23 (362) 22(352)

Planting to tuberization 34 (622) 29 (516) 30 (502) 27 (431) 26 (414)

Planting to1st leaf senescence 65 (1095) 63 (1022) 61 (947) 62 (934) 60 (910)

Planting to ripening 92 (1520) 91 (1489) 88 (1411) 84 (1321) 80 (1262)

Figures in parenthesis are GDD values

The crop duration vary from 80-92 days and the corresponding accumulated growing degree days vary from 1262-1520. The highest number of days were taken between planting and first leaf senescence, which took about 60 to 65 days among the treatments.

The relationship between transmitted photosynthetically active radiation (TPAR), Intercepted Photosynthetically Active Radiation (IPAR) with Leaf Area Index (LAI) was worked out and presented in the Fig.3.10. With increase in leaf area index, there was a fall in TPAR values while increase in IPAR values was noticed.


43

Fig.3.10. Relationship between transmitted photosynthetically active radiation (TPAR) and Intercepted Photosynthetically Active Radiation (IPAR) with Leaf Area Index (LAI) at Mohanpur

SORGHUM Bijapur

Rabi sorghum (var. M 35-1) was grown under three dates of sowing to study crop-weather relationships. The phenological stages of sorghum in terms of growth are given in the Table 3.25. Primordial initiation took place around 30 days under all sowing condition. Flowering took place between 64-70 days. Maturity varies between 102-108 days after sowing with accumulated growing degree days 1396-1473.Table 3.25. Phenological development of sorghum crop in terms of accumulated growing degree days growth units (AGDD) at Bijapur

Date of sowing/Phenophase 15th Sept 29th Sept 9th Oct

Duration GDD Duration GDD Duration GDD

Sowing to germination 7 102 7 107 7 112

Germination to primordial initiation 29 451 30 460 30 435

Primordial initiation to flowering 34 481 27 397 30 390

Flowering to maturity 36 416 38 432 41 536

Sowing to primordial initiation 36 553 37 567 37 547

Sowing to flowering 70 1034 64 964 67 937

Sowing to maturity 106 1450 102 1396 108 1473

TPAR = 4.2496LAI2 - 31.593LAI + 65.195, R2 = 0.89

0

10

20

30

40

50

60

70

0 1 2 3 4 5LAI

TPAR

(%)

IPAR = -4.2496LAI2 + 31.593LAI + 24.805, R2 = 0.89

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5LAI

IPA

R(%

)

Transmitted photosynthetic active radiation (TPAR)- leaf area index (LAI) relationship

Intercepted photosynthetic active radiation (IPAR) - leaf area index (LAI) relationship


44

SolapurTo study the influence of weather parameters on the growth and

development sorghum crop, three varieties viz., Yasodha (V1), Mauli (V2), M-35-1 (V3) were sown under four dates viz., 36th, 38th, 40th and 42nd Standard weeks. The growing degree-days required to attain various phenological stages as influenced by dates of sowing are given in Table 3.26.

Table 3.26. Growing degree days required to attain phenological stages as influenced by sowing dates at Solapur

Sowing

Time

Phenological stage

Emergence 3

leaf

PI Flag

leaf

50 %

flowering

Soft

dough

Hard

dough

Phy.

Maturity

Total

S1V1 90 84 307 593 202 260 204 243 1983

S1V2 90 84 273 586 181 232 176 185 1807

S1V3 90 84 325 602 238 250 218 276 2083

S2V1 66 67 280 493 197 219 181 203 1706

S2V2 66 67 297 435 143 215 164 161 1548

S2V3 100 84 310 499 206 227 208 263 1897

S3V1 84 67 281 445 157 188 173 212 1607

S3V2 101 67 280 500 245 199 171 131 1694

S3V3 101 84 326 460 181 208 219 272 1851

S4V1 83 82 232 412 130 173 165 218 1495

S4V2 65 68 223 375 128 150 150 189 1348

S4V3 83 82 260 433 150 185 198 242 1633

The number of days to attain physiological maturity is about 129 days and the total growing degree-days vary from 1348–2083. The correlation between the grain yield and different weather parameters recorded during different phenophases in Table 3.27. Significant positive correlation was observed between 3rd leaf stage with temperature Tmax, BSS, Pan evaporation and HTU.


45

Table 3.27. Correlation coefficient between grain yield and different weather parameters/ agrometeorological indices prevailed during different phenophases at Solapur

Weather

parameters

Phenophases

S-E E-3L 3L-PI PI –FL FL- 50

%F

50%F-

SDSD -HD HD-PM

Tmax -0.81** 0.22 0.82** -0.45 0.72** -0.23 0.07 0.10

Tmin 0.57 -0.09 -0.41 -0.07 -0.03 -0.34 0.33 0.06

RHI 0.59* -0.13 -0.67* 0.56 -0.18 -0.18 -0.20 -0.03

RHII 0.76** -0.19 -0.68* 0.37 -0.24 -0.24 -0.06 0.07

WDS -0.01 -0.25 -0.51 -0.02 0.02 0.17 0.23 0.21

BSS -0.56 0.26 0.76** -0.56 0.00 0.42 -0.28 0.33

RAIN -0.22 -0.49 -0.81** 0.42 0.00 0.00 0.00 0.00

PAN -0.86** -0.23 0.71** -0.52 0.15 0.19 0.02 0.09

GDD 0.40 -0.31 0.46 -0.13 0.36 -0.01 0.19 -0.08

HTU -0.28 0.15 0.92** -0.29 0.48 0.19 0.17 -0.06

Significant level *0.05 **0.01 0.575 0.707

SUNFLOWER Kovilpatti To study the thermal regimes and its influence on the growth and development of sunflower crop under rainfed conditions, the sunflower crop variety K-1 was sown in four dates of sowing namely 42nd, 43rd, 44th and 45th standard weeks. It was observed that delayed sowing of sunflower, beyond October, causes reduction in GDD accumulation and also reduced seed yield to almost 50 percent as compared to crop sown in the second week of October. Heat use efficiency (HUE) also reduced with delayed sowing (Table 3.28). The decrease in both maximum and minimum temperature in second date of sowing were found to be beneficial in increasing the seed yield.


46

Table 3.28. Heat use efficiency (HUE) of rainfed sunflower under different dates of sowing at Kovilpatti

Treatments Seed yield (q/ha) Stalk yield

(q/ha) AGDD

HUE seed yield

(kg m-2day-1)

HUE stalk yield

(kg m-2day-1)

T1 (42 SW) 8.2 43.6 1411 0.58 3.09

T2 (43 SW) 9.1 45.8 1407 0.64 3.25

T3 (44 SW) 6.8 41.5 1413 0.48 2.93

T4 (45 SW) 4.8 33.8 1308 0.36 2.59

SUNFLOWERBijapur

To study the influence of weekly weather parameters on crop growth and development of yield, the crop was sown under three dates of sowing. The seed yield of sunflower varieties KBSH-1, GK-2002, SB-275and NSP-92-1 as influenced by different growing environments during the rabi 2006-07 are given in the Table 3.29.

Table 3.29. Seed yield (q/ha) of sunflower in different growing environments at Bijapur

Growing environment Genotype

MeanKBSH-1 GK 2002 SB-275 NSP92-1(E)

15th Sept. 17.8 17.8 18.5 8.8 15.7

29th Sept. 15.8 14.1 16.8 8.1 13.7

9th Oct. 12.9 10.4 11.6 7.0 10.5

Mean 15.5 14.1 15.7 8.0 -

The data indicated that for any genotype early September is an optimum time for sowing of sunflower in rabi season under Bijapur climatic conditions.


47

BLACKGRAM / GREENGRAM Kovilpatti

The influence of weather elements at different phenological stages on growth and yield of blackgram and greengram under rainfed conditions was carried out. The effect of different dates of sowing and varietal performance on the grain yield and energy utilization in green gram and black gram are given in Table 3.30.

Table 3.30. Effect of time of sowing and varieties on light energy utilization and grain yield of black gram and green gram at Kovilpatti

Treatments Blackgram Greengram

PAR Interception Grain yield (kg/ha) PAR Interception Grain yield (kg/ha)

(µ moles/m2/s) (µ moles/m2/s)

D1 0.56 587 0.56 498

D2 0.6 795 0.66 616

D3 0.62 753 0.63 603

D4 0.53 466 0.54 375

SEd 0.01 59 0.03 17.3

CD (0.05) 0.03 144 0.06 42.3

V1 0.58 639 0.59 540

V2 0.63 735 0.61 581

V3 0.53 574 0.55 449

SEd 0.01 41 0.01 15.3

CD (0.05) 0.02 87 0.03 32.5

D1 =39th; D2 = 40th; D3 = 41st and D4 = 42nd standard weeks Blackgram V1 = K1, V2 = Co5, V3 = Vamban 3; Greengram V1 = K-1, V2 = Co6,V3 = Pusa bold

It was observed that delayed sowing reduced the yield. The PAR interception was higher in October first and second week treatment in both the crops. The variety Co5 has also recorded higher intercepted PAR values compared to other two cultivars.


48

CABBAGEDapoli To study the effect of weather parameters on the yield of Cabbage crop was sown with three dates of sowing, viz., 15th Oct, 30th Oct and 15th Nov of 2006 and was harvested on 17th Jan, 20th Feb and 25th Feb. The mean yield of cabbage affected by different dates of sowing indicated that the early sown crop recorded highest yields compared to the other two sowing dates. Accumulated growing degree days (AGDD) were calculated for various growth stages and given in Table 3.31. Further, simple models were developed between cabbage yield and AGDD.

Table 3.31. Growing degree-days and yield in cabbage under different sowing dates at Dapoli.

Treatments Sowing date

15th Oct 30th Oct 15th Nov

Seedling (0-30 days) 531 460 417

Vegetative stage (31-45 days) 641 615 605

Head formation and development (46-94 days) 1354 1310 1282

Total 2526 2395 2303

Yield (q/ha) 312 287 210

To obtain the yield level of 312, 287 and 210 q/ha of cabbage yield under three dates of sowing, the required cumulative growing degree are 2526, 2395 and 2303 respectively. The following regression equation for estimating the cabbage yield under different dates of sowing using accumulated growing degree-days is given in Table. 3.32.

Table 3.32. Crop weather relationship in cabbage under different sowing dates at Dapoli

Regression Equation R2

Y = 1238.0 + 0.423 AGDD 0.53**

Y = -535.2 + 3.24 AGDD Seedling 0.36*

Y = -958.1 + 1.47 AGDD Vegetative 0.48*

Y = 258.3 +0.24 AGDD Head formation and development 0.60*


49

4. CROP GROWTH MODELLING

WHEATUdaipur

To record the growing degree-day requirement for various phenological stages five varieties of wheat viz., Lok-1, Raj 3765, Raj-4037, GW-273 and GW-173 were sown on four dates viz., 4th November (D1), 17th November (D2), 3rd December (D3) and 16th December (D4). The results show that different wheat varieties responded differently in terms of heat units requirement to attained maturity. Higher heat units were required under D1 in all varieties. The heat units requirement decreased continuously as the sowing was delayed from D1 to D4 in all varieties. The maximum heat units (1805.1) was recorded by variety GW-273 under D1. The least units (1545.2) were recorded by variety GW-173 under D3.

FaizabadRegression models for estimating the yield for different genotypes at

different dates of sowing using weather parameters recorded at various crop growth stages are given in Table 4.1.

Table 4.1. Multiple correlations between seed yield and weather parameters in wheat at Faizabad Genotype/

Growth phases

Regression equation R2

HUW-234

90 DAS Y=59.648+(-0.455)Tmax.+(-0.309)Tmin.+(-0.127)RH+( 1.091)SS 0.44

105 DAS Y=92.458+(-0.076)Tmax.+(-0.449)Tmin.+(-0.518)RH+( -0.838 )SS 0.45

120 DAS Y=99.669+(-1.641)Tmax.+(0.954)Tmin.+(-0.659)RH+( 2.630)SS 0.70

HD-2285

90 DAS Y=41.932+(0.003)Tmax.+(-0.055)Tmin.+(-0.070)RH+(0.077 )SS 0.46

105 DAS Y=46.684+(0.094)Tmax.+(-0.211)Tmin.+(-0.150)RH+( )SS 0.58

120 DAS Y=43.267+(-0.124)Tmax.+(0.176)Tmin.+(-0.142)RH+(0.481 )SS 0.57

HP-1633

90 DAS Y=82.641+(-0.736)Tmax.+(0.255)Tmin.+(-0.392)RH+( 0.024)SS 0.55

105 DAS Y=56.125+(0.014)Tmax.+(-0.346)Tmin.+(-0.242)RH+(0.554 )SS 0.53

120 DAS Y=52.06+(-0.178) Tmax. +(0.132) Tmin. +(-0.285) RH+( 1.246)SS 0.67


50

MUSTARDUdaipur

The GDD required for vegetative phase were less as compared to that required for reproductive phase in all sowing dates. The highest heat unit of 1727 degree days were required for maturity under 5th Oct sown crop. However, requirement of heat units were decreased with the delayed sowing. Amongst the different levels of irrigation, it was noted that lowest heat units were required under no irrigation in all sowing dates. However, the requirement of heat units was increased from 1548 to 1689 degree days with the increasing level of irrigation from no irrigation to three irrigations.

Hisar Multiple regression equations were developed between the weather

parameters viz., Tmax, Tmin, RH mean, BSH, EP at different growth stages and yield of mustard. The equations are given in Table 4.2.

Table 4.2. Multiple correlations between seed yield and weather parameters in mustard at Hisar

Growth

phases

Multiple regression equation R2

Vegetative phase

D1 Y= -93575+0x1-450x2+1732x3+0x4+0x5 0.61**

D2 Y= 39816+1262 x1+1571x2-1355x3+0x4+0x5 0.14

D3 Y= 600064.1-8994 x1+4453 x2-5201 x3-14039 x4+23204 x5 0.39*

Reproductive phase

D1 Y= 1008-0 x1+0 x2-1173 x3-4616 x4+5785 x5 0.13

D2 Y= -644274+1802 x1-20111 x2+8243 x3-649 x4+74039 x5 0.84**

D3 Y= -4971453+151035 x1-5080 x2+19704 x3+159633 x4-462923 x5 0.93**

X1= Tmax, X2= Tmin, X3= RH mean, X4= BSSH, X5= Ep ** Significant at (P = 0.01) and * at (P = 0.05) level of significance


51

5. WEATHER EFFECTS ON PESTS AND DISEASES

WHEATHisar

Data pertaining to average infection (%) of Karnal bunt disease and meteorological parameters of 1st to 12th standard meteorological week (1st January to 25th March) for 25 crop seasons (1981-82 to 2004-05) of Karnal station were correlated for most sensitive crop growth period corresponding to ear emergence and subsequent crop growth stages. The meteorological parameters having highly significant correlation with disease were identified and their importance for particular crop growth period was established. The coefficient of correlation obtained between various meteorological parameters individually and average Karnal bunt infection (%) is given in Table 5.1. Rainfall during the 3rd week of January showed strong relationship indicating favorable role in the formation and further multiplication of secondary spordia. However, during 9th SMW, maximum temperature, relative humidity, rainfall and sunshine duration showed considerably high correlations, whereas remaining parameters had weak correlation coefficients.

Table 5.1. Correlation coefficients between meteorological parameters and average Karnal bunt infection (%) in wheat at Hisar Parameter 1st week 2nd week 3rd week 4th week 5th week 6th week

Tmax 0.23 0.35 -0.05 0.24 0.26 0.19

Tmin -0.29 0.21 0.01 0.10 0.01 0.22

AVPm 0.02 0.42 0.05 0.21 0.17 0.27

AVPe 0.12 0.47 0.14 0.19 0.15 0.10

RHm -0.30 -0.08 -0.12 -0.02 0.41 -0.10

RHe -0.43 -0.13 0.09 0.01 -0.07 0.04

WS 0.28 0.16 0.34 0.27 0.24 0.38

SS 0.43 0.18 -0.02 0.19 0.16 -0.01

PE 0.28 0.12 0.03 0.12 -0.08 -0.12

Rain 0.20 0.19 0.51 0.20 0.11 0.11

Cont…


52

7th week 8th week 9th week 10th week 11th week 12th week

Tmax -0.01 0.27 -0.48 -0.31 -0.28 -0.32

Tmin -0.02 0.44 0.14 0.14 -0.10 0.01

AVPm -0.03 0.41 0.32 0.09 -0.02 -0.05

AVPe -0.17 0.32 0.47 0.23 0.09 0.15

RHm -0.11 -0.12 0.59 0.36 0.32 0.34

RHe -0.16 0.11 0.64 0.27 0.13 0.23

WS 0.15 0.16 0.12 0.15 0.13 0.15

SS 0.30 -0.08 -0.28 -0.06 -0.18 -0.09

PE -0.05 -0.03 -0.33 -0.24 -0.19 -0.11

Rain 0.12 0.38 0.32 0.26 0.28 0.07

MUSTARDUdaipur

The infestation of aphid in the mustard crop was monitored during the rabiseason. The first appearance was noticed during 50th standard week when the average temperature and RH were about 7.0°C and 55 percent, respectively. The aphid population was more under early sown crop compared to late sown crop. The aphid infestation for the year 2004, 2005-06 is given in Table 5.2. The data showed that the aphid incidence occurred during 14th Dec to 25th Jan when the mean temperature and RH ranged between 13.7 to 17.2°C and 55-60 percent, respectively. The temperature range of 18-20°C and 56-61percent of RH favoured the multiplication of the aphids.

Table 5.2. Aphid infestation for the year 2004, 2005-06 at Udaipur

Aphids 2004-05

Below ETL

2005-06

Peak infestation

2006-07

Peak infestation

Incidence 25th Jan (4th week)

Green seed in D3

29th Dec

Green seed in D2 &

16th Jan

Flowering in D3

14th Dec (50th week)

Seed initiation in D2

2nd Jan

Flowering in D3

Cont…


53

Range of 17.7 to 20.4 °C and 56 to 61 % RH favoured the multiplication of aphids. D1= 5th October, D2= 20th October, D3= 4th November

AnandTo study the effect of weather on the incidence and spread of aphids and

mustard sawfly population in mustard crop, the crop variety GM-2 was sown in four different dates of sowing. It was found that the mustard aphid intensity and its peak values were found to vary according to dates of sowing. Lowest aphid intensity was recorded in 10th October sowing treatment compared to the rest of sowings dates. The highest aphid index in all the dates of sowing was seen around 11-13 weeks after sowing. Under the low RH values aphid intensity was found higher in all the dates of sowing. In case of mustard sawfly lowest intensity was recorded in the first date of sowing (10th Oct) and highest was observed in second date of sowing (20th

Oct). Lower maximum temperature in the range of 27-32°C coupled with higher relative humidity (>61%) was not suitable for outbreak and growth of the sawfly under the first date of sowing. While higher average maximum temperature and low relative humidity values were found congenial for the better growth of sawfly in the rest of the treatments. It was found that the intensity of mustard white rust was found higher under the late sown conditions and weather in January month is found to be crucial for disease development and its spread. Thus the delay in sowing may cause more infestation of white rust.

Mohanpur The aphid population collected from top 10 cm plant was related to

maximum temperature and the range of temperature during the period of growth. There is no relation between the maximum temperature and the aphid population. However, a positive relationship between the aphid population and the range of

Mean Temperature (°C) 13.7 16.6 17.2

RH (%) 54.5 60 55.3

Peak infestation 12th Feb (7th week) 9th Feb (6th week) 23rd Dec (51st week)

Mean Temperature (°C) 20.4 19.7 17.7

RH (%) 58.0 56.8 61.0


54

temperature has been noticed (Fig.5.1) which means higher daytime temperature and lower minimum temperature are favorable for multiplication of aphid population.

Fig.5.1. Trend line between temperature range and aphid count in mustard at Mohanpur

Palampur

The influence of weather parameters in relation to mustard aphid population in gobhi sarson was studied with three varieties of crop viz., Sheetal, Neelam and Hyloa sown under three dates of sowing viz., 25th Oct, 02nd Nov and 10th Nov. The maximum, minimum temperature and evening relative humidity explained more than 70% variation in aphid population. Based on the weather parameters in the first fortnight of February and the appearance of the aphids over the years 2003-2007 some of the highlights associated with the pests are:

� Flowering stage is most susceptible

� Maximum temperature has positive effect

� Lower minimum temperature has negative effect

� Period of outbreak is observed in second fortnight of February

� Peak population observed by end of March

� Late sown varieties experienced more aphid attack


55

CHICKPEAFaizabad

The incidence of pod borer was monitored at weekly intervals in the chickpea crop from sowing to physiological maturity and the corresponding weather data from agro meteorological observatory was used to study the influence of weather parameters on the incidence and spread of the pod borer. Highest population of pod borer was recorded with evening relative humidity around 35-49% and minimum temperature around 12°C. Relationships between these parameters and the incidence of pod borer were developed, and are shown in Fig.5.2.

