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.

Studies on crop growth modelling and simulation models in China

There is a close relationship between agricultural production and environmental meteorological conditions. In the study of the correlation between them, the simulation models are paid more attention to the crop growth. In this paper the development of the studies on the crop growth dynamic simulation model in China is briefly reviewed. The relationships between meteorological conditions and each process of crop growth (such as photosynthesis, respiration, accumulation and distribution of assimilation products and growth of leaf area) are studied and simulated basing on the results from field experiments. Preliminary models for rice, wheat, maize and soybean have been developed, and some investigations about modelling methods, procedures and parameters in simulation models are made.

Comparison of Mathematical Models for Describing CropResponses to N Fertilizer

Four mathematical models were systematically evaluated in describing responses of four different cropsat 7 rates of nitrogen application. Residual sum of squares and a total point ranking method were used toassess the model fitting for crop responses to nitrogen application. Sparrow’s inverse quadratic polynomialmodel performed the best.

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.

Assessment of Climate Change Impact on Water Requirement and Rice Productivity

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。

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.

作物生长模拟模型研究和应用

作物生长模拟模型研究和应用宇振荣（北京农业大学农业生态环境系，１０００９４）ＳｔｕｄｉｅｓｏｎＣｒｏｐＧｒｏｗｔｈＭｏｄｅｌｌｉｎｇａｎｄＩｔｓＡｐｐｌｉｃａｔｉｏｎ．￥ＹｕＺｈｅｎｒｏｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＡｇｒｏ－Ｅ－ｃｏｌｏｇｉｃａｌＥ...

Quantifying Responses of Winter Wheat Physiological Processes to Soil Water Stress for Use in Growth Simulation Modeling

A deep understanding of crop-water eco-physiological relations is the basis for quantifying plant physiological responses to soil water stress. Pot experiments were conducted to investigate the winter wheat crop-water relations under both drought and waterlogging conditions in two sequential growing seasons from 2000 to 2002, and then the data were used to develop and validate models simulating the responses of winter wheat growth to drought and waterlogging stress. The experiment consisted of four treatments, waterlogging (keep 1 to 2 cm water layer depth above soil surface), control (70%-80% field capacity), light drought (40%-50% field capacity) and severe drought (30%-40% field capacity) with six replicates at five stages in the 2000-2001 growth season. Three soil water content treatments (waterlogging, control and drought) with two replicates were designed in the 2001-2002 growth season. Waterlogging and control treatments are the same as in the 2000-2001 growth season. For the drought treatment, no water was supplied and the soil moisture decreased from field capacity to wilting point. Leaf net photosynthetic rate, transpiration rate, predawn leaf water potential, soil water potential, soil water content and dry matter weight of individual organs were measured. Based on crop-water eco-physiological relations, drought and waterlogging stress factors for winter wheat growth simulation model were put forward. Drought stress factors integrated soil water availability, the sensitivity of different development stages and the difference between physiological processes (such as photosynthesis, transpiration and partitioning). The quantification of waterlogging stress factor considered different crop species, soil water status, waterlogging days and sensitivity at different growth stages. Data sets from the pot experiments revealed favorable performance reliability for the simulation sub-models with the drought and waterlogging stress factors.

The Removal and Remediation of Phenanthrene and Pyrene in Soil by Mixed Cropping of Alfalfa and Rape

The mechanisms and efficiencies of the removal and remediation of polycyclic aromatic hydrocarbons （PAHs） in soils by different planting patterns with rape （Brassica campestris） and alfalfa （Medicago sativa） were studied by pot experiments in a greenhouse. Results showed that the remediation efficiencies under mixed cropping of alfalfa and rape significantly exceeded those under single cropping when the initial concentrations of phenanthrene and pyrene were at 20.05-322.06 mg kg^-1 and 20.24-321.42 mg kg^-1, respectively. After 70 days plantation of crops, the contents of extractable PAHs in soils under mixed cropping were much lower than those under single cropping. About 65.17-83.52% of phenanthrene and 60.09%- 75.34% ofpyrene was removed from the soils under mixed cropping, respectively, which were averagely 43.26 and 40.38% for phenanthrene, and 11.03 and 16.29% for pyren higher than those under single cropping. Alfalfa or rape did absorb and accumulate PAHs from the soils apparently; the PAHs concentrations in plants monotonically increased with the increase of initial PAHs concentrations in soil; the accumulations of PAHs in plants showed following sequence as roots 〉 above parts, phenanthrene 〉 pyrene and single cropping 〉 mixed cropping at same contamination level. Despite the presence of vegetation significantly enhanced the remediation of PAHs in soils, contributions of abiotic loss, plant uptake, accumulation and microbial degradation was much lower than those of plant-microbial interactions in the process of phytoremediation. Thus plant-microbial interactions are the main mechanisms for the remediation enhancement of soil PAHs pollution under mixed cropping models. Results suggested a feasibility of the establishment of multi-species phytoremediation for the improvement of remediation efficiencies of PAHs, which may decrease accumulations of PAHs in crops and thus reduce their risks.

