Agricultural Systems最新文献

{"title":"Agriculture on wet peatlands: the sustainability potential of paludiculture","authors":"Ralph J.M. Temmink ,&nbsp;Kristiina Lång ,&nbsp;Renske J.E. Vroom ,&nbsp;Jens Leifeld ,&nbsp;Christian Fritz ,&nbsp;Walther Zeug ,&nbsp;Daniela Thrän ,&nbsp;Clemens Kleinspehn ,&nbsp;Greta Gaudig ,&nbsp;Josephine Neubert ,&nbsp;Jürgen Kreyling ,&nbsp;Jennifer M. Rhymes ,&nbsp;Chris D. Evans ,&nbsp;Wiktor Kotowski ,&nbsp;Anke Nordt ,&nbsp;Franziska Tanneberger","doi":"10.1016/j.agsy.2025.104561","DOIUrl":"10.1016/j.agsy.2025.104561","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Humanity must overcome the polycrisis of biodiversity loss, climate change and pollution. These challenges are especially urgent in peatlands, which develop slowly under waterlogged conditions, function as landscape filters and store large amounts of carbon. Drainage for agriculture, forestry or peat extraction leads to severe socio-ecological impacts, including greenhouse gas emissions, biodiversity loss, land subsidence, higher flood and drought risks and downstream pollution.</div></div><div><h3>OBJECTIVE</h3><div>This study evaluates paludiculture as an innovative wet agricultural land use that maintains wet peatlands, offers economic alternatives to drainage-based systems and reduces environmental impacts.</div></div><div><h3>METHODS</h3><div>We reviewed and synthesized ecological and socio-economic evidence from low- and high intensity paludiculture practices to assess their potential to balance human needs with peatland conservation.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Paludiculture is a promising new agricultural land use that effectively reduces greenhouse gas emissions, supports biodiversity restoration and contributes to climate mitigation and sustainable development. Our findings show direct and indirect contributions to ten UN Sustainable Development Goals: no poverty, good health, clean water, clean energy, innovation, sustainable cities and communities, responsible production, climate action, life below water, and life on land. Nonetheless, challenges remain regarding economic viability, land-use competition and management.</div></div><div><h3>SIGNIFICANCE</h3><div>Paludiculture shows how wetland agriculture can create new revenue opportunities combined with ecological protection. By contributing to both climate and biodiversity goals, it is a sustainable alternative to drainage-based peatland use.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104561"},"PeriodicalIF":6.1,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568698","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":"Global dynamics of climate smart agricultural practices and technologies: Recent advancements, challenges and potential future pathways - A review","authors":"Belay Tizazu Mengistie,&nbsp;Ram L. Ray","doi":"10.1016/j.agsy.2025.104570","DOIUrl":"10.1016/j.agsy.2025.104570","url":null,"abstract":"<div><div>Climate smart agriculture (CSA) is increasingly promoted as a solution to climate related threats to global food systems. While research on CSA is growing, critical analysis of its evolution, implementation, and future pathways remains limited, especially across diverse geopolitical contexts. Critics argue that several farming practices, interventions, and technologies are being introduced as climate-smart, even though they may not effectively address the issues caused by climate change. This review systematically examines 129 publications to assess challenges, recent advancements, and future directions in CSA practices and technologies. The findings reveal significant barriers to adoption, including policy gaps and technological limitations. This review identified several critical challenges and potential future pathways in the current structure of CSA adoption which includes fragmented definitions, practice vs. policy gap, insufficient integration of socio-economic dimensions; weak monitoring and accountability mechanisms; overreliance on quantitative metrics and fragmented indicator systems among others. CSA has advanced globally through diverse practices and technologies, yet faces political contestation, goal trade-offs, and power imbalances. Its adoption depends on personal, technological, economic, institutional, socio-cultural, and informational factors CSA is not a one-size-fits-all solution. It highlights concerns over CSA being lacking unified criteria, and unevenly addressing its three core pillars. Overall, this review analyzed that CSA implementation often reflects power imbalances, as policies, funding, and technologies are largely shaped by institutions in the Global North, frequently misaligned with the needs and realities of smallholder farmers in the Global South. Effective CSA requires context-specific solutions that optimize synergies and manage the trade-off between core pillars of CSA. The review calls for context specific interventions and broader engagement beyond scientific framing to make CSA more inclusive and effective for farmers, policymakers, and stakeholders globally.