Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm

Wu, Shangrong; Yang, Peng; Ren, Jianqiang; Chen, Zhongxin; He, Li

doi:10.1016/j.rse.2020.112276

Cited by 39 publications

(26 citation statements)

References 60 publications

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“…This resulted in relatively moderate values of the model accuracy (average NRMSE were 41.4%). However, these results are in accordance with other studies used the WOFOST model for LAI simulation for different crops such as: wheat [47], jujube [8] and corn [48]. For the seasonal ETa predictions, an average difference of about 8 mm between estimated and e measured ETa has been found.…”

Section: Discussionsupporting

confidence: 90%

“…The observed over or under estimation is certainly due to a slight bias in the estimation made by the soil model implemented in WOFOST [44]. However, it should be noted that these results are relatively weak compared to the work conducted by Wu et al (2021) [47] on winter wheat, where they found a modeling efficiency greater than 80%. However, Eweys et al With regard to these results, we can recommend an average total amount of irrigation water of 430 mm and an average fertilizer input of 140 kg of N, 80 kg of P and 102 kg of K (these values are the average of the level 4 fertilization of the three fields while respecting the balance) to reach the average potential yield of the region which is 6.270 t/ha.…”

Section: Discussionmentioning

confidence: 96%

“…The observed over or under estimation is certainly due to a slight bias in the estimation made by the soil model implemented in WOFOST [44]. However, it should be noted that these results are relatively weak compared to the work conducted by Wu et al (2021) [47] on winter wheat, where they found a modeling efficiency greater than 80%. However, Eweys et al (2017) worked on maize in the eastern Netherlands and have a modeling efficiency EF <−4 with an associated large error (RMSE > 45%) [48].…”

Section: Discussionmentioning

confidence: 96%

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Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Dewenam¹,

Er‐Raki

Ezzahar

et al. 2021

Agronomy

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The main goal of this investigation was to evaluate the potential of the WOFOST model for estimating leaf area index (LAI), actual evapotranspiration (ETa), soil moisture content (SM), above-ground biomass levels (TAGP) and grain yield (TWSO) of winter wheat in the semi-arid region of Tensift Al Haouz, Marrakech (central Morocco). An application for the estimation of the Yield Gap is also provided. The model was firstly calibrated based on three fields data during the 2002–2003 and 2003–2004 growing seasons, by using the WOFOST implementation in the Python Crop simulation Environment (PCSE) to optimize the different parameters that provide the minimum difference between the measured and simulated LAI, TAGP, TWSO, SM and ETa. Then, the model validation was performed based on the data from five other wheat fields. The results obtained showed a good performance of the WOFOST model for the estimation of LAI during both growing seasons on all validation fields. The average R2, RSME and NRMSE were 91.4%, 0.57 m2/m2, and 41.4%, respectively. The simulated ETa dynamics also showed a good agreement with the observations by eddy covariance systems. Values of 60% and 72% for R2, 0.8 mm and 0.7 mm for RMSE, 54% and 31% for NRMSE are found for the two validation fields, respectively. The model’s ability to predict soil moisture content was also found to be satisfactory; the two validation fields gave R2 values equal to 48% and 49%, RMSE values equal to 0.03 cm3/cm3 and 0.05 cm3/cm3, NRMSE values equal to 11% and 19%. The calibrated model had a medium performance with respect to the simulation of TWSO (R2 = 42%, RSME = 512 kg/ha, NRMSE = 19%) and TAGP (R2 = 34% and RSME = 936 kg/ha, NRMSE = 16%). After accurate calibration and validation of the WOFOST model, it was used for analyzing the gap yield since this model is able to estimate the potential yield. The WOFOST model allowed a good simulation of the potential yield (7.75 t/ha) which is close to the optimum value of 6.270 t/ha in the region. Yield gap analysis reveals a difference of 5.35 t/ha on average between the observed yields and the potential yields calculated by WOFOST. Such difference is ascribable to many factors such as the crop cycle management, agricultural practices such as water and fertilization supply levels, etc. The various simulations (irrigation scenarios) showed that early sowing is more adequate than late sowing in saving water and obtaining adequate grain yield. Based on various simulations, it has been shown that the early sowing (mid to late December) is more adequate than late sowing with a total amount of water supply of about 430 mm and 322 kg (140 kg of N, 80 kg of P and 102 kg of K) of fertilization to achieve the potential yield. Consequently, the WOFOST model can be considered as a suitable tool for quantitative monitoring of winter wheat growth in the arid and semi-arid regions.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 96%

