Agricultural Systems最新文献

{"title":"Climate change impacts on rainfed maize production of small-scale cropping systems in Eastern Cape, South Africa","authors":"Luleka Dlamini ,&nbsp;Olivier Crespo ,&nbsp;Jos van Dam ,&nbsp;Deborah V. Gaso ,&nbsp;Allard de Wit","doi":"10.1016/j.agsy.2025.104627","DOIUrl":"10.1016/j.agsy.2025.104627","url":null,"abstract":"<div><h3>Context:</h3><div>Climate change poses a growing threat to rainfed maize production systems in southern Africa. The region is warming at nearly twice the global average, intensifying climate extremes and disrupting maize development, with small-scale farmers particularly vulnerable due to their limited adaptive capacity and resource access.</div></div><div><h3>Objective:</h3><div>This study assesses the potential impact of projected future climates on actual water-limited maize yield, phenology, and water stress in small-scale farming systems in the Eastern Cape Province of South Africa.</div></div><div><h3>Methods:</h3><div>We used five global climate models and the WOFOST model to simulate maize growth and yield under three emission scenarios. Maize responses were assessed at a farm level for the near future (2026–2055) and compared to the historical baseline period (1985–2014). We considered five planting dates and five maize varieties.</div></div><div><h3>Results and Conclusions:</h3><div>The results show that the annual average temperature is projected to increase by up to 8.3% coupled with a 95% increase in the number of summer days (day with maximum temperature over 30 °C) under SSP585. Precipitation trends were less consistent and highly variable across years and models. Simulations under conventional management predicted shorter growing cycle duration (by up to 25 days) and grain filling period (by up to 15 days), leading to significant yield losses (up to 14%) under high-emission scenarios, particularly on farms with existing high water stress. However, adaptation strategies, such as early planting and the use of medium-maturity varieties, significantly improved yield performance. These results highlight the combined effects of warming, phenological acceleration, and water stress on maize productivity, while emphasizing the value of localized adaptation.</div></div><div><h3>Significance</h3><div>: Adjusting planting dates and selecting suitable varieties offer low-cost adaptation options, but alone may not suffice under future climate conditions. Integrating these with broader strategies is essential for building long-term resilience and ensuring food security under increasingly uncertain agro-climate conditions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104627"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837420","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":"Willing or unable? The cognitive–resource mismatch behind farmers' adaptive behavior under agricultural disasters","authors":"Zhiyuan Zhu,&nbsp;Yongzhong Feng,&nbsp;Binkun Wu,&nbsp;Shuo Zhang,&nbsp;Xu Ma,&nbsp;Guangxin Ren,&nbsp;Gaihe Yang","doi":"10.1016/j.agsy.2025.104612","DOIUrl":"10.1016/j.agsy.2025.104612","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Climate-related disasters have become institutionalized risks in agricultural systems, with smallholder farmers particularly vulnerable. Conventional explanations focusing solely on “lack of perception” or “lack of resources” fail to fully account for under-adaptation. Emerging evidence suggests that structural misalignment between risk perception and resource capacity—termed “cognitive–resource mismatch”—is a critical constraint.</div></div><div><h3>OBJECTIVE</h3><div>This study investigates how cognitive–resource mismatch suppresses adaptive behavior, identifies “willing but unable” (high perception–low resource) and “able but unwilling” (low perception–high resource) groups, and examines their differentiated effects on disaster recovery and household heterogeneity.</div></div><div><h3>METHODS</h3><div>Using survey data from 3240 households in the Guanzhong Plain, China, we constructed indices of risk perception and resource capacity, and developed a mismatch indicator. Econometric models—including OLS, Ordered Probit, 2SLS with instrumental variables, and Lewbel-IV—were employed, alongside heterogeneity and robustness analyses.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Both mismatch types significantly reduce adaptive behavior and weaken post-disaster recovery. The effect is strongest among female-headed, resource-poor, and disaster-inexperienced households. Results reveal non-linear complementarity between cognition and resources, showing that adaptation failure arises from systemic misalignment rather than isolated individual deficiencies.