Spatial patterns of soil crusting and their relationship to crop cover

CATENA ELSEVIER

Catena 26 (1996) 247-260

Spatial patterns of soil crusting and their relationship to crop cover P.J. Farres, J. Muchena Department of Geography, Buckingham Building, Lion Terrace, Universi O, of Portsmouth, Portsmouth, Hants, UK

Received 15 August 1995; accepted 23 January 1996

Abstract

The interactions between the spatial pattern of soil crust development and crop cover conditions are investigated through controlled laboratory experiments using rainfall simulation. The experimental responses are monitored by using terrestrial photogrammetry and results quantified and analysed in a geographical information system. The generated categorical data is used within a stepwise logistic modelling framework. Results obtained show for the experimental conditions that the expected results of a decrease in areal crust development with increase in crop cover do not hold true. These results are being explained in terms of differences in crop morphology of the two crops used: sweet corn and cabbages, and the specific properties of the soil used in the experiments.

1. Introduction

The development of soil surface seals and crusts by raindrop impact is seen as an important trigger response for soil erosion. In particular, crusted surfaces markedly increase the potential for the generation of surface water flow from subsequent storm events, and hence increase the possibility of the activation of accelerated soil erosion (Boardman et al., 1990). The majority of studies focusing on soil crusting and sealing process have considered the interactions of the impacting raindrops on soil surfaces free of vegetation cover (Farres, 1978; Slattery and Bryan, 1992; Bedaiwy and Rolston, 1993). Those studies which have considered vegetation crop cover have in general looked at splash erosion losses rather than the soil crusting (Finney, 1984), or have only dealt with the effects of particular crops on the redistribution of the rainfall within the canopy (Quinn and Laflen, 1993; Brandt, 1989, 1990). 0341-8162/96/$15.00 © 1996 Elsevier Science B.V. All rights reserved Pll S0341-8162(96)0(/006-9

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In the natural field setting of crops in agricultural fields, rainstorms capable of producing crusts and seals can occur at almost any time during any one crop's growth cycle, from newly emerged seedlings right through to fully developed crop forms (Auzet et al., 1990). The research reported here looks at the effect of identical rain storms on the spatial pattern of crust/seal development for soil surfaces on which crops are at different stages in their natural growth cycle. The experiments use two commonly grown, but morphologically different, agricultural field crops of northern Europe - cabbages and sweet corn. Just as Morgan (1982) forced us to question the traditional view of the role of vegetation cover in splash erosion, here we will see the interactions between crops and crusting may also need to be considered within a new framework.

2. Experimental method and design The experiments use a pendant drop rainfall simulator similar to that described in Farres (1987). The rainfall intensity generated was 34 m m / h r , and each experiment subjected the erosion plots to 120 rains of rain. The soil used in the experiments was taken from the Ap horizon of a Dystric Cambisol (FAO, 1988) from the Midhurst area West Sussex, UK. Basic properties of the soil are given in Table 1. This Midhurst area has suffered many episodes of severe erosion and the soil aggregates produced on tillage are particularly vunerable to break down by drop impact so forming natural soil crusts and seals (Farres et al., 1993). The soil was air dried for two weeks and all air dry aggregates greater than 8 mm and smaller than 1 mm were discarded. The remaining soil structural units were poured into experimental plots (75 cm X 45 cm) with free drainage to produce a soil surface resembling that of a well prepared seed bed. As with any seed bed preparation, by its very nature, a degree of spatial sorting of aggregate sizes takes place. The pouring of the dry soil units into the plots also gave this spatially random sorting of sizes. In the centre of each plot a fixed tube (2.5 cm diameter) was used to firmly locate the greenhouse grown plant in its life position before the addition of simulated rainfall. Thirty experiments were undertaken, five for each major phase in the plant's growth cycle (Table 2). The two crops (cabbages and sweet corn) used in the experiments were grown in individual pots in a greenhouse prior to their use in the experiments. At the start of each experiment vertical photographs of the plot were taken to give complete stereographic coverage of the plot. These photographs gave the initial or " b e f o r e " status of the experimental soil surface. One plant from the greenhouse was taken at random, removed from its growing pot, the roots trimmed and tied together so it

