What could promote farmers to replace chemical fertilizers with organic fertilizers?

What could promote farmers to replace chemical fertilizers with organic fertilizers?

Accepted Manuscript What could promote farmers to replace chemical fertilizers with organic fertilizers? Yan Wang, Yuchun Zhu, Shuoxin Zhang, Yongqia...

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Accepted Manuscript What could promote farmers to replace chemical fertilizers with organic fertilizers?

Yan Wang, Yuchun Zhu, Shuoxin Zhang, Yongqiang Wang PII:

S0959-6526(18)32211-X

DOI:

10.1016/j.jclepro.2018.07.222

Reference:

JCLP 13676

To appear in:

Journal of Cleaner Production

Received Date:

20 October 2017

Accepted Date:

23 July 2018

Please cite this article as: Yan Wang, Yuchun Zhu, Shuoxin Zhang, Yongqiang Wang, What could promote farmers to replace chemical fertilizers with organic fertilizers?, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.07.222

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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What could promote farmers to replace chemical fertilizers with organic fertilizers? Yan Wanga,b, Yuchun Zhuc, Shuoxin Zhanga*, Yongqiang Wang c* a. College of Forestry, Northwest A& F University, Yangling, 712100, Shaanxi, PR China b. College of Science, Northwest A& F University, Yangling, 712100, Shaanxi, PR China C. College of Economics & Management, Northwest A& F University, Yangling, 712100, Shaanxi, PR China

Abstract: To lessen the negative environmental impact of chemical fertilizers, replacing chemical fertilizers with more organic fertilizers for famers is a good choice. However, most of the farmers would like to use chemical fertilizers instead of organic fertilizers in developing countries, mainly because they fear that they may lose income if they use organic fertilizers instead of chemical fertilizers. From this point, policy makers need to find strategies to incentivize farmers to use organic fertilizers instead of chemical fertilizers. Therefore, a randomly selected household survey on the use of chemical and organic fertilizers by apple growers in China was conducted from July 2016 to October 2016. Its aim is to find out what could promote farmers to replace chemical fertilizers with organic fertilizers partially or completely. We have analyzed farmers’ choices involving prospect utility, risk, and environment based on Kahneman’s prospect theory and Lewin's field theory. Twelve psychological and socio-economic variables were included in a tobit regression model to explain farmers’ choice between organic fertilizers and chemical fertilizers. We find that membership in agriculture cooperatives, subsidies on organic fertilizers, and farm size play positive roles in influencing farmers' choice of organic fertilizers instead of chemical fertilizers. The results will be helpful to update extension policy of organic fertilizers. Key words: chemical and organic fertilizers; apple growers; choice; tobit model regression; prospect theory; field theory

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What could promote farmers to replace chemical fertilizers with organic fertilizers? 1. Introduction Globally, crop yields have increased steadily and food security has been improved due to increasing

inputs of chemical fertilizers and the adoption of new technologies (Tilman, D., et al., 2002; Boli Ni, et al., 2011; Yinghua Duan, et al., 2016). However, increasing chemical fertilizers not only contributes to food security, but also causes soil deterioration, greenhouse gas emissions, and water contamination (Wen-yuan Huang and Rhona,1992; Tilman et al., 2001; Johanna Link et al., 2006 ; Han and Zhao, 2009; Ju et al., 2009; Wauters et al., 2010; Liu, X. J. et al,2013.; Stuart et al., 2014; Wei-feng Zhang, et al,2014; Sierra et al., 2015; Smith, L. E. D. & Siciliano, G. 2015; Norman Uphoff, Frank B. Dazzo,2016). Studies have shown that crops can take up only 30 to 50% of chemical fertilizers, thus a great amount of the applied components is lost in the soil where it pollutes groundwater (Norse, 2005; MEP, 2010; Zsófia Mózner, et al, 2012). For example, nitrogen use efficiency (NUE) has decreased during the past two decades, with much of this excess nitrogen fertilizer being lost to the environment (MOA, 2009; NBSC, 2013). The efficiency of chemical fertilizer use has decreased because of fertilizer saturation (Zsófia Mózner, et al, 2012; Carter et al., 2012). To ensure food security for nearly one fifth of world population, China uses more chemical fertilizers than any other country (FAOSTAT, 2014). Since the 1970s, Chinese farmers have paid 50 to 75% less for urea fertilizer than the world market price (Li et al., 2013). Some authors assert that this has contributed to excessive chemical fertilizer use (Sun et al., 2012; Li et al., 2013). More chemical fertilizers were used on high value horticultural crops than cereal crops in China (Zhang and Powlson, 2010; Rahn, 2010), and apples rank in the top 2 in acreage and yield in fruit crops (NBSC, 2013-2016). Therefore, we have focused on ways to reduce the use of chemical fertilizers in apple orchards in Shaanxi, which has the largest apple planting area in China. Thus, we face a challenge in the increasing use of chemical fertilizers, and it is essential to develop related strategies for sustainable agriculture (Yinghua Duan, et al., 2016). There are several ways to control the increasing use of chemical fertilizers, which mainly include technical and policy solutions. On the one hand, technical solutions can be used to reduce chemical fertilizer use, including soil testing and fertilizer application, controlled-release fertilizers, crop rotation or intercropping, organic–inorganic compound fertilizers, organic fertilizers, and recycled agriculture (Sun and Huang, 2012; Garnet and Wilkes, 2014; Ji X H, et al, 2007; Boli Ni et al, 2011; Wen-Yuan Huang and Noel D. Uri, 1992; Szumigalski and Van Acker, 2006; Amanullah, et al., 2010; Wipawee K., et al, 2006; Jun Zhao, et al., 2016; Bronick and Lal, 2005; Masto et al., 2006; Ge et al., 2010; Jannoura et al., 2014; Insam et al., 2015; Jian XIE, et al, 2010). The use of organic fertilizer has contributed significantly to environmental sustainability and

