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Simulating the Impacts of Global Warming on Wheat in China Using a Large Area crop model

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Acta meteorologica Sinica 2010年第1期24卷 123-135页

作者：李三爱 Tim Wheeler Andrew Challinor 林而达许吟隆居辉 Department of Meteorology University of Reading Department of Agriculture University of Reading Department of Agriculture University of Reading Institute for Atmospheric Science School of Earth and Environment University of Leeds Agro-Environment and Sustainable Development Institute Chinese Academy of Agricultural Science

Temperature is one of the most prominent environmental factors that determine plant growth,development, and yield.Cool and moist conditions are most favorable for wheat.Wheat is likely to be highly vulnerable to further warming because currently the temperature is already close to or above optimum.In this study,the impacts of warming and extreme high temperature stress on wheat yield over China were investigated by using the general large area model（GLAM） for annual crops.The results showed that each 1℃rise in daily mean temperature would reduce the average wheat yield in China by about 4.6%-5.7% mainly due to the shorter growth duration,except for a small increase in yield at some grid cells.When the maximum temperature exceeded 30.5℃,the simulated grain-set fraction declined from 1 at 30.5℃to close to 0 at about 36℃.When the total grain-set was lower than the critical fractional grain-set（0.575-0.6）, harvest index and potential grain yield were reduced.In order to reduce the negative impacts of warming, it is crucial to take serious actions to adapt to the climate change,for example,by shifting sowing date, adjusting crop distribution and structure,breeding heat-resistant varieties,and improving the monitoring, forecasting,and early warning of extreme climate events.

关键词： climate change warming wheat yield crop model

Performance of classic multiple factor analysis and model fitting in crop modeling

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International Journal of Agricultural and Biological Engineering 2016年第2期9卷 119-126页

作者： Jiang Zhaohui Zhang Jing Yang Chunhe Rao Yuan Li Shaowen School of Information and Computer Anhui Agricultural UniversityHefei 230036China

Multivariate statistical analysis and regression,which are typical methods for crop modeling,have direct influence on the accuracy of model,but the applications of these methods usually depend on experiences.In this research,the performances of some common methods of statistical analysis and regression model were compared and verified,in order to avoid the blindness in crop modeling.The monitoring data of growth environment and photosynthesis of tomato,pumpkin and cucumber were obtained by PTM-48A.For the object variable of CO2 exchange rate,selectivity on the main environmental factors by correlation analysis and path analysis were quantitatively compared.The performances of four kinds of multivariate binomial regression equations were compared using a comprehensive aggregative indicator,and the effectiveness of modeling was verified with the selected optimized multivariate statistical analysis and regression equation.Results showed that path analysis was more comprehensive and effective than correlation to discrimination of the variables,especially the path analysis ruled out some suspected independent variables which were not really independent,and the pure quadratic was more suitable to crop modeling because of its simple structure and high accuracy when the data set was small.The conclusion of this research has a general applicability,and offers a useful reference and guide for the other study and application of crops’modeling.

关键词： crop model multivariate statistical analysis path analysis regression comparison

RESEARCH ON crop SIMULATION model BASED ON PDA

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RESEARCH ON CROP SIMULATION MODEL BASED ON PDA

Differences in parameter estimates derived from various methods for the ORYZA(v3) model

第二届国际计算机与计算技术在农业中的应用研讨会

作者： Jianbing Zhang Yeping Zhu Department of Computer Science and Technology China University of Petroleum-Beijing Institute of Agricultural Information Chinese Academy of Agricultural Sciences

crop model simulates growth and yield of crop in field scale and can be used to provide useful field information for farmers,such as irrigation application dates and amount,fertilizer application dates and amount,the choose of crop variety.Researchers have used crop models for many years,but they have had limited success in packaging these complex models in a framework that make them easy for producers to use.In order to provide an efficient and powerful way for delivering crop models to crop producer,a PDA based crop simulation model is designed and implemented.The paper discussed the structure and key technologies of the PDA based crop model.By using the PDA based crop model,farmers can get quick and effective advices from the model.The PDA based crop models are expected to give a new powerful tool to assess the effect of farm practices in crop production.

关键词： crop model simulation visualization PDA

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Journal of Integrative Agriculture 2022年第2期21卷 375-388页

作者： TAN Jun-wei DUAN Qing-yun GONG Wei DI Zhen-hua College of Water Resources and Civil Engineering China Agricultural UniversityBeijing 100083P.R.China College of Water Resources and Hydrology Hohai UniversityNanjing 210098P.R.China Institute of Land Surface System and Sustainable Development Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875P.R.China

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

关键词： parameter estimation frequentist method Bayesian method crop model calibration

A TOOL DEVOTED TO RECOMMEND SPATIALISED NITROGEN RATES AT THE FIELD SCALE,BASED ON A crop model AND REMOTE SENSING OBSERVATIONS ASSIMILATION

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A TOOL DEVOTED TO RECOMMEND SPATIALISED NITROGEN RATES AT TH...