Table 1 Basic properties of soil used in the experiments

Composition CEC Specfic ions (me/100 g) Loss on ignition

Sand (57%); silt (27%); clay (16%) 23.5 meq/100 g; base saturation 74.5% Ca (9); K (8); Mg (0.23); Na (0.27) 4.86%

P.J. Farres, J. Muchena / Catena 26 (1996) 247-260

Table 2 Selection of plants. Ages in weeks to define the 3 major phases Young post Maturing form emergenceform Cabbages Sweetcorn

1-8 1-5

8-18 5-9

249

Fully developed 18-22 9 13

could be firmly located and sealed in the centre of the experimental plot in its natural growth habit. The experimental plot plus plant was now photographically recorded in exactly the same way as for the bare conditions at the start of the experiment. The experimental plot plus plant was then subjected to simulated rainfall. At the end of the experimental run the plant was removed and stereo photographs taken to give the final "alter" conditions of the soil surface. Five replicates in each of the three growth status for the two different crops were used to complete the experimental design. In addition some of the plots were used for destructive sampling for crust thickness and infiltration rate determinations.

3. Initial measurements and analysis The photographic records of the experimental plots were looked at stereoscopically, reported here are the resulting maps of zones (or polygons) of identical soil surface conditions. For the "before" situation a three-fold classification of soil surface conditions was made. As already noted the seed bed preparation process tends to produce spatial sorting by aggregate size. Zones dominated by large aggregates (4-8 mm) were delineated and coded by the number 3, those areas of medium sized units being dominant (2-4 ram) at the surface code 2, and the small (1-2 mm) 1. The reasoning behind such a mapping system was to see if initial aggregate size patterns before the input of rainfall controlled the pattern of crusting forms after rainfall. Such relationships could be a result of differences in inherent structural stability to impact of the different aggregate sizes, or the different sizes which control the void filling mechanism by the breakdown products; a situation previously shown to be significant (Farres, 1978; Boiffin, 1984). The "after" situation was also initially resolved into a three-fold classification. Those areas where crusting produced an unbroken seal (coded 1), the areas where total crusting is partially complete and ghosts of the original structural units were still visible on the photograph, i.e. partially crusted (coded 2). Finally those areas effectively unaltered by drop impact were coded 3. The other photographs of each plot showing the plant allowed a simple two-fold disaggregation of the surface: those areas covered by the plant code 1; where no cover exists 2. For each experimental run the generated maps were digitised and stored within a geographical information system for further calculations. Specifically, because each map was identically spatially referenced the possibility of overlaying one map on another to form interaction combination maps could be achieved. Plate 1 shows a typical example of these maps for just one experiment.

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250

Fig. 1. An example of a typical coded combination map.

Because of the way each individual polygon map was coded the overlay procedure generated a new combination map in which 18 possible combined codes could exist. For example, an area initially of small aggregates (code 1) coincident with an area ending up crusted (code 1) and not covered by a plant would produce a combination polygon coded 112 (see Fig. 1). The polygons in Fig. 1 could be indicated by codes 1-18 according to the degree of crusting (Table 3). In addition the GIS package can also be used to calculate the area for each and every polygon on the combination map. Each experiment generated a data file (report file) of the area of each polygon and its associated combination code 1-18, this file was transferred to a statistical package for further sorting and basic calculations. Initially, a simple plot was made involving little further calculation, i.e. cumulative area of each polygon by combination code 1-18 (Fig. 2). While this is an easy plot to produce, interpretation is not straightforward and the comparison of one plot to another is awkward as a result of lack of initial spatial standardisation. However, when all such plots were considered they did show one important pattern: codes 4, 5, 6, 10, 11, 12 had significant associated areas i.e. surface crusting zones did exist directly under the plant cover.

Percentage Total Area of Plot

15 ]

o"

~ 1

i

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Combination Codes Fig. 2. Percentage of experimental cover for each of the 18 combination codes.

17

18

P.J. Farres, J. Muchena / Catena 26 (1996) 247 260

cq

~

cq

~r

~=~

~=~

tt~

c q ~ ' q

~q

cq

===

e~

~

~

251

252

P.J. Farres, J. Muchena / Catena 26 (1996) 247-260

Table 4 Disaggregation of areas across all experimentsby plant Cabbages Total area:

Sweetcorn

100

100

% Total crusted % Total uncrusted

35 65

60 40

% of crusted under plant % of crusted outside plant

43 57

13 87

% of uncrustedunder plant % of uncrustedoutside plant

26 74

25 75

Also the number of polygons and their absolute area for codes 7 - 1 2 were very small and therefore for all subsequent analysis a two-fold "after" map classification will be used: combining the crusted and partially crusted together as one code and uncrusted as the other code.

