Climate-Smart Agriculture
Climate smart agricultural practices can refer to both mitigating and adapting to climate change. In collaboration with scientists across the country, I am working to evaluate agricultural practices for their effectiveness in mitigating and/or adapting to climate change. We tested approaches to use eddy covariance measurements of net ecosystem exchange as well as estimates of carbon removed from grain yield against soil samples. Figure 1 shows that eddy covariance approaches perform better than repeated soil samples, which we attributed primarily to the difficulty in capturing spatial heterogeneity in soils with point samples. Additionally, we have used eddy covariance measurements to evaluate the sensitivity of cropping systems to heat waves. We found that an agricultural system with an expanded rotation was able to mimic some properties of a native prairie and become more resilient to environmental stress.
Agricultural Management Impacts on Water and Carbon Fluxes
Overview: Three fields in Missouri have been instrumented with meteorological stations and eddy covariance sensors allowing characterization of water and carbon fluxes from the fields. One field is a “business-as-usual” cropping system that is managed by a local farmer. The second is an “aspirational” cropping system that uses cover crops and is no-till, and the final site is a native tallgrass prairie that has never been plowed. This experiment allows us to gain insight about how management practices impact the plant water use and carbon uptake.
Drought Impacts on Shallow Groundwater
Overview: Droughts are projected to become more frequent and more intense in the future as the global climate warms. Groundwater is often used as a supplemental water source during drought conditions when surface water supplies are limited. Therefore, an important consideration for water management under drought conditions is how long it takes for the meteorological droughts to propagate through the hydrologic system and cause groundwater drought. As part of this project, we are taking a three-step approach to understand groundwater response time to droughts: (1) utilize well-level observations and gridded precipitation datasets across the United States to statistically determine the most important controls on groundwater response to drought; (2) build a synthetic watershed scale model using the ParFlow.CLM to understand how drought propagates through the vadose zone under idealized conditions (no snow with stochastically generated meteorological forcings with known properties, and simple subsurface parameterizations); and (3) utilize a ParFlow.CLM model of the Kaweah River watershed and real forcing datasets from the recent California drought to study drought propagation to groundwater in a complex, real setting.
