Search Results (1,519)

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

The growing demand for renewable energy, together with the need to mitigate climate change and promote more sustainable agriculture systems, has stimulated interest in energy crops. In this context, invasive alien plant species (IAPS), which have progressively colonized abandoned farmland, degraded ecosystems, and marginal areas, represent a key bioresource. IAPS have a dual nature combining high ecological invasiveness and fast growing rate with notable energetic potential. These aspects have generated a still ongoing debate among farm managers, ecologists, and policymakers regarding their role within the future bioeconomy. The present study provides a review of the IAPS black locust (Robinia pseudoacacia L.) on its real benefits as a source of bioenergy, ecological impact, and the management strategies adopted. We examine the trade-offs between containment efforts and use for renewable bioenergy production, particularly in marginal areas where few alternatives exist. This review highlights the need for stratified site-specific approaches that balance biodiversity conservation with bioresource exploitation. Finally, this study also contributes to the ongoing discussion on whether IAPS should be regarded primarily as a management challenge or a multifunctional bioresource, as in the production of bioenergy. Full article

This study investigates the macroeconomic, institutional, and energy-related determinants of agricultural value added in Middle East and North Africa (MENA) countries over the period 2000–2023, with particular emphasis on whether international clean energy finance operates as a conditionally effective driver depending on energy endowments. Using a panel fixed-effects framework with Driscoll–Kraay standard errors to address cross-sectional dependence, heteroskedasticity, and serial correlation, the analysis incorporates an interaction term between clean energy finance and an oil-exporting dummy to capture structural heterogeneity. Robustness is ensured through Panel-Corrected Standard Errors (PCSEs), Granger causality tests, and System GMM estimation. The findings reveal that GDP per capita and clean energy finance are positively and significantly associated with agricultural value added, while trade openness negatively affects the sector. Importantly, the interaction results indicate strong asymmetry: the positive contribution of clean energy finance is concentrated in non-oil economies but becomes weak or insignificant in oil-exporting countries, consistent with diminishing marginal returns in energy-abundant contexts. Inflation captures nominal price effects, while short-run dynamics suggest the presence of adjustment costs. Overall, the study highlights that clean energy finance acts as a structurally conditional mechanism, offering nuanced and policy-relevant insights for sustainable agricultural transformation in MENA economies. Full article

Invasive species, whose geographic distribution is expanding, seeing introduction and establishment in previously pest-free areas, have major environmental and economic impacts. The problem of invasive pests is multidimensional and complex and can only be tackled through strong integration and the use of various approaches [1]. Climate change, intense human mobility, and increased international and transcontinental trading have brought biological invasions to the forefront of the list of threats to agricultural production worldwide. Full article

Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs. Full article

As climate change worsens, irrigation modernization has become critical for better water distribution and maintaining rice production in the face of increasing water constraints. However, there remains a gap in quantification regarding the environmental trade-offs between pump-managed and gravity-based irrigation systems, especially in integrated assessments that relate economic performance, carbon emissions, and water use. This study used an integrated framework of water productivity (WP), consumptive water footprint (WF), carbon footprint, and eco-efficiency to compare gravity-based and pump-managed systems in the Don Chedi Operation and Maintenance Project, Thailand, from 2021 to 2023. The results showed no significant differences in WP and WF between systems. WP averaged 0.39 kg m⁻³ during the wet seasons and 0.54 kg m⁻³ during the dry seasons, while the WF averaged 2517 m³ t⁻¹ and 1854 m³ t⁻¹, respectively. These findings indicate that pump-managed irrigation enhanced operational flexibility and yield stability but did not substantially improve water use efficiency. However, compared with the gravity-based system, the pump-managed system produced much greater carbon emissions, with total carbon footprints ranging from 1.252 to 1.333 tCO₂eq t⁻¹, or five times higher in the irrigation process. Eco-efficiency metrics rose by up to 8.11% despite this environmental burden, indicating enhanced economic resilience amid fluctuating water conditions. These results show a recurring trade-off between low-carbon agricultural development and irrigation modernization. The study therefore emphasizes the importance of integrating renewable energy and low-carbon technologies into pump-based irrigation systems to support climate-resilient and sustainable agricultural transitions. Full article

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R² = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m³ (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges. Full article

