[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-745":3,"related-745":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":60,"slug":63,"view_count":64,"doi":8,"paper":8,"created_at":65},745,"基于UAV与地面高光谱的杜兰小麦氮肥分施策略评价","https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86","设置7组氮肥分施处理,对地面高光谱与UAV多光谱协同监测进行评估;N3分施方案在拔节期UAV-NDVI与籽粒产量相关性最强(r=0.717,p\u003C0.01),实现产量最大化与叶养分动态同步。",null,"## 1. Introduction\n\nWheat (Triticum durum Desf.) is one of the world’s most important cereal crops and plays a central role in global food security by supplying a substantial proportion of dietary calories and protein [[1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B1-nitrogen-07-00086),[2](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B2-nitrogen-07-00086)]. Achieving sustainable wheat production under increasing food demand and climate variability requires improved nitrogen (N) management. Nitrogen is the nutrient most closely associated with wheat growth, photosynthetic capacity, grain yield, and grain protein concentration [[3](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B3-nitrogen-07-00086)]. However, excessive N fertilization reduces the effectiveness of fertilizer utilization and increases environmental risks through nitrate leaching, ammonia volatilization, and nitrous oxide emissions [[4](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B4-nitrogen-07-00086)]. Consequently, modern wheat production aims to optimize nitrogen management by ensuring an adequate nitrogen supply for crop growth while minimizing environmental impacts [[5](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B5-nitrogen-07-00086),[6](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B6-nitrogen-07-00086)]. Improving the synchronization between crop nitrogen demand and fertilizer application has therefore become a major objective of sustainable and precision agriculture [[7](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B7-nitrogen-07-00086)].\n\nAmong the agronomic practices used to improve nitrogen management, split nitrogen application is widely recognized as an effective strategy because it synchronizes fertilizer application with crop nitrogen demand during successive phenological stages. Compared with a single basal application, split nitrogen application generally enhances nitrogen availability throughout the growing season, sustains canopy photosynthetic activity, improves grain yield and quality, and reduces nitrogen losses to the environment. Nevertheless, the effectiveness of split nitrogen applications varies with climatic conditions, soil properties, and crop developmental stages. These factors are particularly important in Mediterranean environments, where irregular rainfall and high evaporative demand strongly influence soil nitrogen dynamics. Therefore, identifying the optimum timing and frequency of split nitrogen applications remains a key challenge for improving wheat productivity while promoting more effective nitrogen management under region-specific conditions [[7](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B7-nitrogen-07-00086),[8](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B8-nitrogen-07-00086)].\n\nAccurate assessment of crop nitrogen status is essential for optimizing split nitrogen fertilization and supporting effective nitrogen management throughout the growing season. Conventional methods, including plant tissue analysis and destructive biomass sampling, provide reliable measurements but are labor-intensive, time-consuming, and unsuitable for repeated field-scale assessments. Consequently, remote sensing has emerged as an effective alternative for rapid, non-destructive, and spatially explicit monitoring of crop physiological responses, enabling timely evaluation of crop nitrogen status and supporting informed nitrogen management decisions [[9](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B9-nitrogen-07-00086)].\n\nAmong available approaches, UAV-based multispectral imaging has been widely adopted because it provides high spatial resolution and flexible data acquisition under field conditions. Vegetation indices derived from multispectral imagery, such as NDVI and SR, have demonstrated strong relationships with canopy vigor, chlorophyll content, biomass accumulation, and nitrogen status. However, their performance is influenced by crop phenology, canopy structure, and environmental conditions, indicating that a single spectral indicator may not adequately characterize crop responses throughout the entire growing season [[10](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B10-nitrogen-07-00086),[11](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B11-nitrogen-07-00086)].\n\nGround-based hyperspectral sensing complements UAV observations by providing detailed spectral information that is highly sensitive to changes in canopy biochemistry, particularly chlorophyll and nitrogen-related traits. These measurements are frequently used to calibrate and validate UAV-derived observations, improving the interpretation of crop physiological responses under different nitrogen management strategies [[12](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B12-nitrogen-07-00086)].