[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5549":3,"related-5549":100},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":99},5549,"Characterising climate mitigation and adaptation performance in oil palm plantation: evidence from Indonesia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1871877","Despite growing demand for ‘climate-friendly’ palm oil, little is known about how existing soil and management conditions align with climate mitigation and adaptation goals. This study characterised the climate mitigation and adaptation performance of oil palm (OP) production across different soil and management conditions based on simulated carbon balance and productivity. The Agricultural Production Systems SIMulator (APSIM)-OilPalm model was used to simulate productivity and carbon balance in 25 industrial OP sites on mineral soils in North Sumatra, Indonesia. K-means clustering was used to group similarity patterns between simulated carbon balance and productivity with observed soil and management conditions data. The model has good skill in simulating OP productivity at sites with standard plant density, achieving coefficient of determination (R 2 ) yield up to 0.79, though performance is poor at plantations with plant density outside the standard range or very low observed yields. Simulated OP productivity exhibits moderate variability from 15.38 to 28.67 t fresh fruit bunch (FFB) ha −1 yr. −1 . Simulated carbon balance, which is comparable to measured values in the region, indicates that all observed OP sites serve as a carbon sink with low variability from −2.09 to −3.86 t C eq. ha −1 yr. −1 , driven by uniformity in climate factors and plant density. Eleven of 25 sites have good mitigation and adaptation performance, indicated by high carbon sinks, yield and soil organic carbon (SOC) increment. These sites are characterised by greater plant density and moderate levels of N fertilisation, sand content and Ganoderma boninense (G. boninense) infection, with low carbon nitrogen (CN) ratio. Over half of sites face climate adaptation challenges, indicated by either low productivity or SOC increment and characterised by low plant density and nitrogen fertiliser with high G. boninense infection and sandy soils. This study contributes information about mitigation and adaptation in determining the management of OP and supports the tracking of climate-friendly palm oil within broader supply chains.","尽管对“气候友好型”棕榈油的需求日益增长，但人们对现有土壤和管理条件如何与气候减缓及适应目标相协调知之甚少。本研究基于模拟碳平衡和生产力，表征了油棕（OP）生产在不同土壤和管理条件下的气候减缓与适应表现。研究采用农业生产系统模拟器（APSIM）-OilPalm模型，模拟了印度尼西亚北苏门答腊25个矿质土壤工业油棕样点的生产力和碳平衡。运用K均值聚类方法，将模拟碳平衡与生产力之间的相似性模式与观测到的土壤和管理条件数据进行分组。该模型在标准种植密度样点的油棕生产力模拟中具有良好能力，决定系数（R²）最高达0.79，但在种植密度超出标准范围或观测产量极低的种植园中表现较差。模拟油棕生产力呈现中等变异性，范围为15.38至28.67 t鲜果串（FFB）ha⁻¹ yr.⁻¹。模拟碳平衡与该地区实测值相当，表明所有观测油棕样点均为碳汇，变异性较低，范围为−2.09至−3.86 t C eq. ha⁻¹ yr.⁻¹，这归因于气候因子和种植密度的一致性。25个样点中有11个具有良好的减缓和适应表现，表现为高碳汇、产量和土壤有机碳（SOC）增量。这些样点的特征是种植密度较大，氮肥施用量、砂含量和灵芝（Ganoderma boninense，G. boninense）感染程度适中，碳氮（CN）比低。超过半数样点面临气候适应挑战，表现为生产力或SOC增量较低，其特征是种植密度和氮肥用量低、灵芝感染率高及砂质土壤。