Damásio Miguel, Barbosa Miguel, Deus João, Fernandes Eduardo, Leitão André, Albino Luís, Fonseca Filipe, Silvestre José
INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal.
SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal.
Plants (Basel). 2023 Dec 12;12(24):4142. doi: 10.3390/plants12244142.
Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
气候变化正在影响全球葡萄栽培，导致热浪和干旱加剧。在地中海盆地的大部分地区，借助强大的水分状况指标（WSIs）进行精准灌溉是不可避免的。最可靠的WSIs之一是叶片水势（Ψleaf），它是通过一种侵入性且耗时的方法测定的。这项工作的目的是识别与植物水分状况相关的最有效变量，并确定能更好预测Ψleaf的变量。选取了在葡萄牙阿连特茹地区种植的五个葡萄品种，并从2018年开始对其进行三种灌溉处理：充分灌溉（FI）、亏缺灌溉（DI）和不灌溉（NI）。2023年对植株进行了监测。测量内容包括气孔导度（gs）、黎明前水势Ψpd、茎水势（Ψstem）、热成像和气象数据。WSIs，即Ψpd和gs，根据灌溉处理的不同表现出不同的响应。上午中旬（MM）和中午（MD）测得的Ψstem无法区分不同处理。MM测量结果显示WSIs之间具有最佳相关性。gs在其他WSIs之间显示出最佳相关性，因此对估计Ψpd具有最佳预测能力。利用气象、热成像和gs数据训练机器学习回归模型来预测Ψpd，集成模型表现出色（极端随机树：R2 = 0.833，平均绝对误差= 0.072；梯度提升：R2 = 0.830；平均绝对误差= 0.073）。