4. Analysis and interpretation of combination maps The generated report files contain a huge amount of information which can be extracted in many ways. For example summing areas associated with codes 1, 2, 3, 7, 8, 9, 13, 14, 15 would give the total area of the plot surface covered by the plant, while the total area of polygons coded 1, 2, 3, 7, 8, 9 expressed as a percentage of the total plant covered area would give proportion of surface under the plants crusted. The first disaggregation of the experimental data is shown in Table 4. In this cross tabulation the role of crop type on percentage of total area crusted can be explored. The data clearly show some differences in response between the two plants across all the experiments. Sweet corn plants show greater areas of crusted surfaces developing compared with cabbages. However, when we look only at these crusted areas the sweet corn plants give the expected result of most of the crusting occurring on areas not covered directly by plant leaves. In contrast, for cabbages we have an almost equal proportion of crusted zones directly under plant cover as those not covered. When the uncrusted zones are considered, although greater in total for cabbages, their relative

Table 5 Differences in cover by growth status for the two plant types. Cabbages Young Mature Old

Sweetcorn

% cover

St. dev.

% cover

St, dev.

10.4 24.0 64.0

3.4 11.6 11.5

3.6 21.0 31.1

3.4 11.6 11.5

253

P.J. Farres, J. Muchena / Catena 26 (1996) 247 260

Table 6 Soil surface areas for plants at different stages in their growth % Soil surface area Covered Crusted by crop Young sweetcorn Young cabbage Mature sweetcorn Mature cabbage Old sweetcorn Old cabbage

4 10 21 24 31 64

Not crusted

Total Underplant

Outsideplant

Total

Underplant

Outsideplant

93 19 33 37 52 47

90 16 28 26 35 17

7 81 67 63 47 53

0 6 16 13 14 33

7 75 51 44 33 20

3 3 5 11 17 30

distribution with respect to the plant cover is almost identical to that for the sweet corn crop. Some of the differences between the two plants might be explained by the total proportion of the experimental plots covered by the plants at different stages in their growth cycles. Table 5 begins to disaggregate the data not only by plant but also by growth stage used in the experiments. As can be seen at each growth stage cabbages offer greater total coverage. In Table 6 the pattern of crusting and non-crusting at different stages of growth as ordered in the table by the average total cover is revealed. One problem with the experimental design needs to be noted at this point. Because of the delicate nature of very small cabbage plants (covers < 10%) it was impossible to use such plants in the experiment. However, logic tells one that if this had been possible cabbage plants with equivalent coverages to the sweet corn (say 3 - 5 % ) would have given similar crusting patterns.

5. Implications and conclusion from initial tabulations If soil crusting was exclusively a result of raindrop impact on an unprotected exposed soil surface, it would be expected that as crops grow, cover increases, and the relative areas of crusting and sealing would decrease. This is not borne out by the data; in general for these experiments the reverse would seem to be the case. Once crops become fully established the proportion of crusting in areal terms increases. The only way this can occur is for crusted areas directly under plant leaves to become increasingly more significant through time. This pattern of response is very clearly seen in Table 6 and also in Fig. 3. To explain such a pattern one has to on the one hand consider possible mechanisms of crust formation, and on the other, the morphological characteristics of the two plants. Under what conditions is it possible to form a soil surface crust or seal without direct drop impact? Soil crusts and seals result from the destruction of the soil structural units; this releases particulate material to fill the original between aggregate voids. The result is a continuous surface particle cover (Boiffin, 1984). Structural crust development (Valentin and Bresson, 1992) have been observed on some soil surfaces despite a mulch