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increasing agricultural production (Dick and Gregorich, 2004; Conway and Barbier, 2013; Li Hui, et al, 2017). While soil testing, fertilizer application, and controlled release fertilizers reduce excessive chemical fertilizer use and nutrient loss in agriculture (Boli Ni, et al, 2011), their effects on environmental sustainability are not superior to organic fertilizer solution. Although crop rotation has positive environmental impacts, results have shown that very low harvest in plots without fertilizer use in long-term crop rotation (Rutunga, et al, 2006). South American countries have shown relatively positive development in organic fertilizer use (Nadia Adnan, et al, 2017). The incorporation of organic fertilizers with chemical fertilizers is a viable alternative to standard chemical fertilizers and is helpful to reduce the environmental impact of chemical fertilizers (Wipawee K., et al, 2006; Yinghua Duan, et al, 2016). Application of organic fertilizers instead of chemical fertilizers is economically feasible and is one of the environmentally sound long-term approaches to sustainable agriculture (Li Hui, et al, 2017; Ning Chuanchuan,et al, 2017). However, the effect of organic fertilizers on crop yield is slow and variable in the short term (Khaliq et al., 2006). The use of organic fertilizers needs more labor and monetary input to compare with the use of chemical fertilizers (Maggio A,2008; Wang Shanshan, Zhang Guangsheng,2013; Hu Hao,Yang Yongbing,2015). Thus, most of the farmers prefer to use chemical fertilizers rather than organic fertilizers to preserve crop yield (Smith et al., 2007), especially with the labor cost increasing rapidly in China. There is no widespread use of green fertilizer technology throughout developing countries (Pingali, 2012; D., Chadwick et al., 2015). On the other hand, the policy solutions to reduce chemical fertilizer use include providing subsidies for reducing chemical fertilizers or using organic fertilizers, tax increases on chemical fertilizers, and extension services of reducing chemical fertilizers or using organic fertilizers (Mark Brady,2003; Sun et al., 2012; Li et al., 2013; Garnet and Wilkes, 2014). To extend the use of organic fertilizers, national policies, including subsidies, are needed to establish the infrastructure necessary to collect, store, treat, distribute, and apply organic wastes to smallholder farms (Li et al., 2013; Luo et al. 2014; Jaza Folefack 2015; Lim et al., 2016). To combat the overuse of chemical fertilizers, China has launched a scheme of no further increase in chemical fertilizers since 2015. Some counties are carrying out an experiment with subsidies on organic fertilizers instead of chemical fertilizers. These sample counties are required by the Ministry of Agriculture to have a large growing area of fruits, vegetables, or tea. Large-scale farms, organic fertilizer factories, and agricultural cooperatives in the experiment counties can apply for subsidies on organic fertilizers instead of chemical fertilizers. The number of sample counties reached 100 in 2017. However, detailed incentive policies are still in exploration in these sample areas and have not been widely implemented. To develop effective incentive policies, we need to understand why farmers would like to

