Location: Water Management and Systems Research
2022 Annual Report
Objectives
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.
Approach
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).
Progress Report
Objective 1 has two broad goals: A) to improve crop water productivity by improving the physiological functioning of crop plants under limited water conditions, and B) to understand how water stress affects interactions between nitrogen (N) uptake and plant growth. We are currently only five months into the current research plan. All required experiments and tasks (see Project Plan 3012-13210-001-000D for details) are currently underway and a detailed report on this research will be written in fiscal year (FY) 2023.
Objective 2 has two broad goals: A) to improve precision variable-rate irrigation (VRI) decisions by integrating soil, weather, and plant stress measurements, and B) use multi-source remote sensing data to detect water and nitrogen stress and use this information to improve irrigation.
Objective 3 seeks to develop and improve tools and models for stakeholders in water limited regions.
Accomplishments
