The role of cryptogams in runoff and erosion control on bariland in the Nepal Middle Hills of the Southern Himalaya

Earth Surface Processes and LandformsEarth Surf. Process. Landforms 26, 1303–1315 (2001)DOI: 10.1002/esp.277

THE ROLE OF CRYPTOGAMS IN RUNOFF AND EROSION CONTROLON BARILAND IN THE NEPAL MIDDLE HILLS OF THE SOUTHERN

HIMALAYA

STEVE GASKIN1* AND RITA GARDNER2

1 Department of Geography, Queen Mary, University of London, Mile End Road, London E1 4NS, UK2 Royal Geographical Society (with The Institute of British Geographers), 1 Kensington Gore, London SW7 2AR, UK

Received 19 October 2000; Revised 31 May 2001; Accepted 3 September 2001

ABSTRACT

Cryptogams are communities of non-vascular plants that live on the soil surface. Numerous functions have been attributedto these crusts, including changes in soil fertility and nutrient status, soil hydrology and soil erosion. Most significantfor this paper is the reported benefit of cryptogams in reducing soil erosion by water in semi-arid areas. However, todate there have been few attempts to understand the soil conservation value of cryptogams in subsistence agriculturalsystems or in humid mountain environments. This paper investigates the potential of cryptogams in soil erosion by wateron agricultural hillslope terraces (bariland) in the Nepal Middle Hills of the southern monsoonal Himalaya. The researchis significant because the loss of fertile topsoil is considered by some to be the biggest threat to the livelihoods ofsubsistence farmers in the area in the medium and long term.

The current study was conducted in the field between two of the weeding events that take place under maize cover,grown in the traditional manner. Three groundcover types which represented (i) maize only (types A), (ii) maize andweed cover (types B), and (iii) maize and cryptogam cover (types C) were monitored utilizing multiple microerosionplots. Measurements of runoff and soil loss data were collected sequentially on a storm-by-storm basis throughout themonitored period from 24 July 1997 to 29 August 1997. Measurements of infiltration rates were also taken on each ofthe groundcover types at selected times.

Results collected from the erosion plots demonstrate that runoff and soil losses over distances of <2 m can besignificantly reduced by up to 50 per cent with cryptogam cover, compared to maize-only canopies. Mean runoff for allstorm events sampled from plot types A, B and C were 3Ð4 l m�2, 1Ð6 l m�2 and 1Ð5 l m�2 respectively. For soil loss, theresults were 21Ð7 g m�2, 11Ð3 g m�2 and 10Ð2 g m�2 respectively. Therefore, cryptogams would appear to offer a similardegree of protection to the soil surface from runoff and raindrop erosion, to that afforded by weed cover. Weed andcryptogam covers protect the soil surface from rainfall kinetic energies and work to preserve surface microtopographies,depressional storage and surface water detention. Terminal infiltration rates taken at the end of the monitored periodshowed that well developed maize- and cryptogam-covered soil surfaces (types C) have a mean terminal infiltrationrate of 35Ð0 mm h�1 compared to 44Ð5 mm h�1 for comparable maize- and weed-covered soil surfaces (types B), and15Ð5 mm h�1 for maize-only soil surfaces (types A). These results show that cryptogams and weeds also have relativelyhigher infiltration rates than comparable maize-only covered plots, devoid of groundcover.

The findings in this study may have implications for traditional weed management practices used by local hill farmers,which often destroy cryptogam soil coatings two to three times during the maize growing period. However, further workneeds to be done to ascertain farmers’ understandings of cryptogams. It is hoped that conservationists will benefit fromincorporating cryptogams into the design of future soil erosion studies relating to development programmes. Copyright 2001 John Wiley & Sons, Ltd.

KEY WORDS: cryptogams; weeds; soil loss; runoff; Nepal; bariland; Himalaya

* Correspondence to: S. Gaskin, Learning and Teaching Support Network Subject Centre for Geography, Earth and Environmen-tal Sciences (LTSN-GEES), University of Plymouth, Room 509, The Moneycentre, Drake Circus, Plymouth, PL4 8AA, UK. E-mail: [email protected]/grant sponsor: Department of Geography, Queen Mary, University of London.Contract/grant sponsor: British Geomorphological Research Group.Contract/grant sponsor: Roy Woodward Education Foundation.Contract/grant sponsor: Dudley Stamp Memorial Fund (Royal Geographical Society with the Institute of British Geographers).

Copyright 2001 John Wiley & Sons, Ltd.

1304 S. GASKIN AND R. GARDNER

INTRODUCTION

Cryptogams are assemblages of micro-organisms that live in close association with the soil surface. Typicalcryptogam components include, algae, cyanobacteria, moss, lichens and liverworts. Cryptogams have beenreported from the semi-arid deserts of North America (Belnap et al., 1999), Australia (Eldridge and Kinnell,1997), Israel (Danin and Barbour, 1982) and on coastal dune systems of The Netherlands (Pluis and DeWinder, 1989) and the UK (Forster and Nicolson, 1981). Cryptogams have also been noted in Antarctica(Cameron, 1972).

There have been several comprehensive reviews of the literature relating to cryptogam form and function,and their reported environmental roles are varied (West, 1990; Johansen, 1993; Belnap et al., 1999). Most rel-evant for this paper is the work on cryptogams and soil erosion in semi-arid areas, even though full attainmentof cryptogam cover typically takes years in such areas (Belnap et al., 1999).

Most studies assessing the effect of cryptogams on soil erosion, runoff and infiltration have been conductedin semi-arid areas of North America and Australia on non-agricultural land where frequent disturbances to thesoil surface are relatively uncommon. In other words, these studies have assessed the impacts of cryptogamson infiltration, runoff and erosion when the cryptogam cover has been in situ for at least several months, ormore commonly years (Belnap et al., 1999).

