Himalayan
JOURNAL OF SCIENCES'
A PEER-REVIEWED JOURNAL Vol 6 Issue 8 2010
www.himjsci.org ISSN 1727 5210
Microbial activity in the cold and dry High Himalayas
Regeneration of Pinus wallichiana
New record of Malaxis biaurita for Nepal
Climate change and disease risk in the Himalayan region
Overuse of agricultural pesticides in Nepal
Himalayan
JOURNAL OF SCIENCES '
Volume 6
Issue 8
2010
Page 1-44
ISSN 1727 5210
Editor
Assistant Editors
Kumar P Mainali
Arjun Adhikari
Prem B Budha
Managing Editor
Prem S Chapagain
Bharat B Shrestha
Debendra Karki
Krushnamegh Kunte
Language Editor
Vibek R Maurya
Seth Sicroff
Shishir Paudel
Himalayan Journal Online
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Advisory Board
Ram P Chaudhary
Professor, Central Department
of Botany, Tribhuvan University,
Kathmandu, Nepal
Monique Fort
Professor, Centre de Geographie
Physique, University of Paris, France
Mohan B Gewali
Professor, Central Department of
Chemistry, Tribhuvan University,
Kathmandu, Nepal
lack Ives
Professor, Department of Geography
and Environmental Studies, Carleton
University, Ottawa, Canada
Pramod Kfha
Professor, Central Department
of Botany, Tribhuvan University,
Kathmandu, Nepal
UdayR Khanal
Professor, Central Department
of Physics, Tribhuvan University,
Kathmandu, Nepal
Bishwambher Pyakuryal
Professor, Central Department of
Economics, Tribhuvan University,
Kathmandu, Nepal
Sahotra Sarkar
Professor, Section of Integrative
Biology and Department of Philosophy,
University of Texas at Austin, USA
Madhusudhan Upadhyaya
Nepal Agricultural Research Council
Khumaltar, Lalitpur, Nepal
Teiji Watanabe
Associate Professor, Hokkaido
University, lapan
Pralad Yonzon
Chairperson, Resources Himalaya
Foundation, Lalitpur, Nepal
Price
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Himalayan
JOURNAL OF SCIENCES *
Volume 6
Issue 8
2010
Page 1-44
ISSN 1727 5210
Himalayan
JOURNAL OF SCIENCES
Himalayan Journal of
Sciences
Volume 6, Issue 8, 2010
Page 1-44
Cover image: microbes in
high Himalayas; story p 11
Mancozeb: growing risk for agricultural
communities?
The short- and long-term health
consequences of pesticides are real
Kishor Atreya and Bishal K Sitaula
Page 9-10
HIMALAYAN
Published by
Himalayan Association for
the Advancement of Science
Kathmandu, Nepal
research paper
Microbial biomass and activity
in high elevation (>5100 meters)
soils from the Annapurna and
Sagarmatha regions of the Nepalese
Himalayas
Andrew I King, Debendra Karki,
Laszlo Nagy, Adina Racoviteanu and
Steven K Schmidt
Page 11-18
Regeneration of Pinus wallichiana AB
Jackson in a trans-Himalayan dry valley of
north-central Nepal
Balkrishna Ghimire, Kumar P Mainali,
Hari D Lekhak, Ram P Chaudhary and
Amal K Ghimeray
Page 19-26
Simulating farm income under the current soil
management regime in the mid-hills of Nepal
Gopal D Bhatta and Nilhari Neupane
Page 27-34
Optimization of RAPD-PCR conditions for the
study of genetic diversity in Nepal's Swertia
chirayita (Roxb. Ex Fleming) H. Karst
Sangita Shrestha, laishree Sijapati,
Neesha Rana, Diwa Malla, Prabha Regmi
and Bhakta Raskoti
Page 35-40
Malaxis biaurita (Lindley) Kuntze
(Orchidaceae): a new record for Nepal
Bhakta B Raskoti and Rita Ale
Page 41-42
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SCIENCES)
scientific information for the advancement of society
Editorial
Climate change and disease risk
in the Himalayas
Climate change is likely to increase the burden of infectious diseases in the
Himalayas; systematic preparation must start now
Sahotra Sarkar
The International Centre for Integrated Mountain Development (ICIMOD, Kathmandu), with
funding from the MacArthur Foundation, has recently performed a detailed analysis of the
impacts of climate change on the Eastern Himalayas (Sharma et al. 2009). Their report focuses
primarily on biodiversity and ecosystem services. As expected, the predictions are dire while
the uncertainties remain large, given the paucity of serious attention to this region compared,
for instance, to the Alps or even to the Andes. While the report notes the implications of climate change
for human well-being, including health, infectious disease gets only passing mention (three paragraphs
out of 25 pages). This is unfortunate because there is ample reason to expect that climate change in the
Himalayas will lead to a significant increase in
the regional infectious disease burden due, in
particular, to vector-borne tropical diseases that A new challenge is unfolding in the Himalayas:
have historically been absent from the Himalayas. a significant increase in the burden of infectious
The reasons are simple. First, climate change djsease drjven by c|jmate change Vect()rs are
is likely to lead to a deterioration of water quality,
partly because the increased initial meltdown and m0V'n9 bey°nd their historic ran9eS to h'9her
excess in water availability will be followed by a very elevations; water quality is deteriorating, and
significant reduction in supply. Resulting shortages the avaj|aD|e supply is diminishing. Preventive
of good quality water will increase the burden of
, , , , c , , and ameliorative measures to address these
diarrheal disease, hven cholera may emerge as a
significant problem because of its known asso- problems require robust quantitative estimates
elation with climate (CoiweU 1996), and given its 0f the size and spatial distribution of disease
current incidence in some parts of the Indian ^ ^ h ^ ^ avaHab| djsease
Himalayas and Nepal. Second, there will also be a
likely decrease in air quality, with a higher concen- "Sk Can be mapped With predictive models SO
tration of pollutants such as nitrogen dioxide and that appropriate policies can be formulated and
airborne-particles (the latter primarily in urban implemented. Unfortunately, there has been
areas), and an increase in lower tropospheric and
ground-level ozone levels, all leading to an in- Vlrtual|y no quantitative epidemiological atten-
creased frequency of cardio-respiratory disease. tion to this region.
Third, and perhaps most important,
systematic temperature increases (0.01-0.04°C
per year, according to the ICIMOD scenarios) will allow tropical diseases to expand their range to
higher elevations at which they did not occur before. These include a large spectrum of vector-borne
(and, often, reservoir-dependent) diseases that have already begun to become problems in the region,
including (visceral) leishmaniasis (locally known as kala-azar), dengue, and malaria. (The ICIMOD report
also mentions schistosomiasis but it is unlikely that this disease will emerge as a major problem in the
Himalayas given its very low frequency in neighboring regions.)
The possible upward spread of leishmaniasis is particularly worrisome because it has recently
recently begun to be established at an altitude greater than 600 m in Garhwal and Himachal Pradesh where
it was previously not endemic (Raina et al. 2009). The implicated vectors are almost certainly Phlebotomus
or Sergentomyia species which have ranges restricted by climate. Range shifts of these vector species to
HIMALAYAN JOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
I Editorial
{
sr of der
Aedes aegypti, the principal vector of dengue, has
already advanced upward from its historic range.
N
o cd
2 s
S-i TO
a<
O Ph
higher elevations may have already been induced by climate
change, as suggested by theoretical analyses of the climatic
dependencies of these species (Cross et al. 1996, Kuhn et al.
1999, Gonzalez et al. 2010). Similarly, clinical cases of dengue,
as well as larvae of its principal vector, Aedes aegypti, have
recently been recorded in the Kumaon hills at elevations
higher than previously reported (Shukla and Sharma 1999).
In the case of falciparum malaria, there is documentation of
its spread into higher altitudes in the Himalayas (Bouma et al.
1996, Bhattacharya et al. 2006). Himalayan populations, with
no prior history of exposure to these pathogens, are likely to
be more vulnerable than their tropical counterparts.
It is critical that concerted action be taken to prepare for
these problems and to investigate if other diseases are also
likely to expand their range due to climate change. Unfortunately, except for malaria (the spread of which has been investigated for all of India), there has as yet been no systematic
analysis of the emergence of previously absent diseases for
any region of the Himalayas. Partly, this reflects the general
problem that these other tropical diseases associated with
poverty have largely been ignored by recent medical research
(Hotez et al. 2007). But it also reflects a lack of quantitative
epidemiological and ecological attention to the Himalayas.
Nevertheless, quantitative predictive models of disease
risk are necessary prerequisites for any health planning for
the region: not only must these models predict the extent of
the risk for each specific disease, they must also predict the
spatial distribution of this risk. Otherwise, efficient allocation
of limited resources to prepare for the future—that is, where
and to what extent preventive and ameliorative medical materials and expertise should be distributed—will be impossible.
Right now, high resolution risk profiles for disease are not available for a single Himalayan region. Moreover, georeferenced
disease incidence data, organized by date, which are necessary
for any analysis, have never been collated. Basic data availability for the Himalayas lags behind even tropical Africa.
It is likely that the data availability problems can be rela
tively easily solved, and the necessary analyses performed, for
some regions, including the Indian Himalayas and Tibet. However, for other regions, including Bhutan and Nepal, basic epidemiological information is typically lacking for the implicated
diseases, partly because these diseases are rare and, therefore,
often unrecognized. Even the World Health Organization's
regional strategy shows a lack of urgency and entirely ignores
the new epidemiological challenges posed by climate change
(WHO 2007). But, without immediate and sustained attention
to these issues, the challenges will not be surmounted.
Sahotra Sarkar is a professor of biology (Section of Integrative
Biology) and philosophy (Department of Philosophy) at
University of Texas at Austin, USA
References
Bhattacharya S, C Sharma, RC Dhiman, and AP Mitra. 2006. Climate
change and malaria in India. Current Science SO: 369-375
Bouma MI, C Dye, and HI van der Kaay. 1996. Falciparum malaria and
climate change in the northwest frontier province of Pakistan.
American Journal of Tropical Medicine and Hygiene 55:131-137
Colwell RR. 1996. Global climate and infectious disease: the cholera
paradigm. Science 274: 2025-2031
Gonzalez C, O Wang, S Strutz, C Gonzalez-Salazar, V Sanchez-
Cordero, and S Sarkar. 2010. Climate change and risk of
leishmaniasis in North America: predictions from ecological
niche models of vector and reservoir species. PLoS Neglected
Tropical Diseases 4(1): e585
Hotez PI, DH Molyneux, A Fenwick, I Kumaresan, S Ehrlich Sachs, ID
Sachs, and L Savioli. 2007. Control of neglected tropical diseases.
New England Journal of Medicine 357: 1018-1027
Raina S, DM Mahesh, R Kaul, KS Satinder, D Gupta, A Sharma, and
S Thakur. 2009. A new focus of visceral leishmaniasis in the
Himalayas, India. Journal ofVector-Borne Diseases 46: 303-306
Sharma E, M Chettri, K Tse-ring, AB Shrestha, F ling, P Mool and M
Eriksson. 2009. Climate change impacts and vulnerability in the
Eastern Himalayas. Report. Kathmandu: ICIMOD
Shukla RP and SN Sharma. 1999. Aedes aegypti survey of Western
Himalayas town of Haldwani, District Nainital, India. Dengue
Bulletin 23 (December)
[WHO] World Health Organization. 2007. WHO country cooperation
strategy, 2006-2011: Nepal. Kathmandu: WHO
8
HIMALAYAN JOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Commentary
Mancozeb: growing risk for agricultural
communities?
The short- and long-term health consequences of pesticides are real
Kishor Atreya and Bishal K Sitaula
Since the 1950s, when DDT was introduced in Nepal
for the purpose of malaria control, many other
pesticides have been registered for use. Chemical
pesticides are used by 25% of Terai households,
9% of mid-hill households, and 7% of mountain
households (CBS 2003). In certain mid-hill pockets close to
urban markets, the penetration of pesticide use is considerably higher. The incorporation of vegetables into Nepal's
cereal-based agricultural production system, especially in
the hills, has stimulated a significant demand for chemical
inputs such as pesticides. Although pesticide import declined
after 2002, that trend has apparently reversed, with import
substantially increasing in 2007 and 2008 (Table 1). Assuming
all the imported pesticide is consumed, the pesticide use at national level for the year 2008 was 151.2 g active ingredient per
ha of arable land (total arable land = 2,357,000 ha; UN [2010]).
Chemical pesticides are known to have deleterious
effects on human health and on the environment. A series
of studies (e.g., Dahal 1995, Pujara and Khanal 2002, Atreya
2007a, b, c, 2008a, b), highlighting the massive use of
chemicals in vegetable growing areas has raised the issue of
possible health risks for our agrarian communities. Studies
conducted abroad have linked mancozeb, a synthetic
pesticide, to serious hazards including cancer. Now we
have evidence that farmers are applying mancozeb at levels
significantly exceeding the manufacturer's recommendations
(Atreya 2007c, 2008b).
Mancozeb, a grayish-yellow powder, is used to control
fungal diseases that afflict many important economic crops,
including potato, tomato, fruits and flowers. It is a broad-
spectrum pesticide that indiscriminately kills a range of
organisms, targeted as well as untargeted (and beneficial)
species. It acts by disrupting lipid metabolism. It has low
volatility at standard temperatures and pressure but can be
found associated with air-borne particulates or as spray drift.
In a moist environment, it hydrolyzes into ethylenethiourea
(ETU), ethyleneurea (EU) and ethylene bisisothiocyanate
sulfide (EBIS) with a half-life of less than 2 days. In moisture-
limited soil conditions, the half-life is 2-7 weeks. The World
Health Organization (WHO 2005) classified it as 'non-
hazardous under normal use.' Mancozeb enters into body
mainly through the skin and from inhalation. Baldi et al. (2006)
observed the highest contamination through the hands.
Vegetable farming, an important source of revenue in
the hills of Nepal, generally entails heavier applications of
pesticides than does cereal farming. In Nepal, a few studies
have shown that households apply more than 90% ofthe total
pesticides into vegetable farming. Atreya (2008b) investigated
In a country where farmers rely on conventional
wisdom to make decisions on farming practices and
the government lacks clear policies based on solid
research, pesticide overuse is emerging as a problem.
Mancozeb, the widely applied pesticide in Nepal's
vegetable farming, has both short- and long-term health
consequences to people exposed to its unsafe levels. A
handful of studies in the hill regions of Nepal suggest
that the pesticide is being sprayed to farms at much
higher level than recommended. The widespread misuse
and dangerous consequences of this pesticide suggest
a need for more thorough study, better instruction, and
more effective control.
