sustainability

Article Does Farmland Rental Contribute to Reduction of Agrochemical Use? A Case of Grain Production in Province,

Ying Liu 1,2,3 , Chenggang Wang 4, Zeng Tang 2,3 and Zhibiao Nan 2,3,*

1 School of Management, University, Lanzhou 730000, China; [email protected] 2 State Key Laboratory of Grassland Agro-ecosystems, , Lanzhou 730020, China; [email protected] 3 College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou 730020, China 4 Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, TX 79409, USA; [email protected] * Correspondence: [email protected]

 Received: 11 March 2019; Accepted: 18 April 2019; Published: 23 April 2019 

Abstract: As a consequence of the new strategy to boost productivity capacity and ensure food security, China’s farmland rental market is developing rapidly, and its impacts on agricultural productivity have been extensively studied. However, the impacts of farmland rental on food safety have not been considered. The aim of this study was to determine the causal effects of farmland rental on fertilizer and pesticide use in wheat and maize production and evaluate the potential effects of this activity on food safety. Survey data obtained from 900 households in eight counties in Gansu province were used in this study, and the propensity score matching (PSM) method was employed to solve selection bias problems with the data. The results showed that farmland rental significantly reduced fertilizer and pesticide use in wheat and maize production, implying a potential reduction in heavy metal contamination of food and drinking water as well as less pesticide residues-remaining in food and contamination of environment. Also, households renting land were more likely to adopt new agricultural technologies and management methods and to acquire more agricultural acknowledges and information than those not renting land and renting out land. Thus, farmland rental is a benefit to the application of new agricultural technologies and management methods, to rational use of agrochemicals, and finally to food safety and environmental conservation. Policies such as encouraging farmland rental, enhancing education of farmers, improving technological innovation, and providing better information transfer should help ensure not only “enough food” but also “safe food”.

Keywords: farmland rental; land consolidation; food security; food safety; fertilizer use; pesticide use; environmental conservation; propensity score matching

1. Introduction For a long time, decreasing farmland and rapidly growing populations have made food security a growing concern worldwide [1,2]. As the largest developing country with the largest population and grain consumption in the world, China has successfully created the “Miracle in China” by feeding 22% of the global human population with less than 9% of the world’s arable land over the past several decades, indicating a substantial contribution to global food security [3,4]. However, with the reduction of arable land and the increase of population and grain consumption, China’s food security is increasingly acute [5].

Sustainability 2019, 11, 2402; doi:10.3390/su11082402 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 2402 2 of 15

To ensure food security, the central government of China is promoting the formation of large-scale and mechanized farms through farmland rental [5,6]. With substantial support from the government and promotion from the booming off-farm labor market, China’s farmland rental market is growing rapidly [7,8]. Meanwhile, issues related to the impacts of farmland rental on agricultural productivity and food security have received special attention by economists and policy makers. Various studies have shown the positive effects of farmland rental and consolidation on food security due to its improving allocative and technical efficiency and reducing land fragmentation [9–12]. Food security requires an access to “sufficient, safe, and nutritious food” [2], i.e., food security not only means “sufficient food” but also “safe food”. In particular, the quality of food products, especially their safety, is an increasing focus for governments, companies, and international trade bodies after the 2008 melamine problem in China. Food safety was ranked first in the top five safety issues that were of concern to the Chinese population in 2011 [13]. Thus, understanding the role of farmland rental in food safety is of fundamental importance not only for agriculture policymakers but also for society stability. China’s agriculture development strongly depends on the use of many kinds of agrochemicals, especially fertilizers and pesticides. China is now the largest consumer of fertilizers and pesticides in the world, using over one-third of the world’s fertilizers and pesticides [14,15]. Overuse of fertilizers and pesticides is widespread in China. The massive use of fertilizers and pesticides not only causes serious contamination of soil and water, but it can be harmful to human health with heavy metals and pesticide residues in food [1,16,17]. Thus, China’s intensive use of fertilizers and pesticides is threating food safety and the environment [14,18]. Theoretically, farmland rental will increase farm size and promote the application of new technologies and management based on scientific knowledge, which may improve use efficiency of fertilizer and pesticides, thereby reducing the risks of agriculture production on food safety. On one hand, the enlargement of farm size and reduction of farmland fragmentation caused by farmland rental will promote the application of new agricultural machines and technologies [19]. On the other hand, the increase of agricultural production cost and risk caused by farmland rent will urge households renting land to acquire more information and acknowledges than those without renting land to reduce this risk. This will finally cause a more rational agrochemical use of households renting land than those without renting land. In addition, the insecure land right of rented farmland will also have effects on agrochemical use. Gao et al. [20] found households had different behaviors on manure and chemical fertilizer use between contracted farmland and rented farmland. Therefore, those changes along with farmland rental will cause fundamental impacts on agrochemical use in agricultural production. Unfortunately, previous studies have mainly focused on the impacts of farmland rental on “sufficient food” with the impact on “safety food” receiving little attention [6,21]. In this study, we investigated the effects of farmland rental on fertilizer and pesticide use in grain production. Results of this study should provide insights on the effects of farmland rental on food safety that can be helpful for policymakers.

2. Background In the past several decades, the household contract responsibility system (HCRS) of farmland starting in 1978 has greatly promoted the economic growth and the transition of rural China [22]. This land system was compatible to the backward agricultural productivity and simple economic structure of China at that time [23]. For a long time, it not only aroused rural households’ enthusiasm for agricultural production and improved agricultural productivity, but also optimized the agricultural production structure and food consumption structure of residents in China [24]. However, with the development of agricultural productivity, the shortcoming of HCRS is increasingly outstanding. The agricultural development of China is facing many new challenges, such as declining farmland area, aging farmers, and deteriorating ecological environment [5,21]. Developing large-scale and mechanized farms is considered a promising approach to address the challenges facing China’s agriculture [6,25]. For this purpose, Chinese government has openly Sustainability 2019, 11, 2402 3 of 15 encouraged farmland rental and large-scale farming by a series of agricultural policies, including subsidies on leased land, allowance for purchasing agricultural machinery, farmland certification, agricultural insurance, and farmland collateral [26]. Furthermore, farmland rental and large-scale farming were also encouraged and emphasized in the Chinese Number 1 Document of 2012 to 2019. In addition, the release of surplus labor from rural areas to urban industrial sectors also contributed to the booming of farmland rental market. China’s farmland rental market grows rapidly, while only 5.2% of contracted farmland was leased in 2007, but this ratio rose to 35.1% in 2016 [27]. The rapid growth of farmland rental market in China attracted considerable attention of academia. Some studies focused on the understanding of the determinants of farmland rental. Plenty of studies on the determinants of farmland rental found that off-farm employment, migration, tenure security, and agricultural ability had significantly positive impacts on the development of the farmland rental market [7,28–31]. Additionally, the productive heterogeneity among farmers, household attributes, contract condition, credit market development, institutional buildings, and property rights also played critical roles on farmland rental [8,32]. Some other studies explored the effects of farmland rental on agricultural production and farmers’ income. The impacts of farmland rental and large-scale farming on agricultural investments to land of farmers, agricultural productivity and efficiency, occupational diversification, farmers’ income and income inequality, as well as farmers’ welfare, were extensively studied [6,20,33–36]. Although the impacts of farmland rental and large-scale farming on agricultural production have been extensively studied, the casual relationship between farmland rental and agrochemical use in agricultural production is still unclear. Thus, it is important to clarify this relationship.

