A Tractor Efficiency Map
An Aid to Land-use Planning in the Coal Producing Counties
Lecture notes... See paper below
Many of us are interested in the concepts of H.T. Odum and the premise that energy can be the means for analyzing almost all systems and, more importantly, designing systems for humans that conserve energy, save costs, and improve criteria in the increasingly difficult decision-making processes.
This is a report on a low key effort in this direction, one which unifies computer based geographics into systems and rudimentary concept of energetics. The paper addresses one aspect of the suitability of land for a broad class of agricultural uses, namely limitations to the use of agricultural implements. Because so many agricultural enterprises require land suitable for generating tractor drawn tillage, cultivation, and harvesting equipment, one good measure of land suitability for these enterprises would be a measure of tractor efficiency. This efficiency is related to energy consumption. As energy costs increase, so do costs of post-mining land use operations. Expressing land suitability as a function of tractor efficiency, is, therefore, one way of relating land characteristics directly to operating costs.
Tractor efficiency is the % of hours used effectively. We based our ideas on Beek's 1978 book from The Netherlands, modifying his classes only slightly. Efficiency is related to different combinations of slope, relief, soil stoniness, soil texture, and effective soil depth.
These are all factors that can be mapped as we shall show. For example, slopes less than 20% but with moderate impediments due to stoniness or shallowness have an efficiency of less than 60%. Slopes less than 8% with slight impediment due to a sandy texture have efficiencies between 60 and 90%.
We used the 1954 soil survey of Wise County, the "Interpretive Guide to Soils of Southwest Virginia." We used digital ____ tapes of the USAS. The soil survey was digitized for half of Wise County at a scale of about 3 acres per cell.
Figure 1 shows the soil map for an area of about 47,000 acres. It demonstrates the range of variability for characteristics of interest. There are shown 19 different nameable soil types. The larges 3 categories are on 30,000 acres of Muskingum story loam, 3000 acres of Coeburn silt loam, and 7500 acres of Coeburn-Muskingum complex.
Figure 2 of the same area shown pre-mining stoniness. The lighter cells are areas in which the dominant soil type is described as stony by the Soil Survey. You will be quick to point out that most reclamation is stony. There are topsoil amendments and a variety of treatments possible so such maps may be adjusted. Here we were looking at total land use planning for an area owned primarily for mining purposes. WE were looking at the 7 mines in the context of larger potential, uses in the post-mine era.
Figure 3 shows surface soil texture. The darker the cell, the heavier the texture (meaning the more clay).
Figure 4 shows depth to bedrock. Lighter cells have depths less than about 40 inches.
Figure 5 shows slopes. The darker cells are the steeper slopes.
Figure 6 is the composite. The darker cells have higher efficiency than lighter ones. The procedure used is a simple decision tree employing the 5 factors and their associated maps. Each cell as analyzed and the results mapped. The map of interest is not the payoff because within each 3-acre all use an "efficiency class," and new way to characterize a small piece of land cell.
The map shows the profound influence of one factor, namely stoniness. In general the analysis shows that for one ownership, 91% of the land is completely unsuited for tractor use. On only 2% will efficiencies greater than 75% be obtained. The procedures can be used manually for small areas. For larger areas, entire coal regions within a state, a centralized database is likely to be very cost effective-for not only can such novel maps be created, but also many others for a variety of purposes from computing overburden to estimating hauling costs.
A Tractor Efficiency Map: Applied Energetics for Coalfield Ownership
Robert H. Giles, Jr. and Jerry W. Ziewitz
Department of Fisheries and Wildlife Sciences
Virginia Polytechnic Institute and State University
Blacksburg, Virginia 24061
Tractors are widely used in the coalfields for land clearing and reclamation. Tractor efficiency is defined as the percentage of tractor hours effectively used. Efficiency is related to many factors, key ones being slope, relief, and soil stoniness, texture, and depth. Using a computerized geographic information system with 3-cells, we studied 189 km2 (46,674 acres) in Wise County, Virginia. By combining maps of the major factors, it was possible to assign each land cell to seven efficiency classes including an "unsuitable" class.
The maps are believed useful in area planning, in explaining cost overruns (and profits), and in improving energy conservation within the comprehensive mining system.
