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Volume 5, Issue 4, October 2019

Journal of Social Sciences and

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J. Soc. Sci. Hum. Stud. 2019., Vol. 5 No. 4: pp. 1-18, Year 2019

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Editorial Board Editor -in–Chief William Ebomoyi Ph.D., Professor, Department of Health Studies, College of Health Sciences, Chicago State University, USA. E-mail: [email protected]

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Dr. Pratap V. Naikwade M.Sc., Ph.D. Head, Department. of Botany, ASP College, Devrukh. Maharashtra, India. Dr. Tarig Osman Khider Associate Professor, University of Bahri-Sudan, College of Applied and Industrial Sciences, Department of Pulp and Paper Technology, Sudan Dr. Hayman Z. Metwally Associate Professor of Space Science cairo University Egypt and Vice Dean of Quality Assurance and Development Hayel University KSA. Dr. Nawfal Jebbor Department of Physics, Moulay Ismail University, Meknes, Morocco. Dr. Eng. Ahmed Kadhim Hussein Assistant Professor, Department of Mechanical Engineering, College of Engineering, University of Babylon, Republic of Iraq. Prof. Dr. Abd El Fady Beshara Morcos Ass. Prof. of Relativistic Astrophysics and Cosmology, National Research In stitute of Astronomy and Geophysics, Egypt. Zohre Bahrami Shahid Beheshti University of Medical Sciences, Tehran, Iran. Researcher and Methodology Adviser. Dr. Ayhan Kapusuzoglu Department of Banking and Finance, Yildirim Beyazit University, Turkey. Dr. Charalambos Tsekeris Department of Psychology, Panteion University of Social and Political Sciences, Athens, Greece. Dr. Mahdi Zowghi Industrial and System Engineering, Management and Soft Computing, London Business and engineering School, United Kingdom. Dr. Tomislav Jurendic Bioquanta Ltd. for Research and Development, Koprivnica, Croatia Dr. Hanna Bolibok-Bragoszewska Warsaw University of Life Sciences, Poland. Dr. Alaa Abdelwahed Abdelbary Prof. of Computational and Applied Mathematics, Arab Academy for Science and Technology & Maritime Transport, Egypt. Dr. N R Birasal Associate Professor, Zoology Department, KLE Society’s G H College, HAVERI – 581 110, Karnataka state, India. Dr. Nawab Ali Khan Professor of Human Resource Management, College of Business Administration, Salman Bin Abdulaziz University, Post Box:165, Al Kharj - 11942 Kingdom of Saudi Arabia

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Abdel Baset Hasoneh, PhD, Associate professor of Marketing, Head of marketing Department Al Isra University - Amman, Jordan Muhamad Fazil bin Ahmad Asst. Prof. Universiti Sultan Zainal Abidin, Terengganu, Malaysia. Shaukat Amer CPA,Assistant Professor, Department of Management Sciences, COMSATS Institute of Information Technology, Attock, Pakistan. Naveed Ahmed Assistant Professor, Department of business administration, Indus International Institute, 2-Km, Jampur Road, Dera Ghazi Khan, Pakistan Rab Nawaz Lodhi PhD (ABD), Management Sciences (Bahria University Islamabad), Lecturer: Department of Management Sciences, COMSATS Institute of Information Technology, Sahiwal, Pakistan. International Licensed Trainer - NVivo Qualitative Research: QSR International Limited Australia Dr. Majid Sharifi Rad Department of Range and Watershed Management, Faculty of Natural Resources, University of Zabol Dr. Muhammad Naeem LECTURER, Department of Information Technology, Hazara University, Mansehra. Dr. Sohrab Mirsaeidi Centre of Electrical Energy Systems (CEES), Faculty of Electrical Engineering (FKE), Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia Farhan Altaee Ministry of Science and Technology, Iraq-Baghdad Dr. Hafiz Abdul Wahab Assistant Professor of Mathematics, Department of Mathematics, Hazara University Mansehra Pakistan

Table of Contents, October 2019

Tariq Mehmood Bhuttah, Chen Xiaoduan, Saima Javed, Hakim Ullah, Li Yan Ping

Karl Marx View of Education and Its Influence on Pakistani Educational System: A Modernistic Approach

J. Soc. Sci. Hum. Stud. 2019 5(4): 1-5. [Abstract] [Full Text PDF]

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Aneela Ashraf, Irfan Hussain Khan, Hasan Raza Jafri

Technical Efficiency and its Determinates in Cotton Production: A Case Study of Tehsil Pirmahal

J. Soc. Sci. Hum. Stud. 2019 5(4): 6-18. [Abstract] [Full Text PDF]

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J. Soc. Sci. Hum. Stud., 5(4)1-5, 2019

© 2019, TextRoad Publication

ISSN 2356-8852


Humanity Studies

www.textroad.com

*Corresponding author: Chen Xiaoduan, School Of Education, Shaanxi Normal University,Yanta campus: No 199, South Road, Yanta District, Xi’an 710062, China.

Karl Marx View of Education and Its Influence on Pakistani Educational

System: A Modernistic Approach

Tariq Mehmood Bhuttah1, Chen Xiaoduan*2, Saima Javed3, Hakim Ullah4, Li Yan Ping5

School Of Education, Shaanxi Normal University,Yanta campus: No 199, South Road,

Yanta District, Xi’an 710062, China

Received: July 6, 2019

Accepted: September 20, 2019

ABSTRACT

This quantitative investigation is an effort to highlight existing status quo in the current education system of Pakistan

through the implication of Karl Marx theory. For this prupose, one private and public school from the urban area and

one private and one public school from the rural areas were examined. The finding of the study is based on the secondary data regarding key position holders (Matric) of Board of Intermediate and Secondary Education (BISE)

Lahore, fee structure and provided facilities in public and private schools. The descriptive analysis concludes with the accurate depiction of existing educational inequality in Pakistan as described by Karl Marx. The findings showed

that it is upper social class in Pakistani society which determines the choice of an educational institution and thus will affect the future of upcoming generation if not handled this issue on time. success. There is an urgency to

overcome the educational disparities in the Pakistani education system.

KEYWORDS: Modernism, Karl Marx, the Pakistani education system, status quo, class inequality.

1. INTRODUCTION

The modern educational approach protects the executives from focus. People educated at schools did not meet the desires like the possibility of an upbeat perfect universe of edification thought; despite the world which is

overwhelmed with alarm, disarray and war after the industrial revolution. Hierarchy, determination of duties of all

school staff, registration based on priority, documentation system for the students and also the dominance of individualistic preferences in addition to the regulations of all central educational system are the basic features of

modern schools. 1979, after the denationalization of private schooling in Pakistan, the share of education in the private sector significantly grow in terms of numbers of schools as well as children enrollment (Tan, 1987; Andrabi,

2002). Although the growing number of private institutions still not enough for a large proportions of the country’s population yet have successfully dissemination the ideology of the ruling class (Tan, 1987).

The industrial revolution is transferring institution, and educational environments through providing the

sustainability of the existent situation (Aslanargun, 2007). In the modern period, every individual can orient in every

kind of circumstances by utilizing his mind. For the modern era, the educational activities are at the lead, whereas personal satisfaction is at the forefront in the postmodern period. These two eras can be adopted if teachers and

managers work together by keeping the vision of stability and flexible understanding. Karl Marx presented the

modernistic view of education and explained how society works. According to him, society has economic contradictions and conflicts because of existing class imbalance. According to Marx, capitalism is the primary

reason for conflict. The Marxist theory, also known as the conflict theory is a part of macro theories. It looks at the

society from a broader view and tries to explain the process of society in terms of conflict. In Marxist sociologists’ context, education is a continuation of the repressive nature of capitalism. It maintains the class structure in the form of the ruling class and the working class.

