Agricultural Systems最新文献

{"title":"AI-powered Pheno-Farm Server: Making adaptive farming decisions","authors":"Kenny Paul , Stefan Schweng , Hans-Peter Kaul , Franz Gansberger , Andreas Holzinger","doi":"10.1016/j.agsy.2025.104563","DOIUrl":"10.1016/j.agsy.2025.104563","url":null,"abstract":"<div><h3>Context</h3><div>Agricultural productivity faces growing challenges due to climate variability, resource constraints, and the demand for sustainable practices. Precision agriculture, powered by artificial intelligence (AI) integrate high-throughput phenotyping, offers practical advancements for monitoring crop traits, growth patterns, and responses to environmental factors. The Pheno-Farm Server (PFS) leverages AI-driven approaches to integrate real-time data, analyse it, and enable adaptive decision making for efficient crop management in diverse farming environments such as glasshouses, rain-out shelters (ROS), experimental farms, and open fields. This empowers stakeholders to make informed, data-driven decisions.</div></div><div><h3>Objective</h3><div>This study evaluates the potential of the PFS in advancing precision agriculture through adaptive AI models. It focuses on the system's ability to collect, process, and analyse data by developing a robust pipeline for integrating phenotyping datasets and optimizing nitrogen application. Additionally, it assesses the effectiveness of AI/ML-based adaptive decision-making in improving crop management and resource utilization.</div></div><div><h3>Methods</h3><div>The PFS was implemented using a structured framework integrating hardware, software, and data processing components. Data from phenotyping platforms, including plant area (PA) measurements and digital biomass (DBM) estimates from four cereal cultivars under varying nitrogen levels (NL) and drought conditions (DC), were aggregated into the PFS. Three regression models such as Ridge Regression, Support Vector Regression (SVR), and Random Forest were trained and evaluated using a structured pre-processing pipeline. Hyperparameters were optimized through grid search with 5-fold cross-validation. Model performance was assessed using the coefficient of determination (R<sup>2</sup>) and normalized root mean squared error (NRMSE). Tests were conducted to validate the system's reliability and applicability.</div></div><div><h3>Results and conclusions</h3><div>The experimental implementation of the PFS demonstrated its capability to collect and analyse data from various sources effectively. The SVR model had the highest accuracy with an R<sup>2</sup> of 0.992 and an NRMSE significantly lower than other models followed by Random Forest and Ridge Regression. SVR was good at understanding complex relationships and worked well with unseen nitrogen levels. Random Forest showed limitations in generalization due to data dependency. The integration of AI-powered servers like PFS improve precision agriculture by making real-time data analysis possible, allowing smart decisions, and using resources more efficiently.</div></div><div><h3>Significance</h3><div>The integration of AI-powered systems like PFS represents a significant advancement in sustainable farming. These systems help drive innovation and tackle important agricultural problems, leading to","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104563"},"PeriodicalIF":6.1,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516442","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":"Farm-level adaptations to harvest logistics constraints in export-oriented grain systems","authors":"Garima ,&nbsp;Doina Olaru ,&nbsp;Brett Smith ,&nbsp;Kadambot H.M. Siddique","doi":"10.1016/j.agsy.2025.104565","DOIUrl":"10.1016/j.agsy.2025.104565","url":null,"abstract":"<div><h3>Context</h3><div>Grain Producers in export-oriented grain systems face mounting pressure to coordinate harvest logistics under growing climatic, institutional, and infrastructure constraints. These stresses are especially acute in regions with decentralised production, long transport routes, and limited receival and labour capacity. In such settings, logistics adaptability is not just an operational concern but a critical factor shaping farmers' access to markets and overall system performance.</div></div><div><h3>Objective</h3><div>This study investigates how Western Australian (WA) grain farmers adapt their storage, freight, and delivery strategies in response to misalignments between on-farm decision-making and centralised grain logistics infrastructure.</div></div><div><h3>Methods</h3><div>Using an abductive, mixed-methods approach—including 48 surveys, 19 interviews, and media and policy document analysis—we explored how farm-level decisions interact with institutional asymmetries and inflexible infrastructure. The design is theoretically informed by Actor–Network Theory to trace translations, enrolment and obligatory passage points among heterogeneous human/non-human actors, and by the Actors–Resources–Activities framework to map actor bonds, resource ties and activity links across inland logistics.