小麦器官间干物质分配动态的定量模拟

干物质分配是作物生长模拟模型中的关键成分。本文根据对大量试验资料的分析与处理，建立了小麦一生中各器官分配指数随生育进程连续变化的动态关系，并以收获指数作为遗传参数调节不同品种穗部的分配比例，从而预测不同品种各器官的物质增长动态。模型的检验结果表明，模拟值与观察值之间具有较高的符合度，平均相对误差小于8%。     

大麦植株干物质分配模型

为给建立大麦产量品质形成预测模型奠定基础,通过定量分析不同品种和氮肥处理大麦干物质分配和转移的变化过程,建立大麦植株干物质分配动态模型.模型采用Richards方程描述大麦干物质分配指数的动态变化,引入叶片潜在分配指数、茎鞘潜在分配指数、籽粒潜在分配指数3个品种遗传参数反映不同品种在器官间的干物质分配差异;采用两段Richards方程来描述大麦茎鞘在灌浆期前后的干物质分配动态,较好地解决了两段方程的衔接问题;运用氮素影响因子来校正不同氮素水平对大麦干物质分配的影响,引入潜在临界含氮量和潜在最小含氮量2个品种遗传参数来表达氮素对不同品种干物质分配影响的差异.利用不同品种、氮肥、播期和种植地域试验数据检验模型.结果表明,大麦干物质在叶片、茎鞘、穗和籽粒间分配模拟值与观测值的绝对预测误差为0.001～0.252 kg·m-4,RMSE为0.007～0.186 kg·m-2,精度良好.模型体现出较好的可靠性.     

磷素水平对不同基因型大豆干物质积累与分配的影响

通过磷素水平对不同基因型大豆干物质积累与分配影响的研究表明:施磷量对不同基因型大豆品种植株干物质积累量有较大影响,提高施磷量能增加大豆各器官干物质积累量,但施磷量过高各器官干物质积累反而减少,高蛋白品种和高油品种分别以P10处理和Ps处理为最佳施P量.干物质分配开花期以前以叶为主,花期以茎为主,花期后生长中心开始转移到豆荚中.     

不同熟期玉米品种在不同生态区的干物质积累、转运与分配特征

以不同熟期的9个玉米品种为试验材料,2014、2015年在哈尔滨和北京同步开展试验,研究不同熟期玉米品种在不同生态区的干物质积累、转运和分配特征.结果表明,不同熟期玉米品种的干物质积累量、花前、花后干物质积累率及产量在不同生态区之间、品种间存在显著或极显著差异,并存在生态区、品种、年际间的显著互作效应.玉米各器官的干物...     

超高产玉米品种干物质积累与分配特点的研究

以4个玉米品种为试验材料,比较研究了超高产玉米干物质积累和分配的特点。结果表明:高的生物产量是获得高产的物质基础;高产和超高产品种的物质生产优势表现在生育中期和后期,玉米植株个体干物质积累呈S形曲线变化;玉米干物质在各器官的分配随生长中心的转移而发生变化,小喇叭口以前干物质主要分配在叶片,之后转为茎、叶;散粉后,各器官干物质开始向子粒转移,高产品种子粒产量主要来源于生育后期叶片制造的光合产物即成为光合产物的分配中心,并与抽雄后具有较高的叶面积指数且持续时间较长密切相关。     

蓖麻器官间干物质分配的预测模型

为实现蓖麻生长的数字模拟和预测,在前人研究基础上,构建了蓖麻器官间干物质分配的预测模型。用Richards方程来表达叶片、茎秆、根和蒴果的分配指数的变化动态。引入分配指数最大值作为遗传参数来区分不同品种的器官间干物质分配特性,对于通蓖5号、通蓖6号、通蓖9号和中东等4个品种的叶片、茎秆、根和蒴果,分别赋予了分配指数的最大值。引入氮肥影响因子来区分不同施肥水平的养分效应。不同地区的检验结果表明,蓖麻各器官干物重观测值与模拟值的绝对预测误差为0.64~19.34g,RMSE为1.01~11.43g。模型显示出较好的预测性。     