In this era, inequity is an unavoidable result of capitalism. Like other institutions, the system of education shows inequality through schools. Nowadays education has become a business, impersonating the similar social thought

over the generations (Greaves, Hill & Maisuria, 2007). On one side where the educational industry has been

burgeoning in Pakistan because of the rapidly growth of private school on the other side the increase in private

institutions is also causing more difference in educational opportunities on the basis of wealth. The existing education system is just an extension of past elites prejudices system, which used to serve for the interest of

1

Citation: Tariq Mehmood Bhuttah, Chen Xiaoduan, Saima Javed, Hakim Ullah, Li Yan Ping (2019); Karl Marx View of Education and Its

Influence on Pakistani Educational System: A Modernistic Approach, Journal of Social Sciences and Humanity Studies, 5(4)1-5.

influential and the public level were for the lower middle-class (Reay, 2006). Private schools with higher fees are

concomitant with the elite people of the society and these schools maintain certain standards of education whereas government schools’ education standards are based on the availability of teachers and resources (Bari & Sultana,

2011; Bowles & Gintis, 2013).

Inspite of higher fee of the private sectors, people want to send their children to private schools. Literature have shown that income status is the most important reason behind the selection of school, students belongs to

government schools are usualy have poor family background and rich families admit their children in private school.

Private school charge higher fee and children studying in private schools perform better than public schools’ students, (Cox, Donald & Jimenez, 1991; Kingdon, 1996 : Ball, 2004; Lawler, 2005; Skeggs, 2004). Besides

maintaining good standards of education, the socioeconomic status of the household, access to school, fee of school,

parents’ perceptions about the education quality in school, and parents' perceptions of the available employment

opportunities in the region.socioeconomic status of the household, the degree of a school’s accessibility, the cost of schooling, parents’ perceptions of school quality, and their perceptions of the available employment opportunities in

the region also play a very crucial role behind the increasing choice of private schools (Awan, 2018).

2. Theoretical Framework Marx favored the idea of the utilization of state power as an only mean for providing adequate public education. He

protested against for the provision of elementary school by the state. For Marx education means the mental

education, physical education, and technological education. Technological training informs the general principles of all processes of production, and thus initiates the persons for the practical use and handling of the elementary skills.

According to his mental, physical and polytechnic training will raise the level of working class than the level of the

higher and middle classes" (Marx's Inaugural Address of the International Working Man's Association,89). He

considered productive labor and polytechnic education as an essential aspect of education. According to Marx "There can be no doubt," wrote Marx in Capital, "that when the working class comes to power, as inevitably it must,

technical instruction, both theoretical and practical, will take its proper place in the working-class schools"( Capital,

Vol. I.494). For Karl Marx society is an arena of social conflict where the functions and roles of social institutions can be

understood easily through its economic system. According to Karl Marx, education system as social institution

strengthens the existing class system. There are two main classes: bourgeoisie, haves; and proletariat, have nots. w “haves” indicates source of production, and “have not” shows the labor force, as a base of the social institution.

According to him, educational institutions are in charge of bourgeoisie class for providing the workforce. The ruling

class establishes the status quo which is dispersed through education system in form of public and private. People who

can not afford the high fee of private schools they admit their children in public schools. Private schools students belong to rich families so they can pay high fee of private institutions. In this way, private schools serve the maintain

the status quo for elites. In contrast, the public schools depict the ideology of submission in the children of proletariats.

Figure 1: Karl Marx Conflict Theory and Educational System

According to Karl Marx, the education system creates the social classes by determining the attitudes as well as division of labor Bowls and Gintis (2013). He claimed that the education system favor the rich people of the society

Economic

institutions

Education

system

Disseminatio

n the

ideology of

the ruling

class

Educational

Divide

Private and

public

schools

Status quo

of elites

2

J. Soc. Sci. Hum. Stud., 5(4)1-5, 2019

because they helps in propagating status quo. That is why students registered in private school get more exposure,

facilities, and chances of getting higher position as well as social prestige than the students passed out from the public schools. This study is an attempt to know the implication of Karl Marx theory in the education system of

Pakistan through analyzing facilities, fee structure and position in annual results at the secondary and higher

secondary level of private and public school.

3. The objective of the study The main objective of this study is to analyze the Karl Marx’s conflict theory from the perspective of the public and

private schools system in Pakistan education. This quantitative investigation intend to find out whether private

schools are successfully maintaining status quo in the Pakistani education system or not through analyzing the fee structure and position in annual results at the secondary and higher secondary level of private and public school.

Research Questions

Are private schools maintaining the status quo in Pakistan’s education system?

3. Research procedure This research planned to see the education standards, based on Karl Marx class-based theory through analyzing the difference in private and public schools. In order to measure this differences, last five years of secondary level

education result details were obtained from the Board of Intermediate and Secondary Education (BISE) Lahore to

compare the position holders’ record. This data was obtained through personal visit of the BISE Lahore office. List of fees was also collected from Lahore board for verification and information about the facilities was also gathered

by visiting schools. Once the data was collected, it was analyzed through descripitive statistics and observations

were described in qualitative way.

In this study, two public sectors and two private sector schools registered under the Board of Intermediate and Secondary Education (BISE) Lahore were visited. From the examined schools, one public and one private school

was located in the city and one public and one private school were situated in the rural areas. The facilities like

library and laboratory were examined. It was observerd there was no proper mechanisms. Libraries had outdated books and students also can not access those available books. Students did not have reading habits and they rarely issue any book from the library. Due to limited instuments available in laboratory, students were not able to do

perform their practical work. technical staff was also not able to guide the students. In rural areas, Schools were

hardly equipped. Libraries have small number of books and laboratory have few equipment which were not sufficient to meet the needs of students as well as without proper technical staff.

The researcher also visited two private schools. School located in urban areas of Lahore had many equipments in

laboratory and there were many books available in the library. 1-2 library period in a week were compulsory for students, in these periods students were given a topic from a book to read, later teacher also discuss about that topic.

This is how the habit of reading is developed among students in private schools. Matriculation students also had

labaotary period on a regular basis for doing different kind of experiments under the guidance of teachers and

technical staff. Private schools in rural areas di not have better equipments than urban private schools. The school in rural areas’ school were very low level school without any library. Some schools had a laboratory in some but lab

equipments were not there or that labaortory was not utilized. Moreover; Some schools also take their students to

other school for practical.

4. RESULTS

Table 1: Frequency distribution of position holders at Matric level ( 2013-2017)

Year Public Schools Private Schools

Arts Science Arts Science

Male Female Male Female Male Female Male Female

2017 0 0 1 0 3 3 2 3

2016 2 0 1 0 1 3 2 3

2015 1 0 0 1 2 3 3 2

2014 0 0 1 0 3 3 2 3

2013 1 0 0 0 2 3 3 3

Total 4 0 3 1 11 15 12 14

3

Citation: Tariq Mehmood Bhuttah, Chen Xiaoduan, Saima Javed, Hakim Ullah, Li Yan Ping (2019); Karl Marx View of Education and Its

Influence on Pakistani Educational System: A Modernistic Approach, Journal of Social Sciences and Humanity Studies, 5(4)1-5.

The above-mentioned table 1 is indicating the frequency distribution of public and private schools’ results from

2013 to 2017. Overall in these five year analysis, public schools’ students remained unable to provide good

performane as compared to private schools’ students at the matriculation level. Most of the positions were obtained by private schools’ students took most of the position in this five years’ period.

Table 2: Descriptive statistics of position holders at Matric level for science and Arts subjects ( 2013-2017)

Source: BISE Lahore

Table 2 explains the descriptive statistics of position holders at Matric level for science and Arts subjects ( 2013-

2017). In the science group, public schools students’ got only 12.2% of positions whereas private schools’ students got 78.8% positions. In the Arts group, the situation is more worse, majority as 96.67% of the position were secured

by private schools and only 3.33% of positions were held by public schools.

Table 3: Fee structure of Public and private schools at the matriculation level

Source: BISE Lahore and Private schools association

Table 3 is showing a fee structure of registered public and private schools under BISE Lahore. At present, the fee cost is same as 20 rupees per month for public schools in rural or urban areas for matriculation students. As far as

private schools are conserned, fee structure of private schools in villages and urban level varies a lot. The results

show that private schools in rural areas have less fee as compared to urban private schools. In villages private schools fee varies between Rs. 500/- and Rs. 3000/-. In urban areas, private schools are very expnsive, the fee of

private schools in cities is between Rs. 2000/- and Rs. 15000/- per month. The private school fee structure includes

the charges of generator, lab equipments, clothes, books, stationary, sports, and, security, etc.

