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NM-Manure: A Seasonal Prediction Model of Manure Excretion for Lactating Dairy Cows in New Mexico

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2007 ASAE Annual Meeting  074171.(doi:10.13031/2013.23271)
Authors:   Victor E Cabrera
Keywords:   Markov-chain, dynamic stochastic simulation, herd dynamic simulation, environmental impacts

Environmental concerns from dairy farms arise primarily because of potential impacts to air, soil, and water resources of excretions (manure and urine) by dairy cattle. In addition, increasing opportunities are arising for using manure as a source of renewable energy. Consequently, it is important to assess the amounts of manure excreted by dairy herds. Regulatory agencies use only a few animal groups and average herd characteristics to estimate steady manure excretion. However, manure excretion varies seasonally and should be predicted based on dynamic herd group characteristics. In addition, prediction parameters are periodically revised and improved. This study describes the creation of a stochastic dynamic herd model to predict seasonal manure excretion that matches current regulatory standards by adjusting improved predictor parameters. The Markov-chain model defines more than 1,400 cow states according to parity, month in milk, and pregnancy; and includes season of the year according to New Mexico pregnancy and culling rates. It uses a well-known parameter of milk rolling herd average to estimate milk productivity by any cow in the herd in any month and from it to predict manure excretion. Results indicated strong seasonal variations of manure excretion. This is the lowest during February (3,870 tons it is lower during November (+5.8%) and September (+6.2%); it is medium during April (+8.0%), June (+8.2%), December (+9.2%), and October (+9.6); it is higher during August (+10.6%), January (+10.9%), March (+11.1%), and July (+11.7%); and it is the highest in May (+11.9%).

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