\n\nIn addition to spectral information, UAV photogrammetry enables the extraction of structural crop characteristics, including plant height and canopy architecture. Structural traits provide complementary information on crop growth and biomass that cannot be obtained from spectral data alone. Therefore, integrating spectral and structural information offers a more comprehensive assessment of crop development and nitrogen status than either approach independently [[13](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B13-nitrogen-07-00086)].\n\nAlthough the combined use of UAV multispectral imagery, ground-based hyperspectral sensing, and structural canopy traits has shown considerable potential, relatively few studies have applied these complementary techniques to evaluate different split nitrogen application strategies throughout successive wheat growth stages, particularly under Mediterranean environmental conditions. This limitation highlights the need for integrated sensing approaches capable of supporting more efficient, data-driven nitrogen management.\n\nDespite substantial advances in nitrogen management and remote sensing technologies, several important knowledge gaps remain. Most previous studies have evaluated nitrogen fertilization rates or remote sensing techniques independently, with limited attention to the combined effects of different split nitrogen application schedules and multi-source sensing approaches. Moreover, many investigations have relied on a single sensing platform, providing either spectral or structural information, which may not fully capture the temporal physiological responses of wheat throughout the growing season. These limitations are particularly evident under Mediterranean environments, where variable rainfall and temperature strongly influence nitrogen availability and crop uptake.\n\nTo address these gaps, this study integrates UAV-based multispectral imagery, ground-based hyperspectral measurements, vegetation indices, and UAV-derived canopy structural traits within a unified experimental framework. Unlike previous studies, which generally focused on individual sensing techniques or nitrogen application rates, the proposed approach simultaneously evaluates biochemical and structural crop responses to six split nitrogen application strategies across successive Zadoks growth stages. This integrated framework provides a more comprehensive understanding of crop nitrogen dynamics and supports the development of more efficient and environmentally sustainable nitrogen management practices.\n\nWe hypothesized that: (i) synchronizing nitrogen applications with crop developmental stages would enhance crop growth and generate distinct spectral and structural responses in durum wheat; and (ii) integrating UAV multispectral imagery with ground-based hyperspectral sensing and canopy structural measurements would provide a more comprehensive and reliable assessment of crop responses to split nitrogen application strategies than individual sensing techniques.\n\nAccordingly, the objectives of this study were to: (i) evaluate the physiological responses of durum wheat to six split nitrogen application strategies under Mediterranean conditions; (ii) assess the performance of integrated UAV multispectral and ground-based hyperspectral sensing for monitoring crop development across different growth stages; (iii) quantify the relationships among spectral, structural, and agronomic traits under different nitrogen management strategies; and (iv) identify the optimum split nitrogen application schedule for enhancing crop growth, maintaining plant nutritional status, and supporting sustainable wheat production.\n\n## 2. Materials and Methods\n\n### 2.1. Study Area and Experimental Design\n\nThe field experiment was conducted at the Agricultural Research Farm of the Faculty of Agriculture, Akdeniz University, located on the Akdeniz University campus in Antalya, Türkiye (36°53′54″ N, 30°38′13″ E), during the 2019–2020 growing season (November 2019 to May 2020) ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#fig_body_display_nitrogen-07-00086-f001)). The experimental field was located on a clay loam soil classified as a Typic Xerochrept, with a soil pH ranging from 7.94 to 8.02. The durum wheat (Triticum durum Desf.) cultivar Svevo was used in this study. Svevo is a widely cultivated, high-yielding Italian spring durum wheat cultivar developed by Società Produttori Sementi (SPS). It is characterized by its high grain quality, good adaptation to Mediterranean agroecological conditions, excellent pasta-making quality, and relatively high yield potential. Owing to its agronomic performance and broad adaptation, Svevo has been extensively used in breeding programs and scientific studies as a reference durum wheat genotype.