Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain

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1 July, 2018 Journal of Resources and Ecology Vol. 9 No.4 J. Resour. Ecol (4) DOI: /j.issn x Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain WANG Jiayue 1,2, XIN Liangjie 1,* 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing , China; 2. University of Chinese Academy of Sciences, Beijing , China Abstract: Small-scale household agricultural production has been in conflict with China s agricultural modernization. In the context of vigorously promoting rural land circulation and moderate scale management nationwide, research on the relationship between farm size and scale efficiency has become increasingly important. In this paper, we use the DEA-OLS two stage method to analyze data from 368 surveys of rice farming households in the Jianghan Plain. The scale efficiency of rice farming was calculated, and the relationship between farm size and scale efficiency investigated. The results indicate that (1) the rice farm size is generally small in Jianghan Plain, with an average of 0.77 ha. The average scale efficiency is 0.88, and it is the main factor limiting an increase in comprehensive technical efficiency. Moreover, 88.32% of households are in the stage of increasing returns to scale. (2) There is a stable inverted U type relationship between rice farm size and scale efficiency. Considering characteristics of the householder, the household and land quality, the maximum scale efficiency corresponds to a household with 5 ha of rice farm. (3) Among householder characteristics, age has a significant negative influence on scale efficiency, and scale efficiency is lower for a household whose householder is engaged in non-farm work than for one whose householder is devoted to farming. As for land quality, there was a significant positive effect of irrigation on scale efficiency. Among family characteristics, the application of a machine during the seeding process increased scale efficiency by 3.07%. Therefore, we suggest that local government should actively promote rural land circulation, implement a staged-scale management subsidy, and other forms of support for the purchase of agricultural machinery and technical popularization. Increased investment in irrigation improvements and mechanical facilities and encouragement of farmers to enlarge their farm size could improve scale efficiency and farming profit and lead to the development of moderate scale management. Key words: Jianghan Plain; rice farming; farm size; scale efficiency; moderate scale management; data envelopment analysis 1 Introduction Agricultural production costs are increasing with the increase in labor and land costs. The most common smallscale household agricultural production mode has been unable to meet the needs of farmers to increase their income. In addition, there is a conflict between small-scale agricultural production and agricultural modernization. The development of modern agriculture needs to be matched with modern operation methods; consequently, the transformation of agricultural production mode has become one of the most important challenges for China s agricultural development. The conference report of the eighteenth National Congress of the Communist Party of China explicitly proposed to cultivate new types of agricultural business entities Received: Accepted: Foundation: National Natural Science Foundation of China ( ). *Corresponding author: XIN Liangjie, xinlj@igsnrr.ac.cn Citation: WANG Jiayue, XIN Liangjie Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain. Journal of Resources and Ecology, 9(4):

2 WANG Jiayue, et al.: Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain 353 and develop scale management of various types. Although the number of new types of agricultural business entities, such as professional farmers and family farms, is growing, under the restriction of a large population and scarce land resources, small household contract management is still the basic mode of agricultural management. Under the impetus of scale management, households tend to expand their farm size to improve production efficiency, access the benefits of scale, and increase agricultural income. Because of the constraints of China s rural land system, households can only enlarge their farm size through land circulation. At present, with the increase in non-agricultural employment opportunities and in farming labor opportunity cost, the transfer of agricultural labor to non-agricultural industries has created conditions favoring rural land circulation. China s rural land circulation area accounted for 30.4% of the contracted land area in 2014 (Han, 2015), which does provide the possibility for households to increase their farm size. The scale of management cannot be blindly expanded; otherwise it will cause diseconomies of scale. Therefore, the key is to find the appropriate scale of management. In this context, the study of the relationship between farm size and efficiency of household management is particularly important. Research on the relationship between farm size and production efficiency has been a hot topic worldwide. Chayanov first noted that a negative relationship between farm size and productivity exists in Russia s farms (Chayanov, 1926). Subsequently, Sen s study showed that small-scale management is more efficient than large-scale management in India (Sen, 1962). Whether an inverse relationship between farm size and production efficiency exists has been fervently discussed since. An inverse relationship has been found in Asia (Rios and Shively, 2005), Europe (Alvarez and Arias, 2004), Latin America (Berry and Cline, 1979) and Africa (Barrett, 2010). Doubt appeared in the 1980s, when it was considered that the existence of the inverse relationship is due to backward technical conditions. This inverse relationship may disappear with the introduction of chemical fertilizer, the popularization of machinery, and the application of modern irrigation equipment and other advanced technologies (Ajit, 1979). In early studies of the inverse relationship between farm size and efficiency, the definition of efficiency is relatively simple; most studies use land productivity as a measurement. The definition is becoming increasingly comprehensive. Li believes that land productivity is negatively related to farm size, while labor productivity and cost profit margins are positively related to farm size (Li et al., 2010; Li et al., 2013). Some studies also use scale efficiency as a measurement. Anang found that farm size had a significantly positive effect on scale efficiency in northern Ghana s smallscale rice farming sector (Anang et al., 2016). There are also studies that treat the input factor as a penetration