本研究为确定油棕管理中的减缓和适应提供了信息，并支持在更广泛供应链中追踪气候友好型棕榈油。",null,"Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于APSIM模型对印尼25个油棕园碳平衡与生产力的模拟研究，方法清晰、数据具体，对气候友好型油棕管理与供应链追踪有参考价值，但属区域性论文，影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57,63],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},6,true,[],[49,33],"土壤",[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":56},"H4",4,[],[29],[29],{"code":58,"score":53,"supported":46,"title":59,"abstract":60,"tags":62},"H7",[],[61],"气候适应",[27],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I2",[],[],[26],[69,70],"APSIM-OilPalm 油棕 碳平衡","印尼 油棕 气候适应","APSIM-OilPalm油棕碳平衡-5549",0,"10.3389\u002Ffsufs.2026.1871877",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":72,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7223112249",[77,80,83,86,89],{"name":78,"orcid":79},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":81,"orcid":82},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":84,"orcid":85},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":87,"orcid":88},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":90,"orcid":91},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"用APSIM模型模拟印尼油棕园碳平衡与产量，评估其气候减缓与适应表现。","APSIM-OilPalm模型模拟25个站点，结合K-means聚类分析土壤与管","所有站点均为碳汇，11个站点减缓与适应表现良好，过半站点面临适应挑战。","农业绿色发展与碳","可扩展至不同气候带与泥炭地油棕园，结合遥感数据构建区域尺度气候友好型油棕认证体系。","openalex","2026-10-22T11:20:15.883770Z",{"total":45,"page":21,"page_size":45,"items":101},[102,220,284,382,485,560],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":10,"source_url":105,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":115,"tags":117,"primary_topic":122,"secondary_topics":123,"classification":132,"search_phrases":193,"slug":196,"view_count":72,"doi":197,"paper":198,"created_at":219},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。","2026-10-20T00:00:00Z",70,{"impact":111,"substance":112,"depth":113,"authority":19,"freshness":20,"relevant":21,"comment":114},12,20,16,"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[116],{"name":10,"url":105},[27,118,119,120,121],"碳交易","干旱农业","土壤碳汇","作物选择",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[124,128],{"code":58,"slug":125,"name":126,"path":127,"parent_code":36},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":52,"slug":129,"name":130,"path":131,"parent_code":36},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",{"status":39,"locked":14,"version":40,"method":41,"evidence":133},{"candidates":134,"agricultural_evidence":46},[135,140,147,154,161,168,175,181,187],{"code":32,"score":136,"supported":46,"title":137,"abstract":138,"tags":139},14,[33],[49,33],[],{"code":58,"score":13,"supported":46,"title":141,"abstract":142,"tags":146},[],[143,144,145],"气候变化","干旱","climate change",[27],{"code":52,"score":148,"supported":46,"title":149,"abstract":150,"tags":153},8,[],[151,152],"农业碳","carbon sequestration",[120,118],{"code":155,"score":45,"supported":46,"title":156,"abstract":157,"tags":160},"A2",[],[158,159],"小麦","wheat",[],{"code":162,"score":45,"supported":46,"title":163,"abstract":164,"tags":167},"B1",[],[165,166],"番茄","黄瓜",[],{"code":169,"score":170,"supported":46,"title":171,"abstract":172,"tags":174},"A5",3,[],[173],"马铃薯",[],{"code":176,"score":170,"supported":46,"title":177,"abstract":178,"tags":180},"B2",[],[179],"葡萄",[],{"code":182,"score":170,"supported":46,"title":183,"abstract":184,"tags":186},"H6",[],[185],"农业生态",[],{"code":188,"score":170,"supported":46,"title":189,"abstract":190,"tags":192},"I1",[],[191],"smart agriculture",[],[194,195],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":197,"openalex_id":199,"authors":200,"venue":10,"cited_by_count":72,"oa_url":105,"card":213,"direction":218,"ingested_from":98},"W7223064172",[201,203,205,208,210],{"name":202,"orcid":9},"Tasnim Althalaj",{"name":204,"orcid":9},"Ross. M. Thompson",{"name":206,"orcid":207},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":209,"orcid":9},"Dan