[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3870":3,"related-3870":120},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":90,"slug":93,"view_count":22,"doi":94,"paper":95,"created_at":119},3870,"Livestock-driven agricultural expansion and the transformation of a dryland food system in northern Mexico","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1923361","Introduction Agricultural expansion and intensification are major drivers of land use and land cover change, with significant implications for ecosystems and natural resource sustainability. In dryland regions, the expansion of livestock-oriented production systems can drive the specialization of agricultural production systems and accelerate landscape transformation. This study examines how livestock-driven agricultural expansion is reshaping land-use patterns in the Comarca Lagunera, a major dryland food-producing region in northern Mexico and one of the country’s most important dairy basins. Methods Agricultural and livestock production trends were analyzed using official statistics, while land cover changes for 2003, 2013, and 2023 were mapped from Landsat imagery using Google Earth Engine. Future land use and land cover trajectories to 2050 were simulated under two scenarios representing the long-term historical trend and recent historical trend, using a spatially explicit model incorporating biophysical and socioeconomic drivers. Results The expansion of forage crops associated with intensive livestock production became the dominant change, primarily replacing native shrubland and food crops destined for human consumption. Between 2003 and 2023, forage crops expanded from 51 to 70% of the harvested area, while crops destined for human consumption declined from 23 to 4%. Land conversion was strongly associated with infrastructure accessibility, climatic suitability, and livestock-sector activity. Under the recent historical trend, more than 2,000 km2 of natural cover could be lost by 2050, with potential implications for water availability and ecosystem conservation in a region already affected by groundwater stress. Discussion These findings illustrate how livestock-oriented production can drive the specialization of agricultural systems and reconfigure landscapes, while highlighting the need for integrated land-use planning and water management.","引言 农业扩张与集约化是土地利用和土地覆盖变化的主要驱动力，对生态系统和自然资源可持续性具有重要影响。在干旱地区，以畜牧业为导向的生产体系扩张可推动农业生产系统的专业化，并加速景观转型。本研究考察了畜牧业驱动的农业扩张如何重塑科马卡·拉古内拉（Comarca Lagunera）的土地利用格局。该地区是墨西哥北部主要的干旱区粮食产区，也是该国最重要的奶业基地之一。方法 利用官方统计数据分析了农业和畜牧业生产趋势，同时基于Landsat影像，通过Google Earth Engine绘制了2003年、2013年和2023年的土地覆盖变化图。采用纳入生物物理和社会经济驱动因素的空间显式模型，在代表长期历史趋势和近期历史趋势的两种情景下，模拟了至2050年的未来土地利用和土地覆盖轨迹。结果 与集约化畜牧生产相关的饲草作物扩张成为主导变化，主要取代了原生灌丛和供人类消费的粮食作物。2003年至2023年间，饲草作物占收获面积的比例从51%扩大至70%，而供人类消费的作物占比从23%下降至4%。土地转换与基础设施可达性、气候适宜性和畜牧业部门活动密切相关。在近期历史趋势情景下，到2050年可能有超过2，000 km²的自然覆盖丧失，这对一个已受地下水压力影响的地区的水资源可用性和生态系统保护具有潜在影响。讨论 这些发现表明，以畜牧业为导向的生产如何推动农业系统专业化并重塑景观，同时凸显了综合土地利用规划和水资源管理的必要性。",null,"Frontiers in Sustainable Food Systems","2026-09-30T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,8,1,"基于Landsat与Google Earth Engine的干旱区土地利用模拟研究，数据扎实、结论明确，对农业资源与生态管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"畜牧业","水资源管理","遥感监测","土地利用变化","干旱区农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,60,67,73,80,85],{"code":37,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},15,true,[49],"livestock",[51,49],"畜牧",[27],{"code":33,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},6,[],[57,58],"饲草","forage",[],{"code":61,"score":62,"supported":47,"title":63,"abstract":64,"tags":66},"H3",4,[],[65],"水资源",[28],{"code":68,"score":62,"supported":47,"title":69,"abstract":70,"tags":72},"H7",[],[71],"干旱",[31],{"code":74,"score":75,"supported":47,"title":76,"abstract":77,"tags":79},"C2",3,[],[78],"奶业",[],{"code":81,"score":22,"supported":14,"title":82,"abstract":83,"tags":84},"H6",[],[],[30],{"code":86,"score":22,"supported":14,"title":87,"abstract":88,"tags":89},"I3",[],[],[29],[91,92],"Comarca