Search Results (529)

The growth of rural residents’ disposable income is essential for narrowing the income gap between urban and rural areas and promoting integrated development. This study explores how China’s National Main Functional Zoning Plan influences rural household income through its regulatory impact on construction land expansion. Using data from county−level administrative units across China, the research identified the construction land regulation index as a key mediating variable linking zoning policy to changes in household income. By shifting the analytical perspective from a traditional urban–rural classification to a framework aligned with the National Main Functional Zoning Plan, the study reveals how spatial planning tools, particularly differentiated land quota allocations, influence household income. The empirical results confirm a structured causal chain in which zoning policy affects land development intensity, which in turn drives rural income growth. This relationship varies across different functional zones. In key development zones, strict land control limits income potential by constraining land supply. In main agricultural production zones, moderate regulatory control enhances land use efficiency and contributes to higher income levels. In key ecological function zones, ecological constraints require diverse approaches to value realization. The investigation contributes both theoretical and practical insights by elucidating the microeconomic effects of national spatial planning policies and offering actionable guidance for optimizing land use regulation to support income growth tailored to regional functions. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

Under China’s “Rural Revitalisation” strategy, contract farming faces challenges including farmers’ limited access to advanced technologies and high operational risks for agricultural support enterprises. The collaborative involvement of scientific research institutions offers potential solutions but remains underexplored. This study employs Stackelberg game theory to model a contract farming supply chain under two agricultural assistance modes: enterprise-led (EL) and collaborative assistance with scientific research institutions (CI). We further propose two government subsidy mechanisms: subsidies to enterprises and subsidies to scientific research institutions. The models analyze optimal decisions, supply chain performance, and subsidy efficiency, validated through numerical experiments. Key findings reveal the following: (1) The CI mode enhances agricultural output and farmer revenue but may reduce enterprise profits, deterring collaboration. (2) Government subsidies incentivize enterprise–institution collaboration. Subsidizing scientific research institutions typically improves agricultural productivity and economic benefits more effectively than subsidizing enterprises. (3) Synergistic effects exist among the government subsidy coefficient, cost coefficient of technical assistance, consumer preferences for agricultural quality, and profit-sharing ratio. The latter three parameters significantly influence subsidy model selection. This research provides policy insights for enhancing agricultural assistance efficiency and sustainable contract farming development. Full article

The production of winter wheat, spring barley, spring oilseed rape, and field beans requires detailed experimental data studies to analyze the quality and productivity of spring barley grain under different cultivation and tillage conditions. As the world’s population grows, more food is required to maintain a stable food supply chain. For many years, intensive farming systems have been used to meet this need. Today, intensive climate change events and other global environmental challenges are driving a shift towards sustainable use of natural resources and simplified cultivation methods that produce high-quality and productive food. It is important to study different tillage systems in order to understand how these methods can affect the chemical composition and nutritional value of the grain. Both agronomic and economic aspects contribute to the complexity of this field and their analysis will undoubtedly contribute to the development of more efficient agricultural practice models and the promotion of more conscious consumption. An appropriate tillage system should be oriented towards local climatic characteristics and people’s needs. The impact of reduced tillage on these indicators in spring barley production is still insufficiently investigated and requires further analysis at a global level. This study was carried out at Vytautas Magnus University Agriculture Academy (Lithuania) in 2022–2024. Treatments were arranged using a split-plot design. Based on a long-term tillage experiment, five tillage systems were tested: deep and shallow plowing, deep cultivation–chiseling, shallow cultivation–disking, and no-tillage. The results show that in 2022–2024, the hectoliter weight and moisture content of spring barley grains increased, but protein content and germination decreased in shallowly plowed fields. In deep cultivation–chiseling fields, the protein content (0.1–1.1%) of spring barley grains decreased, and in shallow cultivation–disking fields, the moisture content (0.2–0.3%) decreased. In all fields, the simplified tillage systems applied reduced spring barley germination (0.4–16.7%). Tillage systems and meteorological conditions are the two main forces shaping the quality indicators of spring barley grains. Properly selected tillage systems and favorable climatic conditions undoubtedly contribute to better grain properties and higher yields, while reducing the risk of disease spread. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

