温室黄瓜叶面积、光合作用及干物质生产对叶片含氮量响应的模拟模型
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摘要
叶面积指数(LAI)是作物冠层光合作用及水分利用模拟的重要参数,光合作用和干物质生产是作物产量和品质形成的物质基础。叶片含氮量直接影响了作物的LAI和光合作用速率,从而影响作物的干物质生产与产量。定量研究作物LAI、叶片光合作用速率以及干物质生产过程对叶片含氮量的响应,是实现作物氮素精确与优化管理的重要前提。黄瓜是我国和世界上的主要设施栽培作物。本研究以温室黄瓜为研究对象,于2005年到2008年间,在上海(121.5°E,31.2°N)农科院Venlo型现代化自控温室和上海孙桥现代化农业示范园区的2,4-连栋温室中进行了不同播期、不同栽培基质下的温室黄瓜氮素处理试验。在对试验数据进行系统分析的基础上,以光温指数为尺度,以叶片含氮量为参数,建立了叶片形态发生指标以及叶面积指数对叶片含氮量响应的模拟模型；以叶片光合作用的FvCB模型为基础,确定了FvCB模型的各个参数对叶片含氮量的响应函数；将叶面积指数对叶片含氮量响应模型与基于FvCB的叶片光合作用对叶片含氮量响应模型相结合,建立了温室黄瓜干物质生产对叶片含氮量响应模拟模型。主要结果如下：
(1)温室黄瓜叶面积对叶片氮含量响应的模拟模型。通过温室黄瓜的不同氮素施用浓度处理试验,定量研究了黄瓜冠层叶片氮累积量的季节变化以及叶片含氮量在冠层中的垂直分布,并描述了黄瓜坐果开始时冠层叶片氮积累量及特定叶片含氮量对叶片形态发生指标参数的定量影响。在此基础上,以光温指数为尺度,以叶片含氮量为参数,建立了叶片形态发生指标以及叶面积指数对叶片含氮量响应的模拟模型。利用独立的试验数据对本模型和基于叶干重和SLA的叶面积指数预测模型进行评估和比较,结果表明,对于本模型,用坐果开始时冠层叶片氮累积量作为参数时,出叶数、叶长、LAI的预测值和实测值的r2分别为0.95、0.93、0.87,RMSE分别为2.9、0.05m、0.18 m2m-2；而用坐果开始时倒8叶的叶片含氮量作为参数时,对应的实测值和预测的r2分别为0.93、0.92、0.8,RMSE分别为3.2、0.05 m、0.39m2 m-2。对于基于叶干重和SLA的LAI预测方法,用实测的SLA作为输入,冠层LAI预测值和实测值的r2和RMSE分别为0.74和0.43 m2 m-2。表明,本文提出的模型能够较好的模拟温室黄瓜在不同温室和氮素施用浓度下的叶片生长对叶片含氮量的响应。
(2)光合速率对叶片含氮量响应的模拟模型。以FvCB光合作用模型为基础,结合温室黄瓜叶片的净光合速率和叶绿素荧光活体测定数据,对FvCB模型的参数[暗呼吸速率(Rd)、限制光强下光系统Ⅱ中光强转化为电子传递的转化速率(κ2(LL))、最大电子传递速率(Jmax)、电子传递速率对光照响应的非直角曲线的曲率因子(θ)、Rubisco酶位点上CO2/O2的特定因子(Sc/o)、Rubisco酶的羧化能力(Vcmax)以及叶肉导度(gm)]进行了估算,确定了这些参数对叶片含氮量的响应函数。结果表明,K2(LL)、Vcmax(T0)、Jmax和Rd与叶片含氮量有较好的线性关系,而θ和gm与叶片氮含量的关系不明显。将估算出的参数作为模型输入,用独立的试验数据对模型进行检验,并与基于光合有效辐射、叶片最大光合速率以及初始光能利用率的光合作用函数(负指数方程)的预测效果相比较。结果表明,使用FvCB模型对单叶光合作用的预测值与实测值的r2和RMSE分别为0.63和5.28 mol CO2 m-2 s-1,而基于负指数方程的光合作用模型的预测值与实测值之间的r2和RMSE分别为0.65和4.94 molCO2 m-2 s-1。从总体的r2和RMSE来看,基于负指数方程的光合速率模型的预测效果略好于FvCB模型,但进一步的分析表明其对与建模环境相差较大的情况下的黄瓜单叶净光合作用速率的预测效果较差,因而稳定性和广适性差;而FvCB模型的预测精度虽稍差一些,但其对不同条件下的预测结果一致,稳定性和广适性好,且其预测精度可以通过利用翔实的数据对参数进行进一步的校正而获得提高。
(3)将叶面积指数对叶片含氮量响应模型与基于FvCB模型的叶片光合作用对叶片含氮量响应模型相结合,建立了温室黄瓜干物质生产对叶片含氮量响应的模拟模型。将第二章中所讨论的的两种叶面积对叶片含氮量响应的模拟模型(基于叶片形态发生和基于SLA)分别与第三章中所讨论的两种光合速率对叶片含氮量响应的模拟模型(FvCB模型和负指数方程)相结合,分别用来模拟不同氮素条件下温室黄瓜的每日冠层总光合量,再与常用的呼吸作用模型和干物质生产计算方法相结合,组成四种干物质生产对叶片含氮量量响应的模拟模型,用来预测不同条件下黄瓜的干物质生产过程。用独立的试验数据进行检验,结果表明,基于叶片形态发生和FvCB模型的干物质生产模型、基于叶片形态发生和负指数方程的干物质生产模型、基于SLA法和FvCB模型的干物质生产模型、基于SLA法和负指数方程的干物质生产模型对黄瓜总干物质量的预测值与实测值之间的r2分别为0.62、0.66、0.53、0.71,RMSE分别为58.9 g m-2、54.3 g m-2、77.2 g m-2、44.8 g m-2。虽然基于叶片形态发生和FvCB模型的干物质生产模型的预测效果仅排在第三位,但其相对于基于SLA的模型,避免了破坏性取样；相对于基于叶片形态发生和负指数方程的干物质生产模型,预测结果具有更好的一致性。因此,基于叶片形态发生和FvCB模型的干物质生产模型具有较好的机理性、易用性、稳定性和广适性,且通过改进FvCB模型参数的预测方法,提高光合速率的预测精度,其对干物质生产的预测精度还可以有进一步的提高。
本研究建立的模型可以根据定植日期、种植密度、施氮水平等栽培措施,温室内的太阳辐射、温度和CO2浓度等环境资料,定植时的叶片数、去除的老叶数、定植时的顶部叶片含氮量等易于获取的作物参数来预测上海地区单蔓整枝方式下温室黄瓜的出叶数、各叶位叶片的含氮量、各叶叶长、群体叶面积指数、光合作用速率以及干物质累积量,具有较强的机理性和简单易用的特点,可以为温室黄瓜生产中氮素精确与优化管理提供理论依据与决策支持。
Leaf area index (LAI) is an important parameter for modelling canopy photosynthesis and crop water status, photosynthesis and dry matter production are necessary to guarantee crop yield and quality formation. Leaf nitrogen content directly affects the crop LAI and photosynthesis rate, thus affects crop yield and dry matter production. Quantitative researches on the responses of crop LAI, leaf photosynthesis rate and dry matter production to leaf nitrogen are the important premise for the accuracy and optimization of nitrogen management in