【作者】 徐寿军；

【导师】 庄恒扬；

【作者基本信息】 扬州大学 ， 作物栽培学与耕作学， 2007， 博士

【摘要】 在生长发育与环境因素关系研究的基础上,系统地构建了大麦顶端发育阶段和物候期的预测模型、节间生长和穗部伸长动态模型、光合生产、干物质积累与分配及产量形成的模型、大麦氮素吸收与转移的模型、啤酒大麦品质形成模拟模型。并在不同品种、播期、氮肥处理和种植地域间进行了检验。对大麦生育热效应Beta模型的特殊形式的模型特征和参数意义进行了系统分析,认为该模型满足关于温度效应函数符合温度对作物发育影响的三基点规律、较好反映发育速率对温度变化的响应特征、温度三基点在模型中应比较明确的三个规范性要求,具有较强的变化特征表达能力,可以近似表达二次函数、高斯函数、正弦函数等函数的变化。当k≤1、P<1或当k > 1时, P<1/k时,函数为凸变化,分析了Beta模型与积温法计算结果的关系,指出参数P取值范围。利用生理发育时间恒定的原理,建立了系统预测大麦顶端发育阶段和物候期的预测模型。引入温度敏感性、生理春化时间、光周期敏感性、基本早熟性和灌浆因子5个遗传参数,用生理发育时间来确定大麦一生的各个发育阶段,从而建立了衡量大麦发育阶段的统一标准,模型考虑了氮素效应的影响。检验结果表明,扬州地区2004～2006年各生育阶段的绝对预测误差一般小于6 d,RMSE为0.9～2.9 d。连云港地区2004～2006年各物候期的绝对预测误差均小于5 d,RMSE为1.0～2.8 d。模型表现出较好的机理性、解释性和预测性。通过对不同株型大麦的生长过程的连续观测和定量分析,在探明生理发育时间（PDT）与大麦穗和茎秆生长关系的基础上,运用Richards方程,以生理发育日为时间步长,构建了大麦穗增长、节间伸长和节间增粗的动态模型。模型以生理发育日衡量穗和茎秆的生长进程与生长次序,考虑了氮素、温度和光照等环境因素对大麦生长发育的影响,将机理性和经验性有机地结合起来。检验结果表明,不同品种、播期和氮肥处理大麦穗长模拟值与观测值的绝对预测误差为0.05～1.66 cm, RMSE为0.28～0.75 cm。节间长度模拟值与观测值的绝对预测误差为0.03～6.08cm, RMSE为0.23～4.43 cm。节间粗度观测值与模拟值的绝对预测误差为0.002～0.112 cm,RMSE为0.016～0.048 cm。在连续观测和定量分析的基础上,采用单株叶面积最大值作为品种遗传参数,用两段非线性方程构建了大麦叶面积指数随PDT变化的动态模型。模型将经验性和机理性有机结合,考虑了温度和氮素营养对叶片生长的影响,较好的解决了两段非线性方程的衔接问题;在借鉴小麦光合生产模型的基础上,构建了大麦光合生产模拟模型;以PDT为尺度,构建了大麦绿叶、茎鞘、穗和籽粒等器官的分配指数模型,在籽粒分配指数模型中,引入了籽粒分配指数最大值作为遗传参数,从而体现了不同品种籽粒的不同灌浆特性。不同品种、氮肥处理、播期以及不同种植地域间检验结果表明,叶面积指数模拟值与观测值的绝对预测误差为0.007～1.486,RMSE为0.109～0.718。植株地上部干物质积累模拟值与观测值的绝对预测误差为0.002～0.264 kg·m^-2,RMSE为0.019～0.206 kg·m^-2。叶重模拟值与观测值的绝对预测误差为0.001～0.199 kg·m^-2,RMSE为0.013～0.060 kg·m^-2。茎鞘重模拟值与观测值的绝对预测误差为0.001～0.689 kg·m^-2,RMSE为0.006～0.227 kg·m^-2。穗重模拟值与观测值的绝对预测误差为0.001～0.056 kg·m^-2,RMSE为0.004～0.018 kg·m^-2。籽粒重模拟值与观测值的绝对预测误差为0.000～0.178 kg·m^-2,RMSE为0.007～0.090 kg·m^-2。对大麦氮积累过程做了如下假设。（1）抽穗开花前,大麦从土壤吸收的氮素按一定比例分配到各器官（叶、茎鞘和穗）中去。（2）抽穗后,大麦从土壤中吸收的氮素都被用来供籽粒蛋白质的形成。（3）从抽穗起,储存于叶、茎鞘、穗的氮素开始向籽粒转移。基于上述假设,在干物质积累与氮素积累关系研究的基础上,建立了大麦花前氮素积累的动态模型。模型简化了氮素开花前在大麦体内运转的复杂过程,从而避免了复杂的计算。引用了土壤氮素供应因子来反映氮素供应的满足程度,确立以土壤硝态氮和铵态氮浓度为氮素供应状态基础,在养分吸收的计算中,避免对与根系有关的计算。在所测试验数据的基础上,构建了氮素养分分配指数随生理发育时间的动态变化模型,模拟了开花前氮素在大麦各器官的分配状态。不同品种、播期、氮素处理和种植地域间检验结果表明,植株体氮素积累模拟值与观测值的绝对预测误差为0.030～3.300 g·m^-2,RMSE为0.330～1.586 g·m-2。叶片氮素积累模拟值与观测值的绝对预测误差为0.015～1.839 g·m^-2,RMSE为0.190～0.871 g·m^-2。茎鞘氮素积累模拟值与观测值的绝对预测误差为0.004～1.732g·m^-2,RMSE为0.197～0.676 g·m^-2。穗氮素积累模拟值与观测值的绝对预测误差为0.007～0.782 g·m^-2,RMSE为0.053～0.191 g·m^-2。表明模型具有较好的预测性和实用性。本研究假定在大麦籽粒灌浆过程中,籽粒氮素积累有趋于最大化的倾向,所需氮素由营养器官贮藏的氮和土壤供给,其中营养器官转移的氮包括绿叶、茎鞘和穗转移的氮。从土壤中吸收的氮,全部用来供应籽粒蛋白质的形成。氮素供应不足时,则从营养器官中获取较多的氮。在此基础上建立了大麦花后氮素吸收转移模型,其中,叶片氮的转移与叶面积指数呈指数关系。茎鞘和穗部的氮转移与其氮浓度的降低呈非线性函数关系,籽粒从土壤中吸收的氮量则随干物重呈指数增加。模型将经验性与机理性有机结合,综合反映了籽粒氮素积累、营养器官氮素输出以及与温度效应之间的关系。以生理发育时间为尺度,构建了大麦千粒重的预测模型。模型除了考虑温度、氮素对千粒重的影响外,还引入了光照因子。不同品种、氮肥水平、播期和种植地域检验结果表明,大麦籽粒氮积累模拟值与观测值的绝对预测误差为0.004～2.529 g·m^-2,RMSE为0.664～1.343 g·m^-2。籽粒增重模拟值与观测值的绝对预测误差为0.01～6.00 g,RMSE为0.266³.800 g。成熟期籽粒蛋白质含量模拟值与观测值的绝对预测误差为0.06%³.23%,RMSE为0.51%¹.57%。千粒重模拟值与观测值的绝对预测误差为0.04⁶.00 g,RMSE为3.08 ³.80 g。更多还原

【Abstract】 Based on the relationship between growth and environmental factors in barley, the models were constructed systematically to predict the apical and phenological development stages, the internode and spike growth, the photosynthetic production, the dry matter accumulation and partitioning, the yield components, the nitrogen absorption and translocation and grain quality. The validations were made in the different genotypes, sowing dates, N rates and regions.The characters of Beta function were analyzed. It is widely used to describe the nonlinear effects of temperature on crop phenological development. The