Journal of Sustainable Agriculture and Environment最新文献

{"title":"From Prototype to Sustainable Food Production: Translational Barriers and Design Priorities for Agricultural Biosensing Systems","authors":"Frank Efe Erukainure,&nbsp;David B. Langston,&nbsp;Zachary Easton,&nbsp;Blake N. Johnson,&nbsp;Abhilash K. Chandel","doi":"10.1002/sae2.70221","DOIUrl":"https://doi.org/10.1002/sae2.70221","url":null,"abstract":"<p>Agricultural biosensors could advance sustainable food production by enabling timely detection of crop stress, pathogens, nutrient imbalances, and environmental contaminants. Despite decades of research, however, robust commercial biosensing platforms remain uncommon because analytical performance under controlled conditions does not necessarily translate into dependable field operation or measurable sustainability benefits. Sensors affected by fouling, calibration drift, unstable biorecognition elements, ambiguous biological signals, or frequent replacement may increase cost and material consumption without improving management decisions or reducing agricultural inputs. This perspective article examines the technical, biological, operational, and socioeconomic barriers that limit the transition of agricultural biosensors from laboratory prototypes to resource-efficient decision-support systems. It considers performance standards, recognition-element durability, plant–device biocompatibility, validation practices, user education, and uncertainty communication across open fields, controlled-environment agriculture, plant wearables, and in vivo platforms. A sustainability-oriented framework is proposed that connects analytical validity and operational reliability with decision relevance, maintainability, lifecycle considerations, and responsible resource use. Future progress will require biological co-design, realistic multiscale validation, multimodal data integration, modular and repairable devices, actionable user interfaces, and transparent communication of uncertainty. Agricultural biosensors should ultimately be evaluated not only by their ability to detect target analytes but also by whether they enable reliable interventions that reduce resource consumption, environmental burdens, production risks, and avoidable waste.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projected Climate Change Impacts on Phenology, Yield and Water Productivity of Wheat and Potato in Northwest Bangladesh","authors":"Md Belal Hossain,&nbsp;Ketema Zeleke,&nbsp;De Li Liu,&nbsp;K M Shamsul Haque,&nbsp;Bin Wang","doi":"10.1002/sae2.70215","DOIUrl":"https://doi.org/10.1002/sae2.70215","url":null,"abstract":"<p>Assessing climate change impacts on agriculture is essential for developing effective adaptation strategies, with crop simulation models providing valuable tools for evaluating future crop productivity. This study assessed climate change impacts on wheat and potato productivity in northwest Bangladesh using 27 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585). The Agricultural Production Systems sIMulator (APSIM) wheat and potato models were evaluated before simulating phenology, yield and water productivity for two future periods: near future (NF: 2031–2065) and far future (FF: 2066–2100), relative to the baseline period (1988–2022). Climate projections indicated consistent increases in maximum temperature (<i>T</i>_max) and minimum temperature (<i>T</i>_min) during both crop-growing seasons. During FF, mean <i>T</i>_max (<i>T</i>_min) was projected to increase by up to 1.8°C (2.0°C) under SSP245 and 3.5°C (3.8°C) under SSP585 during wheat, and 1.9°C (2.0°C) under SSP245 and 3.7°C (3.9°C) under SSP585 during potato-growing season, with site-specific changes in rainfall and solar radiation. Rising temperatures accelerated crop development, reducing wheat flowering time by up to 12 days and increasing potato tuber initiation time by 4 days. Despite these phenological changes, both crops demonstrated yield gains across scenarios. Wheat yield was projected to increase by up to 21.2% under SSP245, while potato yield increased by 53% under SSP585 in FF. Water productivity also improved, primarily due to increased yield under unchanged irrigation. Regression analysis indicated that T_max exerted negative effect on yield, whereas T_min, solar radiation, and CO₂ had positive effects. Rainfall had limited influence on potato but showed relevance to wheat. Projection uncertainty primarily originated from GCMs, and SSP scenarios used in simulations. Overall, these findings provide valuable insights for policymakers in promoting climate-resilient crop selection under changing climate in Bangladesh.