Lagunera 土地利用","墨西哥 奶牛 饲料作物","ComarcaLagunera土地利用-3870","10.3389\u002Ffsufs.2026.1923361",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":111,"oa_url":6,"card":112,"direction":116,"ingested_from":118},"W7214931064",[98,100,102,105,108],{"name":99,"orcid":9},"Maricruz González-Alvarado",{"name":101,"orcid":9},"Alma Mendoza-Ponce",{"name":103,"orcid":104},"Quetzalcóatl Orozco Ramírez","https:\u002F\u002Forcid.org\u002F0000-0002-3085-7406",{"name":106,"orcid":107},"Lucrecia Arellano","https:\u002F\u002Forcid.org\u002F0000-0001-6364-2447",{"name":109,"orcid":110},"Martha Bonilla‐Moheno","https:\u002F\u002Forcid.org\u002F0000-0002-2403-2002",0,{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"研究墨西哥北部旱区畜牧驱动的农业扩张如何改变土地利用与食物系统。","用官方统计、Landsat影像与Google Earth Engine制图，并模","饲料作物占比由51%升至70%，口粮作物由23%降至4%，2050年或再损失超2000平方公里自然植","农业遥感与作物表型","可结合遥感与水文模型，量化饲料扩张对地下水与粮食安全的耦合影响，支撑旱区土地与水协同规划。","openalex","2026-10-01T23:30:06.894840Z",{"total":54,"page":22,"page_size":54,"items":121},[122,231,328,392,448,489],{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":136,"tags":138,"primary_topic":143,"secondary_topics":144,"classification":151,"search_phrases":179,"slug":182,"view_count":183,"doi":184,"paper":185,"created_at":230},2937,"Modeling the effects of livestock rotation frequency on forage production and soil carbon using a spatially explicit grazing distribution","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17583004.2026.2732593","Livestock grazing strongly influences terrestrial ecosystems and carbon (C) dynamics, but outcomes depend on grazing management. High-frequency rotation (HFR) grazing may reduce the uneven grazing distribution commonly associated with low-frequency rotation (LFR), which can alter forage production and soil organic carbon (SOC). However, most ecosystem models do not explicitly represent uneven grazing distribution, limiting their ability to evaluate management effects. We enhanced the MEMS ecosystem model by incorporating discrete spatial units to simulate grazing distribution driven by key environmental drivers, including forage availability and quality, and distance to water. Using remote sensing-derived enhanced vegetation index (EVI), we verified the simulated grazing distribution at an experimental rangeland site in Oklahoma. We tested the model’s sensitivity to rotation frequency under different management (stocking rate, timing, and duration) and weather (typical, dry, and wet) scenarios. Simulated light and moderate grazing generally sustained forage production and SOC regardless of rotation frequency. However, under heavy grazing, the 30-year projection showed HFR reduced localized overgrazing and sustained higher pasture average forage productivity and SOC stocks than LFR. Long-term experimental data are needed to validate these results. Our study offers guidance for designing grazing management experiments to address this critical knowledge gap and advance C management strategies.","牲畜放牧对陆地生态系统和碳（C）动态具有强烈影响，但其结果取决于放牧管理方式。高频轮牧（HFR）可能减少通常与低频轮牧（LFR）相关的不均匀放牧分布，从而改变饲草产量和土壤有机碳（SOC）。然而，大多数生态系统模型并未明确表征不均匀放牧分布，限制了其评估管理效果的能力。我们通过引入离散空间单元增强了MEMS生态系统模型，以模拟由关键环境驱动因素（包括饲草可获得性和质量，以及与水源的距离）驱动的放牧分布。利用遥感反演的增强植被指数（EVI），我们在俄克拉荷马州一个实验性牧场验证了模拟的放牧分布。