Variation and simulation of tomato transpiration in a greenhouse under different ventilation modes
Greenhouse ventilation is a critical factor in regulating internal microclimates, ensuring optimal crop growth, and improving water use efficiency in facility agriculture. However, the semi-enclosed nature of greenhouses poses challenges for accurately modeling crop water consumption, primarily due...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424006176 |
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| Summary: | Greenhouse ventilation is a critical factor in regulating internal microclimates, ensuring optimal crop growth, and improving water use efficiency in facility agriculture. However, the semi-enclosed nature of greenhouses poses challenges for accurately modeling crop water consumption, primarily due to uncertainties in parameter estimation for widely used models such as the Penman-Monteith (P-M) model. To address these challenges, this study refines the P-M model by incorporating resistance parameters specifically tailored to greenhouse convection regimes. Field experiments were conducted at the Xinxiang Integrated Experimental Base of the Chinese Academy of Agricultural Sciences in northern China from March to July in 2020 and 2021. Drip-irrigated tomatoes were grown under natural sunlight with two ventilation treatments: T1 (top window open) and T2 (top and south windows open). Meteorological conditions inside the greenhouse and water consumption indicators were analyzed to improve the Penman-Monteith (P-M) model’s resistance parameters (canopy resistance, rc and aerodynamic resistance, ra). Results showed that ventilation significantly influenced water consumption across growth stages, with the highest water consumption intensity observed during the fruit enlargement stage (3.39 mm d−1). rc and ra were significantly lower under T2, with forced convection dominating in both cases. The improved P-M model demonstrated high predictive accuracy, underestimating transpiration by 2.15 % for T1 and overestimating by 6.26 % for T2. These findings provide a robust framework for optimizing greenhouse ventilation strategies, enabling precise modeling of crop water consumption and enhancing resource utilization in facility agriculture. |
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| ISSN: | 1873-2283 |