Development of a climate-based computer model to reduce wheat harvest losses in Australia

Grain harvest represents a period of high risk and is also a bottleneck in a grain production. This study develops a climate-based systems simulation model to investigate the economics of high moisture grain harvesting in Australia. The optimum harvesting and drying strategies were determined. The role of grain aeration cooling was also examined. The model software was developed in MATLAB. This model was run on an hourly basis using 15 years of historical weather data (1991-2005) for three main wheat production areas in Australia, represented by Goondiwindi (QLD), Tamworth (NSW) and Scaddan (WA). The Wheat Harvest System Simulation Model (WHSSM) consists of four submodels of weather data, machinery performance, crop loss and economic calculations. Each submodel is represented by mathematical functions and supported by available theoretical and field data. The weather submodel is used to predict dynamic grain moisture contents for a standing crop in the field. Machinery submodel was developed to calculate machinery performance and its operating costs at different grain and weather conditions. The main machinery involved are combine harvester, cooling aerator, and four categories of grain driers. Crop loss submodel is used to quantify grain losses involved during harvest and storage periods, including shedding (yield) losses, header losses, threshing losses, crop quality downgrading losses (due to rainfalls), and storage spoilage losses. The model has been used to predict and compare the possible return for different harvesting and postharvest management strategies. For the reference case (a 1000 ha farm with a high-capacity harvester and medium-capacity drier in Goondiwindi), it is found that the optimum harvest moisture content for using continuous flow drier and batch drier is 14 and 13% (wet basis) respectively. For aeration simulation, it is found that the use of an aeration cooling system would slightly increase grower’s return when the drier capacity is inadequate. No positive impact can be achieved on return if growers use either high or medium capacity driers. Generally, high capacity harvester travelling at lower speed is preferred. It is also demonstrated that local weather conditions/rainfall patterns can have a very significant influence on grower returns. Growers in dry and warm location (e.g. Goondiwindi) will gain better return. It is predicted that at the given model control values, the long-term optimum harvest moisture contents for Goondiwindi, Scaddan and Tamworth are 14, 15 and 17% respectively.