Fig.5.2. Weekly insect population in relation to evening RH (%) and effect of minimum temperature on population of pod borer in chickpea

It was observed that the number of pod borer increased with increase in minimum temperature from 4.5 – 12.1°C.

y = 0.091x2 - 0.341x + 0.832R² = 0.878

0

2

4

6

8

10

12

14

3.3

4.5 5

5.7

5.9

9.3

9.4

9.7

9.9

10.3

11.7

12.1

12.2In

sect

pop

ulat

ion

Min. Temp.(oC)

�

y = -0.0024x4 + 0.1066x3 - 1.4318x2 + 5.307x + 51.343R2 = 0.4586

0

10

2030

40

5060

70

40 42 44 46 48 50 52 2 4 6 8 10

Met. week

RH

(%)

0246810121416

Inse

ctpo

pula

tion

Insectpopulation


56

JabalpurCorrelation coefficient of pest population with different weather parameters

has shown significant relationship with temperature and humidity. Higher temperature and humidity favored pest incidence, which caused maximum pod damage. Evening humidity was not as important as morning humidity. These relationships with weather parameters can help in simulating pest population and prediction of pest incidence based on weather forecast.

POTATOMohanpur

The late blight infestation was measured at weekly intervals and percent disease incidence (PDI) was worked out in the potato crop grown at Mohanpur. It was observed that infestation of late blight mainly started during tuberisation stage. In all the dates of sowing more than 20% of the incidence was observed at 6-12 days after tuberization. The PDI is positively correlated with maximum temperature and minimum temperature but negatively correlated with temperature range (Fig.5.3). This indicates that during February when the minimum temperature rises the infestation of late blight increases.


57

SAFFLOWERAkolaWeather effect on aphid’s population:

The incidence of aphids in safflower was analyzed in an experiment during rabi season with four sowing dates during 39th, 40th, 42nd and 43rd MW. Observations on aphid population on the crop and corresponding weather parameters (temperature and humidity) inside the crop canopy were recorded. The step-wise regression analysis was done and the best fit regression equation of aphid’s population with weather parameters temperature and RH inside the crop canopy is as under:

97.064.4120.5341min5.50min6.14285.1538 2 �� RRHIlagRHIlagTTY

BLACKGRAM Kovilpatti

Regression models were developed between the incidence of powdery mildew disease in blackgram and the accumulated growing degree-days for the years 2003-2005. These models were tested and the percentage disease index was predicted. The observed and predicted values of the disease at different dates after sowing for the year 2003,2004 and 2005 are given in the Fig.5.4. Except during 2005, there is a close agreement between th4e observed and predicted values of PDI during 2003 and 2004.

020406080

100120

10 20 30 40 50 60 70 80 90DAS

PDI (

%)

Observed PDIPredicted PDI

2003


58

Fig.5.4. Observed and Predicted percent disease index during 2003 to 2005 at Kovilpatti

Observed PDI Predicted PDI

0

20

40

60

80

100

120

10 20 30 40 50 60 70 80 90

0

20

40

60

80

100

120

10 20 30 40 50 60 70 80 90

2005

2005

PDI (%)

DAS

PDI (%)

DAS


59

MANGODapoli The incidence of mango hopper was monitored and found that it started from 51st SMW and attained maximum value in the 6th SMW. This increase in incidence of mango hopper was due to increased in minimum temperature and sudden increase in afternoon humidity (RH II) from 46 to 62 percent and decrease in maximum temperature from 33.4 to 30.1�C (Fig.5.5).

Fig.5.5 Effect of temperature on hopper incidence on Mango

Standard Meteorological Week


60

6. AGROCLIMATIC CHARACTERIZATION

Anand Fifty years (1958-2007) of rainfall data and temperature data of Anand was analysed to find out the trends in weather parameter. The data analysis revealed no significant trend in rainfall. In case of temperature, the maximum temperature during winter has increased at the rate 0.036�C per year over its normal of 30.4�C (Fig.6.1). The minimum temperature has also showed an increasing trend of 0.0221�C per year over the normal of 14.9�C during the winter season.

Fig.6.1. Trend of average maximum temperature in winter season at Anand

However during summer, the rate of increase in maximum and minimum temperature was about 0.042°C and 0.032°C per year over its normal of 35.3 °C and 18.5°C during the period 1958-2003 respectively. During monsoon season, the rate of increase in maximum and minimum temperatures was 0.018 and 0.017°C respectively over their mean values.

Fig. 1.4 Trend of average maximum tempertautre in winter season

y = 0.036x - 40.657R2 = 0.2284

28

29

30

31

32

33

19581963

19681973

19781983

19881993

19982003

Winter season

Ave

rage

Max

. T


61

Udaipur The onset and withdrawal of monsoon at Udaipur was computed using 36 years data (1971-2006). The amount of 30 mm rainfall received in a week is considered as starting of rainy season and an amount of 15 mm rainfall accumulated in a week was considered as ending of rainy season. Mean standard meteorological week (SMW) for onset and withdrawal of monsoon were found as 25 SMW and 38 SMW, respectively. Mean monsoon rainfall is found to be 551.5 mm and season extend over a duration of 92 days (13 weeks) (Table 6.1).

Table 6.1. Onset and withdrawal of monsoon at Udaipur (1971-2006)

Particulars Onset week Withdrawal week Duration (weeks) Rainfall (mm)

Mean 25 38 13.2 551.5

Earliest/Minimum 22 32 7 289.4

Latest/Maximum 28 43 18 1120.5

SD 1.6 2.5 2.5 199.1

CV (%) 6.3 6.5 19.3 36.1

Accumulated rain for start of monsoon � 30 mm; Amount of rain for ending the monsoon 15 mm`

Probability analysis of rainfall at Udaipur: The available daily rainfall data for the period of 1971 to 2006 was

analyzed. The centre received an average amount of 601mm rainfall annually in 31 rainy days. The coefficient of variation is 31 per cent and standard deviation is 186mm. Monthly rainfall trends for the period 1971 to 1990 and 1991 to 2006 showed a drastic reduction in rainfall in post monsoon season (October – November) in later group. Initial and conditional probabilities indicated that the chances of higher rainfall i.e. above 30 mm per week were lesser during 23 to 26 weeks. This indicates that week No. 23 to 26 is suitable for undertaking preparatory tillage operations. It also indicated that the monsoon activity is weakened from 35 to 39 week that causes moisture stress in major field crops.


62

Rainfall variability in southern Rajasthan: To study the rainfall variability in southern Rajasthan, thirty-seven

years of rainfall data (1970-2006) of different districts viz. Banswara, Durgapura, Sirohi, Chittorgarh and Udaipur was analyzed. The results indicated that the highest mean annual rainfall of 1101.5 mm in 41.5 rainy days was recorded in Banswara. The lowest rainfall of 507.5 mm in 28 days was recoded in Rajsamand. Banswara, Dungarpur, Sirohi, Chittorgargh and Udaipur receive an average rainfall of 1102, 748, 601, 758 and 601 mm respectively. The coefficients of variation are in the order of 38.4, 34.9, 55.8, 32.4 and 31.0 per cent. Higher coefficient of variation in all the district indicates the lower dependable rainfall. Further, it was noted that the rainfall decreased in recent years (1990-2006) during June and August months in all the districts as compared to earlier 20 years (1970-1989). However, reverse trend was observed during July i.e. in recent 17 years rainfall increased as compared to previous 20 years. It is interesting to note that there is drastic reduction in rainfall during post monsoon season (October and November) in the recent years as compared to previous 20 years in all districts.

Hisar Weather and productivity data of chickpea for 25 seasons (1978-79 to 2002-03) were analyzed for Mahendragarh district of Haryana as a part of agroclimatic characterization study of chickpea. The correlation matrix revealed that minimum temperature of second week of February, morning RH during second fortnight of January and sunshine hours during March showed positive correlation with yield. Whereas wind speed during third week of February, rainfall of March and evaporation during first fortnight of March showed negative correlation.

A linear regression model was developed based on the above weather parameters to predict the grain yield of chickpea is as follows:

Y = -576.8-13.43 X1 +16.46 X2 +40.94 X3 R2=0.72 Where, X1 = Minimum temperature of 8th SMW X2 = Morning relative humidity of 4th SMW X3 = Sunshine hours of 45th SMW


63

Agroclimatic features of Bawal region comprising the arid and semi-arid tracks in the districts of Bhiwani, Gurgaon, Mahendragarh, Rewari and Jhajjar was carried out based on the available agroclimatic, soils and natural vegetation data. The total annual rainfall in this study area varies from 229mm to 1010mm with a mean of 577mm per year. About 83% of the annual rainfall is received during the Southwest monsoon period and there is a large variability in the seasonal rainfall over the study period. The water balance computation revealed a large water deficit of 1144mm on annual basis. The Moisture Adequacy Index (MAI) during 27-38th SW is below 40 and can support only rainfed crops that can withstand moisture stress conditions. The mean maximum temperature during the summer is around 41°C and the minimum temperature during winter is around 4-6°C.

BijapurAgroclimatic analysis in respect of 98 stations located in North

Karnataka region has been completed. From the long period daily rainfall data of weekly, monthly, and annual rainfall totals, their probabilities and other statistical analysis, viz., conditional probabilities, water balance parameters, LGP, etc. have been analyzed. Agroclimatic Atlas for Bijapur and Bagalkot districts has been prepared.

JabalpurAnalysis of Wheat Productivity in Relation to temperature in Four Districts of M. P.

Wheat being an important cereal crop of the state, during rabiseason which is influenced by temperature. Efforts were made to study the effect of temperature in relation to its seed yield. For this purpose, four districts, viz., Jabalpur, Hoshangabad, Chhindwara and Gwalior were selected and analysis was done on the basis of long term available data of temperature and crop productivity. The yield values were detrended and normalized. Yield at Gwalior and Chhindwara shows no change in crop productivity, whereas decreasing trend in yield in Jabalpur and Hoshangabad were observed. Relationship between normal temperature (maximum and minimum) deviations and normal yield of indicated that yield of wheat crop decreased with increasing in temperature in different districts except Chhindwara (Fig. 6.2 and 6.3).


64

Fig.6.2. Normalized wheat yield in relation to normalized T-max deviation at Chhindwara

Fig.6.3. Normalized wheat yield in relation to normalized T-max deviation at Hoshangabad

Relationship between T-max deviation and NYD for wheat at Hoshangabad

y = 1.1229x + 0.0053R2 = 0.0876

-0.3-0.2

-0.10.0

0.10.2

0.30.4

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15T-max deviation

NYD

NYDLinear (NYD)

�

Relationship between T-max deviation and NYD for wheat at Chhindwara

y= -0.4708x - 2E-06�R2 = 0.0456

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

-0.20-0.15-0.10-0.050.00 0.05 0.10 0.15 0.20

T-max deviation�

NYD

NYDLinear (NYD)�


65

Trend analysis of Mustard Crop in Relation to Temperature

Long term analysis of mustard yields and mean temperature was done. It was observed that the yield of mustard was decreased with increased temperature at Gwalior, whereas the reserve was true for morena Fig (6.4 and 6.5).

Fig.6.4. Normalized mustard yield in relation to normalized mean temperature at Gwalior.

Fig.6.5. Normalized mustard yield in relation to normalized mean temperature deviation at Morena.

�

y = -0.764x - 0.042R² = 0.035

-3.0-2.0-1.00.01.02.03.04.05.06.07.0

-1.0 -0.5 0.0 0.5 1.0

NYD

Mean Temp.


66

RaipurIn the northern districts of Chhattisgarh, it was observed that the

maximum temperature showed increasing trend in October and November that resulted in reduction of the rice crop duration. There was also a decline in maximum temperature observed during March. This helped the farmers of the state to take two crops of potato, one before rice crop and the other after rice crop. The increased temperature during reproductive phase of rice proved detrimental by reducing the yields at a rate of 3-4 q/ha for 1° C rise and 5-6 q/ha for 2°C temperature rise in both rainfed as well as irrigated crop conditions. Due to increase in temperature because of forced maturity there is a decline in the yields by almost 15-18 q/ha was observed (Fig 6.6).

Fig. 6.6.Maximum and minimum temperature during rabi season (Nov-Mar) at Labhandi


67

LudhianaThe historical data of maximum, minimum temperature and rainfall

for five locations in three agroclimatic zones, i.e., Zone-1 (Ballowal Saunkhri) Zone-3 (Amritsar, Ludhiana and Patiala) and Zone-4 (Batinda) was collected and analyzed. The annual and seasonal temperature statistics of the above locations are given in the Table 6.2.

Table 6.2. Annual/seasonal temperature statistics at different locations in Punjab

Location/

Annual/

Seasonal

Mean

(°C)

SD

(°C)

CV

(%)

Mean

(°C)

SD

(°C)

CV

(%)

Highest Lowest Highest Lowest

Ballowal Saunkhri

Annual 29.7 0.7 2.3 16.1 0.66 4.1 31.0(2002) 28.5(1997) 17.4(1988) 14.9(1996)

Kharif 34.1 0.65 1.9 22.3 0.62 2.8 35.2(2005) 32.5(1996) 23.3(1988) 20.7(1997)

25.4 0.92 3.6 9.9 0.86 8.7 26.8 23.8 11.2 8.1

Rabi (2001-02) (1991-92) (1985-86) (1995-96)

Amritsar

Annual 30.5 1.15 3.8 15.4 0.61 4 36.0(2004) 28.8(1997) 16.6(2002) 13.6

( 1989)

Kharif 35.6 0.68 1.9 22.3 0.77 3.4 36.9(1974) 33.9(1997) 23.4(2000) 20.4

(1989)

Rabi 25 0.81 3.2 8.3 0.66 7.9 26.4 23.1 9.6 6.7

(1970-71) (1982-83) (1979-80) (1996-97)

Ludhiana

Annual 29.8 0.57 1.9 16.5 0.83 5 30.8(2002) 28.2 (1997) 17.8 (2004) 14.8 (1972)

Kharif 34.9 0.6 1.7 23.1 0.89 3.9 35.9 (1970) 33.5 (1997) 24.5 (2000) 21.1 (1976)

Rabi 24.5 0.78 3.2 9.7 0.88 9.1 25.8 22.6 11.7 8.1

(2001-02) (1981-82) (2003-04) (1975-76)

Patiala

Annual 30.1 0.5 1.6 17.4 0.37 2.1 30.9(1987) 28.5(1997) 18.1(1988) 16.6(1979)

Kharif 34.8 0.52 1.5 23.7 0.45 1.9 35.8(1987) 33.4(1997) 24.5(1998) 22.2(1979)

Rabi 25.1 1.28 4.7 10.9 0.69 6.3 26.5 23.5 11.9 9.96

(1987 -88) (1982-83) (1995-96,

2003-04)

(1996-97)

Bathinda

Annual 31.5 0.8 2.5 16.9 0.63 3.7 32.0(2002) 29.2(1997) 18.3(1999) 15.8(1983)

Kharif 36.9 1.24 3.4 23.9 0.77 3.2 38.7(2000)' 34.1(1993) 26.0(1986) 22.8(1993)

Rabi 25.9 0.84 3.2 9.8 0.8 8.1 27.3 23.9 12.1 8.0

(1984-85) (1997-98) (2002-03) (1983-84)


68

The analysis of rainfall with respect to the above stations for the annual kharif and rabi season are given in Table 6.3. Extreme rainfall events occurred were analyzed. The climatic trends for maximum temperature, minimum temperature and rainfall for annual, kharif and rabiseason have been worked out and presented in Table 6.4, 6.5.and 6.6.

Table 6.3. Annual/seasonal rainfall statistics at different locations in Punjab Location /

Annual/

Seasonal

Rainfall (mm) Extremes of Rainfall (mm)

Mean (mm) SD (mm) CV (%) Highest Lowest

Ballowal Saunkhri

Annual 1122 296.2 26.4 2041 (1988) 753(2003)

Kharif 963 272.5 28.3 1802(1988) 618(2003)

Rabi 160 83.2 52.1 422(1996-97) 54(1983-84)

Amritsar

Annual 713 187.2 26.3 1103 (1984) 336(1972)

Kharif 571 176.2 30.8 934 (1984) 248 (1972)

Rabi 143 81.4 56.8 387 (1982) 33 (2001)

Ludhiana

Annual 750 236.2 31.5 1334 (1988) 379 (1974)

Kharif 624 236.8 37.9 1254 (1988) 293 (1987)

Rabi 127 72.6 57.2 318 (1981-82) 24 (1984-85)

Patiala

Annual 788 288.1 36.5 1497(1988) 275(1987)

Kharif 661 271.9 41.1 1425(1988) 188( (987)

Rabi 129 88.6 68.6 426(82-83) 11 (84-85)

Bathinda

Annual 539 194.1 36.0 984(1990) 202(2000)

Kharif 449 194.5 43.3 876(1990) 133(1982)

Rabi 92 51.4 55.6 191(81-82) Nil (01-02)


69

Table 6.4. Time trend equations for maximum temperature (slope of regression = °C/calendar year) over the past three decades at different locations for annual, kharif and rabi season in Punjab

Station (Latitude, Longitude

& Height a.m.a.l) Annual Kharif Rabi

Ballowal Saunkhri Y = 0.058X - 86.08 Y = 0.045X - 56.17 Y = 0.089X - 152.30

(31 ° 60' N, 76° 23' E, 55m) R2= 0.46 R2 = 0.46 R2= 0.56

Amritsar Y = -0.010X + 50.97 Y = -0.018X + 73.07 Y = 0.007X + 10.39

(31°37'N, 74°53' E, 231 m) R2= 0.13 R2 = 0.32 R2= 0.05

Ludhiana Y = -0.0001X + 30.90 Y = -0.014X + 62.67 Y = 0.017X - 98.40

(30° 56' N 75° 48' E 247 m) R2= 0.00 R2 = 0.21 R2=0.15

Patiala Y = 0.004X + 21.10 Y= -0.007X + 50.33 Y = 0.020X - 16.39

(30° 20' N 76° 28' E 251 m) R2 = 0.04 R2 = 0.17 R2 = 0.26

Bathinda Y = -0.023X + 77.42 Y= -0.040X+117.00 Y = -0.001X + 29.41

(30° 12' N 74° 57' E 211 m) R2 = 0.21 R2 = 0.31 R2= 0.00

Table 6.5. Time trend equations for minimum temperature (slope of regression = °C/calendar year) over the past three decades at different locations for annual, kharif and rabi season in Punjab

Station (Latitude, Longitude

& Height a.m.a.l) Annual Kharif Rabi

Ballowal Saunkhri Y = -0.022X + 61.80 Y = -0.011IX +. 45.87 Y = -0.008X + 27.25

(31° 60' N, 76°23' E, 355m) R2= 0.06 R2 = 0.02 R2= 0.006

Amritsar Y = -0.004X + 25.17 Y = -0.002X + 28.13 Y = -0.006X + 21.01

(31° 37' N, 74° 53' E, 231 m) R2=0.017 R2=0.004 R2= 0.02

Ludhiana Y = 0.071X - 125.00 Y = 0.076X - 129.00 Y = 0.067X - 125.00

(30° 56' N 75° 48' E 247 m) R2 = 0.92 R2= 0.94 R2 = 0.86

Patiala Y = 0.015X - 13.34 Y = 0.022X - 20.81 Y = 0.010X - 9.803

(30° 20' N 76° 28' E 251 m) R2 = 0.43 R2 = 0.59 R2 = 0.15

Bathinda Y = 0.038X - 59.57 Y = 0.025X - 27.30 Y = 0.053X - 97.18

(30° 12' N 74° 57' E 211 m) R2 = 0.59 R2 = 0.25 R2 = 0.71


70

Table 6.6. Time trend equations for rainfall (slope of regression =°C/calendar year) over the past three decades at different locations for annual, kharif and rabi seasons in Punjab

Station (Latitude, Longitude &

Height a.m.a.l) Annual Kharif Rabi

Ballowal Saunkhri Y=-16.11X+3314 Y = -12.50X + 25948 Y = -3.26X + 6675

(31 ° 60' N, 76° 23' E, 355m) R2= 0.39 R2 = 0.34 R2= 0.36

Amritsar Y = -1.94X + 4579 Y = -1.32X + 3210 Y = -1.18X + 2493

(31° 37' N, 74° 53' E, 231 m) R2 = 0.06 R2 = 0.04 R2 = 0.06

Ludhiana Y = 3.193X - 5602 Y = 3.26X - 5860 Y = -0.476X + 1077

(30° 56' N 75° 48' E 247 in) R2=0.12 R2 = 0.13 R2= 0.02

Patiala Y = 1.08X - 1358 Y = 2.25X - 3824 Y = -1.462X+ 3044

(30° 20' N 76° 28' E 251 m) R2 = 0.007 R2 = 0.038 R2 = 0.054

Bathinda Y = -3.015X + 6562 Y = -0.276X + 1009 Y = -2.976X + 6025

(30° 12' N 74° 57' E 211 m) R2 = 0.034 R2 = 0.00 R2 = 0.54

KanpurRainfall characteristics of scarcity zone of central plain, south west

and Bundelakhand zone of Uttar Pradesh was carried out and it was found that the annual average precipitation was 793, 659 and 824mm respectively. Two peaks of rainfall were generally observed in June, July and again in August and September. The annual rainfall variability varies from 26.5 percent at Banda to 56.3 percent at Raibareily. The spatial distribution of annual rainfall indicated that the southwest region received less rainfall compared to other two regions. The annual rainfall characteristics of different stations in three different above agroclimatic regions are given in Table 6.7.


