Groundwater Fluctuations Impact Grain Yields

Year Awarded  2017

FFAR award amount   $300,000

Total award amount   $600,000

Location   Ames, IA

Matching Funders   Iowa State University

Where’s the Water?

Today’s crop models–our primary tools for predicting yields and how crops respond to climate change–ignore the contribution of groundwater, a substantial source of water for agriculture. In part, this is because there is a lack of data on groundwater in agriculture to inform crop model improvements. As a result, future climate change predictions on grain yield are uncertain, especially in environments with wet soils, such as areas of the Midwest Corn Belt.

Most crop models are capable of simulating drought stress but not excess moisture stress, including flooding. A major assumption of these models is that there is no water table–the subsoil layer that is fully saturated with water–in a soil profile. Rather, the models take for granted that water enters the top of the soil profile as rain or irrigation and leaves from the bottom of the profile as drainage. With a $300,000 FFAR grant and matching funds from the Iowa Crop Improvement Association, Iowa State University’s Dr. Sotirios Archontoulis and his research team addressed this problematic assumption in two steps.

First, the team carried out research to enable simulations of excess moisture in crop models. Second, they linked excess moisture stress to soil-root-plant processes so the model can simulate excess moisture’s impacts on grain yields. The researchers not only found that water balances change, but they also determined how the excess moisture stress affects grain yields.

With the results of these breakthroughs, the research team released web-based tools to inform growers and agronomists about soil water and also how this affects the soil nitrogen. A major advantage to growers and researchers is they can use these data to better predict crop yield based on soil water availability.

Better Predictions of Future Yields

To develop excess moisture simulations, the researchers gathered data on water table fluctuations and soil moisture from field experiments. They then used the Agricultural Production Systems sIMulator (APSIM), a commonly used platform for modelling agricultural systems, to simulate soil moisture at scale.

The team also carried out further field experiments and literature review to link excess moisture stress to crop growth and grain yield. In addition, the experiments examined crops’ root functionality in response to crop management, and environmental settings.

“What makes this research exciting is that we are able to more accurately represent the real-world system across scales and see how plants and soil processes respond to the full spectrum of water stress—from too little water to too much water induced by shallow water tables,” explained Dr. Archontoulis. “The simulated grain yield response to precipitation better matched real-world observations, which is a major step towards more accurate future climate change yield predictions and agronomic assessments.

What makes this research exciting is that we are able to more accurately represent the real-world system across scales and see how plants and soil processes respond to the full spectrum of water stress. Dr. Sotirios Archontoulis
Associate Professor, Iowa State University

The research generated several key insights into how water tables influence crop yields and crops’ root growth:

  • The existence of water tables in farmland increases yields and yield stability. In central Iowa, for example, over a 30-year period shallow water tables increased maize grain yields by 15 percent and yield stability by 13 percent over soil with no water table. This shows that the existence of water tables provides a buffer against weather variability such as drought years.
  • On the other hand, excess moisture can limit yield. For row crops such as corn and soybean, root growth ceases when soil moisture approaches 95 percent saturation. Climate change models project future increases in precipitation, meaning more frequent wet springs will limit the potential for crops’ development.
  • Overall, the depth to the water table was the number one factor determining maximum rooting depth and root architecture.

These breakthroughs in the understanding and predicting of soil moisture’s effect on crop roots is significant for optimizing the production and performance of cropping systems.

Dr. Archontoulis and team perform simulations

More about this research

Our $300,000 investment in this research is part of our New Innovator in Food & Agriculture Research Award.

For further reading, please visit these Iowa State University blog posts by Drs. Sotirios Archontoulis and Mark Licht.

FFAR’s New Innovator Award is the best award for young faculty. It is an honor and a grant together. The combination of these two is the key to success. Dr. Archontoulis

Publications resulting from this research can be found here:

[1] 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 U.S. Corn Belt. Crop Science, 60: 721–738.

[2] Ebrahimi-Mollabashi E, Huth NI, Holzwoth DP, Ordonez RS, Hatfield JL, Huber I, Castellano MJ, Archontoulis SV, 2019. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crops Research 236: 58–67.

[3] Pasley HR, Huber I, Castellano MJ, Archontoulis SV, 2020. Modeling flood-induced stress in soybeansFrontiers Plant Science 11:62.

[4] Nichols V, Ordóñez A, Wright E, Castellano M, Liebman M, Hatfield J, Helmers M, Archontoulis SV, 2019. Maize root distributions strongly associated with water tables in Iowa, USA. Plant Soil, 444: 225-238.

[5] Castellano M, Archontoulis S, Helmers M, Poffenbarger H, Six J, 2019. Sustainable intensification of agricultural drainage. Nature Sustainability 2, 914–921.

[6] Shahhosseini M, Hu G, Huber I, Archontoulis S, 2021. Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the U.S. Corn Belt. Nature Scientific Reports, 11:1606.

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