The many years of on-farm data, yield maps and fibre quality data collected by growers and gins have the potential to be utilised to better understand the variability experienced in Australian cotton systems. These diverse datasets describe, and help understand the causes of variability; from why yield or fibre quality is high or low, or the distribution of nutrients in the soil. Big data analytics and machine learning present a promising avenue to process this data into a useful format for growers.

This project supports a PhD student to assess this cotton yield and fibre quality variability through the use of data science with the aim to understand the causes of variation within-fields. This then will assist growers' decision making regarding management practices.