Challenges and opportunities in precision irrigation decision-support systems for center pivots
Challenges and opportunities in precision irrigation decision-support systems for center pivots
复制标题
DOI：
10.1088/1748-9326/abe436
复制
发表时间：
2021-04
影响因子：
6.7
通讯作者：
Jingwen Zhang;K. Guan;B. Peng;Chongya Jiang;Wang Zhou;Yi Yang;M. Pan;T. Franz;D. Heeren;D. Rudnick;O. Abimbola;H. Kimm;Kelly K. Caylor;S. Good;M. Khanna;J. Gates;Yaping Cai
中科院分区：
文献类型：
--
作者：
Jingwen Zhang;K. Guan;B. Peng;Chongya Jiang;Wang Zhou;Yi Yang;M. Pan;T. Franz;D. Heeren;D. Rudnick;O. Abimbola;H. Kimm;Kelly K. Caylor;S. Good;M. Khanna;J. Gates;Yaping Cai
灌溉对于在干旱或半干旱环境中维持农业生产力至关重要，而中心枢轴式灌溉系统由于其多功能性和坚固性，是使用最广泛的灌溉系统。为了有效地使用中心枢轴灌溉系统，生产者需要工具来支持他们关于何时灌溉以及灌溉多少水的决策。然而，目前生产者主要根据经验和/或有限的天气信息做出这些决定。灌溉系统的无效使用会导致水资源的过度使用，损害作物生产力，直接降低生产者的经济回报，并对环境的可持续性带来负面影响。在本文中，我们从同行评审文献、赠地大学推广和行业产品以及美国专利中调查了现有的精准灌溉研究和工具。我们重点关注与精准灌溉决策支持系统相关的四个挑战领域：(a) 数据可用性和可扩展性，(b) 植物水分胁迫的量化，(c) 模型的不确定性和约束，以及 (d) 生产者的参与和动机。然后，我们确定了解决上述四个挑战领域的机会：(a) 增加高时空分辨率卫星融合产品和廉价传感器网络的使用，以扩大精准灌溉决策支持系统的采用； (b) 通过明确考虑土壤供水、大气需水和植物生理调节之间的相互作用，使用“植物水分胁迫”的机械量化作为改进灌溉决策的触发因素； (c) 使用数据模型融合方法来限制每个单独领域的基于流程的统计/机器学习模型，以实现可扩展的解决方案； (d) 开发易于使用、灵活的工具，并增加政府的财政激励和支持。我们在总结本次审查时阐述了我们对中心枢轴精密灌溉决策支持系统的愿景，该系统可以为生产者实现可扩展、经济、可靠且易于使用的灌溉管理。
Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers’ economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers’ participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of ‘plant water stress’ as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments’ financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.