Agricultural Systems 42 (1993) 199-207

Analysis of the Effects of Soil Compaction on Cotton Yield Trends

F. D. Whisler, a V. R. Reddy, b D. N. Baker a & J. M. McKinion c

Department of Agronomy, Mississippi State University, PO Box 5248, Mississippi State, Mississippi 39762, USA

b USDA: ARS: BA: NRI: SRL, Systems Research Laboratory, Building 01 IA, Room 165-B, BARC-WEST, Beltsville, Maryland 20705, USA c Crop Simulation Research Unit, ARS, USDA, PO Box 5367,

Mississippi State, Mississippi 39762, USA

(Received 28 March 1991; revised verson received 6 May 1992; accepted 8 May 1992)

ABSTRA CT

Cotton (Gossypium hirsutum L.), yields in the U.S. Cotton Belt declined from 1960 to 1980 despite improvements in technology and introductions of higher yielding cultivars. As part of the effort to examine possible causes for the yield reduction, the cotton crop simulation model, GOSSYM, was used to analyze the effects of soil compaction on cotton yield trends. Weather, soil and cultural input data from six locations over 20 years were acquired and used or this study.

There were no consistent trends over all locations. Prior to 1974, compaction had some negative effect at Florence, South Carolina, but due to annual in-row subsoiling, had no effect after that time. At Stoneville, Mississippi, the effects of compactions were generally detrimental but the), were often masked by weather. In years of abundant moisture, wheel traffic compaction had little negative effect on yields', since shallow root systems could extract sufficient moisture for plant growth and yield. In extremely dry years, predicted yields were low for both compacted and uncompaeted crops. The effect of wheel compaction on yield was generally favorable at College Station, Texas. The lower yielding crop, however, generally put more of its photosynthate into roots during the boll filling period. This was also true at Phoenix, Arizona, where the results were erratic. At Lubbock, Texas, on a clay soil the effects of simulated compaction were negligible.

199

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200 F. D. Whisler, V. R. Reddy, D. N. Baker, J. M. McKinion

INTRODUCTION

It has been shown by Meredith (1982) that cotton yields have declined from 1960 to 1980. This decline has occurred in spite of several factors, including improvements in technology and the introduction of higher yielding cultivars, which should have had positive influences on yields. Furthermore, atmospheric carbon dioxide levels have increased, and in some areas, cotton acreage has shrunk to the more productive soils. The exact causes for the yield reductions have not yet been identified, but they appear to be attributable to complex interactions and managerial factors.

We have used the cotton simulation model, GOSSYM (Baker et al., 1983), to study this problem from the aspects of weather (Wanjura & Baker, 1988; Reddy & Baker, 1989); atmospheric gases (Reddy et al.,

1989), and herbicide injury (Reddy et al., 1990). Not one of these phenomena could account for all of the yield decline observed at any one location and the impact was different between locations.

One common reason suggested for the yield decline is that soil compaction has increased with the increased weight and size of farm equipment compared to 20-30 years ago (Brooks, 1977). This is a difficult problem to address analytically because: (1) there is such a range of tillage implements used on any one farm in soil preparation and cultivation for cotton production, (2) an even wider assortment of implements is used for these same practices across the U.S. cotton belt, (3) there is little quantitative information on the soil-applied pressures per unit area for each of the implements. However, some data do exist in sizes and sales records of farm implements in various parts of the cotton belt.

Our approach, then, was to use the cotton simulation model, GOSSYM, in a series of studies at different locations to evaluate the effects of soil compaction on cotton growth and yield.

METHODS

As in the other studies, six locations across the cotton belt (Florence, South Carolina; Stoneville, Mississippi; College Station, Texas; Lubbock, Texas; Phoenix, Arizona; and Fresno, California) were selected where field variety tests were located and most of the weather records were known. The weather and soils information for each location and year were used in GOSSYM to show the predicted yields and compared to the field observations (Wanjura & Baker, 1988: Reddy & Baker, 1989). While at all locations the major shifts in yields due to very wet or dry, hot or cold weather were predicted by the model, other more subtle

Effects o f soil compaction on cotton yieM 201

trends were not, thus yield disturbances due to herbicides and ozone were incorporated into the model to improve the fit to the real data (Reddy et al., 1989, 1990). The problem was further complicated in this study because: (1) at all but the California locations, the tillage operations and other soil traffic were done with plot-size implements (2- or 4-row), (2) in California no notes were available as to the size of implements used by the cooperating growers (therefore it was deleted from further study), (3) at Florence, a significant reason given for the lack of a yield decline was that in the early 1970s in-row subsoiling was begun as a standard practice, (4) few notes existed on the actual dates of cultivation; only the general number of cultivations at each location was available, and (5) at all but the College Station locations, the plots were moved during this 20-year period.

