Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

CropRota – A crop rotation model to support integrated land use assessments

Crop rotations influence the sustainability of agricultural systems. Integrated land use modeling frameworks increasingly acknowledge their role when analyzing economic and environmental impacts of agricultural production systems. However, insufficient data on crop rotations often challenge their consideration. In this article, we present the crop rotation model CropRota, which integrates agronomic criteria and observed land use data to generate typical crop rotations for farms and regions. The article describes the model and data requirements as well as an application of CropRota to 579 farms in the Austrian Mostviertel region. Model validation and sensitivity analysis are conducted to reveal robustness and accuracy of model outputs as well as its appropriateness for supporting integrated land use analyses. Comparisons between modeled and observed crop sequences from seven years of field observations show that the average area-weighted deviations range between 11% and 105% depending on the procedure of comparison and model specifications. The results indicate that CropRota is a suitable tool for the estimation of typical crop rotations from observed land use data. In addition, the model is sufficiently flexible to spatial scales and research contexts as frequently required in integrated land use analyses.     

A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland,Australia

New sugarcane cultivars are continuously developed to improve sugar industry productivity. Despite this sugarcane crop models such as the ‘Sugar’ module in the Agricultural Productions System sIMulator (APSIM-Sugar) have not been updated to reflect the most recent cultivars. The implications of misrepresenting cultivar parameters in APSIM-Sugar is difficult to judge as little research has been published on the likely values of these parameters and how uncertainty in parameter values may affect model outputs. A global sensitivity analysis can be used to better understand how cultivar parameters influence simulated yields. A Gaussian emulator was used to perform a global sensitivity analysis on simulated biomass and sucrose yield at harvest for two contrasting sugarcane-growing regions in Queensland, Australia. Biomass and sucrose yields were simulated for 42 years to identify inter-annual variability in output sensitivities to 10 parameters that represent physiological traits and can be used to simulated differences between sugarcane cultivars. Parameter main effect (S_i) and total effect (ST_i) sensitivity indices and emulator accuracy were calculated for all year-region-output combinations. When both regions were considered together parameters representing radiation use efficiency (rue), number of green leaves (green_leaf_no) and a conductance surrogate parameter (k_L) were the most influential parameters for simulated biomass in APSIM-Sugar. Simulated sucrose yield was most sensitive to rue, sucrose_fraction (representing the fraction of biomass partitioned as sucrose in the stem) and green_leaf_no. However, climate and soil differences between regions changed the level of influence cultivar parameters had on simulation outputs. Specifically, model outputs were more sensitive to changes in the transp_eff_cf and k_L parameters in the Burdekin region due to lower rainfall and poor simulated soil conditions. Collecting data on influential traits that are relatively simple to measure (e.g. number of green leaves) during cultivar development would greatly contribute to the simulation of new cultivars in crop models. Influential parameters that are difficult to measure directly such as transp_eff_cf and sucrose_fraction are ideal candidates for statistical calibration. Calibrating crop models either through direct observation or statistical calibration would allow crop modellers to better test how new cultivars will perform in a range of production environments.     

FIELD—A summary simulation model of the soil–crop system to analyse long-term resource interactions and use efficiencies at farm scale

