Support vector machine-based open crop model (SBOCM): Case of rice production in China

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.     

Application of a rice simulation model in high temperature sensitivity study

以调试校正较高精度的ORYZA2000模型参数及高温敏感性模拟验证为目的, 为模型适应性和本地化提供依据, 利用江苏省9个试验点5个水稻(Oryza sativa)品种的田间观测数据及当地逐日气象数据, 采用ORYZA2000最新版本(V2.13)水稻生长模型, 首先挑选出5个试验点3个品种的观测数据进行模型参数适应性调试校正, 确定了水稻发育生长阶段的各项参数, 然后用该参数对独立样本的4个试验点2个水稻品种地上部分各器官生物量、叶面积指数动态变化过程及最终产量进行了动态模拟。通过t检验和质量评价指标对模拟结果进行了显著性检验。利用通过检验的模型及其参数在假设环境温度不同时间段的持续升高条件下, 开展了高温对水稻生物量及产量影响的模拟研究, 模拟结果的影响幅度与实际高温处理结果的影响幅度进行了比较。结果表明: 1)经过调试校正获得较高精度的水稻发育阶段各参数, 较准确地模拟了水稻生物量和叶面积指数的动态累积过程, 模拟值与观测值基本一致, 说明校正后参数的合理性和有效性; 2)调整参数后高温敏感性模拟结果表明, 孕穗期到开花期温度连续3天、5天、7天升高到35 ℃时, 总生物量、穗生物量和总产量与对照(CK)相比分别下降了12%-25%; 不同时间段连续升高到38 ℃时下降18%-31%; 不同时间段升高到41 ℃时, 各生物量与对照相比分别下降了20%-38%。模型模拟值与控制试验室的观测数据的下降幅度基本一致, 表明经过参数校正的ORYZA2000可以应用于水稻对气温升高响应的预测。     

水稻模拟模型在高温敏感性研究中的应用

以调试校正较高精度的ORYZA2000模型参数及高温敏感性模拟验证为目的, 为模型适应性和本地化提供依据, 利用江苏省9个试验点5个水稻(Oryza sativa)品种的田间观测数据及当地逐日气象数据, 采用ORYZA2000最新版本(V2.13)水稻生长模型, 首先挑选出5个试验点3个品种的观测数据进行模型参数适应性调试校正, 确定了水稻发育生长阶段的各项参数, 然后用该参数对独立样本的4个试验点2个水稻品种地上部分各器官生物量、叶面积指数动态变化过程及最终产量进行了动态模拟。通过t检验和质量评价指标对模拟结果进行了显著性检验。利用通过检验的模型及其参数在假设环境温度不同时间段的持续升高条件下, 开展了高温对水稻生物量及产量影响的模拟研究, 模拟结果的影响幅度与实际高温处理结果的影响幅度进行了比较。结果表明: 1)经过调试校正获得较高精度的水稻发育阶段各参数, 较准确地模拟了水稻生物量和叶面积指数的动态累积过程, 模拟值与观测值基本一致, 说明校正后参数的合理性和有效性; 2)调整参数后高温敏感性模拟结果表明, 孕穗期到开花期温度连续3天、5天、7天升高到35 ℃时, 总生物量、穗生物量和总产量与对照(CK)相比分别下降了12%-25%; 不同时间段连续升高到38 ℃时下降18%-31%; 不同时间段升高到41 ℃时, 各生物量与对照相比分别下降了20%-38%。模型模拟值与控制试验室的观测数据的下降幅度基本一致, 表明经过参数校正的ORYZA2000可以应用于水稻对气温升高响应的预测。     

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.     

站点CERES-Rice模型区域应用效果和误差来源

作物区域模拟是利用有限的空间数据,最大限度地反映出生育期、产量等作物性状的时空变化规律.由于目前的作物模型大多是田间尺度的站点模型,把它运用到区域水平的效果如何研究甚少.文章利用CERES-Rice模型,对作物模型在我国的区域应用效果进行了分析.首先利用田间观测数据在各实验点上对模型进行了详细的站点校准,以验证模型在我国的模拟能力;然后以我国水稻生态区(精确到亚区)为单位,运用均方根差(RMSE)法进行了区域校准和验证;最后利用区域校准后的CERES-Rice模型,模拟了1980～2000年的网格(50km×50km)水稻产量,并与同期农调队调查产量进行统计比较,以验证区域应用的效果,为区域模拟的推广和应用提供参考.结果表明:经过空间校准后的CERES-Rice模型,在水稻的主产区1～4区(占种植面积的95%)模拟的平均产量与调查产量相对均方根差在22%以内,两者的符合度也较好,个别区域(5、6) RMSE%在24%～30%之间;1980～2000年水稻各产区模拟的平均产量与调查产量随时间变化趋势也具有一定的一致性;全国1896个网格中,大部分网格(71.01%)模拟的21年水稻年产量与调查产量的RMSE%在30%之内,且大部分分布在水稻主产区,考虑到水稻种植面积的权重后,认为利用区域校准和验证后的CERES-Rice模型进行水稻区域模拟,可以反映出产量的时空分布特征,能够为宏观决策提供相应的信息.但目前区域模拟中还存在着一定的误差,有待今后进一步研究.     

Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms

Machine learning (ML) along with high volume of satellite images offers an alternative to agronomists in crop yield predictions for decision support systems. This research exploited the possibility of using monthly image composites from Sentinel-2 imageries for rice crop yield predictions one month before the harvesting period at the field level using ML techniques in Taiwan. Three ML models, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), were designed to address the research question of yield predictions in four consecutive growing seasons from 2019 to 2020 using field survey data. The research findings of yield modeling and predictions showed that SVM slightly outperformed RF and ANN. The results of model validation, obtained from SVM models using the data from transplanting to ripening, showed that the root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) values were 5.5% and 4.5% for the 2019 second crop, and 4.7% and 3.5% for the 2020 first crop, respectively. The results of yield predictions (obtained from SVM) for the 2019 second crop and the 2020 first crop evaluated against the government statistics indicated a close agreement between these two datasets, with the RMSPE and MAPE values generally smaller than 11.2% and 9.2%. The SVM model configuration parameters used for rice crop yield predictions indicated satisfactory results. The comparison results between the predicted yields and the official statistics showed slight underestimations, with RMSPE and MAPE values of 9.4% and 7.1% for the 2019 first crop (hindcast), and 11.0% and 9.4% for the 2020 second crop (forecast), respectively. This study has successfully proven the validity of our methods for yield modeling and prediction from monthly composites from Sentinel-2 imageries using ML algorithms. The research findings from this research work could useful for agronomists to timely formulate action plans to address national food security issues.     

基于更新和同化策略相结合的遥感信息与水稻生长模型耦合技术的研究

将遥感与作物模型耦合有利于提高作物模型在区域尺度应用时的精度。基于集合平方根滤波算法(Ensemble Square RootFilter,EnSRF)和粒子群优化算法(Particle Swarm Optimization,PSO),以叶面积指数(Leaf Area Index,LAI)和叶片氮积累量(Leaf Nitrogen Accumulation,LNA)共同作为同化耦合点和过程更新点,将同化与更新策略相结合,研究建立了基于遥感信息与水稻生长模型(RiceGrow)耦合的水稻生长与产量预测技术。结果表明,将更新和同化策略结合后,利用RiceGrow模型模拟的水稻生长指标和产量结果更接近于实测值。其中LAI、LNA和产量与实测值间的RMSE分别为0.94、0.47 g/m2和320.15 kg/hm2;RiceGrow模型直接模拟LAI、LNA和产量的RMSE为1.25、1.24 g/m2和516.83 kg/hm2;而单纯基于同化策略模拟LAI、LNA和产量的RMSE为1.01、0.59 g/m2和335.70 kg/hm2。此外,基于该技术的模型区域尺度预测结果能较好地描述水稻生长和产量的时空分布状况,生长指标及区域总产量的模拟相对误差均小于20%。显示基于更新和同化策略相结合的遥感与模型耦合技术具有较高的预测精度,从而为区域尺度作物生长和产量预测提供了技术支撑。     

Quantifying the thermal heat requirement of Brassica in assessing biophysical parameters under semi-arid microenvironments

Evaluation of the thermal heat requirement of Brassica spp. across agro-ecological regions is required in order to understand the further effects of climate change. Spatio-temporal changes in hydrothermal regimes are likely to affect the physiological growth pattern of the crop, which in turn will affect economic yields and crop quality. Such information is helpful in developing crop simulation models to describe the differential thermal regimes that prevail at different phenophases of the crop. Thus, the current lack of quantitative information on the thermal heat requirement of Brassica crops under debranched microenvironments prompted the present study, which set out to examine the response of biophysical parameters [leaf area index (LAI), dry biomass production, seed yield and oil content] to modified microenvironments. Following 2 years of field experiments on Typic Ustocrepts soils under semi-arid climatic conditions, it was concluded that the Brassica crop is significantly responsive to microenvironment modification. A highly significant and curvilinear relationship was observed between LAI and dry biomass production with accumulated heat units, with thermal accumulation explaining ≥80% of the variation in LAI and dry biomass production. It was further observed that the economic seed yield and oil content, which are a function of the prevailing weather conditions, were significantly responsive to the heat units accumulated from sowing to 50% physiological maturity. Linear regression analysis showed that growing degree days (GDD) could indicate 60–70% variation in seed yield and oil content, probably because of the significant response to differential thermal microenvironments. The present study illustrates the statistically strong and significant response of biophysical parameters of Brassica spp. to microenvironment modification in semi-arid regions of northern India.     

