Sandy to clay soils have different hydrological properties such as wilting point, field capacity, and saturation level (Fig 1a). Wilting point means no water is available to plants, field capacity means near optimum moisture for plant growth, and saturation means the soil pores are full of water. Root growth, plant transpiration, and soil nitrogen mineralization are inhibited by too little or too much soil moisture. Nitrogen losses (denitrification and leaching) and soil water evaporation are favored by excess moisture. Thus, knowing the soil moisture level is critically important.
Soil moisture fluctuates between wilting point and saturation (Fig. 1a). A value of 0.30 mm/mm has different meanings in different soil types. For a loam soil, it means soil moisture is near field capacity while for a silty-clay soil, it means soil moisture is near wilting point. To facilitate comparisons across soil types at scale we converted the volumetric soil moisture to a 0–1 relative soil moisture index (Fig 1b):
Relative soil moisture = (soil moisture – wilting point) / (saturation – wilting point)
A value of 0 means soil moisture is at the wilting point (very dry) while a value of 1 means soil moisture is at saturation (very wet). A value of 0.54 means near optimum soil moisture for plant growth while values below 0.2 and above 0.8 indicate drought and excess moisture stress. The suggested optimum relative soil moisture levels hold across major soil types (Fig 1b) but it becomes problematic in extreme sandy soils (> 90% sand) and high clay soils (> 50% clay). However, this is of little concern as there is minimal row crop production in such extremely sandy or very high clay soils.
To estimate relative soil moisture across temporal (1984 to today) and spatial (30,000 fields) scales, we used the Agricultural Production Systems sIMulator (APSIM). The APSIM model has been extensively calibrated, and it is currently adequately simulating soil moisture and fluctuating water table (e.g., Dietzel et al., 2016; Archontoulis et al. 2020). In our regional scale simulation, we consider about 30 points per county. The points are cropland weighted averages and soil data derived from SSURGO. Areas with shallow water tables, tile drainage, and irrigation are factored in. Regional scale simulations of crop yields, soil moisture, and crop phenology were tested against USDA data and proved adequate (Archontoulis et al., in preparation for publication).
Starting March 22, 2021, and every Monday since then, we run APSIM for a corn-soybean rotation (current crop = corn, previous crop = soybean) to estimate soil moisture across the US Corn Belt. The simulation includes Monday’s actual weather. The simulated results are displayed on the FACTS website Soil Conditions every Tuesday. Our simulations and soil moisture updates will continue until November 2021. The benchmarking tool provides options to visualize relative soil moisture at three different depths: 0 to 0.5 feet, 0 to 1.5 feet, 0 to 3.5 feet. Previous week results are stored on the website and users can go back to visualize how the 2021 soil moisture has changed from one week to another. Figure 2 illustrates weekly maps from March 22 until May 17, 2021, for the 0 to 1.5 feet depth. In addition, for different crop reporting districts in Iowa, we benchmark the 2021 soil moisture time series patterns with historical years. Results thus far indicate that the 2021 moisture pattern is close to last year’s soil moisture pattern and, in general, is at or below the 37-yr average.
The soil moisture benchmarking tool provides spatio-temporal data to inform producers on how much water is in the soil profile. Our tool has some similarities and differences with other tools, e.g., US Drought Monitor. The similarity is that all regional scale soil moisture products report estimates of soil moisture, not real measurements. If estimates from different products agree, then the confidence in the results is high. The main difference between regional scale soil moisture products is the way soil moisture is estimated and the source of weather and soil input data. In our case, we estimate soil moisture via APSIM model water balance, which it accounts for water tables and tile drainage in the landscape and major water fluxes such as evaporation, transpiration, runoff, and drainage. Significant efforts have been made to ensure sensible and accurate model simulations, but as with every tool, there is always uncertainty, and we continue to improve the simulation of soil moisture while at the same time releasing weekly updates to assist producers’ decision making.
Dietzel R, Liebman M, Ewing R, Helmers M, Horton R, Jarchow M, Archontoulis SV, 2016. How efficienctly do corn- and soybean-based cropping systems use water? A systems modeling analysis. Global Change Biology, 22: 666–681
Archontoulis SV, Castellano MJ, Licht MA, Nichols V, Baum M, Huber I, Martinez-Feria R, Puntel L, Ordónez RA, Iqbal J, Wright EE, Dietzel RN, Helmers M, Vanloocke A, Liebman M, Hatfield JL, Herzmann D, Cordova SC, Edmonds P, Togliatti K, Kessler A, Danalatos G, Pasley H, Pederson C, Lamkey KR, 2020. Predicting Crop Yields and Soil-Plant Nitrogen Dynamics in the US Corn Belt. Crop Science, 60: 721–738.