Annelies Baert

Variable hydraulic resistances and their impact on plant drought response modelling

Plant drought responses are still not fully understood. Improved knowledge on drought responses is, however, crucial to better predict their impact on individual plant and ecosystem functioning. Mechanistic models in combination with plant measurements are promising for obtaining information on plant water status and can assist us in understanding the effect of limiting soil water availability and drought stress. While existing models are reliable under sufficient soil water availability, they generally fail under dry conditions as not all appropriate mechanisms seem yet to have been implemented. We therefore aimed at identifying mechanisms underlying plant drought responses, and in particular investigated the behaviour of hydraulic resistances encountered in the soil and xylem for grapevine (Vitis vinifera L.) and oak (Quercus robur L.). A variable hydraulic soil-to-stem resistance was necessary to describe plant drought responses. In addition, implementation of a variable soil-to-stem hydraulic resistance enabled us to generate an in situ soil-to-stem vulnerability curve, which might be an alternative to the conventionally used vulnerability curves. Furthermore, a daily recalibration of the model revealed a drought-induced increase in radial hydraulic resistance between xylem and elastic living tissues. Accurate information on plant hydraulic resistances and simulation of plant drought responses can foster important discussions regarding the functioning of plants and ecosystems during droughts.

Model-assisted evaluation of crop load effects on stem diameter variations and fruit growth in peach

Key messageThe paper identifies and quantifies how crop load influences plant physiological variables that determine stem diameter variations to better understand the effect of crop load on drought stress indicators.AbstractStem diameter (Dstem) variations have extensively been applied in optimisation strategies for plant-based irrigation scheduling in fruit trees. Two Dstem derived water status indicators, maximum daily shrinkage (MDS) and daily growth rate (DGR), are however influenced by other factors such as crop load, making it difficult to unambiguously use these indicators in practical irrigation applications. Furthermore, crop load influences the growth of individual fruits, because of competition for assimilates. This paper aims to explain the effect of crop load on DGR, MDS and individual fruit growth in peach using a water and carbon transport model that includes simulation of stem diameter variations. This modelling approach enabled to relate differences in crop load to differences in xylem and phloem water potential components. As such, crop load effects on DGR were attributed to effects on the stem phloem turgor pressure. The effect of crop load on MDS could be explained by the plant water status, the phloem carbon concentration and the elasticity of the tissue. The influence on fruit growth could predominantly be explained by the effect on the early fruit growth stages.

Functional unfold principal component analysis for automatic plant-based stress detection in grapevine

Detection of drought stress is of great importance in grapevines because the plants water status strongly affects the quality of the grapes and hence, resulting wine. Measurements of stem diameter variations show promise for detecting drought stress, but they depend strongly on microclimatic changes. Tools for advanced data analysis might be helpful to distinguish drought from microclimate effects. To this end, we explored the possibilities of two data mining techniques: Unfold principal component analysis (UPCA) - an already established tool in several biotechnological domains - and functional unfold principal component analysis (FUPCA) - a newer technique combining functional data analysis with UPCA. With FUPCA, the original, multivariate time series of variables are first approximated by fitting the least-squares optimal linear combination of orthonomal basis functions. The resulting coefficients of these linear combinations are then subjected to UPCA. Both techniques were used to detect when the measured stem diameter variations in grapevine deviated from their normal conditions due to drought stress. Stress was detected with both UPCA and FUPCA days before visible symptoms appeared. However, FUPCA is less complex in the statistical sense and more robust than original UPCA modelling. Moreover, FUPCA can handle days with missing data, which is not possible with UPCA.

Automatic drought stress detection in grapevines without using conventional threshold values

AimsBecause the water status of grapevines strongly affects the quality of the grapes and resulting wine, automated and early drought stress detection is important. Plant measurements are very promising for detecting drought stress, but strongly depend on microclimatic changes. Therefore, conventional stress detection methods require threshold values which define when plants start sensing drought stress. There is however no unique method to define these values. In this study, we propose two techniques that overcome this limitation.MethodsTwo statistical methods were used to automatically distinguish between drought and microclimate effects, based on a short preceding full-irrigated period to extract plant behaviour under normal conditions: Unfold Principal Component Analysis (UPCA) and Functional Unfold Principal Component Analysis (FUPCA). Both techniques aimed at detecting when measured sap flow rate or stem diameter variations in grapevine deviated from their normal behaviour due to drought stress.ResultsThe models based on sap flow rate had some difficulties to detect stress on days with low atmospheric demands, while those based on stem diameter variations did not show this limitation, but ceased detecting stress when the stem diameter levelled off after a period of severe shrinkage. Nevertheless, stress was successfully detected with both approaches days before visible symptoms appeared.ConclusionsUPCA and FUPCA based on plant indicators are therefore very promising for early stress detection.

UNFOLD PRINCIPAL COMPONENT ANALYSIS AND FUNCTIONAL UNFOLD PRINCIPAL COMPONENT ANALYSIS FOR ONLINE PLANT STRESS DETECTION

To be able to develop accurate plant-based irrigation scheduling tools, automatic and early detection of plant drought stress is of great importance. In this context, measurements of stem diameter variations are very promising as a source of information. These measurements are sensitive for drought stress, but also depend on changing microclimatic conditions. Specific data mining techniques, such as Unfold Principal Component Analysis (UPCA), have been developed to facilitate monitoring and diagnosing of such large-dimensional data sets. A UPCA model is used in this study to determine whether the measured stem diameter variations deviate from normal conditions due to drought stress. A newer technique, Functional Unfold Principal Component Analysis (FUPCA), combines functional data analysis with UPCA. The function parameters instead of the original data are then analysed by UPCA. The resulting FUPCA model is less complex and more robust compared to the original UPCA model. Moreover, FUPCA can handle days with missing data straightforwardly. The performances of UPCA and FUPCA models for online plant stress detection were investigated and compared to each other. Two pilot-scale setups were conducted: one with an herbaceous and one with a woody species. For both species, UPCA and FUPCA were shown to be applicable for stress detection. Both allowed successful detection days before visible symptoms appeared, while FUPCA exhibited a lesser parametric complexity.