71

Table 6.7. Annual rainfall (mm) and its variability (%) in Central Plain, South – West and Bundelkhand Zones of Uttar Pradesh

Zones District Normal

Rainfall Highest Year Lowest Year CV Base year

Central

Plane

Kanpur 892.7 1982.3 1980 437.4 1987 33.8 1976-2006

Kanpur

Dehat675.3 1154.1 2003 350.4 2000 34.7 1988-2006

Farrukhabad 887.5 1731.0 1976 446.8 1987 33.7 1976-2006

Etawah 713.8 1116.0 1983 228.4 2006 34.0 1976-2006

Kannauj 683.5 1189.6 2003 337.0 2002 39.5 1998-2006

Aurriya 454.6 932.9 2003 214.0 2002 43.8 1998-2006

Allahabad 850.3 1689.9 1980 364.4 2006 28.3 1976-2006

Fatehpur 802.7 1345.9 1980 393.1 1993 33.4 1976-2006

Lucknow 809.2 2036.8 1980 292.5 2002 40.8 1976-2006

Unnao 808.7 1532.1 1980 307.2 2004 35.0 1976-2006

Raebareli 726.9 1620.7 1986 158.3 2002 56.3 1976-2006

Sitapur 891.9 1394.6 2003 275.4 1987 30.7 1976-2006

Hardoi 795.0 1517.8 1996 219.0 2006 39.8 1976-2006

Kheri 1105.3 1711.1 2003 573.9 1987 26.8 1976-2006

South–West

Agra 695.1 1400.3 1977 302.7 2000 38.1 1976-2006

Aligarh 717.3 1498.5 1977 140.7 2002 38.6 1976-2006

Mathura 624.2 1337.4 1977 305.5 2006 35.6 1976-2006

Hathrus 500.0 1048.7 2003 239.7 2006 50.4 1999-2006

Mainpuri 716.6 1043.2 1998 239.7 2006 30.8 1976-2006

Firozabad 661.2 1156.5 2003 296.0 2006 33.8 1989-2006

Etah 695.3 1385.1 2003 383.7 1987 37.5 1976-2006

Bundelkhand

Jhansi 849.7 1357.2 1983 399.5 2006 28.4 1976-2006

Jalaun 815.7 1603.2 1980 404.4 2006 32.5 1976-2006

Lalitpur 862.0 1627.6 1982 421.9 2006 33.5 1978-2006

C.K.D.Ngr. 846.6 1221.9 2001 563.5 2003 28.0 1999-2006

Hamirpur 880.8 1847.7 1980 383.5 2006 32.7 1976-2006

Banda 897.8 1547.1 1980 520.2 1979 26.5 1976-2006

Mahoba 617.6 975.9 1998 378.8 2002 31.5 1995-2006


72

RanchiAgroclimatic analysis of temperature for the years 1961-2006 was

analyzed to study the impact of variability in temperature over the productivity of the region. A considerable raise in temperature over the decades has been recorded at Jharkhand, which was attributed as one of the major reason for stagnation of crop productivity level at Jharkhand. Average monthly temperatures for the recent years (2001-06) (Fig. 6.7) have been found considerably higher than that of 1961-70 period. The maximum rise of 1.21°C has been observed in the month of May followed by February (0.82°C), December (0.76°C) April (0.51°C), March (0.43°C), January (0.3°C), June (0.15°C) and November (0.1°C). This indicates increasing level of PET during winter-summer period raising the threat of high-level water stress for rainfed rabi and summer crops. Yield reduction of wheat due to increasing temperature level during April-May. This is the major area of concern from climatological point of view.

Fig.6.7. Monthly average maximum temperature for the period 1961-70 and 2001-06 at Ranchi

SamastipurTrend analysis with respect to monthly maximum and minimum

temperatures of Pusa station for the period 1961-2001 was carried out. It was observed that increase in average mean temperature as well as the minimum temperature was noticed for all the months under the study period. The rate of increase in mean, minimum temperature during winter season over the last 40 years was found to be 0.05°C per year. The centre has also reported week wise mean temperature trend analysis up to 15th

standard week and it was found that except 15th week all the rest of the weeks have shown increasing trend in the mean temperature.

0

5

10

15

20

25

30

35

40

n A n A S D

A m 61 70 A m 2001 06


73

Khairf 2007 7. CROP-WEATHER RELATIONSHIPS

RICEFaizabad

In order to study the impact of growing environments manipulated through different transplanting dates on rice crop, three varieties of rice viz., PANT-12, Sarjoo-52, Usar dhan-3, were transplanted on 5th Jul, 15th

Jul and 25th Jul of 2007. Paddy crop transplanted on 5th July recorded longer duration under each phenophases than the crop transplanted on 15th July and 25th July. Transplanting on 25th July, however, reduced the crop duration by 11 days than 5th July transplanted crop (Table 7.1). The heat use and radiation efficiency of the crop at different growth stages are given in the following Table 7.2 and 7.3.

Table 7.1. Days taken to achieve different phenophases of rice as affected by various treatments at Faizabad

Treatments Tillering Vegetative PI 50%

flowering Milking Dough Maturity

Date of transplanting

July.5 24(460) 50(1014) 60(1214) 67(1342) 79(1553) 83(1633) 96(1808)

July.15 22(448) 49(1016) 56(1146) 64(1285) 73(1435) 79(1549) 89(1698)

July.25 21(419) 45(915) 49(991) 60(1217) 72(1411) 76(1475) 84(1578)

Genotypes:

Pant-12 21(415) 45(988) 55(1163) 65(1215) 70(1397) 80(1583) 88(1664)

Sarjoo-52 22(432) 46(1008) 56(1183) 67(1239) 72(1416) 82(1614) 92(1713)

Usar dhan-

320(393) 43(848) 60(1240) 62(1225) 70(1387) 79(1554) 88(1693)

Figure in parenthesis indicates GDD values

It was seen that the total number of days from transplanting to physiological maturity varied between 84 to 96 days among the treatments.


74

Corresponding GDD vary from 1578 to 1808. Among the genotypes, total number of days and GDD from transplanting to maturity did not vary much.

Table 7.2. HUE (g/m2/ °days) of rice at different phenophases as affected by various treatments at Faizabad

Treatments

Phenophases

Tillering Vegetative PI 50% flowering Milking Dough Maturity


July.5 0.42 0.42 0.47 0.63 053 0.57 0.54

July.15 0.38 0.36 0.47 0.63 0.53 0.56 0.54

July.25 0.37 0.36 0.46 0.62 0.51 0.53 0.52

Genotypes:

Pant-12 0.40 0.36 0.44 0.68 0.52 0.53 0.51

Sarjoo-52 0.45 0.40 0.48 0.73 0.57 0.57 0.58

Usar dhan-3 0.44 0.44 0.42 0.64 0.54 0.56 0.53

Table 7.3. Radiation Use Efficiency (g/MJ) of rice as affected by various treatments at Faizabad

Treatments DAT

15 30 45 60 75 90 AH


July.5 1.8 2.1 2.7 2.3 2.6 2.7 2.8

July.15 1.5 1.7 2.3 2.3 2.5 2.6 2.7

July.25 1.4 1.7 2.3 2.2 2.4 2.4 2.5

Genotypes

Pant-12 1.7 1.9 2.5 2.2 2.5 2.6 2.7

Sarjoo-52 1.7 1.9 2.7 2.3 2.6 2.7 2.8

Usar-3 1.6 1.8 2.5 2.2 2.5 2.6 2.5


75

The above tables revealed that 5th July transplanting recorded higher HUE at all the phenophases followed by 15th July and 25th July. Among the genotypes, Sarjoo-52 produced slightly higher HUE over PANT-12 at all the stages. The effect of daytime temperature during flowering stage on grain yield of rice is given in the Fig.7.1.

Fig.7.1. Effect of day temperature during flowering phase versus grain yield of rice of Faizabad

It is observed that there is increase in the grain yield up to 30°C and beyond this value a decline in yield was noticed.

DapoliTo study the hybrid rice and wal system, rice crop was sown under

two dates of sowing, viz., 24th and 26th standard weeks and sowing of wal was done during 44th, 45th, 46th standard weeks. The growing degree-days and the other indices like hydro thermal and heliothermal units during different crop growth stages of the hybrid rice are given in Table 7.4. It is seen from the table that these heat units are maximum at maturity stage and lowest at seedling stage. There is no variation in the heat units and hydrothermal units at tillering and flowering stages of the crop sown on 24th

and 26th MW. However, wide variation in the heliothermal units was noticed among the dates of sowing.

y = -0.5283x4 + 13.226x3 - 96.727x2 + 248.66x + 3507R2 = 0.8809

3000

3200

3400

3600

3800

4000

4200

4400

4600

4800

28.8

29.1

29.1

7

29.4

5

29.7

5

29.8

6

29.9

8

30.0

3

30.1

2

30.1

8

30.2

5

30.3

4

30.5

3Day temp.(oC)

Gra

in y

ield

(kg/

ha)


76

Table 7.4. Weather derived parameters during different stages of hybrid rice at Dapoli

Sowing

date

Growing Degree Days (GDD)

Seedling Tillering Flowering Maturity Total

24 MW 529 1043 1550 2152 5274

26 MW 522 1010 1525 2045 5102

Hydrothermal units

24 MW 77773 156504 234663 335292 804232

26 MW 74029 150524 233535 330814 788902

Heliothermal units

24 MW 1605 3424 5721 12000 22751

26 MW 1941 3598 6102 13866 25507

RaipurThe data on crop phenology, GDD, photo and heliothermal units of

three varieties of rice viz., Chandrahsani, MTU-1010 and Mahamaya as influenced by different dates of transplanting are given in the Table 7.5.

Table 7.5. Influence of sowing dates on growing degree days, photothermal units and heliothermal units of different rice varieties at Raipur

Physiological

Stages

Chandrahasini MTU 1010 Mahamaya

D1 D2 D3 D1 D2 D3 D1 D2 D3

Phenology

Seedling 25 24 23 25 24 23 25 24 23

Veg. 44 43 41 39 37 36 40 39 39

Repr. 37 36 35 30 29 29 34 34 33

Mat. 28 28 27 24 23 22 26 26 25

Total 134 131 126 118 113 110 125 122 120

Cont…


77

Physiological

Stages

Chandrahasini MTU 1010 Mahamaya

D1 D2 D3 D1 D2 D3 D1 D2 D3

Growing degree-days (Cumulative)

Seedling 442 428 419 442 428 419 442 428 419

Veg. 1228 1188 1136 1140 1082 1051 1158 1118 1101

Repr. 1878 1817 1740 1672 1591 1559 1756 1715 1679

Mat. 2303 2225 2132 2073 1969 1998 2178 2139 2048

Photothermal Units (Cumulative)

Seedling 5853 5646 5524 5833 5646 5524 5853 5646 5524

Veg. 16035 15387 14622 14909 14076 13572 15138 14529 14193

Repr. 24015 23006 21995 21525 20343 19782 2251 21808 21191

Mat. 28981 27716 26399 26298 24782 23750 27521 26188 25469

Heliothermal Units (Cumulative)

Seedling 1360 1016 1142 1360 1016 1142 1360 1016 1142

Veg. 3578 3302 3236 3293 3034 3013 3370 3167 3140

Repr. 6523 6522 6761 5555 5187 5483 5868 5910 6234

Mat. 9950 9989 10083 7943 7857 8215 8871 9015 9351

D1-20 June 2007, D2- 30 June 2007 and D3-10 July 2007

It was observed that the total duration decreased considerably when the sowing was delayed from 20th Jun to 10th July in all the varieties. It was also found that higher GDD accumulated when crop was sown 20th June as compared to other dates of sowing.

Mohanpur Radiation measurements were recorded at 0730, 1130 and 1530 hours at different crop growth stages, viz., maximum tiller, panicle initiation and emergence at 50 percent anthesis in rice crop that was transplanted on five dates (1st July, 8th July, 15th July, 22ndJuly and 29th July). From the incident and the intercepted PAR values, the percentage interception was computed and shown in Fig.7.2. It was observed from the figure that maximum absorption of more than 95 percent was noticed during panicle initiation stage in the afternoon hours. With the advancement of crop growth stages, the percentage absorption decreased. However, the


78

absorption values in the morning hours have shown increasing trend continuously upto the end of 50 percent anthesis.

Fig.7.2. Percent intercepted PAR values for different growth stages at Mohanpur

AMARANTHUSRanichauri

Two varieties of amaranthus was sown in three dates of sowing 16th

Jun, 26th Jun and 6th Jul 2007 to study the impact of weather parameters on the growth and development of the crop. The total number of growing degree days from sowing to harvesting vary between 1002 – 1152°days. From the water balance procedure actual evapotranspiration during different crop stages was estimated and related to plant height. The total dry matter accumulated during crop season was related to growing degree-days and is pictorially presented in Fig.7.3.

Fig.7.3. Relationship between TDM and GDD TDM (g/plant) = 0.227*GDD –106.55 (R2 = 0.776)

50

60

70

80

90

100

Max�T illering P I E merg ence 50% �Anthes isPerc

ent i

nter

cept

ion

PAR

(%)

730hr 1130hr 1530hr

�

04080

120160200

200 400 600 800 1000 1200

Growing Degree Days

Tota

l Dry

Mat

ter

Crop Phenology


79

FINGER MILLET Dapoli

To work out the crop weather relationship in the finger millet crop it was sown on 22nd, 23rd, 24th and 25th SMW with a three types of seedling having different ages, viz., 30, 40 and 50 days. Sowing time affected the grain yield significantly (Table 7.6).

Table 7.6. Grain yield (q/ha)of finger millet as affected by different sowing dates and age of seedling at Dapoli

Treatment Age of seedlings

Mean A1 (30 days) A2 (40 days) A3 (50 days)

D1 = 22 MW (28.05 - 03.06) 14.48 22.16 15.62 17.42

D2 = 23 MW (04.06 - 10.06) 19.23 24.12 20.33 21.23

D3 - 24 MW (11.06 - 17.06) 21.05 29.45 23.22 24.57

D4 - 25 MW (18.06 - 24.06) 18.42 22.46 18.46 19.78

Mean 18.30 24.55 19.41

-Date of

sowing Age of seedling Interaction

SE+ 0.65 0.32 1.27

CD at 5% 1.96 0.97 NS

Sowing on 24th Standard week resulted in higher grain yield (24.6 q/ha) compared to other dates of sowing. Similarly, the age of seedling also significantly affected the grain yield. Planting of 40 days old seedling recorded higher grain yield as compared to 30 and 50 days old seedlings.

BLACKGRAMRanchi

Blackgram, variety Pant U-19, was sown on different dates viz., 15th

Jun, 25th Jun, 5th Jul and 15th Jul to study the impact of weather under different growing environments. The average heat unit requirements of


80

blackgram crop for the years 2004-2006 have been computed and presented in the Fig.7.4.

Fig.7.4. Cumulative heat units in blackgram crop at different growth stages at Ranchi

It is seen from the figure that highest heat units of 1257 were recorded at maturity. The total average heat unit requirement for blackgram at Ranchi worked out to be 2653 units.

SOYBEAN Akola

Crop growing environment in kharif soybean was quantified in three popular varieties viz., JS-335 (V1), TAMS-98-21 (V2) and TAMS-38 (V3)under four micro-environments by sowing them on four dates, viz., 30th

June, 6th July, 13th July and 20th July.

Grain and biomass production Crop sown on 6th July (27MW) recorded significantly higher grain

and biomass yields followed comparably by 30th June sowing (26 MW), and significantly lower yields under 13 (28MW) and 20th July (29MW) sowings.

87

604

705

1257

0

200

400

600

800

1000

1200

1400

Em e rge nce Flow e r ing Pod initiation M atur ity

Acc

umul

ated

Hea

t Uni

ts (D

eg. d

ays)

3

3


81

Soybean variety JS-335 recorded significantly higher grain yield than TAMS - 38 and TAMS 98-21 (Table 7.7).

Table 7.7. Grain yield and biomass production (q ha-1) of soybean varieties as influenced by different dates of sowing at Akola

Treatments Grain yield

(q ha-1)

Biomass yield

(q ha-1)

Straw Yield

(q ha-1)

Dates of sowing

26th MW (30.06.07) 13.28 37.51 24.23

27th MW (06.07.07) 14.10 41.10 27.00

28th MW (13.07.07) 11.57 31.93 20.35

29th MW (20.07.07) 3.02 15.48 12.46

SE m + 0.52 1.39 1.33

CD (0.05) 1.49 4.01 3.80

Varieties

V1 JS- 335 12.61 31.61 18.99

V2 TAMS- 98-21 9.11 34.51 25.39

V3 TAMS- 38 9.76 28.40 18.63

SE m + 0.45 1.20 1.15

CD (0.05) 1.29 NS 3.29

Thermal use efficiency of soybean varieties under different dates of sowing is given in Table 7.8. It was observed that first and second date of sowings (30th Jun and 6th Jul) were recorded higher thermal use efficiency compared to other sowing dates. Delayed sowing beyond 15th Jul reduced thermal use efficiency drastically in all the varieties. Among the varieties JS-335 recorded highest thermal use efficiency compared to two other varieties.


82

Table 7.8. Thermal use efficiency of soybean varieties in terms of grain and biomass production (kg/ha/°C) under different dates of sowing at Akola

VarietySowing date

Mean30 June 06 July 13 July 20 July

JS-335 0.79 (1.90) 0.79 (2.00) 0.8 (1.96) 0.41 (1.21) 0.70 (1.77)

TAMS -98-21 0.60 (1.97) 0.77 (2.55) 0.53 (1.90) 0.04 (1.01) 0.49 (1.86)

TAMS -38 0.74 (2.14) 0.79 (2.28) 0.62 (1.55) 0.10 (0.60) 0.56 (0.61)

JabalpurThree varieties of soybean JS-335 (V1) JS-93-05 (V2) JS-97-52 (V3)

were sown in four dates of sowing viz., 22nd Jun, 3rd Jul, 16th Jul and 4th

Aug to study the influence of weather parameters on growth and development of soybean crop. The heat unit requirements under different growth stages as influenced by different sowing dates in the varieties are given in Fig.7.5. Among the varieties, the newly released variety JS-97-52 took more number of days for maturity thus recording more GDD than other two varieties. The heat unit requirements from sowing to harvest vary much among the varieties. However, delayed sowing resulted in recording less number of growing degree-days for al the crop stages.


83

P1 = 21 June; P2 = 3 July; P3 = 16 July P4 = 4 Aug Fig.7.5. GDD at different stages of soybean as influenced by different

treatments at Jabalpur

ParbhaniThe influence of weather parameter on the growth and development

of soybean crop for the years 2003-04 to 2007-08 were studied. The pooled analysis was carried out between the weather variables prevailed during important growth stages viz., pod formation (P5), grain formation


84

(P6), pod development (P7) and grain development (P8) and the grain yield. The correlation matrix between different weather variables recorded in the above stages and the yield of soybean are presented in Table 7.9. Weather parameters viz., rainfall, relative humidity, played significantly positive role in recording the highest grain yield. Similarly higher temperature, BSS and GDD have significant negative correlation with the grain yield. Among the stages, the weather parameter at reproductive stage (P7 and P8) and found to be negatively influenced by thermal and radiation regime.

Table 7.9. Correlation coefficient between grain yield and weather variables prevailed in different phenophases of Soybean (2003-04 to 2007-08) at Parbhani

Parameters P5 P6 P7 P8 Pooled

Rainfall -0.078 0.188** 0.110 0.331** 0.132**

R.Days 0.053 0.280** -0.107 0.0126 0.061

Max.T. 0.101 -0.044 -0.357** -0.375** -0.153**

Min.T. -0.245** -0.194** -0.264** -0.187 -0.185**

MeanT -0.104 -0.169* -0.385** -0.278** -0.231**

T.Range 0.239** 0.111 -0.141 -0.140 0.017

RHI 0.258** 0.343** 0.242** -0.255** 0.272**

RHII 0.060 0.014 0.167* 0.098 0.049

RHmean 0.049 0.151** 0.186** 0.167* 0.135**

RHRange 0.250** 0.212** -0.026 0.031 0.124**

BSS 0.036 -0.172** -0.269** -0.222** -0.139**

EVP -0.153* -0.166** -0.170** -0.184** 0.169**

GDD -0.141 -0.144 -0.145 -0.146 -0.144**

SMC 0.250** 0.092 0.073 -0.126 0.078*

* = Significant at 5 %; Significant at 1 % P5 = Pod formation; P6 = Grain formation; P7 = Pod development;P8 = Grain development


85

MAIZEUdaipur

Radiation use efficiency in maize based intercropping was studied. The experiment consisted of seven treatments viz. maize, green gram, black gram, soybean, maize + green gram (1:1), maize + black gram (2:2) and maize + soybean (1:1). Treatments were replicated four times in randomized block design. The results indicated that the highest intercepted photosynthetic active radiation (IPAR) was observed in sole soybean. The maximum IPAR was obtained at 60 DAS in all treatments. Thereafter IPAR was decreased up to harvest. In intercropping system, IPAR was more as compared to sole crops. Maize + soybean registered maximum IPAR (92%) followed by maize+ black gram (91%) and maize+ green gram (86%) at 60 DAS. Maize + black gram (2:2) gave significantly highest maize equivalent yield (53.81 q/ha). Intercropping advantage in terms of land equivalent ratio (LER) and area time equivalent ratio (ATER) and monetary advantage index (MAI) showed that maize + black gram was superior as compared to rest of intercropping systems. All intercropping systems showed higher rainwater use efficiency over sole crops. The maximum rainwater use efficiency of 21.19 kg/ha/mm was recorded with maize + black gram. It was also noted that the severity of powdery mildew in green gram was more in intercropping as compared to sole green gram. It was observed that infestation of stem borer in maize (5-8 %) was more in maize + blackgram intercropping than other intercropping systems and sole maize. Infestation of tobacco caterpillar in soybean was more (7 to 15%) under sole soybean than maize + soybean intercropping (7 to 12%).