point, take the regional environment, land quality and pecuniary conditions into consideration, and verify this inverse relationship from the perspective of cultivated land use intensity. Xin s research concluded that the inverse relationship between farm size and land productivity occurred when the farm size increased to 2 ha in Jilin province and that there was a positive relationship between labor productivity and farm size (Xin et al., 2009). Still other studies test this relationship from the comprehensive view of productivity and efficiency. Justin found an inverse relationship between farm size and both agricultural productivity and technical efficiency in rural Mexico (Justin et al., 2015). The majority of scholars who support the existence of an inverse relationship believe that the reasons for the existence of this relationship are that the labor opportunity cost for small-scale farmers is lower (Sen, 1962; Sen, 1964) and that the land cost is much higher. Therefore, more intensive labor use by smaller farms is a key underlying reason. Small farms tend to use a more labor-intensive production method, while large farms are more land-intensive and capital-intensive (Zyl et al., 1995; Frank Ellis, 2006; Ali and Deininger, 2015). As a result, the larger the farm size, the greater the dependence on the employment of the labor force, resulting in a labor supervision problem. Taslim found that this inverse relationship is due to the decline in productivity caused by regulatory issues (Taslim, 1989). Tchale believed that the inverse relationship can be explained as a result of decreasing returns to scale due to the high supervision cost and moral hazards (Tchale et al., 2009). Some scholars hold the opposite opinion; they believe the verification of this inverse relationship should fully consider land use intensity and that one should choose a suitable efficiency index and clarify whether the labor market is perfect and whether the moderate scale can fit local conditions (Graham, 1991). Sen first found that the unobserved difference in land quality has a strong explanatory power in this inverse relationship in Java s rice farming (Sen, 1975). Many subsequent studies have also proposed that the heterogeneity of land quality between farmers of different scales has a certain influence on the inverse relationship (Assunção et al., 2003; Chen et al., 2011). Lamb found that in the random effect model of panel data for the male labor force, the inverse relationship disappears after controlling for the heterogeneous factors of labor, land market failure and soil quality (Lamb, 2001). Ali found that labor market imperfection is a key reason for this inverse relationship in Rwanda (Ali and Deininger, 2015). In summary, there is no consistent conclusion about the relationship between farm size and production efficiency. Most of the studies took households as the basic research unit and based their work on household survey data. The estimation indexes include land productivity, labor productivity, cost profit margin, total factor productivity and technical efficiency, and common methods are parametric and nonparametric. The production function method is the most

3 354 Journal of Resources and Ecology Vol. 9 No. 4, 2018 commonly used parametric method; the most frequently used nonparametric method is the data envelopment analysis method (Aziz et al., 2014; Anang et al., 2016). However, in the current studies, the consideration of land endowment heterogeneity is insufficient, and some researchers have grouped households according to the experience farm scale. This grouping may erase heterogeneous information between different scale groups, resulting in unstable results (Ajit, 1979; Frank Ellis, 2006). The study of this relationship should serve to help households choose a suitable farm size, and this appropriate scale is different in different regions for different crops. Therefore, this study selected Jianghan Plain, which is known as the granary of Hubei, as a focal area and chose rice, the main grain crop grown in the plain, as the study objective. Scale efficiency, which is a production efficiency influenced by farm size, is used as an index to reflect the gap between the actual farm size and optimal farm size. We used the data envelopment analysis method, taking full consideration of the contribution of each sample to the production frontier. Factors that can reflect the heterogeneous information of households, such as land quality and family characteristics, were added to the model as control variables to explore the relationship between rice farm size and scale efficiency. This study provides a theoretical basis for the development of moderate scale management for rice, theoretical guidance for an increase in scale efficiency for rice farming, and policy recommendations for the formulation of a scale management subsidy policy for rice. 2 Method This paper adopts the DEA-OLS two stage method: first, we calculate the rice farming scale efficiency using the data envelopment analysis method; then, we assess the relationship between rice farm size and scale efficiency using the least square regression method. The data envelopment analysis method (DEA) is a nonparametric method for determining production efficiency proposed by Charnes and Cooper in 1978 (Charnes et al., 1978). The DEA method is based on the concept of relative efficiency; it can evaluate the effectiveness of decision-making units (DMU) based on multiple inputs and outputs of the same type. The DEA method constructs a nonparametric data envelope curve, where the point on the production frontier is effective (Wei, 2004). The DEA method requires simple data and does not need the estimation of parameters, which avoids the influence of subjective factors. This study used the DEA model; thus, each household input-output system of rice farming was treated as a decision-making unit, and the production frontier was built depending on the input and output of the DMU. If the actual input or output of the household is close to the production frontier, the production efficiency of the decision-making unit is high, and if the decision-making unit falls on the production frontier, the efficiency value is 1. The DEA model can be divided into constant returns to scale model (DEA-CRS) and variable returns to scale model (DEA-VRS). We chose the DEA-VRS model, the comprehensive technical efficiency (TE) can be broken down into pure technical efficiency (PTE) and scale efficiency (SE). The scale efficiency indicates whether each decision-making unit can achieve maximum productivity; if the value of the scale efficiency is 1, the management scale of that decision-making unit is optimal, and it is in the stage of constant returns to scale. In this study, we used the