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy","authors":"Rongfei Ma,&nbsp;Wenjiao Shi,&nbsp;Minglei Wang,&nbsp;Xiwei Chen,&nbsp;Chenchen Zhang,&nbsp;Baihong Pan,&nbsp;Xiangming Xiao","doi":"10.1002/sae2.70218","DOIUrl":"https://doi.org/10.1002/sae2.70218","url":null,"abstract":"<p>Accurate mapping of rice, one of the world's most important food crops, is critical for monitoring its growth, estimating yields, and supporting global food security. Most deep learning methods for crop classification rely on multi-temporal optical remote sensing data. However, it is difficult to obtain sufficient ground-truth samples for time-series deep learning models through conducting large-scale field surveys, and persistent cloud and rainfall often limit optical observations during the rice-growing seasons in subtropical regions. Rice classification based solely on time-series optical images may therefore be less accurate and reliable. Here, we developed two dual-branch deep learning networks for effective rice identification: one combining a long short-term memory network running in parallel with a multi-layer perceptron (LSTM + MLP), and the other replacing the LSTM with a one-dimensional convolutional neural network (Conv1d + MLP). These models process temporal sequences in parallel with auxiliary data through the MLP, allowing them to automatically extract phenology information while incorporating auxiliary variables such as texture features, digital elevation model (DEM) data, and the standard deviation of the vegetation index time series. We evaluated these models in a representative county in the subtropical region of China, and compared the accuracies with standalone LSTM, Conv1d, MLP, and random forest (RF) models. The results revealed that LSTM + MLP had the highest accuracy, with an F1-score of 95.5%, which can automatically extract rice growth patterns and successfully identify the key phenological information of flood signals during the rice transplanting period. By incorporating auxiliary variables, the model achieved an average accuracy improvement of 8.1% relative to the models using only temporal features. The proposed method provides a useful approach for rice mapping in subtropical regions.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental Sustainability of Soybean Production in the Democratic Republic of Congo: Cases of Production Systems in the South-Kivu and Kasaï-Lomami Provinces","authors":"Réussite Bugale Malembaka,&nbsp;Stephan Pfister,&nbsp;Braida Thom,&nbsp;Kokou Kintche,&nbsp;Johanna Jacobi","doi":"10.1002/sae2.70216","DOIUrl":"https://doi.org/10.1002/sae2.70216","url":null,"abstract":"<p>Soybean is increasingly grown in the Democratic Republic of the Congo (DRC), produced mainly in small-scale systems and supplied locally for human consumption. Prospects of large-scale expansion and intensification, with potential environmental drawbacks, loom and call for a sustainability analysis of soybean agri-food systems. We assessed the environmental impacts of different soybean production systems, and evaluated options for inputs reduction and yields improvement, in the east and centre of the DRC. We mapped soybean value chains of production in small and large-scale systems, used the Life Cycle Assessment (LCA) method for inventory and assessment ecological impacts on climate change, acidification, fossil energy use, particulate matter formation, land use, biodiversity loss, and soil quality. We additionally examined the correlations between productivity and environmental sustainability. Results showed that small-scale systems were mostly with less than 1 hectare, largely intercropped (&gt; 80%) and had better land-use efficiencies. Seed and compost inputs rates oscillated significantly across small-scale farmers' systems, still their ecological footprints were similar. In contrast, large-scale soybean productions were monocropped, with no amendment but relied on mineral fertilizers, and mechanized operations with fossil fuels. Large‑scale production delivered about four times more soybean yield but caused severe environmental impacts of tenfold higher than small‑scale production systems, in all the impact categories assessed except for land occupation. In small-scale production systems, productivity and environmental sustainability indicators correlated positively with each other, but negatively with farm size, pinpointing the need to maintain and support the adoption of better farming practices even as the farm size increases. Our sensitivity analysis suggested that production in small-scale with improved quantity and quality of seeds and compost inputs could achieve an economically viable yield with still much lower ecological impacts compared to the large-scale production. Therefore, a system optimizing both productivity and environmental preservation is to be sought after. Our study and the LCA method implementation limitations are discussed and call for further investigation with larger samples sizes and adaptation of emissions modelling to the region for comprehensive LCA.