Evapotranspiration studies of maize The evapotranspiration pattern of maize variety Aravalli Makka was

studied using lysimeter. The evapotranspiration losses by the crop were 346.4 mm as against the ETo of 412.8 mm with a crop coefficient value of 0.84. The highest ETc was recorded during silking stages to milking stage (153.4 mm). The Kc value reached its peak (1.35) at silking stage then it starts declined upto 0.33 at maturity of the crop (Fig.7.6).


86

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Emergence knee highstage

Tasseling Silking Mliking Maturity

Phenological stages

Kc Kc

Fig.7.6. Pattern of crop coefficient for maize at Udaipur

Jorhat To study the influence of different dates of sowing on growth and dry matter accumulation in kharif maize, an experiment was conducted with three varieties of maize, viz., Mahi Kanchan (V1), Kiran (V2), Keshari (V3)and three planting dates, viz., 28th Feb (MR I), 20th Mar (MR II) and 18th Apr (MR III). The crop duration varied between 103 days (MR I) to 90 days (MR III). With delayed planting, crop duration decreased by about 13 days. Nine types of agroclimatic indices, viz., accumulated maximum, minimum mean temperature, growing degree days, Heliothermal units, photothermal units, average RH, Sunshine and pan evaporation for different crop growth stages during the crop growing period were computed and correlated with the grain yield (Table 7.10).

Table 7.10. Correlation coefficients between seed yield and accumulated agroclimatic indices in different phenophases in maize at Jorhat

Growth Stage AMXT AMIT AMNT AGDD AHTU APTU AMRH ABSH APAN

Variety (V1)

Cob initiation 0.428 0.997 0.547 0.624 0.537 0.966 0.739 0.742 0.941

Cob 100% 0.707 0.919 0.474 0.567 0.705 0.988 0.777 0.830 0.685

Maturity 0.349 0.967 0.584 0.655 0.903 0.987 0.955 0.955 0.753

Cont…


87

Variety (V2)

Cob initiation 0.168 0.742 0.578 0.662 0.397 0.963 0.486 0.541 0.515

Cob 100% 0.263 0.623 0.574 0.659 0.434 0.889 0.452 0.696 0.136

Maturity 0.770 0.915 0.416 0.502 0.939 0.824 0.707 0.954 0.728

Variety (V3)

Cob initiation 0.746 0.845 0.023 0.105 0.990 0.709 0.101 0.182 0.928

Cob 100% 0.454 0.932 0.003 0.098 0.403 0.798 0.078 0.174 0.811

Maturity 0.308 0.552 0.153 0.057 0.964 0.371 0.214 0.601 0.976

GROUNDNUTBangalore The groundnut sown on 23rd Jun has given the higher mean yield of 1390 Kg/ha compared to that crop sown on 20th Jul which has yielded 1308 kg/ha. Among varieties, JL-24 has given higher yield followed by TMV-2 which recorded 1502 kg/ha and 1338 kg/ha respectively in the first date of sowing which was sown on 23rd Jun. Where as in the second date of sowing the variety TMV-2 has recorded higher yield of 1502 kg/ha followed by JL -24 (1338 kg/ha) sown on 20th Jul.

AnandTo study the effect of weather parameters on the groundnut crop,

two varieties of groundnut namely Robout-33-1 and GG-2 were sown under two dates of sowing, viz., 15th June and 30th June, at two levels of irrigation, viz., No irrigation and Irrigation at 50 percent depletion of soil moisture. The results revealed that date of sowing has not affected the groundnut yields and the yield attributes. Due to well distribution of rainfall, during 2007 crop kharif season the effect of irrigation was not recorded. Among the varieties Robout -33-1 recorded significantly higher pod yields (2.37 q/ha) compared to GG-2. The phenological stage wise accumulated heat units (AGDD) are presented in the Table 7.11. The crop growth period was higher in early sown crop compared to late sown crop.


88

Table 7.11. Phenological stage-wise heat units at Anand

PhaseD1V1: 21st Jun (Robut 33-1) D1V2: 05th Jul (GG-2)

Days AGDD APTU AHTU Days AGDD APTU AHTU

Emerg. 5 106 1426 797 5 106 1426 797

Fl. 50% 27 517 6922 1914 24 459 6140 1690

Peg.50% 35 676 9028 2934 32 615 8224 2484

Pod50% 47 904 12015 3522 46 886 12015 3522

Pod dev. 66 1248 16456 5105 64 1211 15973 4819

Maturity 125 2325 29487 12470 125 2325 29487 12470

PhaseD2V1: 21st Jun (Robut 33-1) D2V2: 05th Jul GG-2

Days AGDD APTU AHTU Days AGDD APTU AHTU

Emerg. 5 94 1254 0 5 94 1254 0

Fl. 50% 24 463 6151 1797 22 422 5613 1701

Peg.50% 43 807 10623 2569 37 699 9228 2308

Pod50% 56 1049 13741 4056 54 1013 13270 3979

Pod dev. 64 1198 15594 4566 67 1256 16306 4972

Maturity 116 2133 26757 11789 116 2133 26757 11789

REDGRAMBangalore

To study the impact of weather conditions at different growth stages of three redgram varieties, viz., TTB-7 (V1), HYD-3C (V2), BRG-1 (V3) were sown on three dates of sowing 4th Jun, 22nd Jun and 21st Jul of 2007 under different spacing trials. The effect of date of sowing on varieties and spacing are given in Table 7.12. It is seen that redgram sown on first date recorded higher grain yield (14.2q/ha) compared to second and third date of sowing. The grain yield of redgram was also significantly influenced by


89

different spacing treatments. Among the varieties, TTB-7 recorded higher yields over the other two in all the dates of sowing.

Table 7.12. Effect of date of sowing, varieties and spacing on grain yield of redgram at Bangalore

Varieties

Grain yield (Q ha -1)

(D-1) 04th Jun (D-2) 22nd Jun (D-3) 21st Jul

S1 S2 S3 Mean S1 S2 S3 Mean S1 S2 S3 Mean

TTB-7 (V1) 18.0 21.8 9.9 16.6 14.5 15.1 15.0 14.9 11.9 10.4 10.0 10.8

Hyd-3C

(V2)16.7 12.8 10.6 13.4 11.8 9.3 10.4 10.5 10.2 7.3 7.8 8.4

BRG-1 (V3) 14.1 16.8 9.6 13.5 15.1 8.8 9.8 11.2 8.1 10.0 7.9 8.7

Mean 16.3 17.1 10.0 14.5 13.8 11.1 11.7 9.3 10.1 9.2 8.6 12.2

Spacing: S1- 60 cm x 22.5 cm, S2 - 90 cm x 22.5 cm, S3 - 120 cm x 22.5 cm

COTTONParbhani

Pooled analysis of correlation study between the cotton seed yield and the weather variables at different growth stages for the years 1998-2007 (Table 7.13) revealed that mean relative humidity and bright sunshine hours during the entire crop growth period showed significant positive association with the cotton yield. During the crop growing stage like square formation to flowering (P4), Tmin, RH2, RH mean, and GDD showed highly positive association. RH1, RH2 and BSS played important role during flowering to boll setting stage (P5). Similarly during boll setting to bursting (P6) and boll bursting to picking (P7) also shown a good association with humidity and sunshine hours indicating that sunny days with high humid conditions are conducive for proper boll development and higher cotton yield.


90

Tabl

e 7.

13.

Cor

rela

tion

co-e

ffici

ent

exhi

bite

d by

wea

ther

par

amet

ers

prev

aile

d in

diff

eren

t ph

enop

hase

s w

ith

seed

cot

ton

yiel

d (1

998-

2007

) at P

arbh

ani

Pa

ram

ete

rs

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P

oo

led

Rai

nfal

l -0

.121

0.

010

0.0

63

-0.0

72

0.24

6*

0.21

7 0.

247*

0.

356*

* 0.

098

0.24

7*

-0.0

27

R.D

. -0

.152

0.

186

0.0

36

0.02

2 -0

.105

-0

.45

5**

-0.2

12

-0.1

29

0.05

8 -0

.057

-0

.037

Tm

ax.

0.13

4 0.

155

-0.0

07

0.18

0 -0

.138

0.

219*

-0

.021

-0

.218

* -0

.367

**

-0.2

67**

0.

039

Tm

in.

0.12

0 -0

.225

* -0

.094

0.

319*

* -0

.040

0.

370

**

0.07

1 -0

.093

-0

.323

**

-0.2

50

0.00

1

Tm

ean

. 0.

109

0.08

0 0.

149

0.

096

-0.0

39

-0.1

46

0.13

9 0.

002

-0.1

52

-0.1

02

0.02

2

T.r

ange

-0

.052

-0

.049

-0

.254

**

-0.2

86**

-0

.101

0.

128

0.14

3 -0

.112

-0

.458

**

0.06

5 -0

.041

RH

I 0.

089

0.02

9 -0

.003

-0

.103

-0

.079

0.

058

0.13

1 -0

.098

-0

.353

**

-0.0

19

-0.0

29

RH

II 0.

144

0.09

8 0.

269*

* 0.

248*

0.

016

-0

.201

-0

.100

0.

110

0.42

4**

-0.1

55

0.04

9

RH

mea

n 0.

309

**

0.25

7**

-0.0

13

0.33

5**

0.37

5**

0.6

80**

0.

471*

* 0.

420*

* 0.

118

0.02

8 0.

204

**

RH

rang

e 0.

129

0.11

6 -0

.083

0.

098

0.22

9*

0.4

83**

0.

642*

* 0.

323*

* -0

.181

-0

.258

**

0.05

7

EV

P

0.08

1 0.

108

0.1

02

0.21

2 -0

.014

-0

.27

3**

0.09

8 0.

136

0.27

9**

0.32

2**

0.03

9

BS

S

0.20

8 0.

194

-0.0

55

0.19

0 0.

284*

* 0.

578

**

0.74

3**

0.48

0**

0.02

7 0.

131

0.1

14**

Dur

atio

n

-0.0

41

-0.3

76**

0.

077

0.

444*

* -0

.147

-0

.074

0.

093

-0.1

13

0.24

4*

-0.1

40

-0.0

30

GD

D

-0.0

35

-0.3

58**

0.

056

0.

437*

* -0

.148

-0

.024

0.

129

-0.1

74

-0.3

53**

-0

.141

-0

.037

* =

Sig

nific

ant a

t 5%

leve

l.

** =

Sig

nific

ant a

t 1%

leve

l

P1 =

Sow

ing

to e

mer

genc

e.

P 2

= E

mer

genc

e to

see

dlin

g.

P3 =

See

dlin

g to

squ

are

form

atio

n

P4 =

Squ

are

form

atio

n to

flow

erin

g

P 5 =

Flo

wer

ing

to b

oll s

ettin

g.

P

6 = B

oll s

ettin

g to

bol

l bur

stin

g.

P 7

= B

oll b

urst

ing

to 1

st p

icki

ng.

P 8

= 1

st p

icki

ng to

2nd

pic

king

. P

9 = 2

nd p

icki

ng to

3rd

pic

king

.


91

SORGHUM Parbhani

The impact of different weather variables prevailed in different phenohases of sorghum crop for the years 1996 to 2007 were studied with the help of correlation coefficient values. The correlation of weather parameters recorded during different phenophases with grain yield of sorghum is presented in the Table 7.14.

Table 7.14. Correlation coefficient between weather variables and grain yield at Parbhani

Weather

variables

Stages Pooled

Boot

stage

Flowering

stage

Milk stage Dough

stage

Maturity

RF -0.006 0.450** 0.578** 0.160 0.051 0.129

RD -0.038 0.368** 0.772** -0.036 0.191 0.163

TMax -0.449** -0.268 -0.404** -0.478** 0.281* -0.279

TMin 0.202 0.435** 0.626** 0.314* 0.506** 0.382**

TMean -0.217 0.244 0.448** -0.044 0.422** 0.205

Trange -0.402** -0.389** -0.717** -0.480** -0.378** -0.409**

RHI 0.337** 0.535** 0.545** -0.301* 0.233 0.349**

RHII 0.298* 0.328** 0.659** 0.188 0.430** 0.341**

RHmean 0.360** 0.409** 0.671** 0.243 0.430** 0.374**

RHrange -0.124 -0.156 -0.561** -0.061 -0.252 -0.214

BSS -0.346** -0.357** 0.239 -0.283* -0.175 -0.258

EVP -0.480** -0.257 0.376** -0.300* -0.356** 0.337**

* = Significant at 5 % ** = significant at 1 %

It is observed that number of rainy days, Tmin and relative humidity during the entire crop growth period showed significantly positive association with grain yield, whereas Tmax, Trange, RH Range, BSS and EVP shown significant negative association with grain yield. It is interesting to note that during milk stage of the crop except BSS all other meteorological variables showed significant association with grain yield. Increase in temperature, its range and the relative humidity range during milk stage have shown negative association in the yield.


92

PEARLMILLET Hisar

Micrometeorology studies were conducted in pearl millet and in castor crop under dryland conditions. Observations with respect to temperature and humidity profiles were measured at 1200 hrs in various treatments at different growth stages under Integrated Nutrient Management (INM) strategies. Canopy temperature and the differential of air and canopy temperature were also measured at the same time. The humidity profiles were found to be more irregular at soft dough stage and physiological maturity stages in pearl millet crop. The highest temperature was recorded in N30+P15.A30 treatment during soft dough stage (Fig.7.7). The temperature profile pattern was irregular and the deviations were noticed in various treatments during hard dough and physiological maturity among the treatments.

Jointing stage

0

20

40

60

80

100

120

140

160

180

200

35.0 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 45.0

Temperature (°C)

Hei

ght (

cm)

N30+P15 PSB N30+P15 BiomixN20 + P10 BiomixN40 + P20 N30 +P15 A30sN30 +P15 A30to ControlN30 +P15 N40 + P20 BiomixN40 + P10 Biomix


93

Fig.7.6. Effect of crop age on temperature profile in bajra at different stages and control at Hisar

It is further observed from the figure that the canopy temperature in treatment N40+P20 are higher from jointing to hard dough stage, whereas at physiological maturity N40+P20 Biomass, treatment recorded higher temperature values.

soft dough stage

0

20

40

60

80

100

120

140

160

180

200

32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0

Temperature (°C)

Height (cm

)

Hard dough stage

0

20

40

60

80

100

120

140

160

180

200

32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0

Temperature (°C)

Height (cm

)

Physiological maturity

0

20

40

60

80

100

120

140

160

180

200

30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0

Temperature (°C)

Heigh

t (cm

)


94

8. CROP GROWTH MODELLING

RICEFaizabad

Different regression equations for estimating the rice yield were developed for different growth stages under different dates of sowing.

Regression model for Yield (q/ha.):

July 5th transplanting

60 DAT Y=-87.773+2.705Tmax +1.675Tmin –0.008Rainfall r=0.77 75 DAT Y=-81.486+0.0303Tmax +4.430Tmin –0.005Rainfall r=0.60 90 DAT Y=17.188+0.225Tmax +0.522Tmin+0.002Rainfall r=0.92


60 DAT Y=-71.331+2.283Tmax +1.405Tmin –0.0061 Rainfall r=0.79 75 DAT Y=-82.191+0.403Tmax +4.178Tmin –0.004 Rainfall r=0.72 90 DAT Y=-74.714+2.268Tmax +1.551Tmin-0.006 Rainfall r=0.77


60 DAT Y=-60.159-0.469Tmax-4.572Tmin-0.002 Rainfall r=0.70 75 DAT Y=-55.712+0.0783Tmax +2.452Tmin+0.002 Rainfall r=0.84 90 DAT Y=-21.859+0.865Tmax+1.008Tmin+0.002 Rainfall r=0.73

These equations are being validated for estimating the yield to use in agro advisory services.

Mohanpur Info crop models for rice crop cultivars for which the minimum data

sets are available, the model was validated to obtain the phenology of the crop and the crop growth parameters, viz., LAI, total dry matter, weight of green leaves, number of grains/meter, etc. The other parameters to run the model which were collected from the field survey as well as from the review of literature are (1) base temperature from sowing to germination (2)


95

thermal time for sowing to germination (3) base temperature for germination to 50 percent flowering, (3) thermal time for 50 percent flowering (5) base temperature for 50 percent flowering to physiological maturity (6) thermal time for 50 percent flowering to physiological maturity (7) optimal temperature (8) maximum temperature and (9) sensitivity to photoperiod.

From the model output, the leaf area index value is plotted against the degree-day and the results are under predicted by this model (Fig.8.1). However, the total dry matter predicted at different growth stages (Fig.8.2) is nearer to observed value.

Fig. 8.1. Output of detailed result obtained from Info-crop model

Fig.8.2. Intercepted PAR values for different growth stages at Mohanpur

0500

1000150020002500300035004000

Tota

l Dry

Mat

ter (

TDM

) in

Kg/

Ha

201 211 221 231 241 251 261Julian day

TDM_prd

TDM_obs


96

SOYBEAN AkolaThermal unit requirement:

Quantification of thermal unit requirement of vegetative and reproductive phenophases is useful in phenological modelling. Requirement of thermal units for attaining different phenological stages in three varieties of soybean (Table 8.1) showed that variety TAMS-38 required less number of degree days compared with variety TAMS-98-21 and JS-335. During vegetative period, variety JS-335, TAMS-98-21 and TAMS-38 required 755.1, 794.6 and 703.5 thermal units and during reproductive to maturity period they required 1018.2, 1040.2 and 997.9 thermal units, respectively.

Table 8.1. Thermal units (0C) availed by soybean varieties during different phenophases at Akola

Phenophase JS-335 TAMS-98-21 TAMS_38

Vegetative stage

Emergence 124 123 123

Seedling 467 502 414

Branching 164 170 167

Total Vegetative stage 755 795 704

Reproductive stage

Bud initiation 96 118 95

Flowering 222 240 249

Pod formation 84 96 93

Grain formation 142 174 173

Pod development 292 268 240

Dough stage 107 84 86

Maturity 76 61 63

Total reproductive stage to maturity 1018 1040 998

Total 1773 1835 1701


97

SORGHUM Parbhani

Regression analysis was carried out for prediction of kharif sorghum grain yield on the basis of weather conditions prevailed during critical growth stages i.e., boot stage, 50 percent flowering, milk stage and dough stage as they are quite ideal for planning purposes. The regression equation for above mentioned stages are given in the following Table 8.2. The R2 values for these different regression equations at different growth stages are 0.33, 0.58, 0.82, and 0.56, respectively.

Table 8.2. Regression equation for prediction of sorghum grain yield at Parbhani

Growth stage Regression equation R2

Boot stage Y = 6350.24 + 0.577RF – 54.42RD – 62.18Tmax –245.80Tmin +

316.57Tmean – 256.89Trange – 194.30RHI – 248.45RHII +

428.34Rhmean – 11.04RHrange – 41.54BSS – 422.58EVP.

R2 = 0.33

Multiple R = 0.57

SEY = 1248.92

50% flowering

stage Y = - 8425.28 + 7.95RF – 130.33RD + 62.08Tmax + 526.87Tmin –

378.43Tmean + 74.09Trange + 256.26RHI – 39.01RHII –

143.70RHmean – 80.67RHrange – 75.18BSS + 28.35EVP

R2 = 0.5

Multiple R = 0.76

SEY = 991.97

Milk stage Y = - 2601.14 – 4.56 RF + 424.67 RD - 10584.36 Tmax + 10542.56

Tmin + 176.76 Tmean + 10381.04 Trange – 2359.20 RHI + 2280.97

RHII + 87.91 RHmean + 2367.13 RHrange + 71.83 BSS + 27.21 EVP

R2 = 0.82

Multiple R = 0.90

SEY = 644.64

Dough stage Y = 5436.71 + 0.797RF – 197.02RD – 1206.74Tmax + 3744.57Tmin –

2752.94Tmean + 2207.59Trange – 399.59RHI – 952.91RHII +

1407.97RHmean – 156.93RHrange – 40.40BSS – 220.77EVP

R2 = 0.56

Multiple R = 0.75

SEY = 1006.55

These above equation so developed can predict grain yield of kharifsorghum at milk stage, which is ideal from predication point of view for planning agency of Govt. and also is useful in Agro Advisory Services.