Beta function has many advantages. It can reflect the general requirements of the effectiveness functions of temperature on crop phenological development and well describe the expressions of other types of functions such quadratic function, Gause function and sine function. If k<1 , P<1 or k > 1, P<1/k, the Beta function is certainly convex. The study also demonstrated the relationship between P values and the temperature sums and reasoned the range of parameter P. The models of apical and phenological development stages in barley were constructed by the scale of physiological development time, which was based on the ecophysiological development process. In the models, the Beta function was used to describe the response of barley development to temperature. The genetic parameters of the temperature sensitivity, physiological vernalization time, photoperiod sensitivity, intrinsic earliness, filling fraction were introduced. The validation showed that the absolute prediction errors for development stages were generally less than 6 d, and the root mean square errors （RMSEs） were 0.9 2.9 d in Yangzhou in 2004²006 years. The absolute prediction errors for phenological development stages were less than 5 d, and the RMSEs were 1.0².8 d in Lianyungang in 2004²006 years. The models reflected an enhancement in mechanism, explanation and prediction.Based on physiological development time, the spike and internode growth dynamics were simulated by Richards equation in order to construct systematically architectural models in barley. In the models, growth process and order of spike and internode were judged by physiological development time, with special consideration of the effects of nitrogen, temperature and light. The validating results showed that the absolute prediction error ranges of spike length were 0.05¹.66 cm and the RMSEs were 0.28～0.75 cm. The absolute prediction error ranges of internode length were 0.03⁶.08 cm, and the RMSEs were 0.23～4.43 cm. The absolute prediction error ranges of internode thickness were 0.002⁰.122 cm, and the RMSEs were 0.016～0.048 cm.Based on the experimental observation and quantitative analysis, the model of leaf area index （LAI） in barley was constructed by two nonlinear equations, with the maximal value of per plant leaf area as genetic parameter. The model represented the influence of nitrogen and temperature on leaf growth, and solved how two nonlinear equations joined. With reference to the relative models in wheat, the photosynthetic production models in barley were constructed. Taken the physiological development time as the developmental scale, the models of the partitioning indexes of dry matter for the green leaf, stem, spike and grain were constructed. In the model of dry matter partitioning index for grain, the maximal value of partitioning index was uses as the genetic parameter to reflect the grain-filling characteristics of different genotypes of barley. The validating results showed that the absolute prediction error ranges for LAI were 0.007¹.468, and the RMSEs were 0.109～0.718. The absolute prediction error ranges for dry matter weight were 0.002～0.264 kg·m^-2, and the RMSEs were 0.019～0.206 kg·m^-2.The absolute prediction error ranges of leaf weight were 0.001～0.199 kg·m^-2, and the RMSEs were 0.013～0.060 kg·m^-2. The absolute prediction error ranges for stem weight were 0.001～0.689 kg·m^-2, and RMSEs were 0.006～0.227 kg·m^-2. The absolute prediction error ranges for spike weight were 0.001～0.056 kg·m^-2,and RMSEs were 0.004～0.018 kg·m^-2. The absolute prediction error ranges for grain weight were 0.000～0.178 kg·m^-2, and the RMSEs were 0.007～0.090 kg·m^-2。