| A unit of Lasting Forests
evolving since March 30, 1999 |
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A Total Forest Management Plan
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See related files:
For the long-term well being of the people of the region and their fellow world-citizens, it will be essential that they have a rich, vital soil resource. They need a system to help create
and manage that resource. While soil can be radically changed and formed to meet current needs and perform selected services,
the proper extent of alternation must be judged including likely future uses. This judgement and current action is a prime example of feedforward.
Soil is not one thing but many. "Soil" is a word symbolizing many land use and natural resource system components, both inputs and processes. To list them is to flirt with the error of omission. Some people like to speak of the soil as the "soil ecosystem, " the vegetation it supports with the multitude of organisms within it. We have discovered a peculiar dualism in the soil literature, a kind of mixture or confusion of cause and effect, of uncertainty between dependent and independent variable. On one hand, slope determines the soil type.
On the other, the type specifies the slope. As elsewhere in Virginia, the soils of the mountainous region are highly variable. In many areas, the soils are very poor (by several criteria) or abused in the past. A few areas are rich. As only one of many resources upon which people depend, soils are highly related to other resources. They may not be the key resource but they often are the "conditional resource," one that allows other resources to be fully beneficial to people, conditional upon the soil being well configured and managed.
It is not appropriate to assume that crops, food, and other produce will come from elsewhere, will be grown on rich soils, or all will be imported. There are major reasons to continue to use these less-than-most-rich soils. They can produce positive net returns; they can support families and communities; and they are critical to forestry, rangeland, and watershed management. Their development and improvement, however slight, can stabilize human populations and therefore the socioeconomic structure of the region.
Soil is an evident factor influencing trees and other wildland resources. Not so evident, however, is the difference between knowledge of cropland and wildland soils. Extensive studies over several centuries have provided people working in agriculture and rangeland a rich base of knowledge. Some of this knowledge can be applied directly to wildland soil management. Nevertheless, there are evident gaps in knowledge of wildland soils.
Rather than calling for more research, designers of The Trevey have taken a rationally robust approach to soil. This means that we clarify what knowledge we need, use existing knowledge, look for correlates (analyze the system very well), reduce our needs for confidence and accuracy a little, sample strategically, upgrade when possible, and acknowledge the natural differences in soils but only "if it makes a difference."
Soil Management
This part of The Trevey seeks to describe a system for soil management. The assumptions are that: (1) action on the land is needed very soon (start-up delays and costs can be very great); (2) a system is needed. Piecemeal and partial action, of local value, will not be sufficient for the region or for the state (of which the region can be a contributing or a detracting, burdensome force.) Systems are described in terms of (1) context, (2) objectives, (3) inputs, (4) processes, (5) feedback, and (6) feedforward.
1. Context Blanks are to be filled automatically from processed field form data in a Word document or related software.
A system is being proposed for the people of the region. The area is ___________ acres (i.e. __________ hectares, ___ square kilometers, and _________ square miles.) See Table 1.
There were ____ composite soil samples and analyses of each made and they are believed to be representative of areas shown on the following map. {Insert soil map 1 after this point call it "Soil Region Map Page")}
Organic Matter
No organic matter analyses were performed. The percentage of organic matter is highly variable and most efforts for the future soil management will increase it significantly. In general, we shall bring organic matter to about ___ percent. It is important to achieve this level to hold moisture for nutrient transfer to roots, feed soil organisms, reduce erosion, increase percolation and infiltration of water, and reduce rainfall impact. Now established, decreases in soil base cation and soil pH are attributable to leaching and the sequestration of nutrients in biomass.
Soil pH The average pH throughout the area is ____. The closer to 7.0, the more neutral the soil. When close to 5, the soil is acidic, higher than 7 it is basic. Plants all have their specific pH ranges in which they grow best. Some cannot tolerate acid soils, others can only grow within such rooting conditions.
In yout area, the pH based on samples, ranges from ____ to ____.
pH influences many phenomena, primarily the availability and uptake of the nutrients that are present. If very acidic, aluminum becomes readily available and it is poisonous to many plant species.
Calcium levels
Soil "calcification" occurs when calcium builds up in soils, when water is insufficient to move dissolved amounts theough the soil. This is a bad condition for plants since such accumulations restrict many plant roots and roots seldom penetrate layers of calcium carbonate.
Many plants are very intolerant of calcium. These are the so-called ericaceous plants, the laurel, rhododendron, and azaleas (and others).
In limestone-base soils, calcium levels may be high but calcium is soluble and readily leached from soil in high rainfall areas. Rural System staff are studying the concept that there is little readily-available-to-animals calcium in Mid-Eastern US ecosystems. It has been washed from unprotected soils. Animals must consume large amounts of vegetation and process it at high energy costs to gain enough calcium for hair, bone, flesh, and reproduction. The rapid consumption of deer antlers an danimal bones by rodents suggests the importance of the minerals therein. Low calcium levels in soils where there is no dolomite or limestone parent material may be due to feldspar presence.
In 2003 (Forest Magazine, Spring, p. 12) there were reports of Joel Blum (Univ. Michigan) finding that some trees by their unique fungal symbionts (ectomycorrhizae) can extract calcium from rock, namely apatite (calcium phosphate). Calcium is said to help trees resist temperature changes and insect defoliation. Soil calcium resource measures will not tell the story of the potential richness of the site for certain tree species.
Potassium Levels
The Potassium - Calcium Ratio ___
The Prosphorus - Calcium Ratio ___
The Phosphorus - Potassium Ratio ___
Magnesium and its Relations ___
Calcium and Lime ____
Nitrogen ____
The Carbon - Nitrogen Ratio ____
Phosphorus
Phosphorus is easily dissolved so it is easily lost over time in an area. It is in short supply, expensive, and many sources are in politically unstable areas of the world making supplies uncertain. It is an essential element for plants, especially in flowering and fruiting but throughout plant life. It is fundamental to energy bonding .. the unique solar radiation storage capability of plants.
Nutrients in commercial fertilizer (e.g., 5-10-10) have 5 percent nitrogen, 10 percent phosphorus, and 10 percent potassium.
Applications of just the right amount are needed for each plant species, but it is especially importnat to be precise because of the easy loss, the rich solutions that can cause ponds and streams to become excessively productive of algae, and of course its high cost. It seems morally proper to conserve or provide a limited Earth resource that, while it can be recycled, is in a ponderously slow cycle and one that, for completion required deep-ocean layers to be thrust up into new mountains.
Only soils, broadly taken, are being treated in this part of the text, but the interrelations are known and efforts will be made to address them in other sections.
It has been estimated for the author by a local NRCS officer that average soil mapping costs for a county (1980s) are over $300,000. Given the current regional and national economy, it seems reasonable to assume that such funds will not be available within the next 5 to 10 years.
A regional map providing much information is needed. It will not suffice for planning or other purposes to have only a part of the region under some "information control." It will be better to have the entire region mapped at some level, rather than parts done very precisely! The tradeoff is not easy to make; risks are involved. The premise involved is simple probability. What if 7 things are known with confidence of 0.80 each? This is to say that a score of 80% would be gotten on any test questions about the area. The probability of being correct in all 7 things, their product, is 0.21, pretty low odds of being correct. However, if 6 areas have a very high probability of correctness -- say 0.95, and only one has zero or very low information, then the probability of being right is the product of 0.73 (i.e., 0.956) and zero, or zero. If a person could guess correctly within an unmapped area about 20% of the time, the chances of being correct are still less than in the all-mapped-at-some-reasonable-level condition, i.e., 0.15. Table 1. The potential counties of the region and their areas. ____________________________________________________________________Name Acreage Total Forests Water Urban Other ____________________________________________________________________ Table 2. Soil mapping status within the region. Percent coverage by public surface geologic maps is also estimated. ____________________________________________________________________ County Date of Last Percent of County Soil Map for which Surface Geology Maps are Available ____________________________________________________________________
We now know that geological maps, fundamental to soils maps, are limited and sparse for western Virginia. The soil maps were designed to answer agricultural questions and recently questions about living at the rural-urban interface. Alignment of old with new maps is difficult or impossible (Hamm 1978.) Some are in error (personal communication, DMR staff.) Slope maps are difficult to develop and vary with cartographer, base map, aerial photo quality (if available) and other factors. Land use itself can radically change soil conditions. Agriculture, forestry, roads, dams and mining have changed thousands of acres. The soil taxonomy for such areas is not well developed or widely accepted. Soil taxonomy itself has changed three times in the period of mapping soils in southwestern Virginia. Some older classes cannot be merged or integrated with the new classes.
Soil surveys published after 1981 relate forest site index to productivity by giving the estimate of the average growth rate for a species and the culmination age of mean annual increment. A class of 8 means a growth of 8 cubic meters per hectare; a class of 14 suggests 14 cubic meters per hectare. Site index is a very gross index and while a central number is useful for some expressions, the difference in productivity at different ages on the same site are extreme. Site productivity, while related to soil, is very much related to factors other than classical soil measures and these include past land use, depth to bed rock, land form, and topographic shadow. In the past soil maps have rarely been useful to foresters. They have not been specific.
The result of all of the above is that a new approach to a soil management system is needed, one not based on conventional soil maps. An approach is needed that is completely system-centered. It is one that uses a few area maps, but these are dynamic maps for the management system. The approach does not deny the value of conventional soil maps. It may use them. It seeks to face the practical realities of developing one in less than a year, a practical soil management system for a __________ acre area (known to be highly variable), for the lowest possible cost. "Things desirable" are no longer criteria; "Things essential" and especially these that are highly correlated or mathematically related to low-cost observations are the assumptions for building a resource system. The procedure is grounded in expert-system theory and the perception that describing and evaluating soils is dependent upon the objectives for a site (Knoepp et al. 2000).