\n\n**Figure 1.** Study area located in Antalya, Turkey.\n\nThe experiment was established in a randomized complete block design (RCBD) comprising seven nitrogen treatments with three replications, resulting in a total of 21 experimental plots. Each plot measured 5.0 m × 2.5 m (12.5 m 2) ([Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#fig_body_display_nitrogen-07-00086-f002)).\n\n**Figure 2.** Experimental plot layout and pre-establishment coordinate measurements: (**a**) randomized complete block design (RCBD) showing the treatments (N0–N6) and three replicates (1–3); (**b**) coordinate measurements conducted prior to experimental establishment.\n\nPrior to the establishment of the experiment, the field was surveyed using a Differential Global Positioning System (DGPS). The acquired sub-centimeter-accuracy coordinate data were used to generate vector boundaries for each experimental plot, ensuring the precise positioning and layout of all treatments within the experimental field. This high-precision georeferencing also provided an accurate spatial framework for subsequent UAV image acquisition, photogrammetric processing, and geospatial analyses.\n\nBefore the first nitrogen application, soil samples were collected from the experimental field to determine its physical and chemical properties. Samples were obtained from two soil depths (0–20 and 20–40 cm), air-dried, gently crushed, and passed through a 2 mm sieve prior to laboratory analysis. Soil pH was determined in a 1:2.5 soil-to-water suspension using a digital pH meter following the method of Jackson [[14](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B14-nitrogen-07-00086)], while electrical conductivity (EC) was measured in the same suspension according to Page et al. [[15](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B15-nitrogen-07-00086)]. Total carbonate content was determined using a Scheibler calcimeter [[16](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B16-nitrogen-07-00086)]. Soil texture, organic matter, and total nitrogen contents were determined using the hydrometer method [[17](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B17-nitrogen-07-00086)], the modified Walkley–Black method [[18](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B18-nitrogen-07-00086)], and the modified Kjeldahl method [[19](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B19-nitrogen-07-00086)], respectively. Plant-available phosphorus was extracted with 0.5 M NaHCO 3 and quantified using the molybdate blue colorimetric method with a UV–Vis spectrophotometer Shimadzu UV-1800 (Shimadzu Corporation, Tokyo, Japan) [[20](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B20-nitrogen-07-00086)]. Exchangeable potassium (K), calcium (Ca), and magnesium (Mg) were extracted with 1 M ammonium acetate (NH 4 OAc) and determined by inductively coupled plasma optical emission spectrometry (ICP–OES) [[20](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B20-nitrogen-07-00086)]. Available iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu) were extracted using diethylenetriaminepentaacetic acid (DTPA) and quantified by ICP–OES.\n\nThe physical and chemical properties of the soil samples collected from the 0–20 and 20–40 cm depths are summarized in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#table_body_display_nitrogen-07-00086-t001). The analytical results indicated that the experimental soil had a clay loam texture with generally adequate nutrient availability for durum wheat production. ([Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#table_body_display_nitrogen-07-00086-t001)).\n\n**Table 1.** Physical and chemical properties of the study area soils.\n\nBased on soil analysis results ([Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#table_body_display_nitrogen-07-00086-t001)) and a target grain yield of 7500 kg ha−1, 140 kg ha−1 of phosphorus and potassium were applied as base fertilization during sowing. Phosphorus was applied as diammonium phosphate (DAP) and potassium as potassium sulfate (K 2 SO 4), taking into account commonly applied fertilizers. For nitrogen fertilization, after deducting the amount of nitrogen from DAP fertilizer, a total of 175 kg N ha−1 of urea fertilizer was applied as top dressing. In the control application, no nitrogen fertilization was performed other than base fertilization. To evaluate the effects of split nitrogen application, the total nitrogen rate was divided into different application schedules according to the principal phenological stages of wheat defined by the Zadoks growth scale [[21](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B21-nitrogen-07-00086)]. Split nitrogen applications were carried out at sowing, tillering, stem elongation, booting, heading, and flowering, depending on the treatment.