Stomatal resistance of New Guinea Impatiens pot plants. Part 2: Model extension for water restriction and application to irrigation scheduling

Cannavo, Patrice; Ali, Hacène Bouhoun; Chantoiseau, Etienne; Migeon, Christophe; Charpentier, Sylvain; Bournet, Pierre-Emmanuel

doi:10.1016/j.biosystemseng.2016.07.001

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…However, the PET_r cs often exceeds PET_ref in growing seasons and becomes lower than it in nongrowing seasons (Figure S18 in Supporting Information S1). Important to note that these temporal dynamic patterns of PET_r cs are in line with the observed evapotranspiration in well-watered fields found in previous studies (Cannavo et al, 2016;Uddin et al, 2016). Besides these, the PET_r cs also exceeds PET_ref in arid and semiarid regions, and in the tropic forests.…”

Section: Discussionsupporting

confidence: 89%

Estimating Dynamic Non‐Water‐Limited Canopy Resistance Over the Globe: Changes, Contributors, and Implications

Liu,

Sun,

Lin

et al. 2023

Water Resources Research

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Non‐water‐limited canopy resistance (rcs, also known as the bulk stomatal resistance or surface resistance) is a critical variable in estimating potential evapotranspiration (PET), which is widely used in ecohydrology related fields. However, quantifying rcs is a challenging work. Here we develop an approach for estimating rcs over the globe. Comparing results over the globe and across ten ET datasets (used as inputs), which are based on diverse mechanisms and algorithms, we find that the approach can capture canopy resistance well (mean correlation of 0.84 ± 0.04, mean relative Root Mean Squared Error of 4.4 ± 1.0%, and mean relative Mean Absolute Error of 5.8 ± 1.4%), and the estimated rcs are very close to those estimated using another method (R2 = 0.92), which is based on a quite different hypothesis that is only suitable for saturated regions. Based on these, we find that the rcs shows an overall increasing trend (0.43 ± 0.13 s m‐1 year‐1) over the globe (at 77.6 ± 3.9% of the land grid cells) during 1982‐2014, and the air temperature dominates the variabilities of rcs in regions with decreasing rcs (mean relative contribution of 57.9 ± 11.4%), while air CO2 concentration controls the changes in rcs in regions with increasing rcs (mean relative contribution of 47.3 ± 8.0%). Moreover, we also find that the traditional PET estimator explicitly overestimates the increasing trends in PET, and tends to overestimate (underestimate) the increasing (decreasing) trends in regions with increasing (decreasing) PET. These findings can improve our knowledge on the complex water‐vegetation‐environment interactions and would be helpful for developing more accurate models for quantifying the impacts of global change on water resources.This article is protected by copyright. All rights reserved.

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Section: Discussionsupporting

confidence: 89%

Estimating Dynamic Non‐Water‐Limited Canopy Resistance Over the Globe: Changes, Contributors, and Implications

Liu,

Sun,

Lin

et al. 2023

Water Resources Research

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“…The reverse mechanism takes place under water scarcity. Stomatal opening and closing can be assessed through the evolution of Rs which defines the plant-atmosphere interactions, strongly influencing the rate of gas exchange, and hence, photosynthesis and transpiration [55,56].…”

Section: Resultsmentioning

confidence: 99%

“…More specifically, a linear regression model with indicator variables was applied having R 2 = 74.20% (adjusted R 2 = 70.89%) and a linear model was estimated for each stress level. The equation of the fitted model is: y = 0.72 − 0.44x − 10.70I 40 − 2.69I 55 − 9.46xI 40 − 2.32xI 55 where y is the dependent/response variable (stomatal resistance, Rs), x is the predictor variable (leaf water potential, Ψ) and I i , i = 40, 55 are indicators variables where I i = 1 if the data belongs to stress level T i and I i = 0 otherwise. This equation describes three linear models that are shown in Table 4 and Figure 5.…”

Section: Stress Levelsmentioning

confidence: 99%

Tolerance to Drought and Water Stress Resistance Mechanism of Castor Bean

et al. 2020

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Castor bean (Ricinus communis L.) is a multipurpose crop; its oil has numerous applications worldwide and the last decade demonstrated a growing international demand. The aim of this work was to investigate the level of castor bean tolerance to drought and its possession of a water stress resistance mechanism by applying three different water regimes in a glasshouse pot experiment conducted for two years. The treatments applied were 70% (T70-control), 55% (T55) and 40% (T40) of the available soil moisture. The results showed that the growth parameters height, trunk diameter, and fresh and dry weights of leaves and stems were not affected by the moderate water scarcity (T55), while they were significantly decreased by T40. Significant decrease in leaf number was observed in both T55 (17%) and T40 (27%) plants, with a delay of 4 weeks in the lower treated plants. Leaf area was decreased by 54% and 20% in T55 and T40 respectively, indicating that its reduction was mainly due to a reduction of leaf size than of leaf number. The leaf water potential was increased negatively with increasing stress, showing a water loss and decrease of turgidity in cells. Stomatal resistance was significantly higher at the higher water scarcity and this response indicates a water stress resistance mechanism. This result was also confirmed by the regression analysis performed between stomatal resistance and leaf water potential. In conclusion, castor bean showed a tolerance ability under water stress conditions and its early physiological reaction allows its acclimatization to drought conditions.

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“…The net radiation flux (R n ) absorbed by the crop can be systematically assumed to be equal to the net radiation measured above the crop, thus neglecting the radiation exchanged below the canopy and the ground. Thus, net radiation above the canopy is almost equal to the total shortwave irradiance R sw − (Wm ) 2 during the day (Cannavo et al, 2016).…”

Section: Dynamic Response Of the Plant Canopy Temperaturementioning

confidence: 99%

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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The crop water stress index (CWSI) has been shown to be a tool that could be used for non-contact and real-time monitoring of plant water status, which is a key requirement for the precision irrigation management of crops. However, its adoption for irrigation scheduling is limited because of the need to know the baseline temperatures which are required for its calculation. In this study, the canopy temperature of greenhouse cultivated lettuce plants which were maintained as either well-watered or non-transpiring was continuously monitored along with prevailing environmental conditions during a five week period. This data was applied in developing a dynamic model that can be used for predicting the baseline temperatures. Input variables for the dynamic model included air temperature, shortwave irradiance, and air vapour pressure deficit measured at a 10 s interval. During a follow up study, the dynamic model successfully predicted the baseline temperatures producing mean absolute errors (MAE) that varied between 0.17°C and 0.29°C, and root mean squared errors (RMSE) that varied between 0.21°C and 0.35°C when comparing model predictions with measured values. The model predicted baseline temperatures were applied in calculating an empirical CWSI for lettuce plants receiving one of two irrigation treatments. The empirical CWSI consistently differentiated between the irrigation treatments and was significantly correlated with the theoretical CWSI with correlation coefficient (r) values greater than 0.9. The dynamic model presented in this study requires easily measured input parameters for the prediction of the baseline temperatures. This eliminates the need to maintain artificial reference surfaces required in other empirical approaches for the CWSI calculation and also eliminates the need for computing the complex theoretical CWSI.

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Stomatal resistance of New Guinea Impatiens pot plants. Part 2: Model extension for water restriction and application to irrigation scheduling

Cited by 12 publications

References 17 publications

Estimating Dynamic Non‐Water‐Limited Canopy Resistance Over the Globe: Changes, Contributors, and Implications

Estimating Dynamic Non‐Water‐Limited Canopy Resistance Over the Globe: Changes, Contributors, and Implications

Tolerance to Drought and Water Stress Resistance Mechanism of Castor Bean

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

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