</div></div><div><h3>Results and conclusions</h3><div>The findings reveal that growers rely on on-farm storage, investment in mobile freight capacity, and tactical scheduling to manage seasonal bottlenecks and limited delivery access. These strategies are shaped by market signals, spatial disparities in receival infrastructure and transport options and behavioural heuristics that help farmers navigate institutional constraints. While such adaptations provide short-term resilience, they also create inefficiencies and reinforce systemic inequities in harvest throughput, export timing, and access to price premiums.</div></div><div><h3>Significance</h3><div>This study contributes to ongoing debates on agricultural systems resilience by linking farm-level behavioural adaptation with infrastructure governance and logistics system design. It highlights the need for modelling frameworks—such as agent-based or participatory approaches—that reflect decentralised, spatially differentiated decision-making. Implications are drawn for transport planning, cooperative infrastructure policy, and the development of future decision-support systems tailored to export-reliant agricultural regions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104565"},"PeriodicalIF":6.1,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516465","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":"There is more to it than just adoption: Exploring agricultural mechanisation journeys in South Asia","authors":"B. Brown ,&nbsp;P. Timsina ,&nbsp;A. Chaudhary ,&nbsp;E. Karki ,&nbsp;A. Sharma ,&nbsp;K.K. Das ,&nbsp;A. Ghosh ,&nbsp;M.W. Rahman","doi":"10.1016/j.agsy.2025.104564","DOIUrl":"10.1016/j.agsy.2025.104564","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Agri-mechanisation continues to be a policy and development priority across the Eastern Gangetic Plains of South Asia. However, adoption evaluations are often constrained by methodological limitations, including narrow sampling, restricted analytical scope, and an overreliance on binary adoption metrics. These shortcomings hinder a deeper understanding of the dynamics and quality of mechanisation adoption in the region.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to improve the assessment of agri-mechanisation adoption by introducing and applying a comprehensive analytical framework (‘Five Ps of Adoption Analysis’) to move beyond binary metrics and uncover the underlying patterns, processes, and pathways of mechanisation uptake.</div></div><div><h3>METHODS</h3><div>The ‘Five Ps’ framework was applied to survey data on the use of eight key agricultural machines collected from 5053 households across 55 villages in Nepal, India, and Bangladesh. The framework integrates proportional, temporal, typological, pathway, and process-based analyses to enable a multidimensional evaluation of adoption.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The analysis reveals critical insights into regional mechanisation trends, including significant constraints in extension systems, sub-optimal adoption rates, and the presence of pseudo-adoption. Temporal and typological analyses expose the uneven evolution of adoption processes across contexts and machine types. The framework offers a novel means to capture the diversity and progression of adoption over time, providing a richer understanding than traditional binary approaches.</div></div><div><h3>SIGNIFICANCE</h3><div>This study advances methodological approaches in mechanisation research and offers practical insights for policymakers and development practitioners. By identifying key barriers and dynamic adoption patterns, the findings support more targeted interventions and highlight the need for complementary qualitative research to inform sustainable agri-mechanisation strategies in South Asia.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104564"},"PeriodicalIF":6.1,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516463","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":"Applications of ecological niche and species distribution models in agricultural, livestock, and forestry systems: A comprehensive review","authors":"Lucas A. Fadda ,&nbsp;Rodrigo Lasa-Covarrubias ,&nbsp;Luis Osorio-Olvera ,&nbsp;M. Gabriela Murúa ,&nbsp;Andrés Lira-Noriega","doi":"10.1016/j.agsy.2025.104542","DOIUrl":"10.1016/j.agsy.2025.104542","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Declining global agricultural productivity driven by climate variability and pest proliferation creates unprecedented food security challenges that traditional management approaches cannot adequately address. Ecological Niche Models (ENM) and Species Distribution Models (SDM) have emerged as powerful frameworks for predicting spatial distributions under future climate scenarios. These approaches enable identification of optimal cultivation zones and development of targeted adaptation strategies that enhance resilience across agricultural systems while supporting proactive management for global food security.</div></div><div><h3>OBJECTIVE</h3><div>This review explores the development and use of ENM and SDM in agriculture, livestock, and forestry, emphasizing their role in identifying production areas, assessing risks from pests, diseases, and weeds, and informing management decisions. It also addresses key methodological aspects and their growing importance in sanitary planning, food security, and climate adaptation.