3. Econometric Approach To estimate the effects of a treatment, the difference in an individual’s outcome with and without treatment should be estimated. However, it is not possible to observe both outcomes for the same individual at the same time. Ideally, experimental data could provide information on the counterfactual situation that would solve the problem of causal inference [37]. As this is not the case, there is a problem of “missing data” [38]. Thus, the problem of selection bias arises when taking the mean outcome of nonparticipants as an approximation [39]. In our study, households renting land and renting out land were usually different from those not renting land, even in the absence of farmland rental. For instance, households active on farmland rental market might be more economically oriented or economically rational than those not renting land. The propensity score matching (PSM) approach is one possible solution to selection bias, and it has become a popular approach to estimate causal treatment effects, since it shows a close link to the experimental context [39] and isolates the treatment effects from other socio-economic determinants [37]. The basic idea of PSM is to find in a large group of nonparticipants that are similar to the participants in all relevant pretreatment characteristics. Then, the differences in outcomes of this are well selected and thus act as an adequate control group, and those of the participants can be attributed to the treatment. Firstly, PSM reduces the high level of dimensionality of households by comparing households with probability of farmland rental based on relevant characteristics [40,41]. A definition of the conditional probability of involvement of rural households i in farmland rental can be expressed as a logit form:   exp Xi0βi P(Xi) = Pr(Ti = 1 Xi) =   (1) | + 1 exp Xi0βi where P(Xi) is the conditional probability of household i renting land or renting out land and is defined as the propensity score (p-score); Ti is a binary variable for renting land or renting out land for rural household i, and it represents the treatment here; Xi is the vector of exogenous variables; βi is a vector of parameters to be estimated. All variables are defined in the next section. The p-score estimation Sustainability 2019, 11, 2402 4 of 15 ranks households according to their own behavior toward renting land or renting out land. Thus, this study estimated the effects of farmland rental among groups of farmers with similar behaviors. In our study, households renting land (estimating the effects of renting land) and households renting out land (estimating the effects of renting out land) were considered as treatment groups, and those not involved in farmland rental were considered as control groups. Then, each household in the treatment group was matched with a household in the control group according to their p-scores. Three of the most widely-used matching methods, nearest neighbor matching (NNM), radius-based matching (RBM), and kernel-based matching (KBM) [42], were used in this study. To make two samples with similar p-scores from different matched groups, NNM entails backward and forward searching to identify one or more samples in the control group with the closet p-scores for the samples in the treatment group; RBM entails searching all samples of the control group occurring within a certain radius according to their p-scores for the samples in the treatment group; KBM entails matching each sample in the treatment group with a weighted average of all weighted controls that are inversely proportional to the distance between p-scores of the samples in the treatment and the control groups [42]. After matching, the balancing hypothesis should be tested to check whether the two households from different groups are, in fact, similar. Households with the same or similar p-score are assumed to have the same distribution of Xi, irrespective of their farmland rental status in the balancing hypothesis. Moreover, the common support condition that bounds the p-score away from zero and one and excludes the tails of the distribution of p-score is imposed to improve the quality of matches [37]. The p-score density functions of two groups should be proximate, thus indicating similar characteristics of X in the two groups after matching according to the common support assumption. Two household groups after matching are considered to behave similarly when the balancing hypothesis and the common support assumption are simultaneously satisfied [43]. The main feature of PSM is creating the conditions of a randomized experiment to evaluate a causal effect as in a controlled experiment. Thus, the conditional independence assumption is made that farmland rental is random and unrelated to fertilizer (chemical fertilizer here) and pesticide cost with controlled X. The conditional independence assumption is more plausible than in the case of ordinary least square (OLS), since farmland rental is assumed to be random within households having the same behavior towards farmland rental [37]. Then, the impact of renting land or renting out land on fertilizer and pesticide cost of wheat and maize production is:       1 0 1 0 f (Xi) = E Y Y Xi = E Y T = 1, Xi E Y T = 0, Xi (2) i − i i − i where f (Xi) is the difference of fertilizer or pesticide cost of wheat and maize production between a 1 0 household i from treatment group and its matched household from control group; Yi and Yi represent the fertilizer or pesticide cost of wheat and maize production of a household i from treatment group and those of its matched household from control group, respectively; T = 1 and T = 0 indicate a household from the treatment group and from the control group, respectively. To facilitate comparison, the cost of fertilizer and pesticide were used to represent the use of fertilizer and pesticide in this study. There are several reasons for using agrochemical cost to represent agrochemical use. Firstly, it is improper to use the real agrochemical use amount, since different households used different agrochemical types with different ingredients and concentrations. The amounts of agrochemicals with different ingredients and concentrations cannot be directly compared. Secondly, in the study area, almost all of households brought their agrochemical from local stores of the nearest towns, and the prices of agrochemicals were stable and transparent. In addition, only several households had a huge farm size after renting land, thus households in our study did not get the point of reducing the input price by large volume purchase. For example, only three households in our study had a farm size over one hundred mu (the mu unit is widely used as the unit of farmland in China, and 1 mu is 1/15 hectare). Hence, agrochemical cost should be a good index to represent agrochemical use in our study. Sustainability 2019, 11, 2402 5 of 15

The average effect of farmland rental is:

ATT = E[ f (Xi)] (3) where ATT is the average effect of fertilizer or pesticide cost of wheat and maize production resulting from farmland rental. It is worth noting that small sample bias remained in the statistical analysis of this study.Sustainability To reduce 2019, 11, thisx FOR bias,PEER REVIEW the bootstrap method was employed to estimate standard5 of 16 errors of ATTs. where ATT is the average effect of fertilizer or pesticide cost of wheat and maize production resulting 4. Survey andfrom Datafarmland rental. It is worth noting that small sample bias remained in the statistical analysis of this study. To reduce this bias, the bootstrap method was employed to estimate standard errors of The dataATTs. for this study were derived from a survey of rural households conducted in eight selected counties of4. Gansu Survey and province, Data China (Figure1). Gansu province is a typical grain production area in a less-well developed region of China with relatively poor agricultural production conditions and The data for this study were derived from a survey of rural households conducted in eight low level ofselected urbanization. counties of Gansu It has province, a land China area (Figure of about 1). Gansu 4.7% province of the is total a typical area grain of Chinaproduction and is one of the major grainarea in producing a less-well developed provinces region in of Northwestern China with relatively China. poor Theagricultural planted production grain crops conditions area of Gansu is about 2.5%and oflow the level national of urbanization. level duringIt has a land 2015, area whichof about is4.7% larger of the than total area that of of China most and other is one provincesof in the major grain producing provinces in Northwestern China. The planted grain crops area of Gansu Northwesternis about China 2.5% [of44 the]. Innational addition, level during the farmland 2015, which rental is larger market than that in of Gansu most other province provinces has in developed rapidly. ForNorthwestern example, China only [44]. 4.1% In ofaddition, contracted the farmland farmland rental market had been in Gansu leased province in September has developed 2010, while 24.6% of contractedrapidly. For example, farmland only had 4.1% been of contracted leased fa inrmland September had been 2016 leased [ 45in ].September Thus, the2010, studywhile of Gansu province may24.6% provide of contracted some farmland hints for had not been only leased Northwestern in September 2016 China [45]. but Thus, also the forstudy some of Gansu less developed province may provide some hints for not only Northwestern China but also for some less developed areas similarareas to similar Gansu to inGansu other in other countries. countries.

FigureFigure 1. 1.Location Location of of study study regions. regions. Eight counties with different economic development levels located in different parts of Gansu Eightprovince counties were with selected different as sample economic regions (Figure development 1). These counties levels contain located the majority in different of landform parts of Gansu province weretypes selected and planting as sample models regions in Gansu (Figure province,1). These such as counties contain in the Hexi majority Corridor of landform and types and plantingHuining models county in Gansu on the Loess province, Plateau. such The face-to- as Minleface countyquestionnaire in the survey Hexi was Corridor conducted and with Huining the county household heads during April to September 2015 by students of Lanzhou University after proper on the Loess Plateau. The face-to-face questionnaire survey was conducted with the household heads training, and sample households were randomly selected. The questionnaire contained demographic during Aprilinformation, to September planting 2015 and breeding by students situation, of Lanzhouinputs and outputs University of crops, after farmland proper types training, and area, and sample householdsfarmland were randomly rental situation selected. in 2014, The and questionnaire willingness to take contained part in farmland demographic rental. A information, total of 900 planting and breedinguseable situation, questionnaires inputs andwas collected outputs (144 of crops,from Xifeng farmland county, types 157 from and Yuzhong area, farmland county, 123 rental from situation in Minle county, 28 from , 30 from , 137 from Jingning county, 132 from 2014, and willingnessHuining county, to takeand 149 part from in Min farmland county). rental. A total of 900 useable questionnaires was collected (144 from XifengWheat county, and maize 157 were from selected Yuzhong to be county,included 123in this from study, Minle since they county, were 28two from main Gulanggrain county, 30 from Lingtaicrops county,taking up 137 56.54% from of the Jingning total planted county, area 132 in the from eight Huining counties. county,The farmland and 149rental from and the ). Wheatplanting and maize situations were of selectedwheat and to maize be included in the stud iny thisarea study,are reported since in they Table were 1. There two were main 479 grain crops households that planted wheat, and 84 of them rented and 71 of them rented out some land. The taking up 56.54% of the total planted area in the eight counties. The farmland rental and the planting situations of wheat and maize in the study area are reported in Table1. There were 479 households Sustainability 2019, 11, 2402 6 of 15 that planted wheat, and 84 of them rented and 71 of them rented out some land. The number of households that planted maize was 452, and 78 of them rented and 65 of them rented out some land. Of the 900 surveyed households, 114 were renting land and 209 renting out land, i.e., 12.67% and 23.22%, respectively. The farmland rental market in Gansu province was still at its infancy in 2014.

Table 1. Farmland renting and crop planting situations.

Planting Situations Renting Land Renting Out Land Not Renting Land Total Households planting wheat 84 71 324 479 Households planting maize 78 65 309 452 Households planting both wheat and maize 62 48 226 336 Households not planting either wheat or maize 14 121 170 305 Total 114 209 577 900 Note: “Total” in the row is the number of households with different farmland rental status without regard to the crop planting situation; “Total” in the column is the number of households with different crop planting situations without regard to their farmland rental status.

Some descriptive information of the study area and the definition of variables is reported in Table2. According to previous studies, exogenous variables X including variables of farmer characteristics (head age, age square, occupation, education, health condition, labor ratio, income, and off-farm income), variables of farmland characteristics (land area, irrigation, landscape, and block size), some other factors (farmland reallocation and farmland rental policy) and county dummies (Xifeng, Yuzhong, Minle, Gulang, Lingtai, Jingning, and Huining) were expected to influence farmland rental behaviors of households [7,29,46].

Table 2. Descriptive information of the study area.