254

P.J. Furres, J. Muchena / Catena 26 (1996) 247-260 RATIO =

% crust under plant % crust outside plant

1.8 -

_

~ ~ 7 ~ 1 1

a

1.41.00.6 -

ges

• Sweetcorn O.20

=

t

" I

0

10

I

20

I

I

J

30 40 50 % plotcoverby plant

I

60

I

70

Fig. 3. Relationshipof crust, no crust ratio to crop cover. cover protecting the surface structural units from the effects of direct drop impact (Valentin and Ruiz-Figueroa, 1986). For structurally weak soil units (as used in these experiments) the required disaggregation and in situ collapse can take place (in the absence of forces provided by drop impact) if the initially dug units become rapidly saturated with water or are in some way immersed in water (Yoder, 1936). Under these conditions the mechanisms causing breakdown result from internal weakness by rapidly increasing water bridge lengths and internal slaking stresses set up by escaping air from the inter-aggregate voids being rapidly replaced by water. We require then, even for the very weakest soil structural units, rapid saturation of the soil surface by surface flooding of water directly under the plant cover. The plant cover must therefore actually create localised surface flooding by rerouting the rain hitting the canopy and so capturing and concentrating water on particular zones of the soil surface. In particular three mechanisms can be recognized; (l) stem flow floods (2) leaf flow floods, and slightly differently (3) leaf drip floods. Fig. 4 gives a schematic view of these crop flood crusts forming. Once the initial collapse takes place the crust development diffuses across the flooded surface zone. In the case of the cabbage plants, where development is by leaf size increase in a lateral Partionin9 of input water

i [ l .o. o.

Jil

~Stem Fig. 4. Schematicrepresentationof plant soil surface crust interactions.

[JJ

P.J. Farres, J. Muchena / Catena 26 (1996) 247-260

255

ilii

Fig. 5. An example of the photographs used to obtain combination maps and polygon codes.

rather than vertical growth large amounts of water can be caught by the leaves and directed towards the short squat stem. In addition, for the cabbage it is not unusual for leaves to actually be in contact, or at least very close, to the soil surface; on such occasions water flows directly from the leaf and so becomes concentrated on one area of the soil surface. Careful examination of Fig. 5 (top right corner) shows this even for a small cabbage plant. The larger these cabbages become the greater the effect of stem flow surface flooding is and this explains a large percentage of soil crusting for cabbages being located directly under plant cover. For sweet corn such effects are less developed because the leaves are smaller and of a different form; however, this type of

P.J. Farres, J. Muchena / Catena 26 (1996) 247-260

256

Crusted

Uncrusted

Large

Medium

Small

Large

Medium

Small

18

12

29

82

88

71

Young

2

Mature

16

6

38

6

Old

22 plant

2

30

32

23

39

Under

6

27 44

7

17

33

plant

plant

39

18

54

16

36

Outside Under

plant

plant

75

5

44

6

61

18

36

Outside

Under

plant

plant

83

5

70

62

5

49

Outside Under

7

56

64

16

51

25

28

Outside Under

plant

plant

66

40

47

23

34

Outside Under

plant

plant

13 Outside

plant

Fig. 6. Decomposition of data by total area of large, medium and small initial aggregate size for age of plant and location under or outside direct plant cover.

plant generates stem flow flood crusts rather than leaf flow directly to the surface which is rare (Van Elewijck, 1989a, b). Because sweet corn growth is characterized by vertical rather than lateral development, leaf drip to the surface can also give localised floods associated with spatially localised drop impact. At such sites one might also expect crust development to diffuse across the surface from the locus of impacts. It is important to note here for the conclusions above that the soil in which the crops grow must have naturally weak unstable soil structural units. For many soils in situ breakdown may not occur even during flooding and therefore crusts may not form in this way even though stem flow surface water concentrations exist. It is possible to decompose the data into ever more complex cross tabulations to evaluate different possible interactions. For example, the role of initial soil aggregate size on crusting patterns can be explored. The percentage of the experimental plots crusted and not crusted for the three initial aggregate sizes is tabulated by age and type of plant (Table 7). In this tabulation the young sweet corn experiments reveal that all the plots in this group had initial soil surfaces in which 73% were covered by large soil units, 18% by medium and 10% by small. Of the large aggregate areas 95% of them finally became crusted while 5% remained uncrusted. Fig. 6 shows a slightly different disaggregation of the data considering cabbage plants only: for the young cabbages the 18% of the large units that finally crusted 2% of the crusted areas were under the plant and 16% outside the direct influence of leaf cover. While such tables begin to summarise the data it becomes increasing more complex to tease out any underlying patterns. Specifically, with the data in the form presented so

