فصلنامه اقتصاد کشاورزی
سال سیزدهم شماره 4 (زمستان 1398)

Under-Pressure Irrigation Projects (UPIPs) are the most frequently implemented water projects undertaken by the Agricultural Organization in Khorasan Razavi Province. While an increase in the productivity of agricultural sector is the main goal of these projects, there are few, if any, studies focusing on calculating total factor productivity of UPIPs. Therefore, this study will use an integrated Matching-Malmquist analysis to calculate the effects of implementing UPIPs on the productivity of farms.

This study used a two-stage approach to evaluate the effects of implementing UPIPs on the productivity of farms. First, the Malmquist Productivity Change Index (MPCI) was used to measure changes in the productivity of a treatment and a control group in which the UPIPs was and was not implemented repectively. Second, a Genetic Matching Method was used to compare the MPCI between the two goups.

Results showed that the average difference between the treatment and control groups was 0.29. In other words, implementing UPIPs improved the productivity of farms by 29%.  Suggestions The significant increase of 29 percent in the productivity of farms that implemented UPIP suggests that these projects should be given a higher priority. Furthermore, the results show that actions that increase the level of technology (such as UPIPs) contribute more to improving productivity than actions such as educational programs for better farming practices.

Sustainable economic growth accompanies a reduction in poverty. One of the critical aspects of economic growth is the income distribution issue. The Gini coefficient is one of the indicators of the equal income distribution. The income distribution and economic growth are two critical indicators in the study of economic performance, so they are crucial for policymakers. Every country is trying to improve its welfare state by improving its production, distribution and specialization. The agricultural sector growth is essential to achieve economic development, and it can accelerate economic growth.Villages play an essential role in ensuring food security, whereas rural households earn the majority of their income through agricultural activities; so it is necessary to study the relationship between income distribution and the growth of the agricultural sector. The purpose of this study is to investigate the relationship between rural income distribution and economic growth of the agricultural sector.

According to the study purpose, We collected the data of "GDP per capita" and "per capita value added of the agricultural sector at constant prices" annually from 2001 to 2016 from "the Statistics Center of Iran". "The rural Gini coefficient", for the period 2001-2016, have also been collected from the Statistics Center of Iran. The inflation rate data for the period 1380-1395 is gathered from the Central Bank. Data on higher education and the number of agricultural students have been collected from the yearbook of the Statistics Center of Iran for the period 2001-2016. All data except the inflation rate are provincial form, and they are related to all 30 provinces of Iran. In order to estimate the models, the STATA 15.0 software package was used.  Since this research has two parts, the form of its experimental model will also include two experimental models. The first part is related to the whole economic sector, and the second part is related to the agricultural sector.

Firstly, the stationarity of the variables was investigated, and the results of the unit-root test shown that all variables are Stationary. As can be seen from the estimates obtained in the macroeconomic section of the panel approach, with increasing GDP per capita (economic growth), the Gini coefficient (income inequality distribution) also increases. There is also a significant relationship between the Gini coefficient and higher education, which suggests that the higher the education level, the greater the income share, that it leads to income inequality. This result is consistent with the theory and previous studies conducted. In the PVAR approach, there is a one-way causal relationship of per capita GDP to the Gini coefficient. In other words, a change in income inequality is the cause of changes in economic growth. In the agricultural sector, in the panel approach, there is a positive relationship between the Gini coefficient and the per capita value added of the agricultural sector.  As the per capita value added of the agricultural sector increases, so does the Gini coefficient.  There is a significant positive relationship between the Gini coefficient and the number of agricultural students, which shows that the higher the level of agricultural knowledge, the higher the income from agriculture; resulting in more significant economic growth and income distribution becomes unequal. The PVAR approach also establishes a one-way causal relationship between the per capita value added of the agricultural sector and the Gini coefficient.