Some assumptions were made during the nitrogen accumulation process in barley as following. （ 1） Before anthesis, the nitrogen absorbed from soil was distributed to various organs according to certain proportions. （2） After anthesis, the absorbed nitrogen from the soils all was used for the grain protein formation. （3） After anthesis, the nitrogen stored in leaf, stem and spike starts to remobilize to the grain. Based on these assumptions, the dynamic models of nitrogen accumulation were constructed by relationship between dry matter accumulation and the nitrogen accumulation. In the model, the complex process of nitrogen remobilization after anthesis was simplified. The soil nitrogen factor was used to reflect the nitrogen supply levels in the soil and the contents of NO3--N and NH4+-N were regarded as the criterion of nitrogen supply conditions. In the computation about nutrient absorption, the computation related to the root system was avoided. Based on the experimental data, the dynamics of nitrogen partitioning indexes with the physiological development time was modeled. The validating results showed that the absolute prediction error ranges for nitrogen accumulation in barley were 0.030～3.300 g·m^-2and the RMSEs were 0.330～1.568 g·m^-2.The absolute prediction error ranges for leaf nitrogen accumulation were 0.015～1.839 g·m^-2 and the RMSEs were 0.190～0.871 g·m^-2. The absolute prediction error ranges for stem nitrogen accumulation were 0.004～1.732 g·m^-2, and the RMSEs were 0.197～0.676 g·m^-2. The absolute prediction error ranges of spike nitrogen accumulation were 0.007～0.782 g·m^-2 and the RMSEs were 0.053～0.191 g·m^-2.This research assumpted that during the barley grain filling, the grain nitrogen accumulation had the maximized tendency and the nitrogen demand of grain was supplied from the vegetative organs and the soil. Nitrogen absorbed from the soil was used completely for the grain. When nitrogen supply was insufficient, the grain would gain more nitrogen from the vegetative organ. Based on these hypothesis, the nitrogen absorption and remobilization model after anthesis were constructed. In the model, the relationship between leaf nitrogen remobilization and the leaf area index were described with exponential functions. The nitrogen translocation from stem and spike showed nonlinear relations with the respective nitrogen contents in these two organs. The nitrogen accumulation in grain was closely related to grain dry matter weight and was expressed with exponential functions. Based on physiological development time,the model to predict 1000- grain weight was constructed and in the model the effects of the nitrogen supply,temperature and light were included. The test results showed that the absolute prediction error ranges of nitrogen accumulation in grain were 0.004～2.539 g·m^-2,and the RMSEs were 0.664～1.343 g·m^-2. The absolute prediction error ranges for grain weight were 0.01～6.00 g, and RMSEs were 0.27～3.80 g. The absolute prediction error ranges for grain protein contents were 0.06%～3.23%, and RMSEs were 0.51%～1.57%. The absolute prediction error ranges for 1000-grain weight were 0.04～6.00 g, and the RMSEs were 3.08～3.80 g.更多还原