Search Results (382)

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization. Full article

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article

During the critical period of agricultural green transformation, clarifying the evolutionary logic of farmers’ green production behavior under a multi-stakeholder framework provides significant insights for implementing “Dual Carbon” goals, establishing long-term mechanisms for high-quality agricultural development, and resolving deep-seated contradictions in agricultural non-point source pollution. Based on the social co-governance and public participation framework, this paper constructs a tripartite evolutionary game model involving government departments, farmer groups, and the general public, grounded in cost–benefit analysis, social governance friction, and evolutionary game theory. Through simulation, the study explores the equilibrium states and the specific impacts of varying parameter values on stable points. The findings reveal that: (1) The “interest price scissors” (benefit disparity) between green and conventional production is the key determinant of farmers’ strategic equilibrium. Once this structural contradiction is resolved, green production becomes the optimal strategy. (2) Farmers are highly sensitive to marginal cost–benefit fluctuations, leading to a sequential behavioral cascade: farmers retreat first, followed by the government, and finally the public. (3) Public participation cost is the pivotal variable for activating the co-governance mechanism, and the application of digital governance tools determines the time required to reach equilibrium. (4) A “Success Paradox” exists in government regulation; incentive mechanisms must be adjusted promptly after initial success. (5) Integrated policy combinations outperform single instruments; breaking the “locked-in” state requires a policy shock of sufficient intensity. This research offers a theoretical basis and policy enlightenment for optimizing the social co-governance landscape and promoting sustainable agricultural modernization. Full article

Decades of traditional fertilizer subsidies have yielded modest maize productivity gains for Malawian farmers, mainly due to the twin challenges of soil degradation and intermittent weather patterns. Increasing nitrogen intake through subsidies without addressing these structural constraints has failed to close the country’s yield gap. Although climate-smart agriculture (CSA) technologies offer options for sustainable productivity growth, low and inconsistent adoption among farmers has led to insufficient evidence. Most existing studies that have examined the complementarity between CSA and inorganic fertilizers rely on experimental plot data, with limited evidence from actual farmer-managed fields. We use farm-level data collected in 2022 from 307 smallholder farmers across central and southern Malawi to investigate whether integrating CSA technologies with subsidized inorganic fertilizers enhances maize productivity. We apply the Inverse Probability Weighted Regression Adjustment (IPWRA) model to estimate the effects of CSA adoption and its integration with subsidized fertilizer. Results indicate that CSA adoption increased maize yields by 30%, confirming significant productivity gains from technologies such as mulching, agroforestry, and organic manure. However, integrating these technologies with subsidized fertilizers produced no additional yield advantage, suggesting that farmers often substitute CSA with inorganic inputs rather than combining them effectively. These findings imply that the potential synergies between CSA and subsidy programs remain unrealized under current practices. Policy reforms under Malawi’s current farm input subsidy program (FISP) should therefore emphasize extension and incentive mechanisms that promote complementary—not substitutive—use of CSA technologies and fertilizers at recommended application rates. Full article

To address the issues of high working power consumption and poor structural stability of current ploughing equipment under conditions of straw coverage and heavy clay soil, a 1LFT-450D variable width reversible plough (VWRP) with resistance reduction function is designed. Based on the shark shield scale, a bionic resistance reduction plough body was designed. Through theoretical analysis, the turnover mechanism (TM) and the working width adjustment mechanism (WWAM) were designed, and their main structural parameters were determined. Further research was conducted on key components using simulation software. The discrete element method (DEM) simulation results indicated that arranging bionic ribs on the plough breast achieved the best resistance reduction effect compared with the ploughshare tip and ploughshare. Meanwhile, relative to the conventional plough body, the designed bionic plough body exhibited average reductions in resistance and energy consumption of 12.55% and 12.34%, respectively. The soil bin test further verified the resistance reduction performance of the designed bionic plough body. The kinematic performance of the TM and the WWAM was analyzed using RecurDyn, and their reliability and stability were verified through the mechanism performance test. The results of the field operation performance test showed that under the conditions of forward speed of 8–10 km·h⁻¹ and working width of 1320–2000 mm, the operation performance of the designed VWRP satisfied the requirements of relevant standards. This study can provide a theoretical reference for the resistance reduction optimization of agricultural machinery soil-engaging parts and the design of new ploughs. Full article

This paper examines how rural development support was associated with changes in Lithuania’s agri-food sector between 2000 and 2025 across successive Common Agricultural Policy (CAP) programming periods. Integrating complementary theoretical perspectives, the study assesses whether policy interventions were linked to structural transformation, market upgrading, partial innovation deepening, and sustainability-oriented change, or whether they primarily reinforced existing agri-food development paths. Methodologically, the research employs a quantitative, longitudinal, descriptive–analytical design, combining time-series analysis with comparative policy-cycle analysis. By tracing both incremental adjustments and more pronounced structural shifts over the 2000–2025 period, the paper provides an evidence-based assessment of how rural development support aligned with sectoral change. The findings suggest that the observed trajectory is most consistent with modernization, consolidation, and market upgrading, while innovation-led transformation appears more uneven and concentrated in downstream processing than in primary agriculture. The results contribute to debates on the calibration of rural development instruments and offer implications for future policy design in small open economies undergoing agri-food restructuring. Full article

