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The lack of real-life experiences, such as handling livestock at a production facility (e.g., ranch), exists for a variety of reasons such as availability, liability, time, and cost, amongst others. As more students enter undergraduate animal science programs without prior exposure to animal handling, the necessity for more hands-on, real-life experiences has increased dramatically. Complementary, educational simulation games (edugames) might provide means to overcome the lack of "hands-on" experiential learning by providing similar interactions in a virtual context. The primary goal of this study was to document the design and construction phase of a virtual cattle-handling simulation (CowSim) edugame, and to analyse preliminary survey data. An association exists between students' notion of cattle being mishandled (or not) depending on students' previous opportunity to work with animals (X[superscript 2]P value = 0.0017). Furthermore, students with previous experience handling cattle did not feel more prepared to handle cattle after playing CowSim, but students with previous experience handling cattle indicated they learned more about cattle handling after playing CowSim. After playing the CowSim game, students were, in general, optimistic about their playing experience. They perceived the CowSim game was realistic enough to increase their preparedness towards handling cattle. Our findings suggested there is heightened interest was for the use of an edugame to help visualize difficult concepts. Virtual learning tools such as the CowSim edugame are essential for advancing animal science education through the integration of virtual technology. However, improvements are warranted in the CowSim to capture more realistic scenarios given the complexity of the simulation game.

Simple Summary: In this study, we wanted to find the best way to predict the overall chemical composition of Boer × Saanen male kids by evaluating different parts of their bodies. We conducted two experiments where goats were fed different intake levels and slaughtered at various weights. We used various body parts such as the neck, ribs, leg, shoulder, loin, hide, head + feet, and organs to see which ones best estimate the whole body composition regarding nutrients such as fat, protein, and minerals. We found that the neck, loin, and 9–11th ribs could precisely predict the body composition. However, using the loin or 9–11th ribs to do this can lower the price one can obtain from selling the meat because they are valuable parts of the carcass. Our study suggests that the neck can be used as effectively as the 9–11th ribs to estimate the chemical body composition. This finding is useful for farmers, nutritionists, and meat processors as it helps them choose a cost-effective method for evaluating body composition without sacrificing valuable parts of the goat carcass. Two experiments were conducted to determine which part of the empty body of Boer × Saanen intact male kids can be used to predict the chemical composition of the whole body. In the first experiment, kids were fed ad libitum and slaughtered at 5, 10, and 15 kg body weight (BW). Eighteen animals were group-fed at three intake levels (ad libitum or restricted to 30% and 60% of the ad libitum level). When the ad libitum animal in the group reached 15 kg BW, all animals in the group were slaughtered. In the second experiment, kids were fed ad libitum and slaughtered at 15, 20, and 25 kg BW. Twenty-one animals were group-fed at three intake levels and slaughtered when the ad libitum animal within the group reached 25 kg BW. Analyzed body parts included head + feet, hide, organs, neck, shoulder, ribs, loin, leg, 9–11th ribs, and half carcass. Principal component and cluster analyses showed that the neck, 9–11th ribs, and loin had the highest frequency of grouping with the empty body. These body parts were used to develop prediction models for estimating body composition. The neck, loin, and 9–11th ribs accurately and precisely predicted the dry matter, ash, fat, protein, and energy body composition of goats, with most models also incorporating BW as a predictor variable. The equations showed root mean squared error (RMSE) lower than 13.5% and a concordance correlation coefficient (CCC) greater than 0.84. Fat and protein concentrations in the loin and neck were also reliable predictors of empty body energy composition (RMSE = 2.9% of mean and concordance correlation coefficient = 0.93). Removing the loin and 9–11th ribs could reduce the carcass retail price. Using the neck to estimate body composition in growing Boer × Saanen goats provides a valuable alternative for nutrition studies, given its low commercial value. [ABSTRACT FROM AUTHOR]

