Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-23T17:30:53.918Z Has data issue: false hasContentIssue false

The application of organic fertilizers and farmers' income increase

Published online by Cambridge University Press:  15 October 2024

Bowei Li*
Affiliation:
College of Economics and Management, Zhejiang A&F University, Hangzhou City, Zhejiang, China Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou City, Zhejiang, China
Congming Zhang
Affiliation:
College of Economics and Management, Zhejiang A&F University, Hangzhou City, Zhejiang, China
Yanjun Qian
Affiliation:
College of Economics and Management, Zhejiang A&F University, Hangzhou City, Zhejiang, China
*
Corresponding author: Bowei Li; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

The extension of organic fertilizers helps improve soil quality and reduces non-point source pollution caused by excessive use of fertilizers, however, whether the application of organic fertilizers (OFA) contributes to an increase in farmers' income is a matter of debate. This paper discussed how the application of soil-testing formulas and outsourcing services that some or all links of agricultural production to professional organizations moderate the income-increasing effect of OFA, and Multinomial Endogenous Switching Regression Model (MESR) is selected to do the empirical test. The results indicate that OFA with soil-testing formula and OFA with outsourcing service can effectively increase farmers' income, in specific, OFA with soil-testing formula increases the net monetary income of wheat growing on per hectare (ha) of land by 2150 Renminbi (RMB), and OFA with outsourcing service increases the net monetary income of wheat growing on per ha of land by 3950 RMB, however, OFA has no effectiveness on increasing farmers' income if neither soil-testing formulas nor outsourcing services is available. The influence mechanism of OFA to improve farmers' income is to increase crop yield, but OFA has no effectiveness on increasing the price of products. A systematic extension services including the extension of organic fertilizers, soil testing formulas and outsourcing services should be formed in the future.

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Long-term Overuse of fertilizers has led to soil quality degradation, non-point-source pollution, and high production costs (Uhunamure et al., Reference Uhunamure, Kom, Shale, Nethengwe and Steyn2021). Despite this, the amount of chemical fertilizer used per hectare of planting area in 2020 was 313.5 kg/ha, significantly exceeding the internationally recognized upper limit of chemical fertilizer input of 225 kg/ha (Li, Reference Li2019). To address this, the Chinese government encourages the use of organic fertilizers as an alternative to reduce the excessive use of chemical fertilizers (Zhan et al., Reference Zhan, Shao, He and Shi2021). The adoption of organic fertilizers is mainly driven by market forces, and farmers are more likely to use them if organic fertilizer application (OFA) increases their income (Gao et al., Reference Gao, Yao, Chen and Niu2022). Numerous agronomic experiments have demonstrated that OFA positively impacts crop yield and product quality, enhancing the competitiveness and market price of these products (Choudhary et al., Reference Choudhary, Bairwa, Kumar, Javed, Asad, Lal, Mahawer, Sharma, Singh, Hassan, Abo-Shosha, Rajagopal and Abdelsalam2022; Du et al., Reference Du, He, Zhang, Lu, Mao and Zhai2022; Jiang et al., Reference Jiang, Li, Chen, Fu, Feng and Zhuang2022a; Tao et al., Reference Tao, Liu, Wu, Wu, Liao, Shah and Wu2022).

In contrast to the consistent results obtained from agricultural experiments, social science studies have reached varied conclusions. Some empirical studies have found a positive effect of OFA on farmers' income (Chen, Fu, and Liu, Reference Chen, Fu and Liu2022), while others have found that OFA does not significantly improve farmers' income (Su, Zhou, and Zhou, Reference Su, Zhou and Zhou2022). Some technical considerations are necessary to ensure that OFA positively impacts farmers' income. First, since organic fertilizers have a lower nutrient content and release nutrients more slowly than chemical fertilizers (Fertahi et al., Reference Fertahi, Ilsouk, Zeroual, Oukarroum and Barakat2021), it is essential to use both organic and chemical fertilizers in precise proportions to enhance crop yield and quality (Hauck and Bremner, Reference Hauck and Bremner1976). Second, artificial fertilization methods cannot deliver fertilizers to the deep soil, reducing fertilizer utilization efficiency (Wang et al., Reference Wang, Li, Yu, Wang, Lian, Du, Zhao, Wang, Miao, Ding, Wang, Zhou, Zhang, Li and Gui2021). However, farmers face various social and economic constraints which may reduce the effectiveness of OFA on increasing farmers' income. Firstly, since most farmers in China have not systematically studied professional agricultural knowledge, they mainly rely on ancestral teachings and personal experience to decide how much fertilizer to use, which is often imprecise (Zheng et al., Reference Zheng, Li, Zhao and Qian2023). Secondly, diseconomies of scale prevent farmers from buying machinery, and mechanical fertilization is not widespread among them (Baruah, Mohanty, and Rola, Reference Baruah, Mohanty and Rola2022). Thirdly, farmers face information asymmetry and often purchase inferior fertilizers, while the scattered layout of fertilizer retail stores makes it difficult for the government to regulate the quality of fertilizers (Amfo and Baba Ali, Reference Amfo and Baba Ali2021). Finally, many smallholders have limited access to technical support because the government tends to be more inclined to provide technical support for large-scale farmers (Qing et al., Reference Qing, Zhou, Song, Deng and Xu2023). These constraints prevent OFA from achieving the expected technical performance. However, this issue can be alleviated by extending soil-testing formulas and outsourcing services. Soil-testing formulas, a type of precision fertilization technique, are widely promoted in rural China (Dong et al., Reference Dong, Zhang, Chen and Li2023). By measuring the nutrient content in the soil, the precise amount of fertilizer needed is determined, avoiding the excessive or insufficient fertilizer inputs typically caused by farmers' decisions. Outsourcing services involve delegating some or all aspects of agricultural production to professional organizations such as cooperatives or agricultural machinery stations, which can mitigate the limitations of individual farmers through the division of labor (Chen, Zhong, and Lu Reference Chen, Zhong and Lu2023). Therefore, theoretically, the effectiveness of OFA may be enhanced if organic fertilizers are applied using soil-testing formulas or outsourcing services.

Previous studies have overlooked the potential impact of extending soil-testing formulas and outsourcing services on moderating the effectiveness of OFA on farmers' income. This article's innovation mainly revolves around two aspects. Firstly, it analyzes how soil-testing formulas and outsourcing services enhance the effectiveness of OFA in augmenting farmers' income, utilizing survey data for empirical testing. Secondly, it examines the mechanisms by which OFA increases farmers' income, testing both its effects on yield and price.

Theoretical analysis

The influence mechanism by which soil-testing formulas and outsourcing services enhance the effectiveness of OFA in increasing farmers' income is illustrated in Figure 1.

Figure 1. The influence mechanism that soil-testing formula and outsourcing service drive the effectiveness of OFA.

The effectiveness of OFA improved by the soil-testing formula

As a form of precise fertilization technology, soil-testing formulas, vigorously promoted by the Chinese government, help farmers enhance the accuracy of OFA (Sun and Li, Reference Sun and Li2021; Zheng et al., Reference Zheng, Li, Zhao and Qian2023). Based on soil test field experiments, these formulas can be made by local fertilizer stations or cooperatives, utilizing soil nutrient content measurements to precisely replenish missing nutrients in the soil (Zhang et al., Reference Zhang, Antonangelo, Grove, Osmond, Slaton, Alford, Florence, Huluka, Hardy, Lessl, Maguire, Mylavarapu, Oldham, Pena-Yewtukhiw, Provin, Sonon, Sotomayor and Wang2021). Consequently, the application of soil-testing formulas not only determines precise fertilizer inputs, leading to improved crop yields, but also reduces fertilizer residue, thereby enhancing crop quality (Li et al., Reference Li, Zhang, Song, Ren, Jin, Fang, Yan, Li, Wang and Cao2022b). Theoretically, the economic benefits of OFA can be realized through the use of soil-testing formulas, and this article proposes the following hypothesis:

H1: The application of soil-testing formulas improves the effectiveness of OFA in increasing farmers' income.

