Farmland Scale Research Articles

Assessing the Coordination Development Level of Agricultural Economy and Ecology in China: Regional Disparities, Dynamics, and Barriers

Achieving sustainable rural development in China requires effectively integrating agricultural growth with ecological balance. However, existing research on the coordination between agricultural economy and ecosystems has often focused on isolated aspects, such as economic growth or ecological sustainability, or has been limited to specific provinces or regions, lacking a comprehensive nationwide analysis. To address this gap, this study uses spatial data from 31 provincial-level regions in China from 2008 to 2022, developing a multidimensional framework that encompasses economic input, structure, efficiency, benefits, vitality, ecological conditions, and pressure. Using multi-factor econometric methods, we comprehensively evaluate the coordination between China’s agricultural economy and ecosystems, revealing regional disparities and spatiotemporal variations in their coupling coordination, and analyzing the barriers affecting this coordination. Our findings show that: First, coupling coordination has steadily improved, narrowing regional disparities. Second, regional differences are primarily driven by variations between the eastern, central, and western regions, with structural disparities shifting from interregional to hyper-variable density. Third, development exhibits a “club convergence” pattern, where upward transitions are difficult and downward mobility is a risk. Key barriers include farmland scale, land efficiency, afforestation area, and soil erosion control. Based on these findings, we recommend regional development strategies, dynamic monitoring mechanisms, optimized land use, and enhanced ecological protection. This study provides valuable insights for policymakers and practitioners to promote the coordinated and sustainable development of agricultural economies and ecosystems in China.

How Does China’s Agricultural Subsidy Policy Drive More Commercially Productive Small Farmers? The Role of Farmland Scale, Labor Supply, and Cropping Structural Change

This study investigated the impact of China’s agricultural subsidy policy on farmers’ commercial production, indicated by sales market participation. A nationally representative survey data, panel Tobit model, IV Tobit model, and the Correlated Random Effects (CRE) approach were used. The findings reveal that farmers receiving agricultural subsidies have improved crop sales’ share of total output value. Mechanism analysis reveals that agricultural subsidies have driven up farmland scale and increased agricultural labor supply in production, incentivizing the motive for profit maximization among farmers. Meanwhile, agricultural subsidies also encourage farmers to cultivate more grain crops. Policymakers should continue deepening the reform of agricultural subsidies and promoting farmer specialization and commercial production. Moderate-scale operation of farmland should be one of the targets of agricultural subsidies.

Leveraging digital infrastructure for sustainable grain production: evidence from China

IntroductionAgriculture faces significant challenges in ensuring global food security while minimizing resource costs and environmental impacts. The development of digital infrastructure offers transformative potential for agricultural systems and aligns with the United Nations Sustainable Development Goals. This study examines the role of digital infrastructure in enhancing grain production capacity in China, a key player in the global food system.MethodsWe analyzed data from 277 prefecture-level cities in China from 2011 to 2021. A double machine learning model was employed to empirically assess the impact of digital infrastructure on grain production capacity, allowing for robust insights into causal relationships.ResultsResults reveal that digital infrastructure significantly enhances grain production. Mechanism analysis results indicate that digital infrastructure construction drives agricultural technological advancements and farmland scale, contributing to increased production capacity. Heterogeneity analysis results show that the impact of digital infrastructure construction is significant in major grain-producing regions and the central-eastern regions, while its effects are relatively limited in grain production-consumption balanced regions, main grain consumption regions, and the western regions.DiscussionThe results underscore the importance of strengthening digital infrastructure in rural areas to improve grain production capacity. Tailored policy implications are suggested to enhance sustainable food production and contribute to global food security, particularly in regions with varying agricultural dynamics.

Reconstruction of Fine-Spatial-Resolution FY-3D-Based Vegetation Indices to Achieve Farmland-Scale Winter Wheat Yield Estimation via Fusion with Sentinel-2 Data

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data.