257

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Table 7 A disaggregation of data by initial soil aggregate size and age of plants %

Large

Medium

Small

area Total % % Total % % Total % % crop crusted not crusted not crusted not cover crusted crusted crusted Young sweetcorn Young cabbages Mature sweetcorn Mature cabbages Old sweetcorn Old cabbages

4 10 21 24 31 64

73 61 72 68 65 64

95 18 30 38 46 39

5 82 70 62 54 61

18 20 11 5 13 12

94 12 27 30 77 49

6 88 73 70 23 51

10 19 17 27 22 24

100 29 47 44 50 54

0 71 53 56 50 47

far, it is not possible to directly address the more fundamental question: for any given p o i n t / a r e a on the soil surface is it possible to predict the possibility of that area crusting or not? To begin to approach this question we need to recast the data in a rather different form.

6. Recasting the data A grid was placed over each photographic image (the grid was 15 by 9 cells representing 5 cm squares on each experimental plot). For each cell the final outcome dominating the cell was coded; 1 = crust, 2 = no crust. In addition for each cell the type of plant, covered or not covered, the age of the plant (young, mature, old) and the relative size of the initial surface soil aggregates dominating the cell were also given a numerical code. All this data can be directly obtained from the combination code maps previously created. From the analysis already given an implied interaction between crusting and distance from plant stem was seen. A further column of data was therefore generated in which if the cell on the grid was close to the plant stern (centre of cell 0 - 1 5 cm from stem) it was coded with a number 1, medium distance ( 1 5 - 3 0 cm) code 2 and far away ( > 30 cm) code 3. The final data matrix was 4050 lines long (30 experiments by 135 cell per experimental plot) by 5 categorical column variables wide. The question now is how much of the variation in the crusting outcome of each area can be attributed to, or explained by, the proposed set of interacting control variables? The form of this question is one normally treated by the parametric multiple regression technique. However, in this case the data is totally categorical. Although such data is potentially common in environmental science, rarely is it ever treated in a predictive way, although techniques are readily available to do this and are commonly applied in the social sciences (Wrigley, 1985). The analysis of the data involves the use of the logit transformation of the dependent variable, the so called logodds ( l o g e ( P i / ( l - P i ) , where Pi is probability of crusting). The modelling procedure is known generally as Logistic Regression and has been completed here using the step-wise logistic module of B M D P package (Dixon, 1990).

258

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Table 8 Results from stepwise logistic modelling Variables Cover Plant Age Distance

(1) (2) (1) (2)

Constant log likelihood =

Coeff.

Coeff./S. Error

0.3353 0.2032 - 3.937 -3.032 0.6231 -0.8452 3.095

3.92 2.3 - 14.0 10.9 - 6.8 - 8.28 10.3

1969.493

The exact details of the modelling procedure are inappropriate here and can be found fully documented in Wrigley (1985). However, some explanation would be useful so that an interpretation of the statistical significance of the results might be offered. Of the 4,000 plus observations the analysis identified 87 distinct combinations of the categorical codes for the five independent variables. It is the consideration of these unique combinations that provide the central focus of the analysis. For each combination the proportion of the observations that are in this case crusted and not crusted are calculated and these values are then compared with the estimated proportions obtained from the best fit equation obtained from the complete data set. The difference between the observed proportion and that generated from the best fit model give a standardised residual which can be interpreted and used in exactly the same way as residuals from traditional regression. Likewise, the coefficients for the estimated model, along with their associated t statistics, can also in general be interpreted in exactly the same way as for a multiple regression equation. The stepwise procedure is also similar to the traditional continuous response case. The most efficient final model parameters are shown in Table 8. The first interpretation possible is to state that all the independent variables contained in the model are statistically significant at the 0.05 level. Secondly, this model does not contain the variable describing the initial soil aggregate size. What this means is that this particular variable plays a statistically insignificant role in predicting the logodds (hence probability) of an area being crusted or not. Interpreting the coefficients for each variable in a logistic regression is slightly different fi'om standard regression. The constant term is used to estimate the logodds of crusting for the so called base category. In this particular case these are the first category codes for each independent variable, i.e. no plant cover (Cover), sweet corn (Plant), young plants (Age) and close to plant stem (Distance). The values of the coefficients show how this estimated probability changes for changes away from this base category. For example, because Cover and Plant give positive values it implies the probability of crusting increases for change to under plant and for cabbages, i.e. the other categorical codes for these two variables. In addition, because these values are small (thought significant), the affects of the alternative codes on the base probability are small. This contrasts with the other two independent variables; here the base probability reduces for the other categories in each variable. So as the plant increases in