\n\nThe experimental treatments consisted of seven split nitrogen application strategies: N0 (no nitrogen application), N1 (100% of the total nitrogen applied at sowing), N2 (50% at sowing + 50% at tillering), N3 (33% at sowing + 33% at tillering + 33% at stem elongation), N4 (25% at sowing + 25% at tillering + 25% at stem elongation + 25% at booting), N5 (20% at sowing + 20% at tillering + 20% at stem elongation + 20% at booting + 20% at heading), and N6 (16.7% at sowing + 16.7% at tillering + 16.7% at stem elongation + 16.7% at booting + 16.7% at heading + 16.7% at flowering ([Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#fig_body_display_nitrogen-07-00086-f003)).\n\n**Figure 3.** Experimental scheme of split nitrogen application treatments across durum wheat phenological stages.\n\nIn the agricultural ecological conditions of Antalya province, durum wheat (Triticum durum Desf.) is typically sown in mid-November. However, due to prevailing weather conditions, wheat sowing was carried out on 16 December 2019. Harvesting took place on 15 June 2020.\n\n### 2.2. Leaf Sampling and Remote Sensing Studies\n\nPlant sampling was initiated at the tillering stage and continued at five subsequent phenological stages throughout the growing season. During each sampling event, representative leaf samples were collected from every experimental plot for laboratory analyses following the procedures described by Kacar and Inal [[22](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B22-nitrogen-07-00086)]. For each plot, the youngest fully expanded leaves were collected from 20 randomly selected plants. Rather than sampling all leaves on the shoot, only the youngest fully expanded leaves were used, and the collected leaves were pooled to obtain a representative composite sample for each experimental plot ([Figure S1](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#app1-nitrogen-07-00086)). Total nitrogen (N) concentration was determined using the modified Kjeldahl method, while phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu) concentrations were measured in the filtrate obtained by wet digestion using an inductively coupled plasma optical emission spectrometer (ICP-OES; PerkinElmer Avio 2000, PerkinElmer, Shelton, CT, USA).\n\nGround-based hyperspectral measurements and UAV-based multispectral image acquisition were conducted concurrently with each plant sampling period to ensure consistency between field observations and remote sensing data ([Figure 4](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#fig_body_display_nitrogen-07-00086-f004) and [Figure S2](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#app1-nitrogen-07-00086)). Hyperspectral reflectance measurements were acquired using an ASD FieldSpec® 3 spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA), covering the 325–1075 nm spectral range. Measurements were collected under clear-sky conditions by positioning the sensor at a height of 1.35 m above the crop canopy, producing a ground field of view (GFOV) with a diameter of approximately 60 cm ([Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#fig_body_display_nitrogen-07-00086-f003)). The GFOV was calculated according to the method described by Mac Arthur et al. [[23](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B23-nitrogen-07-00086)], as expressed in Equation (1).\n\n$$\nG F O V = 2 \\textrm{ } \\left(h \\times tan \\frac{\\theta}{2}\\right)\n$$\n\n(1)\n\nwhere GFOV is the ground field of view, h is the height above the target, and θ is the field of view (FOV).\n\n**Figure 4.** Hyperspectral measurement setup. (**a**) Field spectroradiometer; (**b**) ground field of view (FOV) during spectral measurements.\n\nUAV-based multispectral image acquisition was performed at flight altitudes of 10 and 45 m during each sampling period. Multispectral imagery was collected using a MAPIR Survey3W (RGN) camera mounted on the UAV platform. The camera captures reflectance in the green, red, and near-infrared (NIR) spectral bands and is equipped with a 12-megapixel sensor weighing 76 g. The MAPIR Survey3W RGN (Red, Green, and Near-Infrared) camera is equipped with three spectral bands centered at 550 nm (green), 660 nm (red), and 850 nm (near-infrared). To ensure the accuracy and temporal consistency of reflectance measurements, radiometric calibration was performed for all multispectral images using a MAPIR Reflectance Calibration Panel before image processing. The calibration procedure converted raw digital numbers (DNs) into surface reflectance values based on reference reflectance targets, thereby minimizing the effects of variations in illumination conditions among different flight dates and enabling reliable temporal comparisons of multispectral imagery [[24](https:\u002F\u002Fwww.mdpi.com\u002F2504-3129\u002F7\u002F3\u002F86#B24-nitrogen-07-00086)].\n\nFollowing radiometric calibration, the images were processed to generate georeferenced multispectral datasets. Orthorectification, resampling, and band stacking were performed to produce four-band multispectral image products for subsequent vegetation index calculations and spatial analyses.