</div></div><div><h3>METHODS</h3><div>We conducted a systematic literature review to examine ENM and SDM applications in productive systems. The analysis recorded specific uses, target organisms, study objectives, and key elements of model construction, parameterization, validation, transferability, and input data.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The review defined the current scope of ENM and SDM in productive systems and identified critical knowledge gaps. It highlights the value of the BAM framework to guide modeling design and interpretation. The findings provide a conceptual base for broader applications and identify future research and implementation opportunities.</div></div><div><h3>SIGNIFICANCE</h3><div>ENM and SDM transform complex ecological and production data into actionable insights that support policy, social, economic, and management decisions across agriculture, forestry, and livestock sectors. Their flexibility across scales enables tailored solutions. Technological advances will enhance their impact, positioning these models as essential tools for sustainable food security.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104542"},"PeriodicalIF":6.1,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515802","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":"Estimating cumulative input effects in annual crop production: A LASSO-based panel data approach from India","authors":"Hiroyuki Takeshima ,&nbsp;Avinash Kishore","doi":"10.1016/j.agsy.2025.104555","DOIUrl":"10.1016/j.agsy.2025.104555","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;In non-experimental settings, evidence of interannual cumulative effects of inputs for annual crops (e.g., the effects of inputs in one year on outputs in the subsequent year) remains limited despite potential implications within dynamic production systems. The scarcity of sufficiently long annual panel data partly explains this. Furthermore, in non-experimental settings, quantities of many inputs are highly correlated with one another and across years, posing challenges when isolating such cumulative effects through conventional estimation methods.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study narrows these knowledge gaps by applying novel methods to unique annual panel datasets at district- and farm household-levels in India. Specifically, we identify whether certain inputs exhibit meaningfully significant cumulative effects on production relative to the non-cumulative effects and the effects of other inputs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We start with flexible translog production functions appropriate for identifying cumulative effects in non-experimental settings. We apply shrinkage methods (LASSO and GMM-LASSO) to approximate production functions with reduced parameter dimensions, addressing multicollinearity among multiple inputs as well as among the same inputs across years, and potential endogeneity in inputs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Throughout the shrinkage process, potassium fertilizer consistently remains a key predictor of outputs, while other inputs (land, labor, capital, irrigation, and other fertilizer nutrients) drop out mainly due to high collinearity with potassium and other inputs. More importantly, the cumulative quantity of potassium from the previous year, as well as the current year, is a consistently more critical determinant of production than the quantity of potassium from the current year alone, demonstrating the significant cumulative effects of potassium. These patterns hold both at district and farm levels across diverse agroecologies and cropping systems. Furthermore, the dynamic panel data analyses further suggest that farmers' use of potassium in the current year is significantly negatively affected by its use in the previous year, potentially stabilizing outputs across years. Earlier agronomic results suggesting residual effects of potassium are potentially relevant across wider geographic regions than previously thought. Simulation exercises reveal that the cumulative effects of potassium translate into a significant carryover of productivity into the following year and, combined with the dynamics, considerable repercussions on production during the subsequent years&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;To the authors' knowledge, this is the first study to verify the interannual cumulative effects of potassium fertilizer in nonexperimental settings, using methods that control for all other relevant production inputs and their potential cumulative effects with","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104555"},"PeriodicalIF":6.1,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515659","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":"Spatial planning based on the modeling the food-energy-water‑carbon nexus: A case study of the Yangtze River basin","authors":"Beibei Guo ,&nbsp;Xian Zou ,&nbsp;Yingxue Cui ,&nbsp;Suchen Ying ,&nbsp;Yinkang Zhou","doi":"10.1016/j.agsy.2025.104552","DOIUrl":"10.1016/j.agsy.2025.104552","url":null,"abstract":"<div><h3>Context</h3><div>The food-energy-water (FEW) nexus is crucial for addressing global resource conflicts and sustainability challenges. It achieves this by securing resources, enhancing synergies, managing competition, and supporting climate adaptation. In the context of China, there is an urgent need to balance FEW securities with carbon neutrality in its food system, particularly given the complex environmental and socioeconomic pressures it faces.