[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3759":3,"related-3759":191},{"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":36,"classification":49,"search_phrases":127,"slug":130,"view_count":105,"doi":131,"paper":132,"created_at":190},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。",null,"Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准施肥","小麦","保护性耕作","水肥一体化",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[37,43],{"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",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,63,68,74,82,89,96,103,110,116,122],{"code":38,"score":56,"supported":57,"title":58,"abstract":60,"tags":62},16,true,[59],"fertigation",[61,59],"施肥",[30,27],{"code":32,"score":64,"supported":57,"title":65,"abstract":66,"tags":67},15,[33],[28,33],[28],{"code":44,"score":69,"supported":57,"title":70,"abstract":72,"tags":73},12,[71],"conservation agriculture",[71],[29],{"code":75,"score":76,"supported":57,"title":77,"abstract":78,"tags":81},"A3",6,[],[79,80],"玉米","maize",[],{"code":83,"score":76,"supported":57,"title":84,"abstract":85,"tags":88},"A8",[],[86,87],"粮食安全","food security",[],{"code":90,"score":76,"supported":57,"title":91,"abstract":92,"tags":95},"H2",[],[93,94],"土壤","soil",[],{"code":97,"score":76,"supported":57,"title":98,"abstract":99,"tags":102},"H3",[],[100,101],"灌溉","irrigation",[],{"code":104,"score":105,"supported":57,"title":106,"abstract":107,"tags":109},"A7",3,[],[108],"轮作",[],{"code":111,"score":105,"supported":57,"title":112,"abstract":113,"tags":115},"H5",[],[114],"秸秆还田",[],{"code":117,"score":105,"supported":57,"title":118,"abstract":119,"tags":121},"H7",[],[120],"干旱",[],{"code":123,"score":21,"supported":14,"title":124,"abstract":125,"tags":126},"I1",[],[],[26],[128,129],"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":131,"openalex_id":133,"authors":134,"venue":10,"cited_by_count":182,"oa_url":6,"card":183,"direction":187,"ingested_from":189},"W7214579192",[135,137,140,142,144,146,148,151,153,155,157,159,161,164,166,168,171,174,177,180],{"name":136,"orcid":9},"Kadagonda Nithinkumar",{"name":138,"orcid":139},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":141,"orcid":9},"Devika A. R.",{"name":143,"orcid":9},"Arjun Singh",{"name":145,"orcid":9},"Susama Sudhishri",{"name":147,"orcid":9},"Vinod Kumar Singh",{"name":149,"orcid":150},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":152,"orcid":9},"Sarvendra Kumar",{"name":154,"orcid":9},"Teekam Singh",{"name":156,"orcid":9},"Jitendra Rajput",{"name":158,"orcid":9},"Pravin Kumar Upadhyaya",{"name":160,"orcid":9},"R. N. Sahoo",{"name":162,"orcid":163},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":165,"orcid":9},"Aye Aye San",{"name":167,"orcid":9},"K. S. Sachin",{"name":169,"orcid":170},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":172,"orcid":173},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":175,"orcid":176},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":178,"orcid":179},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":181,"orcid":9},"Subhash Babu",0,{"tldr":184,"method":185,"finding":186,"direction":187,"opportunity":188},"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",{"total":76,"page":21,"page_size":76,"items":192},[193,258,354,433,496,538],{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":210,"classification":218,"search_phrases":240,"slug":243,"view_count":244,"doi":245,"paper":246,"created_at":257},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":69,"substance":17,"depth":64,"authority":13,"freshness":76,"relevant":21,"comment":203},"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。",[205],{"name":199,"url":196},[26,27,28,207,208],"遥感","NDVI",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[211,212],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":213,"slug":214,"name":215,"path":216,"parent_code":217},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":50,"locked":14,"version":51,"method":52,"evidence":219},{"candidates":220,"agricultural_evidence":57},[221,225,230,236],{"code":32,"score":64,"supported":57,"title":222,"abstract":223,"tags":224},[33],[28,33],[28],{"code":38,"score":69,"supported":57,"title":226,"abstract":228,"tags":229},[227],"fertilizer",[227],[27],{"code":213,"score":231,"supported":57,"title":232,"abstract":233,"tags":235},8,[],[207,234],"remote