Agricultural Systems最新文献

{"title":"Exploring opportunities to improve crop-livestock integration and production in mixed farms with a serious game: The case of semi-arid Burkina Faso","authors":"Gildas G.C. Assogba ,&nbsp;Erika N. Speelman ,&nbsp;David Berre ,&nbsp;Myriam Adam ,&nbsp;Katrien Descheemaeker","doi":"10.1016/j.agsy.2025.104364","DOIUrl":"10.1016/j.agsy.2025.104364","url":null,"abstract":"<div><h3>Context</h3><div>Serious games are increasingly recognised as powerful tools to deepen knowledge and stimulate (social) learning among game participants.</div></div><div><h3>Objective</h3><div>We developed a serious game, called QUEEN, to better understand farmer decision-making and to explore potential options to improve crop-livestock integration and production as seen from the farmer perspective using a case study of a semi-arid farming system in Burkina Faso.</div></div><div><h3>Methods</h3><div>The development of the serious game was an iterative process with several rounds in which local farmers and local researchers played a pivotal role. The game included options for soil fertility management, crop and livestock production, market opportunities and interactions among participants. Three game sessions were organized to collect data on farmers' decision-making and management strategies. Beyond gameplay, the game was used as an experimental platform—its solution space was explored through simulations to assess the outcomes of possible strategies, and the impacts of assumptions and game's mechanics on the game's outcomes.</div></div><div><h3>Results and conclusions</h3><div>We showed that while the participants remained generally close to their real-life farm management, in the game they all applied mulch, cereal-legume strip intercropping and rotations, which is less common in reality. This demonstrates the capacity of the game to encourage farmers to explore alternative practices. The solution space exploration helped identify trade-offs between objectives (e.g., livestock production vs. income generation), and pathways to improve crop-livestock integration.</div></div><div><h3>Significance</h3><div>QUEEN is a valuable learning and research tool for Burkina Faso and other semi-arid systems, engaging stakeholders in critical reflection on system constraints and innovations.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104364"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia","authors":"Habtamu S. Gelagay ,&nbsp;Louise Leroux ,&nbsp;Lulseged Tamene ,&nbsp;Meklit Chernet ,&nbsp;Gerald Blasch ,&nbsp;Degefie Tibebe ,&nbsp;Wuletawu Abera ,&nbsp;Tesfaye Sida ,&nbsp;Kindie Tesfaye ,&nbsp;Marc Corbeels ,&nbsp;João Vasco Silva","doi":"10.1016/j.agsy.2025.104360","DOIUrl":"10.1016/j.agsy.2025.104360","url":null,"abstract":"<div><h3>Context</h3><div>Characterizing crop production environments is essential for targeted interventions, resource allocation, scaling localized findings, and agricultural decision-making. However, existing methods lack the spatial and temporal rigor required to capture spatial and temporal variability in crop production environments.</div></div><div><h3>Objective</h3><div>This study aimed to introduce a data-driven and dynamic spatial framework that integrates crop area mapping with the delineation of agro-ecological spatial units (ASUs) to characterize Ethiopia's rainfed wheat crop production environments.</div></div><div><h3>Methods</h3><div>Annual rainfed wheat areas for the 2021 and 2022 <em>Meher</em> growing seasons were mapped using an ensemble machine-learning approach, leveraging time-series satellite images and environmental data. Dynamic ASUs were delineated using pixel- and object-based clustering methods, considering short-term changes (annual ASUs for 2021 and 2022) and longer-term trends (ASUs developed using data aggregated over the period 2016–2022). Clustering was based on key biophysical variables, including climatic, soil, topographic, and vegetation indices derived from satellite images that capture crop growth and development over space and time.</div></div><div><h3>Results and conclusions</h3><div>The framework captured the spatial and temporal variability of wheat production environments, demonstrating its scalability across space and time. Rainfed wheat area mapping across two growing seasons revealed an expansion in rainfed wheat areas, highlighting the evolving nature of rainfed wheat cultivation in Ethiopia. The integration of rainfed wheat area mapping with dynamic ASU delineation identified five main production environments for wheat in Ethiopia, allowing to better target future research and development activities toward increasing wheat productivity in the country.