</div></div><div><h3>SIGNIFICANCE</h3><div>The study introduces the concept of alignment-sensitive governance, emphasizing differentiated policies to reduce mismatch. Financial and insurance instruments can empower the “willing but unable,” while behavioral activation and risk communication can mobilize the “able but unwilling.” This framework advances adaptation theory, highlights equity and climate justice dimensions, and provides actionable insights for precision governance in agriculture and beyond.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104612"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836966","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":"Soil health and agricultural land suitability assessment of highlands of the Eastern Ghats using geospatial index","authors":"Rakesh Paul,&nbsp;Rangabhatla Saishree,&nbsp;Monalisa Ghadei,&nbsp;B. Anjan Kumar Prusty","doi":"10.1016/j.agsy.2025.104622","DOIUrl":"10.1016/j.agsy.2025.104622","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Land Suitability assessment emphasizes integrating the evaluation of biophysical and environmental attributes to determine sustainable land use. Koraput district of the Eastern Ghats, one of India's agrobiodiversity hotspots, lacks studies on land suitability mapping and index-based micro-scale quantification. This study addresses these gaps by developing a novel Agricultural Land Suitability Index (ALSI) integrating soil physicochemical, topographic, and climatic variables.</div></div><div><h3>OBJECTIVE</h3><div>The study aimed to assess soil health status and to understand its interrelationship with the agriculturally suitable areas through development of geospatial index using hybrid modelling.</div></div><div><h3>METHODS</h3><div>A total of 24 soil parameters and derived indices were analysed following standard protocols. Multi-criteria Analytic Hierarchy Process (AHP) was used along with the Weighted Overlay Modelling (WOM), incorporating key geospatial indices like Normalised Difference Red Edge (NDRE) and Rainfall Erosivity (R-factor), to derive the index, i.e. ALSI. The said index was developed using the assigned weights and raster values of each variable and socio-economic information, collected through semi-structured interview, were also integrated to establish an interrelationship.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Soil health indicators have shown spatial heterogeneity across the Eastern Ghats highlands<em>.</em> A strong positive correlation (R² = 0.883) between Agricultural Land Suitability and ALSI confirms that soil health is the primary determinant of land suitability. Out of 1078 low suitable grids (1 km² dimension), there exist low (958 grids), moderate (114 grids), and high (06 grids) ALSI categories suggesting localized areas of better potential within a generally unsuitable landscape. A similar pattern was observed in case of the Moderate and High suitability classes. Approximately 70 % of high-suitability grids have shown low to moderate ALSI. This indicates that edaphic factors are controlling the agricultural output in the agrobiodiversity hotspot, along with the influence of climatic and topographic parameters. These areas have been identified as Priority Management Zones, which was also supported by the socio-economic factors, highlighting their implications in site-specific soil resilience planning and management.</div></div><div><h3>SIGNIFICANCE</h3><div>The findings provide a region-specific and soil-specific perspective of land evaluation. This approach enables targeted agricultural interventions and land suitability-based strategic crop management. Together, these approaches promote sustainable agriculture in the agriculture-dominated areas of the Eastern Ghats and beyond.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104622"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836969","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":"Pathogen and pest communities in agroecosystems across climate gradients: Anticipating future challenges in the highland tropics","authors":"Romaric A. Mouafo-Tchinda ,&nbsp;Aaron I. Plex Sulá ,&nbsp;Berea A. Etherton ,&nbsp;Joshua S. Okonya ,&nbsp;Gloria Valentine Nakato ,&nbsp;Yanru Xing ,&nbsp;Jacobo Robledo ,&nbsp;Ashish Adhikari ,&nbsp;Guy Blomme ,&nbsp;Déo Kantungeko ,&nbsp;Anastase Nduwayezu ,&nbsp;Jan F. Kreuze ,&nbsp;Jürgen Kroschel ,&nbsp;James P. Legg ,&nbsp;Karen A. Garrett","doi":"10.1016/j.agsy.2025.104619","DOIUrl":"10.1016/j.agsy.2025.104619","url":null,"abstract":"<div><h3><em>CONTEXT</em></h3><div>Tropical agricultural systems must respond to current and future pathogen and pest communities. An important research gap is how climate change may shift the geographic distribution of tropical pathogens and pests.