3637 spray operations performed by 291 households in
two VDCs of Kavre district. In 3464 of those operations,
mancozeb was used either alone or in combination with
other pesticides, making it the most widely used of the
sixteen pesticides studied.
Due to its low acute toxicity and short environmental
persistence, the amount of mancozeb used is increasing
worldwide (Colosio et al. 2002), and so far there are no
recorded incidents of acquired resistance to the chemical.
In Nepal, mancozeb is marketed under various commercial
names such as Dithane M-45 75% wettable powder (WP),
Kishan M-45 75% WP, Indofil M-45 75% WP. Mancozeb is also
marketed in combination with 8% metalaxyl as Krinoxyl Gold
72% WP and Mateo 72% WP.
Farmers use mancozeb primarily to control late blights
of potato and tomato at 5-7 day intervals, although a study
claims that a 14-day interval application at the recommended dose is sufficient for the purpose (Apel et al. 2003).
The recommended dose for control of potato blight is 1125—
1500 grams of mancozeb dissolved in 750 liters of water per
hectare, at regular intervals of 7-10 days (PRMS 2006). This
amounts to a concentration of 1.5-2 grams/liter. It is generally agreed that mancozeb is being used at levels exceeding this
recommendation. For instance, Atreya (2007c) calculated the
Himalayan lournal ofSciences 6(8): 9-10, 2010
doi: 10.3126/hjs.v6i8.1794; published online: Nov 04, 2010
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Commentary
Table ]
. Pesticide's active ingredients import in Nepal ('000 kg)
Year
Insecticides*
Fungicide
Herbicides
Roden-
ticides
Oth-
ers++
Total
1999
43.46
54.53
2.68
4.06
3.69
108.43
2000
62.44
102.77
14.94
3.42
12.48
196.06
2001
60.32
75.44
3.26
4.30
2.83
146.15
2002
60.39
90.57
6.84
1.24
18.55
177.59
2003
85.61
55.20
11.24
7.87
16.46
176.37
2004
35.36
97.04
6.39
1.14
14.17
154.08
2005
65.00
47.70
11.03
1.46
6.08
131.27
2006
46.55
74.37
5.70
1.81
2.85
131.28
2007
60.28
237.37
6.57
37.30
5.97
347.49
2008*
105.81
203.39
11.12
31.09
4.93
356.35
+ includes organochlorines, organophosphates, synthetic pyrithroids,
carbamates, botanical insecticides, mixed-insecticides, and other
insecticides; ++ bactericides, acaricides, bio-pesticides, pesticide
used in public health, and others
Source: Various official records of Pesticide Registration and Management Unit, Crop Protection Directorate, Department of Agriculture,
Ministry of Agriculture; * Sabitri Baral, Pesticides Registrar, Office of
the Pesticide Registrar, Kathmandu, Nepal.
average concentration of mancozeb as applied to vegetable
crops in two VDCs of Kavre to be 4.26 grams per liter of water,
more than twice the recommended concentration.
Health effects
Although it has been characterized as 'non-hazardous
under normal use,' is less acutely toxic, and less persistent
in the environment, it degrades into other products, of
which ethylethiourea (ETU) is of greatest toxicological
concern. ETU has been linked to sperm abnormalities (Cox
1996). It affects the central and peripheral nervous systems
and causes endocrine disruption (Solomon et al. 2000).
Many studies have confirmed that ETU is carcinogenic
- particularly affecting the thyroid (Solomon et al. 2000, EPA
2001), teratogenic (interfering with embryonic development),
and a general irritant (WHO 2005).
Nepal Government recommends that the maximum
residue limit on food be 0.20 mg/kg food at maximum
(CBS 2008). However, Nepal lacks toxicological studies on
the mancozeb induced health hazards. A few survey based
studies have revealed immediate and short-term effects from
various pesticides. Farmers who spray pesticides suffer a
range of symptoms. For those involved directly in pesticide
applications, the predicted probability of acute illness in the
day of operation is 0.41 (Atreya 2007c). Atreya (2008b) found
that headache, skin and eye irritation and throat discomfort
increase significantly with exposure to fungicides. The study
calculates that the likelihood of developing headache and
skin irritation after a single pesticide spray operation are 19%
and 8%, respectively.
The demand for pesticides in Nepal is likely to increase.
A handful of studies show mancozeb to be the most widely
used pesticide in Nepal. Studies around the world have
documented a range of health hazards associated with
it. Because farmers rarely use any kind of protection gear
during spray operations, they are likely to expose themselves
to unsafe concentration of pesticides. Long-term effects of
the pesticide have not yet been studied in Nepal. However,
the application of pesticides at levels much higher than those
recommended entails serious risk. As most of the vegetables
sold by farming families are grown by independent farmers
who determine their own protocols for pesticide application
without reference to standard recommendations, significantly
high residues are likely to be passed on to consumers.
The authors are at Department of International Environment
and Development Studies (Noragric), Norwegian University of
Life Sciences (UMB), 1432, Aas, Norway,
e-mail: k.atreya@gmail.com (KA)
References
Apel H, MS Paudyal and O Richter. 2003. Evaluation of treatment
strategies of the late blight Phytophthora infestans in Nepal by
population dynamics modeling. Environmental Modeling and
Software 18: 255-364
Atreya K. 2007a. Farmers' willingness to pay for community
integrated pest management training in Nepal. Agriculture and
Human Values 24: 399-409
Atreya K 2007b. Pesticide use knowledge and practices: a gender
differences in Nepal. Environmental Research 104(2): 305-311
Atreya K. 2007c. Pesticide use in Nepal: understanding health costs
from short-term exposure. Kathmandu, Nepal: South Asian
Network for Development and Environmental Economics. 26 p
Atreya K 2008a. Health costs from short-term exposure to pesticides
in Nepal. Social Science & Medicine 67: 511-519
Atreya K 2008b. Probabilistic assessment of acute health symptoms related to pesticide use under intensified Nepalese agriculture. International Journal of Environmental Health Research 18(3): 187-208
Baldi I, P Lebailly, S lean, L Rougetet, S Dulaurent, P Marquet. 2006.
Pesticide contamination of workers in vineyards in France. Journal
of Exposure Science and Environmental Epidemiology 16:115-124
CBS 2003. National sample census of Agriculture, Nepal, 2001/02:
Highlights. Kathmandu, Nepal: Central Bureau of Statistics
CBS 2008. Environment statistics of Nepal 2008. Kathmandu, Nepal:
Central Bureau of Statistics
Colosio C, S Fustinoni, S Birindelli, I Bonomi, G De Paschale, T
Mammone et al. 2002. Ethylenethiourea in urine as an indicator
of exposure to mancozeb in vineyard workers. Toxicology Letters
134:133-140
Cox C. 1996. Pesticides and male fertility: masculinity at risk. Journal
of Pesticide Reform 16(2): 2-7
Dahal L. 1995. A study on pesticide pollution in Nepal. Kathmandu,
Nepal: IUCN/Nepal
EPA [US Environmental Protection Agency]. 2001. The determination
of whether dithiocarbamate pesticides share a common
mechanism of toxicity. 46 p
PRMS [Pesticides Registration and Management Section]. 2006.
Use pattern of registered pesticides. Kathmandu, Nepal: Nepal
Government Department of Agriculture (in Nepali)
Pujara DS and NR Khanal. 2002. Use of pesticides in laishidhi sub-
catchment, Ihikhu khola watershed, Middle Mountain in Nepal.
In: Hermann A and Schumann S (eds.), Proceedings: International
workshop on environmental risk assessment of pesticides and integrated pest management in developing countries. Braunschweig,
Germany: Landschaftsokologie und Umweltforschung. p 168-177
Solomon G, OA Ogunseitan and I Kirsch. 2000. Pesticides and human
health: a resource for health care professionals. California,
USA: Physicians for Social Responsibility and Californians for
Pesticide Reform
UN [United Nations] .2010. Statistical Yearbook - fifty third issue. UN
Department of Economic and Social Affair, p 487-496
WHO 2005. The WHO recommended classification of pesticides by
hazard and guidelines to classification: 2004. Rome, France:
World Health Organization ofthe United Nations
10
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
Microbial biomass and activity in high elevation (>5100 meters)
soils from the Annapurna and Sagarmatha regions of the Nepalese
Himalayas
Andrew J King1, Debendra Karki2, Laszlo Nagy3, Adina Racoviteanu4 and Steven K Schmidt1*
1 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, 80309, USA
2 College of Applied Science Nepal, Anamnagar, Kathmandu, NEPAL
3 EcoScience Scotland, 2/1 27 Glencairn Drive, Glasgow, SCOTLAND
4 Department of Geography, University of Colorado, Boulder, Colorado 80309, USA
* For correspondence, email: steve.schmidt@colorado.edu, phone: (01) 303-492-6248
High elevation subnival-zone soils are increasing in spatial extent in the Himalayas due to glacial retreat and
grazing pressures. These seemingly barren soils actually harbor significant microbial diversity but have remained
mostly unstudied in all of the major mountain ranges of the Earth. Here we describe a preliminary survey of
subnival-zone soils and one vegetated high-elevation soil in the Annapurna and Sagarmatha regions of the
Nepalese Himalayas. We examined microbial biomass and activity as well as key microclimatic and edaphic
variables that may control microbial activity in these soils. Microbial biomass carbon levels were the lowest
ever reported for any soil to date, whereas microbial nitrogen and soil enzyme activities were similar to levels
measured in previous studies of subnival-zone soils of Peru and Colorado. Our initial studies also indicate that
soil water availability is the primary limiting factor for life in these high-elevation soils.
Keywords: Microbial, soil, biogeochemistry, subnival, extracellular enzymes, microbial biomass
On the highest mountain ranges of the Earth, between the
upper zone of year-round snow and ice (the nival zone) and
the zone of continuous vegetation (the alpine zone), exists a
stark expanse of seemingly bare rock and barren soils called
the "subnival zone" or "mountain desert" (Figure la; Nagy
and Grabherr 2009, Troll 1973). Yet, upon close inspection
the subnival zone is revealed to be a landscape mosaic in
which soil development and plant colonization are related
to local variation in snow pack accumulation. This variation
results in a patchy environment with barren soils underlying
high snow areas, sparse plant communities in moderate
accumulation areas, and further barren soils in wind-scoured
locations. When not snow-covered, the soils of this highly
exposed environment are subject to extreme fluctuations in
temperature, solar radiation, and soil moisture. This effect
is particularly pronounced during the fall season when daily
soil temperatures can fluctuate over 40°C, with nighttime
conditions below freezing and surprisingly hot daytime soil
temperatures around 30°C (Figure 2). These factors create
a harsh environment for subnival zone organisms and result
in one of the most barren looking ecosystems on Earth. It is
presently unclear if, given enough time, plants can colonize
the upper-elevations of the subnival zone. However, extant
subnival soils are dominated by abundant and surprisingly
diverse microorganisms even in the highest elevation soils
sampled to date (Costello et al. 2009, Schmidt et al. 2009).
Due to the dependence on snow accumulation, the
area defined as the subnival zone occurs at much higher
elevations in drier mountain ranges such as in the Andes
and in the inner ranges ofthe Himalayas than in more humid
mountain ranges such as the Alps. The subnival zone of the
Bavarian Alps starts at about 2500 m above sea level (masl)
compared to 4700-5000 m in the Andes of southern Peru
and 5000-5600 m in the Himalayas (Chang 1981, Rawat
and Pangtey 1987). Owing to their extremely high elevation
and historical inaccessibility, much less is known about the
subnival zone in ranges such as the Himalayas than in the
comparatively well-studied Alps. Globally, the subnival zone
is thought to have expanded downwards in recent years
due to overgrazing in the upper alpine zone (Ahmad et al.
1990, Del Valle et al. 1998) and upwards, due to the retreat
of glaciers and icepacks at high elevations (Byers 2007), and
is predicted to increase over the next century (Zemp et al.
2006). In Nepal, the subnival zone currently occupies about
6% ofthe land area (Figure lb); yet we know almost nothing
about the organisms that inhabit these environments.
It is still unclear how life forms that survive in the subnival zone obtain the nutrients and energy needed to sustain
life. Swan (1963, 1990, 1992) contended that life at these
extreme elevations subsisted mainly upon aeolian-deposited
r
Himalayan lournal ofSciences 6(8): 11-18, 2010
doi: 10.3126/hjs.v6i8.2303; published online: Sept 27, 2010
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
11
Research paper
Annapurna
Conservation Area
■s£&R
|||^wjLj- Sagarmatha
^■vL
hwfei^FL — National Park
Elevation (m) \^
SmS^m A o7?*_
High : 8505 ^>/"VVv
'~&i£jt ■ vWJhjBCj
Low : 57
~^—\ -7
Sub-nival zone
} \
■i 5000 - 5600m
^__w
____
^^Pv
b ^~\_^s
Precipitation
S_-
^^"""^
^rS""^ /
250 - 500 mm
^L >
■■ 500 - 1000 mm
^
^J\
■i 1000- 1500 mm
^B 1500 - 2500 mm
IH 2500 - 4000 mm
O Field samples 2008
0 H 150
300
Kilometers
Figure 1. a (top): Photograph
ofthe Chulu range taken (10/
19/2008) from above Kangshar
village during our fieldwork in
the ACA. The labels indicate the
distinct zonation of the alpine,
subnival, and nival zones. The
boundary between alpine and
subnival zones is defined as the
upper extent of continuous plant
cover. The boundary between
subnival and nival zones is
defined as the lower extent of
continuous ice and snow cover.
b (middle): An elevational map
of Nepal. The red area represents
the extent of the subnival zone
as estimated by locations having
an elevation of 5000-5600 masl.
Sampling locations are in the
black circles, c (bottom): A map
of average yearly rainfall for
Nepal showing the sampling sites
(data from Nepal Department of
Hydrology and Meteorology,
http://www.dhm.gov.np).
12
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
Table 1. Sampling sites used in this study
Site UTM coordinates (zone)
Elevation (masl)
Rock type
pH
Zun Tal (ACA) 784700E, 3180600N (44)
5101-5289
shale
7.2
Kobresia sward/eroded (ACA) 786770E, 3176125N (44)
4824
soil
6.1
Thorong La (ACA) 787000E, 3188900N (44)
5482-5516
shale
7.3
Gokyo Lake 5 (SNP) 468350E, 3097500N (45)
5094-5111
granite
5.3
Chola Pass (SNP) 493650E, 3092850N (45)
5376-5387
granite
5.3
Island Peak (SNP) 493170E, 3089600N (45)
5272-5291
granite
5.3
ACA, Annapurna Conservation Area; SNP, Sagarmatha National Park
organic matter; that is, organic matter blown from lower
elevations to higher elevations. However, recent studies of
microbial communities at high elevations (Freeman et al.
