Crop Modeler at Gap Recruitment Services Limited

Overview of the Role

We are seeking a passionate and skilled Crop Modeler to join the team and play a vital role in utilizing sophisticated crop models for diverse purposes. These include assessing risk for informed policy development, supporting underwriting and claims teams, and generating impactful agronomic advisories for smallholder farmers. Your dedication will contribute to both the success of our company and the improved livelihoods of these farmers.

Education and Experience

• At least a masters degree Soil Science, Crop Science, mathematics, mathematical biology, plant physiology, meteorology/agronomy, geo-spatial science/statistics/climate/data science with some experience in applied sciences such as Agronomy, Soil Science or a quantitatively oriented science, or a closely related field, with a strong emphasis on crop modeling.
• Minimum of four (4) years of verifiable experience in crop modeling and simulations, preferably in data-limited environments.
• Proven track record of data analysis, statistical modeling, and interpreting complex datasets.

Knowledge & Capabilities

Crop Modeling:

• In-depth understanding of various crop models (e.g., DSSAT, APSIM, AquaCrop, WoFost, CropSim) and their underlying principles.
• Familiarity with different types of models (empirical, mechanistic, statistical) and their strengths and weaknesses in simulating crop growth and yield under various conditions.
• Proficiency in using crop modeling software and tools, including statistical software (e.g., R) and programming languages (e.g., Python).
• Ability to operate specialized software for running simulations, calibrating models to specific regions and crops, and interpreting outputs.
• Knowledge of factors influencing crop growth and yield, including environmental factors (weather, soil, pests, diseases) and agronomic practices (planting decisions, fertilizer application, irrigation).
• Experience with modeling and forecasting yields, climatic and/or environmental systems.
• Experience using Earth Observation data for crop detection and estimation of crop-specific acreage.
• Strong understanding of database architecture, statistics, data visualization, etc.

Data Analysis and Interpretation:

• Ability to analyse large datasets from various sources (weather stations, remote sensing, yield records) and translate them into meaningful insights for model parameterization and validation.
• Expertise in working with large datasets and the ability to integrate and interpret multi-thematic data.
• Experience in Big Data handling and coding for automation.

Agricultural Insurance and Risk Assessment (Desirable):

• Familiarity with agricultural insurance products and risk assessment methodologies.
• Understanding of how insurance companies manage risk associated with agricultural production, loss estimation methods, and claim settlement processes.
• Ability to translate crop model outputs into actionable risk insights.

Communication and Collaboration:

• Effectively communicate complex scientific concepts to both technical and non-technical audiences.
• Ability to work independently and as part of a multidisciplinary team.
• Co-produce peer-reviewed research articles.

Key Responsibilities

Expected Deliverables and Outcomes

• Accurate and reliable crop models for various crops and regions.
• Comprehensive risk assessments and yield predictions.
• Practical agronomic advisories tailored to smallholder farmers.
• Improved decision-making for agricultural stakeholders.
• Enhanced resilience of smallholder farmers to climate change.

Desirable Skillset

• Experience with specific crop models such as DSSAT, APSIM, AquaCrop, WOFOST, EPIC, CropSim, The Hybrid-Maize model or other relevant models, demonstrating the application of the skills
• Programming skills (e.g., Python, R) for data analysis and potentially modifying or developing crop models.
• Geographical information systems (GIS) experience for analysing spatial data and mapping risk across regions.
• Statistical knowledge for analysing field trial data, model validation, and uncertainty quantification.
• Understanding of climate change impacts and their potential effects on crop production and insurance risks.
• Understanding of climate change impacts and their potential effects on crop production and insurance risks.
• Quantitative and analytical skills for interpreting model outputs, assessing uncertainties, and making data-driven decisions.