greenhouses. Cucumber is one of main greenhouse cultivated crops in China and in the world. Experiments of greenhouse cucumber with different planting dates, substrates and nitrogen application rates were conducted in a Venlo-type greenhouse and multi-span plastic greenhouse in Shanghai (121.5 (?),31.2 (?)) from 2005 to 2007 to collect data for model development and validation. Based on the data, first we established a model to simulate the responses of leaf morphological traits and leaf area index to leaf nitrogen content using the index of radiation and temperature (photothermal index, PTI); second, based on the FvCB photosynthesis model, the responses of parameters of FvCB model to leaf nitrogen content was determined; third, integrated the model of responses of LAI to leaf nitrogen content and responses of photosynthesis based on the FvCB model to leaf nitrogen content, we established a model of to simulate the response of dry matter production of greenhouse cucumber to leaf nitrogen content. The main results are:
(1) Simulation of the response of leaf growth of greenhouse cucumber to leaf nitrogen content. Using the experiment data with different nitrogen application rates, seasonal time course of canopy nitrogen content and the distribution of leaf nitrogen content in canopy were quantified, then the effects of canopy nitrogen content and specific leaf nitrogen content on leaf morphological traits of greenhouse cucumber were described. On this basis, a simulation model of responses of leaf morphological traits and leaf area index to leaf nitrogen content was established. Independent experiments data were used to validate our model and SLA-based model. The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values using our method are 0.95 and 2.9 for leaf number,0.93 and 0.05 m for specific leaf length,0.87 and 0.18 m2 m-2 for LAI when using canopy nitrogen content at the start of fruit setting while using specific leaf nitrogen content (the 8th leaf counted downward) at the start of fruit setting, the r2 and RMSE are 0.93 and 3.2 for leaf number,0.92 and 0.05 m for specific leaf length and 0.8 and 0.39 m2 m-2 for LAI. For the SLA-based model, using measured SLA data as input, the r2 and RMSE are 0.74 and 0.43 m2 m-2. So, our model gives satisfactory prediction of the response of leaf growth and LAI of greenhouse cucumber to leaf nitrogen content with different nitrogen application rate in different greenhouses.
(2) Simulation of the response of leaf photosynthesis rate to leaf nitrogen content using the FvCB model. Based on the FvCB model, parameters used for calculating photosynthesis rate (e.g. day respiration (Rd), conversion efficiency of linc into linear electron transport of PSII under limiting light (k2(LL)), electron transport capacity (Jmax), curvature factor (0 for the non-rectangular hyperbolic response of electron flux to Iinc, ribulose 1-5-bisphosphate carboxylase/oxygenase (Rubisco) CO2/O2 specificity (Sc/0), Rubisco carboxylation capacity (Vcmax) and mesophyll conductance (gm) were estimated, using combined measurements of photosynthesis rate and chlorophyll fluorescence, and the responses of these parameters to leaf nitrogen