Development of a grain monitoring probe to measure temperature, relative humidity, carbon dioxide levels and logistical information during handling and transportation of soybeans

In Brazil, over 60% of soybeans are transported by truck from farms to processing, storage and export terminals. Poor road conditions, improper truck maintenance, overloading, and inefficient transfer of grain are major causes of transportation losses. With 1.7 million km of roads and only 20% of which is paved, there is an urgent need to exploit opportunities for reducing postharvest losses from truck transport of grains in Brazil. This study describes the design, fabrication and testing of custom instrumentation for recording grain conditions and logistics during short-haul truck transport from farms to storage. Robust, low-cost instrumented probes containing four vertically-arranged chambers, with on-board data logging and global positioning system capability, were developed and deployed in the Sinop, Mato Grosso region. Representative results from five of 61 trips recorded over two harvest seasons (2014, 2015) are presented to demonstrate the utility of the methodology. Climatic conditions for these periods were: dry bulb temperature, 19.7-35.1 °C and relative humidity, 23.6-94.7%. Soybean harvest moisture content ranged from 10.8% to 25.7% (w.b.). Inside the trailer, grain temperatures remained relatively uniform throughout the trailer and were near to maximum daily ambient temperatures; relative humidity levels remained relatively uniform during transport. Carbon dioxide (CO2) levels resulting from grain respiration ranged from 2,000 to 200,000 ppm and tended to accumulate at the bottom of the trailers over time. Low CO2 levels were also observed with drier beans, shorter trips and fewer damaged beans. Trip durations ranged from 1.1 to 15.8 h, with variability induced by weather (rainfall events) and queues at the storage facility. The instrumented probes developed for this project proved robust, cost-effective and capable of acquiring significant new information to generate baseline information related to postharvest loss during transport.