Sustainable potato farming in Shandong Province, China: a comprehensive analysis of organic fertilizer applications

Introduction: The potato holds the distinction of being the world’s largest non-cereal food crop and ensuring its sustainable production is imperative for global food security. Notably, China leads in both the planting area and output of potatoes globally, cementing its crucial role in the nation’s agricultural economy. A scientific assessment of the effectiveness of organic fertilizers on potato cultivation can significantly contribute to the promotion of sustainable agriculture.

Methods: This study utilizes a Propensity Score Matching (PSM) model and introduces a novel cost-efficiency approach to analyze and evaluate the production efficiency and economic impact of organic fertilizer application among 546 potato growers in Shandong.

Results: The research findings reveal the following: Firstly, compared to the control group without organic fertilizer application, it is evident that the use of organic fertilizers enhances production technology efficiency, labor productivity, land productivity, and net profit per unit by 3.6%, 1588.47 kg/person, 16346.77 kg/ha, and 16135.32 yuan/ha, respectively. Secondly, an examination of cost efficiency among growers with different production scales indicates that those with a planting scale of 0.667-1.333 hectares demonstrate relatively high production efficiency across multiple factors. Additionally, there is an observable inverted U-shaped trend in the relationship between planting scale and production efficiency. Thirdly, the continuous application of organic fertilizers proves advantageous in mitigating inefficiencies in investment techniques, leading to cost savings and efficiency improvements in potato cultivation.

Discussion: Consequently, it is recommended that the government and relevant departments enhance technical support, elevate professional training programs, and optimize the allocation of input factors. These measures aim to encourage farmers to adopt organic fertilizers, thereby promoting sustainable agricultural practices.

1 Introduction

The continuous progression of green agricultural technology plays a crucial role in achieving China’s dual objectives of reducing the quantity and enhancing the efficiency of chemical inputs (Xu et al., 2014; Yi et al., 2021), while effectively mitigating non-point source pollution in agriculture (Adnan et al., 2017a; Luthra et al., 2022). As per statistical findings from the Ministry of Agriculture and Rural Affairs, the policy initiated in 2015 to decrease chemical fertilizer and pesticide quantities while improving efficiency resulted in a 13.8% decrease in China’s application of agricultural chemical fertilizers (51.91 million tons, actual purity) and a 16.8% reduction in pesticide use (248,000 tons) in 2021 compared to 2015 (Li and Shen, 2021; Fan P. et al., 2023). Despite this apparent decline in the usage of chemical products, persistent issues related to non-point source pollution and concerns about food quality and safety due to long-term excessive application continue to significantly impact both product quality and sustainable agricultural development (Ma et al., 2018; Wang et al., 2019). The No. 1 Central Document issued in 2023 emphasizes the urgency of accelerating the adoption and implementation of technologies that facilitate agricultural product quantity reduction and efficiency improvement, thereby promoting the green development of agriculture (Chadwick et al., 2015; Zhang et al., 2020). Subsequently, an increasing number of scholars have provided evidence supporting the viabilities of green agricultural technology such as green fertilizers and green control measures to foster sustainable development in agriculture (Asfaw et al., 2016; Ikram et al., 2021; Moli et al., 2021).

Seemingly, the existing literature studies have already demonstrated the efficacy of employing various eco-friendly technologies in agricultural production to mitigate non-point source pollution (Naher et al., 2021; Movahedi et al., 2023). These technologies include the substitution of chemical fertilizers with organic fertilizers, soil improvement measures, cover crops, and better straw and residue amendments. The implementation of these green agricultural practices not only enhances the quality of agricultural products but also significantly improves the ecological environment in production areas in the long run (Mao et al., 2005; Dong et al., 2022). For example, a study of green fertilizer application in Maize production in sub-Saharan Africa, (Sileshi et al., 2009) indicated that green manure application indicated a significant increase in yield response. Egodawatta et al. (2012), Baweja et al. (2020), and Krasilnikov et al. (2022) advocated that green fertilizers such as manure are derived from natural sources the application of these eventually foster better soil organic matter, safeguard ecosystems and maintain soil quality. In a study of apple growers in China Wang et al. (2018) found that organic fertilizer significantly lowered the cost and maintain profitability. Epule (2019) outlined critical prospects of green technologies such as organic fertilizer in maintaining a safer food supply for the community. Therefore, it can be concluded that the promotion of green agricultural technology holds significant importance in ensuring both production attributes such as a better and a sustainable environment, fostering sustainable agricultural development. The progress of green agricultural technology faces a notable challenge in terms of limited adoption among smallholder farmers (Bukchin and Kerret, 2018; Adnan et al., 2019b; Mao et al., 2021), indicating a substantial path ahead to comprehend these sustainable agricultural practices (Adnan et al., 2018, 2019a; Tanko et al., 2023). Challenges also persist in achieving widespread adoption among marginal farmers, attributed to factors such as high labor costs, time requirements, investment expenditures, and equipment demands associated with production processes (Chen et al., 2018; Wang X. et al., 2021).

Currently, the application quantity of agricultural chemical fertilizers significantly exceeds the optimal amount required for agricultural production. However, due to the influence of the law of diminishing marginal returns, excessive use of chemical fertilizers impedes improvements in agricultural productivity (Harraq et al., 2022). Organic fertilizer application, as a prominent technology in sustainable agriculture, not only provides substantial economic benefits to farmers but also aligns with environmentally-friendly farming practices for the enhancement of the ecological environment (Daadi and Latacz-Lohmann, 2021b). The issue of food security consistently poses a significant constraint on the economic development of our nation. Given China’s substantial population and ever-decreasing trends of agricultural land, achieving substantial increases in both total output and planting area for traditional food crops within a short timeframe is indeed challenging (Yi et al., 2021). However, potato cultivation in China currently holds the top position globally in terms of both planting area and production volume, playing a significant role in the agricultural economy (Yin et al., 2023). According to statistics, China’s potato planting area accounts for approximately one-fourth of the global total. Moreover, an annual consumption of at least 655,000 tons of chemical fertilizers is dedicated solely to potato cultivation (Shi M. et al., 2023); however, the efficiency of fertilizer utilization remains suboptimal (Jiang L. et al., 2023; Yang et al., 2023). The application of excessive fertilization practices can result in a decline in potato yield, compromised quality, and adverse impacts on economic profitability (Zhang F. et al., 2023a). Therefore, judicious control of chemical fertilizer dosage and optimization of fertilization methods have emerged as crucial strategies for enhancing potato production. In existing agricultural settings, reducing chemical fertilizer usage while incorporating organic fertilizers represents a crucial strategy for enhancing crop quality and efficiency (Shi X. et al., 2023; Xu et al., 2023).

However, existing studies primarily focus on organic fertilizer application on potato agronomic traits, yield, quality, and related factors (Fan H. et al., 2023; Shi X. et al., 2023). Notably, factors influencing the application of organic fertilizers to potatoes encompass various aspects such as new variety considerations, farmer endowment, technical environment characteristics, and so on (Adnan et al., 2019a). Despite the growing interest in the influencing factors of farmers’ behavior regarding organic fertilizer application in existing literature (Amfo and Ali, 2021; Xie et al., 2021; Ochieng et al., 2022; Mwakidoshi et al., 2023), there remains a dearth of documents examining the impact of potato growers’ utilization of organic fertilizers from the perspectives of production efficiency and planting benefits. The current body of research predominantly focuses on assessing the efficiency of potato production (Priegnitz et al., 2019; Imani et al., 2021; Naghdi et al., 2022), often overlooking the crucial role played by the application of organic fertilizers. By incorporating a dedicated emphasis on the dynamic interaction between organic fertilizer and production efficiency, scholars can uncover nuanced insights into the sustainability and environmental impact of potato cultivation. This integration is imperative, particularly in light of the escalating emphasis on sustainable agricultural practices and the imperative to minimize the environmental footprint of farming activities. While extant research touches upon various determinants of adoption [such as Imani et al. (2021), Andati et al. (2022), and Nazziwa-Nviiri et al. (2017)], the connection to operational income remains insufficiently explored. Understanding how the application of organic fertilizers influences the socio-environmental and economic aspects of potato production is pivotal for both farmers and policymakers. This research aims to provide valuable information regarding the economic viability of adopting organic fertilizers, with the potential to influence agricultural practices and policies. However, the Planting scale stands out as a pivotal factor in agricultural operations, and its impact on efficiency should also be explored sufficiently. Therefore, we also explored how different planting scales influence production efficiency through organic fertilizer application to fill a notable research gap. Through the implementation of innovative cost-efficiency decomposition methods, the study tends to provide a though investigation, offering insights into the cost-effectiveness of diverse planting scales and guiding farmers in optimizing their operations. This avenue of investigation aligns with academic rigor and has the potential to contribute meaningfully to both theoretical understanding and practical applications in the field of organic fertilizer application. More specifically, the study seeks to answer the following inquiries: (i) What are the economic implications of organic fertilizer application for potato growers? (ii) Can it enhance potato production efficiency? (iii) Can it mitigate the adverse effects of inefficient technology investment on cost efficiency? The answers to these questions can contribute significantly toward facilitating widespread implementation and effective promotion of green agricultural practices such as organic fertilizers.

2 Literature review

The advancement of agriculture toward high-quality development necessitates a comprehensive improvement in productivity through the application of advanced technologies, equipment, and modern management methods (Wu et al., 2011; Adnan et al., 2017b). This enhancement is crucial for promoting increased agricultural productivity. The investment in various factors of agricultural production, including labor, land, capital, and technology, plays a fundamental role in this development (Daadi and Latacz-Lohmann, 2021a). Some scholars propose that stimulating investments in organic fertilizers can effectively enhance soil quality and land productivity, subsequently leading to an overall improvement in labor productivity (Mueller et al., 2019; Zhang L. et al., 2023). This approach aligns with the emerging trend of intensive cultivation in China’s agriculture, adapting to limited land resources and striving to significantly improve both land and labor productivity over its long history.

In accordance with the theories of factor endowment and comparative advantage, the scarcity of production resources and the resource endowment of producers exert significant influence on the decision-making of farmers regarding the application of organic fertilizers (Amoroso et al., 2011). Acting as rational economic agents, farmers often base their decisions on the use of organic fertilizers on their resource endowment and comparative cost advantages (Li X. et al., 2021; Chen Z. et al., 2022). This decision-making approach facilitates the optimization and allocation of labor, capital, and other production factors, thereby contributing to the further enhancement of potato production efficiency. Therefore, the present study posits the hypothesis one as:

H1: The application of organic fertilizer contributes to the enhancement of labor productivity and land productivity in potato production.

According to the theory of technical efficiency (Färe and Knox Lovell, 1978), when viewed from an investment perspective, the substitution of chemical fertilizers with organic fertilizers, while maintaining constant output scale and market prices, is anticipated to result in a reduction in the cost of organic fertilizer investment relative to chemical fertilizers at the same output level (Ali et al., 2020; Lampach et al., 2021). This transition is expected to bring about advancements in potato production technology efficiency. Furthermore, the strategic application of organic fertilizers holds the potential to mitigate the prospective increase in production costs that may arise from inefficient cost efficiency in agricultural production (Tzouvelekas et al., 2001; Madau, 2007). This implies that adopting organic fertilizers could lead to an enhanced level of productivity among growers. While acknowledging potential variations in technical proficiency levels among potato farmers, especially considering a fixed investment in other production factors, the judicious utilization of organic fertilizers is proposed to play a crucial role in narrowing the gap between actual yield and maximum potential yield (Blanchard et al., 2013; Li C. et al., 2021). This, in turn, is anticipated to contribute significantly to the enhancement of production efficiency (Salam et al., 2021). In light of these considerations, the present study formulates the following hypothesis:

H2: The application of organic fertilizer contributes to the enhancement of technical efficiency in potato production.

Historically, traditional agriculture has been characterized by a predominant emphasis on optimizing agricultural production efficiency to achieve maximal yields (Holden, 2018; Jain et al., 2020). In contrast, the contemporary landscape of modern agriculture is undergoing a paradigm shift, placing primary importance on elevating farmers’ income (Macharia et al., 2016; Triyono et al., 2023). This transformative shift is realized through the active promotion of green agricultural technologies and a committed effort to enhance the overall quality of agricultural products (Li Y. et al., 2021; Qi et al., 2021). An evaluation from the cost–benefit perspective reveals that green agricultural technologies while entailing higher initial costs, promise substantial returns over the long term (Li Y. et al., 2021).

In the context of potato cultivation, the adoption of organic fertilizers emerges as a pivotal practice that frequently leads to noteworthy enhancements in both product quality and output for farmers (Zenbaba, 2021). This dual improvement significantly contributes to an augmented income for agricultural practitioners. This perspective finds support in the work of Zhang L. et al. (2023), who demonstrate that the application of organic fertilizers has the potential to yield a sales income growth rate for agricultural products surpassing the associated costs. This outcome culminates in a definitive enhancement in overall profitability for farmers. The continual refinement and strategic allocation of production factors, coupled with advancements in cost efficiency (Takahashi et al., 2020; Ma et al., 2022), play indispensable roles in fostering a progressive increase in marginal yield (Ogada et al., 2014). Consequently, this positively influences crop productivity per unit area, thereby providing tangible benefits to farmers in terms of planting profitability. In light of these intricate dynamics, the present study posits the following hypothesis:

H3: The application of organic fertilizer can enhance the unit output value and unit net profit in potato production.

