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(SITS)”征集，这是美国国家科学基金会和美国农业部国家粮食和农业研究所(USDA NIFA)之间的合作伙伴关系。根据美国疾病控制中心的数据，砷(As)是最优先考虑的污染物，因为它普遍存在，并与许多慢性疾病有关，包括心脏病、癌症和糖尿病。数以亿计的人通过饮用水和食物长期接触到高水平的天然污染物。稻田覆盖了12%的耕地，提供了人类20%的卡路里摄入量，含有丰富的铁氧化物，可以保留天然的砷。稻田中铁的还原将其动员成水，在那里它可以被水稻吸收。当人类食用这种大米时，就会暴露在这种有毒物质中，而且砷还会降低大米的整体产量，因为它对大米也是有毒的。因此，从稻田土壤中释放的砷对人类健康构成威胁，并威胁到农业社区和世界上最重要的作物之一的供应。这个由哥伦比亚大学、联合学院和圣地亚哥州立大学组成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水稻土壤中砷的释放地点和时间，以及最终如何转化为水稻作物的吸收。这项工作的结果将使用来自田间和卫星测量的实时数据，帮助预测水稻作物中砷风险最高的地区，并确定将水稻作物对砷的吸收降至最低的水稻栽培措施。这些信息将与柬埔寨和得克萨斯州项目研究地区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做法。这项研究的目的是从机制上理解控制水稻土中溶解砷浓度和形态的环境因素，并利用这些信息制定有效的管理解决方案。这一研究目标非常适合SITS，因为这个多学科研究团队融合了土壤地球化学、矿物学、微生物学和水文学的频繁和密集测量，并通过现场传感器、遥感和水稻土壤采样收集，以观察、建模和预测砷在水稻中的固溶体分配和吸收。高分辨率遥感数据将被用于将孔隙尺度的观测提升到田野和景观尺度。这项研究将检验三个假说，检验厌氧条件的发展，铁(Fe)的还原和As的释放，以及水稻对As的吸收：1)外部控制，包括气候、灌溉和施肥决定氧化还原梯度的时间、位置和深度，并最终调节水稻对As的吸收；2)近地表陡峭的梯度，由于Fe和硫酸盐还原重叠而溶解，并创建与Fe还原脱钩的瞬时硫代砷复合体；3)与基于过程的模型相结合，水分和营养胁迫的遥感指标能够准确地将田间观察的氧化还原梯度和水稻吸收扩大到更大的景观。田间地点将从柬埔寨稻田中选择，那里的稻米砷水平经常超过安全水平。全年将对这些地点进行广泛的表征，以测量水稻土和对照中铁、砷和其他关键元素的组成、矿物学和氧化还原状态的变化，促进这些转化的微生物群落和新陈代谢，以及它们与地表水水文、水平衡和灌溉制度的关系。将构建定量模型，以测试潜在的反应网络，并建立影响稻田氧化还原梯度的动力学和热力学控制。新的机器学习、概率模型和淹没、水分和营养胁迫的遥感指标将被用来预测氧化还原过程、水砷和水稻-砷水平的时空分布，范围更广，并在精细的空间尺度上。这一综合方法将为水稻吸收砷的氧化还原过程和环境控制的机制和动态提供新的和强大的见解，这将通过德克萨斯州的田间采样进行测试，那里的水稻-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，以及对代表不足的和第一代高中生的研究经验。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。