Why IMO?

Maximize net profits when the water supply is expensive or limited.

Water is yield. Every field is different. IMO uses predictive analytics to calculate, pre-test, and target the most profitable irrigation strategies field by field.

The Economic Optimum

The first 10% and the last 10% of water applied to a crop have very limited impacts on yields. It’s the irrigation in between that makes the difference between profits and losses.

The graphic above shows a typical water production function (this one is for wheat). When a field is fully irrigated (top of the curve) the last increments of applied water produce relatively little yield. If a farm has more land than water, the last 10% or 20% might be better used on additional land. IMO models and calculates these relationships field by field. It defines and schedules irrigation strategies to target any point on the curve.”

What is IMO?

IMO is an analytical tool that models and evaluates irrigation system performance for the unique conditions of a field and farm. It analyzes the multiple variables, constraints, and opportunities that influence how irrigation impacts the bottom line.

Once calibrated to a field, it calculates how different amounts of applied water will impact yields. It is then used to pre-test how different irrigation timing strategies will influence soil moisture volume at any point in the season. It has a high degree of predictive accuracy when targeting different amounts of irrigation.

“…IMO reduces uncertainty when planning and targeting water.”

Originally a research tool, IMO reduces uncertainty when planning and targeting water. It is used to maximize net returns to irrigation, season to season, and over the life of the asset. It is adaptable to any irrigation system.

Predictive Analytics when Water is Expensive or Limited

IMO defines the productivity of water per field or management unit. This is used to identify optimal water use volumes based on the economics of the farm. The user determines how much water to target. IMO translates this into highly accurate, full-season schedules.

For further explanation check out these case studies >