Crop–livestock integrated production systems (CLISs) combine cash-crop production and forage production in succession. There are plenty of options of forage cultivars with differences in production aspects and seeds cost, and there is little information on how the choice of forage cultivar can affect the results of a CLIS. We hypothesized that different forage cultivars can have important economic impacts on production systems. Thus, we evaluated the two-year economic results of using three forage species in a CLIS: (1) Urochloa ruziziensis; (2) Megathyrsus maximus cv. BRS Zuri e; and (3) Megathyrsus maximus cv. BRS Tamani. The system was evaluated during 2018 and 2019 with no-tillage soybean (Glycine max) cultivation from November to March and grazing of cattle from May to August. The seed costs were, on average, USD 25.27 ha⁻¹ for Ruziziensis grass, USD 39.97 ha⁻¹ for Zuri guinea grass, and USD 64.13 ha⁻¹ for Tamani guinea grass. Animal production varied from 96.4 to 147.5 kg of live weight per hectare per year and mean two-year soybean yields varied from 3849 to 4217 kg per hectare, both without differences between forage cultivars. However, the lowest values for animal and soybean yields were obtained with Ruziziensis grass, and the highest were obtained with Zuri grass. Thus, Zuri guinea grass presented a net income (NI) of USD 1039.87 ha⁻¹ with an annual return on equity (ROE) equal to 11.19%, while Ruziziensis grass obtained an NI equal to USD 612.65 ha⁻¹ with an ROE of 6.47%, demonstrating the economic impact of forage resource choice in CLISs. Therefore, the choice of forage cultivars adequate for the conditions of an individual farm can correspond to an increase of 69.7% in net income, which highlights the importance of continuing efforts to develop new cultivars and the simultaneous evaluation of these cultivars in different production scenarios in order to better recommend forage genetic resources for particular production environments.

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings—8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature–humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

The aim of this study was to predict carcass traits of Santa Inês lambs finished in tropical pastures by using biometric measurements. Data originated from two experiments involving 56 lambs (32 in experiment I and 24 in experiment II). In both experiments, the sheep were finished in that were finished in pastures of Panicum maximum and Brachiaria brizantha, experiment I being conducted in the rainy season and experiment II in the dry season. The following biometric measurements were recorded before slaughter: body length (BL), withers height (WH), rump height (RH), thorax width (TW), rump width (RW), chest width (CW), heart girth (HG), thigh circumference (TC), rump circumference (RC) and leg length (LL), in addition to live weight at slaughter (SW). After slaughter, hot carcass weight (HCW), cold carcass weight (CCW) and the weights of primal cuts (shoulder, neck, loin, leg and rib) were recorded. In the equations generated to predict SW, HCW and CCW, R² ranged from 0.58 to 0.91 and the measurements of WH, TC, CW, HG and RW were the most relevant. In the equations developed to predict the weight of primal cuts, in turn, R² ranged from 0.26 to 0.99. In these models, SW, BL, CW, TC, LL and HG explained most of the variation in the weight of primal cuts. Biometric measurements can be used to accurately and precisely predict HCW, CCW and the weight of primal cuts from the carcass of Santa Inês sheep finished in tropical pastures, since the equations presented R² and correlation coefficient and agreement above 0.8.

This investigation aimed to assess how the somatic cell count (SCC) impacts milk production and the physicochemical quality of milk, including the effects of centrifugation and microfiltration on SCCs and the yield of fresh cheese. Milk production was analysed at different somatic cell counts (SCCs) to observe how centrifugation and microfiltration affected the removal of SCs from the milk and the yield of fresh cheese. Tukey’s test was employed at a 5% significance level to compare the chemical composition of the milk, milk production, and fresh cheese yield when using microfiltered and/or centrifuged milk. Milk with an SCC of ≤200,000 somatic cells (SCs) per mL exhibited higher yields. Although centrifugation and microfiltration influenced milk fat content, total dry extract and SCC, they did not impact fresh cheese yield. It is important to note that these processes reduced the fat content and SCC, making skim milk a potential option for future research studies.

SUMMARY Sunflower as silage is an alternative to be used in the off-season, when water shortage makes traditional crops unfeasible. However, exclusive sunflower silages have levels above 70 g/kg DM ether extract, which may impair ruminal fermentation. Thus, ensiling sunflower with tropical forages can bring benefits to the quality of the silage. This study evaluated the fermentation profile and nutritional value of sunflower silage with cultivars of Urochloa brizantha in the off-season. This was a completely randomized experimental design with four replications. The treatments were composed of the sunflower silage with and without addition of cultivars of Urochloa brizantha in the ensiling process: sunflower silage; sunflower silage + 30% Marandu palisadegrass; sunflower silage + 30% Xaraes palisadegrass; sunflower silage + 30% Piata palisadegrass and sunflower silage + 30% Paiaguas palisadegrass, totaling 20 experimental silos. The addition of the cultivars of Urochloa brizantha to sunflower silage contributed to improve the fermentation profile and nutritional value of the silages. Silages with 30% Piata and Paiaguas palisadegrass showed higher levels of CP and lower levels of ADF and lignin, and these cultivars are the most recommended for ensiling with sunflower.

Although belonging to the same species, different Brachiaria brizantha varieties used for pasture vary significantly in their morphological structure. Therefore, this study starts from the central hypothesis that the defoliation dynamics during the period of occupation by animals in pastures formed by Brachiaria brizantha under rotational stocking varies among cultivars during the year. The experimental design used was randomized blocks with three treatments and three repetitions, with the cultivars consisting of Brachiaria brizantha cv. Xaraés, BRS Paiaguás, and Ecótipo B4. The defoliation patterns during the grazing periods were estimated using the marked tiller technique. Despite alterations in the tiller and forage mass components in the wet and dry seasons, no variations were observed in the tiller defoliation dynamics and the respective structural components. In the wet season, the area grazed only once was greater for BRS Paiaguás compared to the other two cultivars, but the other areas representing the grazing horizons were equally exploited among the grass varieties during the year. Therefore, based on the defoliation dynamics of pastures in Brazil’s tropical climate, it is possible to suggest that pastures formed by grasses with different structures and growth habits can be submitted to similar management strategies without impairing the forage canopy exploitation by the animals.