沟谷地春玉米干物质积累、分配与转移规律的研究

1996～1998年在山西省隰县黄土高原残垣沟壑区进行了沟谷地春玉米干物质积累、分配与转移规律的研究.结果表明:春玉米植株个体干物质积累与生育进程间的关系呈S形曲线变化.春玉米干物质在各器官的分配随生长中心的转移而发生变化,小喇叭口以前干物质主要分配在叶片中,之后转为茎、叶;散粉后,各器官干物质开始向子粒转移,果穗成为光合产物的分配中心.在栽培上,应使各器官向子粒转移的干物质总量控制在20%以下。     

不同种植方式对冬小麦花后干物质积累与分配特征及产量的影响

为探索不同种植方式对冬小麦干物质积累、转运与分配特征及产量的影响,选用大穗型小麦品种农大1108为试验材料,研究在大田生产中主要的4种种植方式(穴播、撒播、窄幅条播和宽幅条播)对冬小麦花后各器官干物质积累、分配及产量的影响。结果表明,4种种植方式下花后各器官的干物质积累量变化趋势基本一致,且均表现为窄幅条播显著高于其他3组处理;窄幅条播主要通过提高花后干物质同化量及其对籽粒的贡献率而获得高产;与宽幅条播相比,窄幅条播花后营养器官干物质积累量及干物质在籽粒中的积累量分别增加45.59%和19.44%;与穴播和撒播相比,窄幅条播产量分别提高17.25%和11.39%;花后干物质对籽粒的贡献率表现为穴播最高,达到80.26%。不同种植方式对冬小麦籽粒的灌浆速率影响不大,表现为各处理间千粒重差异不显著。     

杂种小麦干物质积累及其杂种优势的研究

对6个澳大利亚杂种小麦在不同生育期植株及各器官的干物质积累、分配及干物质积累的杂种优势进行了研究。结果表明,植株总干物质积累在籽粒灌浆末期达到最大值,茎秆和叶片干物质积累均在开花期达到最大值。挑旗期茎秆、叶片各占植株总干物质的50％;从孕穗王灌浆末期,茎秆为干物质的主要贮存器官,占植株总干物质的 39.87％～66.67％;在成熟期,籽粒成为干物质贮存的最主要部位,占37.65％,茎秆占35.23％。从6个杂交种平均值来看,干物质积累多表现为正向杂种优势,尤其在营养生长期优势最明显。相关分析表明,营养生长期植株总干物质积累量较大且主要分配在茎秆部位的杂交品种,一般具有较高的籽粒产量和较强的产量优势。     

不同类型玉米饲用栽培的营养品质研究

试验于2001～2002年对三个不同类型5个玉米品种饲用栽培的营养品质进行了系统研究.结果表明不同类型玉米品种各器官营养成分含量不同,营养成分在各器官中的分配也不相同,植株干物质各营养成分含量也因品种而异.就营养成分含量而言,粗蛋白含量科多8号最高,而科青1号最低,中单9409、东陵白及掖单13居中;粗脂肪含量粮饲兼用型玉米品种较高,普通型玉米品种其次,二者均高于青贮专用型;粗纤维含量青贮专用型玉米品种较高,粮饲兼用型玉米品种居中,普通型玉米品种最低;无氮浸出物的含量普通型玉米品种＞粮饲兼用型玉米品种＞青贮专用型玉米品种;粗灰分含量青贮专用型玉米品种大于普通型玉米品种与粮饲兼用型玉米品种.由于干物质中营养成分含量的不同及干物质积累量的不同,营养成分积累量不同类型品种间也存在一定的差异性.     

大豆对磷素吸收规律的研究

以北丰-14为供试品种,采用五因素三水平实施方案,系统的研究了大豆在不同密度、施肥量处理下,各器官磷素含量和对磷素的吸收规律.结果表明:随着生长发育进程的推进,大豆各器官磷素浓度大小因器官而异,均呈下降趋势;大豆对磷素最高吸收速率出现在结荚至鼓粒期,最高吸收速率为5.28mg/株·d,吸收量约占全生育期总吸收量的30%～50%左右;适宜密度和氮磷钾适量配施,可显著提高大豆磷素的吸收量与吸收速度,并能显著提高大豆的经济产量.     