The effectiveness of OFA improved by outsourcing services

Outsourcing services help alleviate the inefficiencies of OFA resulting from farmers' limitations in accessing information, machinery, and technical support. Firstly, professional organizations have stronger market negotiation skills than farmers, enabling them to purchase fertilizers at lower prices (Rutsaert et al., Reference Rutsaert, Chamberlin, Oluoch, Kitoto and Donovan2021). In addition, these organizations can mitigate the information asymmetry regarding fertilizer quality that often hinders farmers, thus ensuring the acquisition of high-quality fertilizers (Li et al., Reference Li, Yang, Shi, Zang and Liu2021). Secondly, professional organizations employ mechanized fertilization, facilitating the delivery of fertilizers to deep soil layers and thus enhancing operational efficiency (Chen, Zhong, and Lu Reference Chen, Zhong and Lu2023). Thirdly, professional organizations have better access to government-provided technical support compared to individual farmers (Mattila et al., Reference Mattila, Hagelberg, Söderlund and Joona2021). In theory, the economic effects of OFA can be enhanced through the implementation of outsourcing services, and this article proposes the following hypothesis:

H2: The implementation of outsourcing services improves the effectiveness of OFA in increasing farmers' income.

Data and method

Empirical model

The dependent variable in this paper is farmers' income, measured by the variable ‘net income of wheat (Triticum aestivum L.) per hectare of land’ (NIW). This variable is calculated by subtracting total costs, including the cost of seedlings, fertilizers, pesticides, irrigation, machinery, labor, and land rent, from total revenue. The key independent variables represent farmers' behaviors regarding OFA, encompassing several choices: applying no organic fertilizers, OFA with soil-testing formula, OFA without soil-testing formula, OFA with outsourcing service, and OFA without outsourcing service, with farmers applying no organic fertilizers considered the control group. The two key independent variables are ‘whether organic fertilizer is applied with soil-testing formula (OSF)’ and ‘whether organic fertilizer is applied with outsourcing service (OUS)’. Farmers' selections of OFA are not random behaviors. They are influenced by various factors, such as technical training, which may also impact farmers' income, potentially leading to self-selection bias (Ma and Abdulai, Reference Ma and Abdulai2016). Reliable estimates of OFA's effect on farmers' income cannot be obtained without addressing self-selection bias (Vigani et al., Reference Vigani, Kathage, Khanna, Roe, Vercammen and Wu2019). The Propensity Score Matching Model (PSM) cannot correct for self-selection bias resulting from unobservable factors (Abdulai, Reference Abdulai2016), and the Difference-in-Difference Model (DID) cannot be applied to cross-sectional data (Pan, Lu, and Kong, Reference Pan, Lu and Kong2022). Therefore, the Multinomial Endogenous Switching Regression Model (MESR) is chosen to address the self-selection bias issue (Deb and Trivedi, Reference Deb and Trivedi2006), and the IV-2sls method is selected to test the robustness of the results.

The MESR comprises three stages. In the first stage, a Multinomial Logit Model (Mlogit) is used to estimate the probability of farmers choosing various behaviors related to OFA. This article assumes that there are k selections in total.

(1)$$S = \left\{{{\rm \;}\matrix{ {1\;\;\;if\;U_{i1}^{\rm \ast } \;< \;\;\;\mathop {\max }\limits_{n\ne k} ( {U_{{\rm in}}^{\rm \ast } } ) \;\;} \hfill \cr \cdots \hfill \cr {k\;{\rm if}\;U_{ik}^{\rm \ast } \;\;> \;\;\mathop {\max }\limits_{n\ne k} ( {U_{{\rm in}}^{\rm \ast } } ) } \hfill \cr } } \right.\quad n\ne k$$

S represents all the k selections made by famers. ‘S = 1’ represents the absence of OFA, serving as the control group, while ‘S = 2, 3■■■k’ represents the other selections, including OFA with soil-testing formula, OFA without soil-testing formula, OFA with outsourcing service, and OFA without outsourcing service. $U_{ik}^{\rm \ast } {\rm \;}$represents the utility attained by farmers from the kth selection. The selection equation can be represented by Equation (2).

(2)$$U_{ik}^{\rm \ast } = \beta _kX_{ik}{\rm \;} + \varepsilon _{ik}$$

X ik represents the observable factors that may influence farmers' selection, including the characteristics of the household head and family (Lee, Reference Lee2005). Household head characteristics that may impact OFA include the ‘age of the household head (AGE)’ (Oyetunde-Usman, Olagunju, and Ogunpaimo, Reference Oyetunde-Usman, Olagunju and Ogunpaimo2021), ‘gender of the household head (GEN)’ (Makate and Mutenje, Reference Makate and Mutenje2021), ‘years of education of the household head (EDU)’ (Ojo and Baiyegunhi, Reference Ojo and Baiyegunhi2021), ‘risk preference of the household head (RPH)’ (Qiao and Huang, Reference Qiao and Huang2021), ‘whether the household head has received technical training (HRT)’ (Maertens, Michelson, and Nourani, Reference Maertens, Michelson and Nourani2021), ‘whether the household head is a village cadre (HVC)’ (Li et al., Reference Li, Tang, Che, Shi and Ma2022a), and ‘whether the household head is a member of a cooperative (HMC)’ (Zhang et al., Reference Zhang, Yang, Zhao and Kong2023). Family characteristics include ‘per capita household income (PHI)’ (Setsoafia, Ma, and Renwick, Reference Setsoafia, Ma and Renwick2022), ‘number of household labor force (NHL)’ (Qian et al., Reference Qian, Lu, Gao and Lu2022), ‘size of farm (SOF)’ (Hu et al., Reference Hu, Li, Zhang and Wang2022), and ‘proportion of non-farm income to total household income (PNT)’ (Wesenbeeck et al., Reference Wesenbeeck, Keyzer, Veen and Qiu2021). Additionally, the instrumental variable ‘distance between farmers and the nearest store selling organic fertilizers (WOV)’ is included in Equation (2). This variable reduces transaction costs associated with accessing organic fertilizer by allowing farmers to purchase it from nearby stores (Jiang et al., Reference Jiang, Zhang, Zhang, Su, Cong and Deng2022b), which also provide farmers with information related to OFA, aiding in the rational use of organic fertilizers (Li et al., Reference Li, Tang, Che, Shi and Ma2022a). Therefore, ‘WOV’ and OFA are highly correlated, but ‘WOV’ does not directly impact farmers' income, making it a suitable instrumental variable for this article.

β k is the estimated coefficient of X ik. $\varepsilon _{ik}$ represents unobservable variables assumed to follow an independent and identical Gumbel distribution. Thus, the probability of the kth selection by the farmer, characterized by X i can be calculated by Equation (3) (McFadden, Reference McFadden1974).

(3)$$P_{ik} = P{\rm r}\left({U_{ik}^{\rm \ast } > \mathop {\max }\limits_{n\ne k} ( {U_{{\rm in}}^{\rm \ast } {\rm \vert }X_i} ) } \right) = \displaystyle{{{\rm exp}( {X_i\beta_k} ) } \over {\mathop \sum \nolimits_{n = 1}^k {\rm exp}( {X_i\beta_n} ) }}$$

In the second stage, an income determination equation is constructed to estimate the effects of various OFA selections on farmers' income. In this article, farmers have k different selections, with each corresponding to its own income determination equation.

(4)$$\eqalign{& S = 1\colon {\rm \;}NIW_{i1} = \alpha _1Z_{i1} + \mu _{i1} \cr & \cdots } $$
(5)$$S = K\colon NIW_{ik} = \alpha _kZ_{ik} + \mu _{ik}$$

S = 1 represents the condition where farmers do not apply organic fertilizers, considered the control group. NIW i1 represents the income of those farmers who do not use organic fertilizer, while NIW ik represents the income of farmers employing the kth selection of OFA methods. Z represents all factors that may impact farmers' income. The variable μ ik satisfies the equation E(μ ik|X, Z) = 0 and ${\rm var}( {\mu_{ik}{\rm \vert }X, \;Z} ) = \sigma _k^2$. If OLS is used to estimate equation (5), biased results may be obtained (Teklewold et al., Reference Teklewold, Kassie, Shiferaw and Köhlin2013). Therefore, a correction item is added to equation (5) to replace the selection of OFA (Kumar et al., Reference Kumar, Mishra, Saroj and Joshi2019). This article assumes that μ ik is highly correlated with farmers' selections, leading to the modification of Equations (4) and (5) into Equations (6) and (7).

