Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method

Proper estimation of model parameters is required for ensuring accurate model predictions and good model-based decisions. The generalized likelihood uncertainty estimation (GLUE) method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the closeness-of-fit of modeled and observed data. Various likelihood functions and methods of combining likelihood values have been used in previous studies. This research was conducted to determine the effects of using previously reported likelihood functions in a GLUE procedure for estimating parameters in a widely-used crop simulation model. A factorial computer experiment was conducted with synthetic measurement data to compare four likelihood functions and three methods of combining likelihood values using the CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT). The procedure used an arbitrarily-selected parameter set as the known “true parameter set” and the CERES-Maize model to generate true output values. Then synthetic observations of crop variables were randomly generated (four replicates) by using the simulated true output values (dry yield, anthesis date, maturity date, leaf nitrogen concentration, soil nitrate concentration, and soil moisture) and adding a random observation error based on the variances of corresponding field measurements. The environmental conditions were obtained from a sweet corn (Zea mays L.) experiment conducted in 2005 in northern Florida. Results showed that the method of combining likelihood values had a strong influence on parameter estimates. The combination method based on the product of the likelihoods associated with each set of observations reduced the uncertainties in posterior distributions of parameter estimates most significantly. It was also found that the likelihood function based on Gaussian probability density function was the best among those tested. This combination accurately estimated the true parameter values, suggesting that it can be used when estimating CERES-Maize model parameters for real experiments.     

改进CERES-Maize模型在玉米膜下滴灌模式下的适用性

针对CERES-Maize模型没有覆膜处理模块而无法从机理上实现对覆膜玉米生长发育及产量形成过程进行模拟的问题,依据作物生长发育的有效积温原理,利用膜地增温对有效气积温的补偿效应,量化覆膜地温对气温的补偿值,改进模型气象模块中的气温输入数据,同时将模型中影响腾发量及水量平衡计算的冠层能量消光系数K调整为0.5,构建了适宜于膜下滴灌的改进型CERES-Maize模型,并依据2014和2015年膜下滴灌玉米田间试验数据对改进模型进行验证.结果表明覆膜地积温对气积温的增温补偿系数C_c:播种-出苗期为0.45,出苗~抽雄前期为0.20;随着K降低,地上生物量与籽粒产量的相对误差绝对值ARE降低并趋近于0;改进后的模型能够较好地模拟覆膜玉米开花期天数、成熟期天数、收获期地上生物量和籽粒产量,其模拟值和实测值的ARE分别为0.58%,0.37%,7.65%和16.95%,相对均方根误差RRMSE分别为0.84%,0.51%,8.75%和17.50%.     

基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析

在对DSSAT4.0中CERES-Maize模型进行参数校正和验证的基础上,进一步利用华北地区具有代表性的10个气象站30年(1976～2005年)的气象资料以及华北地区典型的土壤数据展开模拟.结果表明,在一年一季的生产条件下,华北平原各地区玉米多年平均光温生产潜力为13.53～22.56 t/hm2;各地区玉米产量在4月下旬至6月中旬的播期范围内均呈随播期的延迟而增加的趋势,对这一趋势和各气象指标进行相关分析表明,在华北北部主要驱动因子是灌浆期平均日辐射量,而华北中南部主要驱动因子是灌浆期的温度.华北平原自北向南,优化播期呈逐渐推迟的趋势:北部怀来地区5月上旬播种较为适宜,北京、乐亭和天津地区以5月下旬至6月初播种产量最高;中南部以6月中上旬播种(夏播)较适宜.     

气候变化对河南省夏玉米主栽品种发育期的影响模拟

为模拟气候变化对夏玉米发育期影响,本文将河南省划分为4个夏玉米主栽区,分区进行主栽品种遗传参数调试验证,确定各区域品种平均遗传参数。将未来气候变化情景(A2和B2)下,2020s、2050s和2080s各时段的温度和降水增量加上基准值,模拟未来气候变化对河南省夏玉米发育期的影响。模型调参验证结果表明：各区域品种遗传参数存在一定差异,豫西地区当前种植品种播种-开花所需积温高于其它地区,而豫北和豫东当前种植品种开花-成熟所需积温高于其它地区;各区开花期调参和验证误差RMSE为2～4d,相对误差NRMSE均小于10%;各区域成熟期调参误差RMSE均小于4d,验证误差RMSE为3～7d,除豫西区外,各区域调参及验证期间的成熟期相对误差NRMSE均小于10%。表明CERES-Maize模型对河南省各区域夏玉米发育期模拟精度均较高。未来气候变化影响模拟结果表明：A2和B2情景下,夏玉米营养生长期平均缩短4.7d和3.1d,全生育期平均缩短12.9d和8.6d。夏玉米生育期缩短日数与各时段增温幅度趋势一致,全省4个区域中豫西区生育期日数缩短最多。     

