Project : USDA ARS

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

2024 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 1a: Sunflower, maize, and barnyard grass were grown in a greenhouse and in a “walk-in” growth chamber to assess their ability to recovery from embolism, i.e., gas obstructions arising in the water-conductive cells of plant species during drought. It is currently well understood that embolism is linked to plant death in many wild and domesticated species, but the ability to reverse/repair embolism has not been documented using non-destructive methods, free from methodological artifacts. We used two state-of-the-art non-destructive methods (one developed in our lab) to directly observe embolism in maize (reversal not observed), sunflower (reversal not observed), and in Barnyard grass (reversal observed). These results are the first of their kind and are helping us understand how crops survive severe drought, and how embolism tolerance/avoidance traits differ among crop and weed species. The traits identified in this study - traits associated with either tolerating or avoiding embolism - have potential to improve the performance and survival of crop species grown in water limited environments because these traits are directly linked with plant death and could markedly improve future germplasm. Objective 1b: To understand water and fertilizer controls on mineralized nitrogen (N) rates in maize, we measured gross nitrogen mineralization using soil enzyme activity as a proxy and net mineralized nitrogen using in situ incubations under full and near-dryland levels of water availability and three levels of N fertilization, low (22 kg/ha), optimal (224 kg/ha), and excessive (262 kg/ha) across two growing seasons. Additionally, grain production and water and N use was assessed under full and near-dryland water availability and six levels of N fertilizer from 22–262 kg/ha. Water use efficiency (grain yield ÷ total crop water use) and N fertilizer use efficiency (grain ÷ N fertilizer rate) were calculated. Our data shows a proportional increase in grain yield as N fertilizer increases when water is fully available. However, when water was limiting, higher applications of N fertilizer tended to reduce grain yields and water use efficiency, likely because accumulated N in plant roots acts as a signal to close stomata. These results highlight the need to adjust N fertilizer rate based on water availability and plant growth potential to maximize yields and water use efficiency, as well as economic fertilizer use. More work is needed to explore how crop management might be modified to support the potential use of mineralized N as a N source for maize. These results were presented at an international meeting. Objective 1c: We examined vascular traits in monocotyledon and dicotyledon species that could potentially lead to improved drought tolerance in crops. We did this at two separate levels of investigation: at the level of the individual organ (e.g., maize “nodes”), as well as across species (33 crop, weed, and wild selections). These studies included three separate experiments undertaken with collaborators at the University of Colorado-Boulder and Colorado State University. Experiments performed at ARS laboratories included testing the hypothesis that maize nodes serve to prevent gas embolism from spreading from leaves (where embolism if first observed) to stems, and potentially to reproductive organs. This is important because it is well understood that embolism (gas obstruction in the water-conducting cells), and the spread of embolism among cells and tissues, is causally linked to plant death. Using state of the art methods, one of which was developed in our lab, we quantified the death of living tissue in nodes during plant development and maturation, and whether or not node death was associated with embolism spread. We found that node death was indeed associated with embolism spread, with the spread of embolism greatly accelerating after node death. At the across-species level, we evaluated the anatomical characteristics of xylem and phloem tissues conferring fast rates of growth (water and sugar transport) in the vasculatures of crop and weed species. Taken together, these three studies identified key stem and leaf vasculature features associated with fast growth and low/high carbon construction costs. Our findings suggest that although there is no clear growth advantage between monocotyledon and dicotyledon vascular designs, breeding for enhanced (and aligned) sugar and water transport would likely result in improved climate resilience and drought tolerance. Objective 2a: Field experiments of real-time irrigation scheduling were successfully conducted in the 2023 field season at the Limited Irrigation Research Farm (LIRF). Treatments included both full and limited irrigation treatments, using several evapotranspiration (ET) and water balance methods, including water balance, standardized ET methods (FAO-56; Food and Agriculture Organization, Technical Paper 56), energy balance, canopy temperature and Degrees Above Non-Stressed index, and a combination of remote sensing and the Root Zone Water Quality Model (RZWQM2). Each of these methods showed promise to characterize crop water use under various stress levels. Several important datasets are being collected for this objective, with many of them integral in water balance modeling that has quick turnaround (e.g. within a day) for near real-time irrigation decisions. To streamline this process, a related side-project was completed to streamline all data collection and processing into a customized programmable and repeatable database model, serving as a template for researchers, irrigation system manufacturers, mesonet managers, crop consultants, and other stakeholders interested in estimating ET and irrigation needs. Preliminary data from the project was presented at the U.S. Committee on Irrigation and Drainage Conference. Objective 2b: We collected spectral reflectance data from leaves of maize under various conditions – with six different N levels and two irrigation levels at the ARS Akron, Colorado, research station. Our analysis revealed distinct spectral signatures for plants depending on the N and water treatments. By identifying specific patterns, we were able to determine key indicators and spectral regions that differentiate maize performance based on these factors at different growth stages. Furthermore, we developed a statistical model to analyze even more detailed data (hyperspectral) to predict crucial plant parameters – leaf carbon and N content, crop biomass, yield, and water use efficiency. This information, initially gathered at the leaf level, has the potential to be scaled up. By linking it with data from UAVs equipped with various sensors (Red-Green-Blue, multispectral, and thermal), we can potentially detect N and water stress across entire fields. We also collected time-series UAV images over maize fields at LIRF through the growing season. Machine learning models were then developed to predict crop yield at different growth stages. Notably, thermal information from the UAV images proved to be crucial for predicting yield when crops were experiencing water stress. Overall, machine learning methods significantly improved yield prediction accuracy, especially in scenarios where less spectral information was available. Objective 3: Test Root Zone Water Quality Model 2 (RZWQM2) for irrigation scheduling and simulated ET. Simulate planting date risk for sites across the Central Great Plains region using the Unified Plant Growth Model. RZWQM2 was successfully used to inform irrigation in the 2023 growing season at LIRF.