Bumblauskas",{"name":211,"orcid":212},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":214,"method":215,"finding":216,"direction":96,"opportunity":217},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:07.383006Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":9,"content":9,"source_name":225,"source_url":223,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":231,"tags":233,"primary_topic":237,"secondary_topics":242,"classification":244,"search_phrases":260,"slug":263,"view_count":72,"doi":264,"paper":265,"created_at":283},5229,"Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105017","Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation。Agricultural Systems","Agricultural Systems","2026-10-17T00:00:00Z",82,{"impact":17,"substance":18,"depth":229,"authority":136,"freshness":20,"relevant":21,"comment":230},19,"耦合HYDRUS-2D与AquaCrop模拟覆膜滴灌下水盐与作物生长，方法有创新、结论可靠，属农业信息化与节水灌溉领域值得精选的论文。",[232],{"name":225,"url":223},[26,234,235,236],"节水灌溉","覆膜滴灌","水盐调控",{"code":238,"slug":239,"name":240,"path":241,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[243],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":245},{"candidates":246,"agricultural_evidence":46},[247,251,256],{"code":238,"score":111,"supported":46,"title":248,"abstract":249,"tags":250},[239],[239],[234],{"code":32,"score":252,"supported":46,"title":253,"abstract":254,"tags":255},11,[33],[33],[],{"code":64,"score":21,"supported":14,"title":257,"abstract":258,"tags":259},[],[],[26],[261,262],"HYDRUS-2D AquaCrop 覆膜滴灌","土壤水盐 作物生长 模拟","HYDRUS-2DAquaCrop覆膜滴灌-5229","10.1016\u002Fj.agsy.2026.105017",{"doi":264,"openalex_id":266,"authors":267,"venue":225,"cited_by_count":72,"oa_url":223,"card":9,"direction":9,"ingested_from":98},"W7222801463",[268,270,272,274,276,279,281],{"name":269,"orcid":9},"Yunfeng Li",{"name":271,"orcid":9},"Jun Wang",{"name":273,"orcid":9},"Hexiang Zheng",{"name":275,"orcid":9},"Delong Tian",{"name":277,"orcid":278},"Jie Ren","https:\u002F\u002Forcid.org\u002F0000-0001-8795-0372",{"name":280,"orcid":9},"Hao Liu",{"name":282,"orcid":9},"Jingsheng Sun","2026-10-18T23:30:04.248249Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":290,"source_url":287,"published_at":291,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":227,"score_detail":292,"sources":294,"tags":296,"primary_topic":301,"secondary_topics":306,"classification":308,"search_phrases":344,"slug":347,"view_count":45,"doi":348,"paper":349,"created_at":381},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。","Precision Agriculture","2026-09-25T00:00:00Z",{"impact":17,"substance":18,"depth":229,"authority":136,"freshness":20,"relevant":21,"comment":293},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[295],{"name":290,"url":287},[297,298,26,299,300],"智慧农业","玉米","遥感监测","数据同化",{"code":302,"slug":303,"name":298,"path":304,"parent_code":305},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[307],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":309},{"candidates":310,"agricultural_evidence":46},[311,316,320,325,331,335,339],{"code":302,"score":312,"supported":46,"title":313,"abstract":314,"tags":315},15,[303],[298,303],[298],{"code":32,"score":136,"supported":46,"title":317,"abstract":318,"tags":319},[33],[49,33],[],{"code":58,"score":45,"supported":46,"title":321,"abstract":322,"tags":324},[],[144,323],"drought",[],{"code":326,"score":53,"supported":46,"title":327,"abstract":328,"tags":330},"I3",[],[329],"遥感",[299],{"code":188,"score":21,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[297],{"code":64,"score":21,"supported":14,"title":336,"abstract":337,"tags":338},[],[],[26],{"code":340,"score":21,"supported":14,"title":341,"abstract":342,"tags":343},"I5",[],[],[300],[345,346],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":348,"openalex_id":350,"authors":351,"venue":290,"cited_by_count":72,"oa_url":287,"card":375,"direction":379,"ingested_from":98},"W7214405444",[352,354,357,360,363,365,367,370,372],{"name":353,"orcid":9},"Manuela