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Special episode · Data · West Africa · multiple sources
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Crop yields

Four crops that feed West Africa: maize, rice, sorghum and millet. Can their yields be predicted from multi-source data, climatic, socio-economic and agronomic?

See the code on GitHub →

A field of red-eared sorghum, as far as the eye can see.
Photo : Zarate123 · CC BY-SA 4.0
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Acte I · The context

1. e4 e5

Four crops that feed West Africa: maize, rice, sorghum and millet. And one regression question: can their yields be predicted from multi-source data, climatic, socio-economic and agronomic? All of it in R.

Acte II · The problem

2. Nf3 Nc6

Section in preparation

Acte III · The approach

3. Bc4 Bc5

The repository follows a seven-step pipeline, each step an R script:

  1. Integrating the data sources.
  2. Cleaning.
  3. Exploring the raw data.
  4. Exploring the cleaned data.
  5. Modelling on the raw data.
  6. Modelling on the cleaned data.
  7. Comparing both approaches.

that raw versus cleaned split is the interesting move of the project: measuring what cleaning actually changes, instead of assuming it

Section in preparation

Acte IV · The results

4. O-O

Section in preparation

Acte V · What I learned

5. d4

Section in preparation