Resources for crop production are often scarce in smallholder farming systems in the tropics, particularly in sub-Saharan Africa (SSA). Decisions on the allocation of such resources are often made at farm rather than at field plot scale. To handle the uncertainty caused by both lack of data and imperfect knowledge inherent to these agricultural systems, we developed a dynamic summary model of the soil–crop system that captures essential interactions determining the short- and long-term crop productivity, while keeping a degree of simplicity that allows its parameterisation, use and dissemination in the tropics. Generic, summary functions describing crop productivity may suffice for addressing questions concerning trade-offs on resource allocation at farm scale. Such functions can be derived from empirical (historical) data or, when they involve potential or water-limited crop yields, can be generated using process-based, detailed crop simulation models. This paper describes the approach to simulating crop productivity implemented in the model FIELD (Field-scale Interactions, use Efficiencies and Long-Term soil fertility Development), based on the availability of light, water, nitrogen, phosphorus and potassium, and the interactions between these factors. We describe how these interactions are simulated and use examples from case studies in African farming systems to illustrate the use of detailed crop models to generate summary functions and the ability of FIELD to capture long-term trends in soil C and crop yields, crop responses to applied nutrients across heterogeneous smallholder farms and the implications of overlooking the effects of intra-seasonal rainfall variability in the model. An example is presented that evaluates the sensitivity of the model to resource allocation decisions when operating (linked to livestock and household models) at farm scale. Further, we discuss the assessment of model performance, going beyond the calculation of simple statistics to compare simulated and observed results to include broader criteria such as model applicability. In data-scarce environments such as SSA, uncertainty in parameter values constrains the performance of detailed process-based models, often forcing model users to ‘guess’ (or set to default values) parameters that are seldom measured in practice. The choice of model depends on its suitability and appropriateness to analyse the relevant scale for the question addressed. Simpler yet dynamic models of the various subsystems (crop, soil, livestock, manure) may prove more robust than detailed, process-based models when analysing farm scale questions on system design and resource allocation in SSA.     

Developmental Dynamics and Early Growth Vigour in Rice 2. Modelling Genetic Diversity Using Ecomeristem

Early vigour is an important objective in rice breeding. A previous study reported strong positive effects of development rate (DR, 1/phyllochron) on early growth vigour in two rice panels. This study provided a model‐based analysis of DR effects on rice early vigour and underlying source–sink processes during exponential growth, using Ecomeristem model. Relevant model parameters were fitted to panel observations, and their effect on early vigour was quantified. A sensitivity analysis was performed to quantify the impact of model parameters on simulation outputs. The simulated behaviour of a population of virtual genotypes defined by the combination of model parameter values was compared with that of diversity panel. Finally, a simulation experiment was conducted to analyse source–sink adjustments constituting early vigour across a range of DR. Parameters governing structural development, particularly DR, had greater impact on vigour than parameters for resource acquisition. High DR was associated with rapid dry weight accumulation and low transitory carbohydrate reserves in both real and virtual populations. Genotypic DR is thus a major driver of early vigour in rice under stress‐free conditions. To evaluate traits contributing to vigour, the capacity of crop models to simulate interactions between structural development and resource acquisition must be improved.     

Evaluation of regional climate scenario data for impact assessment of climate change on rice productivity in Korea

Spatial evaluation of the uncertainty associated with climate data would allow reliable interpretation of simulation results for regional crop yield using gridded climate data as input to a crop growth model. The objective of this study was to examine the spatial uncertainty of regional climate model data through determining optimal seeding date with the ORYZA2000 model for assessment of climate change impact on rice productivity in Korea. The optimal seeding date was determined at each grid point using regional climate model outputs under the RCP 8.5 scenarios. In major rice production areas such as inland plain regions, where temperatures of regional climate data were relatively accurate, the optimal seeding date determined using those gridded data were reasonable. However, areas with complex terrains including areas near bodies of water, e.g. coastal areas, riverbasins, lakes, and mountainous areas, had a relatively large uncertainty of the optimal seeding date determined using the regional climate data. These results indicated that the uncertainty of regional climate data at a high spatial resolution of 12.5 km should be taken into account in the regional impact assessment based on crop growth simulations in Korea. In addition, further studies would be merited to assess the impact of climate change on rice yield at an ultra-high spatial resolution of 1 km in Korea. Crop yields were projected to decrease after the 2020s when crop yield simulations from inland plain areas were considered, which suggested that adaptation strategies should be established and implemented in the near future.     