98

MAIZEJorhat

Predictive regression equations between grain yield and physic mean meteorological parameters in maize crop for three varieties, viz,Kanchan Mahi, Kiran and Keshari were developed at given in Table 8.3.

Table 8.3. Predictive models between grain yield and phasic mean meteorological parameters in maize at Jorhat

Variety PE Model R2

V1 Cob initiation Y = 4905.89 - 28.53MMIT 0.932

Y = 3896.86 + 95.0 I MBSH 0.962

Cob 100% Y = 7993.25 - 151.21MMIT 0.883

Y = 4144.29 +18.80MBSH 0.891

Maturity Y = 5550.71 - 52.56MMIT 0.496

V2 Cob initiation Y = 4619.82 - 28.08MMIT 0.918

Y = 3731.26 + 50.69MERH 0.938

Cob 100% Y = 264.12 + 152.30MMIT 0.997

Y = 3731.26 + 50.69MBSH 0.938

Maturity Y = 5076.77 - 44.58MMIT 0.417

V3 Cob initiation Y = -2045.3 + 66.06MERH 0.731

Cob 100% Y = 8411.41 - 199.98MM1T 0.667

Maturity Y = 13808.42 - 120.57MERH 0.944

Y = 2946.5 + 176.01 MBSH 0.670

It is observed from the above table that mean minimum temperature, bright sunshine hours and mean relative humidity could explain the variability in the yield upto 90 percent in varieties Kanchan and Kiran for the crop initiation stages. However, the same parameters could not explain the variability significantly with respective yield for the Keshari variety at the same growth stage.


99

PalampurHistorical weather data for the past 24 years during the rice growth

period was analyzed to work out the range of weather parameters conducive for development of rice blast. It was found that the maximum temperature between 23.2 -26.7°C was found to be favorable for rice blast disease under mid hill region of Palampur.

CashewnutThrissur

Pest forecasting models for prediction of incidence of Tea-mosquito bug at two locations, viz., Madakkathara and Pilicode using various meteorological parameters observed from November to February are given below:

Prediction equations for Madakkathara

Equations Variance explained

(%)

November – December

Y = -16.7524-0.01003X12+0.012335X2

2+0.002837X32-0.00241X4

2-

0.0021X52+0.076383X6

2+0.036141X72-14.1434X8

2

79

January

Y = -7.463347-0.01602X12+0.008064X2

2+0.001009X32-

0.00142X42-0.17641X5

2+0.076383X62

75

February

Y = -4.70092-0.00557X12+0.004041X2

2+6.4x10-6

X32+0.001236X4

2+0.00297X52+0.22697X6

2

71

Cont…


100

Prediction equations for Pilicode

Equations Variance explained

(%)

November

Y = 35.08047+0.00548X12+0.00793X2

2+0.00076X32-0.00433X4

2-

0.05205X52-3.77462X6

2+0.01006X72-5.77618X8

2

92

December

Y = 60.7937-0.00084X12-

.10902X22+0.00874X3

2+0.00326X42+0.15904X5

2-3.89004X62

94

January

Y = 21.2148+0.00432X12+0.01632X2

2+0.00973X32-0.00482X4

2-

.17192X52-1.1475X62

76

February

Y = 45.5252-0.00767X12-0.00765X2

2+6.7x10-5X32-

0.00327X42+0.00629X5

2-0.31714X62

93

Where Y = Predicted TMB population, X1 = Minimum temperature,

X2 = maximum temperature, X3 = Forenoon relative humidity,

X4 = Afternoon relative humidity, X5 = sunshine hours, X6 = Rainfall.

X7 = Wind speed, X8 = Rainy days


101

y = -0.0022x4 + 0.0535x3 - 0.1371x2 + 0.1174x - 0.1395R2 = 0.9917

0

10

20

30

40

50

60

70

80

90

27 28 29 30 31 32 33 34 35 36 37 38 39 40

Met. Week

RH

%

0

5

10

15

20

25

30

35

40PD

I(%)

Evening RH PDI(%)

Poly. (Evening RH) Poly. (PDI(%))

9. WEATHER EFFECTS ON PESTS AND DISEASES

RICEFaizabad

The incidence of foliar blight disease was monitored on weekly basis and the corresponding weather parameters were related to the incidence of the disease. It was observed that highest blight intensity was recorded during the reproductive phase when the relative humidity vary between 56-85% and maximum and minimum temperature was around 33 and 23°C respectively. The relationship between evening relative humidity and Percentage Disease Intensity (PDI) were developed from historical data and shown in Fig.9.1.

Fig.9.1. Effect of evening RH percent on the leaf blight in rice at Faizabad

The PDI increased from 30th week to 40th week corresponding to physiological maturity when the evening relative humidity was around 65 percent.


102

COTTONParbhani

Regression analysis was conducted to assess the impact of different weather variables on the outbreak of bollworm in cotton cultivar NHH-34. Since the development of pest was also influenced by the earlier weeks weather conditions, the current weeks of meteorological data along with previous weeks data was used in developing regression equations. The following are the step-wise regression equation developed for forewarning of pink bollworm, where the parameters with suffix 1 values are corresponding to previous week and the values without suffix are of the current week value. In the last equation, the weather variable showed additive affect on the outbreak of bollworm.

Step: 1

Y = 58.18 – 0.502 Tmin1 – 0.581RHI + 0.151 RHII R2 = 0.37 SE = 4.06

Step: 2

Y = 64.37 – 0.250 Tmin1 – 0.621 Tmin – 0.687 RHI + 0.328 RHII R2 = 0.39 SE = 4.01

Step: 3

Y = 53.04 + 0.548Tmax – 1.30Tmin – 0.806RHI + 0.558RHII R2 = 0.40 SE = 3.98

Step: 4

Y = 51.68 - 0.012Tmax1 + 1.47Tmax - 0.35Tmin1-1.84Tmin - 0.21RHI1 - 0.73RHI + 0.18RHII1 +

0.65RHII - 0.29BSS1 - -1.31BSS

R = 0.46 SE = 3.91

Step: 5

Y = 33.73 + 45.59Tmax1 + 0.86Tmax + 46.77Tmin1 + 4.71Tmin -93.40MeanT1 - 8.05Mean +

2.85RHI1 + 3.26RHI + 1.86RHII1 + 3.79RHII - 4.37Rhmean1 - 7.04Rhmean + 5.37BSS1 +

2.89BSS

R2 = 0.59 SE = 3.48


103

GROUNDNUTAnantapur Leaf webber is a major pest of groundnut in Andhra Pradesh. An empirical model was developed to predict incidence of leaf webber at Anantapur using groundnut variety TMV-2 which was sown under five dates of sowing. The model is shown as follows: Y = 0.47 + 0.004 X1 + 0.12 X2 – 0.019 X3 R2 = 0.91 Where Y = Predicted leaf webber damage X1= Minimum temperature X2= RH I X3= RH II

Leaf Webber infestation occurs when maximum temperature raises by 2�Cin a day followed by dry spell.

Emergence of red hairy caterpillar moth is very closely associated with soil moisture at different depths and different periods during rainy season. As soil moisture status is governed by rainfall or irrigation, a regression model has been developed to predict the pest in relation to rainfall received during June-September.

Y = 163.5 + 4.34 X R2 = 0.45 Where Y = Predicted number of RHC moths emerged X = Rainfall during June-September (mm)

BangaloreTo study the effect of weather parameters on tikka disease

incidence at flowering, pegging initiation, pod formation and at maturity stages, three varieties of groundnut viz., TMV-2 (V1), JL-24, (V2), K-137 (V3) were sown under two dates of sowing viz., 23rd Jun and 20th Jul of 2007.

This year the incidence of Tikka disease was low perhaps due to occurrence of better rainfall that must have washed the Tikka disease spores. However, it was observed at pod filling and maturity stages, the maximum incidence of Tikka was observed in the first date of sowing at pod formation stage (19.0 %) as well as maturity stage (24.9 %). Among


104

varieties the maximum incidence of Tikka was noticed in TMV-2 followed by JL -24 and this is shown in the Table 9.2.

Table 9.2. Incidence of Tikka disease of Groundnut at different stages of crop growth (%) at Bangalore

Treatment Varieties

V1- TMV-2 V2-JL-24 V3- K-137 Mean

At Flowering

D-I - - - -

D-II - - - -

Mean - - - -

Peg initiation

D-I - - - -

D-II - - - -

Mean - - - -

Pod formation

D-I 19.3 21.0 16.8 19.0

D-II 13.5 5.5 5.8 8.3

Mean 16.4 13.3 11.3

At maturity

D-I 25.3 27.0 22.3 24.9

D-II 22.5 15.0 9.8 15.8

Mean 23.9 21.0 16.1

BERAnantapur

The regression equations which were developed earlier for prediction of powdery mildew in ber plant varieties viz., Gola, Sev, Umran, Kaithili Mundia and local were tested during the year 2007. It was observed that none of the predicted equation could able to predict the occurrence of the powdery mildew, as their percentage deviation between the actual and predicted is very high. These models, which were predicted earlier need to be modified further.


105

GRAPESBijapur

Weekly observation on the incidence of different insect pests in grape vine were taken during the year 2007 and were correlated with the meteorological parameters at different lead times of 0,1,2and 3 weeks peak time during kharif and rabi season. The correlation tables for major pests during rainy and post rainy season for the year 2007 are given in Table 9.3 and 9.4.

Table 9.3. Correlations between weather variables at different lead weeks and insect incidence on grape during rainy season of 2007 at Bijapur

Insect Lead weeksMaxT (C) MinT (C) BSS (hrs) RH1 (%) RH2 (%) RF (mm)

Flea beetle / vine

0 -0.56 -0.25 -0.57 0.42 0.56 -0.25

1 -0.54 -0.28 -0.61 0.35 0.53 0.24

2 -0.49 -0.26 -0.59 0.33 0.51 0.29

3 -0.45 -0.16 -0.57 0.34 0.52 0.33

Thrips /3 leaves

0 0.67 0.56 0.53 -0.60 -0.55 0.04

1 0.77 0.52 0.64 -0.57 -0.69 0.15

2 0.80 0.61 0.76 -0.62 -0.75 -0.28

3 0.73 0.47 0.80 -0.68 -0.77 -0.29

Mealy bug (col./vine)

0 -0.49 -0.29 -0.45 0.60 0.59 0.19

1 -0.31 -0.40 -0.23 0.32 0.25 0.45

2 -0.25 -0.18 -0.18 0.39 0.23 -0.42

3 -0.32 -0.16 -0.35 0.31 0.35 0.08

Table 9.4. Correlations between weather variables at different lead weeks and insect incidence on grape during post rainy season of 2007 at Bijapur

Insect Lead weeksMaxT (C) MinT (C) BSS (hrs)RH1 (%) RH2 (%) RF (mm)

Flea beetle / vine

0 -0.26 -0.55 0.41 -0.16 -0.42 -0.58

1 -0.52 -0.37 0.06 0.05 -0.01 -0.23

2 -0.37 -0.30 -0.06 -0.24 -0.03 0.11

3 0.04 -0.21 0.06 -0.58 -0.23 -0.11

Cont…


106

Thrips /3 leaves

0 -0.26 -0.67 0.46 -0.31 -0.51 -0.62

1 -0.58 -0.54 0.26 -0.09 -0.14 -0.35

2 -0.56 -0.52 0.08 -0.25 -0.19 -0.08

3 -0.19 -0.43 0.17 -0.49 -0.37 -0.28

Mealy bug (col./vine)

0 -0.04 -0.22 0.21 -0.17 -0.20 -0.30

1 0.02 0.09 -0.13 0.17 0.18 0.04

2 0.20 0.36 -0.39 0.20 0.42 0.40

3 0.35 0.46 -0.54 -0.07 0.29 0.26

During the kharif season, amongst the various insect pests of grape, flea beetle was influenced negatively by maximum temperature at zero and one week lead-time and by sunshine duration at all lead times of 0 to 3 weeks. Conversely, it was favored by higher afternoon relative humidity at all the four lead weeks. On the other hand, thrips were influenced positively by maximum temperature, minimum temperature and sunshine duration at all lead times of 0 to 3 weeks, but negatively by relative humidity at both morning and afternoon time. The highest correlations were noticed at two and three weeks lead-time. It is inferred that the role of weather variables is opposite for occurrence of flea beetle and thrips on grape vines. Current week relative humidity of both morning and afternoon showed positive association with incidence of mealy bug, which indicated that the lead-time available for forecast of the pest was very short, and observations need to be made at shorter time intervals.

During the rabi season, amongst the various insect pests of grape, flea beetle was influenced negatively by maximum temperature at one week lead time, by minimum temperature and rainfall at zero lead week and by morning relative humidity at three weeks lead time. The role of weather variables on thrips was also similar. On the other hand, sunshine at three weeks lead-time was the sole influencing variable for mealy bug incidence. Collection of more intensive data is continued.


107

REDGRAMBangalore

The incidence of pod borer was monitored among the three popular varieties of redgram, viz., TT B-7, HYD-3C and BRG-1 sown under three dates of sowing 4th Jun, 22nd Jun and 21st Jul of 2007. The percentage incidence of pod borer per plant is given in the Table 9.5.

Table 9.5. Incidence of pod borers in different varieties, dates of sowing and spacing of red gram (percent/ plant) at Bangalore

Varie

-ties

Pod borer incidence /Plant (%)

(D-1) (04th Jun) (D-2) (22nd Jun) (D-3) (21st Jul)

S1 S2 S3Mea

nS1 S2 S3

Mea

nS1 S2 S3

Mea

n

TTB-

7

21.8

0

10.4

1

39.2

4

23.8

2

19.4

1

18.8

0

23.4

5

20.5

59.37

39.3

1

38.6

3

29.1

0

Hyd -

3C

48.8

4

20.2

3

14.5

0

27.8

6

13.8

7

47.0

0

42.6

7

34.5

1

42.0

3

42.3

8

42.4

8

42.3

0

BRG-

1

33.7

4

21.7

5

44.7

7

33.4

29.76

34.5

8

37.2

4

27.1

9

40.7

8

49.7

3

24.9

8

38.5

0

Mean 34.7

9

17.4

6

32.8

4

28.3

6

14.3

5

33.4

6

34.4

5

27.4

2

30.7

3

43.8

1

35.3

6

36.6

3

The results showed that highest pod borer incidence per plant were observed in HYD-3C compared to the other varieties in the second and third dates of sowing. The highest incidence was observed in the spacing of 90 x 22.5 cm in the third date of sowing which can be attributed to closed canopy area.


108

AnandVegetables (Ladyfinger)

To study the effect of weather on the incidence and development of major pests in ladyfinger during kharif season, variety GUJ.Okra-2 was sown in five dates of sowing, viz., 15th May, 1st June, 15th June, 1st July and 15th July 2006. The observations on incidence on population of jassids were correlated with the weather parameters (Table 9.6).

Table 9.6. Correlation coefficient of weather parameters with number of jassids per leaf of okra during different date of sowing at Anand

Dates of

sowing Weather parameters

EP BSS RF WS MAXT MINT RH1 RH2

15th May -0.605* -0.600 0.085 0.186 -0.654* -0.353 0.488 0.565

1st June -0.560 -0.427 0.477 0.190 -0.651* -0.513 0.390 0.464

15th June -0.130 -0.054 -0.473 -0.132 -0.270 -0.461 0.185 0.038

1st July 0.206 0.463 -0.142 -0.454 0.445 -0.245 -0.031 -0.229

15th July 0.831** 0.884** -0.494 -0.779** 0.857** -0.325 -0.861** -0.903**

*Correlation is significant at 0.01 level (2-tailed); **Correlation is significant at 0.05 level (2-tailed).

The results showed that maximum temperature was negatively correlated with no. of jassids under both dates of sowing. However, when the crop was delayed beyond 15th July temperature is found to be related positively. The step-wise regression for the treatment that was sown on 15th

July for prediction of Jassids population is as follows:

Y= 29.100 -0.3423RH2 R2: 0.815

Nearly 81percent of jassids population can be predicted from the afternoon relative humidity values. Similarly the prediction of pests namely jassids, whitefly and diseases, viz., macrophonia and shoot damage in Okra crop


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raised under four dates of sowing, viz., 25th Feb, 10th Mar, 25th Mar, 10th

Apr 2007 during summer season using weather data are given in Table 9.7.

Table 9.7. Regression models for pest-disease in okra crop at Anand during summer 2007

Pest/disease Dates of sowing Regression model R2

Jassids

25th Feb Y=-9.437 + 0.608 MinT 0.652

10th Mar Y=-13.144 + 1.464WS +0.888BSS 0.915

25th Mar Y=-39.754 + 5.189EP 0.826

25th Apr Y=-39.682 + 1.601 MinT + 6.062 RF 0.948

White fly 25th Mar Y= 3.557 -0.379 EP 0.551

Macrophonia

25th Feb Y= 73.216 +2.246 MinT -2.931 MaxT 0.951

10th Mar Y= -32.437 + 4.378MinT-0.806RH1 0.980

25th Mar Y=-82.007 + 2.268MinT + 3.933 BSS 0.975

25th Apr Y=-112.356 + 5.080MinT 0.863

Shoot damage 10th Mar Y= -3.818 + 0.188 RH2 0.733

It is seen that rainfall and minimum temperature have positive relationship with Jassids that were sown in 10th April. Similarly, the temperature both maximum and minimum and sunshine has positive relation with macrophonic disease.


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10. Economic Impact of Agromet Advisory Services

All the centres of AICRPAM have participated in issuing weather-based agro-advisories prepared based on the forecast received from NCMRWF and the current crop condition. The information was disseminated through different mass media including website operating at CRIDA and also at their respective Agricultural Universities. The economic gains due to follow up of agro-advisories obtained by farmers as evaluated by different centres are presented in this chapter.

Bangalore Agro advisories, regular estimation of benefit/loss on each item of the advisory realized at the farmers level, economic benefit/loss on adoption of the agro advisory issued by the Agromet division compared to the non-AAS farmers and identification of the ITK’s at farmers level related to weather forecast. For this purpose two villages have been identified and about 80 farmers have been identified as beneficiaries. Two crops in rabi have identified for this study. The data on weekly basis collected from the farmers. The economic impact studies revealed that AAS villages benefited as 1:1.31 compared to the Non AAS villages of 1:1.20 in Tomato and 1:5.16 and 1:4.13 in Field Bean crop respectively.

Tomato AAS NAAS DIFF % Increase

Seed 2663.291 2731.034 -67.74 -2.54

FYM 974.68 1011.5 -36.83 -3.77

Fertilizer 1032.32 1180.13 -147.90 -14.32

Micronutrient 0 0 0 0

Herbicide 0 0 0 0

Pesticide 222.2 365.52 -143.4 -64.53

Human labour 2442.4 2587.15 -145.5 -5.95

Bullock labour 100 200 -100 -100

Machine labour 1200 1200 0 0

Cont…


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Irrigation 1000 1000 0 0

Associated Cost 0 0 0 0

Total cost (Sum of all above) 9634.7 10276.76 -641.34 -6.50

Main Product 12645.57 12241.38 404.19 3.20

By Product 0 0 0 0

Total VOP 12645.57 12241.38 404.19 3.20

Net Return (Total VOP-Total cost) 3010.6 1965.24 1045.53 34.72

BCR 1.31 1.20

Field bean AAS NAAS DIFF % Increase

Seed 297.0 324.4 -27.4 -9.2

FYM 975.0 1120.4 -145.4 -14.9

Fertilizer 857.7 1006.7 -149.0 -17.4

Micronutrient 0.0 0.0 0.0 -

Herbicide 0.0 0.0 0.0 -

Pesticide 107.5 181.9 -74.4 -69.3

Human labour 2700.0 3179.6 -479.6 -17.8

Bullock labour 100.0 200.9 -100.9 -100.9

Machine labour 916.3 900.0 16.3 1.8

Irrigation 1300.0 1300.0 0.0 0.0

Associated Cost 0.0 0.0 0.0 -

Total cost (Sum of all above) 7253.4 8214.0 -960.6 -13.2

Main Product 37400.0 33925.9 3474.1 9.3

By Product 0.0 0.0 0.0 -

Total VOP 37400.0 33925.9 3474.1 9.3

Net Return (Total VOP-Total cost)

BCR

30146.6

5.16

25712.0

4.13

4434.6

-

14.7

-


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Bhubaneswar The economic benefit due to use of the AAS, in term of increased net return,

ranged from Rs.846.00 in okra to Rs.4592.00 in brinjal per acre. For rice it was Rs.1886.00/acre.

JabalpurOn set of South-West Monsoon

Monsoon season is important for the farmers who cultivate crops particularly during kharif season under rainfed conditions. Total amount of rainfall during kharif season of 2007 was less than the average rainfall (152.0 mm). However, through agro-advisory bulletin farmers were regularly advised about progress of rainfall and advised accordingly about delayed field operation and sowing which resulted into considerable success in saving (Rs. 1200 to 1800 ha-1) in kharif crop cultivation.

