Apple tree transpiration estimated using the Penman-Monteith model integrated with optimized jarvis model

Accurate estimates of plant transpiration (Tsf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (rc), the Penman-Monteith (PM) model is commonly used in Tsf modeling. However, the rc is difficult to measure but it can be accurately estimate...

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Veröffentlicht in:Agricultural water management Jg. 276; S. 108061
Hauptverfasser: Xing, Liwen, Zhao, Lu, Cui, Ningbo, Liu, Chunwei, Guo, Li, Du, Taisheng, Wu, Zongjun, Gong, Daozhi, Jiang, Shouzheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2023
Schlagworte:
air temperature
algorithms
apples
canopy
Canopy resistance
China
Constraint function
decision making
developmental stages
Growth period segmentation
leaf area index
light intensity
meteorological data
orchards
soil water content
Swarm intelligence optimization algorithms
transpiration
trees
vapor pressure deficit
water management
water use efficiency
China
Canopy resistance
Constraint function
Swarm intelligence optimization algorithms
Growth period segmentation
ISSN:0378-3774, 1873-2283
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Abstract Accurate estimates of plant transpiration (Tsf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (rc), the Penman-Monteith (PM) model is commonly used in Tsf modeling. However, the rc is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the rc of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (Rn), and air temperature (Ta), and of soil water content (θa) on the accuracy of JA sub-models to calculate apple tree rc and Tsf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate rc. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of Tsf estimates with five empirical rc models, combined with the PM model. The results showed that sub-model JA3322, i.e., the third constraint for VPD, the third constraint for Rn, the second constraint for Ta, and the second constraint for θa attained the best estimate of rc with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of rc estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of rc, followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree Tsf, and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. •Jarvis was superior to other empirical models for canopy resistance estimation.•Jarvis segmented by canopy growth stage gave a more accurate estimation.•Finding the most appropriate Jarvis model for apple tree canopy resistance.•Intelligence optimization algorithms improved the accuracy of the Jarvis models.
AbstractList Accurate estimates of plant transpiration (Tsf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (rc), the Penman-Monteith (PM) model is commonly used in Tsf modeling. However, the rc is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the rc of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (Rn), and air temperature (Ta), and of soil water content (θa) on the accuracy of JA sub-models to calculate apple tree rc and Tsf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate rc. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of Tsf estimates with five empirical rc models, combined with the PM model. The results showed that sub-model JA3322, i.e., the third constraint for VPD, the third constraint for Rn, the second constraint for Ta, and the second constraint for θa attained the best estimate of rc with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of rc estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of rc, followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree Tsf, and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. •Jarvis was superior to other empirical models for canopy resistance estimation.•Jarvis segmented by canopy growth stage gave a more accurate estimation.•Finding the most appropriate Jarvis model for apple tree canopy resistance.•Intelligence optimization algorithms improved the accuracy of the Jarvis models.
Accurate estimates of plant transpiration (Tₛf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (rc), the Penman-Monteith (PM) model is commonly used in Tₛf modeling. However, the rc is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the rc of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (Rₙ), and air temperature (Tₐ), and of soil water content (θₐ) on the accuracy of JA sub-models to calculate apple tree rc and Tₛf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate rc. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of Tₛf estimates with five empirical rc models, combined with the PM model. The results showed that sub-model JA₃₃₂₂, i.e., the third constraint for VPD, the third constraint for Rₙ, the second constraint for Tₐ, and the second constraint for θₐ attained the best estimate of rc with a coefficient of determination (R² = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m⁻¹), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m⁻¹ and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m⁻¹ and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of rc estimation, where the median R² and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of rc, followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree Tₛf, and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously.
ArticleNumber 108061
Author Liu, Chunwei
Guo, Li
Zhao, Lu
Cui, Ningbo
Jiang, Shouzheng
Du, Taisheng
Wu, Zongjun
Gong, Daozhi
Xing, Liwen
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  givenname: Lu
  surname: Zhao
  fullname: Zhao, Lu
  organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China
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  givenname: Ningbo
  surname: Cui
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  email: cuiningbo@126.com
  organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China
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  givenname: Chunwei
  surname: Liu
  fullname: Liu, Chunwei
  organization: Jiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, PR China
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  surname: Guo
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  organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China
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  givenname: Taisheng
  surname: Du
  fullname: Du, Taisheng
  organization: Center for Agricultural Water Research in China, China Agricultural University, Beijing 100091, PR China
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  givenname: Zongjun
  surname: Wu
  fullname: Wu, Zongjun
  organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China
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  givenname: Daozhi
  surname: Gong
  fullname: Gong, Daozhi
  organization: State Engineering Laboratory of Efficient Water Use of Crops and Disaster Loss Mitigation, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Science, Beijing 100081, PR China
– sequence: 9
  givenname: Shouzheng
  surname: Jiang
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  organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China
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Keywords Canopy resistance
Constraint function
Swarm intelligence optimization algorithms
Growth period segmentation
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Snippet Accurate estimates of plant transpiration (Tsf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance...
Accurate estimates of plant transpiration (Tₛf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance...
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StartPage 108061
SubjectTerms air temperature
algorithms
apples
canopy
Canopy resistance
China
Constraint function
decision making
developmental stages
Growth period segmentation
leaf area index
light intensity
meteorological data
orchards
soil water content
Swarm intelligence optimization algorithms
transpiration
trees
vapor pressure deficit
water management
water use efficiency
Title Apple tree transpiration estimated using the Penman-Monteith model integrated with optimized jarvis model
URI https://dx.doi.org/10.1016/j.agwat.2022.108061
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