The impact of agricultural machinery services on farmland transfer: a analytical perspective based on the profitability of grain production

IntroductionIn the context of the deceleration of farmland scale management, a crucial question arises in agricultural development theory and practice: will service driven scale management in the production process become a viable alternative to farmland scale management, serving as the secondary pathway to achieve scale management in China?MethodsTo address this issue, an empirical analysis on the impact of agricultural machinery services on farmland transfer was conducted in this article using data from the China Rural Revitalization Survey database comprising 302 villages and 2,871 micro-farmers in 2020 by utilizing the ordinary least squares method and instrumental variable method.Results and discussionThe results show that the implementation of agricultural machinery services will increase the rate of farmland transfer in villages. The effect of agricultural machinery services on increasing the profitability of large-scale farmers surpasses its effect on smallholder farmers, thereby motivating smallholder farmers to transfer farmland to large-scale farmers, consequently fostering the consolidation of farmland. In addition, power-intensive service links are more effective in increasing the rate of farmland transfer and fostering the concentration of farmland transfer compared to control-intensive service links. The impact of agricultural machinery services on enhancing the rate of farmland transfer and fostering the consolidation of farmland transfer is more pronounced on the plain areas compared to the hillmountainous areas. Consequently, this article demonstrates that service-driven scale management and farmland scale management are not merely alternatives but rather complementary strategies that mutually reinforce each other.

Effect of farmland scale on agricultural green production technology adoption: Evidence from rice farmers in Jiangsu Province, China

Agricultural green production technology (AGPT) is crucial for mitigating farmland pollution and degradation, as well as promoting green agricultural development. Using data from 774 rice farmers in the 2021 China Land Economy Survey (CLES), the study analyzes the impact mechanisms and mediating pathways of farmland scale on AGPT adoption at both the farmer and plot levels. The study also reveals the differences in impact based on the heterogeneity of plot concentrations and technological attributes. The results show that: (1) Farmland scale positively impacts AGPT adoption, both at the farmer and plot level. (2) Farmland scale positively impacts AGPT adoption at two levels by affecting commercialization rate, time preference, and machinery investment. (3) The positive impact of farmland scale are heterogenous at two levels. When rice is scatteringly planted, plot scale has a more significant impact on AGPT adoption than operation scale; however, when rice is concentratedly planted, the situation is the opposite. Additionally, when faced with "capital-increasing, labor-saving, and risks-increasing" technology, operation scale has a more significant impact on AGPT adoption than plot scale; however, when faced with "capital-stabilizing, labor-increasing, and risks-controlling" technologies, the situation is the opposite. These findings help to provide policy implications for promoting AGPT adoption and sustainable farmland use.

Towards Sustainable Development: The Impact of Agricultural Productive Services on China’s Low-Carbon Agricultural Transformation

In the context of carbon neutrality, the low-carbon transition in agriculture is crucial to achieving carbon mitigation through clean production. The provision of agricultural productive services (APS) is pivotal for modernizing farming practices in China. However, the impact of this on the low-carbon transformation has received limited attention. This research examined the non-linear relationship between agricultural productive services and low-carbon development, including verifying a threshold effect with APS as the threshold variable, employing panel data for 31 provinces in China from 2010 to 2021. The results of the study suggested that the effect of services associated with agricultural productivity on the transition of the agricultural sector to low-carbon practices varied across threshold ranges. Specifically, when the APS exceeded the threshold of 2.4396, a significant suppressive effect was observed on carbon emission intensity. Further analysis revealed that APS indirectly influenced the farmland scale and agricultural technological advancements, thereby promoting the low-carbon transition of China’s agriculture. Based on these results, it is recommended to intensify the development of APS in key cereal-producing regions, while emphasizing the harmonious progress of these services in conjunction with large-scale farmland management.

Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images

Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images

Continuous crop rotation increases soil organic carbon stocks in river deltas: A 40-year field evidence

River deltas, as important food production centers, support 66 % of the world's population, together with other coastal areas. However, agriculture in river deltas is negatively affected by soil salinization and agricultural intensification. Improving the soil carbon pool is a mutually beneficial solution for maximizing crop production and improving climate resilience to secure food production. In this study, long-term croplands in the Yellow River Delta (YRD), with a wheat-maize (WM) rotation system and a single cotton (SC) cropping system, were selected to explore the changes in soil organic carbon (SOC) stocks and the driving mechanisms at 0–20 cm depth from 1980 to 2020. We found that, over the past 40 years, the SOC stocks in WM and SC croplands had increased by 10.05 Mg C ha−1 and 7.44 Mg C ha−1, respectively. The Random forest model revealed that in the WM croplands, soil N stock and available K were the most important driving factors of SOC stocks, while in SC croplands, soil type and salinity were the most important driving factors of SOC stock dynamics. An increase in soil salinity to 2.0 ‰ caused a 17.5 % loss in SOC stocks in SC croplands. Our results show that, in the long run, croplands with a WM rotation system have stronger carbon sequestration potential. Depending on the planting system, promoting crop carbon input under high soil nutrients and affecting SOC decomposition by soil salinity are two different pathways of SOC sequestration in delta croplands. We propose that nutrient management and organic fertilizer application are crucial for increasing SOC stocks in the WM and SC croplands, respectively. This study confirms that it is of practical significance to take measures to promote soil carbon sequestration at the farmland scale and to provide scientific guidance for the sustainable development of river delta agriculture.

How does agricultural production outsourcing services affect chemical fertilizer use under topographic constraints: a farm-level analysis of China.

Agricultural production outsourcing services (APOS) are an important means to promote green and low-carbon development of agriculture in China. Topography is the key factor limiting the development of APOS. There is little research on the influence relationship between APOS and farmers' chemical fertilizer use from the perspective of topography. Based on the theory of economies of scale, this study empirically evaluated the effect of APOS on chemical fertilizer use and their differences from the perspective of nutrient elements under different topographic conditions by using micro survey data for farmers in China and econometric methods. The results show that APOS can reduce farmers' chemical fertilizer use, and this effect is more obvious for farmers with large farmland sizes and high education levels. Farmland topography directly affects the fertilizer reduction effect of APOS in China. Compared with plain areas, hilly and mountainous areas weaken this effect. To enhance the fertilizer reduction effect of APOS, China should fully consider farmland topography when promoting APOS development. In plain areas, farmland scale management should be further encourage to better utilize APOS as a means of reducing chemical fertilizer use. In hilly and mountainous areas, high-standard farmland construction and small agricultural machinery use can alleviate the constraints of topographical on the scale expansion of APOS. It is necessary to fully consider the differences in farmers' resource endowments and their awareness of purchasing agricultural services in promoting APOS development, especially in regard to the farmers with small farmland sizes and low education levels.

Agricultural machinery service adoption and farmland transfer-in decision: evidence from rural China

With the deepening of China’s agricultural labor division, the rapid development of agricultural machinery service has had a profound impact on farmland scale management. Based on the new classical economic and transaction cost theories, this paper investigates the impact mechanism of agricultural machinery service on farmers’ farmland transfer-in decision from the dual perspectives of transaction cost and benefit of labor division, and uses the national rural survey data on the Chinese Family Database to conduct empirical research by using the ordinary least square (OLS) model and limited information maximum likelihood (LIML) method. The study finds that, first, agricultural machinery service expands farmers’ farmland transfer-in decision by encouraging them to obtain the economic efficiency of labor division. Not only agricultural machinery service can alleviate the constraint of farmers’ farmland transfer in labor and capital but also reduce the service transaction cost and increase farmers’ share of the division of labor economy, which further stimulates farmers to expand the scale of management. Second, with the deepening of labor division of agricultural machinery services, the higher transaction efficiency of agricultural machinery service promotes farmers’ farmland transfer-in participation. As long as the marginal net benefit obtained is greater than zero, farmers have incentives to expand the scale of farmland. Therefore, in order to promote China’s farmland scale management, the professional, whole-process, and large-scale development of agricultural machinery services should be further promoted, and the service capacity and fields should be expanded so that outsourcing services can be better provided to agricultural production demanders. At the same time, the technical standards, supervision system, and service platform of agricultural machinery should be established and improved so as to enhance the bargaining position of farmers in service outsourcing transactions, thus promoting the development of agricultural division of labor to a higher level and realizing the mutual promotion and mutual benefit of agricultural machinery service subjects and agricultural land scale management subjects.

