NIR-Prediction of water-soluble carbohydrate in white clover and its genetic relationship with cold tolerance

Abstract

In temperate climates, cold stress constrains productivity of white clover (Trifolium repens L.), the most important perennial forage legume in intensive grazing systems for ruminants. Metabolism of water sugar carbohydrate (WSC) has been proposed as an important trait conferring cold tolerance to white clover. Conventional methodologies for WSC determination are considered high-cost and timeconsuming. Near-infrared (NIR) spectroscopy is a robust, reliable, and high-throughput methodology to estimate chemical composition of forage species. The objectives of this work were to determine the accuracy of NIR spectroscopy for predicting WSC in stolon samples of white clover, and to evaluate the genetic relationship between WSC and cold tolerance. A white clover association mapping (WCAM) population was stablished in three location that represent a winter low temperature gradient associated with altitude. Dry matter production and some morphological traits were evaluated during three growing seasons. Samples for WSC determination were collected three time during a winter period. Samples were scanned with a NIR system, and a prediction model for WSC was fitted using partial least squares (PLS) regression. The adjusted prediction model achieved suitable predictive ability (R 2 > 0.85). The WSC per se did not show significant genetic relationship with morphological and agronomically important traits. However, the WSC degradation rate (WSCdr) across the winter period showed significant genetic correlation with DM production during spring (r g = 0.64), which is the result of genetic/ physiological mechanism expressed during the cold period. The NIR spectroscopy is a reliable and high-throughput methodology to predict WSC in stolon samples of white clover. The metabolism of WSC, evaluated as WSCdr, is involved in the cold tolerance of the WCAM population. The methodology implemented in this work is suitable to be applied in a plant breeding program routine.

Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy

“…The calibration error obtained in this study for OMD was lower than that reported by Corson et al (1999) in pasture samples with a similar concentration range. The coefficient of determination obtained in this study for WSC was lower than that reported by Alomar et al (2009) and Lobos-Ortega et al (2014) in fresh forage samples and by Inostroza et al (2017) in dried white clover. Finally, the results for R 2 c and the RPD values for the WSC/CP ratio were lower in the present study than those shown by Rivero et al (2014).…”

“…This technology is broadly accepted as a fast and reliable method for evaluating the nutritional qual-ity of pasture silages (Ibáñez and Alomar, 2008;Restaino et al, 2009), as well as dried and fresh pastures (Cozzolino and Labandera, 2002;Alomar et al, 2009;Burns et al, 2013;Lobos-Ortega et al, 2013;Moscoso and Balocchi, 2016). It has also been used to evaluate how forage sample preparation affects nutrient composition analysis (Alomar et al, 2003), green forage intake and digestibility in ruminants (Decruyenaere et al, 2009), and water-soluble carbohydrates (WSC) in stolon samples of white clover (Inostroza et al, 2017). NIRS has also been used to characterize and quantify isoflavones and phenolic acid contents in red and white clover (Krähmer et al, 2013).…”

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

“…Historically, prediction models have been fitted with linear regression techniques that use multivariate inputs (Hernandez et al, 2015; Inostroza et al, 2017). Partial least square (PLS) regression is a widely used methodology to regress predictor data against target prediction traits based on the assumption that the response variables are from a process generated by unobserved latent variables (Rosipal and Krämer, 2006).…”

Rapid monitoring of flavonoid content in sweet tea (Lithocarpus litseifolius (Hance) Chun) leaves using NIR spectroscopy

“…Thus, the characteristic information of hydrogen-containing groups in organic molecules can be obtained by scanning the near-infrared spectra of samples [ 25 ]. It has been widely used in agriculture, [ 12 , 17 , 19 ], petrochemicals [ 1 , 14 ], food [ 28 ] and pharmaceuticals [ 6 ]. The use of NIR spectroscopy in plant leaf tissue analysis started in the mid-1970s [ 20 ], and there are an increasing number of papers on NIR spectroscopy application on leaf tissues [ 4 , 13 ].…”