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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Research Project #441925

Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Project Number: 3012-13210-001-000-D
Project Type: In-House Appropriated

Start Date: Feb 5, 2022
End Date: Feb 4, 2027

Objective 1: Identify crop physiological trait networks and soil nitrogen processes that improve the performance of agricultural systems under water and nutrient stress. Sub-objective 1.A: Identify physiological trait networks that advance process-based plant growth models, artificial intelligence (AI)/statistical models, and conceptual understanding of crop stress physiology. Sub-objective 1.B: Identify plant and soil processes that determine crop nitrogen requirements under varying water availability. Sub-objective 1.C: Develop rapid and cost-effective phenotyping methods to quantify complex physiological traits across genotypes. Objective 2: Develop methods to guide precision agricultural water management using remote-sensing, climate and soil data. Sub-objective 2.A: Develop algorithms and tools that integrate in-situ sensor and remotely sensed image data with soil and weather data to inform precision variable-rate irrigation (VRI) decisions. Sub-objective 2.B: Link multi-source remote-sensing data for detection of crop abiotic and biotic stress and estimation of crop water use using machine learning and AI techniques to support precision irrigation. Objective 3: Build better field- to farm-scale decision support datasets, tools, and models for stakeholders in water-limited regions to optimize water use, profitability, and sustainability.

Urban demand for water will increase ca. 80% over the next 30 years, independent of climate change (Florke et al. 2018). Considering the combined effects of urban demand and the changing climate, we can expect an increase in the needs for agricultural water and a decrease in the supply of agricultural water over the next several decades, resulting in decreased food security world-wide (Wallace 2000, Harmel et al. 2020, Hasegawa et al. 2020, Qin et al. 2021). There is therefore an urgent need to make crop species and agricultural practices more water efficient in the face of these challenges. The research proposed herein addresses key knowledge gaps and confronts these challenges with a multifaceted approach. Specifically, we aim to improve scientific understanding of which crop traits should be targeted to increase crop water productivity (crop production per unit water) and nitrogen use efficiency under limited water (Objectives 1.A, 1.B, & 1.C). This will be achieved through a truly broad multidisciplinary approach combining plant physiology, genetics, soil biogeochemistry, and process modeling. In parallel, we will develop novel irrigation scheduling techniques that will leverage newly emerging technologies (i.e., plant stress sensing, proximal sensing, airborne remote sensing, precision agriculture, machine learning) to improve the spatial and temporal application of both water and nitrogen (Objectives 2.A & 2.B). Lastly, these plant, soil, and irrigation data streams will be woven together to build new decision support datasets, tools, and models for stakeholders in water-limited regions (Objective 3).