Collaborative Research: SitS: Improving Rice Cultivation by Observing Dynamic Soil Chemical Processes from Grain to Landscape Scales

This award was made through the "Signals in the Soil (SitS)" solicitation, a collaborative partnership between the National Science Foundation and the United States Department of Agriculture National Institute of Food and Agriculture (USDA NIFA). According to the US Center for Disease Control, arsenic (As) is the highest priority contaminant due to its prevalence and association with numerous chronic diseases, including heart disease, cancer, and diabetes. Hundreds of millions of people are chronically exposed to high levels of naturally occurring As through both drinking water and food. Paddy rice fields, which cover 12% of all arable land and provide 20% of human caloric intake, contain abundant iron oxides that retain natural As. Iron reduction in paddy fields mobilizes this As into water where it can be absorbed into rice crops. Humans are exposed to this toxic As when they consume this rice, and the As also reduces overall rice yields because it is toxic to rice too. Thus, As release from rice paddy soils poses a human health risk and threatens farming communities and the supply of one of the world’s most important crops. This collaborative research team from Columbia University, Union College, and San Diego State University aims to identify how rice cultivation practices, along with climate, affect where and when As is released from rice paddy soils and how this ultimately translates into absorption into the rice crop. Findings from this work will use real-time data from field and satellite measurements to help predict areas of greatest risk of As in the rice crop and to identify rice cultivation practices that minimize As uptake by the rice crop. This information will be shared with farming communities in the project study areas of Cambodia and Texas as well as with the broader scientific community to help promote better rice cultivation practices. The goal of this research is to develop a mechanistic understanding of the environmental factors that control the dissolved As concentration and speciation in rice paddy soils, and to use this information to develop effective management solutions. This research goal is well-suited to SitS because this multidisciplinary research team fuses frequent and dense measurements of soil geochemistry, mineralogy, microbiology, and hydrology collected with in situ sensors, remote sensing, and sampling in rice paddy soils to observe, model, and predict arsenic solid-solution partitioning and uptake into rice. High-resolution remote sensing data will be used to upscale pore-scale observations to field and landscape scales. The research will test three hypotheses examining the development of anaerobic conditions, iron (Fe) reduction and As release, and rice uptake of As: 1) External controls including climate, irrigation and fertilization drive the timing, location and depth of the redox gradients, and ultimately regulate As uptake in rice; 2) Steep near-surface gradients in dissolved As result from overlapping Fe and sulfate reduction, and create transient thioarsenic complexes that decouple As solubility from Fe reduction; and 3) When integrated with process-based models, remotely sensed indicators of water and nutrient stress can accurately scale field observations of redox gradients and rice uptake to larger landscapes. Field sites will be selected from working rice farms in Cambodia where rice-As levels frequently exceed safe levels. These sites will be extensively characterized throughout the year to measure changes in the composition, mineralogy, and redox state of Fe, As, and other key elements in the paddy soil and controls, the microbiological communities and metabolisms that facilitate those transformations, and their relationship to surface water hydrology, water balance, and irrigation regimens. Quantitative models will be constructed to test potential reaction networks and to establish the kinetic and thermodynamic controls affecting redox gradients in rice paddies. Novel machine learning, probabilistic models, and remotely sensed indicators of inundation, water, and nutrient stress will be used to predict the spatial and temporal distribution of redox processes, aqueous As, and rice-As levels more widely, and at a fine spatial scale. This integrated approach will provide new and powerful insight into the mechanism and dynamics of redox processes and environmental controls on As uptake by rice that will be tested with field sampling in Texas, where rice-As is also variable and frequently elevated. Broader Impacts activities include training of graduate and undergraduate students, and also research experiences for underrepresented and first-generation high school students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.