The effects of changes in China’s final demand structure on its forestry sector and associated supply chains have not been thoroughly examined. This study aims to provide a detailed analysis of the quantitative relationships and underlying mechanisms between these interactive changes. Using China’s 153-sector input–output tables from the National Bureau of Statistics and applying a Leontief-based input–output model, we conducted scenario simulations through three distinct schemes, generating both quantitative and qualitative results. Our findings indicate that (1) For China’s forestry sector and its entire value chain to thrive, policymakers should boost consumer demand. This can better stimulate the development of forestry and the “agriculture-forestry-animal husbandry-fishery services” sector and related service industries; (2) Increased investment demand effectively stimulates the development of tertiary industries and secondary industries within the forestry supply chain and boosts the demand and production of intermediate products; (3) Changes in net exports have a significant impact on forestry and the forestry industry chain. To reduce dependence on foreign timber resources, China should strategically expand commercial plantation development; (4) Regarding intermediate product production, investment has a more pronounced effect on increasing total volume compared to consumption. Additionally, the Sino–US tariff disputes negatively impact the forestry industries of both countries. China needs to accelerate import substitution strategies for timber products, adjust international trade markets, and expand domestic consumption and investment to ensure the healthy and stable development of its forestry sector. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

The development of technology has influenced agricultural production and the establishment of the Agriculture 4.0 system in practice. This research is focused on the selection of equipment and machinery suppliers for the needs of the MAMEX Company. When selecting suppliers, an approach based on the application of an intuitionistic fuzzy set for decision-making was used. This approach allows the uncertainty present in decision-making to be incorporated, considered, and, hopefully, reduced in order to make a final decision on which of the observed suppliers is the most suitable for this company. Ten criteria were used that enable the application of sustainability in the supply chain. Eight local suppliers of equipment and machinery were observed with these criteria. The results obtained by applying the SWARA (Step-wise Weight Assessment Ratio Analysis) method showed that the most important criterion for selecting suppliers is the reliability and quality of equipment and machinery, while the results of the CORASO (COmpromise Ranking from Alternative Solutions) method showed that the SUP2 supplier is the best choice for establishing partnership relations with the MAMEX company. This supplier should help the MAMEX company improve its business and achieve better results in the market. The contribution of this research is to improve the application of intuitionistic fuzzy sets in decision-making, and to emphasize the importance of equipment and machinery in agricultural production in the Agriculture 4.0 system. Full article

With the growth of international trade, concerns over the safety of imported agricultural products in South Korea have intensified due to factors such as the COVID-19 pandemic, radiation contamination risks, and the prevalence of GMOs. In response, this study develops two composite indices—the Agricultural Product Safety Index (APSI) and the Imported Food Safety Index (IFSI)—to quantitatively assess food safety risks across major exporting countries and apply them to Korea’s import structure. The indices integrate production and distribution risk indicators based on publicly available data and adhere to five key principles, including applicability, reliability, boundedness, independence, and representativeness. Empirical results from 2014 to 2021 indicate that Australia consistently demonstrates the highest food safety level, followed by the United States, Argentina, Ukraine, and Brazil. While the indices provide a structured and transparent framework for monitoring import-related safety, their scope is limited to selected countries and excludes biological hazards due to data limitations. Future research should expand the geographical coverage and incorporate empirical validation techniques. These findings contribute to the development of evidence-based policy instruments aimed at enhancing food safety governance in global supply chains. Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

The efficacy of government subsidy programs in agriculture is frequently compromised by internal policy conflicts that arise between competing government departments. This challenge is addressed herein, with a focus on the policy environment in China, through the development of a game-theoretic model of an agricultural supply chain. This model explicitly incorporates two competing government bodies—the Agriculture and Rural Affairs Department (ARAD) and the Development and Reform Commission (DRC)—each with distinct objectives and performance indicators. Within this framework, the strategic interactions of four subsidy types are analyzed: production and cold-chain subsidies (ARAD), and platform operation and blockchain subsidies (DRC). The findings reveal that department-specific performance indicators can significantly distort the overall effectiveness of subsidies. While individual subsidies may achieve their intended departmental goals, their combined impact is shown to be complex and frequently suboptimal in the absence of higher-level coordination. Notably, a subsidy portfolio combining production and platform operation subsidies is found to consistently yield superior performance in maximizing social welfare. Ultimately, this research contributes a new framework for understanding subsidy policies and provides actionable insights for optimizing interdepartmental coordination to enhance supply chain performance. Full article

