基于生理生态过程的棉籽品质模拟模型研究
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摘要
本文综合国内外棉铃发育和棉籽品质形成的生理生态研究成果,基于2006-2007年在江苏南京进行的不同熟性棉花品种与施氮量试验和2005年在长江流域下游棉区和黄河流域黄淮棉区多个试点同时进行的异地分期播种试验,在统计分析基础上,确定影响棉籽品质的主要生态因子,并建立棉籽品质的生态预测模型。运用作物模型学原理和系统分析法,综合量化棉铃发育和棉籽品质形成过程及其与环境因子间的动态关系,并在此基础上构建基于生理发育时间(PDT)的棉花铃期模拟模型和基于生理生态过程的棉籽干物质积累与品质形成模拟模型。利用不同熟性棉花品种多试点分播期的试验资料对模型的预测精度和广适性进行了检验。1.棉花铃期模拟模型
基于不同熟性棉花品种和施氮量试验,综合量化品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)对棉花铃期的影响,在作物生育期模拟研究基础上,改进温度效应的计算方法,增加太阳辐射和氮素效应函数,以生理发育时间(PDT)作为尺度,建立棉花铃期模拟模型。利用田间试验资料对模型进行检验的结果表明：铃期模拟模型对德夏棉1号、科棉1号和美棉33B铃期预测值与实测值的根均方差(RMSE)分别为2.25 d、2.61 d和2.75 d,说明铃期模拟模型预测精度高,机理性强,模型实现了棉铃发育进程的逐日模拟和棉花铃期的准确预测,可为棉籽生长和品质形成的模拟模型提供时间变量。
2.棉籽干物质积累模拟模型
基于不同熟性棉花品种的异地分期播种试验,综合量化棉籽干物质积累过程及其对品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)的响应,基于棉籽干物质积累的“库限制”假设,结合棉花铃期模拟模型,建立基于生理生态过程的棉籽干物质积累与籽指形成的模拟模型。通过量化铃期棉铃对位叶氮浓度的变化,为模型构建氮素效应函数。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：科棉1号和美棉33B的棉籽干重模拟值与实测值的根均方差(RMSE)分别为9.5 mg·seed-1和8.2 mg·seed-1。模型预测精度高,机理性强,实现了棉籽干物质重、棉铃对位叶氮浓度的逐日模拟,为进一步模拟棉籽品质的形成过程奠定了基础。
3.棉籽蛋白质和油分含量的生态因子效应研究
基于不同熟性棉花品种的异地分期播种和施氮量试验,综合分析品种特性、主要气象条件和栽培措施对棉籽蛋白质和油分含量的影响,确定了影响棉籽品质的主要因子：基因型、日均温、太阳辐射量和施氮量。除品种因素外,铃期温度对棉籽品质影响最大,棉籽蛋白质和油分形成的最适宜铃期日均温分别为26.1℃和25.7℃；较高的太阳辐射降低了棉籽蛋白质和油分含量；增加施氮量提高棉籽蛋白质含量,降低油分含量。在统计分析基础上,综合棉籽品质的主要影响因子,建立了棉籽蛋白质和油分含量的生态预测模型。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：棉籽蛋白质含量和油分含量预测值与实测值的根均方差(RMSE)分别为2.03%和2.54%,模型具有综合性强、预测精度高、简便易行等特点,较好地描述了品种因素、主要气象条件和栽培措施与棉籽蛋白质和油分含量的关系。
4.棉籽蛋白质形成的模拟模型
基于不同熟性棉花品种的异地分期播种和施氮量试验,综合量化棉籽蛋白质形成过程及其对品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)的响应,在棉花铃期模型、棉籽干物质积累的模拟模型和棉籽品质生态模型基础上,运用农业模型学原理,通过模拟棉籽氮素吸收、结构蛋白和储藏蛋白的合成,建立基于过程的棉籽蛋白质形成模拟模型,实现了不同生态条件下棉籽蛋白质积累及含量变化的模拟预测。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：供试品种科棉1号和美棉33B棉籽蛋白质积累的模拟值与实测值的根均方差(RMSE)分别为2.5mg·seed-1和1.8 mg·seed-1,蛋白质含量预测的RMSE分别为2.05%和2.33%。
5.棉籽油分形成的模拟模型
基于不同熟性棉花品种的异地分期播种试验,综合量化品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)对棉籽油分形成的影响,在棉籽油分形成的生理生态研究基础上,建立基于生理生态过程的棉籽油分形成模拟模型。模型与棉花铃期模型和棉籽干物质积累模型结合,通过模拟棉籽油分合成过程及其对品种和环境因子的响应,实现了棉籽生长过程中棉籽油分积累和油分含量的逐日模拟。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：供试品种科棉1号和美棉33B棉籽的油分积累量模拟值与实测值的根均方差(RMSE)分别为1.9 mg·seed-1和2.0 mg·seed-1,棉籽油分含量模拟值与实测值的RMSE分别为2.45%和2.95%。
本研究系统模拟了棉铃和棉籽的发育、棉铃对位叶氮浓度变化、棉籽干物质积累、氮素吸收、蛋白质和油分合成等生理过程以及棉籽品质的温度效应、太阳辐射效应、氮素效应等生态过程。模型通过棉铃发育进程(生理发育时间PDT)将各子模块紧密结合,应用面向对象的程序设计与软构建技术在Visual Basic平台上构建棉花铃期模型系统和棉籽生长与品质形成模型系统,系统主要用于气象数据的统计分析、模型参数调试以及模型的运行、应用和检验。本研究是对棉花生长模型的补充完善,填补了国内外在该领域的研究空白,为进一步进行棉籽产量、棉籽蛋白质和油分产量的预测及其形成过程的模拟研究奠定了基础,为棉花生产的辅助调控提供了技术支撑。
