Search Results (14,961)

Current agricultural irrigation management practices are often extensive, and traditional soil moisture content (SMC) monitoring methods are inefficient, so there is a pressing need for innovative approaches in precision irrigation. This study proposes a Multi-Feature Fusion Network (MFF-Net) for SMC inversion. The model uses a designed Channel-Changeable Residual Block (ResBlockCC) to construct a multi-branch feature extraction and fusion architecture. Integrating the Channel Squeeze and Spatial Excitation (sSE) attention module with U-Net-like skip connections, MFF-Net inverts root-zone SMC from summer maize leaf images. Field experiments were conducted in Zhengzhou, Henan Province, China, from 2024 to 2025, under three irrigation treatments: 60–70% θ_fc, 70–90% θ_fc, and 60–90% θ_fc (θ_fc denotes field capacity). This study shows that (1) MFF-Net achieved its smallest inversion error under the 60–70% θ_fc treatment, suggesting the inversion was most effective when SMC variation was small and relatively low; (2) MFF-Net demonstrated superior performance to several benchmark models, achieving an R² of 0.84; and (3) the ablation study confirmed that each feature branch and the sSE attention module contributed positively to model performance. MFF-Net thus offers a technological reference for real-time precision irrigation and shows promise for field SMC inversion in summer maize. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with excessive fertilizer use. Variable-rate application (VRA) offers a precision-based approach to matching N inputs with crop demand, yet winter wheat responses to reduced N rates are still underexplored. This study evaluated winter wheat (Triticum aestivum L.) performance under variable and uniform N application strategies using canopy greenness (NDVI), grain yield, plant N content, nitrogen use efficiency (NUE), and fertilizer costs as indicators. Reduced N treatments (40% and 60% VRA rates) were compared with a uniform (100%) application. Canopy greenness increased across all treatments over time, with NDVI values ranging from 0.855 early in the season to approximately 0.94 at later growth stages, and statistically significant among N rates (p < 0.05). Grain yield was highest under the low N rate (1676.81 kg ha⁻¹), although yield differences among treatments were not statistically significant (p > 0.05). Similarly, plant N content varied slightly across treatments, ranging from 1.73% to 1.82%, with no significant differences. In contrast, NUE declined sharply with increasing N rates, decreasing from 71% under the lower rate to 28% under the uniform rate. Overall, variable-rate treatments used just over half the fertilizer input and cost of the uniform rate while supporting comparable yield and plant N status. These results prove that VRA can improve nitrogen efficiency and reduce input costs without compromising winter wheat productivity, supporting its practical value for sustainable fertilizer management. Full article

There is little evidence of the implementation of the Principles for Responsible Investment in Agriculture and Food Systems between universities and businesses, and there is even less research that prioritizes people and implements sustainable development with a territorial focus. In this article, we address a form of collaborative work that integrates academia with business, where the Principles for Responsible Investment in Agriculture and Food Systems (CFS-RIA) are seen as an opportunity to promote and strengthen the management of a business in the communities where it operates, and determine a new way of working from its links with the university. The experience is developed in the provinces of Santiago Rodríguez, Valverde (Mao), and Dajabón in the Dominican Republic, with the aim of contributing, using this new approach, to economic, social, environmental, and governance development in the territory. The conceptual and methodological basis for the university–business link is Working With People, a model that integrates key elements of planning such as social learning, collaborative participation, and project management models. The main catalysts of the experience are the business values and the stakeholders who insert the principles into their programs and projects. Among these is an innovative Family Social Responsibility Program with female entrepreneurs and organic banana production. It is concluded that the implementation of the CFS-RIA Principles has a significant impact on the sustainable development of the region and that the university–business link reinforces the social responsibility of companies, providing an opportunity for the entry of new actors. Full article

The aim of the study was to investigate the effect of vegetation cover and height on soil and plant nutrients across managed and unmanaged agricultural land in a temperate climate. Fresh soil and vegetation samples were collected during the years 2023 and 2024 from 125 different land parcels in the southwest of the UK. Land was either managed for grazing and/or feed production or not managed for agricultural use, and had a range of grass, crop, legume, herb, and flower species. A linear mixed model was used to assess the effect of vegetation height (in cm) and cover (tonnes of dry matter per hectare) on soil and plant nutrients. The results showed plant dry matter (DM) digestibility, acid detergent fibre (ADF), water-soluble carbohydrate, and oil contents increased with vegetation height, and soil DM and neutral detergent fibre (NDF) decreased with vegetation height. The ratio of soil-to-plant OM reduced and ADF increased with increasing vegetation cover. Interactions between vegetation height and cover (i.e., density) were found for the ratio of soil-to-plant OM, ADF, NDF, DM, DM digestibility, oil, water-soluble carbohydrate, and crude protein nutrients. Measuring the interaction between soil and plant properties showed soil OM stocks increased and soil pH decreased with increased vegetation cover across agricultural land. Full article