(6)$$\eqalign{& S = 1{\rm \;}NIW_{i1} = \alpha _1Z_{i1} + \sigma _1{\hat{\lambda }}_{i1} + \omega _{i1} \cr & \cdots } $$
(7)$$S = k\;NIW_{ik} = \alpha _kZ_{ik} + \sigma _k\hat{\lambda }_{ik} + \omega _{ik}$$

σ k represents the covariance of $\varepsilon _{ik}$ and μ ik, and unbiased estimates of σ k can be obtained. ω ik represents the error term with an expected value of zero. $\hat{\lambda }_{ik}$ represents the Inverse Mills Ratio, which is calculated based on the probability of the kth selection by farmers.

(8)$$\hat{\lambda }_{ik} = \mathop \sum \limits_{n\ne k}^k \rho _k\left[{\displaystyle{{{\hat{\rho }}_{ni{\rm \;}}{\rm ln}( {{\hat{\rho }}_{ni{\rm \;}}} ) } \over {1-{\hat{\rho }}_{ni{\rm \;}}}} + {\rm ln}( {{\hat{\rho }}_{ik{\rm \;}}} ) } \right]$$

ρ represents the correlation coefficient of $\varepsilon _{ik}$ and μ ik. The standard error in Equation (6) can be obtained using the bootstrap method to account for the heteroscedasticity that arises when generating $\hat{\lambda }_{ik}$.

In the third stage, the average treatment effects (ATT) of different OFA methods on farmers' income are estimated by comparing their income under factual and counterfactual scenarios.

The expected income value of farmers who made the kth selection can be calculated using Equation (9).

(9)$$E( {NIW_{ik}{\rm \vert }U = k, \;Z_{ik}, \;{\hat{\lambda }}_{ik}} ) = \alpha _kZ_{ik} + \sigma _k\hat{\lambda }_{ik}$$

The expected income value of farmers making the kth selection in the counterfactual state can be calculated using Equation (10).

(10)$$E( {NIW_{i1}{\rm \vert }U = k, \;Z_{ik}, \;{\hat{\lambda }}_{ik}} ) = \alpha _1Z_{ik} + \sigma _1\hat{\lambda }_{ik}$$

The unbiased estimation results of the ATT can be calculated using Equation (11).

(11)$${\rm ATT} = E( {NIW_{ik}{\rm \vert }U = k, \;Z_{ik}, \;{\hat{\lambda }}_{ik}} ) -E( {NIW_{i1}{\rm \vert }U = k, \;Z_{ik}, \;{\hat{\lambda }}_{ik}} ) = Z_{ik}( {\alpha_k-\alpha_1} ) + \hat{\lambda }_{ik}( {\sigma_k-\sigma_1} ) $$

When examining the influencing mechanism, the variable ‘yield of wheat on per ha of land (YWH)’ is chosen to assess the impact of ‘OSF’ and ‘OUS’ on crop yield, while the variable ‘proportion of wheat price exceeding the village's average wheat price (PWA)’ is selected to evaluate the effects of ‘OSF’ and ‘OUS’ on wheat price. We do not use wheat price as a dependent variable due to its significant regional variation (Langridge and Reynolds, Reference Langridge and Reynolds2021).

Study site

The study was conducted in Anhui Province, China, in 2021. Anhui is one of the most important grain-producing provinces in eastern China, encompassing the production of cereals, legumes, and tuber within its grain production sector. Data from the China Statistical Yearbook indicates that Anhui Province's grain production in 2022 reached 411,001 million kg, ranking fourth among all provinces in China. Firstly, 18 counties with the highest grain output in Anhui Province in 2022 were selected as our sample sources. Second, these 18 counties were ranked based on their grain output in 2022. From this ranking, 6 counties were chosen as sample counties in this paper using equidistant sampling method. Among these 6 sample counties, 3 are situated north of the Huai River, comprising Funan County, Lixin County, and Yongqiao County, where wheat and maize are the primary crops. The remaining 3 counties are located south of the Huai River and north of the Long River, namely Feixi County, Mingguang County, and Dingyuan County, where rice (Oryza sativa L.) and wheat are the predominant crops. Figure 2 depicts the geographic locations of the sample counties.

Figure 2. Location of Anhui Province and the province's 6 grain-producing counties that are the study sites.

Data

Data were collected through a survey of grain-growing households located in the county study sites in 2021. The survey involved face-to-face questionnaires with household heads. A multi-stage clustered random sampling strategy was employed to derive the household sample. Specifically, within each sample county, high-, middle-, and low-income towns were identified based on the index of per capita disposable income, with one town of each income level selected. Subsequently, all villages within each selected town were categorized into high- and low-income villages, from which one village of each type was chosen. Within each village, rural households were further divided into large-scale farmers managing at least 3.33 hectares of land and smallholders managing less than 3.33 ha (Note. Anhui province belongs to the region of two-harvest-a-year. According to the standard set by Ministry of Agriculture and Rural Affairs of the People's Republic of China, farmers who operate on at least 3.33ha of farmland in the region of two-harvest-a-year are classed as large-scale farmers (Guo, Zhong, and Ji, Reference Guo, Zhong and Ji2019)). 10 large-scale farmers and 10 smallholders were then selected from each village. This sampling strategy resulted in 720 households being selected for the survey (36 villages in 18 towns across 6 counties). Among them, 104 surveyed households did not grow wheat and were therefore excluded from the total sample, leaving 616 effective samples.

Results

Results of descriptive statistics

The results of the descriptive statistics for all the variables are presented in Table 1. The net income and yield of wheat cultivation vary significantly among farmers. However, the price of wheat fluctuates within a small range, with the selling price typically within 10% above or below the average price in their villages. Among the farmers, 257 (41.72%) use organic fertilizers. Of these, 135 (52.53%) combine organic fertilizers with soil-testing formulas, and 86 farmers (33.46%) use organic fertilizers with outsourcing services. This indicates that organic fertilizers are not yet widely used in rural China, and the combined application of organic fertilizers with soil-testing formulas and outsourcing services needs further promotion.

Table 1. Descriptive statistics of the variables

Validation of the instrumental variable

The variable ‘the distance between farmers and the nearest store selling organic fertilizers (WOV)’ is selected as the instrumental variable. We test its validity, and the results are presented in Table 2. The outcome of the KPrkLM test rejects the null hypothesis at the 1% significance level, indicating that WOV is correlated with both OSF and OUS. Additionally, the value of CDWF exceeds the Stock Yogo weak ID test's critical value of 16.38 at the 10% level, indicating that WOV is not a weak instrumental variable.

Table 2. The validation of instrumental variable

Note. *, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

The results of T-test

The T-test was conducted to identify differences between farmers who apply organic fertilizers and those who do not. The results are presented in Table 3. It is evident that the differences in AGE, EDU, RPH, HRT, HMC, PHI, NHL, SOF, and WOV between farmers not applying organic fertilizers and those applying organic fertilizers with soil-testing formulas are statistically significant at the 5% level. Similarly, the differences in EDU, RPH, HRT, HMC, PHI, NHL, SOF, and WOV between farmers not using organic fertilizers and those using organic fertilizers with outsourcing services are statistically significant at the 5% level. These results indicate that the behavior of farmers regarding OFA is not random and may be influenced by numerous factors. Therefore, MESR is suitable for solving the estimation bias caused by the non-randomness of this behavior.

Table 3. The results of T-test

A, Not applying organic fertilizers; B, OFA without soil-testing formula; C, OFA with soil-testing formula; D, OFA without outsourcing service; E, OFA with outsourcing service.

*, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

The results of Mlogit

Mlogit is chosen in the first stage of MESR to estimate the probability of OFA, and the results are reported in Table 4. The results of marginal effects indicate that most variables significantly influence farmers' OFA behavior. These results demonstrate that OFA is not a random behavior among farmers, justifying the use of MESR over OLS. As farmers age, the probability of OFA without soil-testing formula and OFA without outsourcing service decreases, while the probability of OFA with soil-testing formula and OFA with outsourcing service increases. This indicates that older farmers accumulate more planting experience than younger farmers, which helps them enhance technology application (Thar et al., Reference Thar, Ramilan, Farquharson, Pang and Chen2021). The probability of OFA with outsourcing service is higher in males than in females, indicating that men are more adventurous than women in terms of technology application (Qing et al., Reference Qing, Zhou, Song, Deng and Xu2023). Risk preference increases the likelihood of OFA, and receiving technical training encourages farmers to use organic fertilizers with soil-testing formulas, consistent with previous studies (Ambali, Areal, and Georgantzis, Reference Ambali, Areal and Georgantzis2021). The probability of OFA with the soil-testing formula is higher among village cadres than among ordinary farmers. This is because village cadres have more positive attitudes toward technology application, as they need to maintain their prestige in rural society by leading in technology application (Peng and Yang, Reference Peng and Yang2021). Being a member of a cooperative increases the probability of OFA with outsourcing services but decreases the probability of OFA without outsourcing services. This is because cooperatives usually provide outsourcing services to farmers (Xie, Luo, and Zhong, Reference Xie, Luo and Zhong2021). Conversely, an abundant household labor force reduces the likelihood of OFA with outsourcing services, as their own labor force is enough to support agricultural production, thereby decreasing the demand for outsourcing services (Brown et al., Reference Brown, Kovács, Herzon, Villamayor-Tomas, Albizua, Galanaki, Grammatikopoulou, McCracken, Olsson and Zinngrebe2021). An increase in farm size increases the probability of OFA. This is because land consolidation through transfer reduces land fragmentation, thereby enhancing the economies of scale for OFA (Helfand and Taylor, Reference Helfand and Taylor2021). The instrumental variable WOV has a negative impact on farmers' OFA behavior, aligning with expectations and confirming the validity of the instrumental variable.

Table 4. The results of Mlogit

Note. Standard errors are reported in parentheses, and *, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

The results of ATT

The results of the ATT estimated by MESR are presented in Table 5. They indicate that neither OFA without a soil-testing formula nor OFA without an outsourcing service significantly increases the net income of wheat cultivation per hectare. However, OFA with a soil-testing formula increases the net income of wheat cultivation per hectare by 2150 RMB, and OFA with an outsourcing service increases it by 3950 RMB. Both results are statistically significant at the 1% level. These results confirm H1 and H2, suggesting that the effectiveness of OFA in increasing income can be enhanced by the application of a soil-testing formula or an outsourcing service.

Table 5. The results of ATT

Note. Standard errors are reported in parentheses, and *, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

The Kernel density (K-density) of farmers not applying organic fertilizers, applying organic fertilizers without a soil-testing formula, and applying organic fertilizers with a soil-testing formula are depicted in Figure 3. When compared with farmers not using organic fertilizers, the shift of Kdensity for farmers using organic fertilizers without a soil-testing formula to the right is not noticeable. However, there is a clear rightward shift in the Kdensity for farmers applying organic fertilizers with soil-testing formulas. These results indicate that OFA without a soil-testing formula has no significant effect on the net income of wheat cultivation, whereas OFA with a soil-testing formula has a significantly positive impact on the net income of wheat cultivation.

Figure 3. The Kdensity of OFA with soil-testing formula.

The K-density of farmers not applying organic fertilizers, applying organic fertilizers without outsourcing service, and applying organic fertilizers with outsourcing service are presented in Figure 4. When compared with farmers not applying organic fertilizers, the shift in K-density for farmers applying organic fertilizers without outsourcing service to the right is not apparent. However, the shift in K-density for farmers applying organic fertilizers with outsourcing services to the right is evident. These results indicate that OFA without outsourcing service has no significant effect on the net income of wheat cultivation, whereas OFA with outsourcing service has significant positive effects on the net income of wheat cultivation.

Figure 4. The Kdensity of OFA with outsourcing service.

Heterogeneity analysis

We divide the total sample into two subsamples: smallholders managing land less than 3.33 hectares and large-scale farmers managing land of at least 3.33 hectares. MESR is used, respectively, to test the effects of OFA on income across different farm sizes, and the results of ATT are presented in Table 6. OFA with a soil-testing formula increases the net income of wheat cultivation per hectare by 510 RMB for smallholders and 2680 RMB for large-scale farmers. Similarly, OFA with an outsourcing service increases the net income of wheat cultivation per hectare by 640 RMB for smallholders and 4860 RMB for large-scale farmers. It is evident that the income-increasing effect of OFA is more pronounced for large-scale farmers compared to smallholders. Additionally, OFA proves ineffective in increasing income when neither soil-testing formula nor outsourcing service is used with organic fertilizers, aligning with the findings in Table 5.

Table 6. The effects of OFA on income in different levels of farm size

Note. Standard errors are reported in parentheses, and *, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

The test of influence mechanism

The effects of OFA on crop yield and price are tested in order to elucidate the influence mechanism. The results are presented in Table 7. OFA with soil-testing increases wheat yield by 931 kg per hectare, and OFA with outsourcing service increases wheat yield by 1058 kg per hectare. Both of the results are statistically significant at the 1% level. However, neither OFA with soil-testing formula nor OFA with outsourcing service significantly increases the price of wheat.

Table 7. The test of influence mechanism

Note. Standard errors are reported in parentheses, and *, ** and *** means passing the test at the significance levels of 10%, 5%, and 1%, respectively.

Discussion

The effectiveness of OFA on increasing income improved by soil-testing formula and outsourcing service

The results of this article reveal that both OFA with soil-testing formulas and OFA with outsourcing services have positive impacts on income. Conversely, the positive impact of OFA on farmers' income is minimized when neither of them is applied. Our results explain why the effectiveness of OFA in enhancing farmers' income remains uncertain in real agricultural production. This is because farmers cannot ensure the effectiveness of technology adoption (Fang et al., Reference Fang, Hu, Mao and Chen2021). In rural China, most farmers lack professional and systematic training in agricultural knowledge and skills, relying instead on practical experience and social networks for knowledge acquisition (Qin et al., Reference Qin, Wang, Zhou, Guo, Jiang and Zhang2022). However, excessive reliance on experiences may lead to knowledge solidification (Li and Li, Reference Li and Li2023), making it challenging for farmers to gain valuable knowledge and skills from social networks formed by village acquaintances (Elahi et al., Reference Elahi, Zhang, Lirong, Khalid and Xu2021). Therefore, the lack of knowledge and skills results in farmers' inability to use organic fertilizers rationally, which limits the positive impact of OFA on their income (Daadi and Latacz-Lohmann, Reference Daadi and Latacz-Lohmann2021). As a result, farmers need to rely on external technical support, or even division of labor in order to enhance the effectiveness of OFA (Niu et al., Reference Niu, Chen, Gao, Wang, Chen and Zhao2022). Fertilizer stations or cooperatives provide soil-testing formulas to enable precise fertilization for farmers (Li et al., Reference Li, Zhuo, Ji and Zhu2022c). On the other hand, specialization and standardization of fertilization operations can be achieved through the application of outsourcing services (Cui et al., Reference Cui, Ba, Dong and Fan2022). The Average Treatment Effect on the Treated (ATT) of OFA with outsourcing service exceeds that of OFA with soil-testing formula, indicating that outsourcing service is more effective than soil-testing formula in enhancing the income-increasing effectiveness of OFA. This suggests that introducing division of labor in the fertilization process is more beneficial for improving fertilization efficiency compared to providing soil-testing formulas to farmers (Slaton et al., Reference Slaton, Lyons, Osmond, Brouder, Culman, Drescher, Gatiboni, Hoben, Kleinman, McGrath, Miller, Pearce, Shober, Spargo, Volenec and J2022). Soil-testing formulas are by no means infallible. For example, soil K testing is seriously flawed because the exchangeable fraction estimated by NH4OAc extraction does not necessarily equate to plant-available K (Das et al., Reference Das, Sahoo, Raza, Barman and Das2022). Despite fertilizer recommendations based on soil testing being provided to farmers, farmers ultimately remain the decision-makers and implementers of production. This means that the effectiveness of OFA in increasing income may still be compromised by poor decisions made by farmers (Antwi-Agyei and Stringer, Reference Antwi-Agyei and Stringer2021). For example, farmers may distrust soil-testing formulas and refuse to adhere to the recommended fertilizer amounts, thereby potentially reducing the effectiveness of OFA due to improper operations (Wu, Li, and Ge Reference Wu, Li and Ge2022). With the implementation of outsourcing services, professional organizations take over agricultural production tasks from farmers, introducing a division of labor into the industry. Through this division of labor, the shortcomings in farmers' production capacity can be addressed, facilitating more rational fertilization practices. Empirical studies have already demonstrated that outsourcing services help reduce excessive fertilizer usage and enhance fertilization efficiency (Rahman and Connor, Reference Rahman and Connor2022).