Agricultural economic resilience plays a pivotal role in the integrated development of agriculture and rural areas, and carries great significance for ensuring national food security and advancing sustainable agricultural development in the context of complex risks and challenges. Using panel data covering 41 cities in the Yangtze River Delta region from 2011 to 2023, this paper empirically investigates the impact mechanism of fiscal support for agriculture on agricultural economic resilience. The results demonstrate that fiscal support for agriculture in the Yangtze River Delta exerts a significant positive effect on agricultural economic resilience, especially with a pronounced promoting influence on resistance capacity. Mechanism analysis indicates that fiscal support for agriculture indirectly affects agricultural economic resilience through channels including agricultural industrial agglomeration and the urban–rural income gap. Accordingly, to strengthen agricultural economic resilience, it is necessary to optimize the allocation and expenditure structure of fiscal funds, adopt differentiated strategies with dynamic and timely adjustments, allocate funds to boost agricultural industrial agglomeration, enhance investment in human capital to narrow the urban–rural income gap, and facilitate sustainable agricultural development. Full article

Carbon emissions from crop farming are a critical component of carbon emissions from land use. This study focuses on crop farming in the Yangtze River Economic Belt. The carbon emission coefficient method, the LMDI model, the Tapio decoupling model, and the GM(1,1) gray forecasting model were employed to systematically analyze the spatiotemporal evolution, driving mechanisms, decoupling effects, and future trends of carbon emissions from crop farming in the Yangtze River Economic Belt, based on panel data from 11 provinces (municipalities) covering the period 2013–2024. The results show that the total carbon emissions from crop farming in the Yangtze River Economic Belt exhibit an inverted “U”-shaped pattern, rising initially and then declining, while carbon emission intensity continues to decrease. In terms of emission sources, methane emissions from paddy fields account for the highest proportion, emissions from agricultural inputs show a steady decline, and emissions from soil use continue to rise. Regarding driving factors, crop farming efficiency is the most significant negative driver, while regional economic development serves as the primary positive driver; the decoupling pattern has gradually transitioned from “weak decoupling” to a predominantly “strong decoupling” pattern; projection results indicate that both carbon emissions and emission intensity from crop farming in the Yangtze River Economic Belt will generally decline in the future, though regional pressure for emission reductions remains significant; agricultural industrial structures should be optimized and adjusted, with efforts focused on promoting the standardized and scaled development of organic and ecological agriculture to facilitate the green and low-carbon transformation of agriculture. Full article

Sweet potato (Ipomoea batatas (L.) Lam) is an important food and energy crop. Soil salinization is a major abiotic stress that limits agricultural productivity and severely reduces yield of crops. Protein hydrolysates, as a class of natural biostimulants, have gained increasing attention for their potential to improve crop yield, quality and stress tolerance. This study investigated the effects of peptides from swine blood (PSB) on high salinity stress tolerance in sweet potato. Application of PSB promoted the growth of both aerial and underground parts of sweet potato under normal and high-salinity conditions. Further analysis revealed that, under high salinity stress, exogenous PSB up-regulated the expression of genes associated with stress responses, increased the accumulation of organic osmotic adjustment compounds such as free amino acids, promoted K⁺ uptake to elevate the K⁺/Na⁺ ratio, and enhanced the activity of key antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) involved in the reactive oxygen species-scavenging system. These biochemical responses contributed to maintaining cellular osmotic balance and redox homeostasis, protecting the cell membrane from damage while preserving its structural integrity and normal physiological functions, and improving photosynthetic efficiency, thereby enhancing high salinity stress tolerance in sweet potato. Thus, PSB holds significant potential as an effective natural biostimulant for sweet potato cultivation in saline soils. Full article

Regional trade liberalization via preferential agreements increasingly shapes economic outcomes in small open economies embedded in overlapping regional frameworks. This study evaluates the short-run economy-wide effects of tariff and non-tariff measure (NTM) reforms under the South Asian Free Trade Area (SAFTA) and the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) using a Computable General Equilibrium (CGE) model calibrated to the GTAP 10 database. Gravity-based estimates of ad valorem equivalents (AVEs) of NTMs are integrated into the CGE framework, enabling explicit modeling of regulatory barriers alongside tariff reductions. Policy simulations examine scenarios involving a 90 percent tariff cut and a 50 percent NTM reduction, applied individually and jointly, under a short-run closure with fixed factor endowments and a trade balance for Nepal. Results indicate that combined liberalization yields positive macroeconomic adjustments, with real GDP rising by about one percent and exports increasing by over 14 percent, driven primarily by the manufacturing sector, particularly textiles, while agricultural responses vary by exposure to NTMs. These findings provide policy-relevant evidence on the relative effectiveness of tariff and regulatory reforms, informing strategies for deeper regional integration and enhanced competitiveness in small, structurally constrained economies. Full article