Water-quality degradation from nutrient pollution remains a major challenge for resource managers. Developing effective strategies requires tools to characterize nutrient sources and transport. This study used the RSPARROW framework to develop and assess new, smaller-scale models for Total Nitrogen (TN) and Total Phosphorus (TP) transport across Mississippi (MS). These state-level models were built using 15 years (2005–2020) of observation data and considered variables including multiple nutrient sources, land characteristics, and attenuation processes. The MS models demonstrated comparable accuracy to larger regional SPARROW models, validating the use of smaller-scale models for local management. Results showed agricultural sources are the major contributors to TN, dominated by fertilizer in northern MS and livestock manure in the south. Urban land cover also significantly influenced TN and was the second most significant source of TP, following geologic material (background P). Fertilizer and manure were also important TP sources. This study provides valuable, spatially explicit data on nutrient distribution in MS streams, supporting the state’s nutrient reduction planning. It concludes by highlighting the need for future model improvements via updated source data and mean annual flow estimates. Full article

The black soil region of Northeast China (NEC) is China’s most important food production base. Long-term inefficient land use has made its ecosystem vulnerable to widespread degradation, prompting the implementation of ecological restoration projects (ERPs) to enhance ecosystem service (ES) resilience. Yet, the complex interactions among key ESs, including grain production (GP), water yield (WY), soil conservation (SC), and carbon storage (CS), as well as the spatial non-stationarity of their driving factors post-ERPs, have caused spatially heterogeneous, scale-dependent ES relationships. To address these gaps, this study aims to analyze temporal changes in ESs across multiple scales in NEC from 2000 to 2020. By mapping the interactions and quantifying their intensities, we revealed spatial variations in driving factors under different ERPs. The results show that the Natural Wetland Conservation Project (NWCP) and Three-North Shelterbelt Program (TNSP) have led to overall improvements in all ESs. In contrast, the Grain for Green Program (GFGP), the Land Salinity/Sodicity Amelioration Project (LASP), and the Natural Forests Conservation Program (NFCP) are associated with trade-offs between ESs. Interactions between ESs exhibited clear spatial scale dependence, and the dominant drivers varied across scales and restoration contexts. These findings highlight the importance of considering spatial scale and non-stationarity when evaluating ecological restoration outcomes. This study provides a scientific basis for the development and management of ecological restoration programs in intensively managed agricultural regions worldwide, particularly those undergoing multiple, overlapping restoration interventions, from a multi-scale spatial perspective. Full article

The adoption of conservation systems in agriculture has been increasingly explored as a strategy to improve soil quality and potentially influence greenhouse gas (GHG) emissions. This study reports the first assessment of GHG emissions within a long-term (14 years) agroecological field experiment evaluating soil management systems for onion (Allium cepa L.) production in a Humic Dystrudept (Cambissolo Húmico Distrófico, Brazilian Soil Classification System) in Southern Brazil. Three management systems based on permanent soil cover and crop diversification were evaluated in an onion–maize rotation: conventional tillage (CT) without cover crops, no-till (NT) without cover crops, and a no-till vegetable system (NTV) with a summer cover crop mixture of pearl millet (Pennisetum americanum), velvet bean (Mucuna aterrima), and sunflower (Helianthus annuus). Short-term GHG emissions were monitored during one onion growing season (106 days), while soil chemical and physical properties reflect long-term management effects. Evaluations included (i) daily and cumulative GHG (N₂O, CH₄, and CO₂) emissions, (ii) soil carbon (C) and nitrogen (N) stocks, (iii) soil aggregation, porosity, and bulk density in different soil layers (0.00–0.05, 0.05–0.10, and 0.10–0.30 m), and (iv) onion yield and cover crop dry matter production. The NTV system improved soil physical and chemical quality and increased onion yield compared to NT and CT. However, higher cumulative N₂O emissions were observed in NTV, highlighting a short-term trade-off between increased N₂O emissions and long-term improvements in soil quality and crop productivity. All systems acted as methane sinks, with greater CH₄ uptake under NTV. Despite higher short-term emissions, the NTV system maintained a positive C balance due to long-term C accumulation in soil. Short-term greenhouse gas emissions were assessed during a single onion growing season, whereas soil carbon stocks reflect long-term management effects; CO₂ fluxes measured using static chambers represent ecosystem respiration rather than net ecosystem carbon balance. These results provide an initial baseline of GHG dynamics within a long-term agroecological system and support future multi-year assessments aimed at refining mitigation strategies in diversified vegetable production systems. Full article

Accurate classification of tomato ripening stages and quality estimation is pivotal for optimizing post-harvest management and ensuring market value. This study presents a rigorous comparative analysis of morphological and colorimetric features extracted via two state-of-the-art deep learning-based instance segmentation frameworks—Mask R-CNN and YOLOv8n-seg—and their efficacy in machine learning-driven ripening stage classification and quality prediction. Using 216 fresh-market tomato fruits across four defined ripening stages, we extracted 27 image-derived features per model, alongside 12 laboratory-measured physio-morphological traits. Multivariate analyses revealed that R-CNN features capture nuanced colorimetric and structural variations, while YOLOv8 emphasizes morphological characteristics. Machine learning classifiers trained with stratified 10-fold cross-validation achieved up to 95.3% F1-score when combining both feature sets, with R-CNN and YOLOv8 alone attaining 96.9% and 90.8% accuracy, respectively. These findings highlight a trade-off between the superior precision of R-CNN and the real-time scalability of YOLOv8. Our results demonstrate the potential of integrating complementary segmentation-derived features with laboratory metrics to enable robust, non-destructive phenotyping. This work advances the application of vision-based machine learning in precision agriculture, facilitating automated, scalable, and accurate monitoring of fruit maturity and quality. Full article

