Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far o...Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.展开更多
Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only desi...Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.展开更多
In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean...In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean observation）, r （correlation）, and R2 （determination coefficient）, are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap〈5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects：（1） calculating da and dap; （2） calculating the RMSE or Sd; （3） calculating r and R2, at the same time, plotting 1：1 diagram.展开更多
In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were c...In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.展开更多
The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer.Coupling crop growth model in land surface model is an importan...The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer.Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland.However,the previous work mainly focused on crops in single cropping system,less work was done in multiple cropping systems.This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI),latent heat flux(LHF)and sensible heat flux(SHF)of winter wheat growing in double cropping system in the North China Plain(NCP).The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP.The phenophases,LAI and heat fluxes in 2004–2006 at Yucheng Station,Shandong Province,China were used to calibrate the model.The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station,Hebei Province,China in 2009–2010.The results showed the significant accuracy of the calibrated model in simulating these variables,with which the R2,root mean square error(RMSE)and index of agreement(IOA)between simulated and observed variables were obviously improved than the original code.The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model.Overall,the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.展开更多
Information is limited on the potential of cowpea-wheat double cropping in the southern United States to enhance soil health and increase net returns. Using the Decision Support System for Agrotechnology Transfer (DSS...Information is limited on the potential of cowpea-wheat double cropping in the southern United States to enhance soil health and increase net returns. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data spanning 80 years, we assessed the effects of soil type (Darco: Grossarenic Paleudults and Lilbert: Arenic Plinthic Paleudults), N application rate (0, 100, and 200 kg•ha<sup>−1</sup>), and El Niño-Southern Oscillation (ENSO) on the grain yields of double-cropped cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in this region. Yield differences were tested using the pairwise Wilcoxon rank sum test. Results showed that yields of wheat that followed cowpea (<sup>c</sup>wheat) were greater than those that followed fallow (<sup>f</sup>wheat). The soil type effects on <sup>c</sup>wheat and <sup>f</sup>wheat yields decreased with an increase in N rate. The soil type effect on cowpea yields was greater during La Niña. The ENSO impact on cowpea yields was greater on the less fertile soil Darco. Yields of <sup>c</sup>wheat and <sup>f</sup>wheat increased with an increase in N rate up to 100 and 200 kg•ha<sup>−1</sup>, respectively. The yield response of <sup>c</sup>wheat to N rate was less than that of <sup>f</sup>wheat. The N rate effects on <sup>c</sup>wheat and <sup>f</sup>wheat yields were greater on Darco and under El Niño. Yields of cowpea were greatest under El Niño, whereas those of wheat were greatest under La Niña. The ENSO effect on cowpea yields was greater on Darco. With an increase in N rate, the effect of ENSO was diminished.展开更多
To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measur...To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measure cultivar specific parameters by using DSSAT (v4.7) Cropping Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a Completely Randomized Design (CRD) with split plot design. Results showed that five upland rice genotypes had significantly affected each other by different temperature treatments (28°C, 30°C, 32°C) with grain yield, tops weight, harvest index, flowering, and maturity date. At the same time, all the phenological traits had highly significant variation with the genotypes. The cultivar specific parameters obtained by using a temperature tolerant cultivar (Basmati 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model evaluation results indicated that utilizing the estimated cultivar coefficient parameters, model simulated well with varying temperature treatments as indicated by the agreement index (d-statistic) closer to unity. Hence, it was estimated that model calibration and evaluation was realistic in the limits of test cropping seasons and that CSM fitted with cultivar specific parameters can be used in simulation studies for investigation, farm managing or decision making. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.展开更多
Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the...Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.展开更多
Information is limited on the potential of double-cropping cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in the semiarid region of the southern United States. Using the Decision Support System for Agr...Information is limited on the potential of double-cropping cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in the semiarid region of the southern United States. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data of 80 years, we assessed the possibility of cowpea-wheat double-cropping in this region for grain purpose as affected by planting date and N application rate. Results showed that the possibility of double-cropping varied from 0% to 65%, depending on the cropping system. The possibility was less with systems comprising earlier planting dates of wheat and later planting dates of cowpea. Results indicated that cowpea-wheat double-cropping could be beneficial only when no N was applied, with wheat planted on October 15 or later. At zero N, the double-crops of cowpea planted on July 15 and wheat planted on November 30 were the most beneficial of all the 72 double-cropping systems studied. With a delay in planting cowpea, the percentage of beneficial double-cropping systems decreased. At N rates other than zero, fallow-wheat monocropping systems were more beneficial than cowpea-wheat double-cropping systems, and the benefit was greater at a higher N rate. At 100 kg N ha<sup>-1</sup>, the monocrop of wheat planted on October 15 was the most beneficial of all the 94 systems studied. Results further showed that fallow-wheat yields increased almost linearly with an increase in N rate from 0 to 100 kg∙ha<sup>-1</sup>. Fallow-wheat grain yields were quadratically associated with planting dates. With an increase in N rate, wheat yields reached the peak with an earlier planting date. Wheat yields produced under monocropping systems were greater than those produced under double-cropping systems for any cowpea planting date. Cowpea yields produced under monocropping systems were greater than those produced under any double-cropping system. The relationship between cowpea grain yields and planting dates was quadratic, with July 1 planting date associated with the maximum yields.展开更多