Montella",{"name":355,"orcid":356},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":358,"orcid":359},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":361,"orcid":362},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":364,"orcid":9},"Jean François Iffly",{"name":366,"orcid":9},"Thomas Udelhoven",{"name":368,"orcid":369},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":371,"orcid":9},"Zoltan Szantoi",{"name":373,"orcid":374},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":376,"method":377,"finding":378,"direction":379,"opportunity":380},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","农业遥感与作物表型","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":393,"tags":395,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":428,"slug":431,"view_count":170,"doi":432,"paper":433,"created_at":484},3351,"Integrated Land, Soil and Crop Information Systems in Ethiopia, Kenya, and Rwanda: Institutional Readiness and Implications for Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101776","Integrated land–soil–crop information systems are increasingly important for supporting climate-smart agricultural planning and implementation, yet their development in Eastern Africa is constrained by fragmented mandates, weak technical capacity, and limited data interoperability. This study assesses how institutional readiness, user demand, and technical and human-capacity conditions shape the integration of such systems into national Agricultural Knowledge and Innovation Systems (AKIS). A mixed-methods, multi-country assessment was conducted in Ethiopia, Kenya, and Rwanda (2022–2024), drawing on 145 semi-structured key-informant interviews, stakeholder mapping, national workshops, and a regional synthesis consultation. The analysis addressed three objectives: (i) diagnose institutional readiness and user demand; (ii) assess technical, infrastructural, and human-capacity requirements; and (iii) identify governance and design principles for embedding integrated information systems within AKIS and Climate-Smart Agriculture (CSA) strategies. The results show consistently high demand for spatially explicit soil and crop data but persistent fragmentation of mandates, uneven coordination, and substantial subnational capacity gaps. Despite these constraints, emerging digital-agriculture strategies, open-data policies, and regional soil-health initiatives provide potential entry points for integration. The study offers a comparative evidence base and design principles—covering governance, interoperability standards, co-production processes, and capacity strengthening—needed to transition from project-driven fragmentation toward interoperable and sustainable information systems. These findings provide diagnostic and design-oriented insights for national and regional efforts to strengthen agricultural information systems that support CSA implementation in Eastern Africa.","综合的土地—土壤—作物信息系统在支持气候智慧型农业规划与实施方面日益重要，但其在东非的发展受到职责分散、技术能力薄弱和数据互操作性有限的制约。本研究评估了制度准备度、用户需求以及技术和人力能力条件如何影响此类系统融入国家农业知识与创新系统（AKIS）。研究于2022—2024年在埃塞俄比亚、肯尼亚和卢旺达开展了混合方法、多国评估，基于145次半结构化关键知情人访谈、利益相关方映射、国家研讨会以及一次区域综合磋商。