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
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
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
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...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.展开更多
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 objective...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.展开更多
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） v...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.展开更多
Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental...Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.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
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 w...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.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
The Soil and Water Assessment Tool(SWAT) has been widely used throughout the world to model crop growth and nutrient uptake in various types of soils.A greenhouse experiment was performed to validate the process equat...The Soil and Water Assessment Tool(SWAT) has been widely used throughout the world to model crop growth and nutrient uptake in various types of soils.A greenhouse experiment was performed to validate the process equations embedded in SWAT for describing the growth and nutrient uptake of tomatoes in south Florida.The scaled growth curve of greenhouse-grown tomatoes was in close agreement with the theoretical model for field conditions,with the scaling factors being the maximum canopy height and the potential heat units.Similarly,the scaled leaf area index(LAI) growth curve and the scaled root depth curve for greenhousegrown tomatoes agreed with the SWAT functions,with the scaling factors being the maximum LAI and maximum root depth.The greenhouse experiment confirmed that the growth of biomass is a linear function of the intercepted photosynthetically active radiation.The fractions of nutrients in the plant biomass under greenhouse conditions were found to be on the order of 60% of those fractions observed in the field.Values of the initial P distribution(0.2 mg kg -1),initial ratio of mineral stable P to mineral active P(50:1),and initial ratio of humic N to humic P(2.4:1) were determined from soil measurements and can be used for field simulations.The conventional saturation-excess model for soil-water percolation was used to predict the movement of water in the top 10 cm of the greenhouse containers and the results agreed well with measurements.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
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
In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate m...In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate model（EPIC）, the simulated results of FCS model for maize, rice and soybean were spatialized with 1 km×1 km grids to obtain cropping pattern. The reference map of spatial distribution for the three staple crops acquired by remote sensing imageries was applied to validate the simulated cropping pattern. The results showed that（1） the total simulation accuracy for the study area was 78.62%, which proved simulation method was applicable and feasible;（2） simulation accuracy for Jilin Province was the highest among the three provinces with a rate of 82.45% since its simple cropping system and not complex topography;（3） simulation accuracy for maize was the best among the three staple crops with a ratio of 81.14% because the study area is very suitable for maize growth. We hope this study could provide the reference for cropping pattern forecasting and decision-making.展开更多
To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irr...To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.展开更多
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 com...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.展开更多
The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N)rates.RGB(red,green and blue)dat...The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N)rates.RGB(red,green and blue)data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates.A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values.The results indicated that leaf color changes went through three distinct stages,including early development stage(ES),early maturity stage(MS)and early senescence stage(SS),with respective color characteristics of light green,dark green and yellow for the three stages.In the ES stage,the R and G colors gradually decreased from their initial values to steady values,but the B value generally remained unchanged.RGB values remained steady in the MS,but all three gradually increased to steady values in the SS.Different linear functions were used to simulate the dynamics of RGB values in time and space.A cultivar parameter of leaf color matrix(MRGB)and a nitrogen impact factor(FN)were added to the color model to quantify their respective effects.The model was validated with an independent experimental dataset.RMSEs(root mean square errors)between the observed and simulated RGB values ranged between 7.0 and 10.0,and relative RMSEs(RRMSEs)ranged between 7 and 9%.In addition,the model was used to render wheat leaves in three-dimensional space(3 D).The 3 D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat.The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time.展开更多
Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equi...Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.展开更多
Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the imp...Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model. The simulation results were close to the measured county yields with relative error only 2.6% for maize, and - 0.6% for sorghum. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields to those with the original, standard data sets. Runoff curve number changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for the dryland condition. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables for maize was larger than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.展开更多
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.
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Project supported by the National High Technology Research and Development Program of China (863 Program) (No. 2003AA209030) High Technology Research and Development Program of Jiangsu Province (No. BG2004320) the National Natural Science Foundation
文摘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.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘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.
基金supported by the National Natural Science Foundation of China (41401491,41371396,41301457,41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2016-X38)+1 种基金the Agricultural Scientific Research Fund of Outstanding Talentsthe Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘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.
基金This work is supported by New Zealand Ministry of Foreign Affairs and Trade PhD Scholarship and the University of Auckland’s Postgraduate Research Student SupportMinistry of Foreign Affairs and Trade,New Zealand,University of Auckland.
文摘Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.
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.
文摘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.
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.