In order to try to evaluate some of the effects of increased implement size and weight, we simulated the effects of tractor tire compactions on cotton growth and yield. We followed the logic that the rear tractor tire will contact the widest area of soil between the row of any implement used prior to harvesting, and that yield effects predicted for compaction of every middle (old 2-row, tricycle-type equipment) could be linearly averaged with effects in non-compacted middles for larger or wider operation, i.e. 4-row equipment would average 1/2 compacted + 1/2 uncompacted, 6-row equipment would average 1/3 compacted + 2/3 un- compacted, etc. We have reported such analyses in more limited studies elsewhere (Baker et aL, 1979; Whisler et al., 1982).

The basic tractor weights, tire sizes, and soil-applied pressures were supplied by Deere and Co. for the period 1962-1984 for the different regions of the cotton belt (Lyle Stephens, Deere and Co., personal communication). These data were used in a model provided by Gupta & Allmaras (1987) that predicted the soil bulk density from various applied pressures and bearing surfaces for soils of different textures and water contents as a function of depth and distance from the tire tread. These values of bulk density, depth, and porosity were then used to change the soil hydraulic and impedance properties within the wheel track zone and used in GOSSYM. Soil hydraulic properties were measured on samples taken from as close to the plot area as possible.

RESULTS AND DISCUSSION

The comparisons of the effects of wheel traffic compaction to non- compacted conditions are shown for all locations for certain years in Tables 1 and 2. In addition to yield, several other related parameters

202 F. D. Whisler, V. R. Reddy, D. N. Baker, J. M. McKinion

TABLE1 Simulated LintYields(kghal) of Non-compacted(NC) versusCompacted(C)Soils

~ a r Horence, SC Stoneville, MS CollegeStation, TX Lubbock, TX Phoen~,AZ

NC C NC C NC C NC C NC C

1962

1963 1853 1613 1964 1 217 1 270

1965 1479 1498 1966 1072 1090

1967 1 547 1 664 1968 1 605 1 627 1969 1 292 1 492 1970 2 061 2 094 1971 1 539 1 678

1972 1 565 1 635 1973 1993 1808

1974 1975 1976 1977 1978 1979 1456 1456 1980 1 642 1 642

1981 1 981 1981 1982 1838 1 838 1983 1240 1240 1984

688 524

1410

1868 1902 1 654 1336

711 1 370 500 1204 767 1101

521 1420 777 1 297

679 1 201 437 1435 643 1326 976 1517 734 1258 712 1 366 817 1 581

354 1251 568 1473 302 1 203

601 413

333 515 748 232 299

1479 1463

1539 1662

1368 1472

1335 1368

1739 1756

1665 1674 1310 1442

860 1002 1 547 1642 t444 1545

878 872

627 627

817 817

2088 2032 2 104 2 136 2 138 2 116

2 399 2 283 1 720 1 720 2 563 2 526 I 933 1 970 2 603 2 592 2 461 2 423 2 602 2 495 2057 I 910 2 155 2 073 2316 2273 2 048 ! 959 1 939 I 780 2657 2581 2 110 2 109 1 947 1 844

2 148 1 910 2 480 2 620

were examined. Since weather varies so much from year to year, the interaction of weather effects and soil compaction must also vary greatly from year to year, and thus yield and plant growth varies. We tested for the trends of each parameter using the sign test (Siegel, 1956). Thus, if the yield difference between non-compacted versus compacted soils was positive in any one year and the difference between non-compacted versus compacted soils resulted in a greater number of bolls, then they agreed (Table 2). If they agreed in all years, for example in Florence, they are called highly significant (or the 0.6% level of significance) (Siegel, 1956). Rooting depth and distribution were not clearly related to yield responses. Boll numbers in some cases, but not in others, seemed to be related to yield. Total plant transpiration for the season versus yield was not significant at Florence (Table 2). Total plant nitrogen uptake versus yield was not significant at Florence and College Station (Table 2). The root weight/total plant weight ratio was significant or highly significant at all locations.