Variable Mean SD Min. Max. Observations Renting land 0.127 0.333 0 1 900 Renting out land 0.232 0.422 0 1 900 Head age 50.761 11.141 21 81 900 Age square 2700.681 1154.397 441 6561 900 Occupation 0.811 0.392 0 1 900 Education 6.143 3.809 0 16 900 Health condition 0.757 0.429 0 1 900 Labor ratio 0.660 0.231 0 1 900 Income 11.648 9.879 0.180 101.155 900 Off-farm income 0.689 0.323 0 1 900 Land area 2.585 2.136 0.057 16.500 900 Irrigation 0.360 0.431 0 1 900 Landscape 0.718 0.293 0 1 900 Block size 1.746 0.882 0.584 3.946 900 Farmland reallocation 1.113 0.952 0 3 900 Farmland rental policy 0.490 0.500 0 1 900 Xifeng 0.160 0.367 0 1 900 Yuzhong 0.174 0.380 0 1 900 Minle 0.137 0.344 0 1 900 Gulang 0.031 0.174 0 1 900 Lingtai 0.033 0.180 0 1 900 Jingning 0.152 0.359 0 1 900 Huining 0.147 0.354 0 1 900 Min 0.166 0.372 0 1 900 Note: The mu unit is widely used as the unit of farmland in China, and 1 mu is 1/15 hectare; renting land = 1 if a household rented in some land, otherwise = 0; renting out land =1 if a household rented out some land, otherwise = 0; head age is the age of household head; head age square is the square of head age; occupation = 1 if the household head was farming, otherwise = 0; education is the education year of household head; health condition =1 if the health condition of household head was good, otherwise = 0; labor ratio is the quotient of labor numbers to family size and family labors, including all family members who were able-bodied, not in school, and between 16 and 65 years old [7]; income is the annual income per capita, measured in thousand yuan; off-farm income is the quotient of off-farm income to total income of a household; land area is the original owned farmland area per capita (the contracted farmland), measured in mu; irrigation is the ratio of irrigable land at village levels; landscape is the ratio of farmland in level areas at village levels; block size is the average block size at village levels, measured in mu; farmland reallocation is the frequency of which the farmland has been reallocated by any kind of reallocation since the start of Household Contracted Responsibility System (HCRS); farmland rental policy is the answer of households to the question, “Do you know that the Chinese Central Government is encouraging farmland rental?”. Sustainability 2019, 11, 2402 7 of 15

The descriptive information of wheat and maize production is presented in Table3. The average planted wheat and maize area was 4.580 mu and 4.213 mu, respectively. The average output of wheat was 296.718 kg mu 1, which is approximately in line with the data of Rada et al. [6], and the average · − output of maize was 579.153 kg mu 1. The data suggest a significant variability in fertilizer and · − pesticide use as well as crop outputs in the study area.

Table 3. Fertilizer and pesticide use and output of wheat and maize production.

Variable Mean SD Min. Max. Observations Wheat analysis Planted area 4.580 13.920 0.300 300.000 479 Output per mu 296.718 98.933 45.000 570.000 479 Fertilizer cost per mu 100.754 47.786 0.000 260.000 479 Pesticide cost per mu 11.745 13.886 0.000 73.000 479 Fertilizer cost per kg wheat 0.360 0.224 0.000 1.895 479 Pesticide cost per kg wheat 0.041 0.050 0.000 0.322 479 Maize analysis Planted area 4.213 4.968 0.100 80.000 452 Output per mu 579.153 159.784 220.000 1050.000 452 Fertilizer cost per mu 126.914 61.756 0.000 330.000 452 Pesticide cost per mu 16.566 16.873 0.000 95.000 452 Fertilizer cost per kg maize 0.230 0.137 0.000 1.222 452 Pesticide cost per kg maize 0.030 0.033 0.000 0.259 452 Note: 1 mu is 1/15 hectare; planted area is measured in mu; output per mu is measured in kg; all the input costs are measured in yuan.

5. Empirical Results

5.1. Estimation Results of the Propensity Score The Logit model was used to estimate propensity scores of rural households for renting land and renting out land. A condition number test was made to test the multicollinearity of explanatory variables before estimating models. The condition index of explanatory variables in the Logic models of renting land (149.85) and renting out land (140.80) were both lower than 30 [47], indicating there was no multicollinearity in the Logic regressions (Table4). Robust standard errors were applied to reduce the heteroscedasticity in the regressions. The pseudo R2 value, which was widely used to measure the good fitness of a model, was 0.171 (renting land) and 0.337 (renting out land) in this study, showing the models had good fit with micro data [42]. Additionally, the AUC indicator (area under the receiver operating characteristic curve) greater than 0.7 (0.785 for renting land and 0.866 for renting out land) could be considered a good indicator that the model’s specifications were appropriate in this study [48]. Results of Logit formulations for the propensity scores of renting land and renting out land are reported in Table4. Some farmer and farmland characteristics significantly influenced farmland rental. For farmer characteristics, an inverted U-shaped relationship was found between renting land behavior and household head age with a peak point of 50.430 years old. Households whose heads were not involved in farming were less likely to rent out their land ( 1.749). Off-farm income ratio was − negatively ( 1.022) correlated with renting land behavior but positively (0.815) correlated with renting − out land behavior. These results are in line with the study of Kung [28] and Huang et al. [7]. For farmland characteristics, contracted land area per capita was positively (0.136) correlated with renting out land behavior of the household, indicating households lacking labors were more likely to rent out their land than those with sufficient labors. The significant positive coefficients of irrigation (1.172) and landscape (2.121) indicated a high demand for high quality farmland. The significant negative coefficient ( 1.110) of block size indicated households tended to rent out their land with small area. − The significant negative effect of farmland reallocation ( 0.556 for renting land and 1.121 for renting − − out land) and significant positive effect of rural households’ perceptions of farmland rental policy (1.170 for renting land and 1.395 for renting out land) on households’ renting land and renting out Sustainability 2019, 11, 2402 8 of 15 land behaviors indicate the importance of farmland right security and government support on the development of farmland rental market in China. The location of rural households also had some effects on households’ farmland rental behaviors.

Table 4. Estimation of the propensity score for renting land and renting out land.

Renting Land Renting Out Land Variable Coefficient Robust Standard Error Coefficient Robust Standard Error Intercept 11.882 *** 3.770 0.046 2.062 − − Head age 0.403 *** 0.147 0.112 0.077 Age square 0.004 *** 0.002 0.001 0.001 − − Occupation 0.579 0.460 1.749 *** 0.261 − Education 0.050 0.350 0.047 0.033 − − Health condition 0.004 0.322 0.502 * 0.279 − Labor ratio 0.166 0.546 0.486 0.502 − Income 0.014 * 0.010 0.019 * 0.010 Off-farm income 1.022 *** 0.374 0.815 *** 0.146 − Land area 0.026 0.072 0.136 ** 0.053 − Irrigation 1.172 * 0.627 0.720 0.64 Landscape 2.121 ** 1.050 1.151 0.981 − Block size 0.310 0.503 1.110 ** 0.442 − Farmland reallocation 0.556 *** 0.179 1.121 *** 0.172 − − Farmland rental policy 1.170 *** 0.304 1.395 *** 0.254 Xifeng 2.960 * 1.661 2.863 ** 1.419 − Yuzhong 0.241 0.588 0.084 0.592 − Minle 0.163 0.833 0.628 0.869 − − Gulang 0.063 0.993 0.145 1.147 − Lingtai 0.340 0.724 0.790 * 1.054 − Jingning 0.277 0.678 1.155 ** 0.662 Huining 0.386 0.660 1.602 0.738 − Observations 691 786 Pseudo R2 0.171 0.337 Log pseudolikelihood 256.598 301.945 − − Condition index 149.85 140.80 AUC 0.785 0.866 Notes: *, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively. AUC= area under the receiver operating characteristic curve.

