Effect of row spacing, growth stage, and nitrogen rate on spectral irradiance in winter wheat

Lukina, E. V.; Raun, W. R.; Stone, M. L.; Solie, John B.; Johnson, G. V.; Lees, H. L.; LaRuffa, J. M.; Phillips, S. B.

doi:10.1080/01904160009382001

Cited by 24 publications

(23 citation statements)

References 17 publications

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“…This study reaffirmed the correlation between vegetative coverage and dry biomass found by Lukina et al (1999Lukina et al ( , 2000 and Ter-Mikaelian and Parker (2000). High correlation (R 2 = 0.91 to 0.96) was observed between vegetative coverage of the spinach as measured with digital imagery and biomass as measured in the laboratory.…”

Section: Conclusion and Discussionsupporting

confidence: 72%

“…High correlation (R 2 = 0.91 to 0.96) was observed between vegetative coverage of the spinach as measured with digital imagery and biomass as measured in the laboratory. The findings of Lukina et al (1999Lukina et al ( , 2000 and Sembiring et al (1998) were also supported regarding NDVI producing a stronger estimate of chlorophyll yield than of chlorophyll concentration. These correlations indicate the appropriate use of the sensing equipment to estimate biomass and chlorophyll yield in row crop spinach.…”

Section: Conclusion and Discussionsupporting

confidence: 62%

“…NDVI, in turn, was found to have a strong correlation with dry biomass (r 2 = 0.52) and with nitrogen content (r 2 = 0.66). The studies of Ter-Mikaelian and Parker (2000) and Lukina et al (1999Lukina et al ( , 2000 support the hypothesis that percent vegetative coverage may be useful in estimating the biomass of a plant canopy.…”

supporting

confidence: 49%

“…Sembiring et al (1998) found NDVI to be a better predictor of plant nitrogen content than nitrogen concentration. Studies by Lukina et al (1999Lukina et al ( , 2000 also support NDVI as a better predictor of nitrogen content than nitrogen concentration.…”

mentioning

confidence: 99%

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Remote Sensing to Estimate Chlorophyll Concentration in Spinach Using Multi-Spectral Plant Reflectance

Jones¹,

Weckler²,

Maness³

et al. 2007

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ABSTRACT.etermination of optimal levels for nitrogen fertilizer application to agricultural crops is not a straightforward process and carries significant uncertainties. Traditionally, pre-plant nitrogen requirements have been estimated by utilizing soil samples and crop-yield levels from previous years. The estimated application rate is then applied uniformly to the field (Sawyer, 1994). Lack of soil homogeneity and difficulty in implementing effective sampling strategies can lead to misapplication of nitrogen. An under-application of nitrogen may diminish crop production, while over-application can lead to negative environmental impacts, including nitrogen leaching and groundwater contamination . Fertilizer in excess of plant needs may result in surface runoff and pollution of lakes and streams (Daughtry et al., 2000;Wood et al., 1993). In either case, the economic returns to the producer are reduced. Therefore, a method that would allow real-time, in-field detection of crop nitrogen needs could be useful in crop nitrogen management.Chlorophyll a and b and carotenoid concentrations correlate to the photosynthetic potential of a plant and give some indication of the physiological status of the plant (Danks et al., 1983;Gamon and Surfus, 1999;Young and Britton, 1990 mass per unit area or per plant (kg/ha or kg/plant). Chlorophyll concentration (C conc ) is defined as the chlorophyll mass per unit mass of dry plant material (mg/kg). C yld may be used to evaluate the overall photosynthetic capacity or productivity of the plant canopy. C conc may be an indicator of plant physiological status or level of stress (Blackburn, 1998).Chlorophyll a content is mainly determined by nitrogen availability (Moorby and Besford, 1983). Light reflectance by leaves in the visible region of the spectrum depends primarily on the concentration of chlorophylls and carotenoids. A deficiency in nutrients such as nitrogen decreases pigment formation and leaf color, which subsequently increases reflectivity due to reduced radiation absorption.The normalized difference vegetative index (NDVI) was proposed by Rouse et al. (1974) where I NIR = near-infrared irradiance I RED = red irradiance. NDVI has a range of -1 to +1, with bare soil surfaces having an NDVI of approximately 0 and heavy vegetative cover having an NDVI of near 1 (Thiam, 1998). Reflected red irradiance (I RED ) is strongly diminished through chlorophyll absorption, with peak chlorophyll absorption occurring at 647Ănm. Both red and NIR irradiance are strongly influenced by plant cover. Red irradiance (I RED ) decreases with plant cover, and NIR irradiance (I NIR ) increases. Given these relationships, NDVI from vegetated surfaces is heavily influenced by chlorophyll content of materials in the vegetation. NDVI was introduced as a measure of biomass and is successful because chlorophyll concentration in plants is relatively constant. NDVI has been correlated with such plant properties as leaf area index, fractional vegetation canopy, vegetative condition, biomass, nitrogen cont...

