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УДК 330, 338.23 ТАДЖИЕВ АБДУСАМЕ АБДУХАМИДОВИЧ
аспирант кафедры «Агроэкономики и бухгалтерского учета» Самаркандского Института ветеринарной медицины, e-mail: abdusame1983@yahoo.com
ДЕТЕРМИНАНТЫ ФЕРМЕРСКОГО СОТРУДНИЧЕСТВА В ИРРИГАЦИИ: ПРИМЕР САМАРКАНДСКОЙ ОБЛАСТИ, УЗБЕКИСТАН
Аннотация. Цель работы. В этом исследовании мы фокусируемся на изучении детерминант сотрудничества фермерских хозяйств в ирригации, отвечая на главный вопрос исследования: «Каковы факторы, влияющие на решение фермеров сотрудничать в ирригации?». Метод или методология проведения работы. Мы используем пробитную модель, которая является одной из моделей бинарных решений, для оценки сотрудничества. Общая формула модели приведена в литературе следующим образом
Где G - функция, принимающая значения строго между нулем и единицей: 0 <G (z) <1 для всех действительных чисел z. Результаты. Полученные данные свидетельствуют о том, что уровень образования руководителей хозяйств имеет значение при принятии решения о сотрудничестве фермеров. Более того, наши результаты показывают, что управляющие ферм среднего возраста влияют на сотрудничество в области ирригации. Мы обнаружили, что «фермеры, выращивающие хлопок» негативно влияют на «сотрудничество в области орошения», которое было статистически значимым. Кроме того, плодородие участков негативно влияет на сотрудничество в области ирригации. Результаты нашей модели показывают, что если фермер выступил с собственными инициативами по участию в неформальной мобилизации рабочей силы (хашар) и неофициальных соглашениях с домашними хозяйствами и другими фермерами, он имеет положительную динамикам на сотрудничество в области ирригации. Кроме того, существует положительная тенденция между режимом орошения рек или каналов (без насосов) и сотрудничеством в ирригации в области. Область применения результатов. Результаты исследования могут быть широко использованы в развитии сотрудничества между фермерами Самаркандской области. Выводы. Фермеры, выращивающие хлопок в Самаркандской области, реже сотрудничают в принятии решений о распределении воды с другими водопользователями. Если фермер орошает свое поле из канала, он с большей вероятностью будет сотрудничать с другими водопользователями в Самаркандской области. Расположение фермерского поля в ирригационной системе имеет значение для решения фермеров о сотрудничестве.
Ключевые слова: кооперация, пробит-модель, характеристики фермы, сельскохозяйственные активы, хашар, режим орошения
TADZHIYEV ABDUSAME ABDUKHAMIDOVICH
graduate student of department "Agroeconomy and accounting" Samarkand Institute of veterinary medicine, e-mail: abdusame1983@yahoo.com
DETERMINANTS OF FARMERS' COOPERATION IN IRRIGATION: THE CASE OF SAMARKAND PROVINCE, UZBEKISTAN
Abstract. Objectives. In this study, we focus on learning determinants of farm cooperation in irrigation by answering main research question that "what are the factors influencing farmers' decision to cooperate in irrigation?". Methodology. We use probit model, which is the one of the binary decision models, to estimate cooperation. General formula of the model given as follows in the literatures
Where G is a function taking on values strictly between zero and one: 0<G(z)<1, for all real numbers z. Results. The findings suggest that education level of farm managers matters in farmers' cooperation decision. Moreover, our findings show that middle age farm managers impact on cooperation in irrigation. We uncovered that "cotton growing farmers" perform negatively in "irrigation cooperation" which the relationship was statistically significant. Furthermore, fertility of plots has negative impact on irrigation cooperation. Our model results show that if a farmer took own initiatives in participation in informal labor mobilization (hashar) and informal agreements with households and other farmers, it has a positive association with irrigation cooperation. Moreover, there is a positive relationship between river or canal irrigation regime (without pumps) and irrigation cooperation in the province. Scope of results. The results of the research can be widely used in the development of cooperation between farmers in Samarkand province. Findings. Cotton-growing farmers in Samarkand province are less likely to cooperate in water allocation decisions with other water users. if a farmer irrigates its field from canal, he/she is more likely to cooperate with other water users in Samarkand province. Location of the farm field within the irrigation system matters in farmers' decision to cooperate. Keywords: cooperation, probit model, farm characteristics, farm assets, hashar, irrigation regime
Introduction. After the collapse of Soviet Union decentralizing irrigation system management was the main problem in all Central Asian (CA) countries. Furthermore, lack of financial support on repairing irrigation canals lead to less supply water to the tail water users [8]. Establishing water user associations (WUA) was one the main water reforms in CA countries. First WUA was established in 1999 in Uzbekistan [17] [14]. Followed by the farm restructuring, the government of Uzbekistan took efforts to decentralize the agricultural water supply and improve the role of WUA in water management. Yet, the outcome of this reform has been disappointing. As many scholars and observers reported, the water management in Uzbekistan failed to organize around WUA and farmers' cooperation in water use.