5.2. Tests of Sample Matching Estimating p-scores enabled similar samples from the treatment and the control groups to be obtained after sample matching. Three matching methods (NNM, RBM, and KBM) were used to estimate the effects of farmland rental on fertilizer and pesticide costs of wheat and maize production in this study, and results from different methods were the same (Table 7). Thus, only the test results for the matching quality with NNM are reported here. Tables5 and6 report the results of balancing hypothesis tests to the sample matching with NNM. The results indicated that significant differences based on characteristics of X between the treatment groups and the corresponding control groups did not exist after matching [49], and all the biases after matching were below 10% [50,51]. Thus, the balancing hypothesis was satisfied in this study. The kernel density functions before and after matching for the treatment and the control groups are shown in Figure2. It was apparent from the figures that di fferences in the density of functions between treatment groups and their corresponding control groups were highly significant prior to conducting the matching procedure. After matching, the distribution density functions of treatment groups and their corresponding control groups were very similar, and there was an evident decrease in their deviations. This evident contrast indicated that the common support assumption was satisfied in this study [37,42]. Sustainability 2019, 11, 2402 9 of 15

Table 5. Balancing hypothesis test showing characteristics of variables before and after matching in wheat analysis.

Renting Land Renting Out Land U Mean of Mean of Variable Mean of Mean of M Treatment Bias (%) Treatment Bias (%) Control Group Control Group Group Group U 49.440 50.985 15.6 50.000 50.985 8.1 Head age − − M 49.617 49.512 1.1 49.941 50.457 4.7 − U 2508.000 2730.900 21.9 2610.800 2730.900 10.0 Age square − − M 2526.100 2525.500 0.1 2609.400 2660.700 4.6 − U 0.964 0.883 31.0 ** 0.845 0.883 10.9 Occupation − M 0.963 0.954 3.5 0.868 0.834 9.7 U 6.595 6.556 1.1 6.592 6.556 1.0 Education M 6.728 6.963 6.4 6.574 6.863 7.6 − − U 0.833 0.716 28.3 ** 0.718 0.716 0.5 Health condition M 0.852 0.824 6.7 0.735 0.717 4.1 U 0.674 0.648 12.1 0.686 0.648 16.1 Labor ratio M 0.673 0.656 7.9 0.683 0.684 0.5 − U 12.147 11.781 2.7 11.351 11.781 3.8 Income − M 10.907 12.108 8.9 11.035 11.328 2.6 − − U 0.571 0.707 43.1 *** 0.736 0.707 11.5 Off-farm income − M 0.592 0.576 5.0 0.732 0.736 1.3 − U 2.819 2.697 6.4 2.803 2.697 5.5 Land area M 2.861 2.709 7.9 2.765 2.789 1.3 − U 0.452 0.342 24.5 ** 0.209 0.342 32.9 Irrigation − M 0.456 0.485 6.4 0.218 0.218 0.1 − U 0.749 0.769 7.3 0.793 0.769 9.7 Landscape − M 0.748 0.756 2.9 0.802 0.797 1.6 − U 1.655 2.042 50.6 *** 2.155 2.042 12.8 Farmland reallocation − M 1.662 1.632 4.0 2.177 2.169 0.9 U 0.679 1.083 50.4 *** 0.479 1.083 79.3 *** Farmland rental policy − − M 0.679 0.757 9.7 0.657 0.683 -6.8 − U 0.690 0.404 59.8 *** 0.394 0.404 2.0 Block size − M 0.674 0.662 5.7 0.338 0.339 0.2 − U 0.060 0.250 54.5 *** 0.324 0.250 16.3 Xifeng − M 0.062 0.056 1.8 0.338 0.338 0.1 − U 0.071 0.130 19.4 0.056 0.130 25.4 * Yuzhong − − M 0.074 0.108 9. 0.059 0.063 1.5 − − U 0.214 0.105 30.1 *** 0.127 0.105 6.8 Minle M 0.610 0.207 0.8 0.132 0.135 0.8 − U 0.036 0.068 14.5 0.000 0.068 38.1 ** Gulang − − M 0.037 0.034 1.4 0.000 0.000 0.0 U 0.071 0.062 3.9 0.028 0.062 16.2 Lingtai − M 0.049 0.040 3.7 0.029 0.032 1.2 − U 0.286 0.154 32.0 *** 0.423 0.154 61.7 *** Jingning M 0.296 0.291 1.5 0.397 0.378 4.5 U 0.202 0.173 7.5 0.028 0.173 49.5 *** Huining − M 0.210 0.210 0.0 0.029 0.035 2.0 − U 0.060 0.059 0.4 0.014 0.059 23.9 Min − M 0.062 0.056 2.6 0.015 0.019 2.4 − U 84 71 Treated observations M 74 62 U 324 324 Control observations M 302 264 U 408 395 Total observations M 376 326 Note: U and M indicate before and after matching, respectively; *, ** and *** indicate that the difference between the treated and control groups is significant at 10%, 5%, and 1% levels, respectively; bias is the standardized difference [50]. The same in Table6. Sustainability 2019, 11, x FOR PEER REVIEW 11 of 16