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use organic fertilizers instead of chemical fertilizers, that is to say, the driving forces of the farmers’ behavior change need to be explored. Different theories from psychology, social science, and economics are used to analyze individual behavior change. Socio-psychological methods are widely used to identify farmers' behavior change of adopting improved technology and environmental conservation behavior (Wauters and Mathijs, 2012; Poppenborg and Koellner, 2013; Borges et al., 2014; Yazdanpanah et al., 2014; Truelove et al., 2015; Jian Deng et al., 2017). The typical socio-psychological methods analyze farmers' intention and behavior change based on the theories of reasoned action and planned behavior (Fishbein and Ajzen, 1975; Ajzen ,1991). These methods analyze farmers’ behavior change according to psychological variables, such as attitudes, intention, and subjective norms. They do not analyze farmers’ behavior change according to economic variables. While economic methods show that economic risk aversion influences farmers’ decisions on replacement of chemical fertilizers with organic fertilizers (Sri Ramaratnam et al., 1987; Babcock, 1992; Morris and Potter, 1995; Aimin, 2010; Yang et al., 2012; Bowman and Zilberman, 2013; Stuart et al., 2014), farmers' risk preference to avoid yield loss is one of the key factors that lead to farmers’ overuse of chemical fertilizer instead of organic fertilizers (Smith et al., 2007; Luan Hao, Qiu Huanguang,2013). These studies do not consider the psychological variables or social environment variables. The prospect theory analyzes individual decisions considering both psychology and economics (Kahneman &Tversky,1979; Tversky & K ahneman,1992). It tries to model real-life choices of farmers, rather than optimal decisions or other sole psychological models. This paper attempts to analyze farmers’ behavior change based on prospect theory. On the other hand, the replacement of chemical fertilizers with organic fertilizers is only possible when farmer’s economic and non-economic goals are satisfied (Reimer et al., 2012). Some researchers suggest that farmers’ behavior can not only be changed by economic incentive, but also by external environment incentive, such as legal regulations, provision of advice and voluntary collective actions (Wondolleck and Yaffee,2000; K.L. Blackstock et al, 2010). Therefore, this paper also considers the environmental effect on farmer’s behavior change, which is emphasized in Lewin's field theory (Lewin, K. ,1943; Muuss, R. E. 1988). Prior studies conducted on the reduction of chemical fertilizers or farmers’ behavior change have focused on technological studies, policy studies, psychology and social studies, and economic studies. However, few studies have been conducted to evaluate farmers’ choices based on theories combining economics, psychology and social studies. Here, we have combined the prospect theory with field theory to analyze what could promote farmers to replace chemical fertilizers with organic fertilizers. Based on Kahneman’s prospect theory and Lewin's field theory, this research mainly considers the comprehensive effect of prospect utility, farmers’ risk preference, environment, and farmers’ traits on their behavior change. It provides a Page | 4

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different theoretical framework to analyze what could promote farmers' use of organic fertilizers instead of chemical fertilizers. The results will be helpful to create and modify incentive policies to promote sustainable agriculture development in developing countries. 2. Methods 2.1 Theoretical Foundation Prospect theory describes that individuals’ behavior is the choice between different prospect utilities that involve risk. The prospect theory (Kahneman &Tversky,1979; Tversky & K ahneman,1992;Tanaka, Camerer, and Nguyen,2010; Wang Y., et al,2015) can be expressed by a formula

v( y )   ( p )[v( x)  v( y )], x  y  0 or U ( x, p; y, p )  {  ( p)v( x)   (q)v( y ), x  0  y

x y0

.

where

x1 , x  0 v( x)  {   ( x)1 , x  0 , and w( p )  exp[( ln p ) ]

v denotes expected utility of the outcomes to the individual making the decision, x and p denote the potential outcomes and their respective probabilities,  denotes degree of risk aversion,  measures the sensitivity to loss versus gain, w( p ) is the probability weighting function. The theory contains two decision stages. In the initial phase, termed editing, the individual often uses heuristics to quickly make decisions, which is called "cognitive bias". This is a mental shortcut that usually focuses on one aspect of a complex problem and ignores others. It works well under most circumstances. The risk preference will affect individual’s choice in the second evaluation phase. Here, we used  to denote farmers’ risk preference. When farmers decide which fertilizer is chosen, they will simplify the prospect output change and price change according to their experience when they consider using organic fertilizers instead of chemical fertilizers. According to the heuristic bias in prospect theory, we have simplified the prospect utility of replacing chemical fertilizers with organic fertilizers, namely the prospect output change of agriculture products, price change of agriculture products, and farmer’s risk preference in our econometric model. Field theory claims that an individual’s behavior is the interaction between the individual’s personality and the environment (Lewin's,1943; Muuss,1988). It can be expressed in a function

Behavior  function( Personality, Environment ) . In this formula, individual personality includes age, farming years, risk preference, and acreages of farm. The environment includes the natural environment, market environment, and policy environment (Yongqiang Wang, Yuchun Zhu, 2012). The Page | 5

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natural environment here mainly refers to the soil fertility of the farm. We have denoted market environment by sale modes of agriculture products. The sale modes of agriculture products can be expressed in signing sale contracts, serving as members of agriculture cooperatives, or owning traceable code. For policy environment, we use subsidies for organic fertilizers, quality certification of apple production area, and extension guidance of organic fertilizers. 2.2. Econometric Model To understand the influence of the external environment and inner incentive factors on choice of organic fertilizers, we have detailed our econometric model and propose the following assumptions to estimate what could promote farmers to use organic fertilizers instead of chemical fertilizers.