We used systems thinking (ST) to identify the critical components of beef cattle production through the lens of ecosystem services (ES), offering a holistic approach to address its adverse externalities. We identified eight critical feedback loops in beef production systems: (i) grazing and soil health, (ii) manure management and soil fertility, (iii) feed efficiency and meat production, (iv) water use and soil moisture, (v) cultural services and community engagement, (vi) energy use, (vii) carbon sequestration and climate regulation, and (viii) environmental impact. Our analysis reveals how these interconnected loops influence each other, demonstrating the complex nature of beef production systems. The dynamic hypothesis identified through the loops indicated that improved grazing and manure management practices enhance soil health, leading to better vegetation growth and cattle nutrition, which, in turn, have a positive impact on economic returns to producers and society, all of which encourage the continuation of interlinked beef and ecosystem stewardship practices. The management of beef production ES using ST might help cattle systems across the globe to contribute to 9 of the 17 different United Nations' Sustainable Development Goals, including the "zero hunger" and "climate action" goals. We discussed the evaluation framework for agrifood systems developed by the economics of ecosystems and biodiversity to illustrate how ST in beef cattle systems could be harnessed to simultaneously achieve the intended environmental, economic, social, and health impacts of beef cattle systems. Our analysis of the literature for modeling and empirical case studies indicates that ST can reveal hidden feedback loops and interactions overlooked by traditional practices, leading to more sustainable beef cattle production outcomes. ST offers a robust framework for enhancing ES in beef cattle production by recognizing the interconnectedness of ecological and agricultural systems, enabling policymakers and managers to develop more effective and sustainable strategies that ensure the long-term health and resilience of humans and ES. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate and precise predictions of microbial crude protein (MCP) synthesis are crucial to predicting the supply of metabolizable protein in cattle. Inaccurate estimates of MCP can lead to over- or underfeeding of protein in beef cattle diets, which can have important animal performance and economic consequences. Extant MCP prediction equations are generally based on the intake of energy (e.g., total digestible nutrients (TDN) or digestible energy) and protein. We developed new equations based on the intake of organic matter, which yielded similar fit statistics to the extant equations for observed vs. predicted values, but the precision of all MCP predictions was less than desired, with errors averaging more than 28% of the observed mean. A simple approach of calculating MCP as 10% of the TDN intake was as effective as more complex equations. To move forward and improve the accuracy and precision of MCP prediction equations, research is needed to develop consistent and more precise techniques to measure MCP synthesis in cattle that will yield reliable estimates across a wide range of diets and production situations. Meta-omics tools might be a useful component of these new methods, but additional research is needed. Predictions of microbial crude protein (MCP) synthesis for beef cattle generally rely on empirical regression equations, with intakes of energy and protein as key variables. Using a database from published literature, we developed new equations based on the intake of organic matter (OM) and intakes or concentrations of crude protein (CP) and neutral detergent fiber (NDF). We compared these new equations to several extant equations based on intakes of total digestible nutrients (TDN) and CP. Regression fit statistics were evaluated using both resampling and sampling from a simulated multivariate normal population. Newly developed equations yielded similar fit statistics to extant equations, but the root mean square error of prediction averaged 155 g (28.7% of the mean MCP of 540.7 g/d) across all equations, indicating considerable variation in predictions. A simple approach of calculating MCP as 10% of the TDN intake yielded MCP estimates and fit statistics that were similar to more complicated equations. Adding a classification code to account for unique dietary characteristics did not have significant effects. Because MCP synthesis is measured indirectly, most often using surgically altered animals, literature estimates are relatively few and highly variable. A random sample of individual studies from our literature database indicated a standard deviation for MCP synthesis that averaged 19.1% of the observed mean, likely contributing to imprecision in the MCP predictions. Research to develop additional MCP estimates across various diets and production situations is needed, with a focus on developing consistent and reliable methodologies for MCP measurements. The use of new meta-omics tools might improve the accuracy and precision of MCP predictions, but further research will be needed to assess the utility of such tools. [ABSTRACT FROM AUTHOR]