The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of inter...The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.展开更多
A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overest...A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.展开更多
Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simula...Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.展开更多
Water is essential for agricultural production;however,climate change has exacerbated drought and water stress in arid and semi-arid areas such as Iran.Despite these challenges,irrigation water efficiency remains low,...Water is essential for agricultural production;however,climate change has exacerbated drought and water stress in arid and semi-arid areas such as Iran.Despite these challenges,irrigation water efficiency remains low,and current water management schemes are inadequate.Consequently,Iranian crops suffer from low water productivity,highlighting the urgent need for enhanced productivity and improved water management strategies.In this study,we investigated irrigation management conditions in the Hamidiyeh farm,Khuzestan Province,Iran and used the calibrated AquaCrop and WinSRFR(a surface irrigation simulation model)models to reflect these conditions.Subsequently,we examined different management scenarios using each model and evaluated the results from the second year.The findings demonstrated that combining simulation of the AquaCrop and WinSRFR models was highly effective and could be employed for irrigation management in the field.The AquaCrop model accurately simulated wheat yield in the first year,being 2.6 t/hm^(2),which closely aligned with the measured yield of 3.0 t/hm^(2).Additionally,using the WinSRFR model to adjust the length of existing borders from 200 to 180 m resulted in a 45.0%increase in efficiency during the second year.To enhance water use efficiency in the field,we recommended adopting borders with a length of 180 m,a width of 10 m,and a flow rate of 15 to 18 L/s.The AquaCrop and WinSRFR models accurately predicted border irrigation conditions,achieving the highest water use efficiency at a flow rate of 18 L/s.Combining these models increased farmers'average water consumption efficiency from 0.30 to 0.99 kg/m^(3)in the second year.Therefore,the results obtained from the AquaCrop and WinSRFR models are within a reasonable range and consistent with international recommendations.This adjustment is projected to improve the water use efficiency in the field by approximately 45.0%when utilizing the border irrigation method.Therefore,integrating these two models can provide comprehensive management solutions for regional farmers.展开更多
An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crop...An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crops but not yet in protected environments.We present a case study of data assimilation in a greenhouse,updating growth estimates of the Reduced State TOMGRO model.We assimilated data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras,using the Unscented Kalman Filter and the Ensemble Kalman Filter.In some cases,assimilation led to improvements of more than 40%in the RMSE of yield estimates of the non-calibrated model,within a validation set.The improvements were more noticeable when there was a need to adjust the estimates to a condition the model does not represent.In these situations,we noted the RMSE decreased by almost 80%,depending on the variable being assimilated.However,in some cases,the results were also impaired by assimilation,and we highlight the impacts on the filter performance caused by the quality of observations and of observation models.Overall,the employed measurements,i.e.,area of organs observed in pictures and plant-water mass,seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model.As with other studies,it was not the case that assimilating one state was useful for improving the value of others,including yield.As the first study using filters and non-destructive observations in a process-based crop model in a protected environment,we identified a lot of potential,but to identify the best use of these techniques with real-time data,more studies are needed.By making all data and code from this study available,we hope to ease future research in this area.展开更多
Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explici...Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.展开更多
Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in ...Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in order to estimate biomass according to relationship between biomass and backscattering coefficients from SAR data. Based on cost function, parameters of growth model were optimized as per conjugate gradient method, minimizing the differences between estimated biomass and inversion values from SAR data. The results indicated that the simulated biomass using the revised growth model with SAR data was consistent with the measured one in time distribution and even higher in accuracy than that without SAR data. Hence, the key parameters of crop growth model could be revised by real-time growth information from SAR data and accuracy of the simulated biomass could be improved accordingly.展开更多
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 furth...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.展开更多
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 r...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.展开更多
基金co-supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2021B0301030007)the National Key Research and Development Program of China (Grant Nos. 2017YFA0604302 and 2017YFA0604804)+1 种基金the National Natural Science Foundation of China (Grant No. 41875137)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)。
文摘Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.
