[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3108":3,"related-3108":72},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":67,"slug":70,"view_count":62,"doi":8,"paper":8,"created_at":71},3108,"安徽省农科院作物所\"一种小麦抗倒伏肥料及其制备方法\"等 2 项专利赋权转化公示——转让总金额 5.3 万元","https:\u002F\u002Fwww.aaas.org.cn\u002F4303171\u002F71129984.html","9-16 安徽省农科院作物所发布小麦栽培与耕作团队张向前、杜世州、乔玉强、曹承富、李玮、陈欢、赵竹等 7 位完成人 2 项职务科技成果赋权转化公示：专利\"一种小麦抗倒伏肥料及其制备方法\"（ZL202211176053X）、\"一种大豆高产肥料及其制备方法\"（ZL202410297486.3），赋权方式均为职务科技成果长期使用权（10 年），完成人占 70%、转化方占 20%、院占 10%；分别以 5 万元、0.3 万元转让给垦浩农业科技（广州）有限公司。公示时间 9-16 至 10-2 共 15 个工作日。",null,"安徽省农业科学院","2026-09-16T00:00:00Z","报道",10,false,53,{"impact":16,"substance":17,"depth":12,"authority":18,"freshness":19,"relevant":20,"comment":21},8,16,13,6,1,"省级科研机构职务成果赋权转化公示，含具体专利号、金额与权益分配，信息实在但影响限于行业层面。",[23],{"name":9,"url":6},[25,26,27,28,29],"安徽省农科院","专利转化","小麦抗倒伏","大豆高产","成果赋权",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,55,60],{"code":31,"score":50,"supported":51,"title":52,"abstract":53,"tags":54},11,true,[33],[33],[],{"code":38,"score":50,"supported":51,"title":56,"abstract":58,"tags":59},[57],"肥料",[57],[],{"code":61,"score":62,"supported":51,"title":63,"abstract":64,"tags":66},"A4",3,[],[65],"大豆",[],[68,69],"安徽省农科院 小麦抗倒伏肥料 专利","小麦抗倒伏肥料 大豆高产肥料 赋权转化","安徽省农科院小麦抗倒伏肥料专利-3108","2026-09-22T00:05:35.824526Z",{"total":19,"page":20,"page_size":19,"items":73},[74,240,325,387,477,528],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":8,"source_name":80,"source_url":77,"published_at":81,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":89,"tags":91,"primary_topic":96,"secondary_topics":97,"classification":105,"search_phrases":176,"slug":179,"view_count":62,"doi":180,"paper":181,"created_at":239},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","2026-09-28T00:00:00Z","论文",80,{"impact":85,"substance":86,"depth":85,"authority":18,"freshness":87,"relevant":20,"comment":88},18,22,9,"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。",[90],{"name":80,"url":77},[92,93,33,94,95],"智慧农业","精准施肥","保护性耕作","水肥一体化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[98,99],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":44,"locked":13,"version":45,"method":46,"evidence":106},{"candidates":107,"agricultural_evidence":51},[108,114,119,125,132,139,146,153,159,165,171],{"code":38,"score":17,"supported":51,"title":109,"abstract":111,"tags":113},[110],"fertigation",[112,110],"施肥",[95,93],{"code":31,"score":115,"supported":51,"title":116,"abstract":117,"tags":118},15,[32],[33,32],[33],{"code":100,"score":120,"supported":51,"title":121,"abstract":123,"tags":124},12,[122],"conservation agriculture",[122],[94],{"code":126,"score":19,"supported":51,"title":127,"abstract":128,"tags":131},"A3",[],[129,130],"玉米","maize",[],{"code":133,"score":19,"supported":51,"title":134,"abstract":135,"tags":138},"A8",[],[136,137],"粮食安全","food security",[],{"code":140,"score":19,"supported":51,"title":141,"abstract":142,"tags":145},"H2",[],[143,144],"土壤","soil",[],{"code":147,"score":19,"supported":51,"title":148,"abstract":149,"tags":152},"H3",[],[150,151],"灌溉","irrigation",[],{"code":154,"score":62,"supported":51,"title":155,"abstract":156,"tags":158},"A7",[],[157],"轮作",[],{"code":160,"score":62,"supported":51,"title":161,"abstract":162,"tags":164},"H5",[],[163],"秸秆还田",[],{"code":166,"score":62,"supported":51,"title":167,"abstract":168,"tags":170},"H7",[],[169],"干旱",[],{"code":172,"score":20,"supported":13,"title":173,"abstract":174,"tags":175},"I1",[],[],[92],[177,178],"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":180,"openalex_id":182,"authors":183,"venue":80,"cited_by_count":231,"oa_url":77,"card":232,"direction":236,"ingested_from":238},"W7214579192",[184,186,189,191,193,195,197,200,202,204,206,208,210,213,215,217,220,223,226,229],{"name":185,"orcid":8},"Kadagonda