Abstract
In the coming decades, a changing climate, growing global population, and rising food prices will have significant yet uncertain impacts on both water and food security. The loss of high-quality land, the slowing in annual yield of major cereals, and increasing fertilizer use, all indicate that strategies are needed for monitoring and predicting ongoing and future water deficits on farms for better agricultural water management decisions. Most such activities are based on in-situ measurements which are costly, hard to scale, and ignore the wealth of spatial and temporal information from remotely-sensed data. In this study, we designed FarmCan, a novel and robust climate-informed machine learning (ML) framework to predict crop water demand at the farm scale with up to 14 days lead time. We use a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with inputs from farmers, a Random Forest (RF) algorithm, and precipitation (P) prediction from MSWEP to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our study shows that SM and RZSM are the variables that are more correlated with P, while PET and ET do not show a strong correlation with P, SM, and RZSM. Our case study of 4 farms in the Canadian Prairies Ecozone (CPE) using R2, RMSE, and KGE indicators, shows that our algorithm was able to forecast crop water requirements 14 days in advance reasonably well. We also found that during 2020, RF forecasted ET and PET and needed irrigation (NI) with more accuracy than SM and RZSM, although this might vary based on the soil type, location, year of study, and crop type. Due to the forecasting capability and transferability of the mechanism developed, FarmCan is a promising tool for use in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government' policy concerns.
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