WOFOST Model Research Articles

Enhancing global agricultural monitoring system for climate-smart agriculture

Global agricultural monitoring systems face unprecedented challenges due to intensifying climate change. This paper reviews the advancements in existing global agricultural monitoring systems, highlighting deficiencies in addressing extreme weather events, data integration, and real-time analysis. To overcome these limitations, we introduce the Earth System Model-Coupled Global Agricultural Monitoring System (ESM-GAMS), an advanced framework that combines satellite and near-surface remote sensing, artificial intelligence-driven modeling, supercomputing, and crop model to enhance the accuracy and timeliness of crop monitoring and yield predictions under diverse climate scenarios. By integrating multi-source remote sensing data, ESM-GAMS mitigates delays caused by satellite revisit cycles and weather interference, enabling near real-time monitoring with results available at hourly or minute-level intervals. Additionally, the system demonstrated high accuracy in yield simulations under extreme weather, with the improved WOFOST model achieving robust R2 values ranging from 0.55 to 0.77, indicating its reliability in predicting yields across diverse conditions. ESM-GAMS not only enables detailed daily monitoring of crop growth, but also provides early-warning capabilities for extreme weather and its impact on prediction. By optimizing resource allocation, supporting climate resilience, and enabling global data computing, ESM-GAMS represents a further step toward achieving climate-smart agriculture.

Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

Simulating water-limited potato yields across the Netherlands with (SWAP-)WOFOST: Experimentation, model improvement and evaluation

Water availability explains a large part of the spatial and temporal yield variability of ware potato in the Netherlands. Climate change is projected to lead to greater variability in water availability. Therefore, an accurate simulation of crop yield as a function of water availability is of high importance. Many crop models can simulate water-limited potato yields, but as detailed experimental data on drought and oxygen stress are scarce, models are often not well calibrated and evaluated. We set up a large experiment and an on-farm observational trial to calibrate and evaluate the simulation of water-limited yield of different potato cultivars across the Netherlands. In addition, we investigated the required model complexity to accurately simulate water-limited potato yields. The crop growth model WOFOST was used for simulations either as a single model or coupled with SWAP. Yields were simulated applying either only drought stress or by including both drought and oxygen stress. We employed two methods for simulating oxygen stress in SWAP-WOFOST. The calibration improved accuracy of simulated water-limited yields as demonstrated in our model evaluation. Both on sandy and clayey soils, water-limited yields were best simulated using SWAP-WOFOST while simulating oxygen stress using a process-based method in combination with a rooting density that decreases linearly with rooting depth. On sandy soils, the bottom boundary condition of free drainage was applicable. On clayey soils measured groundwater levels (or soil water pressure heads when measurements are not available) should be used as bottom boundary condition. On sandy soils both WOFOST and SWAP-WOFOST may be used, as results were similar. On clayey soils, WOFOST is less suitable, as it cannot simulate capillary rise. SWAP-WOFOST however has many options and parameters, which can result in large differences in results. Hence, a careful model set up and evaluation is required for each application.

The necessity of coupling the legacy effect with temperature response in crop phenology models

Global climate change has changed vegetation phenology substantially around the world. However, the necessity of coupling legacy effects with temperature responses in phenological models remains unclear. The objective of this study was to demonstrate that legacy effects [based on day of year (DOY) of phenology events] have substantial positive impacts on crop phenology. Data from 1883 crop×site combinations across Germany and China were analyzed. DOY was found to be a temperature-independent factor for both vegetative (VGP) and reproductive growth periods (RGP) (based on variance inflation factor values). Partial correlation analysis suggested that DOY explained almost the same variability in date of phenology events as temperature. Akaike information criterion showed the cost-effectiveness of coupling DOY with temperature in 71.2% and 59.1% of sites in VGP and RGP, respectively. A model that coupled a linear legacy effect and a temperature response mechanism (LETM) improved fitting efficiency by an average of 57%. LETM was observed to outperform the well-calibrated WOFOST and WE models in VGP and RGP for all crops. Averaged over all crops, root mean square errors for WOFOST, WE, and LETM were 4.0, 3.9, and 3.7 d in VGP, respectively, and are 5.3, 4.6, and 4.0 d in RGP, respectively. Our results verified the necessity of coupling the legacy effect with temperature responses in phenology models. Given that the results were consistent for all of the crops investigated, we believe that our conclusions can apply to other field crops. Results of this study expand the knowledge of crop phenology responses to environment, and are helpful for accurately predicting crop growth and development responses under future global climate change.