Nithinkumar",{"name":187,"orcid":188},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":190,"orcid":8},"Devika A. R.",{"name":192,"orcid":8},"Arjun Singh",{"name":194,"orcid":8},"Susama Sudhishri",{"name":196,"orcid":8},"Vinod Kumar Singh",{"name":198,"orcid":199},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":201,"orcid":8},"Sarvendra Kumar",{"name":203,"orcid":8},"Teekam Singh",{"name":205,"orcid":8},"Jitendra Rajput",{"name":207,"orcid":8},"Pravin Kumar Upadhyaya",{"name":209,"orcid":8},"R. N. Sahoo",{"name":211,"orcid":212},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":214,"orcid":8},"Aye Aye San",{"name":216,"orcid":8},"K. S. Sachin",{"name":218,"orcid":219},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":221,"orcid":222},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":224,"orcid":225},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":227,"orcid":228},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":230,"orcid":8},"Subhash Babu",0,{"tldr":233,"method":234,"finding":235,"direction":236,"opportunity":237},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-29T23:30:06.859116Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":8,"source_name":246,"source_url":243,"published_at":247,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":252,"tags":254,"primary_topic":258,"secondary_topics":259,"classification":261,"search_phrases":294,"slug":297,"view_count":62,"doi":298,"paper":299,"created_at":324},2655,"Response of Wheat Crop to The Application of Different Micronutrients and Their Influence on Growth and Development","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.9.2026.605","Micronutrients are required in small quantities but play essential roles in wheat growth, physiological processes, yield formation, and grain nutritional quality. Deficiencies of zinc (Zn), iron (Fe), manganese (Mn), copper (Cu), and boron (B) can restrict plant development and reduce productivity, particularly in soils with limited nutrient availability. This study summarizes the roles of major micronutrients in wheat growth and development, with emphasis on their effects on germination, root development, tillering, biomass production, photosynthesis, nutrient uptake, enzyme activity, yield components, and grain quality. The study also evaluates major approaches for micronutrient management, including soil application, seed treatment, foliar application, and integrated fertilization. Particular attention is given to the influence of soil properties, environmental conditions, wheat genotype, fertilizer dose, application timing, and application method on micronutrient availability and utilization efficiency. Recent evidence indicates that balanced micronutrient management can improve wheat physiological performance, yield components, grain yield, and nutritional value, while agronomic biofortification offers an important strategy for increasing grain Zn and Fe concentrations. However, the effectiveness of micronutrient application varies considerably with soil conditions, climatic stresses, genotype, and management practices. Therefore, site-specific