Agriculture In China Research Articles

Dense planting and nitrogen fertilizer management improve drip-irrigated spring maize yield and nitrogen use efficiency in Northeast China

Farmers in China often use nitrogen (N) fertilizers to ensure adequate crop growth. However, injudicious applications have increased the risk of environmental pollution, lower maize yields, and reduced profits for farmers. Appropriate N fertilizer management is crucial for improving yield and nitrogen use efficiency (NUE). This study conducted a three-year experiment involving nine N treatments (0, 45, 90, 135, 180, 225, 270, 315, and 360 kg ha-1) on a field under nitrogen fertilizer precision management (NFPM) in Northeast China. These results were compared with studies published within the past decade that analyzed yield and dry matter (DM) content under two management practices in Northeast China: conventional nitrogen fertilization management (CNFM) and water-saving fertilization management (WSFM). The findings reveal that maize yield increases with rising N application rates up to 270 kg ha-1, after which yield decreases. The kernel number (KN) and kernel weights (KW) of maize grown under NFPM were 13.7 and 14.7% higher than those grown under WSFM, respectively. Furthermore, they surpassed crops grown under CNFM by 38.4 and 21.2%, respectively. The maximum total yield of the NFPM treatment was 41.8 and 78.8% higher than WSFM and CNFM, respectively. Additionally, compared with CNFM and WSFM, NFPM significantly increased nitrogen use efficiency (NUE) across various N-level treatments. Optimizing nitrogen management could help farmers achieve higher yields and promote sustainable agricultural development.

Re-designing a yield-improving strategy of pear orchards in the Yangtze River Basin, China.

Pear yield is a primary source of income for smallholder farmers in China, yet significant yield disparities exist among different smallholders. Systematic analyses of limiting factors and the feasibility of solutions at the smallholder level are limited. This study employs a novel DEED (Describe, Explain, Explore, and Design) research cycle centered on smallholders to formulate yield-improving strategies. A comprehensive survey of 173 smallholders in the Yangtze River pear district, encompassing Zhejiang province, Jiangxi province, and Shanghai city, was conducted to delineate the current yield status. The boundary line analysis model was applied to elucidate the contributions of various yield-limiting factors. Findings reveal an average yield ranging from 16.7 to 19.3 t ha-1, with a potential highest yield of 37.5 t ha-1. Fertilizer nitrogen (N) was identified as the most pervasive yield-limiting factor, constituting 62.7% of the average limitation at the regional level and 37.7% at the individual smallholder level, surpassing other yield-related factors in all three regions. Subsequently, a 2-year field optimization experiment was conducted to explore the potential for yield improvement through adjustments in N fertilizer rates. Compared with traditional farmer management (FM), the N fertilizer optimization treatment (OPT) resulted in a 38.1% and 22.5% increase in yield for 2022 and 2023, respectively. These results identify N fertilizer application rate as the most important yield-limiting factor and verify the feasibility of optimizing N fertilizer management practices for improving pear yield. This study integrates farmer surveys, boundary line models, and field experiments to provide valuable insights into addressing yield disparities among smallholders in the pear industry. © 2024 Society of Chemical Industry.

ICT adoption, commercial orientation and productivity: Understanding the digital divide in Rural China

This study investigates the impact of Chinese smallholders’ adoption of Information and Communication Technologies—the use of smartphones and computers connected to the internet—on their commercial orientation, land, and labor productivity. Commercial orientation is the share of farm output for sales in the market. We used a control function approach and a selectivity-corrected model. The study uses national survey data from rural sample households, the China Household Database, and the China Household Finance Survey and Research Center. Findings reveal that the adoption of information and communication technologies by Chinese farmers increased the commercial orientation of farming. Furthermore, adopting information and communication technologies increases land and labor productivity by about 21.3 % and 28.2 %, respectively. Farm households’ commercial orientation improved labor productivity by about 35.9 %. Heterogeneity indicates that the adoption of information and communication technologies has a more significant effect on improving productivity for young household heads and small farmers. Policymakers should establish information and communication technologies training programs, develop digital infrastructure, and promote smallholder commercial production to increase agricultural productivity.