There is a substantial literature describing water use problems from farmers' cooperation perspective in various parts of the region, particularly in Ferghana and Khorezm provinces of Uzbekistan (e.g., [1]; [16]; [5]; [13]; [3]). Furthermore, in the case of Zerafshan valley (Samarkand) one of the few sources, Zinzani (2015) studied WUAs in selected three districts of Samarkand province. General ideas of the most scientists are to decrease the role of government, should be bottom-up approach in water distribution. As well as, most studies emphasized that WUAs have difficulties on supporting finance [12].
In this study, we focus on learning determinants of farm cooperation in irrigation by answering main research question that "what are the factors influencing farmers' decision to cooperate in irrigation?".
Material and Methods. Our study is based on farm survey data collected within the AGRICHANGE1 project. In total 450 individual farms were surveyed from Paiarik, Pastdargom
1 Institutional change in land and labour relations of Central Asia's irrigated agriculture (AGRICHANGE). Project duration 1 July 2015 - 30 June 2018
and Jambai districts of Samarkand province. In our study we test age of farm manager, female farm, education of farm manager, farm size, crop type variables that tested by several researchers ([11]; [6]; [2]; [10]; [4]; [7]) before. Furthermore, we are going to test additional variables that assuming to impact on cooperation (Table 1). We use cooperation in irrigation as a dependent variable which is binary variable.
Based on farm questionnaire, we picked up first five questions which could represent activities related to cooperation in irrigation such as (1) irrigation of fields, amelioration of the farm land; (2) control of water distribution for irrigation; (3) repair and cleaning of irrigation canals; (4) repair and cleaning inter-farm irrigation or drainage canals; (5) joint maintenance, utilization, and repair of irrigation equipment (hashar); (AGRICHANGE farm survey, 2017). For each participation in collective action, farmer response is recorded as 1 and non-participation as 0. These responses are then aggregated into 1 if farmer participated in one of those, and zero a farmer responded about nonparticipation. We gave a value zero if a farmer chose "Do not carry out such kind of activity" response.
In our case, the independent variables are divided into three groups: (1) Farm manager characteristics; (2) Farm characteristics and (3) Farm assets. We use farm size which indicated total land area variable. Here, we get farms that have irrigated land area. Furthermore, we use district dummy variable to estimate attitudes cooperation of districts.
Based on data collected via the AGRICHANGE farm survey we identify a list of possible variables to be used as determinants of farmer's participation in cooperation in irrigation. These determinants as well as dependent variables are summarized in Table 1.
Table 1
Descriptive statistics of farmers from the survey data
Samarkand
Variables Units Obs Mean Min Max
Dependent variables
Cooperation in irrigation (ircoop) Dummy 406 0.14 0 1
Independent variables
Farm manager characteristics
Age of farm manager (age) Year 406 46.30 27 63
Female farmer Dummy 406 0.09 0 1
Education of farm manager (Edu) categorical 406 5.29 1 6
Risk aversion (riskaversion) categorical 406 2.59 0 9
Farm manager is likely to punish for unfair behavior to him (punish) categorical 406 5.06 0 10
Farm characteristics
Total farm size (landsum) ha 406 23.40 1 200
Average size of plots (avsizeplt) ha 406 17.78 0.5 170
Family labour input (family_ft) persons 406 4.72 0 59.5
Hired labour input (hired_fte) persons 405 4.34 0 61.8
Farmer grows cotton (y_cot) Dummy 406 0.35 0 1
Farmer grows high value crops (y_hvc2) Dummy 406 0.31 0 1
Average fertility of plots (sfertil) categorical 406 2.57 1 4
Farm assets
Number of tractors (tractor) Dummy 406 0.76 0 1
Decisions about informal agreements made by farmer himself (infvolt) Dummy 406 0.34 0 1
Self-interested people in the village (selfint) categorical 406 3.27 1 5
Average irrigation regime (river or canal = 1, oth-er=0 watsce) Dummy 368 0.65 0 1
Location of first plot (locat) categorical 368 2.14 1 3
Source: Author's calculation based on the AGRICHANGE farm survey data.
