|Abstract||This project was a continuation of previous SRDC/BSES grub monitoring projects in Mulgrave and Mackay, and aimed to value add the previous findings of the very high importance of thorough grub monitoring Grower involvement to spread the message was identified as a key factor in grub management, so the Herbert Cane Grub Management Group was formed through this Grower Group Project. This concept has proven to be very successful due to the active grower involvement, grower feedback and data collection, which aided in data interpretation, decision making and dissemination of information to the wider growing community beyond the actual growers in the project. 15 Herbert growers initially participated in the project , with six growers (Geoff Morley, Mario Porta and his two farm managers, Bert Bonassi, Frank and Alan White and Darren Harragon) being very actively involved. Staff from Herbert Productivity Services Ltd ( Graeme Holzberger, Lawrence Di Bella, Ash Benson and Ron Kerkwyk) as well as BSES Ltd was heavily involved. The actual field work and data collection was carried out by HCPSL and BSES staff with BSES entomologist Dr Nader Sallam and his entomology research team, processing and interpreting data and also making the district and farm predictions. 41 sugarcane blocks were used to monitor grub numbers and damage levels as well as to predict greyback cane grub numbers and potential damage across the Herbert district over 3 consecutive years (2010-2012). Some of these blocks were not sampled in 2011 due to the effects of 5 flood events and as many blocks had been left as standover in 2010.Due to the extreme weather associated events (cyclone Yasi and the we prolonged wet weather coupled with low grub numbers in dug fields which made the predicted grub numbers and the likely area that may be damaged the following year less reliable an extension of the project was requested and granted so data could be collected for the whole of the 2012 calendar year Emphasis had been placed on “Managing Grubs across the Whole Farm”. The growers mentioned above plus others in predicted “Likely damage Areas” had their predictions of future population dynamics and potential damage levels conducted for their whole farms. Strip trials with new product formulations, and comparing existing products were also undertaken after discussions with the growers within the grower group. Predicting the future grub dynamics and damage levels was made possible through the prediction models developed by Dr Frank Drummond, Maine University, USA. Dr Drummond used monitoring results generated through previous GrubPlan projects to build and develop the models. During the 3 seasons the selected blocks were dug for grubs. All grubs found were identified and recorded and then raised in the laboratory in the Herbert and Meringa and regularly checked for diseases. Various other factors were recorded (these are recorded in the methodology section) and results entered into the prediction models. The predictions and damage estimates that were generated for each season were discussed with growers at GrubPlan meetings and during one on one extension activities. Grower’s actions for managing their farms grub issues were recorded and compared to the BSES recommendations. This project proved to be very successful as it engaged the growers in a planned approach to grub management, reinforced the need for continual monitoring of population dynamics, and also raised the profile and awareness of grub levels and damage across the whole Herbert region. Previous to this, management tended to be reactionary with the rise and fall in insecticide treatment following the rise and fall in area damaged. This project has let to growers and indeed whole of districts treating to prevent grub damage, based on the predictions of increasing grub damage. Most Herbert growers can see the benefits of the current project in assisting to predict grub damage as well as assist in selecting areas at highest risk to treat, and seek to continue this work as a part of the district work program.