• Energy Research

Abstract Purpose Life cycle assessment studies on smallholder farms in tropical regions generally use data that is collected at one moment in time, which could hamper assessment of the exact situation. We assessed seasonal differences in greenhouse gas emissions (GHGEs) from Indonesian dairy farms by means of longitudinal observations and evaluated the implications of number of farm visits on the variance of the estimated GHGE per kg milk (GHGEI) for a single farm, and the population mean. Methods An LCA study was done on 32 smallholder dairy farms in the Lembang district area, West Java, Indonesia. Farm visits (FVs) were performed every 2 months throughout 1 year: FV1–FV3 (rainy season) and FV4–FV6 (dry season). GHGEs were assessed for all processes up to the farm-gate, including upstream processes (production and transportation of feed, fertiliser, fuel and electricity) and on-farm processes (keeping animals, manure management and forage cultivation). We compared means of GHGE per unit of fat-and-protein-corrected milk (FPCM) produced in the rainy and the dry season. We evaluated the implication of number of farm visits on the variance of the estimated GHGEI, and on the variance of GHGE from different processes. Results and discussion GHGEI was higher in the rainy (1.32 kg CO2-eq kg−1 FPCM) than in the dry (0.91 kg CO2-eq kg−1 FPCM) season (P < 0.05). The between farm variance was 0.025 kg CO2-eq kg−1 FPCM in both seasons. The within farm variance in the estimate for the single farm mean decreased from 0.69 (1 visit) to 0.027 (26 visits) kg CO2-eq kg−1 FPCM (rainy season), and from 0.32 to 0.012 kg CO2-eq kg−1 FPCM (dry season). The within farm variance in the estimate for the population mean was 0.02 (rainy) and 0.01 (dry) kg CO2-eq kg−1 FPCM (1 visit), and decreased with an increase in farm visits. Forage cultivation was the main source of between farm variance, enteric fermentation the main source of within farm variance. Conclusions The estimated GHGEI was significantly higher in the rainy than in the dry season. The main contribution to variability in GHGEI is due to variation between observations from visits to the same farm. This source of variability can be reduced by increasing the number of visits per farm. Estimates for variation within and between farms enable a more informed decision about the data collection procedure.

Dairy production across the world contributes to environmental impacts such as eutrophication, acidi-fication, loss of biodiversity, and use of resources, such as land, fossil energy and water. Benchmarkingthe environmental performance of farms can help to reduce these environmental impacts and improveresource use efficiency. Indicators to quantify and benchmark environmental performances are generallyderived from a nutrient balance (NB) or a life cycle assessment (LCA). An NB is relatively easy to quantify,whereas an LCA provides more detailed insight into the type of losses and associated environmentalimpacts. In this study, we explored correlations between NB and LCA indicators, in order to identify aneffective set of indicators that can be used as a proxy for benchmarking the environmental performanceof dairy farms. We selected 55 specialised dairy farms from western European countries and determinedtheir environmental performance based on eight commonly used NB and LCA indicators from cradle-to-farm gate. Indicators included N surplus, P surplus, land use, fossil energy use, global warming potential(GWP), acidification potential (AP), freshwater eutrophication potential (FEP) and marine eutrophicationpotential (MEP) for 2010. All indicators are expressed per kg of fat-and-protein-corrected milk. Pear-son and Spearman Rho’s correlation analyses were performed to determine the correlations betweenthe indicators. Subsequently, multiple regression and canonical correlation analyses were performed toselect the set of indicators to be used as a proxy. Results show that the set of selected indicator, includingN surplus, P surplus, energy use and land use, is strongly correlated with the eliminated set of indicators,including FEP (r = 0.95), MEP (r = 0.91), GWP (r = 0. 83) and AP (r = 0.79). The canonical correlation betweenthe two sets is high as well (r = 0.97). Therefore, N surplus, P surplus, energy use and land use can be usedas a proxy to benchmark the environmental performance of dairy farms, also representing GWP, AP,FEP and MEP. The set of selected indicators can be monitored and collected in a time and cost-effectiveway, and can be interpreted easily by decision makers. Other important environmental impacts, such asbiodiversity and water use, however, should not be overlooked.