温室甜椒生长发育模拟模型的研究
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摘要
作物模型是辅助温室作物生产环境优化调控和栽培管理的有力工具。甜椒(Capsicum annuum L.)经济价值较高,是重要的温室栽培物之一,因此建立温室甜椒生长发育模拟模型对提高我国温室甜椒种植水平,增加种植者经济效益,提高生态效益具有重要意义。本研究于2005年分春茬和秋茬间,分别在上海孙桥现代化农业园区PVC连栋温室内和新疆石河子大学农学院试验站加温日光温室内进行了不同品种、播期的试验。通过对试验数据的系统分析,以温室甜椒的发育生理生态过程为基础,以作物生理发育时间(Physiological development time, PDT)为尺度,建立了温室甜椒生育期模拟模型。以辐热积(Product of thermal effectiveness and PAR, TEP)为尺度,建立了温室甜椒叶面积指数模拟模型。将叶面积模型与已有的光合作用和干物质生产模型相结合,建立了光合生产模拟模型。利用分配指数(Partitioning index, PI)和采收指数(Harvest index, HI)建立了温室甜椒干物质分配和产量预测模拟模型。将上述四个子模型综合集成,建立了温室甜椒生长发育模拟模型。
在发育子模型中,根据甜椒发育对光温的反应,建立了基于生理发育时间的甜椒生育期模拟模型,并利用不同品种和播期与建模相独立的试验资料对模型进行了检验。结果表明,从出苗到现蕾、开花、成熟所需的生理发育时间分别为29、35、53个PDT天。模型对甜椒从出苗到现蕾、开花、成熟不同物候期的模拟值与观测值的回归估计标准误差(RMSE)和相对误差(RE)分别为0.8、1.6、4天和1.9%、3.1%、5.1%,对现蕾、开花、成熟三个物候期预测的平均RMSE和RE为2.1天和3.7%。模型预测的模拟值与观测值呈较好的1：1关系,基于1：1直线的决定系数R2为0.99,本模型能够用生理发育时间准确地预测甜椒各个生育期的起止日期和成熟上市日期。
在叶面积子模型中首次利用综合光温指标—辐热积模拟了甜椒叶面积指数形成过程,建立了甜椒叶面积指数模拟模型。辐热积指的是相对热效应(Relative thermal effectiveness, RTE)与光合有效辐射PAR的乘积。累积辐热积的具体计算过程公式如下：
式中Tb为生长下限温度,Tm为生长上限温度,Tob为生长的最适温度下限,Tou为生长的最适温度上限,T为每小时的平均温度。
DTEP(i)=(∑RTE(i,j)/24)×PAR(i)
式中DTEP(i)表示第i日辐热积(MJ·m-2d-1)；RTE(i,j)为第i日第j小时的相对热效应,PAR(i)分别为第i日总光合有效辐射(MJ·m-2·d-1)。
TEP(i+1)=TEP(i)+DTEP(i+1)
TEP(i+1)为从出苗到第i+1天的累积辐热积(MJ·m-2),TEP(i)为从出苗到第i天的累积辐热积(MJ·m-2),DTEP(i+1)为第i+1天的日总辐热积(MJ·m-2·d-1).