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104570"},"PeriodicalIF":6.1,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568697","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":"Natural capital enhances farm production, profitability and financial resilience: findings from a study on 230,000 ha of farmland in Australia","authors":"Elizabeth Heagney ,&nbsp;Daniel Gregg ,&nbsp;Dan Hill ,&nbsp;James Radford ,&nbsp;Grace Sutton ,&nbsp;Fred Rainsford ,&nbsp;Daniel O'Brien ,&nbsp;Angela Hawdon ,&nbsp;Imogen Semmler ,&nbsp;Mark Gardner ,&nbsp;Milly Taylor ,&nbsp;Sue Ogilvy","doi":"10.1016/j.agsy.2025.104553","DOIUrl":"10.1016/j.agsy.2025.104553","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Ambitious targets under the Paris Climate Agreement and the Kunming-Montreal Global Biodiversity Framework bring increasing urgency for agriculture to play an active role as a nature-based solution to climate and biodiversity loss. But widespread uptake of nature-based solutions by the agriculture sector has proved elusive. This paper presents the results of the <em>Farming for the Future Livestock Program</em>, a large-scale program that sought to quantify the financial implications of natural capital for farm business performance in Australia's broadacre livestock sector, which covers 350 million ha and contributes more than 50% of the country's gross value of agricultural production.</div></div><div><h3>OBJECTIVE</h3><div>We aim to build a better understanding of the financial implications of natural capital on farms - a critical knowledge gap that limits effective policy and landholder adoption of nature-based solutions in the agriculture sector. We aim to quantify the effect of on-farm natural capital on farm business performance.</div></div><div><h3>METHODS</h3><div>We collected natural capital data from 114 farms via satellite imagery analysis and on-ground vegetation surveys, alongside production and financial data collected via detailed producer surveys. We used five natural capital metrics (<em>Ecological Condition</em>, <em>Aggregation</em>, <em>Proximity</em>, <em>Ground Cover</em>, and <em>Forage Condition</em>) to understand the effect of natural capital on farm business performance (productivity efficiency, profitability and financial resilience) on farms with a combined land area of &gt;230,000 ha, in the largest analysis of its kind to date.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Our multi-region models tested a total of 20 natural capital – farm business performance relationships (4 business performance measures x 5 natural capital metrics). There was moderate or strong evidence for 6 of these (5 positive, one negative) and weak statistical evidence for a further 6 relationships (4 positive, 2 negative). Region-specific models yielded similar results to the multi-region model. This suggests that high-performing livestock businesses benefit from high levels of natural capital. High levels of specific types of natural capital were associated with increased production efficiency of up to 3%, improved livestock gross margin, higher farm earnings, and higher levels of climate resilience.</div></div><div><h3>SIGNIFICANCE</h3><div>We highlight the important role that integrating robust information about the financial implications of natural capital in production systems can play in shaping appropriate and adoptable nature-based climate solutions for the agriculture sector.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104553"},"PeriodicalIF":6.1,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516443","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":"AI-powered Pheno-Farm Server: Making adaptive farming decisions","authors":"Kenny Paul ,&nbsp;Stefan Schweng ,&nbsp;Hans-Peter Kaul ,&nbsp;Franz Gansberger ,&nbsp;Andreas Holzinger","doi":"10.1016/j.agsy.2025.104563","DOIUrl":"10.1016/j.agsy.2025.104563","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;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.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;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.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;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&lt;sup&gt;2&lt;/sup&gt;) and normalized root mean squared error (NRMSE). Tests were conducted to validate the system's reliability and applicability.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results and conclusions&lt;/h3&gt;&lt;div&gt;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&lt;sup&gt;2&lt;/sup&gt; 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.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;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}