Existing literature such as Adnan et al. (2018, 2019a) and Takahashi et al. (2020) highlight a complex interplay of factors shaping farmers’ decisions to expand their planting scale and the consequent implications for the cost efficiency of organic fertilizer application. Moreover, some researchers highlight that, as farmers navigate the dynamic landscape of scale expansion, the initial positive correlation between increasing returns to scale and reduced unit area production cost sets the stage for an intriguing inquiry into the nuanced factors influencing agricultural decision-making (Li J. et al., 2020; Mao et al., 2021). According to Li et al. (2023), beyond the theoretical framework of economies of scale lies a critical juncture marked by a specific threshold in planting scale and the corresponding rise in the number of operated parcels. The intricate relationship between scale expansion, labor availability, and the diminishing marginal effect resulting from reduced production costs signifies a pivotal transition from a phase of cost-efficient scale expansion to a potential era of diseconomies of scale. This transition reflects the complexities inherent in optimizing production efficiency as factors of production are strategically invested (Avane et al., 2022; Chen Y. et al., 2022).

Upon the implementation of organic fertilizer, a consequential decline in cost efficiency emerges, gaining prominence amidst the continuous expansion of farmers’ planting operations. This decline, notably pronounced in the context of scaling agricultural activities, suggests a shifting dynamic in the efficiency of organic fertilizer application. The emerging trend mirrors an inverted U-shaped trajectory, characterized by an initial ascent in efficiency followed by a subsequent descent. This distinctive pattern prompts an exploration of the underlying factors contributing to the observed fluctuations in efficiency during the expansion of the planting scale. In light of these multifaceted dynamics, we posit the following hypothesis to encapsulate the intricate relationship between planting scale, organic fertilizer application, and cost efficiency:

H4: The cost efficiency of organic fertilizer application exhibits an inverted U-shaped trend with the expansion of planting scale.

Based on the aforementioned arguments, we have derived a theoretical framework for analyzing the economic impact of organic fertilizer application, as depicted in Figure 1.

Figure 1

Figure 1. The conceptual framework for analyzing the economic effects of organic fertilizer application.

3 Materials and methods

3.1 Data sources

In 1937, Austrian scholar Von Bertalanffy (1950) defined a system as a set of interrelated elements, thus formally putting forward the concept of general system theory. Systems theory provides a comprehensive framework for quantifying the elements of a system as a whole and explains its structure, function, behavior, and dynamics (Rice, 2013). The theory’s core idea is to emphasize any particular system’s overall concept (Drack and Apfalter, 2007). System thinking has transformed people from a systematic conception of looking at isolated and scattered and a disciplinary notion of looking at connected, organic, and holistic things. It is a new paradigm of scientific thinking, each element in the system does not exist in isolation, and each part plays a specific role in the system (Biazzo, 2002). The external conditions of the system refer to the environment, which denotes the collection of factors outside the system that affect the system itself. The external environmental element and the system are inseparable and interconnected (Järvilehto, 2009; Virapongse et al., 2016).

The data for this paper were gathered through an on-site questionnaire survey conducted in the primary potato-producing region of Tengzhou City, Shandong, between March and April 2023. Notably, we employed a multistage random sampling approach in our study. Initially, Shandong Province was purposefully selected due to its status as one of the major provinces facilitating modern potato processing industries (Wang Z. et al., 2023). Similarly, Tengzhou City was chosen purposefully as our primary data collection area.

Tengzhou, situated in the southern part of Shandong Province, is a county-level city covering approximately 1,495 km2, with a resident population of around 1.73 million. Nestled within the Huaihe River basin and the Beijing-Hangzhou Grand Canal water system, Tengzhou boasts fertile soil and unique conditions conducive to potato cultivation. This geographical advantage has established it as a crucial agricultural production region, often acknowledged as “The home of the Chinese potato” (Zheng et al., 2023). According to Xinhua Silk Road (Xinhua Silk Road, 2023), with a nearly century-old history of potato plantation, Tengzhou is also referred to as the hometown of potatoes in China.

The projected potato cultivation area in Tengzhou City is expected to reach approximately 30,300 hectares by 2023. Furthermore, there has been widespread local promotion of advanced cultivation techniques, such as “two potatoes and one dish” and “two potatoes and one grain.” Consequently, farmers predominantly engage in intensive cultivation to achieve high potato yields, recognizing the substantial investment costs associated with large-scale farming operations. The core planting areas in Tengzhou typically yield between 60,000 to 75,000 kg per hectare, positioning them among China’s most productive regions for potato production.

In the third stage of our sampling process, we employed the method of probability proportional to size sampling (Rosén, 1997) to ensure a representative sample of Tengzhou City’s potato farming landscape. Initially, we identified and randomly selected six townships: Jiehe Town, Dawu Town, Jiangtun Town, Longyang Town, Dongguo Town, and Jisuo Town, out of a total of 16 townships in the region. These six townships were chosen based on their cumulative contribution, which accounts for an impressive 79.21% of the entire potato planting area of approximately 24,000 hectares across Tengzhou City. Following the township selection, we proceeded to sample villages within each selected township. Five sample villages were chosen per township, totaling 30 villages across all six townships. From these villages, we applied a systematic random sampling method to select between 15 to 20 households per village. This approach ensured that the sample of households was both comprehensive and unbiased. Ultimately, we conducted face-to-face interviews with a total of 560 farmers selected from the sampled households. Through rigorous adherence to a structured questionnaire, we gathered data on various aspects of potato farming practices, challenges faced by farmers, and their perspectives on agricultural policies and innovations. The interviews yielded 508 responses meeting the criteria for completeness and accuracy.

3.2 Variable selection

3.2.1 Treatment variable

The paper focuses on the economic impact of organic fertilizer application by potato growers, with particular emphasis on their current practices. Therefore, the key variable in this study is the status of organic fertilizer application by growers. To assess this condition accurately, a specific indicator question was designed: “Does your family apply organic fertilizers?” The response options were coded as 1 for “yes” and 0 for “no.”

3.2.2 Outcome variable

The improvement of the economic effect through organic fertilizer application is assessed based on research design, with five indicators including production technology efficiency and unit net profit as outcome variables.

3.2.3 Control variable

Previous studies show that farmers’ individual characteristics, operational attributes, cognitive traits, and environmental factors significantly influence the economic outcomes of using organic fertilizers. Differences in gender, age, and education affect growers’ understanding (Xie and Huang, 2021). Operational features like duration, income distribution, and labor force size impact their needs and abilities (Mathur et al., 2016). Knowledge of fertilization, environmental policies, and training enhances their comprehension application (Wang et al., 2019; Fan P. et al., 2023). Environmental factors such as fertility level and market distance also influence adoption (Naher et al., 2021). This study uses four control variables: individual characteristics, operational features, cognitive traits, and orchard environmental features, alongside factors for propensity score matching (Oyetunde-Usman et al., 2021; Daadi and Latacz-Lohmann, 2021b). The variables and their descriptions are presented in Table 1.¹

Table 1

Table 1. Variable meaning and descriptive statistics.

3.3 Model building

To assess the impact of organic fertilizer application by growers, this study employs propensity score matching (PSM) methodology to account for heterogeneity between growers who apply organic fertilizers and those who do not (Wordofa et al., 2021). The objective is to ascertain whether organic fertilizer application influences production efficiency and planting income.

3.3.1 Multivariate linear regression model

$\begin{array}{ll}{Y}_i={\beta }_0+\alpha \eta +{\beta }_i{X}_i+e_i& (1)\end{array}$

The dependent variable $Y_i$ in Equation 1 stands for the $i$ the grower’s potato production efficiency or planting benefits; $\eta$ stands for the dummy variable. When $\eta =1$ , it indicates that the grower applies organic fertilizers, otherwise $\eta =0$ . $X_i$ stands for the control variable. ${\beta }_0$and $e_i$respectively stand for the intercept term and residual term. $\alpha$and $\beta$stand for coefficients to be estimated of explanatory variables.

3.3.2 Propensity score matching model

In the empirical analysis, matching methods can be divided into covariate matching and propensity score matching (PSM), with the latter being an improvement of the former (Fortin et al., 2021). However, to accurately measure the production efficiency and planting benefits of growers who fertilizers versus those who do not, this study employs the propensity score matching (PSM) model to construct a counterfactual framework for estimating the application of organic fertilizers. By comparing the differences in production efficiency and planting benefits between these two groups this study utilizes propensity matching to mitigate sample selection bias caused by “self-selection.” Specifically, it identifies a group of potato growers who do not use organic fertilizers but possess similar resource endowment characteristics as those who do, in order to obtain an accurate assessment of the actual effects of organic fertilizer application.

Rosenbaum and Rubin (1983) proposed the concept of Propensity Score as: the propensity score of individual $i$ is the conditional probability that individual $i$ enters the treatment group given $X_i$ , The propensity score is a one-dimensional variable with a value between [0.1], which reduces the dimensions of multiple covariates into one dimension and includes the information of all covariates. The basic principle of PSM is to assume that individuals choose whether to participate in a project based on observable characteristics, and unobservable characteristics will not affect whether individuals participate in a project. For individual $i$ who participates in the project, non-participating items with the same observable characteristics are matched. As a better method than covariate matching, propensity score matching (PSM) has been widely used in sub-medicine, economic and policy evaluation. In recent years, it has become one of the most popular methods in the evaluation of policy effects (Wordofa et al., 2021). The specific steps are outlined as follows:

First of all, the Logit model is primarily employed to estimate the fitted value, i.e., the propensity score (PS value), representing the conditional probability of organic fertilizer application by each potato grower given their endowment feature $X_i$ :

$\begin{array}{ll}P\left(X_i\right)=P_r\left[D=1|X_i\right]=\frac{exp\left(\beta X_i\right)}{1+exp\left(\beta X_i\right)}iϵ\left(I+J\right)& (2)\end{array}$

In the Equation 2, $D$ represents the treatment variable. $D=1$means the grower applies organic fertilizers (treatment group $I$ ), and $D=0$means the grower does not apply organic fertilizers (control group $J$ ). $X_i$ stands for the matching variable that may influence the grower’s application of organic fertilizers. Through estimating the Equation 2, the score, namely the possibility that the grower may apply organic fertilizers, is obtained.

Subsequently, five matching methods were and control groups were based on propensity scores of matching variables. The robustness of the matching result was confirmed if similar results were obtained across different methods. Afterwards, the study computes the average treatment effects (ATT) of both the treatment group and control group, thereby validating the impact of organic fertilizer application on potato planting efficiency and yield benefits. The specific model is presented as follows in Equation 3:

$\begin{array}{ll}ATT=E\left[Y_1^i-Y_0^i\right]=E\left[Y_1^i-Y_0^i|D=1\right]=E\left[Y_1^i|D=1\right]-E\left[Y_1^i|D=0\right]iϵ\left(I+J\right)& (3)\end{array}$

In the Equation 3, $Y_1^i$ and $Y_0^i$ respectively stand for the production efficiency or planting benefits of growers applying organic fertilizers and those who do not apply organic fertilizers. Since only the production efficiency and planting income of farmers applying organic fertilizer can be observed in the actual investigation, namely $E\left[Y_1^i|D=1\right]$ . However, the production efficiency and planting income of farmers who applied organic fertilizer could not be observed when they did not apply organic fertilizer., namely $E\left[Y_0^i|D=1\right]$ . The method of propensity score matching can be used to find a group of growers who do not apply organic fertilizers and have features similar to those owned by growers who apply organic fertilizers to make a comparison between the two parts in terms of production efficiency and planting benefits and estimate the production efficiency and planting benefits $E\left[Y_0^i|D=1\right]$ of the growers who apply organic fertilizers at the time when they do not apply organic fertilizers. In this way, the difference value between production efficiency and planting benefits $E\left[Y_0^i|D=1\right]$ and the estimated production efficiency and planting benefits $E\left[Y_0^i|D=1\right]$ can be obtained, and the economic effect brought by organic fertilizer application to production efficiency and planting benefits of potato planting can be obtained.

