Calibrating Canopy Reflectance Sensors to Predict Optimal Mid‐Season Nitrogen Rate for Cotton

Oliveira, Luciane F.; Scharf, Peter C.; Vories, Earl D.; Drummond, S. T.; Dunn, David B.; Stevens, W. E.; Bronson, K. F.; Benson, N. R.; Hubbard, V. C.; Jones, Andrea

doi:10.2136/sssaj2012.0154

Cited by 21 publications

(25 citation statements)

References 24 publications

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“…Oliveira et al (2013) found that sensor measurements were more reliably related to optimal N rate when taken from 50 cm above a cotton canopy than when taken from 25 or 100 cm above the canopy. At 100 cm, reflectance from the soil around small plants is likely to interfere with the ability of sensors to discern the N status of the plants.…”

Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 90%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

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Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 90%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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“…Oliveira et al (2013) developed equations relating vegetative indices to optimal N fertilizer rate, with the intent that these could be used to support real-time variable-rate N applications. Cropscan data were averaged for each 2-min period, temperature-corrected using custom software supplied by Cropscan, and formatted appropriately for POSTPROC software.…”

Section: Methodsmentioning

confidence: 99%

“…Cropscan wavelengths used for this study were green (560 nm) and near-infrared (810 nm), chosen based on the results of Oliveira et al (2013). The Crop Circle sensor emits yellow (590 nm) and near-infrared (880 nm) wavelengths, while the GreenSeeker sensor emits red (656 nm) and near-infrared (774 nm).…”

Section: Reflectance Measurementsmentioning

confidence: 99%

“…Standard deviations for GreenSeekerbased N rates were higher: 53 kg N ha -1 for Vis/NIRbased N rate (higher than Crop Circle with P < 0.0001) and 19 kg N ha -1 for NDVI-based N rate (higher than Crop Circle with P = 0.09). Nitrogen rates were derived from reflectance values using equations from Oliveira et al (2013 which is reflected less (absorbed more) by plants than the yellow wavelength used by Crop Circle and the green wavelength that we used from the Cropscan. 2 that the effect of diurnal changes in the GreenSeeker indices resulted in much larger changes in N rate than for the other sensors, especially with Vis/NIR.…”

Section: Standard Deviations For Recommended N Rates For Crop Circle mentioning

confidence: 99%

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Diurnal Variability in Reflectance Measurements from Cotton

Oliveira

Scharf

2014

Crop Science

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Diurnal variability of reflectance measurements on crop plants has been documented for passive sensors, but minimally studied for active sensors. Active sensors are now commercially available for crop management decisions including diagnosis and control of N rate. Our objective was to quantify variability during the day for both passive and active sensors mounted above a cotton canopy, and to assess the effect that this variability would have on N rate decisions. Measurements were taken from 0600 to 2000 h at early square, midsquare, and early flower growth stages using Cropscan (passive), Crop Circle (active), and GreenSeeker (active) reflectance sensors. Average CV for GreenSeeker was higher than for Crop Circle for visible/near‐infrared (Vis/NIR) (P = 0.01) and possibly for normalized difference vegetative index (NDVI) as well (P = 0.15). Variability in Cropscan reflectance was much higher in north‐south than in east‐west rows. When restricted to east‐west rows, Cropscan CVs were equal to or lower than the other sensors. Nitrogen rates calculated from sensor measurements also were more variable for GreenSeeker (SD ≥ 19 kg N ha–1) than for Crop Circle (SD ≥ 12 kg N ha–1) (P < 0.09). GreenSeeker NDVI followed a consistent diurnal pattern: lower in midday and higher in morning and evening. A regression equation using temperature, solar radiation, and solar time explained 45 to 50% of this variability, and could potentially be used to correct values during field use. Additional study to isolate causal factors of variability is justified. Strategies to compensate for drift in reflectance measurements will be needed when using sensors to control applications of N fertilizer or other inputs, even for active sensors.

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“…The use of the CC sensors has been directed toward development of algorithms for in-season N application. Relationships of sensor readings to yield and in-season N status, as well as algorithms for use in determining in-season N rates have been developed by several researchers Solari et al, 2008;Sripada et al, 2008;Scharf and Lory, 2009;Barker and Sawyer, 2010;Solari et al, 2010;Oliviera et al, 2013;Franzen et al, 2014).…”

Section: Crop Circlementioning

confidence: 99%

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Sharma

Denton

et al. 2017

Agronomy Journal

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Algorithms using active-optical (AO) sensors have been developed to direct in-season N application to crops. Many farmers in the United States have a large number of farm fi elds to manage. Farmers using AO technology must visit each fi eld and operate the sensor across the entire fi eld in order to conduct in-season N application. A fi eld might be driven over with an on-the-go N fertilizer applicator, but the application might not be required. Th e objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunfl ower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fi elds that would benefi t from in-season N application. Two N-rate studies in sugar beet, spring wheat, corn and sunfl ower, were conducted with experimental unit size of 9 by 9 m large enough to fi t a satellite pixel of 5 by 5 m size within each unit. Th e AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunfl ower in some site-years. Th e problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. Th ese results indicate that even though satellite imagery could be used as a fi eld screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).

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Calibrating Canopy Reflectance Sensors to Predict Optimal Mid‐Season Nitrogen Rate for Cotton

Cited by 21 publications

References 24 publications

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Diurnal Variability in Reflectance Measurements from Cotton

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

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