HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Steiner, J.L. Articles by Cresap, J. Search for Related Content PubMed Articles by Steiner, J.L. Articles by Cresap, J. Agricola Articles by Steiner, J.L. Articles by Cresap, J.

DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

Biomass and Residue Cover Relationships of Fresh and Decomposing Small Grain Residue

^a USDA-ARS, 1420 Experiment Station Road, Watkinsville, GA 30677 USA
^b USDA-ARS, P.O. Drawer 10, Bushland, TX 79012 USA

jsteiner{at}arches.uga.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
Maintaining residue cover provides diverse conservation benefits. Exponential relationships have been developed to estimate cover from biomass of randomly distributed, flat residues, but a large portion of crop biomass remains standing after harvest. Our objective was to determine how relationships between biomass and soil cover change in no-tillage small grain fields as residues decompose and shift from standing to flat. Winter and spring wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and spring oat (Avena sativa L.) were grown at Bushland, TX, on Pullman clay loam (fine, mixed thermic Torrertic Paleustoll) in 12 field plots in three randomized complete blocks. For each crop, differential seeding rate, fertilization, and irrigation produced a range of biomass. During decomposition, differential irrigation increased environmental variability (13, 5, and 0 applications to sub-sub-plots). Ash-free residue biomass was measured seven times in 14 mo, after taking photographs to determine soil cover of 1-m² sites. For crop-date combinations, coefficients were determined from total (k_t, m² g^-1) or flat (k_f, m² g^-1) biomass. Regression indicated k_t increased with time (P < 0.0001 for all crops, except spring wheat with P < 0.0041). Across crops, the relationship indicated that decomposition affects cover provided by total biomass. Across crops, the weak relationship indicated that cover could be estimated from flat biomass with k_f 0.0175 for extended periods. These findings can improve estimation of residue cover for no-tillage fields and indicate that residue orientation should be considered in biomass-to-cover relationships.

Abbreviations: DAH, days after harvest • k_f, flat biomass cover coefficient (m² g^-1) • k_t, total biomass cover coefficient (m² g^-1) • M, residue biomass (g m^-2)

	INTRODUCTION

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
CONSERVATION TILLAGE SYSTEMS are adopted for a wide variety of reasons, including decreased production costs, decreased labor, and resource conservation. Many natural resource conservation benefits are attained by retaining increased crop residue cover over longer periods of time, including increased infiltration, reduced evaporation, and reduced soil erosion in the short term as well as long-term enhancements in soil organic matter and structure (Steiner, 1994).

Maintaining surface residue cover is often recommended to reduce erosion by water and wind. Residues contribute to erosion control both through sheltering the soil with a nonerodible material (cover) or through changing the surface conformation in ways that change the flow of water and wind across the surface (roughness or resistence). Both aspects are important for both wind and water erosion. The fraction of soil covered by crop residue also influences raindrop impact on soil surface properties (aggregation, crusting, etc.) and on the surface aerodynamic properties (Hagen, 1991). The processes of wind and water erosion are interactive—changes in soil or residue surface properties by either wind or water impacts the erodibility of that surface when exposed to future wind or water erosive forces.

In spite of this complexity, where erosion is primarily by water, the required amount of residue has been based on surface cover (with 30% cover required after planting the subsequent crop) with less focus on conformational aspects. Wind erosion control programs have also often been based on cover, in spite of the fact that the height and number of standing residue elements are also related strongly to the degree of erosion control achieved (Bilbro and Fryrear, 1994; Nielsen and Aiken, 1998). Standing residues contribute less to cover than flat residues, but persist longer because of slower decomposition rates. Additionally, standing stems probably provide more soil protection than a vertical-view cover estimate indicates, because erosive rains often come in storms where rain is blown by strong winds at low angles to the surface. Standing stems intercept blown rain along the length of the stem, reducing the direct impact of raindrops on soil particles (Morrison et al., 1984).