</div></div><div><h3>Significance</h3><div>The developed framework can facilitate agronomic assessments and inform the targeting of agricultural interventions, with potential applications that extend beyond this case study of rainfed wheat in Ethiopia.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104360"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of farm household resilience and its impact on climate-smart agriculture performance: Insights from coastal and non-coastal ecosystems in Odisha, India","authors":"Usha Das ,&nbsp;M.A. Ansari ,&nbsp;Souvik Ghosh ,&nbsp;Neela Madhav Patnaik ,&nbsp;Saikat Maji","doi":"10.1016/j.agsy.2025.104370","DOIUrl":"10.1016/j.agsy.2025.104370","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Climate change presents severe challenges to agricultural production systems, particularly for smallholder farmers in developing nations like India. Strengthening the resilience of farm households is crucial for sustaining agricultural productivity in the face of climatic uncertainties. To enhance the effectiveness and upscaling of Climate Smart Agriculture (CSA) interventions, agricultural systems must be restructured and reformed considering resilience of farm households. Understanding the influencing factors of farm household resilience as well as effect of resilience pillars in improving the CSA performance is essential.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;Present study aims to identify the key determinants of farm household resilience across coastal and non-coastal ecosystems in Odisha, India, a highly climate-vulnerable state. It seeks to analyse the interrelationships between resilience determinants and explore the link between farm household resilience and CSA performance in terms of effectiveness and implementation feasibility as perceived by the farmers.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;The study investigates the resilience of farm households across coastal and non-coastal ecosystems, focusing on three dominant livelihood groups: crop farming, livestock farming, and combined crop-livestock farming. It employs the Resilience Index Measurement and Analysis (RIMA) framework to assess resilience through four key pillars: Access to basic services (ABS), Assets (AST), Social safety nets (SSN), and Adaptive capacity (AC). A Multiple Indicators Multiple Causes (MIMIC) model is used to examine resilience determinants, while Structural Equation Modeling (SEM) is applied to assess the relationship between household resilience and CSA performance. Additionally, multiple regression and path analysis are conducted to identify resilience drivers in terms of livelihood indicators across coastal and non-coastal ecosystems.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results and conclusions&lt;/h3&gt;&lt;div&gt;Findings indicate that crop-livestock farming households exhibit the highest resilience in both coastal and non-coastal regions, while crop farmers demonstrate higher resilience than livestock farmers. The study uncovers distinct resilience drivers between coastal and non-coastal areas. SEM analysis highlights a differential relationship between resilience and CSA performance, revealing how resilience influences CSA outcomes. It suggests a moderately fit model highlighting AC and SSN pillars contributing to resilience. Multiple regression and path analysis have revealed key livelihood indicators (such as infrastructure, connectivity, community network, landholding, irrigation access, and income) determining the resilience of the farmers. These insights contribute to a deeper understanding of micro-level resilience among farm households and its relationship with CSA performance.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;By identifying key resilience d","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104370"},"PeriodicalIF":6.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cropland displacement results in changes in cropland site conditions and quality in China during 1990–2020","authors":"Haoyang Wang ,&nbsp;Chaoqing Chai ,&nbsp;Wenhao Niu ,&nbsp;Yuan Qi ,&nbsp;Weiwei Zheng ,&nbsp;Xiangbin Kong ,&nbsp;Bangbang Zhang","doi":"10.1016/j.agsy.2025.104362","DOIUrl":"10.1016/j.agsy.2025.104362","url":null,"abstract":"<div><h3>Context</h3><div>Knowledge of cropland displacement patterns and their impacts contributes to cropland conservation, thus ensuring national food security. Despite the growing number of studies focusing on cropland displacement, how cropland displacement affects cropland quality is still not thoroughly explored.