Potential Impact of Future Climate Change on Crop Yield in Northeastern China

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China （NEC） by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5 （CMIP5） under the Representative Concentration Pathway 4.5 scenario （RCP4.5）, the projected maize yield changes for three future periods [2010-39 （period 1）, 2040-69 （period 2）, and 2070-99 （period 3）] relative to the mean yield in the baseline period （1976-2005） were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase （but with great uncertainty） against the background of global warming, which may offer some valuable guidance to government policymakers.

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

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.

Simulating Phenology,Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000

Rice crop growth and yield in the north Iran are affected by crop duration and phenology.The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental data for simulating and quantifying the phenological development,crop duration and yield prediction of rice crop influenced by different seedling ages.In order to calibrate and validate the crop parameters of ORYZA2000 model,a two-year field experiment was conducted under potential growth condition for transplanted lowland rice during the 2008-2009 rice growing seasons,using three rice varieties with three seedling ages(17,24 and 33 days old).The results showed that the seedling age changed crop duration from 7 to 10 d.The ORYZA2000 model could predict well,but consistently underestimated the length of growing period.The range in normalized root mean square error(RMSEn) values for each phenological stage was between 4% and 6%.From our evaluation,we concluded that ORYZA2000 was sufficiently accurate in simulation of yield,leaf area index(LAI) and biomass of crop organs over time.On average,RMSEn values were 13%-15% for total biomass,18%-21% for green leaf biomass,17%-20% for stem biomass,16%-23% for panicle biomass and 24%-26% for LAI.The RMSEn values for final yield and biomass were 12%-16% and 6%-9%,respectively.Generally,the model simulated LAI,an exceeded measured value for younger seedlings,and best-fit was observed for older seedlings of short-duration varieties.The results revealed that the ORYZA2000 model can be applied as a supportive research tool for selecting the most appropriate strategies for rice yield improvement across the north Iran.

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

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.

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.

Simulation and Validation of Rice Potential Growth Process in Zhejiang Province of China by Utilizing WOFOST Model

A crop growth model of WOFOST was calibrated and validated through rice field experiments from 2001 to 2004 in Jinhua and Hangzhou, Zhejiang Province. For late rice variety Xiushui 11 and hybrid Xieyou 46, the model was calibrated to obtain parameter values using the experimental data of years 2001 and 2002, then the parameters were validated by the data obtained during 2003. For single hybrid rice Liangyoupeijiu, the data recorded in 2004 and 2003 were used for calibration and validation, respectively. The main focus of the study was as follows： the WOFOST model is good in simulating rice potential growth in Zhejiang and can be used to analyze the process of rice growth and yield potential. The potential yield obtained from the WOFOST model was about 8100 kg/ha for late rice and 9300 kg/ha for single rice. The current average yield in Jinhua is only about 78% （late rice） and 70% （single rice） of their potential yield. The results of the simulation also showed that the currant practice of management at the middle and late growth stages of rice should be reexamined and improved to reach optimal rice growth.

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.

A Method for Upscaling Genetic Parameters of CERES-Rice in Regional Applications

To upscale the genetic parameters of CERES-Rice in regional applications, Jiangsu Province, the second largest rice producing province in China, was taken as an example. The province was divided into four rice regions with different rice variety types, and five to six sites in each region were selected. Then the eight genetic parameters of CERES-Rice, particularly the four parameters related to the yield, were modified and validated using the Trial and Error Method and the local statistical data of rice yield at a county level from 2001 to 2004, combined with the regional experiments of rice varieties in the province as well as the local meteorological and soil data （Method 1）. The simulated results of Method 1 were compared with those of other three traditional methods upscaling the genetic parameters, i.e., using one-site experimental data from a local representative rice variety （Method 2）, using local long-term rice yield data at a county level after deducting the trend yield due to progress of science and technology （Method 3）, and using rice yield data at a super scale, such as provincial, ecological zone, country or continent levels （Method 4）. The results showed that the best fitness was obtained by using the Method 1. The coefficients of correlation between the simulated yield and the statistical yield in the Method 1 were significant at 0.05 or 0.01 levels and the root mean squared error （RMSE） values were less than 9% for all the four rice regions. The method for upscaling the genetic parameters of CERES-Rice presented is not only valuable for the impact studies of climate change, but also favorable to provide a methodology for reference in crop model applications to the other regional studies.

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.