Search Results (1,755)

This paper presents industry-specific time series for GHG emissions, land use, and complete protein production in Canada from 1990 to 2023. This analysis relies on an updated version of the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). Whereas ULICEES was developed to compare Canada’s livestock industries based on 2001 and 2006 Agricultural Census data, ULICEES-T relies mainly on national agricultural Greenhouse Gas (GHG) emissions reported by Environment and Climate Change Canada (ECCC) to compare all Canadian agronomic land uses within the farm gate. The national CH₄ and N₂O emissions from all livestock are re-aggregated into livestock-specific crop complexes. Fossil CO₂ emissions are simulated using the Farm Fieldwork and Fossil Fuel Energy and Emissions (F4E2) model. Between 2005 and 2020, crop areas that supported livestock decreased from 14 to 10 Mha, whereas in Western Canada, the areas growing non-livestock feed crops increased from 20 to 25 Mha. Over the same 15-year interval, GHG emissions from crop areas not supporting livestock increased from 18 to 27 MtCO₂e, while GHG emissions from livestock decreased from 51 MtCO₂e in 2005 to 42 MtCO₂e in 2020, a drop of 18%. Meanwhile, protein from all Canadian livestock decreased by only 12% over that interval. Reducing N₂O emissions associated with N fertilizer and reduced beef consumption are the two best options for achieving a lower agricultural carbon footprint in Canada. Full article

Bacillus thuringiensis Cry toxins are well known for their high insecticidal activity against Lepidoptera, Diptera, and Coleoptera and have been widely used in Bt transgenic crops. However, their activity against Hemipteran aphids remains relatively low. Identifying novel Cry proteins and elucidating their action mechanisms can facilitate the development of effective aphid control strategies. In this study, we found that ingestion of Cry2Ab12 did not kill Myzus persicae adults but significantly reduced their offspring number and exerted a lethal effect on M. persicae nymphs. After identifying Cry2Ab12 toxin-binding proteins in M. persicae, we further characterized the interaction with Obg-like ATPase 1 (OLA1), a conserved protein involved in growth regulation. Bio-layer interferometry (BLI), ELISA, and enzyme activity assays revealed that Cry2Ab12 and OLA1 do not interact directly. Interestingly, heat shock protein 60 (HSP60) was shown to mediate the interaction among Cry2Ab12, HSP60, and OLA1, leading to inhibition of OLA1 enzymatic activity. Based on these findings and bioinformatics simulations, we proposed a mechanistic model for Cry2Ab12 toxicity against M. persicae: upon ingestion of a sufficient amount of Cry2Ab12, the formation of the Cry2Ab12–HSP60–OLA1 complex impairs the cellular stress response, disrupts normal OLA1 expression, and ultimately restricts larval growth and development, resulting in lethality. This study provides new insights into the action of Cry toxins in aphids and offers a basis for developing enhanced aphid biocontrol strategies. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Horticultural intensification in Mediterranean areas has increased the risk of nitrate pollution due to inefficient irrigation and nitrogen fertilisation management. The availability of simulation models aimed at rational nitrogen management in outdoor crops is limited. The objective of this study is to adapt the VegSyst model, initially developed for greenhouse vegetables, for use in open-field conditions in relevant crops, such as processing peppers and broccoli in Extremadura. VegSyst simulates dry matter production and nitrogen uptake by incorporating the influence of evaporative demand (TUE approach) in addition to the effect of radiation (RUE approach). Experimental field data obtained in five campaigns (peppers: 2020–2022; broccoli: 2020 and 2022) under different nitrogen doses were used. The model was calibrated, and critical N dilution curves were developed for each crop. Subsequently, the simulation of fi-PAR, dry matter production (DMP) and N uptake was validated using statistical indices (RMSE, RE, d, EF) and regression analysis. The model showed a high predictive capacity for N uptake in both crops, with values of d ≥ 0.98 and EF ≥ 0.90 in the validation campaigns. The fi-PAR simulation was acceptable in peppers and excellent in broccoli. In contrast, the DMP prediction showed notable deviations in peppers, especially in 2022, attributable to interannual variations in weather conditions and physiological limitations not considered by the model. In both crops, the TUE-based strategy was a better fit for the measurements than the RUE-based strategy, indicating that under semi-arid Mediterranean conditions, transpiration is the limiting factor for biomass production. The adaptation of the VegSyst-Outdoors model proved to be robust for simulating N uptake and sufficiently accurate to be integrated into decision support tools aimed at efficient fertilisation and irrigation management. Full article

Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

The azimuth of a solar greenhouse affects the lighting and the amount of solar radiation received. To investigate the influence of greenhouse azimuth angles on the thermal environment and to ensure an optimal temperature for the growth of warm-season crops such as tomatoes and cucumbers, a naturally ventilated solar greenhouse in Urumqi, Xinjiang was examined. Using computational fluid dynamics (CFD) software (ANSYS 2020), a model of the greenhouse under natural ventilation was constructed. Taking the indoor temperature as the evaluation index, the temperature field inside the greenhouse was simulated at two time points (11:00 and 17:00) during the daytime in spring, under different azimuths (8° west of south, 4° west of south, due south, 4° east of south, and 8° east of south). The indoor measured point temperatures at 11:00 and 17:00 over four consecutive days were compared with the simulated results. The MaxRE, ARE, RMSE, and MAE were all remained within a low range, verifying the accuracy of the constructed CFD greenhouse model. The temperature contour maps of different sections, as well as the indoor average temperature and temperature uniformity in each case, were compared and analyzed. The results indicated that, at 11:00, the greenhouses with azimuths of 8° and 4° east of south exhibited higher average indoor temperatures than those with azimuths of due south and west of south. At 17:00, however, the highest average indoor temperatures occurred in the greenhouses with azimuths of 8° and 4° west of south, exceeding those with azimuths of due south and east of south. The differences in temperature uniformity among different azimuths at the same time were small, but there were significant differences in the temperature uniformity at different times for the same azimuth. According to the climatic characteristics and the temperature requirements of crops of Urumqi, Xinjiang, an azimuth of 4–8° west of south is recommended for solar greenhouses in this region. Full article

This study aimed to identify areas at risk of deforestation in the Cerrado biome of the Brazilian Midwest (states of Mato Grosso, Mato Grosso do Sul, and Goiás) and to estimate potential soil losses due to water erosion under land-use change scenarios. The methodology integrated the Universal Soil Loss Equation (USLE), spatializing rainfall erosivity (R), soil erodibility (K), topographic factor (LS), and cover-management factor (CP), with the ACEU (Accessibility, Cultivability, Extractability and Unprotected/protection status) model to assess deforestation risk based on accessibility, agricultural suitability, extractive activities, and legal protection status. Results indicated an average soil loss of 0.11 t ha⁻¹ year⁻¹ under natural vegetation cover, with 90% of the area presenting losses below 0.25 t ha⁻¹ year⁻¹. However, 27.5% of the remaining natural cover is located in areas classified as high or very high deforestation risk, indicating significant environmental vulnerability. Simulated scenarios of land-use conversion to pasture and annual crops revealed substantial increases in soil loss, particularly under annual cropping systems, potentially exceeding soil loss tolerance thresholds across millions of hectares. The findings demonstrate that integrating deforestation risk assessment with erosion modeling is a strategic tool for environmental planning, reinforcing the importance of preserving native vegetation to maintain ecosystem services and ensure long-term environmental sustainability. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

Saline irrigation is increasingly practiced in semi-arid regions to cope with freshwater scarcity; however, it strongly affects crop growth, water use, and soil salinity. This study aims to calibrate and validate the AquaCrop model to simulate key growth parameters of winter wheat (cv. Achtar) under saline irrigation conditions in the Tadla Plain, Morocco, focusing on canopy cover (CC), actual evapotranspiration (ETa), soil water content (SWC), biomass (B), and grain yield (GY). The model was first calibrated using observed data from the 2023 growing season and subsequently validated using data from the 2022 growing season. Overall, AquaCrop effectively reproduced crop growth during both calibration and validation phases. During calibration, canopy cover was accurately simulated, with average RMSE values below 1%, while biomass and grain yield were also well reproduced, with low RMSE values (0.25 t ha⁻¹ for B and 0.10 t ha⁻¹ for GY), confirming the robustness of the calibrated parameters. The model also performed well in simulating ETa and SWC, capturing the seasonal dynamics of crop water use and soil moisture. During validation, ETa was satisfactorily reproduced, with an RMSE of approximately 0.80 mm day⁻¹, while SWC showed good agreement with observations, with NRMSE values ranging from 7.9 to 10.5%. Grain yield and biomass were reliably predicted, with NRMSE values below 4%. These results demonstrate that AquaCrop is a reliable tool for simulating winter wheat under saline irrigation and for assessing crop response under salt-affected conditions, providing an integrated evaluation of crop performance, water use, and soil salinity dynamics to support improved irrigation management and water-use efficiency under semi-arid conditions. Full article

Optimizing nitrogen management is essential for maintaining high spring maize yield while mitigating nitrous oxide (N₂O) emissions in irrigated areas. However, the interactive effects of total nitrogen application rate and starter nitrogen proportion on yield and N₂O emissions remain insufficiently quantified. Reliable assessment of these interactions requires well-calibrated DeNitrification–DeComposition (DNDC) simulations, yet existing calibration studies often emphasize crop parameters while neglecting soil parameters critical for soil hydrothermal dynamics and N₂O production. In this study, field data from shallow-buried drip-irrigated spring maize in Tongliao during 2024–2025 were used to conduct Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis on 12 crop and 13 soil parameters of the DNDC model. Sensitive parameters were calibrated using the differential evolution algorithm, and 64 nitrogen management scenarios were simulated by combining eight total nitrogen application rates (100, 150, 200, 250, 300, 350, 400, and 450 kg N ha⁻¹) with eight starter nitrogen proportions (0%, 15%, 25%, 30%, 35%, 40%, 45%, and 50% of the total nitrogen rate). The results showed that DNDC outputs were jointly controlled by crop and soil parameters, among which maximum yield, leaf carbon-to-nitrogen ratio, stem fraction, grain carbon-to-nitrogen ratio, thermal degree days for maturity, grain fraction, soil organic carbon (SOC) decrease rate below topsoil, soil clay content, soil porosity, wilting point and depth of top soil with uniform SOC content were dominant. Compared with the conventional crop-parameter calibration, the sensitivity-screened parameter set improved the simulation of both cumulative N₂O emissions and yield. Across the 64 scenarios, cumulative N₂O emissions ranged from 0.42 to 4.87 kg [N]/ha, while simulated maize yield ranged from 1597 to 6347 kg [C]/ha. N₂O emissions increased with total nitrogen rate, whereas yield increased initially and then reached a plateau. Increasing the starter nitrogen proportion did not substantially enhance yield but increased N₂O emission risk under high nitrogen rates. Overall, the scenario with 300 kg/ha and no nitrogen applied at sowing achieved a relatively high yield of 5519 kg [C]/ha while maintaining a low cumulative N₂O emission of 0.98 kg [N]/ha and was therefore identified as the preferred trade-off strategy under shallow-buried drip irrigation. This study provides an EFAST–DNDC framework for optimizing nitrogen management to sustain spring maize yield while reducing N₂O emissions in the West Liaohe Plain. Full article

