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.     

基于Richards扩展方程提取水稻灌浆结实光温特性参数

以籼型两系杂交稻陵两优268和两优培九为材料,进行了为期两年(2012-2013)的大田分期播种试验,每年分7期播种,旨在研究水稻灌浆期光温要素对籽粒灌浆结实的影响,并提取与此有关的光温特性参数。因此,以Richards方程拟合观测数据得到的平均灌浆速率为纽带,通过引入光温订正方程,建立Richards扩展方程。结合全局优化算法SCE-UA (Shuffled Complex Evolution Algorithm),优化方程参数,估算2个品种灌浆结实的光温特征参数和响应曲线。结果显示,光照主要通过影响籽粒最终重量而间接作用于平均灌浆速率,温度则通过影响灌浆进程的快慢作用于平均灌浆速率。陵两优268灌浆结实期的光照阈值(R₀)、最低(T_min)、最高(T_max)和最适温度(T_op)分别为18.94 MJ m^-2 d^-1、6.81℃、30.28℃和33.29℃; 两优培九上述参数分别为21.71 MJ m^-2 d^-1、6.10℃、24.16℃和33.74℃。形成的光照和温度响应曲线,能够较好地反映两品种平均灌浆速率对光温条件的响应差异,其中陵两优268平均灌浆速率表现为“温度敏感型”,而两优培九表现为“光照敏感型”。本文运用数学模型方法定量分析和比较了光温要素对有效灌浆期内平均灌浆速率的影响,其方法和结论为建立相应的农业气象指标,评估气候资源对水稻产量的影响提供了重要参考依据。     

Estimation of annual suspended sediment yield from a Japanese cypress (Chamaecyparis obtusa) plantation considering antecedent rainfalls

Japanese cypress (Chamaecyparis obtusa) is one of the most common plantation species in Japan. In most Japanese cypress plantations, closure of the forest canopy hinders understory growth because of insufficient management practices. Thus leaf litter barely covers the soil surface. Such plantations are vulnerable to surface erosion triggered by rainfalls, and could yield large amounts of suspended sediment (SS). However, few studies have investigated the annual SS yield. This study aimed to develop a modified model of SS yields, and to accurately estimate and characterize the annual SS yield from a Japanese cypress plantation. For this, hydrological surveys were conducted for 5 years, and stream water was sampled weekly as well as sequentially at 15–60 min intervals in each of 14 rain events in a small forested watershed of Japanese cypress. The root mean square error (RMSE) and the annual SS yield estimates were obtained using three different equations: the rating curve derived from instantaneous SS concentration versus stream discharge relationship (SSC − Q equation); the rating curve derived from cumulative SS yield versus cumulative specific discharge relationship (∑SS − ∑Q equation); and the multiple regression of cumulative SS yield expressed as functions of cumulative specific discharge and antecedent rainfalls (∑SS − ∑Q × AR equation). The ∑SS − ∑Q × AR equation is a modified model considering the effects of antecedent rain history on the SS yield. The parameters in each equation were determined by a nonlinear least square method. Of the three equations, the RMSE was the highest using the SSC − Q equation. Although the ∑SS − ∑Q equation and the ∑SS − ∑Q × AR equation gave similar RMSE values, the SS yields extrapolated by the ∑SS − ∑Q equation would be overestimated. Thus, the 5-year average of annual SS yield estimated by the ∑SS − ∑Q equation was three times larger than that estimated by the ∑SS − ∑Q × AR equation. These results indicate that the ∑SS − ∑Q × AR equation more accurately estimates the annual SS yield, compared with the other two equations. The results also suggest that antecedent rain history, as well as stream discharge, should be considered for appropriately estimating the annual SS yield. The annual SS yield estimated by the ∑SS − ∑Q × AR equation was the largest among 25 undisturbed forested watersheds, except for two watersheds dominated by sandstone. Our results suggest that the annual SS yield is greater from a Japanese cypress plantation under current management practices than from other undisturbed forests.     

