Soil Condition Analysis and Prediction
Weather, both directly and indirectly, is the critical factor in the success of a harvest and farmers' livelihoods. Severe weather events, such as hail, high winds, tornados, and flash floods can destroy an entire harvest in a very short period. However, many agricultural decisions simply require more accurate forecasts of the weather and the resultant soil conditions. Precise soil temperature and soil moisture forecasts are critical to the timely application of pesticides, seed and fertilizer selection, and to efficient irrigation practices. RAL has been collaborating with industry to develop agricultural decision support capabilities that optimizes the timing of pesticide application and irrigation. These projects typically utilize advanced weather and land surface models and an intelligent data fusion technology that continuously optimizes the weather and soil predictions. This research has led to improvements in the High-Resolution Land Data Assimilation System (HRLDAS), Dynamic, Integrated Forecast System (DICAST®), and Noah Land Surface Model. This research is instrumental in providing critical feedback to the weather and land surface modeling, and satellite communities and represents a cross disciplinary effort. Continued work in this area will lead to more precise prediction of weather and soil condition and more efficient and profitable agricultural operations.
NASA Agriculture DSS
The US agricultural sector suffers from a lack of accurate soil temperature and moisture forecasts. Crude agriculture–specific models have been developed, often in academic environments, to address the sector's problems. For example, phenological pest models have been developed based only on average daily air temperatures. This NASA funded program addresses this problem by combining advanced weather and soil forecast systems (DICast and HRLDAS) with the goal of producing an accurate soil temperature and moisture forecast at high temporal and spatial resolution. The output of the land–surface model will be communicated to Meteorlogix (RAL's commercial partner in this project). After an evaluation period, the output will be used to drive agriculture–specific models and incorporated into the Meteorlogix agricultural DSS which has roughly 80,000 subscribers. NASA's interest in funding the project is to determine whether incorporation of their MODIS products into the land–surface model can improve the soil forecasts.