3.3.3 Decomposition model of new cost efficiency

Data Envelopment Analysis (DEA), also known as the data envelopment model, encompasses the traditional concept of cost efficiency proposed into technical efficiency and allocative efficiency (Xue et al., 2020). However, when the invested values differ, the efficiency of different decision-making units may paradoxically be equal, which contradicts the principle of cost minimization. To address the limitations of traditional cost efficiency measures, this study employs a novel approach that accounts for varying prices of invested elements to calculate cost efficiencies across decision-making units with distinct unit costs resulting from disparate element prices (Tone, 2002). The basic idea is to decompose the actual investment cost into Minimum invested cost (optimal invested cost) + inefficient loss of the invested technology + inefficient loss of the price + inefficient loss of the allocation.²

Whereas t is set there are n decision-making units (DMU), ${\mathrm{x}}_{\mathrm{o}}$production investments and ${\mathrm{y}}_{\mathrm{o}}$outputs. The number of the invested elements of specific DMU ( ${\mathrm{x}}_{\mathrm{o}},{\mathrm{y}}_{\mathrm{o}}$ ) is ${\mathrm{x}}_{\mathrm{iO}}$ , and its price is ${\mathrm{c}}_{\mathrm{iO}}$ . Then the actual invested cost ${\mathrm{C}}_{\mathrm{O}}$can be presented as follows:

$\begin{array}{ll}{\mathrm{C}}_{\mathrm{O}}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{c}}_{\mathrm{iO}}{\mathrm{x}}_{\mathrm{iO}}\left(\mathrm{o}=1,\dots ,\mathrm{n}\right)& (4)\end{array}$

In regard to the production-possibility set P of traditional cost efficiency:

$\begin{array}{ll}\mathrm{P}=\left\{\left(\mathrm{x},\mathrm{y}\right)|\mathrm{x}\ge X\lambda ,\mathrm{y}\le Y\lambda ,\lambda \ge 0,\mathrm{X}=\left({\mathrm{x}}_{1,}\dots {\mathrm{x}}_{\mathrm{n},}\right),\mathrm{Y}=\left({\mathrm{y}}_{1,}\dots {\mathrm{y}}_{\mathrm{n}\text{,}}\right)\right\}& (5)\end{array}$

The investment-oriented CCR-I model is operated to obtain the optimal investment quantity ${\mathrm{x}}_{\mathrm{io}}^{\ast }$of DMU₀, and the target cost ${\mathrm{C}}_{\mathrm{O}}^{\ast }$ of technical efficiency investment can be presented as follows:

$\begin{array}{ll}{\mathrm{C}}_{\mathrm{O}}^{\ast }={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{c}}_{\mathrm{iO}}{\mathrm{x}}_{\mathrm{io}}^{\ast }\left(\mathrm{o}=1,\dots ,\mathrm{n}\right)& (6)\end{array}$

Then the loss ${\mathrm{L}}_{\mathrm{O}}^{\ast }$ of inefficient technology invested can be presented as follows:

$\begin{array}{ll}{\mathrm{L}}_{\mathrm{O}}^{\ast }={\mathrm{C}}_{\mathrm{O}}-{\mathrm{C}}_{\mathrm{O}}^{\ast }\left({\mathrm{L}}_{\mathrm{O}}^{\ast }\ge 0\right)& (7)\end{array}$

3.3.4 Inefficient loss of the price

The study makes the efficiently invested quantity x* multiply by the actual price c to obtain the equation, ${\overline{x}}_{\mathrm{i}\mathrm{j}}={\mathrm{c}}_{\mathrm{i}\mathrm{j}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\ast }$ , and combines ${\overline{x}}_{\mathrm{i}\mathrm{j}}$with the output data to form a new production-possibility set. The investment-oriented CCR-I model is operated on the basis of the new data set, and the obtained target investment value in the column of technical efficiency values is used to calculate the target cost ${\mathrm{C}}_{\mathrm{O}}^{\ast \ast }$ of technical price efficiency. Then the inefficient loss of price ${\mathrm{L}}_{\mathrm{O}}^{\ast \ast }$can be presented as follows:

$\begin{array}{ll}{\mathrm{L}}_{\mathrm{O}}^{\ast \ast }={\mathrm{C}}_{\mathrm{O}}^{\ast }-{\mathrm{C}}_{\mathrm{O}}^{\ast \ast }\left({\mathrm{L}}_{\mathrm{O}}^{\ast \ast }\ge 0\right)& (8)\end{array}$

3.3.5 Inefficient loss of the allocation

The study decomposes the efficient investment shown in the datasheet of Step II as quantity and price, and the data of the decomposed quantity and price are applied to the new cost efficiency model. Then it adds the optimal invested costs ${\mathrm{C}}_{\mathrm{O}}^{\ast \ast \ast }$of each DMU in the target value together to obtain the optimal invested cost. Then the loss ${\mathrm{L}}_{\mathrm{O}}^{\ast \ast \ast }$caused by inefficient allocation can be presented as follows:

$\begin{array}{ll}{\mathrm{L}}_{\mathrm{O}}^{\ast \ast \ast }={\mathrm{C}}_{\mathrm{O}}^{\ast \ast }-{\mathrm{C}}_{\mathrm{O}}^{\ast \ast \ast }\left({\mathrm{L}}_{\mathrm{O}}^{\ast \ast \ast }\ge 0\right)& (9)\end{array}$

3.3.6 Decomposition of actual invested cost and new cost efficiency

At last, based on the aforesaid decomposition, the actual invested cost (C₀) can be decomposed as follows:

Average actual invested cost ( $C_O$ ) = Optimal investment cost ( $C_O^{\ast \ast \ast }$ ) + Inefficient loss of the invested technology ( $L_O^{\ast }$ ) + Inefficient loss of the price ( $L_O^{\ast \ast }$ ) + Inefficient loss of the allocation ( $L_O^{\ast \ast \ast }$ ) (10).

New cost efficiency (CE) = Technical efficiency (TE) × Allocative efficiency (AE) × Scale efficiency (11).

4 Results

4.1 Analysis of the grower operational effect of organic fertilizer application

To assess the economic impact resulting from the application of organic fertilizers by growers, this study employs ordinary least squares (OLS) regression analysis based on Equation 1, without accounting for heterogeneity. The results (Table 2) reveal that the utilization of organic fertilizers exerts a significant and positive influence on various indicators, including labor productivity (755.02 ha/person), land productivity (3379.65 kg/ha), production technical efficiency (1.8%), unit output (6609.06 yuan/ha), and unit net profit (4824.08 yuan/ha).

Table 2

Table 2. Multiple linear regression models were used to estimate the results.

4.2 Analysis of influencing factors of organic fertilizer adoption by growers

Initially, the study employs the Logit model to estimate the propensity score and identify the matching variable that significantly influences growers’ application Subsequently, a stepwise regression method is employed to eliminate variables with no significant impact on growers’ organic fertilizer application in models I-V. Finally, 8 control variables such as growers’ educational background and planting scale are selected as matching variables, and these results are presented in model V as depicted in Table 3.

Table 3

Table 3. The estimation results of the decision equation for growers to apply organic fertilizer based on the Logit model.

In terms of individual characteristics of growers, at a significant level of 10%, the educational background positively influences the application of organic fertilizers by growers. This suggests that growers with a higher level of education, who prioritize land and demonstrate a better comprehension of green agricultural policies, are more inclined to utilize organic fertilizer (Blanchard et al., 2013).

Concerning growers’ features of production and operation, at the significance level of 5%, the planting scale has a negative influence on growers’ organic fertilizer application. The possible reason may be that growers with a larger scale of production have scattered land parcels, and the behavior of organic fertilizer application is easily influenced by adverse factors, such as the high cost of transportation. At the significance level of 5%, the planting period also has a negative influence on growers’ behavior of organic fertilizer application. The reason may be that more elements are required to be invested in organic fertilizer application. Although growers with long-term experience in growing potatoes have abundant planting experience, they may not be proficient in adopting new technology. The various elements invested may impede the increase of production efficiency and income of potato planting. Therefore, the longer the planting period, the weaker the growers’ willingness to organic fertilizer application.

At the significance level of 1%, the degree of specialization has a positive influence on growers’ behavior of organic fertilizer application. In other words, growers with the income of potato production sharing a higher proportion of the family’s total income are more likely to apply organic fertilizers. The reason may be that growers who are more specialized can be more dedicated when they engage in agricultural production, and they generally hope to improve the planting benefits of potato planting by improving production efficiency.

In terms of growers’ cognitive features, at the significance level of 5%, understanding the hazards of excessive fertilization has a positive influence on growers’ behavior of organic fertilizer application. This suggests that growers recognize the potential long-term harm to soil and the environment resulting from excessive fertilization. They acknowledge that adopting organic fertilizers can mitigate these adverse impacts. Consequently, a deeper understanding of the hazards associated with excessive fertilization enhances their comprehension of environmental protection and promotes more effective utilization of organic fertilizers.

At the significance level of 10%, understanding policies related to soil and environmental protection has a positive influence on growers’ behavior of organic fertilizer application. Through strengthening the publicity of policies related to soil and environment protection and governance, the government and relevant departments can deepen growers’ understanding of soil and environmental protection. This can encourage them to engage in the behavior of green production and apply organic fertilizers.

At the significance level of 1%, professional training in organic fertilizer application has a positive influence on growers’ behavior of organic fertilizer application. This indicates that professional training can convey technical information related to organic fertilizer application, reduce the risk of adopting new technology, enhance growers’ ability, and foster their awareness of scientific fertilization (Liao and Chen, 2017). Additionally, professional training can help growers master the correct methods of organic fertilizer application, reduce the cost of unit production, and encourage growers to make the decision of organic fertilizer application by improving quality, efficiency, and growers’ planting benefits.

At the significance level of 5%, the degree of fertility of the soil among the environmental characteristics of orchards has a negative influence on growers’ behavior of organic fertilizer application. In other words, growers facing more infertile soil are more likely to apply organic fertilizers (Salam et al., 2021). The reason may be that organic fertilizers’ prices are high, and their effects can only be slowly shown. When growers perceive their soil as fertile, they tend to reduce organic fertilizer application to save the cost of fertilization.

4.3 Empirical result analysis of propensity score matching

4.3.1 K value matching

The k-nearest Neighbor matching method is employed for the preliminary analysis. Initially, the analysis is conducted using k-nearest neighbor matching with three different matching ratios (refer to Table 4, 5). Simultaneously, the study utilizes the Bootstrap method to select neighbor matching with a 1:3 ratio based on critical values generated from data under conditions of small or medium-sized samples.

Table 4

Table 4. Comparison of results calculated using different nearest neighbor matching.

Table 5

Table 5. PSM matching results.

4.3.2 Overlap inspection

The study assesses the balance between the treatment group and the control group by comparing the matching variables before and after matching. Table 6 reveals that the p-values for various variables significantly increase after matching, and the inspection results are not significant. This indicates that the matching has a positive effect, validating the rationality and effectiveness of the matching process.

Table 6

Table 6. Balance hypothesis testing before and after propensity matching.

4.3.3 Balance inspection

To ensure result accuracy, the study adopts the method proposed by Jiang B. et al. (2023) as a reference and utilizes parameters such as pseudo R2, the test value of the likelihood ratio, Mean Bias, etc., to assess the matching quality of different matching methods (refer to Table 7). The findings demonstrate a notable improvement in matching quality:

• Pseudo R² decreases from 0.901 before matching to 0.004–0.005.

• The test value of the likelihood ratio decreases from 513.31 before matching to 14.61–19.94.

• Mean Bias decreases from 105.4 to 4.9–9.8%.

• Median Bias decreases from 97.3 to 2.8–6.5%.

• B-value decreases from 3318.3 before matching to 19.8–13.1, which is 20 lower than the boundary line’s standard.

Table 7

Table 7. Matching quality test of different matching methods.

The variation in these indicators before and after matching indicates that systematic differences between the treatment group and the control group, caused by disparities in observable variables, are essentially eliminated after propensity matching. The matching quality of the model is deemed good. The results across the five types of matching methods are relatively consistent, affirming the robustness of the balance inspection results.

4.3.4 Sensitivity analysis

The key hypothesis to support the PSM method is that growers only depend on observable variables to make the decision of organic fertilizer application. Although as much as matching variables are selected in the paper, there are still some unobservable factors that may influence growers’ decisions about organic fertilizer application. Therefore, relying on the research methods proposed by Zhao et al. (2021) and Ren et al. (2021), the study adopts Rosenbaum’s boundary method to conduct sensitivity analysis, and the result is shown in Table 8. The parameter gamma (≥1) refers to the measurement of the degree of deviation freedom of (gamma = 1) without the condition of concealing bias. The larger the gamma value is, the lower the degree of sensitivity is, and the result can be more robust. According to the documents, the researchers conduct sensitivity analysis within gamma’s range between 1 and 2 (Dillon, 2011). During the process of analyzing the influence of organic fertilizer application on growers’ economic effect, it is shown that both sig + and sig- are 0, indicating that unobservable factors are not sensitive to the influence of the result of estimation, which has a certain robustness.

Table 8

Table 8. Rosenbaum bounds sensitivity analysis.

4.3.5 Average treatment effect estimates

The use of multiple matching methods helps ensure the reliability and validity of the findings (Pirracchio et al., 2016). By comparing various matching techniques, we can be more confident that the observed effects are genuinely due to the organic fertilizer application and not artifacts of a particular matching method. Additionally, the robustness test helps identify any potential biases or inconsistencies across different methods, providing a more comprehensive understanding of the treatment effect (Abdia et al., 2017). Thus, to further verify the estimated results of the above-mentioned k-nearest neighbor matching, five additional methods, including k-Nearest neighbor matching, radius matching, K-nearest neighbor matching within calipers, Kernel Matching and Local linear regression matching, were adopted for a robustness test. This involved comparing the results (production efficiency and planting benefit) between matched groups to obtain the average treatment effect (ATT) of the five outcome variables, thereby confirming the impact of organic fertilizer application (Table 9).

Table 9

Table 9. Results of average treatment effect (ATT) estimates for the outcome variables in 2022.

The estimated results presented in Table 9 indicate that the outcomes from the five matching methods are consistent with each other. This suggests that the research findings possess proper robustness, affirming that organic fertilizer application indeed has a significant and positive influence on planting outcomes. Regarding production efficiency, the Average Treatment Effect on the Treated (ATT) of the treatment group’s labor productivity is 1588.47 kilograms per person. This signifies that the per capita potato output of growers who apply organic fertilizers increases by 1363.47–1829.07 kilograms, and it is statistically significant at the 1% level. Similarly, the average ATT of land productivity is 16346.77 kilograms per hectare, indicating a significant increase in potato output per hectare for growers who apply organic fertilizers (14566.69–15318.09 kilograms) at the 10% significance level. This validates Hypothesis H1.