</div></div><div><h3>Objective</h3><div>This study aims to analyze correlations within the food-energy-water‑carbon (FEWC) nexus, characterize its resources, identify spatial allocation patterns and synergistic relationships, determine obstacles to agricultural sustainability, land planning, and cultivated land protection within the nexus, and propose tailored protection strategies for the basin.</div></div><div><h3>Methods</h3><div>Utilizing multi-source remote sensing data, FEWC resource potentials were characterized (integrated food production, power plant/renewable energy potential, total regional water production, carbon storage). Principal component analysis (PCA) was applied to the Yangtze River basin data to study the FEWC nexus, using representative indicators providing a robust scientific characterization.</div></div><div><h3>Results and conclusions</h3><div>PCA showed the first principal component indicates FEWC synergies (reflecting overall synergy intensity), while the second reveals FEWC trade-offs. FEWC nexus evolution enhanced overall synergy but displayed an inverted U-shape pattern in terrestrial ecosystems, marked by growing energy dominance and a shift of food status. Consequently, food security's strategic importance decreased amid rising energy consumption, persistent water scarcity, and increasing carbon neutrality demands. Future conflicts concentrate in cultivated land areas. Spatial zoning requires prioritizing farmland protection and developing energy-agriculture linkages. Monitoring should track food production polarization and conflict zone changes, with FEWC synergy projected to rise in more than 70 % of cities. Carbon storage dynamics must also be monitored, while agricultural buffer zones are needed to reduce ecological risks.</div></div><div><h3>Significance</h3><div>The study provides valuable insights applicable to territorial spatial planning efforts at the basin scale. Furthermore, it offers tools with which to analyze complex resource nexuses and strategies for sustainable agricultural land management on a global scale.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104552"},"PeriodicalIF":6.1,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473191","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":"Comprehensive review of optimization and surrogate models for agricultural water resources and reservoir water management","authors":"Ankita Kumari ,&nbsp;Tinesh Pathania","doi":"10.1016/j.agsy.2025.104562","DOIUrl":"10.1016/j.agsy.2025.104562","url":null,"abstract":"<div><h3>Context:</h3><div>The efficient and coordinated management of water resource systems is crucial to address increasing demands and resource uncertainties. As a result, good water resources management in agricultural and reservoir systems is vital for ensuring water security, better resource utilization, and sustaining agricultural productivity.</div></div><div><h3>Objective:</h3><div>In this respect, optimization models are employed to analyze several management scenarios, allowing decision-makers to identify optimal strategies under varying conditions. Therefore, we examined optimization-based approaches to address interconnected challenges to promote better understanding of systemic balances between agricultural water and reservoir-based systems.</div></div><div><h3>Methods:</h3><div>We conducted a comprehensive review of more than 400 research articles. The review article is grouped into sections, which include the applications of optimization techniques in irrigation, surface water and groundwater management, water-energy- food (WEF) nexus, and reservoir management.</div></div><div><h3>Results and conclusions:</h3><div>The review emphasizes the importance of agricultural water resources and reservoir management for developing an efficient water resource system. This includes simulation–optimization (SO) models that facilitate sustained water policies for conflicting objectives. It also highlights the increasing applications of machine learning (ML) based surrogate models to reduce computational efforts. Further, several reviewed studies indicate that the participation of different stakeholders in the optimization process leads to better water resource utilization.</div></div><div><h3>Significance:</h3><div>The review presents an overview containing numerous case studies throughout the world, demonstrating the applicability of optimization and ML methodologies. This serves as an important document for academicians, industry experts, and policymakers with the advantages of sustainable water system operations and advances in optimization modeling techniques.