[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2632":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":52},2632,"AquaCrop modelling with frost risk in Southern Africa for bambara groundnut suitability and optimal planting dates","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1829034","Bambara groundnut ( Vigna subterranea ) is a resilient indigenous African legume with potential for improving food and nutrition security in semi-arid environments. However, conventional land suitability assessments often rely on static climatic averages, which fail to capture the dynamic interactions between climate variability and crop phenology. This study proposes a novel, process-based framework that integrates the AquaCrop simulation model with a custom frost-trigger algorithm to map land suitability and optimal planting dates across 5,838 altitude zones in Southern Africa. By simulating crop growth dynamics over 49 consecutive seasons, the framework explicitly accounts for inter-seasonal variability and extreme events, specifically frost and water stress, that are typically overlooked in conventional assessments. A sequential filtering procedure was applied to exclude unsuitable areas based on seasonal rainfall thresholds, risk of crop failure, and variability in water productivity. Findings indicate that October is the most favourable planting month, as later sowings face increased yield reductions due to potential frost risk. Accounting for frost occurrence reduced suitable growing areas by 28%, with refined land-use filtering further reducing viable area by 20%. These results highlight the critical role of dynamic environmental stressors in accurately assessing crop suitability. Ultimately, this framework provides a scalable tool for climate-smart agricultural planning, offering a more robust approach to identifying viable production zones for resilient underutilised crops.","班巴拉花生（Vigna subterranea）是一种具有韧性的非洲本土豆科作物，在改善半干旱环境的粮食和营养安全方面具有潜力。然而，传统的土地适宜性评估往往依赖静态气候平均值，无法捕捉气候变率与作物物候之间的动态相互作用。本研究提出了一种新颖的基于过程的框架，将AquaCrop模拟模型与自定义霜冻触发算法相结合，以绘制南部非洲5，838个海拔区域的土地适宜性和最佳播种日期。通过在连续49个生长季中模拟作物生长动态，该框架明确考虑了传统评估中通常被忽视的季节间变率和极端事件，特别是霜冻和水分胁迫。研究采用序贯筛选程序，基于季节性降雨阈值、作物歉收风险和水生产力变异性排除不适宜区域。研究结果表明，10月是最有利的播种月份，较晚播种因潜在霜冻风险而面临更大的产量损失。考虑霜冻发生使适宜种植面积减少了28%，进一步的土地利用精细化筛选又使可行面积减少了20%。这些结果凸显了动态环境胁迫因素在准确评估作物适宜性中的关键作用。最终，该框架为气候智慧型农业规划提供了一种可扩展的工具，为确定具有韧性的未充分利用作物的可行生产区提供了更为稳健的方法。",null,"Frontiers in Sustainable Food Systems","2026-09-15T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"该研究将AquaCrop模型与霜冻风险算法结合，在南部非洲5838个海拔带模拟49季作物生长，方法新颖、数据规模大，对气候智慧型农业规划有参考价值，但区域聚焦非洲，国内影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","非洲农业","气候智慧型农业","作物模型","种植适宜性",0,"10.3389\u002Ffsufs.2026.1829034",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":44,"card":45,"direction":49,"ingested_from":51},"W7213270406",[37,39,41],{"name":38,"orcid":9},"Simon Lake",{"name":40,"orcid":9},"Richard Kunz",{"name":42,"orcid":43},"Tafadzwanashe Mabhaudhi","https:\u002F\u002Forcid.org\u002F0000-0002-9323-8127","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1829034\u002Fpdf",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"用AquaCrop结合霜冻算法评估南部非洲班巴拉花生适宜性与最佳播期。","AquaCrop作物模型+霜冻触发算法，模拟49季5838个海拔带。","10月最适播种，考虑霜冻使适宜区减少28%，土地利用过滤再减20%。","农业人工智能与决策模型","可将该动态胁迫框架迁移到其他未充分利用作物或区域，并耦合未来气候情景。","openalex","2026-09-16T23:30:08.115404Z"]