The relationship between residue biomass and residue cover has been described for many crops and exhibits an exponential relationship, assuming randomly distributed, flat residue elements (Gregory, 1982). Additionally, Gregory (1982) showed that the exponential cover coefficient was shown to be related to the area/biomass ratio of individual residue elements (Greb, 1967) that provide the cover. The biomass-to-cover relationship plateaus at high biomass levels, so considerable biomass decomposition may occur before cover decreases. If crop biomass is low, decomposition will be associated with loss of cover. For leafy residues, there may be loss in soil cover with relatively little change in biomass, because leaf material decomposes rapidly and provides significant cover per unit biomass compared with stem material (Gregory, 1982). Erosion processes can also redistribute residue elements, particularly the light material, and decrease their effectiveness in providing cover. Leaves contribute relatively less biomass than stems. Soluble carbohydrates, which comprise as much as 20% of the biomass at harvest, can leach into the soil with little change in cover. Decomposition may occur in the stem's interior, leaving the stem exterior (and soil cover) relatively intact. For a given species, a similar percent cover can be achieved in field environments with a wide range of residue biomass and distribution. The change of cover over time depends on the initial amount and distribution of residue. McCool et al. (1995) analyzed relationships between reduction in cover and reduction in surface biomass associated with tillage operations, but no similar analysis is available for changes in cover with reduction in surface biomass by decomposition.

Estimating soil cover from biomass remains useful, despite the difficulties described above, because field measurements are also problematic. Recommended line-intercept measurement methods are often subjective and produce highly variable results (Morrison et al., 1995). Available remote sensing techniques that are economically feasible cannot reliably separate background soil from residue cover across the wide range of conditions found in agricultural fields (Daughtry et al., 1996).

Erosion models include residue decomposition submodels (Stott et al., 1995; Steiner et al., 1995) because of the extreme sensitivity of erosion prediction to surface residues. Residue decomposition models usually predict biomass loss (Stroo et al., 1989; Ghidey et al., 1985; Douglas and Rickman, 1992), and sometimes maintain separate pools of residue such as standing and surface biomass (Stott et al., 1995; Steiner et al., 1995). In semiarid regions, residue may remain standing for over a year following harvest (Tanaka, 1986; Steiner et al., 1994).

Different researchers have had different results when trying to compare the relationship among residue biomass and surface cover for different small grain crops. Greb (1967) analyzed several small grains and indicated the highest coefficient for spring barley and lower coefficients for winter wheat and oat. McCool et al. (1990) found the highest coefficient for spring wheat, a lower coefficient for winter barley, and three different lower coefficients for three winter wheat varieties. Data of Sloneker and Moldenhauer (1977) indicate exceptionally high cover per unit biomass of oat for field residue samples collected over several months as they decomposed and were degraded by tillage operations.

To estimate adequately soil cover from residue biomass, better information is needed about cover provided in no-tillage fields where a portion of the biomass may be near vertical. Our objective was to determine how residue biomass and surface cover relationships change in no-tillage small grain fields as residues decompose and shift from standing to flat orientation.

	Materials and methods

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
Field Experiments
Residue biomass and cover over time were monitored under no-tillage management for `TAM-107'<sup>1</sup> winter wheat, `Oslo' spring wheat, `Post' winter barley, and `Lew' spring oat at the USDA, Agricultural Research Service, Conservation and Production Research Laboratory, Bushland, TX (35° N lat., 102° W long., 1170 m elev., 476 mm mean annual precipitation; 13.3°C mean annual temperature). The climate is continental, with precipitation falling predominately in the summer months and potential evaporation more than double precipitation in all months. Crops were grown on a Pullman clay loam (fine, mixed thermic Torrertic Paleustoll) in 0.25 m north-south rows. Twelve main plots (12 by 70 m) were arranged in three randomized complete blocks of the four crop treatments. High, medium, and low initial biomass subplots were established for each crop by differentially managing seeding rate, fertilization, and growing season irrigation (Steiner et al., 1994, 1999). Harvest height was varied in each plot to leave as much residue standing as possible, while still removing the lowest heads. The highest biomass plots tended to have the highest combine header height, but height of standing stubble was not measured by plot. Following harvest, each crop-density subplot was divided into thirds for decomposition-period treatments consisting of nonirrigated, full irrigation, and alternate-date irrigation, randomly assigned to sub-sub-plots. Full-irrigation plots were irrigated to maintain a moist surface (as often as weekly) as described in Steiner et al. (1999) and alternate-date irrigation was on roughly every other full-irrigation date (in some cases rainfall following the full-irrigation application made the alternate-date application unnecessary). No irrigation was applied when daily mean air temperature was at or below freezing.

Residue biomass samples were collected from controlled traffic areas of the plots and processed seven times in 14 mo as described in Steiner et al. (1999). Briefly, standing residues (>10° from horizontal) were collected and processed separately from flat surface residues. Standing residues were washed to remove soil, if needed. All samples were dried at 60°C and weighed. Surface residue samples were ground to pass a 0.635-mm screen and subsamples were weighed, ashed in a muffle furnace at 500°C for 4 h, and weighed to determine the soil fraction of the sample. Residue mass for the surface samples were corrected to ash-free mass and summed with standing-stem biomass obtain total biomass. Initial biomass and cover measurements were made in July 1991 (24 d after harvest, DAH) and continued until August, 1992 (401 DAH). Harvest of all crops occurred over about a 3 wk period in June 1991 during an extremely hot, dry period. Though the actual days from harvest was different for different crops and plots, residues had not undergone decomposition on any plot and the decomposition time was treated as the same for all plots.