Laboratory-based rainfall simulation experiments conducted by Eldridge and Greene (1994), using circularerosion plot monoliths, showed that rates of soil erosion under bare plots were 15Ð3 g m�2 min�1 compared toless than 1 g m�2 min�1 for plots completely covered by cryptogams. Other experiments have been conductedwhich reinforce these findings (Brotherson and Rushforth, 1982; Mucher et al., 1988; Chartres and Mucher,1989; Kinnell et al., 1990; Williams et al., 1995; Eldridge and Kinnell, 1997). However, Savoury (1983) andWest (1990) are unconvinced that cryptogams are beneficial in mitigating soil loss by water erosion, anddiscount much of the evidence as being a result of poor methodologies.

Cryptogams have also been shown to influence infiltration rates and runoff; the results are variable. Forexample, Greene and Tongway (1989) showed how non-eroded soil surfaces with high cryptogam cover hadinfiltration rates of 49Ð2 mm h�1 compared to 7Ð8 mm h�1 for unstable, crusted soils devoid of cryptogamcover in the semi-arid rangelands of New South Wales, Australia. Conversely, Williams et al. (1995), duringfield trails in Utah utilizing rainfall simulation, showed that cryptogams have little effect on infiltration rates.With respect to runoff, Alexander and Calvo (1990) showed during field trials in the Spanish badlands thatcryptogam-covered surfaces reduce surface water detention and surface runoff times, but increase runoffvolumes.

The authors are unaware of any published research that has attempted to ascertain the effect on soil erosionof faster growing cryptogams that can attain full cover in a matter of weeks (Gardner et al., 1995; Gaskin,1999). Similarly, no studies have experimentally investigated the effect of cryptogams on soil erosion, runoffand infiltration on arable land, where disturbances to the soil surface from land management practices arefrequent. This study, on the rain-fed agricultural terraces (bariland) in the Nepal Middle Hills, offers a firstinsight.

A substantial farmer-based study revealed that the loss of relatively fertile topsoil by erosion is consideredto be the biggest single threat faced by subsistence farmers on bariland in the medium and long term (Turtonet al., 1994).

AIMS

The aims of this study are (i) to evaluate soil losses under different groundcover types, including cryptogamcover, that are prevalent under maize on bariland in the Nepal Middle Hills during the monsoon, and (ii) toaccount for any observed differences that may occur between these groundcover types. The selection ofdifferent groundcovers is explained in the methods section below.

THE STUDY AREA

Nepal is situated within the mid-belt of the Himalaya, and occupies an area of 147 181 km2 (Sharma,1996). The Middle Hill zone stretches for approximately 80 km in an east–west direction, and has an

Copyright 2001 John Wiley & Sons, Ltd. Earth Surf. Process. Landforms 26, 1303–1315 (2001)

CRYPTOGAMS IN RUNOFF AND EROSION CONTROL 1305

elevational range of about 300 m to 2300 m (Ives and Messerli, 1989). The Middle Hills, like most ofNepal, receive intense seasonal precipitation during the monsoon between June and September. Rainfall canexceed 11 000 mm per annum in some pocket areas, although it is typically less than 2500 mm per annum(Ives and Messerli, 1989).

Bandipur, the field location chosen, lies within the Tanahun district in the Western Development zone ofNepal (Figure 1). It is also within the Research Command Area (RCA) of the Agricultural Research Sta-tion (ARS), Lumle. This fieldsite was selected after evaluation of five sites (Gaskin, 1999). The coverand distribution of cryptogams at Bandipur enabled scientific investigation of their role in soil erosioncontrol. In addition, Bandipur was equipped with an automatic rain gauge, allowing storm-by-stormmonitoring.

Bandipur lies at an elevation of between 800 and 1500 m a.s.l. and is classified as ‘mid-altitude’ by ARS,Lumle. Past rainfall records suggest that rainfall is in the region of 1500–2500 mm per year, although thisprobably varies considerably from year to year (Gardner et al., 2000). Lithology is medium- to high-grademetamorphic rock with phyllite/mica-schist sequences dominant. The alfisol soils (LRMP, 1986) of Bandipurare highly weathered as a result of the acid hydrolysis of the metamorphic regolith. The soils generally havea low to moderate stone content (<25 per cent), and a low to moderate organic carbon contact (<5 per cent)(Gaskin, 1999).

MYAGDIMYAGDI

MUSTANG

MANANG

BAGLUNGBAGLUNG

GULMI

KAPILVASTU RUPANDEHINAWALPARASI

ARGH AKHUNCHIPALPA

SYAHGTASYAHGTA

KASKIKASKI

TANAHUNTANAHUN

LAMJUNGLAMJUNG GURKAGURKA

TalbariTalbari

ChambasChambas

LARCLARC

LandrukLandrukKimchaurKimchaur

BaglungBaglung

Tansen

PokharaPokhara

DumreDumre

TurtureTurture

GandrukGandruk

Jomson

BandipurBandipur

N

0 15 30 45 60 75

kilometres

International boundary

Regional boundary

Study area

Main town and ARC, Lumle

WesternDevelopment Region

Central Development Region

Central Development Region

EasternDevelopment Region

Mid WesternDevelopment Region

Far WesternDevelopment Region

Mid WesternDevelopment Region

CH

I NA

I N D I A

KathmanduKathmanduKathmandu

MYAGDI

BAGLUNG

PA

RB

AT

SYAHGTA

KASKI

TANAHUN

LAMJUNG GURKA

Talbari

ARC, Lumle

BaglungBeniBeniBeni

Pokhara

Dumre

Gandruk

Bandipur

Pokhara

Figure 1. Location of Bandipur fieldsite in Western Development Zone of Nepal (Source: Agricultural Research Centre, Lumle, 1997).Reproduced by permission of ARS Lumle (Formally LARC, DFID)



The cropping pattern in Bandipur is typical of the Middle Hills generally. Maize is planted in late April/earlyMay and is grown throughout the summer months on rain-fed bariland. Maize is underplanted with finger-millet typically one to two weeks before the maize harvest in late August/early September, although this canvary considerably. Finger-millet, harvested in November, may be followed by winter wheat grown betweenDecember and April. The cycle is generally repeated, although farmers from time to time set aside land forfallow for one to two years, for soil nutrient replenishment purposes. Chemical fertilizers are rarely used inthe Nepal Middle Hills. However, farmyard manure is applied to the soil surface just prior to the time ofmaize planting in late April/early May.

