[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3218":3,"related-3218":104},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":98,"slug":101,"view_count":102,"doi":8,"paper":8,"created_at":103},3218,"国家重点研发计划项目《盐碱地智慧生态农业关键技术研发与集成应用》应用场景验收会在山东昌邑召开","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F22\u002F991029269.html","9-22 中国农网报道：9-19 由山东省农业科学院主持的\"十四五\"国家重点研发计划（部省联动）项目\"工厂化农业关键技术与智能农机装备\"重点专项\"盐碱地智慧生态农业关键技术研发与集成应用\"应用场景验收会在山东潍坊昌邑召开。会议邀请北京工商大学食品与健康学院杨信廷研究员、农业农村部南京农机化所科技管理处处长常春研究员、南京农业大学工学院院长肖茂华教授、山东省农科院阮怀军研究员及湿地农业与生态研究所所长李宗新研究员组成的专家组及项目骨干成员70余人参会。现场演示了智慧水肥一体化灌溉系统和变量喷药技术，展示了水肥及智能精准调控技术、土壤质量提升绿色精量配肥、玉米花生带状复合种植生态高效等核心技术的规模化示范应用；陕西大荔、内蒙古达拉特旗及山东武城、东营、无棣5处示范基地现场实时查看。",null,"山东省农业科学院","2026-09-19T00:00:00Z","报道",10,false,82,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},24,20,16,14,8,1,"国家重点研发计划项目在昌邑完成应用场景验收，多基地联动示范智慧水肥与变量喷药技术，专业性与示范价值突出，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","智能农机","水肥一体化","盐碱地治理","生态农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,62,68,74,81,87,93],{"code":32,"score":50,"supported":51,"title":52,"abstract":54,"tags":56},15,true,[53],"盐碱地",[55,53],"土壤",[29],{"code":39,"score":58,"supported":51,"title":59,"abstract":60,"tags":61},12,[30],[30],[30],{"code":63,"score":64,"supported":51,"title":65,"abstract":66,"tags":67},"F3",4,[],[28],[28],{"code":69,"score":64,"supported":51,"title":70,"abstract":71,"tags":73},"F5",[],[72],"农机",[27],{"code":75,"score":76,"supported":51,"title":77,"abstract":78,"tags":80},"A3",3,[],[79],"玉米",[],{"code":82,"score":76,"supported":51,"title":83,"abstract":84,"tags":86},"A4",[],[85],"花生",[],{"code":88,"score":76,"supported":51,"title":89,"abstract":90,"tags":92},"H3",[],[91],"灌溉",[],{"code":94,"score":21,"supported":13,"title":95,"abstract":96,"tags":97},"I1",[],[],[26],[99,100],"盐碱地智慧生态农业 昌邑","山东农科院 盐碱地 水肥一体化","盐碱地智慧生态农业昌邑-3218",7,"2026-09-23T00:04:29.434147Z",{"total":105,"page":21,"page_size":105,"items":106},6,[107,204,297,403,492,634],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":114,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":116,"score_detail":117,"sources":123,"tags":125,"primary_topic":130,"secondary_topics":136,"classification":139,"search_phrases":183,"slug":186,"view_count":21,"doi":187,"paper":188,"created_at":203},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z","论文",80,{"impact":118,"substance":119,"depth":120,"authority":121,"freshness":20,"relevant":21,"comment":122},18,22,19,13,"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[124],{"name":113,"url":110},[26,126,127,128,129],"农业人工智能","精准农业","无线传感器网络","土壤监测",{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[137,138],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":140},{"candidates":141,"agricultural_evidence":51},[142,148,152,157,162,167,173,178],{"code":131,"score":50,"supported":51,"title":143,"abstract":145,"tags":147},[144],"wireless sensor",[146,144],"无线传感",[128],{"code":32,"score":19,"supported":51,"title":149,"abstract":150,"tags":151},[33],[55,33],[],{"code":39,"score":20,"supported":51,"title":153,"abstract":155,"tags":156},[154],"sustainable agriculture",[],[],{"code":94,"score":20,"supported":51,"title":158,"abstract":159,"tags":161},[],[127,160],"precision agriculture",[26,127],{"code":75,"score":105,"supported":51,"title":163,"abstract":164,"tags":166},[],[79,165],"corn",[],{"code":82,"score":105,"supported":51,"title":168,"abstract":169,"tags":172},[],[170,171],"大豆","soybean",[],{"code":69,"score":76,"supported":51,"title":174,"abstract":175,"tags":177},[],[176],"harvester",[],{"code":179,"score":21,"supported":13,"title":180,"abstract":181,"tags":182},"I2",[],[],[126],[184,185],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":187,"openalex_id":189,"authors":190,"venue":113,"cited_by_count":194,"oa_url":195,"card":196,"direction":200,"ingested_from":202},"W7214927134",[191],{"name":192,"orcid":193},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489",0,"https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":197,"method":198,"finding":199,"direction":200,"opportunity":201},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","智慧农业 \u002F 农业物联网","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","openalex","2026-10-03T23:30:17.793958Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":8,"source_name":210,"source_url":207,"published_at":211,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":212,"score_detail":213,"sources":216,"tags":218,"primary_topic":221,"secondary_topics":222,"classification":225,"search_phrases":279,"slug":282,"view_count":214,"doi":283,"paper":284,"created_at":296},1305,"IoT-Based Soil Condition Monitoring System for Supporting Precision and Sustainable Agriculture in West Java","https:\u002F\u002Fdoi.org\u002F10.55981\u002Fjet.807","West Java Province is one of the largest horticultural producers in Indonesia. The horticulture subsector in West Java covers tens of thousands of hectares of farmland and contributes approximately 1.8% to the province’s total Gross Regional Domestic Product (GRDP). However, this sector faces serious challenges. Climate change has increased the frequency of droughts and floods, disrupting horticultural production. In addition, soil fertility has declined due to unsustainable farming practices; many agricultural lands in West Java contain less than 2% organic matter as a result of long-term chemical fertilizer use. These challenges have led to decreased productivity and threaten the sustainability of horticultural farming. Precision agriculture emerges as a solution to these issues. This concept leverages IoT technologies such as the ESP32 microcontroller with SPIFFS, soil sensors, and a web-based dashboard to monitor land conditions in real time and manage inputs more efficiently. The IoT-based soil monitoring system developed in this study was tested in a farmer’s yard in Pameungpeuk Subdistrict, Garut. The results demonstrated practical benefits, including improved irrigation efficiency (up to ~30% water savings) and reduced risk of crop losses through early detection of drought conditions. Moreover, the application of IoT in potato cultivation in West Java has been shown to double seed tuber yields from 15,000 to 30,000 tubers. These findings are consistent with regional policy directions, as reflected in the West Java Regional Medium-Term Development Plan (RPJMD) and the “Petani Milenial” (Millennial Farmers) program, which promote sustainable agricultural modernization through IoT-based technologies to enhance the productivity of young farmers.","西爪哇省是印度尼西亚最大的园艺生产地之一。该省的园艺分部门覆盖数万公顷农田，约占全省地区生产总值（GRDP）总额的1.8%。然而，该部门面临严峻挑战。气候变化增加了干旱和洪水的发生频率，扰乱了园艺生产。此外，由于不可持续的耕作方式，土壤肥力下降；长期使用化肥导致西爪哇许多农业用地的有机质含量低于2%。这些挑战导致生产力下降，并威胁到园艺农业的可持续性。精准农业应运而生，成为解决这些问题的方案。这一概念利用物联网（IoT）技术，如搭载SPIFFS的ESP32微控制器、土壤传感器和基于网页的仪表盘，实时监测土地状况并更高效地管理投入。本研究中开发的基于物联网的土壤监测系统在加鲁特县帕梅翁佩乌克分区的一位农民庭院中进行了测试。结果表明其具有实际效益，包括提高灌溉效率（节水约30%）以及通过早期检测干旱状况降低作物损失风险。此外，物联网在西爪哇马铃薯种植中的应用已被证明可使种薯产量翻倍，从每公顷15,000个增加到30,000个。这些发现与区域政策方向一致，正如西爪哇省中期发展规划（RPJMD）和“千禧农民”计划所反映的那样，这些政策通过基于物联网的技术促进可持续农业现代化，以提高年轻农民的生产力。","Jurnal Elektronika dan Telekomunikasi","2026-08-31T00:00:00Z",61,{"impact":50,"substance":118,"depth":18,"authority":12,"freshness":214,"relevant":21,"comment":215},2,"论文展示IoT土壤监测系统在西爪哇的应用，节水30%并提升马铃薯产量，对区域精准农业有参考价值，但时效性较低。",[217],{"name":210,"url":207},[26,219,127,220,129],"物联网","可持续农业",{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},[223,224],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":226},{"candidates":227,"agricultural_evidence":51},[228,233,237,241,251,255,262,267,274],{"code":131,"score":50,"supported":51,"title":229,"abstract":231,"tags":232},[230],"IoT",[219,230],[219],{"code":32,"score":19,"supported":51,"title":234,"abstract":235,"tags":236},[33],[55,33],[],{"code":39,"score":58,"supported":51,"title":238,"abstract":239,"tags":240},[154],[220],[220],{"code":242,"score":243,"supported":51,"title":244,"abstract":245,"tags":250},"H7",9,[],[246,247,248,249],"气候变化","干旱","climate