Estimation of winter wheat yield in Hebi city based on crop models and remote sensing data assimilation

Abstract Assimilating remote sensing data with crop models is an effective approach to improve the accuracy of crop model applications at the regional scale. Sobol global sensitivity analysis and Markov Chain Monte Carlo methods were used to calibrate parameters in the WOFOST and WheatSM models within a gridded model framework in the study area. Time series data reconstruction techniques were employed to correct MODIS LAI and ET data. Four assimilation scenarios were tested, including assimilation of only leaf area index (LAI), assimilation of only evapotranspiration (ET), simultaneous assimilation of LAI and ET and a control scenario without assimilation. These scenarios were applied to model winter wheat yields in Hebi City, China, from 2013 to 2018. Statistical validation demonstrated that assimilating ET or LAI individually significantly improved model accuracy compared to the control scenario with similar levels of improvement. The highest model accuracy was achieved when assimilating both ET and LAI simultaneously, showing the highest correlation coefficients and the lowest root mean square errors among the four scenarios. This research provides a basis for selecting assimilation variables when applying crop models at the regional scale. The coupling of crop growth models with remote sensing data empowers governments and agricultural producers to devise more effective agricultural strategies, allocate resources efficiently, and implement disaster response measures. This enhances scientific management within the agricultural sector, promotes increased food production and elevates farmers’ incomes.

Improving crop yield estimation by unified model parameters and state variable with Bayesian inference

Data assimilation techniques integrating crop growth models and remote sensing technologies offer a feasible approach for large-scale crop yield estimation. Previous research has primarily focused on either recalibrate the uncertain model parameters or updating model state variables independently using remotely sensed observations. In this study, we developed a two-step inference algorithm that couples the parameter inference and the state update, to solve the joint posterior distribution of uncertain parameters and state variables given the remote sensing observations. An Observing Simulation System (OSS) experiment was first performed based on the WOFOST crop model to validate the effectiveness of the parameter inference method. The results indicate that, the parameter inference method successfully improved the estimation of different types of model parameters and enhanced yield estimation. Furthermore, leaf area index (LAI) retrieved from Sentinel-2 was assimilated into the WOFOST model to simulate winter wheat yield at the plot scale in the northeastern part of Henan Province. The results demonstrated that the proposed two-step inference algorithm can more effectively correct model simulation biases and improve winter wheat yield estimation accuracy (R²=0.58, MAPE=12.75 %, and RMSE=1112 kg·ha⁻¹), outperforming the standard EnKF algorithm (R²=0.51, MAPE=14.62 %, RMSE=1328 kg·ha⁻¹). Overall, attributed to its unified approach to estimating both model parameters and state variables, the proposed two-step inference algorithm shows promising application prospects for data assimilation-based crop yield estimation.

Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Development of a yield monitoring model based on analysis of surveys and images of the field

The study is devoted to the construction of a mathematical model of the yield of agricultural crops, which includes three components: trend, seasonal, and random. The developed model shows the dependence of yield on phenological indicators, the quality of land resources, management efficiency, and other random factors. The trend and seasonal components of the yield model do not depend on random factors and can therefore be used to predict yield. It is proposed to consider the trend component of the model as a piecewise linear function, and the seasonal component of the model as a linear harmonic regression. A method for analyzing multispectral images with consideration of geoinformation data has been developed to assess phenological indicators. This method includes determining the threshold value using Otsu method to find the density of the agricultural crop in the field. Data on crop density, supplemented with geodata about the plot boundaries, are used to calculate the yield. A comparison of yield forecasts for three crops in the Chernihiv region was made using observations of the phenological indicators of crops throughout the year and over 3 months. It was found that yield is significantly determined by plant development in the first months after germination. The comparison of yield forecasts was made with data from the State Statistics Service of Ukraine and forecasts made using the WOFOST simulation model. It was established that the average relative error of yield prediction using the developed model is 2.96% when using observations of the phenological indicators of crops throughout the year and 4.51% when observing over 3 months. This accuracy is sufficient and comparable to the average accuracy of predictions based on the WOFOST model, which is 3.62%).

