Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on September 9, 2009. Major professor: Bruce L. Golden, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Agriculture, specialization in Animal Science." "June 2009." Includes bibliographical references (p. 75-83). Also available on microfiche.

Thesis (M.Agr.Sc.) - University of Queensland, 1999. / Includes bibliography.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (doctoral)--Universiteit Utrecht, 2003. / Includes bibliographical references (158-171).

No description available.

No description available.

Microcomputer simulation was used to evaluate effects of all combinations of two levels of involuntary culling, heifer rearing, and sire selection against dystocia in heifers on timing and magnitude of net income in dairy cattle. Time to cumulative payoff of expenses and net income per day of herdlife were measured for herds and individual cows. Net income was accumulated monthly, and expressed per day of life and per day to 96 mo. Twenty herds of 80 cows were simulated for 20 yr under eight options in the herd study. More than 1000 cows with complete herdlives, from a single herd, were individually simulated for each of eight options in the cow study, with no voluntary culling of cows. For the herd study, milk yield per cow averaged 6838 kg ± 858 kg/yr, and net income per cow was $671 i $193/yr. Mean time to payoff was 60.0 mo, and mean net income to 96 mo, including salvage value, was $.36/d. Heifers calving at 26 mo had rearing expenses of $1030, time to payoff of 54.6 mo, and net income of $.432/d, compared with rearing expenses of $1200, time to payoff of 70.0 mo, and net income of $.285/d for heifers calving at 32 mo. Options with 12% involuntary culling paid off 2.3 mo earlier and had $.081 more net income per day than 24% involuntary culling. Differences in response variables due to sire dystocia and PD Dollars were minimal. Options with 26 mo age at first calving, 12% involuntary culling, and random mating had earliest time to payoff (54.0 mo) and highest net income ($.485/d). Options with 32 mo age at first calving, 24% involuntary culling, and random mating had latest payoff (74.3 mo) and lowest net income (S.246/d). Herds and years had large effects on time to payoff and net income due to differences in herd production and genetic trend of sires for production over time. Including salvage value for cows surviving 96 mo increased net income $.064/d for herds, and $.25/d for cows. Twenty-one percent of the value of increased milk yield was attributed to increased feed costs. For the cow study, cost of rearing heifers was $141 ± $127, mean time to payoff was 53.4 mo, and mean time to cumulative profit was of 56.5 mo. Cumulative profit represented positive cumulative net income for 12 consecutive mo, which included 69% of cows with time to payoff. Net income was $.19/d for all cows, $.46/d for cows with a first calving, $.60/d for cows surviving 96 mo, and $.85/d for cows surviving 96 mo including salvage value. Heifers calving at 26 mo paid off expenses at 47.1 mo, compared with 60.6 mo for heifers calving at 32 mo. Heifers calving at 26 mo cost $.07/d more to raise to first calving, but paid off by 70 d in milk into their second lactation, compared with payoff by 140 d in milk of the third lactation for cows calving at 32 mo. Regressions of time to payoff and net income per day at 96 mo on cow production were -.0077 d/kg and .00028 $/kg, respectively. The regression of time to payoff on PD Dollars was -.0035 d/PDS, and the regression of net income per day on PD Dollars was .00072 S/PDS for cows that calved. Differences did not exist in time to payoff between levels of involuntary culling and selection against dystocia. Heifer rearing was most important in this study due to large differences in time to payoff and net income as age at first calving changed. Sire selection against dystocia in heifers was least important due to the mating program used, with intermediate differences in payoff and net income between levels of involuntary culling. / Ph. D.