were determined. The results showed that K2(LL), Vcmax(To), Jmax and Rd increased linearly with leaf nitrogen content, but (?) and gm could be independent. Using estimated parameter as input, independent experimental data was used to validate and give the comparison between this model and the photosynthesis model which is based on the radiation, the maximum gross photosynthesis rate and initial light use efficiency (the asymptotic negative-exponential fucntion). The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values are 0.63 and 5.28μmol CO2 m-2 s"1 when using FvCB based model; r2 and RMSE between the predicted and measured values are 0.65 and 4.94μmol CO2 m-2 s-1 when using the the asymptotic negative-exponential fucntion. Though the prediction accuracy of the negative-exponential function is better than the FvCB model, but further analyses showed that the prediction accuracy of net photosynthesis rate is poor when the environment is differ from the model established, thus stability and adaptability is poor; the prediction accuracy of FvCB model is lower, but under different environment, the prediction accuracy is the consistent thus has stability and adaptability. The prediction accuracy can be improved when using more data to correction the parameters used in the model.
(3) Integrating simulation of the responses of leaf growth and photosynthesis rate to leaf nitrogen content developed in chapter 2 and chapter 3, the responses of dry matter production to leaf nitrogen content were established. Integrating prediction of the effect of nitrogen on greenhouse cucumber leaf area developed in chapter 2 (leaf morphological traits based LAI model and SLA-based LAI model) and prediction of the effect of nitrogen on leaf photosynthesis using FvCB model and an asymptotic negative-exponential function used in Chapter 3, dry matter production of greenhouse cucumber was predicted. Independent experimental values were used to validate the model. The results show that the r2 and RMSE between the predicted and measured total dry matter based on the 1:1 line are 0.62 and 58.9 g m-2 when using leaf morphological traits based LAI model and FvCB model; 0.66 and 54.3 g m-2 when using leaf morphological traits based LAI and the asymptotic negative-exponential function; 0.71 and 44.8 g m-2 when using SLA-based LAI model and FvCB model; 0.53 and 77.2 g m-2 when using SLA-based LAI model and the asymptotic negative-exponential function, respectively. Though, using leaf morphological traits based LAI model and FvCB model did not give a better prediction as using leaf morphological traits based LAI and the asymptotic negative-exponential function and using SLA-based LAI model and FvCB model, it can avoid destructive sampling with respect to SLA-based model and prediction accuracy has a better consistency than leaf morphological leaf traits and the asymptotic negative-exponential function gives. Therefore, leaf morphological traits based LAI model and FvCB model is more applicable, stable and adaptable, the prediction accuracy can be improved through the improvement of the parameters in FvCB model to raise the prediction accuracy of photosynthesis rate.