How Do Digital Capabilities Impact the Sustained Growth of Entrepreneurial Income: Evidence from Chinese Farmer Entrepreneurs

How Do Digital Capabilities Impact the Sustained Growth of Entrepreneurial Income: Evidence from Chinese Farmer Entrepreneurs

Characteristics of air-borne and feces-borne ARGs and microbial community in different livestock farms in China

Livestock farms are hotspots of antibiotic resistance due to the intensive use of antibiotics, in which the characteristics of air-borne and feces-borne antibiotic resistance genes (ARGs) and microbial communities are of great significance. This study delves into the distribution of ARGs and microbial communities across various livestock farms in China, and the correlation of microorganisms between livestock farms and other global environments was investigated. The concentrations of ARGs and mobile genetic elements (MGEs) in air samples were basically at the same level, but those in fecal samples collected from chicken farms were universally higher than those in pig and cattle farms. There was significant ability of ARGs to spread easily among different bacteria in all samples in livestock farms. Additionally, there may be more possible host bacteria of airborne ARGs in chicken farms. In the global-scale analysis of highly similar microbial communities, the database matching with the highest number of similarities to microbial communities collected from livestock farms is genes related to human sources (54.8%). This study advances our understanding of ARG dynamics in different livestock farms and contributes to the development of sustainable livestock management practices.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

The impact of the digital economy on land transfer-out decisions among Chinese farmers: evidence from CHFS micro-data

Land transfer is essential for advancing moderate-scale agriculture, and the digital economy plays a key role in facilitating these transfers. Based on microdata from four rounds of the China Household Finance Survey spanning from 2013 to 2019, this study employs a Linear Probability Model (LPM) to examine the impact of digital economy on rural land transfer-out decisions. The results indicate that: (1) The development of the digital economy significantly increases the likelihood of rural households transferring land out. Specifically, for each unit increase in the digital economy index, the probability of a household transferring land out increases by 3.39%. These conclusions remain robust even after accounting for endogeneity and conducting robustness checks. (2) The digital economy facilitates rural land transfer-out by promoting non-agricultural employment and entrepreneurship among farmers, as well as enhancing online social interactions and information acquisition. (3) There are heterogeneous impacts due to different scales and transferees, variations in physical and human capital, as well as differences in village topographies. This study deepens the understanding of the digital economy's impact on land transfer decision-making and provides relevant policy references for facilitating land transfer and agricultural modernization.

Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events

With wind power penetration increases, accurate and reliable wind power forecasting is becoming gradually critical, and data driven model is a promising solution to implement this task. However, limited by deficient data samples under extreme conditions, scarcity of feature measurements fails to meet the number of training samples required, making the forecasting model exhibits low adaptability. This paper proposes a physics-informed reinforcement learning based method for probabilistic forecasting. Analytical physical expression of wind power output under extreme event is established to construct the error evaluation function for abrupt feature. Deep deterministic policy gradient-based quantile fitting model is then proposed, with abrupt feature embedded as the auxiliary input data for neural network. On this basis, parameter training technique under small data set is proposed to deal with the effect of extreme conditions on correction process of network. It extracts key historical transitions from experience replay pool to establish effective training samples, and model parameters are updated through the small-batch learning strategy to minimize long-term error feedback. Test result on the practical cold wave event of wind farms in China shows effectiveness of the proposed method.

The impact mechanisms of intermediate water depth on the coupled dynamic responses of large-capacity floating wind turbines

ABSTRACT The currently planned floating wind farms in China mostly have water depths in the intermediate range. Therefore, investigating the impact mechanisms of intermediate water depths on the coupled dynamic responses of floating wind turbines is of significant importance. In comparison to deep water, mooring systems in intermediate water depths are more prone to experiencing catenary shape loss and stiffness. The contribution of difference-frequency effects increases with decreasing water depth, resulting in a more substantial impact on low-frequency responses. The Full Quadratic Transfer Function (QTF) method accurately captures low-frequency responses. Placing clump weights in the bottom section of the mooring line effectively restricts the displacement range of floating turbines while maintaining average fairlead tensions at levels similar to the other two forms. On this basis, focusing on the most optimal mooring system performance, experiments were conducted to validate the coupled dynamic responses of floating wind turbines in intermediate water depths.

