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A quantitative case study assessment of biophysical and economic effects from altering season of calving in temperate pasture-based dairy systems. | LitMetric

A quantitative case study assessment of biophysical and economic effects from altering season of calving in temperate pasture-based dairy systems.

J Dairy Sci

DairyNZ, Private Bag 3221, Hamilton, New Zealand 3240; School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. Electronic address:

Published: December 2019

In theory, a late winter-early spring calving date in temperate grazing systems best matches pasture supply and herd demand, thereby minimizing the need for nonpasture feeds and maximizing profitability. We used a quantitative case study approach to define the effects of season of calving on biophysical and financial performance in a grazing system without the confounding effects of imported feeds (i.e., milk production directly from grazed pasture). A 2-yr production system experiment was established to quantify the effects of changing onset of seasonal calving (i.e., planned start of calving; PSC) from winter (July in the Southern Hemisphere) to spring (October), summer, (January), or autumn (April) on pasture and animal production and profitability. Eighty Holstein-Friesian cows were randomly allocated to 1 of 4 PSC treatments, each of which had a different PSC [mean calving date of January 10 (JAN), April 10 (APR), July 10 (JUL), or October 10 (OCT)]. Data were analyzed for consistency of treatment response over years using ANOVA procedures with year, PSC treatment, and year × PSC treatment interactions as fixed effects. Collated biological data and financial data extracted from a national economic database were used as fixed variables to model the financial performance for the different treatments. A stochastic risk analysis was undertaken, where historical pasture growth and milk price data were used to estimate the probability distributions for stochastic input variables. Gross farm revenue and operating profit per hectare were modeled under 2 scenarios: (A) milk price did not include a premium for milk supplied during the winter, and (B) milk price included a realistic premium for milk supplied in winter. Annual and seasonal pasture growth did not differ between treatments, but the pasture growth (kg of dry matter/ha) and profile of the JUL treatment best matched the lactation nutrient demand profile. In comparison, profiles for JAN, APR, and OCT calving treatments had periods of greater surplus and deficit due to the time of calving and herd demand relative to the pasture growth profile. As a result, the JAN and OCT treatments conserved more pasture as silage and cows consumed a larger proportion of their annual diet as silage. Although the amount of silage conserved and consumed did not differ between the JUL and APR calving treatments, the timing of the silage consumption was different, with silage making up a greater proportion of the diets in the APR treatment 1 to 90 and 91 to 180 d postcalving and being offered to the JUL calving treatment only 271 to 365 d postcalving. As a result of differences in the quantity and proportion of pasture and pasture silage in the lactating diet, the JUL treatment herd tended to produce greater milk, 4% fat-corrected milk, fat, protein, and lactose yields (kg/cow) than the other PSC treatments, which did not differ from each other. Operating expenses per hectare did not differ materially between calving date scenarios, but operating expenses per kilogram of fat-corrected milk and kilogram of fat and protein were 15 to 20% less in the JUL treatment. With or without a realistic winter milk premium, gross farm revenue and operating profit per hectare were greater in the JUL treatment than in the APR treatment, which had greater revenue and profitability than the remaining 2 calving date treatments. In summary, our results indicate that a PSC in late winter is most profitable in a grazing system not importing feed, with or without a realistic price incentive scheme.

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Source
http://dx.doi.org/10.3168/jds.2018-15911DOI Listing

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