Corn stover is one of the most abundant agricultural residue, typically yielding 8 to 10 t of dry matter (DM)/ha. Leaving stover on the ground is beneficial for erosion control and soil organic matter renewal. However, several studies suggest that 50% of stover could be removed sustainably under appropriate conditions (negligible slope, no-till system, adequate rotation). Two major factors limit corn stover removal: cost of harvest and transport, and final end use. This paper addresses the first factor by considering several harvest options, mainly related to spring harvest. The objective is to develop a corn stover harvest, transport and storage simulation model by a systems approach. The model takes into account field machinery, field area, yield, distance and timeliness of operations. The weather data are used to take into account delay in stover collection. Empirical data collected during in-field operations, transport and handling are used as input for the dynamic simulation model. Other input data include DM loss and stover moisture content. The model is implemented using Extendsim®. The main operations modeled are: corn stover mowing, corn stover windrowing, baling, loading trailers in the field, transport of bales, and unloading bales at the point of use. The model considers sharing available equipment and tractors among different operations modeled to carry out the whole corn stover harvest. The model can operate with either a low level or a high level of input data. The low level data allow deterministic modeling, with average work times. The high level data allow stochastic modeling, with statistical distribution of work times. For example at windrowing, low level of input includes tractor, windrower width, capacity and labour. High level of input requires in addition the windrower speed, the turning time, the type of turning, and the field pattern. Output from the model includes resources required and total operation cost. The model will be useful in identifying total system cost, the biomass retrieved, the bottlenecks and potential improvements.
Modeling corn stover harvest operations
BERRUTO, Remigio;BUSATO, Patrizia;
2010-01-01
Abstract
Corn stover is one of the most abundant agricultural residue, typically yielding 8 to 10 t of dry matter (DM)/ha. Leaving stover on the ground is beneficial for erosion control and soil organic matter renewal. However, several studies suggest that 50% of stover could be removed sustainably under appropriate conditions (negligible slope, no-till system, adequate rotation). Two major factors limit corn stover removal: cost of harvest and transport, and final end use. This paper addresses the first factor by considering several harvest options, mainly related to spring harvest. The objective is to develop a corn stover harvest, transport and storage simulation model by a systems approach. The model takes into account field machinery, field area, yield, distance and timeliness of operations. The weather data are used to take into account delay in stover collection. Empirical data collected during in-field operations, transport and handling are used as input for the dynamic simulation model. Other input data include DM loss and stover moisture content. The model is implemented using Extendsim®. The main operations modeled are: corn stover mowing, corn stover windrowing, baling, loading trailers in the field, transport of bales, and unloading bales at the point of use. The model considers sharing available equipment and tractors among different operations modeled to carry out the whole corn stover harvest. The model can operate with either a low level or a high level of input data. The low level data allow deterministic modeling, with average work times. The high level data allow stochastic modeling, with statistical distribution of work times. For example at windrowing, low level of input includes tractor, windrower width, capacity and labour. High level of input requires in addition the windrower speed, the turning time, the type of turning, and the field pattern. Output from the model includes resources required and total operation cost. The model will be useful in identifying total system cost, the biomass retrieved, the bottlenecks and potential improvements.
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