温室甜椒单株叶面积(LA)计算模型为：
N=23.29×exp(TEP/290.391-21.35
Li=Lmaxi[1-exp(-ki×TEPi/Lmaxi)]+2
式中N为植株上已展开的叶片数,i为叶序,Li为第i叶的实际叶长(cm),Lmaxi是第i片叶的最大叶长(cm),TEP为出苗后的累积幅热积,TEP1为第i片叶展开后的累积幅热积,ki是无量纲参数,No为已摘除的老叶数,ALo为摘除老叶的总面积(cm2),ALi表示第i叶的面积(cm2)。
LAI=LA×d/10000
式中LAI为叶面积指数；d为种植密度(株·m-2)；10000为将cm2换算成m2的单位换算系数。
模型对甜椒出叶数、叶片长度和叶面积指数的模拟结果与实测值之间基于1：1直线的决定系数R2和回归估计标准误差RMSE分别为0.94、0.89、0.93和3.4、2.15cm、0.15。该模型能够利用气温、辐射、种植密度和出苗日期准确地预测温室甜椒叶面积指数动态,模型参数少实用性强,可以为温室甜椒生长模型和蒸腾模型提供所必需的叶面积指数动态信息。
在甜椒生长与产量预测子模型中,首先将本研究建立的叶面积模型与已有的光合作用和干物质生产模型相结合,建立温室甜椒生长动态预测模型。然后定量分析辐射和温度对甜椒干物质分配和果实采收指数的影响,以及果实干物重增长和鲜重增长的关系,建立以辐热积为预测指标的甜椒干物质分配模型和产量预测模型。温室甜椒干物质分配指数和采收指数与辐热积的函数关系为：
地上部分分配指数：PISH=0.91-0.33×0.99TEP
地下部分分配指数：PIR=1-PISH
茎分酯指数
叶分配指数：
果分配指数：PIF=1-PIS-PIL
采收指数：HI=0.94×(1-EXP x (-(TEP-245.16)/58.18))
式中PISH、PIR、PIS、PIL、PIF分别为地上部分、地下部分、茎、叶和果分配指数,HI为采收指数。
基于温室甜椒干物质分配指数和采收指数与辐热积的函数关系构建了温室甜椒产量预测模型：
WF=BIOMASS x PISH x PIF
Y=(WF×HI)/0.05
式中WF表示预测的果实总干重(kg·ha-1), BIOMASS为甜椒的总干重(kg·ha-1)Y表示甜椒的产量(kg·ha-1),0.05为甜椒果实的干物质含量(g·g-1)。
利用与建模相独立的试验资料对模型进行检验,结果表明,模型对Venlo型连栋温室和日光温室中种植的不同品种甜椒的干物质生产与分配和产量的预测结果较好。模型对甜椒总干物质量和各个器官干重的模拟预测值与实测值之间基于1：1直线的决定系数R2和相对预测误差RE,总干物质量分别为0.93和8.4%,地上部分干重、地下部分干重分别为0.92和8.4%、0.91和9.0%,地上部分茎、叶、果干重分别为0.85和11.5%,0.80和11.2%,0.86和12.9%,甜椒产量分别为0.79和16.1%。本研究建立的模型参数少且易获取,模型的实用性较强,可以为我国温室甜椒生产中光温的精准管理提供决策支持。
Crop growth and development simulation model is a useful tool for the optimization of greenhouse crop and climate management. Sweet pepper is one of the most important greenhouse vegetable crops. In this study, experiments with different cultivars and substrates were conducted in a venlo-type greenhouse in Shanghai (E121.5°, N31.20) and a solar greenhouse in Xinjiang shihezi (E58.70, N49.90) in2005to collect data for model development and validation. Firstly, a module for greenhouse sweet pepper development simulation was developed based on the concept of physiological development time (PDT). Secondly, a module for greenhouse sweet pepper leaf area simulation was developed using the product of thermal effectiveness and PAR (TEP). Thirdly, a photosynthesis based dry matter production module was developed by combining the leaf area module developed in this study with photosynthesis and dry matter production simulation model. Fourthly, a module for greenhouse sweet pepper matter partitioning and yield prediction was developed using dry matter partitioning index and harvest index. Finally, the four modules mentioned above were integrated to develop the greenhouse sweet pepper growth and development simulation model.
In the phonological development submodel, the PDT value for the duration from emergence date to flower bud emergence date was29days, with35,53days, respectively, for the duration from emergence date to flowering date and from emergence date to maturation. The root mean squared error (RMSE) and relative prediction error (RE) between the simulated and observed results for duration of flower bud emergence, flowering, maturity respectively, was0.8,1.6,4days and1.9%,3.1%,5.1%respectively. Based on the1:1line, the coefficient of determination (R2), RE and RMSE between simulated and measured are0.99,3.7%, and2.1days for total development stages. The model developed in this study can be used for simulation of the development stages of greenhouse sweet pepper.
