Agricultural Systems最新文献

{"title":"Strategies to reduce CH4 and N2O emissions whilst maintaining crop yield in rice–wheat system under climate change using SPACSYS model","authors":"Shuhui Wang ,&nbsp;Nan Sun ,&nbsp;Zhijian Mu ,&nbsp;Fa Wang ,&nbsp;Xiaojun Shi ,&nbsp;Chuang Liu ,&nbsp;Shuxiang Zhang ,&nbsp;Joost Wellens ,&nbsp;Bernard Longdoz ,&nbsp;Jeroen Meersmans ,&nbsp;Gilles Colinet ,&nbsp;Minggang Xu ,&nbsp;Lianhai Wu","doi":"10.1016/j.agsy.2025.104337","DOIUrl":"10.1016/j.agsy.2025.104337","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Climate change is projected to threaten food security and stimulate greenhouse gas emissions. Hence, adaptation measures without sacrificing food production are required.</div></div><div><h3>OBJECTIVE</h3><div>To assess possible consequences of rice–wheat system under climate change and to propose possible practices for mitigation.</div></div><div><h3>METHODS</h3><div>The Soil-Plant-Atmosphere Continuum SYStem (SPACSYS) model was tested using datasets from long-term experiment (1991–2019) assessing the impact of different fertilisation on crop production, crop nitrogen (N) content, soil organic carbon (SOC) stock, methane (CH₄) and nitrous oxide (N₂O) emissions in a Cambisol under rice–wheat system. The validated SPACSYS was then used to investigate the possible mitigation strategies from 2024 to 2100 under climate change scenarios (SSP1–2.6 and SSP5–8.5) and the baseline scenario and mitigation management scenarios, i.e., (i) reduced N application rate by 20 % (RNA), (ii) the introduction of mid-season drainage (MSD) and (iii) integrated management combining RNA with MSD (IM).</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Results showed that SPACSYS performed effectively in simulating yield and N content in grain and straw, SOC stock and CH₄ and N₂O emissions. Scenarios analysis elucidated that RNA would not decrease grain yields for either rice or wheat under the two climate change scenarios. Compared to the baseline scenario, low level of climate change scenario considering the CO₂ fertilisation effects (SSP1–2.6_CO₂) may benefit wheat yield (28 %) and had no effects on rice yield. In contrast, under the SSP5–8.5 scenario, whether CO₂ fertilisation effects are considered or not, both rice and wheat yield could face great loss (i.e., 11.8–29.9 % for rice, 8.3–19.4 % for wheat). The winter wheat would not be suitable for planting in the distant future (2070–2100) due to the incomplete vernalisation caused by warming. The switching from winter wheat to spring wheat from 2070 onward could avoid the yield loss by 8.3–19.4 %. Climate change could decrease SOC sequestration rate. Under future climate change scenarios, IM could significantly decrease CH₄ emissions by 56 % and N₂O emissions by 24 %, as such reducing the net global warming potential by 69 % compared to no adaptation. Our simulations suggest that under climate change, crop switching in rice–wheat system combining integrated mitigation practices is possible to mitigate global warming and maintain crop production.</div></div><div><h3>SIGNIFICANCE</h3><div>Our results underscore the significance of integrated adaptation of agricultural systems to climate change.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104337"},"PeriodicalIF":6.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760357","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":"Use of atypical plant resources for cattle farming in Western Europe to drive agroecological transition","authors":"T. Puech ,&nbsp;A. Farruggia ,&nbsp;D. Durant ,&nbsp;J.F. Glinec ,&nbsp;S. Novak ,&nbsp;F. Signoret ,&nbsp;F. Stark ,&nbsp;D. Sterling","doi":"10.1016/j.agsy.2025.104329","DOIUrl":"10.1016/j.agsy.2025.104329","url":null,"abstract":"<div><h3>CONTEXT</h3><div>In the efforts to optimize their production processes and yields, livestock systems overlook less productive on-farm areas and resources. Given the challenges of feed self-sufficiency, climate change mitigation and adaptation, and biodiversity conservation, it makes sense to revalue these resources to support agroecological transition in livestock systems.</div></div><div><h3>OBJECTIVE</h3><div>This paper introduces the concept of ‘atypical resources’, defined as plant resources that are part of the farm environment but are not conventionally used. The aim is to explore their nature, access, use and potential contribution to the performance of livestock systems.</div></div><div><h3>METHODS</h3><div>The study examines four unique and under-researched cattle farming systems (dairy or beef) using atypical resources in Western France through comprehensive analysis, and assessment of nitrogen metabolism.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Our results show a rich diversity of atypical resources such as abandoned land, hedgerow slopes, woody leaves, ditch bottoms and marsh reeds. These resources, coming from the farms themselves or their surroundings, are used for animal feed and/or bedding. The contribution of atypical resources in the nitrogen metabolism of the system ranges from almost 0 % to 12 %, while their contribution to animal feed varies for almost 0 % to 29 %. In addition, the management practices and grassland-based farming systems associated with these resources may limit N waste, preserve habitats and enhance biodiversity.