Accomplishments
1. Customized and demonstrated an open-source standardized evapotranspiration and water balance model for irrigation scheduling. ARS researchers in Fort Collins, Colorado, and Temple, Texas, advanced development of a Python-based software package called “pyfao56” which calculates standardized crop and reference evapotranspiration (ET) and water balance. The tool was customized for data acquisition, processing, and modeling at the ARS Limited Irrigation Research Farm, and demonstrated functionality for use in monitoring crop water use and scheduling irrigation. The team developed a template to assist ET researchers, hydrologists, agronomists, irrigation manufacturers, and software developers determine water balance thus optimizing added water. This team is also developing the tool for integration into smartphone apps for producers. This tool is a crucial next step for use in real-time variable rate irrigation management, which will apply the right amount of water exactly where it is needed based on real-time feedback, which will boost agricultural sustainability in arid and semi-arid regions.

2. Root traits align with productivity, growth, and carbon allocation. ARS researchers in Fort Collins, Colorado, reported that suites of root morphological and physiological traits are aligned with fast- versus slow-plant growth (such as thinner diameters, greater length per mass ratio, higher respiration rate, and faster and greater capacity for phosphorus absorption). The researchers also illustrated the collective effect of these traits on phosphorus acquisition through modeling; determined that root morphological traits were intrinsic among species, while root physiological activity shifted in concert with concurrent plant growth rate; linked root hydraulic traits with plant growth strategies; and identified root traits aligned with plant nitrogen acquisition and root tissue lifespan. This ground-breaking work is critical for identifying important relationships and tradeoffs between root traits and plant growth strategies for plant biologists with crucial implications for geneticists and breeders developing the next generation of crops to maintain productivity in the face of climate change. Additionally, linkages between tissue chemistry, lifespan, and plant growth strategies are central to understanding plant effects on carbon storage pools in ecosystems, which is an important component of the global carbon cycle, and therefore global warming potential.

U.S. DEPARTMENT OF AGRICULTURE

Water Management and Systems Research: Fort Collins, CO