A Quantitative Analysis for Improving Harvest Productivity for Biomass Crops

Harvest cost is a major concern for making biomass a viable option. Unproductive time in-field significantly contributes to this cost. Variability of harvest timeliness is largely due to maneuvering equipment in-field, operator experience, equipment failures, and field and crop conditions, among other reasons. These are particularly important for farm management to know how to best handle interruptions during harvest. Consequently, there is a serious need to better account for harvest untimeliness. For this research, the crops of interest are Miscanthus and shrub willow. These crops are attractive for several reasons. They do not compete with cash crops because they grow on marginal land and have the potential normalize feedstock qualities. In general, three aspects of harvest productivity will be focused on, which include: equipment maneuverability at the headlands, operator performance, and equipment reliability. More specifically, maneuvering equipment during harvest operations can have a significant impact on production cost; therefore, the fieldwork pattern is critical for optimal productivity and a cost-efficient harvest. Harvest pattern influences time wasted due to excessive unproductive time and distances traveled during operational tasks. Equipment is maneuvered at the skill of the operator. Often, operator experience is a bottleneck for operations and a key factor influencing productivity. In addition, unproductive times are largely due to repair and maintenance on the equipment caused by unexpected harvest complications. The uncertainty of these factors cause inconsistency in productivity. It is crucial to achieve optimum harvest efficiency for the feasibility of the biomass supply chain. Evaluating these aspects will allow us to better understand and model for these limitations

Decision making for Farmers: A Case Study of Agricultural Routing Planning

Agricultural business is shifting to a stronger integration of information technology and data analysis to optimise the management and operations of small- and large-scale farms. In particular, computer support for decision-making is critical for farmers who want to decrease the cost of operations and control their (semi-)automated fleet of agricultural machines. This paper develops an optimisation module for decision support in Agricultural Routing Planning (ARP). The output is expected to help farmers to decide on the most efficient route for their harvesting machines. Specifically, the aim of this study is to contribute to optimisation solutions by introducing a new methodology called a Lovebird Algorithm, to address the routing problem. The Lovebird Algorithm acts as an optimisation tool to screen alternatives and focus only on efficient ones. The experimental results show that the proposed algorithm can save 8% of the non-working distance compared to the Genetic Algorithm and Tabu Search

Analysis of Influencing Factors and Decision Criteria on Infield-Logistics of different Farm Types in Germany

In future increasing production efficiency in agriculture will not only be achieved by rising machinery working widths but more and more by optimization of entire production process chains. The more machines are interacting the higher will be the specific optimization potential.Navigation not only to the fields but also within the fields will certainly contribute to make use of these efficiency reserves. Necessary therefore is the knowledge of potential influences on infield-logistics to be able to navigate agricultural machinery in the fields effectively and process optimized.Preliminary studies based on GPS-lane analysis in different German agricultural regions and in Central Canada show that decisions on specific infield patterns to a certain degree depend on unchangeable factors such as field geometry or field access points. Nevertheless regarding infield-logistics farm managers and staff members mostly act farm specifically as well as depending on technology or situation and furthermore often intuitive.The examination is based on expert interviews with farmers of all agricultural regions in Germany. Rural mixed farms with simple machinery are considered as well as large agricultural cooperatives which farm thousands of hectares using track guidance and other electronic assistance systems. By aerial images of their arable land the individual decision behavior should be analyzed to specify the “soft” influencing factors.First results show that farm managers using guidance tracking or SectionControl increasingly attune their infield-logistics to direction giving obstacles such as power lines. Livestock farmers rather focus on the application of organic manure, where road conditions and possible field access points become important due to the required supply logistics. Sugar beets make great demands on infield patterns because of relatively low bunker sizes compared to the mass to be transported as well as the positioning of the beet clamp.Afterwards the obtained influences can be integrated into a navigation tool for optimizing infield logistics. Thus process efficiency can be further increased