How do outsourcing services affect agricultural eco-efficiency? Perspectives from farmland scale and technology substitution

In-depth research on whether the development of agricultural outsourcing services (AOS) can improve agricultural eco-efficiency (AEE) and the mechanisms underlying the impact of AOS on AEE is still lacking. This study uses micro survey data from Jiangxi Province in China in 2021 to explore the impact of AOS on AEE in terms of farmland scale management and agricultural technology substitution. The results indicate that AOS in general can effectively improve AEE, but this effect varies across the different stages of production. Plough services, fertilizer application services, and pest control services can significantly improve AEE, but seeding services and harvesting services have no significant effect. Farmland scale management and agricultural technology substitution are two effective channels by which AOS improves AEE. The government should improve policies to support AOS, strengthen the positive effects of AOS on AEE, and implement differentiated support policies for AOS related to different production activities.

The causal mechanism of farmers’ chemical fertilizer reduction: an empirical perspective from farmland transfer-in and digital extension

In the context of rural revitalization, farmland transfer for the purpose of scale operation has not significantly promoted green agricultural development, and it is urgent to find ways to further promote farmland transfer-in to achieve chemical fertilizer reduction. Using the data of 1,298 farmers surveyed in the 2020 China Rural Revitalization Survey, this paper incorporates digital extension into the analytical framework between farmland transfer-in and chemical fertilizer reduction, analyzing their relationship in-depth. The results showed that: (1) From the basic regression results, farmland transfer-in and digital extension significantly reduced farmers’ chemical fertilizer application. (2) From the influence mechanism, digital extension played a negative moderating role in the effect of farmland transfer-in on chemical fertilizer application, reinforcing the chemical fertilizer reduction effect of farmland transfer-in. (3) From the heterogeneity analysis, farmland transfer-in has no significant heterogeneity in food function areas, but there is heterogeneity in different terrain and fertilizer dosage. (4) In terms of the scale of farmland transfer-in, when the area of transferred farmland was less than 100 mu, farmland transfer-in significantly reduced chemical fertilizer application; otherwise, farmland transfer-in did not achieve chemical fertilizer reduction. The above findings provide a reference for promoting moderate-scale farmland transfer and synergistic construction of farmland scale operation and digital extension.

Influence of Climate Change on Carbon Emissions during Grain Production and Its Mechanism

Abnormal climatic changes and related disasters are increasing in prevalence, with many negative impacts on ecosystems and agricultural production. The area of land in China is vast, including diverse terrain and climate types, and a substantial area is used to grow food crops. Therefore, climate change is having a huge impact on China’s grain production. Currently, the relationship between climate change and carbon emissions during grain production and the underlying mechanism have not been fully clarified. Therefore, this study used an ordinary least squares regression (OLS) model and the system generalized method of moments (SYS-GMM) to examine the influence of climatic change and carbon emissions during grain production, and we constructed mediation effect models to explore the mechanism of influence between them by utilizing panel data in China from 2000 to 2020. In addition, we also examined the adjustment effect of green technology progress and farmland scale. The study found that China’s carbon emissions during grain production increased from 2000 to 2015 and then presented a decreasing trend after 2015. We found that the annual average temperature has a prominent positive effect on carbon emissions during grain production, while the annual average rainfall has a negative effect. Among them, temperature changes mainly lead to the increase in carbon emissions during grain production through the increase in “fertilizer use” and “multiple cropping index”, but the mechanism of rainfall changes’ impact on carbon emissions during grain production is still unclear. In addition, green technology progress and farmland scale play adjustment roles in the impact of climate change on carbon emissions during grain production, and they could significantly suppress carbon emissions. On the basis of the conclusions in this paper, we propose that strengthening climate change adaptation is an important prerequisite for reducing carbon emissions during grain production. Furthermore, China should continue to reduce fertilizer use, facilitate the application of agriculture green technology, and expand the scale of farmland to achieve agricultural carbon emission reduction.