分析围绕三个目标展开：（i）诊断制度准备度和用户需求；（ii）评估技术、基础设施和人力能力需求；（iii）确定将综合信息系统嵌入AKIS和气候智慧型农业（CSA）战略的治理与设计原则。结果表明，对空间显式土壤和作物数据的需求持续较高，但职责分散、协调不均衡以及地方层面能力差距显著等问题长期存在。尽管存在这些制约，新兴的数字农业战略、开放数据政策和区域土壤健康倡议为整合提供了潜在切入点。本研究提供了比较性证据基础和设计原则——涵盖治理、互操作性标准、共同生产过程和能力建设——这些是从项目驱动的碎片化转向可互操作且可持续的信息系统所必需的。这些发现为国家和区域层面加强支持东非CSA实施的农业信息系统提供了诊断性和设计导向的见解。","Land","2026-09-23T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":136,"freshness":20,"relevant":21,"comment":392},"基于三国145位关键知情人访谈的混合方法研究，为东非农业信息系统整合与气候智慧农业提供治理与设计原则，方法扎实、结论可靠，具区域政策参考价值。",[394],{"name":388,"url":385},[396,297,397,27,398,399],"数字农业","土壤健康","农业数据","数据互操作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":403},{"candidates":404,"agricultural_evidence":46},[405,409,413,419,423],{"code":188,"score":113,"supported":46,"title":406,"abstract":407,"tags":408},[191],[396,191],[396,297],{"code":32,"score":312,"supported":46,"title":410,"abstract":411,"tags":412},[33],[49,33],[397],{"code":414,"score":170,"supported":46,"title":415,"abstract":416,"tags":418},"J1",[],[417],"农业规划",[],{"code":58,"score":21,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[27],{"code":424,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},"I6",[],[],[398],[429,430],"埃塞俄比亚 肯尼亚 卢旺达 农业信息系统","气候智慧农业 土壤作物数据","埃塞俄比亚肯尼亚卢旺达农业信息系统-3351","10.3390\u002Fland15101776",{"doi":432,"openalex_id":434,"authors":435,"venue":388,"cited_by_count":72,"oa_url":385,"card":478,"direction":218,"ingested_from":98},"W7214079859",[436,439,442,444,446,449,452,455,457,460,463,466,469,472,475],{"name":437,"orcid":438},"John Walker Recha","https:\u002F\u002Forcid.org\u002F0000-0002-1146-7197",{"name":440,"orcid":441},"A. Kooiman","https:\u002F\u002Forcid.org\u002F0000-0001-8208-6781",{"name":443,"orcid":9},"Thaïsa van der Woude",{"name":445,"orcid":9},"Hanneke Heesmans",{"name":447,"orcid":448},"Ermias Aynekulu","https:\u002F\u002Forcid.org\u002F0000-0002-1955-6995",{"name":450,"orcid":451},"Angela Nduta Gitau","https:\u002F\u002Forcid.org\u002F0000-0002-8963-2375",{"name":453,"orcid":454},"Pascal Debons","https:\u002F\u002Forcid.org\u002F0000-0001-6314-9935",{"name":456,"orcid":9},"Frank van Weert",{"name":458,"orcid":459},"Michael Okoti","https:\u002F\u002Forcid.org\u002F0000-0002-9550-8258",{"name":461,"orcid":462},"Elizabeth A. Okwuosa","https:\u002F\u002Forcid.org\u002F0000-0001-5941-7423",{"name":464,"orcid":465},"Kennedy Were","https:\u002F\u002Forcid.org\u002F0000-0002-8012-6812",{"name":467,"orcid":468},"Girma Mamo Diga","https:\u002F\u002Forcid.org\u002F0000-0002-2593-3187",{"name":470,"orcid":471},"Dejene Abera","https:\u002F\u002Forcid.org\u002F0000-0003-3692-8620",{"name":473,"orcid":474},"Pierre Celestin Ndayisaba","https:\u002F\u002Forcid.org\u002F0000-0002-8400-9146",{"name":476,"orcid":477},"Jules Rutebuka","https:\u002F\u002Forcid.org\u002F0000-0002-5236-3503",{"tldr":479,"method":480,"finding":481,"direction":482,"opportunity":483},"评估埃塞俄比亚、肯尼亚和卢旺达三国土地-土壤-作物综合信息系统的机构准备度与整合路径。","2022-2024年三国混合方法评估，含145个关键知情人访谈、利益相关方映射与","三国对空间化土壤作物数据需求高，但机构职责碎片化、协调不均、地方能力缺口大。","数字乡村与农业信息化","可研究开放数据政策与区域土壤健康倡议如何作为切入点，推动跨部门互操作标准与联合生产能力建设。","2026-09-24T23:30:10.120978Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":490,"content":9,"source_name":491,"source_url":488,"published_at":492,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":493,"sources":495,"tags":497,"primary_topic":502,"secondary_topics":508,"classification":510,"search_phrases":537,"slug":540,"view_count":21,"doi":541,"paper":542,"created_at":559},2928,"Biochar 2.0 in Climate-Smart Agriculture: A Critical Review of Designer