nutrient management, soil testing, genotype selection, and stage-specific application are essential for improving micronutrient-use efficiency. Integrating micronutrient management with wheat breeding and precision agriculture can contribute to sustainable productivity and improved grain nutritional quality. Future research should focus on genotype-specific micronutrient requirements, nutrient interactions, precision application technologies, and climate-resilient strategies for efficient wheat production and enhanced food and nutritional security.","微量营养元素虽然需求量小，但在小麦生长、生理过程、产量形成和籽粒营养品质中发挥着重要作用。锌（Zn）、铁（Fe）、锰（Mn）、铜（Cu）和硼（B）的缺乏会限制作物发育并降低生产力，尤其是在养分有效性有限的土壤中。本研究综述了主要微量营养元素在小麦生长发育中的作用，重点阐述了其对萌发、根系发育、分蘖、生物量积累、光合作用、养分吸收、酶活性、产量构成和籽粒品质的影响。研究还评估了微量营养元素管理的主要途径，包括土壤施用、种子处理、叶面喷施和综合施肥。特别关注了土壤性质、环境条件、小麦基因型、肥料用量、施用时期和施用方法对微量营养元素有效性和利用效率的影响。近期证据表明，平衡的微量营养元素管理可以改善小麦生理性能、产量构成、籽粒产量和营养价值，而农艺生物强化是提高籽粒锌和铁浓度的重要策略。然而，微量营养元素施用的效果因土壤条件、气候胁迫、基因型和管理措施的不同而有很大差异。因此，针对特定地点的养分管理、土壤测试、基因型选择和分期施用对于提高微量营养元素利用效率至关重要。将微量营养元素管理与小麦育种和精准农业相结合，有助于实现可持续生产力并改善籽粒营养品质。未来研究应聚焦于基因型特异性微量营养元素需求、养分互作、精准施用技术以及气候韧性策略，以实现高效小麦生产并增强粮食和营养安全。","Kashmir Journal of Academic Research and Development","2026-09-14T00:00:00Z",68,{"impact":250,"substance":85,"depth":17,"authority":120,"freshness":16,"relevant":20,"comment":251},14,"综述性论文，系统梳理微量元素对小麦生长与品质的作用及管理策略，对科学施肥与营养强化有参考价值，但非原创试验数据，影响力偏细分领域。",[253],{"name":246,"url":243},[136,33,255,256,257],"生物强化","微量元素","科学施肥",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[260],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":262},{"candidates":263,"agricultural_evidence":51},[264,268,275,279,285,289],{"code":31,"score":115,"supported":51,"title":265,"abstract":266,"tags":267},[32],[33,32],[33],{"code":38,"score":12,"supported":51,"title":269,"abstract":270,"tags":274},[],[112,57,271,272,273],"养分管理","fertilizer","fertilization",[257],{"code":140,"score":19,"supported":51,"title":276,"abstract":277,"tags":278},[],[143,144],[],{"code":172,"score":19,"supported":51,"title":280,"abstract":281,"tags":284},[],[282,283],"精准农业","precision agriculture",[],{"code":133,"score":20,"supported":13,"title":286,"abstract":287,"tags":288},[],[],[136],{"code":290,"score":20,"supported":13,"title":291,"abstract":292,"tags":293},"F2",[],[],[255],[295,296],"微量元素 生物强化 科学施肥 粮食安全","微量元素 生物强化","微量元素生物强化科学施肥粮食安全-2655","10.71317\u002Fkjard.2.9.2026.605",{"doi":298,"openalex_id":300,"authors":301,"venue":246,"cited_by_count":231,"oa_url":316,"card":317,"direction":323,"ingested_from":238},"W7213313809",[302,304,306,308,310,312,314],{"name":303,"orcid":8},"Muhammad Adnan Rafique",{"name":305,"orcid":8},"Aftab Ahmad Sheikh",{"name":307,"orcid":8},"Syed Asghar Hussain Shah",{"name":309,"orcid":8},"Saima Nazar",{"name":311,"orcid":8},"Asia Shaheen",{"name":313,"orcid":8},"Muhammad Imran",{"name":315,"orcid":8},"Hafiz Abu Bakar Amin","https:\u002F\u002Frjsaonline.org\u002Findex.php\u002FKJARD\u002Farticle\u002Fdownload\u002F605\u002F852",{"tldr":318,"method":319,"finding":320,"direction":321,"opportunity":322},"综述小麦对锌铁锰铜硼等微量元素的响应及其对生长、产量和品质的影响。","文献综述，分析土壤施用、种子处理、叶面喷施及综合施肥等管理方式。","平衡微量元素管理可提升小麦生理、产量和营养品质，农艺强化可增加籽粒锌铁。","农业绿色发展与碳","可结合基因型与精准农业，研究特定基因型微量元素需求及气候适应型精准施用技术。","农业遥感与作物表型","2026-09-16T23:30:21.993931Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":8,"source_name":331,"source_url":328,"published_at":332,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":333,"score_detail":334,"sources":336,"tags":338,"primary_topic":341,"secondary_topics":342,"classification