-The objective of this study was to evaluate the mass of the forage and of the morphological components, and the sward structure in Piata palisadegrass (Brachiaria brizantha cv. Piata) deferred in function of different initial heights and nitrogen levels. Two levels of nitrogen (75 and 150 kg/ha) and one control without fertilization associated to three initial deferring heights (20, 30, and 40 cm) were evaluated. The design was of completely randomized blocks with three replications in a subdivided plot scheme. A raise in the initial deferment height and in the fertilization elevated the sward and extended tiller heights, the leaf area index, and the light interception by the sward. The piata palisadegrass deferred with an initial height of 40 cm presented a higher forage mass with lower percentage of leaf blades, and higher stem and dead tissue quantity.Additionally, this management strategy resulted in lower bulk density of leaf blade and in higher bulk densities of stems and dead tissue. The utilization of 150 kg/ha of nitrogen raised the production and the bulk density of deferred piata palisadegrass forage and increased production and bulk density of leaves and stems. In the region of Viçosa, Minas Gerais, the piata palisadegrass deferred with an initial height of 30 cm and fertilized with 75 kg/ha of nitrogen in the beginning of the fall presents adequate mass and structure to animal production in the winter.

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( R), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.

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