https://github.com/jabulente/soil-health-compliance-analysis

A data analysis project evaluating soil health indicators to assess their compliance with established agronomic standards. Includes data cleaning, statistical analysis, and insights for sustainable soil management.
https://github.com/jabulente/soil-health-compliance-analysis

agriculture agronomy ai data-science jabulente machine-learning matplotlib-pyplot python python3 real-world-project scipy-stats seaborn seaborn-plots soil-properties soil-science statistics ttest

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A data analysis project evaluating soil health indicators to assess their compliance with established agronomic standards. Includes data cleaning, statistical analysis, and insights for sustainable soil management.

• Host: GitHub
• URL: https://github.com/jabulente/soil-health-compliance-analysis
• Owner: Jabulente
• License: mit
• Created: 2025-07-08T09:29:23.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-07-08T09:39:49.000Z (4 months ago)
• Last Synced: 2025-07-08T10:39:02.513Z (4 months ago)
• Topics: agriculture, agronomy, ai, data-science, jabulente, machine-learning, matplotlib-pyplot, python, python3, real-world-project, scipy-stats, seaborn, seaborn-plots, soil-properties, soil-science, statistics, ttest
• Language: Jupyter Notebook
• Homepage:
• Size: 9.77 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


# 🧪 Analysis of Soil Health Compliance with Agronomic Standards

This repository presents a data-driven assessment of soil health by analyzing various physical, chemical, and biological soil parameters and evaluating their compliance with standard agronomic benchmarks. The project aims to support sustainable land management and precision agriculture by identifying nutrient deficiencies, soil imbalances, or potential risks to crop productivity.

The analysis utilizes real or simulated soil test data, applying descriptive statistics, visualizations, and threshold-based evaluations to determine whether soil samples meet agronomic standards for essential factors such as pH, nitrogen (N), phosphorus (P), potassium (K), organic matter content, electrical conductivity, and others.

---

## 📌 Objectives

* Assess the quality of soil samples against agronomic benchmarks.

* Identify soil health issues such as nutrient imbalance or acidity.

* Provide actionable insights for soil improvement and crop planning.

* Promote evidence-based recommendations for sustainable farming.

---

## 📂 Project Structure

```

Soil-Health-Compliance-Analysis/

│

├── data/                     # Raw and processed soil datasets

├── notebooks/                # Jupyter notebooks for analysis and visualization

├── scripts/                  # Python scripts for data processing and automation

├── reports/                  # Generated reports and visual summaries

├── results/                  # Output files and evaluation results

├── requirements.txt          # Python dependencies

├── README.md                 # Project overview and documentation

└── LICENSE                   # License information

```

---

## 📊 Key Features

* ✅ **Data Cleaning & Preprocessing**: Handling missing values, unit conversion, and variable standardization.

* 📈 **Descriptive & Comparative Analysis**: Statistical summaries and comparison with agronomic threshold values.

* 🌿 **Soil Health Index Scoring**: Classification of soil condition based on key nutrients and properties.

* 🧩 **Visualization Tools**: Heatmaps, histograms, bar plots, and compliance dashboards.

* 💡 **Interpretation & Recommendations**: Data-backed insights for improving soil fertility.

---

## 📘 Agronomic Standards Referenced

The analysis relies on internationally recognized and regionally adapted agronomic guidelines, including:

* FAO Soil Fertility Standards

* USDA Natural Resources Conservation Service (NRCS)

* Tanzanian Agricultural Research Institute (TARI) benchmarks

* Peer-reviewed soil science literature

---

## ⚙️ Installation & Setup

1. **Clone the repository:**

   ```bash

   git clone https://github.com/yourusername/Soil-Health-Compliance-Analysis.git

   cd Soil-Health-Compliance-Analysis

   ```

2. **Install dependencies:**

   ```bash

   pip install -r requirements.txt

   ```

3. **Launch Jupyter Notebook:**

   ```bash

   jupyter notebook

   ```

---

## 📈 Sample Parameters Analyzed

| Parameter          | Description                    | Ideal Range |

| ------------------ | ------------------------------ | ----------- |

| Soil pH            | Acidity/alkalinity level       | 5.5 – 7.5   |

| Organic Matter (%) | Soil fertility indicator       | > 2.5       |

| Nitrogen (N)       | Macronutrient for plant growth | ≥ 0.2%      |

| Phosphorus (P)     | Root and fruit development     | ≥ 15 ppm    |

| Potassium (K)      | Disease resistance and growth  | ≥ 120 ppm   |

---

## ✅ Usage

You can reuse or extend this project to:

* Conduct regional soil fertility assessments

* Monitor the effectiveness of soil management practices

* Support decision-making in agricultural extension services

* Train machine learning models for predictive soil diagnostics

---

## 🔍 Future Improvements

* Integration of **geospatial data** for mapping soil quality

* Development of **interactive dashboards** using Plotly or Streamlit

* Machine learning classification of **soil fertility classes**

* Multi-season data integration to track changes over time

---

## 📄 License

This project is licensed under the MIT License – see the [LICENSE](./LICENSE) file for details.

---

## 🤝 Acknowledgments

Special thanks to:

* Soil science experts and agronomists for benchmark standards

* Local agricultural institutions and farmers for sample data

* Open-source contributors and the Python data community