WOFOST模型对东北春麦区春小麦生长和产量的模拟效果

为了解WOFOST模型在东北春麦区的适用性,利用哈尔滨、绥化、嫩江、呼玛、牙克石等地春小麦生长监测点收集到的数据,对WOFOST模型进行了本地化参数校正,建立相应的模型运行数据库,并对各个监测点2010-2013年春小麦生长发育情况进行了模拟分析。结果表明,运用校正过的WOFOST模型对各监测点春小麦各生育时期需要的天数、地上部鲜重、经济产量、干物质积累模拟的效果较好,R2分别为0.968、0.904~0.974、0.911~0.944、0.853~0.931,均在可信区间,说明经过校正的WOFOST模型在东北春麦区的生产中具有一定的适用性。     

基于EDEM的大豆窝眼轮式排种盘型孔参数优化与验证

对品种的适应性是大豆育种专用排种器技术研究的关键,主要取决于大豆种子与排种盘型孔间的关系,因此,如何精准构建排种盘型孔参数关于大豆种子物理参数的数学模型具有非常重要的意义。本研究基于东农52大豆种子的物理参数,理论分析并确定其各种型孔充填状态的型孔均径比范围(1.43~1.87)及对应状态概率。建立了大豆小区育种插装式排种器的EDEM仿真模型,并以型孔均径比及充种倒角为试验因素,漏播率及重播率为试验指标,进行了二因素五水平二次回归通用旋转组合设计试验,建立了试验指标的数学模型,以优化型孔的最佳结构参数。结果表明:对漏播率,型孔均径比极显著(P0.01),充种倒角显著(0.01P0.05);对重播率,型孔均径比极显著(P0.01),充种倒角有一定影响(0.05P0.1)。在试验范围内,随型孔均径比的增大,漏播率减小及重播率增大,随充种倒角的增大,漏播率先减小后增大,重播率无变化。型孔最佳参数为型孔均径比1.64,充种倒角1.54 mm,此时,漏播率4.95%,重播率6.49%。验证试验结果表明:优化型孔的排种盘具有相对最佳的作业性能。该研究为其它作物排种器型孔的设计研究提供了参考。     

公路水泥混凝土路面设计中的问题探讨

结合交通部新颁布的公路水泥混凝土路面设计规范和公路路面设计中存在的问题,对设计参数和标准的选择进行分析论证,提出了合理的路面设计思想和方法。     

Bertalanffy生长方程对峦大杉生长的拟合

在阐述Bertalanffy生长方程基本性质基础上,运用单向差分最小二乘法、双向差分最小二乘法、中心差分最小二乘法对其模型参数进行估计;应用Bertalanffy生长方程对峦大杉(Cunninghamia konishii Hayata)生长进行拟合,验证参数估计有效性及适用性。结果表明:单向差分最小二乘法、双向差分最小二乘法、中心差分最小二乘法对Bertalanffy生长方程参数估计,有效、适用;应用Bertalanffy生长方程对峦大杉生长拟合优度,验证了Bertalanffy生长方程不仅适用于海洋生物,同样适用于拟合峦大杉的生长规律;由3种参数估计法对应的统计量(R2)表明,中心差分最小二乘法得到的回归模型拟合度,优于单向差分最小二乘法、双向差分最小二乘法。     

生物量模型方差非齐性研究

以长白落叶松（Ｌａｒｉｘ ｏｌｇｅｎｓｉｓ）为实例,验证树木生物量模型中存在方差非齐性,并提出以模型自身作为权函数来消除方差非齐性的参数估计方法。     

轮胎非稳态侧偏理论模型的参数辨识

根据空间域内轮胎非稳态侧偏试验数据计算出试验频率特性,由考虑胎体复杂变形的轮胎非稳态侧偏理论模型与试验数据建立了参数辨识数学模型,并辨识出模型的结构参数。理论结果与试验数据的一致,验证了模型的正确性,辨识出的参数还可以用于轮胎力学和汽车动力学的分析与仿真计算。     

饥饿胁迫对饲喂槐属植物黄酮的罗非鱼肝胰脏抗氧化能力的影响

为了研究饥饿胁迫对饲喂槐属植物黄酮的罗非鱼肝胰脏抗氧化能力的影响,将400尾罗非鱼（平均体重为(11.52±2.85) g/尾）随机分为5个处理,每个处理4个重复。试验饲料SF添加量分别为0、200、400、800、1600 mg/kg,试验鱼正常饲喂7周后饥饿胁迫15天,测定饥饿胁迫第1天、第8天和第15天罗非鱼肝胰脏的丙二醛（MDA）含量、总抗氧化能力（T-AOC）以及超氧化物歧化酶（SOD）和过氧化氢酶（CAT）活性。结果表明：饲料中添加200~1600 mg/kg的SF可显著降低饥饿胁迫试验期末罗非鱼肝胰脏MDA含量和CAT活性（P＜0.01）,显著提高应激时T-AOC水平（P＜0.01）和SOD活性（P＜0.05）;随SF添加水平的升高,罗非鱼肝胰脏抗氧化能力升高,1600 mg/kg SF添加组最佳。表明,SF可提高饥饿胁迫状态下罗非鱼肝胰脏的抗氧化能力。