Development of an NDVI‐Based Nitrogen Rate Calculator for Cotton

Arnall, Daryl B.; Abit, M. Joy M.; Taylor, Randal K.; Raun, W. R.

doi:10.2135/cropsci2016.01.0049

Cited by 20 publications

(15 citation statements)

References 31 publications

(91 reference statements)

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“…Arnall et al. (2016) developed a VI‐based N rate calculator for cotton. It entailed dividing NDVI by growing degree days, and the use of a response index relative to N‐rich strips.…”

Section: Discussionmentioning

confidence: 99%

“…Interest in canopy reflectance using active optical sensors (AOS) continues to grow for the management of N fertilizer in field crops such as cotton (Arnall, Abit, Taylor, & Raun, 2016; Bronson, Malapati, Scharf, & Nichols, 2011; Chua et al., 2003; Oliveira et al., 2012; Raper, Varco, & Hubbard, 2013; Stamatiadis et al., 2019). The most common AOS technology measure reflectance in visible (450–670 nm, but typically red, i.e., 650–670 nm) and NIR (780‐870 nm) wavebands, and vegetation indices like the normalized difference vegetation index (NDVI) are calculated (Tucker, 1979) from the reflectance values.…”

Section: Introductionmentioning

confidence: 99%

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Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

et al. 2020

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Use of active optical sensors (AOS) in nitrogen (N) management of row crops continues to grow. Since the first studies in the mid-1990s, several commercial AOS are now available. Typically, canopy reflectance in red and near infrared (NIR) bands are used to calculate the normalized difference vegetation index (NDVI). More recently, commercially available AOS include a third, red-edge band that allows the calculation of additional vegetation indices (VIs). We present two studies of five site-years of N management studies in Maricopa, AZ, on a Casa Grande sandy loam with cotton (Gossypium hirsutum L.). The 2014-2015 study was conducted under an overhead sprinkler irrigation system (OSI), and the 2016-2018 study was in subsurface drip irrigation (SDI). The study objective was to compare the ability of 12 different VIs to detect N deficiency among N treatments from shortly after emergence to midbloom. In the OSI study, which showed delayed, small N treatment effects, NDRE and the chlorophyll index using amber (CIRE) detected N deficiency in zero-N and in reduced N-reflectance-based plots 7-23 d before other VIs did. With SDI, the choice of VI was less critical as several VIs could distinguish N deficiency in zero-N and in reflectance plots. The commonly used NDVI red (NDVIR) only detected N deficiency in reflectance plots in one of five site-years. In conclusion, we recommend the use of AOS with NIR and red-edge bands and the calculation of NDRE or CIRE to guide AOS-based in-season N management of irrigated cotton.

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“…Arnall et al. (2016) developed a VI‐based N rate calculator for cotton. It entailed dividing NDVI by growing degree days, and the use of a response index relative to N‐rich strips.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

et al. 2020

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“…Several researchers have developed algorithms for N fertilization based on optical sensors [4] [7] [8] [9]. However, due to higher annual precipitation, significant variation in soil type and texture, low soil organic matter content, and low nutrient holding capacity of soils in the Coastal Plain region, N-application algorithms developed in other regions, either under-or over-estimate N rates for crop production.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to higher annual precipitation, significant variation in soil type and texture, low soil organic matter content, and low nutrient holding capacity of soils in the Coastal Plain region, N-application algorithms developed in other regions, either under-or over-estimate N rates for crop production. During a three-year study at Clemson [4] [10], N fertilizer applied based on the algorithm developed at the Oklahoma State University (OSU) [9], reduced irrigated cotton yields by 15% compared to standard farmer practices (one uniform N rate across the entire field). The OSU algorithm, recommended 66% less N. During a 2013 study at Clemson, the algorithm developed at OSU [11], overestimated N rates by 11% in winter wheat production, without affecting crop yields.…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Algorithm for Mid-Season Nitrogen Application in Corn

