Article

Optimal management of molds in stored corn

Department of Agricultural Economics, Purdue University, 403 West State Street, West Lafayette, IN 47907, USA; Department of Grain Science and Industry, Kansas State University, 201 Shellenberger Hall, Manhattan, KS 66506, USA; Department of Botany and Plant Pathology, Purdue University, 915 West State Street, West Lafayette, IN 47907, USA; Department of Entomology, Purdue University, 901 West State Street, West Lafayette, IN 479070, USA; Department of Agricultural and Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN 47907, USA
Agricultural Systems DOI:10.1016/j.agsy.2008.07.003 pp.220-227

ABSTRACT Long term storage of corn is becoming more common due to the recent increase in the demand for corn by ethanol plants. Infection of maize kernels by toxigenic fungi remains a challenging storage problem despite decades of research. Experts in storage management propose the use of a combination of preventive and monitoring-based responsive strategies in response to mold risks. In this paper, a stochastic dynamic programming model is solved to determine the expected profitability and optimal combination, timing, and intensity of the proposed mold management strategies using farmers’ existing infrastructure. The results show that even with relatively high monitoring costs, maintaining high quality grain using a monitoring-based optimal mold management strategy costs less than the benefit it fetches. The current typical practice by Indiana farmers of aerating the grain until the end of December and doing nothing thereafter bears a high risk of economic losses if grain is to be stored until later during the summer. Generally, the optimal mold management strategy depends on monitoring the biophysical conditions of the grain and the time period under consideration. If the in-bin temperature is high and less than 5% of kernels are mold damaged, then aerating when the outside temperature is at least 3 °C less than the in-bin temperature and continuing to store the grain is the optimal strategy.

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Keywords

biophysical conditions
 
current typical practice
 
decades
 
economic losses
 
expected profitability
 
in-bin temperature
 
Indiana farmers
 
mold risks
 
monitoring-based optimal mold management strategy costs
 
monitoring-based responsive strategies
 
optimal combination
 
optimal mold management strategy
 
optimal strategy
 
proposed mold management strategies
 
recent increase
 
stochastic dynamic programming model
 
storage management
 
term storage
 
time period
 
toxigenic fungi