</div></div><div><h3>SIGNIFICANCE</h3><div>This article examines an under-explored but critical issue that is essential to address the current challenges of livestock systems in Western Europe. We advocate for further research to generate knowledge and methods that harness the multiple services provided by atypical resources, thereby facilitating agroecological transition and addressing spatial management challenges.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104329"},"PeriodicalIF":6.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of total factor productivity and methane efficiency of beef cattle producers worldwide","authors":"Rebecca H. Chung ,&nbsp;Sholih Nugroho Hadi","doi":"10.1016/j.agsy.2025.104334","DOIUrl":"10.1016/j.agsy.2025.104334","url":null,"abstract":"<div><h3>CONTEXT</h3><div>To align with SDG 12, emphasizing responsible consumption and production, it has become imperative for the global beef cattle industry to engage in sustainable production. One of the substantial challenges for future beef production is the increasing competition for resources between animal feed and human food. Moreover, there is a pressing need to address the impact of climate change, given that beef cattle production is a major contributor to methane gas emissions.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate beef cattle production across 20 major-beef producing countries from 2011 to 2020 by 1) Assessing total factor productivity. 2) Estimating both technical efficiency and methane efficiency. 3) Estimating potential reduction of resource uses and methane emission. 4) Investigating the relationship between total factor productivity and methane efficiency.</div></div><div><h3>METHODS</h3><div>The assessment utilizes the Malmquist productivity index and data envelopment analysis with an undesirable output. This study considers cattle population, permanent pasture, and labor productivity as inputs, with beef production and methane emissions as desirable and undesirable outputs, respectively.</div></div><div><h3>RESULTS AND CONCLUSION</h3><div>The results indicate that the annual productivity growth for these 20 countries is 1.3 %, primarily attributed to technological advancements. The technical and methane efficiency scores stand at 0.645 and 0.476, respectively. Overall, the potential reduction in beef cattle, pasture area, and methane emissions are 283,257,995 heads, 933,802,815 ha, and 13,414,544 tons, respectively. There is a positive relationship between total factor productivity and methane efficiency. Moreover, the findings reveal that the US and India demonstrated relatively higher productivity growth and full methane efficiency in their beef cattle production. Accordingly, the US could serve as valuable references for other countries implementing pasture-based systems, while India's approach can be particularly insightful for countries employing smallholder farmer-based systems.</div></div><div><h3>SIGNIFICANCE</h3><div>This study addresses a pressing environmental concern of methane emissions from beef cattle production. Given that beef cattle production is a major contributor to methane gas emissions, estimating methane efficiency of beef cattle production while considering the undesirable output of methane is critical. This study is expected to enrich the existing literature on improving sustainability in beef production and raise collective awareness about reducing greenhouse gas emissions, particularly methane. Ultimately, it will foster collaboration and synergy among stakeholders to achieve sustainable beef production.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104334"},"PeriodicalIF":6.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746526","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":"Benefits and trade-offs from the diversification of rangeland farms in Northern Queensland, Australia","authors":"Lipy Adhikari ,&nbsp;Peter de Voil ,&nbsp;M. Fernanda Dreccer ,&nbsp;Daniel Rodriguez","doi":"10.1016/j.agsy.2025.104331","DOIUrl":"10.1016/j.agsy.2025.104331","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;In Northern Queensland, farmers have limited information on options to increase resilience to climate variability and market volatility by diversifying production activities in large-scale rangeland grazing farms.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To quantify benefits and trade-offs of co-designed diversification options of large-scale rangeland grazing farms in Northern Queensland.