The model developed in this study could predict the number of appeared leaves per plant, leaf nitrogen content of each rank, specific leaf length, leaf area, leaf photosynthesis rate and dry matter production of greenhouse cucumber with the single-stem pruning in Shanghai using the easily gained crop and environmental parameters such as planting date, planting density, nitrogen application rate, PAR, temperature and CO2 concentration inside the greenhouse, the number of leaves on planting date and the removed leaves as input, therefore has strong mechanism and is easy to use, can be used for optimization nitrogen management for greenhouse fruit cucumber prediction.
(1)温室黄瓜叶面积对叶片氮含量响应的模拟模型。通过温室黄瓜的不同氮素施用浓度处理试验,定量研究了黄瓜冠层叶片氮累积量的季节变化以及叶片含氮量在冠层中的垂直分布,并描述了黄瓜坐果开始时冠层叶片氮积累量及特定叶片含氮量对叶片形态发生指标参数的定量影响。在此基础上,以光温指数为尺度,以叶片含氮量为参数,建立了叶片形态发生指标以及叶面积指数对叶片含氮量响应的模拟模型。利用独立的试验数据对本模型和基于叶干重和SLA的叶面积指数预测模型进行评估和比较,结果表明,对于本模型,用坐果开始时冠层叶片氮累积量作为参数时,出叶数、叶长、LAI的预测值和实测值的r2分别为0.95、0.93、0.87,RMSE分别为2.9、0.05m、0.18 m2m-2；而用坐果开始时倒8叶的叶片含氮量作为参数时,对应的实测值和预测的r2分别为0.93、0.92、0.8,RMSE分别为3.2、0.05 m、0.39m2 m-2。对于基于叶干重和SLA的LAI预测方法,用实测的SLA作为输入,冠层LAI预测值和实测值的r2和RMSE分别为0.74和0.43 m2 m-2。表明,本文提出的模型能够较好的模拟温室黄瓜在不同温室和氮素施用浓度下的叶片生长对叶片含氮量的响应。
(2)光合速率对叶片含氮量响应的模拟模型。以FvCB光合作用模型为基础,结合温室黄瓜叶片的净光合速率和叶绿素荧光活体测定数据,对FvCB模型的参数[暗呼吸速率(Rd)、限制光强下光系统Ⅱ中光强转化为电子传递的转化速率(κ2(LL))、最大电子传递速率(Jmax)、电子传递速率对光照响应的非直角曲线的曲率因子(θ)、Rubisco酶位点上CO2/O2的特定因子(Sc/o)、Rubisco酶的羧化能力(Vcmax)以及叶肉导度(gm)]进行了估算,确定了这些参数对叶片含氮量的响应函数。结果表明,K2(LL)、Vcmax(T0)、Jmax和Rd与叶片含氮量有较好的线性关系,而θ和gm与叶片氮含量的关系不明显。将估算出的参数作为模型输入,用独立的试验数据对模型进行检验,并与基于光合有效辐射、叶片最大光合速率以及初始光能利用率的光合作用函数(负指数方程)的预测效果相比较。结果表明,使用FvCB模型对单叶光合作用的预测值与实测值的r2和RMSE分别为0.63和5.28 mol CO2 m-2 s-1,而基于负指数方程的光合作用模型的预测值与实测值之间的r2和RMSE分别为0.65和4.94 molCO2 m-2 s-1。从总体的r2和RMSE来看,基于负指数方程的光合速率模型的预测效果略好于FvCB模型,但进一步的分析表明其对与建模环境相差较大的情况下的黄瓜单叶净光合作用速率的预测效果较差,因而稳定性和广适性差;而FvCB模型的预测精度虽稍差一些,但其对不同条件下的预测结果一致,稳定性和广适性好,且其预测精度可以通过利用翔实的数据对参数进行进一步的校正而获得提高。
(3)将叶面积指数对叶片含氮量响应模型与基于FvCB模型的叶片光合作用对叶片含氮量响应模型相结合,建立了温室黄瓜干物质生产对叶片含氮量响应的模拟模型。将第二章中所讨论的的两种叶面积对叶片含氮量响应的模拟模型(基于叶片形态发生和基于SLA)分别与第三章中所讨论的两种光合速率对叶片含氮量响应的模拟模型(FvCB模型和负指数方程)相结合,分别用来模拟不同氮素条件下温室黄瓜的每日冠层总光合量,再与常用的呼吸作用模型和干物质生产计算方法相结合,组成四种干物质生产对叶片含氮量量响应的模拟模型,用来预测不同条件下黄瓜的干物质生产过程。用独立的试验数据进行检验,结果表明,基于叶片形态发生和FvCB模型的干物质生产模型、基于叶片形态发生和负指数方程的干物质生产模型、基于SLA法和FvCB模型的干物质生产模型、基于SLA法和负指数方程的干物质生产模型对黄瓜总干物质量的预测值与实测值之间的r2分别为0.62、0.66、0.53、0.71,RMSE分别为58.9 g m-2、54.3 g m-2、77.2 g m-2、44.8 g m-2。虽然基于叶片形态发生和FvCB模型的干物质生产模型的预测效果仅排在第三位,但其相对于基于SLA的模型,避免了破坏性取样；相对于基于叶片形态发生和负指数方程的干物质生产模型,预测结果具有更好的一致性。因此,基于叶片形态发生和FvCB模型的干物质生产模型具有较好的机理性、易用性、稳定性和广适性,且通过改进FvCB模型参数的预测方法,提高光合速率的预测精度,其对干物质生产的预测精度还可以有进一步的提高。
本研究建立的模型可以根据定植日期、种植密度、施氮水平等栽培措施,温室内的太阳辐射、温度和CO2浓度等环境资料,定植时的叶片数、去除的老叶数、定植时的顶部叶片含氮量等易于获取的作物参数来预测上海地区单蔓整枝方式下温室黄瓜的出叶数、各叶位叶片的含氮量、各叶叶长、群体叶面积指数、光合作用速率以及干物质累积量,具有较强的机理性和简单易用的特点,可以为温室黄瓜生产中氮素精确与优化管理提供理论依据与决策支持。
