Case Study Overview
An agricultural consultancy advises farmers about irrigation practices and timing of water management. A focus was on influencing the depth of soil moisture. Their data analytics methods (correlational) were helpful but gave only narrow and limited insights. They did not identify multiple cause and effect relationships in data enabling management to more reliably and precisely identify the factors that have a strong causal impact on others and when, predictively to improve crop yields.
Twelve different meteorological factors (temperatures, wind strength/ direction, humidity, etc) and five soil moisture measures were input to the BDC algorithms.
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 and when.
One causal driver was identified by the our analysis. This single driver was shown to strongly and adversely impact water quality. This was unexpected information not know before.