In the leaf area submodel, the concept of the product of thermal effectiveness and PAR (TEP) was proposed. TEP was calculated by formula as follows:
RTE is the relative thermal effectiveness. Tb, Tob, Tou, Tm are the base temperature, the base optimum temperature, the upper optimum temperature, the maximum temperature, respectively, for greenhouse sweet pepper growth.
DTEP(i)=(∑RTE(i,j)/24) x PAR(i)
DTEP(i) is the daily total product of thermal effectiveness and PAR to day i. RTE(i,j) is the relative thermal effectiveness on day i, hour j. PAR(i) is the daily total PAR on day i.
TEP(i+1)=TEP(i)+DTEP(i+l)
TEP (i+1) is the cumulative TEP from emergence date to day i+1. TEP (i) is the cumulative TEP from the ith leaf unfolding date to day i, DTEP(i+1) is the daily total TEP on day i+1.
Then, the relationships between plant leaf area (LA) and TEP were calculated as follows:
N=23.29×exp(TEP/290.39)-21.35
N is the number of leaves unfolding, i is leaf order, Li is the leaf length of leaf i (cm), LMAXi is the maximum leaf length of leaf i, TEP is the cumulative TEP after emergence (MJ·m-2), TEPi is the cumulative TEP after unfolding of leaf i (MJ·m-2), ki is a parameter, No is the number of old leaves removed, ALo is the total area of old leaves removed (cm2), ALi is the leaf area of leaf i (cm2). When TEP=274.10MJ·m-2, old leaves were removed for the first time. LAI=LA×d/10000
LAI is the leaf area index, d is the planting density (plant·m-2),10000is the conversion coefficient from cm2to m2.
Based on the experimental data, the relationships between the TEP accumulated after emergence to the number of unfolding leaves per plant, the number of old leaves removed per plant and the length of each leaf were determined. Based on these quantitative relationships, a leaf area simulation model for greenhouse sweet pepper was developed. Independent experimental data were used to validate the model. The results showed that the coefficient of determination (R2) and the root mean squared error (RMSE) between the simulated and the measured leaf number, leaf length and leaf area index(LAI) based on the1:1line are, respectively,0.94.0.89.0.93and3.4.2.15cm、0.15. The model can predict LAI satisfactorily using air temperature, radiation, date of emergence and planting density for greenhouse sweet pepper growth and water evapotranspiration simulation models.
In the biomass production submodel and dry matter partitioning and yield prediction submodel, the LAI model based on TEP was combined with photosynthesis and dry matter production model to predict dry matter production. The effects of radiation and temperature on dry matter partitioning and fruit harvest index were quantified based on the experimental data. A greenhouse sweet pepper growth and yield simulation model was developed by integrating those quantitative relationships with a general photosynthesis driven biomass production model.
In the dry matter partitioning and yield prediction submodel, the partitioning index (PI) of organs and the harvest index (HI) were predicted using TEP:
PI of shoot:PISH=0.91-0.33×0.99TEP
PI of root:PIR=1-PISH
PI of fruit:PIF=1-PIS-PIL
HI=0.94x (1-EXP x (-(TEP-245.16)/58.18))
Then, greenhouse sweet pepper yield were predicted using PI and HI as follows:
WF=BIOMASS x PISH x PIF
Y=(WF x HI)/0.05
WF is the fruit total dry weight (kg·ha-1), BIOMASS is the plant total dry weight (kg·ha-1), Y is the yield (fresh weight),0.05is the dry matter content of fruit (g·g-1).