RCP情景下河南省夏玉米发育期变化及可调节热量资源估算

为模拟未来气候变化对夏玉米发育期影响并估算增温背景下夏玉米收获至冬小麦播种间可调节热量资源,将河南省划分为4个夏玉米主栽区,引入未来气候变化情景(RCPs)数据驱动参数本地化后的CERES-Maize模型开展研究。结果表明:2006—2060年夏玉米生长季积温呈显著上升趋势,较基准条件(1951—2005年)平均增加179(RCP 4.5)和235℃·d(RCP 8.5)。未来情景下夏玉米播种—开花和播种—成熟日数均呈缩短趋势,其中豫西(Ⅱ区)的变化率高于其他地区。2050s夏玉米播种—开花期全省平均缩短2.7(RCP 4.5)和3.4d(RCP 8.5),播种—成熟期平均缩短9.4(RCP 4.5)和11.6d(RCP 8.5),其中豫西(Ⅱ区)缩短最多。夏玉米可调节热量资源估算结果表明,未来气候变化情景下夏玉米成熟后—冬小麦播种前可调节热量资源豫东(Ⅲ区)增加最多,分别增加244.6(RCP 4.5)和296.8℃·d(RCP 8.5),豫西(Ⅱ区)增加最少,分别增加152.3 (RCP 4.5)和215.8℃·d(RCP 8.5)。未来气候变化情景下夏玉米可生长日数豫西南(Ⅳ区)增加最多,分别增加9(RCP 4.5)和11d(RCP 8.5),其他各区夏玉米可生长日数在RCP 4.5情景下分别增加8 (豫北Ⅰ区)、6 (豫西Ⅱ区)和8d(豫东Ⅲ区);RCP 8.5情景下分别增加9(豫北区)、8(豫西Ⅱ区)和10d(豫东Ⅲ区)。秋收秋种间可调节热量资源的增加将对提高玉米产量产生重要作用。     

用CERES玉米生长模型预测优质蛋白玉米生物产量的形成

在泰国北部清迈大学农学院的多种作物中心(北纬18°47＇,东经99°57＇,海拔300m)进行品种×播种期的双因素试验,用CERES玉米生长模型模拟栽培管理措施对不同品种的生长发育的影响。参试种为优质蛋白玉米Across8763,PozaRica8763和普通玉米Suwan1;播种期分别为1994年12月20日(PD1)、1995年1月5日(PD2)、1995年1月20日(PD3)。结果表明用CERES玉米模型模拟,试验3个品种与播种期无相关关系,对叶片氮百分含量模拟较为准确。但对叶片数、地上部分生物产量的模拟值高于实测值。     

干旱胁迫对玉米物候及产量组成的影响及模拟研究

为了研究产量关键期干旱胁迫对玉米物候及产量和产量组成的影响,评估作物生长模型对干旱胁迫下玉米物候和产量模拟的效果,基于锦州农业气象试验站2011-2015年分期播种试验玉米产量和产量组成观测资料,尤其是2014年和2015年天然干旱胁迫试验数据和2015年玉米开花、吐丝物候加密观测资料,分析了产量关键期干旱胁迫对玉米物候及产量和产量组成的影响,评估了CERES-Maize模型对不同降水年型玉米产量和产量组成的模拟效果,提出了模型改进的方向。结果表明,2014年和2015年辽宁省西部地区在玉米开花期前后经历了较严重的干旱胁迫过程,干旱胁迫导致玉米吐丝延迟程度大于开花,90%以上的植株能到达开花阶段,但仅有45%~88%的植株能到达吐丝阶段,直接影响株籽粒数（不同播期下的玉米株籽粒数相差32%）及最终产量（产量下降33%~78%）。CERES-Maize模型对正常年玉米产量及产量组成的模拟效果较好,对干旱年的模拟效果较差,部分原因在于模型在模拟玉米物候时不对开花和吐丝期加以区别,只考虑了温度对花期的影响,而没有考虑干旱胁迫下玉米因开花-吐丝间隔增大、雌穗发育异常、物候期推迟而造成的减产作用。因此,玉米产量关键期干旱胁迫直接影响玉米物候（开花-吐丝期）,进而影响玉米穗粒数及最终产量;为提高干旱胁迫下作物模型的模拟评估能力,亟待开展干旱胁迫下基于冠层吐丝动态的玉米产量模拟研究。     

