Abstract

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

Highlights

  • Wheat is one of the most widely cultivated cereal crops in the world

  • The ability of spectral data to scale across different genotypes within species and years is not well understood and needs further study to identify the range of applications for the use of hyperspectral data in agronomic practices [30,31,37]. To address these knowledge gaps, we examined the ability of hyperspectral data to identify traits related to nitrogen dynamics in different wheat lines, across two growing seasons, and assessed the ability of hyperspectral data in estimating nitrogen uptake efficiency (NUpE) in a wheat breeding population

  • The 2017 model accurately predicted nitrogen at both leaf and canopy levels, there was a decline in external validation performance (Table 1, Figure 1), and these declines were comparable

Read more

Summary

Introduction

Million ha of wheat are planted, producing over 670 million tons of grain, which is mostly used as a food source for humans and animals [1]. Because of the widespread consumption, this cereal plays an essential role in global food security, representing ~20% 4.0/). Projections estimate that the demand for wheat will increase by nearly 60% within the 30 years due to projected human population growth [3]. This predicted increase in consumption poses a significant challenge for wheat breeders and producers, especially considering the estimated decrease in available areas for cultivation and an increase of extreme weather events [1]

Objectives
Methods
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.