5. DISCUSSION AND CONCLUSION

This study aims to track the implication of Karl Marx conflict theory of education in the current educational institutions’ situation of Pakistan. In order to achieve the aim of the study, Karl Marx’s idea of class and the social division was applied on the public and private educational institutions in Pakistan. Secondary data as well as

observational data was used for the analysis. Findings approved the Karl Marx theory’ s implication in Pakistani

educational system as results showed that elites maintain status quo in the education system of pakitan through private schools. The upper class of Pakistan has more resources and opportunities to avail the quality of education so

students who belong to private institution not only get higher positions in the examinationa but also their chances of

getting higher education and highly paid jobs are more than the students who passed from government school.

The position holders data also confirms the dominacy of private institutions over public institutions as they have

succeeded in establishing their status quo. This is mainly because of the lack of facilities availbilities at public

schools. Private schools are getting money from the people so they are able to provide the educational facilities to the students which is the reason of their good performance. On the other hand, public schools are funded by

government and Pakistan’s only 2 % of GDP is spent on its education expenditures, so in this situation how students can get better facilities like private schools.whereas government schools students in rural areas are tottaly ignored.

Students’ enrollment is high in public school because pakistan’s majority belongs to lower-middle class so they can

Types of school Science group Arts group

f % F %

Public schools 04 12.2 1 3.33

Private schools 26 78.8 29 96.67

Total 33 100 30 100

Private schools expenditures per student Public schools expenditures per student

Urban Rural Urban Rural

2000-15000 500-3000 20 20

4

J. Soc. Sci. Hum. Stud., 5(4)1-5, 2019

not afford to admit their childprivate schools. On the other side government teachers are very few, it is impossible

for a teacher to pay attention to the class of 80 to 100 students. This is also the reason of bad performance of public schools. At private institutions, schools are accountable to parents; they charge high fees and in return maintain a

certain mechanism to maintain certain standard of education.

In private schools, every student is attended individually because of the less students in the class. Every student get the chances for grooming their personality through innovative activities. Based on the findings of the studies, it can

be concluded that Pakistani private institutions are successfully maintaining status quo as described by Karl Marx

conflict theory. This status quo is increasing every year which is a biggest threat for the upcoming generation of

lower-middle class. Thus in order to bring economic development of Pakistan, there is a need to reduce this class discrimination from the education sytem of Pakistan. To bring this change, there is a need to revise the educational

policy of Pakistan as well as the carefully implementation of a uniform policy of education.

REFERENCES

Awan, Dr.Abdul. (2018). Comparison of the achievements of Private and Public School in District Khanewal-

Pakistan.

Andrabi, Tahir & Das, Jishnu. (2002). The rise of private schooling in Pakistan: Catering to the urban elite or educating the rural poor?.

Aslanargun, E. (2007). Modern Eğitim Yönetimi Anlayışına Yönelik Eleştiriler ve Postmodern Eğitim Yönetimi.

Kuram ve uygulamada eğitim yönetimi, 50, 195-212

Ball, S. J. (2004). Class strategies and the education market: The middle classes and social advantage. Routledge.

Bari, F., & Sultana, N. (2011).Inequality in Education. Foundation Open Society Institute, Pakistan.

Bowles, S., &Gintis, H. (2013).Schooling in capitalist America: Educational reform and the contradictions of

economic life. Haymarket Books.

Capital, Vol. I, ed. Dona Torr (1946). 494.

Cox, D., & Jimenez, E. (1990). The relative effectiveness of private and public schools: Evidence from two

developing countries. Journal of Development Economics, 34(1), 99-121.

Greaves, N., Hill, D., &Maisuria, A. (2007). Embourgeoisment, Immiseration, Commodification-Marxism

Revisited: a Critique of Education in Capitalist Systems. Journal for Critical Education Policy Studies, 5(1).

Jimenez, E., & Tan, J. (1987). Decentralised and Private Education: The Case of Pakistan. Comparative

Education, 23(2), 173-190. Retrieved from http://www.jstor.org/stable/3098985

Kingdon, G. G. (1996). Private schooling in India: Size, nature, and equity-effects. Economic and Political Weekly,

3306-3314.

Lawler, S. (2005). Disgusted subjects: The making of middle‐class identities. The Sociological Review, 53(3), 429-

446.

Marx's Inaugural Address of the International Working Man's Association,89

Reay, D. (2006). The zombie stalking English schools: Social class and educational inequality. British journal of

educational studies, 54(3), 288-307.

Skeggs, B. (2004). Class, self, culture. Routlede…

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J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

© 2019, TextRoad Publication

ISSN 2356-8852


Humanity Studies

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*Corresponding author: Irfan Hussain Khan, PhD Scholar, Government College University Faisalabad. Email: [email protected]

Technical Efficiency and its Determinates in Cotton Production:

A Case Study of Tehsil Pirmahal

Aneela Ashraf1, Irfan Hussain Khan1, Hasan Raza Jafri2

1MPhl Scholar, Government College University Faisalabad 2PhD Scholar, Government College University Faisalabad

Received: July 17, 2019

Accepted: September 25, 2019

ABSTRACT

Efficiency and determinants of cotton production in Pakistan. Data were collected from cotton farms through a

well-structured questionnaire. To this end, different villages were randomly selected from all over Pakistan, and

data on cotton crops were randomly selected from 299 respondents. To measure and estimate the technical

efficiency of cotton growers, we applied Data Envelopment Analysis (DEA). In addition, we used Tobit regression analysis to determine the impact of technical efficiency determinants on cotton production. Our

results show that the age of the peasants, the number of workers and the sales price of the farmers have a

significant impact on the yield of cotton, while the method of sowing has a negative impact on the productivity of cotton. The study shows that the government should provide more available inputs at a lower price, while the

cotton market should be reasonably priced. This research will help improve the living standards of agricultural

communities and help increase investment in the agricultural sector.

KEY WORDS: Technical Efficiency, Cotton, Determinants, Data Envelopment Analysis, Tobit Model

INTRODUCTION

In the 1947 free period, agriculture contributed about 50% of GDP, because agriculture was a

developed industry at the time. Other sectors that have been developed using time lapses have therefore

declined and are therefore associated with the contribution of agriculture to GDP (Looney 1997). But in 1997,

agriculture remained the biggest factor affecting gross domestic product. Its share is 24%. However, it is indeed the largest industry, it uses 50% of the workforce, specifically supports or even does not directly support 70% of

the entire population, and 80% is related to foreign trade. Therefore, it is clear how the benefits associated with

most people really depend on the effective use of national agricultural assets (government related to Pakistan, 1997).

Technical efficiency shows that natural resources often turn into products, and suppliers without waste

become natural people, in which case producers often undertake the most beneficial work associated with

consolidating resources to create goods and services. There is absolutely no waste of investment. Absolutely no employees are standing idle and looking forward to free parts. The absolute value most relevant to the physical

output will be extracted from the specific provided input. Essentially, the output will be completed at the

cheapest opportunity cost.

Pakistan is the fourth largest producer of yarn, the second and third largest exporter of the United States (Innocent and Independent Commission), and the seventh largest cloth producer in the world. In Pakistan,

nearly 60% of overseas income is dependent on cotton products. However, at least 2% of GDP in the Pakistani

economy depends on cotton, while cotton has a 10% agricultural added value (Bakhsh et al., 2009; Sial et al., 2014).

