Tải bản đầy đủ - 0 (trang)
II. Decomposition of Surface-Managed Crop Residues

II. Decomposition of Surface-Managed Crop Residues

Tải bản đầy đủ - 0trang



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.



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

be inputs.


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.







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.



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

Tài liệu bạn tìm kiếm đã sẵn sàng tải về

II. Decomposition of Surface-Managed Crop Residues

Tải bản đầy đủ ngay(0 tr)