sensing",[208,207],{"code":123,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[26],[241,242],"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",2,"10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":245,"openalex_id":247,"authors":248,"venue":199,"cited_by_count":182,"oa_url":9,"card":251,"direction":255,"ingested_from":189},"W7212289620",[249],{"name":250,"orcid":9},"Mohammed A. Naser",{"tldr":252,"method":253,"finding":254,"direction":255,"opportunity":256},"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。","农业遥感与作物表型","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":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":270,"tags":272,"primary_topic":276,"secondary_topics":277,"classification":279,"search_phrases":321,"slug":324,"view_count":182,"doi":325,"paper":326,"created_at":353},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":17,"substance":18,"depth":17,"authority":19,"freshness":268,"relevant":21,"comment":269},4,"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。",[271],{"name":264,"url":261},[26,28,273,274,275],"精准农业","生命周期评价","意大利",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[278],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":280},{"candidates":281,"agricultural_evidence":57},[282,287,291,297,304,310,316],{"code":123,"score":56,"supported":57,"title":283,"abstract":285,"tags":286},[284],"precision agriculture",[273,284],[26,273],{"code":32,"score":64,"supported":57,"title":288,"abstract":289,"tags":290},[33],[28,33],[28],{"code":38,"score":20,"supported":57,"title":292,"abstract":293,"tags":296},[],[61,294,227,295],"肥料","fertilization",[],{"code":298,"score":76,"supported":57,"title":299,"abstract":300,"tags":303},"F4",[],[301,302],"农药","pesticide",[],{"code":117,"score":76,"supported":57,"title":305,"abstract":306,"tags":309},[],[307,308],"气候变化","climate change",[],{"code":311,"score":105,"supported":57,"title":312,"abstract":313,"tags":315},"F5",[],[314],"农机",[],{"code":317,"score":21,"supported":14,"title":318,"abstract":319,"tags":320},"H4",[],[],[274],[322,323],"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":325,"openalex_id":327,"authors":328,"venue":264,"cited_by_count":182,"oa_url":261,"card":347,"direction":187,"ingested_from":189},"W7208758947",[329,331,334,337,339,341,344],{"name":330,"orcid":9},"Claudia Camplone",{"name":332,"orcid":333},"Luisa Paolotti","https:\u002F\u002Forcid.org\u002F0000-0002-8264-1674",{"name":335,"orcid":336},"Francesco Romagnoli","https:\u002F\u002Forcid.org\u002F0000-0003-1814-7310",{"name":338,"orcid":9},"Sara Allahverdiveva",{"name":340,"orcid":9},"Renzo Santi",{"name":342,"orcid":343},"Lorenzo Covarelli","https:\u002F\u002Forcid.org\u002F0000-0002-4568-6160",{"name":345,"orcid":346},"Lucia Rocchi","https:\u002F\u002Forcid.org\u002F0000-0002-3386-2390",{"tldr":348,"method":349,"finding":350,"direction":351,"opportunity":352},"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。","农业绿色发展与碳","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":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":362,"score_detail":363,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"classification":375,"search_phrases":404,"slug":407,"view_count":105,"doi":408,"paper":409,"created_at":432},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":364,"substance":17,"depth":56,"authority":69,"freshness":231,"relevant":21,"comment":365},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。",[367],{"name":360,"url":357},[86,28,369,370,371],"生物强化","微量元素","科学施肥",