Figure 2.FigureCommon 2. Common support support assumption assumption test test to to densitiesdensities of of propensity propensity score score of wheat of wheat farmers farmers with with different land rental status before matching (A,B) and after matching (E,F), and maize farmers with different land rental status before matching (A,B) and after matching (E,F), and maize farmers with different land rental status before matching (C,D) and after matching (G,H). different land rental status before matching (C,D) and after matching (G,H). 5.3. Effects of Farmland Rental on Fertilizer and Pesticide Cost The estimated results of the effects of farmland rental on fertilizer and pesticide costs of wheat and maize production are presented in Table 7. Overall, renting land significantly reduced fertilizer and pesticide costs per mu for both wheat and maize production, meanwhile significantly reducing the fertilizer and pesticide costs per kg output of maize production. Renting out land significantly reduced the output per mu, pesticide cost per mu, and pesticide cost per kg output of both wheat and maize production. Households renting land used significantly less fertilizer and pesticides per unit of land area for both wheat and maize production than those not renting land (Table 7). The “nearest-neighbor” casual effects of renting land on fertilizer and pesticide costs per mu of wheat production were significant and equal to −16.281 yuan and −4.239 yuan, respectively, which were the average differences between fertilizer and pesticide cost per mu for wheat production for similar pairs of households belonging to different groups (renting-land group and not-renting-land group). In other words, the average fertilizer and pesticide costs per mu of wheat production of households renting land were 16.281 yuan and 4.239 yuan less than those of households not renting land, respectively. Similarly, the average fertilizer and pesticide costs per mu of maize production of households renting land were 32.981 yuan and 5.502 yuan less than those of households not renting land, respectively. This resulted in a reduction of 0.060 yuan and 0.010 yuan in fertilizer and pesticide cost per kg maize, respectively, for households renting land compared with those not renting land.

Table 7. Matching estimates for effects of renting land and renting out land on fertilizer and pesticide uses.

Renting Land Renting Out Land Variable NNM RBM KBM NNM RBM KBM −14.599 −15.210 −14.077 −37.904*** −37.848 *** −38.706 *** Output per mu (13.785) (11.961) (11.454) (13.622) (12.716) (12.495) −16.281 ** −14.738 ** −14.789** −6.765 −8.290 −8.960 Fertilizer cost per mu (7.495) (6.653) (6.551) (6.971) (6.169) (6.177) −4.239 * −3.240 * -3.236* −7.325*** −6.723 *** −6.623 *** Pesticide cost per mu (2.174) (1.957) (1.840) (1.949) (1.342) (1.411) Fertilizer cost per kg −0.046 −0.056 -0.067 0.052 0.031 0.020 wheat (0.085) (0.063) (0.062) (0.040) (0.038) (0.046) Pesticide cost per kg −0.008 −0.056 -0.006 −0.017*** −0.017 *** −0.017 *** wheat (0.009) (0.008) (0.007) (0.006) (0.005) (0.006) −84.631 −91.262 −87.233 −93.306 * −75.290* −74.900 * Output per mu (59.621) (59.331) (59.094) (47.373) (43.170) (42.652) −32.981 *** −36.982 *** −36.985 *** −8.992 −9.114 −9.666 Fertilizer cost per mu (11.201) (10.361) (9.984) (9.894) (8.570) (8.385) Pesticide cost per mu −5.502 ** −5.916 ** −5.775 ** −7.708 *** −7.139 *** −7.218 *** Sustainability 2019, 11, 2402 10 of 15

Table 6. Balancing hypothesis test showing characteristics of variables before and after matching in maize analysis.

Renting Land Renting Out Land U Mean of Mean of Variable Mean of Mean of M Treatment Bias (%) Treatment Bias (%) Control Group Control Group Group Group U 48.821 51.638 30.2 ** 51.108 51.638 4.7 Head age − − M 49.014 48.840 1.8 51.083 51.872 7.1 − U 2445.900 2789.400 35.1 ** 2736.700 2789.400 -4.5- Age square − M 2467.300 2460.800 0.7 2742.800 2820.000 6.7 U 0.949 0.880 24.6 * 0.815 0.880 18.0 Occupation − M 0.945 0.946 0.4 0.850 0.830 5.5 − − U 7.423 6.641 22.6 * 6.369 6.641 7.3 Education − M 7.370 7.537 4.8 6.500 6.378 3.3 − U 0.876 0.741 26.1 * 0.692 0.741 10.8 Health condition − M 0.849 0.821 6.9 0.733 0.706 6.0 U 0.649 0.655 2.7 0.664 0.655 3.8 Labor ratio − M 0.650 0.639 5.1 0.671 0.659 5.5 U 11.459 11.805 2.9 12.288 11.805 3.9 Income − M 11.508 11.951 3.8 11.989 11.801 1.5 − U 0.597 0.668 21.7 * 0.717 0.668 17.0 Off-farm income − M 0.590 0.597 2.2 0.713 0.716 -1.1 − U 2.944 2.825 6.3 2.923 2.825 4.9 Land area M 2.926 2.893 1.7 2.571 2.754 9.2 − − U 0.446 0.344 23.7 * 0.222 0.344 31.1 ** Irrigation − M 0.463 0.463 0.1 0.222 0.255 8.5 − − U 0.744 0.693 16.8 0.794 0.693 36.2 ** Landscape M 0.746 0.746 0.2 0.805 0.806 0.5 − − U 1.731 2.085 47.7 *** 2.195 2.085 12.4 Block size − M 1.751 1.742 1.2 2.235 2.197 4.3 U 0.731 1.256 58.0 *** 0.462 1.256 94.4 *** Farmland reallocation − − M 0.731 0.718 1.4 0.500 0.694 8.3 − U 0.756 0.453 65.0 *** 0.308 0.453 30.2 ** Farmland rental policy − M 0.577 0.513 2.7 0.349 0.367 4.0 − U 0.077 0.217 40.2 *** 0.338 0.217 27.3 ** Xifeng − M 0.082 0.083 0.4 0.367 0.350 3.7 − U 0.103 0.188 24.3 * 0.108 0.188 22.6 Yuzhong − − M 0.110 0.109 0.2 0.117 0.139 6.4 − U 0.141 0.052 30.4 *** 0.062 0.052 4.2 Minle M 0.137 0.153 5.5 0.050 0.069 8.2 − − U 0.051 0.068 7.0 0.000 0.068 38.1 ** Gulang − − M 0.055 0.055 0.3 0.000 0.000 0.0 − U 0.051 0.006 26.8 *** 0.000 0.006 11.4 Lingtai − M 0.000 0.006 3.3 0.000 0.000 0.0 − U 0.269 0.168 24.5 *** 0.415 0.168 56.2 *** Jingning M 0.288 0.274 3.3 0.400 0.370 6.7 U 0.308 0.301 1.5 0.077 0.301 59.5 *** Huining − M 0.329 0.319 2.0 0.067 0.071 1.2 − U 78 65 Treated observations M 76 62 U 309 309 Control observations M 282 226 U 387 374 Total observations M 358 288