Exploring opportunities for reducing complexity of genotype-by-environment interaction models

Heterogeneity in genetic effects among environments (G × E) is a common phenomenon in crop plants and can arise from heterogeneity in variance (scale effects) and/or crossover interaction. Here, a study of yield of macadamia progeny in 15 trials established at 9 locations and assessed for yield at 7 years is used to explore the impact on prediction of clonal values (additive + dominance effects) from (i) scaling observations by phenotypic standard deviation of each trial, and (ii) reducing complexity of the pattern of genotype-by-environment interaction. The initial fit of an unconstrained G × E model to unscaled observations indicated significant G × E, which was supported by the fit of the same model to scaled data. Scaling observations reduced heterogeneity of genetic parameter estimates among locations. Clustering of the additive and dominance genetic-by-environment covariance matrices from the fit of G × E models to scaled observations and log-likelihood testing was used to identify reduced models where locations with apparent homogeneous genetic effects (genetic variance not significantly different, and genetic correlations not significantly different from 1) were grouped into single environments. Complexity reduction condensed the additive genetic-by-environment covariance matrix to 3 environments, and 4 environments for the dominance matrix, and the accuracy of parameters estimates increased, although accuracy of prediction as assessed by generalised heritability only improved for a few locations. On the other hand, accuracies of clonal values predicted from a main effects only G + E model were lower. Nevertheless, correlations of the averages of predicted clonal values across locations from different models were very high suggesting models are robust to parameter estimates. These results support the use of scaling by the phenotypic standard deviation to reduce heterogeneity in parameter estimates, and complexity reduction to improve accuracy of estimating parameters required to predict genetic effects.     

作物模型中单叶最大光合作用速率的温度响应修订

作物的光合作用对温度变化敏感, 其温度依存性随品种、生长环境的变化而改变。基于光效率模型的作物生长模型, 在应用中很少对光合作用的温度影响参数值进行订正, 且在全生育期使用相同的参数值, 难免会增加干物质模拟的误差。为此, 本文以ORYZA2000模型为例, 提出了一种修订光合作用温度影响参数值的方法。为确定方法的有效性, 结合2012年和2013年水稻品种两优培九的温度梯度控制实验, 首先利用抽穗开花期光合作用观测曲线提取了不同温度水平的光合作用参数值, 然后结合Arrhenius方程和Peaked方程建立了温度敏感性参数的温度影响方程。将这些方程代入机理性光合作用模型, 模拟了单叶最大光合作用速率与温度的曲线关系。最后, 以归一化后的曲线关系修订作物模型参数值, 并利用两年地上部分生物量(WAGT)观测值对其验证。结果显示, 两优培九单叶最大总光合作用速率随温度的变化关系不同于ORYZA2000的默认设置, 修订后的最适温度为38~40°C, 高于默认值。在10~20°C的低温段, 修订后的温度影响系数低于默认值。从WAGT模拟值的相对误差看, 修订后较修订前平均降低约3.3%。本研究为改进干物质模拟精度和分析不同品种光合作用的温度依存性提供了重要参考。     

Implications of climate model biases and downscaling on crop model simulated climate change impacts

In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50 × 50 km) and bias corrected downscaled (site-specific) hindcast (1960–1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles.     

河北省主要作物田地下害虫种类及其分布

为了明确现有耕作制度下,河北省农田地下害虫发生种类及其在不同作物田分布特点,笔者采用挖土法,于2011~2014年调查了河北省不同区域内主要作物地下害虫发生情况。总体来看,河北省作物田地下害虫年度间发生差异较大。发生种类上,蛴螬和金针虫占全部地下害虫种类的80～90%,但不同种类蛴螬年度间发生也存在较大差异。不同区域地下害虫发生量存在较大差异,坝上地区和沿海地区发生虫量相对较低,分别为0.96±0.11头/m₂和2.31±0.75头/m₂,山前平原发生虫量较大,达到了8.09±3.28头/m₂。不同作物田地下害虫发生情况来看,马铃薯和莜麦田以金针虫为主,占全部地下害虫的60～70%,小麦和玉米田内以蛴螬为主,占75～80%,花生田内以蛴螬为优势种,占90%以上。笔者根据地下害虫发生危害受分布地域、土壤结构和作物种类等多因素影响,提出了不同区域地下害虫分区分类防治策略。     