Suggestion

Based on the results,  policymakers need to put appropriate policies on the agenda to reduce income inequality. In this regard, the best policy is to redistribute income. Wealth should be taken from wealthy people in the form of mechanisms such as tax collection and redistribution among the poor in the form of subsidies or the provision of public goods. Given the positive relationship between education and economic growth, it is suggested that the government invest in education and improve educational facilities. Therefore, allocating more money from the government in the field of education is one of the appropriate strategies to reduce income inequality. Inflation is one of the phenomena that can have positive and negative effects, and one of its most important effects is the change in income distribution so that it is in favour of high-income deciles and to the detriment of low-income groups and decimators. In other words, inflation hurts people with stable monetary incomes and reduces their purchasing power, and in contrast, it benefits most people who keep their wealth non-monetary. According to the results of this study, there is a negative relationship between inflation and economic growth, so it is necessary to adopt policies that reduce inflation in society.

Climate change is an irreversible change in average climate. This phenomenon has a wide temporal and spatial range and, consequently, has a major impact on various economic sectors including water and agriculture. Economic implications of climate change affect water resources, taking into account opportunity costs and benefits of allocating and storing water resources during climate change. In order to achieve this goal, considering sensitive climate conditions in arid regions and multiple consequences of climate change in these areas, this study aimed to investigate economic effects of climate change and water deficiency in arid regions on the production, price and income of the agricultural sector in Iran.

  For this purpose, the present study used an Agricultural Multi-market Model (AMM) that is superior to other methods because of its endogenous price considerations. It is also designed for agriculture with high geographical distribution (Ponce et al., 2014). The core of the AMM consists of two sets of equations. The first set depicts the behavior of agricultural producers (supply) and the second set depicts a picture of consumer behavior (demand).

The results showed a potential decrease as a result of climate change in the area under cultivation of maize crop under warm, cold and dry climates, as well as in barley, rainfed wheat and wheat under warm, cold and dry climates until 2025. The results also showed that with climate change, a decrease would be observed in maize crop production in hot dry, cold dry and temperate dry sub-climates as well as in wheat and rainfed wheat in temperate dry sub-climates until 2025, as compared to 2016. Wheat, barley and maize crops would also increase under hot, cold and temperate climates until 2025 compared to 2016. Climate change is projected to increase farmers' income in hot, cold, and temperate sub-climates. Moreover, the results revealed that with climate change, net exports of wheat, barley and maize would remain negative for 2025 and the country would remain an importer of these agricultural products.

According to the results, the average price of agricultural products would increase for the sub-climates by increasing climate change. Since products under consideration are inelastic commodities, it is emphasized that a price protection policy should be adopted to increase the consumer purchase power. The results showed that the temperature response coefficient is more for wheat, rainfed wheat and barley in warm and dry sub-climates, for rainfed wheat, barley and rainfed crops in cold dry climate and for rainfed barley in dry temperate climate, as compared to rainfall. Therefore, it is necessary to increase the cultivation level of these products in the above sub-climates.

The optimized cropping pattern can not only sustainably preserve water resources but bring more income as well. Therefore, since no research has been done on optimizing cropping pattern in the Shahdad county, this study identifies the optimized cropping pattern in this region with the goal of both maximizing farmers’ gross profit and decreasing water consumption.

Since more than 90 percent of the current cropping pattern in Shahdad is cultivated with the four following crops: irrigated barley and wheat, garlic, and Alfalfa, the needed data were collected during 2016-17 crop year, from 450 farmers who cultivate these four crops simultaneously. 106 farmers were selected for face-to-face interview by using questionnaire and based on simple random sampling. The ant colony meta-heuristic model based on binary knapsack problem to achieve the optimized cropping pattern was used.

The ACO algorithm showed that the cultivated area of irrigated barley, irrigated wheat, garlic, and Alfalfa changed from 509, 408, 617, and 1124 Ha in the observed cropping pattern to 421, 588, 998 and 651 Ha in the optimized cropping pattern, respectively. Therefore, the gross profit, by 282.96%, has increased from 201.59 billion Rials in the observed cropping pattern to 772 billion Rials in the optimal cropping pattern.

Suggestion

Results showed that optimized cropping pattern in addition to saving 5% of water consumption, will increase gross profit to 282.96%. Therefore, it is suggested to change cropping pattern based on results of this study.

Over the past decade, water has become a strategic commodity, so that it has not only economic and social importance, but also has security importance; and it is predicted to become a major factor some conflicts in next years. This highlights the necessity for water resources management.