Elucidating the mechanisms by which cropland fragmentation impacts production and ecological functions is critical for ensuring food security and ecological sustainability. Using Jilin Province as a case study, this research develops a cropland fragmentation evaluation framework based on landscape pattern indices. A restricted cubic spline model is employed to quantify nonlinear relationships and identify critical thresholds between fragmentation and both production and ecological functions. Furthermore, the PLUS model is utilized to simulate land-use patterns for 2030 under three scenarios: natural development, cropland protection, and ecological protection. The primary findings are as follows: (1) From 2000 to 2023, cropland fragmentation displayed pronounced spatial heterogeneity. Fragmentation was consistently high in the eastern mountainous areas and showed significant spatial clustering; the central region maintained relatively contiguous cropland, while the western region exhibited marked spatial variability. (2) Cropland fragmentation exhibits a nonlinear negative correlation with production functions, wherein the marginal negative impact attenuates beyond a threshold of 0.340. Conversely, its association with ecological functions follows a U-shaped trajectory, with a critical inflection point at 0.363 marking a directional shift in the fragmentation–ecology nexus. (3) Based on these nonlinear thresholds, the study area was delineated into production-ecology synergy zones, dysfunctional sensitive zones, and ecosystem landscape trade-off zones. Specifically, the central agricultural core is characterized by functional synergy; the ecologically fragile western zone resides near the nadir of the U-shaped curve, rendering its balance between production and ecological functions highly vulnerable to shifts in development intensity; and the eastern ecological barrier zone manifests a distinct trade-off prioritizing ecological functions. (4) Multi-scenario simulations reveal that the natural development scenario exacerbates the expansion risk of dysfunctional sensitive zones. While the cropland protection scenario enhances production capacity, it concurrently introduces risks of ecological instability. Conversely, the ecological protection scenario effectively steers sensitive zones toward ecological recovery. Consequently, we propose a differentiated spatial regulation strategy: prioritizing land consolidation in the central region, integrating ecological restoration with capacity enhancement in the west, and sustaining ecological barriers in the east, thereby fostering sustainable regional development. Full article

The pollution of water, including salt and fresh water, has become an emergency problem. Pollutants come from different sources and have various characteristics, starting from industry and fertilizers used in agriculture, sewage related to human living, and other sources. Diverse sources of pollution require a comprehensive approach to water purification. One possible approach may be the use of appropriate sorbents. Currently, one of the most promising materials used is zeolites. This is because they can come from various sources, including waste raw materials such as fly ash, and, therefore, allow for the use of a circular economy approach. Moreover, these materials can be modified, which enables their selective use for selected types of pollutants. Eventually, these materials become economically viable options. The main aim of this article is to present and analyze possible solutions to water pollution based on zeolite materials. For this purpose, a critical literature review was prepared. The review reveals that zeolites perform particularly well in ion-exchange-driven removal of inorganic contaminants, while their effectiveness for organic micropollutants under realistic conditions is often limited. The identified trade-offs between removal efficiency, regeneration stability, and scalability indicate that zeolites are best applied as function-specific rather than universal sorbents. From a sustainability perspective, this targeted applicability is supported by advantages, such as low material cost, long service life, and the possibility of using naturally occurring or waste-derived precursors, which, together, enable resource-efficient water treatment processes, reduced reliance on energy-intensive technologies, and the valorization of industrial byproducts within circular economy frameworks. Full article

This study investigates consumer knowledge, perceptions, and purchasing behaviors regarding products with geographical indications and certifications in the Campania region. Traditional Agri-Food Product (PAT) is the regional label used in Italy to identify traditional products whose distribution is so limited that they do not qualify for PDO or PGI designation. In this view, this research examines the diffusion of such products, their economic and sustainability attributes, and alignment with modern objectives, including environmental impact reduction, rural development, and the European Common Agricultural Policy (CAP) 2023–2027. Using a structured questionnaire administered to a sample of 706 respondents, the study combines descriptive statistics and econometric analysis, trying to identify key factors influencing Willingness to Pay (WTP) for certified products and knowledge of certifications. Findings reveal that education, knowledge of certifications, and lifestyle factors positively affect WTP, highlighting opportunities for targeted marketing and awareness campaigns, also emphasizing critical issues in view of new trade scenarios (e.g., Mercosur agreement) and climate change. Full article

Intensive agricultural production contributes significantly to greenhouse gas (GHG) emissions, accounting for between 10 and 12% of global anthropogenic emissions, at a time when the agricultural sector is facing increasing pressure to adapt to ever-stricter environmental regulations. This study develops and applies a multi-objective Goal Programming model to identify the optimal mix of crops and management practices that simultaneously minimize the carbon footprint and maximize productivity, at the level of a 300-hectare (ha) model agricultural system in Romania. The life cycle assessment (LCA) methodology, in accordance with ISO 14040/14044 standards and Ecoinvent 3.8 emission factors, was applied to nine crops distributed across three soil types, within four management scenarios, over an annual planning horizon. The unit of measurement used is a ton of CO₂ equivalent per agricultural system. The results show that the optimized configuration achieves near-zero total carbon emissions (0.33 t CO₂eq for the entire farm), reduces synthetic nitrogen inputs to 35.7% of the limit set by the EU Nitrates Directive, and generates water savings of 48%. However, these environmental gains entail a 52.9% production trade-off relative to the maximum target of 3000 tons, highlighting a Pareto-optimal structural conflict between climate and food security objectives. The sensitivity analysis identifies the nitrogen emission factor and crop yield as the most influential parameters. The results confirm the technical feasibility of the European Green Deal targets through systematic mathematical optimization, while also demonstrating that achieving economic parity requires policy support of 110–165 EUR/ha/year. Full article

