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

合作研究：SitS：通过观察从谷物到景观尺度的动态土壤化学过程来改善水稻种植

• 批准号: 2226648
• 负责人: Mason Stahl
• 金额: $ 16.9万
• 依托单位: Union College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226648&HistoricalAwards=false
• 关键词: Collaborative; Research; SitS; Improving; Rice

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.

该奖项是通过“土壤中的信号（坐着）”招标颁发的，这是国家科学基金会与美国农业部国家食品和农业研究所（USDA NIFA）之间的合作伙伴关系。根据美国疾病控制中心的说法，砷（AS）由于其患病率而与许多慢性疾病（包括心脏病，癌症和糖尿病）相关，因此是最高的优先污染物。通过饮用水和食物，成千上万的人长期暴露于高水平的天然发生。帕迪稻田占所有可耕地的12％，并提供20％的人类热量摄入量，其中含有保留天然AS的丰富的铁氧化物。稻田中的减铁将其动员到水中，可以吸收在稻米作物中。人类会像食用这种米饭一样暴露于这种有毒的毒性，并且同时也会降低大米的产量，因为它也对米饭有毒。这是从稻田中释放出来的人类健康风险，威胁着农业社区以及世界上最重要的农作物之一的供应。来自哥伦比亚大学，联合学院和圣地亚哥州立大学的合作研究团队旨在确定水稻种植实践以及气候如何影响稻田土壤中的何处和何时释放的何时以及如何最终将其转化为滥用稻米作物。这项工作的发现将使用现场和卫星测量的实时数据，以帮助预测大米作物中最大风险的领域，并确定将水稻作物摄入的水稻种植实践。这些信息将与柬埔寨和德克萨斯州项目研究领域的农业社区以及更广泛的科学界共享，以帮助促进更好的水稻种植实践。这项研究的目的是对控制溶解的浓度和稻田土壤规范的环境因素进行机械理解，并使用此信息来开发有效的管理解决方案。这项研究目标非常适合坐着，因为这个多学科研究团队经常融合，并密集地测量了土壤地球化学，矿物学，微生物学和水文学，并在水稻帕迪土壤中收集的原位传感器，遥感和采样，以观察，模型，并预测砷的固体固态固体固态分析和uptecipition和Uptake和uptake和uptake。高分辨率遥感数据将用于将孔尺度观测到现场和景观尺度。这项研究将检验三个假设，以研究厌氧条件的发展，减少铁（Fe）的释放和释放，以及大米的吸收AS：1）包括气候，灌溉和受精在内的外部控制，推动了氧化还原梯度的时间，位置和深度，并最终调节米在大米中的摄取； 2）由于重叠的Fe和硫酸盐还原而导致溶解的陡峭的近表面梯度，并创建瞬时硫索复合物，将其作为Fe还原的溶液脱酸； 3）当与基于过程的模型集成时，远程感知的水和养分应力指标可以准确地缩放氧化还原梯度和大米吸收到较大景观的现场观测。将从柬埔寨的水稻农场中选择野外地点，那里的水稻水平经常超过安全水平。这些地点将在全年中广泛特征，以衡量稻田和控制中的Fe的组成，Mineraology和FE状态的变化，以及促进这些转变的微生物学群落和代谢中的其他关键要素，以及它们与地表水水文，水平平衡和灌溉方案的关系。定量模型将被构建以测试潜在的反应网络，并建立影响稻田中氧化还原梯度的动力学和热力学控制。新型的机器学习，概率模型以及基础设施，水和营养应力的远程感知指标将用于预测氧化还原过程的空间和临时分布，水性AS水性和水稻AS水平更广泛，并以精细的空间尺度更广泛。这种综合方法将为稻米的摄取氧化还原过程和环境控制的机理和动力学提供新的深入了解，这将通过得克萨斯州的现场采样进行测试，在德克萨斯州，米斯 - AS也是可变的，并且经常升高。更广泛的影响活动包括对研究生和本科生的培训，以及针对代表性不足和第一代高中生的研究经验。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子的智力优点和更广泛的影响来评估NSF的法定任务。