Impact Assessment of Maize Cold Damage and Drought Cross-Stress in Northeast China Based on WOFOST Model

Impact Assessment of Maize Cold Damage and Drought Cross-Stress in Northeast China Based on WOFOST Model

Sensitivity analysis of simulated Lycium barbarum L. yield in the WOFOST model under different climate conditions

Calibrating process-based crop models in a timely manner is often challenging due to the extensive range of parameters involved. Sensitivity analysis (SA) is a useful practice for analyzing model uncertainty in these parameters. In this study, the Morris and extended Fourier Amplitude Sensitivity Test (E-FAST) methods were employed in sub-model level experiments. The yield outputs of WOFOST model under different climate conditions were used to evaluate the impact of altering model parameters in the modeling of Lycium barbarum L. The results indicate that parameters associated with CO2 assimilation rate, leaf area expansion and thermal time during specific periods have a significant impact on the simulated yield. The sensitive parameter rankings obtained from E-FAST exhibited good concordance across each planting site, and both SA methods consistently revealed similar rankings. This study provided a strategy to efficiently verify the applicability of new crop model parameters in different regions.

Climate change impact on potato (Solanum tuberosum) productivity and relative adaptation strategies

WOFOST and InfoCrop crop growth simulation models were used to assess the impact of climate change on potato cultivars and to develop adaptation strategies for future climatic scenarios (2030, 2050 and 2080) under representative concentration pathways (RCP’s) 4.5 and 6.0 in Bihar. Potato cultivars belonging to late (Kufri Badshah), medium (Kufri Jyoti) and early (Kufri Pukhraj) maturity groups were selected. The simulated results revealed variations in potential productivity of potato under both RCP’s (4.5 & 6.0) with baseline yields of 43.80 t ha-1 for Kufri Badshah, 41.5 t ha-1 for Kufri Jyoti and 43.6 t ha-1 for Kufri Pukhraj. Under RCP 4.5, elevated concentration of CO2 projected to increase the productivity of Kufri Badshah, Kufri Jyoti, and Kufri Pukhraj. However, a decline in yield is expected when individual effect of temperature is considered for future climatic scenarios (2030, 2050 & 2080). However, these yield loss is negated when combined effect of CO2 and temperature is considered by 1.3, 0.7 and 0.3 % in 2030, by -0.4, -1.1 and -2.2 % in 2050 and by 3.5, 4.4 and 5.9 % in 2080, respectively. Likewise, for RCP 6.0, combined effect of CO2 and temperature offset the yield losses by 2.6, 2.4 and 2.3% in 2030, 2.1, 1.7 and 1.1 in 2050 and 1.1, -0.1 and -1.8 in 2080. In addition, selection of suitable cultivars, shifting the date of planting and proper irrigation and nitrogen management practices can counterbalance the yield losses.

Monitoring Waterlogging Damage of Winter Wheat Based on HYDRUS-1D and WOFOST Coupled Model and Assimilated Soil Moisture Data of Remote Sensing

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring.

Spatial Distributions of Yield Gaps and Production Increase Potentials of Spring Wheat and Highland Barley in the Qinghai-Tibet Plateau

Low grain yield caused by high altitude; cold climate; small, cultivated land area, and poor soil fertility is the critical factor posing a potential risk to local food security in the Qinghai-Tibet Plateau (QTP). Analyzing spatial distribution of the increase potential of grain production in the QTP could be contributable to developing a regional increase in the space of grains to ensure food security. Taking spring wheat and highland barley as objectives, this study simulated the annual potential yields of spring wheat and highland barley at the site level. They estimated their yield gaps and production increase potentials at the regional and county level and mapped their spatial distribution in 2020, based on the methodologies of the literature data collection, using the WOFOST model and GIS analysis. The yield gaps of spring wheat and highland barley were 3.7 and 2.4 t ha−1 for the whole QTP, accounting for 51.4% and 39.5% of their potential yields, respectively. At the county level, the yield gap ranges of spring wheat and highland barley were 1.5–7.0 t ha−1 and 0.3–5.9 t ha−1 across the QTP, respectively. When the yield gap was fully developed, spring wheat and highland barley productions had the potentials of 497.4 and 717.4 Kt for the whole QTP, equal to 118.2% and 75.2% of their current total production, respectively. Spatially, the counties with a large increase potential of spring wheat were mainly distributed in Haidong, Hainan, Xining, Shannan, Nyingchi, and Lhasa, while those with low potential were located in Xigaze and Shannan. Regarding highland barley, Lhasa, Shannan, Xigaze, Yushu, and Hainan had a larger potential to increase. To increase grain production in the QTP, the priority should be given to the shrinkage of the yield gap in the counties with larger potentials to increase, such as Hainan, Shannan, Lhasa, etc., through improving the irrigation rate and fertilizer usage in the farmland.