</div></div><div><h3>Objective</h3><div>This study aims to analyze the displacement characteristics of newly added and lost cropland in China from 1990 to 2020 and the impacts of cropland displacement on cropland site conditions and quality, to provide a scientific basis for cropland protection.</div></div><div><h3>Methods</h3><div>An analysis framework was developed for the impact of cropland displacement on cropland site conditions and quality. The center of gravity model was used to inscribe the spatial displacement trajectory of cropland, while the geographic information system (GIS) was employed for spatial analyses. Based on the land evaluation and site assessment (LESA) system, we quantitatively assessed changes in climate, topography, locational conditions, and landscape patterns of cropland as well as changes in cropland quality.</div></div><div><h3>Results and conclusions</h3><div>The gravity center of added and lost cropland in China generally shifted toward the southwest during 1990–2020. During this period, the most significant cropland displacement was observed in the Northern arid and semiarid region, the Yunnan-Guizhou Plateau, and the Middle-lower Yangtze River Plain in China. Further analysis revealed that cropland displacement led to changes in topography, climate, and landscape patterns, which were characterized by “occupying low-elevation cropland and compensating high-elevation one”, “occupying gentle-sloped cropland and compensating steep-sloped one”, “occupying superior cropland and compensating inferior one”, and “occupying integrated cropland and compensating fragmented one”. The cropland locational conditions exhibited a trend of “occupying cropland farther from rural roads and compensating one nearer”, and “occupying cropland nearer to rural settlements and compensating one farther”. Additionally, China's LESA (land evaluation and site assessment) scores gradually decreased from 68.91 in 1990 to 66.17 in 2020, indicating an overall decline in cropland quality.</div></div><div><h3>Significance</h3><div>The findings of this study provide useful insights to reveal the impacts of cropland displacement, and are of great significance for optimizing cropland spatial layout and improving cropland conservation policies in China.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104362"},"PeriodicalIF":6.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving crop modeling by simultaneously incorporating dynamic specific leaf area and leaf area index: A two-year experiment","authors":"Yufan Zhang ,&nbsp;Koki Homma ,&nbsp;Liangsheng Shi ,&nbsp;Yu Wang ,&nbsp;Han Qiao ,&nbsp;Yuanyuan Zha","doi":"10.1016/j.agsy.2025.104357","DOIUrl":"10.1016/j.agsy.2025.104357","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Specific leaf area (SLA) is an important trait for quantifying crop growth status and leaf physiological structure. It also serves as a vital crop-specific parameter in the derivation of leaf area index (LAI) and aboveground biomass within crop models.</div></div><div><h3>OBJECTIVE</h3><div>The simplified and empirical consideration of SLA in current models ignores its dynamics. To accurately characterize the leaf structural traits, the SLA in the model need to be modified as an updatable continuous variable.</div></div><div><h3>METHODS</h3><div>This study used a dual-branch neural network for processing two-view digital images to extract leaf-specific traits. A novel approach was then proposed to modify the SLA from a fixed sequence to an updatable variable by introducing the SLA dynamic function (SDF) into WOFOST model. Meanwhile, a data assimilation framework was developed based on the Ensemble Kalman Filter, enabling SLA and LAI observations to be uptaken simultaneously. A two-year drought-controlled experiment was conducted on rice to evaluate the method under varying water stress conditions.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Results demonstrated that the incorporation of SDF and the assimilation of SLA and LAI significantly improved the accuracy of LAI, aboveground biomass, and grain yield estimations compared to the original model (R² = 0.85, RMSE = 1310.05 kg⋅ha⁻¹). The incorporation of SLAs further improves model performance and highlights the complementary roles of SLA and LAI in data assimilation.</div></div><div><h3>SIGNIFICANCE</h3><div>The technical route developed in this study aims to provide a concise and efficient solution for the integration of crop growth simulation and observation while highlighting the importance of considering dynamic leaf traits in crop modeling to better capture the responses to environmental stresses such as drought.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104357"},"PeriodicalIF":6.