The effectiveness of OFA on increasing income stronger in large-scale farmers than in smallholders

OFA with soil-testing formulas and outsourcing services both have a more significant impact on income for large-scale farmers compared to smallholders. Firstly, large-scale farmers stand to benefit more from technological advancements than smallholders, thus they are more motivated to enhance technology application (O'Connor et al., Reference O'Connor, Ehimen, Pillai, Black, Tormey and Bartlett2021). Secondly, large-scale farmers have more human and social capitals than smallholders (Chen, Fu, and Liu, Reference Chen, Fu and Liu2022), resulting in a higher capacity for technology adoption (Mao et al., Reference Mao, Zhou, Ying and Pan2021). Thirdly, large-scale farmers experience less land fragmentation than smallholders, leading to reduced fertilizer wastage and thereby enhancing the effectiveness of OFA in increasing income (Zhang et al., Reference Zhang, Qiao, Lakshmanan, Yuan, Liu, Zhong and Chen2022).

The effectiveness of OFA on increasing income derived from yield-increase rather than price-increase

Our results suggest that both OFA with soil-testing formula and OFA with outsourcing service positively impact crop yield rather than price, and this finding holds true across subsamples of large-scale farmers and smallholders. Comparing our results with other studies reveals that OFA neither drives increases in grain prices (Li et al., Reference Li, Zhuo, Ji and Zhu2022c) nor in cash crop prices (Su, Zhou, and Zhou, Reference Su, Zhou and Zhou2022). Although OFA helps contributes to improving product quality, transitioning from high quality to high prices faces several institutional barriers (Fertahi et al., Reference Fertahi, Ilsouk, Zeroual, Oukarroum and Barakat2021). Firstly, the agricultural product market exhibits characteristics of information asymmetry (Seifert, Kahle, and Hüttel, Reference Seifert, Kahle and Hüttel2021). Due to the absence of effective standards for product quality classification and labeling systems (Abate et al., Reference Abate, Bernard, De Janvry, Sadoulet and Trachtman2021), consumers find it challenging to assess the quality of agricultural products. Consequently, they are reluctant to pay higher prices for potentially high-quality products (He and Shi, Reference He and Shi2021). Secondly, high transaction costs make it difficult for farmers to sell their products directly to consumers (Foster and Rosenzweig, Reference Foster and Rosenzweig2022). Specifically, the difficulty in identifying consumers' preferences for high-quality products results in high search costs, while the uncertainty surrounding product quality complicates price negotiations. In order to save transaction costs, most farmers opt to await middlemen's visits to purchase their products (Ali et al., Reference Ali, Xia, Ouattara, Mahmood and Faisal2021). As a result, the premium generated by product quality improvement is occupied by the middlemen (Sharma et al., Reference Sharma, Cosguner, Sharma and Motiani2021). Thirdly, farmers lack bargaining power in the market (Kopp and Mishra, Reference Kopp and Mishra2022). Since individual farmers occupy a small market share, they often have no advantages in price negotiation (Rogers et al., Reference Rogers, Wilmsen, Han, Wang, Duan, He, Li, Lin and Wong2021).

Concluding remarks

The main conclusions are that both OFA with soil-testing formulas and OFA with outsourcing services effectively increase farmers' income. The effectiveness of OFA with outsourcing services is stronger than that of OFA with soil-testing formulas. However, OFA does not increase income if neither soil-testing formulas nor outsourcing services are available. The mechanism through which OFA enhances farmers' income is by boosting crop yields, yet it does not impact product prices. While OFA effectively increases the income of large-scale farmers, it does not have the same effect on smallholders' income.

There are several policy implications drawn from this article. Firstly, the agricultural technology extension system in China requires further enhancement. The extension of organic fertilizers, soil-testing formulas, and outsourcing services should not operate independently but rather be integrated into a comprehensive extension framework. This integration can be achieved through the design of interconnected subsidies, technical training, and other policies. Secondly, the government needs to consider the challenges farmers encounter in responding to the extension of organic fertilizers. Both the accuracy of soil-testing formulas and the quality of outsourcing service should be enhanced to support the application of technology by farmers. Thirdly, eliminating the information asymmetry of agricultural product quality is crucial to unlock the price-increase effect of OFA. The government should establish quality classification standards for various agricultural products and expand existing quality labels, such as pollution-free, green, organic products, and origin labels, to enhance the richness of quality information. In addition, Internet of Things technology should be integrated into the agricultural product circulation system to enhance product traceability. Fourthly, land-scale operations need to be extended to enhance the effectiveness of technology application. The government should implement measures to further promote land transfer in rural China. Some innovative modes of land transfer should be introduced to incentivize smallholders to lease out their land. Expanding the practice where smallholders rent their land in exchange for shares should be prioritized. Additionally, supportive policies, including subsidies and technical training, should be implemented to encourage capable farmers to engage in land rental for scaled operations.

Acknowledgements

This work was supported by National Natural Science Foundation of China (grant number 72303214).

