Service-grazing is a novel term for grazing done on land not owned or rented by a livestock producer or manager, for the purpose of land management, and for which the owner or land manager receiving grazing services pays compensation to the service provider. This research project seeks to gather detailed information about producers in the Western United States, providing grazing services under this project’s definition of “service-grazing”, with a focus on those operating in California, in order to discover the business models or practices necessary to be successful within a service-based grazing operation. To this end, an online survey consisting of 59 questions was designed and administered to 25 service-grazers operating with the Western United States. Demographic data collected suggested that service-grazers tend to be younger than most sheep producers. They are also highly educated and generally not generational farmers. The majority of service grazers graze mostly goats and are highly dependent on off-farm income. Due to the limited number of responses received to the survey, it is recommended that future work be split into two parts: 1) compiling an accurate and up-to-date list of producers providing grazing services, with detailed demographic information and specific characteristics of each operation; 2) a further survey to question those producers as to the feasibility of transitioning, in whole or in part, from a production-based livestock operation to one providing grazing services.

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Precision livestock farming technologies have been widely researched over the last decade. These technologies help in monitoring animal health and welfare parameters in a continuous, automated fashion. Under this umbrella of precision livestock farming, this study focuses on activity classification and body weight prediction in pigs. Activity monitoring is essential for understanding the health and growth of pigs. To automate this task effectively, we propose efficient and accurate sensor-based deep learning (DL) solutions. Among these, the 2D Residual Networks emerged as the best performing model, achieving an accuracy of 95.6%. This accuracy was 15.6% higher than that of other machine learning approaches. Additionally, accurate pig weight estimation is crucial for pork production, as it provides valuable insights into growth rates, disease prevalence, and overall health. Traditional manual methods of estimating pig weights are time-consuming and labor-intensive. To address this issue, we propose a novel approach that utilizes deep learning techniques on depth images for weight prediction. Through a custom image preprocessing pipeline, we train DL models to extract meaningful information from depth images for weight prediction. Our findings show that XceptionNet gives promising results, with a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%. In comparison, the best performing statistical model, support vector machine, achieved a mean absolute error of 4.51 kg mean absolute percentage error of 15.56%. / Master of Science / With the increasing demand for food production in recent decades, the livestock farming industry faces significant pressure to modernize its methods. Traditional manual tasks such as activity monitoring and body weight measurement have been time-consuming and labor-intensive. Moreover, manual handling of animals can cause stress, negatively affecting their health. To address these challenges, this study proposes deep learning-based solutions for both activity classification and automated body weight prediction. For activity classification, our solution incorporates strategic data preprocessing techniques. Among various learning techniques, our deep learning model, the 2D Residual Networks, achieved an accuracy of 95.6%, surpassing other approaches by 15.6%. Furthermore, this study also compares statistical models with deep learning models for the body weight prediction task. Our analysis demonstrates that deep learning models outperform statistical models in terms of accuracy and inference time. Specifically, XceptionNet yielded promising results, with a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%, outperforming the best statistical model by nearly 8%.

A polyperiod model was developed for investigating production investment decisions associated with firm growth. A fifteen year planning horizon divided into three production periods was used. Initial resources were those of a large-size livestock farm (410 acres of open land) located in Southwest Virginia. The model maximizes the present value of net returns. A twelve percent discount rate was used to obtain a basic solution. The effect of varying the discount rates or maximizing net worth at the end of the planning period were analyzed. Growth was measured in terms of net returns and net worth at the end of the planning period. Family consumption affected capital accumulation by the withdrawal of fixed amounts of capital per period from returns generated during the period. The effect in the amount of initial debt was studied. Growth was associated with changes in enterprise organization, added investments and finance policies. A high discount rate and a high initial debt were the variables that most affected growth. When land purchases were restricted growth was reduced considerably. The dry-lot steer enterprise was more profitable and had a greater potential for expansion than the beef cow enterprise. A major proportion of investments were financed with capital generated within the firm. The greatest amount of investments were done during the last production period. This stresses the importance of time in the capital accumulation process for the growth of the firm. / M.S.

no abstract provided by author / Master of Science

Important implications of the study are: beef cow-calf production is competitive over a wide range of beef prices and it may increase farm returns to feed small amounts of corn silage rather than grow additional pasture even when the weaned steer calf-corn ratio is quite low. / Master of Science