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;A diversification framework, specifically designed for large-scale farms (&lt;span&gt;&lt;span&gt;Adhikari et al., 2023&lt;/span&gt;&lt;/span&gt;), guided a participatory modelling exercise to co-design alternative production options. Two representative case-study farms, a dryland cropping (DC) and a dryland rangeland (DR), were selected from Northern Queensland's Gilbert region. Discussions with farmers were used to parameterise the APSIM model and simulate farmer's proposed alternative production systems. For DC, the baseline scenario was the existing dryland cotton-maize rotation, and the farmer's interest was to diversify into irrigated cotton, legume, and winter cereal rotations. For DR, the baseline scenario was growing dryland forages, and the diversification options included a dryland cotton-mungbean crop rotation. Benefits and trade-offs were quantified on economic, risk, environmental and social indicators.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;Irrigating legumes, and winter crops, alongside the existing cotton-maize rotation, increased the mean gross margin by ∼$1500 ha&lt;sup&gt;−1&lt;/sup&gt; year&lt;sup&gt;−1&lt;/sup&gt; and reduced downside risks by ∼25 % in the DC. Under irrigation, adding double-cropped winter-maize in rotation with cotton tripled ground cover and reduced erosion losses by ∼6 t ha&lt;sup&gt;−1&lt;/sup&gt; year&lt;sup&gt;−1&lt;/sup&gt;. However, this was at the expense of a lower water-use efficiency ($ML&lt;sup&gt;−1&lt;/sup&gt;), which was undesirable to the farmer. For the DR farm, the proposed dryland cropping increased downside risk by &gt;65 % over the existing forage system, and reduced erosion by ∼3 t ha&lt;sup&gt;−1&lt;/sup&gt; year&lt;sup&gt;−1&lt;/sup&gt; reinforcing farmers' preference to maintain the current forage production system. In both cases, “peer-learning” emerged as the preferred method for upskilling as it does not necessitate substantial investment, is based on trust, and yet promises considerable positive impact in the farming business.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;Our results show that diversification doesn't always ensure positive outcomes. By co-designing options and quantifying benefits and trade-offs across multiple dimensions, discussions between researchers and farmers were better informed, allowing for improved decision making. Additionally, aligning diversification options with the local context and farmers' needs improves the likelihood of adoption and facilitates the scaling-up of these efforts. A major constraint includes inherent complexities of the participatory modelling approach that require skilling up of ","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104331"},"PeriodicalIF":6.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and analysis of yield gaps in pasture-based livestock systems: A review of methods","authors":"Patricia Menezes Santos ,&nbsp;Aart van der Linden ,&nbsp;Geraldo Bueno Martha Jr ,&nbsp;Leonardo Amaral Monteiro ,&nbsp;Fábio R. Marin ,&nbsp;Dianne Mayberry ,&nbsp;Sandra Furlan Nogueira ,&nbsp;Gustavo Bayma ,&nbsp;Nicolas Caram ,&nbsp;Gerrie W.J. van de Ven ,&nbsp;Lynn Sollenberger","doi":"10.1016/j.agsy.2025.104323","DOIUrl":"10.1016/j.agsy.2025.104323","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Grazing landscapes cover a substantial portion of global agricultural land and are essential for the provision of ecosystem services, food security, and rural livelihoods. The yield gap concept highlights the potential for increased agricultural production through sustainable intensification by quantifying the difference between current yields and maximum achievable yields. Assessing yield gaps is crucial for targeting public and private interventions and investments in regions with the greatest potential for production increases. However, methods for assessing yield gaps vary, impacting their ability to identify underlying factors and assess yield risks consistently and accurately, particularly in pasture-based systems where interactions between plants and animals add complexity.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;The objectives of this review were to provide an overview of methods used to assess and analyze yield gaps in pasture-based livestock production systems and to discuss how they may aid decision-making processes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Review of literature.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;Different approaches have been applied for yield gap analysis of pasture-based livestock production systems. For benchmarking, climate binning, frontier methods, and production system models approaches we provide a brief description, examples of applications, data requirements, and advantages and disadvantages. The selection of specific approaches depends on the research questions addressed, spatial scale of the study, data availability and computational processing capacity. Benchmarking approaches are commonly used by farmers to compare the performance of their enterprise to others with similar characteristics. The climate binning approach is applied to larger spatial scales for identifying regions where sustainable intensification technically could be an option. Frontier approaches provide insights on both technical and economic efficiencies. Methods based on production system models may be applied for different purposes, according to the characteristics of the models. In general, mathematical models currently used for yield gap analysis in pasture-based production systems rarely account for the effects of different grazing strategies, plant species proportion, pasture nutritive value and selective grazing by animals.