Computerized geographic information systems (GIS) are widely-used tools of natural resource and land use planners. A GIS essentially stores, processes, and retrieves mapped data. An operating GIS can generate maps and new combinations of data at a considerably lower cost in time and dollars than is possible with manual methods, and a GIS can perform some tasks that are technically or economically infeasible otherwise.
POWER, a cellular GIS (data re collected and referenced using a grid system) developed an once operated by the Department of Fisheries and Wildlife Sciences at Virginia Polytechnic Institute and State University (Giles et al. 1976) has been applied to such complex problems as determining minimum-impact routes for high-voltage power lines and siting general-aviation airports. Land evaluation, the process of assessing land performance or suitability for particular land uses, is a promising new set of applications for GIS.
Land evaluation begins by defining the land use of interest and listing the land-related requirements for such use. This paper addresses one aspect of the suitability of land for a broad class of agricultural and post-mining uses, namely, limitations to the use of agricultural implements. Because so many agricultural enterprises required land suitable for operating tractor-drawn tillage, cultivation, and harvesting equipment, one good measure of land suitability for these enterprises would be a measure of tractor efficiency. Tractor efficiency is directly related to energy consumption, and as the cost and the risks of shortages of energy increase so do the operating costs of farming. Expression land suitability as a function of tractor efficiency is, therefore, one way of relating land characteristics directly to the operating costs of a land use. As part of an effort to computerize evaluation of land for forestry, wildlife, and agricultural uses, this paper demonstrates the use of a GIS to produce a map of tractor efficiency.
Tractor efficiency is defined as the percentage of tractor hours effectively used. Several factors influence tractor efficiency. Beek (1978) described five broad classes of tractor efficiency resulting from different combinations of slope, relief, soil, stoniness, soil texture, and effective soil depth on a parcel of land. For example, slopes less than 20% but with moderate impediments due to stoniness (1-15 percent), rockiness (10-25 percent), or shallowness have a tractor efficiency less than 60%. Slopes less than 8% with slight impediments due to sandy texture, or due to clayey texture with montnorillonitric or illicit clays, have a tractor efficiency between 60 and 90 percent. Beek's classes have been modified slightly to produce the classification system summarized in Table 1. Seven classes are described with classes I through VI representing decreasing tractor efficiency, and class VIII representing areas unsuited to the use of tractor-drawn agricultural implements. Table 1. Tractor Efficiency Classification (a "+" in a column indicates that the factor is a class-determining characteristic). CLASS FACTORS Soil Texture Limitations Slope (%) Soil Shallow Soil is Stony Slight Moderate I: >95% 0-4 II: 90-95% 4-8 III: 75-90% 0-4 0-4 8-14 + + IV: 60-75% 4-8 4-8 14-20 + + V: 30-60% 0-4 8-14 20-30 + + VI: 0-30% 4-8 14-20 30-40 + + VII: Unsuited Any Slope 40-100 +
DATA FOR THE STUDY AREA
The Soil Survey of Wise County, Virginia (Perry et al. 1954), was then the main source of mapped data for this study. A Virginia Polytechnic Institute and State University extension publication entitled "Interpretive Guide to the Soils of Southwest Virginia TVA Counties" (Lietzke and Porter 1978) provided additional information regarding depth and stoniness of the soil types mapped in the 1954 survey. Slopes throughout the area were calculated from elevation data supplied by U.S. Geological Survey Digital Terrain Tapes. The digital Terrain Tapes were computer compatible but the Soil Survey had to be digitized before it could be used POWER system.
Soil maps were digitized by aligning a 30 x 30 transparent grid of square cells on the source map, each cell covering 1/9 km2 (about 3 acres), and then entering to a computer file a number corresponding to the dominant soil type in each cell (i.e., the soil type of the mapping unit occupying the greatest area in the cell). Each digitized 30 x 30 grid (900 cells) was given appropriate labels that identified its actual geographic coordinates to the Power system. Some cartographic quality is lost in this process, because more than one soil type can occur in a single cell. The 1954 Wise County Soil Survey, mapped at a scale of 1:24,000 (2.67 inches = 1 mile), did not show areas smaller than 5 to 10 acres unless the area contrasted highly with the surrounding soils (Lietzke and Porter 1978). Digitizing with a cell size of 3 acres preserved most of the detail of the source maps in the resulting computer mapfile. Figure 1 shows the digitized soils map for an area of 188.88 km2 (46,674 acres) containing the northern-most sections of Penn Virginia Resources Corporation holdings in Wise County. All holdings in Wise County were processed (over 65,000 acres), but it would require several pages to present a series of readable maps for this large an area. The area shown in Figure 1 and in the figures that follow was selected because it displays most of the range in variability for the characteristics of interest in mapping tractor efficiency.