Current feeding strategies aim to maximize efficiency at the pen level. However, feed intake varies across animals and in response to diet composition, making it difficult to capture these variations and control feeding effectively. A precision feeding system is required to feed animals individually, continuously monitor responses, and make timely adjustments to feed tailoring. Such a system would efficiently integrate dairy operations to enhance profitability and reduce their environmental footprint. Thus, the objectives of this dissertation were to build, test, and apply a precision feeding system able to tailor feeding strategies to animals more precisely and closely match their individual requirements. In Chapter 3, we describe the precision feeding system framework using directional data streams. The system integrates real-time farm data, segmented into data-analytic modules for independent testing and troubleshooting. It provides feeding instructions to automatic feeders and generates animal and financial monitoring reports. In Chapter 4, we describe the "Animal Performance" system module. This study developed a predictive model to estimate individual dry matter intake (DMI) by integrating markers, animal characteristics, dietary nutrient concentrations, and chewing sensor data. The performance of the developed model was then assessed and contrasted with the NASEM (2021) DMI equations. By incorporating covariates derived from short-term use of external and internal markers we demonstrated a greater accuracy of DMI predictions when using a fixed effects model, supporting its predictive capabilities for further application. In Chapter 5, we describe the "Diet Optimization" systems module, used to maximize profit by optimizing rations using a developed compact-vectorized version of NASEM (2021). The study aimed to simulate optimized diets, evaluate the economic impact of feeding individual diet, compare feed costs and income over feed cost (IOFC) for optimized group diet, and compare optimized diets against pen-averages (PEN). The results showed that IND diets had lower costs, higher milk production, and increased IOFC compared to CLU diets. Additionally, both IND and CLU diets outperformed PEN solutions. This work established methods for deriving efficient diet solutions for individual animals and using clustering techniques for more precise pen-level feeding. In Chapter 6, we describe the application of "Animal Performance", "Diet Optimization", and "Nutrient Titration" system modules. The former DMI model described in Chapter 4 was applied to the experimental data. The middle utilized optimized diets generated by the optimizer developed in Chapter 5, with additional algorithm updates. The latter aimed to investigate individual milk true protein production responses of dairy cows to varying levels of metabolizable protein (MP) and rumen-protected amino acids (RPAA) using automatic feeding systems and rank animals based on their individual gross milk protein efficiencies. Results demonstrated heterogeneous animal responses across MP and RPAA levels, ranging from linear, and quadratic to no response, emphasizing the necessity of addressing individual variability within a common pen. High-efficiency animals behaved consistently across MP treatments with lower variability, while low-efficiency animals showed high variability but consistently remained in the bottom efficiency rank. In conclusion, the precision feeding system underscores true capabilities to tailor nutrient delivery to individual cows, maximizing economic and environmental benefits, and sets the stage for future research focused on further refinement and automation of these technologies / Doctor of Philosophy / Feeding practices for dairy cattle have evolved significantly from manual grain mix offering to group feeding. While pen-level feeding has its benefits, it overlooks opportunities to maximize efficiency and minimize feed waste and nutrient excretion by not using individual animal variation to apply control feeding. With modern farms and increased technology adoption, feeding animals while being individually milked, even when group-housed, is now possible, leveraging this variability to apply precision feeding. In Chapter 3, we described the development of a precision feeding system that leverages technological advancements on dairy farms to gather and analyze data, supporting informed decision-making. This system includes various modules for testing and adjusting feeding strategies according to animal needs, providing feeding instructions to automatic feeders, and generating reports to help farmers monitor their animals and manage costs. Recognizing that precision feeding relies on quality data and accurate predictions of crucial metrics such as dry matter intake (DMI), Chapter 4 focused on developing a mathematical equation to predict DMI on an individual animal basis. This model demonstrated potential for commercial dairy operations due to its use of readily available farm-level predictors and its adequate performance compared to gold-standard field equations. Given the lack of efficient optimizers that incorporate individual animal data, in Chapter 5, we described the development of a new optimizer incorporated into the system to maximize profits. We simulated different feeding strategies, including optimized individual and group diets, and demonstrated that these tailored diets were more cost-effective and led to higher milk production compared to pen-average diets. To complete the development, testing, and application cycle, in Chapter 6, we applied the precision feeding system to determine the metabolizable protein (MP) requirements of dairy cows and assess milk protein production responses to different levels of MP and rumen-protected amino acids (RPAA). The results indicated varied responses among cows, highlighting the importance of individualized feeding to account for animal-to-animal differences within the same pen. Top-efficient animals were consistent in their responses across treatments, whereas bottom-efficient animals exhibited greater variability and consistently underperformed. In conclusion, the precision feeding system demonstrated significant potential to improve the efficiency of dairy farming by more accurately meeting the specific needs of dairy cattle. Future research will focus on refining this system and further automating the process for broader farm applications.

The main goal of this research was to explore the use of benchmarking in the dairy industry. This includes descriptions of the various sectors in North America where benchmarking has been used successfully on a continuous basis or in research. Benchmarking methods that are currently used in the Quebec dairy industry are examined. An improvement to such methods is proposed through the use of visualization, coupled with interactivity, and with a focus on adaptability and usage. The advantages of such an interactive tool are discussed in light of on-farm decision-making, and a further use of visual slider applications is described to help with parameters of known economic importance.

No description available.