Independent experimental data were used to validate the model. The results show that the model developed in this study gives satisfactory predictions of the growth and yield of sweet pepper grown in both the multi-span Venlo-type greenhouse and the solar greenhouse. Based on the1:1line, the coefficient of determination (R2) and the relative prediction error (RE) between simulated and measured are0.93and8.4%, respectively, for total biomass;0.92and8.4%, respectively, for shoot dry weight;0.91and9.0%, respectively, for root dry weight;0.85and11.5%, respectively, for stem dry weight;0.80and11.2%, respectively, for leaf dry weight;0.86and12.9%, respectively, for total fruit dry weight;0.79and16.1%, respectively, for yield (fresh weight). The model developed in this study can be used for light and temperature management for greenhouse sweet pepper production.
在发育子模型中,根据甜椒发育对光温的反应,建立了基于生理发育时间的甜椒生育期模拟模型,并利用不同品种和播期与建模相独立的试验资料对模型进行了检验。结果表明,从出苗到现蕾、开花、成熟所需的生理发育时间分别为29、35、53个PDT天。模型对甜椒从出苗到现蕾、开花、成熟不同物候期的模拟值与观测值的回归估计标准误差(RMSE)和相对误差(RE)分别为0.8、1.6、4天和1.9%、3.1%、5.1%,对现蕾、开花、成熟三个物候期预测的平均RMSE和RE为2.1天和3.7%。模型预测的模拟值与观测值呈较好的1：1关系,基于1：1直线的决定系数R2为0.99,本模型能够用生理发育时间准确地预测甜椒各个生育期的起止日期和成熟上市日期。
在叶面积子模型中首次利用综合光温指标—辐热积模拟了甜椒叶面积指数形成过程,建立了甜椒叶面积指数模拟模型。辐热积指的是相对热效应(Relative thermal effectiveness, RTE)与光合有效辐射PAR的乘积。累积辐热积的具体计算过程公式如下：
式中Tb为生长下限温度,Tm为生长上限温度,Tob为生长的最适温度下限,Tou为生长的最适温度上限,T为每小时的平均温度。
DTEP(i)=(∑RTE(i,j)/24)×PAR(i)
式中DTEP(i)表示第i日辐热积(MJ·m-2d-1)；RTE(i,j)为第i日第j小时的相对热效应,PAR(i)分别为第i日总光合有效辐射(MJ·m-2·d-1)。
TEP(i+1)=TEP(i)+DTEP(i+1)
TEP(i+1)为从出苗到第i+1天的累积辐热积(MJ·m-2),TEP(i)为从出苗到第i天的累积辐热积(MJ·m-2),DTEP(i+1)为第i+1天的日总辐热积(MJ·m-2·d-1).
温室甜椒单株叶面积(LA)计算模型为：
N=23.29×exp(TEP/290.391-21.35
Li=Lmaxi[1-exp(-ki×TEPi/Lmaxi)]+2
式中N为植株上已展开的叶片数,i为叶序,Li为第i叶的实际叶长(cm),Lmaxi是第i片叶的最大叶长(cm),TEP为出苗后的累积幅热积,TEP1为第i片叶展开后的累积幅热积,ki是无量纲参数,No为已摘除的老叶数,ALo为摘除老叶的总面积(cm2),ALi表示第i叶的面积(cm2)。
LAI=LA×d/10000
式中LAI为叶面积指数；d为种植密度(株·m-2)；10000为将cm2换算成m2的单位换算系数。
模型对甜椒出叶数、叶片长度和叶面积指数的模拟结果与实测值之间基于1：1直线的决定系数R2和回归估计标准误差RMSE分别为0.94、0.89、0.93和3.4、2.15cm、0.15。该模型能够利用气温、辐射、种植密度和出苗日期准确地预测温室甜椒叶面积指数动态,模型参数少实用性强,可以为温室甜椒生长模型和蒸腾模型提供所必需的叶面积指数动态信息。
在甜椒生长与产量预测子模型中,首先将本研究建立的叶面积模型与已有的光合作用和干物质生产模型相结合,建立温室甜椒生长动态预测模型。然后定量分析辐射和温度对甜椒干物质分配和果实采收指数的影响,以及果实干物重增长和鲜重增长的关系,建立以辐热积为预测指标的甜椒干物质分配模型和产量预测模型。温室甜椒干物质分配指数和采收指数与辐热积的函数关系为：
地上部分分配指数：PISH=0.91-0.33×0.99TEP
地下部分分配指数：PIR=1-PISH
茎分酯指数
叶分配指数：
果分配指数：PIF=1-PIS-PIL
采收指数：HI=0.94×(1-EXP x (-(TEP-245.16)/58.18))
式中PISH、PIR、PIS、PIL、PIF分别为地上部分、地下部分、茎、叶和果分配指数,HI为采收指数。
基于温室甜椒干物质分配指数和采收指数与辐热积的函数关系构建了温室甜椒产量预测模型：
WF=BIOMASS x PISH x PIF
Y=(WF×HI)/0.05
式中WF表示预测的果实总干重(kg·ha-1), BIOMASS为甜椒的总干重(kg·ha-1)Y表示甜椒的产量(kg·ha-1),0.05为甜椒果实的干物质含量(g·g-1)。