Incidence of frost In this year during the month of February night temperatures reached below

4.0�C continuous for a week, which is very much congenial for frost occurrence. Agro advisory bulletin users (farmers) were advised accordingly and farmers took initiatives in advance as per suggestions and they were benefited and able to save the pulse crops by escape from frost (Agro Advisory bulletin no. 50a and 50 b on dated 12/02/2008).

Animal and Poultry Production Farmers were advised from time to time to take preventive measures for

animal diseases. Vaccination for important animal diseases was also advised to protect the animals from diseases. Poultry production is also very sensitive to weather conditions and taking appropriate measures minimized loss of production.

KovilpattiSpecific instances of benefit/losses due to AAS with cultural practices

modified as per advisories at Kovilpatti are as under: Benefits1. Crop : Pulses

Advice given : Dry weather is expected in next four days of September 29 to October

9. The farmers are requested to postpone the sowing of pulses.

Additional cost : Rs. 525/- per ha

Additional benefit : Saving in seed cost (Rs.525)

Cont…


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2. Crop : Sorghum and Pulses

Advice given : Forthcoming week (October 10-13) is expected to get slight drizzling,

farmers are advised to sow K8 sorghum and pulse crops in their

fields.

Additional cost : Nil

Additional return : Rs. 2500/- per ha due to additional yield of 25% due to timely sowing

3. Crop : Chilli

Advice given : Occurrence of damping off disease in chilli nursery is observed due to

stagnation of rainwater. Hence, adequate drainage should be made

and the nursery should be drenched with Fytolan fungicide @ 2.5

g/litre of water.

Additional Cost : Rs. 75/- per ha

Additional return : Rs. 500/- per ha

PalampurCost of production and net returns of major crops grown at Palampur area

for 2006-07 are given below:

Crop Cost of Production

(Rs/ha) (Sit 1)

AAS

Cost of Production

(Rs/ha)(Sit2)

Recommended

Net Returns

(Rs/ha)

AAS

Net Returns

(Rs/ha)

Recommended

Wheat 12925 13525 15475 19275

Paddy 16108 16708 12642 14592

Maize 11940 11940 19210 21340

Oil seed (Gobhi

sarson)

11545 12895 19400 21705

Pulses 12500 13400 - -

Selling prices: Wheat grain Rs 700, Wheat straw Rs 150, Paddy grain Rs 600, Straw Rs 100, Maize grain Rs 550, Straw Rs 100, Gobhi sarson grain Rs 1800 and Straw Rs 150/

Percent increase/decrease in net returns due to the impact of AAS on different crops of the agro climatic zone assessed in University Farm are as under:


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Crop Increase in rupees due to

AAS/hectare

Increase/Decrease (in %) due to AAS from

recommended practices (net returns basis)

Maize 1400 7.5

Rice 1150 8.4

Wheat 1300 5.8

The economic impact of the AAS on maize indicated an increase of 7.5 and 8.4 percent rice and 5.8 percent in wheat. The kharif season recorded higher rainfall compared to rabi with low correct and useable cases in comparison to rabi season. The net returns obtained due to AAS were not due to higher yields but due to less cost of production.

BijapurA grape farmer of Aheri Village - about 20 km from Bijapur enquired on the

occurrence of rainfall, since he would have to take up spraying on grape to prevent incidence of diseases in case of impending rainfall. The tracking of the movement of the storm over Bijapur since 11th November was carried out and advised the farmer not to go ahead with spraying as probability of occurrence of rainfall is high. By this, the farmer saved Rs. 4000/- per acre by avoiding the spray. For an area of 12 acres one single farmer could save nearly Rs. 50,000/-. He took his fellow grape growers into confidences and prevailed upon them not to take up spray un-necessarily and follow the weather forecasts.

ThrissurEconomic Impact of AAS on major crops obtained over the years 2004, 2005

and 2006 at Thrissur are given below: Season / crop Yield (q/ha)

Economic gain

(Rs/ha)

AAS

farmers

Non-AAS

farmers

% Increase in

yield

Kharif – Paddy

2004 28.0 26.0 7.1 2290.0

2005 27.0 25.3 6.3 1776.0

2006 27.8 25.3 9.0 2125.0

Average 27.6 25.5 7.6 2064.0

Cont…


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Rabi – Paddy

2003-04 36.5 29.5 19.2 2932.0

2004-05 30.0 27.8 7.3 2987.0

2005-06 33.0 28.5 13.6 3581.0

2006-07 31.8 28.8 9.4 3363.0

Average 32.8 28.7 12.4 3216.0

Kharif – Banana

2004 228.0 204.5 10.3 10479.0

2005 252.5 225.0 10.9 10340.0

2006 245.0 215.0 12.2 15365.0

Average 241.8 214.8 11.1 12061.0

Rabi – Banana

2003-04 317.3 278.8 12.1 37690.0

2004-05 312.5 292.5 6.4 40968.0

2005-06 276.8 244.3 11.7 33060.0

Average 302.2 271.9 10.1 37239.0

Coconut (nuts/ha)

2003-04 13460 11025 18.1 4810.0

2004-05 12343 11443 7.3 2888.0

2005-06 12431 11438 8.0 3644.0

Average 12745 11302 11.1 3781.0


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11. CLIMATE CHANGE STUDIES

The atmospheric concentration of carbon dioxide has been increasing at an

alarming rate (1.9 ppm per year) in recent years than the natural growth-rate. The

global atmospheric concentration of methane was at 1774 ppb in 2005 and nearly

constant for a period of time. Nitrous oxide increased to 319 ppb in 2005 from pre-

industrial value of about 270 ppb. Thus warming of climate system is unequivocal,

as it is evident from the recent past trends of twelve warmest over the last 100 years

recorded over the last 18 years (1995-2007). The increase in mean air temperature

over the globe for last 100 years (1850-1899 to 2001-2005) is 0.76°C, which is

influencing reduction of snow cover and discharge of river water in addition affecting

the agricultural production system. The Fourth Assessment report of Inter-

governmental Panel on Climate Change (2007) concluded that ‘there is high

confidence that recent regional changes in temperature have had discernible

impacts on many physical and biological systems’.

In the view of the importance of the creeping problem, the AICRIP on

Agrometeorology has also started the research on climate change with the help of

its coordinated centers and also at the headquarters of the project. Some of the

findings observed at various centres are given below:

Coordinating unit, CRIDA, Hyderabad

Analysis of long-term monthly rainfall data was carried out for all the rainfed

stations (1140 stations) spread across the country. As an example, annual rainfall

variability from 1870 onwards for few stations in the country is shown in Fig.11.1. A

four-degree polynomial equation of the annual rainfall shows a cyclic trend in the

rainfall pattern for a lag period of 40 years in case of Hyderabad and other stations

located in Peninsular and in northern parts. However, similar clear-cut variability is

not observed the stations that are located in eastern and extreme western parts of

the country.


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Fig.11.1. Annual rainfall variability at selected stations in India

Spectral analysis of long-term data indicated majority of the stations have shown a cyclic trend with a certain periodicity. Greater number of stations showed periodicity of less than seven years indicating that 73 percent of stations of rainfed areas in the country are subjected to short-term fluctuations in rainfall. For a sample, it was observed that a periodicity of approximately ten year’s annual rainfall cycle has been noticed for Hyderabad. (Fig.11.2).


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A n n u a l r a in fa l l o f H y d e r a b a d ( 1 8 7 1 -2 0 0 4 )

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

18

71

18

76

18

81

18

86

18

93

18

99

19

04

19

12

19

19

19

24

19

29

19

35

19

40

19

45

19

50

19

55

19

60

19

66

19

72

19

78

19

83

19

96

20

01

Y e a r

Ra

infa

ll

A n n u a l R a in f a l l 1 0 p e r . M o v . A v g . ( A n n u a l R a i n f a l l)

Fig.11.2 Autocorrelation function & Spectrum Analysis (Hyderabad)

The trend analysis of rainfall data from 1140 meteorological stations carried out at CRIDA showed negative trend among the stations situated in deep southern parts, southern peninsular, Central India, Parts of North Indian region and N.E. regions. Positive deviations were seen at Gujarat, Maharashtra, Coastal A.P., Rayalaseema and Orissa. However, part of the country comprising the areas in central parts covering eastern U.P., eastern M.P., west coast and greater parts of North West India did not show any changes. As the above analysis did not indicate the significance of it changes the data has been subjected to statistical significance tests using Mann Kendall test for the same locations significance and then spatial distribution maps were generated and presented in Fig.11.3. Significant negative trends were observed in the eastern parts of Madhya Pradesh, Chhattisgarh and parts of Bihar, Uttar Pradesh, parts of N-W and N-E Indian and a small part in Tamil Nadu.


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Fig.11.3. Rainfall Trend over India (Mann Kendall test of significance)

Droughts and Desertification

Drought is a regular part of the natural cycle affecting agricultural productivity and leading to desertification. Impacts of shifts in climatic pattern becomes more prominent when one considers the climatic spectrum of the dryland and arid regions as these marginal areas provide early signals of the impacts of climate variability and change. From the annual rainfall values of 1140 stations, the probabilities of various intensities of meteorological droughts across the country has been computed and probabilities maps have been prepared and shown in Fig.11.4.


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Droughts

Moderate


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Fig.11.4. Probabilities (%) of drought in different parts of India

Impact Studies

From the Had CM3 model projected temperature data of 2020, potential evapotranspiration (PET) ratio computed for a few locations in the state of Andhra Pradesh estimating the crop water requirement and compared it with the water requirements of the crops that were computed using the measured weather parameters upto the year 2005. The differences in the water requirements provide a clue to the Climate Change Impact Studies. Similarly, change in the total crop duration estimated from the computation of GDD for the years 2005 and 2020 also provides information on impacts of climate change. The increase in crop water requirements and the reduction in length of crop duration of important crops in respect of few selected stations in Andhra Pradesh are given in Table 11.1. It was noticed from the results that overall water requirements of the major crops grown under rainfed conditions in Andhra Pradesh showed increasing trend. This is mainly attributed to the increase in average temperature by ~1�C over the base year.

Severe


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Table 11.1. Projected Crop Water Requirements and Changes in Crop Duration

Station Crop Increase in water

requirements(2020-2005) (mm)

Reduction in crop duration (weeks)

Anakapalli Maize 51.7 1

Groundnut 61.3 1

Anantapur Groundnut 70.1 1

Red gram 174.3 1

Jagityal Cotton 60.5 2

Maize 49.0 1

Rajendranagar Red gram 114.5 2

Groundnut 73.0 1

Tirupathi Groundnut 73.0 1

There is not much significant decrease in the crop duration due to fulfilling of the required growing degree-days at an early date. Over all, the crop duration is expected to decrease by one to two weeks by 2020 for all the major crops of the State.

Climate change studies in Andhra Pradesh

Rainfall

Rainfall data of different stations of Andhra Pradesh were analyzed using the Mann Kendall statistical significance test and a spatial rainfall trend map was generated using the data in GIS (Fig.11.5). Majority of the districts have shown no significant trend. But there is an increasing trend of rainfall was observed in parts of Adilabad, Prakasham and Chittoor districts.


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Fig.11.5. Spatial rainfall trends map of Andhra Pradesh

Temperature

The temperature trends of 8 stations in Andhra Pradesh viz., Anantapur, Arogyavaram, Hyderabad, Khammam, Kalingapatnam, Kurnool, Mahaboobnagar and Visakhapatnam were studied and presented in the following Table 11.2. There was no change in temperatures viz., average annual maximum, minimum and annual temperatures were observed in the coastal station. The average annual maximum temperature was increased in five stations except Khammam where the maximum temperature showed a declining trend. There is no change in average annual maximum temperature was observed in Arogyavaram station.


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The average annual minimum temperatures were showed an increasing trend in six stations except in Visakhapatnam where the declining trend was observed. The annual average temperature showed an increasing trend in 6 stations except Khammam where it has shown a declining trend.

Table 11.2: Temperature trends in different stations in Andhra Pradesh

Climate Change over the Humid Tropics of Kerala

Rainfall trends

Rainfall in June and July was in declining trend over Kerala. A decrease of 142.5 mm and 102.5 mm of rainfall was noticed in June and July, respectively over a period of 135 years (1871 to 2005). In contrast, a marginal increase was noticed in rainfall of August and September. The trend in monsoon and annual rainfall of Kerala was declining since last 60 years though it is stable if we consider annual rainfall since 1871 onwards.


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The monthly rainfall in October and November was increasing while December decreasing. An increase of 51.7 mm and 42.9 mm of rainfall was noticed in October and November, respectively over a period of time. Overall, there was an increase of 96.7 mm in northeast monsoon rainfall over a period of 135 years.

Temperature trends

During the last 43 years, the mean maximum temperature has risen by 0.8 degree Celsius, the minimum by 0.2 degree Celsius and the average by 0.5 degree Celsius over Kerala according to India Meteorological Department in the wake of the Intergovernmental Panel on Climate Change. February and March are the hot months in Kerala; with a mean maximum of 33°C. Palakkad recorded the highest temperature of 41°C on April 26, 1950. This was 8°C more when compared to the normal maximum temperature in March. Similar temperatures were recorded over the Palakkad region in February and March of 2004, which was one of the severe summer droughts in Kerala. The year 1987 was the warmest year over Kerala. The lowest temperature recorded was 12.9°C at Punalur on January 8, 1968. Kerala experienced severe summer droughts in 1983 and 2004 while recently floods during monsoon season in 2005 and 2007. The crop losses were considerably high during the above weather extremes.

Climate shifts

The State of Kerala falls under the climatic type of “B4-humid” based on the moisture index. The moisture index shifted from B4 to B3 - humid climatic type during the study period. It revealed that Kerala state moved from wetness to dryness since last fifty years (1951-2005) (Fig.11.6).

Fig.11.6. Moisture index from 1951 to 2005 over Kerala


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Climate projections

Rainfall and number of rainy days showed declining trend during the southwest monsoon (June-September) at four selected locations viz., Pilicode, Vellanikkara, Amabalavayal and Pampadumpara across Kerala with a maximum rate of 22.0 mm/year at Vellanikkara in the case of rainfall. Significant decline in rainfall from 2000 to 2005 reflected in the above trend at Vellanikkara. Ambalavayal and Pampadumpara showed a rise in maximum temperature at the rate of 0.006°C/year and 0.04°C/year, respectively on annual basis. At Pampadumpara, the difference between maximum and minimum temperatures is likely to rise as maximum temperature was increasing while minimum temperature declining. Cooler summers are expected at all the locations and may be significant at Vellanikkara (0.05°C/ year) due to pre - monsoon showers. Ambalavayal and Vellanikkra are likely to experience warmer nights while cooler nights at Pampadumpara and Pilicode. Warmer days are likely at Ambalavayal and Pampadumpara while cooler days at Pilicode and Vellanikkara. The above trends are based on short - term climatological data. These trends are likely to vary if the analysis is done based on long series of data or in extreme weather events like abnormal surplus rainfall like floods.

Climate Change over Karnataka

Annual rainfall trends

The mean rainfall of the State for the period from 1951 to 2000 is found to reduce to 1140 mm. The time series of the mean annual rainfall of the State indicates a definite cycle of sixteen years starting 1950 to 1964 and so on. The first half of the cycle received less than the normal rainfall for the period from 1950 to 1958 and the second half of the cycle received more than the normal for the period from 1959 to 1964. During this half of the cycle, two or three of eight years have received the rainfall opposite to their trends. This cycle is repeated up to 2004 and the State is in the positive half of the cycle from 2004 and will continue till 2012.

The mean annual rainfall data of few districts have been analyzed and a particular trend in rainfall has been observed. There is a definite declining trend in rainfall in Kodagu, Chikkamagalur and South Canara districts. In Kodagu district, the mean annual rainfall for the period 1901-1950 has been reduced from 2725 mm to 2625 mm during the period 1951-2006. Chikkamagalur district’s mean annual rainfall of 1927 mm has been reduced to 1872 mm and South Canara district’s


127

mean annual rainfall of 3976 mm has been reduced to 3960.3 mm for the corresponding periods which clearly indicated the decline in mean annual rainfall and indicates decline trend.

Further few districts of the State have shown increasing trend in the annual rainfall. Bangalore, Kolar and Tumkur districts have shown the considerable increasing trend in annual rainfall. Their mean annual for the period from 1901 to 1950 are 867 mm, 745 mm and 687.9 mm, respectively and for the period from 1951 to 2006 are 882.8 mm, 767.2 mm and 730 mm, respectively.

The rainfall data collected from 30 raingauge stations belonged to the Eastern Dry zone of Karnataka State has been analyzed for different periods. This zone consists of Bangalore and Kolar districts and parts of Tumkur district. it is also known as the Tank fed region, which constitutes 9.42 per cent of the State’s geographical area. Eighty per cent of the area is at an altitude of 800 -900 meters above mean sea level. 47.16 per cent of its area is under agriculture/ horticulture crops. It has been observed that there is a predominant shift (Fig.11.7) in the initiation and termination of rainfall to supply adequate moisture for crop growing period.

Fig.11.7. Rainfall shift in the Eastern Dry zone of Karnataka

This shift has been observed after 1990 and their mean monthly values also have changed. Before 1990, the annual rainfall ranged from 619 to 1119 mm with a mean of 869.2 mm. After 1990, the annual rainfall ranged between 611 and 1311 mm with a mean of 1011.2 mm. During the first period, on an average, the peaks


128

were observed during May, July and September, and during the second period, the peaks are observed during May, August and October.

Decreasing trend in the mean annual rainfall in the Coorg district was observed. There was decline in rainfall in coastal and hilly zones and increase in some interior zones. Increase in rainfall from 725 mm to 825 mm in the Eastern Dry zone is observed. The seasonal shift in the rainfall for Eastern dry zone and Southern Dry zone has been reported. The change in the seasonal rainfall in a particular zone also affected the rate of sowing which in turn affected the grain yield.

Temperature trends

Studies indicated that the average raise in annual temperature was 1.3°C in the State of Karnataka during 1950 to1990.

Climate Change over northern Karnataka

Northern Karnataka is represented by the four districts viz., Bellary, Bijapur, Gulbarga and Raichur. Rainfall at Bellary in September decreased from 111.4 mm in the first decade to 102.7 mm in the last decade of the twentieth century, while rainfall in October increased from 88.4 mm to 102.1 mm during the same period. Maximum temperature decreased by 1.7°C in October, 0.7°C in November and marginally by 0.4°C in December. On the other hand, there was a drop in minimum temperature by 2°C in all the three months.

Rainfall in September at Bijapur showed a marginal decrease by 7.7 mm (from 144.1 mm to 136.4 mm) from the beginning of the century to the end, whereas the October rainfall indicated a sharp increase by 121.6 mm (from 46.7 to 168.3 mm). Decrease in maximum temperature in October by 2.0°C from 33.4°C to 31.3°C could be ascribed to a large increase in rainfall. Practically no change in maximum temperature could be noticed in November while increased by 0.5°C (29.1°C to 29.6°C) in December. There was an increase in minimum temperature by 0.6oC in October, 2.7°C in November and 1.9°C in December.

Rainfall at Gulbarga decreased by 65.0 mm (from 206.4 mm to 131.0 mm) in September and increased by 55.5 mm (from 76.2 mm to131.7 mm) in October over the 20th century. The maximum temperature increased by 0.8°C in November and 1.1°C in December.


129

At Raichur, rainfall decreased by 64.6 mm in September, from 180.4 mm during 1901-10 to 115.8 mm during 1991-2000. In October, rainfall increased by 89.1 mm, from 69.2 mm to 158.3 mm in the corresponding periods. Maximum temperature decreased by 0.7°C in November and 0.9°C in December. On the other hand, minimum temperature increased by 2.8°C in November and decreased by 0.5°C in December. Thus, there was a general decrease of rainfall in September and large increase in October over northern Karnataka during the 20th century. Corresponding decrease in maximum temperature during October was noticed. An increase of temperature in November and December could be observed, indicating greater thermal energy for better vegetative growth during November while greater thermal stress during flowering period in December for rabi sorghum crop.

Weather extremes

The year 2007 was very eventful, because the heaviest rainfall event in the history of northern Karnataka occurred on June 23. On this day, Bijapur received as high as 180.8 mm rainfall, of which 168.4 mm occurred in just about six hours time, which was an all time record. Lowest minimum temperature of sub-ten degrees prevailed at Bijapur during November 23-26, 2007. The all time record of 6.5°C and 5.6°C were recorded on November 23 and 24, respectively.

Climate Change over Maharashtra

Rainfall variability

It was observed that at Solapur during 1971-1990, the annual rainfall (792.2 mm) showed more stability as compared to normal rainfall (723.4 mm). However, the annual rainfall was decreased by 6 percent (678.6 mm) and 17 percent (601.1 mm), respectively during the decades of 1991-2000 and 2001-2007.

Temperature variability

The annual maximum and minimum temperatures showed an increasing trend since last 39 years (1968-2007) over Solapur. In kharif season, maximum temperature showed steady increase, however minimum temperature showed drastic increase (Fig.11.8) during the study period. Further, it showed the stability in maximum temperature and steady increase in minimum temperature from 1968-1990. There was a drastic increase in maximum and minimum temperatures from 1991 to 2007.