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Beneficial Effects of Sustainable Farming on Multitrophic Biodiversity Are Amplified Under Harsh and Less Predictable Environmental Conditions","authors":"Pablo Homet,&nbsp;Juan Piñeiro,&nbsp;Carmen Poveda-Aguilar,&nbsp;Raúl Ochoa-Hueso","doi":"10.1002/sae2.70183","DOIUrl":"https://doi.org/10.1002/sae2.70183","url":null,"abstract":"<p>The expansion and intensification of agricultural lands are major contributors to the global biodiversity loss. Alternative sustainable management practices, such as integrated and organic farming, have emerged as tools to mitigate this loss and make food production and nature conservation compatible. Multiple studies have assessed how transitioning from conventional to more sustainable farming promotes the diversity and abundance of accompanying organisms; however, there is significant variability in the findings. This variability can be driven by both local environmental contexts and the metrics used to assess biodiversity responses. In this study, we compiled climatic and edaphic data from 55 studies and 101 study cases investigating differences in species richness and abundance across conventional, integrated, and organic farming practices. Using this dataset explored the relationship between biodiversity metrics and climatic or soil conditions and compared trends among types of sustainable management (i.e., organic vs. integrated). Overall, species richness and abundance consistently increased under more sustainable farming practices. However, while organic farming had a positive effect on both abundance and species richness, integrated farming only enhanced richness. The positive effects of sustainable farming generally increased in locations characterised by lower fertility, more alkaline soils and less predictable climates, indicative of harsher environmental conditions within the range of environmental conditions represented in the available dataset. Our study highlights the importance of implementing environmentally friendly management strategies to mitigate the ongoing biodiversity crisis, particularly under environmentally challenging conditions. Moreover, it emphasizes the need to apply adaptive management strategies that consider the environmental variability of farming regions to maximize these positive outcomes on biodiversity.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Livestock Farming Technologies Reduce Greenhouse Gas Emissions on Commercial Beef Farms: A Modelling Study","authors":"Louise C. McNicol,&nbsp;Jenna M. Bowen,&nbsp;Holly J. Ferguson,&nbsp;Julian Bell,&nbsp;Carol-Anne Duthie","doi":"10.1002/sae2.70214","DOIUrl":"https://doi.org/10.1002/sae2.70214","url":null,"abstract":"<p>Agriculture, particularly beef production, accounts for a significant share of global greenhouse gas (GHG) emissions, requiring substantial mitigation. Precision livestock farming (PLF) technologies offer a potential solution to indirectly reduce GHG emissions by increasing production efficiencies. Using data from 18 Scottish beef farms (nine suckler and nine finishing systems) which are representative of many European production systems, this study modelled three PLF technologies: automatic weighing platforms, fertility sensors, and health sensors. Baseline emissions were estimated using the carbon calculator Agrecalc, with assumptions informed by an extensive literature review and expert input. Automatic weighing platforms, reducing age at slaughter by 1–3 months, enabled total emissions to be reduced by 1.1%–2.76% in suckler systems and 6.10%–21.28% in finishing systems on average. Emission intensities also decreased, although to lesser extent due to reduced killing out percentage. Impacts were greater in finishing systems as they have fewer additional emission sources beyond the finishing cattle themselves. Fertility sensors enabled improvements in production efficiency that reduced total emissions by 3.69% and emission intensities by 9.18% on average in suckler systems, driven by increased calves born and lower mortality. Improvements in production efficiencies resulting from the adoption of health sensors slightly increased total emissions (7.96% in suckler systems and 8.24% in finishing systems due to increased calves born, increased DLWG and lower mortality, but delivered the greatest emission intensity reductions (12.63% in suckler herds and 8.61% in finishing systems) as output increased. Overall, the PLF technologies explored in this study show strong potential to reduce GHG emissions in beef systems, though impacts vary by system and technology. These findings could guide targeted incentives and wider adoption.