Assessing changes in potential yields and yield gaps of summer maize in the North China Plain

AbstractIn recent years, China's crop production growth rate has decreased. This has provoked questions on whether the grain yields have reached their potential. Taking the North China Plain (NCP) as the study area, this study first simulated the potential yield of an irrigated maize crop with the WOFOST model, using daily weather data for 1960–2017. The yield gap was quantified for 1998–2017, and finally, the total potential maize production was estimated. The results indicate that during 1960–2017, the average potential yield for the region varied annually from 9.5 to 13.5 t ha−1, showing an obvious downward trend at a rate of 37.6 kg ha−1 per year. The long‐term mean potential yield at all 327 counties ranged from 9.9 to 13.7 t ha−1, increasing from south to north. The annual maize yield gap was between an average of 2.7–6.0 t ha−1 (29.0%–51.3%) of the potential yield for the NCP during 1998–2017. At county‐level, the yield gap varied between 1.9 and 7.8 t ha−1 and exceeded 5.0 t ha−1 for 99 counties (30.3% of all counties). Attainable yields can be increased by 0.1–5.2 t ha−1 at county‐level, and by 2.2 t ha−1 for the whole NCP, implying that the maize production has the potential to increase with 20.8 Mt. To increase maize production, the focus areas should be the southcentral NCP, which have a larger yield gap during 2012–2017.

Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets

CONTEXTThe assimilation of Remote Sensing (RS) data into crop models improves the accuracy of yield predictions by considering crop growth dynamics and their spatial heterogeneity due to the different management practices and environmental conditions. OBJECTIVEThis study proposes a new method for performing sub-regional yield predictions (Nomenclature of territorial units for statistics, NUTS-3 level) using RS time series data and crop models. The primary objective was to release a procedure whereby the heterogeneity of the agricultural landscape (i.e. the agrophenotypes) observed from RS is used to drive crop model calibration. METHODSFine-resolution barley and maize distribution maps (100 m) and related crop calendars have been used to derive the “where” and “when” of the 8-day MODIS NDVI time series data, located in Apulia, Tuscany, and Veneto in 2018–2019. Principal Component Analysis and Hierarchical Clustering have been applied to NDVI seasonal profiles to identify the agrophenotypes, which have been used to derive crop growth and leaf area index dynamics. These data served as the reference to optimize the most relevant parameters of the WOFOST_GT model, using gridded weather data as input (0.25° resolution, Copernicus ERA5). Parameter distributions from multiple automatic calibrations have been sampled to characterize the agricultural heterogeneity within 22 NUTS-3 administrative units. Yield statistics from the Italian National Institute of Statistics have been used as reference data to test the accuracy of the yield simulation by the crop model WOFOST_GT. RESULTS AND CONCLUSIONSThe agrophenotypes reflected the wide north-south latitudinal gradient experienced in Italy by the two crops, leading to a gap of 15–30 days in barley and maize flowering and harvest dates across the study area. Average Nast-Sutcliffe modelling efficiency in reproducing LAI dynamics from RS (0.6) and Relative Root Mean Square Error (RRMSE) in predicting yield data (12.1% for barley, 3.7% for maize) in hindcast simulations demonstrated the effectiveness of our approach. The average RRMSE was reduced by 32.7% (barley) and 8.5% (maize) compared to baseline, decreasing by 0.7–1 Mg ha−1 absolute yield errors on both crops. SIGNIFICANCEThe inclusion of local agrophenotypes in the yield prediction workflow reduced errors in yield prediction compared to unsupervised simulations at NUTS-3 level. The script for agrophenotypes extraction and the model parameter sets are released to the scientific community, to foster improvements and further applications to other crops, ecoclimatic regions, satellite sensors and spatial scales.

Multiple model averaging methods for predicting regional rice yield

AbstractUsing multiple crop models in an ensemble can generate more accurate crop growth predictions than only using an individual crop model. However, few studies have investigated the performances of individual crop models and multiple model averaging (MMA) methods for predicting regional rice (Oryza sativa L.) yield with limited inputs and observations. This study assessed the performances of three individual crop models, that is, AquaCrop, WOFOST, and Oryza version 3 (OryzaV3), and five MMA methods for predicting regional rice yield. Results showed that the AquaCrop model achieved better performances than the WOFOST and OryzaV3 models for predicting regional rice yield, especially for the early‐season rice yield, which implied that a crop model with a simple structure was powerful for making accurate regional rice yield predictions. The MMA methods achieved better performances than the AquaCrop model for predicting the late‐season rice yield; however, the AquaCrop model and three information criterion (IC) methods, that is, Akaike IC (AIC), Bayesian IC (BIC), and AICc (bias‐corrected), achieved more acceptable prediction accuracies of the early‐season rice yield than the other MMA methods. Overall, not all the MMA methods could necessarily produce more accurate regional rice yield prediction than individual crop models. This study suggested using the simple‐structure crop models and the AIC, AICc, and BIC methods for making accurate regional crop yield prediction when limited data are available for model parameters calibration.

Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data

Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data

The applicability evaluation and drought validation of the WOFOST model for the simulation of winter wheat growth in Shandong Province, China

The yield of winter wheat in Shandong Province is of great significance for ensuring regional and national food security. To reduce the risk of production loss, the WOFOST model was used to simulate the winter wheat growth to obtain the quantitative and dynamic information. Based on the observational data, a moisture control experiment and the trial and error method, the applicability and drought simulation of the WOFOST model were evaluated for winter wheat growth. For the simulation of the seedling period, flowering period, and maturity period of winter wheat in Shandong Province, the R2 were 0.95, 0.69, and 0.68 respectively. The D-index were 0.99, 0.89, and 0.86 respectively. The mean absolute error (mAE) were 1.3, 4.3, and 4.1 respectively. And the nRMSE were 0.65%, 4.3%, and 3.2%, respectively. For the yield simulation, the R2, D-index, mean relative error (mRE), and nRMSE were 0.48, 0.82, 10.5% and 12.8%, respectively. For the yield simulation under drought stress, the R2, D-index, mRE, and nRMSE were 0.77, 0.93, 7.1%, and 7.4%, respectively. An evaluation index system was built through four different degrees of drought treatment between the jointing period and the flowering period. With the aggravation of drought, the growth indicators about the total above ground production (TAGP), maximum leaf area index (MAXLAI), total dry weight of leaves (TWLV), and total dry weight of stems (TWST) decreasing by 13.6–41.0%, 37.8–56.5%, 19.4–42.1%, and 20.3–51.2%, respectively. The results showed that this model could adequately simulate the formation process of yield under both normal and drought conditions.

Yield estimation of Lycium barbarum L. based on the WOFOST model

Previous research about crop growth simulation and yield prediction mainly focused on annual crops with very few such studies relating to perennial fruit tree crops. In this study, field experiments were conducted in different growth periods of Lycium barbarum L. in Gansu province, China to determine and improve the initial WOFOST model parameters with combination of the time-series leaf area index (LAI) obtained from Sentinel-2 satellite and predict yields for L. barbarum shrubs at a field scale for summer fruit (SF) and autumn fruit (AF). The results showed that the performance of initial parameters established by previous research and partial measured data was relatively poor, with a relative error (RE) of 20.95% for annual yield. The simulation after model parameter calibration significantly improved model prediction accuracy. The predicted SF was 2588 kg/ha and AF was 601 kg/ha with a RE of -5.51%. This study provided a strategy to improve modelled yield for fruit tree crops through utilization of remote sensed and field measured data in model parameter calibration, which on the other hand provides useful information for regional development planning and crops marketing.

Bayesian Posterior-Based Winter Wheat Yield Estimation at the Field Scale through Assimilation of Sentinel-2 Data into WOFOST Model

Accurate and timely regional crop yield information, particularly field-level yield estimation, is essential for commodity traders and producers in planning production, growing, harvesting, and other interconnected marketing activities. In this study, we propose a novel data assimilation framework. Firstly, we construct the likelihood constraints for a process-based crop growth model based on the previous year’s statistical yield and the current year’s field observations. Then, we infer the posterior sets of model-simulated time-series LAI and the final yield of winter wheat with a Markov chain Monte Carlo (MCMC) method for each meteorological data grid of the European Centre for Medium-Range Weather Forecasts Reanalysis (v5ERA5). Finally, we estimate the winter wheat yield at the spatial resolution of 10 m by combining Sentinel-2 LAI and the WOFOST model in Hengshui, the prefecture-level city of Hebei province of China. The results show that the proposed framework can estimate the winter wheat yield with a coefficient of determination R2 equal to 0.29 and mean absolute percentage error MAPE equal to 7.20% compared to within-field measurements. However, the agricultural stress that crop growth models cannot quantitatively simulate, such as lodging, can greatly reduce the accuracy of yield estimates.