Testing the responses of four wheat crop models to heat stress at anthesis and grain filling

Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop models have been widely used to simulate future crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat models (DSSAT‐CERES‐Wheat, DSSAT‐Nwheat, APSIM‐Wheat, and WheatGrow) were evaluated with 4 years of environment‐controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration (GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days (HDD, degree days over 30 °C), grain filling duration was reduced by 0.30–0.60%, total aboveground biomass was reduced by 0.37–0.43%, and grain yield was reduced by 1.0–1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested crop simulation models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the crop models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested models require improvements in the response to heat stress during grain filling, but all models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies.     

Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?

Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.     

基于ORYZA2000模型的北京地区旱稻适宜播种期分析

确定适宜播种期是制定合理的作物栽培管理方案的关键内容之一。在作物模型ORYZA2000有效性验证的基础上,以北京地区为例,利用该模型结合长期历史气候资料,对确定旱稻适宜播种期做了初步研究。结果表明：在不考虑水分因子条件下,北京地区旱稻297安全播期的范围较广,多年平均为3月26日-6月4日;受温度升高的影响,最早播期有提前趋势,而最晚播种期有延后趋势。在同一年份内,播期不同旱稻的产量也有一定的变化,呈现为先升高而后降低的趋势。播期过早或过晚导致生育期平均温度偏低是影响穗干物质累积且造成减产的主要原因,在适宜的播期范围内才能获得高产。以90%-100%当年最高产量潜力作为适宜播期的产量指标,确定北京地区旱稻297的适宜播期变化在5月11日-5月19日之间,相应的产量变化在6689-7257 kg/hm2范围内。研究方法可为其他地区旱稻的播期研究提供借鉴。     

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.     

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

Background and Aims

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.

Methods

Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions.

Key Results

To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (N_max) among the ILs. Genetic variation in N_max had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se.

Conclusions

This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.     

三化螟种群系统的最优管理决策

以三化螟Tryporyza invertulas(Walker)种群动态模型和水稻产量损失预测模型为基础,根据水稻插植期、品种抗性,保护利用自然天敌和杀虫剂多次使用等控制措施以及它们的各种不同组合对该虫种群动态、水稻产量损失串和净收益的影响,以净收益最大为目标函数,研究三化螟种群的最优管理决策。其中,对昆虫种群动态模拟方法作了一点改进,它综合了前人所提出的种群动态模型的优点。建立的系统模型能够提供包括农业防治、生物防治和化学防治措施在内的、对三化螟种群实施有效管理的最优决策方案。     

长期施肥和不同生态条件下我国作物产量可持续性特征

采用产量可持续性指数（SYI）法,研究了我国不同生态条件下20个长期试验点8个肥料处理的水稻、玉米和小麦产量的可持续性.结果表明：作物SYI值因施肥、作物种类和水热因子不同而呈显著差异.长期不施肥（CK）条件下,水稻、玉米和小麦的SYI值较低,分别为0.55、0.44和0.43;施肥尤其是NPK化肥配施有机肥可显著提高作物产量的可持续性,水稻、玉米和小麦的SYI值分别为0.66、0.58和0.57;单施N肥或NK肥的玉米和小麦的SYI值在0.36～0.47.SYI值大于0.55表明可持续性较好,小于0.45表明可持续性差.经纬度和气象因子对作物SYI也有不同程度的影响,3种作物不施肥时,水稻SYI变异较小,与各因子间没有显著相关性,玉米SYI变异最大且与各因子间存在显著的相关关系,小麦介于两者之间.因此,NPK配施有机肥有利于作物高产稳产,是维持系统可持续性的最优施肥模式.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

模糊数学在研究定西地区胡麻产量预报中的应用

目前,不少科学领域中,模糊数学方法已愈来愈受到重视。例如在气象、地震预报等方面已获得较好的效果。鉴于农作物产量预报的研究,现在尚处于经验估测阶段。但它考虑的模型与识别的对象在很大程度上往往都还是模糊的,因此,采用模糊数学方法来探测农作物产量预报就具有一定的可能性和优越性。近年来,我们在农作物产量预报方面做了一些探索,取得了一些初步结果。这些初步结果表明,模糊数学方法与技巧的应用,具有十分诱人的前景。本文在广义Fuzzy运算的综合决策模型的基础上,提出了一个改进的“综合决策模型”,并给出了其在产量预报中的具体实例。此法意义直观易懂,简便易行,便于群众掌握。最后又采用了逐步回归及逐段回归方法,建立了一个定量的预报模型与综合决策模型以作相互弥补,取得了较好的效果。它们不仅对历史资料模拟率可达88.2％,而且其可靠性也被1983年的大丰收所证实。经各方面探讨,认为采用这两种方法综合对产量趋势预报,其结果是令人满意的。     