scale efficiency of each household to judge whether the farm size was effective. We assumed that there were n DMU, each with m input and k output; the input data set was X j ( x 1,, ) T j xmj, and the output data set was Yj ( y1 j,, y ) T kj, where j 1,, n. The input-oriented DEA-VRS model can be written as a dual linear expression: min[ ε( eˆ T S e T S )] (1) n X jj S X0 (2) j1 n Yj j S Y0 (3) j1 n s.t. j 1 (4) j1 j 0, j 1,2,, n (5) S 0 (6) S 0 (7) where, θ is a dual variable that can determine whether the DMU is effective; is an Archimedes infinitesimal that is an infinitely small positive number; e ˆT and e T are the unit vectors of the m and k dimensions; S is the residual variable and S is the slack variable; and j represents the coefficient of the DMU linear combination. The calculation of the DEA model was achieved using MaxDEA The DEA model was used to obtain the scale efficiency value of rice farming, and following this, the least square regression method was used to determine the relationship between farm size and scale efficiency. In the model, the quadratic term of farm size is added to assess whether there is a nonlinear relationship between the two. Meanwhile, taking the impact of other heterogeneous information on the relationship into account, other possible factors of influence are introduced into the model as control variables. The basic form of the model is as follows: 2 y 0 1farm _ size 2farm _ size βx i (8)

4 WANG Jiayue, et al.: Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain 355 where, y is scale efficiency of rice farming; farm_size is rice farm size; farm_size 2 is the quadratic term of the rice farm size; and x i contains the factors that may have an impact on scale efficiency, such as householder characteristics, land quality and family characteristics. The estimation of OLS model is achieved by Stata Data and sample characteristics The data were collected from a field survey of farming households in Jianghan Plain, south-central Hubei province, in August Jianghan Plain, a land of plenty, lies along the middle reaches of the Yangtze River. It is the alluvial plain of the Yangtze River and the Hanjiang River, and is an important part of the Yangtze River Plain. The plain is relatively flat and rich in water resources. With a subtropical monsoon climate, periods of high rainfall and high temperature occur during the same season; thus, it is suitable for the cultivation of thermophilic crops. Rice, cotton and rape dominate the plain, and rice is the most cultivated and highest produced crop. Jingzhou, Xiantao, Qianjiang, Tianmen and Wuhan are representative cities of Jianghan Plain; this research collected samples in Jianli County and Shishou County (both under the administration of Jingzhou City), Xiantao City and Qianjiang City. Seventeen sample villages were randomly selected from the four counties (Fig. 1). A total of 482 valid questionnaires were collected, including basic household information, cultivated land use information and farming input-output information. According to the needs of this research, 368 rice farming households were selected as the data source for this study. From the distribution and characteristics of the samples (Table 1), the average area of the households rice farms was 0.77 ha, and the median was 0.4 ha. The size of the rice farms of most of the sample households was lower than the average level, and only 24.46% of the households were greater than the average size. This shows that rice farming in the sample villages is still small-scale farming, and the farm size needs to be expanded. We also found that the farm size has increased in the study area since 2000; 9.23% of sample households have a rice farm that is more than 2 ha, and four professional large-scale households have a rice farm that is more than 6 ha. Regarding the sample households participation in land circulation, a quarter of households had transferred-in land. The average rice farm is larger in area where the proportion of households participating incoming land transfer is higher. 4 Analyses 4.1 Variables selection Each household was treated as a decision-making unit, and one output variable and 11 input variables were selected to apply to the DEA model (Table 2). The output variable (Y) is the total yield of rice; the land input variable (X 1 ) is represented by the sown rice area because in the study area, the average multiple cropping index of rice farm land is 1. Thus, the sown rice area is also the scale of the cultivated land. The seed input variable (X 2 ) is represented as the seed weight per unit area; the pesticide input variable (X 3 ) is expressed as the pesticide expenditure per unit area. Because of the different purposes and effects of different fertilizers, the chemical fertilizer input variables (X 4 X 7 ) is divided Fig.1 Location of the study area and distribution of sample villages

5 356 Journal of Resources and Ecology Vol. 9 No. 4, 2018 Table 1 Distribution and characteristics of the sample Sample counties Number of sample villages Number of sample households Average size of rice farm (ha) Yield of rice (kg ha 1 ) Proportion of households involved in land transfer in (%) Xiantao Qianjiang Jianli Shishou Total Table 2 Descriptive statistics of input-output variables in the DEA model Variables Unit Mean Standard deviation Minimum Maximum Output Variable Rice yield (Y) kg Input Variable Sown area (X 1 ) ha Seed (X 2 ) kg ha Pesticide (X 3 ) CNY ha Compound fertilizer (X 4 ) kg ha Nitrogenous fertilizer (X 5 ) kg ha Phosphate fertilizer (X 6 ) kg ha Potash fertilizer (X 7 ) kg ha Employee (X 8 ) CNY Self-employment (X 9 ) day ha Machinery (X 10 ) CNY ha Irrigation and water conservation facilities maintenance (X 11 ) CNY ha into compound fertilizer, nitrogen fertilizer, phosphate fertilizer and potash fertilizer, and these are all represented as the fertilizer weight per unit area. Considering that the labor input intensity of employee labor and self-employed labor is different, the labor input variables (X 8, X 9 ) are divided into employee labor input and self-employment input; the employee labor input is represented as the total cost of employees hired, and the self-employment input is represented as the number of days spent per unit area. Taking into account the different labor input intensity or different quality of self-employed laborers, we assume that the labor input intensity varied by only age and that the input intensity of female laborers is the same as that of male laborers. Therefore, we standardize the self-employment input according to the age of the self-employed laborers. Laborers whose age is less than 65 years (inclusive) are treated as the standard agricultural labor force, the labor input intensity of laborer whose age is greater