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;Methods for yield gap assessment and analysis in pasture-based systems can contribute knowledge and technical conditions to increase productivity and resource use efficiency from existing areas rather than expanding to new ones. This provides opportunities to meet the increasing demand for food while conserving land and natural resources. It is necessary to integrate technical insights from yield gap analysis into a broader social, economic, and political framework to support decision making by policy makers and farmers, highlighti","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104323"},"PeriodicalIF":6.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746471","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":"Planting date adjustment and varietal replacement can effectively adapt to climate warming in China southern rice area","authors":"Yixuan Wang ,&nbsp;Shuo Li ,&nbsp;Jiongchao Zhao ,&nbsp;Chong Wang ,&nbsp;Yupeng Feng ,&nbsp;Mingyu Zhao ,&nbsp;Xiaoyu Shi ,&nbsp;Fu Chen ,&nbsp;Qingquan Chu","doi":"10.1016/j.agsy.2025.104330","DOIUrl":"10.1016/j.agsy.2025.104330","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The southern rice cultivation system in China stands out as the most intricate due to its diverse climate and complex terrain, compounded by the interplay between single and double cropping rice. However, the current regionalization of adaptation strategies of rice cropping systems fails to accurately reflect the evolving dynamics brought about by regional variations in rice production and climate warming.</div></div><div><h3>OBJECTIVE</h3><div>Our aim was to address the challenge of climate change on rice production by evaluating its effects on the rice growth period and potential yield. We also assessed the potential of adjusting planting dates and implementing varietal replacement to mitigate the impacts of climate change on rice production across different maturity stages.</div></div><div><h3>METHODS</h3><div>Using phenological, climate, and land use data, we employed the validated ORYZA model to analyze the impact of climate change on the growth period and yield of rice in southern China from 1961 to 2020. Subsequently, simulations were carried out for seven scenarios to assess the effects of adjusting planting dates and replacing rice varieties on rice production in the region.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Climate change shortened the whole growth period (WGP) by 0.63 ± 0.11 days•10a⁻¹ and decreased potential yield by 239.98 ± 22.76 kg•ha⁻¹•10a⁻¹ over six decades. Changing the planting dates of single and double season rice can lengthen or shorten the growth period of rice, affecting potential yields accordingly. Varietal replacement increased WGP and potential yield by 12.64 days and 2451.07 kg•ha⁻¹ for single rice, but decreased WGP by 13.12 days while increasing yield by 2161.57 kg•ha⁻¹ for double rice. Combined adaptation strategies of planting date and varietal replacement resulted in a 10.7 % increase in WGP and a 19.3 % increase in yield for single rice, and a 7.5 % reduction in WGP but a 9.5 % increase in potential yield for double rice. Our research results show that under changing climatic conditions, adjusting planting dates and implementing varietal replacement can effectively alter the growth period of rice, leading to an increasing trend in potential yield.</div></div><div><h3>SIGNIFICANCE</h3><div>Variety replacement and sowing date adjustments are two of the most important strategies in crop production management. This study provides the first comprehensive analysis of how both measures impact agricultural production. The findings highlight the significant role of planting date adjustments and variety replacement in adapting to climate warming in southern China, offering critical insights for ensuring food security in the region.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104330"},"PeriodicalIF":6.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725393","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 machine learning approach to simulate cattle growth at pasture using remotely collected walk-over weights","authors":"Tek Raj Awasthi ,&nbsp;Ahsan Morshed ,&nbsp;Dave L. Swain","doi":"10.1016/j.agsy.2025.104332","DOIUrl":"10.1016/j.agsy.2025.104332","url":null,"abstract":"<div><h3>Context</h3><div>The growing interest in the use and implementation of remote and automated technologies, such as walk-over (WO) weighing, has made the availability of a large volume of data. However, animal behaviour, sensitivity and repeatability of WO weighing scales and animal's physiological state could impact the accuracy of recorded weights. Therefore, WO weight data needs through processing before it can be utilized effectively for developing livestock management tools.