The Agricultural Risk Assessment Engine was designed to assess risks associated with agricultural credit, integrating diverse data sources such as local epidemics, pest outbreaks, topography, climate and meteorology. Developed by Group 4, this tool calculates a specific risk index for each region, helping financial institutions make informed decisions. The model is intended for direct use in assessing agricultural credit risks and is not suitable for non-agricultural applications. Accuracy depends on the quality of the input data, and there are potential biases and limitations that can affect performance. It is recommended that users regularly update and validate the model with new data to ensure its accuracy. The template is simple to implement, with sample code provided to begin a risk assessment. It was trained on diverse datasets and demonstrated high accuracy in risk prediction, significantly improving traditional methods.

Model Card for Agricultural Risk Assessment Engine

This model card describes the Agricultural Risk Assessment Engine, developed to evaluate and mitigate risks in agricultural credit. The engine integrates various data sources to generate specific risk values for regions, aiding institutions in making informed decisions.

Model Details

Model Description

The Agricultural Risk Assessment Engine is an innovative tool designed to evaluate risks for agricultural credit. It integrates a wide range of data sources, including information on local epidemics and pest outbreaks, topography, climate, and meteorology. By analyzing these data, the engine calculates a specific risk index for each region, helping financial institutions and other stakeholders make informed decisions.

Developed by: Grupo 4
Model type: Risk Assessment
Language(s) (NLP): Not Applicable
License: MIT

Uses

Direct Use

The model can be directly used by financial institutions to assess the risk associated with agricultural credit in different regions.

Out-of-Scope Use

The model is not suitable for non-agricultural risk assessments or any applications outside the scope of agricultural and environmental data.

Bias, Risks, and Limitations

The model's accuracy is dependent on the quality and comprehensiveness of the input data. Biases in data sources can affect the model's performance. The model may not account for unforeseen environmental changes or rare events.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Regular updates and validations with new data are recommended to maintain accuracy.

How to Get Started with the Model

You can the code below to get started with our model.

from risk_assessment_engine import RiskEngine
engine = RiskEngine()
risk_score = engine.evaluate(region_data)
print(f"Risk Score for the region: {risk_score}")

Training Details

Training Data

The model was trained on diverse datasets, including meteorological data, topographical information, and records of local pest outbreaks.

Training Procedure

The training procedure involved data preprocessing, integration of multiple data sources, and the use of advanced algorithms to analyze and calculate risk indices.

Training Hyperparameters

Training regime: [More Information Needed]

Testing Data, Factors & Metrics

Testing Data

Testing data included a diverse set of regions with known historical data on pest outbreaks, climate conditions, and agricultural productivity.

Factors

The evaluation considered factors such as the frequency and severity of pest outbreaks, variations in climate, and topographical features.

Metrics

Metrics included accuracy of risk prediction, false positive rates, and the robustness of the model in different environmental conditions.

Results

The model demonstrated high accuracy in predicting risk levels, with significant improvements over traditional risk assessment methods.

Summary

The Agricultural Risk Assessment Engine provides precise and actionable insights for agricultural credit risk, enabling better decision-making and risk mitigation strategies.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).