Crop yields under no-till farming in China: A meta-analysis

No-till (NT) farming is popular globally, however, the effects on crop yields remain debatable. A meta-analysis was conducted on crop yield responses to NT in China based on 1006 comparisons from 164 studies. Results showed that a decrease of 2.1 ± 1.8% on crop yield was observed under NT with residue removed (NT0) compared with that under plow tillage with residue removed (PT0), but the decreases can be diminished to 1.9 ± 1.0% when residue retention was combined with both the two tillage practices. On the contrary, NT with residue retention (NTR) may significantly increase crop yields by 4.6 ± 1.3% compared with that under PT0 (P < 0.05). Along with improvements in crop yields, increases in soil organic carbon (SOC) by 10.2 ± 7.2%, available nitrogen (N) by 9.4 ± 5.4%, available potassium by 10.5 ± 8.8%, and water storage by ∼9.3 ± 2.4% was observed under NTR compared with PT0, indicating that improvements in soil quality could benefit crop productivity under NTR. Categorically, results on meta-analysis and regression indicated large variations in crop yields under NTR because of differences in crop species, temperature and precipitation, antecedent SOC level, N fertilizer input, duration of adoption, and with or without residue retention. For example, crop yields significantly increased with increase in duration (P < 0.0001) under NTR, by 21.3% after 10 years of continuous NTR compared with PT0. Adoption of NTR under appropriate site-specific conditions can advance China’s food security, improve yield stability and alleviate soil-related constraints.     

Establishment of a crop evapotranspiration calculation model and its validation

In order to contribute knowledge on the method used to calculate the actual crop evapotranspiration, soil, crop, atmosphere, and water spatial structure were integrated into a complete system. Based on the energy balance equation and aerodynamic equation, the meteorological data was reduced and the crop physiological parameter was increased, then the crop evapotranspiration calculation model under natural conditions was derived. The crop evapotranspiration calculation model was verified by the water balance formula using data generated from corn, potato, and flue-cured tobacco grown under field conditions for three consecutive years from 2017 to 2019. The results showed that: from 2017 to 2019, the average root mean square error for measured and calculated evapotranspiration of corn, potato, and flue-cured tobacco at different growth times were 0.5948, 0.4753, and 0.3326, respectively, the mean deviation, mean absolute error, and mean relative error were small, and the coefficient of determination and consistency index were both greater than 0.9100. The measured and calculated crop evapotranspiration of the selected crops increased at first and then decreased gradually as the crops matured, and finally decreased to harvest evapotranspiration, showing a parabolic trend. The crop evapotranspiration calculation model not only reflects the actual evapotranspiration of crops at different growth time but also reflects the change law of actual crop evapotranspiration. The model does not need the correction of soil moisture content, irrigation method, and crop coefficient and can directly calculate the actual crop evapotranspiration. It has the characteristics of consistency between the calculated value and the measured value, strong applicability, simple calculation process, and high accuracy and has the best effect on monitoring soil moisture and crop water shortage sensitivity. The model is significance in that it guides for monitoring soil moisture, determining actual crop evapotranspiration, crop water shortage index, and high yield and efficiency under water-saving conditions.     

Modelling crop growth and biomass partitioning to shoots and roots in relation to nitrogen and water availability, using a maximization principle. II. Simulation of crop nitrogen balance