Digitizing soils maps takes about 30 minutes per 1000 acres and is a tedious job. Once a map is digitized, however, single-factor maps and special interpretive maps can be generated with great ease. For mapping tractor efficiency, three soils mapfiles are needed: stoniness, depth to bedrock or limiting layer, and surface texture.
The presence of stones or rock outcrops on or near the soil surface limits the use of mechanized agricultural equipment. Soil stoniness could potentially act as a very discriminating land characteristic for evaluating tractor efficiency if data on the size and percent occurrence of stones for each soil type in the study area were available. The 1954 Wise County Soil Survey, however, indicates only whether a soil type is stony or not stony, cherty or not cherty, etc. This is why the stoniness mapfile (Figure 2) has only two categories. The guide to the sols of southwest Virginia (Lietzke and Porter 1978) provided more detailed information regarding soil stoniness and it was also used to identify soil types with poor workability due to stoniness. A soil type described as stony in the survey or the interpretive guide was considered unsuited to the use of tractor-drawn agricultural implements. Figure 1. Soils Map
Excessively light or heavy soils also impede tractor efficiency. Soils with a sandy texture (USDA classification) in the surface horizon or a clayey texture due to the presence of montmorillonitic or illitic clays are considered to pose moderate impediments to the use of agricultural implements. When no other impediments are present (i.e., stoniness, slope greater than 4%, or shallowness), a sandy or clayey texture will decrease tractor efficiency to less than 50%, but the use of tractors is still possible. The textural classes of loamy sand and silty clay are considered to pose slight impediments. Without other impediments, these textures limit tractor efficiency to between 75 and 90 percent. Figure 3 shows the distribution of soil surface textures in the study area.
Figure 2. Stoniness map. The lighter cells are areas in which the dominant soil type is described as stony by the Soil Survey
Figure 3. Surface soil texture map. The darker cells are heavier in texture (smaller particle sizes or more clayey) than the lighter cells.
Tractor efficiency decreases with decreasing soil depth to bedrock or other limiting layers and is therefore included as a factor in the evaluation. The USDA uses 100 cm (40in) as the effective depth for Class I and II soils in its Land Capability Classification system, chosen on the basis of its effect on soil manageability (Singer 1978). The guide to the soils of southwest Virginia (Lietzke and Porter 1978) was used to determine whether a soil type was likely to be deep or shallow by this criterion. When no other impediments are present, a shallow soil limits tractor efficiency to between 75-90%. Figure 4 shows the distribution of shallow and deep soils in the study area.
Figure 4. Depth to rock map. The lighter cells are areas with average depth to rock less than 100 cm (40 in).
Given the evaluation of the centroid of each 3 acre cell on the Digital Terrain Tapes, slope is calculated for all cells in the study area except the perimeter cells. A slope mapfile is created (Figure 5). Slope is an important factor influencing tractor efficiency and in the present program is the most discriminating factor. Seven slope classes are used: 0-4%, 8-14%, 14-20%, 20-30%, 30-40%, and 40-100%. There is no one-to-one correspondence between the seven slope classes and the several tractor efficiency classes, since other factors also determine tractor efficiency.
TRACTOR EFFICIENCY MAP
A program written in FORTRAN takes the previously described four mapfiles as input, determines which combination of factors is present in each cell, and assigns each cell in the study area to classes I through VII, as in Table 1. The result is a mapfile that shows tractor efficiency (Figure 6). The lightest cells in Figure 2, the stoniness map, are also the lightest class in Figure 6. This is because soil stoniness is a severe limitation to tractor efficiency, and all cells Designated stony automatically become class VII, land Designated unsuited to the use of agricultural implements. Table 2 shows the percent occurrence of the seven tractor-efficiency classes for the land holdings shown in Figs. 1-6, and for Penn Virginia Resource Corporation holdings throughout Wise County.
Figure 5. Slope Map. The lighter cells have greater slope than the darker cells.