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonizing soil carbon simulation models, emission factors and direct measurements used in LCA of agricultural systems","authors":"Simone Pelaracci ,&nbsp;Pietro Goglio ,&nbsp;Simon Moakes ,&nbsp;Marie Trydeman Knudsen ,&nbsp;Klara Van Mierlo ,&nbsp;Nina Adams ,&nbsp;Fossey Maxime ,&nbsp;Alberto Maresca ,&nbsp;Manuel Romero-Huelva ,&nbsp;Muhammad Ahmed Waqas ,&nbsp;Laurence G. Smith ,&nbsp;Frank Willem Oudshoorn ,&nbsp;Thomas Nemecek ,&nbsp;Camillo de Camillis ,&nbsp;Giampiero Grossi ,&nbsp;Ward Smith","doi":"10.1016/j.agsy.2025.104361","DOIUrl":"10.1016/j.agsy.2025.104361","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The increasing demand for animal products, coupled with the need to reduce greenhouse gas (GHG) emissions from livestock production, highlights the urgency for effective mitigation strategies for livestock systems, including the cropping systems. Soil organic carbon (SOC) sequestration, a crucial approach for reducing atmospheric GHG concentrations, is often underrepresented in Life Cycle Assessments (LCA) of agricultural systems, largely due to methodological challenges in accurately accounting for soil carbon dynamics.</div></div><div><h3>OBJECTIVE</h3><div>The objective of this study was to evaluate soil carbon simulation models, emission factors and direct measurements used in LCA, with the aim of developing a harmonized approach for including soil carbon change in agricultural LCAs. The goals were to: i) assess soil carbon simulation models, emissions factors and direct measurements used in LCAs of agricultural systems; ii) evaluate the strengths and weaknesses of these models; iii) provide recommendations for LCA practitioners; and iv) identify areas for future methodological improvements.</div></div><div><h3>METHODS</h3><div>A systematic review of soil carbon simulation models, emission factors and direct measurements used in LCAs of agricultural systems was conducted, obtaining 263 relevant articles from an initial pool of 29,151. In addition to direct measurements, fifteen soil carbon simulation models and three methods based on emission factors were identified and categorized into three tiers based on complexity and data requirements. A modified Delphi participatory process was used to evaluate each method against established criteria through expert workshops.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results showed an inverse relationship between applicability and accuracy of methods, making the choice of methodology critical to achieving high-quality LCA results. Recommendations emphasize selecting methods based on objectives and data availability, while being aware of the effect of the initial soil carbon level and the assessment time period when using soil carbon simulation models. In addition, this study identified current methodological challenges in assessing soil C dynamics in LCA of agricultural systems.</div></div><div><h3>SIGNIFICANCE</h3><div>This research provides a foundation for improving LCA practices and supports better decision-making in mitigating climate impacts of agricultural systems.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104361"},"PeriodicalIF":6.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“The end point is a… more appropriate innovation ecosystem” Mission-oriented and responsible innovation in Australian agricultural systems","authors":"Simon Fielke,&nbsp;Aysha Fleming,&nbsp;Emma Jakku,&nbsp;Cara Stitzlein,&nbsp;Katie Ricketts,&nbsp;Gillian Cornish,&nbsp;Stephen Snow,&nbsp;Graham Bonnett","doi":"10.1016/j.agsy.2025.104359","DOIUrl":"10.1016/j.agsy.2025.104359","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Responding to global sustainability challenges requires restructuring of systems of production and consumption. Such processes require careful consideration of the mechanisms which coordinate change at scale. We examine an exemplar Mission program, Drought Resilience Mission, and key enabling technology in Australia.</div></div><div><h3>OBJECTIVE</h3><div>With an aim to assist collectives of organisations to deploy programs with ambitions toward systemic change, we introduce a framework to operationalise innovation concepts simultaneously. Namely, experimenting with mission-oriented <em>and</em> responsible innovation ecosystem conceptualisation.</div></div><div><h3>METHODS</h3><div>We utilise an organisational case study of innovation discourse in Australia's national science agency. Secondary data and primary perceptions of expert respondents from a Mission Program, Drought Resilience Mission, and involved with co-development of a digital climate service in the agricultural sector were gathered (<em>n</em> = 52).