The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HG P), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HW I), body length, tail distance to the neck, dorsum area (DA I), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HG p was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HW I (cm) + 0.01929 * DA I (cm 2) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.

Brazil is the second largest beef producer and a leading exporter, contributing to some 3000 t CWE in global markets (27.7% of market share). The sector has experienced substantial development, but yields remain far below potential, and there are growing concerns regarding land use change and greenhouse gas emissions. The need for sustainable technologies, such as sound pasture management and integrated farming systems, is evident, but adoption may be low amongst farmers unable to keep up with technological advances. This article describes the historical developments of Brazilian beef farming towards sustainability and discusses possible socioenvironmental outcomes. We combined an extensive literature review, public data, and our own insights as senior researchers to achieve that. The trajectory shown here evidenced the technological intensification of Brazilian beef farming, with strong support of public policies for decarbonizing agriculture. Nonetheless, the pace of this transition may affect small to medium farmers with limited access to information, technologies, and credit. Our recommendations involve a broad program of technical assistance and training on sustainable technologies, including financial and digital literacy. A novel approach to financing farmers is suggested to support a sustainable and inclusive transition in beef farming in Brazil.

This study evaluated the performance of 21 beef cattle modal production systems that conduct only the raising phase. These production systems were developed in 21 municipalities in seven Brazilian states. We used two different DEA BCC models, corresponding to different points of view. Firstly we used an economic model that measured the ability of a production system to generate revenue with the preservation of native forest, using labor, capital, and current spending as inputs. The socio-environmental approach, corresponding to the second BCC model, uses the production factor labor as an output. In that model our interest was to assess whether the capital costs generate economic, environmental, and social benefits. Weight restrictions were imposed on the output variables of each model to explain the proposed viewpoints and to avoid inconsistent results. The results pointed out sources of inefficiency in terms of labor with low qualification, and use of bulls of questionable quality, factors that are common in extensive systems. These are some of the major bottlenecks in animal production systems as a whole.