Natural cotton is considered a special crop associated with high temperature areas. It is said that the

original cotton was planted from India and Pakistan 1500 years ago, before the beginning of contact with Christ,

especially the characteristics of the ancient Greek historian Herdos, which mentioned that this cultivation is actually India has developed from there. It is actually made of wool. Using these details, information on the

export of Indian cotton to European countries and the Middle East and international regions was obtained. In

addition to the valleys associated with the Indus River, there are the Nile River, the Euphrates River and Due to the historical period, Tegrus has begun to grow cotton. On the other hand, as time went on, the special trend of

6

Citation: Aneela Ashraf, Irfan Hussain Khan, Hasan Raza Jafri (2019); Technical Efficiency and Its Determinates in Cotton Production: A

Case Study of Tehsil Pirmahal, Journal of Social Sciences and Humanity Studies, 5(4)6-18.

planting cotton in other parts of the world began. Today, cotton is being developed in many countries around

the world. The maximum temperature for cotton growth is 28.5 degrees Celsius to 35 degrees Celsius. In

Pakistan, the temperature during the cotton growing season changes from normal to normal. Sometimes it will

change to a maximum of 50C. This is high for both humans and plants. At this temperature, their survival is threatened. In Pakistan, heat stress is also a major problem affecting production (Raza, 2014). According to the survey, an increase in temperature can seriously affect production. Temperatures affect cotton production and

cotton production in previous years. The most important factor affecting cotton leaf curl virus (CLCV) disease

and reducing water use. Pakistan faces many other problems that affect cotton production and production, such

as heat stress and high input costs, which are critical for cotton crops such as pesticides, seeds, pesticides, and fertilizers.

In general, cotton yield improvement is well achieved by simply improving the planting area under

cotton harvesting and the growth of each acre of lint products and both. It is necessary to constantly study the types of improved varieties simply by following modern strategies. Opportunities associated with farming sites

cannot be achieved due to insufficient irrigation water, and the two important cotton growing provinces, Punjab

and Sindh, have also reached their highest levels.

At present, the textile industry is no longer connected to the transmission of fiber or cotton bales to cotton, but to industries that are connected to unconventional fiber materials, in addition to commodities, can

maintain several times higher value returns. The cotton scalp is the starting point for the value associated with

cotton made from fabric. If quality products are needed, natural cotton cultivation from all important horticultural plants will be technically stressful. It is very sensitive to changes in increasingly serious problems.

The problems that need to be solved “how to develop cotton that is beneficial to the ecology” have also brought

about major problems.

Successful technical improvements in improving the environment have helped cotton crops and longer time and quality cotton yarn. The latest methodological studies on various technical and economic efficiencies

produced throughout Pakistan's plants, especially those related to cereals, also point to the special existence of

the production gap. This particular gap suggests that there are significant differences between farms of different

productivity in best practices and, where relevant, different farms and similar learning resources end (Akhtar et al., 1986; Ali and Flinn, 1987; Hussain et al., 1991). Khan et al., 1994).

Unusually, many scientific studies ignore silk wool, even though it has become a particularly important

foreign trade in Pakistan. Popular scientific research on technical and economic efficiency uses only the original parametric techniques to calculate "average" efficiency (Khan and Maki, 1979; Ali and Flinn, 1987; Ali and

Chaudhry, 1990; Ali et al, 1993). Battese et al., 1993; Parikh and Shah, 1994; Parikh et al., 1995). Assessments

associated with this “average” efficiency often seem to ignore arguments that individual farm analysis may be

more important for assessing the utilization of specific deficient learning resources, as specific parameter techniques do not provide sufficient information about the insurance policy (Kalirajan, 1984; Kalirajan and

Shand, 1986). This study was conducted to study the deficiencies of individual farms, which was first analyzed

in connection with the specific nonparametric methods applied by the factual data envelopment analysis (DEA). Ali (1983) identified the biological factors that contributed to the gap in farmer's field production through

farm experiments and socio-economic constraints. These factors have led to low adoption rates of new technologies by farmers, which has led to a production gap. Khan et al. (1986) and Hassan (1991) found that the

lack of well-trained manpower, lack of funds and sales facilities, and high cost of agricultural inputs are responsible for low crop yields. Nabi (1991) studied the relationship between overall productivity and input. He

showed that farm size, labor, seeds, fertilizer, irrigation, farming quantity and working capital are important

variables in the production process. Irrigation water, poor land quality, herbicide costs and fertilizers are important

constraints to crop productivity. Good management of these variables can increase yields (Anwar, 1998).

REVIEW OF LITERATURE

Thian et al. (2001) estimated the efficiency of the agricultural sector in developing countries. The main

purpose of this study was to analyze factors affecting agricultural production and efficiency. To this end, they used data from 51 observations and applied the Tobit model of econometrics to measure technical efficiency by

using the Cobb Douglas production function. They carefully explained the results in the meta-analysis. They concluded that inefficiency has a negative impact on agricultural production.

Maqbool et al. (2005) studied the role of farmer management methods in improving cotton efficiency

through the Sargodha sector procurement. The study is based on the main data collected by a group of 75 cotton

7

J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

growers randomly selected in the Sargodha area in 2003. The effects of these ingredients on the delivered cotton

were studied by extensive regression analysis. The actual Cobb Douglas production function is predicted using all ordinary least squares (OLS) methods. The results of this study showed that the R value of 0.49 was very

suitable, and the variables such as education, land preparation, and fertilizer were statistically significant, while

the variables such as seed amount and irrigation were not statistically significant. Anwer et al. (2009) assessed the output costs of actual farmers affected by cotton production and

estimated the actual efficiency of various inputs during the entire cotton production period. The analysis is

based on the main data collected through the target area through a list of the broad questions of 60 small

farmers, 25 middle classes and 15 large farmers in March 2006. These farmers were randomly selected from

two tehsils in the Multan region, which are Multan and Shugabad. The final results of the Cobb-Douglas production function indicated that these coefficients envisaged for planting (0.113) and seedlings (0.103) were

statistically 1% significant. The cost-benefit ratio of this large grower has been observed to increase (1. 41)

compared to small (1.22) and middle class (1. 24) growers. Gul et al. (2009) analyzed the technical efficiency (TE) of cotton production and described the

technical efficiency differences between farmers in the Çukurova region of Turkey. The input-oriented DEA

approach seems to help to use the DEAP application software to calculate efficiency approximations. Tobit

regression analysis seems to help determine the impact of determinants on technical efficiency. The results point out that although cotton farmers are in a similar stage of production, they will certainly reduce their input

by at least 20%. Through the age of the farmer, the knowledge stage and the multiple cotton growing areas,

factors that have a significant impact on the actual planter's efficiency level can be observed. Chimai (2011) evaluated the technical efficiency of smallholder sorghum growers and also

differentiated the practical aspects that affected the internal inefficiency of sorghum production. The analysis

uses data envelopment analysis (DEA) and general least squares (OLS) regression, in addition to farm

attributes, with DEA scores for the family. The average technical efficiency within the sorghum production of several small farmers appears to be 34%. The efficiency of sorghum production seems to be influenced by

factors such as the size of the family, the number of dependents, the use of biological draft power, and the

offensive value associated with industrial plant yields. Property, income from livestock operations, credit

ratings, seed initiation rates, and whether the household is located in the lowest rainfall area, as well as uncertainty. In general, the output of sorghum farmers in the factory is more useful than non-sorghum farmers.

Therefore, the production of sorghum increases the technical efficiency of the overall industrial plant

production of small farmers.

Solakoglu et al. (2013) used stochastic frontier production to measure the efficiency of organic cotton

production and used a combination of support premium premiums as a background variable to determine the

impact of premium payments on cotton production. The data was collected from 14 cities in Turkey from 2001 to 2008. The study found that the most important determinant of inefficiency was premium payments, which

showed premiums paid to help farmers improve the efficiency of organic cotton production. They concluded

that the Turkish organic cotton industry has been under constant control of scale returns for many years. Actual production in the Mediterranean and other regions Locations have a positive impact on efficiency, while output

in the Southeast has an adverse effect on efficiency. Although some regions of Turkey cannot respond at the same time and exhibit unique qualities in manufacturing, the efficiency gap between these regions is usually

recently closed. Mugera and Watto (2014) used a stochastic boundary model and an unconstrained and constrained

model to analyze the technical efficiency and determinants of groundwater in punctured cotton crops in Punjab,

Pakistan. The data was collected by 172 cotton farmers, including 92 tube well owners and 80 water purchasers

to calculate the technical inefficiency of irrigating groundwater. The results show that the average tube well owner is more productive than the water purchaser. The results of the study show that tube well owners and

water users can increase cotton production by 19% and 28%, respectively, without increasing existing inputs.