References

Abate, G.T., Bernard, T., De Janvry, A., Sadoulet, E. and Trachtman, C. (2021) ‘Introducing quality certification in staple food markets in Sub-Saharan Africa: four conditions for successful implementation’, Food Policy, 105, p. 102173. https://doi.org/10.1016/j.foodpol.2021.102173CrossRefGoogle Scholar
Abdulai, A.N. (2016) ‘Impact of conservation agriculture technology on household welfare in Zambia', Agricultural Economics, 47(6), pp. 729–41. https://doi.org/10.1111/agec.12269CrossRefGoogle Scholar
Ali, A., Xia, C., Ouattara, N., Mahmood, I. and Faisal, M. (2021) ‘Economic and environmental consequences’ of postharvest loss across food supply chain in the developing countries’, Journal of Cleaner Production, 323, p. 129146. https://doi.org/10.1016/j.jclepro.2021.129146CrossRefGoogle Scholar
Ambali, O.I., Areal, F.J. and Georgantzis, N. (2021) ‘Improved rice technology adoption: the role of spatially-dependent risk preference’, Agriculture, 11(8), p. 691. https://doi.org/10.3390/agriculture11080691CrossRefGoogle Scholar
Amfo, B. and Baba Ali, E. (2021) ‘Beyond adoption: the interaction between organic and inorganic fertilizer application, and vegetable productivity in Ghana’, Renewable Agriculture and Food Systems, 36(6), pp. 605–21. https://doi.org/10.1017/S1742170521000235CrossRefGoogle Scholar
Antwi-Agyei, P. and Stringer, L.C. (2021) ‘Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: insights from northeastern Ghana’, Climate Risk Management, 32, p. 100304. https://doi.org/10.1016/j.crm.2021.100304CrossRefGoogle Scholar
Baruah, S., Mohanty, S. and Rola, A.C. (2022) ‘Small Farmers Large Field (SFLF): a synchronized collective action model for improving the livelihood of small farmers in India’, Food Sec, 14, pp. 323–36. https://doi.org/10.1007/s12571-021-01236-xCrossRefGoogle Scholar
Brown, C., Kovács, E., Herzon, I., Villamayor-Tomas, S., Albizua, A., Galanaki, A., Grammatikopoulou, I., McCracken, D., Olsson, J.A. and Zinngrebe, Y. (2021) ‘Simplistic understandings of farmer motivations could undermine the environmental potential of the common agricultural policy’, Land Use Policy, 101, p. 105136. https://doi.org/10.1016/j.landusepol.2020.105136CrossRefGoogle Scholar
Chen, Y., Fu, X. and Liu, Y. (2022) ‘Effect of farmland scale on farmers’ application behavior with organic fertilizer’, International Journal of Environmental Research and Public Health, 19(9), p. 4967. https://doi.org/10.3390/ijerph19094967CrossRefGoogle ScholarPubMed
Chen, S., Zhong, Z. and Lu, H. (2023) ‘Impact of agricultural production outsourcing service and land fragmentation on agricultural non-point source pollution in China: evidence from Jiangxi Province’, Frontiers in Environmental Science, 10, p. 1079709. https://doi.org/10.3389/fenvs.2022.1079709CrossRefGoogle Scholar
Choudhary, R.C., Bairwa, H.L., Kumar, U., Javed, T., Asad, M., Lal, K., Mahawer, L.N., Sharma, S.K., Singh, P., Hassan, M.M., Abo-Shosha, A.A., Rajagopal, R. and Abdelsalam, N.R. (2022) ‘Influence of organic manures on soil nutrient content, microbial population, yield and quality parameters of pomegranate (Punica granatum L.) cv. Bhagwa’, PLoS ONE, 17(4), p. e0266675. https://doi.org/10.1371/journal.pone.0266675CrossRefGoogle Scholar
Cui, N., Ba, X., Dong, J. and Fan, X. (2022) ‘Does farmland transfer contribute to reduction of chemical fertilizer use? Evidence from Heilongjiang Province’, China. Sustainability, 14(18), p. 11514. https://doi.org/10.3390/su141811514CrossRefGoogle Scholar
Daadi, B. and Latacz-Lohmann, U. (2021) ‘Organic fertilizer use by smallholder farmers: typology of management approaches in northern Ghana’, Renewable Agriculture and Food Systems, 36(2), pp. 192206. https://doi.org/10.1017/S1742170520000228CrossRefGoogle Scholar
Das, D., Sahoo, J., Raza, M.B., Barman, M. and Das, R. (2022) ‘Ongoing soil potassium depletion under intensive cropping in India and probable mitigation strategies. a review’, Agronomy for Sustainable Development, 42(1), p. 4. https://doi.org/10.1007/s13593-021-00728-6CrossRefGoogle Scholar
Deb, P. and Trivedi, P.K. (2006) ‘Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment', Stata Journal, 6(2), pp. 246–55. https://doi.org/10.1207/s15327906mbr4104_7CrossRefGoogle Scholar
Dong, H., Zhang, Y., Chen, T. and Li, J. (2023) ‘Acceptance intention and behavioral response to soil-testing formula fertilization technology: an empirical study of agricultural land in Shaanxi Province’, International Journal of Environmental Research and Public Health, 20(2), p. 951. https://doi.org/10.3390/ijerph20020951CrossRefGoogle ScholarPubMed
Du, T., He, H., Zhang, Q., Lu, L., Mao, W. and Zhai, M. (2022) ‘Positive effects of organic fertilizers and biofertilizers on soil microbial community composition and walnut yield’, Applied Soil Ecology, 175, p. 104457. https://doi.org/10.1016/j.apsoil.2022.104457CrossRefGoogle Scholar
Elahi, E., Zhang, H., Lirong, X., Khalid, Z. and Xu, H. (2021) ‘Understanding cognitive and socio-psychological factors determining farmers’ intentions to use improved grassland: implications of land use policy for sustainable pasture production’, Land Use Policy, 102, p. 105250. https://doi.org/10.1016/j.landusepol.2020.105250CrossRefGoogle Scholar
Fang, L., Hu, R., Mao, H. and Chen, S. (2021) ‘How crop insurance influences agricultural green total factor productivity: evidence from Chinese farmers’, Journal of Cleaner Production, 321, p. 128977. https://doi.org/10.1016/j.jclepro.2021.128977CrossRefGoogle Scholar
Fertahi, S., Ilsouk, M., Zeroual, Y., Oukarroum, A. and Barakat, A. (2021) ‘Recent trends in organic coating based on biopolymers and biomass for controlled and slow release fertilizers’, Journal of Controlled Release, 330, pp. 341–61. https://doi.org/10.1016/j.jconrel.2020.12.026CrossRefGoogle Scholar
Foster, A.D. and Rosenzweig, M.R. (2022) ‘Are there too many farms in the world? Labor market transaction costs, machine capacities, and optimal farm size’, Journal of Political Economy, 130, p. 2. https://doi.org/10.1086/717890CrossRefGoogle Scholar
Gao, Y., Yao, X., Chen, C. and Niu, Z. (2022) ‘Can farmers’ participation in contract farming promote the application of organic fertilizer? Empirical evidence from a sample of vegetable farmers in Shandong province of China’, Renewable Agriculture and Food Systems, 37(5), pp. 479–89. https://doi.org/10.1017/S1742170522000205CrossRefGoogle Scholar
Guo, Y., Zhong, F. and Ji, Y. (2019) ‘Economies of scale and farmland transfer preferences of large-scale households: an analysis based on land plots', Chinese Rural Economy, 35(4), pp. 721. in Chinese.Google Scholar
Hauck, R. and Bremner, J. (1976) ‘Use of tracers for soil and fertilizer nitrogen research’, Advances in Agronomy, 28, pp. 219–66. https://doi.org/10.1016/S0065-2113(08)60556-8CrossRefGoogle Scholar
He, M. and Shi, J. (2021) ‘Circulation traceability system of Chinese herbal medicine supply chain based on internet of things agricultural sensor’, Sustainable Computing: Informatics and Systems, 30, p. 100518. https://doi.org/10.1016/j.suscom.2021.100518Google Scholar
Helfand, S.M. and Taylor, M.P. (2021) ‘The inverse relationship between farm size and productivity: refocusing the debate’, Food Policy, 99, p. 101977. https://doi.org/10.1016/j.foodpol.2020.101977CrossRefGoogle Scholar
Hu, Y., Li, B., Zhang, Z. and Wang, J. (2022) ‘Farm size and agricultural technology progress: evidence from China’, Journal of Rural Studies, 93, pp. 417–29. https://doi.org/10.1016/j.jrurstud.2019.01.009CrossRefGoogle Scholar