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Section: Conclusion and Discussionsupporting

confidence: 72%

Section: Conclusion and Discussionsupporting

confidence: 62%

supporting

confidence: 49%

mentioning

confidence: 99%

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Remote Sensing to Estimate Chlorophyll Concentration in Spinach Using Multi-Spectral Plant Reflectance

Jones¹,

Weckler²,

Maness³

et al. 2007

Transactions of the ASABE

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“…Furthermore, mid-season CV of a field can be equated to the response index, which is currently used by various researchers to determine topdress fertilizer needs. Lukina et al (2000) observed that as the vegetation coverage increase, the CV of spectral radiance (NDVI) values decreases. This report suggests that the CV of NDVI readings could be used to estimate stand density.…”

Section: Statistical Parametersmentioning

confidence: 99%

Mid-Season Prediction of Wheat-Grain Yield Potential Using Plant, Soil, and Sensor Measurements

Girma

Martin

Anderson

et al. 2006

Journal of Plant Nutrition

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The components that define cereal-grain yield potential have not been well defined. The objective of this study was to collect many differing biological measurements from a long-term winter wheat (Triticum aestivum L.) study in an attempt to better define yield potential. Four treatments were sampled that annually received 0, 45, 90, and 135 kg N ha −1 at fixed rates of phosphorus (P) (30 kg ha −1 ) and potassium (K) (37 kg ha −1 ). Mid-season measurements of leaf color, chlorophyll, normalized difference vegetative index (NDVI), plant height, canopy temperature, tiller density, plant density, soil moisture, soil NH 4 -N, NO 3 -N, organic carbon (C), total nitrogen (N), pH, and N mineralization potential were collected. In addition, soil texture and bulk density were determined to characterize each plot. Correlations and multiple linear-regression analyses were used to determine those variables that can predict final winter wheat grain yield. Both the correlation and regression analyses suggested mid-season NDVI, chlorophyll content, plant height, and total N uptake to be good predictors of final winter wheat grain yield. Keywords

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Optimizing agronomic practices for hard winter wheat production in the Great Plains with respect to seeding rate, row spacing, and variety

et al. 2023

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Winter wheat (Triticum aestivum L.) yield in dryland areas is optimized by the improvement of genetics in combination with optimum production practices. Different combinations of seeding rate and row spacing can influence the development of wheat plants, impacting wheat yield and its components. To determine the influence of these practices, yield components, water use efficiency, biomass, and subsequent grain yield were evaluated in an experiment conducted at the High Plains Agricultural Laboratory (HPAL), Sidney, NE, from 2019 until 2022, using a split‐plot randomized complete block design with four replications. Treatments consisted of four different row spacings (19, 25, 31, and 51 cm), three seeding rates (1.05, 2.1, and 3.1 million seeds ha −1), and two high performing winter wheat varieties—‘Ruth’ and ‘Robidoux.’ Row spacing showed differences in wheat yield (P < .0001) across all years, with a significant effect of seeding rate only in one year and an interaction effect of the three factors in 2022 alone. Higher wheat yield was achieved with the two narrower row spacings (19 and 25 cm) compared to the wider row spacings of 38 and 51 cm. The treatment combining the 19 cm row spacing and either 2.1 or 3.1 million seed ha−1 seeding rate had the greatest yields in most years. Narrower row spacings had improved water use efficiency in drier years as compared to wider row spacings.This article is protected by copyright. All rights reserved

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Effect of row spacing, growth stage, and nitrogen rate on spectral irradiance in winter wheat

Cited by 24 publications

References 17 publications

Remote Sensing to Estimate Chlorophyll Concentration in Spinach Using Multi-Spectral Plant Reflectance

Remote Sensing to Estimate Chlorophyll Concentration in Spinach Using Multi-Spectral Plant Reflectance

Mid-Season Prediction of Wheat-Grain Yield Potential Using Plant, Soil, and Sensor Measurements

Optimizing agronomic practices for hard winter wheat production in the Great Plains with respect to seeding rate, row spacing, and variety

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