This study analysed of the ability of a crop model to simulate crop nitrogen (N) balance. The model was originally developed to serve as a foundation to develop a decision-making tool to analyse the impact of water management and nitrogen fertilization on crop yield. The model included a dynamic parameter for allocation of dry matter between root and shoot allowing root to shoot ratio to vary according to differing environmental conditions. The new allocation parameter was introduced in order to make the model more applicable under water and nitrogen limited growing conditions. Two wheat (Triticum aestivum L.) data sets were used to test the model simulations. Generally, the model simulations agreed well with the recorded data on crop N uptake. The relationship between the actual and simulated amount of N taken up by the crop was close in the calibration treatments of a greenhouse experiment. The coefficient of determination (r²) of the regression line (simulated value = independent variable, measured value = dependent variable) was 0.90. The r² was 0.83 for the validation data. In the field experiments, the r² values were 0.91 for the calibration data and 0.82 for the validation data. In field data, the model underestimated in some cases the crop N uptake during the period when actual shoot dry weight increased exponentially in spring. Therefore, methods used in computation of nitrogen uptake have to be analysed further. Plant organ N content was simulated satisfactorily for both greenhouse and field data. However, the range over which the simulated values varied was larger than in the actual data.

The results from the study indicate that our model is capable of simulating the crop N balance and we suggest that the model could be used when developing an N application decision tool for field crops. However, the availability of N and soil water were provided as inputs in the present study. Thus, the model should be integrated with models simulating below ground processes in the future. Moreover, the model should be further validated with actual field data.     

An overview of the crop model

is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.     

Two contrasting laboratory methods improve <Emphasis Type="Italic">Silphium integrifolium</Emphasis> Michx. germination rate to agronomically acceptable levels

Organic dormancy, the inability to germinate under favorable conditions, is a common problem in many crop species and their wild relatives, leading to more variable emergence, plant density, and growth rates, thus increasing costs, and lowering yield. To overcome these problems, several different methods have been developed for various crop and model plants. However, in the emerging crop being bred from wild and semi-domesticated Silphium, no such method has been established thus far. The objective of this study was to identify a dormancy-breaking assay to increase the seed germination rate. Seeds of three different Silphium integrifolium Michx. genotypes were treated with five chemical and one cold treatment, and dried before or after the treatment at 27 °C or 40 °C. Untreated, dried seeds were taken as control. Seeds soaked for 24 h in a ethephon/potassium nitrate solution followed by a 72 h drying step at 40 °C showed an increase of germination to up to 90?±?2% compared to control seeds (3?±?0% and 5?±?1%). We identified the minimum time frame of cold stratification needed to enhance seed germination in Silphium, and found that cold stratification was nearly as good as the chemical treatment. Our results provide two alternate ways to treat S. integrifolium seeds for breaking organic dormancy, which will help to facilitate future research in the Silphium domestication community. The fact that our optimal treatments were similar to protocols developed for sunflower suggests that these methods may also be applicable to many related economically important Asteraceae species.     

The interactions between genotype,management and environment in regional crop modelling

Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e. M) to the variability in biomass yield (Y_T, total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25 G x M scenarios were simulated at a 5 arc minute resolution (∼5 km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of Y_T and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (S_i), showed distinct spatial patterns for the two output variables. The Y_T was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of S_i to environmental drivers. For example, the sensitivity of Y_T and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest.     

AquaCrop模型在东北黑土区作物产量预测中的应用研究

东北黑土区是我国玉米和大豆生产基地,为了实现利用AquaCrop模型优化管理和预测产量,本文基于作物小区田间试验和大田观测数据,采用OAT(one factor at a time)法分析了该模型参数的敏感性,率定了敏感性高的参数,并对率定后的模型进行了验证。结果表明:玉米和大豆产量均对影响经济产量的收获指数十分敏感,二者虽然对冠层和根系生长参数都敏感,但有所差异:玉米对冠层衰减系数(canopy decline coefficient,CDC)更为敏感,而大豆则对限制冠层伸展的水分胁迫系数曲线的形状因子(shape factor for water stress coefficient for canopy expansion,Pexshp)更为敏感;玉米因根系深对最大有效根深(maximum effective rooting depth,Zx)更敏感,大豆因根系浅对根区根系伸展曲线的形状因子(shape factor describing root zone expansion,Rexshp)更敏感。由于玉米需水量大,对冠层形成和枯萎前的作物系数(crop coefficient before canopy formation and senescence,KcTr,x)和归一化水分生产力(normalized water productivity,WP*)很敏感,大豆则是一般敏感。率定后模型模拟玉米产量与实测产量的回归系数由0.34提升至0.89,模拟大豆产量与实测产量的回归系数由0.80提升至0.88。进一步用大田实测产量的验证结果表明:预测的玉米与大豆产量与实测产量间回归方程的决定系数(coefficient of determination,R2)分别为0.775和0.779,均方根误差(root mean square error,RMSE)分别为1.076 t hm^–2和0.299 t hm^–2,标准均方根误差(normalized root mean square error,NRMSE)分别为0.097和0.178,模拟效率(model efficiency,ME)分别为0.747和0.730,率定后的AquaCrop模型能较精准地模拟东北黑土区玉米和大豆产量,可用于产量预测或优化管理。     

