Abstract
Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.
Highlights
Malting barley ranks among the most challenging crops, concerning its nitrogen (N) nutrition
Environmental conditions can significantly modulate the translation of N status into grain protein content and should always be carefully considered when making decisions about N nutrition in malting barley, N status is still one of the most critical parameters for the final grain protein content that can be affected by agronomic practices
We demonstrated the considerable potential of artificial neural networks for improving the direct estimation of N content in barley aboveground biomass from hyperspectral data in comparison with best spectral reflectance indices
Summary
Malting barley ranks among the most challenging crops, concerning its nitrogen (N) nutrition. This is mainly due to the very narrow N optima of barley, which is constrained by grain yield on the one hand, and by grain protein content on the other, extensively affecting malting quality [1,2]. The grain protein content is closely related to canopy N due to its extensive translocation to grain and the transformation into grain protein during grain filling These processes are significantly modulated by water availability and temperatures, limiting the starch synthesis and accumulation in grain, resulting in an altered relative proportion of protein and starch in grain [3,5]
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