</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>We propose that considering integration of mission-oriented and responsible innovation could help to unlock transition pathways. We suggest attention to coordination across level components and contexts via capability development, intermediation and integration of these innovation concepts will reduce duplication. Ultimately, increasing the impacts of agricultural research and innovation activities where systemic change is sought.</div></div><div><h3>SIGNIFICANCE</h3><div>The multi-level perspective on socio-technical transitions, mission-oriented innovation and responsible innovation are all frameworks for change, which differ in their backgrounds and agendas, yet all recognise how the spirit of transitions, missions and innovation logics can overlap. We provide an exploratory framework to assist with integration of these overlaps, to suggest integrated investments in agricultural innovation coalition coordination are more likely to result in systemic change ambitions being met.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104359"},"PeriodicalIF":6.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and strategies of resource poor farmers in adoption of innovative farming technologies: A comprehensive review","authors":"S. Vasavi ,&nbsp;N. Anandaraja ,&nbsp;P.P. Murugan ,&nbsp;M.R. Latha ,&nbsp;R. Pangayar Selvi","doi":"10.1016/j.agsy.2025.104355","DOIUrl":"10.1016/j.agsy.2025.104355","url":null,"abstract":"<div><h3>Context</h3><div>India's agricultural sector, with 121 million small and marginal holdings, faces challenges from a growing population, limited land, and restricted resources. Innovative farming technologies like precision agriculture, climate-smart practices, smart irrigation, and digital platforms offer solutions for enhancing productivity, sustainability, and economic viability. However, resource-poor farmers struggle to adopt these technologies due to low awareness, budget limitations, and infrastructure gaps.</div></div><div><h3>Objective</h3><div>This review examines the potential of innovative technologies for resource-poor farmers in India. It aims to identify accessible, effective solutions and explore strategies such as public-private partnerships, cooperative farming, and custom hiring centers to overcome adoption barriers.</div></div><div><h3>Methods</h3><div>A systematic literature review was conducted to gather findings on technologies relevant to smallholders. Studies were selected based on insights into accessibility, barriers, and support systems. The review categorized technologies by suitability for resource-poor settings and analyzed enabling factors like funding, education, and institutional support.</div></div><div><h3>Results and conclusions</h3><div>Technologies that enhance resource efficiency, like precision agriculture, can benefit smallholders. Yet, adoption remains limited due to financial, informational, and infrastructure barriers. Strategies involving partnerships, cooperatives, and custom hiring show promise in reducing these obstacles. A multi-faceted approach with policy, financial, and educational support is essential for adoption.</div></div><div><h3>Significance</h3><div>This review highlights actionable pathways for technology adoption among India's smallholder farmers, emphasizing the need for supportive frameworks and local research. The findings are relevant to policymakers and development practitioners focused on enhancing food security and resilience in smallholder farming.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104355"},"PeriodicalIF":6.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of livelihood diversification in farming systems of the Eastern Gangetic Plains","authors":"Tamara M. Jackson ,&nbsp;Ravi Nandi ,&nbsp;Arifa Jannat ,&nbsp;Arunava Ghosh ,&nbsp;Dilip Kumar Hajra ,&nbsp;Biplab Mitra ,&nbsp;Md Mamunur Rashid ,&nbsp;Sagar Bista ,&nbsp;Anjana Chaudhary ,&nbsp;Pragya Timsina ,&nbsp;Emma Karki ,&nbsp;Kali Rattan Chakma ,&nbsp;Gunjan Rana ,&nbsp;Avinash Kishore","doi":"10.1016/j.agsy.2025.104346","DOIUrl":"10.1016/j.agsy.2025.104346","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The Eastern Gangetic Plains (EGP) is a region characterized by smallholder-dominated farming systems, facing rapid socio-economic and environmental changes. Livelihood diversification away from traditional agriculture is increasingly seen as a strategy to enhance resilience, income stability, and food security among these smallholders. However, comprehensive understanding of diversification patterns and their drivers within the EGP remains limited.