151

The Dynamics of Mountain Agriculture andLand Change in Lamjung District, Nepal
Milan SHRESTHA
School of Sustainability, Arizona State University
PO Box 875502, Tempe, AZ, 85287-5502, USAe-mail: milan@milanshrestha.com

Abstract
Mountain agriculture in Lamjung has changed over the last six decades from a subsistence agropastoralism to a mixed-farming system. This transition is significant from the perspective of land-use andland-cover dynamics, as it has resulted in alterations in forest coverage, livelihoods and overall ecosystem services. This transition is also emblematic of the long-term environmental, economic and socio-culturalchanges occurring in the Nepal Himalaya. Using multi-temporal spatial data (1976 to 2010) in combination
with socio-economic data, this study has detected, quantified and analyzed the land-cover trajectories in
Lamjung District during the last four decades, matching the time period of a highly contested debate on the
Theory of Himalayan Environmental Degradation. The results of this study reveal two significant patterns ofchange: (a) trade-offs and dynamic transitions occurring among forest, agricultural land and shrubland
categories, showing more complex patterns than the common story that massive, irreversible deforestation
has occurred in the Himalayan region due to increased population pressure, and (b) land-use intensification
significantly increasing in the irrigated lands of the lower valleys, accelerating the competition for prime lands,
but disintensification and, in some cases, increasing land abandonment of the upper ridges and remote lands,
emerging in recent years. Finally, using a spatiotemporal, place-based analysis of landscape level transitions,this study strove to develop a better understanding of environmental change in the Nepal Himalaya – widely
considered one of the most fragile ecosystems in the world, where we lack sufficient knowledge of the extent
to which agricultural land-use modifications are contributing to land-cover change and vice versa.
Key words: agricultural change, deforestation, land change, migration, the Himalaya

1. Introduction
Over the last six decades, mountain agriculture in the
Nepal Himalaya has changed from a subsistence agropastoralism to a mixed-farming system (Rhoades, 1997),
as the country opened up to a globalized world and thewider market economy penetrated what was once considered a remote and inaccessible region (Jodha, 2001;
Blaikie et al., 2002). This transition has had significant
implications in land-use and land-cover change (LUCC)
or land change, as it alters forest coverage, livelihoods
and overall ecosystem services. It is also emblematicof the long-term environmental, economic and sociocultural changes occurring in the Himalayan region(Jodha, 1995; Zurick et al., 1999). This study analyzes
land change and its relationship with agricultural andsocio-economic changes in Lamjung District of Nepal
during the last four decades (1976 to 2010). This time
period also overlaps with the highly contested debate of
the Himalayan Environmental Degradation or the HED(Eckholm, 1975; Ives & Messerli, 1989; Metz, 1991;
Global Environmental Research
18/2014: 151-160
printed in Japan

Guthman, 1997), which received worldwide attention as“a classic example” of deforestation and environmental
degradation. One of the key limitations in the debate andperhaps the most constructive lesson learned from it wasthat more empirical dataset and interdisciplinary research(combining natural and social environmental sciences)were needed for a full understanding of the relatively
complex biophysical and socio-cultural variations within
the Himalaya (Bishop, 1990; Brower, 1991; Stevens,
1993; Gurung, 2004). Such studies, in turn, could help
debunk any generalized assumption or so-called “mountain myths” (Forsyth, 1998; Metz, 1989 and 1991; but
also see Ives, 2004 for a counter-argument that the HED
was anything but “myths”). This is important because theHED debate has been mostly dominated by the common
story of deforestation or “environmental orthodoxies”(Fairhead & Leach, 1996) – often perceived as a linear
process of converting forests into agricultural lands withirreversible impacts on the fragile ecosystems such as
those of the Himalaya. To this end, the study upon which
this material is primarily based – dissertation fieldwork©2014 AIRIES152

M. SHRESTHA

conducted in 2005 – analyzed the spatio-temporal dynamics of land change in the district by combining
remote sensing data derived from a Landsat series withsocio-economic data collected from household- andcommunity-level surveys and ethnography (see Shrestha,
2007 for more details). However, in order to bring theland-cover change analysis more up-to-date, a newLandsat image of 2010 was acquired and subsequently
processed and compared with the previously analyzed
dataset.
In any land-cover change analysis, it is imperative to
examine the major land-use strategies practiced in agiven area to make sense of the overall land-cover change
detected from remotely-sensed data (Turner & Meyer,1994). It is more so for a region of predominantly subsistence farming with a diversified livelihood portfolio
covering agriculture, forestry, livestock raising andpasturing, and other economic activities. In the Nepal
Himalaya, where land-use strategies are mostly shapedby subsistence farming and its relationships to forests andlivestock, the changes in agricultural land-use largely
define land-cover change trajectories. Lamjung’s transition from agropastoralism to a mixed-farming system
bears a striking resemblance to how mountain communities across Nepal have been transformed in recent
decades (Gurung, 2004).
In this paper, land-cover change trajectories ofLamjung are detected, quantified and analyzed within the
broader context of changes in farming systems. The primary objectives of this paper are: (1) to detect andanalyze the patterns of land-cover change, and (2) toexamine the spatial and temporal linkages of land-coverchange with the transition in farming systems and otherchanges.