不同时间尺度太阳辐射数据对作物生长模型的影响(英)

逐日太阳辐射数据是作物模拟模型的重要输入参数之一。然而,在很多情况下,候、旬、月尺度的辐射信息相对容易获取。该文利用长时间序列(1961-2000)逐日太阳辐射数据,分别建立研究区候、旬、月不同时间尺度太阳辐射数据库,利用两个常用的作物生长模型(CERES-Maize和CGOPGRO-Soybean),以逐日数据(太阳辐射和模拟结果)为基准,分别探讨在雨养和灌溉条件下,不同时间尺度太阳辐射数据对作物生长模型的影响。结果表明:在不同时间尺度下,模型的输出(花期和作物产量)都接近于基准值。总体来看,两个模型模拟的花期平均误差和平均相对误差均接近于0,均方根误差为3.5d;CERES-Maize模型的模拟产量低于基准值,而CGOPGRO-Soybean的模拟结果高于基准值。在雨养和灌溉条件下,CERES-Maize的平均相对误差和均方根误差分别为-0.59%,120kg/hm2和-0.52%,129kg/hm2,CGOPGRO-Soybean的平均相对误差和均方根误差分别为5%,152kg/hm2和4.7%,165kg/hm2。短期数据误差(RMSE)是影响模型精度的主要因素。CGOPGRO-Soybean模型对不同时间尺度太阳辐射数据和水情信息比CERES-Maize模型敏感。当缺少逐日太阳辐射数据时,在雨养和灌溉条件下,候、旬、月尺度的太阳辐射数据都可以用于作物生长模型。     

Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model

Global/Regional Circulation Models (GCM/RCM) predict the interannual climate variability better than the absolute values of meteorological variables. Statistical bias-correction methods increase the quality of daily model predictions of incoming solar radiation, maximum and minimum temperatures and rainfall frequency and amount. However, when bias-corrected forecasts/hindcasts are used by dynamic crop models, timing of dry-spell occurrences generate the largest uncertainty during the linking process. In this study, we used 20 ensemble members of an 18-year period provided by the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) regional spectral model coupled to the National Center for Atmospheric Research Community Land Model (CLM2). The daily seasonal-climate hindcast was bias-corrected and used as input to the CERES-Maize model, thus producing 20 crop yield ensemble members. Using observed weather data for the same period, a time series of simulated crop yields was produced. Finally, principal component (PC) regression analysis was used to predict this time series using the crop yield ensemble members as predictors. Between 13.7 and 28.8% of the simulated corn yield interannual variability was explained using only one principal component (p < 0.05), and estimated yields were in the correct tercile by margins of 16.7 to 38.2% beyond chance. Predictability of simulated corn yields using principal components was improved relative to the use of bias-corrected daily hindcasts. Bias-correcting all meteorological variables used by the crop model increased predictability skills compared with use of raw hindcasts, individual bias-correction of rainfall, and climatological values.     

Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance

The CERES-Maize model was tested in a semi-arid Mediterranean environment during a period of 2 years under three different soil moisture conditions (well-watered and two limited irrigation regimes). In well-watered plots, growth and yield were adequately simulated by the model (differences between simulated values and observations were less than 10%). Results suggest that the absence of air humidity among the model inputs does not limit the CERES-Maize performance, even under dry-air conditions. On the contrary, under mild soil water shortage, CERES-Maize underestimated the leaf area index (LAI) (up to 26% for maximum LAI), above-ground biomass (up to 23%) and grain yield (up to 15%). Mismatches between observations and predictions increased with water stress level (by up to 46, 29 and 23% for maximum LAI, biomass and grain yield, respectively). It is suggested that the functions describing leaf growth and senescence and those calculating the soil water deficit functions should be modified to adapt CERES-Maize to Mediterranean environments.