The average ATT coefficient of production technical efficiency’s net effect is 0.036, indicating a noticeable increase in production technical efficiency by 1.6–5.0%. This result is statistically significant at the 10% level, verifying Hypothesis H2. Concerning planting benefits, organic fertilizer application exhibits a clear promotional effect on unit output and unit net profit for growers. The average ATT of unit output is 35011.12 yuan per hectare, signifying a significant increase in the total potato output for growers who apply organic fertilizers (31922.62–337475.48 yuan) at the 10% significance level. Similarly, the ATT of unit net profit is 16135.32 yuan per hectare, indicating a substantial increase in potato net profit per hectare for growers who apply organic fertilizers (12641.78–118325.94 yuan) and is significant at the 10% level. This verifies Hypothesis H3.

4.4 Decomposition result of the new cost efficiency

Since different types of growers are mainly limited by their resource endowment when choosing technology, they tend to pursue production factors, family resources and other allocations for optimization to achieve maximum efficiency. Therefore, when facing the real problems of the small planting scale, multiple parcels, and growers with different planting scales have different motivations for adoption and operational costs, which usually influence the decision of organic fertilizer application (Adnan et al., 2017b).

The paper uses MAXDEA7.0 software to decompose the cost efficiency of organic fertilizer application of growers with different scales of planting. This study builds upon the research conducted by Du et al. (2022) and examines the potato planting scale of farmers in Tengzhou City through field investigations. Through extensive discussions with local agricultural technology experts and farmers, it was determined that 0.667 ha and 1.333 ha are widely accepted benchmarks for categorizing farm sizes in the region. Accordingly, this paper defines small-scale farmers as those with a potato planting area less than 0.667 ha, medium-scale farmers as those with a potato planting area between 0.667 ha (inclusive) and 1.333 ha (exclusive), and large-scale farmers as those with a potato planting area exceeding 1.333 ha. This study also calculated the average cost of organic fertilizer applied to production investments of different planting scales, taking the average yield as output, and put it into Equations 4-9, obtaining the following decomposition results: the average actual invested cost ( $C_O$ ), target cost of efficient invested technology ( $C_O^{\ast }$ ), target cost of efficient technology and price ( $C_O^{\ast \ast }$ ), optimal investment cost ( $C_O^{\ast \ast \ast }$ ), inefficient loss of the invested technology ( $L_O^{\ast }$ ), inefficient loss of the price ( $L_O^{\ast \ast }$ ), inefficient loss of the allocation ( $L_O^{\ast \ast \ast }$ ), cost efficiency (CE), technical efficiency (TE), allocative efficiency (AE) and scale efficiency (SE) (Table 10).

Table 10

Table 10. Results of the new cost-efficiency decomposition calculated for the treatment group sample.

It can be seen from Table 10 that compared with the optimal cost invested $C_O^{\ast \ast \ast }$ , the average cost $C_O$invested by growers who apply organic fertilizers includes an inefficient loss reaching 1949.42 yuan. The inefficient loss of the invested technology $L_O^{\ast }$ is 5440.96 yuan. The inefficient loss of the price $L_O^{\ast \ast }$ is 1350.27 yuan, and the inefficient loss of the allocation $L_O^{\ast \ast \ast }$ is 1304.41 yuan. Therefore, the inefficiency loss of invested technology accounts for 67.21% of the total loss, indicating that the inefficiency in technology investment is primarily responsible for the inefficient cost loss in potato production in 2022. In conditions where soil organic matter decreases and nutrient balance is disrupted, crop absorption of nutrients from chemical fertilizers becomes challenging, potentially leading to excessive fertilizer application. However, incorporating organic fertilizer positively impacts the flexibility of chemical fertilizers’ output. The continuous use of organic fertilizer helps mitigate ineffective input technology losses and promotes long-term cost savings and efficiency improvements within the potato industry.

Upon comparing the cost efficiency of growers with different planting scales, a notable pattern emerges. Farmers with small scales ranging from 0.667 to 1.333 hectares exhibit higher values in terms of cost efficiency, technical efficiency, allocative efficiency, and scale efficiency compared to those with smaller or larger planting scales. In contrast, growers with planting scales smaller than 0.267 hectares and over 0.533 hectares demonstrate relatively lower values in the mentioned efficiencies. This implies that the production efficiency indicators, including cost efficiency, technical efficiency, allocative efficiency, and scale efficiency, for growers with planting scales of 0.267–0.533 hectares are higher than those for growers with planting scales smaller than 0.267 hectares and over 0.533 hectares (refer to Table 10). In other words, there is an inverted U-shaped trend between the planting scales of sample growers and various aspects of production efficiency (Salam et al., 2021; Niu et al., 2023). This verifies Hypothesis H4.

5 Discussion

The utilization of agrochemicals such as synthetic fertilizers in modern agriculture has yielded benefits but also poses risks to human health and the environment (Wang et al., 2018). Overuse of chemical fertilizers has significantly impacted the ecosystem of farmlands, human health, and the achievement of sustainable development goals (Patra et al., 2016). Addressing the presence of these chemicals, which include harmful components like nitrites, NH4+ (ammonium ion), and PM2.5 (fine airborne particles), is crucial in the face of today’s triple planetary crisis of climate change, biodiversity loss, and pollution (Naidu et al., 2021). These crises threaten to undo decades of human development progress (Srivastav, 2020). Over the past century, the volume of synthetic nitrogen compounds in water, soil, and the atmosphere has doubled, largely due to the widespread adoption of synthetic fertilizers (Tyagi et al., 2022). The current deviation from Sustainable Development Goals (SDGs) underscores the severity of this issue. Despite its complexities and challenges, effective management of chemicals and waste is essential for building resilient and healthier food systems (Ladha et al., 2020). Exposure to certain chemicals can pose serious health risks, including cancer, reproductive and developmental disorders, and neurological issues (Sharma and Singhvi, 2017). In particular, the latest Global Chemicals Outlook from the UN Environment Program (UNEP) alerts to escalating risks linked to the use of hazardous chemical fertilizers and their potential to contaminate other natural resources such as water, air, and soil (Alpizar et al., 2019).

Nitrogen is crucial for sustaining life on Earth, yet its excessive presence poses significant dangers as a pollutant, contaminating water bodies, plants, and animals, and threatening public health (Bijay-Singh and Craswell, 2021). Despite its low public visibility, experts highlight the inundation of excess nitrogen as one of today’s most pressing pollution challenges (Mashamaite et al., 2024). According to the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), nutrient runoff from farms, often laden with synthetic fertilizers, has notably disrupted land ecosystems (IPBES, 2019). However, freshwater and marine environments have suffered the most severe impacts (Micella et al., 2024).

The key to the current soil and water conservation problems is not the lack of fertility, but the lack of organic matter and microbial bacteria (Savci, 2012), which not only poses a major threat to food security but also significantly hinders the national food safety (Ma et al., 2014). Continuous use of chemical fertilizers leads to soil degradation and diminishes crop productivity. Chemical fertilizers have little impact on the restoration of soil fertility (Bisane et al., 2023). The compaction of soil occurs due to the persistent use of chemical fertilizers. Consequently, this gives rise to other problems such as inadequate oxygenation, subpar water drainage, and the erosion of soil. Additionally, it leads to reduced permeability, hydraulic conductivity, and groundwater recharge (Pahalvi et al., 2021). On the contrary, organic fertilizer, being an exemplary green agricultural technology, primarily functions to supplement soil organic matter, enhance crop root vitality, balance nutrient levels, and improve potato quality (Zheng et al., 2024). By enriching the soil with organic matter, organic fertilizers foster a healthier and more sustainable ecosystem. They contribute to better soil structure, increased microbial activity, and improved nutrient retention, which are crucial for long-term soil fertility and productivity (Lin et al., 2024). In addition to environmental harm, human health is also imperiled. The serious consequences of chemical fertilizers on human health are already well-proven (Loan et al., 2018; Hossain et al., 2022), directly and indirectly, causing various fetal health issues (Nadarajan and Sukumaran, 2021) and posing a serious threat to achieving Chinas promises to achieve optimal sustainable development goals (Loan et al., 2018). Agricultural emissions of ammonia can interact with pollutants from vehicle exhausts, generating harmful particulate matter in the air that exacerbates respiratory diseases (Zhou et al., 2018).

The efficiency of chemical fertilizer application is not optimistic in a country like China (Chen Z. et al., 2022). Excessive chemical fertilizer input has led to serious excessive nitrate content in groundwater in the dryland production system in northern China, which has affected drinking water safety, while the paddy field production system in southern China has led to excessive nitrogen and phosphorus in surface water (Ji et al., 2023). However, China’s agriculture has entered a phase of high-quality development. To achieve the transformation and upgrading of the potato industry, as well as promote its high-quality development, it is imperative to rely on the effective promotion of green agricultural technologies such as organic fertilizers. This will facilitate the transition of potato production toward a direction characterized by technological innovation, sustainable practices, industrial advancement, and increased productivity and income generation. In comparison to solely applying chemical fertilizers, adopting a combined approach of ‘chemical fertilizers + organic fertilizers’ proves more advantageous in augmenting potato yield (Naghdi et al., 2022). This is attributed to the decrease in soil organic matter content and nutrient imbalances, which hinder the absorption of nitrogen, phosphorus, potassium, and other fertilizer nutrients by crops, thereby potentially leading to excessive fertilizer application (Huang et al., 2021). Consequently, augmenting the utilization of organic fertilizers facilitates the enhancement of potato quality and ecological environment while fostering the establishment of an organic potato (Jiang L. et al., 2023).

The decomposition of the new cost-efficiency model reveals significant potential for reducing production costs or enhancing production efficiency in Tengzhou potatoes. Firstly, there is a possibility of achieving cost reduction or improved efficiency by focusing on enhancing technical and configuration aspects. Secondly, by considering the application of organic fertilizers or implementing appropriate scale control measures, there is even greater potential for achieving cost savings and improving production efficiency (Lu et al., 2019). From a technological efficiency perspective, reducing costs or enhancing production efficiency entails minimizing unnecessary inputs in the production process. However, due to farmers’ extensive accumulation of long-term production experience, altering the existing production mode becomes challenging. Consequently, the government, cooperatives, and other relevant departments must provide increased guidance to farmers regarding the application of organic fertilizers, implementation of scientific planting techniques, and promotion of green agricultural technology. Through the comprehensive analysis presented in this article, significant potential still exists for cost reduction and enhanced competitiveness within potato production processes. However, considering the challenges associated with cost reduction, it is imperative to continue implementing agricultural support policies (such as providing financial assistance and subsidies) in the short term to facilitate environmentally sustainable potato production (Fan P. et al., 2023). As cost efficiency improves over time, a gradual reduction in agricultural protection should be pursued to mitigate potential fluctuations in production.

Relatively, the insufficient application of organic fertilizer is the most common phenomenon among farmers (Adnan et al., 2019a). The factors restricting their use of organic fertilizer extend beyond just the scale of operation and various costs. These factors also include the price of fertilizer, income level, risk preference, cognitive level, resource endowment, and other considerations (Chen et al., 2020). For example, in terms of cognition, farmers generally believe that organic fertilizer has low nutrient content, requires a large application rate, has a slow fertilizer effect, and does not provide a significant short-term yield increase. Compared to chemical fertilizers, the perception of organic fertilizer as having insufficient “fertilizer strength” is a key reason for the low enthusiasm among farmers to apply it (Luan et al., 2018). Regarding capital factors, farmers’ low willingness to invest in green production may be influenced by constraints on their capital endowment (Wang H. et al., 2021). In terms of risk preference, the belief that replacing chemical fertilizer with organic fertilizer might reduce production leads farmers with higher risk aversion to applying more chemical fertilizer. They mostly prefer to avoid the potential production reduction associated with adopting organic fertilizers (Belete, 2022).

Specifically, for small-scale growers, limited access to resources such as division of labor, collaboration, and specialized inputs leads to a relatively weak inclination to adopt organic fertilizers (Marenya and Barrett, 2007). Despite significant manpower investments in potato fertilization, most small-scale farmer’s lack enthusiasm for embracing green agricultural technologies like organic fertilizers to reduce expenses (Adebiyi et al., 2020). In rural China, the decentralized operation pattern results in a planting system dominated by scattered, fragmented plots. This fragmentation can cause conflicts among farmers due to differences in planting structures, and neighboring farmers may face high collective decision-making costs when adjusting planting structures or applying fertilizers uniformly (Xu et al., 2014). Additionally, the transportation of organic fertilizers poses challenges due to high costs, long distances, and short shelf life, impacting the environment and human health (Guo et al., 2024). Furthermore, small plots struggle to achieve the scale efficiency of agricultural mechanization, whereas large plots can leverage machinery to achieve economies of scale. Consequently, a combination of mechanization and labor is often employed in the planting process. The fragmented nature of agricultural plots can also lead to inefficiencies in resource utilization, pest and disease control, and overall productivity (Wang et al., 2016). Coordinating numerous smallholders to adopt best practices uniformly is challenging, leading to varied levels of technology adoption and agricultural output (Cui et al., 2018).