2. Study Area and Methods
Lamjung District is located in the north-central part
(officially in the Western Development Region) of Nepal.
The district covers three distinct ecological zones: theMiddle Hills (Pahad), accounting for 43%; High Mountains (Lekh) with 39%; and Himalaya (Himal) with 18%of the total land area (1,692 km2), providing a very
interesting, representative landscape that covers a gradient of socio-cultural and biophysical variations of the
Nepal Himalaya within its borders. The district has 61
village development committees (VDCs) and the landscape rises from approximately 500 to about 7,690 m
within a short distance of about 50 km (Fig. 1), whichmakes it one of the three districts of Nepal with thegreatest elevation variations. To the north is the Himalaya,bordering on Manang District, while the eastern, southern and western boarders are shared respectively with
Gorkha, Tanahun and Kaski districts. The Gurung, orTamu, an indigenous nationality whose language derives
from the Tibeto-Burman language family, are the mostdominant ethnic group in the district. Agropastoralism
was the main livelihood source of the Gurung for centuries, which they complemented well with off-farmincome sources, particularly by serving as mercenary
soldiers in the Indian and British Gurkha regiments andas seasonal traders (Messerschmidt, 1976a, 1976b).
Traditional agropastoralism has virtually disappeared,albeit with some exceptions in remote northern Gurung
villages, in favor of mixed-farming system in the Pahad
and rice-dominated agricultural intensification in thelower ridges and valley floors. Many migrated to theTarai (southern plains) after the eradication of malaria
during the 1960s. In recent years, while agriculture

Fig. 1 Map of Research Site (Lamjung District) in Relation to Nepal. This map also shows
Lamjung district’s neighboring districts, its 61 VDCs and the terrains.

The Dynamics of Mountain Agriculture and Land Change in Lamjung District, Nepal

remains the main source of income and employment,
many are working as laborers in the cities and abroad,especially in India, Malaysia and the Gulf countries,
attracted by non-farm income opportunities that havebeen of increasing significance to the youth in theseremote villages.
This study examined land-cover changes over timeand space with the use and interpretation of satellite
images, and combined them with other spatial data layers(e.g., district and village boundaries, road networks,
digital elevation models or DEM, land capability andland systems) in a Geographical Information System(GIS) platform. Seven Landsat images covering Lamjung
between 1976 and 2010 were acquired (Fig. 2). These
images of 1976, 1984, 1990, 1994, 1999, 2003 and 2010
were processed to enhance detection of land-coverchanges in the district for the last five decades (see Table
1 for satellite data characteristics). The reason for examining LUCC for the last fifty-year period is that it isdifficult to understand the dynamics of LUCC at any
point in time if these are not analyzed within the context
of longer histories of human-environmental interactions(Turner & Meyer, 1994), and mountain communities are
no exception (Price & Thompson, 1997).
In order to eliminate the chance of misregistration of
the spatial data sources, all the vector and satellite images
were first converted to the same datum and coordinate
system – the Universal Transverse Mercator (UTM) Co-

Lamjung Himal(6986m)Himalchuli(7893m)Buddha Himal(6672m)153

ordinate System (44-N in MSS and 45-N in TM andETM+). All the satellite images were subsequently processed, enhanced and checked for their radiometric andspectral corrections using standard processing methods(Jensen, 2000). To minimize the effect of illuminationdifferences on surface reflectance, spectral bands werenormalized by total intensity. A subset of the area ofinterest (AOI), which was about 1,691 km2, was subsequently clipped for each image to cover only the study
area (Fig. 2).
Conducting a LUCC study in the Nepal Himalaya, as
in other mountains, is bound to face several limitations
and challenges. One of the main challenges of analyzing
remote sensing data on sub-tropical mountainous regions
is that near anniversary satellite images are ideally preferred in LUCC studies to minimize the effects ofseasonal phenological variations. However, temporal
resolutions of satellite data for Nepal are relatively low
and the availability of cloud-free images is even rarer.
Most of the images used in this study were captured in
the winter season (November-February), except for the
1994 and 2010 images, which were taken during spring.
Some of these images have clouds, but they are still more
usable than others. Classification of land resources inmountains is still a problem due to variations in altitude
and illumination, as well as the effects of topographic
shadows and small parcel sizes (Millet et al., 1995,
Gautam & Watanabe, 2004). Similarly, the presence ofcloud cover, cloud shadows and snow in the satellite
images in their classification as separate land-covercategories still is a major constraint. Although it ispossible to create a mask (e.g., temporal logic) to separate features like clouds, cloud shadows and terrain
shadows in a LUCC analysis, two or more images fromthe same period are usually required for this (Lillesand &Kiefer, 2000). In this case, however, this option was not
only expensive, but also not feasible for the data-scarce
Himalayan region.
Another concern was the question of “data validation” (or “accuracy assessment” of the land-cover data).
Ideally all the land-cover maps need to be checked forthematic data accuracy, but reference maps were available only for 1984 and 1999 – these reference maps werein the form of aerial photographs from the Land Resource
Mapping Project (LRMP) of 1986 and Survey DepartTable 1 Characteristics of the Landsat data used in this study.