change","drought",[],{"code":94,"score":20,"supported":51,"title":252,"abstract":253,"tags":254},[],[127,160],[26,127],{"code":256,"score":105,"supported":51,"title":257,"abstract":258,"tags":261},"A5",[],[259,260],"马铃薯","potato",[],{"code":88,"score":105,"supported":51,"title":263,"abstract":264,"tags":266},[],[91,265],"irrigation",[],{"code":268,"score":105,"supported":51,"title":269,"abstract":270,"tags":273},"J2",[],[271,272],"农业现代化","agricultural modernization",[],{"code":63,"score":76,"supported":51,"title":275,"abstract":276,"tags":278},[],[277],"fertilizer",[],[280,281],"可持续农业 土壤监测 智慧农业 精准农业","可持续农业 土壤监测","可持续农业土壤监测智慧农业精准农业-1305","10.55981\u002Fjet.807",{"doi":283,"openalex_id":285,"authors":286,"venue":210,"cited_by_count":194,"oa_url":207,"card":291,"direction":200,"ingested_from":202},"W7204810936",[287,289],{"name":288,"orcid":8},"Ayu Latifah",{"name":290,"orcid":8},"Dendi Ardimansah",{"tldr":292,"method":293,"finding":294,"direction":200,"opportunity":295},"开发基于IoT的土壤监测系统，支持西爪哇精准可持续农业，提高灌溉效率并增产。","使用ESP32微控制器、SPIFFS、土壤传感器和Web仪表盘构建实时监测系统。","系统节省约30%灌溉用水，马铃薯种薯产量翻倍至3万块，符合区域政策。","可扩展至更多作物和地区，结合机器学习预测土壤养分，优化施肥策略，提升系统普适性。","2026-09-01T23:30:12.797772Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":8,"source_name":303,"source_url":300,"published_at":304,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":305,"score_detail":306,"sources":308,"tags":310,"primary_topic":316,"secondary_topics":317,"classification":319,"search_phrases":361,"slug":364,"view_count":21,"doi":365,"paper":366,"created_at":402},3756,"Improving soil carbon stabilization within conservation agriculture: An adaptive framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104980","CONTEXT Conservation agriculture (CA) is widely promoted for improving soil ecosystem services, including erosion reduction, water conservation, and soil health. Its contribution to soil organic carbon (SOC) restoration, however, is variable, often modest and concentrated near the surface and strongly dependent on climate, soil, residue management and cropping system. OBJECTIVE To examine why SOC stabilization under CA is limited in some systems and to propose an adaptive framework that increases the probability that crop-residue carbon (CR-C) and root-derived C are converted into more stable SOC pools. METHODS We synthesize evidence on CR-C fate, root-derived C, SOC fractions, nutrient stoichiometry and soil-profile placement, under CA and organize this evidence into a diagnosis-based adaptive framework that links intervention choice with evidence strength, operational feasibility and trade-offs. RESULTS AND CONCLUSIONS A central limitation is the low but highly variable conversion of aboveground CR-C into stabilized SOC, governed by soil texture, mineralogy, climate, residue quality, nutrient availability, rooting depth and time since CA adoption. We propose an adaptive CA framework with six optional, evidence-graded interventions triggered by diagnosed SOC-stabilization bottlenecks: (1) residue retention thresholds to avoid a “problem of plenty”; (2) conditional strategic soil disturbance for compaction, surface stratification and C placement; (3) companion crops that improve residue amount, quality and root inputs; (4) breeding and selection for root traits for deeper C delivery and SOC stabilization; (5) fertilization strategies that better align decomposition with microbial stoichiometric needs while considering trade-offs; and (6) fungal consortia to accelerate lignocellulose breakdown where field conditions support residue turnover without