Estimation of the nutritive value of grasslands with the Yara N‐sensor field spectrometer. Issue 1 (20th October 2022)

• Record Type: Journal Article
• Authors: Morel, Julien; Zhou, Zhenjiang; Monteiro, Leonardo; Parsons, David
• Is Part Of: Plant phenome journal. Volume 5:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-20
• Subjects: Phenotype -- Periodicals; Plant genetics -- Periodicals; Periodicals; 581.35
• DOI: 10.1002/ppj2.20054
• Languages: English
• ISSNs: 2578-2703

Abstract

Abstract: Forage crops are a cornerstone of the agricultural industry in Nordic countries. Economic and ecological performances are directly linked to adapted farming practices, which require timed and precise information on the nutritive value of the forage. Field spectrometers could offer an interesting alternative to time‐consuming laboratory measurements, as they provide near real time information. We used a handheld version of a field spectrometer already commercialized for cereal adjustable rate fertilization, to evaluate its potential for grassland nutritive quality estimation. Spectral data and samples were acquired over experimental fields and plots in four locations in Northern Sweden; samples were analyzed using wet chemistry to determine the crude protein concentration, the in vitro true digestibility, the neutral detergent fiber and the neutral detergent fiber digestibility. Grid‐based adjusted spectral indices, partial least squares, random forest and support vector machine were tested to link the spectral data to the nutritive traits. Partial least squares and support vector machine outperformed the adjusted spectral indices and random forest. Best predictions were obtained with partial least squares for in vitro true digestibility and neutral detergent fiber ( R 2 of 0.64 and 0.78 and normalized root mean square error [nRMSE] of 2.1 and 8.0%, respectively) and with support vector machine for crude protein and neutral detergent fiber digestibility ( R 2 of 0.49 and 0.65 and nRMSE of 13.0 and 3.8%, respectively). These results suggests that there is a potential for this affordable, industry‐ready spectrometer to be used as a practical farming tool, although more comprehensive datasets are needed to ensure that robust models are developed. Core Ideas: A commercial spectrometer, used for cereal fertilization, has been tested for on‐field forage quality estimation. Reflectance spectra were matched against lab measurements using several multivariate regression models. Partial least squares and support vector machine showed best performances. The tested field spectrometer has good potential for practical applications with grasslands.