This paper describes the capability of near infra-reflectance (NIRS) to predict the nutritional quality of pastures from southern Chile (39°-40°S). A Fourier transformed near-infrared (FT-NIR) method for rapid determination of dry matter (DM), crude protein (CP), in vitro digestibility (IVD) and metabolizable energy (ME) was used. Calibration models were developed between chemical and NIRS spectral data using partial least squares (PLS) regression and external validation. The coefficients of determination in calibration (R 2 c) were high varying between 0.89-0.99 and the root mean square errors of calibration (RMSEC) were low, ranging between 0.46-2.55 for the parameters analysed. The Residual Prediction Deviation (RPD) was higher than 2.5. Our results confirmed the convenience of using a wide range of samples applicability in the calibration set. Data also showed that the use of an independent set of samples for external validation increases the robustness of the models to predict unknown samples. Our results indicated RPD values higher than 2.5 which is the minimum recommended for this type of prediction. Thus, the result showed that NIRS was useful to estimate the nutritional quality of permanent pastures, and has a great potential to be used as a rapid decision tool for the studied analysis.

I. Lobos, C.J. Moscoso, and P. Pavez. 2019. Calibration models for the nutritional quality of fresh pastures by near-infrared reflectance spectroscopy. Cien. Inv. Agr. 46(3):234-242.High levels of animal performance and health depend on high-quality nutrition. Determining forage quality both reliably and quickly is essential for improving animal production. The present study describes the use of near infrared reflectance spectroscopy (NIRS) for the quantification of nutritional quality (dry matter (DM), water-soluble carbohydrates (WSC), crude protein (CP), in vitro dry matter digestibility (DMD), organic matter digestibility (OMD), neutral detergent fiber (NDF) and the WSC/CP ratio) in samples from fresh pastures in southern Chile (39° to 40° S). Calibration models were developed with wet chemistry and NIRS spectral data using partial least squares regression (PLSR). The coefficients of determination in the validation set ranged between 0.69 and 0.93, and the error of prediction varied from 0.064 to 2.89. The evaluation of the model confirmed the high predictive ability of NIRS for DM and CP and its low predictive ability for DMD, OMD, NDF and the WSC/CP ratio. It was not possible to obtain a model for WSC because it would have required an increased number of samples to improve the spectral variability and the R 2 value (> 80%).

The chemical composition and quality of honey depend on the floral and geographical origin, extraction techniques, and storage, resulting in a unique product for each area. Currently, consumers are not only concerned about the chemical composition, quality, and food safety of honey, but also about its origin. The objective of this study was to characterize honeys produced in Chile’s central-southern region from a mineral and botanical perspective, thus adding value through differentiation by origin. Two hundred honey samples were used and underwent analysis such as melissopalynological composition, nutritional composition, and color. Forty-seven melliferous floral species were identified, out of which 24 correspond to exotic species and 23 to native species. Fifty-six percent were classified as monofloral honeys, 2% as bifloral, and 42% as multifloral. Moisture mean values (17.88%), diastase activity (15.53 DN), hydroxymethylfurfural (2.58 mg/kg), protein (0.35%), and ash (0.25%) comply with the ranges established by both the national and the international legislation; standing out as honeys of great nutritional value, fresh, harvested under optimal maturity conditions, and absence fermentation. Regarding color, light amber was prevalent in most territories. The territory where honey was produced, denoted relevant differences in all the parameters studied.

One of the challenges of modern grassland systems is to minimize nitrogen (N) fertilization without negatively affecting the forage yield. Therefore, critical N dilution curves (Nc = ac W−b) have been developed in different species to improve N fertilization management. The aim of this study was to validate a critical N dilution curve for hybrid ryegrasses. Two field experiments were conducted in southern Chile. Treatments were the factorial combination of two hybrid ryegrasses (Shogun and Trojan cultivars) and seven N fertilization rates (0, 50, 100, 200, 350, 525 and 700 kg N/ha). Factors were arranged in a split‐plot design, where forage species were assigned to main plots and N rates to subplots that were randomized into four blocks. A wide range in forage yield and plant N concentration was observed (yield: 0.16 and 3.9 Mg DM/ha and N: 1.6% and 5.1%). The variations in these traits were principally explained by the N levels and harvest times. Relative yield responses of both cultivars were significantly (p < 0.001, R2 = 0.81–0.87) related to the nitrogen nutrition index (NNI) calculated with different critical N dilution curves. However, the NNI calculated with N dilution curves from annual ryegrass best described the relative yield response of hybrid ryegrass. Therefore, this validated critical N dilution curve (%Nc = 4.1W−0.38) will serve as a useful diagnosis tool for improving the N fertilization management of grazing systems for hybrid ryegrasses.

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.