5.3. Effects of Farmland Rental on Fertilizer and Pesticide Cost The estimated results of the effects of farmland rental on fertilizer and pesticide costs of wheat and maize production are presented in Table7. Overall, renting land significantly reduced fertilizer and pesticide costs per mu for both wheat and maize production, meanwhile significantly reducing the fertilizer and pesticide costs per kg output of maize production. Renting out land significantly reduced the output per mu, pesticide cost per mu, and pesticide cost per kg output of both wheat and maize production. Households renting land used significantly less fertilizer and pesticides per unit of land area for both wheat and maize production than those not renting land (Table7). The “nearest-neighbor” casual effects of renting land on fertilizer and pesticide costs per mu of wheat production were significant and equal to 16.281 yuan and 4.239 yuan, respectively, which were the average differences between − − fertilizer and pesticide cost per mu for wheat production for similar pairs of households belonging to different groups (renting-land group and not-renting-land group). In other words, the average Sustainability 2019, 11, 2402 11 of 15 fertilizer and pesticide costs per mu of wheat production of households renting land were 16.281 yuan and 4.239 yuan less than those of households not renting land, respectively. Similarly, the average fertilizer and pesticide costs per mu of maize production of households renting land were 32.981 yuan and 5.502 yuan less than those of households not renting land, respectively. This resulted in a reduction of 0.060 yuan and 0.010 yuan in fertilizer and pesticide cost per kg maize, respectively, for households renting land compared with those not renting land.

Table 7. Matching estimates for effects of renting land and renting out land on fertilizer and pesticide uses.

Renting Land Renting Out Land Variable NNM RBM KBM NNM RBM KBM 14.599 15.210 14.077 37.904 *** 37.848 *** 38.706 *** Output per mu − − − − − − (13.785) (11.961) (11.454) (13.622) (12.716) (12.495) 16.281 ** 14.738 ** 14.789 ** 6.765 8.290 8.960 Fertilizer cost per mu − − − − − − (7.495) (6.653) (6.551) (6.971) (6.169) (6.177) 4.239 * 3.240 * 3.236 * 7.325 *** 6.723 *** 6.623 *** Pesticide cost per mu − − − − − − (2.174) (1.957) (1.840) (1.949) (1.342) (1.411) 0.046 0.056 0.067 0.052 0.031 0.020 Fertilizer cost per kg wheat − − − (0.085) (0.063) (0.062) (0.040) (0.038) (0.046) 0.008 0.056 0.006 0.017 *** 0.017 *** 0.017 *** Pesticide cost per kg wheat − − − − − − (0.009) (0.008) (0.007) (0.006) (0.005) (0.006) 84.631 91.262 87.233 93.306 * 75.290 * 74.900 * Output per mu − − − − − − (59.621) (59.331) (59.094) (47.373) (43.170) (42.652) 32.981 *** 36.982 *** 36.985 *** 8.992 9.114 9.666 Fertilizer cost per mu − − − − − − (11.201) (10.361) (9.984) (9.894) (8.570) (8.385) 5.502 ** 5.916 ** 5.775 ** 7.708 *** 7.139 *** 7.218 *** Pesticide cost per mu − − − − − − (2.785) (2.608) (2.338) (2.913) (2.355) (2.300) 0.060 *** 0.060 *** 0.061 *** 0.017 0.009 0.008 Fertilizer cost per kg maize − − − (0.021) (0.020) (0.021) (0.023) (0.022) (0.023) 0.010 ** 0.011 *** 0.010 *** 0.010 ** 0.010 ** 0.012 ** Pesticide cost per kg maize − − − − − − (0.005) (0.003) (0.004) (0.005) (0.005) (0.004) Note: 1 mu is 1/15 hectare; standard errors were derived from bootstrap method with 10,000 replications; *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively; outputs are measured in kg and all costs are measured in yuan. NNM= nearest neighbor matching, RBM= radius-based matching, KBM= kernel-based matching.

Surprisingly, households renting out land also significantly reduced their pesticide cost per mu and output per mu of wheat and maize production, but not fertilizer cost, compared with those not renting land (Table7). Meanwhile, reduction in outputs per mu was also observed in both wheat and maize production of households renting out land. The average pesticide costs per mu of wheat and maize production of households renting out land were 7.325 yuan and 7.708 yuan less than those of households not renting land with NNM, respectively. The significantly lower average outputs per mu of wheat and maize production of households renting out land, which were 37.904 kg and 93.306 kg lower than those of households not renting land, respectively, might partially be attributed to the reduction of pesticide use in wheat and maize production. The reduction on pesticide use also caused a reduction of 0.017 yuan and 0.010 yuan in pesticide cost per kg outputs of wheat and maize, respectively, for households renting land compared with those not renting land.

5.4. Situation of Adoption of New Agricultural Technologies and Management Methods Figure3 shows the situation of adoption of new agricultural technologies and management methods in the study area. The results indicated that households adopting new agricultural technologies or management methods took up 29.79% of the renting-land group, 5.08% of the renting-out-land group, and 10.14% of the not-renting-land group. The percentage of households in the renting-land group adopting new technologies and management methods was almost three times that of households in the not-renting-land group, while the percentage of households adopting new technologies and management methods of renting-out-land group was only around half of that of the not-renting-land group. Sustainability 2019, 11, x FOR PEER REVIEW 12 of 16

(2.785) (2.608) (2.338) (2.913) (2.355) (2.300) Fertilizer cost per kg −0.060 *** −0.060 *** −0.061*** 0.017 0.009 0.008 maize (0.021) (0.020) (0.021) (0.023) (0.022) (0.023) Pesticide cost per kg −0.010 ** −0.011 *** −0.010 *** −0.010 ** −0.010 ** −0.012 ** maize (0.005) (0.003) (0.004) (0.005) (0.005) (0.004) Note: 1 mu is 1/15 hectare; standard errors were derived from bootstrap method with 10000 replications; *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively; outputs are measured in kg and all costs are measured in yuan. NNM= nearest neighbor matching, RBM= radius- based matching, KBM= kernel-based matching.