With the continuous growth of global population and increasing pressure on food security, the transformation toward precise and intelligent agricultural production has become an inevitable trend. In this context, accurate identification of field weeds is crucial for improving crop yields and reducing agricultural inputs. However, agricultural Internet of Things (IoT) edge devices are generally subject to strict constraints in terms of power consumption, storage, and real-time performance. Existing lightweight convolutional neural networks often struggle to simultaneously achieve high accuracy and low resource consumption for fine-grained weed identification tasks. To address this challenge, this paper proposes a hardware aware lightweight convolutional neural network named Weed-Aware Ghost Enhanced Network (WAGENet) for microcontroller deployment. The network synergistically integrates Ghost low-cost feature generation, Mobile Inverted Bottleneck Convolution (MBConv) for deep semantic extraction, Squeeze and Excitation (SE) and Coordinate Attention (CA) dual attention mechanisms for channel space joint calibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context fusion. It constructs a progressive feature abstraction system from shallow textures to high-level semantics. On the public DeepWeeds dataset, WAGENet achieves 95.71% classification accuracy and 93.80% F1 score with only 0.163 M parameters and 2.43 × 10⁸ multiply accumulate operations (MACC), attaining a parameter efficiency of 587.19%/M and significantly outperforming existing mainstream lightweight models. The model has been successfully deployed on the STM32H7B3I microcontroller development board, achieving a single inference latency of 94.63 ms, an internal Flash footprint of only 686.95 KiB, and a single inference energy consumption of 41.45 mJ. Experimental results demonstrate that WAGENet achieves a trade off among accuracy, latency, and energy consumption under strict resource constraints, providing a reproducible microcontroller deployment paradigm for battery powered field robots, drones, and other agricultural IoT edge devices. Full article

Africa’s food systems, dominated by smallholder farmers, have yet to benefit from the transformative potential of digital technologies. While large-scale farms have widely integrated digital solutions, smallholder farms have lagged behind. This disparity creates unequal opportunities in the sector. African governments are not oblivious to the severe impact of the digital divide. Therefore, several policy frameworks and strategies are being adopted to address the challenge. This article examines current policy frameworks and strategies to address the digital divide in Africa’s food systems. It also explores the impact of these policy frameworks and strategies. The article draws from a qualitative review methodology underpinned by a review of primary and secondary sources, both in grey and academic formats. The article reveals that at the regional level, policies such as the African Union (AU) Digital Transformation Strategy, the Comprehensive African Agricultural Development Programme, the AU Digital Agriculture Strategy and the African Continental Free Trade Area Digital Protocol are being adopted to address the digital divide. There are also several Regional Economic Communities Initiatives that focus on several intertwined areas to address the digital divide. In addition, countries adopt notional strategies and policies to address country-specific challenges related to the digital divide. The article concludes that policies aimed at addressing the digital divide have had a limited impact in Africa due to poor implementation. Accountability must be built into the policy lifecycle, from planning and implementation to monitoring and evaluation, to address the digital divide in Africa’s food systems effectively. This can be achieved through a combination of political oversight, legal and regulatory frameworks, civil society engagement, and the establishment of measurable performance metrics. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

This study examines whether financial leverage promotes sustainable agricultural productivity by accounting for the trade-off between profitability and default risk. We construct a risk-adjusted total factor productivity (TFP) measure by incorporating default risk as an undesirable output into a slack-based DEA framework (USBM), with risk estimated via contingent claims analysis (CCA). Using panel data on Chinese listed agricultural firms, we find a robust inverted-U relationship between leverage and TFP, indicating an optimal leverage range. Mechanism analysis reveals a dual-channel effect: leverage improves productivity through profitability and reduced financing constraints at low levels but increases default risk and undermines financial sustainability at high levels. Decomposition results show that leverage promotes efficiency catch-up but inhibits frontier technological progress, implying a trade-off between short-term efficiency gains and long-term sustainability. Substantial heterogeneity across subsectors and market structures further suggests that optimal leverage is context-dependent. This study contributes by developing a risk-adjusted productivity framework, identifying the nonlinear effects of leverage on sustainable TFP, and providing micro-level evidence from agriculture in a developing economy. The findings offer implications for capital structure optimization and sustainable agricultural finance. Full article