We have used for mapping within your region:
The procedure for preliminary soil mapping a Rural System Tract is to digitize the property boundary, sact a window that includes the entire area plus 20 meters on all sides. GIS maps (data)are automatically assembled for the window. Inside the window, exclusion areas are mapped in light colors - roads, structures, mines,borrow pits, security or no-entrance areas, parking lots, ponds and lakes. The proportions removed by each in descending order are reported. Forests are then removed (Forest soil studies are few and these can be specifically done later.). Wilderness or non-use areas are excluded since these will not be treated and soil information will only be needed for later studies if funding become available.Observers go by road or trail to roadsode cuts, foundation digs or other exposed soil cross-section cuts. Soil pits are rarely dug unless equipment is available at a desired site. The observer studies the profile and names the soil and describes the point (representative of a reasonably large area) in terms of slope, aspect, elevation, parent material, slope position, nearness to ridge, nearness to stream. In effect, the computer is being trained about the characteristics of this soil type. A map is then made of all sites having (within 3%) all of these characteristics. Another site is found and named and the process repeated. Three or four such sites are visited and the process repeated. Three soil types have been named and repeated within the potentially-active part of the area of interest (e.g., a Rural System Tract. Next, maps are made of the top 5 most abundant groups of the remaining map cells (alpha units) based on the above listed characteristics. Each is mapped with a roads map and then representative sites are visited nean to roads to give each such yet-unnamed soil a series.name. Progressively over time other soils are identified.
2. Objectives
A primary need within the region is to understand as fully as possible the use-specific potential of every soil, then to relate soils to each other so that they are all related to a maximum potential, i.e., a maximum effectiveness for performing some specified use. The third need is to develop an energy and monetary cost estimate (with confidence bounds) of changing a soil from its present condition to one nearly like (within 10%) the maximum potential.
The objectives are not unlike those of the Soil Survey Manual, Part II, Section 404, (Orvedol 1981:221; Slusher 1981:227).
Amos and Daniels (1982) completed a detailed soil analysis of 3 mine benches over 2 years. The total area involved was only about 200 acres. The study was intensive but suggests the level of investment needed to obtain soil data -- at least at a fair level of precision.
Orvedal (1981:222-223) reported soil maps for the vast interior of Brazil being done in 1966 at a scale of 1:5,000,000. (In the U.S. 1:20,000 is common.) He noted that even at this scale, the map is good enough to serve as a base for technical land classification maps. He said that their experience with the small-scale map demonstrated that they could be informative and useful. "Even though no more than general interpretations are possible, such interpretations nevertheless are useful in agricultural planning for large regions."
By specifying a particular use, the problem of creating and storing a million maps of various scales is eliminated. Also a use may become outdated, e.g., as when one variety of wheat is replaced by a new hybrid or a new tillage makes unsuitable sites feasible for use. The system is dynamic and potentially responsive to such changes, to ideas of new uses, and to new information about soils. It follows the concepts of Williamson ( 198?) for dynamic classification.
Extreme precision is not needed since every soil map carries with it the caveat that on-site investigations are needed. Extreme precision is impossible due to map scale phenomena and to map codes. Extreme precision is impossible due to map scale phenomena and to map codes. Rarely can a spot more precise than 0.2 acres be properly located or designated on a map of 1:20,000 scale. In Hamm's work (1978) she discovered the need for a minimum of 68 "colors" or map codes. The shades of color necessary to distinguish these differences were not available and, even if available, many shades cannot be discriminated. Grouping of the available precision, making it less precise, was done for cartographic as well as communication reasons.
There can hardly be a more important part of land use planning, development, and management than knowledge of soil. Nevertheless, detailed information is often missing, old, or overspecialized for cropland. Forest soils are poorly understood. Available information on soil may be difficult to understand and easily laid aside because of differences in technical language, knowledge, and approaches. Soil resource knowledge is very important but even that which is available may not be used. This unit of The Trevey seeks to solve that problem. It gives specific recommendations based on data provided about a site, then provides access to backup knowledge, explanations, and other material.
The New Soil Map
One of the most often requested maps or data sets for a geographic information system is "soils." This has been the authors' observation in many situations and on many projects and large system designs. Gaining such maps has been a major problem, for there is an intuitive appeal to their request, and a majority vote for it. Staff progressively work on developing a functional map of pseudosoils, partially based on knowledge of geological structures, mineral weathering, and hydrologic phenomena.
| Generalized physical weathering processes (from Derbyshire et al. 1979) suggesting expert system grounds for soil formation within alpha units |
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| Primary process | Mechanism | Geomorphic Process |
| Stress relief (unloading) | Dilation jointing | Sheeting, exfoliation |
| Differential expansion of mineral crystals |
Intergranular and intragranular stress In rock: Thermoclasty |
Granular disaggregation Gelifaction GrGranularisaggregation |
| Ice crystal growth | In soil: Frost Heaving | Ground heaving and cracking Disruption of of bedding (involutions) |
| Salt crystal growth | Holoclasty | Granular disaggregation |
| Adsorption of water (hydration) |
Volume increase of hydrated minerals Swelling of mixed-layer clays (e.g., montmorillonite) |
GrGranularisaggregation Spheroidal weathering Disruption of bedding (involutions), ground heaving and cracking |
Derbyshire et al. (1979) presented a table showing the weathering tendencies of minerals in the geologic column:
| Order of persistence of minerals in the geologic column (least persistent are listed first) |
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| Olivine Actinolite Diopside Hypersthene Sillimanite Argite Zoisite (epidote group) Sphene Topaz Andalusite Hornblende |
Epidote Kyanite Staurolite Magnetite ILmenite? Apatite Biotite Garnet Monazite Tourmaline Zircon Rutile Anatase |
Several companies have formed but have failed to be profitable in doing computer mapping of soils. The detail is very fine, the lines numerous, the storage of data for polygons is difficult and expensive, the graphic output difficult and of variable quality, and the results occasionally subject to adverse legal action. Some soils change due to floods and land use. The enormity of the problem and cost of producing timely maps is conspicuous. Equally as large and more significant is the problem of scope.
The parts of soil include those of Table 3. Each of these parts can be and probably needs to be mapped, i.e., their distribution known so it can be related to one or more problems such as:
There are many parts to soil analyses and to the topic of soils; with study it can be found that many are well correlated. Some factors lead to others by models and even simple logic. The soil triangle, for example, leads from learning two percentages, those of silt and clay, to obviously...the third, the percentage of sand
(i.e., 100 - (% silt) - (% clay) = % sand).
An amazing variety of soil (and plant relations) is unified in the knowledge of bulk density (discussed later).
We have observed significantly different soils in very narrow bands (i.e., soils from a limestone layer with a siltstone over-strata) and very small spots. The problem of scale is profound. In the future we shall seek to move the knowledge now available about soils back from synthetic "names" to precise major discriminating factors that allow generalizations to be made about appropriate soil uses and hazzards of uses. These are now available (e.g., certain soils ararek for septic fields; some are unstable for roads, etc.) by soil name in computer form. Factors used in expert systems and results printed in maps will allow substantial improvements in resource-related decisions.
Mapping unit sizes are decided on practical grounds, and these are usually cartographic, not functional in the field! The cartographic problem is one of scscale-sizef units and line width, and also one of color. The width of a printed line on a map may be equivalent to 50 meters in the field. People cannot readily discriminate more than 10 shades of gray or 20 colors. The large number of types cannot be presented because they cannot be discriminated. An effort to communicate knowledge about soils has a counterintuitive consequence, namely increased loss of information.
| Table 3. A list of soil and plant characteristics. These may be viewed as a set of conditions for which there must be a non-zero probability for a plant to occur in an area. The probability of presence is a product of the factors, some of which have conditional factors. |
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| seed source clear area conditioning climate seed position non predation germination root penetration moisture light root zone-depth absence of toxicants greater than Temp1 and less than Temp2 sufficient seed to withstand predation degree days competition soil volume for root available soil moisture water retention probability vs soil type (presence of stones, etc.) CEH pH N P K |
Soil mapping or data processing problems are often those of deciding about the borders between distinctively different soils. This is a statistical decision because the borders are mixed, blurred, fuzzy, and the task is one of discriminating at some level of confidence -- full-well realizing the high probability of marking the edge along the wrong line--one deviating from the true line a great amount.
These problems have been engaged in past GIS work. They cannot be solved; they must only be engaged. An effort must be made; a least-bad solution found. At present, we operate on the hypothesis that we have such a solution and it is one of cell-level modeling. Soil can be modeled. We have seen soil maps and have seen the components of soil typing, namely:
Type = f (geology, aspect, elevation, slope, nearness to ridge and stream, and vegetative cover.)
If there are minimum subdivisions in each, respectively, as follow: 10, 4, 4, 5, 3, 3, then there are 7,200 nameable, distinctive types possible. There are more categories than this and more units within each. We calculate them to show the magnitude of the problem. These seem to us to be minimum estimates for almost any area. Most of the variables are continuous, so there is possibly an infinite number of types. The chance of one optimum selection from an infinite number of possible maps is very, very small!
We operate on the hypothesis that natural variation is very, very great (for the above reasons and the wild, wild stirrings of capricious nature and those of allegedly rational humankind). We assume that, given geology and topography, then so-called parent material (rocks) is operated upon by rainfall, other precipitation, temperature (namely freezing and thawing episodes), and gravity, and then mixed (or not) with humus and then soaked (or not) with water. The results are several hundred types--at least enough to create an interesting and diverse map. How to group them to minimize internal variability in the newly formed group is more of a problem of discrimination than of soil science. Great discrimination is already possible! Every cubic centimeter of soil is probably unique. The grouping that is done is artificial and functionally unnecessary, but necessary conceptually. Each point on Earth has a unique, computable type number. The problem becomes one of comprehension.
