Abstract

Predictive methods for the nutritional quality of alfalfa are being refined for use in models for whole dairy farms that enhance economic viability and environmental protection on those farms.

Issue

Dairy farmers and environmental managers are concerned about the environmental effects of animal wastes generated on farms. The amount of nutrients in livestock manures can be reduced by not overfeeding dairy cows. This requires information as accurate as possible about the nutrient content and quality of feeds being fed cows. The nutrient levels and the amount of feed needed are particularly difficult to predict for forages such as alfalfa. Accurate predictions will also help dairy farmers optimize farm profitability by helping avoid over or under feeding of cows.

Response

Predictive models for alfalfa quality based on temperature records during the growing period have been developed and applied in dairy-forage systems models. Continuing research indicates that the models can be improved by changing the manner in which the temperature records are analyzed. The next step is to collect a national database of associated temperature and alfalfa quality data so that refined models can be further developed and tested for accuracy.

Impact

Present models have helped dairy farmers and alfalfa producers better estimate the nutritional quality of alfalfa hay or silage before it is harvested. Thus, they have gained more control over the chemical composition of their feed and the management of their feed rations. This work will simply refine and enhance the accuracy of these tools farmers and environmental managers are already using to estimate the needs and effects that result from feeding alfalfa to dairy cows.

Funding Sources

• Federal Formula Funds - Research (e.g., Hatch, McIntire-Stennis, Animal Health)

Collaborators

• University of California (Davis)
• University of Illinois
• Michigan State University
• Pennsylvania State University
• University of Pennsylvania
• Washington State University

Key Personnel

• Gary W. Fick, Crop and Soil Sciences, Cornell University