Besisahar

Fig. 2 A Landsat 5 Thematic Mapper (TM) image in the false
color composite infra-red (IR) of 2010 showing the area ofinterest (AOI). The bottom inset shows the district
headquarter, major rivers, and peaks.

Landsat
MissionSensorSpatialResolution(m)

December 3, 1976

1

MSS

79

February 3, 1984

4

MSS

79

November 10, 1990

5

TM

30

Date

May 13, 1994

5

TM

30

December 13, 1999

7

ETM+

30

January 6, 2003

7

ETM+

30

April 7, 2010

5

TM

30

154

M. SHRESTHA

ment of 1997. A stratified random sampling method was
employed covering 256 sample points and 1,024 searchcounts forced within the AOI. A confusion matrix
together with both kappa statistics and tau coefficientswas calculated (see Shrestha, 2007 for details), showing
an accuracy level of 80.47%, which is relatively low but
acceptable for a mountainous region with the limited data
available.
Finally, the most challenging task in extracting landcover categories from satellite images was to develop a
land-cover classification system that could precisely
capture and fully represent the complex mosaic of landcover types associated with the mixed-farming systems
of mountainous regions. This study followed the LRMP’s
Land-use Classification Scheme, which was modified
from the widely used Anderson’s Classification Scheme
and is considered the standard classification system for
Nepal. For instance, this classification scheme has four
different categories for agricultural lands alone (i.e., level
terraces, sloping terraces, valley floors and footslopes).
Proper identification and extraction of these individual
land-use categories from the moderate resolution Landsat
data would be useful, and yet, it is also quite challenging.
Because of rugged terrain and verticality (i.e., altitudinalvariations within a small distance), smallholders tend to
rely on multiple, often fragmented lands for their farming
activities, resulting in small and non-contiguous landsurface signatures hard to detect in moderate resolution
satellite data. As Zimmerer (2003) argues, mountainagriculture can be best characterized as an overlapped
patchwork of agriculture, forests and pastures (Fig. 3).
For instance, forests can be differentiated by cover types,
such as coniferous, hardwood, shrub and other combinations. Grazing lands, however, are problematic. Gurung(2004: 33) points out that ‘grazing lands’ are often mistaken in LRMP classification, as they include all grassand scrubland adjacent to forests. Gautam and Watanabe(2004) also identify misclassification issues in LRMP.
Nevertheless, based on their spectral characteristics anddominant presence in each of the satellite data, fiveland-cover classes were selected for this study (Table 2).
This study used a hybrid approach of satellite image
classification, combining both unsupervised and supervised classification. This approach enabled: (1) analysisof the spectral properties of various surface features (e.g.,
crops, forests and settlements) in the Landsat data into
spectrally similar categories by the use of predefined,
numerical decision rules, (2) clustering and reclassification based on ground truth data and knowledge of
Lamjung’s environment. Image classification and subsequent extraction of land-cover types were done in threestages.
First, in the unsupervised classification, a set of apredefined number of ‘clusters’ is identified on the basisof their image properties (i.e., pixel DN values) representing a spectral group. The bands 3 (visible red), 4(near infrared) and 5 (mid-infrared) of the Landsat ETM+
image of 1999 were used in the unsupervised classification, which involved two steps: the self-iterative module

for cluster seeding and cluster labeling. The ISODATA(Iterative Self-organizing Data Analysis) algorithm
separated different spectral clusters into 12 classes. Oncethe clusters were established and classes were identified,each of the resultant clusters was labeled and aggregatedinto the five major classes.

Fig. 3 The top North facing picture shows the southern slope andthe bottom South facing picture shows the northern slope.
The overlapped patchworks of agriculture, shrubland, andforest at different altitudes are clearly visible in these two
photographs of circa 2005.

Table 2 Land-cover classes and definitions.
No.

Classes1.

Forest2.

Agricultural
land

3.

Shrubland

4.

Snow

5.