Therefore, high transportation costs and the environmental impact of moving organic fertilizers over long distances highlight the need for localized production and use of organic inputs. Developing localized organic fertilizer production facilities could mitigate these issues, reducing transportation costs and environmental impact while promoting sustainable agricultural practices (Zhang et al., 2021). Additionally, policies and initiatives aimed at consolidating land holdings or promoting cooperative farming could enhance agricultural efficiency and sustainability. Cooperative farming can help achieve economies of scale, reduce conflicts among farmers, and lower collective decision-making costs. It can also facilitate the uniform application of fertilizers and the adoption of mechanization, ultimately improving productivity and sustainability (Zhang et al., 2020). Moreover, investing in agricultural research and development to create more efficient and sustainable farming technologies is crucial. Innovations in organic fertilizer production, storage, and application could address the challenges posed by decentralized and fragmented farming systems (Liu et al., 2022). Training and support for farmers in adopting these new technologies and practices will be essential for their successful implementation. However, higher costs associated with utilizing and managing agricultural machinery result in lower efficiency in factor allocation, leading to relatively high costs for organic fertilizer application (Case et al., 2017). Moreover, labor scarcity can increase internal regulatory costs, hindering the achievement of intensive cultivation and precision farming practices, thereby reducing scale efficiency (Li F. et al., 2020; Fuller et al., 2021). Consequently, growers are more inclined to apply fertilizers on a larger scale to optimize cost savings and enhance efficiency. Potato, as an important food crop, can achieve optimal technical efficiency in organic fertilizer production only when combined with local realities and appropriate planting scales. This approach ensures that the benefits of organic fertilizers are maximized within the specific agricultural context.

Limitations on available resources and the state of the environment pose serious threats to long-term economic and social progress. The agriculture sector of any emerging country like China must pursue goals such as minimizing environmental pollution, contributing to environmental governance, and protecting the environment, especially given the severe resource and environmental restrictions it faces (Du et al., 2023). To address these challenges, China has launched several programs and policies to encourage sustainable agricultural growth (Yang et al., 2022). Since 2015, the Central Committee of the Chinese Communist Party (CCP) has taken significant measures to combat non-point source pollution in agriculture. A major turning point was the release of the “Opinions on Innovating Systems and Mechanisms to Promote Green Agricultural Development.” This policy framework seeks to enhance the capacity for sustainable agricultural growth by establishing systems to prevent and control agricultural non-point source pollution (Liu et al., 2020, pp. 1978–2017; Shen et al., 2020). It also aims to position agriculture as an ecological barrier for a beautiful China and contribute to building the foundation of eco-friendly rural development and revitalisation (Hou and Wang, 2022).

Seemingly, in 2017, the Ministry of Agriculture initiated environmentally friendly agricultural development initiatives (Han, 2019). Among these efforts was the recycling of polluting manure from cattle and poultry into organic fertilizers for crops like tea, fruit trees, and vegetables (Chen Z. et al., 2022). The objective was to decrease non-point source pollution, encourage the comprehensive treatment of agricultural production wastes, and limit the excessive use of agricultural resources (Yu et al., 2020). These measures aim to guide the agricultural sector toward environmentally friendly growth, aligning with the broader national objectives of protecting the environment and responsibly using resources (Lu et al., 2021). A key component of these eco-friendly development measures is the advocacy of organic fertilizers. When applied to soil, organic fertilizers improve crop quality, root vitality, nutrient balance, and soil organic matter content. They help reduce the negative effects of chemical fertilizers on the environment by promoting more balanced soil ecosystems. Enhanced soil structure, higher microbial activity, and greater nutrient retention are critical for long-term soil fertility and productivity (Wang H. et al., 2023; Zhu et al., 2024).

6 Conclusion

This study employs the Propensity Score Matching (PSM) model and a novel cost-efficiency decomposition method to evaluate the economic effects of applying organic fertilizer to potato facilities in Tengzhou City, Shandong Province. The key findings are as follows:

i. Application of organic fertilizer significantly enhances the production efficiency of potato growers. Compared to the control group without organic fertilizer application, the use of organic fertilizer increases average production technical efficiency, labor productivity, and land productivity by 3.6%, 1588.47 kg/person, and 16346.77 kg/ha, respectively. Additionally, organic fertilizer application positively impacts potato planting benefits, leading to an average increase of 35011.12 yuan in output per hectare and 16135.32 yuan in net profit per hectare.

ii. Analysis of cost efficiency among growers with different production scales reveals that those with a planting scale of 0.667 to 1.333 hectares exhibit relatively high production efficiency across various aspects. Furthermore, a notable inverted U-shaped trend is observed between planting scale and production efficiency.

iii. Utilizing a novel cost efficiency decomposition method, it is determined that 67.21% of inefficient losses in invested costs can be attributed to technological inefficiencies. The application of organic fertilizer demonstrates significant potential in mitigating these technological inefficiencies. Moreover, the continuous implementation of organic fertilizer yields substantial long-term benefits, including cost reduction and efficiency enhancement for potato cultivation and other food crops.

Based on these findings, the following policy recommendations are proposed:

i. Strengthen policy support, enhance farmers’ awareness of green production, and promote increased agricultural productivity and income. Given the decentralized household management and dominant presence of small-scale farmers in main potato production areas, local governments should increase support for green production. This involves fostering enthusiasm for sustainable practices, promoting the adoption of eco-friendly agricultural technologies like organic fertilizers, and assisting farmers in achieving enhanced productivity and income.

ii. Enhance professional training in green agricultural technology to promote the improvement of quality and efficiency in potato production. Given the positive impact of professional technical training on farmers’ adoption of organic fertilizers, the agricultural technology department should intensify training efforts. This can be achieved through comprehensive guidance, including household research, training sessions, distribution of teaching materials, creation of instructional videos, and other effective methods aimed at improving traditional practices and elevating technical efficiency in potato cultivation.

iii. Enhance the allocation of factor inputs to facilitate the enhancement of cost efficiency for growers. Optimizing the allocation of input factors through field management practices, such as land leveling, formula fertilization, seed mixing, and ridge covering, can improve labor productivity, reduce average production costs, and enhance mechanization levels in potato cultivation. This ensures the optimal allocation of various production factors and elevates growers’ cost efficiency levels.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics statement

Ethical approval was not required for the studies involving humans because as the study does not involve any personal data and the respondents were well aware that they could opt out anytime during the data collection phase, no written ethical approval is required. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MZ: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. XX: Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Data curation. WN: Writing – review & editing, Formal analysis, Methodology, Validation, Supervision. FZ: Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. AS: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by: (i) the National Social Science Foundation of China (No.:23BJY170).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^Production technical efficiency is the ratio of the actual output of the production unit measured from the perspective investment’s output to the maximum potential output. The paper calculates the technical efficiency of potato planting in the region with MaxDEA software.

2. ^Inefficient loss of the invested technology refers to the inefficient investment caused by the actual investment higher than the optimal cost due to the lagging technology. Inefficient loss of the price refers to the inefficient investment caused by the optimal invested cost of the decision-making unit calculated on the basis of the actual price higher than the optimal invested cost calculated on the basis of the initial price due the rise of the price. Inefficient loss of the allocation refers to the inefficient investment caused by the optimal invested cost of the decision-making unit higher than the optimal invested combination due to the ineffective market allocation.

References

Abdia, Y., Kulasekera, K. B., Datta, S., Boakye, M., and Kong, M. (2017). Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: a comparative study. Biom. J. 59, 967–985. doi: 10.1002/bimj.201600094

PubMed Abstract | Crossref Full Text | Google Scholar

Adebiyi, J. A., Olabisi, L. S., Richardson, R., Liverpool-Tasie, L. S. O., and Delate, K. (2020). Drivers and constraints to the adoption of organic leafy vegetable production in Nigeria: a livelihood approach. Sustain. For. 12:96. doi: 10.3390/su12010096

Crossref Full Text | Google Scholar

Adnan, N., Md Nordin, S., Rahman, I., and Noor, A. (2017a). Adoption of green fertilizer technology among paddy farmers: a possible solution for Malaysian food security. Land Use Policy 63, 38–52. doi: 10.1016/j.landusepol.2017.01.022

Crossref Full Text | Google Scholar

Adnan, N., Nordin, S. M., Bahruddin, M. A., and Tareq, A. H. (2019a). A state-of-the-art review on facilitating sustainable agriculture through green fertilizer technology adoption: assessing farmers behavior. Trends Food Sci. Technol. 86, 439–452. doi: 10.1016/j.tifs.2019.02.040

Crossref Full Text | Google Scholar

Adnan, N., Nordin, S. M., Rahman, I., and Noor, A. (2017b). The impacts and visions of the green fertilizer technologies (GFT): adoption behaviour among Malaysian paddy farmers. World J. Sci. Technol. Sustain. Dev. 14, 336–354. doi: 10.1108/WJSTSD-08-2016-0053

Crossref Full Text | Google Scholar

Adnan, N., Nordin, S. M., Rahman, I., and Noor, A. (2018). The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices: in view of green fertilizer technology. World J. Sci. Technol. Sustain. Dev. 15, 98–115. doi: 10.1108/WJSTSD-11-2016-0062

Crossref Full Text | Google Scholar

Adnan, N., Nordin, S. M., and Rasli, A. M. (2019b). A possible resolution of Malaysian sunset industry by green fertilizer technology: factors affecting the adoption among paddy farmers. Environ. Sci. Pollut. Res. 26, 27198–27224. doi: 10.1007/s11356-019-05650-9

PubMed Abstract | Crossref Full Text | Google Scholar

Ali, D. A., Bowen, D., and Deininger, K. (2020). Personality traits, technology adoption, and technical efficiency: evidence from smallholder Rice farms in Ghana. J. Dev. Stud. 56, 1330–1348. doi: 10.1080/00220388.2019.1666978

Crossref Full Text | Google Scholar

Alpizar, F., Backhaus, T., Decker, N., Eilks, I., Escobar-Pemberthy, N., Fantke, P., et al. (2019). UN environment global chemicals outlook II - from legacies to innovative solutions: Implementing the 2030 agenda for sustainable development.

Google Scholar

Amfo, B., and Ali, E. B. (2021). Beyond adoption: the interaction between organic and inorganic fertilizer application, and vegetable productivity in Ghana. Renew. Agric. Food Syst. 36, 605–621. doi: 10.1017/S1742170521000235

Crossref Full Text | Google Scholar

Amoroso, N., Chiquiar, D., and Ramos-Francia, M. (2011). Technology and endowments as determinants of comparative advantage: evidence from Mexico. North Am. J. Econ. Finance 22, 164–196. doi: 10.1016/j.najef.2011.01.004

Crossref Full Text | Google Scholar

Andati, P., Majiwa, E., Ngigi, M., Mbeche, R., and Ateka, J. (2022). Determinants of adoption of climate smart agricultural technologies among potato farmers in Kenya: does entrepreneurial orientation play a role? Sustain. Technol. Entrep. 1:100017. doi: 10.1016/j.stae.2022.100017

Crossref Full Text | Google Scholar

Asfaw, S., Di Battista, F., and Lipper, L. (2016). Agricultural technology adoption under climate change in the Sahel: Micro-evidence from Niger. J. Afr. Econ. 25, 637–669. doi: 10.1093/jae/ejw005

Crossref Full Text | Google Scholar

Avane, A., Amfo, B., Aidoo, R., and Mensah, J. O. (2022). Adoption of organic fertilizer for cocoa production in Ghana: perceptions and determinants. Afr. J. Sci. Technol. Innov. Dev. 14, 718–729. doi: 10.1080/20421338.2021.1892254

Crossref Full Text | Google Scholar

Baweja, P., Kumar, S., and Kumar, G. (2020). “Fertilizers and pesticides: their impact on soil health and environment” in Soil health. eds. B. Giri and A. Varma (Cham: Springer International Publishing), 265–285.

Google Scholar

Belete, A. A. (2022). Determinants of organic fertilizer adoption in Moretna Jeru District, northern Ethiopia. Adv. Agric. 2022, 1–9. doi: 10.1155/2022/9983782

Crossref Full Text | Google Scholar

Biazzo, S. (2002). Process mapping techniques and organisational analysis: lessons from sociotechnical system theory. Bus. Process. Manag. J. 8, 42–52. doi: 10.1108/14637150210418629

Crossref Full Text | Google Scholar

Bijay-Singh, B., and Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem. SN Appl. Sci. 3:518. doi: 10.1007/s42452-021-04521-8

Crossref Full Text | Google Scholar

Bisane, M., Chakravarty, I., Mehetre, S., Mukherjee, P., and Mandavgane, S. A. (2023). “Biomass ash as a source of nutrient; a case study on cotton (Gossypium)” in Fruit and vegetable waste utilization and sustainability. eds. S. A. Mandavgane, I. Chakravarty, and A. K. Jaiswal (Cambridge, MA: Academic Press), 301–308.