Based on the achievements in physiological and ecological mechanisms of cotton boll development and seed quality formation, the experiment conducted in Nanjing (the lower reaches of Yangtze River Valley) in 2006 and 2007, and the experiment conducted in the Yellow River Valley (Xuzhou and Anyang) and the lower reaches of Yangtze River Valley (Huaian and Nanjing) in 2005, we quantify the effects of cultivar characteristics, weather (temperature and solar radiation), and crop management variables (precisely N supply) on cotton boll development and seed quality formation using agricultural model principle and systematic analysis method. And then we developed the cotton boll maturation period model base on physiological development time, cottonseed quality ecological model, and the simulation model of cottonseed biomass accumulation, protein and oil formation based on the eco-physiology processes. These models were tested with the field experimental data collected from different sits with different cotton cultivars.
1. Cotton boll maturation period model
By the field experiments with different maturity cotton cultivars, the responses of cotton boll development to cultivar characteristics, weather conditions (temperature and solar radiation), and crop management variable (N nutrients) were quantified. Cotton boll development was simulated using the scale of physiological development time. The model was tested using independent field data in 2005. The simulated values of boll maturation period showed reasonable agreement with the observed values, with root mean square error (RMSE) of 2.25 days for DSC-1, of 2.61 days for KC-1, and of 2.75 days for AC-33B. The results showed that the model was sufficiently robust to simulate boll development and predict boll maturation period under diverse environmental conditions. It is the improvement of boll maturation period model and provides the time variable for simulation model of cottonseed growth and quality formation.
2. Simulation model of cottonseed biomass accumulation
By the field experiments with different maturity cotton cultivars and sowing dates conducted at different sites, the responses of cottonseed biomass accumulation to cultivar characteristics, weather conditions (temperature and solar radiation), and crop management variable (N nutrients) were quantified. Based on the hypothesis of sink-determined, the cottonseed biomass accumulation model was then developed. The subtending leaf N concentration of cotton boll was simulated with a semi-empirical equation, which was made as the direct indicator of the N nutrition effect on cottonseed growth and development. The model was tested using independent field data obtained in the Yellow River Valley (Xuzhou and Anyang) and the lower reaches of Yangtze River Valley (Huaian) in 2005. The RMSEs of cottonseed dry weight predictions were 9.5 mg·seed-1 for KC-1 and 8.2 mg·seed-1 for AC-33B. The results showed that the model was sufficiently robust to predict cottonseed biomass accumulation under diverse environmental conditions.
