This study examines the effect of adding coffee husks (CH), cacao by-product (CBP) and passion fruit by-product (PBP) (fresh-matter basis) in the silage of elephant grass cv. Napier on nutritional characteristics. The experiment was laid out in a completely randomized design with a 3 × 4 factorial arrangement represented by three additives (CH, CBP or PBP) and four inclusion levels (0, 12, 24 or 36%). Four replicates were used per treatment. The material was ensiled in experimental mini-silos that were opened 60 days later for chemical analysis of the produced silages. The dry matter content of the silages with CH and PBP included at levels greater than 25% was adequate. The silage with PBP inclusion showed the highest crude protein levels and the best results for in vitro dry matter digestibility. In the silage containing CH, the neutral detergent fiber content decreased linearly with increasing inclusion of the husks. The silage with CBP showed the lowest dry matter and crude protein levels and the highest pH. In conclusion, the inclusion of up to 36% PBP in the ensiling of elephant grass is recommended, as the resulting material has potential for use in ruminant feeding at times of forage scarcity.

This study aims to propose a method to generate growth and degrowth models using differential equations as well as to present a model based on the method proposed, compare it with the classic linear mathematical models Logistic, Von Bertalanffy, Brody, Gompertz, and Richards, and identify the one that best represents the mean growth curve. To that end, data on Undefined Breed (UB) goats and Santa Inês sheep from the works of Cavalcante et al. (2013) and Sarmento et al. (2006a), respectively, were used. Goodness-of-fit was measured using residual mean squares (RMS), Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute deviation (MAD), and adjusted coefficient of determination . The models’ parameters (?, weight at adulthood; ?, an integration constant; ?, shape parameter with no biological interpretation; k, maturation rate; and m, inflection point) were estimated by the least squares method using Levenberg-Marquardt algorithm on the software IBM SPSS Statistics 1.0. It was observed that the proposed model was superior to the others to study the growth curves of goats and sheep according to the methodology and conditions under which the present study was carried out.

Objetivou-se a seleção genotípica da mandioca (Manihot esculenta crantz) para formação de grupos divergentes de genótipos. Inicialmente foram utilizadas 23 variáveis quantitativas de 28 genótipos; após a análise de componentes principais esse número foi reduzido a 7 variáveis (diâmetro do caule, número manivas-sementes por planta, comprimento da raiz, diâmetro da raiz, potencial hídrico foliar antemanhã, potencial hídrico foliar ao meio-dia, produtividade da parte aérea). A análise de agrupamento por meio da distância de Chebyshev e do método de ligação Mcquitty foi utilizada para gerar um dendrograma cuja validação se deu a partir de um coeficiente cofenético de 0,8. Foram formados dois grupos compostos de 24 e 4 genótipos de mandioca. Esses foram indicados no dendrograma por meio dos índices de Ratkowsky, McClain e KL. O segundo grupo que é formado pelos genótipos tussuma, Caititi, Poti Branca e mulatinha apresentou maior frequência das variáveis que descrevem a parte superior da mandioca. Essas informações são importantes para a criação de banco de informações de espécies rústicas e o melhoramento das mesmas.

Assessing the differential behavior of a group of genotypes in various environments is fundamentally important in any breeding program. As sugarcane is the most important crop in the state of Pernambuco, it is of great relevance to study its performance in different cultivation sites to assist in the recommendation of new cultivars that increase the productivity of the cane fields. In view of the new demand from the sugar-energy sector for cultivars with high energy potential, this work aimed to select and recommend new genotypes with high fiber and sucrose percentage in the sugarcane microregions of the state of Pernambuco. The methodologies used to classify genotypes for adaptability and stability were as follows: simple linear regression, the modified centroid method, additive main effects, multiplicative interaction analysis, and linear mixed models. Genotypes with higher productivity and specific adaptability to the tested microregions were identified. The methodologies applied were efficient and complementary in recommending genotypes with favorable prospects for increasing sugar productivity, cogeneration of electric energy and the production of renewable fuels. Genotypes 6, 7, 9, 14, 16, and 18 stand out in terms of the productivity of sugar and fiber, with high potential to be released as commercial cultivars.

The present study aimed to propose new two-compartment models from the combination of the Gompertz, Logistic and Von Bertalanffy models and to identify between Gompertz and Logistic models, in their uni and two-compartiment versions, the one that presents the highest quality of fit to cumulative gas production curves of five cassava genotypes: Brasília, Engana Ladrão, Dourada, Gema de Ovo e Amansa Burro. The gas production readings were 2, 4, 6, 8, 10, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 hours after the start of the in vitro fermentation process. The estimation of the parameters for the models was made by the least squares method through the Gauss-Newton iterative process. The selection of the best model to describe the gas accumulation was based on the adjusted coefficient of determination, residual mean squares, mean absolute deviation, Akaike information criterion and Bayesian information criterion. Among the adjusted models, the proposed models were the best to describe the accumulation of gases over time according to the methodology and conditions under which this study was developed.

This study aimed to propose a model called Two-compartment Logistic-von Bertalanffy (LVB) and to identify among the proposed and Two-compartment Logistic (TL) models the one that has the best goodness of fit to the kinetic curve of cumulative gas production (CGP) of sunflower and corn silages alone and combined using the in vitro semi-automated gas production technique. A random block split-plot experimental design was employed in which the inoculums were the blocks, the incubation times were the split-plots, and the experimental diets were: CS - corn silage, SS - sunflower silage (as single roughage), and their mixtures, i.e., 340SS (660 g kg-1 corn silage and 340 g kg-1 sunflower silage) and 660SS (340 g kg-1 corn silage and 660 g kg-1 sunflower silage). The parameters were estimated by the least squares method using the iterative Gauss-Newton process in the software R version 3.4.1. The criteria adopted were: adjusted coefficient of determination (R2adj.), residual mean squares (RMS), mean absolute deviation (MAD), Akaike information criterion (AIC), Bayesian information criterion (BIC), and relative efficiency (RE). The TL model had higher R2adj. values compared to LVB, however, such difference may be considered negligible. The LVB model had RE above one, which indicates it is superior to the TL model, in addition to the lowest RMS, MAD, AIC, and BIC values, The Two-compartment Logistic-von Bertalanffy model had the best fit to describe the CGP over time according to the methodology and conditions of the present study.