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trichoderma Mobilizes Phosphorus and Cobalt While Excluding Sodium in the Foliar Ionome of High-Andean Grasses Enriched With Biochar","authors":"Alberto Arias-Arredondo,&nbsp;Melina Lopez Rodriguez,&nbsp;Juancarlos Cruz Luis,&nbsp;Richard Solórzano-Acosta","doi":"10.1002/sae2.70188","DOIUrl":"https://doi.org/10.1002/sae2.70188","url":null,"abstract":"<p>Acidic high-Andean soils constrain grassland productivity by limiting nutrient availability and altering plant ionomic balance. Biochar inoculated with plant growth-promoting microorganisms has emerged as a promising strategy to mitigate these constraints; however, the extent to which microbial identity modulates plant nutrient acquisition remains poorly understood. This study evaluated the effects of <i>Trichoderma</i> sp., <i>Bacillus</i> sp., and <i>Pseudomonas</i> sp. as inoculants of guinea pig manure–derived biochar on soil pH and the foliar ionome of <i>Carex</i> sp., <i>Dactylis glomerata</i>, <i>Festuca dolichophylla</i>, and <i>Lolium perenne</i> in a high-Andean Andisol (4150 m a.s.l.). A randomised complete block design (4 × 4 factorial, three replicates) was used. Biochar (20 t ha⁻¹) was applied at establishment, and microbial inoculants (5 L ha⁻¹) were applied at sowing and during crop development. Foliar elemental concentrations were determined by MP-AES and analysed using ANOVA and multivariate approaches. Biochar significantly increased soil pH (<i>p</i> &lt; 0.001). Ionomic responses depended on microbial identity: <i>Trichoderma</i> sp. enhanced P and Co accumulation while reducing Na (<i>p</i> &lt; 0.05), indicating coordinated nutrient regulation. Micronutrients (Fe, Mn, Cu) were primarily species-driven. These findings demonstrate that microbially functionalized biochar can selectively reconfigure plant ionomes, supporting microbiome-assisted strategies to improve nutrient efficiency and resilience in acidic agroecosystems.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paddies' Puzzle: Multi Criteria Decision Aiding (MCDA) Approach for Enhancing Natural and Agrobiodiversity in Italian Rice Farming Districts in Northern Italy","authors":"Michele Salvan,&nbsp;Giuseppe De Santis,&nbsp;Daniela Ponzini,&nbsp;Manuele Mussa,&nbsp;Daniele Seglie,&nbsp;Giovanni Soldato,&nbsp;Cristiana Cerrato,&nbsp;Simona Bonelli","doi":"10.1002/sae2.70191","DOIUrl":"https://doi.org/10.1002/sae2.70191","url":null,"abstract":"<p>Rice cultivation systems in north-western Italy in recent decades have experienced a process of natural biodiversity decline, agro-biodiversity compression and simplification of cultivation practices, reducing the role of paddies as wetland replacement areas. However, this trend can be reversed if farming choices are matched with paddy-associated biodiversity needs. This study aims to analyse the relationship between varietal and agronomic choices of rice growers and their impact on natural biodiversity, through three key wetland-associated taxa, namely butterflies, dragonflies, and amphibians. Starting from historical monitoring data (2014-2022) from the Lombardy-Piedmont rice farming district to obtain a comprehensive list of species, we assessed, through expert evaluations, how different cropping and varietal systems shape natural biodiversity. The varietal analysis focuses on the most cultivated varieties and follows the full life cycle of agronomical rice farming practices. Both agronomical and varietal options have been evaluated for their role in promoting natural biodiversity, through the Multi-Criteria Decision Aiding (MCDA) Approach and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Specifically, it emerges that, both in conventional and organic cultivation conditions, many agronomic choices can have a neutral impact. A limited number of practices or varietal characteristics have a significant positive or negative impact on natural biodiversity in paddies, constituting real bottlenecks or environmental catalysts in agrosystems. The methodology we proposed is flexible, being adaptable to different species and farming systems, and convertible into an app, for direct application by users in farmers and researchers. The final objective is to assess the wide-ranging impact of farmers' choices on wetland-associated biodiversity and promote sustainable farming options with less environmental impact, allowing better reconciliation between anthropic and natural priorities in paddies.