than 65 years is half the labor standard, and this is used to calculate the self-standard labor input days per unit area (X 9 ). The mechanical input variable (X 10 ) is expressed as the total mechanical cost per unit area, including a households own machinery and the employed machinery input, and it is the sum cost of the mechanical input from rice ploughing, seeding, harvesting and transportation. The irrigation and water conservation facilities maintenance input variable (X 11 ) is calculated as the cost of irrigation and the maintenance cost of the irrigation and drainage facilities per unit area. Household rice farming scale efficiency calculated by the DEA model is regarded as the explained variable, the rice farm size is the explanatory variable, and three groups of control variables are selected to successively join the model to assess the relationship between rice farm size and scale efficiency. Householder characteristic control variables include the householder s age, education level and occupation type; if the householder is purely engaged in farming, the value of the occupation type is 0; if the householder is engaged in a concurrent occupation or pure industry, the occupation type is 1. Land quality control variables include the average guaranteed degree of irrigation and drainage of the land parcels, and the average slope of the land parcels. Family characteristic control variables include the land type; if the rice farm land is all contracted land of the household, the variable value is 0, and if there is transferred-in land, the value is 1. As for the household rice sowing method, if the household uses a manual sowing method, the variable value is 0, and if they use a mechanical sowing method, the value is 1. The variable for the proportion of non-agricultural household income is annual non-agricultural household income as a proportion of total household income in The variables involved in the model are shown in Table Analysis of scale efficiency of rice farming We obtained data on the comprehensive technical efficiency,

6 WANG Jiayue, et al.: Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain 357 Table 3 Descriptive statistics of variables Variables Interpretation Mean Standard deviation Minimum Maximum Explained variable: Scale efficiency Scale efficiency Households rice farming scale efficiency Explanatory variable: Farm size Farm size Sown rice area, Unit: ha (Farm size) 2 Square of sown rice area Control variable: Householder characteristics Age Householder s age, Unit: year Education Householder s education level (1=Illiteracy; 2=Primary; 3=Junior; 4=Senior; 5= College or above) Occupation type Householder s occupation type (0=Pure farming; 1=Non-pure farming) Control variable: Land quality Irrigation Average guaranteed degree of irrigation and drainage of the land parcels (1=100%; 2=75-100%; 3=50-75%; 4=25-50%; 5=25% or below) Slope Average slope of the land parcels (1=Gentle; 2=Moderate; 3=Steep) Control variable: Family characteristics Land type Land type (0=No land transferred-in; 1=There is land transferred-in) Sowing method Rice sowing method (0=Manual; 1=Mechanical) Off-farm income The proportion of non-agricultural household income pure technical efficiency, scale efficiency and returns to scale of rice farming from the DEA model, the relationship among the three efficiency values is: comprehensive technical efficiency = pure technical efficiency scale efficiency The comprehensive technical efficiency reflects the production efficiency of DMU under a certain input level; it is a comprehensive evaluation of input configuration, input utilization efficiency and other capabilities of the decision-making unit. The range is from 0 to 1; if the value is 1, the decision-making unit lies on the production frontier. The pure technical efficiency is an assessment of technology and management efficiency; this efficiency varies with the level of technology and management of DMU and reflects whether the input allocation is reasonable and whether each input has achieved most effective use. The range is also from 0 to 1; if the value is 1, it means that the use of input is efficient under the current level of technology and management. The scale efficiency is a production efficiency affected by the management scale of the decision-making unit and reflects the disparity between the actual and optimal management scale under a certain technical and management level. The range is also from 0 to 1; if the value is 1, it means that the management scale is matched with the existing input and output situation, the scale of the decision-making unit is the most effective, and the returns to scale are constant. When the scale efficiency is less than 1, the returns to scale may be increasing or decreasing. If a decision-making unit is in the situation of decreasing returns to scale, then one could continue to increase input factors, but the increase of the output is less than the increase of the input. This decision-making unit should reduce the management scale so that it can receive more output using the same input. From the results (Table 4), the average comprehensive technical efficiency is 0.78, the average pure technical efficiency is 0.90, and the average scale efficiency is Generally speaking, the pure technical efficiency of rice farming is relatively high, and the scale efficiency is the factor restricting the comprehensive technical efficiency of rice farming. From different scale groups, the scale efficiency limits the improvement of comprehensive technical efficiency in households whose farm size is less than 4 ha. That is to say, for the households with this farm size range, the improvement of production efficiency is mainly limited by the farm size, while for the households whose farm size is more than 4 ha, the disparity between scale efficiency and pure technical efficiency decreases, and the scale efficiency is even higher than the pure technical efficiency. In this situation, the pure technical efficiency is the main limitation to comprehensive technical efficiency, and the technology and management level of households will restrict improvement of production efficiency. Regarding returns to scale, 13 households are in the stage of decreasing returns to scale, accounting for 3.53% of the total number of households; 30 households are in the stage of constant returns to scale, accounting for 8.15%, and 88.32% of the households are in the stage of increasing returns to scale. If these households in the stage of increasing returns to scale enlarge their farm size, the increase in yield will be higher than the increase in inputs, which illustrates that from the perspective of returns to scale, the majority of rice farmers should continue to expand their farm size in the study area.