Prior to obtaining each destructive biomass sample, a nadir view photograph was taken of the 1-m² area to be sampled, similar to the method reported by Molloy and Moran (1991). The ends of the 1.0-m plots were marked with white plastic pipe, perpendicular to the rows. A 35-mm camera was supported at 3.5 m above the plot on a boom. Photographs were taken with Kodachrome 64 ASA slide film, an aperture setting of F8 or F11, and the camera shutter speed set on automatic control. A zoom lens was set at 70-mm focal length. A shutter release cable was used to take the picture after the camera was in position. Photographs were taken 1 d and biomass sampling required multiple days, depending on the degree of residue decomposition and number of available workers.

Contrast between soil and residue was poor, particularly when the soil was dry and/or the residues were aged. The best contrast was obtained when skies were overcast. On clear days, the photographs were taken near solar noon to minimize shadows. McCool et al. (1989) used shades to reduce shadows in photographs taken for cover estimates. Because of the low and variable contrast between soil and residue, manual counting (description follows) was determined to be the most reliable method of determining residue cover as compared with image analysis.

Residue cover of each plot was measured by projecting the slide onto a screen for counting. To prepare a screen for counting, a rectangle proportional to the sample site dimensions was drawn on stiff, smooth matte board. (Although the soil area sampled was square, projection of the area was rectangular.) Marks were randomly placed at 1- to 10-cm intervals along the perimeter of the rectangle and lines were drawn at 45° angles to the perimeter line from each mark. Lines drawn from the upper and right side of the rectangle were perpendicular to the lines drawn from the lower and left sides. A 3-mm circle was drawn on the screen at each intersection, with a total of 163 points, consistent with recommendations of Morrison et al. (1989). Our procedure provided randomly located dots to avoid bias associated with rows in determining cover, but the diagonal lines could be traced visually to ensure that all dots were counted only once.

To determine residue cover, a slide was projected onto the screen and adjusted so the 1.0-m² sample area was projected within the rectangle. Each of the 163 dots were examined to determine if soil or residue projected onto the dot. For plots with high residue cover, residue misses were counted with a hand-held inventory counter. For plots with low residue cover, residue hits were counted. Fraction cover was calculated by dividing the number of hits by 163.

Calculating Biomass-to-Cover Coefficients
The exponential relation between cover and biomass developed by Gregory (1982) was fit to determine the cover coefficient for each crop on each date.

where M is residue biomass (g m^-2), and k is a cover coefficient (m² g^-1) and cover is the fraction of soil covered.

Statistical Analysis
Cover coefficients were determined for each crop-sample date combination with total biomass used to calculate k_t or flat biomass to calculate k_f by the MODEL procedure of SAS (1988). For each sample date and crop, data were pooled across density and irrigation treatments to obtain a range of biomass and cover values. We determined relationships of k_t and k_f to DAH using the GLM procedure (SAS, 1989) and analyzed for heterogeneity of slopes for different crops using the procedure described by Freund et al. (1986) for solving linear models within the GLM procedure.

	Results and discussion

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
The biomass relationship to soil cover is shown for all sample dates for winter wheat and oat (Fig. 1) . Spring wheat and barley data produced similar results as wheat and oat (data not shown). As Fig. 1 shows, data collected prior to winter (24 and 92 DAH) tended to produce less soil cover per unit biomass than samples collected later. This is logical because of the large fraction of standing biomass that produced relatively little soil cover (Table 1) , and because little soluble material would have leached from the residues prior to the initial sampling on 24 DAH. For flat biomass, the relationship of biomass to cover changed less over time than with total biomass.