Once comprehension was a mapping and classification problem, a need to develop chunks of information that decision-makers could see and manipulate. This is now no longer as necessary, perhaps not necessary at all, because computers can aid in this manipulation. By delineating an area of interest, the context of the problem, and specifying variables relevant to a particular set of objectives (e.g., minimize risks of slippage), then it is now possible to produce a data set related to each objective (e.g., one of an index to soil slippage) and to complete the now-almost-trivial act of mapping the data in shades of gray or color. The indexes can be combined and the resulting new data set mapped. An answer may be produced, for example, "soils in area A that are suitable for airports for light craft." Some conventional soil type series "name" is not produced but a map of a function or capability. The map (really a list of x, y coordinates and a value for each such point or area) may include 1 to 10 conventional soil types but not all of any type. Thus, functional maps are produced, one for each named, described function. Such maps could be (can now be) produced from soil type or series maps, but they are likely to be overly gross because of the over-aggregation and the associated losses of information that occurred in the first conventional cartographic mapping phase of the soil work, e.g., reducing 7,200 types to 20 mappable units.
The next problem, one rarely pressed even with conventional soil maps, is a use-specific soil map. This is not a type but a use-only map, one grouping soil types that have the same probable suitability for a named used. For example, a map may be requested showing soils that are suitable for growing plant Q. All soils with this suitability would be mapped. It might include 10 types. Where the plant Q may be or should be grown are questions only slightly related to soil but are questions that do relate to costs, growing season, past use, distance to roads, and other factors. The desired map is one of soil suitability only. This is a map of function: "Where are soils probably functionally satisfactory for plant Q?" Everyone knows such a map will be general. It will omit "good" areas (meeting many criteria) and include bad areas. Relative suitability, perhaps along a continuum of 0 to 100, may also be mapped.
We have sought ways to overcome these problems. If not careful, we can adopt concepts, technology, and techniques useful for a pre-computer age and try to build on or around them with possible disasters and at least very great costs. In the case of maps that are generalized and have errors of omission as well as over-inclusion, a well designed computerized geographic information system (GIS) would capture such errors, allow a data base to be improved and, eventually, when enough such spots are identified, allow development of functional equations or rules by which all areas with similar characteristics may be identified and re-classified.
Similarly, broad classes of past use can be included in a database. This discussion leads us farther and farther away from soil mapping and concepts of soil types and series and toward dynamic computer models of the functional lithosphere.
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We have developed preliminary models that convince us that the computer can readily form general soil groups (Hamm 1978) and these relate in meaningful ways to current soil maps. Ziewitz (1982) showed that by using a GIS, maps could be made that closely approximate conventional soil maps. A soil map was made of an area of 100,000 ha adjacent to a similar sized area. Also, a map was made by computer of a part of the area already conventionally mapped. The match was very good. An agronomist suggested that the "error" or mismatch between the maps might have been as much a function of the standard used (the conventional map) as of the computer algorithm.
We now believe that models can be created that generate a soil use suitability index for each cell in a map. That index of use suitability, for example, for roads, or crops, or septic tank drainage areas, can be estimated based on the chemical, physical, and biological characteristics of a soil and its adjacent soils. Efforts (only limited ones), we think, should not be directed at soil types but at (1) mapping these factors (and they will almost all have other uses) and (2) developing a general model to estimate use suitability indexes. Past and current soils work will be essential; it will be the basis of the evolving lithospheric information and decision system. The new soils map will be like a newspaper, good today, but a thing to be thrown away after the first uses, because the unique requirements of each project are likely to quickly become relevant or change. The computer model changes rapidly as scientific advances are made. The soil map is no longer of soil types but of places where soils are suitable for some use (the best or only suitable places shown in 5 to 10 shades of gray.) The map itself may eventually become valueless for it, along with 50 other units of data (50 other maps), will have been integrated by computer to arrive at answers to broad questions, of which soil is only one factor, such as: What is the optimum place to build a factory of type A or a tourist center of type C? To answer, the curious would have to collect all 50 maps for such a decision, only one of which is a display of soil use suitability indexes, and then seek to comprehend them all. The computer system becomes the living, dynamic, evolving subsystem, not the map. The map, once the focus of attention, may now be of only passing interest. The system becomes the focus of attention. The concept is consistent with that of the land use guidance system (Giles and Tsui 1986) and with dynamic classification (Williamson 1981).
Land Form
Throughout the Appalachian region, mountains are very old, quite rounded. The more rounded appear as karst topography, but others are well eroded, having lost elevation estimated as equivalent to the Hymalays Mountains, some 30,000 feet. Throughout there are erosion terraces. One hypothesis is that they are beaver-related. Others are that they are formed by discontinuous down-cutting related to climate changes, changes in baseflow level, and land use including grazing.. Each new incision in a terrace can be made at random, an event such as a directional fall of a large tree followed by a high-intensity storm. The incision can be impeded by large quantities of sediment or organic debris flushed into a channel. Episodic incision is likely to be a normal part of the erosional and soil creation processes of areas within the region.
Surface Medium
"Soil" to certain people is a word with specific meaning and profound connotations. It has been used, studied, described, and changed by thousands of people. We cannot master the complexities of "soil", as a concept or practical reality for the many uses in the region.
We have elected to create a pseudo-soil map, a map of a plant-growing medium, a map of the substance on the land surface, typically the top 3 feet. To some it will be soil, to others a growing medium, to others a platform for a house, parking lot, or roadway.
We map the surface medium based on recent land vegetation as cover (typically to express an organic matter estimate; nearness to water; surface geology layer type (sandstone, limestone, etc.); land form; slope position, aspect, slope, elevation, and disturbed (there being no way to generalize about the admixtures existing at a site). We use these factors (map layers) to describe different sites throughout the region. By selecting superior sites for x, we can map areas identical (or nearly identical) to x. We will progressively unify these observations with NRCS data tapes on soil suitability for a wide range of uses.
Soil Layers
Soil has layers as can be seen at road-bank cuts or when digging a posthole. There usually are 3 very conspicuous layers resulting from presence of decaying organic matter, leaching, oxidation, gravity, past cultivation, and the action of plants and animals. In the past, soils have been named or characterized, in part, based on the depth, color, etc. of these layers.
Starting at the base is level 4, called the C "horizon" or layer. This is the parent material, typically rocks, and is usually equivalent to what is seen on a surface geological map. Luvial (or alluvial, washed in) or colluvial (mass earth slides, gravity influenced) material, both rock and soil, may compose this C layer.
Layer 3 is the lower part of the second conspicuous part of the B-horizon. It is the "flower pot", the lower part of the potential root zone of trees and other plants with substantial roots. It is the zone of support for plants in which nutrients enter water where it has a high probability of being available for plants to take it up. Supplying plant support, nutrients (or poisons), and moisture are the three significant interactive roles of this layer.
Layer 2 is the zone where herbaceous roots are abundant. It is the top few inches of undisturbed soil typically classified as the B-horizon. It is where earthworm and arthropod populations are abundant but where there is little, if any, large organic matter other than roots. Layer 2 is also an important foraging layer of shrews, moles, mice, salamanders, even skunks and bears that dig arthropods living in the zone. Ground hogs have been observed to dig extensively for roots in the layer.
Layer 1 is the top organic layer. It is in various stages of breakdown, varying from a green leaf just-fallen in mid-summer to a complex organic molecules about to be carried by water into Layer 2. This is a seasonally varying layer. It is so complex and variable that intensive quantification is expensive, time consuming, and always results in statements such as "No conclusions could be drawn because the systems were too variable." We know that now. The thickness of the layer (compressed by the boot of an adult or person over 100 pounds (45 kg)) is the measure sought. This is the soil raindrop impact protection layer, the sponge or runoff-resistant layer, and the major faunal layer (arthropods as forage).
Caspall (1975) found that the depth of soil weathering on Illinois surface mines varied, but on well drained soils, weathering reached a depth of 5 inches after 14 years, 6.5 inches after 20 years, and 8.5 inches after 22 years. A so-called A horizon bebins to appear after 20 to 30 years on these mined lands. In forests and grasslands, carbonaceous plant residues are added continually and carbon, as C2 flows out and is lost to the air subsystem. During any period, the change in organic matter equals the gain minus the loss. Under natural conditions, the delicate dynamic equilibrium that depends on climate, parent material, flora, fauna, and other factors, varies enormously from one climate to another. That climate, the micro- or site climate can be unique for each alpha unit. Human activity influences that balance.
These four layers are part of the land volume which we have described before as having many layers ... a volume from one kilometer above the surface to 1 kilometer below the surface. There is a continuum of physical, chemical, and biological phenomena, but there are usually 4 readily seen parts to the soil. These are useful for discussion and some analyses. The above ground layers and pond layers are presented elsewhere in this The Trevey unit. Knowledge of the number and thickness of layers, the soil profile, can be interesting and can allow discrimination within and among the zeta units. Each layer can be treated as a manageable entity. The costs of gaining knowledge for management are very great and generalizations will have to suffice in most wildland situations. Not deciding until all knowledge is available will be destructive.
Analyzing Plant and Soil Relations
Somehow we know that plants are strongly related to soils but we cannot find the strong correlations, mathematical expressions, or predictors. We must be sure that we do not have excessively great expectations for explanatory or predictive models. Numerous past failures suggest that a clear, basic soil and vegetation relationship does not exist or that people have overlooked it.
