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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Vydané v:	Agricultural water management Ročník 276; s. 108061
Hlavní autori:	Xing, Liwen, Zhao, Lu, Cui, Ningbo, Liu, Chunwei, Guo, Li, Du, Taisheng, Wu, Zongjun, Gong, Daozhi, Jiang, Shouzheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.02.2023
Predmet:	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
Author_xml	– sequence: 1 givenname: Liwen surname: Xing fullname: Xing, Liwen organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China – sequence: 2 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 – sequence: 3 givenname: Ningbo surname: Cui fullname: Cui, Ningbo 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 – sequence: 4 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 – sequence: 5 givenname: Li surname: Guo fullname: Guo, Li organization: State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, PR China – sequence: 6 givenname: Taisheng surname: Du fullname: Du, Taisheng organization: Center for Agricultural Water Research in China, China Agricultural University, Beijing 100091, PR China – sequence: 7 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 – sequence: 8 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 fullname: Jiang, Shouzheng 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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CitedBy_id	crossref_primary_10_1016_j_agwat_2024_109193 crossref_primary_10_3390_agronomy14071531 crossref_primary_10_1016_j_resconrec_2024_107528 crossref_primary_10_1002_cem_3515 crossref_primary_10_1002_cem_3537 crossref_primary_10_1016_j_agwat_2024_108745 crossref_primary_10_1016_j_resconrec_2025_108390 crossref_primary_10_1016_j_agwat_2023_108665 crossref_primary_10_1016_j_agwat_2024_109238 crossref_primary_10_1016_j_agwat_2024_108924 crossref_primary_10_1016_j_compag_2023_108253 crossref_primary_10_1016_j_jhydrol_2024_131375 crossref_primary_10_1016_j_plaphy_2023_107939 crossref_primary_10_1016_j_agrformet_2025_110736 crossref_primary_10_1016_j_agwat_2023_108620 crossref_primary_10_1016_j_compag_2023_108139
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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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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
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