2009, King et al. 2008, Schmidt et al. 2009) have caused us to
reevaluate how life is sustained at these elevations. We have
been studying microbial life in soils up to 6000 masl in the high
Andes of South America and the southern Rocky Mountains
of the United States for the past ten years. Although we have
observed pockets of aeolian-supported life (Ley et al. 2004),
we have found much larger areas of wind-swept lands that
do not accumulate high amounts of organic debris from
lower elevations but are nonetheless teeming with previously
unreported microbial life (King et al. 2008, Schmidt et al.
2009). These studies have shown that in many subnival
soils, microorganisms obtain their sustenance not from
wind-blown organic matter but primarily from atmospheric
gases through the processes of microbial photosynthesis
and nitrogen fixation (Freeman et al. 2009, Schmidt et al.
2008, 2009) and that the buildup of microorganisms may
be largely limited by soil water availability (King et al. 2008).
Thus, although we have pushed our understanding of high-
elevation life beyond the pioneering efforts of Swan (1963,
1990, 1992), the question remains as to whether these new
discoveries made in the Andes and Rocky Mountains apply
to even higher mountain ranges ofthe world.
The large area occupied by subnival soils in the
Nepalese Himalayas (Figure lb) makes it particularly
important to understand the activity and abundance of high
elevation microorganisms there. Here we examine microbial
biomass and extracellular enzyme activity in subnival soils
from the Annapurna and Sagarmatha (Everest) regions of
the Nepalese Himalayas. Our study describes microbial
life in four subnival soils: plant-covered, eroded previously
vegetated, fine shale-derived gravel, and fine granite-derived
gravel. This study is the first report of an ongoing research
effort to characterize the microbial activity and diversity of
the subnival soils ofthe Himalayas.
Methods
Study Sites and sample collection Our sampling sites were
in the Annapurna Conservation Area (ACA, Panthi et al. 2007,
Shrestha et al. 2007) and the Sagarmatha National Park (SNP,
Byers 2007) ofthe Himalayan Mountains in Nepal (Figure 1,
Table 1). In each region, we sampled mineral soils from sites
just above the highest plants and from sites as high as were
attainable due to prevailing conditions (e.g. presence of ice
and snow) at the time of sampling. Sampling was conducted
in October and November of 2008 in order to take advantage
of the seasonal lack of precipitation. Low precipitation
seasons are ideal for this type of descriptive study because
it minimizes short term variability due to individual
precipitation events when comparing across sampling sites
and allows access to the highest possible soils due to the
relatively snow-free conditions. In addition to unvegetated
soils of the ACA, we also sampled patches of soil dominated
by the sedge Kobresia cf. pygmaea (C. B. Clarke) C. B. Clarke as
well as soil from eroded areas adjacent to patches of Kobresia.
These sites were located just below the lowest-elevation
plant-free site. Soil samples were collected to a depth of 5 cm
and placed in sterile zip-lock bags. Samples were frozen in
the field by packing sample bags in a cooler along with snow
and ice collected from the landscape and transported back to
the laboratory over about a week. Samples were immediately
extracted for K2S04 dissolvable C and N (Weintraub et al.,
2007) and then stored at -20°C for further analysis.
The low-elevation sites in the ACA are unvegetated,
south-east facing slopes (28°43'N, 83°55'E) at 5122 masl
and approximately 8 km east of the northern edge of Tilicho
Lake. We also collected three samples from patches of
Kobresia covered soil as well as three samples from eroded
areas adjacent patches of Kobresia on a south-south-west
facing slope (28°42'15"N; 83°56'05"E) at 4824 masl. The
high-elevation sites in the ACA are unvegetated south-east
facing slopes from 5503 to 5516 masl, 1 km north of Thorong
Pass (28°48'N, 83°56'E). The Thorong Pass crosses the divide
separating the Marsyangdi River to the east and the Kali
Gandaki River to the west. All ofthe ACA sites are located on
shale bedrock.
The sampling sites in the SNP receive significantly more
precipitation than do the ACA sites (Figure lc). We collected
4 samples from each of three unvegetated south-east facing
slopes in this region, Chola Pass, Gokyo Lake 5, and Island
Peak. All ofthe SNP sites were located on granite bedrock.
Soil temperature Soil temperature measurements were
recorded using HOBO data loggers (Pendant temp/light, UA-
002-64, Onset Computer Corp., Bourne, MA) from October
16th to 19th 2008 at the ACA Kobresisa dominated site. One
data logger was placed flush with the soil surface and another
at a depth of 4 cm.
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
13
Research paper
Microbial biomass carbon and nitrogen Soil dissolved
organic carbon (DOC), total dissolved nitrogen (TDN), and
microbial biomass carbon (MBC) and nitrogen (MBN) were
determined using the methods described in Weintraub et al.
(2007). For soil DOC and TDN, 5 g of each soil sample was
shaken with 25 ml of 0.5 M K2S04 for 1 hour. For microbial
biomass C and N, 5 g of soil was added to a 250 ml glass
flask with 2 ml of chloroform, sealed and fumigated for 24
hours, and then vented for 1 hour; 25 ml of 0.5 M K2S04 was
added to each flask, and then they were shaken for 1 hour.
Solutions were pre-filtered using a 1 |im Pall glass fiber filter
(Pall Corporation, East Hills, NY). Solution C/N analysis was
performed using a Shimadzu total organic carbon analyzer
(TOC 5000) equipped with a total dissolved nitrogen (TDN)
module (Shimadzu Scientific Instruments, Inc., Columbia, MD).
pH determination In order to determine soil pH, 2 g of
each soil was placed to an individual 15 ml conical tube to
which was added 2 mL of distilled water. Conical tubes were
placed horizontally on a shaker table and shaken for 1 hr at
175 rpm. Soil pH was measured using a glass Fisher pH probe
(Fisher Scientific, Pittsburgh, PA).
Extracellular enzyme activity Microbial extracellular
enzyme activities were assayed using a modification by
King et al. (2008) ofthe method of Weintraub et al. (2007).
Enzymes assayed were: N-aceytalglucosaminase, cellulase
(P-glucosidase), ct-glucosidase, (3-xylase, cellobiosidase,
leucine aminopeptidase and organic phosphatase. For
each sample, 2 g of soil was added to 150 ml of buffer at
the average pH for the soil type (0.5M Acetate at pH 5 for
granitic, 0.5M Acetate at pH 6 for eroded and vegetated,
and 0.5M Bicarbonate at pH 7.3 for shale derived soils) and
homogenized at 3000 rpm for 1 minute using a Ultra-Turrax
homogenizer (IKA Works Inc., USA). Soil slurries were
assayed using the same controls, fluorescent substrates, and
solution volumes as in King etal. (2008). Soils were incubated
with substrates for 20 hours at 14°C.
Water holding capacity Tubes for assaying water holding
capacity (WHC) were constructed by cutting the bottom off of
a 1 cm diameter 15 ml conical tube and covering the opening
with 1-mm gauge plastic mesh. The mesh was wetted with
deionized water prior to the addition of soil to the tube so
that particles less than 1 mm in size would clump together
at the bottom of the tube. For each sample we added -4 g of
soil to a tube and then added -2 ml of H20. Wetted sample
tubes were placed in 50 ml conical tubes, which were drained
periodically. When an individual sample stopped dripping,
the mass of the sample was recorded. Samples were then
dried at 100°C for 24 hours. Water holding capacity is
reported as the g H20 at soil saturation divided by g dry soil.
Statistics and data analysis Tukey's honestly significant
difference tests (Devore 2004) were performed in R (version
2.8.1, 12/22/2008, R Foundation for Statistical Computing
http://www.r-project.org/index.html). A correlation test
(Devore 2004) between microbial biomass C (MBC) and
water holding capacity was also performed in R.
Results
Microbial biomass and extracellular enzyme activity were
extremely low in the shale-derived soils from the ACA (Figure
3, 4). The granitic soils of the SNP had significantly higher
biomass and activity than the shale-derived soils (Figure
3, 4). As expected, soils of the lower-elevation eroded area
had higher microbial biomass than either of the two higher-
u
o
<L>
3
ro
\_
Hi
Q.
E
o
40 ■
30 -
20
10
0 -
-10
Surface temperature
Temperature at 4 cm depth
17-Oct-08
18-Oct-08
—* 1
19-Oct-08
Figure 2. Soil temperature measurements from two days
near the 4824 m elevation Kobresia dominated site in the
ACA region.
Shale Granite Eroded Kobresia
Figure 3. Extracellular enzyme activities for the four soil
types. Enzymes activities shown are N-aceytalglucosaminase
(NAG), cellulase (p-glucosidase) (BG), organic phosphatase
(PHOS) and leucine amino peptidase (LAP), a-glucosidase
and f3-xylase activities were at similar levels to NAG and are
not shown. Significant differences are designated by letters
grouping similar levels of activity for an individual enzyme
(Tukey's Test, p < 0.05). Error bars are standard error.
14
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
elevation mineral soils, and the Kobresia soils had the highest
microbial biomass C and N (Figure 4). Surprisingly, while
both the eroded soils and the Kobresia soils had higher
enzyme activity than the mineral soils, the eroded soils had
significantly higher extracellular enzyme activity than the
vegetated soils (Figure 4). The biomass trends were mirrored
by the water holding capacity measurements (Figure 5).
Finally, there was a significant correlation between MBC and
WHC for all samples (Figure 6, Pearson r2= 0.426, correlation
test: p < 0.001). However, this trend was primarily driven by
the increase of WHC and MBC with plant colonization.
Discussion
The subnival soils examined in this study are subject to some
of the most extreme environmental conditions of any soils
on Earth. Nevertheless, we found measurable amounts of
microbial biomass and enzyme activity in even the most
visually barren mineral soils (Figure 3, 4). Perhaps due
to such extreme conditions, the biomass C numbers we
observed were on average very low for both the mineral and
the vegetated soils we sampled. Particularly low microbial
biomass was found in the shale-derived mineral soils of the
ACA. At an average of 20 jig C/g soil, these biomass levels
400
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3 100
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i i Microbial biomass carbon
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Shale Granite Eroded Kobresia
= GO
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ro 20
2
o
□
30
10
■ Total dissolvable nitrogen
3 Microbial biomass nitrogen
Shale
Granite Eroded Kobresia
Figure 4. (a) Dissolved organic carbon (DOC) and microbial biomass carbon (MBC) for the four soil types, (b) Total dissolvable
nitrogen (TDN) and microbial biomass nitrogen (MBN) for the four soil types. Significant differences are designated by letters
grouping similar levels (Tukey's Test, p < 0.05). Error bars are standard error.
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1—*
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7 400 -
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♦
so
3 300 -
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Ul
l/l
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♦
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5 °:
0
2
1 1
0.3 0.4
1
0.5
i
0.6
i
0.7
0.
Water holding capacity (g H20 g l soil)
Figure 5. Water holding capacity (g H20/g dry wt of soil) for
the four soil types. Significant differences are designated by
letters grouping similar levels of activity for an individual
enzyme (Tukey's Test, p < 0.05). Error bars are standard
error.
Figure 6. Microbial biomass C versus water holding capacity
for all samples (Pearson r2= 0.426, correlation test: p < 0.001).
It is apparent that this trend is driven by the increase in
WHC and MBC with plant colonization. Taken individually
the only sample group with a significant positive association
between WHC and MBC was the granitic soils (r2=0.301,
p<0.05). Error bars are standard error.
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
15
Research paper
are the lowest reported to date for subnival or recently
deglaciated soils. Previously, the lowest microbial biomass
numbers were reportedly found in alpine and Antarctic
glacial moraines, which harbor 60 and 100 pig C/g soil,
respectively (Tscherko et al. 2003a, b). Likewise, subnival-
zone soils of Colorado and Peru contained 80 and 140 pg C/g
soil, respectively (King et al. 2008). Thus, subnival soils may
represent environments at the upper boundary of suitable
conditions for sustaining microbial life. More work is needed
to determine what component of the biomass is active
at these sites and what component consists of dormant
organisms blown in from lower elevations.
Our results support the hypothesis put forth by King
et al. (2008) that the primary limiting factor determining
microbial biomass levels in these extreme subnival soils
is water availability (Figure 6). Indeed, the shale-derived
plant-free soils from the ACA had the lowest water contents
of any ofthe soils and the lowest measured levels of microbial
biomass. These soils also had the lowest water holding
capacities of any of the soils examined in this study (Figure
5). However, neither soil water content nor water holding
capacity was significantly different among subnival soil
sampling areas. Interestingly, the granitic soils of the SNP
had similar microbial biomass levels to the granitic soils
of the Rocky Mountains of the central United States that
we have previously studied (King et al. 2008). In that same
study we reported that shale-derived plant-free soils from
the Andes in Peru had higher microbial biomass than the
Rocky Mountain soils, however, the Himalayan soils display
the reverse trend. This discrepancy suggests that rock type
is not the main factor determining microbial biomass levels.
Indeed, this trend is mirrored by the lower extracellular
enzyme activities and DOC content of the shale-derived
soils versus the granite-derived soils from the Nepalese
Himalayas. These results point to possible differences in
soil age and development between the barren subnival soils
of the ACA and the SNP, differences that may originate from
variation in slope stability due to degree of slope, bedrock
hardness, or annual precipitation (Gabet 2004). As seen in
Figure lc, the SNP area receives greater precipitation than
does the ACA, perhaps resulting in higher rates of weathering
and soil formation in the SNP. However, once soil succession
proceeds to the point of plant colonization, significant
accumulation of microbial biomass does occur. Thus,
while water holding capacity may only be a fair predictor
of microbial biomass it appears to be a reasonable proxy for
subnival soil development.
Although there is not a significant influence of soil
bedrock on the overall microbial community biomass, we
can see that the pH ofthe soils varies significantly depending
on bedrock type and degree of plant colonization. Fierer
and fackson (2006) have shown that microbial community
composition can vary significantly with changes in soil pH
and Sinsabaugh et al. (2008) have demonstrated that this
pH variation can result in different rates of extracellular
enzyme activity. Indeed we see that cellulase (p-glucosidase)
activity is very low in the shale barren soils (high pH) while
it is one of the predominant enzymes in the granitic barren
soils (low pH). The opposite trend was seen with protease
(leucine aminopeptidase) activity, wherein activity was high
in the barren shale soils and barely detectable in the granitic
soils. Furthermore, as the soils became more Kobreisia-
dominated there was an increase in extracellular enzyme
activity in concert with a shift in soil pH. Therefore, as soils
development proceeds, the differences in nutrient cycling
between barren soil types may disappear. Moreover, the shift
in patterns of snow accumulation and overall precipitation
predicted to occur as the climate warms (Beniston 2003)
may significantly influence rates of soil weathering and
water holding capacity. Ultimately, said increases in soil
weathering may alter the differences between bed rock types
and result in an overall increase in productivity, biomass, and
activity of these high altitude soils.