Assessment of Climate Change Impact on Water Requirement and Rice Productivity

The 3rd International Symposium on Intelligent Information Technology in Agriculture(ISIITA)

作者： Martine Guérif Vianney Houlès Bruno Mary Unité CSE INRADomaine Saint-PaulSite AgroparcF-84914 AVIGNON Cedex 9 Unité de Laon-Reims-Mons INRARue Femand ChristF-02007 LAON Cedex

A method based on a crop simulation model is proposed to account for within-field variability in recommending nitrogen rates for wheat.A criterion combining gross margin maximization and the limitation of nitrogen balance was proposed to account for economic and environmental constraints.An alternative method was proposed for characterizing soil variability at a high spatial resolution that is based on the assimilation into the crop model of information on leaf area index and nitrogen uptake obtained from remote sensing measurements.This latter enabled us to improve yield simulation RMSE from 1.81 to 0.73 t ha, An evaluation of the interest of the method,in a strategic way,was made thanks to model simulation, taking into account both economic and environmental aspects.

关键词： crop model nitrogen rate recommendation variable rate data assimilation remote sensing agro-environmental criterion

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Rice science 2023年第4期30卷 276-293页

作者： Konan Jean-Yves N’GUESSAN Botou ADAHI Arthur-Brice KONAN-WAIDHET Satoh MASAYOSHI Nogbou Emmanuel ASSIDJO Environmental Training and Research Unit Laboratory of Environmental Sciences and TechnologiesUniversitéJean Lorougnon GuédéBP 150 DaloaCôte d’Ivoire Department of Training and Research of Rural Engineering and Geographic Sciences Institut National Polytechnique FÉLIX HOUPHOUËT-BOIGNYBP 1093 YamoussoukroCôte d’Ivoire University of Tsukuba Ibaraki 305-8577Japan Department of Training and Research of Chemical and Food Engineering Laboratory of ModellingInformation and ProcessesInstitut National Polytechnique FÉLIX HOUPHOUËT-BOIGNYBP 1093 YamoussoukroCôte d’Ivoire

Assessing the impact of climate change(CC)on agricultural production systems is mainly done using crop models associated with climate model outputs.This review is one of the few,with the main objective of providing a recent compendium of CC impact studies on irrigation needs and rice yields for a better understanding and use of climate and crop models.We discuss the strengths and weaknesses of climate impact studies on agricultural production systems,with a particular focus on uncertainty and sensitivity analyses of crop models.Although the new generation global climate models(GCMs)are more robust than previous ones,there is still a need to consider the effect of climate uncertainty on estimates when using them.Current GCMs cannot directly simulate the agro-climatic variables of interest for future irrigation assessment,hence the use of intelligent climate tools.Therefore,sensitivity and uncertainty analyses must be applied to crop models,especially for their calibration under different conditions.The impacts of CC on irrigation needs and rice yields vary across regions,seasons,varieties and crop models.Finally,integrated assessments,the use of remote sensing data,climate smart tools,CO_(2)enrichment experiments,consideration of changing crop management practices and multi-scale crop modeling,seem to be the approaches to be pursued for future climate impact assessments for agricultural systems。

关键词： climate change rice production irrigation crop model climate model

modeling leaf color dynamics of winter wheat in relation to growth stages and nitrogen rates

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Journal of Integrative Agriculture 2022年第1期21卷 60-69页

作者： ZHANG Yong-hui YANG Yu-bin CHEN Chun-lei ZHANG Kui-ting JIANG Hai-yan CAO Wei-xing ZHU Yan School of Computer Engineering Weifang UniversityWeifang 261061P.R.China Texas A&M University System AgriLife Research&Extension CenterBeaumontTX 77713United States College of Information Science and Technology Nanjing Agricultural UniversityNanjing 210095P.R.China National Engineering and Technology Center of Information Agriculture Nanjing Agricultural UniversityNanjing 210095P.R.China

The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N) rates. RGB(red, green and blue) data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates. A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values. The results indicated that leaf color changes went through three distinct stages, including early development stage(ES), early maturity stage(MS) and early senescence stage(SS), with respective color characteristics of light green, dark green and yellow for the three stages. In the ES stage, the R and G colors gradually decreased from their initial values to steady values, but the B value generally remained unchanged. RGB values remained steady in the MS, but all three gradually increased to steady values in the SS. Different linear functions were used to simulate the dynamics of RGB values in time and space.A cultivar parameter of leaf color matrix(MRGB) and a nitrogen impact factor(FN) were added to the color model to quantify their respective effects. The model was validated with an independent experimental dataset. RMSEs(root mean square errors) between the observed and simulated RGB values ranged between 7.0 and 10.0, and relative RMSEs(RRMSEs)ranged between 7 and 9%. In addition, the model was used to render wheat leaves in three-dimensional space(3 D). The 3 D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat. The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time.

关键词： winter wheat crop model virtual crop leaf color crop phenotype RGB model

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

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Journal of Integrative Agriculture 2020年第3期19卷 721-734页

作者： BAI Tie-cheng WANG Tao ZHANG Nan-nan CHEN You-qi Benoit MERCATORIS TERRA Teaching and Research Centre Gembloux Agro-Bio TechLiège UniversityGembloux 5030Belgium Southern Xinjiang Research Center for Information Technology in Agriculture/College of Information Engineering Tarim UniversityAlaer 843300P.R.China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081P.R.China

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

关键词： fruit tree growth simulation yield forecasting crop model tree age

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

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Journal of Integrative Agriculture 2017年第10期16卷 2283-2299页

作者： LI He JIANG Zhi-wei CHEN Zhong-xin REN Jian-qiang LIU Bin Hasituya Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

关键词： winter wheat yield estimates crop model data assimilation ensemble Kalman filter uncertainty leaf area index