Observational and modeling methods to inform ecosystem service markets

Authors: Del Grosso, Stephen - Steve; Stewart, Catherine; Delgado, Jorge; Manter, Daniel; Vigil, Merle

Submitted to: Proceedings Great Plains Soil Fertility Conference
Publication Type: Proceedings
Publication Acceptance Date: 2/10/2020
Publication Date: 3/10/2020
Citation: Del Grosso, S.J., Stewart, C.E., Delgado, J.A., Manter, D.K., Vigil, M.F. 2020. Observational and modeling methods to inform ecosystem service markets. Proceedings Great Plains Soil Fertility Conference. 18:1-265.

Interpretive Summary: Ecosystem service markets provide a way to reduce air and water pollution and can be a source of revenue for land mangers/owners. Interest in quantifying the impacts of land management practices on ecosystem services has grown as governments and corporations have pledged to reduce greenhouse gas emissions, water and air pollution, and other environmental impacts of human activities. Ecosystem service markets were formalized in the 1990s and originally deployed to reduce air and water pollution from factories and other point sources. Associated protocols to quantify benefits and compliance were fairly simple and easy to implement because measuring point sources is easy as is verification of mitigation practices. In contrast, protocols to quantify agricultural sinks and sources of pollutants are more complicated because these sources are more diffuse and often cannot be measured directly because required sampling intensity is not economically or technologically feasible. One approach to transcend this limitation is for protocols to employ pay for practice, i.e., land mangers/owners are paid a standard amount per unit of land area enrolled in a specific conservation practice and no attempt is made to quantify outcomes achieved at the farm level. Another strategy is for protocols to embed models that calculate farm level greenhouse gas, carbon storage, and nutrient loss outcomes. But up to now, estimates generated by these models are not very accurate at the farm level. However, recent advances in data availability, geographic information systems, precision agriculture and remote sensing combined with model applications and ground and atmospheric based measurements can reduce these uncertainties. Protocols that integrate pay for practice and farmand larger scale quantification methods are expected to approach optimal cost:benefit ratios.

Technical Abstract: Interest in quantifying the impacts of land management on ecosystem services has grown as governments and corporations have pledged to reduce greenhouse gas emissions, nutrient leaching, and other environmental impacts of human activities. Ecosystem service markets were formalized in the 1990s and originally deployed to mitigate point sources of air and water pollution. Associated protocols were fairly simple and easy to implement because quantification of point sources is easy as is verification of mitigation practices. In contrast, protocols to quantify agricultural sinks and sources of pollutants are more complicated because these sources are more diffuse and often cannot be measured directly because required sampling intensity is not economically or technologically feasible. One approach to transcend this limitation is for protocols to employ pay for practice, i.e., land mangers/owners are paid a standard amount per unit of land area enrolled in a specific conservation practice and no attempt is made to quantify outcomes achieved at the entity level. Another strategy is for protocols to imbed models that calculate entity level greenhouse gas, carbon storage, and nutrient loss outcomes. But up to now, estimates generated by these models are not very accurate at the entity level. However, recent advances in data availability, geographic information systems, precision agriculture and remote sensing combined with model applications and ground and atmospheric based measurements can reduce these uncertainties. Protocols that integrate pay for practice and entity and larger scale quantification methods are expected to approach optimal cost:benefit ratios.