Greenhouse gas (GHG) emissions and their potential effect on the environment has become an important national and international issue. Dairy production, along with all other types of animal agriculture, is a recognized source of GHG emissions, but little information exists on the net emissions from dairy farms. Component models for predicting all important sources and sinks of CH(4), N(2)O, and CO(2) from primary and secondary sources in dairy production were integrated in a software tool called the Dairy Greenhouse Gas model, or DairyGHG. This tool calculates the carbon footprint of a dairy production system as the net exchange of all GHG in CO(2) equivalent units per unit of energy-corrected milk produced. Primary emission sources include enteric fermentation, manure, cropland used in feed production, and the combustion of fuel in machinery used to produce feed and handle manure. Secondary emissions are those occurring during the production of resources used on the farm, which can include fuel, electricity, machinery, fertilizer, pesticides, plastic, and purchased replacement animals. A long-term C balance is assumed for the production system, which does not account for potential depletion or sequestration of soil carbon. An evaluation of dairy farms of various sizes and production strategies gave carbon footprints of 0.37 to 0.69kg of CO(2) equivalent units/kg of energy-corrected milk, depending upon milk production level and the feeding and manure handling strategies used. In a comparison with previous studies, DairyGHG predicted C footprints similar to those reported when similar assumptions were made for feeding strategy, milk production, allocation method between milk and animal coproducts, and sources of CO(2) and secondary emissions. DairyGHG provides a relatively simple tool for evaluating management effects on net GHG emissions and the overall carbon footprint of dairy production systems.

Accurate assessment of forage mass in pastures is key to budgeting forage in grazing systems. Our objective was to determine the accuracy of an electronic capacitance meter, a rising plate meter, and a pasture ruler in measuring forage mass and to determine the cost of measurement inaccuracy. Forage mass was estimated in grazed pastures on farms in Pennsylvania, Maryland, and West Virginia in 1998 and 1999. Forage mass estimated by each method was compared with forage mass estimated by hand‐clipped samples. None of these indirect methods were accurate or precise, and error levels ranged from 26 to 33% of the mean forage mass measured on the pastures. The computer model DAFOSYM (Dairy Forage System Model) was used to simulate farm performance and the resulting effects of inaccuracies in estimating forage mass on pasture. A representative grazing dairy farm was developed, and the costs and returns from low‐input and conventional managements were calculated. Different scenarios were then simulated, including under‐ or overestimating forage yield on pastures by 10 or 20%. All scenarios simulated resulted in lower returns compared with the optimum farm, with decreases in net return ranging from $8 to $198 ha−1 yr−1. Underestimating forage mass resulted in less hay and silage being harvested, more pasture being consumed, and more forage purchased compared with the optimum scenario. The opposite occurred for overestimation of forage mass. Our results indicate that achieving greater accuracy (to within 10% of actual pasture yield) in estimating pasture yields will improve forage budgeting and increase net returns.

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