View larger version (25K): [in this window] [in a new window]   Fig. 1 Relationship between total and flat biomass for winter wheat and oat crops. Two prewinter sample dates (24 and 92 DAH), the final sample date (401 DAH), and all other sample dates combined, are identified

The relative effectiveness of the biomass of different small grain species in providing soil cover remains unclear when comparing our results to other reports in the literature. Our experiment showed that across the decomposition period, winter wheat provided the most and spring wheat the least cover per unit biomass, differing from the results of Greb (1967) and McCool et al. (1990). Our data indicate that oat provided an average amount of cover compared to other small grain residues, in contrast to the findings of Sloneker and Moldenhauer (1977) that oat provided exceptionally high cover per unit biomass (0.014 m² g^-1). For fresh residues (24 DAH), our k_t values were 0.0026 for spring wheat, 0.0045 for winter wheat, 0.0055 for oat, and 0.0059 for barley. The average k_t across crops on 24 DAH, 0.0046 m² g^-1, is similar to the average of values reported for small grains by Gregory (1982) of 0.0055 m² g^-1 [0.0049 m² g^-1 without the value for oat from Sloneker and Moldenhauer, (1977)], and 0.0057 m² g^-1 reported by McCool et al. (1990) for freshly harvested stem residues from small grain species and cultivars in northwestern USA. Our biomass data included all residue, including fine material such as small leaf fragments and awns.

Our k_t values diverged from published values at later sampling dates when residue elements were leached and highly decomposed. The time trend in the coefficients for each crop is shown graphically in Fig. 2 . The k_t values increased over time, indicating that residue cover decreased more slowly than residue biomass. For both total and flat biomass relationships, winter wheat on 401 DAH provided over 0.8 fraction cover with less than 50 g m^-2 biomass (Fig. 1), indicating that residue elements maintained integrity and provided soil cover when the residue had decomposed to very low levels.

View larger version (21K): [in this window] [in a new window]   Fig. 2 Change in the biomass to cover coefficients for total biomass (kt) and flat biomass (kf) over time

	Conclusions

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
Frequently, soil cover is estimated from total biomass for use in erosion estimation and other applications. Our data showed that in no-tillage conditions, the relationship between total biomass and cover changed significantly as the residues decompose. After harvest, a high proportion of total biomass is standing stems that provide relatively little cover. On a seasonal basis, contribution of the standing biomass to cover is important, because the stems provide soil cover as they fall later in the decomposition season. The relationship between flat biomass and cover was relatively stable over time and our results indicate that the same coefficient could be used through the season with relatively little error.

There is a range of values for cover coefficients for different small grains in this study (k_t and k_f) and in the literature (k_t). There is no consistent trend showing which small grain species is most efficient in providing cover, although data from Sloneker and Moldenhauer (1977) for oat (0.014 m² g^-1) and our winter wheat data (0.042 m² g^-1 on 401 DAH) indicate that an exceptional amount of cover can be provided with a small amount of decomposed small grain residue. Genotype, climate, and other agronomic conditions appear to affect the coefficient value as well as species. Use of an average 0.005 m² g^-1 taken from published values of for freshly harvested small grains appears warranted at this time, unless a coefficient value can be measured for a specific application. However, our data indicate that it would be better to estimate soil cover on the basis of flat residue biomass rather than total residue biomass by means of an average of . Alternatively, a value for k_f varying from 0.015 m² g^-1 for freshly harvested residue to 0.02 m² g^-1 for highly decomposed residue could be used. If total residue biomass is used to estimate cover over extended periods of time, the k_t will vary considerably, from 0.004 m² g^-1 for fresh residues to 0.023 m² g^-1 for highly decomposed residues.SAS Institute 1988; SAS Institute 1989

	NOTES

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
¹ Reference to a trade or company name is for specific information only and does not imply approval or recommendation of the company by USDA to the exclusion of others that may be suitable.

Received for publication May 14, 1999.