Weeds are a common feature on all bariland in Nepal, and Bandipur is no exception (Gaskin, 1999). Weedremoval typically takes place twice during the maize season; it is an integral and widespread land managementpractice on bariland. The process of hand-weeding, whereby weeds are usually pulled out completely, disturbsthe soil. The reason for weed removal is economic; weeds are used for animal fodder and to increase theproductivity of the maize crop, through reduced nutrient competition. The first weeding generally takes placeone month after maize planting (early to mid-June) and the second weeding takes place about four to six weeksafter this (mid- to late July), although wide variability exists between farms. The soil surface is hand-turnedfor a third time, when maize is harvested in late August/early September. Cryptogams are also ubiquitouson bariland in the Nepal Middle Hills. They are frequently disturbed at times of maize weeding and harvest(Gaskin, 1999).

METHODS

Of the various techniques available for the measurements of soil erosion, microerosion plots (2 m2) werechosen for this project. They offered the only possible practical approach in the Nepal Middle Hill envi-ronment. In addition, estimates of soil loss using microerosion plots are much more accurate than erosionpins when measuring erosion over short distances of a few metres (and short time spans), as in this study(Hudson, 1995). Erosion plots also collect and store a quantifiable amount of runoff and sediment, enablingcomparisons of runoff and sediment yield per unit area for each storm event.

The erosion plots tapered at the downslope end towards a 20-litre collection cylinder. Plot boundarieswere constructed from 3 mm thick steel sheets. Soil characteristics on the erosion plots, together with maizeplanting density are shown in Table I. Soil texture was assessed after sodium hexametaphosphate disper-sal (Rowell, 1994). Soil pH was measured in the field using the method described by Goudie (1992). Soilorganic carbon percentage was assessed by using a modified version of the potassium dichromate Walk-ley–Black method, as described in Hesse (1971). Slope angle within each of the plots was determined usinga clinometer.

All plots had topsoil with a sandy silt loam texture: approximately 40 per cent sand, 55 per cent siltand 5 per cent clay, and organic carbon levels were low (<2 per cent). This is typical of the soil typesprevalent in Bandipur more widely (Gaskin, 1999). Soil pH on plots ranged from about 5Ð5 to 5Ð7, slopeangle was approximately 3°d, and there were 3Ð5 maize plants per square metre within each of the erosionplots (Table I). Natural rainfall was used in this study in preference to artificial rainfall simulation techniquesowing to the logistical difficulties in the field and the associated problems of artificially replicating the naturalcharacteristics of monsoon rainfall.

Table I. Mean erosion plot data for each of the three groundcover types investigated

Groundcover type Texture (%) pH Organic Slope Number ofcarbon (degress) maize plants

Sand Silt Clay (%) within plot

Maize only (A) 39Ð7 55Ð2 5Ð1 5Ð6 1Ð80 3 7Maize and weeds (B) 39Ð1 55Ð6 5Ð3 5Ð6 1Ð78 3 7Maize and cryptogams (C) 39Ð0 55Ð3 5Ð7 5Ð7 1Ð83 3 7



Three different groundcover types were considered on the ten plots. With the resources to permit only alimited number of plots, groundcover types were assigned as follows:

1. four plots with maize cover only (referred to as plot types A);2. two plots with maize and weed cover (referred to as plot types B);3. four plots with maize and cryptogam cover (referred to as plot types C).

As farmers use weeds for animal fodder, the number of plots with maize and weed cover was reduced so asto minimize the intrusion of the field programme into the farmers’ management regime. Weed and cryptogamcover were measured throughout the experimental period, as near to the time when soil loss and runoff datawere collected after each storm event, as was practically possible. Percentage cover was estimated using anocular classification system devised by Hodgeson (1974).

Cryptogam growth, where required (on plot types C), was initiated by applying a weak nitrogen fertilizerin solution (2 g m�2 N). Few weeds appeared on either plot types A or C during the monitored period, asthe soil surface was cleared of any residual weed root growth before experimentation commenced. But wherethey did grow they were cut periodically at the base of the stem, so that soil disturbances within the erosionplots was minimal.

The plots, after preparation, were left for ten days to enable any loose debris from installation to washthrough the system. The monitoring period spanned from 24 July 1997 to 29 August 1997 (37 days ofmonitoring). The monitoring period was deliberately selected to simulate an agricultural window, in this casebetween the second weeding event and maize harvest, as described above.

The length of the monitoring period enabled a total of 200 plot events to be captured, all of which weremonitored successfully. The same storm events were monitored on all of the experimental plots, and rain-fall was logged on a minute-by-minute basis with the use of an Environmental Measurements ARG100rain gauge with a 1LX Data Logger. The minimum criterion for recording characteristics of a storm eventwas 2 mm of rainfall. Measurements of runoff and soil loss were made immediately after each storm. Avariety of rainfall parameters were calculated directly from the rainfall results, including total rainfall andthe EI15 index. The EI15 index used in this study is an adjusted form of Wischmeier and Smiths’ (1958)EI30 erosivity index. The index incorporates the kinetic energy (KE) of Hudson (KE D 29Ð8 � �127Ð5�/I)where I D rainfall intensity in mm h�1. It then combines this with the maximum 15-minute intensity, whichhas been found to be more applicable to moderately to sparsely vegetated land, as opposed to using themaximum 30-minute rainfall intensity (Stocking and Elwell, 1973; Gardner et al., 1995). The terraces uponwhich the field experiments were conducted can be considered to be moderately to sparsely vegetated atthe time of year when this study was conducted. Therefore, the EI15 index was chosen in preference toothers. Moreover, in recognition of the fact that ‘to be valid as an index of potential erosion an erosivityindex must be significantly correlated with soil loss’ (Morgan, 1995), the EI15 was also justified empiri-cally in this study, as the Spearman correlation coefficient (r) between EI15 and soil loss was 0Ð85 (Gaskin,1999).