":350,"search_phrases":370,"slug":373,"view_count":374,"doi":375,"paper":376,"created_at":386},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":120,"substance":85,"depth":115,"authority":12,"freshness":19,"relevant":20,"comment":335},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[337],{"name":331,"url":328},[92,93,33,339,340],"遥感","NDVI",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[343,344],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":345,"slug":346,"name":347,"path":348,"parent_code":349},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":44,"locked":13,"version":45,"method":46,"evidence":351},{"candidates":352,"agricultural_evidence":51},[353,357,361,366],{"code":31,"score":115,"supported":51,"title":354,"abstract":355,"tags":356},[32],[33,32],[33],{"code":38,"score":120,"supported":51,"title":358,"abstract":359,"tags":360},[272],[272],[93],{"code":345,"score":16,"supported":51,"title":362,"abstract":363,"tags":365},[],[339,364],"remote sensing",[340,339],{"code":172,"score":20,"supported":13,"title":367,"abstract":368,"tags":369},[],[],[92],[371,372],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332",4,"10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":375,"openalex_id":377,"authors":378,"venue":331,"cited_by_count":231,"oa_url":8,"card":381,"direction":323,"ingested_from":238},"W7212289620",[379],{"name":380,"orcid":8},"Mohammed A. Naser",{"tldr":382,"method":383,"finding":384,"direction":323,"opportunity":385},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":8,"source_name":393,"source_url":390,"published_at":394,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":406,"search_phrases":445,"slug":448,"view_count":231,"doi":449,"paper":450,"created_at":476},1725,"Sustainable Wheat Production Through Precision Agriculture: An LCA Case Study in Italy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091646","The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance of soft wheat production during the transition from conventional to smart and precision farming under real farm conditions. A Life Cycle Assessment (LCA) was conducted following ISO 14040 and 14044 standards using primary data collected from a farm located in Umbria (Italy). Two consecutive three-year production periods were compared: conventional farming (2019–2021) and smart and precision farming (2022–2024). Environmental impacts were assessed using the Environmental Footprint 3.1 method. The transition was associated with a 20.8% reduction in the overall environmental impact. This improvement reflects the combined effects of adopting smart and precision agriculture technologies together with the renewal of farm machinery, which results in lower fuel consumption and more efficient fertilizer and pesticide management. Most impact categories showed improvements, particularly climate change, ecotoxicity, and particulate matter. However, freshwater eutrophication increased due to changes in fertilization strategy, including reduced nitrogen fertilizer use and increased phosphorus fertilizer application. Sensitivity analyses confirmed the consistency of the results; despite some influence on yield variation, the overall comparison remained consistent under the tested scenarios. These findings highlight the potential of smart and precision agriculture to improve the environmental sustainability of soft wheat production under real farm conditions.","日益增长的粮食需求给农业系统带来了越来越大的压力，引发了对农业环境可持续性的担忧。