Analysis of the effect of digital financial inclusion on agricultural carbon emissions in China

<p style="text-align:justify"><font _mstmutation="1">In the context of China's "dual carbon" goal, digital financial inclusion plays an important role in agricultural carbon reduction. Based on the provincial panel data of 31 provinces in China from 2012 to 2022, this paper analyzes the impact of digital financial inclusion on agricultural carbon emissions and its transmission mechanism. The results show that digital financial inclusion has a significant inhibitory effect on agricultural carbon emissions. From the perspective of mechanism, inclusive digital finance can reduce agricultural carbon emissions through the upgrading of agricultural industrial structure. In addition, in the mechanism of digital inclusive finance affecting agricultural carbon emissions, there is a threshold effect, and only when rural human capital is higher than a certain threshold, digital inclusive finance can show an inhibitory effect on agricultural carbon emissions. Therefore, it is necessary to strengthen the multi-dimensional development of digital technology facilities, the development of digital inclusive finance and the upgrading of agricultural industrial structure, promote the green and low-carbon transformation of agriculture, comprehensively reduce agricultural carbon emissions, and realize the low-carbon and sustainable development of China's agriculture.</font></p>

Participation in land rental and labor markets and agricultural economic performance of banana farmers in China

The emerging marketization of agricultural factors allows farmers to expand agricultural production by renting in farmland from land rental market or purchasing services through labor market. Farmers’ transactions in the two markets can stimulate the agricultural transformation in developing countries, but may increase their financial burden in agriculture. Based on the primary data collected from banana farmers in China, this study attempts to clarify farmers categories based on their different choices regarding participation in land rental and labor markets for agricultural production, analyze the determinants of farmers’ simultaneous participation in the two markets, and explore heterogenous effects of farmers’ market participation on agricultural economic performances using the multinomial endogenous switching treatment regression model. The results show that farmer’s tie to retailers is highlighted as an important driving force behind farmers’ simultaneous participation in the two markets, while their participation choices are also varying due to different household head characteristics and family resource endowments. Farmers who participated in the two markets significantly reduce the cost of material input in agricultural production, but the total production cost of participants is higher than that of non-participants due to the high rent of land resource and cost of hired labor for production services. Interestingly, farmers’ participation in land rental market increases the yield of their farms, while participation in labor market has a negative impact on the yield. Farmers’ participation in both land rental and labor markets can significantly increase farm net income by nearly 67 %. In contrast, participation in labor market decreased farm net income by 22 %, whilst participation in land rental market increased farm net income by 186 %, though without statistical significance. The findings underscore the important role of land rental markets in boosting farm economic performance. Policymakers can facilitate land transfer among smallholder farmers in the land rental market and promote agricultural technologies to substitute labor force so as to reduce the adverse effect of labor market, thereby improving agricultural economic performances in developing regions.

Late Holocene rapid paleoenvironmental changes and anthropogenic impacts in central Yunnan, southwest China

Understanding long-term anthropogenic impact on the Earth's surface system is crucial for establishing reference conditions and potentially allowing future trajectories to be more rigorous and tightly constrained. In this study, the evolution of catchment erosion, chemical weathering and bottom-water hypoxia during the late Holocene are investigated using multi-proxy records from an accurately-dated sediment core from Lake Qilu in central Yunnan, southwest China. Through the comparison of our results with other paleoenvironmental records from the study region, we are able to see that the increase in anthropogenic impact on the catchment of Lake Qilu began in 780 CE, which is associated with the large scale expansion of agriculture in China. In the early stages of vegetation disturbance and agricultural land use, soil erosion and chemical weathering within in the catchment was significantly intensified, while the lake gradually changed to a state of anoxia until the period of accelerating eutrophication in 1945 CE. However, the extremely high rate of soil erosion and weak chemical weathering suggest the beginning of a new phase in terms of anthropogenic impact on the landscape. Furthermore, the late Holocene intensification of chemical weathering in monsoonal China can also be linked to increased anthropogenic activities rather than spatial differences in hydroclimate changes. This study highlights the fact that humans have been shaping the Earth's surface for millennia, which means that it is essential to place present environmental concerns into a long-term context.