基于无人机可见光影像与生理指标的小麦估产模型研究

为及时、准确地掌握小麦产量动态信息,基于无人机遥感平台,分别分析了小麦4项生理指标［地面实测叶面积指数、叶片含氮量、叶片含水量及叶片叶绿素相对含量（SPAD值）］及10项植被指数与产量的相关性,以筛选出与产量最为敏感的生理指标与植被指数,并比较了3种建模方法（一元回归UR、多元逐步回归SMLR和主成分回归PCAR）在小麦各生育时期估产的适用性,进而得到小麦最优估产模型。结果表明：（1）不同生育时期两类变量与产量的相关性变化特征一致,均表现为抽穗期>灌浆期>成熟期;不同生理指标、植被指数与产量的相关性在各生育时期均存在差异,生理指标表现为叶片含氮量>LAI>SPAD>叶片含水量;而植被指数在各时期表现不同;（2）以生理指标与植被指数为自变量,采用SMLR模型构建的抽穗期估产模型拟合精度最高,R、RMSE和nRMSE分别为0.828、362.53 kg·hm^-2和12.35%;（3）小麦估产模型在各生育时期的预测精度表现为抽穗期>灌浆期>成熟期。     

水稻颖花与籽粒发育模拟的初步研究

在文献资料与田间试验研究的基础上,建立了基于过程的水稻颖花原基分化与退化数、结实粒数以及灌浆动态预测的机理模型。模型量化了穗部器官发育与生理发育时间（PDT）及环境因子的动态关系,确定了穗部籽粒发育的不均衡性与开花时间和同化物供应状况的关系,进一步模拟了不同类型品种强势粒与弱势粒不同粒位的灌浆动态。模型验证结果表明,此模型对颖花动态和籽粒灌浆过程有较好的预测性。     

Comparison of efficiency between differential evolution and evolution strategy: application of the LST model to the Be River catchment in Vietnam

Parameter calibration is an important step in the development of rainfall–runoff models. Recently, there has been a significant focus on automatic calibration. In this paper, two evolutionary optimization algorithms were applied to calibration of the long- and short-term runoff model (LST model) to simulate the daily rainfall–runoff process in the Be River catchment located in southern Vietnam. The differential evolution (DE) and evolution strategy (ES) algorithms were employed to optimize three objective functions: the Nash–Sutcliffe efficiency coefficient, root mean square error, and mean absolute error, which are indices for evaluating the simulation accuracy of the LST model. Hydrometeorological data for the periods 1985–1989 and 1990–1991 were used for calibration and validation, respectively. The LST model was calibrated for each objective function using five different parent and offspring population conditions. The results show that both the DE and ES algorithms are efficient methods for automatic calibration of the LST model. After 1000 generations, the best values of the fitness indices found by the DE technique were slightly better and more stable than those found by the ES technique in both calibration and validation. The average computation time for each generation using the DE algorithm was approximately two-thirds as long as that using the ES algorithm.     

Simulating Organ Growth in Wheat Based on the Organ–Weight Fraction Concept

Abstract

Quantifying dry matter partitioning into individual organs of plants is a key component for simulating crop growth and yield formation. This study was undertaken to develop a dynamic module of biomass partitioning over the entire duration of growth in wheat. The partitioning fraction of shoot or root was defined as the fraction of its dry weight in plant biomass, and partitioning fraction of green leaf, stem or ear as the fraction of its dry weight in shoot mass of wheat. The functional relationships of the partitioning fraction with physiological development time for the entire growth period were established, in which harvest index (HI) regulated partitioning fraction of ear to shoot biomass as a genetic parameter. The dry weight of individual organ was the product of the respective partitioning fraction and plant weight or shoot weight. Test of the model with the field experiment data sets involving different sowing dates, plant densities and nitrogen fertilization strategies indicated a good agreement between the predicted and observed values.     