The microbial biomass C levels in the Kobresia-
dominated soil patches of the ACA, with an average of 325 pg
C/g soil, were also low for an alpine dry meadow community
and suggestive of a low productivity system; Kobresia
myosuroides dominated soils from the Rocky Mountains
of Colorado, USA average 1260 pg C/g soil (King et al.
unpublished data). Once again, the low precipitation ofthe
ACA region may be the cause of the low microbial biomass
by directly limiting plant photosynthesis and indirectly
limiting the amount of carbon available to soil heterotrophs.
However, relative to the soil C levels, the Nepal Kobresia soils
had very high microbial N content (-54 pg N/g soil); levels
very similar to Kobresia soils in Colorado (75 pg N/g soil, Fisk
etal. 1998). This relationship results in a lower microbial C:N
ratio for the Nepal sites (6.0) versus Colorado (16.8), further
indicating increased microbial C-limitation for the Nepal
soils.
High microbial N levels late in the fall may also be an
adaptation that fosters N retention in this ecosystem over
the winter. A similar retention mechanism occurs in other
ecosystems via microbial growth on senescing plant material
after the plant growing season, resulting in immobilization
of N especially in seasonally cold (laeger et al. 1999, Zak
et al. 1990) or seasonally dry (Singh et al. 1989, Vitousek
and Matson, 1984) systems. This explanation is further
supported by the low extractable nitrogen levels of the bulk
soils from the Kobresia soil patches of the ACA (10 pg/g soil).
Regardless of the mechanism for N storage, the data suggest
that the Kobresia soil communities of the ACA have high N
retention in the face of low productivity.
A final surprising result of our study is the finding that
although Kobresia sward soils had the highest biomass of
subnival Nepalese soils, the eroded soils adjacent to the
Kobresia soils had the highest enzyme activity. This is likely
a result ofthe eroded soils losing their structure, allowing the
microbial community greater access to sequestered organic
matter. This effect would be compounded by cessation of
rhizodeposition of labile carbon sources, which could cause
the microbial community to shift towards breaking down
more recalcitrant organic matter, thus the increase in enzyme
activity. In addition, the lower nitrogen availability in the
eroded soils relative to the Kobresia sward may be responsible
for the concurrent stimulation of N scavenging enzymes such
as NAG and LAP. These effects have been theorized to explain
the shift in dominance of extracellular enzyme production in
16
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
nutrient limited environments (Wallenstein and Weintraub
2008), but the majority of evidence for this phenomenon
comes from studies of microorganisms in culture (Harder
and Dijkhuizen 1983). Our results in the eroded soils support
the long held hypothesis that high microbial enzyme activity
should be higher in soils that have low amounts of labile
nutrients relative to complex organic substrates.
Conclusions
The levels of microbial biomass and activity we observed in
the subnival zone soils of the ACA and SNP regions of the
Nepalese Himalayas are some ofthe lowest ever observed for
this, or any, ecosystem type. It is likely that the subnival zone
will further grow in size as a result of high-elevation glacial
melt and in some areas by overuse of alpine grazing ranges,
significantly increasing the already large area of Nepal that is
occupied by this ecosystem type. The evidence from our work
suggests that expanding areas of the subnival zone will be
characterized by low levels of microbial biomass that increase
gradually with water holding capacity and soil development.
However, specific activity levels will be dependent on
nutrient availability, soil pH, speed of soil development, and
regional precipitation patterns. Our preliminary survey of
microbial biomass and activity in the subnival zone of the
Nepalese Himalayas reinforces the notion that the organisms
that live in this precarious ecosystem are subjected to some
of the most extreme environmental conditions on Earth.
Further study of these areas has the potential to uncover
novel microbial communities in these fascinating soils from
the highest mountain range on Earth.
Acknowledgements
We are grateful to Bharat B. Shrestha, Ram P. Chaudhary, Sasha
Reed, Mark Williams, Nima Sherpa, and Karma Gurung for helpful
discussions and/or for assistance in the field. This work was
supported by grants from the Microbial Observatories Program
of the National Science Foundation of the USA (MCB-0455606)
and the National Geographic Society Committee for Research and
Exploration.
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HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE
2010
Research paper
Regeneration of Pinus wallichiana 1KB Jackson in a trans-Himalayan
dry valley of north-central Nepal
Balkrishna Ghimire1*, Kumar P Mainali2, Hari D Lekhak3, Ram P Chaudhary3 and Amal K Ghimeray4
1 Department of Applied Plant Sciences, Kangwon National University, Chuncheon 200-701, SOUTH KOREA
2 Section of Integrative Biology, The University of Texas at Austin, Texas, USA
3 Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, NEPAL
4 Department of Bio-Health Technology, Kangwon National University, Chuncheon 200-701, SOUTH KOREA
* For correspondence, email: ghimire2ab@gmail.com
We studied the elevational pattern of forest composition and regeneration ofthe subalpine conifer tree species
Pinus wallichiana in Manang, a trans-Himalayan dry valley in north-central Nepal. Thirty-five quadrats (10 m
x 10 m) were laid between 3300 and 4000 masl on both north- and south-facing slopes. We measured diameter
at breast height (DBH) of each mature individual of all tree species (DBH >10 cm), and recorded the number
of seedlings (DBH <10 cm, height <30 cm) and saplings (DBH <10 cm, height >30 cm). We also measured soil
moisture and soil pH, estimated canopy cover, and recorded slope and altitude in each quadrat. For all species
together and for several species individually, tree density, seedling density, sapling density and tree basal area
were found to decrease with elevation on both north and south aspects. This trend is largely explained by the
progressively harsher environment at higher elevations. The north-facing slopes in our study area have denser
forests than the south-facing slopes, the density of all size classes (seedling, sapling and mature plants) and
basal area being greater on the northern aspects. These aspect-wide differences are attributable to the stark
difference in soil moisture between northern and southern aspects, which is in turn due to the difference in
insolation. Irrespective of elevation and aspect, all the forests studied are regenerating, as indicated by inverse
I-shaped density-diameter curves. The elevational pattern of seedling and sapling abundance is explained only
by elevation. Whereas other variables (e.g., canopy) are considered to have an important influence on seed
germination and seedling establishment, they turn out not to be significant predictors of density of seedlings and
saplings. This failure to identify a relationship is probably due to our use of non-parametric test (tree regression
analysis) that we used to establish the relationship between density and its potential explanatory variables or due
to our selection of 1 standard error rule yielding sub-optimal models for regression trees.
Keywords: density-diameter curve, regeneration, seedling, sapling, altitude, canopy, Manang Valley
Pinus wallichiana AB fackson (Himalayan Blue Pine)
is native to the Himalaya, Karakoram, and Hindu Kush
mountains. It has an extensive distribution and grows all
along the Himalayas in an almost continuous range from
eastern Afghanistan and Pakistan through India, Nepal,
Bhutan, Myanmar, and China at elevations between 1800
and 4300 masl (Critchfield and Little 1966, Polunin and
Stainton 1997). The plant is generally found in valleys and
foothills, sometimes in pure stands but often in association
with other conifers including Cedrus deodara, Abies
pindrow, Picea smithiana and Juniperus indica Juniperus
excelsa subsp. polycarpos], and with broadleaved species
including Quercus semecarpifolia, Betula utilis, Acer and Ilex
species (Earle 2009). Pinus wallichiana forests grow in drier
areas susceptible to fire where the plant grows as an early
successional species (Numata 1966, Stainton 1972, Ohsawa
et al. 1986). In wet areas, the plant grows in secondary forests
(Polunin and Stainton 1997). P. wallichiana is an important
source of timber and fuel for villagers in mountain valleys and
is also important in protecting the upper parts of mountain
watersheds (Ives and Messerli 1989, Stainton 1972).
Regeneration of tree stands depends on a combination
of factors controlling seed availability, germination, seedling
growth and establishment (Greene et al. 1999, Dovciak et al.
2003). Whereas environmental conditions play an important
role in establishment and distribution of seedlings (Bonnet
et al. 2005), regeneration of dominant trees in dry valleys is
influenced even by small-scale human impacts. Under such
impacts, the typical inverse I-shaped DBH (diameter at breast
height) class distribution observed among forest species,
where frequency of individuals in larger size classes falls
systematically and progressively, resulting in a non-linear
relationship between frequency and size class, generally gives
r
Himalayan lournal ofSciences 6(8): 19-26, 2010
doi: 10.3126/hjs.v6i8.1798; published online: Dec 27, 2010
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
19
Research paper
Table 1.
Description of study area
Aspect
Transect
Locality
Elevation
(masl)
Dominant tree
species
Number of
quadrats sampled
North
1
Above Humde
3300-3500
Pinus wallichiana
Juniperus indica
10
Above Humde
3500-3800
Pinus wallichiana
Abies spectabilis
15
2
Above Gangapurna
lake
3800-4000
Betula utilis
Pinus wallichiana
10
South
3
Ngawal side
3300-3500
Pinus wallichiana
Juniperus indica
10
Ngawal side
3500-3800
Juniperus indica
15
4
Above Braga
3800-4000
Juniperus indica
10
way to a sporadic and/or unimodal distribution (Wangda and
Ohsawa 2006a). Inverse I-shaped distribution is indicative of
a forest in a state of regeneration (Kimmins 1987). A shift from
inverse I-shape to unimodal or multiple-peaked distribution
is the result of substantial changes in the state and pattern
of forest regeneration, suggesting that the forest is in trouble.
Such a shift is generally caused by both anthropogenic and
natural causes (climatic or biotic), as reported for P. roxberghii
forest on dry valley slopes along the Punatsangchu river in
west-central Bhutan (Wangda and Ohsawa 2006a). Several
studies have focused on the altitudinal and latitudinal forest
zones of the Himalayas in relation to climate (Saxena and
Singh 1984, Ohsawa 1987, Tang and Ohsawa 1997, Wangda
and Ohsawa 2006a). However, little is known about the
comparative ecology and regeneration dynamics ofthe forest
along the typical dry valley slopes, which are particularly
important because the steep environmental gradients are
associated with rapid change in forest types (Wangda and
Ohsawa 2006b).
Regeneration and seedling distribution in conifer
forests have been shown to be influenced by both large-scale
disturbances such as wildfire and forest clearing (Turner et
al. 1998, Bonnet et al. 2005) and also small-scale disturbances
such as animal grazing (wild and domestic), lightening and
disease (Bonnet et al. 2005). Most studies on regeneration of
Pinus species have focused on such disturbances (e.g.,Spanos
1994, Spanos and Spanos 1996, Spanos et al. 2000, Scott et al.
2000, Spanos et al. 2001, Bonnet et al. 2005, Darabant et al.
2007). A few studies investigated the effect of physical factors,
such as aspect and elevation (Wangda and Ohsawa 2006a,
Mong and Vetaas 2006), on forest regeneration. In this study,
we examine forest composition and regeneration patterns of
P. wallichiana along an altitudinal gradient on slopes of the
dry Manang Valley in north-central Nepal, with particular
attention to the differentiation of slopes with northerly and
southerly exposure. Principle objectives of this research were
(a) to determine the community structure of P. wallichiana
forest, (b) to understand the regeneration pattern of P.
wallichiana, and (c) to identify environmental factors that
affect regeneration of P. wallichiana.
Materials and methods
Study area The study area lies in Manang Valley (also
called Nyeshang) of Manang District in north-central
Nepal. This trans-Himalayan Valley (28° 37' - 28° 39' N, 83° 59'
- 84° 08' E) is located in the northern part of the Annapurna
Conservation Area. The valley is traversed by the Marsyangdi
River and is surrounded by the high peaks (>6000 masl) ofthe
Annapurna range to the south, Manasalu to the east, Peri,
Himlung and Choya to the north, and Damodar and Mukti-
nath to the west. The climate ofthe valley is dry, characteristic
of the trans-Himalayan region. It is a rain shadow area with
a mean annual precipitation of 444 mm (at 3420 masl) and
mean annual temperature of 6.2°C (Miehe et al. 2001). The
area is covered by snow from November to March. Snow melt
water is the main source of soil moisture in forested areas
(Shrestha et al. 2007). The south-facing slope (south aspect) is
substantially drier than the north-facing slope (north aspect)
(Bhattarai et al. 2004). Vegetation composition on the northerly and southerly aspects is quite distinct. P. wallichiana,
Abies spectabilis, Betula utilis, Juniperus indica and Salix species are the common tree species on north facing slopes, while
on the southern slope B. utilis and A. spectabilis are found only
in moist gullies. The natural forest in the valley extends from
3000 to 4200 masl on the north aspect, while its upper limit is
below 4000 masl on the south aspect (Panthi et al. 2007).
Our sampling was carried out along four vertical
transects, two in each aspect. Because a single transect never
spanned the elevational limits of our study area (3300-4000
masl), the entire range was represented by transects at
two sites with similar physical environment and biotic
composition. On the northern aspect, the two transects
were laid at Humde village (3300-3800 masl, 28° 37' 39" N,
84° 05' 30" E) and near Gangapurna Glacier Lake (3800-4000
masl, 28° 39' 42" N, 83° 59' 44" E) (Table 1). South-aspect
transects were laid near Ngawal village (3300-3800 masl,
28° 38'23" N, 84° 05'22" E) and above Braga village (3800-4000
masl, 28° 39' 54" N, 84° 03' 24" E - on the way to Ice Lake).