Leaf area index (LAI) is an important parameter for modelling canopy photosynthesis and crop water status, photosynthesis and dry matter production are necessary to guarantee crop yield and quality formation. Leaf nitrogen content directly affects the crop LAI and photosynthesis rate, thus affects crop yield and dry matter production. Quantitative researches on the responses of crop LAI, leaf photosynthesis rate and dry matter production to leaf nitrogen are the important premise for the accuracy and optimization of nitrogen management in greenhouses. Cucumber is one of main greenhouse cultivated crops in China and in the world. Experiments of greenhouse cucumber with different planting dates, substrates and nitrogen application rates were conducted in a Venlo-type greenhouse and multi-span plastic greenhouse in Shanghai (121.5 (?),31.2 (?)) from 2005 to 2007 to collect data for model development and validation. Based on the data, first we established a model to simulate the responses of leaf morphological traits and leaf area index to leaf nitrogen content using the index of radiation and temperature (photothermal index, PTI); second, based on the FvCB photosynthesis model, the responses of parameters of FvCB model to leaf nitrogen content was determined; third, integrated the model of responses of LAI to leaf nitrogen content and responses of photosynthesis based on the FvCB model to leaf nitrogen content, we established a model of to simulate the response of dry matter production of greenhouse cucumber to leaf nitrogen content. The main results are:
(1) Simulation of the response of leaf growth of greenhouse cucumber to leaf nitrogen content. Using the experiment data with different nitrogen application rates, seasonal time course of canopy nitrogen content and the distribution of leaf nitrogen content in canopy were quantified, then the effects of canopy nitrogen content and specific leaf nitrogen content on leaf morphological traits of greenhouse cucumber were described. On this basis, a simulation model of responses of leaf morphological traits and leaf area index to leaf nitrogen content was established. Independent experiments data were used to validate our model and SLA-based model. The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values using our method are 0.95 and 2.9 for leaf number,0.93 and 0.05 m for specific leaf length,0.87 and 0.18 m2 m-2 for LAI when using canopy nitrogen content at the start of fruit setting while using specific leaf nitrogen content (the 8th leaf counted downward) at the start of fruit setting, the r2 and RMSE are 0.93 and 3.2 for leaf number,0.92 and 0.05 m for specific leaf length and 0.8 and 0.39 m2 m-2 for LAI. For the SLA-based model, using measured SLA data as input, the r2 and RMSE are 0.74 and 0.43 m2 m-2. So, our model gives satisfactory prediction of the response of leaf growth and LAI of greenhouse cucumber to leaf nitrogen content with different nitrogen application rate in different greenhouses.