Surprisingly, households renting out land also significantly reduced their pesticide cost per mu and output per mu of wheat and maize production, but not fertilizer cost, compared with those not renting land (Table 7). Meanwhile, reduction in outputs per mu was also observed in both wheat and maize production of households renting out land. The average pesticide costs per mu of wheat and maize production of households renting out land were 7.325 yuan and 7.708 yuan less than those of households not renting land with NNM, respectively. The significantly lower average outputs per mu of wheat and maize production of households renting out land, which were 37.904 kg and 93.306 kg lower than those of households not renting land, respectively, might partially be attributed to the reduction of pesticide use in wheat and maize production. The reduction on pesticide use also caused a reduction of 0.017 yuan and 0.010 yuan in pesticide cost per kg outputs of wheat and maize, respectively, for households renting land compared with those not renting land.

5.4. Situation of Adoption of New Agricultural Technologies and Management Methods Figure 3 shows the situation of adoption of new agricultural technologies and management methods in the study area. The results indicated that households adopting new agricultural technologies or management methods took up 29.79% of the renting-land group, 5.08% of the renting- out-land group, and 10.14% of the not-renting-land group. The percentage of households in the renting-land group adopting new technologies and management methods was almost three times that of households in the not-renting-land group, while the percentage of households adopting new Sustainabilitytechnologies2019 and, 11, 2402management methods of renting-out-land group was only around half of that12 of of 15 the not-renting-land group.

Figure 3. Percentage of households adoptingadopting new agricultural technologiestechnologies or management methods in the recent fivefive years for different different household grou groupsps depending on different different farmland rental status. status. Households renting out landland areare thosethose rentingrenting aa partpart ofof contractedcontracted landland andand stillstill farming.farming.

6. Discussion This studystudy shedshed newnew light light on on food food safety safety by by determining determining the the eff ectseffects of farmlandof farmland rental rental on theon usethe ofuse fertilizer of fertilizer and pesticidesand pesticides in grain in grain production. production. To overcome To overcome the selection the selection bias problem bias problem of the surveyof the data,survey propensity data, propensity score matching score matching (PSM) was (PSM) employed was employed in this study, in this which study, perfectly which perfectly removed removed selection biasselection through bias creating through a creating context closea context to experimental close to experimental conditions conditions [39]. [39]. Our study clearly showed that households renting land significantlysignificantly reduced their fertilizer and pesticide useuse in wheat and maize production without decreasing cropcrop outputsoutputs byby much.much. This is partly explained by the findings of Ju et al. [52] and Pan et al. [14] that increasing farm size sharply reduces the use of fertilizer for agricultural production. One possible explanation is that the increase in farm size enables households to apply some new farming technologies and management methods based on scientific knowledge such as the 4R fertilization technology, which involves applying the right kind and amount of fertilizer at the right time by using the right application methods [53]. Moreover, households renting land often had higher expectations of profit from farming than those not renting land, and this encouraged them to be more specialized. Thus, a more rational amount of fertilizer and pesticide was used by households renting land. Indeed, this is confirmed by the results that households renting land had adopted a significantly higher percentage of new agricultural technologies and management methods than those not renting land. However, it is still worth noting that only 29.79% of the renting-land group had adopted new agricultural technologies and management, suggesting the adoption of new agricultural technologies and management methods adoption is limited in the study area. This may be attributed to the low level of economic development and poor education in the study area, hence farmers’ education about technology should be further improved. On the other hand, households renting out land also significantly reduced their pesticide use of wheat and maize production but not fertilizer use. The numerous incidents of China’s grain food safety, such as the cadmium rice event in 2013 and the rice adulteration problems in 2015 [54], have weakened the public’s confidence in the safety of grain foods sold at markets. Thus, some households renting out land still have small patches of land for growing subsistence crops for their own use [55], which receive reduced amounts of agrochemicals or even no agrochemicals. Therefore, less pesticide use in wheat and maize production of households renting out land was observed. However, subsistence crop growth was not sensitive to fertilizer use, since the harmful effects of chemical fertilizer to human health are indirect. This caused the non-responsiveness of fertilizer use to renting out land. In addition, households renting out land often devoted more time and effort to off-farm working than to farming [9], which was also supported by the result that the percentage of households adopting new technologies and management methods of the renting-out-land group was much lower than that of other groups. Together with the reduction of pesticide use, there was a dramatic reduction in outputs for wheat and maize production among households renting out land. Sustainability 2019, 11, 2402 13 of 15

In general, farmland rental significantly reduced fertilizer and pesticide use for production of both wheat and maize, but renting land and renting out land go different ways. Households renting land reduced their fertilizer and pesticide use by rational use of agrochemicals and by applying new agricultural technologies and management methods, while those renting out land reduced their pesticide use due to concerns about grain food safety and planted food for subsistence. Our results are based on data from a single year, and this is a limitation to understanding the effects of changes in farmland rental on agrochemical use over time. However, our results still provide important references for farmland rental and food safety for policymakers. Besides, this study only determined the impacts of farmland rental on agrochemical use, which is an indirect indicator of food safety. Thus, the impacts of farmland rental to direct indicators of food safety such as pesticide residues and heavy metal content of the food should be further studied.

7. Conclusions and Implication The causal treatment effect of farmland rental on agrochemical use of grain production was estimated in this study. The results indicate that farmland rental can greatly reduce the agrochemical use of grain production. Additionally, the mechanism of households’ renting and renting out land behaviors influencing agrochemical use was also analyzed. The study found that households renting land mainly reduced agrochemical use through rational agrochemicals usage and new agricultural technology and management method applications, while those renting out land mainly reduced agrochemical use with concerns of subsistence grain safety. The results of this study have important implications for farmland rental, food security, and environmental conservation. As a conclusion, our findings suggest that farmland rental contributes to increased food safety and environmental conservation by reducing fertilizer and pesticide use. The development of China’s farmland rental markets will continually benefit not only agricultural productivity but also food safety. With the further development of farmland rental markets in China, farmers’ knowledge on agricultural technologies and management will become increasingly important to the improvement of productivity and food safety in China. To ensure agricultural productivity and food safety, support for farmer education and training should be increased further.

Author Contributions: Y.L. calculated and analyzed the data, and wrote the paper. Z.N. designed the research project. C.W. and Z.T. gave suggestions for the whole study. Funding: This work was funded by the Chinese Center for Strategic Research of Grassland Agriculture Development (SRGAD) and the Chinese Academy of Engineering (2018-XZ-25). Conflicts of Interest: The authors declare no conflict of interest.

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