Parameterization and Application of Stanghellini Model for Estimating Greenhouse Cucumber Transpiration

Haofang Yan, Song Huang, Chuan Zhang, Miriam Coenders Gerrits, Guoqing Wang, Jianyun Zhang, Baoshan Zhao, Samuel Joe Acquah, Haimei Wu, Hanwen Fu

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
68 Downloads (Pure)

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.
Original languageEnglish
Article number517
Pages (from-to)1-14
Number of pages14
JournalWater
Volume12
Issue number2
DOIs
Publication statusPublished - 2020

Keywords

  • Aerodynamic resistance
  • Canopy resistance
  • Different observation heights
  • Micrometeorological data
  • Stanghellini model
  • Transpiration

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