Cloud

Definitions
Areas 75% or more of all coniferous,
hardwood and all other combinations oftree species
All level terraces, sloping terraces, valley
floors and footslopes; often covers
settlements and exposed soil nearsettlements
Shrubs, pastures, grasslands, and scrubs,
shrub vegetation which may include
regenerated vegetation and transitions,and exposed soil in the treeline andsnowline area
All snow-covered area, including ice,rocks and slopes above 4,000 m
Cloud, cloud shadows

The Dynamics of Mountain Agriculture and Land Change in Lamjung District, Nepal

This resultant image was taken to the research sitewhere ‘ground truth’ data or ‘training samples’ werecollected along with socio-economic data through surveys and ethnographic data (see Shrestha, 2007 fordetails). With the help of a handheld global positioning
system (GPS) and a short survey of 12 ‘training samples’
distributed within the research site, detailed observationsof land-cover types, characteristics and structures werecarried out, as accurate detection of land-cover classes ofmountainous areas is conditioned by several factors, suchas relief, shadows, clouds and so forth, and adequate
understanding of the effects of such factors is useful tominimizing their effects (Lillesand & Kiefer, 2000). The
information collected for ‘training samples’ and theauthor’s knowledge of Lamjung’s landscapes were usedin reclassifying and labeling the clusters.
In the second stage of classification, supervised classification of all the satellite images was completed. First,
the areas of known cover type in the image were selected
and were specified to the computer as ‘training signatures.’ A minimum of ten polygons of each training areawith at least 40 pixels were selected. These training
signatures were evaluated by checking signature histograms, followed by applying the ‘signature file’ forclassifying each pixel individually into the class it mostclosely resembled.
The third and final step was the ‘output stage,’ inwhich land-cover maps (Fig. 4), tables of statistics (Table3), and digital data files were prepared. In this classification, the Gaussian Maximum Likelihood system with aparametric rule was preferred as the classifier.
This study used the ‘post-classification’ change detection approach, which uses multi-temporal satellite
images to evaluate differences in land-cover (Yang & Lo,2002). This technique uses the principle of map-to-map
comparisons: it compares two independently produced
classified land-cover maps derived from multi-date images and produces a full matrix of land-cover changes. It
identifies, pixel by pixel, detailed information about theland-cover status and reveals land-cover change patterns
in a ‘from-to’ direction, providing the area gain and loss
for each land-cover type for different years, thus establishing land-cover change trajectories. In this study, allfive land-cover classes were kept separated, mainly

155

because the study focused not on net deforestation, butmore on spatial and temporal changes in forest, agricultural land and shrubland to identify land-cover change
trajectories. Similarly, to ensure the validity of the measurement, total vegetation coverage patterns of the district

Fig. 4 Post-classification change detection system is used toanalyze land-cover type derived from multi- temporalsatellite data of 1976, 1984, 1990, 1994, 1999, 2003
and 2010.

Table 3 Land-cover changes in Lamjung as derived from a time series of Landsat images.
Date of observation
3 December 1976
3 February 1984
10 November 1990
13 May 1994
13 December 1999
6 January 2003
7 April 2010

(ha)
(%)
(ha)
(%)
(ha)
(%)
(ha)
(%)
(ha)
(%)
(ha)
(%)
(ha)
(%)

Forest75582.66
44.62
71078.22
41.9464453.68
38.02
66221.08
39.08
67330.33
39.81
71582.57
42.22
72556.00
42.86

Ag. land30659.93
18.10
41453.82
24.46
53078.50
31.3144904.26
26.50
54961.48
32.50
42047.55
24.80
40847.95
24.14

Shrubland37825.21
22.33
30098.93
17.76
26276.49
15.50
15487.73
9.14
19494.12
11.53
16717.29
9.86
45021.13
26.49

Snow
8130.81
4.80
18388.14
10.85
15969.32
9.42
17199.18
10.15
17021.68
10.07
28551.65
16.84
11021.14
6.51

Clouds17193.28
10.15
8439.904.98
9747.735.75
25620.85
15.12
10301.38
6.0910647.53
6.28
0
0

Total
169391.88
100.00169459.01
99.99
169525.72
100.00169433.09
99.99
169108.99
100.00169546.59
100.00169295.59
100.00156

M. SHRESTHA

for the given time period were calculated using theNormalized Difference Vegetation Index (NDVI): NIRR/NIR+R (where NIR is near-infrared band and R is redband reflectance).

