The DairyPrint model: A decision support model to help dairy farmers and other stakeholders toward improved...

Dairy farmers face increasing pressure to reduce GHG emissions (i.e., carbon dioxide, CO₂; methane, CH₄; and nitrous oxide, N₂O), but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate these emissions. However, current models tend to be not user-friendly, difficult to access, or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal-related variables (i.e., DMI, milk yield, manure excretion, and enteric CH₄ emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade Shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard freestall system with 1,000 dairy cows averaging 40 kg/d of milk production. These factors included 2 NDF-ADF levels in the diet (28%-22.8% and 24%-19.5%), the presence or absence of 3-nitrooxypropanol (3-NOP) dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying (once yearly, only in fall; or twice a year, in fall and spring), and the use or nonuse of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kg CO₂ eq/kg of milk, corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kg CO₂ eq/kg) involved a combination of factors, including a lower NDF-ADF level in the diet in addition to incorporation of 3-NOP, use of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both fall and spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kg CO₂ eq/kg) involved a combination of a higher NDF-ADF level in the diet and excluded incorporation of 3-NOP, use of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in fall. All these scenarios can easily be simulated in the DairyPrint model, with results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.