</div></div><div><h3>Objective</h3><div>This study is designed to implement a machine learning (ML) approach to develop a simulation model capable of predicting cattle growth using remotely collected WO weight data from pasture-based beef production.</div></div><div><h3>Methods</h3><div>WO weight data collected from two beef production farms (Belmont and Tremere) of central Queensland, Australia has been used to train an XGBoost ML model which is then implemented in an algorithm that takes the number of male and females of Belmont Red, Brahman, Composite and unknown breed of animals, number of days to simulate, birth date range of animals and weather conditions at the farm to simulate as input parameters and predicted daily weights as the output.</div></div><div><h3>Results and conclusions</h3><div>The simulation model obtained Lin's concordance coefficient (CCC), root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R²) of 0.84, 32.60 kg, 15.23 % and 0.77 respectively for test set 1 and CCC, RMSE, MAPE and R² of 0.82, 38.30 kg, 15.20 %, and 0.73 respectively for test set 2, when compared with the actual WO weights. The mean difference between the simulated and observed weights is found to be −1.2 kg (SD 27.3 kg), where the 95 % agreement limit (at 1.96 SD) is −54.8 kg to 52.3 kg.</div></div><div><h3>Significance</h3><div>The results suggest that autonomously collected WO weights can be an important data source for developing strategies for livestock management, and the simulation model developed in this study can be used by cattle producers to predict the growth patterns of their herd and by researchers to generate cattle growth data to implement in the development of livestock managements tools.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104332"},"PeriodicalIF":6.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vulnerability of agroecosystems to climate change in the Sahel","authors":"Michael Jordan Twumasi-Ankrah,&nbsp;Jinyan Zhan,&nbsp;Frederick Kwame Yeboah,&nbsp;Linyu Xu,&nbsp;Michael Asiedu Kumi,&nbsp;Sichale Abdissa Bayissa,&nbsp;Ali Raza Otho,&nbsp;Jharna Sharma,&nbsp;Rana Shaker Mohammad Aqel","doi":"10.1016/j.agsy.2025.104327","DOIUrl":"10.1016/j.agsy.2025.104327","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Climate change is influencing the integrity of agroecosystems, with negative impacts on food security, by modifying the crop types used and by altering the plant physiological and biochemical processes that sustain ecosystem service provisioning. In this new environmental context, understanding the spatial patterns and dynamics of climate change agroecosystems face is important for effective conservation and management.</div></div><div><h3>OBJECTIVE</h3><div>This study evaluates agroecosystems vulnerability to climate change through exposure, sensitivity, and adaptive capacity.</div></div><div><h3>METHODS</h3><div>The Shannon Entropy Weight method was employed to estimate the relative weights for individual indicators across the categories of exposure, sensitivity, and adaptive capacity. Indicators of exposure included change in temperature, precipitation variability, and natural disasters. Sensitivity was measured using crop production, livestock density, and distance from deforestation. Adaptive capacity was assessed by evaluating sustainable livelihood assets. These components were integrated into a vulnerability index to inform targeted management approaches.</div></div><div><h3>RESULTS AND CONCLUSION</h3><div>Ethiopia was identified as the country with the highest overall vulnerability, closely followed by Niger and Nigeria. These countries exhibited the highest vulnerability scores in the study, with Ethiopia showing the most pronounced levels of vulnerability.</div></div><div><h3>SIGNIFICANCE</h3><div>Mapping vulnerability assessment informs sustainable agroecosystems in climate change adaptation.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104327"},"PeriodicalIF":6.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704091","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 multi-dimensional approach to assess crop mapping and farmer strategies in drylands of Bamyan, Afghanistan","authors":"Arnaud Caiserman ,&nbsp;Sayed Muhammad Baqer Hussaini ,&nbsp;Aslam Qadamov ,&nbsp;Roy C. Sidle ,&nbsp;Aziz Ali Khan","doi":"10.1016/j.agsy.2025.104322","DOIUrl":"10.1016/j.agsy.2025.104322","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Global warming's impacts on agriculture are well-documented, yet the multiple challenges farmers face, particularly regarding water resources, remain poorly understood. Despite substantial scientific research, regions like Afghanistan, where the level of undernourishment remains very high, still require in-depth investigations into the interactions between climate trends and agriculture. Similar to many arid agricultural regions globally, Afghanistan's agricultural systems have already suffered from depleted groundwater levels due to overuse and increasing drought frequencies due to shifting climate patterns. Therefore, interdisciplinary research on climate trends and adaptive strategies using available datasets is urgently needed in Afghanistan, where food security is one of the most critical challenges to address in the short term.