Paper · 2005 · Communications in Soil Science and Plant Analysis

Optical Sensor‐Based Algorithm for Crop Nitrogen Fertilization

W. R. Raun; J. B. Solie; M. L. Stone; et al.

4
claims
6
verbatim quotes
326
citations
2
research areas
—
Not open access

Abstract

Abstract Nitrogen (N) fertilization for cereal crop production does not follow any kind of generalized methodology that guarantees maximum nitrogen use efficiency (NUE). The objective of this work was to amalgamate some of the current concepts for N management in cereal production into an applied algorithm. This work at Oklahoma State University from 1992 to present has focused primarily on the use of optical sensors in red and near infrared bands for predicting yield, and using that information in an algorithm to estimate fertilizer requirements. The current algorithm, “WheatN.1.0,” may be separated into several discreet components: 1) mid‐season prediction of grain yield, determined by dividing the normalized difference vegetative index (NDVI) by the number of days from planting to sensing (estimate of biomass produced per day on the specific date when sensor readings are collected); 2) estimating temporally dependent responsiveness to applied N by placing non‐N‐limiting strips in production fields each year, and comparing these to the farmer practice (response index); and 3) determining the spatial variability within each 0.4 m2 area using the coefficient of variation (CV) from NDVI readings. These components are then integrated into a functional algorithm to estimate application rate whereby N removal is estimated based on the predicted yield potential for each 0.4 m2 area and adjusted for the seasonally dependent responsiveness to applied N. This work shows that yield potential prediction equations for winter wheat can be reliably established with only 2 years of field data. Furthermore, basing mid‐season N fertilizer rates on predicted yield potential and a response index can increase NUE by over 15% in winter wheat when compared to conventional methods. Using our optical sensor‐based algorithm that employs yield prediction and N responsiveness by location (0.4 m2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization.

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Claims 4

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Winter wheat yield potential prediction equations were reliably established with two years of field data.

Taxon
winter wheatTriticum aestivume0
Outcome
yield potential prediction equationse0
Value
2 yearse0

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Triticum aestivum
Triticum aestivum · TaxonRasbak · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e0

“yield potential prediction equations for winter wheat can be reliably established with only 2 years of field data”

✓ located verbatim in the source · abstract, characters 1492–1605 · show in text ↑
Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Using predicted yield potential and a response index for mid-season nitrogen rates increased winter wheat nitrogen use efficiency by over 15% versus conventional methods.

Taxon
winter wheatTriticum aestivume0
Treatment
basing mid‐season N fertilizer rates on predicted yield potential and a response indexe0
Outcome
NUEe0
Direction
increasee0
Value
over 15%e0
Comparator
conventional methodse0

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Triticum aestivum
Triticum aestivum · TaxonRasbak · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e0

“basing mid‐season N fertilizer rates on predicted yield potential and a response index can increase NUE by over 15% in winter wheat when compared to conventional methods”

✓ located verbatim in the source · abstract, characters 1620–1789 · show in text ↑
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

The optical sensor algorithm can increase wheat yields.

Taxon
Cropt0
Outcome
yieldse0
Direction
increasee0
Value
0.4 m2e0

Verbatim

t0

“Crop”

✓ located verbatim in the source · title, characters 35–39 · show in text ↑
e0

“Using our optical sensor‐based algorithm that employs yield prediction and N responsiveness by location (0.4 m2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization”

✓ located verbatim in the source · abstract, characters 1791–2008 · show in text ↑
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

The optical sensor algorithm can decrease environmental contamination due to excess nitrogen fertilization.

Taxon
Cropt0
Outcome
environmental contaminatione0
Direction
decreasee0
Value
0.4 m2e0

Verbatim

t0

“Crop”

✓ located verbatim in the source · title, characters 35–39 · show in text ↑
e0

“Using our optical sensor‐based algorithm that employs yield prediction and N responsiveness by location (0.4 m2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization”

✓ located verbatim in the source · abstract, characters 1791–2008 · show in text ↑

fertilization · nutrient: efficiency · Triticum aestivum (wheat)Positive and negative results

176 papers: 142 positive, 34 negative or no effect

141 papers in the same direction as this paper · 33 in the opposite direction · 1 reporting no difference from an alternative

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