Role of leaf photosynthesis and stomatal conductance in determining the genotypic variation in whole-plant Transpiration Efficiency (TE) in Sorghum

Water scarcity can limit sorghum (Sorghum bicolor (L.) Moench) production in dryland agriculture, but increased whole-plant transpiration efficiency (TEwp, biomass production per unit of water transpired) can enhance grain yield in such conditions. The objectives of this study were to quantify variation in TEwp for 27 sorghum genotypes and explore the linkages of this variation to responses of the underpinning leaf-level processes to environmental conditions. Individual plants were grown in large lysimeters in two well-watered experiments. Whole-plant transpiration per unit of green leaf area (TGLA) was monitored continuously and stomatal conductance and maximum photosynthetic capacity were measured during sunny conditions on recently expanded leaves. Leaf chlorophyll measurements of the upper five leaves of the main shoot were conducted during early grain filling. TEwp was determined at harvest. The results showed that diurnal patterns in TGLA were determined by vapour pressure deficit (VPD) and by the response of whole-plant conductance to radiation and VPD. Significant genotypic variation in the response of TGLA to VPD occurred and was related to genotypic differences in stomatal conductance. However, variation in TGLA explained only part of the variation in TEwp, with some of the residual variation explained by leaf chlorophyll readings, which were a reflection of photosynthetic capacity. Genotypes with different genetic background often differed in TEwp, TGLA and leaf chlorophyll, indicating potential differences in photosynthetic capacity among these groups. Observed differences in TEwp and its component traits can affect adaptation to drought stress.

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Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach

Remote Sensing ◽

10.3390/rs12244070 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4070

Author(s):

Florian Ellsäßer ◽

Alexander Röll ◽

Joyson Ahongshangbam ◽

Pierre-André Waite ◽

Hendrayanto ◽

...

Keyword(s):

Machine Learning ◽

Stomatal Conductance ◽

Prediction Models ◽

Hydrological Cycle ◽

Machine Learning Algorithms ◽

Sap Flux ◽

Plant Transpiration ◽

Surface Temperatures ◽

Whole Plant ◽

Wide Range

Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r2 = 0.87 for trees and r2 = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.

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