The average slopes ofthe sampling sites on the northern and
southern aspects were 22° and 17°, respectively. The forest on
the northern aspect of the valley had been damaged severely
by forest fire about 35 years prior to our study (Shrestha et
20
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
Table 2. Density (individuals/ha), basal area (BA, m2/ha) and Importance Value Index (IVI, %) oftree species (excluding
seedling and sapling) at
Northern Aspect
Southern
Aspect
Elevation
(masl)
Tree
species*
Density
(per ha)
BA
(m2/ha)
IVI
(%)
Tree
species*
Density
(per ha)
BA
(m2/ha)
IVI
(%)
PW
1028
18.6
75.63
PW
570
11.8
61.64
II
200
0.15
11.86
II
320
5.015
38.35
3300-3500
BU
50
0.06
3
BU
0
NA
NA
S
50
0.02
4.27
S
0
NA
NA
AS
100
0.21
5.33
AS
0
NA
NA
Total
1428
19.04
100.09
Total
890
16.815
99.99
PW
190
2.76
28.09
PW
59
0.42
34.01
II
100
0.037
19.44
II
517
0.22
60.95
3500-3800
BU
267
3.44
9.64
BU
0
NA
NA
S
33
0.02
3.98
S
17
0.017
5.08
AS
137
1.02
38.9
AS
0
NA
NA
Total
727
7.277
100.05
Total
593
0.674
100.04
PW
240
2.26
39.16
PW
0
NA
NA
II
0
NA
NA
II
375
NA
NA
3800-4000
BU
225
1.46
41.82
BU
0
NA
NA
S
0
NA
NA
S
0
NA
NA
AS
150
0.36
19
AS
0
NA
NA
Total
615
4.08
100.08
Total
375
.
.
* PW = Pinus wallichiana; II = Juniperus indica; AS = Abies spectabilis; BU = Betula utilis; S = Salix species
al. 2007); we observed a large number of burnt logs. Because
P. wallichinana is used as a construction material, there were
many stumps, left behind after logs had been extracted.
Field sampling For the sake of analysis, we divided the
elevational range into three bands: 3300-3500, 3500-3800
and 3800-4000 masl. These elevation bands are different
in physical environment (both temperature and moisture)
as well as in biotic composition (Mittermeier et al. 2004,
Chaudhary 1998). For sampling, 35 square quadrats (10 m
x 10 m) were randomly placed on each aspect such that five
of them always occupied a 100 m elevation band. In each
quadrat, we recorded the number and diameter at breast
height (measured at 1.37 m above the ground surface) of
individual trees (DBH > 10 cm) of each species. Canopy
cover was estimated visually. We divided each quadrat into
four sub-quadrats (5 m x 5 m), and recorded the number of
saplings (DBH <10 cm, height >30 cm) and seedlings (DBH
<10 cm, height <30 cm, Rao et al. 1990) of each tree species
in two diagonally located sub-quadrats, selected at random.
Moisture and pH of soil were measured at the four corners
and center of each quadrat with the use of a soil pH and
moisture tester (Model DM 15, Takemura Electric Works Ltd.,
lapan); the values were averaged for data analysis.
Numerical methods and statistical analysis Density
(individuals/ha), frequency (%), basal area (m2/ha), and
the importance value index (IVI) (Holdridge et al. 1971) of
trees were calculated from field data. We also determined
the density of seedlings and saplings of tree species. To
understand the regeneration status of P. wallichiana, trees
in the three elevation bands were divided into 10 cm interval
size classes based on DBH.
How do the physical and biotic differences between
the north and south aspects affect seedling and sapling
abundance? We compared the densities between the two
aspects. Because the assumptions of parametric statistical
tests (in this case, independent sample t-test) were violated
(by non-normality in distribution of cases and heterogeneity
of variances of groups being compared), we performed the
non-parametric Wilcoxon-Mann-Whitney two-sample rank-
sum test in SPSS 16.0 (SPSS Inc.).
Finally, we attempted to establish what determines
density of seedling and sapling. We measured a range of
environmental variables on the studied plots: soil pH, soil
moisture, canopy cover, slope and altitude. Because these
explanatory variables can correlate among themselves -
which is always the case in field-based ecological studies - we
tried to determine the significant predictors of density using
multiple regression. However, the residuals in regression
analysis were severely non-normal, and we observed both
linear and non-linear relationships between explanatory
and dependent variables. This indicated the violation of one
ofthe assumptions of regression analysis and the likelihood
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
21
Research paper
that power transformation would not be able to fix normality.
Because it entails fewer assumptions, tree regression is a
useful alternative to multiple regression analysis. We therefore
performed tree regression analysis of our data in order to
relate explanatory variables to the dependent variables in R
2.9.1 (The R Foundation for Scientific Computation).
One approach to building the best model (simplest
model that best fits the data) is to begin with the simplest
model and make it increasingly complex until the model
keeps on improving in its fit to the data. This would entail
growing the tree with more and more splits. As Breiman
and colleagues have pointed out problems in this approach
in their seminal work (Breiman et al. 1984), we first allowed
the tree to grow to the maximum (full) size and then pruned
it so that only important predictors remain in the simplified
model (Rejwan 1999, De'ath 2000). The minimum cross-
validated error rate was determined for tree models of
various sizes. The smallest tree with an error rate within
1 SE ofthe minimum error rate was chosen as the best model
(Breiman et al. 1984) after 1000 simulations.
All environmental variables (soil pH, soil moisture,
canopy cover, slope and altitude) were accommodated in
the model although cross-validation would eventually select
only the important ones. On top of these, we used density of
sapling as a potentially explanatory variable for explaining
seedling abundance because the germination of seed and
their growth to seedling is likely to be affected by nearby
saplings which are bigger in size and therefore more effective
competitors. For the same reason, we did not use seedling
density as a potentially explanatory variable for explaining
sapling abundance.
Results
Forest Structure Total tree density and basal area
decreased from low to high elevation on both aspects (Table
2). This pattern was generally followed by P. wallichiana.
However, some species, e.g., B. utilis and A. spectabilis, had
their lowest tree density and basal area values in the lowest
elevation band. Because P. wallichiana is the most dominant
species in these forests, its elevational pattern of density and
basal area determined that of the forest as a whole. Forest
density was greater on the northern aspect than on the
southern. Above 3700 masl on the southern aspect no tree
individuals of P. wallichiana were observed, and/, indicawas
represented only by bushes or scrub with minimal effect on
basal area. The treeline occurred at a lower elevation on the
southern aspect (3800 masl) than on the northern aspect
(4100 masl).
Northern aspects were occupied by seedlings and
saplings much more densely than southern aspects (Figure
1). Using the non-parametric Wilcoxon-Mann-Whitney two-
sample rank-sum test, we confirmed that the north-south
differences are highly significant.
Regeneration of Pinus wallichiana The density-
diameter curve of tree populations of P. wallichiana in all
elevation bands resembled an inverse I-shape on both
aspects (Figure 2). We observed larger trees on north
ca
co
zz
•_>
'■>
3000 -
2000 -
•a
■a
CD
CO
1000
P = 0.006
North aspect
South aspect
P = 0.0005
P = 0.0005
I
3300-3500 m 3500-3800 m 3800-4000 m
4000 -
CD
€
P = 0.0005
P = 0.0005
^™ North aspect
South aspect
CD
^ 3000 -
density (indivi
O
O
O
1
1
_? 1000 -
1
^M P = 0.0005
to
n -
I
I u
3300-3500 m 3500-3800 m 3800-4000 m
Figure 1. Seedling (top) and sapling (bottom) densities
(mean ± 1 standard error). All the aspect-wise differences
in the densities as determined by Wilcoxon-Mann-Whitney
two-sample rank-sum test (two-tailed) are statistically highly
significant.
Seedling density: Mann-Whitney U= 14, nl = n2= 10, P= 0.006
for 3300-3500m, Mann-Whitney L/= 15, «, = 15,
n2 =U,P= 0.0005 for 3500-3800 m, Mann-
Whitney U= 11, n = 10, n2 = 11, P= 0.0005 for 3800-4000 m.
Sapling density: Mann-Whitney U= 1.5, nx
n„
10,
P = 0.0005 for 3300-3500 m, Mann-Whitney U= 16.5, n
n2 = 14, P= 0.0005 for 3500-3800 m, Mann-
Whitney U= 0, Wj = 10, n2 = 11, P = 0.0005 for 3800-4000 m.
15,
aspect than on the south aspect (not shown in figure),
and on valley floor and lower elevations than at higher
elevations of both aspects (observation, Figure 1). Altogether
18% of plots on north and 40% of plots on south were devoid
of mature trees.
Among the six variables, five environmental (soil pH,
soil moisture, canopy cover, slope and altitude) and one
biotic (density of saplings as a potential predictor for seedling
density), the only significant predictor of both seedling and
sapling abundance was altitude (Figure 3). On both aspects,
seedling and sapling density decreased with altitude.
Altitude explained approximately half of the total variance
22
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
North aspect
South aspect
CD
SZ
E
50 -
*r
cm
ri
T
uo
5
h-
£
*\
Q
o
o
Q
o
o
rt
*-
CM
pq
%
LO
izD
r^
Figure 2. DBH-class distribution of Pinus wallichiana.
The size class "0-10 cm"
includes seedlings and saplings. To make the other size
classes visible in the graph,
the y axis has been cut out.
We did not observe any tree
with DBH larger than 90 cm.
On the south aspect, seedlings, saplings and trees were
absent in sampling plots
above 3800 masl.
in abundance of seedlings and saplings in all cases except that it explained
three-quarters of the total variance in sapling abundance on the southern
aspect.
Discussion
Our transects were laid in the upper distributional ranges of the tree species.
Towards the upper limit, environmental conditions become increasingly
severe, resulting in the formation of a boundary oftree species called treeline.
Various studies have repeatedly shown that some aspect of temperature
- means or extremes - determine the position of treeline (Korner 1998,
lobbagy and fackson 2000, Korner and Paulsen 2004). Studies have shown that
annual increments in tree ring correlate with temperature at high elevation
boundaries; for example, ring width of
Picea abies and 7? cembra growing near the
alpine timberline in Switzerland positively
correlates with summer temperature
(Meyer 2000). Given the sharp decline in
temperature with elevation - temperature
lapse rate for the western Himalayas being
0.6-0.74°C/100 m for various months of the
year (Iain et al. 2008) - and high sensitivity
of physiological processes to temperature, it
is not surprising that tree density, seedling
density, sapling density and tree basal
area decreased with elevation on both
north and south aspects. High elevations
are also characterized by shorter growing
season, resulting in reduced annual growth
(Tranquillini 1979, Vetaas 2000). This pattern
of decrease in density and basal area with
elevation, however, can vary with species
because biotic interactions, importantly
competition, also play a role in growth rate.
Northern aspects have denser forest
than the southern aspects, with greater
basal area and higher density of all size
classes (seedling, sapling and mature
plants) (Figure 1, Table 2). Five species of
trees were found on northern aspects, but
only three species on southern (Table 2).
Higher species richness on northern aspects
has been reported by previous studies
(e.g., Panthi et al. 2007). The ecological
significance of aspect is important because
it influences diameter growth of tree, forest
productivity, and species distribution
(Hutchins et al. 1976, Verbyla and Fisher
1989). B. utilis and A. spectabilis, which are
absent on the southern aspects, contributed
substantially to the higher total tree density
and, especially, total basal area found on the
northern aspect.
The density-diameter curve of
P. wallichiana populations resembled
an inverse I-shape (Figure 2), indicating
sustainable regeneration (Kimmins 1987,
Shimano 2000). Shrestha et al. (2007) and
Ghimire and Lekhak (2007) found inverse
I-shape size-class distribution for B. utilis
and A. spectabilis, respectively, in nearby
forests on the northern aspect. Tree
regression analyses show that altitude is the
only significant predictor of seedling and
sapling density on both aspects. Altitude
explained half to three-quarters of the total
variance in seedling and sapling density.
Whereas we should not be surprised that
altitude is the most important predictor of
the seedling and sapling abundance, other
variables (including canopy and aspect)
that have been considered important
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
23
Research paper
North aspect
South aspect
Altitude <3595 m
Altitude >=3595 m
Seed density (individuals/ha)
0 2000 4000 600C
•
•
• •
• •
• •
51.8% variance
explained
••
•• ••••
3300 3400 3600 3800 4000
Altitude (m)
Altitude <3480 m Altitude >=3480 m
<D
<D
CO
2693.33
(n=15)
590.00
(n=20)
966.67
(n=9)
52.6% variance
explained
3300
3500 3700
Altitude (m)
3900
26.92
(n=26)
Altitude <3595
m
Altitude >=3595 m
o
s/ha)
1000
•
46.5% variance
as
zn
explained
> _
•
? o
•
.<= o
~-- CO
•
>,
"m
• •
i
c
•
CD
ing c
2000
•
'••*. .
Q.
CO °
3
•— •••VB«»
300 3400
3600 3800 400
0
Alti
tude (m)
Altitude <3500 m
Altitude >=3500 m
3966.67
(n=15)
840.00
(n=20)
800.00
(n=10)
<D
T3
Q.
CO °
73.9% variance
explained
• • •
• m • •••
3300
3500
3700
Altitude (m)
3900
40.00
(n=25)
Figure 3. Regression trees relating abundance of seedlings and saplings to six potentially explanatory variables (soil pH, soil
moisture, canopy cover, slope, altitude, and sapling density). In all cases the only significant explanatory variable was altitude,
explaining approximately half of the total sum of squares except in one instance in which it explained 74% of the variance in
sapling density. The tip ofthe tree branches list the mean ofthe response variable (density) assigned to the tree-branch and the
sample size in parentheses.
in determining environmental conditions suitable for
germination and growth of young plants do not turn out to
be significant predictors.
Seedlings are generally light-demanding; they often
require direct solar radiation (Tilman 1985). High canopy
cover by large trees reduces the amount of direct sunlight
that reaches floor; high canopy cover may also cause litter
accumulation, which is not a favorable condition for seed
germination and seedling establishment for Pinus (Neyisci
1993, Spanos et al. 2001, Bonnet et al. 2005). However, we
failed to establish a relationship between canopy and density
of seedlings and saplings. This finding may possibly reflect the
actual absence of such a relationship in our study systems; we
mi ghthaveobservedacorrelationbetween density and canopy
(r = 0.44 for both seedling and sapling, Pearson Correlation,
P = 0.0005) because both canopy and density covary with
altitude, a more important predictor variable (Pearson
Correlation r = -0.41 between altitude and canopy cover,
-0.5 between altitude and seedling density, -0.43 between
altitude and sapling density; P <0.0005 for all). In such a
situation, variance explained by a less important variable in
bivariate regression is captured by a more important variable
when the variance in the dependent variable is explained
simultaneously by multiple explanatory variables.
