Panicum maximum grasses are among the most used in Brazil because they have high forage production potential, nutritional value and adaptation to different climate conditions and in poor soils, which have been improved by the application of dolomitic limestone and nitrogen fertilizers and K₂O, P₂O₅. The aim was to evaluate the tillering capacity and structural characteristics of six cultivars of Panicum maximum in the Brazilian semiarid region. The test was configured as a randomized block design with six treatments—the cultivars Aruana, Massai, Mombaça, Tamani, Tanzânia, and Zuri—and four replications. The studied variables included tiller population dynamics, tiller population density (TPD), appearance rate (ApR), mortality (MorB), tiller survival (SuvP), tiller stability index, height, light interception (LI), forage mass (FM), and morphological constituents. As to the population dynamics of tillers, seven generations were evaluated, and the largest number of tillers was observed in the first generation, regardless of the cultivar. Furthermore, the Massai cultivar demonstrated the highest TPD in all generations. Interaction between the cultivar and evaluation period was noted for ApR: the cultivars Tamani and Tanzânia indicated higher ApR at 122 and 137 days after establishment. The cultivar (P = 0.380) and evaluation period (P = 0.4469) had no effect on SuvP; however, higher MorB was detected in the cultivars Aruana and Tamani compared to Mombaça and Zuri, with intermediate values detected in the other cultivars. The highest FM was observed in the cultivars Massai, Mombaça, and Tamani, as well as in the leaf blade of Massai and Mombaça. The cultivars Massai, Mombaça, Tamani, Tanzânia, and Zuri have the potential to be cultivated in areas with marked water deficit and high temperatures, such as the Brazilian semiarid region. The cultivar Massai has a rapid recovery after a period of water scarcity. The cultivar Aruana is not recommended for use in the Brazilian semiarid region under rainfed conditions.

Machine learning models such as XGBoost show strong potential for predicting pasture quality metrics like crude protein (CP) content in tamani grass (Panicum maximum). However, their ‘black box’ nature hinders practical adoption. To address this limitation, this study applied SHapley Additive exPlanations (SHAP) to interpret an XGBoost model and uncover how management practices (grazing interval, nitrogen fertilization, and pre- and post-grazing heights) and environmental factors (precipitation, temperature, and solar radiation) jointly influence CP predictions. Data were divided into 80% for training/validation and 20% for testing. Model performance was assessed with stratified 5-fold cross-validation, and hyperparameters were tuned via grid search. The XGBoost model yielded a Pearson correlation coefficient (r) of 0.78, a mean absolute error (MAE) of 1.45, and a coefficient of determination (R²) of 0.57. The results showed that precipitation in the range of 100–180 mm increased the predicted CP content. Application of 240 kg N ha⁻¹ year⁻¹ positively affected predicted CP, whereas a lower dose of 80 kg N ha⁻¹ year⁻¹ had a negative impact, reducing predicted levels of CP. These findings highlight the importance of integrated management strategies that combine grazing height, nitrogen fertilization, and grazing intervals to optimize crude protein levels in tamani grass pastures.

The objective of this study was to evaluate the effect of different protein sources on the performance of lamb ewes and their offspring supplemented in deferred pasture. The treatments were the different protein sources: soybean meal, cotton cake, babassu cake and coconut cake. The ewes were weighed and evaluated for their body condition score during the gestation, parturition and weaning phases. The lambs were weighed at birth and at weaning. The structural variables and chemical composition of the pasture were evaluated during the usage period. Grass height and forage mass were higher when the ewes were in the gestation and weaning stages. The leaf mass was not altered during the pasture usage with a mean value of 566.1kg ha-1 DM. The highest levels of crude protein in the leaf blade were observed during the parturition and weaning phases. The body weight of the ewes did not differ between the sources with averages 45.0, 39.9 and 36.6kg for the gestation, parturition and weaning phases, respectively. Weights at birth and at weaning had an effect from protein sources, with the highest values observed in the lambs of ewes who received soybean meal and babassu cake supplementation. The lambs of ewes supplemented with soybean had higher average daily gain and total gain. Babassu, coconut and cotton cakes can replace soybean meal in supplementation of ewes in deferred pasture.