7 358 Journal of Resources and Ecology Vol. 9 No. 4, 2018 Table 4 Efficiencies of rice farming households Group Scale interval Sample size Percentage (%) Average farm size of samples Comprehensive technical efficiency Pure technical efficiency Scale efficiency Sample size of increasing returns to scale Sample size of decreasing returns to scale Sample size of constant returns to scale 1 (0,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,10) [10,15) [15,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80) [80,90) [90,100] [100,150) Analysis of the relationship between rice farm size and scale efficiency We successively included three groups of control variables to analyze the relationship between rice farm size and scale efficiency (Table 5). From the results of the four models, the scale efficiency of rice farming has a significant positive correlation with rice farm size and a significant negative correlation with the quadratic term of farm size, indicating that there is an inverted U curve relationship between rice farm size and scale efficiency. During the process of adding the control variables, this inverse relationship is stable, which shows that the model is robust. To judge the stability of scale efficiency from the DEA model, we removed 5% (18 households) of the most efficient sample households, recalculated the scale efficiency, and then re-ran the four models; the results remained the same. This shows that there is a stable inverted U shaped nonlinear relationship between rice farm size and scale efficiency in the study area. From models 2, 3 and 4 we can also see the influence of the control variables on scale efficiency. For householder characteristics, age and occupation type of the householder have an influence on scale efficiency, but the education level has no significant influence on scale efficiency. For the factors of land quality and family characteristics, the degree of irrigation and the degree of mechanization of rice sowing have a significant positive impact on scale efficiency, and the slope of the land parcel, whether it is the householder s own land, and the proportion of non-agricultural income have no significant stable impact on scale efficiency. The specific influence is that age has a negative impact on scale efficiency; the older the age of the householder, the lower the scale efficiency. Therefore, it is necessary to include in any decision about the rice farm size reference to the quality of the agricultural labor force. Based on the occupation types of householders, the rice farming scale efficiency of householders who are engaged in non-agricultural work is lower than that of householders who are purely engaged in farming. In the study area, the householders and their spouses are usually the main forces engaged in agricultural work. Compared to households in which the householder is purely engaged in farming, the dependence of household income on agriculture may be relatively small in households in which the householder is engaged in occupations outside farming, and the attention paid to agriculture is relatively low, resulting in a relatively small-scale rice farm where the scale efficiency is low. The education level of the householder has no significant effect on scale efficiency. Rice farming requires much experience and practice and has no significant relationship with a householder s education. According to the results of models 3 and 4, rice farming has a relatively high requirement for irrigation; land parcels with

8 WANG Jiayue, et al.: Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain 359 Table 5 Regression results for the relationship of rice farm size and scale efficiency Variables Model 1 Model 2 Model 3 Model 4 Explanatory variable: Farm size Farming size *** *** *** *** ( ) ( ) ( ) ( ) Farming size *** *** ** ** ( ) ( ) ( ) ( ) Control variable: Householder characteristics Age ** * * ( ) ( ) ( ) Education ( ) ( ) ( ) Occupation type * ** ** ( ) ( ) ( ) Control variable: Land quality Irrigation *** *** ( ) ( ) Slope * ( ) ( ) Control variable: Family characteristics Land type ( ) Sowing method ** ( ) Off-farm income ( ) Constant term *** *** *** *** ( ) ( ) ( ) ( ) R Note: The coefficients in the brackets are standard error; *, **and *** indicate the coefficients significance level of 0.1, 0.05 and 0.01, respectively. better irrigation have greater scale efficiency. Slope has no stable significant impact on scale efficiency; 87.77% of the cultivated land in the study area is gentle, and only 0.82% has a significant slope. In general, land parcels in the study area are relatively flat, and there is little difference in the slope of cultivated land. Thus, slope has no stable significant influence on scale efficiency. From the perspective of family characteristics (Model 4), land parcels sown with machinery are 3.07% more efficient than those that are sown by hand. For large-scale farming, the requirement for machinery is much higher; in the case of the same farm size and input, a manual sowing method will significantly limit any increase in output. Whether the land is transferred-in land has no significant influence on scale efficiency, which shows that a household s investment in rice farming did not significantly differ because of different land ownership. Because of China s farmland system constraints, households can only enlarge their farm size by way of land circulation, and there is no significant difference in scale efficiency between transferred-in land and contracted land under the same management. The proportion of non-agricultural household income has no significant influence on scale efficiency. The intergenerational division is obvious between agricultural and non-agricultural work in sample households; the average age of the family members engaged in agricultural work is years, the quinquagenarian, while the average age of the family members engaged in non-agricultural work is years, the young and middle-aged generation. The two generations have different divisions of labor; the benefit is shared and they do not affect each other. There is a type of instinctive dependence between traditional family members, which is similar to a community of mutual possession and enjoyment (Ferdinand, 1999). Migrant family members who earn wages will not affect the agricultural production of the family members who engage in farming work, so the proportion of non-agricultural household