OFi  f ( Nei , Pril , Somim , Pein , Fcij ,  i ) Where OFi represents different organic fertilizers, organic fertilizers mainly include waste of animals and humans, plant waste, and organic fertilizers made by factory. The dependent variable should be measured as the cumulative amount of different organic fertilizers. However, different organic fertilizers have different nutrients, and different farmers have different farm sizes, so the cumulative amount of organic fertilizers is not comparable among different farms; therefore, we used the weight ratio of organic fertilizers (ROF) to total fertilizers to measure the dependent variables. Because some farmers do not use organic fertilizers, the ratio value of organic fertilizers (ROF) to total fertilizers is from 0 to 1, the tobit model is suitable, and the dependent variable is a choice variable as follows:

 ROF * if ROF *  0 * ROFi    , where ROF is a latent variable: *  0 if ROF  0  ROF *  xi   i ,

xi denotes detailed independent variables in the following paragraph. Nei denotes the natural environment. As soil fertility of different farm plots is hard to quantify, we have eliminated this variable in our model. Where Pril denotes the prospect revenue change of agriculture products, which can be measured by yield change, price change, and farmer’s risk attitude when a farmer replaces chemical fertilizers with organic fertilizers, here l represents different variables of revenue change (simplified yield, price, and risk separately in model). Where Somim denotes sale modes of agriculture products, which include signing sale contract, serving as a member of an agriculture cooperative, and traceable code (simplified contract, cooperatives, and code separately in model), here m represents different

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variables of sale modes stated above. Where Pein denotes policy environment, which includes subsidies on organic fertilizers, quality certification of apple growing area, and extension guidance of organic fertilizers (simplified subsidies, certification and extension in model), here n represents different variables of policy environment stated above. Where Fcij denotes farmer’s characteristics, here j represents different characteristics, which include acreage of farm, years of planting, education, and ratio of apple income to family income (simplified acreage, years, education, and income separately in model). Hence, we have detailed our econometric model as follows:

ROFi  f ( yield i , pricei , risk i , contract i , cooperativesi , codei , subsidesi , certificationi , extensioni , acreagei , yearsi , educationi , incomei ,  i )

Hypothesis 1. Where the dependent variable ratio of organic fertilizers to total fertilizers for farmer i is affected by prospect revenue variables, namely change of apple yield, change of apple price, and farmer’s risk attitudes due to using organic fertilizers instead of chemical fertilizers. On the one hand, if the apple grower thinks that use of organic fertilizers instead of chemical fertilizers could increase the yield of apples or promote the price increase, the farmer would like to choose organic fertilizers. Otherwise, he would choose chemical fertilizers. Therefore, we assume yield increase and price increase are positive to farmer’s use of organic fertilizers instead of chemical fertilizers. On the other hand, if the apple grower uses organic fertilizers instead of chemical fertilizers, the farmer’s judgement on prospect revenue change of apple relies on his risk preference. We assume that the more risk aversion the farmer shows, the more likely he is to choose chemical fertilizers or otherwise. Hypothesis 2. Where the dependent variable ratio of organic fertilizers to total fertilizers for farmer i, is also affected by sale modes of apple variables, which are denoted by signing sale contract, serving as a member of an agriculture cooperative, and traceable code. We suppose the sale modes of apple variables are positive to the apple grower’s choice of organic fertilizers. Hypothesis 3. Where the dependent variable ratio of organic fertilizers to total fertilizers for farmer i, is also affected by policy environment variables, which are denoted by subsidies on organic fertilizers, quality certification of apple production area, and extension guidance of organic fertilizers. We assume these policy variables are positive to farmer’s choice of organic fertilizers. Hypothesis 4. Where the dependent variable ratio of organic fertilizer to total fertilizers for farmer i, is also affected by farmer’s characteristic variables, which are denoted by the acreage of apple orchards,

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years of apple planting, and education. We assume the farmer’s characteristics variables are positive to farmer’s choice of organic fertilizers.

Variables

Table 1. Variable description, dimension, and expected relationship with ROF Detailed variables (abbreviation) Dimension Expected relationship

Prospect revenue

Sale modes

Policy environment

ratio of organic fertilizer to total fertilizers (ROF)

0-1

Yield change (Yield)

Ordinal

Positive

Price change (Price)

Ordinal

Positive

Farmers' risk aversion (Risk)

Ordinal

Negative

Signing sale contract (Contract)

1 = Yes,0 = No

Positive

Member of apple cooperatives (Cooperatives)

1 = Yes,0 = No

Positive

Traceable code (Code)

1 = Yes,0 = No

Positive

Subsidies on organic fertilizers (Subsidies)

Certification of apple production area (Certification) Farmer’s traits

1 = Yes,0 = No Ordinal

Positive Positive

Extension of organic fertilizers (Extension)

Ordinal

Positive

Acreage of farm (Acreage)

Acre

Positive

Years of planting (Years)

Year

Positive

Farmers' education (Education)

Year

Positive

Ratio of apple income to family income (Income)

0-1

Positive

According to preceding assumption, the dependent and independent variables to construct the econometric model are summarized in Table 1. 3. Data Collection To analyze what drives farmer’s choice of organic fertilizers instead of organic fertilizers, a semistructured questionnaire with three sections was designed. The first section is general information of farmer’s household and farm. The second section relates main information of chemical fertilizers and organic fertilizers used on farms, how farmers choose between organic fertilizers and chemical fertilizers, and what factors affect their choice. The third section is an experiment game to measure farmer’s risk preference according to prospect theory. To ensure that the questionnaire was consistent Page | 8