寒地半干旱区鲜食玉米品种适应性和品质性状分析

以18个鲜食玉米品种为试验材料,针对寒地西部半干旱地区的气候条件,研究不同类型鲜食玉米子粒鲜食期的营养成分的积累动态及品质变化。结果表明,鲜食玉米灌浆期子粒的可溶性糖、粗淀粉、蛋白质、子粒含水率和维C含量在不同灌浆期、不同品种间差异极显著。随着子粒的发育,不同类型鲜食玉米品种的可溶性总糖、可溶性蛋白含量和子粒含水率呈下降趋势,粗淀粉含量呈上升趋势,维C含量呈先上升后下降的动态曲线变化。根据鲜食玉米营养成分与品质的关系,通过子粒含水率结合蛋白质含量和粗淀粉含量确定糯玉米的最佳采收期,子粒含水率结合可溶性糖含量和维C含量确定甜玉米的最佳采收期。     

茶跗线螨空间分布型及理论抽样技术研究

在田间调查的基础上,运用聚集度指标法、Iwao回归分析法及Talor幂指数法对茶跗线螨在茶园中的空间分布型、理论抽样数技术进行研究,同时根据1wao的理论抽样数模型确定了茶跗线螨的最适理论抽样数。结果表明：茶跗线螨在茶园中的空间分布为聚集分布,基本成分为个体群,个体间相互吸引,个体群在茶园中呈聚集分布。同时其聚集程度随种群密度的增加而增强。根据各项聚集度指标得出茶跗线螨田间调查的理论抽样数公式为：N=t2/D2（12.557/m＋0.340）.本研究结果为茶园茶跗线螨预测预报及防治提供了科学依据。     

基于同化物分配的冬小麦节间形态参数模型

冬小麦节间形态结构模型是功能-结构小麦模型的重要研究内容。本研究以济麦22、泰农18和鲁原502为材料,于2013-2014和2014-2015年度开展了品种和施氮试验,并结合2013-2014年冬小麦节间主要形态参数和器官生物量等数据,通过分析不同节位节间同化物分配和节间主要形态参数与器官生物量的定量关系,构建了冬小麦节间主要形态结构模型。经2014-2015年小麦生长数据检验,除分蘖节间长模型模拟精度略低外,主茎节间长、主茎节间粗和分蘖节间粗模型精度均较高,所建模型可较好模拟不同品种与施氮水平下冬小麦节间主要形态结构。     

Multivariate global sensitivity analysis for dynamic crop models

Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N₂O gaseous emission in crop fields named CERES-EGC. N₂O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions.The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC.     

Modeling plant carbon flow and grain starch accumulation in wheat

The process of starch accumulation in grain directly influences the yield and quality formation in wheat. Since few studies have been aimed at modeling the grain starch accumulation, this study was undertaken to develop a simplified process model for predicting the rate of starch accumulation in wheat grain by focusing on the variation of plant carbon dynamics post-anthesis. Five different experiments involving genotypes, nitrogen rates and water regimes were conducted to support model development and model evaluation. The model proposed that the starch accumulation rate (STR) in individual grain was determined by the availability of carbon source in plant (GCA_i) and the ability of starch synthesis in grain (f(A_i)), as influenced by the factors of temperature, water and nitrogen conditions within plants. The f(A_i) could be described in a two-section curve with post-anthesis growing degree days (GDD), first exponential increase and then linear decrease. The GCA_i was determined by post-anthesis carbon assimilation and carbon remobilization from vegetative organs to grains. A genotypic parameter was incorporated into the model algorithm, i.e. the maximum rate of individual grain starch accumulation, to differentiate the ability of starch accumulation among cultivars. The overall performance of the model was validated with different data sets from three field experiments spanning 3 years and comprising various genotypes, nitrogen and water levels. The RMSE values for all treatments were averaged as 12.51%, indicating a good fit between the simulated and observed data. It appears that the model can give a reliable prediction for grain starch accumulation of different wheat cultivars under various growing conditions.