Genotypic variation in whole-plant transpiration efficiency in sorghum only partly aligns with variation in stomatal conductance

2019 ◽  
Vol 46 (12) ◽  
pp. 1072 ◽  
Author(s):  
Geetika Geetika ◽  
Erik J. van Oosterom ◽  
Barbara George-Jaeggli ◽  
Miranda Y. Mortlock ◽  
Kurt S. Deifel ◽  
...  
Keyword(s):  
Stomatal Conductance ◽  
Photosynthetic Capacity ◽  
Genotypic Variation ◽  
Vapour Pressure Deficit ◽  
Genotypic Differences ◽  
Transpiration Efficiency ◽  
Plant Transpiration ◽  
Leaf Chlorophyll ◽  
Residual Variation ◽  
Whole Plant

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.

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

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.

scholarly journals Genetic variation for photosynthetic capacity and efficiency in spring wheat

2019 ◽  
Vol 71 (7) ◽  
pp. 2299-2311 ◽  
Author(s):  
Viridiana Silva-Pérez ◽  
Joanne De Faveri ◽  
Gemma Molero ◽  
David M Deery ◽  
Anthony G Condon ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Stomatal Conductance ◽  
Photosynthetic Capacity ◽  
Genotypic Variation ◽  
Genotypic Diversity ◽  
Dry Mass ◽  
Co2 Assimilation ◽  
Leaf Nitrogen ◽  
Yield Potential

Abstract One way to increase yield potential in wheat is screening for natural variation in photosynthesis. This study uses measured and modelled physiological parameters to explore genotypic diversity in photosynthetic capacity (Pc, Rubisco carboxylation capacity per unit leaf area at 25 °C) and efficiency (Peff, Pc per unit of leaf nitrogen) in wheat in relation to fertilizer, plant stage, and environment. Four experiments (Aus1, Aus2, Aus3, and Mex1) were carried out with diverse wheat collections to investigate genetic variation for Rubisco capacity (Vcmax25), electron transport rate (J), CO2 assimilation rate, stomatal conductance, and complementary plant functional traits: leaf nitrogen, leaf dry mass per unit area, and SPAD. Genotypes for Aus1 and Aus2 were grown in the glasshouse with two fertilizer levels. Genotypes for Aus3 and Mex1 experiments were grown in the field in Australia and Mexico, respectively. Results showed that Vcmax25 derived from gas exchange measurements is a robust parameter that does not depend on stomatal conductance and was positively correlated with Rubisco content measured in vitro. There was significant genotypic variation in most of the experiments for Pc and Peff. Heritability of Pc reached 0.7 and 0.9 for SPAD. Genotypic variation and heritability of traits show that there is scope for these traits to be used in pre-breeding programmes to improve photosynthesis with the ultimate objective of raising yield potential.

Genetic variation for leaf carbon isotope discrimination and its association with transpiration efficiency in canola (Brassica napus)

2020 ◽  
Vol 47 (4) ◽  
pp. 355
Author(s):  
Shek M. Hossain ◽  
Josette Masle ◽  
Andrew Easton ◽  
Malcolm N. Hunter ◽  
Ian D. Godwin ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Carbon Isotope ◽  
Carbon Isotope Discrimination ◽  
Genotypic Variation ◽  
Dry Weight ◽  
Transpiration Efficiency ◽  
Isotope Discrimination ◽  
Leaf Chlorophyll ◽  
Total Dry Weight ◽  
Positive Correlations

Drought is a major constraint to canola production around the world. There is potential for improving crop performance in dry environments by selecting for transpiration efficiency (TE). In this work we investigated TE by studying its genetic association with carbon isotope discrimination (Δ) and other traits, e.g. specific leaf weight (SLW) and leaf chlorophyll content (SPAD). Among the 106 canola genotypes – including open-pollinated, hybrid, inbred types and cytoplasmic variants – tested in the field and glasshouse there was significant genotypic variation for TE, Δ, plant total dry weight, SLW and SPAD. Strong negative correlations were observed between TE and Δ (–0.52 to –0.76). Negative correlations between Δ and SLW or SPAD (–0.43 to –0.78) and smaller but significant positive correlations between TE and SLW or SPAD (0.23 to 0.30) suggested that photosynthetic capacity was, in part, underpinning the variation in TE. A cytoplasmic contribution to genetic variation in TE or Δ in canola was also observed with Triazine tolerant types having low TE and high Δ. This study showed that Δ has great potential for selecting canola germplasm with improved TE.

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