Jiang, Y., Li, K., Chen, S., Fu, X., Feng, S. and Zhuang, Z. (2022a) ‘A sustainable agricultural supply chain considering substituting organic manure for chemical fertilizer’, Sustainable Production and Consumption, 29, pp. 432–46. https://doi.org/10.1016/j.spc.2021.10.025CrossRefGoogle Scholar
Jiang, Y., Zhang, R., Zhang, C., Su, J., Cong, W. and Deng, X. (2022b) ‘Long-term organic fertilizer additions elevate soil extracellular enzyme activities and tobacco quality in a tobacco-maize rotation’, Frontiers in Plant Science, 13, p. 973639. https://doi.org/10.3389/fpls.2022.973639CrossRefGoogle Scholar
Kopp, T. and Mishra, A.K. (2022) ‘Perishability and market power in Nepalese food crop production’, Journal of Agricultural Economics, 73(2), pp. 518–40. https://doi.org/10.1111/1477-9552.12463CrossRefGoogle Scholar
Kumar, A., Mishra, A.K., Saroj, S. and Joshi, P.K. (2019) ‘Impact of traditional versus modern dairy value chains on food security: evidence from India’s dairy sector', Food Policy, 83, pp. 260–70. https://doi.org/10.1016/j.foodpol.2019.01.010CrossRefGoogle Scholar
Langridge, P. and Reynolds, M. (2021) ‘Breeding for drought and heat tolerance in wheat’, Theoretical and Applied Genetics, 134, pp. 1753–69. https://doi.org/10.1007/s00122-021-03795-1CrossRefGoogle ScholarPubMed
Lee, D.R. (2005) ‘Agricultural sustainability and technology adoption: issues and policies for developing countries', American Journal of Agricultural Economics, 87(5), pp. 1325–34. https://doi.org/10.2307/3697714CrossRefGoogle Scholar
Li, B. (2019) ‘The effect of the stability of land transfer contract on the fertilization intensity and environmental efficiency of the farmer who tranfers in land’, Journal of Natural Resources, 34(11), pp. 2317–32. In Chinese.Google Scholar
Li, K. and Li, Q. (2023) ‘Towards more efficient low-carbon agricultural technology extension in China: identifying lead smallholder farmers and their behavioral determinants’, Environmental Science and Pollution Research, 30(10), pp. 27833–45. https://doi.org/10.1007/s11356-022-24159-2CrossRefGoogle Scholar
Li, N., Tang, L., Che, X., Shi, X. and Ma, X. (2022 a) ‘Does the democratization level of village governance affect perceptions of security and integrity of land rights? -an analysis from the perspective of social network abundance’, Journal of Rural Studies, 94, pp. 305–18. https://doi.org/10.1016/j.jrurstud.2022.06.013CrossRefGoogle Scholar
Li, J., Yang, Q., Shi, Z., Zang, Z. and Liu, X. (2021) ‘Effects of deficit irrigation and organic fertilizer on yield, saponin and disease incidence in Panax notoginseng under shaded conditions’, Agricultural Water Management, 256, p. 107056. https://doi.org/10.1016/j.agwat.2021.107056CrossRefGoogle Scholar
Li, Q., Zhang, D., Song, Z., Ren, L., Jin, X., Fang, W., Yan, D., Li, Y., Wang, Q. and Cao, A. (2022 b) ‘Organic fertilizer activates soil beneficial microorganisms to promote strawberry growth and soil health after fumigation’, Environmental Pollution, 295, p. 118653. https://doi.org/10.1016/j.envpol.2021.118653CrossRefGoogle ScholarPubMed
Li, B., Zhuo, N., Ji, C. and Zhu, Q. (2022 c) ‘Influence of smartphone-based digital extension service on farmers’ sustainable agricultural technology adoption in China’, International Journal of Environmental Research and Public Health, 19(15), p. 9639. https://doi.org/10.3390/ijerph19159639CrossRefGoogle ScholarPubMed
Ma, W. and Abdulai, A. (2016) ‘Does cooperative membership improve household welfare? Evidence from apple farmers in China', Food Policy, 58(1), pp. 94102. https://doi.org/10.1016/j.foodpol.2015.12.002CrossRefGoogle Scholar
Maertens, A., Michelson, H. and Nourani, V. (2021) ‘How do farmers learn from extension services? Evidence from Malawi’, American Journal of Agricultural Economics, 103(2), pp. 569–95. https://doi.org/10.1111/ajae.12135CrossRefGoogle Scholar
Makate, C. and Mutenje, M. (2021) ‘Discriminatory effects of gender disparities in improved seed and fertilizer use at the plot-level in Malawi and Tanzania', World Development Perspectives, 23, p. 100344.CrossRefGoogle Scholar
Mao, H., Zhou, L., Ying, R. and Pan, D. (2021) ‘Time preferences and green agricultural technology adoption: field evidence from rice farmers in China’, Land Use Policy, 109, p. 105627. https://doi.org/10.1016/j.landusepol.2021.105627CrossRefGoogle Scholar
Mattila, T.J., Hagelberg, E., Söderlund, S. and Joona, J. (2021) ‘How farmers approach soil carbon sequestration? Lessons learned from 105 carbon-farming plans’, Soil and Tillage Research, 215, p. 105204. https://doi.org/10.1016/j.still.2021.105204CrossRefGoogle Scholar
McFadden, D. (1974) ‘The measurement of urban travel demand', Journal of Public Economics, 3(4), pp. 303–28. https://doi.org/10.1016/0047-2727(74)90003-6CrossRefGoogle Scholar
Niu, Z., Chen, C., Gao, Y., Wang, Y., Chen, Y. and Zhao, K. (2022) ‘Peer effects, attention allocation and farmers’ adoption of cleaner production technology: taking green control techniques as an example’, Journal of Cleaner Production, 339, p. 130700. https://doi.org/10.1016/j.jclepro.2022.130700CrossRefGoogle Scholar
O'Connor, S., Ehimen, E., Pillai, S., Black, A., Tormey, D. and Bartlett, J. (2021) ‘Biogas production from small-scale anaerobic digestion plants on European farms’, Renewable and Sustainable Energy Reviews, 139, p. 110580. https://doi.org/10.1016/j.rser.2020.110580CrossRefGoogle Scholar
Ojo, T. and Baiyegunhi, L. (2021) ‘Climate change perception and its impact on net farm income of smallholder rice farmers in South-West, Nigeria’, Journal of Cleaner Production, 310, p. 127373. https://doi.org/10.1016/j.jclepro.2021.127373CrossRefGoogle Scholar
Oyetunde-Usman, Z., Olagunju, K.O. and Ogunpaimo, O.R. (2021) ‘Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria’, International Soil and Water Conservation Research, 9(2), pp. 241–8. https://doi.org/10.1016/j.iswcr.2020.10.007CrossRefGoogle Scholar
Pan, D., Lu, Y. and Kong, F. (2022) ‘The impact of participation degree and time of returning farmland to forest on farmer's household income: empirical analysis based on a multiple endogenous regression model’, Journal of Agrotechnical Economics, 2022(6), p. 14. in chinese. https://doi.org/10.13246/j.cnki.jae.2022.06.003Google Scholar
Peng, L. and Yang, Q. (2021) Social network, environmental cognition and organic fertilizer application behavior of small farmers. In E3S Web of Conferences (Vol. 293, p. 03016). EDP Sciences. https://doi.org/10.1051/e3sconf/202129303016CrossRefGoogle Scholar
Qian, L., Lu, H., Gao, Q. and Lu, H. (2022) ‘Household-owned farm machinery vs. outsourced machinery services: the impact of agricultural mechanization on the land leasing behavior of relatively large-scale farmers in China’, Land Use Policy, 115, p. 106008. https://doi.org/10.1016/j.landusepol.2022.106008CrossRefGoogle Scholar
Qiao, F. and Huang, J. (2021) ‘Farmers’ risk preference and fertilizer use’, Journal of Integrative Agriculture, 20(7), pp. 1987–95. https://doi.org/10.1016/S2095-3119(20)63450-5CrossRefGoogle Scholar
Qin, T., Wang, L., Zhou, Y., Guo, L., Jiang, G. and Zhang, L. (2022) ‘Digital technology-and-services-driven sustainable transformation of agriculture: cases of China and the EU’, Agriculture, 12(2), p. 297. https://doi.org/10.3390/agriculture12020297CrossRefGoogle Scholar
