Optical Sensor‐Based Algorithm for Crop Nitrogen Fertilization

By W. Raun, J. Solie, Marvin L. Stone, K. Martin, K. Freeman, R. Mullen, H. Zhang, James S. Schepers, G. V. Johnson. 2005

320 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/4d9a7487f0ad229b34541bf8293889361c0ffcdf

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Citations in the corpus

Sentinel 2-Based Nitrogen VRT Fertilization in Wheat: Comparison between Traditional and Simple Precision Practices (2019, background): “This would justify that, in some cases, the N rate should be directly related to the NDVI [35], i.”
Generalized Algorithm for Variable‐Rate Nitrogen Application in Cereal Grains (2012, methodology): “Later research by Raun et al. (2005) suggested that midseason N fertilizer rates be based on predicted yield potential and a response index.”
Adjusting Midseason Nitrogen Rate Using a Sensor-Based Optimization Algorithm to Increase Use Efficiency in Corn (2008, methodology): “Raun et al. (2005) used this information to further refine the algorithm.”
Remote Sensing in Agriculture - Accomplishments, Limitations, and Opportunities (2020, background): “For example, nitrogen (N) stress in crops has been linked to RS observations through empirical approaches by comparing spectral signatures in a target field with a reference spectral signature in a well-fertilized plot representative of the target field [21,22], or mechanistically by combining RS-ba”
Crop yield gaps: their importance, magnitudes, and causes. (2009, background): “For example, progress in technologies to measure spatial variability in soil nutrients has been relatively rapid in recent years (62, 63), whereas the ability to forecast growing season weather conditions has arguably progressed more slowly.”
In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index (2006, methodology): “After further evaluation, Raun et al. (2005) reported that the opportunity still existed for their yield potential prediction model to over- or underestimate yield potential.”
Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency (2020, methodology): “This idea has been proficiently utilized to control N application for rice [121], maize [184–188], cotton [189] and wheat [188, 190, 191].”
Independence of yield potential and crop nitrogen response (2011, methodology): “As Raun et al. (2005) showed, the yield achievable with added fertilizer N is the product of estimated yield potential (YP0) and the response index (RI) and given as YPN.”
Modelo para estimativa do potencial produtivo em trigo e cevada por meio do sensor GreenSeeker (2009, background): “A mesma conclusão foi obtida por RAUN et al. (2005), que determinaram que o incremento do potencial produtivo (estimado a partir da leitura do Biomassa acumulada na parte aérea (kg ha-1) - MS 0 200 400 600 800 1000 1200 1400 1600 Ín di ce d e V eg et aç ão p or D ife re nç a N or m al iz ad a (N…”
In-Season Estimation of Corn Yield Potential Using Proximal Sensing (2017, background): “Raun et al. (2005a) suggested that the best time for sensing and to apply in-season N fertilizer was when the variability of the NDVI measurements was maximized stating that treating crops at maximum variability is expected to have the greatest impact.”
Topdressing nitrogen recommendation for early rice with an active sensor in south China (2014, methodology): “2004; Nguyen and Lee 2006) and guiding N fertilization (Raun et al. 2002; Wood et al. 2003; Welsh et al. 2003; Raun et al. 2005).”
Applications of Remote Sensing in Precision Agriculture: A Review (2020, methodology): “, the nitrogen fertilizer optimization algorithm) [236,237] have been developed and successfully implemented in these commercially available sensors to determine vegetation-indices based in-season N-requirements for many crops [238,239].”