\n\nGround-based hyperspectral reflectance measurements were acquired at each phenological stage for every nitrogen treatment and replication, resulting in a total of 751 spectral observations. Spectral measurements were collected using an ASD FieldSpec handheld spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA), operating over the 325–1075 nm wavelength range. All measurements were conducted under clear-sky conditions between 11:00 and 13:00 local time to minimize the effects of variations in solar illumination. The instrument was configured with a 25° field of view, and the distance between the sensor and the crop canopy was maintained at 1.35 m throughout the measurement campaign.\n\nTo facilitate spectral interpretation and reduce data complexity, the measured reflect","MDPI Nitrogen 7(3):86 2026-08-14","2026-08-14T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,4,1,"论文探讨无人机与高光谱遥感在杜兰小麦氮肥管理中的应用，属于智慧农业细分领域，方法有一定新颖性，但影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"精准施肥","高光谱","智慧种植","UAV","杜兰小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},8,true,[34],[],[],{"code":51,"score":21,"supported":14,"title":52,"abstract":53,"tags":54},"F3",[],[],[26],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"I3",[],[],[27],[61,62],"智慧种植 杜兰小麦 精准施肥 高光谱","智慧种植 杜兰小麦","智慧种植杜兰小麦精准施肥高光谱-745",2,"2026-08-19T23:57:32.345125Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,237,315,377,457,514],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":74,"content":8,"source_name":75,"source_url":72,"published_at":76,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":77,"score_detail":78,"sources":83,"tags":85,"primary_topic":89,"secondary_topics":90,"classification":102,"search_phrases":173,"slug":176,"view_count":151,"doi":177,"paper":178,"created_at":236},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",80,{"impact":18,"substance":79,"depth":18,"authority":80,"freshness":81,"relevant":21,"comment":82},22,13,9,"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。",[84],{"name":75,"url":72},[86,26,34,87,88],"智慧农业","保护性耕作","水肥一体化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[91,96],{"code":51,"slug":92,"name":93,"path":94,"parent_code":95},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":103},{"candidates":104,"agricultural_evidence":46},[105,111,116,121,128,135,142,149,156,162,168],{"code":51,"score":19,"supported":46,"title":106,"abstract":108,"tags":110},[107],"fertigation",[109,107],"施肥",[88,26],{"code":32,"score":112,"supported":46,"title":113,"abstract":114,"tags":115},15,[33],[34,33],[34],{"code":97,"score":17,"supported":46,"title":117,"abstract":119,"tags":120},[118],"conservation agriculture",[118],[87],{"code":122,"score":67,"supported":46,"title":123,"abstract":124,"tags":127},"A3",[],[125,126],"玉米","maize",[],{"code":129,"score":67,"supported":46,"title":130,"abstract":131,"tags":134},"A8",[],[132,133],"粮食安全","food security",[],{"code":136,"score":67,"supported":46,"title":137,"abstract":138,"tags":141},"H2",[],[139,140],"土壤","soil",[],{"code":143,"score":67,"supported":46,"title":144,"abstract":145,"tags":148},"H3",[],[146,147],"灌溉","irrigation",[],{"code":150,"score":151,"supported":46,"title":152,"abstract":153,"tags":155},"A7",3,[],[154],"轮作",[],{"code":157,"score":151,"supported":46,"title":158,"abstract":159,"tags":161},"H5",[],[160],"秸秆还田",[],{"code":163,"score":151,"supported":46,"title":164,"abstract":165,"tags":167},"H7",[],[166],"干旱",[],{"code":169,"score":21,"supported":14,"title":170,"abstract":171,"tags":172},"I1",[],[],[86],[174,175],"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":177,"openalex_id":179,"authors":180,"venue":75,"cited_by_count":228,"oa_url":72,"card":229,"direction":233,"ingested_from":235},"W7214579192",[181,183,186,188,190,192,194,197,199,201,203,205,207,210,212,214,217,220,223,226],{"name":182,"orcid":8},"Kadagonda Nithinkumar",{"name":184,"orcid":185},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":187,"orcid":8},"Devika A. R.",{"name":189,"orcid":8},"Arjun Singh",{"name":191,"orcid":8},"Susama Sudhishri",{"name":193,"orcid":8},"Vinod Kumar Singh",{"name":195,"orcid":196},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":198,"orcid":8},"Sarvendra Kumar",{"name":200,"orcid":8},"Teekam Singh",{"name":202,"orcid":8},"Jitendra Rajput",{"name":204,"orcid":8},"Pravin Kumar Upadhyaya",{"name":206,"orcid":8},"R. N. Sahoo",{"name":208,"orcid":209},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":211,"orcid":8},"Aye Aye San",{"name":213,"orcid":8},"K. S. Sachin",{"name":215,"orcid":216},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":218,"orcid":219},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":221,"orcid":222},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":224,"orcid":225},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":227,"orcid":8},"Subhash