Staff of The Trevey and Lasting Forests hold that every spot on Earth, every square yard or square meter, is unique. The probability of predicting what plants will be in any spot is very small. We have over-grouped soils (they are excessively variable within a soil group, series, etc.); we have over-grouped plants according to dominants (with 5-15 subdominants); and we have not grouped sites based on their suitability for the seed and seedling stage. We have tried to find a simple model for a complex phenomenon. The complexity is known; the parts are known; the probability of any site being exactly like another is very small; we do see amazing similarities in nature. One community looks like another (to a significant degree). The task is to list the significant figures and develop a probability space, the probable conditions within which a species or taxon may live. In a place at a time we can then evaluate the probability, at least the relative probability, especially zero probability, of a species being present at a site (with known characteristics.) Different soils are known to support the same group of plants, even with equal productivity. One soil type can support several different plant communities. There is more to community-presence than soil, even though it may be a major factor. The failures of strong correlation are in the variety in both plant groups and soil group and failure to analyze the total, large number of factors within the plant-seedling condition.
Ewing ((2002) observed greater soil weathering under conifer than under deciduous tree cover. She generally confirmed that local interactions between soil and vegetation are important in major ecosystem changes, changes different across a region. There are ®ional drivers" of ecosystem change. Both climate and soil-vegetation relations are important and their relative importance varies over time.
One dominant factor influencing soils... and influenced by them... is major vegetation and past land use. David Morton has created a land use map of the State Of Virginia in 1998. After the work is loaded, click on Fig.25.jpg (the first item in the table after the thesis is down-loaded). The map, besides being beautiful, and reflecting so much effort by Morton, gives a perspective on forest cover and other influences on soils never before available.
An adult plant is a function of time, a sequence of events beyond those of growth. Trying to capture all of these very different events in one or more fixed independent variable measures seems unlikely (and has been the case so far.) We use a set of ranges, exploring the probability that a site may be suitable for a plant, given the limits of a site and the life-long limits of a plant. For example, in a one-factor case, the soil conditions in a forest stand range from a to b.
| a | b | |
| Factor A | ||
| Factor B | ||
| Factor C | ||
| Factor D |
Species B is very likely to be present in a mature community. Species C may be present, will probably be sparse, and will be absent in some pockets even in whole stands with characteristics ranging from a to b (estimates based on small-size, expensive soil samples, where sample sizes are too small based on normal sampling theory in a population with very large variance). Species D may be found as a seeding, but never as an adult; it would experience mortality from one of the many forces in the sequences. Vegetation is strongly related to climate and to soil. Small changes in elevation (less than 5 feet) can result in large changes in soil properties and tree growth. The animals in an area are strongly related to trees and other vegetation. As management intensifies to assure higher profits and lower risks than in the past, the demand for soil information climbs sharply, exponentially. Costs of soil information can be overcome by the increased production that results from using the information. Soil and vegetation relations are very complex. This should not be ignored. Much work remains to understand and learn to apply the results of this work. Much is known, however, and soil knowledge can be used to help understand both forest type and tree growth. Of course soil information can be used in many ways and this will be evident elsewhere in this system. We have analyzed soil-plant relations carefully to balance the needs, costs, time, and staff training required for sampling and analyses. Needs are based on a strong theory and an efficient technological system supports the analyses.
Seeds and Soil
It is well known that plants are highly correlated with soils. Whether plants tell of what soil is present or soils determine what plants may be present can be an exciting debate. Many plants are tolerant of a wide range of soil conditions so what the presence of a plant tells can be easily misunderstood. Other plants are very specific in their soil requirements. If such a plant is growing in an area, then the conditions are "right." There may be compensating factors for some plants. High moisture may compensate for low nitrogen resulting in a community equivalent to that with the conditions reversed.
Many ecologists and managers analyze mature communities usually emphasizing the trees of the forest. In natural communities, there are two start-up rules
These seem elementary but are often forgotten. Conditions for a 50-year old forest (soil, shade, etc.) may be totally different than those that existed and were required when it was in its first days. Analyzing forest to learn what are good or desirable conditions may be almost totally unrelated to necessary conditions for a forest of a particular type. These are the start-up conditions, those for seed germination and rooting.
The more severe the site conditions (e.g., temperature, moisture, pH) the more specifically the vegetation can be classified. Only certain plant seeds can germinate and survive there; over time the seed source itself in such areas becomes limited. A few seeds from other sources do arrive, but one or a combination of factors excludes them. Horn (1968) felt that a pH of 4.0 was a critical value for mined land revegetation success. He found no natural seedlings on spoils with lower values.
Soil Testing
The chemicals of a soil are analyzed to assess the available nutrients and toxic substances affecting plant growth, form, and quality.
A chemically fragile soil does not hold enough of one (or more) biologically essential nutrients in available or recyclable form (slash, litter, ground vegetation) to support the next four forest rotations.
The analyses are made, usually to determine the amounts of fertilizer and lime that are required for optimum grass, tree, or crop growth and density. When proper amounts are are applied, desirable vegetative growth and cover are provided to soil and minimum amounts of fertilizers then will enter the water systems and be judged as pollutants.
As ever, it is critical that landowners apply ferilizers and lime in the right amounts at the right time and conditions if a profit is to be made from such an investment. Soil tests at least every 3 years are recommended.
In situations where sampling and analyses have not been done, typically all wildlands, in which crops will be grown or understory vegetation encouraged, approximately 2 tons of lime per acre should be applied. At this rate, a 60-pound bag of lime, hand applied, should be spread in a band about 6 feet wide over a distance of about ___ feet.) Half should be applied, then disced, then the other half applied.
Hydrogen Ions (pH)
The pH in a 1:1 solid - to - water suspension should be between 6 and 8 to indicate good plant growth. Acidity acts as a regulator of the nutrient and non-nutrient dispensing mechanism of clay minerals. When pH is very low (below 5.5), it can release aluminum that poisons plants. As pH drops, hydrogen ions displace aluminum or manganese in salts, releasing more of these to an exchangable state. In this state, aluminum reacts with phosphorus producing a largely insoluable salt, tieing up phosphorus and preventing its use by plants. If phosphorus is low, the release of aluminum can cause severe phosphorus deficiencies in plants, even to death. At high pH levels (greater that 8) several nutrient deficiencies occur.
Manganase, when released in the exchangable state, can react with iron causing an iron deficiency in plants.
Germination and rooting, i.e., the seedling population, can be a function of climate/microclimate, or soil factors, or soil factors and climate (e.g., abundant frozen moisture in clay loam is not available to seeds; roots desiccate as surely as if they were in a dry desert). Single or combined factors are at work in different ways in each site -- at the surface and with the conditions for the growing seedling.
Years ago we studied county-level soil maps and learned that we could describe significantly different soil groups using slope, elevation, aspect, slope position, geology, and vegetation. This is like Jenny's model of soil
Soil = f (Climate , Relief, Parent Material, [Revise for footnote2] Animals, Vegetation, Time)
Since
Vegetation = f (Climate2, Relief, Soils, 2, Other Vegetation, Animals, Time)
then it is not surprising that foresters and managers assume a strong relationship exists between soil and vegetation, in sum
Vegetation = f (Soil).
The soil scientists recognizes
Soil = f (Vegetation).
As straightforward and logical as these may seem, correlations of vegetation and soil have not been found to be strong. The Trevey and Lasting Forests staff operate on the premises that initial vegetation, seeds and seedlings, are correlated well with soils; (2) that we have over-generalized or studied soil at too broad a scale; (3) that we have similarly over-generalized vegetation into "types" and studied adult vegetation (usually more tolerant, robust, and resilient to a wide variety of conditions than seed and seedling conditions); and (4) that we have not dealt with analyzes of (at least) pairs of conditions (e.g., shade and cold temperature vs. no shade and cold temperature). The staff, with computer assistance, seeks to overcome these in our analyses.
Soil Sampling
Data cost. Costs are in planning collection, collecting, processing, storing, retrieving and analyzing it. Data entry to a computer program seems to take an enormous amount of time (but, compared to the time required for ecological analyses 20 years ago, it is very fast). Nevertheless, the fewest number of low-cost entries that will give an acceptable level of confidence in the results computed seem needed. Many factors are correlated; if one or more are known, some can be estimated. We hold that the dominant factors for measurement and use in models will be
Nitrogen
We know nitrogen is important but see no way to analyze it cost effectively or respond in a timely way to any observed deficiency. (See The Zeta Strategy under development Giles ms). Determining nitrogen in any of its many forms ( NO-3, NH+4, NH3, organic, adsorbed) is rarely an issue. Nitrogen undergoes chemomicrobiological transformations in soils and its availability to plants is subject to the form present. Thus, calibrating nitrogen present in the soil to plant responses is difficult. Nevertheless, we are sure that forest soils are generally very low in total notrogen whereas in agricultural soils the nitrogen pool is relatively high. We know that Nitrogen varies from and is assumed to be 0.02% for subsoils and 2.5% in peat.
One report was that black locust was nitgogen fixing and showed 33-59 kg/ha released nitrogen. To re-vegetate road cuts, mines etc., add 30-45 pounds of nitrogen per acre during seedbed preparation. Additional may be required. Fall seeding for hay or pasture production should be top dressed with 30 pounds of nitrogen per acre the following spring. If a cover or nurse crop has been used for establishment, delay the spring top dressing of nitrogen until after the cover crop has matured in order to reduce competition (by micro-organisms) for the added nitrogen. For spring hay or pasture seedings, apply 30 pounds of nitrogen at seeding and an additional 30 pounds in the fall. For adequate vegetation to control erosion, apply 30 pounds at seeding and 30 pounds during the spring of the second growing year (Evangelou et al. 1985)
Surface medium (soil) samples are costly to collect, process, analyze, and interpret. They may, when well interpreted, communicate about conditions or events on a site such as productivity and fire influences. They may suggest fertilizing and cropping patterns and may explain some water quality differences.
The calcium to magnesium ratio is at an optimum when about 6.5:1. and desireable when between 1:1 to 10:1. In the samples observed, the ratio was about 7:1.