Google Scholar

Blanchard, M., Vayssieres, J., Dugué, P., and Vall, E. (2013). Local technical knowledge and efficiency of organic fertilizer production in South Mali_ diversity of practices. Agroecol. Sustain. Food Syst. 37, 672–699. doi: 10.1080/21683565.2013.775687

Crossref Full Text | Google Scholar

Bukchin, S., and Kerret, D. (2018). Food for Hope: the role of personal resources in farmers’ adoption of green technology. Sustain. For. 10:1615. doi: 10.3390/su10051615

Crossref Full Text | Google Scholar

Case, S. D. C., Oelofse, M., Hou, Y., Oenema, O., and Jensen, L. S. (2017). Farmer perceptions and use of organic waste products as fertilisers – a survey study of potential benefits and barriers. Agric. Syst. 151, 84–95. doi: 10.1016/j.agsy.2016.11.012

Crossref Full Text | Google Scholar

Chadwick, D., Wei, J., Yan'an, T., Guanghui, Y., Qirong, S., and Qing, C. (2015). Improving manure nutrient management towards sustainable agricultural intensification in China. Agric. Ecosyst. Environ. 209, 34–46. doi: 10.1016/j.agee.2015.03.025

Crossref Full Text | Google Scholar

Chen, Y., Fu, X., and Liu, Y. (2022). Effect of farmland scale on farmers’ application behavior with organic fertilizer. Int. J. Environ. Res. Public Health 19:4967. doi: 10.3390/ijerph19094967

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, J., Lü, S., Zhang, Z., Zhao, X., Li, X., Ning, P., et al. (2018). Environmentally friendly fertilizers: a review of materials used and their effects on the environment. Sci. Total Environ. 613-614, 829–839. doi: 10.1016/j.scitotenv.2017.09.186

Crossref Full Text | Google Scholar

Chen, Z., Sarkar, A., Rahman, A., Li, X., and Xia, X. (2022). Exploring the drivers of green agricultural development (GAD) in China: a spatial association network structure approaches. Land Use Policy 112:105827. doi: 10.1016/j.landusepol.2021.105827

Crossref Full Text | Google Scholar

Chen, T., Zhang, S., and Yuan, Z. (2020). Adoption of solid organic waste composting products: a critical review. J. Clean. Prod. 272:122712. doi: 10.1016/j.jclepro.2020.122712

Crossref Full Text | Google Scholar

Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., et al. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366. doi: 10.1038/nature25785

PubMed Abstract | Crossref Full Text | Google Scholar

Daadi, B. E., and Latacz-Lohmann, U. (2021a). Organic fertilizer adoption, household food access, and gender-based farm labor use: empirical insights from northern Ghana. J. Agric. Appl. Econ. 53, 435–458. doi: 10.1017/aae.2021.8

Crossref Full Text | Google Scholar

Daadi, B. E., and Latacz-Lohmann, U. (2021b). Organic fertilizer use by smallholder farmers: typology of management approaches in northern Ghana. Renew. Agric. Food Syst. 36, 192–206. doi: 10.1017/S1742170520000228

Crossref Full Text | Google Scholar

Dillon, A. (2011). The effect of irrigation on poverty reduction, asset accumulation, and informal insurance: evidence from northern Mali. World Dev. 39, 2165–2175. doi: 10.1016/j.worlddev.2011.04.006

Crossref Full Text | Google Scholar

Dong, H., Wang, B., Han, J., Luo, L., Wang, H., Sun, Z., et al. (2022). Understanding farmers’ eco-friendly fertilization technology adoption behavior using an integrated S-O-R model: the case of soil testing and formulated fertilization technology in Shaanxi, China. Front. Environ. Sci. 10:1255. doi: 10.3389/fenvs.2022.991255

Crossref Full Text | Google Scholar

Drack, M., and Apfalter, W. (2007). Is Paul a. Weiss’ and Ludwig von Bertalanffy’s system thinking still valid today? Syst. Res. Behav. Sci. 24, 537–546. doi: 10.1002/sres.855

Crossref Full Text | Google Scholar

Du, Y., Liu, H., Huang, H., and Li, X. (2023). The carbon emission reduction effect of agricultural policy——evidence from China. J. Clean. Prod. 406:137005. doi: 10.1016/j.jclepro.2023.137005

Crossref Full Text | Google Scholar

Du, S., Ma, Z., Chen, J., Xue, L., Tang, C., Shareef, T. M. E., et al. (2022). Effects of organic fertilizer proportion on the distribution of soil aggregates and their associated organic carbon in a field mulched with gravel. Sci. Rep. 12:11513. doi: 10.1038/s41598-022-15110-z

PubMed Abstract | Crossref Full Text | Google Scholar

Egodawatta, W. C. P., Sangakkara, U. R., and Stamp, P. (2012). Impact of green manure and mineral fertilizer inputs on soil organic matter and crop productivity in a sloping landscape of Sri Lanka. Field Crop Res. 129, 21–27. doi: 10.1016/j.fcr.2012.01.010

Crossref Full Text | Google Scholar

Epule, T. E. (2019). “Chapter 1 - contribution of organic farming towards global food security: An overview” in Organic farming. eds. S. Chandran, M. R. Unni, and S. Thomas (Sawston, UK: Woodhead Publishing), 1–16.

Google Scholar

Fan, P., Mishra, A. K., Feng, S., and Su, M. (2023). The effect of agricultural subsidies on chemical fertilizer use: evidence from a new policy in China. J. Environ. Manag. 344:118423. doi: 10.1016/j.jenvman.2023.118423

PubMed Abstract | Crossref Full Text | Google Scholar

Fan, H., Zhang, Y., Li, J., Jiang, J., Waheed, A., Wang, S., et al. (2023). Effects of organic fertilizer supply on soil properties, tomato yield, and fruit quality: a global Meta-analysis. Sustain. For. 15:2556. doi: 10.3390/su15032556

Crossref Full Text | Google Scholar

Färe, R., and Knox Lovell, C. A. (1978). Measuring the technical efficiency of production. J. Econ. Theory 19, 150–162. doi: 10.1016/0022-0531(78)90060-1

Crossref Full Text | Google Scholar

Fortin, S. P., Johnston, S. S., and Schuemie, M. J. (2021). Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research. BMC Med. Res. Methodol. 21:109. doi: 10.1186/s12874-021-01282-1

PubMed Abstract | Crossref Full Text | Google Scholar

Fuller, A. M., Xu, S., Sutherland, L.-A., and Escher, F. (2021). Land to the tiller: the sustainability of family farms. Sustain. For. 13:11452. doi: 10.3390/su132011452

Crossref Full Text | Google Scholar

Guo, X., Zhu, A. L., Zhu, X., An, Z., Xu, Y., Zhuang, M., et al. (2024). Promoting sustainable smallholder farming systems in China. Agric. Syst. 219:104035. doi: 10.1016/j.agsy.2024.104035

Crossref Full Text | Google Scholar

Han, J. (2019). Prioritizing agricultural, rural development and implementing the rural revitalization strategy. China Agric. Econ. Rev. 12, 14–19. doi: 10.1108/CAER-02-2019-0026

Crossref Full Text | Google Scholar

Harraq, A., Sadiki, K., Bourioug, M., and Bouabid, R. (2022). Organic fertilizers mineralization and their effect on the potato “Solanum tuberosum” performance in organic farming. J. Saudi Soc. Agric. Sci. 21, 255–266. doi: 10.1016/j.jssas.2021.09.003

Crossref Full Text | Google Scholar

Holden, S. T. (2018). Fertilizer and sustainable intensification in sub-Saharan Africa. Glob. Food Sec. 18, 20–26. doi: 10.1016/j.gfs.2018.07.001

Crossref Full Text | Google Scholar

Hossain, M. E., Shahrukh, S., and Hossain, S. A. (2022). “Chemical fertilizers and pesticides: impacts on soil degradation, groundwater, and human health in Bangladesh” in Environmental degradation: Challenges and strategies for mitigation. eds. V. P. Singh, S. Yadav, K. K. Yadav, and R. N. Yadava (Cham: Springer International Publishing), 63–92.

Google Scholar

Hou, D., and Wang, X. (2022). Measurement of agricultural green development level in the three provinces of Northeast China under the background of rural vitalization strategy. Front. Public Health 10:202. doi: 10.3389/fpubh.2022.824202

PubMed Abstract | Crossref Full Text | Google Scholar

Huang, Y., Gao, B., Huang, W., Wang, L., Fang, X., Xu, S., et al. (2021). Producing more potatoes with lower inputs and greenhouse gases emissions by regionalized cooperation in China. J. Clean. Prod. 299:126883. doi: 10.1016/j.jclepro.2021.126883

Crossref Full Text | Google Scholar

Ikram, M., Ferasso, M., Sroufe, R., and Zhang, Q. (2021). Assessing green technology indicators for cleaner production and sustainable investments in a developing country context. J. Clean. Prod. 322:129090. doi: 10.1016/j.jclepro.2021.129090

Crossref Full Text | Google Scholar

Imani, B., Allahyari, M. S., Bondori, A., Emami, N., and El Bilali, H. (2021). Adoption of organic potato production in Ardabil plain, Iran: an application of the extended theory of planned behaviour. Potato Res. 64, 177–195. doi: 10.1007/s11540-020-09471-z

Crossref Full Text | Google Scholar

IPBES (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Bonn, Germany: IPBES.

Google Scholar

Jain, M., Solomon, D., Capnerhurst, H., Arnold, A., Elliott, A., Kinzer, A. T., et al. (2020). How much can sustainable intensification increase yields across South Asia? A systematic review of the evidence. Environ. Res. Lett. 15:083004. doi: 10.1088/1748-9326/ab8b10

Crossref Full Text | Google Scholar

Järvilehto, T. (2009). The theory of the organism-environment system as a basis of experimental work in psychology. Ecol. Psychol. 21, 112–120. doi: 10.1080/10407410902877066

Crossref Full Text | Google Scholar

Ji, X., Yin, R., and Zhang, H. (2023). Food security and overuse of agrochemicals: evidence from China’s major grain-producing areas policy. Environ. Sci. Pollut. Res. 30, 64443–64459. doi: 10.1007/s11356-023-26620-2

PubMed Abstract | Crossref Full Text | Google Scholar

Jiang, L., Qiu, S., Ding, W., Xu, X., and He, P. (2023). Synergizing potato productivity and environmental performance with nutrient expert recommendation approach in northern China. J. Clean. Prod. 382:135258. doi: 10.1016/j.jclepro.2022.135258

Crossref Full Text | Google Scholar

Jiang, B., Shi, X., and Qin, Y. (2023). Mechanisms and empirical analysis of the impact of soil and water conservation on the livelihood and well-being of farmer households: a Case study in desert–loess transition zone of China. Sustain. For. 15:6569. doi: 10.3390/su15086569

Crossref Full Text | Google Scholar

Krasilnikov, P., Taboada, M. A., and Amanullah, A. (2022). Fertilizer use, soil health and agricultural sustainability. Agriculture 12:462. doi: 10.3390/agriculture12040462

Crossref Full Text | Google Scholar

Ladha, J. K., Jat, M. L., Stirling, C. M., Chakraborty, D., Pradhan, P., Krupnik, T. J., et al. (2020). “Chapter two - achieving the sustainable development goals in agriculture: the crucial role of nitrogen in cereal-based systems” in Advances in agronomy. ed. D. L. Sparks (Cambridge, MA: Academic Press), 39–116.

Google Scholar

Lampach, N., and To-The, NNguyen-Anh, T. (2021). Technical efficiency and the adoption of multiple agricultural technologies in the mountainous areas of northern Vietnam. Land Use Policy 103:105289. doi: 10.1016/j.landusepol.2021.105289

Crossref Full Text | Google Scholar

Li, Y., Fan, Z., Jiang, G., and Quan, Z. (2021). Addressing the differences in farmers’ willingness and behavior regarding developing green agriculture—a Case study in Xichuan County, China. Land (Basel) 10:316. doi: 10.3390/land10030316

Crossref Full Text | Google Scholar

Li, J., Feng, S., Luo, T., and Guan, Z. (2020). What drives the adoption of sustainable production technology? Evidence from the large scale farming sector in East China. J. Clean. Prod. 257:120611. doi: 10.1016/j.jclepro.2020.120611

Crossref Full Text | Google Scholar

Li, J., Ma, W., Wang, P., and Meng, X. (2023). Farmers’ willingness to transform untreated livestock manure into organic fertilizer: does information technology adoption make a difference? Environ. Dev. Sustain. 26, 5025–5045. doi: 10.1007/s10668-023-02920-y

Crossref Full Text | Google Scholar

Li, F., Ren, J., Wimmer, S., Yin, C., Li, Z., and Xu, C. (2020). Incentive mechanism for promoting farmers to plant green manure in China. J. Clean. Prod. 267:122197. doi: 10.1016/j.jclepro.2020.122197

Crossref Full Text | Google Scholar

Li, X., Sarkar, A., Xia, X., and Memon, W. H. (2021). Village environment, capital endowment, and farmers’ participation in E-commerce sales behavior: a demand observable bivariate Probit model approach. Agriculture 11:868. doi: 10.3390/agriculture11090868

Crossref Full Text | Google Scholar

Li, B., and Shen, Y. (2021). Effects of land transfer quality on the application of organic fertilizer by large-scale farmers in China. Land Use Policy 100:105124. doi: 10.1016/j.landusepol.2020.105124

Crossref Full Text | Google Scholar

Li, C., Shi, Y., Khan, S. U., and Zhao, M. (2021). Research on the impact of agricultural green production on farmers’ technical efficiency: evidence from China. Environ. Sci. Pollut. Res. 28, 38535–38551. doi: 10.1007/s11356-021-13417-4