3. Ecological model of cottonseed protein and oil content
The prediction of cottonseed (Gossypium hirsutum L.) quality by ecological factors is an area of great uncertainty. Our object is to investigate the relationship between the cottonseed protein and oil content and the multi-ecological conditions and develop an ecological model to predict the cottonseed protein and oil content under different environments. A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data at sowing date of 25 April, the effect of cultivar, weather conditions, and crop management variables on cottonseed protein and oil content was analyzed by step regression analysis and non-liner regression analysis. We determined that cultivar characteristics, temperature, solar radiation, and N fertilizer rate are main impact factors on cottonseed quality. The optimum temperature for cottonseed protein formation is 26.1℃, and which for oil accumulation is 25.7℃. Adequate solar radiation will reduce the protein and oil content. Increasing the N fertilizer rate will raise protein content and reduce the oil content in cottonseed. The present study developed a ecological model to predict cottonseed protein and oil content. The model was tested by the data collected at sowing date of 25 May in Nanjing, Xuzhou, and Anyang. The RMSE of the model was 2.03% in prediction of cottonseed protein content, and 2.54% in prediction cottonseed oil content. The results showed that the model is sufficiently robust to accuracy predict cottonseed protein content and oil content under diverse environmental conditions.
4. Simulation model of cottonseed protein formation based on eco-physiology process
The simulation of cottonseed (Gossypium hirsutum L.) growth is an area of great uncertainty, especially in the process of cottonseed quality formation. To simulate the formation of cottonseed protein under different environmental conditions, a simple process-based model was developed driven by the inputs of cultivar parameters, weather, and crop management variable (precisely N supply). A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data collected in Nanjing, the responds functions of cottonseed protein accumulation to weather conditions (temperature, solar radiation), crop management variable (N supply) and boll position were all developed and involved in the model. The model based on the hypothesis that nitrogen accumulation synthesis in cottonseed are mainly sink determined, and was integrated with the cotton boll maturation period model and cottonseed biomass accumulation model. The parameters in the model were calibrated using the field data obtained in Nanjing. The model was tested using the field data obtained in Huaian, Xuzhou and Anyang. The root mean square error (RMSE) of the simulated and measured cottonseed protein content was 2.05% for KC-1 and 2.33% for AC-33B. The results showed that the model is sufficiently robust to accuracy predict cottonseed protein content under diverse environmental conditions. This model is a necessary component of cotton growth model, and provides a good platform for further study in modeling cottonseed protein yield.
5. Simulation model of cottonseed oil formation based on eco-physiology process
The simulation of cottonseed (Gossypium hirsutum L.) growth is an area of great uncertainty, especially in the process of cottonseed quality formation. To simulate the formation of cottonseed oil under different environmental conditions, a simple process-based model was developed driven by the inputs of cultivar parameters, weather, and crop management variable (precisely N supply). A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data collected in Nanjing, the responds functions of cottonseed oil accumulation to weather conditions (temperature, solar radiation), crop management variable (N supply) and boll position were all developed and involved in the model. The model based on the hypothesis that fat synthesis in cottonseed are mainly sink determined, and was integrated with the cotton boll maturation period model and cottonseed biomass accumulation model. The parameters in the model were calibrated using the field data obtained in Nanjing. The model was tested using the field data obtained in Huaian, Xuzhou and Anyang. The root mean square error (RMSE) of the simulated and measured cottonseed oil content was 2.45% for KC-1 and 2.95% for AC-33B. The results showed that the model is sufficiently robust to accuracy predict cottonseed oil content under diverse environmental conditions. This model is a necessary component of cotton growth model, and provides a good platform for further study in modeling cottonseed oil yield.
