Applying Land Data Assimilation to Simulate Days Suitable for Fieldwork for Agricultural Decision-Making

Farm activities such as planting, fertilization, and harvest are both time sensitive and weather dependent. Many farmers use historical averages of the number of days suitable for fieldwork (DSFW) to plan resource allocation during critical time periods. However, such estimates neglect local climate variability and the impacts of long-term climate change, as well as the improving capabilities of extended-range and seasonal forecasts. This study develops a method linking meteorological variables from the North American Land Data Assimilation System to historical records of the number of DSFW, to determine statistical criteria of weather conditions suitable for agricultural fieldwork. We determined state-level workability thresholds across the continental United States, and used these to project DSFW from 1999-2017. Depending on location, 30-81% of the variance in DSFW was explained using soil wetness and precipitation alone. Projections of DSFW during critical periods significantly improved estimations over the use of historical averages. In the future, these thresholds will be linked to a fine-resolution climate model to provide multi-scale forecasts of workable days, and to project future DSFW changes to support agricultural adaptation.