A proposed approach to estimate and reduce net greenhouse gas emissions from whole farms

Greenhouse gas emissions from farms can be suppressed in two ways: by curtailing the release of these gases (especially N 2 O and CH 4 ), and by storing more carbon in soils, thereby removing atmospheric CO 2 . But most practices have multiple interactive effects on emissions throughout a farm. We describe an approach for identifying practices that best reduce net, whole-farm emissions. We propose to develop a “Virtual Farm”, a series of interconnected algorithms that predict net emissions from flows of carbon, nitrogen, and energy. The Virtual Farm would consist of three elements: descriptors, which characterize the farm; algorithms, which calculate emissions from components of the farm; and an integrator, which links the algorithms to each other and the descriptors, generating whole-farm estimates. Ideally, the Virtual Farm will be: boundary-explicit, with single farms as the fundamental unit; adaptable to diverse farm types; modular in design; simple and transparent; dependent on minimal, attainable inputs; internally consistent; compatible with models developed elsewhere; and dynamic (“seeing”into the past and the future). The Virtual Farm would be constructed via two parallel streams - measurement and modeling - conducted iteratively. The understanding built into the Virtual Farm may eventually be applied to issues beyond greenhouse gas mitigation. Key words: CO 2 , N 2 O, CH 4 , agroecosystems, models, climate change