We use probit model, which is the one of the binary decision models, to estimate cooperation. General formula of the model given as follows in the literatures
Where G is a function taking on values strictly between zero and one: 0<G(z)<1, for all real numbers z [15].
Results. We have identified the following results according to (1) Farm manager characteristics; (2) Farm characteristics and (3) Farm assets.
First, the findings suggest that education level of farm managers matters in farmers' cooperation decision. Moreover, our findings show that middle age farm managers impact on cooperation in irrigation. (Table 2);
Table 2
The impact of farm manager characteristics on irrigation cooperation
Irrigation cooperation
Marginal effects
Farm manager characteristics
Age of farm manager 0.044** (0.018)
Age square -0.0005** (0.0002)
Education of farm manager 0.025*** (0.009)
Risk aversion (riskaversion) -0.001 (0.005)
Farm manager is likely to punish for unfair behavior to him (punish) -0.0002 (0.004)
Pseudo R2 0.730
Chi2 (p-value) 69.63 (<0.0000)
N 364
Note: *p<0.1; **p<0.05; ***p<0.01 standard error (se) in parentheses Source: Author's calculation based on the AGRICHANGE farm survey data.
Second, we uncovered that "cotton growing farmers" perform negatively in "irrigation cooperation" which the relationship was statistically significant. High value crop producing farms correlated positively with irrigation cooperation, although the relationship was not statistically significant. Furthermore, fertility of plots has negative impact on irrigation cooperation (Table 3);
Table 3
The impact of farm characteristics on irrigation cooperation
Irrigation cooperation
Marginal effects
Farm characteristics
Total farm size 0.001 (0.001)
Average size of plots -0.001 (0.001)
Family labour input 0.001 (0.003)
Hired labour input -0.005 (0.003)
Farmer grows cotton -0.088** (0.039)
Farmer grows high value crops 0.003 (0.031)
Average fertility of plots -0.080*** (0.013)
Pseudo R2 0.730
Chi2 (p-value) 69.63 (<0.0000)
N 364
Note: *p<0.1; **p<0.05; ***p<0.01 standard error (se) in parentheses Source: Author's calculation based on the AGRICHANGE farm survey data.
Third, our model results show that if a farmer took own initiatives in participation in informal labor mobilization (hashar) and informal agreements with households and other farmers, it has a positive association with irrigation cooperation. Moreover, there is a positive relationship between river or canal irrigation regime (without pumps) and irrigation cooperation in the province (Table 4).
Table 4
The impact of farm assets on irrigation cooperation
Irrigation cooperation
Marginal effects
Farm assets
Number of tractors 0.014 (0.023)
Decisions about informal agreements made by farmer himself 0.285*** (0.026)
Self-interested people in the village (selfint) -0.029*** (0.009)
Irrigation regime 0.071*** (0.021)
Location of first plot (locat) -0.040** (0.017)
Pseudo R2 0.730
Chi2 (p-value) 69.63 (<0.0000)
N 364
Note: *p<0.1; **p<0.05; ***p<0.01 standard error (se) in parentheses Source: Author's calculation based on the AGRICHANGE farm survey data.
Conclusion. By our study we conclude that education level of farm manager matters in farmers' cooperation decision. This can be explained by the manner of land distribution. In Uzbekistan, land was distributed through auctions where applicants' education levels, including in agriculture-related studies, where given certain points. Cotton-growing farmers in Samarkand province are less likely to cooperate in water allocation decisions with other water users. This can be explained by the organization of water distribution through cotton procurement system where cotton growers are prioritized in water access. if a farmer irrigates its field from canal, he/she is more likely to cooperate with other water users in Samarkand province. Location of the farm field within the irrigation system matters in farmers' decision to cooperate. Further away are the farm fields, more likely is the farmer to cooperate in irrigation. When a farmer took own initiatives in participation in informal labor mobilization (hashar) and informal agreements with households and other farmers, he/she would have higher chances to cooperate in irrigation
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