Biochars, Soil Microbiome Responses, Nutrient-Use Efficiency and Greenhouse-Gas Mitigation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajsspn\u002F2026\u002Fv12i4764","Biochar has moved from a broadly promoted soil amendment to a material that is deliberately engineered for defined agronomic and climatic functions. This transition, described here as a shift towards designer biochars, raises questions that the earlier literature was not designed to answer: whether tailoring feedstock, pyrolysis conditions and post-synthesis modification produces reproducible gains in nutrient-use efficiency, whether the accompanying soil microbiome changes are functionally meaningful or largely descriptive, and whether greenhouse-gas benefits observed in short experiments persist under field management and withstand carbon-accounting scrutiny. This critical narrative review examines the evidence linking biochar properties to soil microbial responses, nutrient retention and transformation, and the mitigation of nitrous oxide, methane and carbon dioxide in agricultural soils. Literature was identified through structured searching of open scholarly indexes, agricultural and intergovernmental repositories, and citation tracking, with every retained source verified against an authoritative bibliographic record. The synthesis indicates that the strongest and most reproducible effects are pedoclimatic rather than material-specific: responses concentrate in acidic, coarse-textured, nutrient-depleted and tropical soils, whereas temperate fertile systems frequently show negligible agronomic gain. Microbiome studies consistently report compositional turnover with little change in alpha diversity, and the functional interpretation of these shifts rests heavily on marker-gene abundance rather than demonstrated process rates. Nitrous oxide suppression is well supported in aggregate but varies by a factor of five across syntheses, and recent field work shows that freshly applied high-rate biochar can increase emissions. Methane outcomes in flooded systems depend on water management and application history. Carbon permanence remains contested because the principal proxies used for crediting are informative about carbonisation rather than about in-soil residence under field weathering. Confidence is highest for liming, cation retention and short-term nitrogen conservation, and lowest for long-term net climate benefit at landscape scale. Priorities include multi-year factorial field trials that pair engineered materials with process-level measurement, standardised reporting of biochar properties, and accounting frameworks calibrated on weathered rather than freshly produced material.","生物炭已从一种被广泛推广的土壤改良剂，转变为一种为特定农艺和气候功能而刻意设计的材料。这一转变——本文将其描述为向“设计型生物炭”的转向——提出了早期文献未曾设计回答的问题：定制原料、热解条件和合成后修饰是否能在养分利用效率上产生可重复的增益；伴随而来的土壤微生物组变化在功能上是否有意义，还是主要停留在描述层面；以及在短期实验中观察到的温室气体效益能否在田间管理下持续存在，并经受住碳核算的审视。这篇批判性叙述综述考察了将生物炭性质与土壤微生物响应、养分保持与转化，以及农业土壤中氧化亚氮、甲烷和二氧化碳减排联系起来的证据。文献通过结构化检索开放学术索引、农业和政府间知识库以及引文追踪来识别，所有保留的来源均对照权威书目记录进行了核实。综合结果表明，最强且最可重复的效应是土壤气候性的，而非材料特异性的：响应集中于酸性、粗质地、养分贫瘠和热带土壤，而温带肥沃系统往往显示可忽略的农艺增益。微生物组研究一致报告组成上的更替而α多样性变化甚微，且对这些变化的功能解读在很大程度上依赖于标记基因丰度，而非已证实的过程速率。氧化亚氮抑制在总体上得到较好支持，但不同综合研究之间相差可达五倍，且近期田间工作表明，新施用的高用量生物炭可能增加排放。淹水系统中的甲烷结果取决于水分管理和施用历史。碳永久性仍存争议，因为用于信用核算的主要替代指标所反映的是碳化程度，而非田间风化条件下的土壤内驻留时间。置信度最高的是石灰效应、阳离子保持和短期氮素保全，最低的是景观尺度上的长期净气候效益。