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Abstract
A computer model was developed in cooperation with other researchers to simulate forage systems on dairy farms. The model simulates alfalfa growth, corn silage and corn grain yields, harvest, storage, feeding and ration formulation for a dairy herd. Alfalfa growth is simulated on a daily basis and harvest is simulated on a half-daily basis. Storage, feeding and ration formulation are simulated once per year. A 26-year series of historical weather data from East Lansing, Michigan was used to estimate the average and the distribution of net returns of forage systems.
The analysis focused on alfalfa harvest. Early harvest (May 20 for the first cut) resulted in relatively high quality, low yield and high net return. Low milk producing cows may however use more efficiently an intermediate maturity harvest (June 1 for the first cut) by substituting yield for quality.
Extending the alfalfa harvest period to four weeks reduced the total dry matter and crude protein conserved. The loss in crop value did not however justify the high cost of larger machinery, as long as each harvest is done within a four week period.
More dry matter and a higher crude protein concentration can be conserved by reducing the field-curing delay. Additional curing treatments that would increase the drying rate by 20% increased the feeding value of hay by 10 to 15%. Baling hay at a higher moisture content had a similar effect. Shifting from hay to haylage would yield about 20% more feed per unit area. The feed quality of haylage and hay is practically the same due to the lower dry matter intake of haylage.
The simulation results indicate promising research areas. Applied research could be directed towards the development of conditioning treatments that increase the drying rate without increasing dry matter losses, the improvement of conservation of wet hay and the increase of animal intake of alfalfa haylage. More basic research should consider quality changes in silos during filling and fermentation, modeling animal response to hay, haylage and large variations in feed quality, and improving estimates of drying rates and dry matter losses.