</div></div><div><h3><em>OBJECTIVE</em></h3><div>We evaluated the geographic risk of 27 pathogens and pests in four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and potential future risk under climate change. We analyzed model performance for each pathogen and pest, assessing the potential for changes in geographic distribution, and for decision support systems to facilitate management.</div></div><div><h3><em>METHODS</em></h3><div>Cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests. We surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying associations. We used machine learning to model each species as a function of environmental variables, including host landscape. We also evaluated future temperatures across altitudes under climate change scenarios.</div></div><div><h3><em>RESULTS AND CONCLUSIONS</em></h3><div>Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44 % of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17 % may become less common.</div></div><div><h3><em>SIGNIFICANCE</em></h3><div>These findings indicate how crop health in the region requires adaptation to multiple sustainability challenges. The results also indicate which pathogen and pest species have the potential for development of decision support models.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104619"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836968","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 biogenic life cycle approach towards estimating the carbon intensity of wool production: Evidence from six Australian case studies","authors":"James Blignaut ,&nbsp;Paul Swan ,&nbsp;Lemuel Blignaut","doi":"10.1016/j.agsy.2025.104631","DOIUrl":"10.1016/j.agsy.2025.104631","url":null,"abstract":"<div><h3>Context</h3><div>Life cycle assessments (LCA) of wool typically associate this natural textile fibre with a high greenhouse gas emissions intensity because of enteric emissions. We analysed the application of 14 LCAs published between 2010 and 2024 and found that they focussed exclusively on emissions, disregarding the fact that wool production is embedded in a biogenic system. ISO 14067:2018 recognises biogenic carbon but has not been applied to wool yet. Here we seek to rectify this.</div></div><div><h3>Objective</h3><div>This study explores the application of ISO 14067:2018 to six representative Australian wool enterprises, extending the detailed LCA case study data from <span><span>Wiedemann et al. (2016)</span></span> to define and map the flows of biogenic carbon ingested by grazing sheep. Thereafter we explore the impact of key aspects of system biogenic function on calculated wool emissions intensity within the sheep production system.</div></div><div><h3>Results and Conclusions</h3><div>Mapping of ingested carbon flows across enterprises showed that the major carbon destinations were manure (54.1 %), followed by respiration (22.7 %), urine (7.5 %), and enteric emissions (5.2 %). Exploration of the emissions intensity of wool production showed that while biogenic model outputs closely correlated with those of <span><span>Wiedemann et al. (2016)</span></span> when biogenic carbon was excluded, emissions intensities were reduced by the addition of biogenic functionality, declining by on average 102 % with retention of 66.7 % of manure within the grazing system. We conclude that conducting LCA of biological products without addressing biogenic carbon inflates the emission intensity.</div></div><div><h3>Significance</h3><div>For the first time, the on-farm cradle-to-farm gate flow of biogenic carbon of Australian greasy wool production are comprehensively analysed in a manner that conforms with ISO 14067:2018; it has a major impact on wool's carbon footprint.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104631"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836970","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":"Assessing climate risk and adaptive strategies for forage production in Brazilian pasture-based livestock under future climate scenarios","authors":"H.B. Brunetti ,&nbsp;I.M. Fattori Junior ,&nbsp;T.S.S. Dias ,&nbsp;M.L.A. de Melo ,&nbsp;P.M. Santos ,&nbsp;J.R.M. Pezzopane ,&nbsp;K.J. Boote ,&nbsp;F.R. Marin","doi":"10.1016/j.agsy.2025.104615","DOIUrl":"10.1016/j.agsy.2025.104615","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Brazil hosts the world's largest commercial cattle herd, primarily raised in pasture-based systems that occupy around 164 million ha. Increasing beef production while minimizing environmental impacts is essential. Although climate change is expected to significantly affect global crop yields, comprehensive assessments of its impacts on forage production in Brazil remain scarce.