A cross-scale model coupling approach to simulate the risk-reduction effect of natural adaptation on soybean production under climate change

This study establishes a procedure to couple Decision Support System for Agrotechnology Transfer (DSSAT) and China Agroecological Zone model (AEZ-China). This procedure enables us to quantify the effects of two natural adaptation measures on soybean production in China, concern on which has been growing owing to the rapidly rising demand for soybean and the foreseen global climate change. The parameters calibration and mode verification are based on the observation records of soybean growth at 13 agro-meteorological observation stations in Northeast China and Huang-Huai-Hai Plain over 1981–2011. The calibration of eco-physiological parameters is based on the algorithms of DSSAT that simulate the dynamic bio-physiological processes of crop growth in daily time-step. The effects of shifts in planting day and changes in the length of growth cycle (LGC) are evaluated by the speedy algorithms of AEZ. Results indicate that without adaptation, climate change from the baseline 1961–1990 to the climate of 2050s as specified in the Providing Regional Climate for Impacts Studies-A1B would decrease the potential yield of soybean. By contrast, simulations of DSSAT using AEZ-recommended cultivars with adaptive LGC and also the corresponding adaptive planting dates show that the risk of yield loss could be fully or partially mitigated across majority of grid cells in the major soybean-growing areas.     

温室环境不同灌水水平条件下DSSAT-CROPGRO-Tomato模型的调参与验证

在沈阳地区日光温室试验的基础上,利用番茄生长模型DSSAT-CROPGRO-Tomato模拟了不同灌水水平条件下温室番茄的生长发育和产量形成过程,并确定了参数估计和模型验证的最优方案.试验设4个处理,全育期的灌水上限均为计划湿润层田间持水率,灌水下限分别为计划湿润层田间持水率的50%(W₁)、60%(W₂)、70%(W₃)和80%(CK).利用DSSAT-GLUE参数估计模块得到遗传参数的不同估计结果,通过对比分析番茄物候期、冠层高度、地上干物质量、鲜果产量、叶面积指数(LAI)、土壤含水率的模拟值与实测值之间的差异,来确定该模型模拟精度.结果表明: 番茄遗传参数--最优条件下最终果实负载所需光热时间(PODUR)的估计值具有较大变异性,变异系数为11.5%,将CROPGRO-Tomato模型应用于不同地区日光温室时,应对此参数进行充分估计,否则会影响其模拟精度.在模型应用过程中,应选用充分灌水处理的观测数据进行遗传参数估计,可以提高模型的模拟精度.此时的绝对相对误差和标准均方根误差值分别为8.7%和10.5%.对作物LAI和土壤含水率动态模拟结果可以看出,灌水水平越高,模型模拟精度越高.留一交叉验证法的总体模拟误差在10.5%～12.5%.说明DSSAT-CROPGRO-Tomato模型可以较为准确地模拟沈阳日光温室不同灌水水平条件下番茄生长发育和产量形成过程.     

基于遥感信息的作物模型重新初始化/参数化方法研究初探

作物模型与遥感信息的结合有助于利用遥感监测的大范围植被信息解决作物模型区域应用时模型初始状态和参数值难以确定的问题。该文借助叶面积指数(LAI)将经过华北冬小麦(Triticum aestivium)适应性调整的WOFOST模型与经参数调整检验的SAIL-PROSPECT模型相嵌套,利用嵌套模型模拟作物冠层的土壤调整植被指数(SAVI),在代表点上借助FSEOPT优化程序使模拟SAVI_s与MODIS遥感数据合成SAVI_m的差异达到最小,从而对WOFOST模型重新初始化。结果表明,借助于遥感信息,出苗期的重新初始化使模拟成熟期与按实际出苗期模拟的结果相差在2天以内,模拟的LAI和总干重的误差比按实际出苗期模拟结果的误差降低3~8个百分点;返青期生物量的重新初始化使模拟LAI和地上总干重在关键发育时刻的误差降至16%以内,模拟LAI和贮存器官重在整个生育期内都更加接近实测值;对返青期生物量的动态调整显示返青到抽穗期间较少次数的遥感数据即能有效地提高作物模型的模拟效果。与国外同类研究相比,该文在作物模型本地化、重新初始化变量和优化比较对象的选择上都有所不同,而利用遥感数据动态调整作物模型初始状态或参数值更具有新意。该文对区域尺度上利用遥感信息优化作物模型的研究具有基础性、探讨性意义。