利用与建模相独立的试验资料对模型进行检验,结果表明,模型对Venlo型连栋温室和日光温室中种植的不同品种甜椒的干物质生产与分配和产量的预测结果较好。模型对甜椒总干物质量和各个器官干重的模拟预测值与实测值之间基于1：1直线的决定系数R2和相对预测误差RE,总干物质量分别为0.93和8.4%,地上部分干重、地下部分干重分别为0.92和8.4%、0.91和9.0%,地上部分茎、叶、果干重分别为0.85和11.5%,0.80和11.2%,0.86和12.9%,甜椒产量分别为0.79和16.1%。本研究建立的模型参数少且易获取,模型的实用性较强,可以为我国温室甜椒生产中光温的精准管理提供决策支持。
Crop growth and development simulation model is a useful tool for the optimization of greenhouse crop and climate management. Sweet pepper is one of the most important greenhouse vegetable crops. In this study, experiments with different cultivars and substrates were conducted in a venlo-type greenhouse in Shanghai (E121.5°, N31.20) and a solar greenhouse in Xinjiang shihezi (E58.70, N49.90) in2005to collect data for model development and validation. Firstly, a module for greenhouse sweet pepper development simulation was developed based on the concept of physiological development time (PDT). Secondly, a module for greenhouse sweet pepper leaf area simulation was developed using the product of thermal effectiveness and PAR (TEP). Thirdly, a photosynthesis based dry matter production module was developed by combining the leaf area module developed in this study with photosynthesis and dry matter production simulation model. Fourthly, a module for greenhouse sweet pepper matter partitioning and yield prediction was developed using dry matter partitioning index and harvest index. Finally, the four modules mentioned above were integrated to develop the greenhouse sweet pepper growth and development simulation model.
In the phonological development submodel, the PDT value for the duration from emergence date to flower bud emergence date was29days, with35,53days, respectively, for the duration from emergence date to flowering date and from emergence date to maturation. The root mean squared error (RMSE) and relative prediction error (RE) between the simulated and observed results for duration of flower bud emergence, flowering, maturity respectively, was0.8,1.6,4days and1.9%,3.1%,5.1%respectively. Based on the1:1line, the coefficient of determination (R2), RE and RMSE between simulated and measured are0.99,3.7%, and2.1days for total development stages. The model developed in this study can be used for simulation of the development stages of greenhouse sweet pepper.
In the leaf area submodel, the concept of the product of thermal effectiveness and PAR (TEP) was proposed. TEP was calculated by formula as follows:
RTE is the relative thermal effectiveness. Tb, Tob, Tou, Tm are the base temperature, the base optimum temperature, the upper optimum temperature, the maximum temperature, respectively, for greenhouse sweet pepper growth.