This study deals with water allocation of Gorganrod-Gharahso basin between Golestan, Semnan and North Khorasan provinces and determines the share of each agricultural, industry and environmental sectors based on data of 2015. For this purpose, two methods of Core and Uncertainty Bankruptcy Game are integrated for the first time.

The results of core method showed that the grand coalition creates the net profit equal to 24,211,790 billion rials. By applying Shapely value, it became clear that the Agricultural sector of Golestan province had the highest share (49%) of the generated gains in the top coalition. It should be noted that the Gatley method confirmed the stability of the grand coalition, after satisfying Golestan agricultural sector. In the following, the uncertainty Bankruptcy procedure was used by surveying different allocation rules in 5 scenarios. The results of Plurality index and Stability index suggest that the Sensitivity analysis of Adjusted Proportional rule can be a fair option for determining the share of stakeholders.

According to the above contents, integrating the best coalition of Core and SAP law can fairly supply the needs of stakeholder under different scenario and generate more net profit than the current situation.

Rural non-farm sector(RNFS) comprises all economic activities –such as manufacturing, transport, commerce, banking, service and so on) in rural areas expect cropping, agriculture, livestock, husbandry, hunting, fishing, and forestry. So, RNFE means employment of rural household members in the nonfarm sector, and RNFI is the income thereby generated. Various studies have shown that nonfarm activities play an important role in reducing rural poverty, increasing incomes and improving rural household’s well-being. The present study tries to investigate the effect of non-farm employment on rural household’s consumption expenditures in Neyshabour county. 

The data is gathered through a questionnaire survey carried out on 380 rural households from four districts (Zabarkhan, Markazi, Sarvelayat and Mianjolgeh) in the Neyshabour county. The survey is conducted between September 2017 to February 2018. The household was chosen through a multi-stage stratified random sampling. The propensity score matching approach is used to examine the impact of participation in non-farm activities on householdconsumption expenditures. The method compares the consumption expenditures of non-farm households with their counterfactual group that did not diversify into such activities, depending only on  farm activities. The propensity score is defined P(Xi) as the conditional probability of receiving a treatment (employment in nonfarm activities) given pre-treatment characteristics: (1)   Where Xi denotes a vector of pre-treatment characteristics of household i such as household head characteristics, agricultural characteristics and household characteristics ; b(xi) is the logistic conditional function and F(b(xi)) represents logistic cumulative distribution frequency. In propensity score matching analysis, the average treatment effect on the treated (ATET) is important. It is computed by matching farm and non-farm households that are closest in terms of their propensity scores and calculated as follows. (2) ATET (X) = E(Y1i | Ti = 1) - E (Y0i | Ti = 1) Where, E(Y1i | Ti =1) represents the expected consumption expenditures outcome of households engaged in non-farm activities and E(Y0i | Ti=1) denotes the counterfactual  consumption expenditures of households without these activities. The counterfactual estimates represent what the consumption expenditures outcome of  households engaged in non-farm activities would be if they have not engaged in non-farm activities.

Survey of 380 households showed that 267 households were engaged in non-farm employment and 113 households had only agricultural activity. The results showed that household's characteristics such as the age of household head, household education, farmland size, livestock ownership, amount of credits and agricultural assets (water value and agricultural machinery) and household asset for the households engaged in non-farm activities and they have not engaged this activities were significantly different but after matching propensity scores of two groups, differences were removed. In other words, the mean of independent variables in the two groups wasn't significantly different. Also, results indicated that participation in non-farm employment has a positive effect on household consumption expenditure as a measure of welfare. So that, based on the average treatment effect on the treated (ATET), annual consumption expenditure of the households were engaged in non-farm employment was 1420 thousand Rials more than consumption expenditure of households without these activities. 

The result of examining the impact of participation in non-farm activities on household consumption expenditure showed that participation in these activities has a positive effect on household consumption expenditures. Therefore, in order to, non-farm activities have more sustainable income and less risk than farm income, it is suggested that non-farm activities be expanded by strengthening and expanding existing industrial parks.