Location: Sustainable Agricultural Water Systems Research
Project Number: 2032-13220-002-033-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Dec 1, 2025
End Date: Nov 30, 2027
Objective:
Sustainable intensification of agriculture is an important topic to address growing populations. Adapting cover crop techniques for agricultural fields to enhance sustainability has been accelerating in recent years. Recent publications have reported that cover crops can improve soil’s physical, chemical, and biological properties, conserve soil moisture by increasing water retention and minimizing evaporation, reduce soil erosion from precipitation and wind, increase nitrogen availability for the primary crop on the one hand, while minimizing pollutant leaching on the other. Nevertheless, the contribution of cover crops to the primary crop is a topic of conflict. It was reported that cover crops can damage the primary crop by introducing competition for water, nutrients, and energy. The competition between cover and primary crops is expected to increase when resources are deficient, e.g., lack of irrigation in semi-arid and arid environments. In this proposal, we suggest monitoring wetting and salinity patterns below cover crops almond orchards at a large spatial extent and high resolution, ultimately quantifying roots’ activity. The obtained outcomes of the system will allow the assessment of cover crops’ impact on the primary crop, that is, competition or symbiosis and to what extent, and quantify their impact on the environment regarding solute leaching and water use efficiency. To inspect the environmental conditions, we will compare the outcomes from different orchard variables in the treatment of applied irrigation and cover crops, i.e., deficient and intensive irrigation, and different plots with and without cover crops. This proposal will significantly advance the knowledge and understanding of cover crops tradeoffs at intercropping plantations and can lead to proper and sustainable agricultural practices.
Approach:
ARS will conduct time-lapse, non-invasive geoelectrical measurements to monitor subsurface water fluxes over extended time periods. State-of-the-art machine learning techniques incorporating physical penalties and observational data will be developed and applied to analyze the geoelectrical measurements, separate the electrical signal into water content and solute concentrations, and provide the temporal root water uptake patterns simultaneously. Additional constraints on the machine learning system will involve local point in-situ measurements of the soil state and transpiration rates of the primary crop to be obtained from sap flow and dendrometer measurements.