Does rural e-commerce improve the economic resilience of family farms?

E-commerce and other digital economies are emerging drivers for high-quality development in the Chinese economy. Based on the e-commerce plan in rural China, this paper investigates the effect of e-commerce development on the economic resilience of family farms, using data from 110,574 family farms in China from 2013 to 2021 and applying the staggered DID method. The results show that e-commerce development significantly enhances the resilience of family farms. Mechanism tests reveal that e-commerce policy substantially reduces the financing constraints of family farms and curbs underinvestment, thereby boosting their resilience. However, the effect on family farms with overinvestment is not significant. Additionally, the analysis of heterogeneity indicates that e-commerce has a greater impact on the resilience of family farms in areas with better institutional quality. Regarding the development status of the farm, the impact of e-commerce on resilience is greater in large-scale and capital-intensive family farms. Overall, this study sheds light on the actual impact of rural e-commerce in China on the economic resilience of family farms and offers valuable empirical insights for the revitalization of rural areas in developing countries.

Sustainable Management Practices of State-Owned Forest Farms in Subtropical Regions: Case Study of S Forest Farm in China

Sustainable Management Practices of State-Owned Forest Farms in Subtropical Regions: Case Study of S Forest Farm in China

Study on the Evolution of Spatiotemporal Dynamics and Regional Differences in the Development of Digital Agriculture in China

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum’s Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a “high-high” and “low-low” (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country’s overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region.

The Impact of Pig Futures on the Price Transmission in the Pig Industry Chain during Market Shocks

In recent years, frequent external emergencies have continuously impacted China’s pig industry chain. As the scale and standardization of pig farming in China have increasingly improved, pig futures have met the conditions for good operation and were listed for trading on the Dalian Commodity Exchange on 8 January 2021. To study the impact and influence of African swine fever, COVID-19, and the listing of pig futures on the price transmission mechanism at various stages of China’s pig industry, weekly price data from the pig industry from January 2015 to June 2023 were selected to construct an SV-TVP-VAR model for analysis. The empirical results showed that the shocks of African swine fever and COVID-19 caused price fluctuations at various stages of the pig industry chain, while price fluctuations significantly decreased after the listing of pig futures. Therefore, the introduction of pig futures effectively alleviated the price fluctuations at various stages of the pig industry chain following the shocks of African swine fever and COVID-19, and relevant policy recommendations are proposed accordingly.

How can information and communication technologies influence farmers’ agricultural income and labor productivity? Micro evidence from China

ABSTRACT Based on a panel data from the National Rural Fixed Point Survey of China, this study explored the effects of information and communication technologies (ICTs) on agricultural income and labour productivity of Chinese farmers using fixed effects models and two-stage least square estimations. The results indicate that the deployment of mobile 3 G led to a reduction in both farmers’ agricultural income and agricultural net income by CNY 560.64 and 331.2, respectively, because ICTs prompted farmers to engage in off-farm employment, thereby allocating less time and input to farming activities. Village access to Internet and 3 G increased farmers’ labour productivity, measured by agricultural net income per day, with increments of CNY 2.09 and 11.15, respectively. ICTs also decreased farmers’ grain production, production cost, fertilizer use, and increased grain productivity. Furthermore, ICTs exhibited a spill-over effect, with non-users in Internet-connected villages demonstrating higher labour productivity compared to those in villages without Internet access. These results imply that agricultural labour productivity can be enhanced by employing ICTs for China and countries with small-scale farms. Nonetheless, greater attention should be paid to the potential trade-offs between the income growth facilitated by ICTs in rural out-migration and the decline in agricultural income and grain production.