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Fig.11.8. Variation in minimum temperature in kharif season

at Solapur, M.S (1968-2007)

The rabi season variation in maximum and minimum temperatures showed stability in increase of temperature from 1968-2006. It showed steady increase from 1968-1990 and drastic increase from 1991-2006.

Climate Change over Chhattisgarh

Rainfall trends

Historical rainfall pattern of Eastern Madhya Pradesh revealed that from 1960 onwards, the rainfall anomaly was negative in 28 years out of 40 years indicating decreasing rainfall trend in the entire state/region. When rainfall pattern at district level was analyzed it was found that at some pockets of the 16 districts of Chhattisgarh state (Raigarh, Kankar and Mahasamund) (Fig.11.9) the rainfall is significantly decreasing and as a result the climate is shifting from sub-humid type to semi-arid conditions.

In Mahasamund district, the rainfall showed rapid decline and the climate had changed from moist-sub humid to semi arid conditions. In some part of Chhattisgarh state, rainfall showed a slight reduction.

Fig.11.9. Pattern of annual rainfall and its five year moving average and trend line at Raigarh (1901 to 2000) Kankar (1901 to 2000) and Mahasamund (1927 to 2000)


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In the northern districts of Chhattisgarh, the maximum temperature is increasing in October and November. As a result, the duration of rice crop decreasing. Also, it is observed that the maximum temperature in March decreasing in this area. As a result of decreased crop duration due to increased temperatures in October and November and also due to decreased maximum temperatures in March, the farmers are taking two crops of potato during winter season after rice. This is one favourable condition. But the impact of increased maximum temperature by 1°C to 2°C during reproductive stage is detrimental as the yields decrease by 3- 4 q/ha under 1°C rise of temperature and 5-6 q/ha due to 2°C rice in temperatures during reproductive stage. Such detrimental effect is seen both under rainfed and irrigated conditions. The potential rice yield decreased by about 5 q/ha under both the conditions. Also, if the duration of reproductive stage is decreased by 7 days due to rise in temperatures, the rice yield can down by 10 q/ha. In the same way if there is forced maturity in the crop due to increase in temperatures during maturity stage, the potential rice yields can go down by 15-18 q/ha.

Climate Change over Orissa Rainfall trends

At Bhubaneswar, the mean decadal rainfall was showed an increasing trend. It was increased by 231.3 mm from 1418.5 mm in 1978 to 1649.8 mm in 2006. All the decadal years since 1990 showed above mean decadal rainfall, indicating increasing trend of annual rainfall since 1980s.

Temperature trends

The mean annual temperature showed a discernible increase during 1951 to 1990 at all the five places of the State viz., Balasore, Puri, Gopalpur, Angul and Sambalpur (Table 11.3). However, between 1993 and 2006, coastal places showed increase in temperature, while the interior places recorded decrease in mean temperature. The mean change was 1.0°C in the 40 years from 1951 to 1990 and 0.1°C in the recent past of 14 years. The magnitude of increase was maximum in the coastal town Puri.


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Table 11.3 Change in Temperatures in selected five districts of Orissa over two periods

District

Annual mean temperature (�C) Change in mean temperature (�C)

Upto 1940

(Period I)

1951-90

(Period II)

1993-2006

Period III

During

Period II

During

Period III

Balasore 25.5 26.6 26.8 1.1 0.2

Puri 24.7 27.2 27.7 2.5 0.5

Gopalpur 26.0 26.7 27.2 0.7 0.5

Angul 26.5 26.8 26.5 0.3 -0.3

Sambalpur 26.1 26.7 26.5 0.6 -0.2

Average 25.8 26.8 26.9 1.0 0.1

At Bhubaneswar, mean decadal Tmax increased by 0.5°C from 32.1°C in 1978 to 32.8°C in 2006 with Standard Deviation of 0.13°C. The difference between decadal Tmax and Tmin is continuously increasing from 9.8°C in 1978 to 10.7°C in 2006, indicating about 0.9°C increase in 39 years with SD of 0.25°C. The mean decadal mean temperature (average of Tmax and Tmin) increased by 0.3°C from 27.2°C in 1978 to 27.5°C in 2006 with SD of 0.15°C. During the 1980s as both Tmax and Tmin remained higher and again increasing in the recent years. The mean decadal Tmin decreased by only 0.2°C from 22.3°C in 1978 to 22.1°C in 2006 (Fig.11.10) and the difference is not significant as the SD value is higher.

Fig.11.10. Mean decadal minimum temperature (oC) at Bhubaneswar (1978-2006)


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Weather extremes

The last decade recorded the maximum rainfall of 400.3 mm in one day as against the preceding record of 256.4 mm between 1969-78. Inter-annual variation within the decade also has increased. Number of days with very heavy rainfall (>125 mm) has also increased at Bhubhaneswar.

Highest Tmax recorded sofor was 46.3°C in 2005 at Bhubhaneswar. Number of hot days with >45°C were also increased with such 3 days in 2005, while it was absent in 1970s and1980s except for 1972.

There was no significant variation obsered in tmin. However, the number of cold days with <10°C has increased in the recent years with 3 such days in 2003.

Future climate – Rainfall

The north Orissa is expected to have 98.6 mm more annual rainfall. The maximum increase of 86.6 mm shall be in summer followed by 19.1 mm in monsoon. Both post monsoon and winter shall be drier. It shall decrease in February, June and October and increase in rest of the months. Balasore shall have maximum increase of 150.2 mm annual rainfall and Jharsuguda the minimum of 59.7 mm.

Maximum temperature

The maximum increase of 1.36°C shall be in post monsoon followed by 0.87°C in summer and 0.58°C in winter and no change during monsoon. It shall decrease only in July and increase in the rest of 11 months. Maximum increase (1.6°C) shall be in June. Paradeep shall have minimum increase of 0.68°C in mean annual maximum temperature and Jharsuguda the maximum of 0.75°C.

Minimum temperature

The North Orissa is expected to have 1.90°C more mean annual Tmin. The maximum increase of 3.20°C shall be in winter followed by 2.760C in post monsoon and 1.42°C in summer and minimum of 0.87°C during monsoon. It shall increase in all the 12 months. Maximum increase (3.61°C) shall be in December and minimum of 0.17°C in July. Paradeep shall have maximum increase of 2.18 mean annual minimum temperature and Keonjhar the minimum of 1.72.


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Climate Change over Gujarat


The subdivision-wise rainfall analysis revealed that Saurashtra and Kutch subdivision (Fig.11.11) have mean annual rainfall of 428 mm with coefficient of variation of 44% and decreasing trend of-5% per 100 years while Gujarat sub division has mean annual rainfall of 863 mm with coefficient of variation of 32 percent and decreasing trend of –5 percent per 100 years. However, the recent continuous drought / low rainfall for four years (1999, 2000, 2001 and 2002) followed by five years of heavy rainfall (2003, 2004, 2005, 2006 and 2007) have posed great challenge to planners and managers of water resources, therefore need critical analysis on lower spatial and temporal scales.

The annual rainfall analysis at three locations revealed that Anand and SK Nagar showed slight increase in annual rainfall by 2.86 and 2.16 mm, respectively while Junagadh showed decreasing trend during 1988 to 2002. The discrepancies may slightly be contributed to non-uniformity in data series at three locations. Although, July and August receive high rainfall, the analysis at Anand showed that June and September receive high rainfall than that of July and August. The rainfall intensity in terms of daily maximum rainfall also showed increasing trend at Anand.

Fig.11.11. Annual rainfall variability and trend at Junagadh

Temperature

The maximum temperature at Anand was found to increase (Fig.11.12) in three seasons (summer, monsoon and winter). The rate of increase was between 0.2 to 0.5°C per decade, maximum being in summer season. Similarly, the minimum temperature was found to increase but with slightly lower rate of 0.2 to 0.3°C per


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decade in different seasons. In Saurashtra region (Junagadh) and in north Gujarat (SK Nagar) maximum and minimum temperature during winter season were also found to increase, the rate of increase being highest (0.9°C per decade) for minimum temperature in Junagadh. Thus Saurashtra region may experience warmer nights in times to come.

Fig.11.12. Trends of maximum temperature during different season at Anand


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Impact analysis - Impact on wheat yield

CERES-wheat model was subjected to simulate the wheat yield under hypothetical weather condition that may be arising due to climate change. The climate scenario simulated for temperatures (± 1 to ± 30C), radiation (± 1 to ± 3 MJm-2 day-1) and CO2 (440, 550 and 660 ppm against present concentration of 330 ppm) were well within the range of projected climate scenario by IPCC.

The simulated grain yield of wheat by CERES-wheat model under incremental units of maximum temperature (1 to 3°C) showed gradual decrease in yield ranging from 3546 to 2646 kg ha-1 (8 to 31%) under optimal conditions. Similarly, under sub-optimal conditions, yield reduction was recorded to the extent of 2841 to 2398 kg ha -1(9 to 23 %) (Table 11.4). In general, increase in maximum temperature from 1 to 3°C significantly reduced the wheat yield. The reduction in yield was mainly due to reduction in duration of anthesis and grain filling with rise in ambient temperature and vice versa. Similarly in case of gradual down scale of maximum temperature in the range of -1 to –3°C, totally reverse trend was observed. Similar trend was also noticed in case of increase and decrease in minimum temperature under optimal and sub-optimal conditions. However, the magnitude of effect was quite less (-14 to +19 %) than those due to maximum temperature (-31 to +26 %). Under limited irrigation condition (sub optimal) the effect was less than that under optimal condition. This showed that wheat yield was found to be highly sensitive to change in temperature under irrigated as well as under limited irrigation conditions.

Table 11.4. Simulated wheat yield due to interaction effect of temperature, solar radiation and CO2 concentration under optimal and sub optimal conditions

Temperatures (�C) and

SAR (MJ m-2day-1) and

CO2 (Based value 330 ppm)

Simulated grain yield

(kg ha-1)

% Change from base optimal

(3937 kg ha-1 and sub-optimal

(3112 kg ha-1) yield

Optimal Sub-optimal Optimal Sub-optimal

440 ppm

3 4369 2410 14 -23

2 4699 2890 22 -7

1 4726 3287 23 6

-1 4550 3920 19 26

-2 4255 3929 11 26

-3 3776 3776 -2 21

Cont…


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550 ppm

3 5125 2730 34 -12

2 3593 3307 46 6

1 5778 3784 51 22

-1 5452 4602 42 48

-2 5161 4642 35 49

-3 4707 4695 23 51

660 ppm

3 5781 3015 51 -3

2 6332 3476 65 12

1 6541 4226 70 36

-1 6229 5201 62 67

-2 5950 5262 55 69

-3 5537 5439 44 75

The increase in solar radiation from 1 to 3 MJm-2day-1 resulted in increase in yield of wheat from 18 to 40 per cent while reduction in solar radiation by –1 to –3 MJm-2 day-1 caused decrease in wheat yield to the tune of –18 to –50 per cent. However, under sub-optimal conditions, the yield was found to decrease both under elevated as well as reduced solar radiation. This showed that under limited irrigation condition (sub-optimal), high solar radiation may adversely effect through heating and thereby reduction in yield. It may be noted that under low solar radiation regime, the yields under optimal and sub-optimal conditions are same. The overall response of varying radiation to grain yield of wheat showed that the model was more sensitive to radiation than it was to temperature.

Impact on maize yield

CERES-maize model was used to study the impact of climate change on maize production for two cultivars (Ganga Safed-2 and GM-3) commonly grown in the Gujarat State. Results on effects of minimum and maximum temperatures, solar radiation and CO2 concentration on simulated grain yield of maize (Cv. Ganga Safed-2 and GM-3) indicated that as the maximum temperature increases the yield decrease and vise versa. In case of increase or decrease of minimum temperature


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yield was found to increase, however, there is drastic decrease in yield if minimum temperature falls by more than 3°C. Thus minimum and maximum temperatures influence the maize crop differently.

Higher solar radiation receipt was found to increase the yield of maize while lower radiation may have adverse effect. Higher CO2 concentration was found to have favourable effect on maize yield. Doubling of CO2 may increase its yield by 8-9%.

The combined effect of minimum and maximum temperatures revealed that if both temperatures are increased or decreased by more than 10°C, maize yield was found to decrease. If the increase in temperature is accompanied by increase in solar radiation the favourable effect was noticed only up to two units of increment in the parameters. Otherwise, the negative effect was observed either by increasing the parameters by more than two units or decreasing them.

Climate Change over Madhya Pradesh

Long-term trends in temperature and rainfall

Long-term trends in weekly temperatures for Jabalpur and Gwalior reveal a very different trend; whereas in Gwalior there is a cooling trend for most part of crop growth period but Jabalpur has a warming trends during kharif and cooling trends during rabi season. These places also vary remarkably in terms of occurrence of extreme events of precipitation and temperature.

Biological indicators for climate change

One remarkable indicator that has been recorded for the last more than 10 years is flowering in mango in this region. During last decade early flowering in mango has been a common feature, which has been recorded in the university farm. It is believed to be triggered by generally warmer winters. There are other such examples too which indicate towards a climate change and particularly, towards warmer temperatures. Species diversity evaluation (in situ) under altitudinal gradient at Pachmarhi Biosphere Reserve (PBR) has revealed an upward shift in certain plant communities.


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Climate Change over West Bengal

Climatic variability

West Bengal has mostly a tropical climate. The plains are hot except during the short winter season. The mountainous region in the North is cold on account of its altitude. The hot season lasts from mid-March to mid-June, with the day temperature ranging from 38° C to 45°C in different parts of the State. For climatic variability analysis, twenty raingauge stations covering three districts (namely Bankura, Birbhum and Purulia fig.14) of Red and Lateritic zone of West Bengal were considered for the rainfall pattern and temperature study. The particular zone is cited here due to inherent problems of water holding capacity of soil.

Rainfall

An increasing trend of yearly rainfall and shifting pattern of rainfall were observed in the said zone as a whole. The average yearly rainfall of 1990 – 2000 increased to the tune of 81 mm to 837 mm compared to the average of 1970-80. Rainfall during May decreased in most of the selected stations, whereas in October rainfall amount increased in 75 percent cases and in November, it increased in 95 percent cases. By analyzing the rainfall data of Kalyani and Haringhata of Nadia district (database: 1971 to 2005), it is observed that there is a shift of rainfall and at 41 Standard Meteorological Week (SMW) there is a rainfall peak. Hence in the mid of October, Gangetic West Bengal receives high rainfall (about 100 mm per week). However, during the peak monsoon season, there is less variation of weekly total rainfall (Fig.11.13).

Fig.11.13. Decadal rainfall pattern from May to November at Sunsunia, Bankujra district and Para, Purulia district


140

Maximum temperature

Average monthly maximum temperature of summer (April –May) during 1990-2000 decreased marginally compared to that of 1970-80. The minimum temperature of the zone, as a whole showed an increasing trend. For example in Bankura monthly minimum temperature in January increased by 1°C and in Asansole by 0.5°C. The monthly mean temperature does not show any changed pattern during the study period.

Projected climate scenario

Composite seasonal scenarios (per °C change in global equilibrium mean) for the period 2010-2039 using five General Circulation Models (GCMs), namely, HadCM2, CSIRO-MK2b, CGCM1, GFDLR15 and ECHAM4/OPY3 was developed for West Bengal. The scenarios indicate a warming of about 0.4 ± 0.2°C over Gangetic West Bengal and surrounding region in the eastern part of the Country. There is a warming in the range of 0.3 to 0.7°C in the seasons all over the Gangetic West Bengal except for the monsoon season for HadCM2. In the latter case, there is a warming of more than 1°C in the NW part of the study area.

The spatial distribution shows more change in the eastern and NW part of the said region for both the models. In the pre-monsoon season, changes are between 0.3 to 0.6°C with less value over the coastal belt for ECHAM4. HadCM2 indicates a variation of about 0.4 to 0.7°C with a maximum over the central part of the area. In the monsoon season, HadCM2 shows a variation of 0.3 to 1.1°C with a positive gradient from the sea to inland. ECHAM4 also shows less change in the post monsoon season with a maximum of 0.7°C while ECHAM4 shows a similar maximum of 0.7°C, though both the models indicate a similar gradient, from a minimum over SW to a maximum over the NW part of the Gangetic plain.

Maximum change in rainfall is seen in the winter season for the models HadCM2 and ECHAM4. The values range from 10 to 25% for HadCM2 and from 5 to 35 % for ECHAM4.The pattern indicates a maximum over the coast of the Bay of Bengal and gradually decreases inland for ECHAM4. In the pre-monsoon season, negative changes in rainfall are seen throughout the study region (-13%), while slightly positive changes are seen for ECHAM4 (1 to 6%). The monsoon season rainfall will change very slightly. The maximum change in rainfall over this part of the Country, according to the scenarios developed, would be 4%. In general, an increase in rainfall is estimated in all the seasons except for the pre-monsoon


141

season. Rainfall scenarios indicate a variation of –5 to +9%, except in the winter where it exceeds 20%. This estimation is also higher than that of the projected changes derived directly from the GCMs.

Assessing impact of climate change

It was observed that the yield decrease of Kharif rice due to temperature increase by 1°C (both maximum and minimum) is in the tune of 300 kg/ha. If temperature is increased by 2°C, the potential yield may be reduced to about 800 kg/ha. In the initial period, the above ground biomass is more under increased temperature conditions. Similar results are also observed in the case of dry stem and leaf weight. Due to temperature increase the rate of tillering is more, hence in initial period the predicted biomass and other related parameters are also higher than the normal situation. With the progress of crop growth period, the predicted biomass under normal climatic condition is more. If the mean temperature is 10°Cmore, the model output shows a decrease of dry matter accumulation @ 6kg/ha/day.

It can be stated that as induced by climate change, affecting rhythm of rainfall received in West Bengal, crop and cropping system followed by the farmers have also witnessed a change during the recent years. Pre-monsoon showers and frequency of Nor’wester during April-May decreased and consequently sowing of upland rice and jute grown under rainfed condition has been affected greatly. Further, increased quantum of post-monsoon rainfall has affected sowing of rabipulses and oil-seeds and planting of potato. Therefore, the package of practices (time of sowing/ planting, selection of crops and varieties of crops) needs to be rationalized in tune with the changing climate scenario.

Climate Change over Haryana

Climate change scenario

The 32-year rainfall analysis (1970-2001) in the State indicated an increasing trend for monsoon rainfall. The districts of Sirsa, Hisar, Bhiwani, Narnaul, Rohtak, Gurgaon, Karnal, Ambala and Chandigarh showed increasing trend. The highest increase (5.8 mm/annum) was recorded at Chandigarh and the lowest increase (0.3 mm/annum) was observed at Sirsa. Such general increasing trends in monsoon rainfall in the region could have been partly because of the increased


142

particulate matter in the atmosphere due to intensely debated warming in recent times.

The annual mean rainfall in the State during the 32 years period varied from a low of 422.8 mm at Sirsa to a high of 1132.6 mm at Chandigarh. The mean seasonal rainfall during pre-monsoon and monsoon at Sirsa was lowest (39.2 and 324.8 mm, respectively) whereas at Chandigarh it was highest (84.7 and 906.1 mm, respectively). However, Hisar observed lowest (14.0 and 31.9 mm) and Chandigarh recorded highest (36.6 and 109.8 mm) mean rainfall in post monsoon and winter seasons, respectively. Rainfall during the monsoon season at various stations was quite stable in comparison of other seasons as the CV values were moderate (d” 46 %). The rainfall in the State during the post monsoon season was highly variable as indicated by the higher values of coefficient of variation.

Future climate impacts on crop production

For one of the IPCC scenario (an increase of 1.8°C temperature and 425 ppm CO2 by the year 2030 for India), potential maize yields would be severely affected by 18 per cent. In north India, irrigated wheat yields decreased as temperature increases, a 2.0°C increase resulted in 17 per cent decrease in grain yield but beyond that the decrease was very high. These decreases were compensated by increase in CO2 due to latter’s fertilizing effect on crop growth. CO2 concentration has to rise to 450 ppm to nullify the negative effect of 1.0°C increase in temperature. Studies found that the overall impacts due to the climate change scenario for a 2.0°C rise in temperature and a 7 per cent increase in precipitation are negative for India. On the whole, the negative impacts due to temperature change more than compensate for the small positive impact due to precipitation change.

The recent trends in abnormal rise in temperature (terminal heat stress) during February onward also adversely affected the productivity of rabi field crops more so in case of wheat crop in Haryana. The adverse affect of higher temperature in brassicas will be there but likely to be a marginal one and that too only in case of late sown crop otherwise the productivity level in case of October sown mustard may be normal.

In vegetables, there may be sudden decrease in fruit set and due to that crop growth will increase and expected yield may likely to reduce by 50 per cent in


143

tomato. In onion and garlic too, the unfavourable weather conditions/sudden temperature rise may reduce the yield up to 25 per cent.

Climate Change over Punjab


In general, no significant change was noted for annual and seasonal rainfall at different locations of the State over the past three decades. At Ballowal Saunkhri the annual, kharif and rabi rainfall has decreased at the rate of 16, 12 and 3 mm/year, respectively. At Bathinda rabi season rainfall has decreased at the rate of 2 mm/year over the past three decades.