旱作春玉米农田Priestley-Taylor模式参数的变化

Priestley-Taylor(PT)参考作物蒸散(ET₀)估算模式系数(α)的本地化研究,对于确定水资源高效利用的半旱地农业生产措施及精准灌溉具有非常重要的意义。本文以FAO(1998)推荐的Penman-Monteith (PM)参考作物蒸散估算方法为标准,采用涡度相关技术并根据气象数据信息,监测半干旱气候条件下旱作春玉米农田尺度水、热交换传输过程,以分析Priestley-Taylor模式参数α的变化特征并确定其本地化估算参数值。结果表明,年时间尺度变化过程中高海拔半干旱气候条件下根据PT模式推荐系数α=1.26确定的参考作物蒸散量(ET_{0-PT 1.26})估算值,在11月至来年4月份非作物生长季期间平均偏低21.2%,在5月至9月份旱作春玉米生育期内平均高于PM模式的参考作物蒸散量(ET_0-PM)估算值5.5%,研究站点旱作春玉米生长季本地化适宜的PT模式系数α值为1.15±0.06。在季节变化过程中,旱作春玉米农田近正午时刻实际PT模式系数平均值呈单峰型变化趋势,春玉米抽雄抽穗开花期达到高峰,平均值为0.67±0.08,苗期最低,仅为0.26±0.13,全生育期平均值为0.50±0.21。若要在半干旱气候地区根据PT模式准确估算参考作物蒸散量,需进行PT模式参数的本地化研究。     

Analysis and Application of Creep External Uncertainty in Practice

Based on the measured data from the construction site, the prediction uncertainty of the CEB FIP MC90 due to the variation of the calculation parameters (i.e. the external uncertainty) and their sensitivity are analyzed with the help of statistical method and Latin Hypercube sampling method. Three conclusions can be deduced from the analysis. Those are a)the external uncertainty and its decreasing rate of the CEB FIP MC90 reduce with the time increasing, b)the creep coefficient increases with the increasing temperature while decreases with the increase of other calculation parameters, c)in accordance with the degrees of effect to the creep uncertainty, the first three calculation parameters are relative humidity, loading age and temperature, if the variation of the loading age is not taken into acount, the uncertainty of the creep pridection will be underestimated. Also, a method to analyze the uncertainty of the bridge structure due to the creep prediction uncertainty is proposed after the creep uncertain factor, which is the standard of the uncertainty of the creep, is introduced. An exmaple is analyzed in this paper using the random sampling method and the method presented in this paper. Through drawing a comparison between the results from different methods, the method presented in this paper appears to be feasible and efficient.     

辽宁地区玉米作物系数的确定

玉米作物系数是计算玉米耗水量的重要参数之一。为了准确客观地估算玉米群体的蒸散量,本文利用1980～2002年20个代表站点的日常农业气象土壤湿度观测资料,确定了辽宁5个农业气候区的玉米作物系数,分析作物系数在生育期内的变化规律,并分区建立了玉米作物系数与时间（旬）关系6次多项式函数。结果表明,辽宁各农业气候区的玉米作物系数值存在明显差异,但其变化规律一致。