The impact of labor force aging on agricultural total factor productivity of farmers in China: implications for food sustainability

IntroductionThe rapid aging of the labor force has serious implications for socioeconomic development and poses challenges to food sustainability. Existing studies have focused on the impact of labor force aging on land productivity, but little attention has been paid to the comprehensive capacity of agricultural production, namely the total factor productivity of agriculture.MethodsWe estimate the impact of labor force aging on total factor productivity in agriculture using data from 170,506 sample farm households from the National Fixed Point Survey data from 2003 to 2020. We estimate the results using a panel fixed effects approach and mitigate the endogeneity problem using an instrumental variables approach to ensure the robustness of the estimates.Results and discussionThe results show that labor force aging has an adverse effect on the agricultural total factor productivity of farm households and remains robust after addressing endogeneity and substituting explanatory variables. The aging effect shows significant heterogeneity across different food functional areas with different types of farm households. The negative impact of labor force aging on total factor productivity in agriculture is mainly concentrated in the main grain-production and grain-producing and marketing-balanced areas. We also find that the negative impact of labor force aging is more significant among large-scale farmers. The mechanism analysis finds that the aging of the labor force adversely affects the agricultural total factor productivity by inhibiting technological progress and reducing the efficiency of agricultural resource allocation. These insights, when considered in the light of global trends towards agricultural labor aging, suggest that policy interventions aimed at promoting technological adoption, enhancing resource allocation efficiency, and supporting the transitioning of older farmers may hold promise for maintaining food sustainability and addressing the challenges posed by an aging agricultural workforce in numerous developing nations.

Safety assessment of agricultural products and the pesticide regulation trend in China

The sustainable development of Chinese agriculture needs to coordinate the quality and quantity of agricultural products. To verify whether government regulation can promote agricultural development by controlling pesticides, the indices of government regulation, quantity and quality of agricultural products, and pesticide use per area of cropland were calculated, and a comprehensive evaluation index and the coupling degree and coordination degree of agricultural products were analyzed by coupling function. The results showed that the coupling and coordination degree of the quality and quantity of agricultural products and the comprehensive index are improving. The regulation of pesticide use per area of cropland is a key mechanism in promoting the coordinated development of agricultural product safety. The regulation trend of agricultural development in China is to reduce pesticide usage appropriately and expand the production-possibility frontier of agricultural products.Graphical abstract

Pathways towards low-carbon sustainable agriculture: how farmland size affects net carbon emissions

ABSTRACT Understanding the impact of farmland size adjustment on net crop carbon emissions (NCEs) is crucial for both effective farmland management and sustainable development, which are essential topics in agri-environmental science. However, studies exploring the relationship between farmland size and NCEs remain limited, particularly in developing countries with scarce farmland, such as China. To fill this gap, this paper discusses farmland size’s impact on NCEs using a threshold model combining endogenous switching regression (ESR) and two-stage least squares (2SLS) with survey data from 2600 Chinese farmers. The results show that increasing farmland size curbs the scale and intensity of NCEs. However, their relationship follows a triple threshold effect, where substantial NCEs mitigation occurs only when the per-household farmland size exceeds specific thresholds of 5.1, 15.2, and 46.7 ha. Expanding farmland size can also indirectly reduce NCEs by promoting green agricultural technologies (GAT) adoption. Moreover, lowering farmers’ time preferences and enhancing technical training can increase the positive effect of farmland size expansion on GAT adoption and reduce NCEs further. However, the emission reduction effects vary by GAT type. Soil testing and fertilization technology, pest control technology, and water-saving irrigation technology reduce NCEs, while the other GAT have insignificant effects. Overall, our study sheds light on the intricate relationships between farmland size, GAT adoption, and NCEs, and provides valuable insights for low-carbon, sustainable agriculture policies. We suggest that policymakers can reduce NCEs and facilitate agriculture’s low-carbon transition by promoting moderate farmland size expansion and locally adapted GAT.