Table 2. Acreage and Percent Occurrence of Tractor Efficiency Classes Tractor Efficiency Classes Occurrence in North Wise Co. in Holdings1 Occurrence in All Wise Co. in Holdings1 Acres Total % Acres Total % I: >95% 140 0.6 271 0.4 II: 90-95% 110 0.5 180 0.2 III: 75-90% 403 1.7 756 1.0 IV: 60-75% 503 2.1 790 1.1 V: 30-60% 2138 9.1 2715 3.6 VI: 0-30% 1388 5.9 1711 2.3 VII: Unsuited 18,825 80.1 68,591 9.14 TOTAL 23,506 100.0 75,0152 100.0 1The area shown in the figures of this paper. 2In digitizing the boundaries of Penn Virginia Resource Corporation holdings in Wise Co., a cell was considered an area within the property if any part of the cell contained land inside the source-map boundary. Acreages reported here are therefore slightly greater than the true acreages.
Figure 6. Tractor efficiency map. The darker cells have a higher tractor efficiency rating than the lighter cells.
It would be a simple matter to perform an on-site land evaluation using the same criteria described in this paper for any of the 3-acres cells in the study area. Table 1 summarizes the combination of factors necessary to estimate tractor efficiency and anyone able to determine slope, stoniness, soil depth, and texture of the surface soil horizon in the field could use it. Given soils and topographic maps of an area, the same interpretations could be made by looking at each mapping unit and determining the combination of factors present, although figuring the slope of many mapping units would become very time consuming. The advantage of using a computerized GIS to perform this kind of evaluation is that large areas can be done. Maps can be produced, and special inventories and analyses can be accomplished at a much lower cost in time and dollars than is possible with either of the other approaches. In addition, the data have many other potential uses in other pre- and post-mining permit, reclamation, and land use analysis and decision making.
Tractor efficiency is only one part of land evaluation for post-mining agricultural uses. Given the importance of mechanization inmost modern agricultural operations, it is an important part that enables the planner to define areas that are suited and unsuited for a broad class of land uses. IT is likely that knowledge of such efficiencies can aid in determining the primeness of agricultural land and the long-term cost-effectiveness of farming operations, and can contribute in regional programs of energy conservation.
This paper is based on one by Giles and Jerry W. Ziewitz in the 1985 Symposium on Surface Mining, Hydrology, Sedimentation, and Reclamation, University of Kentucky, Lexington, KY, December, 1985 "A Tractor Efficiency Map: Applied Energetics for a Coalfield Ownership"
|Figure 1. Soils map. Soils key of 19 categories are presented with such figures. Color coding in modern maps is readily done.|
|Figure 2. Stoniness map. Lighter cells are areas in which the dominant soil type is described as "stony" by the Soil Survey|
|Figure 3. Surface soil texture map. The darker cells are heavier in texture (smaller particle sizes or more clayey) than the lighter cells.|
|Figure 4. Depth to bed rock. The lighter cells are areas with average depth to rock less than 100 cm (40 inches).|
|Figure 5. Slope map. The lighter cells have greater slope than the darker cells.|
|Figure 6. Tractor efficiency map. The darker cells have a higher tractor efficiency rating than the lighter cells. printer and map reproduction problems are evident in the righthand column portion.|
ACKNOWLEDGEMENT The support of Penn Virginia Resources Corp. Duffield, VA., is gratefully acknowledged.
Beek, K.J.1978. Land evaluation for agricultural development. ILRI, Wageningen, The Netherlands. 333 p.
Giles, R.H. Jr., A.B. Jones, III, and C.W. Smart. 1976. POWER: a computer system for corridor location. Office of Biological Services." U.S. Fish and Wildlife Service, Washington, D.C. 30 p.
Lietzke, D.A. and H.C. Porter.1978. Interpretive guide to the soils of southwest Virginia TVA counties. Extension Division. Virginia Polytechnic Institute and State University, Blacksburg, 129 p.
Perry, H.H., P.C. Connor, A.M. Baisden, C.S. Coleman, E.F. Henry, and A.W. Sinclair. 1954. Soil survey of Wise County, Virginia," U.S. Department of Agriculture Soil Conservation Service, Virginia Agricultural Experiment Station, and Tennessee Valley Authority.
Singer, M.J. 1978. The USDA land capability classification and Storie index rating: A comparison. J. Soil and Water Conservation. 33(4):178-182.
Perhaps you will share ideas with me about some of the topic(s) above .
Februrary 7, 2007