我们测试了模型在不同管理（载畜率、时间和持续时间）和天气（典型、干旱和湿润）情景下对轮牧频率的敏感性。模拟的轻度与中度放牧无论轮牧频率如何，通常均能维持饲草生产和SOC。然而，在重度放牧下，30年预测显示HFR减少了局部过度放牧，并比LFR维持了更高的牧场平均饲草生产力和SOC储量。需要长期实验数据来验证这些结果。我们的研究为设计放牧管理实验提供了指导，以弥补这一关键知识空白并推进碳管理策略。","Carbon Management","2026-09-17T00:00:00Z",75,{"impact":132,"substance":18,"depth":19,"authority":133,"freshness":134,"relevant":22,"comment":135},12,14,9,"改进MEMS模型显式模拟放牧空间分布，揭示高频轮牧在重度放牧下维持牧草生产与土壤碳的优势，方法新颖但尚缺长期实测验证，属细分领域有价值研究。",[137],{"name":128,"url":125},[139,29,140,141,142],"土壤有机碳","草地碳汇","轮牧管理","生态模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[145],{"code":146,"slug":147,"name":148,"path":149,"parent_code":150},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":152},{"candidates":153,"agricultural_evidence":47},[154,159,163,168,175],{"code":146,"score":46,"supported":47,"title":155,"abstract":156,"tags":158},[147],[157,147],"土壤",[139],{"code":33,"score":133,"supported":47,"title":160,"abstract":161,"tags":162},[58],[57,58],[],{"code":37,"score":164,"supported":47,"title":165,"abstract":166,"tags":167},11,[49],[49],[],{"code":86,"score":169,"supported":47,"title":170,"abstract":171,"tags":174},7,[],[172,173],"遥感","remote sensing",[29],{"code":68,"score":75,"supported":47,"title":176,"abstract":177,"tags":178},[],[71],[],[180,181],"MEMS模型 放牧分布","俄克拉荷马 轮牧 土壤碳","MEMS模型放牧分布-2937",2,"10.1080\u002F17583004.2026.2732593",{"doi":184,"openalex_id":186,"authors":187,"venue":128,"cited_by_count":111,"oa_url":222,"card":223,"direction":229,"ingested_from":118},"W7213466883",[188,190,193,195,198,200,203,205,208,210,212,214,216,218,220],{"name":189,"orcid":9},"Yao Zhang",{"name":191,"orcid":192},"Rafael S. Santos","https:\u002F\u002Forcid.org\u002F0000-0003-4829-0644",{"name":194,"orcid":9},"Emma Hamilton",{"name":196,"orcid":197},"Paige Stanley","https:\u002F\u002Forcid.org\u002F0000-0002-4444-3351",{"name":199,"orcid":9},"Hao Yang",{"name":201,"orcid":202},"Keith Paustian","https:\u002F\u002Forcid.org\u002F0000-0001-5262-5596",{"name":204,"orcid":9},"Erica L. Patterson",{"name":206,"orcid":207},"Stephen M. Ogle","https:\u002F\u002Forcid.org\u002F0000-0003-1899-7446",{"name":209,"orcid":9},"Isabella C.F. Maciel",{"name":211,"orcid":9},"Guilhermo F. S. Congio",{"name":213,"orcid":9},"Hugh Aljoe",{"name":215,"orcid":9},"Alejandro Romero-Ruiz",{"name":217,"orcid":9},"Jeff J. Goodwin",{"name":219,"orcid":9},"Jason Rowntree",{"name":221,"orcid":9},"M. Francesca Cotrufo","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17583004.2026.2732593?needAccess=true",{"tldr":224,"method":225,"finding":226,"direction":227,"opportunity":228},"改进MEMS模型模拟放牧分布，评估轮牧频率对牧草生产和土壤碳的影响。","增强MEMS模型空间离散单元，用EVI遥感验证，模拟不同放牧和天气情景。","重度放牧下高频轮牧减少局部过牧，维持更高牧草生产力和土壤有机碳。","农业绿色发展与碳","需长期实验验证模型结果，并探索不同气候和放牧管理下土壤碳动态的普适性机制。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:13.860699Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":242,"tags":244,"primary_topic":250,"secondary_topics":256,"classification":263,"search_phrases":296,"slug":299,"view_count":111,"doi":300,"paper":301,"created_at":327},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":17,"substance":18,"depth":19,"authority":133,"freshness":13,"relevant":22,"comment":241},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[243],{"name":237,"url":234},[245,246,247,248,249],"智慧农业","农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":251,"slug":252,"name":253,"path":254,"parent_code":255},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[257,262],{"code":86,"slug":258,"name":259,"path":260,"parent_code":261},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":264},{"candidates":265,"agricultural_evidence":47},[266,271,277,281,291],{"code":86,"score":19,"supported":47,"title":267,"abstract":269,"tags":270},[268],"hyperspectral",[172,173,268],[247],{"code":251,"score":46,"supported":47,"title":272,"abstract":274,"tags":276},[273],"alfalfa",[275,273],"苜蓿",[249],{"code":33,"score":13,"supported":47,"title":278,"abstract":279,"tags":280},[],[57,275,58],[249],{"code":282,"score":13,"supported":47,"title":283,"abstract":284,"tags":290},"I2",[],[285,286,287,288,289],"深度学习","机器学习","machine 