increasing C losses. Together these interventions provide an adaptive extension of CA that can improve the likelihood of measurable and more durable SOC stabilization where specific bottlenecks are diagnosed. SIGNIFICANCE Options exist to strengthen the SOC-stabilization component of CA where site-specific bottlenecks are present, while explicitly considering feasibility, costs, trade-offs and the need for empirical validation through long-term monitoring of whole-profile SOC stocks and greenhouse-gas outcomes.","背景 保护性农业（conservation agriculture, CA）被广泛推广以改善土壤生态系统服务，包括减少侵蚀、保持水分和促进土壤健康。然而，其对土壤有机碳（soil organic carbon, SOC）恢复的贡献具有变异性，通常较为有限，且集中于表层，并高度依赖气候、土壤、秸秆管理和种植制度。目的 探讨保护性农业下SOC稳定化在某些系统中受限的原因，并提出一个适应性框架，以提高作物秸秆碳（crop-residue carbon, CR-C）和根系来源碳转化为更稳定SOC库的概率。方法 我们综合了保护性农业下关于作物秸秆碳去向、根系来源碳、SOC组分、养分化学计量学及土壤剖面分布的证据，并将这些证据组织为一个基于诊断的适应性框架，将干预措施选择与证据强度、操作可行性和权衡取舍相联系。结果与结论 一个核心局限在于地上作物秸秆碳向稳定化SOC的转化率低且高度可变，受土壤质地、矿物学、气候、秸秆质量、养分有效性、根系深度及保护性农业采用时间等因素调控。我们提出了一个适应性保护性农业框架，包含六项可选的、按证据分级排序的干预措施，由诊断出的SOC稳定化瓶颈所触发：（1）秸秆还田阈值以避免“过多的问题”；（2）针对压实、表层分层和碳 placement 的条件性战略土壤扰动；（3）改善秸秆数量、质量和根系输入的伴生作物；（4）选育和筛选根系性状以实现更深层的碳输送和SOC稳定化；（5）施肥策略，在考虑权衡取舍的同时更好地使分解过程与微生物化学计量需求相匹配；（6）在田间条件支持秸秆周转且不增加碳损失的情况下，利用真菌菌群加速木质纤维素分解。这些干预措施共同构成了保护性农业的适应性延伸，在诊断出特定瓶颈的情况下，可提高实现可测量且更持久SOC稳定化的可能性。意义 在存在特定地点瓶颈的情况下，现有方案可强化保护性农业的SOC稳定化功能，同时需明确考虑可行性、成本、权衡取舍，以及通过长期监测全剖面SOC储量和温室气体排放结果进行实证验证的必要性。","Agricultural Systems","2026-09-28T00:00:00Z",79,{"impact":18,"substance":119,"depth":118,"authority":19,"freshness":243,"relevant":21,"comment":307},"该研究提出基于诊断的保护性农业自适应框架，为提升土壤有机碳稳定提供可操作路径，专业深度与信息增量突出，值得进入每日精选。",[309],{"name":303,"url":300},[311,312,313,314,315],"保护性农业","土壤健康","农业碳汇","土壤固碳","根际性状",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[318],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":320},{"candidates":321,"agricultural_evidence":51},[322,326,331,338,344,350,356],{"code":32,"score":18,"supported":51,"title":323,"abstract":324,"tags":325},[33],[55,33],[312,314],{"code":39,"score":58,"supported":51,"title":327,"abstract":329,"tags":330},[328],"conservation agriculture",[328],[311],{"code":332,"score":105,"supported":51,"title":333,"abstract":334,"tags":337},"B7",[],[335,336],"温室","greenhouse",[],{"code":63,"score":105,"supported":51,"title":339,"abstract":340,"tags":343},[],[341,342],"施肥","fertilization",[],{"code":345,"score":76,"supported":51,"title":346,"abstract":347,"tags":349},"H5",[],[348],"秸秆还田",[],{"code":351,"score":76,"supported":51,"title":352,"abstract":353,"tags":355},"H6",[],[354],"生态系统服务",[],{"code":357,"score":21,"supported":13,"title":358,"abstract":359,"tags":360},"H4",[],[],[313],[362,363],"保护性农业 土壤有机碳 固碳","保护性农业 根际性状 碳稳定","保护性农业土壤有机碳固碳-3756","10.1016\u002Fj.agsy.2026.104980",{"doi":365,"openalex_id":367,"authors":368,"venue":303,"cited_by_count":194,"oa_url":300,"card":396,"direction":400,"ingested_from":202},"W7214662811",[369,372,375,377,380,383,385,388,391,394],{"name":370,"orcid":371},"Anupam Das","https:\u002F\u002Forcid.org\u002F0000-0001-6548-215X",{"name":373,"orcid":374},"Ashim Datta","https:\u002F\u002Forcid.org\u002F0000-0002-1843-9981",{"name":376,"orcid":8},"Samrat Ghosh",{"name":378,"orcid":379},"Sahely Kanthal","https:\u002F\u002Forcid.org\u002F0000-0001-6550-5227",{"name":381,"orcid":382},"Anwesha Mandal","https:\u002F\u002Forcid.org\u002F0009-0003-0113-211X",{"name":384,"orcid":8},"Dibyendu Sarkar",{"name":386,"orcid":387},"Probir Kumar Ghosh","https:\u002F\u002Forcid.org\u002F0000-0003-1580-5883",{"name":389,"orcid":390},"Richard William Bell","https:\u002F\u002Forcid.org\u002F0000-0002-7756-3755",{"name":392,"orcid":393},"Debashis Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0001-9664-8095",{"name":395,"orcid":8},"Biswapati