The results of the output elasticity indicate that the quality of the seed is critical compared to artificial and irrigated water. They also found that non-agricultural income has a positive impact on technical efficiency, as

farmers with other sources of income have better opportunities to purchase inputs. This study shows that there is a need to improve water allocation to increase efficiency.

8



MATERIALS AND METHODS

Research Design This research is descriptive in nature. The main data used in this study was collected by a random survey of 299

respondents. Basically, research was carried out and the determinant of efficiency was the cotton crop in Tehsil Pir Mahal.In the first section of this study efficiency of cotton crop was determined by using the Data Envelopment Analysis .In the second section the determinants of cotton efficiency was found.

Sampling Procedure The sample is part of any overall data, but it represents the entire data. Data were collected using random sampling techniques. Data were collected from 300 farmers who were randomly selected from the target

population. To this end, a short questionnaire has been prepared, which is divided into three parts. The first part

deals with demographic information, the second part deals with the perception part, and finally deals with

economic variables. All cotton producers in Tehsil Pir Mahal are the target population for this study. The raw data passed through the survey was used to empirically evaluate the results.

Dependent Variable Efficiency of cotton production under constant returns to scale was used as a dependent variable. This efficiency was calculated by using the Data Envelopment Analysis (DEA) Method. DEA technique was employed by using the DEAP software.

3.5.1.1 Data Envelopment Analysis (DEA)

Charnes, Cooper, and Rhodes (1981) proposed methods related to Data Envelopment Assessment (DEA) to address the issue of expected input productivity (DMU) and many input and output results of market prices.

They created this collection of expressions to consolidate non-market companies, such as schools, private

hospitals, and courts, which produce identifiable and measurable results through measurable inputs, but often

lack market prices associated with outcomes. In this study, DEAP software was used for the use of DEA results.

Independent Variables Age, Cotton Cultivation area (CCA), Worker (W), Farmer’s other income (OTHIN); Farmer Education

(FEDU), Use of Pesticides (UPEST), Land Status (LS) and Land Fertility (LF) were used as independent variables. The measurement scales of age was in years; Farmer’s other income was in thousands. Education categories (Above matric=1, under matric=0), Land status (own land=0, on rent land=1 and own land=1, on rent

land=0), Land fertility (fertile=1, less fertile=0 and fertile=0, less fertile=1), Use of Pesticides (if use then=1 and

if not then = 0) were used in this research.Tobit model was presented by James Tobin (1958). It is a statistical model which was used to explain the relationship between dependent and independent variables.

Truncation Truncation means some values of dependent and independent variables were ignored, known as truncation.

Econometric equation of the regression model according to Tobit model is as follows

�� In the above equation, TEC represents the technical efficiency of cotton production, β_0 represents the intercept of the regression model, β_k is called the slope parameter, W represents the independent variable, and μ_i is called the error term.

The Tobit model requires an application similar to the OLS hypothesis. If there is an atopic problem, the

estimation results will be biased and it is difficult to interpret the results. It is very important that the disturbance

term should be uniformly distributed independently. This means that the independent variables should be independent or not related to each other.

RESULTS AND DISCUSSION

The main purpose of this study is to analyze technical efficiency and its determinants. Therefore, this chapter is

an important part of any research because it provides information about achieving goals and discussing results. This chapter basically deals with empirical results and research results in a critical way.

Technical Efficiency of Cotton Production

Technical Efficiency Scores and Frequency Distributions of DEA

Technical efficiency calculated by DEA method shows that the 92.67 percent no. of total farmers growing cotton under increasing Returns to scale (IRS), 4.33 percent under decreasing returns to scale (DRS) and 3

percent under constant returns to scale (CRS). These results were presented in Table No.1

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J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

Table No.1: Technical Efficiency Scores and Frequency Distributions of DEA

The brief summary of the technical efficiency of cotton production was presented in the Table No. 2

Table No.2 Technical Efficiency Scores (Summary) FIRM CRSTE VRSTE SCALE FIRM CRSTE VRSTE SCALE