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with the actual conditions, we asked for the advice of five professors who research apple production or agriculture economics and conducted a pre-survey with 15 apple growers to perfect the questionnaire. After the questionnaire was finalized, we collected multi-stage sampling. In the first stage, primary sample units were selected according to the following three criteria: (1) apple is the major cash crop in the local area; (2) different apple planting areas are selected according to different certified apple growing areas by the Ministry of Agriculture, including non-certified apple growing area, certified harmless apple growing area, certified green apple growing area, and certified organic apple growing area. There are approximately 30 counties which meet these requirements. (3) We used systematic sampling and determined the sample size 15% of 30 counties as the primary sample units according to the apple growing area of the 30 counties. According to the three selection principles, five counties were selected: Baota, Ansai, Luochuan, Baishui, and Changwu. In the second stage, number of secondary units (farmer) in the ith primary unit (county) was determined by simple random sampling.

nij  [

u v 2 ] . Where u represents critical value that corresponds to a 95% confidence level,  1 A

represents the estimated coefficient of variation, which is no more than 0.4 here and A represents range of estimated error, which is no more than 10%. We estimated the least number of secondary units (farmer) in each primary unit sample (county), which reached 62. When we changed the 95% confidence level to a 99% confidence level, the least number of secondary units (farmer) in each primary unit sample (county) reached 106. 2

2

1.96  0.4   2.58  0.4  n  62 , n    106   0.1   0.1  The proportion sample of missing data was 10%; therefore, we determined that the number of secondary units (farmer) in each primary unit sample (county) was 120, and the total sample size reached 600. In every county, we used systematic sampling and selected three villages per county according to different certified apple growing areas and surveyed 40 farmers randomly per village. We investigated a total of 600 farmers. Then, an in-person survey was performed between July 2016 and October 2016. The survey was carried out by eight trained postgraduates and four trained senior college students. To make sure the data collected is in accord with the reality, the interviewers were required to interview the farm labors who are responsible for the use of fertilizers. Based on data collected in five typical apple planting counties in northwest China, there are 359 interviewees who completed the questionnaires finally. 4. Results and Discussion Page | 9

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4.1. Descriptive statistical analysis 4.1.1. Fertilizer classification and organic fertilizers used Table 2 shows the classification of fertilizers used. Chemical fertilizers were used by 30.92% of farmers, while 69.08% of farmers applied organic fertilizers. The average amounts of chemical fertilizers applied by farmers reached 1,099.05,kg/acre. The chemical fertilizers used are mainly compound fertilizers. More than half of the farmers who used both chemical fertilizers and organic fertilizers, which account for 61.56% of farmers. The average amounts of chemical fertilizer and organic fertilizer applied both by farmers reached 3,139.37 kg/acre. Data show that only 7.52% of farmers used organic fertilizers thoroughly. The average amounts of organic fertilizer used by farmers reached 2,974.75 kg/acre. The average amounts of fertilizer, including organic fertilizers and chemical fertilizers, applied by farmers reached 2,496.14 kg/acre. Table 2. Classification of fertilizers used by apple growers Number Classifications of fertilizers Kg/acre Percentage of farmers Chemical fertilizers

1,099.05

111

30.92

Chemical fertilizers, organic fertilizers

3,139.37

221

61.56

Organic fertilizers

2,974.75

27

7.52

Total fertilizers

2,496.14

359

100.00

As shown in Figure 1, the organic fertilizers used by apple growers were sheep manure, organic fertilizers produced by factory, chicken manure, pig manure, human manure, cattle manure, residue of oilseed, biogas slurry, and other organic fertilizers. The top two organic fertilizers used by farmers were sheep manure and organic fertilizer produced by factory, which account for 34.54 and 22.56% of the farmers, respectively. It should be noted that approximately 20 famers applied unfermented manure in

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the orchards.

Note: Some farmers used more than one kind of organic fertilizer Figure 1. Organic fertilizers used by apple growers 4.1.2. Prospect revenue variables As shown in Table 3, farmers are asked to judge the effect on the yield if they replace chemical fertilizers with organic fertilizers. Ninety-one percent of farmers thought it would reduce the yield of their farms, and these farmers applied less organic fertilizers than that of the farmers who thought it would not affect the yield of their farms. However, evidence shows that chemical fertilizers could be partially or completely replaced by manure to maintain high yield (Anwar, M, et al, 2005;Bark, A. K. , et al, 2008; Abedi, Tayebeh , et al, 2010; Liu, Chang-An, et al, 2013; Ros, Amarilis Beraldo, et al, 2014; Herencia, J. F., Maqueda, C.,2016; Li Hui , et al, 2017). Therefore, to increase the use of organic fertilizers used more popularly, we must show the farmers the yield effect of replacing chemical fertilizers with organic fertilizers and instruct them in the correct use of organic fertilizers. As far as the prospect price change is concerned, 66.3% of farmers thought their apple prices would increase if they replace chemical fertilizers with organic fertilizers, but in fact these farmers did not apply more organic fertilizers because a traceable Page | 11

