Calpastatin (CAST) is an endogenous calpain inhibitor and its main function is to modulate the proteolytic action of enzymes responsible for post-mortem myofibril deterioration. The myostatin gene (GDF-8) acts as a negative regulator of skeletal muscle growth. The expression of these two genes, as well as their interaction, affects the quality of the meat, especially the tenderness phenotype. We evaluated the genetic groups Santa Inês, ½ Dorper-Santa Inês and ½ White Dorper-Santa Inês, slaughtered with 2.0 mm, 2.5 mm and 3.0 mm of fat thickness, comparing the levels of expression of the CAST and GDF-8 genes with the weight performance and carcass traits, especially the shear force values. We found significantly higher expression of myostatin and calpastatin in the Santa Inês genetic group. The ½ Dorper-Santa Inês genetic group had the lowest expression of these genes when slaughtered with 2.0 and 2.5 mm of fat thickness. In conclusion, the Santa Inês breed had the lowest phenotype values for meat tenderness, and the ½ Dorper-Santa Inês breed had the best performance for this characteristic. We suggest that high levels of the expression of the CAST and GDF-8 genes are associated with lower values of lamb meat tenderness, and that tenderness is related to the stage of muscular growth and development.

Ingestive behavior of dairy goats fed diets containing increasing levels of neutral detergent fiber and particle size using multivariate analysis

Periods with high precipitation and temporary waterlogging in the humid tropics are challenging to the production and survival of some grasses of the genus Urochloa. This study aimed to evaluate the tolerance of five types of grass belonging to the genus Urochloa under waterlogging conditions through productive and morphological traits. The grasses [U. arrecta (Tanner), U. arrecta x U. mutica (Brachipará), U. brizantha cv. Marandú, U. hybrid cv. Cayman and U. humidicola cv. Llanero] were planted in pots and kept under field capacity for 33 days; then, half of them were submitted to (i) field capacity (33% humidity retention) and the other half were submitted to (ii) waterlogging conditions (2 cm of water above soil level) for 28 days. In this study, Tanner and Brachipará grasses showed higher dry shoot mass under waterlogging conditions, which were followed by Llanero, Cayman, and Marandú, respectively. Llanero, Tanner, and Brachipará presented higher waterlogging tolerance coefficients, 78.7, 76.5, and 64.5, respectively, being less affected than Cayman and Marandú (41.0 and 23.1, respectively). Brachipará, Tanner, and Cayman presented a higher root volume under waterlogging conditions, while Marandú root volume decreased by 88.77%. The Tanner, Brachipará, and Llanero genotypes were more tolerant to poorly drained or waterlogged soils than Cayman and Marandú genotypes.

A dynamic model has been developed to simulate aspects of feedlot lamb growth and body composition, including energy and protein requirements, growth rate, composition of gain, and body mass. Model inputs include initial body mass (kg), standard final mass (kg), age (days), and dietary energy concentration (Mcal·kg⁻¹). The model was assessed as a decision support tool using a dataset of 564 individual measures of final body mass and diet energy. The simulations provide graphical and numerical descriptions of nutrient requirements, composition of gain, and estimates of animal performance over time. The model is accurate and precise, with a root mean squared error of 7.79% of the observed final body mass and a coefficient of determination of 0.89 when simulating the same variable. The model can be used as a reliable decision support tool to estimate final body mass and the days on feed required to reach a certain final mass with precision and accuracy. Moreover, the dynamic model can also serve as a learning tool to illustrate practical principles of animal nutrition, nutrient requirement relationships, and body composition changes. This model holds the potential to enhance livestock management practices and assist producers in making informed decisions about feedlot lamb production.

Este estudio investiga la influencia del horario de cosecha (06:00, 09:00, 12:00, 15:00 y 18:00 horas) sobre la calidad del ensilaje del pasto guinea (Megathyrsus maximus) cv. BRS Zuri. El pasto fue cosechado a los 45 días de edad de rebrote, se picó y almacenó en microsilos de PVC por 45 días. Se evaluaron parámetros de ensilabilidad en el forraje fresco, así como parámetros fermentativos y nutricionales del ensilaje. La concentración de carbohidratos solubles al momento de la cosecha se incrementó de forma lineal en función de la hora y consecuente exposición a la radiación solar, pasando de 1.07 a 2.07% a las 06:00 h y 18:00 h, respectivamente (P=0.001). Los valores más bajos para la pérdida de efluentes, pH y N-NH3 ocurrieron a las 15:00 h, con efectos lineales y cuadráticos significativos (P≤0.05) para las tres variables. El horario de cosecha influenció en forma cuadrática el contenido de MS del ensilaje, obteniendo 17.14% a las 06:00 h y 22.64% de MS a las 15:00 h (P≤0.001). La concentración de proteína bruta fue mayor (13.78%) a las 15:00 h y el menor valor se obtuvo a las 06:00 h (10.37%) (P≤0.05). En las condiciones del estudio, la cosecha del pasto guinea cv. BRS Zuri en horas de la tarde resultó en un ensilaje con mejor calidad en términos sensoriales, fermentativos y nutricionales.

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians.