Efficient irrigation management is crucial to agriculture, forestry and horticulture, especially under climate change. Developments in novel sensors and Internet of Things technology provide an opportunity to carry out real-time monitoring of tree sap flux density, which, when coupled with advanced modelling techniques, enables online prediction of tree water-use suitable for irrigation planning. This manuscript proposes one such pipeline that integrates tree sap flow sensors, weather station sensors, and statistical models to predict tree daily water-use. In particular, an ensemble prediction approach based on additive models has been developed, using weather data as the main predictors of sap flux density. The method simultaneously considers the non-linear relationships and interactions between sap flux density and its environmental drivers, as well as the variability among individual trees over different growing seasons. Using field data collected on nine species of trees over the 2022, 2023 and 2024 growing seasons, this manuscript demonstrates the ability of the proposed ensemble prediction method in producing reliable daily water-use forecasts. The challenge of predicting tree water-use under climate stress, such as heatwaves, and the impact of tree sizes on prediction have also been discussed. Despite the complexity of the problem, the proposed method provides a general framework which can be used in a variety of settings, from commercial tree growers to conversation work. The model can be integrated into an online monitoring platform, assisting real-time decision making on irrigation management.

翻译：高效灌溉管理对农业、林业和园艺至关重要，尤其是在气候变化背景下。新型传感器与物联网技术的发展为实时监测树木液流密度提供了可能，结合先进的建模技术，可实现适用于灌溉规划的树木水分利用在线预测。本文提出了一种整合树木液流传感器、气象站传感器及统计模型的预测流程，旨在预测树木日水分利用量。具体而言，我们开发了一种基于加性模型的集成预测方法，以气象数据作为液流密度的主要预测因子。该方法同时考虑了液流密度与环境驱动因子之间的非线性关系及相互作用，以及不同生长季中个体树木间的变异性。利用2022、2023和2024年生长季期间对九种树木采集的野外数据，本文验证了所提集成预测方法生成可靠日水分利用预测的能力。此外，还讨论了热浪等气候胁迫下树木水分利用预测的挑战，以及树木规模对预测的影响。尽管问题复杂，该方法提供了一个通用框架，可适用于从商业林木种植到生态保护工程等多种场景。该模型可集成至在线监测平台，辅助灌溉管理的实时决策。