DTEP(i)=(∑RTE(i,j)/24) x PAR(i)
DTEP(i) is the daily total product of thermal effectiveness and PAR to day i. RTE(i,j) is the relative thermal effectiveness on day i, hour j. PAR(i) is the daily total PAR on day i.
TEP(i+1)=TEP(i)+DTEP(i+l)
TEP (i+1) is the cumulative TEP from emergence date to day i+1. TEP (i) is the cumulative TEP from the ith leaf unfolding date to day i, DTEP(i+1) is the daily total TEP on day i+1.
Then, the relationships between plant leaf area (LA) and TEP were calculated as follows:
N=23.29×exp(TEP/290.39)-21.35
N is the number of leaves unfolding, i is leaf order, Li is the leaf length of leaf i (cm), LMAXi is the maximum leaf length of leaf i, TEP is the cumulative TEP after emergence (MJ·m-2), TEPi is the cumulative TEP after unfolding of leaf i (MJ·m-2), ki is a parameter, No is the number of old leaves removed, ALo is the total area of old leaves removed (cm2), ALi is the leaf area of leaf i (cm2). When TEP=274.10MJ·m-2, old leaves were removed for the first time. LAI=LA×d/10000
LAI is the leaf area index, d is the planting density (plant·m-2),10000is the conversion coefficient from cm2to m2.
Based on the experimental data, the relationships between the TEP accumulated after emergence to the number of unfolding leaves per plant, the number of old leaves removed per plant and the length of each leaf were determined. Based on these quantitative relationships, a leaf area simulation model for greenhouse sweet pepper was developed. Independent experimental data were used to validate the model. The results showed that the coefficient of determination (R2) and the root mean squared error (RMSE) between the simulated and the measured leaf number, leaf length and leaf area index(LAI) based on the1:1line are, respectively,0.94.0.89.0.93and3.4.2.15cm、0.15. The model can predict LAI satisfactorily using air temperature, radiation, date of emergence and planting density for greenhouse sweet pepper growth and water evapotranspiration simulation models.
In the biomass production submodel and dry matter partitioning and yield prediction submodel, the LAI model based on TEP was combined with photosynthesis and dry matter production model to predict dry matter production. The effects of radiation and temperature on dry matter partitioning and fruit harvest index were quantified based on the experimental data. A greenhouse sweet pepper growth and yield simulation model was developed by integrating those quantitative relationships with a general photosynthesis driven biomass production model.
In the dry matter partitioning and yield prediction submodel, the partitioning index (PI) of organs and the harvest index (HI) were predicted using TEP:
PI of shoot:PISH=0.91-0.33×0.99TEP
PI of root:PIR=1-PISH
PI of fruit:PIF=1-PIS-PIL
HI=0.94x (1-EXP x (-(TEP-245.16)/58.18))
Then, greenhouse sweet pepper yield were predicted using PI and HI as follows:
WF=BIOMASS x PISH x PIF
Y=(WF x HI)/0.05
WF is the fruit total dry weight (kg·ha-1), BIOMASS is the plant total dry weight (kg·ha-1), Y is the yield (fresh weight),0.05is the dry matter content of fruit (g·g-1).
Independent experimental data were used to validate the model. The results show that the model developed in this study gives satisfactory predictions of the growth and yield of sweet pepper grown in both the multi-span Venlo-type greenhouse and the solar greenhouse. Based on the1:1line, the coefficient of determination (R2) and the relative prediction error (RE) between simulated and measured are0.93and8.4%, respectively, for total biomass;0.92and8.4%, respectively, for shoot dry weight;0.91and9.0%, respectively, for root dry weight;0.85and11.5%, respectively, for stem dry weight;0.80and11.2%, respectively, for leaf dry weight;0.86and12.9%, respectively, for total fruit dry weight;0.79and16.1%, respectively, for yield (fresh weight). The model developed in this study can be used for light and temperature management for greenhouse sweet pepper production.
引文
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