1 0.285 0.452 0.631 IRS 151 0.325 0.362 0.899 IRS

2 0.425 0.498 0.852 IRS 152 0.315 0.37 0.851 IRS

3 0.29 0.321 0.903 IRS 153 0.419 0.636 0.659 IRS

4 1 1 1 IRS 154 0.172 0.18 0.954 IRS

5 0.229 0.272 0.841 IRS 155 0.237 0.405 0.587 IRS

6 0.617 0.686 0.899 IRS 156 0.297 0.572 0.519 IRS

7 0.554 0.645 0.858 IRS 157 0.416 0.533 0.779 IRS

8 0.322 0.356 0.905 IRS 158 0.185 0.222 0.835 IRS

9 0.223 0.244 0.912 IRS 159 0.19 0.209 0.907 IRS

10 0.42 0.466 0.901 IRS 160 0.333 0.389 0.857 IRS

11 0.374 0.387 0.965 IRS 161 0.313 0.4 0.783 IRS

12 0.553 1 0.553 IRS 162 0.319 0.379 0.843 IRS

13 0.295 0.297 0.99 DRS 163 0.338 0.372 0.909 IRS

14 0.319 0.338 0.944 IRS 164 0.205 0.212 0.967 IRS

15 0.287 0.324 0.887 IRS 165 0.308 1 0.308 IRS

16 0.29 0.307 0.946 IRS 166 0.238 0.364 0.655 IRS

17 0.52 0.609 0.853 IRS 167 0.101 0.135 0.751 IRS

18 0.279 0.295 0.947 IRS 168 0.264 1 0.264 IRS

19 0.523 0.889 0.588 IRS 169 0 0 0.377 IRS

20 0.2 0.279 0.716 IRS 170 0.358 1 0.358 IRS

21 0.292 0.315 0.928 IRS 171 0.345 0.596 0.578 IRS

22 0.279 0.449 0.62 IRS 172 0.413 0.996 0.415 IRS

23 0.224 0.352 0.638 IRS 173 0.228 0.27 0.844 IRS

Return to scale F Percentage

IRS 280 93.65%

DRS 4 1.34%

CRS 15 5.02%

Total 299 100%

10



24 0.335 1 0.335 IRS 174 0.38 0.498 0.762 IRS

25 0.235 0.382 0.614 IRS 175 0.305 0.333 0.915 IRS

26 0.382 1 0.382 IRS 176 0.402 0.503 0.799 IRS

27 0.268 1 0.268 IRS 177 0.143 0.144 0.998 IRS

28 0.247 0.474 0.52 IRS 178 1 1 1 IRS

29 0.214 0.307 0.696 IRS 179 0.181 0.193 0.94 IRS

30 0.17 0.221 0.771 IRS 180 0.267 0.273 0.981 IRS

31 0.157 0.299 0.524 IRS 181 0.287 0.386 0.741 IRS

32 0.096 0.111 0.863 IRS 182 0.232 0.396 0.586 IRS

33 0.243 0.258 0.942 IRS 183 0.191 0.207 0.925 IRS

34 0.246 0.342 0.719 IRS 184 0.349 0.436 0.802 IRS

35 0.161 0.255 0.631 IRS 185 0.436 0.536 0.814 IRS

36 0.229 0.266 0.861 IRS 186 0.31 0.358 0.866 IRS

37 0.602 1 0.602 IRS 187 0.244 0.258 0.947 IRS

38 0.282 0.361 0.782 IRS 188 0.22 0.292 0.754 IRS

39 0.241 1 0.241 IRS 189 0.218 0.219 0.997 DRS

40 0.174 0.276 0.628 IRS 190 0.245 0.264 0.931 IRS

41 0.153 0.171 0.894 IRS 191 0.258 0.271 0.952 IRS

42 0.295 0.451 0.654 IRS 192 0.198 0.215 0.924 IRS

43 0.13 0.277 0.47 IRS 193 0.278 0.328 0.848 IRS

44 0.208 1 0.208 IRS 194 0.306 0.317 0.965 IRS

45 0.156 0.397 0.393 IRS 195 0.365 0.382 0.957 IRS

46 0.117 1 0.117 IRS 196 0.337 0.397 0.85 IRS

47 0.299 1 0.299 IRS 197 0.35 0.399 0.876 IRS

48 0.238 0.252 0.946 IRS 198 0.36 0.418 0.86 IRS

49 0.243 0.256 0.949 IRS 199 0.324 0.336 0.965 IRS

50 0.183 0.21 0.871 IRS 200 0.173 1 0.173 IRS

51 0.262 0.286 0.915 IRS 201 0.152 0.224 0.678 IRS

52 0.571 0.646 0.884 IRS 202 0.196 0.21 0.934 IRS

53 0.26 0.4 0.649 IRS 203 0.832 1 0.832 IRS

54 0.17 0.965 0.176 IRS 204 0.181 0.201 0.901 IRS

55 0.199 0.312 0.637 IRS 205 0.173 1 0.173 IRS

56 0.13 0.215 0.607 IRS 206 0.173 1 0.173 IRS

57 0.19 1 0.19 IRS 207 0.172 0.173 0.993 IRS

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J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

58 0.131 0.156 0.836 IRS 208 0.346 0.393 0.882 IRS

59 0.099 0.108 0.917 IRS 209 0.222 0.235 0.947 IRS

60 1 1 1 - 210 0.179 0.203 0.881 IRS

61 1 1 1 - 211 0.284 0.285 0.996 IRS

62 0.177 0.837 0.212 IRS 212 0.397 0.397 0.998 IRS

63 0.145 0.269 0.539 IRS 213 0.082 0.083 0.991 IRS

64 1 1 1 - 214 0.173 1 0.173 IRS

65 0.126 0.166 0.763 IRS 215 0.154 0.163 0.943 IRS

66 0.067 1 0.067 IRS 216 0.217 0.246 0.879 IRS

67 0.118 0.132 0.9 IRS 217 0.09 0.094 0.951 IRS

68 0.162 0.259 0.626 IRS 218 0.398 0.444 0.897 IRS

69 0.453 0.491 0.923 IRS 219 0.499 0.646 0.773 IRS

70 0.324 1 0.324 IRS 220 0.24 0.262 0.918 IRS

71 0.254 0.259 0.982 DRS 221 0.361 0.38 0.95 IRS

72 0.138 0.154 0.891 IRS 222 0.348 1 0.348 IRS

73 0.105 0.132 0.797 IRS 223 0.25 0.355 0.705 IRS

74 0.15 0.152 0.986 DRS 224 0.233 0.25 0.933 IRS

75 0.153 0.154 0.992 IRS 225 0.093 1 0.093 IRS

76 0.232 0.243 0.954 IRS 226 0.222 1 0.222 IRS

77 0.127 0.178 0.713 IRS 227 0.442 1 0.442 IRS

78 0.261 0.273 0.958 IRS 228 0.41 0.695 0.589 IRS

79 0.181 0.191 0.949 IRS 229 0.42 0.69 0.608 IRS

80 0.254 0.268 0.946 IRS 230 0.153 0.166 0.924 IRS

81 0.166 0.229 0.724 IRS 231 0.423 0.471 0.899 IRS

82 0.303 0.339 0.894 IRS 232 0.402 0.452 0.888 IRS

83 0.272 0.284 0.955 IRS 233 0.631 1 0.631 IRS

84 0.144 0.163 0.88 IRS 234 0.497 0.558 0.892 IRS

85 0.23 0.253 0.908 IRS 235 0.244 0.287 0.85 IRS

86 0.336 0.354 0.948 IRS 236 0.281 0.331 0.85 IRS

87 0.179 0.19 0.946 IRS 237 0.263 0.315 0.834 IRS

88 0.191 0.269 0.708 IRS 238 0.288 0.404 0.713 IRS

89 0.183 0.199 0.919 IRS 239 0.279 0.292 0.956 IRS

90 0.226 0.242 0.934 IRS 240 0.299 0.313 0.954 IRS

91 0.246 0.272 0.906 IRS 241 0.311 0.361 0.862 IRS

12



92 0.203 0.262 0.775 IRS 242 0.279 0.292 0.957 IRS

93 0.164 0.195 0.842 IRS 243 0.277 0.294 0.942 IRS

94 0.284 0.292 0.971 IRS 244 0.181 0.187 0.972 IRS

95 0.24 1 0.24 IRS 245 0.239 0.24 0.996 DRS

96 0.261 1 0.261 IRS 246 0.326 0.381 0.856 IRS

97 0.179 0.31 0.58 IRS 247 0.398 0.471 0.845 IRS

98 0.159 0.184 0.866 IRS 248 0.311 0.361 0.861 IRS

99 0.204 0.512 0.4 IRS 249 0.295 0.31 0.951 IRS

100 0.288 1 0.288 IRS 250 0.281 0.312 0.901 IRS

101 0.236 1 0.236 IRS 251 0.317 0.329 0.964 IRS

102 0.151 1 0.151 IRS 252 0.279 0.291 0.959 IRS

103 0.12 0.299 0.402 IRS 253 0.288 0.291 0.992 DRS

104 0.214 1 0.214 IRS 254 0.207 0.208 0.994 DRS

105 0.172 0.21 0.821 IRS 255 0.29 0.293 0.99 DRS

106 0.23 0.397 0.58 IRS 256 0.32 0.374 0.854 IRS

107 0.236 0.33 0.714 IRS 257 0.28 0.324 0.865 IRS

108 0.135 0.16 0.842 IRS 258 0.263 0.307 0.856 IRS

109 0.163 0.216 0.756 IRS 259 0.299 0.311 0.961 IRS

110 0.138 0.152 0.909 IRS 260 0.243 0.272 0.894 IRS

111 0.14 0.192 0.731 IRS 261 0.333 0.387 0.862 IRS

112 0.21 0.223 0.943 IRS 262 0.588 0.609 0.966 IRS

113 0.423 1 0.423 IRS 263 0.646 0.745 0.867 IRS

114 0.22 0.221 0.993 DRS 264 0.531 0.649 0.818 IRS

115 0.163 0.324 0.504 IRS 265 0.567 0.606 0.937 IRS

116 0.296 1 0.296 IRS 266 0.715 0.753 0.95 IRS

117 0.237 0.291 0.813 IRS 267 0.763 0.824 0.926 IRS

118 0.195 0.195 0.998 - 268 0.814 0.926 0.879 IRS

119 0.196 1 0.196 IRS 269 0.575 0.634 0.907 IRS

120 0.141 0.148 0.955 IRS 270 0.893 1 0.893 IRS

121 0.225 0.288 0.78 IRS 271 0.623 0.655 0.951 IRS

122 0.347 0.66 0.526 IRS 272 0.32 0.429 0.746 IRS

123 0.315 0.372 0.845 IRS 273 0.531 0.649 0.818 IRS

124 0.282 0.366 0.772 IRS 274 0.348 0.509 0.685 IRS

125 0.235 0.335 0.7 IRS 275 0.332 0.493 0.673 IRS

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126 0.203 0.209 0.97 IRS 276 0.285 0.383 0.745 IRS