3. Results
Figure 4 and Tables 3 and 4 show the area gain andloss for forest and agricultural lands between 1976 and2003, and the updated figures for 2010. Similarly, moredetailed information on the trade-offs between eachland-cover class for different periods is given in Table 4.
One of the most visible changes is the significant lossof shrubland and pasture coverage, decreasing from37,825 ha (22.33%) in 1976 to 16,717 ha (9.86%) in 2003.
However, shrubland increased dramatically to 45,021 ha(26.49%) in 2010, mainly in the northern areas. This
land-cover change detection needs to be further verified
with ground truthing. At the same time, there are some
visible patches of shrubland in areas near the settlements,
which are speculated to be abandoned lands. What is sosignificant about this particular pattern of change is thatshrubland decreased regularly in each observation year,
except for a slight gain of about 2% in 1999 and a significant increase in 2010. From 1976 to 1984, for instance,
shrubland lost about 27,408 ha (21.48%), while this classgained only about 30,116 ha (17.77%) in the same period.
It lost significant area, approximately 10,684 ha (6.30%),
to forests between the years 1994 and 1999; this is higher
than the overall loss of 6,041 ha (3.56%) to forests from1976 to 2003. It lost only 4,751 ha (2.80%) to agriculture
during the same period. The overall areal gain in shrubland between 1976 and 2003 was 22,442 ha (14.03%), of
which 9,738 ha (5.57%) was intact as shrubland and6,789 ha (4.79%) was gained from forest.
Overall the forest area decreased slightly from75,582 ha (44.62%) in 1976 to 72,226 ha (42.86%) in
2010. Forest coverage was lowest in 1990 with only
64,453 ha (38.02%) and started to gain slightly in thesubsequent observation years. Forest coverage was66,221 ha (39.08%) in 1994, 67,330 ha (39.81%) in 1999,
71,583 ha (42.22%) in 2003, and 72,226 ha (42.86%) in
2010. In terms of areal gain and loss, there was no significant change for forest coverage between 1976 and 2003,
as it gained 71,575 ha (42.23%) and lost 70,334 ha(41.50%). In the forest area of each observation year, amajor loss of 15,658 ha (9.24%) to agriculture did occur
between the years 1976 and 1984, which is quite significant when the same category is compared for the period
1976-2003. Only about 8,125 ha (4.79%) of forest wasconverted to agricultural land in this period. The spatial
expansion of forests between 1976 and 2003 camemainly from agricultural lands and shrubland, 10,451 ha(6.17%) and 6,041 ha (3.56%), respectively.
Another significant and visible change was the gain inareal coverage of agricultural lands, which increased
from 30,660 ha (18.10%) to around 42,048 ha (24.80%)
in 2003 and 40,847 ha (24.14%) in 2010. It is noteworthy
that the 1994 and 2003 images have considerable area
under cloud cover in the southwestern part of the district
and these are presumed to be agricultural areas – all otherimages of 1984, 1990 and 1999 confirm the pattern.
Similarly, the 1976 image leaves out a small portion ofsouthern Lamjung, where agricultural land is noticeably
the dominant class. These certainly have slightly reduced
agricultural land coverage shown for those years and it
slightly affected both the total gain and loss. While agricultural lands expanded from 30,660 ha (18.10%) in
1976 to 41,454 ha (24.46%) in 1984 and to 53,079 ha(31.31%) in 1994, they also lost a total of 40,369 ha(23.81%) from 1976 to 2003, mainly to forest as mentioned above.

1976

1976

Table 4 Land-cover change matrix between 1976 to 2003 and 1976 to 2010.
Forest(%)
Ag. land(%)
Shrubland
(%)
Snow
(%)
Clouds(%)
Area Gained(%)

Forest48454.81
28.59
10451.17
6.176040.53
3.56
1075.490.63
5553.483.28
71575.48
42.23

Ag. land8125.26
4.79
17938.22
10.58
4901.712.89
1066.95
0.63
6281.123.71
38313.26
22.60

2003
Shrubland
6789.04
4.79
3021.231.78
9437.54
5.57
333.090.20
2860.901.6922441.80
14.03

Snow
4090.252.41
5122.553.02
6847.354.04
4928.122.91
7688.60
4.5428676.87
16.92

Clouds
2874.971.70
3835.60
2.26
180.710.11
764.230.45
803.550.47
8459.07
4.99

Area Lost
70334.33
42.28
40368.77
23.81
27407.83
16.17
8167.884.82
23187.66
13.69
169466.47

Forest(%)
Ag. land(%)
Shrubland
(%)
Snow
(%)
Clouds(%)
Area Gained(%)

Forest48257.89
28.98
11199.47
6.73
4570.562.74
1188.830.71
6145.60
3.6971362.35
42.86

Ag. land6849.05
4.11
18857.72
11.33
4769.37
2.86
1756.461.05
7964.124.7840196.71
24.14

2010
Shrubland
13589.34
8.16
7796.82
4.68
17194.76
10.33
803.110.48
4728.492.84
44112.53
26.49

Snow
1163.850.70
1336.910.80
1131.770.68
3765.752.26
3441.46
2.07
10839.73
6.51

Clouds
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Area Lost
69860.13
41.9639190.93
23.54
27666.46
16.62
7514.144.51
22279.67
13.38
166511.33