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F16\u002Fcontent_597377.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 盐碱地里，青贮玉米“争气”了\n\n 2026-10-16 15:09:54 来源: 科技日报 点击数：\n\n![Image 6](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n暮秋的黑龙江省安达市天高云淡，一垄垄盐碱旱田刚翻过土，泛着浅浅的白霜。10月13日，记者跟随黑龙江省农科院草业研究所研究员林红，穿过连片田块，走进安达绿生源现代养殖场。还没进牛舍，一股青贮特有的酸香就扑面而来。黑白花奶牛次第低头，把脑袋埋进食槽，咀嚼声此起彼伏。饲养员抄起一捧青贮料，深绿的茎叶裹着饱满的籽粒，“这就是咱们的‘龙育15’‘龙育17’。”\n\n![Image 7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F16\u002F597377_a90e9d31-3cb7-4c4d-b464-ab39a784e19ccopy.jpg)\n\n安达素有“中国牛城”之名，可松嫩平原西部这片土地，盐碱是绕不开的坎。土壤碱化重、地力弱，青贮玉米一度种不出像样的品质，干物质、淀粉指标上不去，优质饲草供不应求，牛越养越“挑食”。能不能在盐碱地里种出合格的青贮玉米，成了当地草畜产业躲不开的考题。而由黑龙江省农科院草业研究所承担的黑龙江省农业科技创新跨越工程项目——青贮玉米新品种示范推广及产业化应用，正是冲着这道题来的。\n\n“高产、优质，适合青贮饲料用途，这是我们选育最核心的标尺。”站在牛舍边，林红介绍，青贮玉米拼的是高淀粉、高干物质，“龙育15”“龙育17”的培育目标就是“产量和品质两头都要”。它们在盐碱地块上，平均增产超一成，亩产全株三四吨，粗淀粉含量能到32%，肉牛吃、长肉快，奶牛吃、奶量高。\n\n良种到手，技术指导一路跟到地头。项目组围绕保全苗、稳产量、提品质，给种植企业做全流程服务。“青贮玉米每亩达不到5000株，高产就没有前提。”林红说，从整地保墒到精准播种，再到田间管护，环环盯紧。\n\n![Image 8](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F16\u002F597377_a95e303c-16eb-4ad2-bd50-703dac8cdb0acopy.jpg)\n\n最难拿捏的，是收割那一刀。收矮了，产量看着高，可地不平，土跟着卷进饲料，营养也打了折扣；收高了，品质上去了，种植户又心疼收入。此前绿生源养殖场总经理周现平就为这一刀找过项目组。林红最终给出折中方案——留茬30公分，“既保产量，又守品质，现在牧场都认这个数”。牛舍里的青贮窖已经装得满满当当，新收的料压得瓷实，来年开窖，又是一窖好饭。\n\n品种在田里扎根，推广也在向外铺开。“龙育15”“龙育17”两个品种，项目建成1.2万亩高产优质栽培示范，每年在安达市推广3万亩以上，还走进内蒙古等周边饲草产区，带动松嫩平原盐碱化旱田地力提升、绿色高效增收。千亩套种示范田里，项目组在玉米垄沟里套种燕麦，青贮玉米产量和常规种植持平，每亩却多收600多公斤燕麦，亩增收300多元；燕麦蛋白含量高，混进青贮正好补齐蛋白短板，饲料品质和喂饲效果都跟着上了台阶。\n\n一方面，项目把良种良法送进地里；另一方面，企业把产业链拉得更长。绿生源依托自有的一万多亩基地，从种到收，再到加工销售，一气呵成，牛吃不完的青贮还能外销；养殖产生的粪污经处理后还田，反哺盐碱地，把“种好草、养好牛、肥好地”串成了闭环。\n\n夕阳把草料场镀成金色，青贮裹包在田埂边排开，“龙育15”“龙育17”在这扎下根。牛槽里多了料，田里多了收成，松嫩平原的青贮玉米，在这片曾经的薄地上“争气”了。\n\n（科技日报记者 朱虹 摄）\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 9](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 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日","2026-10-16T00:00:00Z",{"impact":19,"substance":402,"depth":17,"authority":133,"freshness":134,"relevant":22,"comment":498},"省级科研机构在盐碱旱田推出青贮玉米新品种并配套栽培技术，数据具体、多方信源，具行业推广价值。",[500],{"name":495,"url":492},[461,502,503,504,505],"盐碱地治理","饲草产业","良种良法","青贮玉米",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[508,513],{"code":509,"slug":510,"name":511,"path":512,"parent_code":255},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",{"code":146,"slug":147,"name":148,"path":149,"parent_code":150},{"status":40,"locked":14,"version":41,"method":42,"evidence":515},{"candidates":516,"agricultural_evidence":47},[517,521,525,530,535],{"code":33,"score":17,"supported":47,"title":518,"abstract":519,"tags":520},[429],[57,429],[505,503],{"code":509,"score":132,"supported":47,"title":522,"abstract":523,"tags":524},[511],[511],[505],{"code":146,"score":132,"supported":47,"title":526,"abstract":528,"tags":529},[527],"盐碱地",[527],[502],{"code":251,"score":62,"supported":47,"title":531,"abstract":532,"tags":534},[],[533],"套种",[503],{"code":480,"score":183,"supported":14,"title":536,"abstract":537,"tags":538},[],[],[461,504],[540,541],"黑龙江农科院 龙育15 龙育17","松嫩平原 盐碱地 青贮玉米","黑龙江农科院龙育15龙育17-5123","2026-10-17T00:13:40.216554Z"]