After each storm event, runoff volume was measured and a sample of thoroughly mixed sediment insuspension was collected for analysis. Volumes of suspended sediment ranged from 100 ml to 1000 ml,depending on the amount of runoff generated from the plots. The suspended samples of runoff were subse-quently filtered thorough pre-weighed oven-dry (24 h at 105 °C) filter papers (Whatmans wet-strengthened,180 mm), and oven-dry sediment weight calculated. This was multiplied up proportionately to total runoffvolume.

For comparative purposes, all soil loss and runoff results are expressed as grams per square metre (g m�2)and litres per square metre (l m�2) respectively. Runoff results were measured to an accuracy equivalent to0Ð05 mm m�2 of rainfall, while soil loss was measured to an accuracy of 0Ð1 g m�2.

Terminal infiltration rates were measured on 23 July and 30 August 1997 (the day before and the day aftermonitoring commenced and ceased respectively), with a double-ring infiltrometer, using the methodologydescribed by Burt (1987).



In order to have a better understanding of soil surface condition on the experimental plots during monitoring,which can help in explaining soil surface processes, a qualitative classification system was devised, based onthat of Mucher et al. (1988). The following features are easily recognizable on bariland in the field and theywere noted at the beginning and end of the sampling period.

(a) Microtopography:1. high: surface irregularities present and clearly defined; significant surface water detention;2. medium: maize mounds compacted but surface irregularities still present; little rugosity but slight

crenularity; little surface water detention;3. low: smooth surface, little rugosity; water shedding.

(b) Soil sealing (physical crusts):1. original disturbed surface, no splash, no smoothing, no transient pinnacles, no sealing;2. surface pitted by splash, transient pinnacles evident, slight smoothing, incipient sealing;3. sealing present, quite extensive; well-developed in places;4. smooth, well-developed seals; slaked appearance, may be cracked on drying.

Several statistical tests were used to analyse the field results. Multiple linear regression was used to relaterunoff and soil loss with rainfall and vegetation parameters. This technique has already been shown to havestrong predictive powers in soil erosion research (Stocking and Elwell, 1973; Jeje and Agu, 1990; Gardneret al., 1995; Eldridge and Kinnell, 1997). Analysis of variance (ANOVA) was used to assess the significanceof differences between mean runoff and mean soil loss from the different combinations of groundcover typesinvestigated (plot types A, B and C).

RESULTS AND DISCUSSION

Rates of growth of cryptogams and weeds

The growth rate of cryptogams in this study is very fast, in fact several orders of magnitude greaterthan growth rates reported in semi-arid areas. Here, full attainment can take several years, and in somecircumstances even centuries (Belnap et al., 1999). In contrast, cryptogams on bariland in Nepal can attainin excess of 90 per cent cover, after disturbance (weeding), in a short period of no more than five weeks,as shown in Figure 2. The increased growth rate of cryptogams on bariland in Nepal, compared to semi-arid

0

10

20

30

40

50

60

70

80

90

100

24.07.97

25.07.97

28.08.97

29.07.97

30.07.97

1.08.97

5.08.97

7.08.97

8.08.97

10.08.97

11.08.97

12.08.97

13.08.97

14.08.97

15.08.97

16.08.97

18.08.97

23.08.97

26.08.97

29.08.97

Date

Mea

n w

eed

co

ver

(%)

0

10

20

30

40

50

60

70

80

90

100

Mea

n c

ryp

tog

am c

ove

r (%

)Mean Weed Cover

Mean Cryptogam Cover

Figure 2. Percentage weed cover and cryptogam cover sequentially throughout the monitored period



areas, is likely to be due to an abundance of water on bariland during the monsoon (Belnap et al., 1999).Shortage of water can be a severe limiting factor for cryptogam development. The rate of weed growth onbariland compared to cryptogams is not dissimilar, again seen in Figure 2. Attainment of a 90 per cent weedcanopy is reached within five weeks after the last weeding event, demonstrating the vigour with which weedsalso grow in this environment.

Rainfall variability and representativeness

As seen from Table II, the rainfall events captured during the sampling period were wide-ranging andbroadly representative of the monsoon season at Bandipur fieldsite in 1997, although the extreme event of176Ð5 mm was not captured during the monitored period. These events are important in terms of generatingrunoff and soil loss but unfortunately none were sampled. Notwithstanding, the storm events in terms of meantotal rainfall and the EI15 index are comparable for both periods.

Runoff and infiltration

Runoff on bariland in the Nepal Middle Hills is generally of the ‘infiltration excess type’ (Gardner et al.,1995, 2000). This is generated when the rainfall intensity exceeds the infiltration capacity of the soil.

Table III shows the percentage of the rainfall events monitored that resulted in runoff for all plot typesinvestigated. The results serve to demonstrate the general picture of differential plot runoff responses to totalrainfall. Overall, only 9 per cent of the storm events failed to generate runoff from the experimental plots.On average, plot types A produced runoff for 95 per cent of rainfall events compared to an average of <85per cent for plot types B and C combined. Plot types C had the lowest runoff responses: only 70 per cent ofevents produced runoff.

Mean runoff coefficients (the percentage of rainfall that runs off the plots) for all groundcover types areshown sequentially throughout the monitored period in Figure 3. The results show that for plot types A, thegeneral trend is an increase in the runoff coefficient over time, as a result of soil surface sealing (Table III).On the other hand, plot types B and C show a general decrease in runoff coefficients over time, as the weedand cryptogam cover increasingly intercept rainfall and increase surface water detention and depressionalstorage, through the preservation of soil surface rugosity (Table III).