优先事项包括：将工程化材料与过程水平测量相结合的多年度析因田间试验、生物炭性质的标准化报告，以及基于风化而非","Asian Journal of Soil Science and Plant Nutrition","2026-09-18T00:00:00Z",{"impact":17,"substance":18,"depth":229,"authority":19,"freshness":148,"relevant":21,"comment":494},"系统评述设计型生物炭在土壤微生物、养分效率与温室气体减排上的证据强度与不确定性，学术增量扎实，对农业绿色低碳与土壤健康方向有参考价值。",[496],{"name":491,"url":488},[498,397,27,499,500,501],"生物炭","土壤微生物","温室气体减排","养分利用效率",{"code":503,"slug":504,"name":505,"path":506,"parent_code":507},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[509],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":511},{"candidates":512,"agricultural_evidence":46},[513,518,524,528,533],{"code":32,"score":514,"supported":46,"title":515,"abstract":516,"tags":517},17,[33],[49,33],[397,499,498],{"code":503,"score":136,"supported":46,"title":519,"abstract":521,"tags":523},[520],"greenhouse",[522,520],"温室",[],{"code":188,"score":148,"supported":46,"title":525,"abstract":526,"tags":527},[191],[],[],{"code":529,"score":21,"supported":14,"title":530,"abstract":531,"tags":532},"F3",[],[],[501],{"code":58,"score":21,"supported":14,"title":534,"abstract":535,"tags":536},[],[],[27],[538,539],"生物炭 温室气体 减排","养分利用效率 气候智慧农业 温室气体减排 土壤微生物","生物炭温室气体减排-2928","10.9734\u002Fajsspn\u002F2026\u002Fv12i4764",{"doi":541,"openalex_id":543,"authors":544,"venue":491,"cited_by_count":72,"oa_url":488,"card":554,"direction":218,"ingested_from":98},"W7213554252",[545,547,550,552],{"name":546,"orcid":9},"K. G. Rosin",{"name":548,"orcid":549},"Aditya V Machnoor","https:\u002F\u002Forcid.org\u002F0009-0001-5777-9188",{"name":551,"orcid":9},"Rakesh Kokatnoor",{"name":553,"orcid":9},"Arun S. Kalasad",{"tldr":555,"method":556,"finding":557,"direction":96,"opportunity":558},"综述设计型生物炭在气候智慧农业中的效果，指出其农艺与减排效益高度依赖土壤气候条件。","结构化文献检索与引文追踪，整合生物炭性质、土壤微生物、养分与温室气体证据。","最强效应集中于酸性贫瘠热带土壤，温带肥沃土壤增益有限；氧化亚氮抑制变异大，碳持久性存疑。","需多年多因子田间试验，结合过程速率测量与风化态碳核算，验证设计型生物炭的长期净气候效益。","2026-09-19T23:30:11.160909Z",{"id":561,"title":562,"url":563,"summary":564,"summary_zh":565,"content":9,"source_name":566,"source_url":563,"published_at":567,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":568,"score_detail":569,"sources":572,"tags":574,"primary_topic":577,"secondary_topics":579,"classification":581,"search_phrases":605,"slug":608,"view_count":609,"doi":610,"paper":611,"created_at":633},1540,"Three-way debate – measurements, pedotransfer functions, and inverse modeling – input strategy for soil hydraulic parameters in wheat simulations","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1912611","Accurate characterization of soil hydraulic parameters is crucial for simulating soil-plant-water dynamics in agroecosystem models. Hydraulic parameters, specifically the field capacity and permanent wilting point, govern the soil water balance and define the limits of plant-available water. This study evaluated the impact of soil hydraulic parameters in the Decision Support System for Agrotechnology Transfer (DSSAT) by comparing simulated soil water dynamics, wheat growth, and yield with observed data. To this end, a detailed 3-yr field dataset (2022, 2023, and 2025) from South Germany was employed, distinguished by its integration of wheat growth with an extensive daily record of soil water content and matric potential measurements at multiple depths. Within the DSSAT shell, permanent wilting point and field capacity were derived using both the Saxton and Rawls pedotransfer functions (PTFs); these estimates were then evaluated against lab-measured and sensor data using retention curves and soil-water simulations. In particular, the Saxton PTF introduced higher uncertainty in soil-water simulations and was selected as the starting point for an inverse modeling approach using