Soil samples need to be representative. Random samples rarely work because soils are not randomly distributed. It is easy to over-sample one soil type, under-sample another. Sampling, however, is usually an effort to find out what are the proportions present, a value not available to help in the design of the sampling effort.
Representative samples are taken. Usually 100 grams is adequate. Usually 8 samples (separate, named, representative) of above and below 4 sites are taken. The cost of analysis per sample is about $50 (1997).
Surface samples (composites) are taken (the top 8 inches after the litter and humus layer is kicked away) 4 inches, 4 inches below a point. Each sample (composite) is taken to represent a map pixel. Location centers are found with a GPS. Dates are carefully recorded; e.g., pH drops from spring to autumn because of soluble salt effect resulting from mineralization of organic residues or weathering of minerals. Phosphorous and potassium levels also drop over the same period. All are standardized to the low, October 1, day 1 of the hydrologic year.
Soils are air dried, usually for greater than 4 days (and up to 8 days), on non-metallic trays after being crumbled by hand (but rock are not crushed). Samples are stored in labeled plastic bags.
Analyses are conventional but also key is the total potassium/clay content ratio. The P / Clay ratio was 0.002.
Also, the cation exchange capacity of the clay fraction has been measured. It was 35me/100 see below). Coarse fragments are difficult to measure and interpret. These fragments are all stones, rocks, concretions, nodules etc. "Stoniness" is based on the residue left on a 2 mm sieve (i.e., greater than 2 mm).. The stoniness was 6% and was based on:
Coarse Fragments (% weight)= (Weight of parts not passing the sieve/wt of total) x 100
Volume estimates are used -- one for bulk density for fragments, one for fine earth. Stoniness can be used to interpret
Fines are the proportion of the sample less than 2 mm.
Clay particle size is viewed as the key variable. Linked to it must be organic matter and cations on the exchange complex.
Soil particle size can be limiting to vegetation if less than 20 percent of the soil is smaller than 2 microns in size (Bramble 1952)
We compute a ratio of change for each measured soil component, e.g., potassium
| Above | Below | Ratio | |
| 1 | 3 | 2 | 1.0 |
| 2 | 4 | 3 | 1.0 |
| 3 | 6 | 4 | 1.5 |
| 4 | 1 | 2 | 0.5 |
Only in sample 4 does it appear that potassium is leaching to a lower level. The ratio of magnesium to potassium is important. An optimum ratio is 2:1. (We continue to study to find the acceptable range.) If the ratio is too high, magnesium can restrict the uptake of potassium.
Potassium fluxes are difficult to measure and understand because of the long times and great variabilities involved. Potassium is tightly retained in the pine forest system (Stone and Kszystyniak 1977).
We typically use sample results:
We get only percent clay and percent sand, then use "other" as a category.
Cation Exchange Capacity (CEC)
Agricultural and forst soils may have been leaching over many years so they may have lower soluable salts than soils from roadcuts or mine sites. Except for this minor difference, soils can be considered functionally alike in supporting and feeding plants. Feeding (supplying nutrients) is the mechanism of clay minerals absorbing or releasing (desorbing) nutrients (cations and anions) with the exchange capacity of the soil. The exchange capacity is like a reservoir in regulating nutrient release to plants. Without such a capacity, so many nutrients would dissolve into water in soil that all soils would be toxic to plants. The exchange capacity of the clays in soil eliminates this toxicity by holding the nutrients and dispensing them at a rate with which the growing plants can cope (Evangelou et al. 1985). Grasses, for example, pick up more Ca, K, and Mg than do trees from the same soils. In general, K may be defficient but it should not be correcte until after pH correction, i.e., brought to between 6 and 8. Lime can increase the amount of avaoilable K, thus reducing the need for additions being made.
As another example of the role of cation exchange capacity, P may be deficient for a plant crop. A Bray P-1 test is best used for the area (mildly acidic; high rainfall)
| Table 4. Interpretation of K, Mg, and Ca extracted with neutral 1N Ammonium Acetate, expressed as pounds per acre available based on soil analysis | |
| Calibration range for K | |
| Very low | less than 75 |
| Low | 75 - 164 |
| Medium | 165 - 250 |
| High | greater than 250 |
| No deficiency problem for Mg unless level is below 50 lbs/acre; no deficiency problem for Ca unless pH is low | |
| Table 5. Interpretation of Phosphorus tests with Bray P-1 showing calibration ranges and results in pounds per acre. | |
| Very low Low Medium High Very high |
0-10 11-301 32-49 50-60 greater than 60 |
| Table 6. Interpretation of boron and specific conductance (EC) of saturation extract. | ||
| range | mg/liter | mmhos cm-1 |
| Normal Toxic Very Toxic |
less than 0.5 0.05 - 5 greater than 5 |
less than 2.50 2.5 - 6 greater than 6 |
The definition of CEC is the total number of exchangeable cations that a soil sample can retain on its adsorbent complex at a given pH. Expression: centimoles per kg or moles
1 me/100 g = 1 mmol+ /100 g = 1 cmol+/kg
CEC is also called base-exchange capacity, total exchange capacity, or cation-adsorption capacity. CEC indicates the potential fertility of a soil. It also indicates or is correlated with the amount of clay minerals. Average CECs are:
Bulk soil-22 me/100 g
Silty soil-35 me/100 g
Clayey soil-50 me/100 g
CEC is highest in alkaline conditions. When pH is 7, CEC measurements have little meaning. CEC is high in the organic matter where there are many negatively charged sites. Organic matter is a major source of soil CEC. Correlations should be studied between CEC and %clay, %fines, and pH.
In the uncut hardwood forest, the forest floor has 6 to 8 times greater CEC than surface mineral soil.
"Base saturation" is the percentage of a soil's CEC that is saturated with exchangeable bases such as calcium and magnesium.
Maximum measurable levels of magnesium are typically 120 ppm.
Phosphorus was at 15ppm. Phosphorus has been found limiting to vegetation growth on Kentucky mine spoils and thus similar responses may be expected along road sides and trails. Phosphorus levels can be assumed to be high in late autumn and low in mid summer.
Soil Texture
Texture of soil is an expression of the proportions in a sample of sand, silt, and clay. It is an expression of the feeling and appearance of soil. It expresses the relative sandyness or clayiness. A triangular graph is often used to depict all possible combinations of the properties with "loam being the name of the soil texture with a mixture most useful in crop production.".
You find the percent clay reported for a soil and read across to the right (draw a line if necessary), then find the percent silt and read downward and to the left until that line intersects with the the "clay line". The area marked on the graph where that point lies is the name of the soil texture. To check that name or the progression along the two lines, use the percent clay and read up. That line upward from the percent of clay should intersect the other two lines at the same point.
| Degrees of Freedom Students learning statistics will find this graph a useful example for thinking about and comprehending "degrees of freedom." Where there are three things (sand, silt, and clay) and that number is symbolized as n, then there are n-1 degrees of freedom within this system. There are 2 degrees of freedom. When you know the percent of sand and the percent of clay, then you know the percent of silt. One unit is known if the others are specified. The search is on for the measure or estimate of only two units. |
Texture is a way of expressing a distribution of particle sizes, the proportions of particles from 0.002 mm in size (clay) to 2.0mm or greater (gravel). It is a continuum well expressed mathemetically that will one day (with hand-held computer) provide more useful information for decision makers than current knowledge about the proportions in the three particle-size classes.
(Potential Fig 2 related to sandstone, shales, and limestone.)
Sand has no significant nutrients for plants but is important in air and water movement in soils. Clay, the surface of which is the seat of physical and chemical reactions in soil affest plant growth. Silt, intermediate in particle size, contributes to soil tilth and may play a small role in nutrient availability and delivery. In combination, texture influences soil behavior in every way - management, compaction, moisture balance, and plant production potentials.
Amazing differences in behavior as particle size differs. FIG 3
For a given sample of soil, if you can determine the percent of any two particle sizes, then the third percentage is self-evident. Sand and silt are the most easily measured. Texture is highly variable, so gross estimates ar all that can be afforded in field work. Few decisions depend on precise estimates of these three factors of the zeta unit. Texture analyses are time consuming, thus expensive. Lasting Forests staff take samples for making estimates. They kick ayay organic matter from a smapling spot, take a 2-inch (5cm) plug of soil that is 1 inch wide (2.5cm) with a pocket knife and place it on a piece of white paper. They remove roots and plant parts and put the soil in a blank tube to the specified depth, then add a prepared tube of Lasting Forests Texture Solution. The tube is shaken and the percentages read from the tube.
Things we know after we know texture (and the basis for an expert system):
| Sand | Light Low water-holding capacity Drains rapidly |
| Clay | Heavy Drains slowly Poor aeration Expands on wetting Releases heat |
While the soil texture triangle (above) is well known, it is not well perceived that the soil classes are not equal; the areas within the triangle are unequal. These show, for example, that discrimination is difficult within the sand soil type, it occupying a small percentage of the total possible soil conditions.
A basic computer program is available to enable large data sets of soil sample texture data to be placed in named textural classes. It the margins it would seem that a lab analysis with a discrepancy of 1 percent (say 21% sand, 29% clay) could throw a soil into any one of three soil classes. A probability statement by each name assigned is needed.For example, something called "sandy loam" could be anything from 81% silt, 19% clay to 51% sand, 49% silt. Adopting a 90% confidence convention with the samples, it is possible to express soil textural class with a probability level.