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259

age the probability of crusting decreases and as one gets further from the stem of the plant this also shows a decrease in comparison to the base category model. Finally the log likelihood parameter for this model can be used in conjunction with the same parameter for a constant only model to produce an equivalent r : value. For the model shown here an equivalent y2 of 0.86, highly significant at the 0.05 level, was obtained.

7. Conclusions As Morgan (1982) states " t h e most important interactive effect is plant c o v e r " . This interaction between crop cover and soil surface crust development has been investigated using low altitude photogrammetry, GIS and logistic modelling. The results have shown the power of such an approach in the investigation of spatial pattern. Specifically, the results show that, for a soil with a delicate, naturally unstable soil structure, the simple model of crop cover protecting a soil surface from the effects of direct drop impact and so retarding the spatial development of crust need not be true. In fact, for the soil system conditions used significant areal development of crusts took place directly under leaf cover and the relative proportion of such " p r o t e c t e d soil crusts" actually increased with crop age (i.e. % total cover). As expected differences exist between crop types as a result of plant morphology and growth habit, These variations being a result of the relative importance of leaf flow, leaf drip and stem flow as controlled by the re-routing and concentration of water by the plant structure. In addition, by using the technique of logistic regression it is clearly shown the probability of an area crusting or not can be explained by the variables crop type, crop age and distance of the area under consideration is from the plant stem. For the particular conditions considered within this research the role of initial soil structure unit size was shown to have little effect on the probability of crusting. It would seem the initial soil surface conditions were overpowered by the controlling variables describing the plant.

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FAO, 1988. Legend for the Soil Map of the World. UNESCO, Rome, 59 pp. Fan'es, P.J,, 1978. The role of time and aggregate size in the crusting process. Earth Surf. Process. Landforms, 3: 223-231. Farres, P.J., 1987. The dynamics of rainsplash erosion and the role of solid aggregate stability. Catena, 14: 119 130. Farres, P.J., Wood, S.J. and Seeliger, S., 1993. A conceptual model of soil deposition and its implication for enviromnental reconstruction. In: M. Bell and J., Boardman (Editors), Past and Present Soil Erosion. Oxbow Books, Oxti~rd, pp. 217-226. Finney, H.J., 1984. The effect of crop covers on rainfall characteristics and splash detachment. J. Agric. Eng. Res., 29: 337-343. Morgan, R.P.C., 1982. Splash detachment under plant covers: Results and implications of a field study. Trans. ASAE, 25: 987-991. Quinn, N.W. and Laflen, J.M., 1993. Characteristics of raindrop throughfall under corn canopy. Trans. ASAE, 36: 1445-1450. Slattery, M.C. and Bryan, R.B., 1992. Laboratory experiments on surface seal development and its effect on interrill erosion processes. J. Soil Sci., 43:517 529. Van Elewijck, L., 1989a. Influence of leaf and branch slope on stemflow amount. Catena, 16: 525-533. Van Elewijck, L., 1989b. Stemflow on maize: a stemflow equation and the influence of rainfall intensity on stemflow amount. Soil Technol., 2:41 48. Valentin, C. and Ruiz-Figueroa, J.F., 1986. Effects of Kinetic energy and water application rate on the development of crests in a fine sandy loam soil using sprinkling irrigation and rainfall simulation. In: N. Federoff and L.M. Bressons (Editors), Soil Micromorphology. AISS/AFES, Paris, pp. 401-408. Valentin, C. and Bresson, L.M., 1992. Morphology, genesis and classification of surface crusts in loamy and sandy soils. Geoderma, 55: 225-245. Wrigley, N., 1985. Categorical Data Analysis fbr Geographers and Environmental Scientists. Longman Harker. Yoder, R.E., 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. J. Am. Soc. Agron., 28: 337-351.