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Microbial Traits to Predictive Management of Multifunctional Soils","authors":"Armando Cavalcante Franco Dias,&nbsp;Simone Raposo Cotta,&nbsp;Yiqi Luo,&nbsp;Nico Eisenhauer","doi":"10.1002/sae2.70212","DOIUrl":"https://doi.org/10.1002/sae2.70212","url":null,"abstract":"<p>Soil microbial ecology is data-rich yet struggles to predict ecosystem functions under ongoing environmental change, including climate warming, altered precipitation patterns, and land-use intensification. The core challenge lies in linking micron-scale microbial metabolism to field-scale outcomes. We argue that progress requires: (i) shifting from taxonomic inventories to functional traits as predictors; (ii) integrating machine learning with ecological theory, rigorous uncertainty quantification, and cross-validation; (iii) developing hybrid models that merge mechanistic understanding with data-driven approaches; and (iv) building new theoretical and mathematical frameworks to bridge spatiotemporal scales. We distinguish soil function (ecosystem-level outcomes) from microbial function (organismal or community-level activities), as the relationship between them remains poorly understood. We focus on agricultural soils as a primary application domain, given their global significance for food security, climate mitigation, and the urgent need for management-relevant predictions. Realising predictive microbiology demands standardised data, harmonised metadata, and transdisciplinary collaboration among soil scientists, ecologists, microbiologists, bioinformaticians, and data scientists. This commentary outlines a pathway from descriptive characterisation toward predictive, microbially informed management of multifunctional agricultural soils.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Year Monitoring of the Arthropod-Based Soil Biological Quality Index (QBS-ar) in Arable Fields of the Sentina Reserve: Spatial and Temporal Patterns and Environmental and Management Correlates","authors":"Martina Coletta,&nbsp;Aurora Torresi,&nbsp;Aldo D'Alessandro,&nbsp;Marco Monticelli,&nbsp;Daizy Bharti,&nbsp;Santosh Kumar,&nbsp;Stefano Chelli,&nbsp;Antonietta La Terza","doi":"10.1002/sae2.70213","DOIUrl":"https://doi.org/10.1002/sae2.70213","url":null,"abstract":"<p>Although the arthropod-based Biological Soil Quality Index (QBS-ar) is widely used to assess soil biological quality in agroecosystems, interpreting its variation under field conditions remains challenging. We characterised spatial and temporal patterns in QBS-ar and complementary arthropod-based indicators across four arable fields belonging to two farms within the Sentina Reserve (Central Italy): Fattoria Ferri (FE1 and FE2) and Selva Giurata (RL1 and RL2). Each field was sampled on six occasions, in June and October of each year from 2021 to 2023. Six exploratory multiple linear regression models examined associations between sub-sample level QBS-ar (partial QBS-ar) and six predictor groups: agricultural practices, soil chemistry, textural parameters, soil environmental factors, weather conditions and landscape composition. Across the 24 field-by-occasion assessments, total QBS-ar ranged from 102 to 209. Values were generally higher in the Selva Giurata fields, with RL2 and FE2 showing the highest and lowest mean values, respectively. The complementary biotic metrics followed a similar field-level pattern. At least one term in each predictor-group model met the significance threshold. These terms included exchangeable potassium, tillage for seedbed preparation, harvesting, very fine sand, temperature, relative humidity, soil moisture and the proportions of land classified as agricultural areas and of forest and semi-natural areas. Together, these results show the value of considering both management and environmental context when interpreting QBS-ar variation in field monitoring. The field-specific patterns identified here provide testable hypotheses for broader studies with independent farm-level replication.</p>","PeriodicalId":100834,"journal":{"name":"Journal of Sustainable Agriculture and Environment","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/sae2.70213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}