“…The observed over or under estimation is certainly due to a slight bias in the estimation made by the soil model implemented in WOFOST [44]. However, it should be noted that these results are relatively weak compared to the work conducted by Wu et al (2021) [47] on winter wheat, where they found a modeling efficiency greater than 80%. However, Eweys et al (2017) worked on maize in the eastern Netherlands and have a modeling efficiency EF <−4 with an associated large error (RMSE > 45%) [48].…”

Section: Discussionmentioning

confidence: 96%

See 1 more Smart Citation

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Dewenam¹,

Er‐Raki

Ezzahar

et al. 2021

Agronomy

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show abstract

“…The results demonstrated that applies the Bayesian network for genotype efficacy by the interaction of the environment under plant diseases, the model of land types and climate variables as a synthesis framework enables the agronomist to establish the uncertainty that characterizes the definition and implementation of the GEI analysis. As observed in other studies, Bayesian networks can investigate appropriate and interpretable framework for the simultaneous modeling of multiple quantitative traits in better predictive power result in the context of additive genetic models [35] Among the other studies that had good performance for example [36] used VW-4DEnSRF algorithm to study the area and winter wheat yield estimation based on the WOFOST crop model and a crop yield assimilation system. Wei, et al [25], study highlighted that despite of the impact machine learning approaches in understanding and exploiting GEI for prediction, there is still some room for expanding and improving their use in applications not yet explored.…”

Section: Multi Target Performancementioning

confidence: 96%

Stochastic Stability and Analytical Solution with Homotopy Perturbation Method of Multicompartment Non-Linear Epidemic Model with Saturated Rate

Chahrazed¹

2021

AJAMS

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In this work, we consider a nonlinear epidemic model with a saturated incidence rate. we consider a population of size N(t) at time t, this population is divided into six subclasses, with N(t)=S(t)+I(t)+I₁(t)+I₂(t)+I₃(t)+Q(t). Where S(t), I(t), I₁(t), I₂(t), I₃(t), and Q(t) denote the sizes of the population susceptible to disease, infectious members, and quarantine members, respectively. We have made the following contributions: 1. The local stabilities of the infection-free equilibrium and endemic equilibrium are; analyzed, respectively. The stability of a disease-free equilibrium and the existence of other nontrivial equilibria can be determined by the ratio called the basic reproductive number. 2. We find the analytical solution of the nonlinear epidemic model by Homotopy perturbation method. 3. Finally the stochastic stabilities. The study of its sections are justified with theorems and demonstrations under certain conditions. In this work, we have used the different references cited in different studies in the three sections already mentioned.

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“…Wheat is one of the most widely cultivated food crops in the world, with China ranking first in terms of yield and sales [1]. Therefore, a timely and accurate prediction of grain yield (GY) can not only help guide agricultural production management and assist relevant government departments to formulate scientific food policies, but also has significance for mitigating climate risks, ensuring national food security and economic stability [2][3][4][5][6][7]. The essence of the formation of the yield is the accumulation of dry matter produced by photosynthesis in different growth stages.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

Wang

et al. 2021

Applied Sciences

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Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

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Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm

Cited by 39 publications

References 60 publications

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Stochastic Stability and Analytical Solution with Homotopy Perturbation Method of Multicompartment Non-Linear Epidemic Model with Saturated Rate

Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

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