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104562"},"PeriodicalIF":6.1,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473265","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":"Integrated land-use and agricultural emissions modeling reveals untapped mitigation potential in livestock feed substitution","authors":"Xue Yang ,&nbsp;Yijie Yao ,&nbsp;Yingxu Fan ,&nbsp;Vilma Sandström","doi":"10.1016/j.agsy.2025.104558","DOIUrl":"10.1016/j.agsy.2025.104558","url":null,"abstract":"<div><div>CONTEXT</div><div>With rising global demand for animal-sourced foods, controlling greenhouse gas (GHG) emissions from livestock production has become critical to mitigating climate change. While prior research has quantified the global potential of using agricultural by-products to replace crop feeds, a comprehensive evaluation of the associated GHG mitigation benefits—particularly when considering both land-use change (LUC) and agricultural production emissions—has not been addressed.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to evaluate the global integrated GHG mitigation potential of replacing livestock crop feeds with agricultural by-products, by combining emissions from both land-use change and agricultural activities, while also identifying regional variations in this potential.</div></div><div><h3>METHODS</h3><div>Five major agricultural by-products (cereal bran, molasses, sugar beet pulp, citrus pulp, and distillers' grains) were selected to replace six energy-intensive crop feeds across 19 global regions. The methodology comprised four key steps: (1) developing region-specific substitution matrices based on previous literature; (2) estimating land-use change emissions of replaced crop feeds and replacing by-products using spatially explicit data; (3) assessing agricultural emissions of replaced crop feeds and replacing by-products with statistical data; and (4) calculating global GHG mitigation potential as the difference between emissions from replaced crop feeds and those from replacing by-products.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Globally, replacing crop feeds with agricultural by-products was projected to reduce GHG emissions by 167 Mt. CO₂-eq. (80–276 Mt. CO₂-eq) annually over a 30-year timeframe, which accounts for 9 % (4 %–14 %) of the total annual emissions from crop feeds; 80 % of this reduction is attributed to land carbon restoration. Regionally, South-eastern Asia (31 Mt. CO₂-eq) and Southern Asia (30 Mt. CO₂-eq) emerge as mitigation hotspots, primarily due to their status as tropical and subtropical regions with high native land carbon stocks. Eastern Asia, Eastern Europe, Southern America, and Western Europe exhibit mitigation potentials ranging from 14 to 20 Mt. CO₂-eq, driven mainly by their large volumes of replaced crop feeds.</div></div><div><h3>SIGNIFICANCE</h3><div>This study advances the development of a framework for assessing land-use and agricultural emission reductions from crop feed substitution with by-products. It provides a foundational reference for further research on mitigation strategies in the global livestock feed sector.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104558"},"PeriodicalIF":6.1,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473192","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 novel framework for evaluating farmer resilience: Methodological advances and theoretical foundations","authors":"Kieron Moller ,&nbsp;A. Pouyan Nejadhashemi ,&nbsp;Mohammad Tirgari ,&nbsp;Nilson Vieira Junior ,&nbsp;Ana Julia Paula Carcedo ,&nbsp;Ignacio Ciampitti ,&nbsp;P.V. Vara Prasad ,&nbsp;Amadiane Diallo","doi":"10.1016/j.agsy.2025.104554","DOIUrl":"10.1016/j.agsy.2025.104554","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Many challenges, such as climate change, conflict, and economic fluctuations, pose a significant threat to global agriculture and food systems. Therefore, it is crucial to develop and evaluate methods to enhance agricultural resilience.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVES&lt;/h3&gt;&lt;div&gt;This study aims to identify knowledge gaps in commonly used methods for determining farm household resilience. It addresses these gaps by developing a novel method for determining resilience. Therefore, the objectives of this study were to (1) identify knowledge gaps within existing resilience determination literature and develop a resilience quantification approach and (2) incorporate the shock-failure-recovery sequence to measure resilience. Incorporating this sequence allows understanding the dynamic relationship between failure and recovery phases and how it varies across study units.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;A comprehensive resilience measure for farmers' livelihoods is established by integrating two fundamental dimensions: (1) the system's failure dynamics caused by external shocks and (2) the recovery path following these shocks. This study extends these aspects within the theoretical framework of the agricultural household model, which simultaneously captures farmers' production and consumption decisions within rural economies. The agricultural household model is particularly relevant in contexts characterized by incomplete or missing markets, which often arise due to high transaction costs. By leveraging this framework, we conceptualize failure and recovery as intrinsic components of farmers' resilience, systematically linking household decision-making processes to resilience assessment.