</div></div><div><h3>OBJECTIVE</h3><div>Evaluate (i) the effects of climate change on forage yield, seasonality, and climate risk for Marandu palisadegrass (<em>Urochloa brizantha</em> cv. BRS Marandu) and Mombaça guineagrass (<em>Megathyrsus maximus</em> cv. BRS Mombaça) by 2050, and (ii) the effectiveness of pasture deferment and forage ensiling as mitigation strategies.</div></div><div><h3>METHODS</h3><div>We used the process-based CROPGRO-Perennial Forage Model (CROPGRO-PFM) driven by 10 Global Circulation Models under SSP2–4.5 and SSP5–8.5 scenarios for the 2035–2065 period, compared to a baseline (1989–2019). For the deferment simulation, pastures were left ungrazed for 75 days preceding the three consecutive months with the lowest herbage accumulation rates (HAR), assuming that 50 % of the accumulated dead material remained available for intake. Ensiling was simulated for 90 days during the three months with the highest HARs, assuming 75 % dry matter recovery, which was subsequently allocated to the three months with the lowest HAR. Both management practices were applied to 30 % of the pasture area.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Results indicate a slight decline in annual forage yield, increased drought stress during winter and spring, and intensified seasonality. Climate risk, however, is projected to decrease as the magnitude and period of drought stress and forage deficits and supply will be more predictable, facilitating feed planning. Deferment (Marandu) and ensiling (Mombaça) were effective in reducing seasonality. Ensiling also reversed projected yield declines, whereas deferment improved yield, though not enough to reverse declines. Projected drought stress may require renewed focus on drought-tolerant cultivars and strategic use of rainy-season surpluses to buffer dry-season deficits.</div></div><div><h3>SIGNIFICANCE</h3><div>This study provides the first robust, multi-model, process-based evaluation of climate change impacts on Brazilian forage systems, offering valuable guidance for breeders, policymakers, and producers aiming to enhance the resilience and sustainability of pasture-based livestock systems under future climate conditions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104615"},"PeriodicalIF":6.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836967","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":"Algorithm aversion in agricultural decision-making: Trust dynamics, barriers, and fertiliser-related decision support","authors":"Jack H. Grant ,&nbsp;Dorothee Scharpenberg ,&nbsp;Louise Manning","doi":"10.1016/j.agsy.2025.104630","DOIUrl":"10.1016/j.agsy.2025.104630","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Algorithm-based fertiliser recommendations offer substantial potential to improve Nitrogen Use Efficiency (NUE) and support economic and environmental sustainability. However, adoption among farmers in the United Kingdom (UK) remains limited, partly due to algorithm aversion, i.e., the tendency to distrust or avoid algorithmic-generated recommendations, even when they provide benefits.</div></div><div><h3>OBJECTIVE</h3><div>This study examines algorithm aversion in fertiliser-related decision-making among UK farmers and agronomists. Aiming to identify key barriers to adopting decision-support tools (DSTs), improving understanding of stakeholder trust dynamics, and exploring strategies to improve uptake.</div></div><div><h3>METHODS</h3><div>An online survey of 50 farmers and 26 agronomists assessed confidence in algorithmic recommendations versus human advice, understanding of NUE, perceived adoption barriers, and openness to non-traditional fertiliser recommendations. A follow-up workshop with 10 participants in DSTs trials provided qualitative insights into trust and usability.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Farmers reported significantly greater trust in human advice compared to algorithmic recommendations (median 8 vs. 6, <em>p</em> &lt; .001), whereas agronomists showed the reverse pattern (median 8 vs. 7.0, <em>p</em> &lt; .001). Perceived barriers included cost concerns, poor system integration, complexity, and confusion over metrics. Whilst some farmers showed low levels of NUE literacy, agronomists demonstrated higher NUE literacy. Farmers relied on advice grounded in social trust and shared beliefs, while agronomists viewed algorithmic outputs as complements to technical expertise. Workshop participants found DST dashboards informative but often overwhelming.