All plots showed a reduction in infiltration rates over the monitored period. The greatest mean reduction ininfiltration rates occurred on the maize-only plots (a 63 per cent reduction, Table III). In comparison, on plottypes B and C, infiltration rates reduced by 27 per cent and 22 per cent respectively. These smaller reductionsin infiltration rates over time on plot types B and C, can be attributed to a combination of less surface sealingand preservation of soil surface microtopographies, beneath weeds and cryptogams. The presence of complexmicrotopographic forms at the soil surface tends to slow water. This in turn increases the residence times ofwater at the soil surface, which can promote infiltration (Belnap et al., 1999). In addition, for plot types C,the hummocked and pinnacled effect of the cryptogams themselves may also increase the surface area overwhich infiltration can occur (Beymer and Klopatek, 1992). In contrast, on the maize-only plots, the soil surfacebecomes increasing more degraded and sealed over time, as surface microtopographies become reduced. Soilsealing has long been known to impede soil infiltration capacities (Moore, 1981). The results indicate that

Table II. Summary of rainfall characteristics for the whole monsoon and for the sampled period (24 July to 29 August)at Bandipur

Descriptive statistic Total rainfall (mm) EI15 index (J m�2)

Whole monsoon Sampled period Whole monsoon Sampled period

Mean 13Ð6 12Ð6 8628Ð0 7334Ð3Coefficient of variation (%) 162Ð6 94Ð4 229Ð2 185Ð9Minimum 2Ð1 2Ð1 0Ð0 0Ð0Maximum 176Ð5 46Ð8 137 607Ð0 49 048Ð0



Tabl

eII

I.Pe

rcen

tage

and

actu

alnu

mbe

rof

rain

fall

even

tsge

nera

ting

runo

fffr

omth

eer

osio

npl

ots,

sing

le-f

acto

rA

NO

VA

resu

ltsof

mea

nru

noff

for

each

ofth

eth

ree

grou

ndco

ver

type

sav

erag

edfo

ral

lra

infa

llev

ents

mon

itore

d,pe

rcen

tage

and

actu

alnu

mbe

rof

rain

fall

even

tsge

nera

ting

soil

loss

from

the

eros

ion

plot

s,te

rmin

alin

filtr

atio

nra

tes

atse

lect

edtim

esth

roug

hout

the

field

peri

od,p

erce

ntag

ean

dac

tual

num

ber

ofra

infa

llev

ents

gene

ratin

gso

illo

ssfr

omth

eer

osio

npl

ots,

sing

le-f

acto

rA

NO

VA

resu

ltsof

mea

nso

illo

ssfo

rea

chof

the

thre

egr

ound

cove

rty

pes

aver

aged

for

all

rain

fall

even

tsm

onito

red,

and

soil

surf

ace

feat

ures

onth

eer

osio

npl

ots

ontw

oda

tes

thro

ugho

utth

efie

ldpe

riod

Gro

undc

over

type

Rai

nfal

lA

ctua

lM

ean

Term

inal

infil

trat

ion

Rai

nfal

lA

ctua

lM

ean

Mic

roto

pogr

aphy

†Se

alin

g†

even

tsnu

mbe

rof

runo

ffra

te(m

mh�1

)ev

ents

num

ber

ofso

ilpr

oduc

ing

even

ts(l

m�2

)Łpr

oduc

ing

even

tslo

ss25

July

30A

ug25

July

30A

ugru

noff

(%)

prod

ucin

g23

July

30A

ugso

illo

sspr

oduc

ing

(gm

�2�Ł

runo

ff19

9719

97(%

)so

illo

ss

Mai

zeon

ly(A

)95

Ð076

/80

3Ð4ab

42Ð0

15Ð5

96Ð3

77/8

021

Ð7ab

13

13Ð5

Mai

zean

dw

eeds

(B)

82Ð5

33/4

01Ð6

a61

Ð044

Ð560

Ð024

/40

11Ð3

a1

1Ð51

2M

aize

and

cryt

ogam

s(C

)70

Ð056

/80

1Ð5b

45Ð3

35Ð0

55Ð0

44/8

010

Ð2b

12

12Ð5

Ł Mea

nsfo

llow

edby

the

sam

ele

tter

are

sign

ifica

ntly

diff

eren

tw

hen

PD

0Ð001

.†

Ref

erto

clas

sific

atio

nsy

stem

inm

ain

body

ofte

xt.



0

10

20

30

40

50

60

24.07.97

25.07.97

28.08.97

29.07.97

30.07.97

1.08.97

5.08.97

7.08.97

8.08.97

10.08.97

11.08.97

12.08.97

13.08.97

14.08.97

15.08.97

16.08.97

18.08.97

23.08.97

26.08.97

29.08.97

Date

Eve

nt

To

tal R

ain

fall

(mm

)

0

10

20

30

40

50

60

Eve

nt

Ru

no

ff C

oef

fici

ent

(%)

Event Total RainfallMaize and weeds (B)Maize and cryptogams (C)Maize only (A)

Figure 3. Event total rainfall and runoff coefficient sequentially throughout the monitored period on maize-only plots (types A), maize-and weed-covered plots (types B), and maize- and cryptogam-covered plots (types C)

maize alone offers little protection to the soil surface from raindrop impact forces, and the importance ofgood groundcover is highlighted.

Table III also presents mean runoff in litres per square metre for the three groundcover types investigated.The ANOVA results show a significant difference in mean runoff between plots types A and B, and A and C,and no significant difference between plot types B and C. Mean total runoff (per storm event) on the maize-only plots (types A) is >3 l m�2, whereas it is <1Ð6 l m�2 for the two other plot types (B and C). This couldbe viewed as an approximate 50 per cent reduction in mean runoff on plot types B and C, compared to plottypes A.

Soil loss

Although limited in its applicability, the soil on the experimental plots can be classed as moderatelyerodible, using the soil erodibility (K factor) nomograph developed by Wischmeier (1978). The low organiccarbon values (<2 per cent) obtained in this study reinforce this (Table I). Thus, the rapid attainment ofa good groundcover within a few weeks is potentially important on such erodible soils during periods ofmonsoon rain.

