Effects of climate change on agriculture.

Introduction

Study on effects of climate change on global food production from 1961-2014.

In the coming decades the agricultural sector faces many challenges stemming from growing global populations, land degradation, and loss of cropland to rapid urbanisation. Although food production has been able to keep pace with population growth on the global scale, periodically there are serious regional deficits, and poverty related nutritional deficiencies affect close to a billion people globally. In this century climate change is one factor that could affect food production and availability in many parts of the world, particularly those most prone to drought and famine.

Therefore, it is important to study the effects of climate change on global food production.

Contents

1. Need and Scope of Project
2. Objectives
3. Instructions to Run
4. References

Need and Scope Of Project

The objective of this project is to identify and understand the global consequences of climate change on crop yields, gross production and agricultural land use patterns. During the course of the project, we have applied programming knowledge to achieve the following objectives:

• Dataset being imported as CSV, and viewing it as a pandas dataframe,
• Manipulating it using Pandas and Numpy for feature selection.
• Implementing for loops and user defined functions to obtain parseable data structures.
• Using standard data visualisation libraries such as Matplotlib and Seaborn to represent the data graphically.

Objectives:

Visualizing trends and patterns how climate change affects agriculture.
This is a part of Class XII Informatics Practices (065) Project Work.

Instructions to Run

1. Install Python 3.6 or above (The project should work on lower versions as well but has not been tested on them).
2. Install pandas, matplotlib, seaborn and numpy using pip or conda. e.g. $ pip install pandas, matplotlib, seaborn, numpy
3. Install jupyter, you can take help from the docs here
4. Clone this repo using git clone or downloading zip from browser.
5. Navigate into the project folder and run $ jupyter notebook.
6. Select the climate_change_visualize.ipynb file.

Collaborators:

• Ayush Anand: Data Mining, Processing and cleaning
• Rida Yumn Ahmed: Data Visualization

References

1. Pandas Documentation - Pandas 1.2.0 documentation [https://pandas.pydata.org/pandas-docs/stable], The Pandas Development Team
2. John Nguyen, Mingda Tang, Stacey Chen, Atreya Iyer, Nick Rubel, Patrick Hogan NASA Ames Research Center, [https://github.com/WorldWindLabs/AgroSphere]
3. Seaborn 0.11.0 Documentation, Michael Waskom and the seaborn development team, https://doi.org/10.5281/zenodo.592845/