{"paper_id":"080b18fb-3e1f-43ba-85e7-60add6c0433b","body_text":"Developing an improved remote sensing and calibration strip-based nitrogen management strategy for corn | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing an improved remote sensing and calibration strip-based nitrogen management strategy for corn Junjun Lu, Yuxin Miao, Xiong Du, Fabián G. Fernández This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9568200/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Remote Sensing and Ramp Calibration Strip (RSCS) is a practical on-farm in-season nitrogen (N) management strategy. However, it has not been widely adopted across the U.S. Midwest Corn Belt due to uncertainties about its performance under diverse environmental conditions. This study aimed to evaluate the performance of the RSCS strategy across the U.S. Midwest and develop an improved RSCS (IRSCS)-based N management strategy incorporating genetic × environmental × management (G×E×M) information, and to evaluate its potential agronomic, economic, and environmental benefits. Data from 49 site-years of N rate experiments across eight U.S. Midwest states were analyzed. Conducted from 2014 to 2016, these experiments involved eight pre-plant N treatments (0–315 kg N ha⁻¹ in 45 kg increments) for calibration plots, and six split N treatments (45 kg N ha⁻¹ pre-plant + 45–270 kg N ha⁻¹ sidedress in 45 kg N ha -1 increments) for evaluation plots. Corn canopy reflectance data were collected around the V9 developmental stage using proximal active canopy sensors. Optimal N rates exhibited substantial spatial variability (113–315 kg N ha⁻¹; Coefficient of Variation (CV)=17–40%), far exceeding the narrow ranges of maximum return to N (MRTN) and farmer’s N rate (FNR). The RSCS strategy consistently outperformed MRTN/FNR and was generally aligned with economic optimum N rates (EONRs). Incorporating simple thresholds and bias corrections, the IRSCS strategy further improved EONR prediction accuracy and reduced systematic bias relative to RSCS. The IRSCS strategy would increase corn yield (0.2 t ha⁻¹) and profit ($35-41 ha⁻¹) and reduce post-harvest residual soil mineral N (11–16 kg N ha⁻¹) over FNR and MRTN and achieve the highest N use efficiency. It is concluded that the IRSCS strategy provides a practical and transparent framework for improving the agronomic, economic, and environmental performance of in-season corn N management across diverse conditions in the U.S. Midwest. Agronomy Optimal nitrogen rate In-season nitrogen recommendation Active canopy sensor Precision nitrogen management Sustainability. Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementalMaterial.pdf Supplemental Material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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However, it has not been widely adopted across the U.S. Midwest Corn Belt due to uncertainties about its performance under diverse environmental conditions. This study aimed to evaluate the performance of the RSCS strategy across the U.S. Midwest and develop an improved RSCS (IRSCS)-based N management strategy incorporating genetic × environmental × management (G×E×M) information, and to evaluate its potential agronomic, economic, and environmental benefits. Data from 49 site-years of N rate experiments across eight U.S. Midwest states were analyzed. Conducted from 2014 to 2016, these experiments involved eight pre-plant N treatments (0–315 kg N ha⁻¹ in 45 kg increments) for calibration plots, and six split N treatments (45 kg N ha⁻¹ pre-plant + 45–270 kg N ha⁻¹ sidedress in 45 kg N ha\\u003csup\\u003e-1\\u003c/sup\\u003e increments) for evaluation plots. Corn canopy reflectance data were collected around the V9 developmental stage using proximal active canopy sensors. Optimal N rates exhibited substantial spatial variability (113–315 kg N ha⁻¹; Coefficient of Variation (CV)=17–40%), far exceeding the narrow ranges of maximum return to N (MRTN) and farmer’s N rate (FNR). The RSCS strategy consistently outperformed MRTN/FNR and was generally aligned with economic optimum N rates (EONRs). Incorporating simple thresholds and bias corrections, the IRSCS strategy further improved EONR prediction accuracy and reduced systematic bias relative to RSCS. The IRSCS strategy would increase corn yield (0.2 t ha⁻¹) and profit ($35-41 ha⁻¹) and reduce post-harvest residual soil mineral N (11–16 kg N ha⁻¹) over FNR and MRTN and achieve the highest N use efficiency. 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