Khalilian¹,

Rogers²,

Williams³

et al. 2017

OJSS

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Applying insufficient nitrogen (N) in a highly responsive crop, such as corn, results in lower grain yield, quality, and profits. On the other hand, when nitrogen is applied in excess of crop needs, profit is reduced and negative environmental consequences are likely. The objective of this study was to develop and employ a sensor-based algorithm to determine the mid-season N requirements for deficit-irrigated corn in Coastal Plain soils. The algorithm was developed using varied prescription rate N plot on two soil types. The test plots received nine different rates of N fertilizer, replicated 5 times in plots of each soil type using a Randomized Complete Block design. A 6-row GreenSeeker optical sensor was used to measure plant NDVI, between the V6 to V8 growth stages. The sensor readings were used to develop an algorithm to be used in the estimation of side-dress N application in corn. The NDVI sensor readings were collected at the V6 to V8 growth stage during the 2015 and 2016 growing seasons correlated with actual corn yields (R 2 > 0.68, p < 0.001). In-Season Estimated yield (INSEY) was used along with the actual yield to produce a yield potential for each growing season for deficit-irrigated corn crop. In summary, the algorithm developed from the NDVI readings reduced N application rates by 21% and 34% in soil types 1 and 2, respectively, compared to the normal grower practice (226 kg N/ha) with no reduction in corn yields.

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“…Soil inorganic N and leaf N concentration have been the widely used measurements in guiding in-season N application. However, during the past decade, the optical sensing technology for measuring crop canopy vegetation index non-destructively and at high resolutions has been increasingly investigated for its utilization to obtain information on crop N nutrition status, and thus for in-season precision N applications during the growing season [3]- [8]. The Normalized Difference Vegetation Index (NDVI) is a commonly used vegetation index based on the near-infrared and visible red radiation reflected from crop canopy using optical sensors [9] [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Small Scale Spatio-Temporal Variabilities in Soil Nitrogen, Leaf Nitrogen, and Canopy Normalized Difference Vegetation Index of Cotton

Yin¹

2016

GEP

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Strip plots have been increasingly used in agricultural field experiments to better reflect the true situation of crop production on farmers' fields, but failure to account for spatially and temporally related errors when present in the data analysis of strip plot field experiments may cause inefficient assessment of treatment effect significance. The objective of this study was to investigate patterns and degrees of the spatial and temporal variabilities in soil inorganic N level, leaf N concentration, and Normalized Difference Vegetation Index (NDVI) of cotton under no-tillage and the influences of N treatments on these variabilities. A strip plot experiment was conducted on a private farm near Brazil, Gibson County, Tennessee from 2009 through 2011. Five N treatments of 0, 45, 90, 134, and 179 kg N ha −1 were implemented as side dress N in strip plots under a randomized complete block design with three replicates after 45 kg N ha −1 was applied in the form of chicken litter before cotton planting. Spatial variability was present in soil inorganic N before cotton planting and after harvest, and in leaf N and canopy NDVI at the early square and early, mid-, and late bloom stages although the patterns and degrees of the spatial variabilities sometimes varied with growth stages and years. Application of the in-season side-dress N treatments often reduced the spatial variations of leaf N and NDVI, but increased those of post-harvest soil inorganic N. Out results suggest that the spatial and temporal variabilities of soil inorganic N, leaf N, and NDVI are high, and should be taken into account if possible in the data analyses of N treatment effects on related soil properties and plant characteristics of cotton in strip plot field experiment research.

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Development of an NDVI‐Based Nitrogen Rate Calculator for Cotton

Cited by 20 publications

References 31 publications

Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

Sensor-Based Algorithm for Mid-Season Nitrogen Application in Corn

Small Scale Spatio-Temporal Variabilities in Soil Nitrogen, Leaf Nitrogen, and Canopy Normalized Difference Vegetation Index of Cotton

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