The percentage of rainfall events generating soil loss on plot types A is approximately 96 per cent(Table III). For plot types B, 60 per cent of rainfall events generated soil loss, compared to 55 per centfor plot types C. These results serve to demonstrate the comparable mediating effect of either weed or cryp-togam cover on the erosion process, over distances of a few metres. This can be examined in more detailthrough the use of multiple regression analysis.

Table IV shows the coefficients of determination (adjusted R2 values) for soil loss with total rainfall andthe EI15 index. The table also shows these same relationships with the addition of a vegetation parameter(weeds or cryptogams). The R2 values are those obtained from aggregating rainfall–soil loss results from allplots with the same groundcover conditions, for all storm events monitored. As seen, total rainfall and theEI15 index can explain 60–95 per cent of the variation in soil loss on the experimental plots. Interestingly,the EI15 index appears to explain slightly more variation in soil loss on the maize-only plots, comparedto the maize and weed, and maize and cryptogam plots (types B and C). This is not surprising as Poesen(1992) suggests that on interrill areas, raindrop impact has been recognized to be the dominant erosive forceaffecting erosion. These differences probably reflect the manner in which soil particles are being eroded and



Table IV. Coefficient of determination results for soil loss with rainfall, EI15,cryptogam and weed independent variables

Groundcover type S/P S/EI15 S/EI15/C S/EI15/W

Maize only (A) 0Ð84 0Ð95 Ł ŁMaize and weeds (B) 0Ð60 0Ð60 Ł 0Ð65Maize and cryptogams (C) 0Ð71 0Ð70 0Ð76 Ł

Key: S D soil loss; P D precipitation; EI15 D kinetic energy index; C D cryptogam cover;W D weed cover.Ł Not applicable.

entrained on the different plot types. In the case of the maize-only plots (types A), better correlations witha parameter that incorporates the kinetic energy of the rain (EI15) would suggest that a significant volumeof soil is being detached by rainsplash. In contrast, under a good groundcover, such as under plot types Band C, more detachment is likely to occur from overland flow. This suggestion finds support in the existenceof rainsplash pinnacles on the bare areas, and widespread evidence of splash deposits on vegetation and anyartificial obstacles nearby.

As seen from Table IV, the addition of percentage cryptogam and weed cover into the S/EI15 regressionrelationships raises the R2 values by about 6 per cent for plot types B and C. This additional explanation,although significant when P D 0Ð001, is not great. Reinforcing changes that occur throughout the monitoredperiod include changes in surface rugosity, in particular (transient) surface seals and compaction, as alreadydescribed. These are likely to work to lessen the effect of vegetation in the regression models. Although theseother factors were monitored, they were not the focus of this study. Another reason for the low improvementin the R2 coefficients when percentage weeds and cryptogam cover are incorporated into the regressionmodels, is that the two largest rainfall events sampled, of 46Ð75 mm and 37Ð05 mm, occurred in the first twoweeks of the sampling period. In other words, at a time when vegetation differences were greatest, towardsthe end of the monitored period, event total rainfall and the kinetic energies of the rainfall were reduced.This is an unfortunate but unavoidable constraint when working with natural rainfall over short periods ofexperimentation.

0

10000

20000

30000

40000

50000

60000

24.07.97

25.07.97

28.08.97

29.07.97

30.07.97

1.08.97

5.08.97

7.08.97

8.08.97

10.08.97

11.08.97

12.08.97

13.08.97

14.08.97

15.08.97

16.08.97

18.08.97

23.08.97

26.08.97

29.08.97

Date

Eve

nt

EI1

5 (J

m-2

)

0

20

40

60

80

100

120E

ven

t S

oil

Lo

ss (

g m

-2)

Event EI15Maize only (A)Maize and weeds (B)Maize and cryptogams (C)

Figure 4. Event EI15 and soil loss sequentially throughout the monitored period on maize-only plots (types A), maize- and weed-coveredplots (types B), and maize- and cryptogam-covered plots (types C)



Table V. Mean soil loss from plot types A, B and C for two stormevents monitored

Groundcover type Soil loss (g m�2) for storm of

5 July 1997 23 August 1997

Maize only (A) 82Ð9 45Ð0Maize and weeds (B) 59Ð8 2Ð8Maize and cryptogams (C) 39Ð1 0Ð1

Storm statistics: 5 July 1997, total rainfall 37Ð05 mm, EI15 40 637 J m�2;23 August 1997, total rainfall 17Ð46 mm, EI15 17 990 J m�2.

The differences in soil loss response with respect to rainfall kinetic energies, between all three plot types isshown in Figure 4. This demonstrates the relative orders of magnitude of soil loss. Throughout the monitoredperiod, less soil loss is generated from plot types B and C, whereas for plot types A, soil loss remainsrelatively high (mean soil loss >20 g m�2). It is unfortunate that the largest storm event was captured atthe beginning of the monitoring period, when vegetation differences were less. One way to overcome this isto assess mean soil loss per storm event for all three plot types, for all rainfall events monitored. This canbe achieved by using ANOVA (Table III). Mean soil loss per storm event for plot types A was 21Ð7 g m�2,compared to less than 12 g m�2 for plot types B and C. These differences are significant when P D 0Ð001.Thus, it may be concluded that cryptogams and weeds (plot types B and C) reduce soil losses, like runoff,by about 50 per cent on average, compared to maize-only plots (plot types A).

The effect of cryptogam and weed cover on soil loss can also be examined by looking at soil loss responsesfor individual storms. Table V shows mean soil loss generated from all plot types, and storm characteristicsfor two rainfall events on 5 July and 23 August 1997. These storms were the second and third highestmagnitude events sampled during the experimental period respectively, in terms of the EI15 index, whichhas already been shown to be highly correlated with soil loss in this study. As seen from Table V, soil losson plot types B and C was approximately 60 g m�2 and 40 g m�2 respectively, compared to approximately83 g m�2 for plot types A for the rainfall event on 5 July 1997. This can be translated into a reduction insoil loss of about 30 per cent on plot types B and C, compared to plot types A. Cryptogam and weed coverwere approximately 30 per cent on this date also (Figure 2).