在此背景下，精准农业（precision agriculture）代表了一种在保持高产的同时提高资源利用效率的有前景的方法。本研究旨在评估在真实农场条件下，软质小麦（soft wheat）生产从传统农业向智能与精准农业转型过程中的环境绩效。依据ISO 14040和ISO 14044标准，利用从意大利翁布里亚（Umbria）地区一个农场收集的一手数据，进行了生命周期评价（Life Cycle Assessment, LCA）。研究比较了两个连续的三生产周期：传统农业（2019—2021年）和智能与精准农业（2022—2024年）。采用环境足迹3.1方法（Environmental Footprint 3.1）评估环境影响。转型与总体环境影响降低20.8%相关联。这一改善反映了采用智能与精准农业技术以及农机更新的综合效应，从而降低了燃料消耗，并实现了肥料和农药的更高效管理。大多数影响类别均有所改善，尤其是气候变化、生态毒性和颗粒物。然而，淡水富营养化（freshwater eutrophication）有所增加，原因是施肥策略的变化，包括减少氮肥使用和增加磷肥施用。敏感性分析证实了结果的一致性；尽管产量变化对结果有一定影响，但在所测试情景下，总体比较结果保持一致。这些发现凸显了智能与精准农业在真实农场条件下改善软质小麦生产环境可持续性的潜力。","Land","2026-09-04T00:00:00Z",75,{"impact":85,"substance":86,"depth":85,"authority":18,"freshness":374,"relevant":20,"comment":397},"基于真实农场数据的LCA研究，量化了精准农业转型的环境效益，对农业可持续发展具有参考价值。",[399],{"name":393,"url":390},[92,33,282,401,402],"生命周期评价","意大利",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[405],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":407},{"candidates":408,"agricultural_evidence":51},[409,413,417,421,428,434,440],{"code":172,"score":17,"supported":51,"title":410,"abstract":411,"tags":412},[283],[282,283],[92,282],{"code":31,"score":115,"supported":51,"title":414,"abstract":415,"tags":416},[32],[33,32],[33],{"code":38,"score":87,"supported":51,"title":418,"abstract":419,"tags":420},[],[112,57,272,273],[],{"code":422,"score":19,"supported":51,"title":423,"abstract":424,"tags":427},"F4",[],[425,426],"农药","pesticide",[],{"code":166,"score":19,"supported":51,"title":429,"abstract":430,"tags":433},[],[431,432],"气候变化","climate change",[],{"code":435,"score":62,"supported":51,"title":436,"abstract":437,"tags":439},"F5",[],[438],"农机",[],{"code":441,"score":20,"supported":13,"title":442,"abstract":443,"tags":444},"H4",[],[],[401],[446,447],"生命周期评价 智慧农业 精准农业 意大利","生命周期评价 智慧农业","生命周期评价智慧农业精准农业意大利-1725","10.3390\u002Fland15091646",{"doi":449,"openalex_id":451,"authors":452,"venue":393,"cited_by_count":231,"oa_url":390,"card":471,"direction":236,"ingested_from":238},"W7208758947",[453,455,458,461,463,465,468],{"name":454,"orcid":8},"Claudia Camplone",{"name":456,"orcid":457},"Luisa Paolotti","https:\u002F\u002Forcid.org\u002F0000-0002-8264-1674",{"name":459,"orcid":460},"Francesco Romagnoli","https:\u002F\u002Forcid.org\u002F0000-0003-1814-7310",{"name":462,"orcid":8},"Sara Allahverdiveva",{"name":464,"orcid":8},"Renzo Santi",{"name":466,"orcid":467},"Lorenzo Covarelli","https:\u002F\u002Forcid.org\u002F0000-0002-4568-6160",{"name":469,"orcid":470},"Lucia Rocchi","https:\u002F\u002Forcid.org\u002F0000-0002-3386-2390",{"tldr":472,"method":473,"finding":474,"direction":321,"opportunity":475},"通过LCA评估意大利软小麦从传统向精准农业转型的环境影响，发现总影响降低20.8%。","基于农场实地数据，采用LCA（ISO标准）和Environmental Foot","精准农业结合机械更新使环境影响降低20.8%，但磷肥增加导致淡水富营养化上升。","可研究精准农业下施肥策略优化，平衡产量与富营养化风险，或扩展至其他作物和地区。","2026-09-05T23:30:09.493129Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":8,"content":8,"source_name":482,"source_url":8,"published_at":483,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":484,"sources":486,"tags":488,"primary_topic":490,"secondary_topics":491,"classification":496,"search_phrases":524,"slug":525,"view_count":87,"doi":526,"paper":8,"created_at":527},150,"Residual-corrected decision-level fusion model for collaborative irrigation-fertilization decision support in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.111908","中国农业科学院农田灌溉研究所节水高效灌溉技术与装备团队融合冬小麦无人机多光谱遥感数据与田间水肥管理数据,研发出残差校正决策层融合模型(RC-DLF),为小麦关键生育期水肥协同高效利用提供智慧决策支撑。该模型依托田间定位试验采集小麦关键生育期无人机多光谱冠层信息,整合水肥管理指标构建多源数据集,创新构建RC-DLF,内嵌权重优化、二阶校准、残差校正模块有效消解单一模型预测偏差。