Agricultural carbon footprint is farm specific: Case study of two organic farms

Making reliable inferences from agricultural carbon footprint (aCF) estimates require increased understanding of factors behind differences in carbon footprint (CF) of different farms and systems. Establishing sources and drivers of differences in aCF accounting can improve the level of accuracy of aCF modeling and help identify meaningful areas for decision-making for greenhouse gas mitigation efforts. Currently, a lack of consideration for different styles, sizes and location of organic farms in many studies comparing CF for agricultural products from organic and conventional farming systems limit our understanding of the factors behind the observed inconsistencies in their results. This study used OFoot tool, a Life Cycle Assessment and CropSyst based carbon footprint calculator for organic farms, to estimate the aCF of two certified organic farms growing several crops in common; one is a small-scale farm located in a maritime climate and the other is a large-scale farm located in a semi-arid climate. The study identified the hotspots of each farm's CF and the underlying reasons for differences in hotspots of the two farms and those of two mutual crops, onion and winter squash, grown by the farms.The CF of the small scale and the large scale farms were 7144 and 3410 kg CO2e ha-1 yr-1 respectively. Identified hotspots differ between the two farms as well. Critical CF hotspots in the small scale farm were fuel use (47.6%), greenhouse facilities (11.3%), and net soil emissions (10.3%), whereas those in the large scale farm were electricity used for irrigation (47.5%), fuel use (26.1%), and soil amendments (20.1%). The estimated CFs of onion and winter squash produced in the small scale farm were 0.188 and 0.276 kg CO2e per kg of crops, respectively, whereas the CFs of the same crops produced in the large-scale farm were 0.05 and 0.07 kg CO2e per kg of crops, respectively. This study identified farm size and site-specific soil and climatic conditions as factors influencing decision-making that have great impact on agricultural CF of the whole farms and individual crops of onion and squash. This study reiterates the importance of using site-specific emission factors in developing models and tools for aCF estimation and echoes the need for whole-farm CF analysis in understanding the context behind major hotspots in the CF of farms and farms products.