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I grew up listening to my grandparents tell stories about their short time in Montana – they only stayed a year. Once, I asked why they left. Grandma laughed and told me about that winter.
Silage storage and feedout dry matter losses not only deal a wallop to the wallet; they also negatively affect the nutritional value of the remaining silage.
Livestock producers fully understand the benefits derived from applying genetic selection decisions in both cattle and corn. The tremendous improvements in milk and meat production and corn yield would not have been possible without a strong focus on selecting superior genetics.
Silage storage and feedout dry matter losses (shrink) not only deal a wallop to the wallet, but also negatively affect the nutritional value of the remaining silage. That’s because shrink consumes the most valuable silage nutrients, which must then be replaced with an equal energy source, such as corn grain.
I grew up on a diversified crop and livestock farm in central Iowa during a time when corn silage was the predominant ration ingredient for both growing and finishing beef cattle. It was and is a consistent, high-quality source of both energy and forage.
The relationship of corn silage harvest moisture and maturity to its quality and performance is often underestimated. It’s important to minimize fermentation and storage dry matter losses because it’s difficult and costly to compensate for lower-quality forage in ration balancing and animal performance.
The management of large-scale forage production systems presents many challenges that affect preservation of forage quality. The USDA estimated that in 2013 the amount of U.S. corn harvested as silage was 117,851,000 tons, with a conservative value of $7.5 billion.
Sorghums grown as forages include sorghum-sudangrass, forage sorghum and grain sorghum hybrids. Sorghum-sudangrass hybrids are used mainly for pasture or hay.
Anyone who has taken a managerial economics class knows all about using profit maximization models to determine the investment and output levels that return the greatest profit.
A profit maximization model for dairy rations requires an algorithm using a production function between net energy intake and milk production. This function increases at a decreasing rate.