This research provided a systematic process-based simulation model modeling subtending leaf N concentration of cotton boll, cottonseed development, biomass accumulation, N uptake, protein formation, and oil synthesis. This research is an effective supplement for cotton growth model, which fills the vacuity in the research areas, provides a well platform for further study in modeling the formation of cottonseed yield, protein yield and oil yield, and provides also technical supports for regulation and control in cotton production.
基于不同熟性棉花品种和施氮量试验,综合量化品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)对棉花铃期的影响,在作物生育期模拟研究基础上,改进温度效应的计算方法,增加太阳辐射和氮素效应函数,以生理发育时间(PDT)作为尺度,建立棉花铃期模拟模型。利用田间试验资料对模型进行检验的结果表明：铃期模拟模型对德夏棉1号、科棉1号和美棉33B铃期预测值与实测值的根均方差(RMSE)分别为2.25 d、2.61 d和2.75 d,说明铃期模拟模型预测精度高,机理性强,模型实现了棉铃发育进程的逐日模拟和棉花铃期的准确预测,可为棉籽生长和品质形成的模拟模型提供时间变量。
2.棉籽干物质积累模拟模型
基于不同熟性棉花品种的异地分期播种试验,综合量化棉籽干物质积累过程及其对品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)的响应,基于棉籽干物质积累的“库限制”假设,结合棉花铃期模拟模型,建立基于生理生态过程的棉籽干物质积累与籽指形成的模拟模型。通过量化铃期棉铃对位叶氮浓度的变化,为模型构建氮素效应函数。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：科棉1号和美棉33B的棉籽干重模拟值与实测值的根均方差(RMSE)分别为9.5 mg·seed-1和8.2 mg·seed-1。模型预测精度高,机理性强,实现了棉籽干物质重、棉铃对位叶氮浓度的逐日模拟,为进一步模拟棉籽品质的形成过程奠定了基础。
3.棉籽蛋白质和油分含量的生态因子效应研究
基于不同熟性棉花品种的异地分期播种和施氮量试验,综合分析品种特性、主要气象条件和栽培措施对棉籽蛋白质和油分含量的影响,确定了影响棉籽品质的主要因子：基因型、日均温、太阳辐射量和施氮量。除品种因素外,铃期温度对棉籽品质影响最大,棉籽蛋白质和油分形成的最适宜铃期日均温分别为26.1℃和25.7℃；较高的太阳辐射降低了棉籽蛋白质和油分含量；增加施氮量提高棉籽蛋白质含量,降低油分含量。在统计分析基础上,综合棉籽品质的主要影响因子,建立了棉籽蛋白质和油分含量的生态预测模型。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：棉籽蛋白质含量和油分含量预测值与实测值的根均方差(RMSE)分别为2.03%和2.54%,模型具有综合性强、预测精度高、简便易行等特点,较好地描述了品种因素、主要气象条件和栽培措施与棉籽蛋白质和油分含量的关系。
4.棉籽蛋白质形成的模拟模型
基于不同熟性棉花品种的异地分期播种和施氮量试验,综合量化棉籽蛋白质形成过程及其对品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)的响应,在棉花铃期模型、棉籽干物质积累的模拟模型和棉籽品质生态模型基础上,运用农业模型学原理,通过模拟棉籽氮素吸收、结构蛋白和储藏蛋白的合成,建立基于过程的棉籽蛋白质形成模拟模型,实现了不同生态条件下棉籽蛋白质积累及含量变化的模拟预测。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：供试品种科棉1号和美棉33B棉籽蛋白质积累的模拟值与实测值的根均方差(RMSE)分别为2.5mg·seed-1和1.8 mg·seed-1,蛋白质含量预测的RMSE分别为2.05%和2.33%。
5.棉籽油分形成的模拟模型
基于不同熟性棉花品种的异地分期播种试验,综合量化品种特性、主要气象条件(温度、太阳辐射)和栽培措施(施氮量)对棉籽油分形成的影响,在棉籽油分形成的生理生态研究基础上,建立基于生理生态过程的棉籽油分形成模拟模型。模型与棉花铃期模型和棉籽干物质积累模型结合,通过模拟棉籽油分合成过程及其对品种和环境因子的响应,实现了棉籽生长过程中棉籽油分积累和油分含量的逐日模拟。利用不同生态点分品种、播期和施氮量的田间试验资料对模型进行检验的结果表明：供试品种科棉1号和美棉33B棉籽的油分积累量模拟值与实测值的根均方差(RMSE)分别为1.9 mg·seed-1和2.0 mg·seed-1,棉籽油分含量模拟值与实测值的RMSE分别为2.45%和2.95%。
本研究系统模拟了棉铃和棉籽的发育、棉铃对位叶氮浓度变化、棉籽干物质积累、氮素吸收、蛋白质和油分合成等生理过程以及棉籽品质的温度效应、太阳辐射效应、氮素效应等生态过程。模型通过棉铃发育进程(生理发育时间PDT)将各子模块紧密结合,应用面向对象的程序设计与软构建技术在Visual Basic平台上构建棉花铃期模型系统和棉籽生长与品质形成模型系统,系统主要用于气象数据的统计分析、模型参数调试以及模型的运行、应用和检验。本研究是对棉花生长模型的补充完善,填补了国内外在该领域的研究空白,为进一步进行棉籽产量、棉籽蛋白质和油分产量的预测及其形成过程的模拟研究奠定了基础,为棉花生产的辅助调控提供了技术支撑。
