Detecting Mild Water Stress in Olive with Multiple Plant-Based Continuous Sensors

Abstract

A comprehensive characterization of water stress is needed for the development of automated irrigation protocols aiming to increase olive orchard environmental and economical sustainability. The main aim of this study is to determine whether a combination of continuous leaf turgor, fruit growth, and sap flow responses improves the detection of mild water stress in two olive cultivars characterized by different responses to water stress. The sensitivity of the tested indicators to mild stress depended on the main mechanisms that each cultivar uses to cope with water deficit. One cultivar showed pronounced day to day changes in leaf turgor and fruit relative growth rate in response to water withholding. The other cultivar reduced daily sap flows and showed a pronounced tendency to reach very low values of leaf turgor. Based on these responses, the sensitivity of the selected indicators is discussed in relation to drought response mechanisms, such as stomatal closure, osmotic adjustment, and tissue elasticity. The analysis of the daily dynamics of the monitored parameters highlights the limitation of using non-continuous measurements in drought stress studies, suggesting that the time of the day when data is collected has a great influence on the results and consequent interpretations, particularly when different genotypes are compared. Overall, the results highlight the need to tailor plant-based water management protocols on genotype-specific physiological responses to water deficit and encourage the use of combinations of plant-based continuously monitoring sensors to establish a solid base for irrigation management.

Plant phenotyping relevance

複数の連続植物センサーによる葉の膨圧、果実成長、樹液流の測定を用い、軽度水ストレス検出の有効性・感度・測定頻度の限界を評価しており、植物状態の取得方法が中心です。

Code and data availability

The supplied article blocks describe sensor-based phenotyping (LPCP turgor probes, sap flow, fruit gauges) and R-based statistical analysis, but contain no data availability statement, no public repository deposit, no author code URL, and no supplement reference for the underlying measurements or analysis scripts. No a

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