However, given the established relationship between
canopy cover and density of seedlings and saplings (Neyisci
24
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
1993, Spanos et al. 2001, Bonnet et al. 2005) and our
observation in the field that seedlings and saplings grew
more densely in canopy gaps, the more likely explanations
are the following. The non-parametric test (tree regression
analysis) that we used may not have been powerful enough to
detect the relationship between canopy cover and density of
young plants. Another possibility is that the rule we adopted
to determine the best model (tree) was very conservative
yielding a sub-optimal tree that could not capture all the
important predictor variables. Whereas Breiman et al. (1984)
proposed both a minimum cross-validation rule and a
1 standard error (SE) rule, there is a lack of clear reason
on the choice of the rule for determining best model. The
minimum cross-validation rule assumes that the optimal
tree is the one with the smallest cross-validation error; the
1-SE rule states that the optimal tree is the smallest tree
within 1 standard error of the minimum cross-validation
error. The negative relationship between cross-validation
error and tree complexity (cross-validation error declines as
tree grows until a minimum error is reached) makes it likely
that 1 SE rule can yield a simpler model than minimum rule
does, especially when multiple predictor variables exist.
In such a situation, the 1 SE rule could yield sub-optimal
trees. However, to avoid any potential false positives
and to remain consistent with many studies in the practice
of model selection, we applied the more conservative
1-SE rule, which might have been unable to capture some
of the important predictor variables, such as canopy and
moisture.
The quantity and duration of soil moisture depend
on aspect because of the stark difference in the amount of
sunlight received by north and south aspects (Parker 1991).
As southern aspects receive more intense insolation than
northern aspects, the northern aspects retain more soil
moisture, which provides more suitable conditions for seed
germination, seedling establishment and regeneration of
P wallichiana. The northern slopes in our study sites are
moister - and have more canopy cover - than the southern
slope; these two factors both have a positive influence on
seedling germination. Some plots in moist gullies on the
southern aspect also had higher seedling abundance than
other dryer plots on the same aspect which were relatively
dry. In contrast to the density pattern that we observed,
Schickhoff (1996) found that P. wallichiana is more abundant
on the relatively dry south-facing slopes than on the
north-facing slopes in the Kaghan Valley of northern
Pakistan.
Fire damage to forests has been reported to accelerate
regeneration of Pinus species and increase seedling density;
this is the case, for instance, with P. brutia on the Thasos
Island of north Greece (Spanos et al. 2001) and P. ponderosa
in the Black Hills of South Dakota, USA (Bonnet et al. 2005).
Schweinfurth (1957) and Stainton (1972) have argued for the
significant role of fire in the maintenance of Pinus forests in
the Himalaya. Fire damage that occurred about 35 years ago
on the north-facing slopes of our study site probably changed
the course of forest regeneration. However, without detailed
historical data, we are unable to relate that event to today's
state of forest regeneration.
Acknowledgements
Our research work was financially supported by NUFU (the
Norwegian Council for Higher Educations' Programme for
Development Research and Education) funded collaborative
research project between Tribhuvan University, Nepal, and University
of Bergen, Norway (2002-2006) to the Central Department of Botany,
Tribhuvan University, Kirtipur, Nepal. We thank VNP Gupta,
(Tribhuvan University) and OR Vetaas (University of Bergen) for their
suggestions during fieldwork, K Heo (Kangwon National University,
South Korea) for comments on early drafts of manuscript, MP Panthi
(Butwal Multiple Campus, Rupandehi, Nepal) and BB Shrestha
(Tribhuvan University) for their suggestions and cooperation
during data collection. We extend sincere thanks to Annapurna
Conservation Area Project (ACAP), Pokhara, for the permission to
conduct research.
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Research paper
Simulating farm income under the current soil management regime
in the mid-hills of Nepal
Gopal D Bhatta1*, and Nilhari Neupane2
1 Himalayan College of Agricultural Sciences and Technology, Purbanchal University, NEPAL
2 Institute for Agricultural Policy and Market Research, University of Giessen, GERMANY
* For correspondence, email: bhattagopal@gmail.com
Farmers in the mid-hills of Nepal follow diverse farming systems. The peri-urban area of this region, where
population density is higher, faces several problems in farming. While hills suffer from erosion because they
are erodible, the peri-urban areas face the problem of decline in factor productivity, particularly in intensively
cultivated farmlands. The present study is concerned with simulating farm income on a regional scale based on
soil management practices. Spatial explicit simulation shows that the loss of farm income due to degradation
is substantially higher in hills while it is lower in valley bottoms. Strategy formulation and testing in the spatial
environment indicates that Geographic Information System is an appropriate methodological tool for simulating
the consequences of particular interventions.
Keywords: Mid hills, Nepal, spatial modeling, soil quality index, farm income
The mid-hills cover about 43% of Nepal's land area (Shrestha
1992) and accommodate 46% of Nepal's population. There is
a great diversity of land use due to variations in topography,
population density and market demand (Bhatta 2010a). The
fulfillment of subsistence requirements has for centuries
been the primary objective of the majority of the farmers
in the mid-hills (Carson 1992; Brown 1997). However, in
recent decades market-oriented production has emerged as
a key driving force for land-use intensification in the densely
populated urban fringes of Nepal (Brown and Shrestha 2000).
While subsistence farming is characterized by the integration
of livestock and forestry with agriculture and traditional
modes of production, intensification is characterized
by double or triple crop rotations, expanded cultivation
of vegetable cash crops, and the imprudent use of agro-
chemicals (Bhatta 2010a).
Road access, along with proximity to input markets,
is the main catalyst for expansion of commercial farming
(Brown 2003; Brown and Shrestha 2000) and consequent use
of agro-chemicals (Bhatta and Doppler 2011). In the early
1980s agro-chemicals first appeared in newly accessible areas
and their use quickly accelerated (Pokhrel and Pant 2008). The
environmental and health costs of inorganic farming have by
now been widely felt in Nepal, raising awareness of the issue
of sustainability (Bhatta et al. 2009); meanwhile, agriculture
based on organic practices and balanced application of inputs
on family-owned farms in the peri-urban and rural areas
has shown a great deal of resilience (Sharma 2006). This is
because sustainable farming addresses many environmental
and social concerns and offers innovative and economically
viable opportunities for growers, laborers, consumers, and
other stakeholders, as well as policymakers.
The problem of soil degradation exists in almost
all parts of the mid-hills of Nepal, but the severity varies
depending on different factors. Cultivation of the sloping
marginal hills leads to severe soil erosion, while the scars of
the green revolution are visible in the urban and peri-urban
flat lands. Bio-physical factors such as variations in weather,
landforms, soil types and resource availability (Verbung et
al. 2004) as well as socio-economic factors such as social
structure, family composition and needs, have combined
with economic opportunities, technological availability and
political systems to affect land use evolution (Briassoulis
2000).
Spatial methodologies are commonly used for the
analysis of socio-economic phenomena and their distribution
along the spatial gradient (Bhatta et al. 2009; Codjoe 2007;
KC 2005; Evans and Moran 2002; Schreier and Brown 2001;
Bowers and Hirschfield 1999; foshi et al. 1999). The present
study integrates micro-surveys in a Geographic Information
System (GIS) in order to model the current situation and
predict future economic viability of family farms, assuming
the persistence of prevailing soil management practices.
Materials and methods
Location and physical aspects of the study area The study was
undertaken in the Lalitpur and Bhaktapur Districts, which
have biophysical and socio-economic characteristics typical
ofthe mid-hill region of Nepal (Figure la). The low-lying flat
plains of this region are characterized predominantly by the
Himalayan lournal ofSciences 6(8): 27-34, 2010
doi: 10.3126/hjs.v6i8.3243
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
27
Research paper
rice-wheat cropping pattern, while cultivation in the rainfed
uplands is typically based on maize. Both of these cropping
patterns are exhaustive in nature. Since most farmers in
the mid-hills lack irrigated land, they must rely on maize as
their major food source. Currently, farmers are producing
several species of vegetables, both for in situ consumption
and for market. However, commercial vegetable farming
based on widespread use of agro -chemicals has had negative
repercussions on agro-ecology
The peri-urban part of the study area is located mostly
in the Bhaktapur District and partly in the Lalitpur District,
while the rural area is located in the hilly part of Lalitpur
District. Figure lb shows that the study area is characterized
by an altitudinal gradient ranging from 900 to 2500 meters
above sea level (masl). Elevation ranges from 1500 to 1800
masl cover much ofthe area, with only negligible land surface
at less than 1000 masl.
Slope of the study area, derived from digital elevation
model (DEM), is expressed in percent. Slope at a given grid
cell is estimated from elevation ofthe surrounding eight grid
cells. The following grid consists of nine grid cells labeled "A"
through "I". If "a" represents elevation ofthe gird cell "A", "b"
elevation ofthe grid cell "B", and so on, then the slope for cell
"E" (central cell) in the following grid is determined as follows
(equation [i]):
A
B
C
D
E
F
G
H
I
% Slope = V(Az2+Az2).
where,
Az2--
X
tsz2--
(i)
[(a+2d+g)-(c+2f+i)]l'(8 x cell size)
[(a+2b+c)-(g+2h+i)]l'(8 x cell size)
Mini river
Roads
/\/HueIiwi\
/\yDeliKi Hind
Cart trail
I II MM
Alluvial Plants mid Fans iDcposilioiial)
Anttettt ].;ii;c .t Rivet Tcmccs iT.it>' Einsionali
Matkratelv to Steqtly Slopping Mountainous Tci i .1111
! !■ l| I I" VclvSlrqih ;;I->|>iii-' Mount mum- Tcitntll
Figure 1. The study area: (a) map of Nepal showing the Lalitpur and Bhaktapur Districts, (b) digital elevation model ofthe
study area, (c) slope (%) in the study area, and d) dominant landforms
28
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE
2010
Research paper
A sizeable part of the study area is flat or nearly flat (0
to 5%); most ofthe land has a steep slope (>30%) (Figure lc,
Table 1). The rural hills, in general, have steeper slopes than
do the urban and peri-urban areas. Slope along with fragile
landscape leads to severe soil erosion in hill farming systems
throughout the country (Brown and Shrestha 2000).
Four dominant landforms are found in the study area
(Figure Id). Alluvial plains and fans, generally with a slope
of less than 5%, are composed of deposits from floodwater or
runoff and tend to be rich in nutrients. Most ofthe Kathmandu
Valley bottom is comprised of these formations. Another
group of landforms, the ancient lake and river terraces
(locally referred to as tars), are formed by water erosion; they
have a gentle slope. The forth type of landform is composed
of moderate and steep slopes which are prevalent primarily
in hilly part of the study area.
Sampling and the data The study was based on a cross-
sectional study of 130 farms. Ninety households were selected
through spatial sampling; the remaining 40 were selected at
random. Spatial sampling was adopted in rural and peri-
urban areas because little information was available about
these scattered households.
Household data were collected using a standard
questionnaire prepared subsequent to a pilot study and
administered through personal interview. The spatial data
were collected from already available maps. These data
include elevational contours, dominant landforms, soil
types, roads and other infrastructure.
Integrating socio-economic data into the GIS This paper
represents an attempt to develop a model by which farm
income over years can be simulated based on a degradation
scenario. The methodological framework entails integrating
micro-survey data into a spatial environment. Farm income
was calculated taking into account many facets of the farm
economy, including production costs and market prices of
crops. Farm income was integrated into the GIS database
using Global Positioning System (GPS). Prior to spatial
integration, the significance of the farm income variable
was subjected to a test of spatial autocorrelation using
Geary's Ratio and Moran's I. It was then subjected to spatial
interpolation using Inverse Distance Weighting (IDW) to
generate the output grid surfaces in which the value of
each cell was 25 meters by 25 meters. The interpolation was
performed based on the values of 12 neighboring sample
points and their distance to the point of estimation. A linear
trend in the sample data was assumed in the IDW.
We produced the digital elevation model (DEM)
incorporating terrain parameters such as slope and
elevation. Cost-distance analysis and dominant landform
with land management practices along the spatial gradient
were incorporated in the regional spatial model. Cost
distances from farms to market center were calculated
using the GIS-based cost weighted distance model (ESRI
1997). Biophysical variables such as road infrastructure and
slope were considered in the cost-distance modeling. This
technique is based on the idea that a relative "cost" can be
associated with moving across each cell in a map (ESRI 1992).
Table 1. Area distribution under different
slopes in the
Slope range
(percentage)
Area (ha)
Percentage of
total
<5
8708
48.6
5-10
1338
7.5
10-20
1450
8.1
20-30
1945
10.9
>30
4463
24.9
Total
17903
100.0
The cost of moving across a cell is calculated as the cell size
(in meters) times a weighting factor based on the quality of
the road and associated factors of the cell such as slope. The
least-cost model evaluates the cost of moving between two
designated source areas (from household location to market
center) by calculating, for each cell, the cumulative weighted
distance between the cells and the two sources.
Soil quality weighting In preparing a comprehensive soil
quality weighting for the study area, we considered dominant
landforms available and four soil management practices
commonly followed by the farmers.
Landforms are composed of typical varieties of soil
with varying production potentials. Dark soils containing
alluvial deposits, for instance, have good capacity to retain
water and to supply nutrients (Singh et al. 2007), the essential
requirements of the majority of crops (Rajbhandari and
Bhatta 2008). Lands rich in this first type of soil were given
a high score. The second group is composed of soils around
ancient lakes and river terraces, which have a higher rate of
erosion than the first class. These lands are composed of hills
with narrow valleys and elongated ridges; predominantly
occupied by soil that is well to excessively drained, loamy
skeletal in texture, slightly acidic (pH 5.2 to 6.9), with a
shallow rooting depth (Singh et al. 2007). Lands dominated
by these types of soils grow food crops successfully, but yields
are not comparable to those of the higher-weighted class
described above.
A third landform type is composed of mountainous
terrains with moderate slope, generally suitable for
subsistence farming; the cost of land management is greater
here than on flat lands. This landform was given lower weight
than the classes discussed above. The fourth landform group
is composed of mountain terraces with steep to very steep
slopes, thin soil with stony subsoil; they are subject to severe
erosion by both wind and water (Muller-Boker 1991).The cost
of land management is excessively high due to the rugged
terrain. This group of lands has been given the lowest score.
The difference between scores for alluvial flat lands and for
mountainous terrain was calculated using the gross margin
of rice. The ratio of the gross margin of rice in both classes
is approximately to 1.5. The difference in the productive
potential of two landforms composed of alluvial soils is very
slight. They were, therefore, given higher values with narrow
difference. Similarly, in weighting mountainous terrains with
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
29
Research paper
moderately steep and very steep slopes, we considered the
gross margin of maize; the ratio between the gross margins
of maize in steep and very steep slope lands turned out to be
1.2. Therefore, we assigned a value of 1.70 to steep land and
1.40 to very steep land (Table 2).
Soil management practices Fertility management practices
followed currently are balanced fertilizer application, use of
agro-chemicals only, use of manure only and unbalanced
application of manure and fertilizers.
