The impact of changing toward higher welfare broiler production systems on greenhouse gas emissions: a Dutch case study using life cycle assessment.

In the Netherlands, the Dutch Retail Broiler (DRB) and Better Life one Star (BLS) production systems have been introduced with the aim to improve broiler welfare. Simultaneously, retailers set targets for reduction of greenhouse gas (GHG) emissions in the whole broiler production chain. The GHG emissions of DRB and BLS may differ from conventional systems because of differences in slaughter age, feed intake, and diet composition. The aim of this study was to estimate GHG emissions of the conventional, DRB, and BLS production systems. A deterministic, spreadsheet based model was developed that included the breeder, hatchery, and broiler farm stages. First, the model calculates feed intake of different diets and energy use, based on performance objectives and literature. Selection of feed ingredients for the different types of diets was based on least cost formulation with nutritional constraints for each diet. Second, GHG emissions were estimated from cradle to broiler farm gate for processes along the broiler production chain by using life cycle assessment, and expressed as kg CO2-equivalents per kg live weight (kg CO2-eq/kg LW). Results showed that BLS (3.55 kg CO2-eq/kg LW) had lower GHG emissions compared to conventional (3.65 kg CO2-eq/kg LW) and DRB (3.98 kg CO2-eq/kg LW) at the broiler farm gate. Emissions from land use change (LUC) from feed production, mainly from soybean products, had highest impact on total GHG emissions (>50%) for the systems and these soybean products had the lowest inclusion in the diets of the BLS production system. Sensitivity analyses showed that variation in slaughter weight and feed intake could result in overlap of GHG emissions between systems. When soybean products were sourced from a country with low LUC emissions, conventional (1.37 kg CO2-eq/ kg LW) had the lowest GHG emissions and BLS (1.79 kg CO2-eq/kg LW) the highest. This study showed that origin of and including or excluding LUC emissions from soybean production results in different conclusions for achieving the GHG emissions reduction targets set by retailers.

Estimating the impact of clinical mastitis in dairy cows on greenhouse gas emissions using a dynamic stochastic simulation model: a case study.

The increasing attention for global warming is likely to contribute to the introduction of policies or other incentives to reduce greenhouse gas (GHG) emissions related to livestock production, including dairy. The dairy sector is an important contributor to GHG emissions. Clinical mastitis (CM), an intramammary infection, results in reduced milk production and fertility, increases culling and mortality of cows and, therefore, has a negative impact on the efficiency (output/input) of milk production. This may increase GHG emissions per unit of product. Our objective was to estimate the impact of CM in dairy cows on GHG emissions of milk production for the Dutch situation. A dynamic stochastic simulation model was developed to simulate the dynamics and losses of CM for individual lactations. Cows receive a parity (1 to 5+), a milk production and a calving interval (CI). Based on the parity, cows have a risk of CM, with a maximum of three cases in a lactation. Pathogens causing CM were classified as gram-positive bacteria, gram-negative bacteria, or other. Based on the parity and pathogen combinations, cows had a reduced milk production, discarded milk, prolonged CI and a risk of removal (culling and mortality) that reduce productivity of dairy cows and therefore increase GHG emissions per unit of product. Using life cycle assessment, emissions of GHGs were estimated from cradle to farm gate for processes along the milk production chain that are affected by CM. Processes included were feed production, enteric fermentation, and manure management. Emissions of GHGs were expressed as kg CO2 equivalents per ton of fat-and-protein-corrected milk (kg CO2e/t FPCM). Emissions of cows with CM increased on average by 57.5 (6.2%) kg CO2e/t FPCM compared with cows without CM. This increase was caused by removal (39%), discarded milk (38%), reduced milk production (17%) and prolonged CI (6%). The GHG emissions increased by 48 kg CO2e/t FPCM for cows with one case of CM, by 69 kg CO2e/t FPCM for cows with two cases of CM and by 92 kg CO2e/t FPCM for cows with three cases of CM compared with cows without CM. Preventing CM can be an effective strategy for farmers to reduce GHG emissions and can contribute to sustainable development of the dairy sector, because this also can improve the income of farmers and the welfare of cows. The impact of CM on GHG emissions, however, will vary between farms due to environmental conditions and management practices.

Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model.

The objective of this study was to estimate the economic impact of subclinical ketosis (SCK) in dairy cows. This metabolic disorder occurs in the period around calving and is associated with an increased risk of other diseases. Therefore, SCK affects farm productivity and profitability. Estimating the economic impact of SCK may make farmers more aware of this problem, and can improve their decision-making regarding interventions to reduce SCK. We developed a dynamic stochastic simulation model that enables estimating the economic impact of SCK and related diseases (i.e. mastitis, metritis, displaced abomasum, lameness and clinical ketosis) occurring during the first 30 days after calving. This model, which was applied to a typical Dutch dairy herd, groups cows according to their parity (1 to 5+), and simulates the dynamics of SCK and related diseases, and milk production per cow during one lactation. The economic impact of SCK and related diseases resulted from a reduced milk production, discarded milk, treatment costs, costs from a prolonged calving interval and removal (culling or dying) of cows. The total costs of SCK were €130 per case per year, with a range between €39 and €348 (5 to 95 percentiles). The total costs of SCK per case per year, moreover, increased from €83 per year in parity 1 to €175 in parity 3. Most cows with SCK, however, had SCK only (61%), and costs were €58 per case per year. Total costs of SCK per case per year resulted for 36% from a prolonged calving interval, 24% from reduced milk production, 19% from treatment, 14% from discarded milk and 6% from removal. Results of the sensitivity analysis showed that the disease incidence, removal risk, relations of SCK with other diseases and prices of milk resulted in a high variation of costs of SCK. The costs of SCK, therefore, might differ per farm because of farm-specific circumstances. Improving data collection on the incidence of SCK and related diseases, and on consequences of diseases can further improve economic estimations.