基金supported by the National Natural Science Foundation of China (40701120)the Beijing Natural Science Foundation, China (4092016)the Beijing Nova, China (2008B33)
文摘Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
基金supported by the National High-Tech R&D Program (2006AA10Z230,2006AA10Z219-1)the National Natural Science Foundation of China (31171455)+3 种基金the Jiangsu Province Agricultural Scientific Technology Innovation Fund, China (CX(10)221, CX (11)2042)the Agricultural Scientific Technology Support Program, Jiangsu Province, China (BE2008397,BE2011342)the No-Profit Industry (Meteorology) Research Program, China (GYHY201006027, GYHY201106027)the Jiangsu Government Scholarship for Overseas Studies, China
文摘In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean observation）, r （correlation）, and R2 （determination coefficient）, are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap〈5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects：（1） calculating da and dap; （2） calculating the RMSE or Sd; （3） calculating r and R2, at the same time, plotting 1：1 diagram.
基金Under the auspices of Major State Basic Research Development Program of China(No.2007CB714407)National Natural Science Foundation of China(No.40801070)Action Plan for West Development Program of Chinese Academy of Sciences(No.KZCX2-XB2-09)
文摘In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.
基金This study was supported by the National Natural Science Foundation of China(41801020.41901128)the China Postdoctoral Science Foundation(2016M601115).We also appreciate the advices from Jiangsu Academy ofAgricultural Sciences,China.
文摘The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer.Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland.However,the previous work mainly focused on crops in single cropping system,less work was done in multiple cropping systems.This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI),latent heat flux(LHF)and sensible heat flux(SHF)of winter wheat growing in double cropping system in the North China Plain(NCP).The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP.The phenophases,LAI and heat fluxes in 2004–2006 at Yucheng Station,Shandong Province,China were used to calibrate the model.The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station,Hebei Province,China in 2009–2010.The results showed the significant accuracy of the calibrated model in simulating these variables,with which the R2,root mean square error(RMSE)and index of agreement(IOA)between simulated and observed variables were obviously improved than the original code.The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model.Overall,the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.
基金co-supported by the Guangdong Major Project of Basic and Applied Basic Research [grant number 2021B0301030007]the National Key Research and Development Program of China [grant number 2017YFA0604302]+1 种基金the National Natural Science Foundation of China [grant number 41875137]the National Key Scientific and Technological Infrastructure project"Earth System Science Numerical Simulator Facility"(EarthLab)
文摘Information is limited on the potential of cowpea-wheat double cropping in the southern United States to enhance soil health and increase net returns. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data spanning 80 years, we assessed the effects of soil type (Darco: Grossarenic Paleudults and Lilbert: Arenic Plinthic Paleudults), N application rate (0, 100, and 200 kg•ha<sup>−1</sup>), and El Niño-Southern Oscillation (ENSO) on the grain yields of double-cropped cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in this region. Yield differences were tested using the pairwise Wilcoxon rank sum test. Results showed that yields of wheat that followed cowpea (<sup>c</sup>wheat) were greater than those that followed fallow (<sup>f</sup>wheat). The soil type effects on <sup>c</sup>wheat and <sup>f</sup>wheat yields decreased with an increase in N rate. The soil type effect on cowpea yields was greater during La Niña. The ENSO impact on cowpea yields was greater on the less fertile soil Darco. Yields of <sup>c</sup>wheat and <sup>f</sup>wheat increased with an increase in N rate up to 100 and 200 kg•ha<sup>−1</sup>, respectively. The yield response of <sup>c</sup>wheat to N rate was less than that of <sup>f</sup>wheat. The N rate effects on <sup>c</sup>wheat and <sup>f</sup>wheat yields were greater on Darco and under El Niño. Yields of cowpea were greatest under El Niño, whereas those of wheat were greatest under La Niña. The ENSO effect on cowpea yields was greater on Darco. With an increase in N rate, the effect of ENSO was diminished.