Balanced fertilizer application
This refers to soil management practices employed by organic
growers around the peri-urban areas. Farmers using these
practices apply organic manure and other locally available
resources. They also follow other fertility management
practices such as intercropping, terracing and application of
farm waste to crops. Pest control is generally implemented by
means of local materials and botanicals.
Use of agro-chemicals only
Commercial vegetable growers in the peri-urban areas follow
this practice. Most farmers using agro-chemicals are near
input markets. Generally, exhaustive crops and their rotations
are followed. Farmers experience decline in partial factor
productivity of fertilizers and pesticides in their farmlands.
Unbalanced application of manure and agro-chemicals
This is a kind of intermediate practice and is followed by
some farmers in the peri-urban area. Farmers apply both
organic manure and inorganic fertilizers. Although farmers
do understand the value of organic manure in agriculture,
chemical fertilizers are applied in concentrations so high that
the buffering capability ofthe manure is overwhelmed.
Use of farm manure only
Farmers in rural areas follow this practice, in which crop
nutrients are derived solely from locally-produced manure.
Some farmers apply inorganic inputs, but the amount applied
is so negligible that we would not characterize the practices as
"inorganic farming." Rather, this mode of agriculture is more
often referred to as organic by default or organic by neglect.
The quantity of nutrient supplied to the crops is far below
the crops' requirements, and the organic manure applied in
the field is not enough to prevent soil erosion. Therefore, this
form of soil management is not considered sustainable.
Balanced input application is important for good yields,
and is considered one of the key components of sustainable
agriculture. Consequently it is assigned a high value (2.00),
followed by intensive land management based on inorganic
inputs (1.90). Application of higher amount of inorganic
fertilizer can make good yields likely, but a small amount of
farm manure applied is unable to improve edaphic environment. Therefore, unbalanced application of organic manure
and inorganic fertilizers this soil management practice is
weighted at 1.80. The last category of management is traditional
subsistence farming. Manure application is not enough
to provide the nutrients required for a good yield. Lands
managed in this way are accorded a low weighting (1.50).
After assigning a weight to each farm based on
dominant landform and land management, we produced
a map representing these characteristics using GIS overlay
Table 2. Land quality weighting based on landforms and farmers' practices of soil fertility management under current and
the future scenarios (degradation scenario)
Current scenario
Land
Soil degradation scenario
Landform
management
Landform
Management
Combined
Degradation
Combined
Unbalanced
1.40
1.80
2.52
1.62(10)
2.27
Alluvial plains
and fans
(depositional)
Manure
1.40
1.50
2.10
1.35(10)
1.89
Balanced
2.00
2.00
4.00
2.00
4.00
Chemical
2.00
1.90
3.80
1.77(7)
3.54
Unbalanced
2.00
1.80
3.60
1.71(5)
3.42
Lake and river
terraces (tars,
erosional)
Manure
2.00
1.50
3.00
1.50
3.00
Balanced
1.90
2.00
3.80
2.00
3.80
Chemical
1.90
1.90
3.61
1.71(10)
3.25
Unbalanced
1.90
1.80
3.42
1.67(7)
3.17
Mountain
terrains with
moderate slope
Manure
1.90
1.50
2.85
1.46(3)
2.77
Balanced
1.70
2.00
3.40
2.00
3.40
Chemical
1.70
1.90
3.23
1.58(15)
2.69
lVtniintftin
Unbalanced
1.70
1.80
3.06
1.62(10)
2.75
1V1U 1-lllLdlll
terrains with
Manure
1.70
1.50
2.55
1.35(10)
2.30
steep to very
steep slope
Balanced
1.40
2.00
2.80
2.00
2.80
Chemical
1.40
1.90
2.66
1.58(15)
2.21
Note: Values in tl
le parentheses indicate the reduction
in the score due to degradation by a
given percentage
30
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE
2010
Research paper
technique. The generalized formula to calculate the
combined index is:
(SQ )=(W,xW J (ii)
1 ^-present i v / mjr i v '
(SQr^J^lw^fw-w^xmf.
(iii)
where each of the following values is associated with
the i'h cell: SQ., soil quality; Wf weight attributed to
the landform; W , weight associated with the current
soil management; %R, reduction in weight due to soil
management practices.
Following equation (ii), altogether 16 classes were formed
in which the highest weight (4.00) was attributed to alluvial
plain lands with balanced fertility management, while the
lowest weight was assigned to steep-sloped mountainous
terrains in the only fertilizer applied is locally-produced
manure (2.10) (Table 2).
Results and discussion
Soil degradation scenario and land quality weightings Prac
tices such as continuous deployment of an exhaustive
cropping pattern without prudent use of chemical
fertilizers, abstinence from conservation measures and
multiple cropping, and exploitation of marginal lands can
exacerbate the problem of fertility degradation (Brown
and Shrestha 2000). The single greatest cause of declining
crop production is unbalanced fertilization (Rattan and
Singh 1997). Unbalanced fertilizer application has led to
a chronological emergence of macronutrients such as
phosphorus and potash (P and K) and micronutrients such
as zinc, sulfur and manganese (Zn, S and Mn) deficiencies.
Even balanced application of macronutrients devoid of
organic materials has been implicated in the deterioration
ofthe physical, chemical and biological health of soil (Rattan
and Singh 1997). Most farmers have realized that prolonged
overapplication of fertilizers is not sustainable in the medium
to long run (foshi et al. 1996).
A decline in the partial factor productivity of nitrogen
is generally due to a decrease in the nitrogen-supplying
capacity of intensively cultivated lowlands (Cassman et al.
1994). A series of long-term experiments initiated in India
and Nepal indicated the superiority of organic materials such
as Sesbania aculeata (Mandal et al. 1992; Singh et al. 2000;
Kundu and Samui 2000), FYM (Prasad and Sinha 2000) and
residue (Singh et al. 2000; Prasad and Sinha 2000; Garni and
Sah 1999; Bhatta and Subedi 2006) in enhancing soil quality
and maximizing crop yields.
The rate of degradation in fertility also varies according
to landform. For instance, the rate of soil decline is lower
on plains than in hilly areas because of the compounding
effects of steep slope and land structure. The decreasing
use of organic matter and the land use shift from traditional
subsistence farming towards intensive vegetable farming
in the hill terraces will exacerbate land quality degradation
in the future (Tiwari et al. 2009). Nevertheless, factor
productivity on plains is declining because of the excessive
use of agro-chemicals and continued monocropping of
exhaustive crops (Bhatta 2010a). Soil acidification caused
by urea is a common concern in most intensively cultivated
areas (Brown and Shrestha 2000). Therefore, farmland with
t value
-57**
-135**
301**
Table 3. Model summary ofthe multiple regression
(dependent variable: farm income in ^Rs-ha-1)
Parameters B p SE(B)
Constant -110504 2273
Cost-distance (minute) -2615 -0.25 19
Land quality 163200 0.56 0.05
R2= 61%, F-statistics (2, 282214) = 212500 (p<0.01)
Note: ** significant at 1% level; +73 NRs = 1$
balanced fertilizer management (application of substantial
quantities of organic manure along with a small proportion
of inorganic fertilizer, as well as legume intercropping, for
example) would have almost same quality weighting in the
future. By contrast, production practices that are heavily
dependent on agro-chemicals will result in fertility decline
(Bhatta 2010b). While in the alluvial lands the reduction
due to intensive agro-chemical use is expected to be 5%, on
river terraces with erosional land where agro-chemicals are
abused, the fertility decline is assumed to be around 10%.
Results provided by running a soil erosion assessment
model (Morgan et al. 1984) in the GIS environment show that
annual soil loss rates are highest (up to 56 tonnes-ha^-year1)
on terraced slopes in hilly areas. Erosion from cultivated and
grazing lands is a serious problem, and marginal upland
agricultural sites are prone to a higher erosion rate (Brown
and Shrestha 2000). If farm production is based solely on
agro-chemicals, we would assign a weighting 15% lower
than would otherwise be attributed to that land. Similarly,
with unbalanced land management practices, weighting
reductions of 5%, 7% and 10% from the basic land quality
are attributed to farmland on alluvial plains, river terraces
and moderate to sloping terrain, respectively. In the alluvial
plains, farming based solely on the application of an ample
amount of farm manure is an ideal strategy to restore fertility
and produce an acceptable yield. No reduction in weight is
considered under this management practice; it is weighted
at the same value. The amount of manure applied by the
farmers, however, cannot meet the nutrient requirements of
crop plants and cannot prevent soil erosion to same extent it
would on river terraces and higher sloped lands. Therefore,
such practices entail weight reductions of 3% and 10% from
the baseline for river terraces and sloped lands, respectively.
For the purposes of our calculations, the prices of
inputs as well as outputs were held constant: we assume
that the impact of future inflation will be roughly equal
on both sides of the ledger. It is also assumed that there
will be no technological change in crop production for the
time span considered and that farmers will use the same
amount of inputs as in 2007, our base year. Consequently,
land management is the single largest factor influencing the
performance of production systems in our projections.
Following equation (iii), alluvial plains with balanced
fertility management are accorded the highest quality
score followed by river terraces with balanced application
and alluvial plains where agro-chemicals are used. Under
degradation scenario, there is no effect on soil quality under
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
31
Research paper
balanced fertilizer application while there has
been substantial decrement in soil quality on the
sloping landforms (Table 2).
Base model
The GIS-based multiple regression model, in
which farm income is the dependent variable
and land quality and cost-distance to primary
market are the independent variables, shows
significant trends. All variables in the model have
the expected direction of relationship (Table 3).
The predictive power of the model is 61%. Higher
predictive power ofthe model signifies its better fit
in simulating farm income. A unit increase in cost-
distance reduces farm income by NRs 2,615; a unit
increase in land quality, ceteris paribus, increases
farm income by NRs 163,200.
Interpolated observed farm income (NRs-ha1)
along the spatial gradient is shown in Figure 2a
and estimated farm income using an emperical
regression model are shown in Figure 2b. Both
observed and estimated income have similar
trends in accessible areas, while there is a mixed
tendency at higher altitudinal gradients. This is
because the regresion function underestimates
income in the inaccessible areas. Similarly, both of
the figures show declining farm income as one goes
towards the rural setting from peri-urban areas.
Simulated model under land degradation scenario
Farm income under the soil degradation scenario
was estimated using multiple regression, and the
resulting functional form is presented in equation
(iv). We used estimated farm income under the
current regime (as shown in Figure 3a), and farm
income under the degradation scenario was
deducted from current income while the difference
was taken as the impact of degradation. The
explanatory power of the independent variables
is slightly higher (R2=65%) under future scenario
(degradation situation) than that under the current
situation (R2=61%). All of these coefficients are
highly significant in predicting the changes in farm
income per hectare (ha). One unit increase in the
cost distance, in terms of travel time in minutes,
would reduce farm income by NRs 2,244 while
a unit increase in land quality weighting would
increase farm income by NRs 174,400, as given by
the following equation:
Y= -135692 (-73**) -2244X1 (-129**) +
174400X2(376**) (iv)
(values in parentheses indicate t-statistic; **
indicates statistically highly significant with
p<0.01)
R2= 0.65, F stat (2, 282212) = 260500 (p<0.01)
where Fis farm income, X} cost distance and
X2 land quality.
Figure 3a shows estimated farm income (NRs-ha-1)
• Mam market
Roads
/^Highway
/\/ Dtftnct road
'/X^/Cait trail
/ywaittttaa
__ Forest land
Faini mcowe (NR.* lui I
I I 42250
__ 42250 - "9984
"J "9984 -1382:9
3 ns::>j-is(.j-j
I isf.j-j-:<:-i9
■ 252715-J23664
322664-391209
■ 391209-469454
■ -469454
• Maui market
Roads
/\/ Highway
/\y Dish id road
'/\/ Cart trail
/\/ Maui trad
I I Forest land
Estimated I um income (NRs ha)
| | 42250
_2 42250 --99S4
B"99S4-138229
I3S229-1S64_4
B1S64_4-2?2"19
H252"19-322664
■ 322664 -391209
|391209-469454
■ -J()9J54
Kllomeleis
Figure 2. Farm income (NRs-ha-1) based on current scenario: (a, top)
as obtained from the household survey, and (b, bottom) as estimated
using regression model
regressed by assumed land quality weighting and cost-distance to
primary market. Figure 3b depicts loss in farm income due to the future
scenario (degradation situation) as compared to the present situation.
The current situation shows three distinct areas with respect to farm
income, viz.: high, medium and low income zones, the high-income
zone being located in the peri-urban areas while medium- and low-
income zones are located in rural areas.
32
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
1 -11 - JIKUI-
• Maui market
Roads
/\/Highway
/\y District road
/\/Cart trail
Main tlall
I I Forest laird
Sn i ml ali-'I tat in income (NRsltal
due to land degradation scenario
42250
42250-"9984
"9984-13S229
13S229-1864_4
1S64"4-252^19
252~19-322664
322664-391209
391209-469454
-469454
Kilonietets
• Mam market
Roads
/\/Highway
/\y District road
/\/Carl hail
Mam trail
_] Forest land
% loss in faun income
due to land degradation
□ 0-2
□ :--»
|6-10
| m-15
I 15
Figure 3. Farm income (NRs-ha-1) associated with the future strategy
of land degradation simulated using a spatial explicit model: (a, top)
simulated farm income (NRs-ha1) under soil degradation scenario, and
(b, bottom) impact of soil degradation on farm income (% loss)
The loss of farm income due to degradation is substantially higher
in the low income zone (mainly in the hills), where it goes higher than
15%, while it is very small (0-2%) in high-income areas. Within the high-
income accessible region, there is almost negligible loss of income,
particularly where farmers follow organic practices (apply ample amount
of organic manure), whereas income loss goes as high as 10% in the
commercial inorganic farming area. This is due to the fact that farming
in this zone is based solely on inorganic inputs
whose continued use would reduce soil quality in
the future. The higher loss of income in the rural
area is basically attributable to low quality of the
land associated with high erosion exacerbated by
steep slopes.
The rural area is characterized by
subsistence farming with poor standard of living.
Income in most of remote areas ranges from less
than 42,250-1,864,747 NRs-ha^-year1 and a loss
of 10-15% income would have a substantial
impact on the standard of living. This shows that
rural life depends heavily on local resources,
especially soil, and their degradation would have
enormous effects on the income generation
potential of farmers.
