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: 2226647
• 负责人: Benjamin Bostick
• 金额: $ 85.85万
• 依托单位: Columbia University
• 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=2226647&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“土壤信号”征集活动获得的，该征集活动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，因为它与心脏病、癌症和糖尿病等多种慢性疾病有关。数亿人长期通过饮用水和食物接触到高水平的天然砷。稻田占所有可耕地的12%，为人类提供20%的热量摄入，含有丰富的氧化铁，可以保留天然的砷。稻田中铁的减少将这些砷动员到水中，然后被水稻作物吸收。当人们食用这种大米时，就会接触到这种有毒的砷，砷也会降低大米的总产量，因为它对大米也有毒。因此，水稻土壤中砷的释放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小组旨在确定水稻种植方式以及气候如何影响砷从稻田土壤中释放的地点和时间，以及这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来帮助预测水稻作物中砷风险最大的地区，并确定可以最大限度地减少水稻作物吸收砷的水稻种植方法。这些信息将与柬埔寨和德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，以帮助推广更好的水稻种植方法。本研究的目的是建立对控制水稻土壤中溶解砷浓度和形态的环境因素的机制理解，并利用这些信息制定有效的管理解决方案。这个研究目标非常适合sit，因为这个多学科研究团队融合了频繁和密集的土壤地球化学、矿物学、微生物学和水文学测量，这些测量是通过原位传感器、遥感和稻田土壤采样收集的，以观察、建模和预测砷固溶分配和水稻吸收。高分辨率遥感数据将用于将孔隙尺度观测提升到野外和景观尺度。本研究将检验厌氧条件的发展、铁（Fe）还原和砷释放以及水稻对砷的吸收三个假设：1)气候、灌溉和施肥等外部控制因素驱动氧化还原梯度的时间、位置和深度，并最终调节水稻对砷的吸收；2)由于铁和硫酸盐的重叠还原，溶解的砷在近地表有陡峭的梯度，并产生瞬态硫砷配合物，使砷的溶解度与铁的还原分离；3)当与基于过程的模型相结合时，水分和养分胁迫遥感指标可以准确地将氧化还原梯度和水稻吸收的田间观测结果与更大的景观相结合。田间地点将从柬埔寨的水稻农场中选择，那里的水稻砷含量经常超过安全水平。这些地点将在一年中进行广泛的特征描述，以测量稻田土壤和对照物中铁、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代谢，以及它们与地表水水文、水平衡和灌溉方案的关系。将建立定量模型来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、水和营养胁迫的遥感指标将用于更广泛和更精细的空间尺度上预测氧化还原过程、水中砷和水稻砷水平的时空分布。这种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力学提供新的有力的见解，这些将在德克萨斯州进行田间抽样测试，那里的水稻砷也是可变的，经常升高。更广泛的影响活动包括培训研究生和本科生，以及为代表性不足的第一代高中生提供研究经验。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Zinc localization and speciation in rice grain under variable soil zinc deficiency

• DOI: 10.1007/s11104-023-06140-1
• 发表时间: 2023-07
• 期刊: Plant and Soil
• 影响因子: 4.9
• 作者: Yating Shen;Elizabeth G Wiita;A. Nghiem;J. Liu;E. Haque;R. Austin;Chheng Y. Seng;K. Phan;Yanjia Zheng;B. Bostick