income has no significant impact on the scale efficiency of rice farming. According to the results of the four models, Fig. 2 shows the relationship between rice farm size and scale efficiency. The abscissa is rice farm size, and the ordinate is scale efficiency normalized to (0, 1). We can see from the figure that there is a stable nonlinear relationship between rice farm size and scale efficiency; with expansion of farm size the scale efficiency increases first and then decreases. According to model results, the scale efficiency is highest when the rice farm size reaches approximately 5 ha. The control variables were not included in model 1, and the best rice farm size was 5.09 ha. Householder characteristics were added in model 2, and the best farm size was 5.10 ha. In model 3, land quality factors were added and the best farm size decreased to 5.08 ha due to the mixed degree of irrigation in the study area. Family characteristics were added in model 4, and because 47.28% of households in the study area do not utilize mechanical sowing and mechanization is an important factor restricting scale management, the optimal farm size decreased to 5.01 ha after the degree of mechanization was taken into consideration. From variation Fig.2 Relationship of rice farm size and scale efficiency

9 360 Journal of Resources and Ecology Vol. 9 No. 4, 2018 of the curve, the rates of scale efficiency increase for the four models are almost the same in the first half of the curve. In the latter half, the reducing rate of scale efficiency in model 4 is approximately 1.1 times the average reducing rate of models 1, 2 and 3, which reflects the importance of mechanization in large-scale management. In general, the scale efficiency reaches 0.9 when the rice farm size reaches 3.4 ha in the study area. The average rice farm size in the study area is only 0.77 ha; only 4.62% of the households have a rice farm size of more than 3.4 ha. From the perspective of improving scale efficiency, the study area should continue to promote land circulation to enlarge the rice farm size of households. For large-scale farmers, if technical conditions and mechanization levels cannot keep up, the blind expansion of farm size will lead to a decrease in scale efficiency. In the survey we also found that large-scale farmers have an aspiration to continue expanding their farm size, but the most limiting factor is the problem of employee supervision. Local farmers will generally hire laborers during the rice transplanting process, and the employment wage is calculated based on labor days, not according to the actual working area. If the farm is oversized, hired labor may increase, and there will appear to be an inevitable slowdown and problems of poor regulation. The slowdown of hired laborers will result in the reduction of rice output, and poor regulation will lead to additional labor input, so that when the farm size is increased, the input increases, but the output decreases, as there is a high cost of supervision. As the farm size increases, the input continues to increase, while the per unit area yield decreases, leading to a decrease in scale efficiency. To achieve scale management on the basis of the guarantee of the per unit area yield, decisions about the suitable farm size should be made according to the quantity and quality of self-employed laborers, limiting the farm size to a size that can function without hired laborers or to a size that requires fewer hired laborers (Sun, 2013). Households with farm sizes of approximately 3.33 ha in the study area can achieve an optimal state where they fully input their own labor without hiring employees. 5 Discussion and conclusions 5.1 Discussion Rice is a crucial crop for national food security. The area of rice sown accounted for nearly one third of grain crop sown area in China in In this study, the optimal farm size range of rice is ha in Jianghan Plain according to the criterion of scale efficiency optimization; scale efficiency can reach more than 0.9 in this range. According to the principle of flattening the income gap between urban and rural residents, the country put forward that households whose scale of land management is times the local average area of contracted land, and whose farming income is equivalent to the local work income of secondary and tertiary industries should be given priority support. On the basis of this standard, the moderate farm size of rice farming is ha in the study area. In our sample, there is only one household whose rice farm size is more than 7.7 ha, accounting for 0.27% of the household sample. According to our survey, agricultural subsidies are all owned by contractors, the actual operators cannot get any subsidies from transferred-in land. The risk of planting is relatively high for large-scale farmers who have transferred-in more land. Obviously, in accordance with this support standard, it can only cover large-scale farmers, and it is difficult to drive the enthusiasm of medium-scale farmers. And as for transfer land according to this standard, it calls for a land transfer rate of more than 90%, and centralization of land to 10% of households. Whether we take into account natural condition constraints such as hills and mountains, or take into account the employment problems of landless peasants, the rate of land circulation in some parts of China is difficult to reach more than 90%. That is to say, the moderate scale management of China will be dominated by medium-scale farmers, and supplemented by large-scale farmers. From this point of view, whether the national standards are too high remains to be further demonstrated. To determine the moderate scale of management, we should take into account the following conditions. First, local natural conditions should be considered. According to Yang, under the circumstance of profit maximization, the moderate scale of farmers cultivated land management in plain areas is ha, which is larger than that of 1.896ha in hilly and mountainous areas (Yang et al., 2011). Second, the level of local urbanization and transfer situation of agricultural labor force should be considered. In Wang s study, the moderate rice farm size is 2.13 ha in Jiangxi in 2009 when set the farm income equivalent to work wage (Wang, 2011). And in the study of Huang et al., if only considering the rice farmers and migrant workers have the same income, the moderate rice farm size is 4.64 