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;This study aims to explain the current challenges faced by Afghan farmers related to their agricultural strategies - specifically, their crop choices and the reasons behind them - and the impacts of climate change on water resources using a holistic approach. After characterizing the crop choices, the agricultural practices and their related challenges using interviews and remote sensing, we aim to use CORDEX models, specifically downscaled for Northern Afghanistan, to analyze future predictions of key climate variables for agriculture. Our goal is to highlight future challenges and formulate recommendations to improve the adaptation of agricultural systems to climate change.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We applied crop mapping using Sentinel-2 imagery to quantify farmers' crop choices. Classification was performed using Google Earth Engine, following the Rapid Classification of Croplands instructions and ground-collected crop type data during the growing season in 2023. Along with remote sensing quantification, we conducted in-depth interviews with farmers to understand the economic and climate pressures they face in their agricultural systems. While the crop map shows the area for each crop type, it does not reveal the significant socio-economic reasons behind crop selection. To extract century-long trends of key climate variables for agriculture, we used projections from the World Climate Research Programme's Coordinated Regional Downscaling Experiment (CORDEX-CORE), adjusted to the local context of Afghanistan. Representative Concentration Pathways 8.5 and 2.6 were selected to illustrate the most optimistic and pessimistic greenhouse gas emission scenarios for the coming century.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results and conclusions&lt;/h3&gt;&lt;div&gt;Farmers predominantly cultivate potatoes (52 %), followed by wheat (37 %) and alfalfa (11 %), based on a crop map with an overall accuracy of 83 %. Family subsistence and profitability are the main reasons for their crop choices, and historical climate trends and droughts did not significantly influence the agric","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104322"},"PeriodicalIF":6.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685641","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":"Analysis of mathematical modelling approaches to capture human behaviour dynamics in agricultural pest and disease systems","authors":"Nadine Aschauer ,&nbsp;Stephen Parnell","doi":"10.1016/j.agsy.2025.104303","DOIUrl":"10.1016/j.agsy.2025.104303","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The integration of dynamic human behaviour into epidemiological models aims to improve the effectiveness of pest and disease management in crops and livestock, crucial for sustainable food and agriculture systems, especially amid current challenges such as climate change, globalisation and chemical use.</div></div><div><h3>OBJECTIVE</h3><div>This structured scoping review focuses on how dynamic human behaviour is integrated into mathematical models for pest and disease control, using mathematical frameworks such as game theory and agent-based modelling, providing a comprehensive analysis of literature from the last decade.</div></div><div><h3>METHODS</h3><div>To identify and assess relevant studies, an extensive and systematic search for literature was conducted using the Web of Science database, followed by a manual screening process.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Most studies focused on stochastic and spatially explicit types of models that capture the complexity of agricultural pest and disease systems. Various control strategies, including chemical control, cultural methods, and integrated pest management, are identified, highlighting the presence of context-specific approaches in epidemiological models, considering diversity of agricultural systems and heterogeneity in farmer behaviour. The review also identifies gaps in current research, such as the limited focus on dyadic behaviours (e.g. farmer-to-advisor interactions), limited interdisciplinary collaboration, and reliance on secondary data sources. By addressing these gaps, a more comprehensive and practical understanding of the dynamics within agricultural systems, particularly in relation to human behaviour can be achieved.</div></div><div><h3>SIGNIFICANCE</h3><div>Modellers are encouraged to foster interdisciplinary work with social scientists and collection of primary social data to better capture human behaviour for modelling agricultural pest and disease systems.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"226 ","pages":"Article 104303"},"PeriodicalIF":6.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}