The objective was to investigate the effects of increasing levels of extruded urea (EU) in the supplementation of lambs grazing. Crossbred Texel lambs ( n = 45) were used and kept in continuous grazing in paddocks with Brachiaria spp. The design used was completely randomised, with five of EU replacing soybean meal (0; 6; 12; 18 and 24 g/kg) and two sexes (male and female). The animals were slaughtered, and qualitative and quantitative evaluations of the carcass, non‐carcass components, meat quality and chemical composition were carried out. There was an interaction between level*sex for ether extract (EE) and crude protein (CP), so than the meat of the females from the treatment without EU had a higher content of EE than that used EU and the meat of the males from the treatments with 12 g and 24 g 100 kg ⁻¹ LW higher levels of CP than other treatments. For all measurements performed up to 18 g 100 kg ⁻¹ LW of EU replacing bran and soy, the results were promising, and can be fed to finishing lambs under grazing, without negatively influencing most of the carcass characteristics, components non‐carcass, meat quality and chemical composition.

The objective was to evaluate the effects of potassium fertilization on the morphogenetic, structural, and productive characteristics of Panicum maximum (cvs. Tanzania, Quênia, Mombaça, Zuri, Massai, and Tamani). The design was in randomized blocks with four doses of potassium (K) 0, 205, 410, and 820 mg dm⁻³, divided into 5 applications. The analyzed variables were leaf appearance rate (LAR), leaf elongation rate (LER), stem elongation rate (SER), leaf senescence rate (LSR), leaf life span (LLS), phyllochron (PC), number of live leaves (NLL), final leaf length (FLL), tiller population density (TPD), and forage mass (FM). LAR increased by 0.00216 leaves tiller on day-1 (p = 0.0354) and LER increased by 0.00980 cm tiller on day-1 for each milligram of K (p = 0.0402). There was an increase in FLL of 0.16, 0.08, and 0.07 days for the cultivars Mombaça, Massai, and Tamani, respectively, for each milligram of K applied (p = 0.0034). The TPD of the cultivar Tamani increased linearly by 0.074 tillers/pot for each milligram of K (p = 0.0226), and the cultivar Massai showed a quadratic behavior. The TPD of the other cultivars was not influenced by the increase in the K doses. For forage mass (FM), the cultivars Mombaça and Quênia increased by 0.16 and 0.39 g DM/pot for each milligram of K added to the soil. The cultivars Tanzânia, Zuri, Massai, and Tamani showed maximum point at doses of 261.35, 279.45, 300.57, and 275.86 mg dm⁻³ K, respectively. Potassium fertilization linearly increased leaf appearance and elongation, with maximum productivity reached at a K dose of 430 mg dm⁻³, except for the cultivars Mombaça and Quênia, which responded up to a K dose of 820 mg dm⁻³.

Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. The objectives of this study were to identify ML models able to differentiate P. maximum cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. The experiment was carried out at the experimental area of the Forage Sector of the School Farm belonging to the Federal University of Mato Grosso do Sul (UFMS). The leaf samples of the cultivars Massai, Mombaça, Tamani, Quênia, and Zuri were collected from experimental plots in the field. Analysis was carried out on 120 leaf samples from the P. maximum cultivars using a VIS/NIR hyperspectral sensor. After obtaining the spectral data and separating them into bands, the data were submitted for ML analysis to classify the cultivars based on the spectral variables. The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. Two input models were evaluated in the algorithms: the entire spectrum band provided by the sensor (ALL) and another input configuration using the calculated bands. The reflectances from the P. maximum cultivars showed different behavior, especially in the green and NIR regions. RL and ANN algorithms using all information in the spectrum are able to accurately classify the cultivars, reaching accuracies above 70 for CC and above 0.6 for kappa and F-score. VIS/NIR leaf reflectance can be a powerful tool for low-cost, non-destructive, and high-performance analysis to distinguish P. maximum cultivars. Here, we achieved better model accuracy using only 40 leaf samples. In the present study, the J48 decision tree model proved to have good classification performance regardless of the sample size used, which makes it a strategic model for forage cultivar classification studies in smaller or larger datasets.