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Variable Yield

Price

Risk

Contract

Cooperation

Code

Subsidies

Certification

Extension

Acreage

Planting

Education Page | 12

Table 3. Organic fertilizers used by farmers according to variable group Number Percentage Variable group (average, interval) of farmers 1. No yields reduce (0, 0-0) 8.91 32

Kg/acre 2,991.20

A few yields reduce (2.7, 1-3)

54

15.04

2,231.58

Significant yields reduce (4.5, 4-5)

273

76.04

1,131.84

121

33.70

1,710.38

A few price rise (2.4, 1-3)

146

40.67

1,691.53

Significant price rise (4.2, 4-5)

92

25.63

1,369.45

78

21.73

1,471.85

Farmers' risk neutral (0.25, 0-0.5)

59

16.16

1,591.93

Farmers' risk aversion (0.75, 0.5-1)

222

62.11

1,672.01

4. Signing sale contract (0, 0-0)

121

33.70

2,060.11

Signing sale contract (1, 1-1)

238

62.30

1,389.25

5. Member of apple cooperatives (0, 0-0)

322

89.69

1,535.55

Member of apple cooperatives (1, 1-1)

37

10.31

2,309.92

6. Traceable code (0, 0-0)

359

100.00

1,615.36

Traceable code (1, 1-1)

0

0.00

0.00

8. Subsidies on organic fertilizer (0, 0-0)

311

86.63

1,580.20

Subsidies on organic fertilizer (1, 1-1)

48

13.37

1,843.23

9. Non-Certification of apple area (0, 0-0)

202

56.27

1,380.69

Certification of harmless apple area (1, 1-1)

101

28.13

1,922.72

Certification of green apple area (2, 2-2)

43

11.98

1,798.74

Certification of organic apple area (3, 3-3)

13

3.62

2,267.36

10. Extension of fertilizers (1, 1-1)

204

35.38

1,336.76

Extension of fertilizers (0, 0-0)

155

64.62

1,982.04

11. The acres of orchards(0.68, 0-1)

87

24.23

2,020.50

The acres of orchards(2.76, 1-13.18)

272

75.77

1,485.78

12. years of planting (12.38, 0-20)

283

78.83

1,575.10

years of planting (27.38, 21-40)

76

21.17

1,761.24

13. Farmers' education (4.5, 0-9)

208

57.94

1,614.70

2. No price rise (0, 0-0)

3. Farmers' risk preference (-0.25, -0.5-0)

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Farmers' education (12, 10-15) Income

151

42.06

1,612.28

14. Ratio of apple income (0.18, 0-0.5)

150

41.78

1,912.92

Ratio of apple income (0.94, 0.5-1)

209

58.22

1,401.80

system of apples of different origins is not implemented in most of the sample area, their apples did not get higher price in market even if they used organic fertilizers instead of chemical fertilizers. As for the farmers’ risk preference, we conducted a choice experiment to derive the risk parameter

(((( designed by TCN and Wang (Tanaka, Camerer, and Nguyen, 2010; Wang Y., et al, 2015), in which is included in the related references. Farmers were proposed to face the different prospect payoffs involving different probabilities from series 1 to series 3. In every series, their choice between options A and B decided their risk parameters in the formula of prospect theory. According to the experiment result, we divided the farmers into three groups based on the risk preference parameter  , as shown in Table 4. The value of  equal to -0.5 to 0 belong to the risk preference group, which accounts for 21.73% of sample farmers. Data show that farmers in the risk aversion group (62.11%) applied a little more organic fertilizer than that of risk neutral and risk preference groups.

4.1.3. Sale mode variables According to the data in Table 3, 62.3% of sample farmers signed the sale contract with dealers before they sold their apples, and these farmers applied less organic fertilizers than those of the group who did not sign the sale contract with the dealers. In the apple sale contract, most of the dealers usually require the apple size, apple shape, supplying amount, price, and supplying time. The dealers usually do not require the specified fertilizers and pesticides or the certification of green apples or organic apples in the contract. Therefore, it is reasonable that the farmers of the group who signed the sale contract did not apply more organic fertilizers than those of the group who did not sign the sale contract. Although data show that signing a sale contract or not does not affect the use of organic fertilizers, being a member of an apple cooperative or not was found to affect the use of organic fertilizers. As shown in Table 3, the farmers of the group who joined the agriculture cooperatives applied 774.37 kg/acre more organic fertilizers than that of the group farmers who did not join the agriculture cooperatives. Theoretically speaking, the traceable code should affect the use of organic fertilizers or chemical fertilizers if the traceable system of green apples or organic apples is established. Unfortunately, no farmers knew whether their apples were traceable or not when they sold their apples to the dealers. 4.1.4. Policy environment variables