The effect of agricultural subsidies on chemical fertilizer use: Evidence from a new policy in China

This study uses a nationally representative rural household survey from China, the difference-in-difference, three-step approach, and Seemingly Unrelated Regression methods to assess the impacts of China's new agricultural subsidy on chemical fertilizer use, heterogeneity effect, and mechanism. The results show that, first, the new agriculture subsidy reduces the use of chemical fertilizer by about 7.2 percent. A series of robustness tests confirm the finding. Second, the heterogeneity analysis shows that the subsidy's negative impact on fertilizer use is substantially greater among younger farmers than among older farmers. The negative effect also is significantly more in the main grain-producing areas than in non-grain-producing regions in China. Third, the mediating effect analysis shows that farmland scale mediates 8.3 percent of fertilizer use, and the adoption of agricultural machinery mediates 48.6 percent of fertilizer use. Our findings underscore the positive role that reforming the agrarian subsidy policy plays in sustainable development.

Study on Rapid Inversion of Soil Water Content from Ground-Penetrating Radar Data Based on Deep Learning

Ground-penetrating radar (GPR) is an efficient and nondestructive geophysical method with great potential for detecting soil water content at the farmland scale. However, a key challenge in soil detection is obtaining soil water content rapidly and in real-time. In recent years, deep learning methods have become more widespread in the earth sciences, making it possible to use them for soil water content inversion from GPR data. In this paper, we propose a neural network framework GPRSW based on deep learning of GPR data. GPRSW is an end-to-end network that directly inverts volumetric soil water content (VSWC) through single-channel GPR data. Synthetic experiments show that GPRSW accurately identifies different VSWC boundaries in the model in time depth. The predicted VSWC and model fit well within 40 ns, with a maximum error after 40 ns of less than 0.10 cm3 × cm−3. To validate our method, we conducted GPR measurements at the experimental field of the Academy of Agricultural Sciences in Gongzhuling City, Jilin Province and applied GPRSW to VSWC measurements. The results show that predicted values of GPRSW match with field soil samples and are consistent with the overall trend of the TDR soil probe samples, with a maximum difference not exceeding 0.03 cm3 × cm−3. Therefore, our study shows that GPRSW has the potential to be applied to obtain soil water content from GPR data on farmland.

Farmland Hydrology Cycle and Agronomic Measures in Agroforestry for the Efficient Utilization of Water Resources under Karst Desertification Environments

Severe soil-water loss and unfertile soil frequently occur under karst desertification environments. The surface-underground dual structure in these areas allows the surface water to leak into the subsurface through cracks and sinkholes, as well as other conduits, causing a special “karst drought”. Hence, water-resource shortage has become a challenge for local agricultural development. To realize efficient utilization of water resources, an urgent need is to clearly understand and study the law of farmland hydrological cycles under agroforestry practices, which is still understudied. Here, we focused on the hydrological cycle at the farmland scale and water-saving measures under agroforestry in three study areas representing different degrees of karst desertification. First, a significant positive correlation was found between total and available precipitations as well as land evapotranspiration (LET). Second, under agronomic measures, the soil water content in the three areas was all higher than that of the control group while soil evaporation was all lower. This indicates that agronomic measures can contribute to the efficient use of water resources by halting soil evaporation and increasing soil water content. Third, dwarf dense planting and pruning technologies were helpful in inhibiting crop transpiration and reducing vegetation interception. Fourth, in the farmland hydrological cycle of agroforestry, 77.45% of precipitation transformed into soil water storage, 24.81% into soil evaporation, 20.73% into plant transpiration, 17.40% into groundwater, and 5.18% into vegetation interception. However, their sum was greater than 100%, suggesting that the farmland-scale water cycle is an open system. The implication is that different agronomic practices under agroforestry bring certain water-saving benefits by constraining the conversion of ineffective water and promoting the storage of effective water, thus opening up promising opportunities for efficiently utilizing water resources in karst desertification areas. The finding is also significant to the control of karst desertification, soil and water conservation, and karst drought alleviation.

