Browsing by Author "Oulaid, Bader"

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    Investigating the impact of coupling process-based and data-driven models on wheat crops in arid and semi-arid regions
    (Cranfield University, 2024-07) Oulaid, Bader; Corstanje, Ronald; Milne, Alice E.; Waine, Toby W.; El Alami, Rafiq
    Accurate prediction of wheat yields in arid and semi-arid regions is challenging due to water scarcity, varying environmental conditions, and the dynamic nature of factors influencing crop growth. This thesis aims to enhance scalable wheat yield prediction by integrating remote sensing (RS) data into process-based and data-driven models for more precise and accurate yield prediction in these regions, supporting both tactical and strategic decision-making in agriculture. AquaCrop was chosen for its robust simulation of crop yield response to water. Four interlinked research questions are addressed in this study. First, I identify key factors impacting wheat yield prediction based on sensitivity and SHAP analysis for process-based and data-driven models, respectively. Second, I compare the trade-offs between calibrating process-based models using ground- based hemispherical data and freely available remotely sensed data, highlighting the trade-offs between accuracy and practicality. Third, I evaluate the effectiveness of early-season data-driven yield prediction models across two geographic regions, emphasising the need for region-specific calibrations to maintain accuracy and quantifying accuracy loss due to model transferability. Model performance improved as the season progressed, with Support Vector Regressors achieving an RMSE of 0.23 t ha⁻¹ in the arid regions and Random Forests achieving 0,50 t ha⁻¹ and 0.46 t ha⁻¹ in semi-arid and global models. Fourth, I examine the integration of data-driven models into process-based models through data assimilation techniques, demonstrating how Bayesian assimilation and high-temporal resolution data improve yield prediction accuracy. Bayesian assimilation reduced the prediction errors, decreasing RMSE and MAPE by 25% and 76.5%, respectively, compared to no assimilation approach. This research contributes to the body of knowledge by providing a comprehensive framework for integrating remote sensing data into yield prediction models, supporting precise and timely agricultural decision-making to optimise productivity in water-limited environments.
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    Stepwise model parametrisation using satellite imagery and hemispherical photography: tuning AquaCrop sensitive parameters for improved winter wheat yield predictions in semi-arid regions
    (Elsevier, 2024-04-01) Oulaid, Bader; Milne, Alice E.; Waine, Toby; El Alami, Rafiq; Rafiqi, Maryam; Corstanje, Ron
    Crop models are complex with many parameters, which has limited their application. Here we present an approach which both removes the model complexity through reducing the parameter dimensionality through sensitivity analysis, and presents a subsequent efficient approach to model parameterisation using swarm optimisation. We do this for two key model outputs, crop canopy and yield, and for two types of observational data, hemispheric photographs and Landsat7 imagery. Importantly we compare the usefulness of these two sources of data in terms of accurate yield prediction. The results showed that the dominant model parameters that predict canopy cover were generally consistent across the fields, with the exception of those related water stress. Although mid-season canopy cover extracted from Landsat7 was underestimated, good agreement was found between the simulated and observed canopy cover for both sources of data. Subsequently, less accurate yield predictions were achieved with the Landsat7 compared to the hemispherical photography-based parametrizations. Despite the small differences in the canopy predictions, the implications for yield prediction were substantial with the parametrization based on hemispherical photography providing far more accurate estimates of yield. There are, however, additional resource implications associated with hemispherical photography. We evaluate these trade-offs, providing model parametrization sets and demonstrating the potential of satellite imagery to assist AquaCrop, particularly on large scales where ground measurements are challenging.
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    Trade-offs associated with changing cropping patterns in semi-arid areas of Morocco
    (Elsevier, 2025-06-01) El Fartassi, Imane; Milne, Alice E.; Oulaid, Bader; Bezrhoud, Youssef; Metcalfe, Helen; Alonso Chavez, Vasthi; Coleman, Kevin; Diarra, Alhousseine; El Alami, Rafiq; Prout, Jonah; Waine, Toby W.; Zawadzka, Joanna Ewa; Corstanje, Ronald
    We developed a model-based framework to support land-use and management decision-making. This framework integrates data and models to support an assessment of scenarios related to crop choices and irrigation management. The framework includes the IPCC models to describe nutrient losses, the Rothamsted carbon model to predict soil organic carbon and Cornel's Environmental Impact Quotient model to predict impacts from pesticides (fungicides, herbicides and insecticides). We used Monte Carlo simulations to quantify model uncertainties. Shaded arrays were used to communicate the uncertainties to end users of the framework. We parameterised our framework to explore outcomes for an irrigated agricultural area in a semi-arid region of Morocco. We used the framework to explore scenarios that were codesigned with farming stakeholders. The scenarios related to crop diversification, and to recent policies on the expansion of olive cultivation and the adoption of efficient irrigation technologies. For the outcomes considered (production, profitability, soil carbon, nutrient losses, pesticide impacts), there were clear trade-offs associated with the cropping system choice. Compared to the baseline scenario of rotated crops, olive production led to greater carbon sequestration (average 4 % increase by doubling olive production), reduced water use (average 3 % reduction by doubling olive production), and reduced emissions (average 42 % reduction by doubling olive production) but was less profitable and provided fewer edible calories. Additionally, olive cultivation was associated with higher environmental impacts from pesticides. Diversified systems, while less profitable, were associated with less harmful pesticide use. Drip irrigation was associated with positive outcomes for profit (average 23 % increase), water use (average 13 % reduction in water use), and reduced nitrogen leaching (average 40 % reduction) with negligible changes in other metrics. However, we did not account for factors associated with increased groundwater depletion. We conclude that such frameworks are a useful means for policy-stakeholders to explore the outcomes of their decisions, thereby, helping to minimise unintended consequences.