Evaluation of best practices associated with enhanced performance in Illinois dairy farms: a dairy focus team approach

Management;survey;nutrition;reproduction;young stock;sand;organic matter;somatic cell count (SCC)

Rivelli Bixquert, Maria Ines ; Cardoso ; Felipe C.

关键词: Management;    survey;    nutrition;    reproduction;    young stock;    sand;    organic matter;    somatic cell count (SCC);   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/90608/RIVELLIBIXQUERT-THESIS-2016.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

It is expected that, by the year 2030, the world will have approximately 9.3 billion inhabitants, and by the year 2050 more productive agricultural land would be required to cover the extra 70 to 100% food needed. Although more land would be required, the world would be facing land deficiency since in the last decades higher proportions of good quality land has been allocated to human purposes (e.g. urbanization, emerging generation of biofuels) or lost because of environmental problems (e.g. climate change, desertification, and soil erosion). In an effort to improve efficiency and milk production, the U.S. dairy industry has faced several changes over the last years. During the last three decades milk production increased 59.4% and the total number of dairy cows and operations decreased 17% and 74%, respectively. Improvements in genetic selection and cow nutrition have helped dairy farms to keep dairy farms profitability by increasing average milk yield per cow and average herd size. Illinois' average herd size and average milk yield per cow increased 39.7% and 28%, respectively, from 1991 to 2006. On the other hand, IL total number of dairy operations decreased almost 57%, and the total number of licensed operations (grade A or B) decreased approximately 18% from 2002 to 2006. Therefore, the objectives of this study were to identify nutritional, reproductive, young stock, and on-farm practices associated with milk quality in IL dairy farms; and to study potential geographical differences between the North (N) and South (S) regions in IL. Both objectives could help IL dairy farmers to improve their operations’ performance. For the purpose of this study, 20 dairy farms, located in N (n = 6) and S (n 14) were visited by the Dairy Focus Team. The Dairy Focus Team is made up of a CEO, a president, dairy mentors (e.g., faculty and industry members), and five graduate students that serve as chairmen. Each chairman, helped by one or two graduate and undergraduate students, is in charge of one of the following sections: nutrition, management, milk quality, reproduction, and young stock. To standardize data collection, a questionnaire was developed. Also, five forms were developed in order to make data collection easier and to record specific information from each section (e.g., number of stalls on each pen, number of cows drinking on each pen). All students were trained on how to use these forms, and also on how to collect samples, and to record measurements (e.g., wind speed, relative humidity, and temperature). During the visits, a questionnaire, DHI records, and the individual farm dataset (PCDART, Dairy Comp 305, Dairy Plan, and AgriTech Analytics) were collected from May through June 2014. Also samples related to nutrition (e.g., corn silage, TMR), young stock, lactating (LACT, and dry (DRY) cows housing environmental measurements [relative humidity (RH), d wind speed measurements (WS), and ambient temperature], and cow comfort assessments about LACT and DRY cows were collected. Average herd size was 413 ± 192 and 451 ± 949 LACT for N and S regions, respectively. Average milk yield per cow/d was 37.9 ± 6.7 kg and 33.8 ± 5.7 kg for N and S, respectively. The mean density of corn silage was higher for S than N (221.16 ± 8.24 vs 168.55 ± 12.22 kg/m3, respectively). Dry matter (DM) content of the TMR offered to both LACT and DRY was higher for N than S (48.73 ± 1.72 vs. 44.06 ±1.00 %, respectively). Yearly pregnancy rate (19.75 ± 2.19 vs. 12.57 ± 1.65, respectively), and service per conception rate (2.5 ± 0.13 vs. 1.88 ± 0.10, respectively) were higher for cows and heifers in N than S. A tendency for S for being less likely to use hormones in their breeding programs could explain why N had higher pregnancy rate (PR) than S. The RH percentage was higher for S than N (62.56 ± 2.05 vs. 41.08 ± 4.00 %, respectively). Southern IL had 12.93 time higher odds for calves being fed equal or less than 3.78 L of milk per day.Sand bedding quality was evaluated by quantifying DM and organic matter (OM) contents, as well as particle size distribution (PS). Lactating cows’ bedding DM was 95.95 ± 0.49 % and 96.40 ± 0.35 % for N and S, respectively. Lactating cow’s bedding OM was 3.08 ± 0.51 % and 2.61 ± 0.36 % for N and S, respectively. Average tank SCC was 196 ± 21 and 207 ± 109 for N and S, respectively. A higher OM content was found in DRY cows’ bedding (5.36 ± 0.62) than LACT (2.85 ± 0.31). A linear association between OM and bulk tank SCC was found (SCC = 133.75 + 15.00 × OM). Hence, surveillance of bedding OM used in dairy farms may improve herd milk quality. In conclusion, there were nutritional, reproductive, young stock, and milk quality management differences between N and S. Farms in S were more efficient at the moment of ensiling their silos since higher CSD implies less losses (e.g., CS oxidation) and less costs (e.g., more DM stored with the same volume) than farms in N. Sand bedding DM content was higher for S than for N. Sand bedding OM content was higher for DRY cows than for LACT cows. Organic matter content in the sand used for bedding in dairy farms was positively associated with bulk tank SCC. Surveillance of bedding OM used in dairy farms may improve herd milk quality (SCC). The geographical differences between N and S, hereby presented, have to be taken in consideration by dairy farmers, policymakers, and the dairy industry at large for consistent improvement in dairy farms' efficiency.