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.
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.
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).
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).
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.
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).
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.
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).
The expected income value of farmers making the kth selection in the counterfactual state can be calculated using Equation (10).
The unbiased estimation results of the ATT can be calculated using Equation (11).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).