文摘The Soil and Water Assessment Tool(SWAT) has been widely used throughout the world to model crop growth and nutrient uptake in various types of soils.A greenhouse experiment was performed to validate the process equations embedded in SWAT for describing the growth and nutrient uptake of tomatoes in south Florida.The scaled growth curve of greenhouse-grown tomatoes was in close agreement with the theoretical model for field conditions,with the scaling factors being the maximum canopy height and the potential heat units.Similarly,the scaled leaf area index(LAI) growth curve and the scaled root depth curve for greenhousegrown tomatoes agreed with the SWAT functions,with the scaling factors being the maximum LAI and maximum root depth.The greenhouse experiment confirmed that the growth of biomass is a linear function of the intercepted photosynthetically active radiation.The fractions of nutrients in the plant biomass under greenhouse conditions were found to be on the order of 60% of those fractions observed in the field.Values of the initial P distribution(0.2 mg kg -1),initial ratio of mineral stable P to mineral active P(50:1),and initial ratio of humic N to humic P(2.4:1) were determined from soil measurements and can be used for field simulations.The conventional saturation-excess model for soil-water percolation was used to predict the movement of water in the top 10 cm of the greenhouse containers and the results agreed well with measurements.
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funded by the National Natural Science Foundation of China (41001049, 2011–2013)the China Regional Arable Land Resources Changes and its Warning-A Case Study in Northeast China, Ministry of Science and Technology of China (2004DIB3J092, 2003–2008)
文摘In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate model（EPIC）, the simulated results of FCS model for maize, rice and soybean were spatialized with 1 km×1 km grids to obtain cropping pattern. The reference map of spatial distribution for the three staple crops acquired by remote sensing imageries was applied to validate the simulated cropping pattern. The results showed that（1） the total simulation accuracy for the study area was 78.62%, which proved simulation method was applicable and feasible;（2） simulation accuracy for Jilin Province was the highest among the three provinces with a rate of 82.45% since its simple cropping system and not complex topography;（3） simulation accuracy for maize was the best among the three staple crops with a ratio of 81.14% because the study area is very suitable for maize growth. We hope this study could provide the reference for cropping pattern forecasting and decision-making.
基金funded by the Special Fund for Agro-scientific Research in the Public Interest of China (201203031,201303133)the National Natural Science Foundation of China (31071367)
文摘To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.
基金Consejería de Transformación Económica,Industria,Conocimiento y Universidades"("Junta de Andalucía",Spain)through a project cofunded by ERDF[grant number 27425]part of the work was conducted under two projects funded by"Ministerio de Ciencia,Innovación y Universidades"+7 种基金Spain[grant numbers PID2019-110575RB-I00 and PCI2019-103621]one of which into the framework of the MAPPY project(JPIClimate ERA-NET,AXIS call)financial support from"Ministerio de CienciaInnovación y Universidades",through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D[grant number CEX2019-000968-M]granted to the first and second authors by Consejería de Transformación Económica,IndustriaConocimiento y Universidades"("Junta de Andalucia",Spain)[grant number POSTDOC-21-00381]"Ministerio de Universidades(’María Zambrano’scholarship)[grant number 2021/86493],respectively。
文摘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.
基金supported by the National Natural Science Foundation of China(31872847)the Higher Educational Science and Technology Program of Shandong Province,China(J18KA130)+1 种基金the Science and Technology Benefiting People Plan Project of Weifang High-Tech Zone,Shandong Province,China(2019KJHM13)the Natural Science Foundation of Shandong Province,China(ZR2019PF023)。
文摘The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N)rates.RGB(red,green and blue)data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates.A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values.The results indicated that leaf color changes went through three distinct stages,including early development stage(ES),early maturity stage(MS)and early senescence stage(SS),with respective color characteristics of light green,dark green and yellow for the three stages.In the ES stage,the R and G colors gradually decreased from their initial values to steady values,but the B value generally remained unchanged.RGB values remained steady in the MS,but all three gradually increased to steady values in the SS.Different linear functions were used to simulate the dynamics of RGB values in time and space.A cultivar parameter of leaf color matrix(MRGB)and a nitrogen impact factor(FN)were added to the color model to quantify their respective effects.The model was validated with an independent experimental dataset.RMSEs(root mean square errors)between the observed and simulated RGB values ranged between 7.0 and 10.0,and relative RMSEs(RRMSEs)ranged between 7 and 9%.In addition,the model was used to render wheat leaves in three-dimensional space(3 D).The 3 D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat.The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time.
基金supported by the National Natural Science Foundation of China(NSFC 51909004)。
文摘Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.
文摘Crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model. The simulation results were close to the measured county yields with relative error only 2.6% for maize, and - 0.6% for sorghum. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields to those with the original, standard data sets. Runoff curve number changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for the dryland condition. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables for maize was larger than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.
