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II. Decomposition of Surface-Managed Crop Residues
INFLUENCE OF NO-TILL CROPPING SYSTEMS
tively constant between growing seasons and yield differences; however, the individual components of the residues decompose at different rates. The total C,
total N, soluble C, and nonstructural carbohydrate concentrations of the wheat
residues were not correlated with residue or grain yields (Collins et al., 1990b).
The decomposition rates of the individual parts were closely related to the carbohydrate, lignin, C, and N contents. When the individual residue components were
mixed in the ratios found in the intact residues the residue mixes decomposed
approximately 25% more rapidly than what was predicted from the decomposition
rates of the individual parts (Collins et al., 1990b). McClellan et al. (1987) observed that more than 30% of the wheat residue mass after harvest consisted of
The potential decomposition rate of wheat straw can be based on the size of the
readily available C and N pools (Knapp et al., 1983a,b; Reinertsen et al., 1984).
From this work, it was postulated that microbial extracellular materials such as
polysaccharides might dominate the aggregation process shown by decomposing
straw if the wheat straw contained low N and if alternate sources of N were unavailable. Elliott and Lynch ( I984a) aerobically degraded three wheat straws containing 1.09,0.5,and 0.25% N in the absence of added N. The 0.25% N straw treatment produced significantly more aggregation in the soils tested than the other
treatments. The 0.5% N straw treatment generally caused more aggregation than
the I .095% N straw. The largest microbial biomass would be generated from the
straw containing the most N; thus, these results confirm the postulate that the increased aggregation resulted from the microbial production of extracellular gums.
Electron micrographs also showed more gum production from the 0.25% N containing straw than the 1.09% N straw (Figs. 1 and 2). Polysaccharides have long
been implicated in having a positive effect on the soil aggregation process (Tisdall
and Oades, 1982). The results of these studies show that there is potential for improving soil aggregation through residue management on the soil surface and by
reducing tillage because tillage increases the rate of soil organic matter mineralization and N availability (Rovira and Greacen, 1957). Improved soil aggregation
increases water infiltration, resistance to wind and water erosion, and probably soil
Reinertsen et ul. (1984) and Stroo et al. (1989) considered cereal substrates to
consist of three separate fractions (pools) based on availability to microorganisms.
They designated the pools as readily available, intermediately available (cellulose
and hemicellulose), and resistant (lignin). With their model, they were able to predict wheat residue decomposition across climatic zones. The amount of readily
available C and N controls the size of the initial microbial biomass and the initial
decomposition rate. Readily available C and N content of the residues increases as
the C/N ratio decreases (Reinertsen et al., 1984). Residues high in total N tend to
be high in soluble N (Iritani and Arnold, 1960).
Most models treat decomposers entirely as microbes. When fauna was exclud-
Figure 1 Microbial growth and gum production during the decomposition of wheat straw containing 0.25% N.
Figure 2 Microbial growth with no visible gum production during the decomposition of wheat
straw containing 1.09% N.
INFLUENCE OF NO-TILL CROPPING SYSTEMS
ed from litter bags, decomposition rates in grasslands, in contrast to forests, are little affected (Curry, 1969). Stroo er al. (1989) showed fauna accounted for 5% or
less of the CO, respired during wheat straw decomposition. It must be noted these
studies were conducted under a specific set of conditions and the environment will
vary greatly depending on location. For example, in warmer climates, termites
play a significant role in residue degradation. Distinguishing each group of decomposers and their relative contributions would be almost impossible with current technology (bacteria, fungi, actinomycetes, fauna, etc.). This precision is unnecessary for current predictive needs. Therefore, models generally assume that
under optimal environmental conditions there is an adequate decomposer population present to sustain the maximum decomposition rate.
There must be favorable moisture and temperature conditions for biological activity. However, each group, and even subgroup, of decomposers has a range of
climatic conditions in which they are active and an optimum at which they are most
active. Fluctuating climatic conditions, especially those that go beyond the range
of the decomposer, are more deleterious to decomposers than constant conditions
(Parr and Papendick, 1978). For practical considerations in the field, it is unlikely
that much biological decomposition occurs above 20°C. Effects of climatic conditions are different even with different phases of decomposition. For example,
Stott et al. (1986) found that low water potentials or low temperatures had significant effects on microbial activity only during the initial phase of decomposition.
Thus, both long-term and diurnal fluctuations of temperature and moisture must
III. MODELING CROP RESIDUE DECOMPOSITION
Generally, decomposition is evaluated across some time frame. Decomposition
can be related to degree days (DD) and calculated from air temperature (Douglas
and Rickman, 1992). Degree days are determined by measuring the daily mean air
temperature in "C and summing over the desired period. If the mean daily temperature is less than O"C, it is considered as zero. Zero is used as a base value because of reports by Wiant (1967) that microbial reactions follow the Van't Hoff
and Arrhenius' laws at temperatures below 40°C. Reiners (1968) confirms that this
is true down to 0°C. However, these relationships must be viewed with caution because dramatic change occur in the flora makeup and activity as temperatures
change. Stott et al. ( I 986) showed the response to temperature was more likely related to changers in the microflora as temperature was changed and this was the
reason the system did not respond according to Van't Hoff's and Arrhenius' laws.
Also, degree days may accurately predict in a defined climatic zone but will likely be inaccurate as moisture varies.
L. F, ELLIOT AND D. E. STOTT
In 1989, Stroo et al. published a mechanistic-based model for surface-managed
wheat residue decay in the field. The model simulates decay under constant environmental conditions using C and N dynamics. It then determines the impact of
environmental conditions, calculating the fraction of an optimum “decomposition
day” occurring in a4-h period. Information concerning initial residue C and N pool
availability is required for this model. The model was developed from mechanistic relationships developed in the laboratory and a series of field decomposition
studies in different climatic zones (Stott el al., 1990).
Once the theoretical model for crop residue decomposition was developed, the
next step was to simplify the model for use as a component in an expert system on
residue management (RESMAN) (Stott and Rogers, 1990; Stott et al., 1988; Stott,
1991). The goal in developing RESMAN was to incorporate residue decomposition knowledge with site- and situation-specific tillage considerations including
residue burial. Inputs for the expert system needed to be relatively simple and readily available to a wide variety of users.. Another feature was a user-specified need
for quick run times; thus, the expert system was unable to deal with the complexity of a true research model.
Since its release in 1990, RESMAN has been used widely by industry personnel, extension and soil conservation advisors, and private consultants throughout
the United States, Canada, and several other countries to develop crop residue
management strategies for soil conservation. Due to its wide acceptance, the theory
and equations used in RESMAN were incorporated into several erosion models
being developed by the USDA, including Revised Universal Soil Loss Equation
(RULSE), Revised Wind Erosion Equation (RWEQ), Water Erosion Prediction
Project (WEPP), and Wind Erosion Prediction System (WEPS), RUSLE was
implemented by the USDA-NRCS in its southeastern field offices in 1995 and will
be implemented in the remainder of the United States in 1996. RWEQ is to be implemented in late 1996. WEPP and WEPS, utilizing new erosion prediction technologies, are expected to be completed and implemented by the end of the decade.
USEDIN THE RESMAN
To simulate the decomposition process, the decomposition day concept as presented by Stroo et al. (1989) for winter wheat residue decomposition was used as
a basis for the residue mass loss calculation. The Stroo et al. (1989) model simulates residue decay under constant environmental conditions using C and N dynamics based on Knapp et al. ( 1983a,b)and Bristow er nl. (1986). The residue C is
split into three pools based on availability for use by the soil microbial population
and chemically defined. This information is not readily available for a wide variety