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Since 2015, subsidies on organic fertilizers have been explored in selected counties, which include part of the sample areas. In 2015subsidies were $136.68/acre for the farmers who used organic fertilizers produced by fertilizer factories in large-scale orchards of Luochuan County and Baishui County. As shown in Table 3, 13.37% of sample farmers got subsidies on organic fertilizers. These farmers applied 260.04 kg/acre more than that of the farmers who did not get subsidies on organic fertilizers. We can see that subsidies on organic fertilizers do affect the use of organic fertilizers. Other than subsidies on organic fertilizers, the certification of apple planting areas should also affect the use of organic fertilizers. According to the data in Table 3, the farmers of the group whose apple orchards were certified applied more organic fertilizers than those of the group whose apple orchards were not certified. Among the farmers whose orchards were certified, the farmers whose apple orchards were certified organic applied more organic fertilizers. Lastly, the farmers of the group who got extension service of fertilizers applied less organic fertilizers than those of the farmers who did not get extension service of fertilizers. The reason for this is that the extension service of fertilizers on apple orchards in recent years has mainly focused on chemical fertilizers through soil testing and formula fertilization, integration of water and chemical fertilizers, or how to apply chemical fertilizer precisely. 4.1.5 Farmer’s traits variables As shown in Table 3, farmers in the group whose apple orchards were larger applied less organic fertilizers than those of the farmers whose apple orchards were smaller. The reason may be that the organic fertilizers require more human labor to apply and cost more time than chemical fertilizers. Secondly, the farmers in the group who had more apple planting years applied more organic fertilizers farmers with fewer apple planting years. This means that farmers who grow apples for longer are more likely to realize the positive effect of organic fertilizers compared to chemical fertilizers. Thirdly, data show that there is almost no difference in organic fertilizer use between the more educated group and the less educated group. Lastly, the farmers who received more apple income applied slightly more organic fertilizers than farmers who received less apple income. 4.2. Econometric estimation According to the previous model, the dependent variable involves continuous choice data, and the value of the dependent variable is 0 and above 0 to 1. The tobit model regression is a suitable analysis. Therefore, we estimated the econometric model by tobit regression. Parameters  were estimated using Eviews version 8.0. Table 4 presents the results from the tobit model regression. Comparing Hypothesis 1 with regression results, the three sub-variables in prospect revenue variables were rejected at the 10% level according to the Z-statistic test. This means that prospect revenue does not affect farmer’s choice of organic Page | 14

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fertilizers instead of chemical fertilizers significantly. Comparing Hypothesis 2 with regression results, the sub-variable cooperatives in sale mode variables has been confirmed at the 5% level according to the Z-statistic test, while the other sub-variable contracts in sale mode variables were rejected at the 10% level according to the Z-statistic test. Comparing Hypothesis 3 with regression results, the subvariable subsidies in policy environment variables were confirmed at the 5% level according to Z-statistic test, while the other two sub-variables in policy environment variables were rejected at the 10% level according to the Z-statistic test. Comparing Hypothesis 4 with regression results, the sub-variable acre in farmer’s traits was confirmed at the 5% level according to the Z-statistic test, while the other three subvariables in farmer’s traits variables were rejected at the 10% level according to the Z-statistic test. This means that being a member of an agriculture cooperative, subsidies on organic fertilizers for farmers, and size of the farm significantly affect choice of organic fertilizers instead of chemical fertilizers, while the other nine estimated coefficients of variables have an insignificant effect on farmers’ choice of organic fertilizers instead of chemical fertilizers. Table 4. Tobit regression of organic fertilizer choice (QML)

Variable

Prospect revenue Sale modes Policy environment Farmer’s traits

Method: ML - Censored Normal (Tobit) (Quadratic hill climbing) Sub-Variable Coefficient Std. Error z-Statistic Prob. C Yield Price Risk Contract Cooperatives** Certification Subsidies** Extension Acreage** Years Education Income

0.410449 -0.023851 -0.023261 -0.148782 0.013507 0.222229 0.051178 0.181560 0.078616 0.042785 -0.003428 -0.000296 -9.60E-07

0.156683 0.020232 0.016990 0.094036 0.013743 0.092089 0.033418 0.085865 0.058064 0.018271 0.003669 0.007982 1.25E-06

2.619606 -1.178850 -1.369086 -1.582186 0.982882 2.413204 1.531452 2.114475 1.353954 2.341665 -0.934321 -0.037037 -0.769138

0.0088 0.2385 0.1710 0.1136 0.3257 0.0158 0.1257 0.0345 0.1758 0.0192 0.3501 0.9705 0.4418

Note: ** indicates 5% significance level The positive sign on the cooperative coefficient indicates that farmers who joined the agriculture cooperatives were more likely to choose organic fertilizers instead of chemical fertilizers. Specifically, the positive coefficient regarding membership in an apple cooperative (cooperation) of 0.2222 indicates that the probability of an apple grower choosing organic fertilizers increased if he was a member of the apple cooperation, in other words, showing that the apple cooperatives organized by farmers does some work of promoting the use of organic fertilizers. This result implies that we could encourage more apple growers to join the apple cooperatives to promote the use of organic fertilizers instead of chemical fertilizers. From this point, the government should develop incentive policies to promote Page | 15

