Model-predicted ammonia emission from two broiler houses with different rearing systems

Ammonia (NH3) emissions from broiler production can affect human and animal health and may cause acidification and eutrophication of the surrounding environment. This study aimed to estimate ammonia emissions from broiler litter in two systems of forced ventilation, the tunnel ventilation (TV) and the dark house (DH). The experiment was carried out on eight commercial broiler houses, and the age of the birds (day, d), pH and litter temperature were recorded. Broilers were reared on built-up wood shaving litter using an average flock density of 14 bird m–2. Temperature and relative humidity inside the broiler houses were recorded in the morning during the grow-out period. A factorial experimental design was adopted, with two types of houses, four replicates and two flocks with two replicates each. A deterministic model was used to predict ammonia emissions using the litter pH and temperature, and the day of grow-out. The highest litter temperature and pH were found at 42 d of growth in both housing systems. Mean ambient air temperature and relative humidity did not differ in either system. Mean model predicted ammonia emission was higher in the DH rearing system (5200 mg NH3 m−2h−1 at 42 d) than in the TV system (2700 mg NH3m−2 h−1 at 42 d). TV presented the lowest mean litter temperature and pH at 42 d of growth. In the last week of the broilers’ grow-out cycle, estimated ammonia emissions inside DH reached 5700 mg m−2h−1 in one of the flocks. Ammonia emissions were higher inside DH, and they did not differ between flocks. Assuming a broiler market weight in Brazil of close to 2 kg, ammonia emissions were equivalent to 12 g NH3 bird-marketed−1. Model-predicted ammonia emissions provided comprehensible estimations and might be used in abatement strategies for NH3 emission

Dynamic behavior of PH in fresh urine puddles of dairy cows

Modern livestock farming is an important contributor to ammonia (NH3) emissions. In the Netherlands, 94% of NH3 emissions originate from agriculture, of which 34% is emitted from commercial dairy cow barns. From current mechanistic modeling, it is known that the pH of urine puddles from cows is one of the most important variables in estimating NH3 emissions. However, little pH data are available from commercial cow barns. Therefore, the objective of this study was to investigate pH values and to study their dynamic behavior in fresh, on-floor urine puddles in these barns. To do this, the pH of urine puddles was measured for 4 h per puddle, and a model was developed to describe the pH behavior. In total, 26 fresh puddles were measured from cows at three commercial dairy farms in summer and winter. At farm level, we found initial pH values of 8.1 through 8.4, which increased to 8.9 through 9.4 after 4 h. The pH difference between summer and winter was 0.3 (p <0.05), but this was not confirmed by comparisons at farm level. The pH curves of individual puddles varied substantially and could be fitted by a nonlinear regression model. This model contained correlated coefficients that were able to describe the main, known chemical processes of a urine puddle. However, no linear relationship was found between initial and final pH and thus between coefficients. On average, pH quickly increased initially, declined after 1 h, and became stable around a pH of 9.15. We conclude that a pH curve will better describe the input variable in NH3 emission modeling than the current situation of using a static pH value. Based on this study, we recommend using the mean measured pH curve as input for puddle simulation during NH3 emission modeling of dairy cow barns