China’s National Famous, Special, Excellent, and New Agricultural Products are key rural economic assets, yet their spatial patterns and sustainability drivers remain underexplored. Based on the geospatial data of 1932 National Famous, Special, Excellent and New Agricultural Products in China, this study systematically analyzes their spatial distribution pattern by using GIS spatial analysis techniques, including the standard deviation ellipse, kernel density estimation, geographic concentration index and Lorenz curve, and quantitatively explores the driving factors of sustainable development by using geographic detectors. The research results of this paper are as follows. (1) The spatial distribution shows a significant non-equilibrium characteristic of “high-density concentration in the central and eastern part of the country and low-density sparseness in the western part of the country” and the geographic concentration index (G = 22.95) and the standard deviation ellipse indicate that the center of gravity of the distribution is located in the North China Plain (115° E–35° N), and the main direction extends along the longitude of 110° E–120° E. (2) Driving factor analysis showed that railroad mileage (X10) (q = 0.5028, p = 0.0025 < 0.01), highway mileage (X11) (q = 0.4633, p = 0.0158 < 0.05), and population size (X3) (q = 0.4469, p = 0.0202 < 0.05) are the core drivers. (3) Three-dimensional kernel density mapping reveals that the eastern coast and central plains (kernel density > 0.08) form high-density clusters due to the advantages of the transportation network and market, while the western part shows a gradient decline due to the limitation of topography and transportation conditions. The study suggests that the sustainable development of National Famous, Special, Excellent, and New Agricultural Products should be promoted by strengthening transportation and digital logistics systems, enhancing cold-chain distribution for perishable goods, tailoring regional branding strategies, and improving synergy among local governments, thereby providing actionable guidance for policymakers and producers to increase market competitiveness and income stability. The study provides a quantitative, policy-oriented assessment of China’s branded agricultural resource allocation and its sustainability drivers, offering specific recommendations to guide infrastructure investment, e-commerce logistics enhancement, and targeted subsidy design for balanced regional development. The study highlights three key contributions: (1) an innovative integration of geospatial analytics and geographical detectors to reveal spatial patterns; (2) clear empirical evidence for policymakers to prioritize transport and digital logistics investments; and (3) practical guidance for producers and brand managers to enhance product market reach, optimize supply chains, and strengthen regional competitiveness in line with sustainable development goals. Full article

Agroecology has traditionally been implemented using conventional methods. However, the integration of precision equipment, advanced methodologies, and digital technologies (DT) is now essential for transitioning to a more modern and efficient approach. While agroecological principles remain fundamental for planning and managing sustainable food systems by optimizing natural resources, technological tools can significantly support their implementation and adoption by farmers. This transition, however, must also consider socioeconomic factors and policy frameworks to ensure that technological advancements lead to meaningful improvements in farms and agroecosystems. Across both industrialized and emerging economies, various initiatives, such as precision agriculture, digital platforms, and e-commerce, are driving the digitalization of agroecology. These innovations offer clear benefits, including enhanced knowledge generation and direct improvements to the food supply chain; however, several barriers remain, including limited understanding of digital tools, high-energy demands, insufficient financial resources, economical constrains, weak policy support, lack of infrastructure, low digital learning by framers, etc. to facilitate the transition. This review looks for the understanding of how digitalization can align or conflict with local agroecological dynamics across distinct political frameworks and reality contexts because the information about DT adoption in agroecological practices is limited and it remains unclear if digital agriculture for scaling agroecology can considerably change power dynamics within the productive systems in regions of Europe and Latin America. In South America, among countries like Ecuador, with strong potential for agroecological development, where 60% of farms are less than 1 ha, and where farmers have expressed interest in agroecological practices, 80% have reported lacking sufficient information to make the transition to digitalization, making slow the adoption progress of these DT. While agroecology is gaining global recognition, its modernization through DT requires further research in technical, social, economic, cultural, and political dimensions to more guide the adoption of DT in agroecology with more certainty. Full article