Babu",0,{"tldr":230,"method":231,"finding":232,"direction":233,"opportunity":234},"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":238,"title":239,"url":240,"summary":241,"summary_zh":8,"content":8,"source_name":242,"source_url":240,"published_at":243,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":250,"tags":252,"primary_topic":256,"secondary_topics":257,"classification":258,"search_phrases":280,"slug":283,"view_count":64,"doi":284,"paper":285,"created_at":314},2622,"Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104983","Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",79,{"impact":18,"substance":246,"depth":247,"authority":248,"freshness":13,"relevant":21,"comment":249},20,17,14,"将过程模型与数据驱动学习融合用于冬小麦精准氮肥优化，方法新颖、发表于核心期刊且时效性强，具备进入每日精选的价值。",[251],{"name":242,"url":240},[86,253,26,254,255],"农业人工智能","作物模型","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":259},{"candidates":260,"agricultural_evidence":46},[261,267,272,276],{"code":32,"score":262,"supported":46,"title":263,"abstract":265,"tags":266},23,[264,33],"winter wheat",[264,33],[255],{"code":268,"score":64,"supported":14,"title":269,"abstract":270,"tags":271},"I2",[],[],[254,253],{"code":51,"score":21,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[26],{"code":169,"score":21,"supported":14,"title":277,"abstract":278,"tags":279},[],[],[86],[281,282],"农业人工智能 作物模型 智慧农业 精准施肥","农业人工智能 作物模型","农业人工智能作物模型智慧农业精准施肥-2622","10.1016\u002Fj.agsy.2026.104983",{"doi":284,"openalex_id":286,"authors":287,"venue":242,"cited_by_count":228,"oa_url":240,"card":8,"direction":8,"ingested_from":235},"W7213352238",[288,290,293,296,298,300,302,304,306,308,310,312],{"name":289,"orcid":8},"Yuru Ye",{"name":291,"orcid":292},"Qian Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0750-7843",{"name":294,"orcid":295},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",{"name":297,"orcid":8},"Kang Yu",{"name":299,"orcid":8},"Siva K. Balasundram",{"name":301,"orcid":8},"Wei Li",{"name":303,"orcid":8},"Xiuli Li",{"name":305,"orcid":8},"Xiaojun Liu",{"name":307,"orcid":8},"Yongchao Tian",{"name":309,"orcid":8},"Yan Zhu",{"name":311,"orcid":8},"Weixing Cao",{"name":313,"orcid":8},"Qiang Cao","2026-09-16T23:30:05.340754Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":8,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":339,"search_phrases":360,"slug":363,"view_count":20,"doi":364,"paper":365,"created_at":376},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":17,"substance":18,"depth":112,"authority":13,"freshness":67,"relevant":21,"comment":325},"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。",[327],{"name":321,"url":318},[86,26,34,329,330],"遥感","NDVI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[333,334],{"code":51,"slug":92,"name":93,"path":94,"parent_code":95},{"code":56,"slug":335,"name":336,"path":337,"parent_code":338},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346,351,356],{"code":32,"score":112,"supported":46,"title":343,"abstract":344,"tags":345},[33],[34,33],[34],{"code":51,"score":17,"supported":46,"title":347,"abstract":349,"tags":350},[348],"fertilizer",[348],[26],{"code":56,"score":45,"supported":46,"title":352,"abstract":353,"tags":355},[],[329,354],"remote sensing",[330,329],{"code":169,"score":21,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[86],[361,362],"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","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":364,"openalex_id":366,"authors":367,"venue":321,"cited_by_count":228,"oa_url":8,"card":370,"direction":374,"ingested_from":235},"W7212289620",[368],{"name":369,"orcid":8},"Mohammed A. Naser",{"tldr":371,"method":372,"finding":373,"direction":374,"opportunity":375},"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":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":8,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":389,"tags":391,"primary_topic":395,"secondary_topics":396,"classification":404,"search_phrases":434,"slug":437,"view_count":228,"doi":438,"paper":439,"created_at":456},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":19,"substance":387,"depth":247,"authority":80,"freshness":13,"relevant":21,"comment":388},21,"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。",[390],{"name":383,"url":380},[86,392,393,394,255],"机器学习","遥感监测","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[397,400],{"code":136,"slug":140,"name":398,"path":399,"parent_code":101},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":268,"slug":401,"name":402,"path":403,"parent_code":338},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":405},{"candidates":406,"agricultural_evidence":46},[407,412,416,421,426,430],{"code":32,"score":408,"supported":46,"title":409,"abstract":410,"tags":411},26,[264,33],[34,264,33],[255],{"code":136,"score":112,"supported":46,"title":413,"abstract":414,"tags":415},[140],[139,140],[394],{"code":268,"score":112,"supported":46,"title":417,"abstract":419,"tags":420},[418],"machine 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