Assessment of the Long-Term Impacts of Climate Change on Alfalfa Production in California

Abstract

Alfalfa is a crucial feed-supplier for California’s livestock and dairy industries, which are among the most profitable commodities in the state. Alfalfa production is projected to be impacted by climate change due to rising temperatures and the increasing frequency and intensity of droughts. As a C3 legume, alfalfa yields are expected to increase as atmospheric concentration of carbon dioxide (CO2) increases; however, less than optimal irrigation can also reduce alfalfa yields. Therefore, it is still unclear how alfalfa yields in California will ultimately be impacted due to climate change as there is still much uncertainty regarding future greenhouse gas emissions.

To address this, I conducted climate simulations for the near- (2020-2039), mid- (2040-2067), and long- (2072-2095) term using four General Circulation Models (GCMs) under two representative concentration pathway (RCPs) scenarios, RCP4.5 and RCP8.5. The research objectives were to (1) assess performance of DSSAT models built using variety trial data; (2) develop DSSAT models for thirteen counties in California; and (3) assess how increased atmospheric CO2 concentrations and water stress impact yields under RCP4.5 and 8.5 scenarios. The results align with previous studies that show increases in yields due to increase atmospheric CO2 concentrations as the average yield is projected to increase through the near (3%), mid- (10%), and long-term (11%) across all thirteen counties. RCP8.5 also had higher increases compared to RCP 4.5 for the near- (4 vs 3%), mid- (13 vs 8%), and long-term (14 vs 7%). The negative impact on yield from a water deficit was minimal and was counterbalanced by the increase in yield from elevated atmospheric CO2 concentrations. The larger increases in yield for RCP8.5 was likely due to the expected higher atmospheric CO2 concentrations for the high emission scenario.

The results from the DSSAT model align with previous studies; however, future studies can incorporate additional crop models and GCMs to achieve more robust results. Overall, the results of this study (based on DSSAT simulation models across four GCMs and two RCPs under water stressed conditions) project an increase in yield for all thirteen counties in California.