Parameterization and Application of Stanghellini Model for Estimating Greenhouse Cucumber Transpiration

Abstract

Accurate estimation of transpiration (Tr) is important in the development of precise irrigation scheduling and to enhance water-use efficiency in agricultural production. In this study, the air temperature (Ta) and relative humidity (RH) were measured at three different heights (0.5, 1.0, and 1.8 m above the ground near the plant canopy) parameterize aerodynamic resistance (ra) based on the heat transfer coefficient method and to estimate Tr using the Stanghellini model (SM) during two growing seasons of cucumber in a greenhouse. The canopy resistance (rc) was parameterized by an exponential relationship of stomata resistance and solar radiation, and the estimated Tr was compared to the values measured with lysimeters. After parameterization of ra and rc, the efficiency (EF) and the Root Mean Square Error (RMSE) of the estimated Tr by the SM based on micrometeorological data at a height of 0.5 m were 95% and 18 W m−2, respectively, while the corresponding values were 86% and 29 W m−2 at a height of 1.8 m for the autumn planting season. For the spring planting season, the EF and RMSE were 92% and 34 W m−2 at a height of 0.5 m, while the corresponding values were 81% and 56 W m−2 at a height of 1.8 m, respectively. This work demonstrated that when micrometeorological data within the canopy was applied alongside the data measured above the canopy, the SM led to better agreement with the lysimeter measurements.

A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications

“…Findings of this study showed an enhanced ET prediction with the Stanghellini model with a model efficiency (R 2 ) of 0.872 as compared to 0.481 achieved through the Penman-Monteith model [15]. Overestimations of the evapotranspiration rates were observed, and few studies explained the potential cause of these overestimations including the challenging parametrisation of the canopy and aerodynamic resistances and the somewhat non-homogeneous microclimate data collected in greenhouse environments [13]. The Penman-Monteith model assumes homogeneity in collected climatic data which can be appropriate for open field settings but can rather introduce discrepancies for ET estimates in greenhouse mediums.…”

“…r R = ρ a C p 4σ(T a + 273.15) 3 (12) The external or aerodynamic resistance r e for the Stanghellini model is defined as shown in Equation (13). N u = 0.37 Gr + 6.92Re 2 0.25 (14) where l represents the characteristic dimension of a leaf (m), λ a is the thermal conductivity, ρ a and C p are the air density (kg m −3 ) and air specific heat capacity (J kg −1 •C −1 ).…”

“…Instead of adopting the FAO56 assumptions related to internal and aerodynamic resistances, direct measurements of the stomatal and boundary layer resistances can be conducted and integrated into the original Penman-Monteith equation [40,79]. These measurements can also be used to calibrate existing models to specific climatological and growing practices [13]. Measurement systems include weighing lysimeters, eddy covariance and gas exchange systems that can estimate ET rates, however, these systems are associated with high capital and maintenance costs.…”

“…Moreover, adapting these models to the specific growing conditions of the evaluated system can enhance the accuracy of ET estimates. This can be achieved through the parametrisation of observed relationships through direct measurements [13]. The vast majority of reviews conducted around evapotranspiration address the main differences between certain empirical ET models in terms of input data, accuracy, and limitations [3,14,15].…”

Simulation of Daily Transpiration of Tomatoes Grown in Venlo-Type Greenhouse Substrates

“…However, the versatility of the model may be poor as it lacks consideration for the changes in water consumption intensity and greenhouse microenvironment during crop growth and development. In addition, due to the interaction between crop transpiration and the greenhouse microenvironment, the effect of the former on the latter gradually increases with the growth and development of the crops [28]. Therefore, the sensitivity of the crop to the greenhouse microenvironment is different at different growth stages [29].…”