Model Adaptation and Validation for Estimating Methane and Ammonia Emissions from Fattening Pig Houses: Effect of Manure Management System

Simple Summary Predicting emissions from livestock housing is crucial for helping farmers understand how various factors impact emissions at the pig house level. This knowledge empowers decision-makers and the farmers to make informed choices that encourage emission reduction and improve air quality in pig houses. To create innovative, low-emission pig housing systems, the development of a prediction model appears to be a straightforward solution. This paper introduces such a model for predicting methane and ammonia emissions from two commercial pig houses with two manure management systems: one with long storage in deep pits and the other with short storage, through daily flushing of a shallow pit with sloped walls (a reduced emitting surface) and partial manure dilution. This simulation model, which considers factors like animal weight, age, feed, and pen dimensions as input data, calculates manure quantity and quality, as well as methane and ammonia emissions over the growing period. In this model, the calculation of ammonia emission involves aggregating emissions from various sources identifiable within pig houses. These sources include manure channels, slatted floors, and solid floors, encompassing fouled areas, pigs, and pen partitions. This model successfully predicts the development of methane and ammonia emissions based on animal and farm characteristics.Abstract This paper describes a model for the prediction of methane and ammonia emissions from fattening pig houses. This model was validated with continuous and discrete measurements using a reference method from two manure management systems (MMS): long storage (LS) in deep pits and short storage (SS) by daily flushing of a shallow pit with sloped walls and partial manure dilution. The average calculated methane and ammonia emissions corresponded well with the measured values. Based on the calculated and measured results, the average calculated CH4 emission (18.5 and 4.3 kg yr-1 per pig place) was in between the means from the continuous data from sensors (15.9 and 5.6 kg yr-1 per pig place) and the means from the discrete measurements using the reference method (22.0 and 3.1 kg yr-1 per pig place) for the LS and SS systems, respectively. The average calculated NH3 emission (2.6 and 1.4 kg yr-1 per pig place) corresponded well with the continuous data (2.6 and 1.2 kg yr-1 per pig place) and the discrete measurements using the reference method (2.7 and 1.0 kg yr-1 per pig place) from LS and SS, respectively. This model was able to predict the reduction potential for methane and ammonia emissions by the application of mitigation options. Furthermore, this model can be utilized as a predictive tool, enabling timely actions to be taken based on the emission prediction. The upgraded model with robust calculation rules, extensive validations, and a simplified interface can be a useful tool to assess the current situation and the impact of mitigation measures at the farm level.