PAM is a bio-economic model focussed on simulations of Palmer amaranth control practices that can be used to control this devastating weed in US crop production farming systems. PAM was produced at the University of Arkansas (M Bagavathiannan now at Texas A & M), particularly involving the program led by Jason Norsworthy.
In a welcome collaboration, the PAM model has its genesis in the ryegrass bio-economic control RIM model developed here at the University of Western Australia (Pannell et al 2004 and subsequent Lacoste & Powles papers). However, extensive work in Arkansas was required to create the PAM model.
The PAM model, like the RIM model, can be very useful in simulating the effects of control practices on Palmer amaranth long-term dynamics in farming systems and the economic impacts within US farming systems. As such the PAM model can be of assistance in simulating and ultimately hopefully contributing to achieving sustainable control of the very damaging Palmer amaranth in US cropping.
Palmer amaranth is the most troublesome weed problem in mid-southern US crop production. Herbicides continue to be the most commonly employed method for managing Palmer amaranth, despite the weed’s widespread resistance to them. Therefore, farmers need research and extension efforts that promote the adoption of integrated weed management (IWM) techniques.
Producers, crop consultants, educators, and researchers would be more likely to deploy diversified chemical and nonchemical weed management options if they are more informed about long-term biological and economic implications via user-friendly decision-support software.
Described within is a recently developed software that demonstrates the effects of Palmer amaranth management practices on soil seedbank, the risk of resistance evolution, and economics over a 10-year planning horizon. Aiding this objective is a point-and-click interface that provides feedback on resistance risk, yield potential, profitability, soil seedbank dynamics, and error checking of management options.