The Dynamics of Mountain Agriculture and Land Change in Lamjung District, Nepal

4. Discussion
Overall, the patterns of land-cover changes detectedin this study are found to be consistent with the results of
the ICIMOD case study (1996) and LRMP of 1986. The
results obtained with the change detection method usedin this study exhibit three district-level land-cover change
patterns (Fig. 5). First, there was a steady decline in
shrubland coverage in each observation year because asubstantial expansion of forests occurred in remote partsof northern VDCs like Ghermu, Bhulbhule, Bahundanda,
Bansar, Faleni and Dhodeni. This particular pattern ofland-cover change, however, did not contribute much tothe overall forest coverage expansion, because the forest
area in fact decreased from 1976 to 1990 before startingto gain area at regular intervals in 1994, 1999, 2003 and2010.
The second land-cover pattern is dynamic changes inforest cover. Although the overall forest coverage decreased slightly during 1976–2003, there were significant
changes in-between. There were subtle and dynamic
trade-offs among forest, agricultural land and shrubland,as noted in the land-cover change matrices (Tables 3 and
4). Following the declining trend in early observation
years, especially from 1984 to 1990, forest coverage
started to gain after 1990. As detected in the land change
analysis, in the early period (1976–1994), most of theforest loss was due to the expansion of agricultural lands
in Bhoje, Bangre, Khudi, Faleni and Dhodeni VDCs. Allof these VDCs except Khudi experienced deforestationon the footslopes and valley floors. Forest loss inlower valley areas, such as Beshishahar, Bhotewodhar,
Sundarbazar, Dhamilikuwa, Gaunshahar and Hiletaxar
directly corresponded with the timing of the newly built
Dumre-Beshishahar Road, which opened up new settlements and towns along the highway. However, this trend
did not match the widely-reported deforestation scenarios portrayed in the HED. On the contrary, forest coverage increased starting from the mid-1990s, especiallyafter the initiation of the community forestry program,which enabled local communities to conserve local forest
coverage and forest resources. Aside from existing forests, any shrubland, grassland, barren land or public land

Fig. 5 Land-cover change patterns (1976–2010) in Lamjung
District.

157

on slopes was now within the scope of community forestry programs.
The major concentration of forests in recent years has
been in the middle part of the district, mainly in the lower
part of Bansar, Faleni, Bhanjhakhet, Ghanpokhara andKhudi. The highest proportion of VDC area under forest
in 2003 was recorded in Bansar (68.76%) and the lowest
in Parewadanda (9.5%). It was, however, reported elsewhere in Nepal that modifications in forest cover and thedegradation of forest resources had taken place, eventhough the forest cover had not been decreasing at
the presumed rate. Carson (1992) made an important
observation that the total crown cover of forests was
already decreasing during 1965-1979. Similarly, a casereported from central Nepal (Schreier et al., 1994) suggested that forests were either ‘stable’ or gaining in total
area cover after the late 1960s. Even where deforestation
occurred, the losses had been reversed in terms of bothforest area and tree density; however, the quality of theexisting forest was suspect. However, the total cover andquality of grasslands and shrublands were also decreasing, mostly converted to croplands.
The third and perhaps most important trend was thegradual expansion of agricultural land in each observation year from 1976 to 1994. In this period, the agricultural area expanded mostly on the upper slopes of Khudi,
Bhoje, Bangre, Faleni and Dhodeni VDCs. In the early
1990s, agriculture on valley floors and footslopes along
the Dumre-Beshishahar Road and major rivers like the
Marsyandi, Madi, Midim, Dordi, Nyagdi and Sandhi
expanded in favor of paddy-based intensification. This
trend is to be expected in areas experiencing a growing
penetration of the wider market economy, which increases opportunities for higher value crops such as,
paddy, wheat, fruits, vegetables and cash crops. On theother hand, in remote villages such as Bhujung, Pasagaun,
Ghermu, Taghring, Maling, and Ghanpokhara, where themajority of land areas consists of rugged terrain andrain-fed agriculture systems, there was a growing trend ofland abandonment. There were several small patches ofshrubland seen in the 2010 image that were identified as
farming or cultivated land in 1999 and earlier. This trend
is consistent with a report from Kaski – an adjacent district (Khanal & Watanabe, 2006), as most of the young
people find few incentives to continue subsistence
agriculture and they seek non-farm income opportunities
in cities and abroad, which eventually leads to labor
shortages and land abandonment. Clearly, there is a needfor a more in-depth study to examine these complex
relationships further.
These three land-cover change patterns can be best
explained within the broad socio-cultural and economicchanges that have occurred in the district, especially inthe last four decades. As discussed in Fox et al. (2003),
there are advantages as well as limitations to linking
remote sensing with socio-economic data. A detailed
analysis of these socio-cultural and economic changes isreported elsewhere (Shrestha, 2007), but these changes
can be broadly categorized into three major types:

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M. SHRESTHA

1. increasing accessibility due to the expansion of theDumre-Beshishahar Road and other routes has accelerated the penetration of the wider market economy in the district, allowing subsequent village andtrekking tourism development in the Annapurna
Conservation Area;
2. enormous changes have occurred in agricultural systems, particularly transitioning from subsistence
agropastoralism to mixed farming systems dominatedby agricultural intensification of valley-bottom irrigated lands, which in turn, has helped consolidate
forest areas after the initiation of community forestry
programs in the 1990s; and3. the growing attractiveness of non-farm income opportunities in the cities and abroad for the youth has
created labor shortages in the villages. This trend
accelerated and severely affected the district during
the Maoist Insurgency period (1996 to 2006) when
young people started to leave their homes after gettingcaught between the government security forces andthe Maoist guerillas.
As Rhoades (1997) argues, agricultural growth in the
Himalaya generally takes place either through expansion– the extension of land under cultivation – or intensification – the increased utilization or productivity ofland currently under production. The expansion strategy
is more common than the intensification strategy underconditions of low population density and greater availability of lands. In contrast, in the areas with a fierce
competition for prime cultivated land and higher population density, such as at lower altitudes and in valleybottom areas, agricultural growth often comes from theintensive use of agricultural lands. Some of the villages
with easy access to roads and markets have experienced
this trend in recent decades.
The increasing penetration of the wider market economy and road networks, especially after the 1990s, havetransformed Lamjung’s cultural and ecological landscape.
This ‘modernization process’ resulted not only in development of market towns in the valley, it also changed theconsumption-production based (subsistence agriculture)system to a more market-integrated agricultural system,transforming every economic aspect of the villages. The

demand for cash income increased tremendously forgaining access to basic services like education, medicalcare and extension services, which in turn have put amonetary value on every good/product, service (i.e.,labor, cooperation) and resource exchanged in the villages. Changes are also visible in the way houses are built,
natural resources are used and the food system issustained.
Migration has had enormous impacts on the agricultural transition in Lamjung. Although agropastoralism
among the Gurungs evidently started to decline as earlyas the early 19th century when they began to move tolower ridges and valleys (Messerschmidt, 1976a), theGurungs were found to have taken immense pride in theiragropastoralism, which was still the most dominant agricultural practice as late as the 1970s (Messerschmidt,
1976a). Herding and animal husbandry, as a unit, were
well complemented by farming, trade and soldiery, forwhich the Gurungs were renowned (Messerchmidt,
1976b). This practice has disappeared from all but a
few remote villages, such as Bhujung, Ghanpokhara,
Uttarkanya, and Tanghring. As shown in Table 5, theearly mass migration to the Tarai helped relieved the
population pressure on the natural resources to a great
extent, but the recent migration of the young people has
had negative impacts on the agricultural production systems. In the recent years, subsistence agropastoralism
and farming in general have become impractical, and thevillagers now have no choice but to supplement theirincome from agriculture with remittances and income
from village tourism and trekking in the Annapurna
Conservation Area. Moreover, the recent labor shortage
has triggered a land abandonment process: the farmers
have stopped attending to the lands that are remote andfurther away than a few hours walk from their household
due to their lower productivity and the heightened senseof insecurity during the Maoist Insurgency. Major
agricultural activities are now concentrated on the lower
ridges and in valleys with easier access to irrigation.
Most of the remote villages can no longer afford to sustain their livelihood solely on the basis of subsistence
agriculture.

Table 5 Population trends in Lamjung District.
Census year

Population

Decennial change

1941

107,543

1954

133,627+ 26,084+ 24.3

1961

130,935–2,692–2.0

Tarai resettlement begins

1971

140,226+9,291+7.0

Malaria eradicated in Tarai-

Percent change-

Remarks
Unreliable census data
Heavy monsoon (1954, 1955)

1981

152,720+12,494

+8.9

1991

153,697

+977

+0.6

Democracy restored

2001

177,149+23,452+15.3

Highway to Beshisahar completed

2011

167,724–9,425–5.3

Outmigration of young people(+36,789)+28.09+ 107.9% national average

1961-1991(Source: Gurung (2004) based on various censuses conducted by the Central Bureau of Statistics, Government of Nepal)

