Sap flow of sweet cherry reveals distinct effects of humidity and wind under rain covered and netted protected cropping systems

Stone, Cameron H.; Close, DC; Corkrey, Ross; Goodwin, Ian

doi:10.1038/s41598-022-25207-0

Cited by 3 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sap flow technology has been widely applied for the estimation of tree transpiration, analysis of soil water content level, management of water resources and analysis of tree health status. The main external factors affecting the sap flow rate of trees are environmental factors, such as saturated vapor pressure deficit [28], air relative humidity [69], air temperature [70], wind speed [26], soil moisture content [65], and soil temperature [71]. In this paper, an improved sap flow prediction model was built with environmental factors by factor analysis and the CNN-GRU-BiLSTM network we designed.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have shown that sap flow is significantly related to environment factors. Stone et al used the method of generalized additive modelling to predict the influence of individual climate parameters on sap flow, and the results show a 60% relative humidity (RH) critical threshold of predicted sap flow and the indirect effect that wind speeds have on sap flow under protected cropping systems [26]. Su et al used principal component analysis (PCA) to understand the response of sap flow to water stress and microclimatic variables, and the results showed that there was a positive correlation between vapor pressure deficit and daytime sap flow [27].…”

Section: Introductionmentioning

confidence: 99%

“…Dukat et al investigated the effects of drought on forest function by analyzing ecosystem evaporation under normal and dry conditions and identified key drivers of these processes in Pinus sylvestris [42]. As a result, we can conclude that sap flow is correlated with environmental factors, such as Ta [28,36], RH [26], wind speed [26], SWC [37], VPD [28,32,34,41], RAD [34] and PAR [28]. Therefore, it is possible to predict sap flow using various meteorological factors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Sap Flow Prediction Model Based on CNN-GRU-BiLSTM and Factor Analysis of Historical Environmental Variables

Guo

Wang

et al. 2023

Forests

View full text Add to dashboard Cite

Sap flow is widely used to estimate the transpiration and water consumption of canopies and to manage water resources. In this paper, an improved time series prediction model was proposed by integrating three basic networks—CNN, GRU and BiLSTM—to assess sap flow with historical environment variables. A dataset with 17,569 records of each, including 9 environment variables and 1 sap flow, was applied from a public database of SAPFLUXNET. After normalization, the environment variables were analyzed and composed with the factor analysis method. After the CNN-GRU-BiLSTM structure was designed, N records of three main factors were computed from environment variables, which were measured at N previous moments, and the sap flow was measured at the current moment, and they were applied for each training, validation, and testing cycle. To improve and compare the CNN-GRU-BiLSTM-based model, nine other models, using the methods of multiple linear regression, support vector regression, random forest, LSTM, GRU, BiLSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM, were constructed in this study, respectively. Results show that the performance of the CNN-GRU-BiLSTM-based model has more accuracy than the other nine models we built in this paper, with the mean absolute error, mean squared error, mean absolute percentage error, and coefficient of determination (R2) being 0.0410, 0.0029, 0.2708 and 0.9329, respectively. Furthermore, for a comparison of the descending dimension method of factor analysis, principal component analysis (PCA) and singular value decomposition (SVD) methods were applied and compared, respectively. Results show that the performance of the factor analysis-based model is better than the PCA- or SVD-based model, with the R2 results of the factor analysis-based model being higher than the PCA- and SVD-based models by 5.06% and 10.63%, respectively. This study indicates that the CNN-GRU-BiLSTM-based sap flow prediction model established with a factor analysis of historical environmental variables has optimistic applications for analyzing the transpiration of trees and evaluating water consumption.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Sap Flow Prediction Model Based on CNN-GRU-BiLSTM and Factor Analysis of Historical Environmental Variables

Guo

Wang

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

A review of the production challenges for sweet cherries grown under protected cropping systems

Stone,

Sidhu,

Swarts

et al. 2024

Acta Hortic.

View full text Add to dashboard Cite

Water Needs of Sweet Cherry Trees in the Light of Predicted Climate Warming in the Bydgoszcz Region, Poland

Rolbiecki

Jagosz

et al. 2023

Atmosphere

View full text Add to dashboard Cite

The Bydgoszcz region (Poland) is located in an area with a very high demand for supplementary irrigation during the vegetation period of plants. The projected global warming will bring a rise in the water needs of crops, and thus a further increase in irrigation needs. The goal of the study was an attempt to estimate the water needs of sweet cherry trees in 2021–2050 (forecast period) in the region of Bydgoszcz. The years 1981–2010 were adopted as the reference period. The water needs of sweet cherry trees were calculated on the basis of air temperature using the Treder method, in which water needs are equated with the potential evapotranspiration of a given fruit tree species. It was found that in the growing season of the forecast period, the relative diversity of sweet cherries’ water needs was relatively small (7%). The highest variability of monthly water needs was in April, May, and June. The seasonal water needs amounted to 573 mm, with very high monthly water needs noted in July (139 mm) and August (134 mm). A significant trend of the time variability of water needs was calculated only in August. During this month, it is predicted that the water needs will rise by 5 mm in each subsequent decade. These results will be helpful in the design of sweet cherry irrigation treatments.

show abstract

Sap flow of sweet cherry reveals distinct effects of humidity and wind under rain covered and netted protected cropping systems

Cited by 3 publications

References 43 publications

An Improved Sap Flow Prediction Model Based on CNN-GRU-BiLSTM and Factor Analysis of Historical Environmental Variables

An Improved Sap Flow Prediction Model Based on CNN-GRU-BiLSTM and Factor Analysis of Historical Environmental Variables

A review of the production challenges for sweet cherries grown under protected cropping systems

Water Needs of Sweet Cherry Trees in the Light of Predicted Climate Warming in the Bydgoszcz Region, Poland

Contact Info

Product

Resources

About