Mandal",{"tldr":397,"method":398,"finding":399,"direction":400,"opportunity":401},"提出诊断式自适应保护性农业框架，提升秸秆与根系碳向稳定土壤有机碳的转化。","综合秸秆碳去向、根系碳、有机碳组分与养分化学计量证据，构建六项分级干预框架。","保护性农业固碳受质地、气候、残体质量等制约，需按诊断瓶颈选择干预以提升稳定性。","农业绿色发展与碳","可针对不同土壤-气候区量化各干预的固碳效率与权衡，并开展全剖面长期监测验证。","2026-09-29T23:30:04.622148Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":8,"source_name":409,"source_url":406,"published_at":410,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":411,"score_detail":412,"sources":415,"tags":417,"primary_topic":419,"secondary_topics":424,"classification":431,"search_phrases":476,"slug":479,"view_count":214,"doi":8,"paper":480,"created_at":491},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":18,"substance":17,"depth":413,"authority":121,"freshness":20,"relevant":21,"comment":414},17,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[416],{"name":409,"url":406},[26,127,418,29,129],"遥感",{"code":420,"slug":421,"name":422,"path":423,"parent_code":135},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[425,430],{"code":426,"slug":427,"name":428,"path":429,"parent_code":135},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":432},{"candidates":433,"agricultural_evidence":51},[434,440,448,452,456,462,466,470],{"code":420,"score":118,"supported":51,"title":435,"abstract":437,"tags":439},[436],"remote sensing",[418,436,438],"multispectral",[418],{"code":426,"score":413,"supported":51,"title":441,"abstract":443,"tags":447},[442],"GIS",[444,445,442,446],"地理信息","空间分析","geographic information",[],{"code":32,"score":50,"supported":51,"title":449,"abstract":450,"tags":451},[33],[55,33],[29],{"code":94,"score":12,"supported":51,"title":453,"abstract":454,"tags":455},[160],[],[26,127],{"code":179,"score":105,"supported":51,"title":457,"abstract":458,"tags":461},[],[459,460],"机器学习","machine learning",[],{"code":88,"score":76,"supported":51,"title":463,"abstract":464,"tags":465},[],[91],[],{"code":242,"score":76,"supported":51,"title":467,"abstract":468,"tags":469},[],[247],[],{"code":471,"score":76,"supported":51,"title":472,"abstract":473,"tags":475},"I7",[],[474],"methodological framework",[],[477,478],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":8,"openalex_id":481,"authors":482,"venue":409,"cited_by_count":194,"oa_url":406,"card":485,"direction":489,"ingested_from":202},"W7221843179",[483],{"name":484,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":486,"method":487,"finding":488,"direction":489,"opportunity":490},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","农业遥感与作物表型","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":8,"source_name":498,"source_url":495,"published_at":304,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":116,"score_detail":499,"sources":501,"tags":503,"primary_topic":507,"secondary_topics":512,"classification":519,"search_phrases":573,"slug":576,"view_count":76,"doi":577,"paper":578,"created_at":633},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。","Frontiers in Sustainable Food Systems",{"impact":118,"substance":119,"depth":118,"authority":121,"freshness":243,"relevant":21,"comment":500},"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[502],{"name":498,"url":495},[26,504,505,506,28],"精准施肥","小麦","保护性耕作",{"code":508,"slug":509,"name":505,"path":510,"parent_code":511},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[513,518],{"code":63,"slug":514,"name":515,"path":516,"parent_code":517},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":520},{"candidates":521,"agricultural_evidence":51},[522,527,531,535,540,547,551,555,561,565,569],{"code":63,"score":18,"supported":51,"title":523,"abstract":525,"tags":526},[524],"fertigation",[341,524],[28,504],{"code":508,"score":50,"supported":51,"title":528,"abstract":529,"tags":530},[509],[505,509],[505],{"code":39,"score":58,"supported":51,"title":532,"abstract":533,"tags":534},[328],[328],[506],{"code":75,"score":105,"supported":51,"title":536,"abstract":537,"tags":539},[],[79,538],"maize",[],{"code":541,"score":105,"supported":51,"title":542,"abstract":543,"tags":546},"A8",[],[544,545],"粮食安全","food