The Dynamics of Mountain Agriculture and Land Change in Lamjung District, Nepal

5. Conclusions
In the Nepal Himalaya, which is considered one of themost fragile ecosystems in the world (Jodha, 1995),
smallholder agriculture historically co-evolved with acomplex socio-ecological system largely built around theinterrelationships among forests, crops, livestock, andshrubland. Hence, a comprehensive examination of
LUCC dynamics, rather than a singular and exclusive
focus on “deforestation” is necessary for an accurate
analysis of changes in the regional environmental. The
material presented here, which draws upon the past andon-going research efforts, clearly demonstrates the comparative advantage of combining a geospatial analysis
with socio-economic data in detecting and characterizing
the long-term changes in a particular landscape. In
Lamjung and other mountainous areas of Nepal, the mostsignificant environmental change in the recent decades
has been dynamic transitions in land-cover; the trade-offs
among forest, agricultural land and shrubland over the
decades show a highly dispersed land-cover change patterns. This does not support the common assumptions of
the HED that deforestation in the Himalaya is rampant. In
contrast, there have been reversals in deforestation,
especially after the 1990s. More specifically, this study
reveals that there has been no linearity of land-coverchange as generally assumed (i.e., irreversible conversions of forest and shrubland to agricultural land). Theseland-cover changes are in fact non-continuous in space,leading to complex landscape mosaics and overlapped
patchworks in the district. The results also reiterate thebasic premise of this study that we must look beyond the
popular notion that perceives land-cover change as simple and irreversible conversions from one cover type toanother.
The land-cover change patterns detected in this casealso highlight the significance of land modification
activities with regard to land-cover change. Changes inforest, agricultural land and shrubland have occurred
during the same period when the district was going
through a transition in agricultural practices and theinitiation of forest conservation efforts like community
forestry. In other words, explanations for such complex
patterns of land-cover change arguably are not in theland-cover conversion process, but in the modification
activities that create a highly dispersed pattern ofland-cover change. There is clearly a need to focus on
why and how the modifications in agricultural land-use
strategies occurred within the historical context of thismountainous landscape, and how those are interconnected with the wider changes occurring at the regional
level, such as the changes in the local economy, globalization, poverty and vulnerability, and climate change
and variability.
The significance of this study is that the major landuse inventories (e.g., LRMP and Survey Department)
carried out in other Pahad and Himal regions of Nepal
also reveal similar patterns of land-cover changes. Major
losses of forests have occurred in the lowland part of159

Nepal, the Tarai and the Inner Tarai of the Siwalik hills(Ives & Messerli, 1989; Metz, 1991). It is clear that theHED dramatized deforestation in Nepal so much thatmany important changes and trade-off within land-use
categories were overshadowed. Oversimplification andovergeneralization of deforestation simply took the attention away from the changes occurring in grasslands
and shrublands – two land-cover categories that are vital
components of mountain smallholdings in Nepal. This
study, hence, emphasized identification and quantification of the trade-off between different land-cover categories because it helps provide a better understanding of
the complexity of smallholders’ land-use strategies andtheir consequences to land-cover (Ives, 2004). Satellitedata, nonetheless, are an invaluable resource for mapping
land resources of mountainous areas, where accessibility
is limited and carrying out land inventories is costly.

Acknowledgements
The results presented herein are primarily based on
the dissertation fieldwork supported by the National
Science Foundation (NSF) Dissertation Improvement
Grant (Cultural Anthropology BCS 0350127) and thegenerous financial support provided by the National
Aeronautics and Space Agency (NASA) throughthe Earth System Sciences Fellowship (Grant No.
NNG04GQ16H). This author is grateful to his dissertation advising committee members: Robert Rhoades(Chair), Ted Gragson, Virginia Nazarea and C.P. Lo, fortheir inspiration, mentorship and support. All of theiradvice contributed to this work; however, the author issolely responsible for any errors and inadequacies in thisreport. Comments and feedback received from two
anonymous reviewers are greatly appreciated. The author
would also like to thank Birendra Bajracharya for lending
his expertise to resolving several issues involving
Nepal’s GIS dataset, Anil Shrestha and Dwight Haase for
their editorial assistance, and lastly, Kashi Jung Ghale
and Ganga Chhetri for their fieldwork assistance.
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Milan SHRESTHA
Milan SHRESTHA is a lecturer at the School of
Sustainability and senior sustainability scientist
at the Julie Ann Wrigley Global Institute of Sustainability at Arizona State University (ASU).
He received his PhD in environmental anthropology from the University of Georgia and his
MA in international development from Clark
University. He also completed post-doctoral research work with ASU’sCentral Arizona – Phoenix Long-term Ecological Research (CAP LTER)
focusing on a cross-site comparative study of urban sprawl and land
fragmentation in five southwestern cities of the USA. With a long-term
research focus in Nepal and the US Southwest, he is interested in sustainability and socio-ecological dynamics in five core research areas: landchange and fragmentation; food security and vulnerability; adaptation toclimate change; indigenous knowledge and community-based institutions;and the anthropology of urban dynamics.

(Received 4 June 2014, Accepted 4 November 2014)