Investigation into converting a combine grain loss signal into a grain loss rate
Term: 1 year, beginning in 2017
Status: Complete (CLICK HERE for the performance story)
Funding Amount: $ 23,848
Lead Researcher(s): Derek Rude (Prairie Agricultural Machinery Institute – PAMI)
Funding Partners: Saskatchewan Ministry of Agriculture - Agriculture Development Fund (ADF); Saskatchewan Pulse Growers (SPG) and Saskatchewan Canola Development Commission (SCDC)
Project Description
The project is designed to assess whether current loss monitor sensing systems in combines can accurately provide an absolute loss level with reasonable accuracy.
Grain loss monitoring technology for combines has experienced minimal advancement since being introduced in the market around 1975. A real-time indication of actual grain loss to the operator and farm manager is needed to keep harvest losses as a priority for producers, and to give them a tool to help them optimize the settings and ground speed of their combines. This research is targeted to jumpstart the advancement of current grain loss monitoring technology and will build on the previous research findings by providing the exploratory work necessary for the future development of a tool that combine operators and farm managers can use to quantify harvest losses in real time across every acre of their farm.
The specific objective is to correlate existing harvester loss sensor data with actual loss curves to optimize the harvest loss sensor and to determine if current loss sensing technology is adequate and to decrease harvest losses for all Saskatchewan crops.
The research results from this project will support potential commercial development of a grain loss indication system that displays the loss rate in bushels per acre and dollars per acre, as well as provides the capability to log harvest loss data along with yield data. The loss data could easily be made available to mobile devices via existing telematics systems. The combination of harvest losses and harvested yield are needed to understand the true available yield for specific field areas and improve precision agriculture practices.