Development of a Seasonal Prediction Model for Manure Excretion by Dairy Cattle

Abstract

More accurate estimates of dairy cattle manure excretion are needed for manure management, nutrient plans, renewable bioenergy production, and environmental assessment. Consequently, it is important to quantify manure excretion by dairy herds. Current estimates use only a few animal groups and average herd characteristics to estimate fixed rates of manure excretion throughout the year. However, manure excretion varies seasonally and should be predicted based on dynamic herd group characteristics. In addition, improved prediction parameters should be incorporated. This study describes the creation of a stochastic dynamic herd model to predict seasonal manure excretion according to herd characteristics regarding milk production, pregnancy rates, and culling rates using improved prediction parameters. The Markov-chain model employed defines more than 1,400 cow states according to parity, month in milk, and pregnancy status, and includes season of the year according to pregnancy and culling rates. Although overall estimates of our model were not substantially different from commonly used approaches, strong seasonal variations of manure excretion found with our model are missed when using other approaches. Our model also gives the opportunity to tailor dairy farm specific parameters such as milk productivity, pregnancy rates, and culling rates to more accurate predictions. Predictions of the seasonal variation in dairy manure excretion aids in addressing issues related to manure use and recycling needs, bioenergy production, and assessment of environmental impacts.