Qing, C., Zhou, W., Song, J., Deng, X. and Xu, D. (2023) ‘Impact of outsourced machinery services on farmers’ green production behavior: evidence from Chinese rice farmers’, Journal of Environmental Management, 327, p. 116843. https://doi.org/10.1016/j.jenvman.2022.116843CrossRefGoogle ScholarPubMed
Rahman, M.M. and Connor, J.D. (2022) ‘Impact of agricultural extension services on fertilizer use and farmers’ welfare: evidence from Bangladesh’, Sustainability, 14(15), p. 9385. https://doi.org/10.3390/su14159385CrossRefGoogle Scholar
Rogers, S., Wilmsen, B., Han, X., Wang, Z.J.H., Duan, Y., He, J., Li, J., Lin, W. and Wong, C. (2021) ‘Scaling up agriculture? The dynamics of land transfer in inland China’, World Development, 146, p. 105563. https://doi.org/10.1016/j.worlddev.2021.105563CrossRefGoogle Scholar
Rutsaert, P., Chamberlin, J., Oluoch, K.O., Kitoto, V.O. and Donovan, J. (2021) ‘The geography of agricultural input markets in rural Tanzania’, Food Security, 13, pp. 1379–91. https://doi.org/10.1007/s12571-021-01181-9CrossRefGoogle Scholar
Seifert, S., Kahle, C. and Hüttel, S. (2021) ‘Price dispersion in farmland markets: what is the role of asymmetric information?’, American Journal of Agricultural Economics, 103(4), pp. 1545–68. https://doi.org/10.1111/ajae.12153CrossRefGoogle Scholar
Setsoafia, E.D., Ma, W. and Renwick, A. (2022) ‘Effects of sustainable agricultural practices on farm income and food security in northern Ghana’, Agricultural Economics (Amsterdam, Netherlands), 10, p. 9. https://doi.org/10.1186/s40100-022-00216-9Google Scholar
Sharma, A., Cosguner, K., Sharma, T.K. and Motiani, M. (2021) ‘Channel intermediaries and manufacturer performance: an exploratory investigation in an emerging market’, Journal of Retailing, 97(4), pp. 639–57. https://doi.org/10.1016/j.jretai.2020.09.005CrossRefGoogle Scholar
Slaton, N.A., Lyons, S.E., Osmond, D.L., Brouder, S.M., Culman, S.W., Drescher, G., Gatiboni, L.C., Hoben, J., Kleinman, A., McGrath, P.J., Miller, J.M., Pearce, R.O., Shober, A., Spargo, A.L., Volenec, J.T. and J, J. (2022) ‘Minimum dataset and metadata guidelines for soil-test correlation and calibration research’, Soil Science Society of America Journal, 86(1), pp. 1933. https://doi.org/10.1002/saj2.20338CrossRefGoogle Scholar
Su, S., Zhou, X. and Zhou, Y. (2022) ‘Can organic fertilizer substitution increase farmers’ income? -A case study of vegetable growers in Shandong’, Journal of Arid Land Resources and Environment, 36(04), pp. 2431. in Chinese. https://doi.org/10.13448/j.cnki.jalre.2022.088Google Scholar
Sun, Z. and Li, X. (2021) ‘Technical efficiency of chemical fertilizer use and its influencing factors in China's rice production’, Sustainability, 13(3), p. 1155. https://doi.org/10.3390/su13031155CrossRefGoogle Scholar
Tao, Y., Liu, T., Wu, J., Wu, Z., Liao, D., Shah, F. and Wu, W. (2022) ‘Effect of combined application of chicken manure and inorganic nitrogen fertilizer on yield and quality of cherry tomato’, Agronomy, 12(7), p. 1574. https://doi.org/10.3390/agronomy12071574CrossRefGoogle Scholar
Teklewold, H., Kassie, M., Shiferaw, B. and Köhlin, G. (2013) ‘Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: impacts on household income, agrochemical use and demand for labor', Ecological Economics, 93, pp. 8593. https://doi.org/10.1016/j.ecolecon.2013.05.002CrossRefGoogle Scholar
Thar, S.P., Ramilan, T., Farquharson, R.J., Pang, A. and Chen, D. (2021) ‘An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar’, The Electronic Journal of Information Systems in Developing Countries, 87(2), p. e12159. https://doi.org/10.1002/isd2.12159CrossRefGoogle Scholar
Uhunamure, S.E., Kom, Z., Shale, K., Nethengwe, N.S. and Steyn, J. (2021) ‘Perceptions of smallholder farmers towards organic farming in South Africa’, Agriculture, 11, p. 1157. https://doi.org/10.3390/agriculture11111157CrossRefGoogle Scholar
Vigani, M., Kathage, J., Khanna, M., Roe, B.E., Vercammen, J. and Wu, J.J. (2019) ‘To risk or not to risk? Risk management and farm productivity', American Journal of Agricultural Economics, 101(5), pp. 1432–54. https://doi.org/10.1093/ajae/aaz020CrossRefGoogle Scholar
Wang, T., Li, Z., Yu, Z., Wang, Z., Lian, Z., Du, W., Zhao, X., Wang, M., Miao, C., Ding, M., Wang, Y., Zhou, L., Zhang, X., Li, X. and Gui, J. (2021) ‘Production of YY males through self-fertilization of an occasional hermaphrodite in Lanzhou catfish (Silurus lanzhouensis)’, Aquaculture, 539, p. 736622. https://doi.org/10.1016/j.aquaculture.2021.736622CrossRefGoogle Scholar
Wesenbeeck, C., Keyzer, M.A., Veen, W. and Qiu, H. (2021) ‘Can China’s overuse of fertilizer be reduced without threatening food security and farm incomes?', Agricultural Systems, 190, p. 103093. https://doi.org/10.1016/j.agsy.2021.103093CrossRefGoogle Scholar
Wu, H., Li, J. and Ge, Y. (2022) ‘Ambiguity preference, social learning and adoption of soil testing and formula fertilization technology’, Technological Forecasting and Social Change, 184, p. 122037. https://doi.org/10.1016/j.techfore.2022.122037CrossRefGoogle Scholar
Xie, L., Luo, B. and Zhong, W. (2021) ‘How are smallholder farmers involved in digital agriculture in developing countries: a case study from China’, Land, 10(3), p. 245. https://doi.org/10.3390/land10030245CrossRefGoogle Scholar
Zhan, X., Shao, C., He, R. and Shi, R. (2021) ‘Evolution and efficiency assessment of pesticide and fertilizer inputs to cultivated land in China’, International Journal of Environmental Research and Public Health, 18(7), p. 3771. https://doi.org/10.3390/ijerph18073771CrossRefGoogle ScholarPubMed
Zhang, H., Antonangelo, J., Grove, J., Osmond, D., Slaton, N.A., Alford, S., Florence, R., Huluka, G., Hardy, D.H., Lessl, J., Maguire, R., Mylavarapu, R., Oldham, J.L., Pena-Yewtukhiw, E.M., Provin, T., Sonon, L., Sotomayor, D. and Wang, J. (2021) ‘Variation in soil-test-based phosphorus and potassium rate recommendations across the southern USA’, Soil Science Society of America Journal, 85(4), pp. 975–88. https://doi.org/10.1002/saj2.20280CrossRefGoogle Scholar
Zhang, W., Qiao, Y., Lakshmanan, P., Yuan, L., Liu, J., Zhong, C. and Chen, X. (2022) ‘Combing public-private partnership and large-scale farming increased net ecosystem carbon budget and reduced carbon footprint of maize production’, Resources, Conservation and Recycling, 184, p. 106411. https://doi.org/10.1016/j.resconrec.2022.106411CrossRefGoogle Scholar
Zhang, Y., Yang, R., Zhao, K. and Kong, X. (2023) ‘Fertilizer application in contract farming: a risk analysis’, Land, 12(8), p. 1495. https://doi.org/10.3390/land12081495CrossRefGoogle Scholar
Zheng, L., Li, L., Zhao, Z. and Qian, W. (2023) ‘Does land certification increase farmers’ use of organic fertilizer? Evidence from China’, Journal of Land Use Science, 18(1), pp. 3954. https://doi.org/10.1080/1747423X.2023.2178536CrossRefGoogle Scholar
Figure 0

Figure 1. The influence mechanism that soil-testing formula and outsourcing service drive the effectiveness of OFA.

Figure 1

Figure 2. Location of Anhui Province and the province's 6 grain-producing counties that are the study sites.

Figure 2

Table 1. Descriptive statistics of the variables

Figure 3

Table 2. The validation of instrumental variable

Figure 4

Table 3. The results of T-test

Figure 5

Table 4. The results of Mlogit

Figure 6

Table 5. The results of ATT

Figure 7

Figure 3. The Kdensity of OFA with soil-testing formula.

Figure 8

Figure 4. The Kdensity of OFA with outsourcing service.

Figure 9

Table 6. The effects of OFA on income in different levels of farm size

Figure 10

Table 7. The test of influence mechanism