ha in Jiangxi in 2012 (Huang et al., 2013). The moderate scale increased by % in three years. According to the statistical yearbook of Jiangxi and the data given in the studies, the population urbanization rate in Jiangxi increased by 10.02% from , and the proportion of employees in primary industry decreased by 9.87%. Jiangxi has achieved rapid development of urbanization and rural surplus labor force transfer during this period, and the monthly wage of migrant workers increased by 51.31%. Therefore, with the development of urbanization, employment opportunities for agricultural labor force have increased and the opportunity costs of farming have increased, thus the moderate scale of agricultural management has also increased. Third, comprehensive consideration should be given to the degree of agricultural modernization, including the level of mechanization and the management and the technical level of agricultural practitioners. With the improvement of the level of agricultural

10 WANG Jiayue, et al.: Is Larger Scale Better? Evidence from Rice Farming in Jianghan Plain 361 mechanization, machinery will gradually replace manpower, the moderate scale of management will be further expanded, and production efficiency will increase. At the same time, we should also consider whether the expanded management scale matches the management and technical level of local farmers, and if the level cannot keep up with the scale, resulting in increased cost of supervision and other inputs, will also lead to a decline in production efficiency. Rice is widely planted countrywide, the natural environment, social development and management ability of each region are different, so the moderate scale of this study is difficult to extend to the whole country from the study area. At present, there are some studies on the moderate management scale of rice in different regions in China. Existing studies mainly determine the moderate scale from two aspects which are income and yield of farming. The following table shows the moderate scale of rice farming in different studies (Table 6). It can be seen from the table that the moderate scale varies widely in different regions, ranging from 1 10 ha. The study area and period of this study and the study of Liu et al. are quite close; their study suggests that the technical efficiency of rice is the highest when the farm size range is ha, which is somewhat different from our results. This difference is mainly because they only divided the farm size into three groups: small, medium and large, the average situation of households in each group masks heterogeneity of households, resulting in unstable results, and if they subdivided the farm size groups they would get different conclusions. This study does not select the technical efficiency as the measurement of moderate scale, but taking into account the scale economy effect in the process of farm size expansion and choose scale efficiency, considering that the inputs and outputs are not proportionately increased with the expansion of farm size. Through our calculations, profits from rice farming for different scales are not increasing proportionally, so in terms of whether income or yield, economies of scale exist in the process of scale expansion. Since 2016, the country has comprehensively pushed forward the reform of Three Subsidies for agriculture and has merged the three subsidies into agricultural support and protection subsidies. The policy objective is to support cultivated land fertility protection and grain moderate scale management. The national policy points to the promotion of rural land circulation and the development of moderate scale management. The policy gives key support to large-scale farmers, family farms and other new types of agricultural business entities, to encourage the local government to support the new types of agricultural business entities develop various forms of grain moderate scale management in the form of subsidized loans, technology popularization and service subsidies, and it is not encouraged to take direct cash compensation for new types of agricultural business entities. Subsequently, there have been various forms of subsidies and preferential policies for scale management households. Most of the area using the cash subsidy set the lower bound of subsidy standard at 3.33 ha, but most did not implement the staged subsidy, there is no difference of the subsidy standards between medium-scale and large-scale farmers (Table 7). In this way, the cash subsidies will easily lead farmers to blindly expand their farm size for subsidies, which is not conductive to the effective implementation of policies and the protection of grain production. The cash subsidy policy in Ningxiang county in Hunan is implemented by stages, which can mobilize the enthusiasm of farmers with all management scale more effectively, and avoids blind expansion of farm size, and guarantees grain production at the same time. In Jiangxi there is support scale management entities around the purchase of agricultural machinery and subsidized loans. Jiangxi province has subsidized the purchase of agricultural machinery and the use of mechanical services for large-scale rice farmers whose farm size is ha in Support in Hubei province for large-scale farmers is mainly aimed at farmers whose grain area is more than ten times the average grain area of China. In accordance with this standard, farm size of rice farmers who enjoy preferential policies should reach 7.7 ha in the study area, a relatively high standard compared with Table 6 Research results of moderate farm size of rice in China Authors Study area Data collection time Evaluating basis Moderate farm size (ha) Wu, 2011 Hunan Profit maximization and yield maximization Yang et al., 2011 Jianghan Plain, Hubei Profit maximization Yang et al., 2011 Dabie Mountains, Hubei Profit maximization Wang, 2011 Linchuan district, Jiangxi 2009 Farm income is equivalent to wage 2.13 Huang et al., 2013 Jiangxi Farm income is equivalent to wage 4.64 Huang et al., 2013 Jiangxi Farm income is equivalent to wage and profit maximization per unit area Xin et al., 2015 China Farm income is equivalent to wage 4.93 Li et al., provinces in China 2011 Yield maximization and profit maximization Liu et al., 2016 Jianghan Plain 2015 Technical efficiency Note: The study by Yang et al. focuses on grain crops, and rice is the main grain crop in the study area.