127 0.49 0.657 0.745 IRS 277 0.218 0.253 0.861 IRS

128 0.254 0.272 0.933 IRS 278 0.278 0.328 0.848 IRS

129 0.255 0.305 0.838 IRS 279 0.247 0.263 0.939 IRS

130 0.23 0.25 0.918 IRS 280 0.523 0.889 0.588 IRS

131 0.351 0.493 0.712 IRS 281 0.192 0.202 0.948 IRS

132 0.322 0.341 0.944 IRS 282 0.262 0.269 0.973 IRS

133 0.223 0.231 0.967 IRS 283 0.139 0.141 0.987 DRS

134 0.275 0.278 0.99 DRS 284 0.143 0.158 0.906 IRS

135 0.327 0.559 0.584 IRS 285 0.17 0.187 0.907 IRS

136 0.393 0.584 0.672 IRS 286 0.145 0.179 0.812 IRS

137 0.199 0.209 0.953 IRS 287 0.247 0.263 0.939 IRS

138 0.388 0.422 0.918 IRS 288 0.523 0.889 0.588 IRS

139 0.386 0.704 0.547 IRS 289 0.192 0.202 0.948 IRS

140 0.251 0.297 0.845 IRS 290 0.262 0.269 0.973 IRS

141 0.286 0.452 0.632 IRS 291 0.139 0.141 0.987 DRS

142 0.244 0.254 0.958 IRS 292 0.143 0.158 0.906 IRS

143 0.305 0.312 0.979 IRS 293 0.17 0.19 0.894 IRS

144 0.265 0.281 0.942 IRS 294 0.145 0.18 0.805 IRS

145 0.245 0.296 0.829 IRS 295 0.274 0.307 0.893 IRS

146 0.236 0.239 0.99 DRS 296 0.173 0.41 0.423 IRS

147 0.211 0.244 0.864 IRS 297 0.249 0.517 0.482 IRS

148 0.22 0.253 0.869 IRS 298 0.217 0.324 0.67 IRS

149 0.212 0.232 0.915 IRS 299 0.139 0.15 0.932 IRS

150 0.395 0.516 0.766 IRS 300 0.152 0.164 0.928 IRS

0.29 0.431 0.771

Maximum 1 1 1

Minimum 0 0 0.067

SD 0.160552 0.275136 0.230323

According to Table No. 2. The efficiency ranges from 0 to 1 under variable and constant scale returns. A constant return on scale means the same input-output ratio. In this study, the average CRS is 0.29. The

minimum efficiency score and the maximum efficiency score are 0 and 1, respectively. The median estimate is

0.160552. The efficiency under variable scale return (VRS) was also estimated using the DEA method. Variable size returns mean different the proportionality between the input and output ratios. The average value under

VRS is estimated to be 0.431. The maximum and minimum values under VRS are 1 and 0, respectively. The

average of scale efficiency is estimated to be 0.771. The maximum and minimum values of the scale efficiency

are 1 and 0.067, respectively. The average technical efficiency under VRS was found to be greater than CRS. The reason behind this is that technical efficiency is severely limited compared to CRS (Coelli, 1996). The

14



frequency distribution indicates that the efficiency of cotton farmers under CRS 5 and VRS is close to or equal

to 1. In addition, the technical efficiency of cotton production by cotton farmers under CRS 7 and VRS 3 is almost zero (0 to 0.10). Results shows the frequency distribution of cotton yield is listed in the table. The above

table shows that in the case of constant scale returns, most farmers are in the range of 0.20 to 0.30, while the

lowest farmers are in the range of 0.00 to 0.10. Under variable-scale income (VRS), most farmers have a yield of 0.90 to 01, and the lowest farmers have a yield of 0.80 to 0.90. No one is in the range of 0.00 to 0.10. In addition, the largest farmers have a scale efficiency of 0.90 to 1, and the smallest farmers have a scale efficiency

of 0 to 0.10.

Table No. 3: The frequency distribution of cotton yield

Figure No. 1 Histogram of Technical Efficiency of Cotton Production

Characteristics of Cotton Cultivated Farmers

The average age of farmers is 41.13. The survey shows that 59% of farmers own their own land and lease land. Most cotton producers are people who own their own land than those who own their land in Tehsil Pirmahal.

The average value of peasant education was found under the matrix. The average value of professional

0

10

20

30

40

50

60

70

80

90

100

Efficiency Scores DEA

CRS VRS SE

0.90-1 11 87 82

0.80-0.90 5 9 59

0.70-0.80 9 14 40

0.60-0.70 12 10 29

0.50-0.60 11 16 19

0.40-0.50 27 33 12

0.30-0.40 81 60 20

0.20-0.30 93 53 24

0.10-0.20 49 17 13

0-0.10 1 0 1

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J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

education is 0.277592, which indicates that most farmers do not have professional education and training. The

survey also showed that fertile farmers produce cotton more efficiently than those with fertile land. In addition, farmers who have both (tube well and canal) irrigation systems are found to be more productive to cotton

production. It has also been found that farmers with more than five years of experience have higher technical

efficiency than those who have less experience in cotton production. Most farmers are dissatisfied with the market price of cotton sales. For this reason, according to analysis, this practice has discouraged farmers and affected their production capacity.

Determinants of Technical Efficiency of Cotton Production of Tehsil Pirmahal

Results Obtained By Tobit Analysis

RESULTS AND DISCUSSION

The technical efficiency under the CRSTE condition obtained by applying DEA is used as a variable and

regression with the independent variable to find the determinants or factors that affect the technical efficiency of

the cotton crop. The scoring efficiency is between 0 and 1. Analyze results or results using the Tobit model. The

results obtained are shown in the table. The results show that the age of the farmer (AGE) has an important impact on technical efficiency, as its z-statistic 1.815111 shows significances. The age coefficient is 0.001434,

indicating that it has a positive impact on technical efficiency, which only means an increase in the age of

farmers. The technical efficiency of cotton production has also increased. The number of workers (workers) used in cotton production also has a significant positive impact on the technical efficiency of cotton production.

The worker coefficient is 0.020063 and its z statistic is 2.877028, indicating that it is significant at the 1, 5%,

and 10% significance levels. Other sources of income for farmers have a major impact on technical efficiency,

as its z statistic of 2.331900 is very important. The coefficient of other sources of income for farmers (OTHINC) is 0.045767, indicating that it has a positive impact on technical efficiency, which only means that

as the income of farmers increases, the technical efficiency of cotton production also increases. Farmers'

education (FAREDU) also has a positive impact on the technical efficiency of cotton production, but the impact

is small. Its z statistic is 1.438230, which does not support the significance level of 5%, 10% and 10%. The statistical value of Z pesticide use (UPEST) is -3.067686, which has a significant impact on the efficiency of

cotton production technology. The (UPEST) coefficient is -0.167941, indicating a negative impact on technical

efficiency; in short, this means that as farmers increase the use of pesticides, the technical efficiency of cotton production will decline. Compared with farmers who own two types of land, the farmers' own land conditions

have a great impact on the technical efficiency of cotton. LS1 and LS2 are further divided into two categories.

In the LS1 category, first, own your own land and rent Followed by farmers who own the land. In LS2, the first

is the farmer who owns and rents the land, and the second is the farmer who has rented the land. For LS1 and Ls2, both (own and leased land) are used as reference categories. The results show that LS1 has a significant

positive impact on the technical efficiency of cotton production, with significance levels of 1, 5 and 10. The

coefficient of LS1 is 0.049806, and the value of the z statistic is 2.177507. The Z statistic indicates that it has a significant impact. The LS1 is more efficient at technical efficiency in cotton production than the reference

categories (two, leased and owned land). LS2 also has a positive effect on technical efficiency, but the significance is not significant. The coefficient of LS2 is 0.015263, and the z statistic is 0.536150. Compared to

the reference category, LS2 has little effect on the technical efficiency of cotton production (reference, lease and own land). In short, this means that LS1 is more efficient than LS2 and has a greater impact on technical

efficiency. The impact of cotton production area (CPA) on technical efficiency is not significant, as its z

statistic is 1.416763, which is negligible. The coefficient of cotton production area (CPA) is 0.002330,

indicating that it has a positive impact on technical efficiency, but has little effect. Land fertility is divided into two categories: LF1 and LF2. LF1 and LF2 are further divided into two categories. In the LF1 category, first,

farmers with normal fertile land are assigned zero and second, and farmers with more fertile land are allocated 1

. In LF2, first, farmers with normal land are assigned 1 and 2, and farmers with more fertile land are assigned zero. For LF1 and LF2, less fertile land is used as a reference category. The results show that LF1 has a

significant positive impact on the technical efficiency of cotton production with a 10% significance level. The coefficient of LF1 is 0.043026 and the value of the z statistic is 0.0834. The Z statistic indicates that it has a

significant impact. Compared to the lower fertility (reference category), LF1 was found to be more efficient at technical efficiency in cotton production. LF2 also has a positive impact on technical efficiency, but the

significance is not significant. The coefficient of LF2 is 0.022635, and the z statistic is 0.939075. Compared

with the lower fertility (reference category), LF1 was found to be ineffective in the technical efficiency of

16



cotton production. In short, it can be said that LF1 is more effective than LF2 and has a significant impact on

technical efficiency.