田间试验结果显示,相较传统模型,小麦开花期、灌浆期产量预测决定系数R²分别提升至0.62、0.67,整体预测误差明显降低。","Computers and Electronics in Agriculture 2026 (中国农科院农田灌溉研究所)","2026-07-29T00:00:00Z",{"impact":85,"substance":86,"depth":85,"authority":250,"freshness":16,"relevant":20,"comment":485},"国家级科研机构在智慧农业水肥协同领域的创新模型，数据详实，具有行业参考价值。",[487],{"name":482,"url":480},[489],"中国农科院",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[492,495],{"code":147,"slug":151,"name":493,"path":494,"parent_code":104},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":497},{"candidates":498,"agricultural_evidence":51},[499,505,509,513,519],{"code":31,"score":500,"supported":51,"title":501,"abstract":503,"tags":504},19,[502,32],"winter wheat",[33],[],{"code":147,"score":50,"supported":51,"title":506,"abstract":507,"tags":508},[151],[150],[],{"code":38,"score":16,"supported":51,"title":510,"abstract":511,"tags":512},[273],[],[],{"code":514,"score":16,"supported":51,"title":515,"abstract":517,"tags":518},"I2",[516],"decision support",[],[],{"code":345,"score":19,"supported":51,"title":520,"abstract":521,"tags":523},[],[339,522],"无人机多光谱",[],[],"Residual-correcteddecision-levelfusionmo-150","10.1016\u002Fj.compag.2026.111908","2026-08-01T06:01:55.489617Z",{"id":529,"title":530,"url":531,"summary":532,"summary_zh":533,"content":8,"source_name":534,"source_url":531,"published_at":535,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":536,"score_detail":537,"sources":541,"tags":543,"primary_topic":548,"secondary_topics":549,"classification":557,"search_phrases":587,"slug":590,"view_count":231,"doi":591,"paper":592,"created_at":609},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":17,"substance":538,"depth":539,"authority":18,"freshness":12,"relevant":20,"comment":540},21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[542],{"name":534,"url":531},[92,544,545,546,547],"机器学习","遥感监测","土壤墒情","冬小麦",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[550,553],{"code":140,"slug":144,"name":551,"path":552,"parent_code":104},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":514,"slug":554,"name":555,"path":556,"parent_code":349},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":13,"version":45,"method":46,"evidence":558},{"candidates":559,"agricultural_evidence":51},[560,565,569,574,579,583],{"code":31,"score":561,"supported":51,"title":562,"abstract":563,"tags":564},26,[502,32],[33,502,32],[547],{"code":140,"score":115,"supported":51,"title":566,"abstract":567,"tags":568},[144],[143,144],[546],{"code":514,"score":115,"supported":51,"title":570,"abstract":572,"tags":573},[571],"machine learning",[544,571],[544],{"code":345,"score":12,"supported":51,"title":575,"abstract":576,"tags":578},[],[339,364,577],"multispectral",[545],{"code":147,"score":19,"supported":51,"title":580,"abstract":581,"tags":582},[],[150,151],[],{"code":172,"score":374,"supported":51,"title":584,"abstract":585,"tags":586},[],[92],[92],[588,589],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":591,"openalex_id":593,"authors":594,"venue":534,"cited_by_count":231,"oa_url":531,"card":604,"direction":236,"ingested_from":238},"W7223156204",[595,598,600,602],{"name":596,"orcid":597},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":599,"orcid":8},"Yi Zhang",{"name":601,"orcid":8},"WEI Lei",{"name":603,"orcid":8},"Chengcai Zhang",{"tldr":605,"method":606,"finding":607,"direction":323,"opportunity":608},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z"]