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;The proposed resilience measurement procedure offers two key advantages over conventional methodologies: (1) it conceptualizes resilience as an explicitly quantifiable variable rather than a latent construct; and (2) it mitigates biases stemming from perception-based assessments and subjective valuations. The econometric framework improves methodological flexibility. It estimates the marginal effect of the farmer indicator variable on recovery probabilities and accommodates diverse data structures. However, ensuring the procedure's accuracy requires further refinement through rigorous validation studies, multidimensional assessment, context-specific indicator selection, and the development of standardized analytical models to enhance methodological consistency and policy relevance.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;This study contributes to the field of agricultural resilience by introducing a novel, data-driven framework. The framework can integrate farmers' indicator variables related to economic, nutritional, risk, and other livelihood aspects to measure resilience. The results from this resilience measurement approach can enhance farmers' resilience, helping achieve sustainable dev","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104554"},"PeriodicalIF":6.1,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462000","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":"Identification of synergies and trade-offs in cropping system performance in southern Australia","authors":"Li Luo ,&nbsp;Matthew J. Knowling ,&nbsp;Aaron C. Zecchin ,&nbsp;Glenn K. McDonald","doi":"10.1016/j.agsy.2025.104556","DOIUrl":"10.1016/j.agsy.2025.104556","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Expectations are mounting on cropping systems to satisfy objectives across social, economic, and environmental dimensions. To identify cropping systems with an enhanced capacity to deliver on these diverse objectives, it is essential to understand both how these systems perform for a wide range of quantifiable performance metrics and the extent of synergies and trade-offs between these metrics, as they describe the underlying system's processes and properties. Such knowledge is presently lacking for dryland cropping systems in the southern grains region of Australia (SGR), one of the largest global grain production regions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study (1) evaluates the performance of different cropping systems in the SGR for a range of performance metrics and (2) explores the relationships between these metrics for the range of case studies considered.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Using the process-based crop model, APSIM, we simulated the water-limited production potential of different cropping systems across diverse environments, from which performance metrics were computed to describe key system processes and properties. A total of 16 systems, with varying cropping diversity and intensity, were evaluated using 14 performance metrics spanning six objectives. Three case studies collectively represent key variabilities in climate and soil within the SGR.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;No single cropping system consistently outperformed others across all performance metrics. The ‘baseline’ systems, which reflect the most commonly adopted system in given regions, were generally not the top performers across several key metrics, highlighting some potential opportunities for performance improvements. Systems achieving higher productivity (e.g., water use efficiency (WUE)), were generally associated with improved environmental outcomes across all study areas, including lower CO&lt;sub&gt;2&lt;/sub&gt;-e emissions and decreased relative soil loss. In addition, while higher WUE was linked to greater gross margins (GM) in the low- and high-rainfall zones, this relationship was not as evident in the mid-rainfall zone. Synergies were strongest for the high rainfall zone case study, where the correlation coefficients were 0.94 between WUE and GM, and −0.72 between WUE and CO&lt;sub&gt;2&lt;/sub&gt;-e emissions. Trade-offs and synergies were influenced by both site- and system-specific factors.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;Our findings highlight that identified synergies between economic and environmental metrics could serve to enhance confidence in growers regarding strategic cropping decisions. Our findings offer region-specific insights that can help inform decisions towards more sustainable and profitable cropping systems, while some relationships that were found to be independent of system and sites may be applicable to other cropping regions in similar dryland farming worldwide.&lt;/div&gt;&lt;/","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104556"},"PeriodicalIF":6.1,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462342","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}