</div></div><div><h3>SIGNIFICANCE</h3><div>Addressing algorithm aversion through improved interface design, transparency, and tailored education, particularly via trusted advisors, may bridge the trust gap and facilitate digital tool adoption.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104630"},"PeriodicalIF":6.1,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837421","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":"Revealing the diversity of collective experimentation in agriculture: Constructing idealtypes from French case studies","authors":"Maïté de Sainte Agathe ,&nbsp;Chantal Loyce ,&nbsp;Lorène Prost ,&nbsp;Quentin Toffolini","doi":"10.1016/j.agsy.2025.104623","DOIUrl":"10.1016/j.agsy.2025.104623","url":null,"abstract":"<div><h3>Context</h3><div>The agroecological transition underscores the need to rethink knowledge production in agriculture, especially in relation to experimentation. This includes involving a wider range of stakeholders and exploring diverse and complementary forms of experimentation.</div></div><div><h3>Objective</h3><div>This article aims to shed light on the diversity of existing collective experimentations, in order to document the ongoing renewal of experimental approaches and to propose benchmarks for understanding and supporting them.</div></div><div><h3>Methods</h3><div>We conducted 34 semi-structured interviews and 10 observant participations, leading to the identification of 28 case studies that we define as collective experimentations. We define collective experimentation as the process of implementing and monitoring an intervention with uncertain outcomes, which leads to the production of knowledge. We did a comprehensive analysis of these collective experimentations, to understand how and why they are conducted. To do so, our analysis considered both the physical design of the experimental setups and the questions addressed, as well as the collective organization of the actors involved.</div></div><div><h3>Results and Conclusions</h3><div>We propose six idealtypes of collective experimentations: Idealtype A: Replicating experimental situations to generate standardized data, Idealtype B: Integrating data from diverse experimental practices in a joint analysis, Idealtype C: Distributing questions to generate knowledge on a common topic, Idealtype D: Pooling a diversity of experiences to explore a common subject, Idealtype E: Distributing activities within a single experimental situation and Idealtype F: Gathering human and material resources on a single site to experiment jointly on several experimental situations.</div></div><div><h3>Significance</h3><div>These idealtypes shed light on the diversity of collective experimentation approaches in agriculture, which are often under described in the literature. By offering a set of structured reference points, it can support researchers, facilitators, and practitioners in recognizing, designing and valuing collective experimentations adapted to their contexts. It opens new perspectives for rethinking how experimental knowledge is produced, shared, and valued to support agroecological transitions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104623"},"PeriodicalIF":6.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823136","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":"Optimising crop calendars with management practices promotes climate-smart agriculture in wheat-maize rotations of the North China Plain","authors":"Deyao Liu ,&nbsp;Baobao Pan ,&nbsp;Huarui Gong ,&nbsp;Jing Li ,&nbsp;Enli Wang ,&nbsp;Jinxi Zhao ,&nbsp;Yan Xu ,&nbsp;Shu Kee Lam ,&nbsp;Deli Chen","doi":"10.1016/j.agsy.2025.104626","DOIUrl":"10.1016/j.agsy.2025.104626","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Optimising crop calendars by adjusting sowing dates and the timing and frequency of key management practices can enhance crop productivity while reducing greenhouse gas (GHG) emissions. However, limited research has explored how farmers dynamically adapt crop calendars and practices in response to climate shifts to support climate-smart agriculture.</div></div><div><h3>OBJECTIVE</h3><div>This study developed a DNDC-based hybrid modelling framework to evaluate adaptive management strategies for supporting climate-smart agriculture under future climate scenarios.</div></div><div><h3>METHODS</h3><div>We assessed three management levels: fertiliser application rates (level 1); fertiliser rates combined with crop calendar adjustments, including fertiliser timing, frequency, as well as sowing and harvesting dates (level 2) and level 2 plus irrigation and residue retention (level 3). The framework was designed to optimise management under multiple objectives, including increasing crop yield, SOC sequestration, while simultaneously reducing N input and GHG emissions.