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to elucidate the patterns of livelihood diversification in farming systems across the EGP, and identify the key factors driving diversification.</div></div><div><h3>METHODS</h3><div>Utilizing data from the <em>Rupantar</em> project involving a baseline survey of 1400 households across India, Nepal, and Bangladesh, and a subsequent analysis employing the Simpson's Index of Diversity (SID) to quantify diversification levels. Multivariate regression models were used to explore the determinants of diversification, while disaggregating the analysis by country and diversification components (crop plot, crop non-plot, and non-crop non-plot).</div></div><div><h3>RESULTS AND CONCLUSION</h3><div>The study revealed moderate levels of diversification across the EGP, with significant geographical and contextual variability. Key drivers of diversification included access to resources, gender, education, market access, and institutional support, with notable differences across countries and diversification types. Specifically, non-ownership of irrigation pumps, female household headship, and engagement in off-farm and non-farm activities emerged as significant predictors of higher diversification levels.</div></div><div><h3>SIGNIFICANCE</h3><div>This study contributes to a nuanced understanding of livelihood diversification in the EGP, highlighting the complexity of diversification patterns and the multifaceted nature of its determinants and impacts. By identifying specific drivers of diversification, the findings provide valuable insights for policymakers, development practitioners, and researchers aiming to support rural livelihoods in the region. Emphasizing the role of gender, resource access, and institutional support, the study underscores the importance of tailored interventions to enhance the resilience and sustainability of smallholder farming systems in the face of changing environmental and socio-economic conditions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104346"},"PeriodicalIF":6.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale","authors":"Raniero Della Peruta ,&nbsp;Valentina Mereu ,&nbsp;Donatella Spano ,&nbsp;Serena Marras ,&nbsp;Rémi Vezy ,&nbsp;Antonio Trabucco","doi":"10.1016/j.agsy.2025.104353","DOIUrl":"10.1016/j.agsy.2025.104353","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Climate change may lead to negative impacts on coffee production, such as reduced yields. Addressing this issue requires identifying climate risks and assessing the adaptation potential of agronomic practices across spatial and environmental gradients.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to evaluate climate change impacts on arabica coffee yields at continental scale and evaluate a specific adaptation measure, i.e. increasing shade tree density in agroforestry settings, by simulating the physiological links between coffee growth, climatic factors and agronomic management.</div></div><div><h3>METHODS</h3><div>After evaluating the performance of the process-based model DynACof in simulating arabica yields (using data from previous studies), we developed a new tool called G-DynACof, a modelling framework for spatializing DynACof on a regional scale using extensive climate projections and soil geodata. We used G-DynACof to simulate trends of potential coffee yields in Latin America and Africa using an ensemble of downscaled and bias-corrected climate projections for the period 2036–2065 compared to a historical period 1985–2014.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Despite considerable uncertainties due to the scarcity of information on agronomic management at the regional scale, our results indicate that potential yields could decrease between 23 % and 35 % in Latin America and between 16 % and 21 % in Africa, depending on the Shared Socioeconomic Pathway (SSP) considered (SSP1–2.6 and SSP5–8.5, respectively). Yield variations were very heterogeneous in space, with yields increasing at high altitudes and low latitudes, indicating a possible future shift of production areas. In our simulations, the effect of increased shade tree density on productivity was also spatially variable, and its potential for adaptation to climate change remains uncertain, requiring further investigation.</div></div><div><h3>SIGNIFICANCE</h3><div>Impact analyses and adaptation modelling of coffee agrosystems, together with socio-economic indicators, can delineate realistic, comprehensive, integrated risk assessments and support effective adaptation recommendations.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"227 ","pages":"Article 104353"},"PeriodicalIF":6.1,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}