Innovative Remote Sensing Techniques to Increase Nitrogen Use Efficiency of Corn

Bausch, Walter C.; Diker, Kenan

doi:10.1081/css-100104117

Cited by 59 publications

(39 citation statements)

References 10 publications

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“…The NRI was highly correlated with an N sufficiency index calculated from SPAD chlorophyll meter data and provided a rapid assessment of corn plant N status for mapping purposes. A more recent study using the NRI to monitor in-season plant N resulted in reducing applied N using fertigation by 39 kg N ha Ϫ1 without reducing grain yield (Bausch and Diker, 2001). Because this index was based on the plant canopy as opposed to the individual leaf measurements obtained with SPAD readings, it has potential for larger scale applications and direct input into variable rate fertilizer application technology.…”

Section: Nitrogenmentioning

confidence: 99%

“…In practice, however, the seedling plants are usually too small and their signal is overwhelmed by that of the soil. Acquiring imagery very early in the day (i.e., large solar zeniths) or with off-nadir viewing angles offers a potential solution to plant detection at low leaf area levels (Pinter et al, 1983b;Bausch and Diker, 2001). As more sensitive sensors are deployed and techniques for calibration and removing effects of changes in soil background improve p.…”

Section: Photogrammetric Engineering and Remote Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing for Crop Management

Pinter¹,

Hatfield²,

Schepers³

et al. 2003

photogramm eng remote sensing

491

254

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Section: Nitrogenmentioning

confidence: 99%

Section: Photogrammetric Engineering and Remote Sensingmentioning

confidence: 99%

Remote Sensing for Crop Management

Pinter¹,

Hatfield²,

Schepers³

et al. 2003

photogramm eng remote sensing

491

254

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“…In both systems, a small N application via irrigation water is triggered whenever relative reflectance/transmittance falls below 0.95 of the value observed in a non-N-limited reference area. This system has produced numerous positive outcomes (Bausch and Diker, 2001;Bausch and Delgado, 2003) but is limited in application to irrigated fields with the capability to add N to irrigation water. This limitation was removed by Varvel et al (2007) by developing chlorophyll meter interpretations that produce a rate recommendation for a one-time application, which became the basis for the reflectance sensor interpretations developed by Solari et al (2010).…”

Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 99%

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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“…Confounding effects can originate from changes in canopy architecture, i.e. leaf rolling and leaf angle (Moran et al 1989), reflectance coming from the bare soil (Bausch 1993;Clarke et al 2001), and probably from changes in properties of leaf surfaces (Chaves et al 2003). It has been suggested that measuring the proportion of the targetted component (i.e.…”

Section: Remote Sensing Of Nitrogen Deficiencymentioning

confidence: 99%

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

Rodrı́guez¹,

Fitzgerald²,

Belford³

et al. 2006

Aust. J. Agric. Res.

130

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Abstract. We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSR Pi ) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSR Pi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.Additional keywords: water stress, plant density, remote sensing, nitrogen stress index.

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Innovative Remote Sensing Techniques to Increase Nitrogen Use Efficiency of Corn

Cited by 59 publications

References 10 publications

Remote Sensing for Crop Management

Remote Sensing for Crop Management

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

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

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