As it turns out, there are five textual classification systems used internationally. The chaos created is very unfortunate and unjustified based on the natural variance in soil samples, the general uses to which the conclusions about texture are put, and elementary statistical considerations that are used about pooled soil sampls, confidence levels, and requisite accuracy.
| Soil Particle Size Name | USDA Diameter Limit (mm) | International |
| Gravel | greater than 2 | greater than 2 |
| Very coarse sand | 2.00 - 1.00 | |
| Coarse sand | 1.00 - 0.50 | |
| Medium Sand | 0.50 - 0.25 | "Sand" 2.0 - 0.2 |
| Fine sand | 0.25 - 0.10 | 0.2 - 0.02 |
| Very fine sand | 0.10-0.05 | |
| Silt | 0.05 - 0.002 | 0.02 - 0.002 |
| Clay | less than 0.002 | less than 0.002 |
Bulk Density
Measuring soil compaction may soon be easier for farmers (April, 2003), according to Agricultural Research Service scientists who are evaluating a new sensor that attaches to a tractor and measures compaction at six different depths as it moves across a field. Researchers at the ARS Cropping Systems and Water Quality Research Unit in Columbia, Mo., led by agricultural engineer Kenneth A. Sudduth, have designed and are evaluating the sensor.
Soil texture is the perceived feeling and appearance of soil. It is an expression of the relative sandiness or clayiness.
The texture is usually observed in the top 10 inches. A person may look away from soil once it is in hand and run fingers over it. Sandy soils have a gritty, grainy feel. Silt fills like powder or flour. Clay feels greasy and rubbery when wet.. These three are the three components of texture.
When damp and rolled between the palms, soil that forms a "worm" is called clayey. Loamy soils will roll into a ball or cylinder but fall apart easily. Sandy loam will roll into a ball but not a cylinder and falls apart quickly. Silty loam feels smooth, silky and forms a weak ring when soil is rolled between the palms.
Strongly related to texture is bulk density. Bulk density is the weight of dry soil in grams in one cubic centimeter or (mass (g) / volume (cc)) or its equivalents. (A cubic centimeter of water weighs one gram. A special core sampler of Lasting Forests is used to get an estimate. It is the ratio of the weight of soil to its volume, the mass per unit volume (g cc In soils analyses there are many ways to get the same results(equifinality). Not only "many" but "too many" ways, and this results in a situation in which it is impossible to determine precisely the causes of events and conditions such as plant growth or wilting. If true, and we believe it is, then efforts to discover that which is impossible seems irrational.
For more specifics, see the following table. Australian workers suggest bulk density numbers (above) should be 0.4 units greater.
The inverse of bulk density (1/p) should be studied for this is an expression of porosity. Giles believed (2001) that ecological relations with soils will become more clear if the specific gravity of the parent material (e.g., quartz rock has a sg of 2.65) is assigned a value of 1.0 and the weight of an equivalent volume given a proportional value (column 4 above) and the value (V) conceived as a unitary measure, i.e., as the cube root of the volume (colume 5).
Bulk density is related inversely to pore spaces. About one-half of oven-dry soil is composed of air. The lower the bulk density, the higher percentage of pore space or porosity. Bulk density increases, straight line, with the logarithm of particle size. About 50% of the variation in bulk density is due to variation in the organic content of soil and this is changing over time. The rate can be influenced.
Knowing the variability of bulk density is needed to convert accurately (or standardize) concentrations of nutrients in a soil to absolute quantities. Cultivation tends to to increase bulk density and to reduce its variability. This reduced variability may persist for 20-100 years without management. Bulk density is related to the total surface area of particles within a soil mass and the area influences adsorbing power, swelling, plasticity, cohesion, heat of wetting, and thermal conductivity.
Trying to prevent soil compaction (which increases bulk density) is the typical strategy of land managers. Standards (at least before-work bulk density) are needed. Wildland managers want bulk density to be low. Increases tend to reduce root penetration, reduce aeration, reduce infiltration and percolation, and thus increase runoff. Increases change the physical and nutritional conditions that affect the growth of plants. Seedling oak root penetration and later growth has been seen markedly reduced by increases in bulk density suggesting impaired site regeneration. Site preparation work that does not minimize weight of vehicles, passes over an area, and use during wet periods can increase bulk density. Managing bulk density includes preventing its increase, and encouraging its decrease.
Prevention
Encouragement
The number of seasons of frost action to change bulk density is a needed statistic.Preliminary work suggests possible natural change with plant and animal work of 0.1 per year. The role of animals can be quantified; the relations of bulk density to tree growth can be quantified; tree growth and harvest financial values can be expressed; thus, the relations of animals to tree value can be quantified.
Bulk density is a highly integrative factor and many studies have shown its predominant, significant effect on tree and other plant growth. It needs continued study and use in models as an "independent factor."
Field Capacity and Wilting Point
Field capacity is the maximum amount of water that a soil can hold against the force of gravity alone. It's the water left in soil after drainage by gravity. Another way to see it: maximum capillary water. It indicates water storage capacity and thus potential moisture reserves. When the pores are completely filled with air, field capacity is the "bound water," microscopic, molecular. It is an index (since a lab sample rarely is the same as that sample in the wild and because gravity and other forces act differently on the small sample in the lab.)
Permanent wilting point is the water in soil that plants cannot extract. It is "held" too tightly by several physical forces. It occurs when plant water absorption is less than transpiration. When "permanent" there is no plant recovery after that point is reached.
Hydrogen Ion Concentration - pH
Overall Salinity
Salinity is measured as electrical conductivity of water. Conductance is measured as mhos and conductivity as mhos/cm. New units are suggested:
S = siemens = 1 ds/m = 1 mmho/cm
Electrical conductivity for plants ranges from 0 to 16 mmho/cm. Eight species of trees can tolerate salt levels of 15-40 S. If the soil solution conductivity is greater than 8 S, the yield of most plants is reduced. A soil is said to be saline if its electrical conductivity of the saturation extract is greater than 2 S.
Plants in managed and wild conditions are increasingly subjected to increases in salinity. Irrigation is a major factor. Natural salinity is widespread. Roadside salting; coastal storms; and air pollution all are involved.
Plants seem to react to high salinity by passive exclusion of ions because of membrane permeability, active extrusion by ion pumps, or dilution through developing succulent tissue. Tolerance of salt usually implies salt ion accumulation in tissue, usually in vacuoles, with other cellular accommodations (Allen et al. 1994).
Soil Productivity
"Soil productivity," like many phrases in forest ecosystems, must be defined very carefully and precisely if it can have any meaning and can be used to test a situation. For example, "Is this site more productive than that one?" or "After treatment, is the area more productive than before?" The well-accepted definition is: The capacity of a soil in its normal environment for producing a specified plant, or sequence of plants, under a specified system of management." Perhaps it is merely the ability of soil to supply water and nutrients necessary to produce plant material. This is a statement with a physical or biological base. A soil that will produce limited corn useful only for fodder in a region where cattle are not abundant will not produce a profit. It will produce corn. The Trevey staff recognize "yield" or biological productivity (e.g., named pounds or bushels) but we prefer to do analyses only of crops, products, and services that may conceivably, within a region, within a planning period, produce profit or satisfy objectives.
"Land unsuitable for forestry", usually a function of slope or wetness may be a soil productivity question and when it is answered in The Trevey, it usually contains the element "probable profit", or "probable necessary benefit production."
Conventional soil productivity is seen as the fundamental 6-part model
P = f (M, A, N, L, H, V).
The manager usually works on M, A, and N.
P = productivity (measured as carbon or dry-matter); Some of the key relationships and managerial effects are as follows:
Soil Moisture
Plot work can begin to show soil moisture but as vanRooyen (1972) observed "...water content itself cannot describe the rate and direction of soil- water movement and is not sufficient to evaluate the water balance of a site. Tensiometric measurements can, however, indicate the directions and magnitudes of the hydraulic gradients through the profile and allow us to compile fluxes from the knowledge of moisture retaining properties and of the hydraulic conductivity versus soil moisture tension of a particular site."
Soil formation or rock weathering is best expressed as the relative ratio of average annual precipitation to evaporation (readily available soil moisture). The ratios are 1.5 for London, 1.47 for an area in Michigan, and 5.7 for Tatoosh, Washington; 0.035 for Helwan Egypt. The ratios are important since most of the world's basic rock type is silicate and dissolution in free water occurs. Part of the precipitation percolates through the parent rock and dissolves the bases in preference to the acid radicals. The water carries them away, eventually to the ocean. A residue is left that is slightly acid in reaction and is mainly hydrous silica and hydrous sesquioxides, e.g., oxides of aluminum and iron.
Soil Color
Dark colored soils can cause high surface temperatures, some being lethal to seedlings. Slope ansd aspect influence these temperatures, there being 15 degree C difference between North- and South-facing slopes.
Albedo values (adsorption/reflectance) are 5 % for black soils and about 45 % for light colored soils.
Soil and Solar Relations
It has been shown that albedo (the ratio of reflected to incoming solar radiation), once adjusted to remove effects of the solar zenith angle, is a linear function of the water content of the soil surface (Bowers and Hanks 1965, Clark and Bowen 1967). The albedo of a wet soil is about half that of the same soil when dry (Idso et al. 1975:549). Idso et al.(1975:55l) said that albedo techniques (such as are inherent in Landsat analyses) for estimating soil moisture always require specific knowledge of the soil type being viewed.
Cloud-cover thwarts such efforts as does vegetation. Soil typing, therefore,due to problems with1l1oisture, cloud-cover, and vegetation is not possible with Landsat imagry and even with IDicrowave emission studies there are major problems. As Idso et al. (1975:552) said, "Only intensive experimentation on the ground will determine whether our hopes are well-founded or wishful thinking,"
Solar radiation affects the hydrologic cycle mainly through its influence on temperature. Air temperature is dependent on many factors affecting the
heat balance of an air layer near the ground. The important factors are length of growing season, evapotranspiration proportion of precipitation which is snow,and frequency of freezing and thawing (days less than 30 degrees F in Winter).