For the rainfall event on 23 August, nearer the end of the monitored period when vegetation differencesbetween plot types A and B, and B and C, were greater than on 23 July (by approximately 50 per cent;Figure 2), soil loss on plot types A was approximately 45 g m�2 compared to 3 g m�2 and 0 g m�2 on plottypes B and C respectively. This can be viewed as an almost 100 per cent reduction in soil loss on plottypes B and C compared to plot types A. This figure becomes more apparent when one looks at the meantotal rainfall for these two events (Table V). The storm event on 5 July had over twice as much rainfalland kinetic energy, compared to the event on 23 August. Therefore, these data show the importance of ahigh percentage groundcover of either weeds or cryptogams in reducing soil loss during the relatively highermagnitude events. This can only be examined using this one high magnitude event towards the end of themonitored period, but provides an area for further investigation.

CONCLUSIONS AND FURTHER WORK

This study has shown that both weed and cryptogam cover act positively and approximately equally onreducing soil loss, as compared to non-vegetated surfaces beneath maize canopies on rain-fed agriculturalterraces (bariland). On average, cryptogams and weeds can reduce soil loss by 50 per cent compared tomaize-only surfaces. However, this figure may be greater during higher magnitude events when cryptogamsand weeds have attained almost full cover, although this supposition is based on one storm event and thereforerequires verification. Soil surfaces with cryptogams and weeds also have higher rates of infiltration and lowermagnitudes of runoff than bare surfaces.



The removal of a weed canopy, in terms of increasing soil erosion, must be viewed with respect to whethera cryptogam cover is present and, if so, whether the cryptogam cover is disturbed during a weeding event(from hand-turning the soil). If no cryptogam cover is present beneath a maize canopy, or if cryptogamsare destroyed during weeding, then soil loss can be expected to increase, as the soil surface is not affordedprotection from raindrop kinetic energies by groundcover. Cryptogams have a comparable effect to weeds inreducing runoff and soil loss, but unlike weeds are not removed. Weeds are harvested as fodder, or removedto reduce competition for the maize.

In semi-arid areas, the distribution of cryptogam communities persist as a result of minimal or no distur-bance. In contrast, in the humid Nepal Middle Hills, cryptogams on agricultural land flourish despite beinglocked into a continual cycle of disturbance by hand-weeding, crop planting and harvesting. Cryptogamsrapidly re-establish their cover, like weeds, between disturbance events. Thus, they offer some potential forerosion mitigation when subject to disturbance.

The findings in this study support the work of Alexander and Calvo (1990), Eldridge and Greene (1994)and Williams et al. (1995), in that they demonstrate the importance of cryptogams in reducing soil erosionby water. The study has shown such findings to be applicable in humid agricultural environments for the firsttime. In addition, this study has also shown that cryptogams reduce infiltration rates and increase surface waterdetention compared to bare soil surfaces, and reduce runoff volumes, in contrast to the work of Alexanderand Calvo (1990). The reasons for the different effects on surface water detention and runoff are likely tobe attributed to differences in cryptogam form, which vary widely (Belnap et al., 1999; Gaskin, 1999). Suchdifferences may have a considerable effect on soil hydrology (West, 1990), and would benefit from additionalstudies.

It is outside the scope of this paper to make definite recommendations on agricultural weed management,such as controlling cryptogam disturbance by local Nepalese hill farmers. Nevertheless, cryptogams potentiallyhave an important role in soil conservation and further studies are to be encouraged, especially in relation toweeding practices. For example, would a more selective weeding regime, whereby weeds are only removedfrom those areas that have a good cryptogam cover beneath them, help to reduce soil losses? Similarly, couldland management under a variety of cropping regimes benefit from removing weeds by cutting, as opposed tohand-turning the soil? It has been demonstrated that cryptogams can grow very rapidly after disturbance, andenabling them to develop over longer periods of time and therefore actively preserving their surface covermay reinforce their pedalogical conservation status. Moreover, further work would need to be done in parallelto understand farmers’ attitudes towards changing traditional practices, perceptions and understandings ofcryptogams and their role in soil conservation.

Additional research needs to centre around a greater understanding of cryptogam development seasonally.This includes extending the study forwards into the dry season in September/October and backwards intothe pre-monsoon rains in late April/early May, when single storms can generate over 15 per cent of totalannual soil loss (Gardner et al., 1995). In addition, there would be benefit in raising the spatial scale of theexperiments to the scale of several terraces, although given field conditions, this may be best achieved byhillslope modelling.

This study has produced verifiable results highlighting that cryptogams warrant attention in their own righton bariland in the humid Nepal Middle Hills in development-orientated soil erosion research. Agriculturalpractice may benefit from encouraging cryptogam growth and alleviating limiting factors. Such factors byand large are known, and include soil texture, soil stone content, pH and weeding practices (Gaskin, 1999).It is hoped that the results from this project will have transferability to other humid mountain environmentsalso susceptible to soil erosion. This would seem possible given the ubiquity of cryptogams world-wide.

Acknowledgements

This research was funded by the Department of Geography at Queen Mary, University of London, theBritish Geomorphological Research Group, The Roy Woodward Education Foundation and The Dudley StampMemorial Fund (Royal Geographical Society with The Institute of British Geographers). Thanks are affordedto Murray Gray, John Moore, Kevin Mawdesley, Benedicte Zoetelief, Helen Dangerfield and staff at theAgricultural Research Station, Lumle. Martin Kent is also thanked for statistical assistance. Particular gratitude



is given to Ramchundra Tripathi and Baba Tripathi for assistance with field logistics. Lal Bahada Thapa andfamily are also thanked for their generous hospitality during our stay at Bandipur.

REFERENCES

Alexander RW, Calvo A. 1990. The influence of lichens on slope processes in some Spanish badlands. In Vegetation and Erosion ,Thornes JB (ed.). John Wiley: Chichester.

Belnap J, Rosentreter R, Kaltenecker J, Williams J, Leonard S, Luehring P, Eldridge D. 1999. Biological Soil Crusts: Ecology andManagement. http://www.soilcrust.org [1 October 2000].