Effects o f soil compaction on cotton yield 203

TABLE 2 Test for Significance of Simulated Yields Versus Other Simulated Plant Parameters Being

Influenced in the Same Direction (Positively or Negatively) by Soil Compaction

YieM versus

Total Seasonal Seasonal Root wt/total bolls sum of sum o f plant wt

transpiration N uptake (100 DAE) ('

Florence, SC Disagreement/total years 0/11 5/10 6/11 2/11 Significance level % 0-6 62.3 72.6 3.3

Stoneville, M S Disagreement/total years 3/22 3/22 0/22 1/22 Significance level % <0.2 <0-2 <0.2 <0.2

College Station, TX Disagreement/total years 2/10 2/10 3/10 1/10 Significance level % 5.5 5.5 17.2 1-1

Phoenix, A Z Disagreement/total years 4/18 4/18 3/19 4/19 Significance level % 1.5 1.5 0.2 1-0

Difference between compacted versus non-compacted is of opposite sign compared to yield difference for a given year. DAE is days after emergence.

In Florence (Tables 1 and 2), there was little or no effect of com- paction on yield or any other closely related parameter after 1974 due to in-row subsoiling. The same was true at Lubbock (Table 1). Thus, only three years of data from Lubbock were analyzed. The reasons for this are quite different, however, for these two locations. The soils used in the Florence study were Norfolk sandy loam and Wagram sandy loam. The inherent bulk densities of these soils are high and the water content at 'field capacity' or dryer is relatively low, so that wheel traffic was pre- dicted to increase only the bulk density and to decrease the total porosity relatively little after the practice of in-row subsoiling was started in 1974. At Lubbock the soil was an Olton clay. This soil has an inherently high bearing strength, and thus wheel traffic was predicted not to increase the bulk density or lower the porosity appreciably.

At the Stoneville location, the soil was a Dundee silt loam. Wheel traffic was predicted to increase the bulk density and decrease the poros- ity quite severely (in some cases bulk densities greater than 2-0 Mg m 3 were predicted from the Gupta & Allmaras model). In all cases the yields, total nitrogen uptake, total transpiration, and final boll counts

204 F. D. Whisler, K R. Reddy, D. N. Baker, J. M. McKinion

O3 1.0 03 I.M n,,"

0 . 8 U')

n.," 0 .6 ILl I-.--

N o.4

~ 0 . 2

~ 0 . 0

+- -+ SIMULATED NORMAL * ' " " * SIMULATED WITH SOIL COMPACTION

S ;

/ . / i i - - - - - - - . . . . ~ ¢ ~ r i i

0 20 40 60 80 1 O0 120 140 160 180

DAYS AFTER EMERGENCE

Fig. 1. Water stress parameter (1.0 = no water stress, 0.0 -- 100% of time under water stress) versus time for Stoneville, MS, in 1982 for compacted and uncompacted soil.

were predicted to be lower due to the compacted conditions. In some years this response was not very severe due to the overriding weather conditions.

At College Station, the soil was a Norwood silty clay. In most cases compaction was predicted to increase the yield and other related parameters, whereas the opposite effect was predicted for Stoneville. In order to understand this apparent anomaly, we looked at several factors, such as rooting depth, root weight : total plant weight ratio, root distribution with relation to water and nitrogen distributions, plants water stress, etc. As an example, we can consider the results for 1978, shown in Table 1. The compacted soils were predicted to yield much less at Stoneville than the uncompacted soils, but the opposite was true at College Station. In both cases, the lower yielding crop had more roots on the final day of the season due to more water stress during the boll filling period, and this resulted in less partitioning of photosynthate to the bolls in favor of the roots. As examples, consider Figs 1 and 2, where the water stress is plotted versus time for Stoneville and College Station in 1982 and 1978, respectively. At Stoneville (Fig. 1), wheel traffic com- paction caused the roots to grow deeper, faster and not to spread as much horizontally. This put the roots in soil with a lower (more negative) water potential and thus resulted in more water stress in the plant (i.e. WSTRSD, a parameter indicating the level of plant water stress in the GOSSYM model, was lower than 1.0). This showed up only between days 50 and 60, and later between days 80 and 100. When the plant goes into water stress as programmed in the model (based upon some experimental data), it shifts more photosynthate into root produc- tion and thus less is available for reproductive growth. As stated earlier,

Effects o f soil compaction on cotton yield 205

U3 V) LiJ (Z F- V)

Q~ L=J I-- <C

V

C3 U3 t~ k - (/3

1.0

0 .8

0 .6

0 .4

0 .2

0.0 0

+ - + SIMULATED NORMAL

e,

*- . .4 SIMULATED WITH SOIL COMPACTION

k . . . .

20 40 60 80 100 120 140 160 180

DAYS AFTER EMERGENCE

Fig. 2. Water stress parameters (1.0 -- no water stress, 0-0 -- 100% of time under water stress) versus time for College Station (Texas A&M), TX, in 1978 for compacted and

uncompacted soil.

at the end of the season the plants in the wheel-compacted soil had more roots and less yield. At College Station (Fig. 2), the opposite was true. The uncompacted soil had a deeper root system and the majority of the roots between days 80 and 100 were in dryer soil. This caused more root growth, less boll growth, and lower yield. These differences were never very great on any one day, but when accumulated over the total boll filling period, they produced detectable yield differences. In other years, such as 1983 at Stoneville and 1975 at College Station, the predicted yields of compacted and uncompacted soils were much closer together.