Bayesian and Grid search algorithms to optimize field capacity and permanent wilting point. Using soil water content, wheat growth, and biomass data as target variables in the optimization procedure significantly reduced the normalized Root Mean Square Error (nRMSE) for both optimization algorithms. Overall model performance improved significantly, as shown by a reduction in nRMSE from 20% of the Saxton PTF to ≤ 10% for Bayesian targeting of grain weight and Grid search targeting of above-ground biomass, thereby enhancing the accuracy of wheat growth and yield predictions. This study provides a realistic evaluation of how measured, estimated, and calibrated parameters affect the soil water balance, demonstrating that plant response data can serve as an effective target variable for optimizing field capacity and permanent wilting point.","土壤水力参数的准确表征对于农业生态系统模型中土壤-植物-水分动态的模拟至关重要。水力参数，特别是田间持水量和永久萎蔫点，控制着土壤水平衡，并界定了植物可利用水的范围。本研究通过在农业技术转让决策支持系统（DSSAT）中比较模拟的土壤水分动态、小麦生长和产量与观测数据，评估了土壤水力参数的影响。为此，采用了来自德国南部的一项详细的3年田间数据集（2022年、2023年和2025年），该数据集的独特之处在于将小麦生长与多个深度土壤含水量和基质势的密集逐日记录相结合。在DSSAT框架内，使用Saxton和Rawls土壤传递函数（PTFs）推导了永久萎蔫点和田间持水量；随后，利用持水曲线和土壤水分模拟，将这些估计值与实验室测量和传感器数据进行了对比评估。特别是，Saxton PTF在土壤水分模拟中引入了较高的不确定性，因此被选为逆建模方法的起点，采用贝叶斯和网格搜索算法优化田间持水量和永久萎蔫点。在优化过程中，以土壤含水量、小麦生长和生物量数据作为目标变量，显著降低了两种优化算法的归一化均方根误差（nRMSE）。整体模型性能显著提升，表现为nRMSE从Saxton PTF的20%降至贝叶斯算法以籽粒重量为目标时的≤10%，以及网格搜索算法以地上生物量为目标时的≤10%，从而提高了小麦生长和产量预测的准确性。本研究对实测、估算和校准参数如何影响土壤水平衡进行了现实评估，证明植物响应数据可作为优化田间持水量和永久萎蔫点的有效目标变量。","Frontiers in Agronomy","2026-09-01T00:00:00Z",72,{"impact":312,"substance":112,"depth":17,"authority":111,"freshness":570,"relevant":21,"comment":571},7,"研究评估不同土壤水力参数获取方式对小麦模拟的影响，方法严谨，数据详实，对精准农业有参考价值。",[573],{"name":566,"url":563},[297,26,575,576],"土壤水动力学","参数优化",{"code":155,"slug":159,"name":158,"path":578,"parent_code":305},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[580],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":582},{"candidates":583,"agricultural_evidence":46},[584,588,592,597,601],{"code":155,"score":136,"supported":46,"title":585,"abstract":586,"tags":587},[159],[158,159],[],{"code":32,"score":136,"supported":46,"title":589,"abstract":590,"tags":591},[33],[49,33],[],{"code":64,"score":53,"supported":46,"title":593,"abstract":594,"tags":596},[],[595],"decision support",[26],{"code":182,"score":170,"supported":46,"title":598,"abstract":599,"tags":600},[],[185],[],{"code":188,"score":21,"supported":14,"title":602,"abstract":603,"tags":604},[],[],[297],[606,607],"土壤水动力学 作物模型 参数优化 智慧农业","土壤水动力学 作物模型","土壤水动力学作物模型参数优化智慧农业-1540",2,"10.3389\u002Ffagro.2026.1912611",{"doi":610,"openalex_id":612,"authors":613,"venue":566,"cited_by_count":72,"oa_url":626,"card":627,"direction":96,"ingested_from":98},"W7204969062",[614,617,620,623],{"name":615,"orcid":616},"Jonas Trenz","https:\u002F\u002Forcid.org\u002F0000-0002-4850-5767",{"name":618,"orcid":619},"Emir Memic","https:\u002F\u002Forcid.org\u002F0000-0002-0184-5481",{"name":621,"orcid":622},"Dennis Gekeler","https:\u002F\u002Forcid.org\u002F0009-0003-3725-205X",{"name":624,"orcid":625},"Simone Graeff‐Hönninger","https:\u002F\u002Forcid.org\u002F0000-0001-9996-961X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fagronomy\u002Farticles\u002F10.3389\u002Ffagro.2026.1912611\u002Fpdf",{"tldr":628,"method":629,"finding":630,"direction":631,"opportunity":632},"比较测量、传递函数和反演建模获取土壤水力参数对小麦模拟的影响。","使用DSSAT模型，结合Saxton和Rawls PTF、贝叶斯和网格搜索反演优","反演优化显著降低nRMSE至≤10%，提高小麦生长和产量预测精度。","农业人工智能与决策模型","可探索利用作物生长数据反演土壤参数在其他作物和区域的应用，或开发更高效的优化算法。","2026-09-03T23:31:04.621734Z"]