Socialized Farmland Operation—An Institutional Interpretation of Farmland Scale Management

Farmland scale management is an important approach for developing countries to ensure food security in the face of the COVID-19 pandemic. At present, the realization of farmland scale management through the path of farmland use rights trading encounters obstacles in practice; moreover, the new model of farmland scale management has rarely been systematically discussed. Considering the farmland trusteeship practice implemented in Shandong Province of China as the research case, this study discusses the essence and realization premise of the new farmland scale management model represented by farmland trusteeship based on case analysis. The conclusions are as follows. (1) The high cost generated from farmland scale management is the main obstacle to realize this model. (2) The process of realizing farmland scale management through farmland trusteeship is actually the process of meeting the requirements of the socialization of farmland use, the socialization of the farmland management process, and the socialization of farmland output. Thus, in the context of the existence of a large number of small and scattered farmers in China, the socialized farmland operation is the essence of farmland scale management. (3) Effective collective action is the premise of realizing socialized farmland operation. Undeniably, a lot more systematic explorations are further demanded to strengthen the irrigation management and infrastructures, promote and ensure stable village leadership, and comprehensively improve the ability of rural collective action to ensure the further strengthening of socialized farmland operation so as to realize stable farmland scale management, which will be pursued in the future.

How Can Digital Financial Inclusion Promote High-Quality Agricultural Development? The Multiple-Mediation Model Research.

In order to balance the relationship between economics, society and environment, the Chinese government has stated that China's economy should shift from high-speed growth to high-quality development. Since agriculture is the foundation of the national economy, high-quality development of agriculture plays significant roles in the achievement of food security, social stability and environmental sustainability in China. In practice, the expansion of digital financial inclusion (DFI) seems to provide valuable opportunities for the development of high-quality agriculture. Nevertheless, in theory, the extant literature ignores exploration of the close relationships between DFI and high-quality agricultural development (HQAD). Hence, using Chinese provincial panel data from 2011 to 2020 and structural equation model (SEM) in STATA 16.0, this paper attempts to investigate whether and how DFI can enhance HQAD. Analysis reveals that (1) DFI can directly promote HQAD; (2) DFI can indirectly facilitate HQAD through the mediator of farmland transfer (FLT); (3) DFI can indirectly promote HQAD through the mediator of farmland mechanization level (FML); (4) compared with the benefits brought by "high-mechanization", the benefits brought by "large-scale" farmland transfer policies are much greater. To our knowledge, our research is one of the first to investigate the direct and indirect effecting mechanisms of DFI's influence on HQAD from the perspectives of farmland scale and farmland technology.

Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms

Timely and accurate estimation of soil moisture content (SMC) is essential for precise irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing with a high spatiotemporal resolution has become a promising method for SMC monitoring. Many existing SMC models have only been tested at a specific crop growth stage using a single type of sensor, and the effects of growth stage and irrigation variation on SMC estimation accuracy remain unclear. To address these limitations, this study used UAV-based multimodal data to quantify SMC in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR). The results demonstrated that multimodal data fusion improves the SMC estimation accuracy regardless of the MLA, especially the joint use of thermal and multispectral data. Among three SMC regression models, the RFR model produced the most accurate SMC estimation for the two growing seasons regardless of sensor combinations. The RFR model using all three types of data generated the most accurate and robust SMC estimation at the vegetative stage with R2 of 0.68 and 0.78, and rRMSE of 20.82% and 19.36% for 10- and 20-cm soil depths, respectively; it also produced the best SMC estimation accuracy under well-watered and mild to modest deficit irrigation treatments for both soil depths. The study shows that the high spatial–temporal maps of SMC using UAV-based multimodal data has promising potential for supporting decision-making in irrigation scheduling at the farmland scale.