Beymer RJ, Klopatek J-M. 1992. Effects of grazing on cryptogamic crusts in Pinyon-Juniper woodlands in Grand Canyon NationalPark. American Midland Naturalist 127: 139–148.

Brotherson JD, Rushforth SR. 1982. Cryptogamic soil crusts in the deserts of North America. The American Biology Teacher 44(8):472–475.

Burt T. 1987. Measuring infiltration capacity. Practical Geography (Nov.) 1: 37–39.Cameron RE. 1972. Ecology of blue-green algae in Antarctic soils. In The Taxonomy and Biology of Blue-Green Algae, Desikachary TV

(ed.). Bangalore Press: Bangalore, India.Chartres CJ, Mucher HJ. 1989. The effects of fire on the surface properties and seed germination in two shallow monoliths from a

rangeland soil subjected to simulated raindrop impact and water erosion. Earth Surface Processes and Landforms 14: 407–417.Danin A, Barbour MG. 1982. Microsuccession of cryptogams and phanerogams in the Dead Sea area, Israel. Flora 172: 173–179.Eldridge DJ, Greene RSB. 1994. Assessment of sediment yield by splash erosion on a semi-arid soil with varying cryptogam cover.

Journal of Arid Environments 26: 221–232.Eldridge DJ, Kinnell PIA. 1997. Assessment of erosion rates from microphyte-dominated calcareous soils under rain-impacted flow.

Australian Journal of Soil Research 35: 475–489.Forster SM, Nicolson TH. 1981. Microbial aggregation of sand in a maritime dune succession. Biology and Biochemistry 13: 205–208.Gardner R, Thapa K, Tripathi R. 1995. Soil loss from overland flow on hillslopes. In Land Use, Soil Conservation and Water Resource

Management in the Nepal Middle Hills , Gardner R, Jenkins A (eds). Final Project Report, Institute of Hydrology and the RoyalGeographical Society (with IBG): London.

Gardner R, Mawdesley K, Tripathi B, Gaskin S, Adams S. 2000. Soil Erosion and Nutrient Loss in the Middle Hills of Nepal. ARSLumle and Nepal Agricultural Research Council: Nepal; and Queen Mary, University of London: London.

Gaskin SE. 1999. The distribution and role of cryptogams in soil erosion control in the Nepal Middle Hills. PhD Thesis, University ofLondon.

Goudie AS. 1992. Geomorphological Techniques . Allen and Unwin: London.Greene RSB, Tongway DJ. 1989. The significance of grazing on vegetation, soil structure and determining soil surface condition of red

earths in rangelands. Australian Journal of Soil Research 27: 213–225.Hesse PR. 1971. A Textbook of Soil Chemical Analysis . John Murray: London.Hodgeson JM. 1974. Soil Survey Field Handbook Technical Monograph No. 5 . Soil Survey of England and Wales: Harpenden.Hudson NW. 1995. Soil Conservation (3rd edn). Batsford Books: London.Ives JD, Messerli B. 1989. The Himalayan Dilemma–Reconciling Development and Conservation . Routledge: London.Jeje LK, Agu AN. 1990. Runoff from bounded plots in Alakowe in south-western Nigeria. Applied Geography 10: 63–74.Johansen JR. 1993. Cryptogamic crusts of semiarid and arid lands of North America. Journal of Phycology 29: 140–147.Kinnell PIA, Chartres CJ, Watson CL. 1990. The effects of fire on the soil in a degraded semi-arid woodland. II. Susceptibility of the

soil to erosion by shallow rain-impacted flow. Australian Journal of Soil Research 28: 779–794.LRMP (Land Resources and Mapping Project) 1986. Land Systems Report. The Soil Landscapes of Nepal. Kenting Earth Sciences

Limited: Nepal.Moore ID. 1981. Effect of surface sealing on infiltration. American Society of Agricultural Engineers 24: 1546–1552.Morgan RPC. 1995. Soil Erosion and Conservation . Longman: London.Mucher HJ, Chartres CJ, Tongway DJ, Greene RSB. 1988. Micromorphology and significance of the surface crusts of soils in rangelands

near Cobar, Australia. Geoderma 42: 227–244.Pluis JLA, De Winder B. 1989. Spatial patterns in algal colonisation of dune blowouts. Catena 16: 499–506.Poesen JWA. 1992. Mechanisms of overland-flow generation and sediment production on loamy and sandy soils with and without rock

fragments. In Overland Flow: Hydraulics and Erosion Mechanics , Parsons AJ, Abrahams AD (eds). UCL Press, London.Rowell DL. 1994. Soil Science: Methods and Applications . Longman: Harlow.Savoury A. 1988. Holistic Resource Management . Island Press: Washington, DC.Sharma CK. 1996. Overview of Nepal’s energy sources and environment. Atmospheric Environment 30(15): 2717–2720.Stocking M, Elwell HA. 1973. Prediction of subtropical storm soil losses from field plot studies. Agricultural Meteorology 12: 193–201.Turton CN, Vaidya A, Tuladhar JK, Joshi KD. 1994. Towards sustainable soil fertility management in the hills of Nepal. Lumle Agri-

cultural Research Centre: Lumle, Nepal; Natural Resources Institute Chatham, UK.West NE. 1990. Structure and function of soil microphytic crusts in wildland ecosystems of arid and semi-arid regions. Advances in

Ecological Research 20: 179–223.Williams JD, Dobrowolski JP, West NE. 1995. Microphytic crust influence of interill erosion and infiltration capacity. Transactions of

the American Society of Agricultural Engineers 38(1): 139–146.Wischmeier WH. 1978. Use and misuse of the Universal Soil Loss Equation. Journal of Soil and Water Conservation 31: 5–9.Wischmeier WH, Smith DD. 1958. Rainfall energy and its relationship to soil loss. Transactions of the American Geophysical Union

39(2): 285–291.


Documents

The role of cryptogams in runoff and erosion control on bariland in the Nepal Middle Hills of the Southern Himalaya