	REFERENCES

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 

• Bilbro J.D., Fryrear D.W. Wind erosion losses as related to plant silhouette and soil cover. Agron. J. 1994;86:550-553.[Abstract/Free Full Text]
• Daughtry C.S.T., McMurtrey J.E., III, Chappelle E.W., Hunter W.J., Steiner J.L. Measuring crop residue cover using remote sensing techniques. Theor. Appl. Climatol. 1996;54:17-26.
• Douglas C.L., Jr., Rickman R.W. Estimating crop residue decomposition from air temperature, initial nitrogen content, and residue placement. Soil Sci. Soc. Am. J. 1992;56:272-278.[Abstract/Free Full Text]
• Freund, R.J., R.C. Littell, and P.C. Spector. 1986. SAS System for linear models. p. 154–157. SAS Institute, Inc. Cary, NC.
• Ghidey F., Gregory J.M., McCarty T.R., Alberts E.E. Residue decay evaluation and prediction. Trans. ASAE 1985;28:102-105.
• Greb B.W. Percent soil cover by six vegetative mulches. Agron. J. 1967;59:610-611.[Abstract/Free Full Text]
• Gregory J.M. Soil cover prediction with various amounts and types of residue. Trans. ASAE 1982;25:1333-1337.
• Hagen L.J. A wind erosion prediction system to meet user needs. J. Soil Water Conserv. 1991;46:104-111.
• McCool D.K., Kok H., McClellan R.C. Cover versus mass relationships for small grain residues. St. Joseph, MI: ASAE, 1990 Paper 90-2040..
• McCool D.K., Stott D.E., Laflen J.M., Schertz D.L. Residue incorporation by tillage—Interaction of mass and cover. J. Soil Water Conserv. 1995;50:563-567.
• McCool D.K., Young F.L., Papendick R.I. Crop and tillage effects on residue cover. St. Joseph, MI: ASAE, 1989 Paper 89-2155..
• Molloy J.M., Moran C.J. Compiling a field manual from overhead photographs for estimating crop residue cover. Soil Use Manage. 1991;7:177-183.
• Morrison J.E., Jr., Chichester F.W., Escobar D.E. Measurement of residue cover with dot screens. J. Soil Water Conserv. 1989;45:542-544.
• Morrison J.E., Jr., Gerik T.J., Bartek L.A. Raindrop view of soil and residue. St. Joseph, MI: ASAE, 1984 Paper 84-2042..
• Morrison J.E., Jr., Lemunyon J., Bogusch H.C., Jr. Sources of variation of nine devices when measuring percent residue cover. Trans. ASAE 1995;38:521-529.
• Nielsen D.C., Aiken R.M. Wind speed above and within sunflower stalks varying in height and population. J. Soil Water Conserv. 1998;53:347-352.
• SAS Institute, Inc. SAS/ETS user's guide, Vers. 6, 1st ed Cary, NC: SAS Institute, 1988.
• SAS Institute, Inc. SAS/STAT user's guide, Ver. 6, 4th ed., Vol. 2. Cary, NC: SAS Institute, 1989.
• Sloneker L.L., Moldenhauer W.C. Measuring the amounts of crop residue remaining after tillage. J. Soil Water Conserv. 1977;32:231-236.
• Steiner J.L. Crop residue effects on water conservation. In: Unger P.W., ed. Managing agricultural residues. Boca Raton, FL: Lewis Publishers, 1994:41-76.
• Steiner J.L., Schomberg H.H., Douglas C.L., Jr., Black A.L. Standing stem persistence in no-tillage small-grain fields. Agron. J. 1994;86:76-81.[Abstract/Free Full Text]
• Steiner, J.L., H.H. Schomberg, and P.W. Unger. 1995. Residue decomposition submodel. p. D1–D12. In L.J. Hagen, et al. (ed.) Wind erosion prediction system (WEPS). Technical Documentation. Beta release 95-08. Wind Erosion Research Unit, USDA-ARS, 2004 Throckmorton Plant Science Center, Kansas State University, Manhattan, KS.
• Steiner J.L., Schomberg H.H., Unger P.W., Cresap J.L. Crop residue decomposition in no-tillage small-grain fields. Soil Sci. Soc. Am. J. 1999;63:1817-1824.[Abstract/Free Full Text]
• Stott, D.E., E.E. Alberts, and M.E. Weltz. 1995. Residue decomposition and management. p. 9.1–9.16. In D.C. Flanagan and M.A. Nearing (ed.) WEPP Technical Documentation. USDA- Water Erosion Prediction Project (WEPP). NSERL Report No. 10. Nat. Soil Erosion Res. Lab., USDA-ARS, 1196 SOIL Building, West Lafayette, IN.
• Stroo H.F., Bristow K.L., Elliott L.F., Papendick R.I., Campbell G.S. Predicting rates of wheat residue decomposition. Soil Sci. Soc. Am. J. 1989;53:91-99.[Abstract/Free Full Text]
• Tanaka D.L. Wheat residue loss for chemical and stubble-mulch fallow. Soil Sci. Soc. Am. J. 1986;50:434-440.[Abstract/Free Full Text]

This article has been cited by other articles:

				K. Oorts, P. Garnier, A. Findeling, B. Mary, G. Richard, and B. Nicolardot Modeling Soil Carbon and Nitrogen Dynamics in No-till and Conventional Tillage Using PASTIS Model Soil Sci. Soc. Am. J., March 12, 2007; 71(2): 336 - 346. [Abstract] [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Steiner, J.L. Articles by Cresap, J. Search for Related Content PubMed Articles by Steiner, J.L. Articles by Cresap, J. Agricola Articles by Steiner, J.L. Articles by Cresap, J.