Soil Aeration
Soil Nutrients
Humus
Humus' role in soil is to serve as the energy source for bacteria, fungi, and actinomycetes, but its most important single function is to provide the greatest surface area of its colloidal particles. The particles, along with clay, are the principal sources of surfaces for exchange of ions, the charged particles of nutrients. There are about 120 to 240 tons of humus per acre (US average). There are 18 to 45 grams, dry weight, per square meter of soil animals (earthworms, nemetodes, mites, insect larvae, etc.) in deciduous forests, only 5.4 to 9 grams in coniferous forests (without protozoa). All of these are parts of the humus production system. Humus is an "antifreeze", allowing the soil layer in which it is located to freeze at a temperature only below 32 degrees F.
Mulch
Mulching materials that may be used in various practices on road sides, gardens, and restoration work:
Resilience
Soils differ in so many ways and so greatly that it is often peculiar to speak of one thing, "the soil." For example, consider the difference in deep peat and sand of a coastal dune, both properly called soil. The ability of soil to sustain long term productivity of trees or other resources is highly variable and a function of its properties, states, and processes. Soils vary in their ability to rebound or recover from management activities, resource use, or natural disturbance. Resilience to disturbances, thus reduced risks, is positively related to
While expert soils scientists can integrate these factors, the costs of collecting the data as well as interpreting it in the face of
Availability of soil experts, especially for each wildland area, is very limited.
Soil Management
Herein are the first efforts within The Trevey to create a soil management system that can help overcome those limitations. The assumptions are that:
Piecemeal and partial action, while of local value, will not be sufficient for the land, the region, or the state (of which the region can be a contributing or detracting, burdensome force).
Soils are not soils. There is such great difference between wildland and cropland or agricultural soils that we believe they sould be separately analyzed and managed. By example, ducks and geese are "waterfowl" but they are not managed the same. The preliminary draft of objectives of soil management (based on FAO of the UN documents) are:
Degradation means (FAO) deterioration in stability or potential biotic productivity of land resources currently used for cropping and grazing systems beyond that which might occur naturally, but not typically associated with erosion.
The Management Unit: The Alpha Unit
An Alpha unit is the mapped space in a designated wildland area of 10 meters by 10 meters and is the volume between fallen leaves and twigs to a depth of 2 meters. (The alpha unit used elsewhere includes the entire column, 1 km above and 1 km below the surface.) It is a volume of 200 cubic meters of solids, wet or dry, on which and within which most known terrestrial life exists. It is a named surface volume. It is part of an alpha unit.
Soil is a system and thinking about it as an Alpha unit can be misleading. The unit is dynamic, a function of its past and present and of its neighbors. It is full of diverse life and very much a function of seasonal and climatic changes. Herein we do not belabor this very important point but there may seem to be undue emphasis on singular factors within the system and not enough on the relations among them. (Recall that to write the minimum amount about the known relations within a 30 factor system, there must be at least 870 paragraphs.)
Soil to certain people is a word with specific meaning and profound connotations. It has been used, studied, described, and changed by thousands of people. We cannot master the complexities of "soil" as a concept or practical reality for the emany uses within the region. To some, soil will be a complex biological system, to others a physico-chemical growing medium, to others a platform for a house, parking lot, roadway, or satellite launch. We address the the surface medium based on recent land vegetation as cover (typically to express an organic matter estimate: nearness to water; surface geology layer (sandstone, limestone, etc.); landform; slope position; aspects; slope,; elevation; and disturbed (there being no way to generalize about the admixtures existing at a site). We use these factors in map layers to describe different sites throughout the region. The Alpha unit may be a "solid" rock mass, so it will not be dealt with inconventional soil terms. Coarse fragments (pebbles, rocks, etc.) are usually not considered soil. It may be the space below a pond, river, or stream. It may be a narrow riparian zone. It may be the volume under a road, parking lot, dam, or building.
Every Alpha unit is probably unique. This is a challenging statement, but we contend that the probability of any unit being equal within 20 descriptive terms is so small that reasonable people will call them different.
On the other hand, very different Alpha units can have almost exactly equal plant or animal conditions. For example, low fertility and high moisture may produce the same observable conditions in a group of plants as moderate fertility and low moisture. This is called "equifinality", meaning that many different conditions can produce the same end condition.
Alpha units can be considered a type of classification but this will be to misinterpret the concept. They are a concept, not a thing, type, or class. They reflect a relatively new idea possible with computers. Much work has been devoted to classifying soils and mapping the classes. Now a group of factors can be listed for a specific project or need and a map can be made of where these factors within stated limits exist. This is called "dynamic classification." (Williamson 1981).
In 1982 (Ziewitz 1982) we demonstrated that a pseudosoil map can be made for a county. By concentrating on the major aspects of soil classificationm, we mapped the different areas. The major factors are:
These seven factors, we believe, primarily determine the notably different conditions to which crops and trees respond and which determine the physical, chemical, and biologic differences in which people are interested. They form the criteria previously used to identify soil units, "types" and other classifications. Geology, distance to stream (correlated with slope), and topographic shape are the three most explanatory variables for past soil unit classification.
Mapping experts know that rarely can more than 20 colors or patterns be distinguished on a map. We noted that if we had a minimum of three gross categories for each of the six factors, we would have 729 classes or types that are known to be significantly different (because we defined each category that way!)
The size of the mapping unit was decided on practical grounds and these are cartographic, not functional in the field. The cartographic problem is one of scale -- the size of units and line width and also one of color and pattern on the maps. The width of a printed line on a map may be equivalent to 50 meters in the field. People cannot discriminate more than 10 shades of gray or 20 colors. The large number of types that might be separated cannot be discriminated on a manageable desk map. Regrouping was needed to make maps. Information already in hand in computer data bases was lost. The Alpha units can be used or developed in at least two ways. They can be defined as multifactor units, typically with the above seven factors included. Another use is to find conditions that are desireable (e.g., 30 places where white pine grows exceptionally well), then to have the computer analyze the conditions in the Alpha units where these trees grow -- then to make a map of all similar units (Alpha units having conditions within the limits of , or having a probability of similar conditions.)
We'll not quibble over whether we "classify" soils or not. What we do is unconventional but practical. It uses the knowledge we have and uses the available data bases. Unusual requests for "soil information" seem to arise annually. Existing soil classes do not seem to work well. They must be grouped or an appeal made such as "somewhere within this class the conditions are suitable." Computer mapping soil class boundaries has been expensive and difficult. We have avoided mapping classical soil type polygons and have used the Alpha unit. We have developed preliminary models that convince us that general soil groups can readily be formed by computer (Hamm 1978) and these relate in meaningful ways to current soil maps. Ziewitz (1982) showed that by using GIS, maps can be made that closely approximate conve ntional soil maps. A soil map was made of a area of 100,000 ha adjacent to similar sized area. Also, a map was made by computer of a part of the area already conventionally mapped. The computer-produced maps was verified at the edges by comparison with the conventional maps and by comparison with the conventional map.In one case, comparing Alpha unit work to conventional maps as a basis for accuracy, the question advanced by a soil expert was ... perhaps we should be comparing published maps to the computer maps?
We remind the reader that we are discussing wildland conditions and conditions in the relatively mature soil development of the eastern US. In the western states, soils are of volcanic origin and recent geologic action. Erosion and slides are conspicuous and climatic differences make profoundly different soil genesis and results there.
It has been estimated that average soil mapping costs for a county are about $300,000. Given the current regional and national economy, it seems reasonable to assume that such funds will not be available for extensive mapping soon. We can now produce pseudosoil maps for most of the region.
The premise within the Alpha unit use and work within Lasting Forests is that of simple probability. What if 7 things are known with confidence of 0.80 each. This is to say, a score of 80% would be gotten on any test questions about the area. The probability of being correct in all 7 things, their product, is 0.21, pretty low odds of being correct. However, if 6 areas have a very high probability of correctness -- say 0.95, and only one has zero or very low information, then the probability of being right is the product of 0.73 (i.e., 0.95) and zero, the answer being zero. If a person could guess correctly within an unmapped area about 20% of the time, the chances of being correct are still less than in the all-mapped-at-some-reasonable-level condition, i.e., 0.15.
We now know that geological maps, fundamental to soils maps, are limited and sparse. Alignment of old with new maps is difficult or impossible (Hamm 1978). Some are in error. Land use itself can radically change soil conditions. Agriculture, forestry, roads, dams and mining have changed thousands of acres. The soil taxonomy for such areas is not well developed or widely accepted. Soil taxonomy itself has changed three times in the period of mapping soils in southwestern Virginia. Some older classes cannot be merged or integrated with the new classes.
The result of attempting to integrate the above is to realize that a new approach to knowledge of soils is needed, one not based on conventional soil maps. An approach is needed that is system-centered, with dynamic maps to serve a management system. The approach does not deny the value of conventional soil maps. It may use them. It seeks to face the practical realities of developing one cost effectively in less than a few months. A primary need within the rigion is to understand as fully as possible the use-specific potential of every soil, then to relate soils to each other so that they are all related to a maximum potential, i.e., a maximum effectiveness for performing some specified use. The third need is to develop an energy and monetary cost estimate(with confidence bounds) of changing a soil from its present condition to one nearly like (within 10%) the maximum potential.The objectives are not unlike those of the Soil Survey Manual Part II, Section 404, (Orvedol 1981:221; Slusher 1981:227).
Amos and Daniels (1982) completed a detailed soil analysis of 3 mine benches over 2 years. The total area involved only about 200 acres. The study was intensive but suggests the level of investment needed to obtain soil data -- at least at a fair level of precision.