PubMed Abstract | Crossref Full Text | Google Scholar

Liao, C.-N., and Chen, Y.-J. (2017). Farmers’ information Management in Developing Countries—a Highly Asymmetric Information Structure. Prod. Oper. Manag. 26, 1207–1220. doi: 10.1111/poms.12678

Crossref Full Text | Google Scholar

Lin, D., Xu, J.-Y., Wang, L., Du, S., and Zhu, D. (2024). Long-term application of organic fertilizer prompting the dispersal of antibiotic resistance genes and their health risks in the soil plastisphere. Environ. Int. 183:108431. doi: 10.1016/j.envint.2024.108431

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, Y., Shi, K., Liu, Z., Qiu, L., Wang, Y., Liu, H., et al. (2022). The effect of technical training provided by agricultural cooperatives on farmers’ adoption of organic fertilizers in China: based on the mediation role of ability and perception. Int. J. Environ. Res. Public Health 19:14277. doi: 10.3390/ijerph192114277

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, Y., Sun, D., Wang, H., Wang, X., Yu, G., and Zhao, X. (2020). An evaluation of China’s agricultural green production: 1978–2017. J. Clean. Prod. 243:118483. doi: 10.1016/j.jclepro.2019.118483

Crossref Full Text | Google Scholar

Loan, T. T. H., Ba, V. N., Van Thai Bang, N., Thy, T. H. N., Hong, H. T. Y., and Huy, N. Q. (2018). Natural radioactivity and radiological health hazard assessment of chemical fertilizers in Viet Nam. J. Radioanal. Nucl. Chem. 316, 111–117. doi: 10.1007/s10967-018-5719-2

Crossref Full Text | Google Scholar

Lu, H., Zhang, P., Hu, H., Xie, H., Yu, Z., and Chen, S. (2019). Effect of the grain-growing purpose and farm size on the ability of stable land property rights to encourage farmers to apply organic fertilizers. J. Environ. Manag. 251:109621. doi: 10.1016/j.jenvman.2019.109621

PubMed Abstract | Crossref Full Text | Google Scholar

Lu, X., Zhang, Y., Lin, C., and Wu, F. (2021). Analysis and comprehensive evaluation of sustainable land use in China: based on sustainable development goals framework. J. Clean. Prod. 310:127205. doi: 10.1016/j.jclepro.2021.127205

Crossref Full Text | Google Scholar

Luan, J., Qiu, H., and And Zhao, J. (2018). Excessive use of fertilizer and its difference of geographical distribution in China agricultural production. J. Qingdao Agric. Univ. (Natural Science) 35, 40–48. doi: 10.3969/J.ISSN.1674-148X.2018.01.007

Crossref Full Text | Google Scholar

Luthra, S. K., Mankar, P., Rawal, S., and Lal, M. (2022). “Organic potato cultivation: a sustainable green technology opportunity” in Handbook of research on green Technologies for Sustainable Management of agricultural resources. ed. S. K. Luthra (Pennsylvania, US: IGI Global), 437–452.

Google Scholar

Ma, W., Abdulai, A., and Goetz, R. (2018). Agricultural cooperatives and Investment in Organic Soil Amendments and Chemical Fertilizer in China. Am. J. Agric. Econ. 100, 502–520. doi: 10.1093/ajae/aax079

Crossref Full Text | Google Scholar

Ma, L., Feng, S., Reidsma, P., Qu, F., and Heerink, N. (2014). Identifying entry points to improve fertilizer use efficiency in Taihu Basin, China. Land Use Policy 37, 52–59. doi: 10.1016/j.landusepol.2013.01.008

Crossref Full Text | Google Scholar

Ma, Q., Zheng, S., and Deng, P. (2022). Impact of internet use on farmers’ organic fertilizer application behavior under the climate change context: the role of social network. Land 11:1601. doi: 10.3390/land11091601

Crossref Full Text | Google Scholar

Macharia, M., Oduor, G., Kayuki, C., and Rware, H. (2016). Fertilizer use optimization approach: An innovation to increase agricultural profitability for African farmers. Afr. J. Agric. Res. 11, 3587–3597. doi: 10.5897/AJAR2016.11408

Crossref Full Text | Google Scholar

Madau, F. A. (2007). Technical efficiency in organic and conventional farming: evidence from Italian cereal farms. Agric. Econ. Rev. 1:141. doi: 10.22004/ag.econ.42141

Crossref Full Text | Google Scholar

Mao, X., Sun, K., Wang, D., and Liao, Z. (2005). Controlled-release fertilizer (CRF): a green fertilizer for controlling non-point contamination in agriculture. J. Environ. Sci. 17, 181–184.

Google 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 109:105627. doi: 10.1016/j.landusepol.2021.105627

Crossref Full Text | Google Scholar

Marenya, P. P., and Barrett, C. B. (2007). Household-level determinants of adoption of improved natural resources management practices among smallholder farmers in western Kenya. Food Policy 32, 515–536. doi: 10.1016/j.foodpol.2006.10.002

Crossref Full Text | Google Scholar

Mashamaite, C. V., Motsi, H., Manyevere, A., and Poswa, S. B. (2024). Assessing the potential of biochar as a viable alternative to synthetic fertilizers in sub-Saharan Africa smallholder farming: a review. Agronomy 14:1215. doi: 10.3390/agronomy14061215

Crossref Full Text | Google Scholar

Mathur, M. R., Dias, M. F., and Mathur, M. P. (2016). Importance of green Technology in Fertilizer Quality Improvement. Proc. Eng. 138, 308–313. doi: 10.1016/j.proeng.2016.02.089

Crossref Full Text | Google Scholar

Micella, I., Kroeze, C., Bak, M. P., and Strokal, M. (2024). Causes of coastal waters pollution with nutrients, chemicals and plastics worldwide. Mar. Pollut. Bull. 198:115902. doi: 10.1016/j.marpolbul.2023.115902

PubMed Abstract | Crossref Full Text | Google Scholar

Moli, D. R. R., Hafeez, A. G., Majumder, S., Mitra, S., and Hasan, M. (2021). Does biogas technology adoption improve the livelihood and income level of rural people? Int. J. Green Energy 18, 1081–1090. doi: 10.1080/15435075.2021.1891907

Crossref Full Text | Google Scholar

Movahedi, R., Ataei-Asad, M., and Sarami-Foroushani, T. (2023). Changing potato farmers’ behavior against only the chemical fertilizers to promote sustainable agricultural practice in Hamedan Province, Iran. Environ. Dev. Sustain. 1:3647. doi: 10.1007/s10668-023-03647-6

Crossref Full Text | Google Scholar

Mueller, V., Masias, I., and Vallury, S. (2019). Labor-saving technologies and structural transformation in northern Ghana. Agric. Econ. 50, 581–594. doi: 10.1111/agec.12511

Crossref Full Text | Google Scholar

Mwakidoshi, E. R., Gitari, H. H., Muindi, E. M., Wamukota, A., Seleiman, M. F., and Maitra, S. (2023). Smallholder farmers’ knowledge on the use of bioslurry as a soil fertility amendment input for potato production in Kenya. Land Degrad. Dev. 34, 2214–2227. doi: 10.1002/ldr.4601

Crossref Full Text | Google Scholar

Nadarajan, S., and Sukumaran, S. (2021). “Chapter 12 - chemistry and toxicology behind chemical fertilizers” in Controlled release fertilizers for sustainable agriculture. eds. F. B. Lewu, T. Volova, and S. Thomas (Cambridge, MA: Academic Press), 195–229.

Google Scholar

Naghdi, A. A., Piri, S., Khaligi, A., and Moradi, P. (2022). Enhancing the qualitative and quantitative traits of potato by biological, organic, and chemical fertilizers. J. Saudi Soc. Agric. Sci. 21, 87–92. doi: 10.1016/j.jssas.2021.06.008

Crossref Full Text | Google Scholar

Naher, U. A., Biswas, J. C., Maniruzzaman, M., Khan, F. H., Sarkar, M. I. U., Jahan, A., et al. (2021). Bio-organic fertilizer: a green technology to reduce synthetic N and P fertilizer for Rice production. Front. Plant Sci. 12:2052. doi: 10.3389/fpls.2021.602052

PubMed Abstract | Crossref Full Text | Google Scholar

Naidu, R., Biswas, B., Willett, I. R., Cribb, J., Kumar Singh, B., Paul Nathanail, C., et al. (2021). Chemical pollution: a growing peril and potential catastrophic risk to humanity. Environ. Int. 156:106616. doi: 10.1016/j.envint.2021.106616

PubMed Abstract | Crossref Full Text | Google Scholar

Nazziwa-Nviiri, L., Van Campenhout, B., and Amwonya, D. (2017). Stimulating agricultural technology adoption: lessons from fertilizer use among Ugandan potato farmers. Intl. Food Policy Res. Inst., 1: 36.

Google Scholar

Niu, K., Guo, H., and Liu, J. (2023). Can food security and low carbon be achieved simultaneously? —An empirical analysis of the mechanisms influencing the carbon footprint of potato and corn cultivation in irrigation areas. J. Integr. Agric. 22, 1230–1243. doi: 10.1016/j.jia.2023.02.010

Crossref Full Text | Google Scholar

Ochieng, J., Afari-Sefa, V., Muthoni, F., Kansiime, M., Hoeschle-Zeledon, I., Bekunda, M., et al. (2022). Adoption of sustainable agricultural technologies for vegetable production in rural Tanzania: trade-offs, complementarities and diffusion. Int. J. Agric. Sustain. 20, 478–496. doi: 10.1080/14735903.2021.1943235

Crossref Full Text | Google Scholar

Ogada, M. J., Mwabu, G., and Muchai, D. (2014). Farm technology adoption in Kenya: a simultaneous estimation of inorganic fertilizer and improved maize variety adoption decisions. Agric. Food Econ. 2:12. doi: 10.1186/s40100-014-0012-3

Crossref Full Text | Google Scholar

Oyetunde-Usman, Z., Ogunpaimo, O. R., Olagunju, K. O., Ambali, O. I., and Ashagidigbi, W. M. (2021). Welfare impact of organic fertilizer adoption: empirical evidence from Nigeria. Front. Sustain. Food Syst. 5:1667. doi: 10.3389/fsufs.2021.691667

Crossref Full Text | Google Scholar

Pahalvi, H. N., Rafiya, L., Rashid, S., Nisar, B., and Kamili, A. N. (2021). “Chemical fertilizers and their impact on soil health” in Microbiota and biofertilizers, Vol 2: Ecofriendly tools for reclamation of degraded soil environs. eds. G. H. Dar, R. A. Bhat, M. A. Mehmood, and K. R. Hakeem (Cham: Springer International Publishing), 1–20.

Google Scholar

Patra, S., Mishra, P., Mahapatra, S. C., and Mithun, S. K. (2016). Modelling impacts of chemical fertilizer on agricultural production: a case study on Hooghly district, West Bengal, India. Model. Earth Syst. Environ. 2, 1–11. doi: 10.1007/s40808-016-0223-6

Crossref Full Text | Google Scholar

Pirracchio, R., Carone, M., Rigon, M. R., Caruana, E., Mebazaa, A., and Chevret, S. (2016). Propensity score estimators for the average treatment effect and the average treatment effect on the treated may yield very different estimates. Stat. Methods Med. Res. 25, 1938–1954. doi: 10.1177/0962280213507034

PubMed Abstract | Crossref Full Text | Google Scholar

Priegnitz, U., Lommen, W. J. M., Onakuse, S., and Struik, P. C. (2019). A farm typology for adoption of innovations in potato production in southwestern Uganda. Front. Sustain. Food Syst. 3:68. doi: 10.3389/fsufs.2019.00068

Crossref Full Text | Google Scholar

Qi, X., Liang, F., Yuan, W., Zhang, T., and Li, J. (2021). Factors influencing farmers’ adoption of eco-friendly fertilization technology in grain production: An integrated spatial–econometric analysis in China. J. Clean. Prod. 310:127536. doi: 10.1016/j.jclepro.2021.127536

Crossref Full Text | Google Scholar

Ren, Y., Peng, Y., Campos, B. C., and Li, H. (2021). The effect of contract farming on the environmentally sustainable production of rice in China. Sustain. Prod. Consump. 28, 1381–1395. doi: 10.1016/j.spc.2021.08.011

Crossref Full Text | Google Scholar

Rice, A. L. (2013). The Enterprise and its environment: A system theory of management organization. London: Routledge.