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>From 1990 to 2100, the optimised crop calendars were identified: delaying wheat basal fertilisation (+5 d,) while advancing top-dressing (−5 d), postponing wheat sowing (+5 d) and advancing maize sowing (−9 d); advancing both fertilisation events in maize (−9 d, −3 d); aligning irrigation with fertilisation; and adding one irrigation event during the maize bell stage. Compared with historical practices, these adjustments increased annual crop yields and NUE by 4.2 % and 15.8 %, respectively, while reducing net GHG emissions and GHG intensity by 5.1 % and 8.5 %, respectively. The optimised management reduced N inputs, irrigation water and residue retention by 17.2 %, 6.7 % and 20.0 %, respectively.</div></div><div><h3>SIGNIFICANCE</h3><div>These findings demonstrate that adaptive crop calendars can significantly advance climate-smart agriculture and should be incorporated into climate change impact assessments.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104626"},"PeriodicalIF":6.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823105","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":"Developing scalable farm typologies to guide sustainable intensification in the fragile agroecosystems of the Indian Sundarbans","authors":"Kalyan Roy ,&nbsp;Marta Monjardino ,&nbsp;Mohammed Mainuddin ,&nbsp;Sukamal Sarkar ,&nbsp;Krishnendu Ray ,&nbsp;Poulami Sen ,&nbsp;Srijan Samanta ,&nbsp;Akash Panda ,&nbsp;Sanchayeeta Misra ,&nbsp;Argha Ghosh ,&nbsp;Esmat Ara Begum ,&nbsp;Rupak Goswami","doi":"10.1016/j.agsy.2025.104624","DOIUrl":"10.1016/j.agsy.2025.104624","url":null,"abstract":"<div><h3>Context</h3><div>Sustainable Intensification (SI) aims to boost smallholder productivity while conserving natural resources. However, farm-level heterogeneity often limits equitable access to SI benefits. While most typology studies rely on quantitative methods, few use integrated mixed methods to develop scalable typologies from small samples, especially in fragile agroecosystems.</div></div><div><h3>Objectives</h3><div>This study aimed to: a) classify heterogenous farms using participatory and statistical methods; b) construct a flexible decision support tree to scale out farm typologies to locations beyond initial study area; and c) examine the validity and usefulness of the scalable farm types through stakeholder engagement.</div></div><div><h3>Methods</h3><div>A mixed-methods design was used. First, Focus Group Discussions in four villages used a participatory card-sorting exercise where farmers classified 202 beneficiary households by eight visualized parameters (cropping pattern, landholding, off-farm income, etc.). The resulting farmer-defined groups formed the basis for respondent sampling in the questionnaire survey. Quantitative data from survey were subjected to Principal Component Analysis and Cluster Analysis to identify statistical farm types. Farm types were characterized using the household data and five-year recall data on changing livelihoods trends. For wider application, a Classification and Regression Tree (CRT) analysis was performed to generate a decision tree, using the identified farm types as target variable The tree was refined and successfully applied in new locations, with validation from local experts. Yield differences of SI technology (Zero-Tillage Potato) across farm types were compared between expert and empirical classifications.</div></div><div><h3>Results and conclusions</h3><div>Statistical analysis identified five dynamic farm types, including a distinct landless group, ranging from resource-rich to resource-poor. The CRT classified seven types with 86.6 % accuracy using binary splits on landholding, livestock, income, irrigation, and crop diversity, with additional branches for unique configurations. Expert validation showed strong concordance. Field testing revealed yield differences across farm types, aligning with expert classifications. The typology illustrates a progression from low-input systems to diversified, resource-rich farms integrating crops, livestock, fisheries, and innovations in water, nutrients, mechanization, and markets.</div></div><div><h3>Significance</h3><div>The study demonstrates the utility of typologies not only for classification but also for effectively targeting SI interventions. This scalable, context-sensitive framework supports innovation upscaling in heterogeneous agroecosystems, especially where longitudinal data are unavailable.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104624"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837423","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}