Analysing the impact of weather, including extreme weather events and climate change, on Queensland's agricultural industries.

Taryn

Hypotheses

• Flood data will be highly seasonal from year to year
• La Nina / El Nino multi-year cycles will be evident across multi-year cycles
• The frequency of floods will increase over the last few decades

Data sources

• Open Meteo Flood API (GloFAS), 1990-2020, Queensland

Visualisation

• Seasonality time series
• Autoregression

Anna

Hypotheses

• Excess flooding will decrease cattle head counts
• Excess flooding will increase cattle prices

Data sources

• Open Meteo Flood API (GloFAS), yearly 1990-2020, Queensland
• Ag Output / price data:
• Saleyard Prices 1991-2017
• Herd Size 1974-2021

Visualisation

• Time series line graphs
• Regression

Van

Hypotheses

• Hotter weather leads to more flooding (more severe, more frequent)

Data sources

• Open Meteo Flood API (GloFAS) 1990-2020
• Open Meteo Weather API

Visualisation

• Time series
• Linear regression

Tom

Hypotheses

• Rising global temperatures will increase sugar cane yields in cooler climates

Data sources

• Open Meteo Weather API
• Australian Sugar Milling Council production data (2014 to 2021)
• Open Meteo Climate Change forecast API (from 2040)

Visualisation

• Prediction of sugar cane yields in Queensland

Each of the above questions has been investigated in its Jupyter notebook, as follows:

• Q1 Flooding Over Time (Taryn).ipynb
• Q2 Flooding on Cattle Industry (Anna).ipynb
• Q3 Temperature and Flooding (Van).ipynb
• Q4 Sugarcane and Climate Change (Tom).ipynb

The questions utilise data saved in the /Data directory, primarily flooddata.csv. This data table has been retrieved from the Open Meteo API in ingest.ipynb, using tooling exposed in queryscripts.py.

Other files stored in the /Data directory include:

• ORIGINAL_abs_cattle_totals_2020.xlsx - herd size file downloaded from Australia Bureau of Statistics
• abs_cattle_totals_2020.csv - excerpt taken from original herd file
• abs_cattle_totals_2020_transform.ipynb - notebook that transforms herd size excerpts and creates abs_qld_cattle_counts_excerpt.csv
• abs_qld_cattle_counts_excerpt.csv - used in the analysis in Q2
• abs_meat_prices.xlsx - original file of saleyard prices downloaded from Australian Bureau of Statistics
• abs_meat_prices_saleyard_prices_excerpt.csv - cleaned up excerpt from original saleyard prices file that is used in the analysis in Q2
• sugar_weather_future.csv and sugar_weather_history.csv - weather data exported via queryscripts.py
• sugar_raw_yield_data.csv - retrieved from the Australian Sugar Milling Council, and reshaped into sugar_production.csv in Q4

Plots have also been exported to /visuals

• Open Meteo Global Flood API
• Open Meteo Weather API
• Open Meteo Climate Change API
• Agricultural Data: Saleyard Prices, Outlooks and Herd Size

The environment requirements is stored in environment.yml and can be recreated with conda env create -f environment.yml.

Saved as Flood_Analysis_final.pptx