Based on the achievements in physiological and ecological mechanisms of cotton boll development and seed quality formation, the experiment conducted in Nanjing (the lower reaches of Yangtze River Valley) in 2006 and 2007, and the experiment conducted in the Yellow River Valley (Xuzhou and Anyang) and the lower reaches of Yangtze River Valley (Huaian and Nanjing) in 2005, we quantify the effects of cultivar characteristics, weather (temperature and solar radiation), and crop management variables (precisely N supply) on cotton boll development and seed quality formation using agricultural model principle and systematic analysis method. And then we developed the cotton boll maturation period model base on physiological development time, cottonseed quality ecological model, and the simulation model of cottonseed biomass accumulation, protein and oil formation based on the eco-physiology processes. These models were tested with the field experimental data collected from different sits with different cotton cultivars.
1. Cotton boll maturation period model
By the field experiments with different maturity cotton cultivars, the responses of cotton boll development to cultivar characteristics, weather conditions (temperature and solar radiation), and crop management variable (N nutrients) were quantified. Cotton boll development was simulated using the scale of physiological development time. The model was tested using independent field data in 2005. The simulated values of boll maturation period showed reasonable agreement with the observed values, with root mean square error (RMSE) of 2.25 days for DSC-1, of 2.61 days for KC-1, and of 2.75 days for AC-33B. The results showed that the model was sufficiently robust to simulate boll development and predict boll maturation period under diverse environmental conditions. It is the improvement of boll maturation period model and provides the time variable for simulation model of cottonseed growth and quality formation.
2. Simulation model of cottonseed biomass accumulation
By the field experiments with different maturity cotton cultivars and sowing dates conducted at different sites, the responses of cottonseed biomass accumulation to cultivar characteristics, weather conditions (temperature and solar radiation), and crop management variable (N nutrients) were quantified. Based on the hypothesis of sink-determined, the cottonseed biomass accumulation model was then developed. The subtending leaf N concentration of cotton boll was simulated with a semi-empirical equation, which was made as the direct indicator of the N nutrition effect on cottonseed growth and development. The model was tested using independent field data obtained in the Yellow River Valley (Xuzhou and Anyang) and the lower reaches of Yangtze River Valley (Huaian) in 2005. The RMSEs of cottonseed dry weight predictions were 9.5 mg·seed-1 for KC-1 and 8.2 mg·seed-1 for AC-33B. The results showed that the model was sufficiently robust to predict cottonseed biomass accumulation under diverse environmental conditions.