Impaction of the mandibular third molar in proximity to the mandibular canal increases the risk of inferior alveolar nerve injury. Panoramic radiography is routinely used to assess this relationship. Automated classification of molar–canal overlap could support clinical triage and reduce unnecessary CBCT referrals, while Federated Learning (FL) enables multi-center collaboration without sharing patient data. We compared Local Learning (LL), FL, and Centralized Learning (CL) for binary overlap/no-overlap classification on cropped panoramic radiographs partitioned across eight independent labelers in a simulated heterogeneous multi-center setting. A pretrained ResNet-34 was trained under each paradigm and evaluated using per-client metrics with locally optimized thresholds and pooled test performance with a global threshold. Performance was assessed using area under the receiver operating characteristic curve (AUC) and threshold-based metrics, alongside training dynamics, Grad-CAM visualizations, and server-side aggregate monitoring signals. On the test set, CL achieved the highest performance (AUC 0.831; accuracy ≈ 0.782), FL showed intermediate performance (AUC 0.757; accuracy ≈ 0.703), and LL generalized poorly across clients (AUC range ≈ 0.619–0.734; mean ≈ 0.672). Training curves suggested overfitting, particularly in LL models, and Grad-CAM indicated more anatomically focused attention in CL and FL. Overall, centralized training provided the strongest performance, while FL offers a privacy-preserving alternative that outperforms LL. Full article

Amid intensifying extreme climate events, geopolitical conflicts, and sudden trade policy disruptions, the resilience and vulnerability of global staple food trade systems have emerged as pressing governance concerns. This study constructs directed weighted trade networks for wheat, maize, and rice from 2015 to 2024 and evaluates their vulnerability and resilience evolution using a three-dimensional structural resilience framework and underload cascading failure models. The results reveal that all three networks display scale-free and disassortative properties. The wheat network gradually recovered following the Russia–Ukraine conflict, whereas structural imbalance continues to deepen in the maize network, and the rice network faces persistent resilience pressure arising from excessive dependence on core exporters. Cascading failure simulations indicate that targeted attacks on key exporting countries can trigger large-scale network collapse. Introducing cross-crop substitution effects markedly enhances the resilience of individual food trade networks through cross-layer substitution and supplementation; yet under simultaneous attacks, crop substitution effects instead serve as a conduit for cross-layer cascading failure propagation, and even a minimal willingness to substitute can weaken network resilience. Accordingly, this study proposes policy recommendations to strengthen the resilience of the global staple food trade network. Full article

Watermelons account for 7% of the world’s fruit vegetable production. In the European market, Spain contributes around 35% of total watermelon supply, with the majority grown in greenhouses in Almería, Southern Spain. This study presents experimental results from the first agrivoltaic watermelon trial conducted in a raspa-y-amagado greenhouse during the 2024 growing season in Almería, Spain. Watermelons were cultivated under two shading treatments with 30% and 50% of the roof area covered with PV modules and compared against an unshaded control group. Throughout the experiment, temperature values in the 30% and 50% zones were 2.2 °C and 4.3 °C lower than in the control zone, respectively. The unshaded control zone and the 30% shading treatment maintained DLI conditions within the optimal range between 21 mol m⁻² d⁻¹ and 32 mol m⁻² d⁻¹ for most of the crop cycle, while the 50% shading zone remained largely above the minimum threshold of 15 mol m⁻² d⁻¹ required for adequate crop growth. No statistically significant differences were observed in fruit weight, rind width, fruit firmness, or soluble solids content at harvest. In addition, the experimentally measured irradiance data from this study were compared with simulations from a previously established irradiance model. The model was applied to the raspa-y-amagado greenhouse, and the experimental data were used to perform a long-term comparison between simulated and measured irradiance for 265 days of data. The irradiance model accurately reproduced shading effects from both the PV modules and greenhouse structure, achieving nRMSE values of 0.09, 0.18, and 0.27 for the control, 30% shading, and 50% shading zones, respectively. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article