Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

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.

Evaluation of global gridded crop models in simulating sugarcane yield in China

中国是全球第三大甘蔗生产国,中国甘蔗产量模拟可服务于全球食糖和乙醇的生产和贸易.全球格点作物模式CLM5-crop和LPJmL已实现对甘蔗的模拟,但它们在中国的模拟能力未知.本文评估结果表明:两个模式均严重低估了甘蔗产量,模拟均不足观测的1/4.CLM5-crop能有技巧地模拟产量的空间分布特征,而LPJmL不能.两个模式均不能合理模拟产量的年际变化,且低估了产量的上升趋势.模式低估甘蔗产量的部分原因是模式假设收割的是甘蔗的穗而非茎.

Exploring the Potential of Cowpea-Wheat Double Cropping in the Semi-Arid Region of the Southern United States Using the DSSAT Crop Model

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-1, 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-1. 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.

Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

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.

Comparison of Crop Model Validation Methods

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.

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

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.

Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

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.

Assessing Cowpea-Wheat Double Cropping Strategies in the Southern United States Using the DSSAT Crop Model

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−1), 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 (cwheat) were greater than those that followed fallow (fwheat). The soil type effects on cwheat and fwheat 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 cwheat and fwheat increased with an increase in N rate up to 100 and 200 kg•ha−1, respectively. The yield response of cwheat to N rate was less than that of fwheat. The N rate effects on cwheat and fwheat 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.

Determination of Upland Rice Cultivar Coefficient Specific Parameters for DSSAT (Version 4.7)-CERES-Rice Crop Simulation Model and Evaluation of the Crop Model under Different Temperature Treatments conditions

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.

Study on Growth Monitoring and Yield Prediction of Winter Wheat in the South of Shanxi Province Based on MERSI Data and ALMANAC Crop Model

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.

Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined

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.

Introducing a drought index to a crop model can help to reduce the gap between the simulated and statistical yield

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.

Modelling the impacts of cover crop management strategies on the water use,carbon exchange and yield of olive orchards

Cover crops have long been proposed as an alternative soil management for minimizing erosion rates in olive stands while providing additional ecosystem services.However,the trade-off between these benefits and the competition for water with the trees makes the definition of optimal management practices a challenging task in semiarid climates.This work presents an improved version of OliveCan,a process-based simulation model of olive orchards that now can simulate the main impacts of cover crops on the water and carbon balances of olive orchards.Albeit simple in its formulation,the new model components were developed to deal with different cover crop management strategies.Examples are presented for simulation runs of a traditional olive orchard in the conditions of southern Spain,evaluating the effects of different widths for the strip occupied by the cover crop(Fcc)and two contrasting mowing dates.Results revealed that high Fccresulted in lower olive yields,but only when mowing was applied at the end of spring.In this regard,late mowing and high Fccwas associated with lower soil water content from spring to summer,coinciding with olive flowering and the earlier stages of fruit growth.Fccwas also negatively correlated with surface runoff irrespective of the mowing date.On the other hand,net ecosystem productivity(NEP)was substantially affected by both Fccand mowing date.Further simulations under future climate scenarios comparing the same management alternatives are also presented,showing substantial yield reductions by the end of the century and minor or negligible changes in NEP and seasonal runoff.

基于AquaCrop和WinSRFR组合的夏玉米沟灌方案优化

确定作物合理的灌溉制度和灌水技术要素组合是科学管理农业水资源的基础,可有效缓解水资源短缺矛盾和保障区域粮食安全。基于此,该研究利用在陕西省杨陵区(2022年)和武功县(2017年)进行的夏玉米田间试验,分别对AquaCrop模型和WinSRFR软件进行校准和验证,确定了研究区夏玉米典型水文年(丰水年、平水年和干旱年)的灌溉制度;通过反演沟灌土壤入渗参数和田面糙率,结合确定的灌溉制度,优化了沟灌灌水技术要素组合(入沟流量和灌水时间),并量化评价了优化灌溉制度和灌水技术要素组合对夏玉米的增产能力。结果表明,AquaCrop模型能准确模拟研究区夏玉米生长过程,其中产量模拟值与实测值的相对误差绝对值均值分别为1.85%(校准)和7.47%(验证);研究区夏玉米丰水年(灌浆期)和平水年(拔节期)需灌水1次,干旱年(拔节期和灌浆期)需灌水2次,单次灌水量均为55 mm;反演所得研究区沟灌土壤入渗参数k和α取值范围分别为是55.416~98.437 mm/h^(α)和0.351~0.858,田面糙率n均值为0.056;合理的入沟流量和停水时间取值范围分别为2.2~3.3 L/s和35~16 min,可获得高灌水质量(综合灌水质量指标C_(i)≥85%);以2022年夏玉米优化的灌溉制度和灌水技术要素优化组合为基础,模拟所得夏玉米产量为7.819 t/hm^(2),与无灌溉(5.972 t/hm^(2))、现状条件(7.424 t/hm^(2))和仅对灌溉制度优化(7.659 t/hm^(2))情景相比较,分别提高了30.9%、5.3%和2.1%,且所需灌水量较现状条件可减少59 mm。研究结果可为研究区域夏玉米灌溉制度制定和沟灌方案设计提供理论基础和技术支撑。

Evaluation of Productive Plant Landscapes in Cold Regions Based on a Multiple Cropping Model

Four varieties of each rapeseed and buckwheat were planted in different sowing periods to explore a variety of planting patterns.A theoretical foundation was provided for the innovative application of cold region productive plant landscapes.The analytic hierarchy process was employed to develop a model for the evaluation of multiple cropping systems.A comprehensive evaluation was conducted to study 10 indicators in plant type,flower color,flowering period,flower volume,branch coverage,plot average yield,number of grains per plant,yield per plant,thousand-grain quality and ecological adaptability in four different varieties of each rapeseed and buckwheat.The results indicated that flower color,ecological adaptability,plot average yield and flower volume were the most important indicators for the value of productive plant landscapes in cold regions.Concerning the sowing period,the optimal combination of varieties and planting times were March 31 for Qingza No.5(rapeseed)and July 18 for Xinong T1211(buckwheat).

SAR Data Assimilation for Crop Biomass Simulation Based on Crop Growth Model

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.

Simulating the Impacts of Global Warming on Wheat in China Using a Large Area Crop Model

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.

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

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.

Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary.The model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of Hungary.Plant-specific model parameters were determined by inverse modeling.Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator.Using the available and the generated data,the present and the prospective agro-ecological characteristics of Hungary were determined.According to the simulation results,average yields will decrease considerably(-30%)due to climate change.The rate of nitrate leaching will prospectively decrease as well.The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century.On the basis of the simulation results,the role of autumn crops is likely to become more significant in Hungary.The achieved results can be generalized for more extended regions based on the concept of spatial(geographical)analogy.

Reducing deep learning network structure through variable reduction methods in crop modeling

Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.