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions. Full article

The flower thrips, Frankliniella intonsa, is a major pest threatening citrus production. However, chemical control remains the primary management measure, which poses significant risks on ecosystems. Hence, it is urgent to prioritize more eco-friendly measures to efficiently control thrips. The ladybird, Cheilomenes sexmaculata, is a predominant natural enemy in the local citrus agroecosystem and could play a key role in suppressing thrips in agricultural landscapes. Although some ladybirds are known to be attracted to herbivore-induced plant volatiles (HIPVs), little is known about the specific attractive compounds and the effect of F. intonsa-infested lemon plants on the predatory response of C. sexmaculata. Here, we studied the chemical interaction between F. intonsa, C. sexmaculata, and lemon plants. In dual-choice behavioral assays, C. sexmaculata adults significantly preferred volatiles from F. intonsa-infested plants over those from healthy plants. Volatile collection and analysis identified six monoterpenes, five of which (α-pinene, β-pinene, sabinene, myrcene, and eucalyptol) individually attracted C. sexmaculata at specific concentrations. Moreover, a blend of these five compounds, formulated at their optimal attractive concentrations, elicited a stronger attraction in C. sexmaculata than individual compounds, indicating a synergistic interaction. This attractive blend can thus be used to develop a kairomone-based lure to enhance biological control and to complement existing integrated pest management approaches against thrips in lemon agroecosystems. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

In Northeast China, increasing agricultural activities has led to severe soil compaction, reducing soil aeration and water infiltration capacity. Conservation tillage, through multiple approaches, alleviates this compaction while simultaneously enhancing crop yields and promoting sustainable agricultural production. In light of domestic and international developments, this paper provides a detailed elaboration on conservation tillage (CT) as a sustainable agricultural practice system. It examines its core technical measures, global adoption status, and impacts on soil physicochemical properties. Furthermore, by analyzing the causes and detrimental effects of soil compaction, it proposes approaches and elucidates the significance of using CT to alleviate compaction in black soils. Integrating considerations of its influence on climate change, economic benefits, future development, challenges, and trends, the paper offers a forward-looking perspective. Full article

This study evaluates the agri-environmental performance of the EU-27 Member States using the PROMETHEE multiple-criteria decision analysis method, based on three Eurostat indicators linked to the sustainability pillars: Harmonized Risk Indicator 1 (HRI1, social pillar), pesticide sales intensity (kg/ha UAA, environmental pillar), and environmental protection investments (% GDP, economic pillar). The analysis uses the most recent available Eurostat data (primarily from 2023) and examines three weighting scenarios: (i) equal weights, (ii) higher emphasis on the economic pillar, and (iii) higher emphasis on the environmental and social pillars. Across all scenarios, Slovenia ranked first (net flow, φ = 0.4173 to 0.4734), followed by Czechia (φ = 0.2796 to 0.3260) and France (φ = 0.1886 to 0.2240), while Malta (φ = −0.3356 to −0.3691), Cyprus (φ = −0.2916 to −0.3027), and Estonia (φ = −0.2641 to −0.2903) consistently occupied the lowest positions. The stability of rankings across alternative weighting schemes indicates robust performance patterns, with minimal shifts for most Member States. Overall, the results highlight persistent cross-country differences in agri-environmental performance despite common EU regulatory frameworks, underlining the relevance of national implementation capacity and investment strategies. The proposed PROMETHEE-based ranking provides a transparent and policy-aligned benchmarking tool that can support monitoring and prioritization of interventions related to pesticide risk reduction and environmental investment across EU Member States. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

This review systematically examines the latest research progress and diverse applications of direct evaporative cooling and indirect evaporative cooling across five core sectors: industrial and energy engineering, the built environment, agriculture and food preservation, transportation and aerospace, and emerging interdisciplinary fields. While existing research often focuses on single application silos, this paper distills two common foundational challenges: climate adaptability and water resource management. Quantitative analysis demonstrates significant performance gains. Hybrid systems in data centers increase annual energy-saving potential by 14% to 41%, while precision root-zone cooling in greenhouses boosts crop yields by 13.22%. Additionally, passive cooling blankets reduce post-harvest losses by up to 45%, and integrated desalination cycles achieve 18.64% lower energy consumption compared to conventional systems. Innovative strategies to overcome humidity bottlenecks include vacuum-assisted membranes, advanced porous materials, and hybrid radiative-evaporative systems. The paper also analyzes sustainable water management through rainwater harvesting, seawater utilization, and atmospheric water capture. Collectively, these advancements provide a comprehensive framework to guide the future development and commercialization of sustainable cooling technologies. Full article