文摘To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measure cultivar specific parameters by using DSSAT (v4.7) Cropping Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a Completely Randomized Design (CRD) with split plot design. Results showed that five upland rice genotypes had significantly affected each other by different temperature treatments (28°C, 30°C, 32°C) with grain yield, tops weight, harvest index, flowering, and maturity date. At the same time, all the phenological traits had highly significant variation with the genotypes. The cultivar specific parameters obtained by using a temperature tolerant cultivar (Basmati 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model evaluation results indicated that utilizing the estimated cultivar coefficient parameters, model simulated well with varying temperature treatments as indicated by the agreement index (d-statistic) closer to unity. Hence, it was estimated that model calibration and evaluation was realistic in the limits of test cropping seasons and that CSM fitted with cultivar specific parameters can be used in simulation studies for investigation, farm managing or decision making. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
文摘Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.
文摘Information is limited on the potential of double-cropping cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in the semiarid region of the southern United States. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data of 80 years, we assessed the possibility of cowpea-wheat double-cropping in this region for grain purpose as affected by planting date and N application rate. Results showed that the possibility of double-cropping varied from 0% to 65%, depending on the cropping system. The possibility was less with systems comprising earlier planting dates of wheat and later planting dates of cowpea. Results indicated that cowpea-wheat double-cropping could be beneficial only when no N was applied, with wheat planted on October 15 or later. At zero N, the double-crops of cowpea planted on July 15 and wheat planted on November 30 were the most beneficial of all the 72 double-cropping systems studied. With a delay in planting cowpea, the percentage of beneficial double-cropping systems decreased. At N rates other than zero, fallow-wheat monocropping systems were more beneficial than cowpea-wheat double-cropping systems, and the benefit was greater at a higher N rate. At 100 kg N ha<sup>-1</sup>, the monocrop of wheat planted on October 15 was the most beneficial of all the 94 systems studied. Results further showed that fallow-wheat yields increased almost linearly with an increase in N rate from 0 to 100 kg∙ha<sup>-1</sup>. Fallow-wheat grain yields were quadratically associated with planting dates. With an increase in N rate, wheat yields reached the peak with an earlier planting date. Wheat yields produced under monocropping systems were greater than those produced under double-cropping systems for any cowpea planting date. Cowpea yields produced under monocropping systems were greater than those produced under any double-cropping system. The relationship between cowpea grain yields and planting dates was quadratic, with July 1 planting date associated with the maximum yields.
文摘The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.
基金supported by the National Natural Science Foundation of China(Grant Nos.41775156 and 41590875)
文摘A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.
基金supported by the National Key Research and Development Program of China(2022YFD2001001)the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology[CX(21)1006]+1 种基金the Jiangsu Collaborative Innovation Center for Modern Crop Production(JCICMCP)the 111 Project.
文摘Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.
基金The study was funded by the Soil and Water Research Institute of Iran.