3. Ecological model of cottonseed protein and oil content
The prediction of cottonseed (Gossypium hirsutum L.) quality by ecological factors is an area of great uncertainty. Our object is to investigate the relationship between the cottonseed protein and oil content and the multi-ecological conditions and develop an ecological model to predict the cottonseed protein and oil content under different environments. A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data at sowing date of 25 April, the effect of cultivar, weather conditions, and crop management variables on cottonseed protein and oil content was analyzed by step regression analysis and non-liner regression analysis. We determined that cultivar characteristics, temperature, solar radiation, and N fertilizer rate are main impact factors on cottonseed quality. The optimum temperature for cottonseed protein formation is 26.1℃, and which for oil accumulation is 25.7℃. Adequate solar radiation will reduce the protein and oil content. Increasing the N fertilizer rate will raise protein content and reduce the oil content in cottonseed. The present study developed a ecological model to predict cottonseed protein and oil content. The model was tested by the data collected at sowing date of 25 May in Nanjing, Xuzhou, and Anyang. The RMSE of the model was 2.03% in prediction of cottonseed protein content, and 2.54% in prediction cottonseed oil content. The results showed that the model is sufficiently robust to accuracy predict cottonseed protein content and oil content under diverse environmental conditions.
4. Simulation model of cottonseed protein formation based on eco-physiology process
The simulation of cottonseed (Gossypium hirsutum L.) growth is an area of great uncertainty, especially in the process of cottonseed quality formation. To simulate the formation of cottonseed protein under different environmental conditions, a simple process-based model was developed driven by the inputs of cultivar parameters, weather, and crop management variable (precisely N supply). A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data collected in Nanjing, the responds functions of cottonseed protein accumulation to weather conditions (temperature, solar radiation), crop management variable (N supply) and boll position were all developed and involved in the model. The model based on the hypothesis that nitrogen accumulation synthesis in cottonseed are mainly sink determined, and was integrated with the cotton boll maturation period model and cottonseed biomass accumulation model. The parameters in the model were calibrated using the field data obtained in Nanjing. The model was tested using the field data obtained in Huaian, Xuzhou and Anyang. The root mean square error (RMSE) of the simulated and measured cottonseed protein content was 2.05% for KC-1 and 2.33% for AC-33B. The results showed that the model is sufficiently robust to accuracy predict cottonseed protein content under diverse environmental conditions. This model is a necessary component of cotton growth model, and provides a good platform for further study in modeling cottonseed protein yield.
5. Simulation model of cottonseed oil formation based on eco-physiology process
The simulation of cottonseed (Gossypium hirsutum L.) growth is an area of great uncertainty, especially in the process of cottonseed quality formation. To simulate the formation of cottonseed oil under different environmental conditions, a simple process-based model was developed driven by the inputs of cultivar parameters, weather, and crop management variable (precisely N supply). A set of field experiments were conducted in the lower reaches of Yangtze River Valley (Nanjing, Huaian) and the Yellow River Valley (Xuzhou, Anyang) in 2005, where KC-1 and AC-33B were selected, two sowing dates and three N rates were set. According to the data collected in Nanjing, the responds functions of cottonseed oil accumulation to weather conditions (temperature, solar radiation), crop management variable (N supply) and boll position were all developed and involved in the model. The model based on the hypothesis that fat synthesis in cottonseed are mainly sink determined, and was integrated with the cotton boll maturation period model and cottonseed biomass accumulation model. The parameters in the model were calibrated using the field data obtained in Nanjing. The model was tested using the field data obtained in Huaian, Xuzhou and Anyang. The root mean square error (RMSE) of the simulated and measured cottonseed oil content was 2.45% for KC-1 and 2.95% for AC-33B. The results showed that the model is sufficiently robust to accuracy predict cottonseed oil content under diverse environmental conditions. This model is a necessary component of cotton growth model, and provides a good platform for further study in modeling cottonseed oil yield.
This research provided a systematic process-based simulation model modeling subtending leaf N concentration of cotton boll, cottonseed development, biomass accumulation, N uptake, protein formation, and oil synthesis. This research is an effective supplement for cotton growth model, which fills the vacuity in the research areas, provides a well platform for further study in modeling the formation of cottonseed yield, protein yield and oil yield, and provides also technical supports for regulation and control in cotton production.
引文
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