Sensors for Detection of Nitrogen in Winter Wheat

Stone, M. L.; Solie, John B.; Whitney, R. W.; Raun, W. R.; Lees, H. L.

doi:10.4271/961757

Cited by 23 publications

(21 citation statements)

References 31 publications

(43 reference statements)

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“…Early work at Oklahoma State University (OSU) recognized positive correlation between passive NDVI measurements and plant biomass (Stone et al, 1996). This work went further to identify that these NDVI values were correlated with N uptake, by site, and by year.…”

Section: Oklahoma State Universitymentioning

confidence: 91%

Algorithms for In‐Season Nutrient Management in Cereals

et al. 2016

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The demand for improved decision‐making products for cereal production systems has placed added emphasis on using plant sensors in‐season, and that incorporate real‐time, site specific, growing environments. The objectives of this work were to describe validated in‐season sensor‐based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields. A review of research programs in the central Great Plains that have developed sensor‐based N recommendations for cereal crops was performed. Algorithms included multiple land‐grant university, government, and industry programs. A common thread in this review is the use of active sensors, particularly those using the normalized difference vegetation index (NDVI) for quantifying differences in fertilized and non‐fertilized areas, within a specific cropping season. In‐season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in‐season environment‐specific inputs have also been used. Early passive sensors have advanced to by‐plant N fertilization using active NDVI and by‐plant statistical properties. Most recently, sensor‐based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. The development and promotion of fee‐based modeling approaches for nutrient management continues. Nonetheless, several algorithms using active sensors for in‐season N management are available from state and government sources at no cost and that have been extensively field tested and can be modified by producers.Core Ideas Normalized difference vegetation index algorithms can improve fertilizer N efficiency. Normalized difference vegetation index sensors currently sold employ these algorithms. Algorithms rely on knowledge that increased yields increase fertilizer N demand. Yield potential and N response are independent. Nitrogen‐rich strips help to predict in‐season grain yields.

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Section: Oklahoma State Universitymentioning

confidence: 91%

Algorithms for In‐Season Nutrient Management in Cereals

et al. 2016

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“…However, a ramped NOVI-N rate scale was utilized at Tipton in 1997-98 to account for variation in percent coverage or stand density. The linear NOVI -N rate scale was based on previous experience reported by Stone et al (1996a).…”

Section: Methodsmentioning

confidence: 99%

“…Reflectance values are calculated as a ratio of the incident and reflected radiation. Measurements taken at these wavelengths can be used to calculate a normalized-difference-vegetative-index (NDVI), which has been demonstrated to be highly correlated with plant N uptake (Stone et aL, 1996a), and a reliable predictor oftopdress N needs (Roth et aI., 1989).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum Field Element Size for Maximum Yields in Winter Wheat, Using Variable Nitrogen Rates

LaRuffa

Raun

Phillips

et al. 2001

Journal of Plant Nutrition

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“…Several studies used remotely sensed spectral measurements to evaluate plant biomass (Wallburg et al, 1982;Kleman and Fagerlund, 1987;Wanjura and Hatfield, 1987;Casanova et al, 1998;Felton et al, 2002;Bronson et al, 2003) and plant N content (Blackmer et al, 1994;Stone et al, 1996a;Bronson et al, 2003). Some researchers used spectral data to estimate crop yields using simple regression equations (Moran et al, 1997;Raun et al, 2001).…”

mentioning

confidence: 99%

“…The NDVI was found to be a useful index to estimate crop yield of wheat (Colwell et al, 1977;Tucker et al, 1980;Pinter et al, 1981), millet, and sorghum (Bartholome, 1988). Stone et al (1996a) and Solie et al (1996) reported that NDVI can reliably predict both biomass and N uptake in winter wheat when measurements were done between Feekes physiological growth stages 4 and 5. Similarly, Lukina et al (1999) were able to show high correlations between percentage of soil coverage by wheat and NDVI at these growth stages.…”

mentioning

confidence: 99%