(2) Simulation of the response of leaf photosynthesis rate to leaf nitrogen content using the FvCB model. Based on the FvCB model, parameters used for calculating photosynthesis rate (e.g. day respiration (Rd), conversion efficiency of linc into linear electron transport of PSII under limiting light (k2(LL)), electron transport capacity (Jmax), curvature factor (0 for the non-rectangular hyperbolic response of electron flux to Iinc, ribulose 1-5-bisphosphate carboxylase/oxygenase (Rubisco) CO2/O2 specificity (Sc/0), Rubisco carboxylation capacity (Vcmax) and mesophyll conductance (gm) were estimated, using combined measurements of photosynthesis rate and chlorophyll fluorescence, and the responses of these parameters to leaf nitrogen were determined. The results showed that K2(LL), Vcmax(To), Jmax and Rd increased linearly with leaf nitrogen content, but (?) and gm could be independent. Using estimated parameter as input, independent experimental data was used to validate and give the comparison between this model and the photosynthesis model which is based on the radiation, the maximum gross photosynthesis rate and initial light use efficiency (the asymptotic negative-exponential fucntion). The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values are 0.63 and 5.28μmol CO2 m-2 s"1 when using FvCB based model; r2 and RMSE between the predicted and measured values are 0.65 and 4.94μmol CO2 m-2 s-1 when using the the asymptotic negative-exponential fucntion. Though the prediction accuracy of the negative-exponential function is better than the FvCB model, but further analyses showed that the prediction accuracy of net photosynthesis rate is poor when the environment is differ from the model established, thus stability and adaptability is poor; the prediction accuracy of FvCB model is lower, but under different environment, the prediction accuracy is the consistent thus has stability and adaptability. The prediction accuracy can be improved when using more data to correction the parameters used in the model.
(3) Integrating simulation of the responses of leaf growth and photosynthesis rate to leaf nitrogen content developed in chapter 2 and chapter 3, the responses of dry matter production to leaf nitrogen content were established. Integrating prediction of the effect of nitrogen on greenhouse cucumber leaf area developed in chapter 2 (leaf morphological traits based LAI model and SLA-based LAI model) and prediction of the effect of nitrogen on leaf photosynthesis using FvCB model and an asymptotic negative-exponential function used in Chapter 3, dry matter production of greenhouse cucumber was predicted. Independent experimental values were used to validate the model. The results show that the r2 and RMSE between the predicted and measured total dry matter based on the 1:1 line are 0.62 and 58.9 g m-2 when using leaf morphological traits based LAI model and FvCB model; 0.66 and 54.3 g m-2 when using leaf morphological traits based LAI and the asymptotic negative-exponential function; 0.71 and 44.8 g m-2 when using SLA-based LAI model and FvCB model; 0.53 and 77.2 g m-2 when using SLA-based LAI model and the asymptotic negative-exponential function, respectively. Though, using leaf morphological traits based LAI model and FvCB model did not give a better prediction as using leaf morphological traits based LAI and the asymptotic negative-exponential function and using SLA-based LAI model and FvCB model, it can avoid destructive sampling with respect to SLA-based model and prediction accuracy has a better consistency than leaf morphological leaf traits and the asymptotic negative-exponential function gives. Therefore, leaf morphological traits based LAI model and FvCB model is more applicable, stable and adaptable, the prediction accuracy can be improved through the improvement of the parameters in FvCB model to raise the prediction accuracy of photosynthesis rate.