Reducing agricultural emissions is vital for climate mitigation, yet evidence on green finance’s potential to facilitate agricultural decarbonization—particularly in China—remains scarce. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this study employs a staggered difference-in-differences design and complementary Callaway-Sant’Anna estimates. Using a balanced panel of 282 prefecture-level and above cities spanning 2012–2022—a window covering five pre-policy years before the initial 2017 pilot rollout and sufficient post-policy years to capture dynamic effects for the 2017, 2019, and 2022 cohorts—this study assesses the policy impact on agricultural carbon emission intensity. The findings reveal that the pilot policy reduces emission intensity by approximately 9.2% on average. This result is robust across event-study analyses, placebo tests, PSM-DID, policy interference checks, and alternative outcome specifications. Channel-consistent evidence suggests that the effect operates through three mechanisms: greener credit allocation, stronger green technological innovation, and lower-carbon adjustment of the agricultural production structure. The effect is larger in eastern China, major grain-producing regions, and cities with higher levels of financial development, and exhibits a strengthening trend over time. By analyzing China’s city-based pilot approach, this study demonstrates how financial policy can support agricultural decarbonization in settings characterized by dispersed emitters, imperfect environmental monitoring, and strong food-security constraints. The findings extend beyond China to inform other developing economies seeking non-price-based pathways to greener agriculture. Full article

This study investigates the determinants of capital structure in Moroccan agricultural SMEs, with particular emphasis on the distinction between interest-bearing debt and non-interest-bearing liabilities in a context characterized by persistent credit constraints. While traditional capital structure theories typically treat debt as a homogeneous aggregate, such an approach may obscure important financing dynamics in financially constrained environments. Using a panel dataset of 52 agricultural SMEs observed over the period 2017–2022, the analysis employs a correlated random effects model to control for unobserved heterogeneity. The results indicate a negative relationship between profitability and both total and short-term debt, consistent with the predictions of the Pecking Order Theory. Liquidity, asset tangibility, and firm size are negatively associated with non-interest-bearing current liabilities, suggesting that trade-based financing may serve as an adjustment mechanism when access to formal credit is limited. In contrast, long-term debt is only weakly explained by firm-level characteristics, pointing to potential supply-side constraints in agricultural credit markets. Overall, the findings suggest that financing patterns in agricultural SMEs appear to be more closely associated with credit market imperfections than with optimal trade-off considerations. By distinguishing between different debt components, this study contributes to the literature by highlighting the importance of debt composition when analyzing capital structure in emerging and financially constrained economies. Full article

Regional differences in land use transitions and urban expansion patterns have become increasingly pronounced under rapid urbanization. However, conventional land use and land cover change (LUCC) analyses often rely on independent graphical presentations, limiting systematic cross-regional comparison and the identification of spatial heterogeneity. To address this limitation, this study constructs a comparative land use transition analytical framework integrating LUCC contrastive transition patterns, the landscape expansion index (LEI), and the PLUS model. The framework enables structured identification of transition directions, intensity differentials, and stage-specific characteristics, thereby enhancing the reproducibility and comparability of cross-regional land use analysis. Using Xi’an (inland) and Quanzhou (coastal) as representative cases, this study analyzed their land use changes from 1990 to 2020 based on Intensity Analysis and LUCC contrastive transition patterns and quantified the differences in urban expansion using the urban expansion intensity index and expansion pattern metrics. The results show that the urban expansion of Xi’an and Quanzhou was active during 1990–2020, with crops as the main stable source of urban expansion. This urban expansion mainly took the form of edge-expansion and infilling, with urban development transitioning from disorderly expansion to intensive utilization. Notable regional disparities were observed: Forest conversion to urban land was substantially higher in Quanzhou, reflecting stronger ecological land pressure in coastal areas, whereas grass conversion to crops was more prominent in Xi’an, suggesting agricultural spatial adjustment under food security constraints in inland regions. The PLUS model further demonstrates that urban expansion is jointly influenced by topographic conditions (DEM) and economic growth (GDP), highlighting the coupled effects of natural constraints and development dynamics. This study clarifies the differentiation characteristics and driving forces of coastal and inland urban expansion, providing a scientific basis for differentiated territorial spatial planning, ecological protection, and farmland management in eastern and western regions. It also helps formulate more targeted urban development policies based on regional resource endowments, promoting regional coordination and sustainable urbanization. Full article