security",[],{"code":32,"score":105,"supported":51,"title":548,"abstract":549,"tags":550},[],[55,33],[],{"code":88,"score":105,"supported":51,"title":552,"abstract":553,"tags":554},[],[91,265],[],{"code":556,"score":76,"supported":51,"title":557,"abstract":558,"tags":560},"A7",[],[559],"轮作",[],{"code":345,"score":76,"supported":51,"title":562,"abstract":563,"tags":564},[],[348],[],{"code":242,"score":76,"supported":51,"title":566,"abstract":567,"tags":568},[],[247],[],{"code":94,"score":21,"supported":13,"title":570,"abstract":571,"tags":572},[],[],[26],[574,575],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":577,"openalex_id":579,"authors":580,"venue":498,"cited_by_count":194,"oa_url":495,"card":628,"direction":200,"ingested_from":202},"W7214579192",[581,583,586,588,590,592,594,597,599,601,603,605,607,610,612,614,617,620,623,626],{"name":582,"orcid":8},"Kadagonda Nithinkumar",{"name":584,"orcid":585},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":587,"orcid":8},"Devika A. R.",{"name":589,"orcid":8},"Arjun Singh",{"name":591,"orcid":8},"Susama Sudhishri",{"name":593,"orcid":8},"Vinod Kumar Singh",{"name":595,"orcid":596},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":598,"orcid":8},"Sarvendra Kumar",{"name":600,"orcid":8},"Teekam Singh",{"name":602,"orcid":8},"Jitendra Rajput",{"name":604,"orcid":8},"Pravin Kumar Upadhyaya",{"name":606,"orcid":8},"R. N. Sahoo",{"name":608,"orcid":609},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":611,"orcid":8},"Aye Aye San",{"name":613,"orcid":8},"K. S. Sachin",{"name":615,"orcid":616},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":618,"orcid":619},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":621,"orcid":622},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":624,"orcid":625},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":627,"orcid":8},"Subhash Babu",{"tldr":629,"method":630,"finding":631,"direction":200,"opportunity":632},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","2026-09-29T23:30:06.859116Z",{"id":635,"title":636,"url":637,"summary":638,"summary_zh":639,"content":8,"source_name":640,"source_url":637,"published_at":641,"category":115,"cover_url":8,"hotness":12,"is_selected":13,"score":642,"score_detail":643,"sources":645,"tags":647,"primary_topic":651,"secondary_topics":652,"classification":657,"search_phrases":673,"slug":676,"view_count":194,"doi":677,"paper":678,"created_at":693},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":20,"substance":17,"depth":413,"authority":243,"freshness":12,"relevant":21,"comment":644},"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[646],{"name":640,"url":637},[26,126,648,649,650],"主动学习","线虫检测","土壤采样",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[653],{"code":179,"slug":654,"name":655,"path":656,"parent_code":135},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":13,"version":45,"method":46,"evidence":658},{"candidates":659,"agricultural_evidence":51},[660,664,669],{"code":32,"score":19,"supported":51,"title":661,"abstract":662,"tags":663},[33],[55,33],[],{"code":179,"score":243,"supported":51,"title":665,"abstract":667,"tags":668},[666],"artificial intelligence",[],[126],{"code":94,"score":102,"supported":51,"title":670,"abstract":671,"tags":672},[],[127,160],[26],[674,675],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":677,"openalex_id":679,"authors":680,"venue":640,"cited_by_count":194,"oa_url":637,"card":687,"direction":691,"ingested_from":202},"W7223082195",[681,683,685],{"name":682,"orcid":8},"Amine Ben Salah",{"name":684,"orcid":8},"Ines Trabelsi",{"name":686,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":688,"method":689,"finding":690,"direction":691,"opportunity":692},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z"]