11 362 Journal of Resources and Ecology Vol. 9 No. 4, 2018 Table 7 Moderate scale management subsidy standards for grain-growing farmers in different regions in 2016 Region Scale standard (ha) Subsidy standard (CNY ha -1 ) Region Scale standard (ha) Subsidy standard (CNY ha 1 ) Sha county, Fujian Lanxi city, Zhejiang Jinjiang city, Fujian Yongjia county, Zhejiang Shandong Xiaoshan district, Zhejiang Ningxiang county, Hunan Ruian city, Zhejiang > other areas. According to our survey the rate of rural land circulation has not reached a high level in the study area, so this support standard is slightly higher in the current stage of land circulation development. And we also found that support for large-scale farmers in Jianghan Plain is lacking special support funds, and the implementation of relevant preferential policies should be strengthened. 5.2 Conclusions The average comprehensive technical efficiency of rice farming in Jianghan Plain is 0.78, the average pure technical efficiency is 0.90, and the average scale efficiency is The limiting factor to the improvement of comprehensive technical efficiency is scale efficiency, which is mainly because rice farming is dominated by small-scale farmers. The average farm size is 0.77 ha, the management scale is relatively small, and 88.32% of the households are in the stage of increasing returns to scale. Therefore, based on scale efficiency and returns to scale, we propose the continued promotion of land circulation in the study area to enlarge rice farm size. There is a stable U shaped relationship between rice farm size and scale efficiency in the study area; with the expansion of farm size, scale efficiency increases first and then decreases. Taking householder characteristics, land quality and family characteristics into account, the scale efficiency can reach 0.9 if the rice farm size is 3.4 ha, and when the farm size reaches 5 ha, the scale efficiency is 1. When expanding the farm size beyond 5 ha, the scale efficiency will begin to decrease. The main reason for the decline in the scale efficiency is that oversized farms bring with them the problem of poor supervision of employees, which results in an increase in the input cost and a decrease in per unit area yield. In the formulation of policies to encourage land circulation, this nonlinear relationship between farm size and scale efficiency should be given full consideration. The assessment of the moderate management scale should also vary according to different regions and crops. We knew that, by the survey time, governments in the study area had not set a scale management subsidy for large-scale farmers. With the promotion of land circulation and development of moderate scale management, a trend could be set in the study area through the use of subsidies to encourage farmers to actively join in land circulation and moderate scale management. According to our study results, we suggest that in the formulation of subsidy policy standards, the management scale threshold should not be set to oversized, and should be a staged subsidy. The per unit area subsidy standards for the part that exceeds the size limit should be reduced to encourage locally applicable moderate scale management. Governments should not encourage scale management that blindly expands farm size and sacrifices the per unit area yield. This could prevent a phenomenon of exploiting a policy that blindly expands farm size for subsidies. Regarding the factors that affect scale efficiency, among householder characteristics, the age of the householder has a significant negative influence on scale efficiency; the scale efficiency of a household in which the householder is engaged in non-agricultural work is lower than that in which the householder is engaged in pure farming. Among land quality factors, the degree of irrigation of land parcels has a significant positive effect on scale efficiency. Taking family characteristics into account, the scale efficiency of households employing mechanical sowing is 3.07% greater than in households that use a manual sowing method. We propose that the study area should support agriculture that focuses on the improvement of irrigation and popularity of mechanical facilities. To develop moderate scale management, supporting facilities should be perfect. For rice farming in Jianghan Plain, irrigation and sowing mechanization are important factors affecting scale efficiency. The government should increase investment in construction and maintenance of farmland water conservation facilities and improvement of tractor ploughing roads. The government can also, according to local demand, increase subsidies for the purchase of machinery frequently used in the process of rice farming. In summary, there is great potential for the expansion of rice farm size for farmers in Jianghan Plain, but farm sizes should not be expanded blindly, as there is a U shape relationship between farm size and scale efficiency in rice farming in the region. At the present time, rice farms in Jianghan Plain are small. The government should actively promote land circulation, cooperate with a staged-scale management subsidy, and other forms of support for the purchase of agricultural machinery and technical popularization, and increase investment in irrigation and mechanical facility popularization. These measures would encourage farmers to expand farm size, improve scale efficiency and farming profit, and develop moderate scale management.