Temperature variability

In general, the maximum temperature has decreased from the normal at Ballowal Saunkhri and Bathinda regions of Punjab, however, for other locations no trend could be established. The kharif maximum temperature decreased at the rate of 0.04°C/year at Ballowal Saunkhri and Bathinda.

The annual and seasonal minimum temperature has increased at the rate of 0.07°C/year over the past three decades at Ludhiana. At Patiala the annual and kharif minimum temperature has increased at the rate of 0.02°C/year and at Bathinda the annual, kharif and rabi minimum temperature has increased at the rate of about 0.03, 0.02 and 0.05°C/year, respectively. However no trend of change in minimum temperature was observed at Ballowal Saunkhri and Amritsar.

Climate change impact on rice and wheat production

Simulated yields of both wheat and rice decreased as temperature increased. A 2°C increase resulted in 15-17% decrease in grain yield of both crops but beyond that the decrease was very high in wheat. These decreases were compensated by increase in CO2 due to latter’s fertilizing effect on crop growth. CO2 concentration has to rise to 450 ppm to nullify the negative effect of 1°C increase in temperature, and to 550 ppm to nullify 2°C increase in temperature. Super-imposing different climate change scenarios on these isolines can guide us on the magnitude of the potential impact of change on crop productivity.


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In another analysis revealed that an increase of temperature from normal decreased grain yields of wheat at the following rates:

� Temperature increase in 4th week of January decreased the grain yield by 0.99, 0.66 and 0.70 percent per degree Celsius for wheat sown in 4th week of October, 1st week of November and 2nd week of November, respectively.

� Temperature increase in 1st fortnight of February decreased the grain yield by 2.88 and 1.87 percent per degree Celsius for wheat sown in 4th week of October and 1st week of November, respectively.

� Temperature increase in 2nd fortnight of February decreased the grain yield by 2.40, 3.30, 2.15, 1.26 and 0.69 percent per degree Celsius for wheat sown in 4th

week of October, 1st week, 2nd week, 4th week of November and 1st week of December, respectively.

� Temperature increase in 1st fortnight of March decreased the grain yield by 2.40, 2.10, 2.98, 3.51 and 3.15 percent per degree Celsius for wheat sown in 4th

week of October, 1st week, 2nd week, 4th week of November and 1st week of December, respectively.

� Temperature increase in 2nd fortnight of March decreased the grain yield by 1.24, 2.15 and 3.40 percent per degree Celsius for wheat sown in 2nd week, 4th

week of November and 1st week of December, respectively.

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RESEARCH PUBLICATIONS (2006-2007)

AnandV.J. Patel, H.R. Patel and V. Pandey. Evaluation of CERES-wheat model for

estimation of wheat yield under middle Gujarat conditions Asian J. of Env. Sci (Jun & Dec., 2007) Vol.2 (1&2):89-93.

H.R. Patel, V.J. Patel and V. Pandey. Estimation of wheat yield gap in Anand and Mehsana districts using CERES-wheat model in Gujarat State. Asian J. of Env. Sci (Jun & Dec., 2007) Vol.2 (1&2): 81-84.

M C Varshney, V. Pandey, V.B. Vaidya and B I Karande. Preparation of Agro-climatic calendar. In: Food and water Security, Ed U Aswathanarayana. Taylor and Francis, UK, 2007, pp.45-49

V. Pandey, H.R. Patel and V.J. Patel. Impact assessment of climate change on wheat yield in Gujarat using CERES-wheat model. J. Agrometeorol. 2007, 9(2): 149-157

V Pandey, R P Vadodaria, B K Bhatt V J Patel, H R Patel, J G Talati and A M Shekh. Influence of environmental parameters on mustard yield and its quality. J. Agrometeorol. 2007, 9(1): 49-55

M Kumar, V Pandey and A M Shekh. Surface layer simulation of semi arid region of India using LASPEX-97 data. J. Agrometeorol. 2007, 9(1): 115-117

M.M. Lunagaria and A.M. Shekh. Radiation use efficiency of wheat (Triticum aestivum L.) crop as influenced by date of sowing, row spacing and row orientation. J. of Agrometeorology, 2007, 9(1):282-285

BangaloreDiurnal and monthly UV-B radiation received at Bangalore, Journal of

Agrometeorology 9(1): 108 – 110 (June 2007). Application of weather based agro advisories in eastern dry zone of Karnataka.

Journal of Agrometeorology 9(2): 259 – 264 (Dec 2007). “Havamana Adharitha krushiya pramukyathe. ” Published in Silver jubilee Souvenir

-2007 of ZARS, Brahamvar, Udupi Dist. Held on 23-24 October 2007 pp.88-89.

“Climatic features of Main Research Station, Hebbal, Bangalore“ Published in Centenary Celebrations Souvenir -2008 of MRS, Hebbal, UAS, Bangalore February 2008 pp. 121-125

Faizabad Tripathi,P., and Chaturvedi A. (2006). Temporal variation of rainfall intensity,

rainfall partitioning and its correlation with meteorological element for eastern U.P. submitted to International symposium entitled ”Rainfall rate and radio waves propagation” on January,2007 at Tamil Nadu.

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Tripathi P. Chaturvedi A. and Singh A.K. (2007). Agroclimatic analysis of rice productivity in rainfed areas of eastern U.P. Asian Journal of Environmental Science.Vol. 2, No. (1 & 2) pp. 28-35.

Singh, A.K. , Tripathi, P. and Shabd Adhar(2007). Heat unit requirement for phenophases of wheat genotypes as influenced by sowing dates ,Journal of Agrometeorology, Anand, Gujrat(Revised).

Singh, A.K., Tripathi, P. , Shabd Adhar and Sheobardan(2007). Study on Heat and Radiation use of Chickpea (Cicer arietinum L.). Cultivars under varying sowing dates. Journal of Agrometeorology, Anand, Gujrat (Revised).

Singh, A.K.,Tripathi,P., Sheobardan and Shabd Adhar(2007). Growth and Thermal unit of Chickpea (Cicer arietinum L.).genotypes under variable Weather condition of Eastern U.P.(Revised).

Singh, R.P., Singh, P.K. and Singh A.K. 2007. Comparative performance of green manuring of Dhaincha (Sesbania aculeata) and Sunhemp.(Crotalaria Juncea) on physico-chemical properties of soil, N uptake and productivity of rice. Oryza (Communicated).

Kumar,S. Tripathi,P. and Mishra, J.P. (2007): Study of economic impact of medium range weather forecast on rice and wheat production in eastern U.P. Jr. of Agrometeorology, Anand.

Mishra A.K., Tripathi P., Mishra S.R. and Raj Kumar(2007). Heat unit requirement of wheat cultivars as affected by moisture application frequencies. Journal of Agrometeorology 9(2):295-298.

Hisar Bhan S.C, Attri SD, Rao VUM and Diwan Singh. 2006. Dry matter production in

gram (Cicer arietinum L.) in relation to thermal environment. Bulletin of Indian Meteorological Society 32 (1-2):37-40.

Raj Singh, Mohmmad Shamim, Diwan Singh and Ghanshyam. 2006. Effect of different planting patterns on yield and yield attributes in soybean and pearl millet intercropping system. Haryana Journal of Agronomy. 22(1):18-20.

Raj Singh, V.U.M.Rao and Diwan Singh. 2006. Effect of sowing time and planting density on radiation use efficiency of Indian brassicas. J. Phytol. Res. 19(2): 319-322.

M.K.Tripathi, Diwan Singh, V.U.M.Rao and Raj Singh. 2006. Leaf appearance and chlorophyll content in Indian Mustard (Brassica juncea L.) under different growing environments. J. Phytol. Res.19(2):275-279.

Surender S, Rao VUM and Diwan Singh. 2006. Optimal use of Agrometeorological Information for Sustainable Agricultural Production in Semi-Arid Regions of India. AS ICTP/UNESCO/IAEA, Trieste, Italy, IC/2006/032. Pp 1-12.

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Niwas R, Surender S, Diwan Singh, Khichar ML and Singh R. 2006. A Text Book on Agricultural Meteorology. Dept of Agril Meteorology, CCS HAU Hisar, India. Pp 169.

Prem Prashant, A.K. Jain, Diwan Singh, Anurag and Smita Bhutani. 2007. GIS applications in soil data analysis: a case study of Kurukshetra district (Haryana). Proceedings of 1st Chandigarh Science Congress, Panjab University, Chandigarh. 10th and 11th March2007.

Jabalpur Agrawal, K. K. and Upadhyay, A. P. (2007). Production potential of rice varieties

under rainfed and irrigated condition in Kymore Plateau of Madhya Pradesh. Abstract published in International conference on sustainable agriculture foe food, bio energy and livelihood security held at JNKVV, Jabalpur during 14-16 February, 2007

Agrawal, K. K. (2007). Assessing the climate based productivity potential of rice in rainfed agro ecosystem of Madhya Pradesh. Abstract published in Indian Analytical Science Congress, 2007, Nagpur during 28-29 December, 2007.

KovilpattiT. Saravanan, T. Ragavan, N. S. Venkataraman and V. Subramanian. 2007.

Degree days based model for predicting the occurrence of Powdery mildew disease in Vigna mungo L. In Proceedings of Eighth Agricultural Science Congress: Science for Food, livelihood security and rural prosperity; (eds.), Samiyappan, R. Sathiah, N. Suresh, S. Subramanian, S., Karthkeyan, G. and Radhajeyalakshmi, National Academy of Agricultural Sciences, 5-17, Feb., 2007, Tamil Nadu Agricultural University, Coimbatore, India , 266p

Ludhiana Prabhjyot-Kaur, Harpreet Singh and S.S. Hundal 2006. Frost-its causes, effects

and prevention. Progressive Farming, January, 2006 (PAU), pp 13 & 16. Prabhjyot-Kaur and Harpreet Singh 2006. (Kohre de karan, parbaw ate bachao)

Changi Kheti, January, 2006 (PAU), pp-18. Prabhjyot-Kaur and Harpreet Singh 2006. (Kohre de karan, parbaw ate bachao)

Daily Ajit, January 9, 2006. Prabhjyot-Kaur and Harpreet Singh 2006. (Kohre pan de karan-usde parbaw ton

bachao kiven ? Akali Patrika , December 31, Prabhjyot-Kaur and Harpreet Singh 2006. (Kohre de karan, parbaw ate bachao)

Jagbani, December 27

iv

Prabhjyot-Kaur and S.S. Hundal. 2007. Comparison of phenological development and growth dynamics of oilseed brassica species under different growing environments. J.of Oilseed Research 24(1):107-111.

Prabhjyot-Kaur and S.S. Hundal 2007. Effect of temperature rise on growth and yield of wheat : A simulation study. J. of Research (PAU) 44(1): 6-8.

Hundal S S and Prabhjyot-Kaur. 2007. Climatic variability and its impact on cereal productivity in Indian Punjab : A simulation study. Current Science: 92 (4): 506-511.

Prabhjyot-Kaur and S.S. Hundal 2006. Prediction of growth and yield of Brassica species using thermal time indice J. of Agrometeorology 8(2): 179-185

Harpreet Singh, Prabhjyot-Kaur and S S Hundal. 2006. Spatial and temporal changes in area, production and productivity of rice in Punjab. J.of Agrometeorology 8(1):137-140

Prabhjyot-Kaur, Harpreet Singh and S.S. Hundal. 2007. Application of CERES-Wheat model in evaluating the impact of within-season temperature rise on wheat yield in Punjab. Proceedings of National Conference on “Impacts of Climate Change with Particular Reference to Agriculture” held at Tamil Nadu Agricultural University, Coimbatore from 22-24 August 2007.

Prabhjyot-Kaur and S.S. Hundal. 2007. Climate change and its effect on crop growth and yield : A simulation study. Proceedings of National Conference on “Impacts of Climate Change with Particular Reference to Agriculture” held at Tamil Nadu Agricultural University, Coimbatore from 22-24 August 2007.

Prabhjyot-Kaur and R. Khera. 2007. Modes of Education at Punjab Agricultural University, Ludhiana, India. Proceedings of Workshop on “Innovative E-technologies for Distance Education, Extension / Outreach in Efficient Water Management” held at ICRISAT, Hyderabad from 5 – 9 March 2007.

T S Bharaj, N. Singh, R. Kaur and Prabhjyot-Kaur. 2007. Increasing temperature trends and its effect on rice in Punjab. In pp. 68-70: Proceedings of International workshop on “Cool rice for a warmer world” held at Huazhong Agricultural University, Wuhan, China from 26-30 March 2007.

Mohanpur Khan. S. A. and Bhowmick. M. 2006. Forecasting seed yield of rapeseed and

mustard using climatic variables. Environment & Ecology 24(1 ): 123-127. Khan. S. A., Bhowmick. M. and Das. I. 2006. Predicting seed yield of rape and

mustard by agrometeorological parameters. Journal of Interacademicia 10 (1): 54-59.

Choudhuri. S., Khan S.A. and Jha. S. 2006. Influence of aphid population during different growth phases on yield and growth attributes of mustard. Journal of Interacademicia 10(2): 225-230.

v

Mandal. N., Dey. S., Saha. G., Khan, S.A. and Sarkar, B. 2006. Agrome-teorological indices based forewarming system for jute Bihar hairy caterpillar. Paper presented at the Xiiith State Science and Technology Congress. West Bengal held during 28th Feb. to 2nd March 2006.

Maity, K., Gupta, A. and Khan, S.A. 2006. Application of climatic water balance ethod for evaluation of water harvesting potential. Paper presented at the Xiiith state Science and Technology Congress. West Bengal held during 28th Feb. to 2nd March 2006.

Khan, S.A., Maity, G.C. and Das, L. 2006. Phasic development models of Kharif rice based on heat unit. Journal of Interacademicia 10(1):60-64.

Banerjee, S., Asis Mukherjee, S. A. Khan and Saha, G. 2007. Climate change impact on rice production grown in the New Alluvial Zone of West Bengal. Proc. of the National Conf. on Impacts of Climate Change with Particular Reference to Agriculture (in press), 22-24 August, 2007. Coimbatore, India.

Banerjee, S., Asis Mukherjee, S. A. Khan and Saha, G. Infection of late blight of potato influenced by some weather parameters in the New Alluvial Zone of West Bengal. Submitted to J. of Agrometeorol. as short communication.

Banerjee, S., Asis Mukherjee, S. A. Khan and Saha, G. Aphid infestation in mustard grown in the New Alluvial Zone of West Bengal. Submitted to J. of Agrophysics as short communication.

Mukherjee, A., Banerjee, S., S. A. Khan and Saha, G. Effect of date of planting on performance and water use pattern of potato in New Alluvial Zone of West Bengal. Submitted to the International Symposium on Agrometeorology and Food security-2008.

Mukherjee, A. and Banerjee, S., 2007. Rainfall and temperature trend analysis in the Red and Lateritic zone of West Bengal. Proc. of the National Conf. on Impacts of Climate Change with Particular Reference to Agriculture (in press), 22-24 August, 2007. Coimbatore, India.

Mukherjee, A., Banerjee, S., S. A. Khan and Saha, G. Effect of date of sowing and weather parameter on growth and yield of mustard. Will be submitted to Env. And Ecol. Shortly.

Palampur Rajendra Prasad and Ranbir Rana (2006). A study of maximum temperature

during March 2004 and its impact on rabi crops in Himachal Pradesh. Journal of Agrometeorology 8(1): 91-99.

M.C.Rana, G.D.Sharma, R.S Rana and S.S Rana.2006. Response of seed potato to neem, sea weed extract and fertility levels under dry temperate high hills of Himachal Pradesh. Himachal Journal of Agricultural Research. Vol. 32 (2):25-28.

vi

Rajendra Prasad and Vedna Kumari (2006). Performance of karan rai (Brassica carinata) in relation to rainfall pattern in north western Himalayas. Journal of Agrometeorology: 8(2) 230-236.

Rajendra Prasad and Vedna Kumari (2007). Crop Plans to mitigate the effects of aberrant rainfall situation in Himachal Pradesh. Indian Farming June 2007:26-30.

R. Prasad and R.S. Rana 2006. A study of ambient temperature during March 2004 and its impact on rabi crops in Himachal Pradesh vol.8 (1) (Accepted).

RanichauriR.K.Singh and Shailesh Tripathi, 2007, Krishi Paristhitiki Par Himalaya Ka

Prabhav, Pahari Kheti Bari, 13(1-2),101-104. R.K.Singh, 2007. Agriportal (Weather & Climate), Uttarakhand developed (GOUA). Solapur A.N.Deshpande, J.D.Jadhav and S.V.Khadtare 2006. Harbhara lagwadichi

sudharit padhat daily Sakal 8 Nov. 2006 pp 6 A.N.Deshpande, J.D.Jadhav and S.V.Khadtare 2006. Harbhara Mashagat and Kid

niyantran daily Sakal 15 Nov. 2006 pp 6 S.V.Khadtare, D.D.Mokashi, A.N.Deshpande and J.D.Jadhav 2006. Tan

Nniyantran Ka daily Lokmat 10 Dec. 2006 pp 8 S.V.Khadtare, D.D.Mokashi, A.N.Deshpande and J.D.Jadhav Rasayanik Tan

Niyantran – Kalachi garaj daily Lokmat 17 Dec. 2006 pp 8 S.V.Khadtare, M.V.Patel, D.D.Mokashi and J.D.Jadhav. 2006. Effect of Vermi

compost treatment on yield and quality of sweet corn (2006). J of Soils and crops Dec. 2006 pp 214-219

S.V.Khadtare, M.V.Patel, D.D.Mokashi and J.D.Jadhav. 2006. Economics of Sweet corn as influenced by vermi compost treatment (2006). J of Soils and crops Dec. 2006 pp 227-232

D.D. Mokashi ., S.V. Khadtare and J. D. Jadhav. 2007. Nakshtrawise Rainfall Variability and Probability Analysis for Drought Areas (2007) J of Bioinfolet 4(1) pp 1-7

Mokashi, D.D. , Deshpande, A.N., Khadatre S. V. and Jadhav J.D 2007. Sorghum climatology (2007). Agriculture Update pp 65-67

vii

�Staff Position of AICRPAM during 2006-07

Center

Positions Sanctioned and Filled (F) / Vacant (V)

Agrometeorologist Junior Agronomist

SeniorTechnicalAssistant

Meteorological Observer

FieldAssistant

Junior Clerk

Akola F - - V F -

Anantapur F F F F F V

Anand F F F F F F

Bangalore F F F F F F

Bhubaneswar F - - F F -

Bijapur F - - F F -

Dapoli F - - F F -

Faizabad F F F F F F

Hisar F F V F F F

Jabalpur F F F V F F

Jorhat F - - F F -

Kanpur F - - V F -

Kovilpatti F F F F F F

Ludhiana F F F F F F

Mohanpur F F F F F F

Palampur F - - F F -

Parbhani F - - F F -

Ranchi F F F V F F

Ranichauri F V F F V V

Raipur F - - F F -

Rakh Dhiansar F - - F F -

Samastipur F - - V - -

Solapur F F V F F F

Thrissur F - - F F -

Udaipur F - - V F -

Total posts sanctioned 25 12 12 25 25 12

Total posts filled 25 11 10 19 24 10

viii

�

All India Coordinated Research Project on Agrometeorology

Budget Allocation 2007-08

S.No. Centre Head Total lCARSharePay &

Allowances TA Contingencies N.R.C. I.T.

1. Akola 90000 6500 60000 0 20000 176500

2. Anand 929000 22500 120000 0 20000 1091500

3. Anantapur 579000 22500 120000 20000 741500

4. Bangalore 1250000 22500 120000 100000 20000 1512500

5. Bhubaneswar 979000 18750 140000 200000 20000 1357750

6. Bijapur 729000 18750 200000 200000 20000 1167750

7. Dapoli 376000 18750 60000 0 20000 474750

8. Faizabad 1020000 22500 200000 0 20000 1262500

9. Hisar 715000 22500 120000 0 20000 877500

10. Jabalpur 1079000 22500 120000 0 20000 1241500

11. Jorhat 648000 22500 120000 100000 30000 920500

12. Kanpur 454000 18750 120000 0 20000 612750

13. Kovilpatti 1029000 37750 200000 200000 20000 1486750

14. Ludhiana 1249000 22500 200000 0 20000 1491500

15. Mohanpur 796000 22500 200000 400000 20000 1438500

16. Palampur 549000 18750 160000 180000 20000 927750

17. Parbhani 404000 18750 120000 200000 20000 762750

18. Raipur 479000 18750 120000 0 20000 637750

19. Rakh Dhiansar 1010000 22500 180000 200000 20000 1432500

20. Ranchi 1077250 18750 120000 0 20000 1236000

21. Ranichauri 898000 22500 120000 0 20000 1060500

22. Samastipur 179000 15750 60000 0 20000 274750

23. Sola pur 1129000 18750 180000 200000 20000 1547750

24. Thrissur 1330000 18750 120000 0 20000 1488750

25. Udaipur 399000 18750 140000 200000 20000 777750

19376250 513750 3420000 2180000 510000 26000000

all india coordinated research project on agrometeorology

Documents