The model developed in this study could predict the number of appeared leaves per plant, leaf nitrogen content of each rank, specific leaf length, leaf area, leaf photosynthesis rate and dry matter production of greenhouse cucumber with the single-stem pruning in Shanghai using the easily gained crop and environmental parameters such as planting date, planting density, nitrogen application rate, PAR, temperature and CO2 concentration inside the greenhouse, the number of leaves on planting date and the removed leaves as input, therefore has strong mechanism and is easy to use, can be used for optimization nitrogen management for greenhouse fruit cucumber prediction.
引文
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[1]Yin, X.Y., Struik, P.C., Romero, P., Harbinson, J., Evers, J.B., van der Putten, P.E.L., vos, J.. Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C3 photosynthesis model:a critical appraisal and a new integrated approach applied to leaves in a wheat (Triticum aestivum) canopy[J]. Plant, Cell and Environment,2009,32:448-64.
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[1]Xu, R., Dai, J., Luo, W., Yin, X., Li, Y., Tai, X., Han, L., Chen, Y., Lin, L., Li, G., Zou, C., Du, W., Diao, M.. A photothermal model of leaf area index for greenhouse crops[J]. Agricultural and Forest Meteorology,2010,150:541-552.
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[7]Marcelis, L.F.M., Gijzen, H.. A model for prediction of yield and quality of cucumber fruits[J]. Acta Horticultrae,1998,476:237-242.
[8]李娟,郭世荣,罗卫红.温室黄瓜光合生产与干物质累积模拟模型[J].农业工程学报,2003,19(4)：241-244.
[9]谢祝捷,陈春宏,余纪柱,等.上海自控温室黄瓜干物质生产和分配模拟模型研究[J].上海农业学报,2004,1：75-79.
[10]孙忠富,陈人杰.温室作物模型研究基本理论与技术方法的探讨[J].中国农业科学,2002,35：320-324.
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[14]Peng SB, Rebecca M, Laza C, Garcia FV, Cassman KG.1995. Chlorophyll meter estimates leaf area-based nitrogen concentration of rice[J]. Communication in Soil Science and Plant Analysis, 26(5&6):927-935.
[15]Chiba, A., Ishida, H., Nishizawa, N.K., Makino, A., Mae, T.. Exclusion of ribulose-1,5-bisphosphate carboxylase/oxygenase from chloroplasts by specific bodies in naturally senescing leaves of wheat[J]. Plant and Cell Physiology,2003,44:914-921.
[16]Evans, J.R.. Nitrogen and photosynthesis in the flag leaf of wheat (Triticum aestivum L.)[J]. Plant Physiology,1983,72:297-302.
[17]Evans, J.R.. Partitioning of nitrogen between and within leaves grown under different irradiances[J]. Australian Journal of Plant Physiology,1989a,16:533-548.
[18]Yin, X.Y., Lantiga, E.A., Schapendonk, ADH.C.M., Zhong, X.H.. Some quantitative relationship between leaf area index and canopy nitrogen content and distribution[J]. Annals of Botany,2003,91:893-903.
[19]Goudriaan, J.. The bare bones of leaf angle distribution in radiation models for canopy photosynthesis and energy exchange[J]. Agricultural and Forest Meteorology,1998,43: 155-169.
[20]Gifford, R.M.. Whole plant respiration and photosynthesis of wheat under increased CO2 concentration and temperature:long-term vs. short-term distinctions for modeling[J]. Global Change Biology,1995,1:385-396.
[21]Amthor, J.S.. The role of maintenance respiration in Plant growth[J]. Plant Cell and Environment,1984,7:561-569.
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