Infrastructure and societal pressures drive growth within the IVM market

Global Industrial Vegetation Management
Global Industrial Vegetation Management

Sales of pesticides to the segments of the industrial vegetation management (IVM) market covered in this study are estimated at over $490 million at the manufacturers’ level in 2017. This represents an average annual increase of 0.7% from sales in 2013. Herbicides, including aquatic herbicides, constitute the largest product category in 2017, accounting for over 95% of the total sales.

Rangeland and pastureland is the largest segment covered in this study, with estimated pesticide purchases valued at over $200 million at the manufacturers’ level in 2017, representing over 40% of total sales to the IVM segment. Roadways ranks second with over 15% of the total.

The overall picture for the pure IVM sector is one of a steady state infrastructure, comprising roadways, railroads, and utility right-of-way that need vegetation control. The emphasis is on low budgets and, in some cases, societal pressures to minimize pesticide applications. Growth will come from a slow, steady spreading of noxious weeds, sporadic increases in invasive weed problems, or switching from mechanical to chemical controls to save money. This growth will be offset to some degree from the use of lower-cost generic versions of historically favorite products, although that trend may have just about reached its pinnacle.

In the range, pasture, and forestry sector, spending in the most likely forecast scenario depends more on the individual landowners’ or beef producers’ decisions to increase spending for vegetation or weed control in a geographic area controlled by the owner, who in turn acts, based on variables such as timber prices (forestry), beef prices, and weather (range and pasture).

As the market is free to spend based on a local return on investment, decision-making is less complex, and market growth is more likely to continue. Kline’s forecast projects a slow outlook over the five-year forecast period.