The Client

BDC was engaged by an Agricultural Consultancy firm to assist with their client who was a large Vinyard operator within the Margaret River region of Western Australia.

They had a significant size estate, although some fields were under delivering on their target yield.

They approach BDC to help analyse their time series data to find the underlying root cause required to develop a strategy to increase the yield.

The Challenge

The client was experiencing challenges on irrigation practices and water management relating to the depth of soil moisture. Their existing data analytics methods (correlational) gave only narrow and limited insights and did not identify multiple cause and effect relationships. This limited their ability to reliably and precisely identify the factors that have a strong causal impact on others and ‘when’.

The Solution

The client was able to provide time series data including 12 different meteorological factors (temperatures, wind strength/ direction, humidity, etc) and five soil moisture measures. These were ingested within the Causal AI Engine and using minimum coefficient and confidence levels we identified true causal relationships within the dataset.

The Results

Results enabled the client to more precisely advise which factors (not only singularly but in combination and ‘when’) had the most impact on the depth of soil moisture for different soil types and crops.

This helped the client establish an optimal irrigation regime and timing of water management to optimise yield from the underperforming field and reduce water wastage.