Towards a coupled crop-climate seasonal prediction system for dry-land wheat grain yield in Israel

Wheat production accounts for the largest portion of agricultural land in Israel, and it is the 2^nd most productive crop worldwide after Maize. Spring wheat which is mostly grown under rain-fed conditions, is highly susceptible to changes in climate conditions. As a result, wheat grain yields (GY) are suffering from high climate-dependent year-to-year variability, particularly under changes in precipitation patterns. This large variability stresses the need for accurate seasonal predictions of wheat yield, which may assist farmers in better agro-system planning, making the right management decisions (crop rotation, sowing dates and application of irrigation), and the right varietal choice. As a widespread crop, wheat also has the potential to impact regional climate conditions through an interactive feedback loop by exchanging heat and water with the land surface and the atmosphere above. Yet, current seasonal crop yield prediction systems do not account for climate-crop feedback, and their prediction skill is lacking.

The current study hypothesizes that using a high-resolution regional climate model (WRF) coupled with a crop model (Noah-MP-Crop) may increase seasonal crop yield prediction skill, providing a practical tool for farmers to increase their revenues and increase food security. To confirm this hypothesis, we have adapted the Noah-MP-Crop model for the spring wheat cultivars grown in Israel and conducted coupled simulations using the updated observed crop model parameters. In this presentation, the in-situ calibration process of the crop model to the spring wheat cultivars grown in Israel will be presented together with several simulated results from the calibrated coupled crop-climate model. A focus will be put on the exchange of heat, water, and carbon between the land surface and the lower atmosphere.