Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status

Prost, Lorène; Jeuffroy, Marie‐Hélène

doi:10.1051/agro:2007032

Cited by 84 publications

(77 citation statements)

References 26 publications

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“…NNI was positively related to SPAD values. The increase in NNI with increasing N fertilization has been previously reported in corn (Plénet and Cruz, 1997;Ziadi et al, 2008) and in wheat (Debaeke et al, 2006;Prost and Jeuffroy, 2007). This positive relationship varied across different developmental stages and leaf positions.…”

Section: Correlation Of Spad and Rspad Values With Nni Atsupporting

confidence: 72%

“…The non-linear relationship between SPAD values and NNI has been proved in fescue (Errecart et al, 2012), corn (Ziadi et al, 2008), wheat (Vouillot et al, 1998;Prost and Jeuffroy, 2007) and cocksfoot (Duru, 2002), and the correlation of RSPAD values with NNI, and relative grain yield has been studied only in corn (Ziadi et al, 2008) and wheat (Debaeke et al, 2006). Debaeke et al (2006) also found that the non-linear relationship between RSPAD values and NNI was not significantly affected by years, genotypes, and developmental stages.…”

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confidence: 99%

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SPAD Values and Nitrogen Nutrition Index for the Evaluation of Rice Nitrogen Status

Yang

et al. 2014

Plant Production Science

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Section: Correlation Of Spad and Rspad Values With Nni Atsupporting

confidence: 72%

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confidence: 99%

SPAD Values and Nitrogen Nutrition Index for the Evaluation of Rice Nitrogen Status

Yang

et al. 2014

Plant Production Science

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“…Adoption of this approach as a practical tool has been limited due to the substantial effort required in obtaining the plant samples. However, this approach is effective as a research tool, and has been used to develop critical petiole nitrate curves for potatoes (Be´langer et al 2003b) and to improve interpretation of SPAD-502 meter readings in wheat (Prost and Jeuffroy 2007).…”

Section: Plant-based Diagnostic Testsmentioning

confidence: 99%

Opportunities for improved fertilizer nitrogen management in production of arable crops in eastern Canada: A review

Zebarth¹,

Drury²,

Tremblay³

et al. 2009

Can. J. Soil. Sci.

176

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There is increasing public pressure to reduce the environmental impacts of agricultural production. Therefore, one key challenge to producers is to manage their crop production systems in order to minimize losses of nitrogen to air or water, while achieving crop yield and quality goals. Many strategies have been developed in recent years to meet this challenge. These include: development of new tools to measure crop N status in order to refine in-season fertilizer N management, development of new soil N tests to improve prediction of soil N supply, development of new fertilizer N products with release patterns more closely matched to crop N uptake patterns, and development of site-specific N management strategies. We review the opportunities and limitations to these new strategies within different arable crop production systems under the humid and sub-humid soil moisture regimes present in eastern Canada. Future research opportunities to improve the efficiency of fertilizer N utilization include development of practical methods to predict the magnitude of soil N mineralization; refinement of decision-making processes which take into consideration the crop N status and soil properties as a basis for variable rate fertilizer N application; development of affordable controlled-release fertilizer N products with improved N release characteristics; development of practical methods for capturing and recycling nutrient-laden drainage water; development of gene expression profiling based techniques to identify crop N stress; and application of crop genomics and molecular breeding techniques to accelerate the development of new cultivars with increased N use efficiency. Key words: Soil N tests, plant N tests, nitrogen fertilizers, nitrogen cycling

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“…The N status cannot be estimated from the leaf nitrogen content alone but biophysical parameters of the wheat canopy should also be taken into account. For example, the nitrogen nutrition index (NNI), i.e., the ratio between the actual N concentration (N t ) observed in the plants and the critical N concentration related to dry biomass (N ct ), has been used to reliably characterize the N status of wheat crops during the vegetative period [14]. NNI values >1 indicate excessive N supply (over-fertilization), whereas suboptimal N supply is indicated by values <1.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status

Abstract: absen

Cited by 84 publications

References 26 publications

SPAD Values and Nitrogen Nutrition Index for the Evaluation of Rice Nitrogen Status

SPAD Values and Nitrogen Nutrition Index for the Evaluation of Rice Nitrogen Status

Opportunities for improved fertilizer nitrogen management in production of arable crops in eastern Canada: A review

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

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