There can hardly be a more important part of land use planning, development, and management than knowledge of soil. Nevertheless, detailed information is often missing, old, or overspecialized for cropland. Forest soils are poorly understood. Available information on soil may be difficult to understand and easily laid aside because of differences in technical language, knowledge, and approaches. Soil resource knowledge is very important but even that which is available may not be used. The Alpha unit work seeks to solve that problem. It gives specific recommendations based on data provided about a site, then provides access to backup knowledge, explanations, and other material. Soil mapping or data processing problems are often those of deciding about the borders between distinctively different soils The boundary line must be mapped. This is a statistical decision because the borders are mixed, blurred, fuzzy, and the task is one of discriminating at some level of confidence--full-well realizing the high probability of marking the edge along the wrong line--one deviating from the true line a great amount.
These problems have been engaged in past GIS work. They cannot be solved; they must only be engaged. An effort must be made; a least-bad solution found. At present, we operate on the hypothesis that we have such a solution and it is one of cell-level modeling. Soil can be modeled.
We operate on the hypothesis that natural variation is very, very great (for the above reasons and the wild, wild stirrings of capricious nature and those of a more allegedly-rational humankind). We assume that, given geology and then so-called parent material (rocks) is operated upon by rainfall, other
precipitation, temperature (namely freezing and thawing episodes), and gravity, and then mixed (or not) with humus and then soaked (or not) with water. The results are several hundred types - at least enough to create an interesting and diverse map. How to group them to minimize internal variability in the newly formed group is more of a problem of discrimination than of soil science. Great discrimination is already possible! Every cubic centimeter of soil is probably unique. The grouping that is done is artificial and functionally unnecessary. Each point on Earth has a unique, computable type number. The problem becomes one of comprehension.
Once comprehension was a mapping and classification problem, a need to develop chunks of information that decision-makers could see and manipulate. This is now no longer as necessary, perhaps not necessary at all, because computers can aid in this manipulation. By delineating an area of interest, the context of the problem, and specifying variables relevant to a particular set of objectives (e.g., minimize risks of slippage), then it is now possible to produce a data set related to each objective (e.g., one of an index to soil slippage) and to complete the now-almost-trivial act of mapping the data in shades of gray or color. The indexes can be combined and the resulting new data set mapped. An answer may be produced, for example, "soils in area A that are suitable for airports for light craft." Some conventional soil type series "name" is not produced but a map of a function or capability. The map (really a list of x, y coordinates and a value for each such point or area) may include I to 10 conventional soil types but not all of any type. Thus,functional maps are produced, one for each named, described, function. Such maps could be (can now be) produced from soil type or series maps, but they are likely to be overly gross because of the over-aggregation and the associated losses of information that occurred in the first conventional cartographic mapping phase of the soil work, e.g., reducing 7,200 types to 20 mappable units.
The next problem, one rarely pressed even with conventional soil maps, is a use-specific soil map. This is not a type but a use-only map, one grouping soil types that have the same probable suitability for a named used. For example, a map may be requested showing soils that are suitable for growing plant Q. All soils with this suitability would be mapped. It might include 10 types. Where the plant Q may be or should be grown are questions only slightly related to soil but are questions that do relate to costs, growing season, past use, distance to roads, and other factors. The desired map is one of soil suitability only. This is a map of function: "Where are soils probably functionally satisfactory for plant Q?" Everyone knows such a map will be general. It will omit "good"" areas (meeting many criteria) and include bad areas. Relative suitability perhaps along a probability or score continuum may also be mapped.
We have sought ways to overcome these problems. If not careful, we can adopt concepts, technology, and techniques useful for a pre-computer age and try to build on or around them with possible disasters and at least very great costs.
Alternatively, systems now can capture errors, allow a data base to be improved , and then develop functional equations or rules by which all areas with similar characteristics may be identified.
Similarly, broad classes of past use can be included in a data base. This discussion leads us farther and farther away from soil mapping and concepts of soil types and series and toward dynamic computer models of the functional lithosphere.
The lithosphere is one of the four major, all-inclusive, interactive taxa of the world. These are shown in Fig. 1. The "functional lithosphere" means those aspects serving or potentially serving people, in other words, Earth-related resources. Everything is related; importance is relative; most things in nature are continuous. We do not argue for strict limits in a world view or in soil types, only for ability to designate areas that are probably useful for a particular purpose. Within the lithospheric component can be included geology, earth minerals, mining, and soil. Groundwater is a conspicuous element and an example of the relations symbolized by the arrows between the litho- and hydrosphere in Fig 1. We can now make an estimate of soil differences, unique Alpha units, region wide, with the previously-listed seven factors ... without ever having stepped onto the ground! This estimation is the" first cut" and we do go to the field, but the point being made here is that we have far greater precision and power of discrimination than in current soil maps. We believe, based on limited use, that our map of Alpha units will serve the landowner well, cost effectively, when soil is a component of a resource decision.
In 1972, Epperson and probably others before him were telling people that virtually all crops can be grown without soil in various culture media. Soil is thus not essential for plants but is essential for economically feasible production of many species of plants for people. Good soils produce; the better they are perceived to be. The better the soil, the lower is the production costs. Reducing soil to a notion of monetary worth is bothersome because for many people there are other dimensions, some almost spiritual, but temporarily for analysis, it is useful to make some reductions. The worth of soil can be viewed as "opportunity cost"; how much more must I invest to produce the same crop in the same environment without the soil as with it? Then what is the net difference?
Limited strategic sampling, high technology analyses, and expert system interpretations are currently available and improvements underway. The intent of a soil resource management system is to reduce undesirable change in soils; expedite desired change; assure that plants are on the proper (productive) soils; assure maximum resilience for the future; overcome inherent limitations to productivity; rehabilitate or restore soil to productivity; all at minimum or acceptable costs. A flexible objective is needed, one that includes the potential financial returns from a particular alpha unit minimizing cost to achieve a stated consdition and desired level of control.
The principles are simple but devising a clear objective and unique strategies for each alpha unit are difficult. Key parts of an overall strategy (which might be formulated as weighted objectives) are:
Although some procedures are intimate to stating and describing objectives (above), there are many possible ways pf reducing erosion as part of a soil management system:
Every harvest level can increase annual water yield, longterm runoff (cumulative) and annual sediment yield. Every cut-back in such levels (at presumably some loss or sub-optimization) can reduce sediment. The cost of sediment reduction can thus be estimated.
See IFDC - International Center for Soil Fertility and Agricultural Development and ISFM (integrated soil fertility management - improved fallows, legume rotation, animal feeding, agroforestry, phosphate rock use)).
Literature Cited
Stone, E.L. and R. Kszystyniak. 1977. Conservation of potassium in the Pinus resinosa ecosystem. Science 198:192-194.
FAO. Digital soil map of the world and derived soil properties on References
Blake, G. R. 1965. Particle density. Soil Analysis. Agronomy 9:371-373. Wis. In C. A. Black (ed.) Methods of Amer. Soc. of Agron., Madison,
Brasher, B. R., D. P. Franzmeier, V. Valassis, and S. E. Davidson. 1966. Use of Saran resins to coat natural soil clods for bulk density and water retention measurements. Soil Sci. 101:108.
Bremner, J. M. and D. R. Keeney. 1964. Steam distillation methods for determination of ammonium, nitrate and nitrite. Analytica Chemica Acta. Elsevien Publishing Company, Amsterdam.
Ewing, H.A. 2002. Influence of substrate on vegetation history and ecosystem development. Ecology 83(10):2766-2781
Green, R. E. and J. C. Corey. 1971. Calculation of hydraulic conductivity: a further evaluation of some predictive methods. Soil Sci. Soc. Amer. Proc. 25:3-8.
Jenny, H. 1941. Factors of soil formation. McGraw-Hill, New York.
Nielsen, G. A. and F. D. Hole. 1963. A study of the natural processes of incorporation of organic matter into soil in the University of Wisconsin Arboretum. Wis. Acad. Sci. Arts and Letters. Trans. 52:213-227.
This Web site is maintained by R. H.
Giles, Jr.
Bulk Density
General Texture
equal or greater than 2.6(quartz rock)
mineral soils
> 1.8
parent material
1.8 - 1.3
coarse texture
1.3 - 1.0
fine texture
0.2 - 0.6
organic soils
General relationship among texture, bulk density, and porisity of soils
Texture class
Bulk density
Porosity(%)
Relative density
Cube root of
relative
density
Sand
1.55
42
0.58
0.83
Sandy loam
1.40
48
0.53
0.81
Fine sandy loam
1.30
51
0.49
0.79
Loam
1.20
55
0.45
0.77
Silt loam
1.15
56
0.43
0.75
Clay loam
1.10
59
0.41
0.74
Clay
1.05
60
0.39
0.73
Aggregated
clay1.00
62
0.38
0.72
M = available soil moisture;
A = soil aeration;
N = available soil nutrients (some are present but cannot be taken up by plants);
L = light (both quantity and quality) spectra);
H = heat (often degree days);
V = vegetation (species and genetic traits);
They are related. There are 30 possible relationships.
Soil Formation and Rock Weathering
are affected by soil texture and organic matter.
Mineral
Composition: Ranges for
ModelingRegional Example
Organic Matter
10.0 - 0.40
4.0
Nitrogen
0.50 - 0.02
0.15
Phosphorus
0.20 - 0.01
0.04
Potassium
3.30 - 0.17
1.70
Calcium
3.60 - 0.07
0.40
Magnesium
1.50 - 1.12
0.30
Sulfur
0.20 - 0.01
0.04
can be very high.
TheRural System staff seeks advice on improving the knowledge base for this work, for improving understanding (and avoiding misunderstanding) and for making the ideas more logical, reasonable, and finally useful -- achieving objectives from the soil resource as part of a total system.
CD-ROM. FAO. Instructions for ordering may be found at:
http://www.fao.org/WAICENT/FAOINFO/AGRICULT/agl/agls/T1.HTM
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Last revision November 15, 2000.