Google Scholar

Rosén, B. (1997). On sampling with probability proportional to size. J. Stat. Plann. Inference 62, 159–191. doi: 10.1016/S0378-3758(96)00186-3

Crossref Full Text | Google Scholar

Rosenbaum, P. R., and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55. doi: 10.1093/biomet/70.1.41

Crossref Full Text | Google Scholar

Salam, M. A., Sarker, M. N. I., and Sharmin, S. (2021). Do organic fertilizer impact on yield and efficiency of rice farms? Empirical evidence from Bangladesh. Heliyon 7:e07731. doi: 10.1016/j.heliyon.2021.e07731

PubMed Abstract | Crossref Full Text | Google Scholar

Savci, S. (2012). Investigation of effect of chemical fertilizers on environment. APCBEE Proc. 1, 287–292. doi: 10.1016/j.apcbee.2012.03.047

Crossref Full Text | Google Scholar

Sharma, N., and Singhvi, R. (2017). Effects of chemical fertilizers and pesticides on human health and environment: a review. Int. J. Agric. Environ. Biotechnol. 10, 675–680. doi: 10.5958/2230-732X.2017.00083.3

Crossref Full Text | Google Scholar

Shen, J., Zhu, Q., Jiao, X., Ying, H., Wang, H., Wen, X., et al. (2020). Agriculture green development: a model for China and the world. Front. Agric. Sci. Eng. 7, 5–13. doi: 10.15302/J-FASE-2019300

Crossref Full Text | Google Scholar

Shi, M., Guo, A., Kang, Y., Zhang, W., Fan, Y., Yang, X., et al. (2023). Partial substitution of chemical fertilizer with organic manure enhances yield attributes and tuber quality in potato. J. Soil Sci. Plant Nutr. 23, 3932–3943. doi: 10.1007/s42729-023-01312-5

Crossref Full Text | Google Scholar

Shi, X., Song, X., Yang, J., Zhao, Y., Yuan, Z., Zhao, G., et al. (2023). Yield benefits from joint application of manure and inorganic fertilizer in a long-term field pea, wheat and potato crop rotation. Field Crop Res. 294:108873. doi: 10.1016/j.fcr.2023.108873

Crossref Full Text | Google Scholar

Sileshi, G., Akinnifesi, F. K., Ajayi, O. C., and Place, F. (2009). Evidence for impact of green fertilizers on maize production in sub-Saharan Africa: a meta-analysis. ICRAF Occasional Paper, No. 10.

Google Scholar

Srivastav, A. L. (2020). “Chapter 6 - chemical fertilizers and pesticides: role in groundwater contamination” in Agrochemicals detection, treatment and remediation. ed. M. N. V. Prasad (Oxford, UK: Butterworth-Heinemann), 143–159.

Google Scholar

Takahashi, K., Muraoka, R., and Otsuka, K. (2020). Technology adoption, impact, and extension in developing countries’ agriculture: a review of the recent literature. Agric. Econ. 51, 31–45. doi: 10.1111/agec.12539

Crossref Full Text | Google Scholar

Tanko, M., Muhammed, M. A., and Ismaila, S. (2023). Reshapping agriculture technology adoption thinking: Malthus, Borlaug and Ghana’s fail green revolution. Heliyon 9:e12783. doi: 10.1016/j.heliyon.2022.e12783

PubMed Abstract | Crossref Full Text | Google Scholar

Tone, K. (2002). A strange case of the cost and allocative efficiencies in DEA. J. Oper. Res. Soc. 53, 1225–1231. doi: 10.1057/palgrave.jors.2601438

Crossref Full Text | Google Scholar

Triyono, T., Al Uth’aini, T., Nur Mila, A., Fauzi bin Kamrudin, M., and Mamnuah, M. (2023). Optimization of production factors in organic Rice farming in Sleman regency, Yogyakarta. E3S Web Conf. 444:02050. doi: 10.1051/e3sconf/202344402050

Crossref Full Text | Google Scholar

Tyagi, J., Ahmad, S., and Malik, M. (2022). Nitrogenous fertilizers: impact on environment sustainability, mitigation strategies, and challenges. Int. J. Environ. Sci. Technol. 19, 11649–11672. doi: 10.1007/s13762-022-04027-9

Crossref Full Text | Google Scholar

Tzouvelekas, V., Pantzios, C. J., and Fotopoulos, C. (2001). Technical efficiency of alternative farming systems: the case of Greek organic and conventional olive-growing farms. Food Policy 26, 549–569. doi: 10.1016/S0306-9192(01)00007-0

Crossref Full Text | Google Scholar

Virapongse, A., Brooks, S., Metcalf, E. C., Zedalis, M., Gosz, J., Kliskey, A., et al. (2016). A social-ecological systems approach for environmental management. J. Environ. Manag. 178, 83–91. doi: 10.1016/j.jenvman.2016.02.028

Crossref Full Text | Google Scholar

Von Bertalanffy, L. (1950). An outline of general system theory. Br. J. Philos. Sci. 1, 134–165. doi: 10.1093/bjps/I.2.134

Crossref Full Text | Google Scholar

Wang, N., Gao, Y., Wang, Y., and Li, X. (2016). Adoption of eco-friendly soil-management practices by smallholder farmers in Shandong Province of China. Soil Sci. Plant Nutr. 62, 185–193. doi: 10.1080/00380768.2016.1149779

Crossref Full Text | Google Scholar

Wang, Z., Liu, H., Zeng, F., Yang, Y., Xu, D., Zhao, Y.-C., et al. (2023). Potato processing industry in China: current scenario, future trends and global impact. Potato Res. 66, 543–562. doi: 10.1007/s11540-022-09588-3

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, H., Wang, X., Sarkar, A., and Zhang, F. (2021). How capital endowment and ecological cognition affect environment-friendly technology adoption: a Case of apple farmers of Shandong Province, China. Int. J. Environ. Res. Public Health 18:7571. doi: 10.3390/ijerph18147571

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, X., Xu, M., Lin, B., Bodirsky, B. L., Xuan, J., Dietrich, J. P., et al. (2023). Reforming China’s fertilizer policies: implications for nitrogen pollution reduction and food security. Sustain. Sci. 18, 407–420. doi: 10.1007/s11625-022-01189-w

Crossref Full Text | Google Scholar

Wang, H., Xu, J., Liu, X., Zhang, D., Li, L., Li, W., et al. (2019). Effects of long-term application of organic fertilizer on improving organic matter content and retarding acidity in red soil from China. Soil Tillage Res. 195:104382. doi: 10.1016/j.still.2019.104382

Crossref Full Text | Google Scholar

Wang, X., Zhang, J., He, K., and Li, W. (2021). Place attachment, environmental cognition and organic fertilizer adoption of farmers: evidence from rural China. Environ. Sci. Pollut. Res. 28, 41255–41267. doi: 10.1007/s11356-021-13509-1

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, Y., Zhu, Y., Zhang, S., and Wang, Y. (2018). What could promote farmers to replace chemical fertilizers with organic fertilizers? J. Clean. Prod. 199, 882–890. doi: 10.1016/j.jclepro.2018.07.222

Crossref Full Text | Google Scholar

Wordofa, M. G., Hassen, J. Y., Endris, G. S., Aweke, C. S., Moges, D. K., and Rorisa, D. T. (2021). Adoption of improved agricultural technology and its impact on household income: a propensity score matching estimation in eastern Ethiopia. Agric. Food Secur. 10:5. doi: 10.1186/s40066-020-00278-2

Crossref Full Text | Google Scholar

Wu, J. Y., Martinov, M., and Sardo, V. I. (2011). “Human labour and green manure, two overlooked factors for energy analysis in agriculture” in Genetics, biofuels and local farming systems. ed. E. Lichtfouse (Dordrecht: Springer Netherlands), 215–229.

Google Scholar

Xie, H., and Huang, Y. (2021). Influencing factors of farmers’ adoption of pro-environmental agricultural technologies in China: Meta-analysis. Land Use Policy 109:105622. doi: 10.1016/j.landusepol.2021.105622

Crossref Full Text | Google Scholar

Xie, J., Yang, G., Guo, Z., and Wang, G. (2021). Exploring the influence mechanism of farmers’ organic fertilizer application behaviors based on the normative activation theory. Land 10:1111. doi: 10.3390/land10111111

Crossref Full Text | Google Scholar

Xinhua Silk Road . (2023). China’s Tengzhou hosts potato expo to seek opening-driven development. Xinhua Silk Road. Available at: https://en.imsilkroad.com/p/336811.html (Accessed December 14, 2023).

Google Scholar

Xu, H., Huang, X., Zhong, T., Chen, Z., and Yu, J. (2014). Chinese land policies and farmers’ adoption of organic fertilizer for saline soils. Land Use Policy 38, 541–549. doi: 10.1016/j.landusepol.2013.12.018

Crossref Full Text | Google Scholar

Xu, J., Wang, Y., Chen, Y., He, W., Li, X., and Cui, J. (2023). Identifying the influencing factors of plastic film mulching on improving the yield and water use efficiency of potato in the Northwest China. Water 15:2279. doi: 10.3390/w15122279

Crossref Full Text | Google Scholar

Xue, L., Hou, P., Zhang, Z., Shen, M., Liu, F., and Yang, L. (2020). Application of systematic strategy for agricultural non-point source pollution control in Yangtze River basin, China. Agric. Ecosyst. Environ. 304:107148. doi: 10.1016/j.agee.2020.107148

Crossref Full Text | Google Scholar

Yang, F., He, B., Dong, B., and Zhang, G. (2023). Autumn film mulched ridge microfurrow planting improves yield and nutrient-use efficiency of potatoes in dryland farming. Agronomy 13:1563. doi: 10.3390/agronomy13061563

Crossref Full Text | Google Scholar

Yang, H., Wang, X., and Bin, P. (2022). Agriculture carbon-emission reduction and changing factors behind agricultural eco-efficiency growth in China. J. Clean. Prod. 334:130193. doi: 10.1016/j.jclepro.2021.130193

Crossref Full Text | Google Scholar

Yi, X., Yu, L., Chang, S.-H.-E., Yin, C., Wang, H., and Zhang, Z. (2021). The effects of China’s organic-substitute-chemical-fertilizer (OSCF) policy on greenhouse vegetable farmers. J. Clean. Prod. 297:126677. doi: 10.1016/j.jclepro.2021.126677

Crossref Full Text | Google Scholar

Yin, J., Song, Z., Guo, X., Ding, F., Shen, Y., Zhang, J., et al. (2023). Adjusting the fertilizer application structure for nutrient saving and yield increase in potato. Int. J. Plant Prod. 17, 379–387. doi: 10.1007/s42106-023-00246-3

Crossref Full Text | Google Scholar

Yu, S., Sial, M. S., Tran, D. K., Badulescu, A., Thu, P. A., and Sehleanu, M. (2020). Adoption and implementation of sustainable development goals (SDGs) in China—agenda 2030. Sustain. For. 12:6288. doi: 10.3390/su12156288

Crossref Full Text | Google Scholar

Zenbaba, O. S. (2021). Empirical review on determinants of potato and onion production technology packages adoption in Ethiopia. Int. J. Agric. Sci. Food Technol. 7, 285–291. doi: 10.17352/2455-815X.000121

Crossref Full Text | Google Scholar

Zhang, F., Chen, M., Fu, J., Zhang, X., Li, Y., Shao, Y., et al. (2023). Coupling effects of irrigation amount and fertilization rate on yield, quality, water and fertilizer use efficiency of different potato varieties in Northwest China. Agric. Water Manag. 287:108446. doi: 10.1016/j.agwat.2023.108446

Crossref Full Text | Google Scholar

Zhang, Q., Chu, Y., Xue, Y., Ying, H., Chen, X., Zhao, Y., et al. (2020). Outlook of China’s agriculture transforming from smallholder operation to sustainable production. Glob. Food Sec. 26:100444. doi: 10.1016/j.gfs.2020.100444

Crossref Full Text | Google Scholar

Zhang, T., Hou, Y., Meng, T., Ma, Y., Tan, M., Zhang, F., et al. (2021). Replacing synthetic fertilizer by manure requires adjusted technology and incentives: a farm survey across China. Resour. Conserv. Recycl. 168:105301. doi: 10.1016/j.resconrec.2020.105301

Crossref Full Text | Google Scholar

Zhang, L., Meng, T., Zhang, Z., and Mu, Y. (2023). Effects of organic fertilizer substitution on the technical efficiency among farmers: evidence from Bohai rim region in China. Agronomy 13:761. doi: 10.3390/agronomy13030761

Crossref Full Text | Google Scholar

Zhao, X., Zhang, Z., and Ren, J. (2021). Do agricultural productive services have an impact on farmers’ income. J. Agrotech. Econ. 1, 32–45. doi: 10.13246/j.cnki.jae.2021.01.003

Crossref Full Text | Google Scholar

Zheng, X., Wei, L., Lv, W., Zhang, H., Zhang, Y., Zhang, H., et al. (2024). Long-term bioorganic and organic fertilization improved soil quality and multifunctionality under continuous cropping in watermelon. Agric. Ecosyst. Environ. 359:108721. doi: 10.1016/j.agee.2023.108721

Crossref Full Text | Google Scholar

Zheng, S., Yu, L., and Fu, H. (2023). Has rural E-commerce increased potato farmers’ income? Evidence from the potato home of China. Potato Res. 1, 1–21. doi: 10.1007/s11540-023-09614-y

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, L., Chen, X., and Tian, X. (2018). The impact of fine particulate matter (PM2.5) on China’s agricultural production from 2001 to 2010. J. Clean. Prod. 178, 133–141. doi: 10.1016/j.jclepro.2017.12.204

Crossref Full Text | Google Scholar

Zhu, Z., Chen, Y., Ning, K., and Liu, Z. (2024). Policy setting, heterogeneous scale, and willingness to adopt green production behavior: field evidence from cooperatives in China. Environ. Dev. Sustain. 26, 1529–1555. doi: 10.1007/s10668-022-02773-x

Crossref Full Text | Google Scholar