Agricultural systems, including livestock agriculture (i.e., dairy and beef production), are leading contributors to greenhouse gas emissions. Projects like the ‘Net Zero Initiative’ are gaining traction to reduce or offset such emissions. Some major drawback of these projects are that quantifying entire agriculture greenhouse gas budgets is challenging, subject to high uncertainty, and expensive. Such budgets require full accounting of the greenhouse gas fluxes from imports, exports, energy, enteric fermentation, manure, land management, and vegetation. We quantified seasonal and annual greenhouse gas (GHG) budgets using an integrated crop-livestock system (ICLS) dairy farm in Wisconsin USA as a case study. An eddy covariance (EC) tower measured CO2 and CH4 exchange of vegetation types continuously and data were used to compare vegetation net primary productivity (NPP) from two remote sensing sources, as well as Intergovernmental Panel on Climate Change (IPCC) guidelines to calculate farm emissions. Both satellite and eddy covariance NPP partitioning models included satellite data on enhanced vegetation index (EVI) and land surface temperature (LST) to quantify differences in photosynthetic activity and ecosystem respiration by vegetation type (i.e., crop systems versus forest, shrub and grass vegetation).
Results/Conclusions
Annual vegetation NPP from satellite models correlated well with farm harvest NPP (R2 ~0.85). The highest uncertainties were found with highly managed crops (e.g., alfalfa), where harvest was not evident with EVI data. Accordingly, satellite models underestimated ecosystem respiration of alfalfa by ~20% during regrowth periods. Both satellite and EC NPP results indicated that the farm offset GHG emissions that it produced – with the majority originating from enteric fermentation of dairy cows – by over 100% owing to natural vegetation carbon sinks and harvest products staying within the farm boundaries. Combining satellite and EC modeling approaches helped validate NPP and lowered the uncertainty around whole farm carbon budget estimates. Agricultural NPP estimates may be further improved by including data such as evaporative stress index and LST products from NASA’s ECOSTRESS mission on board of the international space station (ISS), allowing for a greater temporal resolution compared to Landsat. Overall, satellite NPP models are a reliable and cost-efficient way of quantifying farm greenhouse gas budgets, that could be used to help farmers and policy makers understand carbon footprints of agricultural systems and encourage the implementation of better management strategies to reduce emissions and improve the sustainability of a farm’s land base.