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farmers to join the agriculture cooperatives and strengthen the function of agriculture cooperation to extend the use of organic fertilizers. The positive coefficient of subsidies on organic fertilizers (subsidies) of 0.1816 indicates that the probability of using organic fertilizers would increase if a farmer received subsidies. Therefore, the government should explore practical methods of providing subsidies on organic fertilizers for more farmers. Subsidies on organic fertilizers should not be limited to organic fertilizers produced by fertilizer factories for large-scale farms. They should be implemented for all kinds of organic fertilizers for all farms, so that the use organic fertilizers could be expanded. Subsidies on organic fertilizers should be given in the form of organic fertilizers instead of money for farmers, or subsidies on organic fertilizers could be given to farmers to make manure or compost for themselves. Because different subsidies on organic fertilizers for farmers have different costs, policy makers should choose the least cost of subsidies to promote the use of organic fertilizers instead of chemical fertilizers. The positive coefficient on acreage of the apple orchards (acre) of 0.0428 indicates that the probability of an apple grower using organic fertilizers would increase if he owned a larger apple orchard. From this point, the government should strengthen the policy of land circulation to incentivize farmers to own bigger farms. Therefore, related larger farm incentive policies should be made to promote the use of the organic fertilizers. In China, it should be noted that an increasing number of young rural laborers have migrated to cities in search of work in recent years, while elderly laborers and women laborers are left in the rural villages to run the farms. Most of the elderly and women laborers left in the village are not capable of running larger farms. Thus, the policy makers should consider the current rural situation to develop policies to attract potential farmers who are capable of running larger farms. Otherwise, the current policy of land circulation may not work to make more farms larger. 5. Conclusion The use of a tobit regression model based on Kahneman’s prospect theory and Lewin's field theory was very helpful to explain farmers’ choice of organic fertilizers instead of chemical fertilizers. We found that prospect revenue did not significantly affect farmer’s choice of organic fertilizers instead of chemical fertilizers. However, being a member of an agriculture cooperative, subsidies on organic fertilizers, and acreages of the farms are helpful to significantly promote a farmer’s use of organic fertilizers instead of chemical fertilizers. Therefore, first, the government should develop incentive policies to promote farmers to join agriculture cooperatives and strengthen the function of agriculture cooperatives to extend the use of organic fertilizers. Secondly, the government should explore how to provide practical subsidies on organic fertilizers for more farmers . Subsidies on organic fertilizers should be provided in the form of organic fertilizers instead of money for farmers, or subsidies on organic fertilizers could be given for farmers to make manure or compost themselves. Policy makers should choose the least cost of Page | 16

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subsidies to promote the use of organic fertilizers instead of chemical fertilizers. Thirdly, the government should perfect the current policy of rural land circulation to incentivize more farmers to run larger farms. The policy of attracting potential farmers who are capable of running larger farms must be made. 5. Acknowledgment This research was funded by the Social Science Foundation of China (Grant No.16BGL135). The authors thank our colleague Mr. Lu Qian, Mrs. Zhao Minjuan, Mr. Huo Xuexi for their comments on the paper. The authors thank graduate students Zhang Ci, Deng Jianfeng, Xu Baoshi, Wang Taiyun, Li Yanfang, He Yamei, Zhang Huili, and Xingyan and undergraduate students Liang Yingying, Li Jiaojie, Wang Xinhao, and Kuang Xiaoyu for their data collection at farmers’ homes or on their farms. The authors particularly appreciate the amending advice of the editor and four anonymous reviewers on the refinement of the paper. Reference Abedi, Tayebeh , et al, 2010. Effect of organic and inorganic fertilizers on grain yield and protein banding pattern of wheat, Australian Journal of Crop Science, 4: 384-389 Anwar, M, et al, 2005. Effect of organic manures and inorganic fertilizer on growth, herb and oil yield, nutrient accumulation, and oil quality of French basil, Communications in Soil Science and Plant Analysis,36: 1737-1746 Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50, 179-211. Amanullah K. E. Z., et al,2010. Residual Effects of Compost and Green Manure of Pea with Other Organic Wastes on Nutrient-Use Efficiency of Successive Rice after Wheat. Communications in Soil Science and Plant Analysis, 41:2154–2169 Aimin, H., 2010. Uncertainty, risk aversion and risk management in agriculture. Agriculture and Agricultural Science Procedia,1: 152–156. Babcock, B.A., 1992. The effects of uncertainty on optimal nitrogen application. Review of Agricultural Economics. 14(2):271–280. Bark, A. K. , et al, 2008. Yield performance, economics and soil fertility through organic sources (vermicompost) of nitrogen as substitute to chemical fertilizers in wet season rice, Crop Research,36: 4-7 Boli Ni, et al,2011. Environmentally Friendly Slow-Release Nitrogen Fertilizer. Journal of Agricultural and Food Chemistry, 59: 10169–10175. Page | 17

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Highlights (for review) Membership in agriculture cooperatives plays an important positive role on farmer's use of organic fertilizers instead of chemical fertilizers; Subsides on organic fertilizers play a minor positive role on farmer's use of organic fertilizers instead of chemical fertilizers; Enlarging the acreage of the farm plays a minor positive role on farmer's use of organic fertilizers instead of chemical fertilizers.