CONCLUSION

The main purpose of this study was to determine the determinants of the efficiency and technical efficiency of cotton crops. The data was obtained through an investigation. In the survey data, 300 cotton farmers who

produced cotton in Tehsil Pirmahal were collected. These farmers are randomly chosen. A short questionnaire is

used to collect data. Once the data is collected, it is tabulated. The technical efficiency of cotton production was

calculated using Deap software. The result is that the average efficiency of cotton production under constant scale return (CRS) is... and the average efficiency of cotton production under variable scale return (VRS) is....

The technical efficiency of cotton production under CRS is relative to the independent variable. Age has a

significant positive impact on the technical efficiency of cotton production. The number of workers engaged in cotton production has a positive and significant impact on the technical efficiency of cotton production. Farmers

with other sources of income also have a positive impact on the technical efficiency of cotton crops. Farmers’

education has a positive impact on the productivity of cotton crops. But the results show that it is also trivial.

Land conditions can be divided into two categories. Land Status 1 (LS) has a positive impact on the technical efficiency of cotton production. Farmers with land have higher cotton yields than farmers who own land and

rent land. Land Status 2 (LS) has a positive impact on the technical efficiency of cotton production without

significant impact. Farmers with cultivated land produce less cotton than farmers who own land and rent land. Land fertility is divided into two categories. Land fertility 1 (LF) has a positive and significant impact on the

technical efficiency of cotton production. LF1 shows that fertile farmers produce cotton more efficiently than

fertile farmers. Land fertility 2 (LF) has no positive impact on the technical efficiency of cotton production.

This is not significant. LF2 shows that farmers with normal fertile land are more efficient than farmers with fertile land.

REFERENCES

Adanacioglu, H., & Olgun, A. (2012). Evaluation of the efficiency of organic cotton farmers: A case study from

turkey. Bulgarian Journal of Agricultural Science, 18(3), 418-428.

Adzawla, W., Fuseini, J., & Donkoh, S. A. (2013).Estimating technical efficiency of cotton production in Yendi Municipality, Northern Ghana. Journal of Agriculture and Sustainability, 4(1).

Ahmad, M., Muhammad, R., & Ali, A. (1999).An analysis of technical efficiency of rice farmers in Pakistani

Punjab.

Anwar, M., Chaudhry, I. S., & Khan, M. B. (2009). Factors affecting cotton production in Pakistan: empirical evidence from Multan district.

Bakhsh, K. H. U. D. A., Hassan, I., &Maqbool, A. (2005). Factors affecting cotton yield: a case study of

Sargodha (Pakistan). Journal of Agriculture and Social Sciences, 1(4), 322-334. Bravo-Ureta, B. E., & Evenson, R. E. (1994). Efficiency in agricultural production: the case of peasant farmers

in eastern Paraguay. Agricultural economics, 10(1), 27-37. Chimai, B. C. (2011). Determinants of Technical Efficiency in Smallholder Sorghum Farming in

Zambia (Doctoral dissertation, The Ohio State University). Dlamini, S., Rugambisa, J. I., Masuku, M. B., &Belete, A. (2010). Technical efficiency of the small scale

sugarcane farmers in Swaziland: A case study of Vuvulane and Big bend farmers. African Journal of

Agricultural Research, 5(9), 935-940.

Gul, M., Koc, B., Dagistan, E., Akpinar, M. G., &Parlakay, O. (2009). Determination of technical efficiency in cotton growing farms in Turkey: A case study of Cukurova region. African journal of Agricultural

research, 4(10), 944-949.

Huang, J., Hu, R., Rozelle, S., Qiao, F., & Pray, C. E. (2001). Small holders, transgenic varieties, and production efficiency: the case of cotton farmers in China. UC-Davis, Dept. of Agricultural &

Resource Economics Working Paper, (01-015). Hussain, N., Ali, S., Miraj, N., &Sajjad, M. (2014). An Estimation of Technical Efficiency of Garlic Production

in Khyber Pakhtunkhwa Pakistan. International Journal of Food and Agricultural Economics (IJFAEC), 2(2).

Isgin, T., Bilgic, A., & Ipekcioglu, S. (2010). An Analysis of Cotton Production Technology on the Harran

Plain. Journal of Agricultural Sciences, 16, 254-261.

17

J. Soc. Sci. Hum. Stud., 5(4)6-18, 2019

Javed, M. I., Adil, S. A., Hassan, S., & Al, A. (2009).An efficiency analysis of Punjab’s cotton-wheat system.

Kibaara, B. W. (2005). Technical efficiency in Kenyan’s maize production: An application of the stochastic frontier approach (Doctoral dissertation, Colorado State University).

Madau, F. A. (2005). Technical efficiency in organic farming: an application on Italian cereal farms using a

parametric approach. Maganga, A. M. (2012). Technical efficiency and its determinants in irish potato production: evidence from

Dedza District, Central Malawi. African journal of agricultural research, 7(12), 1794-1799.

Ogundele, O. O., & Okoruwa, V. (2006).Technical efficiency differentials in rice production technologies in

Nigeria (No. RP_154). Nairobi: African Economic Research Consortium.

Ogunniyi, L. T., & Oladejo, J. A. (2011).Technical efficiency of tomato production in Oyo State Nigeria. Agricultural Science Research Journal, 1(4), 84-91.Adewumi, M. O., & Adebayo, F. A.

(2008).Profitability and technical efficiency of sweet potato production in Nigeria. Journal of Rural

development, 31(5), 105-120. Rahman, S. A., & Umar, H. S. (2009). Measurement of technical efficiency and its determinants in crop

production in Lafia Local Government Area of Nasarawa State, Nigeria. Journal of Tropical

Agriculture, Food, Environment and Extension, 8(2), 90-96.

Rios, A. R., & Shively, G. E. (2005, July).Farm size and nonparametric efficiency measurements for coffee farms in Vietnam. Technical Report 19159, American Agricultural Economics Association, 2005

Annual meeting, July 24-27, Providence, RI.* http://ideas.Repec.org/p/ags/aaea05/19159. Html.

Shafiq, M., & Rehman, T. (2000). The extent of resource use inefficiencies in cotton production in Pakistan's Punjab: an application of Data Envelopment Analysis. Agricultural Economics, 22(3), 321-330.

Shapiro, K. H., & Müller, J. (1977). Sources of technical efficiency: the roles of modernization and information.

Economic Development and Cultural Change, 293-310.

Solakoglu, E. G., Er, S., & Solakoglu, M. N. (2013). Technical Efficiency in Cotton Production: The Role of Premium Payments in Turkey. Transition Studies Review, 20(3), 285-294.

Theriault, V., & Serra, R. (2014). Institutional environment and technical efficiency: A Stochastic frontier

analysis of cotton producers in West Africa. Journal of Agricultural Economics, 65(2), 383-405.

Thiam, A., Bravo‐Ureta, B. E., & Rivas, T. E. (2001). Technical efficiency in developing country agriculture: a meta‐analysis. Agricultural Economics, 25(2‐3), 235-243.

Watto, M. A., & Mugera, A. (2014).Measuring efficiency of cotton cultivation in Pakistan: a restricted

production frontier study. Journal of the science of food and agriculture, 94(14), 3038-3045. Wossink, A., & Denaux, Z. S. (2006). Environmental and cost efficiency of pesticide use in transgenic and

conventional cotton production. Agricultural Systems, 90(1), 312-328.

18

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