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[374],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":376},{"candidates":377,"agricultural_evidence":57},[378,382,387,391,395,399],{"code":32,"score":64,"supported":57,"title":379,"abstract":380,"tags":381},[33],[28,33],[28],{"code":38,"score":13,"supported":57,"title":383,"abstract":384,"tags":386},[],[61,294,385,227,295],"养分管理",[371],{"code":90,"score":76,"supported":57,"title":388,"abstract":389,"tags":390},[],[93,94],[],{"code":123,"score":76,"supported":57,"title":392,"abstract":393,"tags":394},[],[273,284],[],{"code":83,"score":21,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[86],{"code":400,"score":21,"supported":14,"title":401,"abstract":402,"tags":403},"F2",[],[],[369],[405,406],"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":408,"openalex_id":410,"authors":411,"venue":360,"cited_by_count":182,"oa_url":426,"card":427,"direction":255,"ingested_from":189},"W7213313809",[412,414,416,418,420,422,424],{"name":413,"orcid":9},"Muhammad Adnan Rafique",{"name":415,"orcid":9},"Aftab Ahmad Sheikh",{"name":417,"orcid":9},"Syed Asghar Hussain Shah",{"name":419,"orcid":9},"Saima Nazar",{"name":421,"orcid":9},"Asia Shaheen",{"name":423,"orcid":9},"Muhammad Imran",{"name":425,"orcid":9},"Hafiz Abu Bakar Amin","https:\u002F\u002Frjsaonline.org\u002Findex.php\u002FKJARD\u002Farticle\u002Fdownload\u002F605\u002F852",{"tldr":428,"method":429,"finding":430,"direction":351,"opportunity":431},"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。","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。","2026-09-16T23:30:21.993931Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":9,"content":438,"source_name":439,"source_url":9,"published_at":440,"category":441,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":451,"secondary_topics":452,"classification":454,"search_phrases":490,"slug":493,"view_count":494,"doi":9,"paper":9,"created_at":495},219,"黑龙江农科院齐齐哈尔分院与省黑土体系联学共建——聚焦保护性耕作深层增碳与水肥一体化","https:\u002F\u002Fwww.haas.cn\u002Finfo\u002F1071\u002F334861.htm","7月20日,黑龙江省黑土地保护创新协同推广体系2026年工作推进会在黑龙江省农业科学院齐齐哈尔分院召开,聚焦黑土地保护工作部署。会上开展大豆高产栽培中后期管理、大豆生产机械化装备、大豆品种选择及单产提升、黑土地保护性耕作技术等专项培训。分院现场展示新型保护性耕作深层增碳技术、保护性耕作水肥一体化技术、精准施肥、秸秆还田、有机肥施用等黑土保护关键技术的田间应用成效。","为深入践行正确政绩观，深化党建与农业科研、黑土地保护工作深度融合，以党建红引领农业绿、守护耕地黑、筑牢粮食安全底线，7月20日，黑龙江省黑土地保护创新协同推广体系2026年工作推进会在黑龙江省农业科学院齐齐哈尔分院召开。省黑土体系专家、市农业技术推广中心有关同志、分院领导班子、科研骨干及部分农业合作社代表全程参与各项活动。\n\n![Image 1](https:\u002F\u002Fwww.haas.cn\u002F__local\u002F6\u002FC9\u002F03\u002F52FD879A53C1C13E33B1E9D2603_962AAC9C_92D93.jpg)\n\n会上，黑龙江省农业科学院齐齐哈尔分院党委书记、院长申惠波，黑土耕地保护协同创新推广体系首席专家邹文秀先后致辞，明确黑土地保护工作的重要意义与年度工作核心方向，强调要坚持科技赋能、协同发力，筑牢黑土地保护屏障、夯实粮食安全根基。\n\n![Image 2](https:\u002F\u002Fwww.haas.cn\u002F__local\u002F2\u002F9C\u002F0E\u002F7710B25F6599E7A64B5CF09B461_B86F6C38_B2591.jpg)\n\n在技术专题培训环节，来自黑龙江八一农垦大学、省农科院齐齐哈尔分院的行业专家依次开展专项授课，围绕大豆高产栽培中后期管理、大豆生产机械化装备与技术、大豆品种选择及单产提升、黑土地保护性耕作技术等关键领域，结合龙江寒地农业种植特点与黑土地保护现状，分享前沿科研成果、实操技术方案和高产提质经验，内容贴合生产实际、针对性极强，为参会科研人员搭建了高效的学习交流平台。\n\n![Image 3](https:\u002F\u002Fwww.haas.cn\u002F__local\u002F0\u002F61\u002FF4\u002F3251885D63476C23D3456E0730F_CBC52AC5_82A26.jpg)\n\n理论授课结束后，全体参会人员前往齐齐哈尔分院田间示范基地开展实地观摩活动。现场重点观摩新型保护性耕作深层增碳技术、保护性耕作水肥一体化技术、精准施肥、秸秆还田、有机肥施用等黑土保护关键技术的田间应用成效，技术负责人王宇先现场讲解技术原理、应用优势及推广要点，让参会人员直观感受黑土地保护创新技术的实践成果，实现理论学习与田间实操深度结合。\n\n此次联学共建活动有效打通科研协作、技术推广、产业服务链条，构建起党建引领、科创赋能、惠农增效的良好工作格局。全体科研人员在沉浸式现场教学中锤炼初心使命，进一步树牢正确政绩观，坚定扎根一线、护土兴农、保障粮安的责任担当。