An Adalanto silt loam was used at Phoenix and the results were variable. In some years, such as 1981, the simulated compacted soil yields were less than the yields on the uncompacted soil, while in 1982 the opposite was true. However, as in the discussion above, the partition- ing of photosynthate to the roots was proportionately less for the higher yielding crop due to lower water stresses during the boll filling period.

CONCLUSION

In order to evaluate the contribution of soil compaction due to larger, heavier equipment on the cotton yield decline, the models of Gupta & Allmaras (1987) and GOSSYM were used. Soils were sampled for their physical properties at six locations across the cotton belt where long term variety trials had been conducted from 1960 to 1980. These properties were input to the Gupta & Allmaras model, which predicted the amount and depth of tire compaction in terms of bulk density. This new informa- tion for each year was input into the GOSSYM model along with the

206 F. D. Whisler, V. R. Reddy, D. N. Baker, J. M. McKinion

weather and cultural practices that were used and predictions were given as to the yield. These new yields were combined with uncompacted yields and row sizes of the tillage equipment to give the overall yields.

In looking at the overall effect of wheel traffic compaction (Figs 1 and 2, Table 1), there were no consistent trends. Compaction effects were masked and complicated by the weather and varied from location to location. In some areas, such as at College Station, wheel traffic may even have enhanced yield by changing the root/shoot partitioning in response to water stresses. In other areas and soils, such as the Dundee silt loam of the Mississippi delta, it appears that compaction generally reduced yields.

This study shows that a mechanistically, physiologically, physically based model such as GOSSYM can be used to study the effects of the environment on crop growth and yield. The model has been used to show the interaction of soils and weather on crop yield. Soil compaction effects can, in some cases, be masked by weather conditions; while in other years, compaction may increase or decrease cotton yields. Such studies can be used to study the feasibility of conducting field studies such as controlled traffic and in-row subsoiling experiments.

R E F E R E N C E S

Baker, D. N., Lambert, J. R. & McKinion, J. M. (1983). GOSSYM: A simulator of cotton crop growth and yield. South Carolina Agric. Exp. Tech. Bull. 1089.

Baker, D. N., Landivar, J. A., Whisler, F. D. & Reddy, V. R. (1979). Plant responses to environmental conditions and modeling plant development. In Proc. Weather and Agriculture Symp., ed. W. L. Decker. Kansas City, Mo., pp. 69-135.

Brooks, O. L. (1977). Effects of soil on cotton yields. In Proc. Beltwide Prod-Mech. Conf., Atlanta, GA. 10-13 January 1977. National Cotton Council, Memphis, Tenn., pp. 68-9.

Gupta, S. C. & Allmaras, R. R. (1987). Models to assess the susceptibility of soils to excessive compaction. Advances in Soil Science, 6, 65-100.

Meredith, W. R., Jr. (1982). The cotton yield problem: changes in cotton yields since 1950. In Proc. Beltwide Cotton Prod-Mech. Conf., Las Vegas, Nevada, ed. J. M. Brown, 6-7 January 1982. National Cotton Council. Memphis, Tenn., pp. 35-8.

Reddy, V. R. & Baker, D. N. (1989). Application of GOSSYM to analysis of the effects of weather on cotton yields. Agric. Sys., 32, 83-95.

Reddy, V. R., Baker, D. N. & McKinion, J. M. (1989). Analysis of effects of atmospheric carbon dioxide and ozone on cotton yield trends. J. Environ. Qual:, 18, 427-32.

Reddy, V. R., Baker, D. N., Whisler, F. D. & McKinion, J. M. (1990). Analysis of the effects of herbicides on cotton yield trends. Agric. Sys., 33, 347-59.

Effects of soil compaction on cotton yield 207

Siegel, S. (1956). Nonparametric Statistics for the Behavioral Sciences. McGraw- Hill, New York.

Wanjura, D. F. & Barker, G. L. (1988). Simulation analysis of declining cotton yields. Agric. Sys., 27, 81-98.

Whisler, F. D., Lambert, J. R. & Landivar, J. A. (1982). Predicting tillage effects on cotton growth and yield. In Predicting Tillage Effects on Soil Physical Properties and Processes, eds P. W. van Doren & D. M. Unger, Jr. Spec. Pub. 44. American Society of Agronomy, Madison, Wis., pp. 179-98.