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

Abstract

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.

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

“…The traditional WOFOST model is written in FORTRAN language, but with the development of computer technology, FORTRAN language is difficult to integrate with tools such as databases and has poor flexibility. Therefore, researchers such as De Wit have re-implemented the WOFOST model using Python language ( de Wit et al., 2019 ).PCSE (The Python Crop Simulation Environment) is a Python software package that includes models such as WOFOST and LINGRA ( Dewenam et al., 2021 ). Compared to traditional WOFOST written using FORTRAN language or FORTRAN Simulation Environment (FSE), PCSE is more versatile.…”

Effects of local farming practices on soil organic carbon content, enzymatic activities, and microbial community structure in semi-arid soils of Morocco

“…Eighty percent of agricultural land in Morocco is rain-fed (52), and sustainable yet productive cultivation practices are needed to allow reliable food production for an increasing population (8). Increased resiliency against drought, soil degradation (53), and Urease activity, alkaline phosphatase activity, and B-glucosidase activity of soils sampled from wheat fields treated with fallowing for long-term (FAL), short-term (FAS), and Eucalyptus plantation (EU). A degraded, abandoned field was sampled as a baseline control (DS).…”

“…In Morocco, a harsh climate and population expansion require crop yields which may exceed the potential of current systems, and necessitate improvement of soil fertility (53). According to previous work comparing different fallowing lengths (17,75,76), durations of two years still can result in significant gains.…”

Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process

“…When the NRMSE is ≤10% and MRE is ≤10%, the simulation result accuracy is considered excellent; when 10% < NRMSE ≤ 20% and 10% < MRE ≤ 20%, the simulation result accuracy is considered good; when 20% < NRMSE ≤ 30% and 20% < MRE ≤ 30%, the simulation result is considered average; and when NRMSE > 30% and MRE > 30%, the accuracy of the simulation result is considered poor. The NRMSE value is given priority in the assessment of model accuracy [36][37][38]. The specific formulas are as follows:…”