下一步，分院将持续深化党建与科研业务融合，深耕黑土地保护、大豆产业提质、耕地增效等重点领域，强化科技创新与成果落地，以科技力量护航现代农业高质量发展。","黑龙江省农业科学院 2026-07-20","2026-07-30T00:00:00Z","报道",47,{"impact":64,"substance":69,"depth":231,"authority":13,"freshness":244,"relevant":21,"comment":444},"省级农业科研机构活动报道，涉及黑土地保护技术，有一定信息量但深度和时效性一般。",[446],{"name":439,"url":436},[448,449,450,29],"黑龙江省农科院","黑土地保护","齐齐哈尔分院",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[453],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":455},{"candidates":456,"agricultural_evidence":57},[457,461,465,470,476,481,486],{"code":38,"score":364,"supported":57,"title":458,"abstract":459,"tags":460},[30],[61,30],[],{"code":44,"score":69,"supported":57,"title":462,"abstract":463,"tags":464},[29],[29],[29],{"code":466,"score":268,"supported":57,"title":467,"abstract":468,"tags":469},"H1",[],[449],[449],{"code":471,"score":105,"supported":57,"title":472,"abstract":473,"tags":475},"A4",[],[474],"大豆",[],{"code":83,"score":105,"supported":57,"title":477,"abstract":478,"tags":480},[],[479],"单产提升",[],{"code":311,"score":105,"supported":57,"title":482,"abstract":483,"tags":485},[],[484],"机械化",[],{"code":111,"score":105,"supported":57,"title":487,"abstract":488,"tags":489},[],[114],[],[491,492],"黑龙江省农科院 齐齐哈尔分院 保护性耕作 黑土地保护","黑龙江省农科院 齐齐哈尔分院","黑龙江省农科院齐齐哈尔分院保护性耕作黑土地保护-219",5,"2026-08-04T23:54:46.696741Z",{"id":497,"title":498,"url":499,"summary":500,"summary_zh":9,"content":9,"source_name":501,"source_url":9,"published_at":502,"category":441,"cover_url":9,"hotness":13,"is_selected":14,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":514,"secondary_topics":515,"classification":517,"search_phrases":533,"slug":536,"view_count":105,"doi":9,"paper":9,"created_at":537},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。","安徽省农业科学院","2026-09-16T00:00:00Z",53,{"impact":231,"substance":56,"depth":13,"authority":19,"freshness":76,"relevant":21,"comment":505},"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。",[507],{"name":501,"url":499},[509,510,511,512,513],"安徽省农科院","专利转化","小麦抗倒伏","大豆高产","成果赋权",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[516],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":518},{"candidates":519,"agricultural_evidence":57},[520,525,529],{"code":32,"score":521,"supported":57,"title":522,"abstract":523,"tags":524},11,[28],[28],[],{"code":38,"score":521,"supported":57,"title":526,"abstract":527,"tags":528},[294],[294],[],{"code":471,"score":105,"supported":57,"title":530,"abstract":531,"tags":532},[],[474],[],[534,535],"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",{"id":539,"title":540,"url":541,"summary":542,"summary_zh":543,"content":9,"source_name":544,"source_url":541,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":545,"score_detail":546,"sources":549,"tags":551,"primary_topic":554,"secondary_topics":555,"classification":561,"search_phrases":590,"slug":593,"view_count":105,"doi":594,"paper":595,"created_at":609},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals",62,{"impact":69,"substance":17,"depth":547,"authority":76,"freshness":20,"relevant":21,"comment":548},17,"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[550],{"name":544,"url":541},[26,552,27,553,30],"农业人工智能","可解释AI",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[556],{"code":557,"slug":558,"name":559,"path":560,"parent_code":217},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":50,"locked":14,"version":51,"method":52,"evidence":562},{"candidates":563,"agricultural_evidence":57},[564,569,575,579,583],{"code":38,"score":565,"supported":57,"title":566,"abstract":567,"tags":568},19,[59],[61,294,227,59],[30,27],{"code":557,"score":19,"supported":57,"title":570,"abstract":572,"tags":574},[571],"artificial intelligence",[573],"机器学习",[552,553],{"code":123,"score":20,"supported":57,"title":576,"abstract":577,"tags":578},[284],[],[26],{"code":97,"score":76,"supported":57,"title":580,"abstract":581,"tags":582},[],[100,101],[],{"code":584,"score":76,"supported":57,"title":585,"abstract":586,"tags":589},"I5",[],[587,588],"物联网","IoT",[],[591,592],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":594,"openalex_id":596,"authors":597,"venue":544,"cited_by_count":182,"oa_url":602,"card":603,"direction":187,"ingested_from":189},"W7212934364",[598,600],{"name":599,"orcid":9},"Shraddha S. Tayade",{"name":601,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":604,"method":605,"finding":606,"direction":607,"opportunity":608},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","农业人工智能与决策模型","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z"]