文摘Water is essential for agricultural production;however,climate change has exacerbated drought and water stress in arid and semi-arid areas such as Iran.Despite these challenges,irrigation water efficiency remains low,and current water management schemes are inadequate.Consequently,Iranian crops suffer from low water productivity,highlighting the urgent need for enhanced productivity and improved water management strategies.In this study,we investigated irrigation management conditions in the Hamidiyeh farm,Khuzestan Province,Iran and used the calibrated AquaCrop and WinSRFR(a surface irrigation simulation model)models to reflect these conditions.Subsequently,we examined different management scenarios using each model and evaluated the results from the second year.The findings demonstrated that combining simulation of the AquaCrop and WinSRFR models was highly effective and could be employed for irrigation management in the field.The AquaCrop model accurately simulated wheat yield in the first year,being 2.6 t/hm^(2),which closely aligned with the measured yield of 3.0 t/hm^(2).Additionally,using the WinSRFR model to adjust the length of existing borders from 200 to 180 m resulted in a 45.0%increase in efficiency during the second year.To enhance water use efficiency in the field,we recommended adopting borders with a length of 180 m,a width of 10 m,and a flow rate of 15 to 18 L/s.The AquaCrop and WinSRFR models accurately predicted border irrigation conditions,achieving the highest water use efficiency at a flow rate of 18 L/s.Combining these models increased farmers'average water consumption efficiency from 0.30 to 0.99 kg/m^(3)in the second year.Therefore,the results obtained from the AquaCrop and WinSRFR models are within a reasonable range and consistent with international recommendations.This adjustment is projected to improve the water use efficiency in the field by approximately 45.0%when utilizing the border irrigation method.Therefore,integrating these two models can provide comprehensive management solutions for regional farmers.
文摘An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crops but not yet in protected environments.We present a case study of data assimilation in a greenhouse,updating growth estimates of the Reduced State TOMGRO model.We assimilated data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras,using the Unscented Kalman Filter and the Ensemble Kalman Filter.In some cases,assimilation led to improvements of more than 40%in the RMSE of yield estimates of the non-calibrated model,within a validation set.The improvements were more noticeable when there was a need to adjust the estimates to a condition the model does not represent.In these situations,we noted the RMSE decreased by almost 80%,depending on the variable being assimilated.However,in some cases,the results were also impaired by assimilation,and we highlight the impacts on the filter performance caused by the quality of observations and of observation models.Overall,the employed measurements,i.e.,area of organs observed in pictures and plant-water mass,seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model.As with other studies,it was not the case that assimilating one state was useful for improving the value of others,including yield.As the first study using filters and non-destructive observations in a process-based crop model in a protected environment,we identified a lot of potential,but to identify the best use of these techniques with real-time data,more studies are needed.By making all data and code from this study available,we hope to ease future research in this area.
基金supported by United States Department of Agriculture,Agricultural Research Service(No.58-8042-9-072)United States Department of Agriculture-National Institute of Food and Agriculture(No.2019-34263-30552)+1 种基金Management Information System(No.043050)United States Department of Agriculture-Agricultural Research Service-Non-Assistance Cooperative Agreement(No.58-6066-2-030).
文摘Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.
基金Supported by National High-tech R & D Program of China (863 Program)(2007AA12Z174)~~
文摘Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in order to estimate biomass according to relationship between biomass and backscattering coefficients from SAR data. Based on cost function, parameters of growth model were optimized as per conjugate gradient method, minimizing the differences between estimated biomass and inversion values from SAR data. The results indicated that the simulated biomass using the revised growth model with SAR data was consistent with the measured one in time distribution and even higher in accuracy than that without SAR data. Hence, the key parameters of crop growth model could be revised by real-time growth information from SAR data and accuracy of the simulated biomass could be improved accordingly.
基金Supported by Dorothy Hodgson Postgraduate-NERC-Hutchinson Whampoa Ph.D.Scholarship and Key Projects in the National Science & Technology Pillar Program in the 11th Five-Year Plan Period:Demonstration of Adaptation to Climate Cnange in Vulnerable Region of China(2007BAC03A06)
文摘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.
基金Natural Science Foundation of Anhui Province(1508085MF110&1608085QF126)Key Programs for Science and Technology of Anhui Province(1501031102)+1 种基金Open Foundation of Key Laboratory in Application and Integration of Internet of Things(IOT)in Agriculture of Ministry of Agriculture(2015-kf01)International S&T Cooperation Project of Ministry of Agriculture(2015-Z44).
文摘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.