Conclusion
Four dominant practices of soil fertility
management are assumed in this study. The
baseline spatial explicit model shows a clear
variation in farm income along the spatial
gradient. Balanced application is considered
a sustainable way of enriching soil and hence
restoring its fertility over time. Relatively
inaccessible rural areas have lower farm income
than peri-urban areas. Farm-families living in the
higher altitude relatively inaccessible areas have
a lower standard of living and they are highly
dependent on farming for their subsistence
needs. The low lying valley hinterlands with good
road access and other infrastructure are more
tractable in terms of agricultural enterprise, but
agro-ecological degradation should be taken
seriously. GIS-based socio-economic analysis
and modeling is a key approach to the study of
complex phenomena and formulation of policies
for future development.
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Research paper
Optimization of RAPD-PCR conditions for the study of genetic
diversity in Nepal's Swertia chirayita (Roxb. Ex Fleming) H. Karst
Sangita Shrestha1*, Jaishree Sijapati1, Neesha Rana1, Diwa Malla2, Prabha Regmi2 and Bhakta Raskoti3
1 Molecular Biotechnology Unit, Nepal Academy of Science and Technology, Khumaltar, Lalitpur, NEPAL
2 Department of Biotechnology, Kathmandu, University, Dhulikhel, Kavre, NEPAL
3 World Conservation Union, Lalitpur, NEPAL
* For correspondence, email: sangita7@hotmail.com, tel: 977-1-5547714
Of the 30 species (including five varieties) of the genus Swertia in Nepal, nine have been reported to possess
medicinal properties. Among these, S. chirayita is the most valuable species, with high demand in domestic
and international markets. Nepal's S. chirayita and related species are being recklessly exploited for commercial
purposes. Two problems that have emerged with this lucrative market are (a) adulteration and fraudulent
labeling of S. chirayita, and (b) depletion of S. chirayita and allied species from their natural habitats. To address
the problem of adulteration and conservation, we studied molecular genetic diversity in S. chirayita populations
and developed a molecular diagnostic tool for the purposes of authentication. We studied intra-specific genetic
diversity in S. chirayita using Polymerase Chain Reaction (PCR)-based Random Amplified Polymorphic DNA
(RAPD) technique. As a preliminary step, we identified optimal RAPD-PCR reaction and cycling conditions
by varying PCR reaction parameters such as concentration of template DNA, MgCl2, dNTPs, primer, Taq DNA
polymerase and RAPD-PCR programs. The optimized PCR reaction and cycling conditions were then used in
subsequent RAPD profiling experiments for the study of genetic diversity within S. chirayita populations from
various geographical locations. Genetic diversity characterization of S. chirayita populations at the molecular
level would furnish information with significant applications in the conservation and sustainable utilization of S.
chirayita and its allied species in Nepal.
Key words: Polymerase Chain Reaction, Random Amplified Polymorphic DNA, DNA fingerprinting, genetic
diversity
Globally, genus Swertia is represented by approximately
100 species (Willis 1996). Of the 30 species, including five
varieties, that have been reported in Nepal (Press et al. 2000),
nine are being traded for their medicinal properties, viz.: (1) S.
chirayita (Roxb. Ex Fleming) H. Karst, (2) S. angustifolia Buch.
- Ham. Ex D. Don, (3) S. tetragona Edgew, (4) S. racemosa
(Griseb) C. B. Clark, (5) S. cilaita (D. Don ex G. Don) B. L.
Burtt, (6) S. dilatataC. B. Clarke, (7) S. multicaulisD. Don, (8)
S. alata (Royle ex D. Don) C. B. Clarke, and (9) S. nervosa (G.
Don) C. B. Clarke (loshi and loshi 2001, Rajbhandari 2001).
Among these, S. chirayita is considered superior to all the
others in quality and is in high demand for trade (Pant 2011).
S. chirayita is distributed in the hills of eastern, central and
western Nepal at an altitude of 1200-3000 m in open forests
and shady habitats. Bhattarai (1996) reports the species in 40
districts of Nepal; Barakoti et al. (1999) reports distribution
in 54 districts.
Eighty to 90% ofthe total harvest is exported to India as
a crude drug and about 9% to China, Malaysia, Singapore,
Germany, Italy, France, Switzerland, Srilanka, Bangladesh,
Pakistan and the United States. Nepal consumes only 1%
ofthe total harvest and supplies 45-50% ofthe world's total
volume (loshi and Dhawan 2005). It has been reported that
more than 27.2 tons of S. chirayita (valued at more than
2.3 million Indian rupees) was exported from Nepal during
1997-1998 (loshi and Dhawan 2005).
The medicinal properties of S. chirayita are attributed
to a number of chemical compounds with therapeutic
value; these include chiratanin, gentianine, amarogentin,
amaroswerin, and several xanthone, iridoid, triterpenoid
and glycoside derivatives (Bajpai et al. 1991, Chakravarty
et al. 1991, Benerjee et al. 2000, loshi and Dhawan 2005).
The whole plant is used in crude form, and it is also used
to manufacture various ayurvedic, herbal, and allopathic
medicines. The importance of S. chirayita as a multipurpose
drug was perceived already in ancient times (Kiratatikta2001).
In "Ayurvada," S. chirayita is said to contain anti-cancerous
properties (Chiraito Swertia chirayita, www.ansab.org).
In Ayurvedic preparations, it is used as antipyretic,
hypoglycemic, antifungal and antibacterial agent (loshi and
Dhawan 2005). Although the entire plant has medicinal
r
Himalayan lournal ofSciences 6(8): 35-40, 2010
doi: 10.3126/hjs.v6i8.2699
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
35
Research paper
properties, in traditional medicines the root is considered
the most powerful part and is used in the treatment of
chronic and malarial fever, joint pain, ulcers, cough, cold,
asthma, scabies, leucoderma etc. (Chiraito Swertia chirayita,
www.ansab.org). One of the constituents, Swerchirin, has
been found effective in lowering human blood sugar level
(Bajpai et al. 1991, Saxena et al. 1993) through the stimulation
of insulin release from the islets of Langerhans. Its anti-
leishmanial property has been studied in hamster model
(Medda et al. 1999), and its anti-inflammatory and hepato-
protective effects have been studied in mice model (Islam et
al. 1995, Mukherjee et al. 1997).
S. chirayita has been listed as one ofthe top priority medicinal plants of Nepal as well as of Asia (DPR 2005). The great
demand, both in Nepal and abroad, has led to its rapid depletion in the wild. Because ofthe availability of multiple species
of Swertia in Nepal, a number of other allied species are also
being traded. This trend of adulteration, from human health
safety viewpoint is a malpractice. The World Conservation
Union (IUCN) has categorized this species as 'Vulnerable'
(IUCN 2004). An urgent need has been felt to conserve its
existing diversity in different geographical gradients of Nepal
while sustainably utilizing it for the economic benefit.
Patterns of genetic diversity in plants can be studied
by analyzing morphological, anatomical, embryological,
biochemical and molecular traits (Weising et al. 1995).
Historically, the method of choice has been to focus on
morphological characters as the basis on which different
plant species are identified and classified into different
taxonomical hierarchical groups. However, the classical
tools are being increasingly complemented by advanced
biochemical tools (focusing on isozymes, allozymes, seed
proteins, and secondary metabolites) as well as molecular
tools based on restriction fragment length polymorphism
(RFLP) and hybridization, polymerase chain reaction
(PCR), and DNA sequencing (Murphy et al. 1996, fudd et al.
2002, Chawla 2003). Various PCR-based molecular marker
techniques, have already been widely used for genetic
diversity analyses, identification of genotypes in genebank
management and molecular phylogenetic studies
and development of species diagnostic protocols;
these techniques include those based on Random
Amplified Polymorphic DNA (RAPD), microsatellites
or Simple Sequence Repeats (SSRs), Inter-Simple
Sequence Repeats (ISSRs), Amplified Fragment
Length Polymorphism (AFLPs), and Internal
Transcribed Spacers (ITS) sequences (Edwards 1998,
Matthes et al. 1998, Wetzel et al. 1999, Yasuda et al.
2002, Shrestha et al. 2003, loshi et al. 2004, Shrestha
et al. 2005, Qian et al. 2006, loshi and Dhawan 2007).
RAPD analysis allows detection of polymorphism in
closely related organisms (e.g., different populations
of single species or individuals within a population)
and therefore provides a powerful tool for tasks
such as population and pedigree analysis, genetic
diversity and molecular diagnostic development
(Micheli et al. 1997).
In the present study, an attempt has been
made to optimize RAPD-PCR reaction and cycling
parameters for the study of genetic diversity in S. chirayita
collections from four districts of Eastern Nepal (flam,
Terhathum, Dhankuta and Sankhusabha), two districts
of Central Nepal (Lalitpur and Kathmandu) and one
district from Western Nepal (Kaski). PCR-based molecular
genetic diversity studies furnish valuable information
regarding genetic diversity in S. chirayita populations from
various geographical locations and their inherent genetic
relationship. A study of molecular genetic diversity in
various populations of S. chirayita would not only help us
understand the evolutionary aspect of the species but also
provide valuable insights for its conservation and sustainable
utilization. Such a study would also identify a number of
taxonomic units for conservation purposes and would
enable the linkage of genetic diversity information with data
regarding chemical properties (Alam et al. 2008). In addition,
since molecular tools hold great promise for diagnostic
development (Shrestha et al. 2005, Qian et al. 2006, Shrestha
et al. 2010) and medicinal plant authentication (loshi et al.
2004, Sucher and Carles 2008, Vural and Eri 2009), molecular
markers specific to S. chirayita could be generated and this in
turn could yield a molecular diagnostic tool for authentication
purpose. Such a diagnostic tool would be valuable for
pharmaceutical applications and for pharmacognosy-based
research (loshi et al. 2004) as well as for intellectual property
rights protection (Sharma et al. 2009).
Materials and methods
Plant materials For the RAPD PCR optimization, fresh
DNA samples were collected from Godawari, Lalitpur; the
rest of the DNA samples were collected in silica gel (Table
1). Collected samples from various part of the country were
brought to the NAST Biotechnology Laboratory for DNA
extraction and analysis.
DNA extraction and DNA estimation Two main DNA
extraction techniques (Doyle and Doyle 1990, Graham et al.
1994) were assessed for their usefulness in generating DNA
profiles of S. chirayita using RAPD-PCR. DNA quantification
Table 1. Details of S. chirayita collections for the present study
District/Locality
Ham/ Maipokhari
to Maimayuwa
Lalitpur/ Phulchowki
Kathmandu/ Nagarjun
Terhathum/ Tirikhimti
to Guphapokhari
Dhankuta/ Pakhribas
Sankhuwasabha/
Lampokhari,
Shreemane, Manlabre
and Chauki
Kaski/Sikles W 15 2000-2500 m
Region in
Nepal*
Number of
samples
Altitudinal
range
E
15
2100-2600m
C
15
2150 m
C
15
2000 m
E
15
1500-2800 m
E
15
1750 m
E
15
2600-2950 m
W= Western, C = Central, E = Eastern
36
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE
2010
Research paper
as well as quality assessment was carried out using a
Biophotometer (Eppendorf- AG 22331, Germany).
Gel electrophoresis The quality of extracted DNA was also
assessed using 1.5% agarose gel electrophoresis (in an EMBI
TEC Santiago, CA gel tank) in IX TBE buffer [10XTBE; 108 gm
Tris base, 55 gm Boric Acid and 40 mL of (0.5 M) EDTA pH
8.0] at 50 V (8.47 V/cm) for half an hour. PCR amplification
products were analyzed at 25 V (4.2 V/cm) for 1.5 h using the
same buffer system.
The gels were stained with ethidium bromide (lOmg/ml
solution) for 45 minutes and de-stained for 15 minutes in
water prior to visualization and photography using UV trans-
illuminator (UVITEC, lapan) and Polaroid Gelcam (UK).
RAPD-PCR optimization The RAPD-PCR
reaction conditions were optimized by varying
key parameters (MgCl2, dNTPs, template DNA,
primer and Tag polymerase concentration; Table
2). Selection ofthe best RAPD cycling conditions
was carried out through the assessment of two
randomly selected RAPD-PCR cycling conditions
(viz. Yu and Pauls 1992, Edwards 1998). All PCR
reactions were carried out in the final volume of
25 pL.
Results and discussion
We first compared two DNA extraction protocols
in some of our samples. The method developed
by Graham et al. (1994) produced multiple sharp
bands on electrophoresis gels (Plate 1). Doyle
and Doyle's (1990) method produced fewer
bands. Both techniques produced reasonably
pure DNA (A260/280 ratio ranged from 1.8-2.0)
for PCR amplification. Graham et al.'s technique
was used in all subsequent DNA extractions
from multiple collections. The Graham et al.
extraction buffer is comprised of 2% CTAB, 1.4
M NaCl, 0.1 M EDTA and 0.1 M Tris HC1 pH 8.0.
Principal components of this DNA extraction buffer are cetyl
trimethyl ammonium bromide (CTAB), sodium chloride
(NaCl), ethylene diamine tetra acetic acid (EDTA) and Tris-
HC1. CTAB may bind to poly-phenolic compounds during
extraction by forming a complex with hydrogen bonds and
may help in removing impurities to some extent (Padmalatha
and Prasad 2006). RNA that can be co-isolated with DNA can
chelate Mg2+ and reduce the yield of the PCR. In the present
investigation, RNAse has been incorporated in the TE Buffer,
which was used to re-suspend DNA pellets at the end of DNA
extraction procedure.
Optimization of RAPD reaction conditions was obtained
by varying parameters and selection of the best concentrations for each ofthe constituent of PCR. The optimized RAPD-
PCR conditions comprised of 3.0-3.5 mM of MgCl2, 12.5 ng of
__
M
i
2
3 4 5 6 7 8 9
10 11 12 13 14 15 16
i
l
!
:
:
:
t j
i
1
3
f --if
#
Plate 1. RAPD profile of S. chirayita genotypes generated by primer UBC
2 and optimized RAPD reaction parameters. Lane marked M is 100 bp
ladder molecular weight marker; lanes 1-2: CTAB extracted DNA (Graham
et al. 1994) sample from Godawari, Lalitpur; lanes 3-4: DNA extracted from
Godawari, Lalitpur sample using Doyle and Doyle (1990) method; lanes 5-6,
7-8, 9-10,11-12 and 13-14 are CTAB extracted DNA sample N-l, N-4, N-6,
N-8 andN-9 from Pakhribas, Dhankuta respectively. Lane 16 represents
negative control.
Table 2. PCR parameters tested and optimized parameters
PCR parameters Tested range
DNA concentration (ng) 12.5, 25, 50, 75,100,
125, 150, 175
MgCL, concentration
(mM)
dNTPs concentration
(mM)
Primer concentration
(mM)
1.5,2.0,2.5,3.0,3.5,
4.5,5.0
0.1,0.2,0.3,0.4,0.5
0.1,0.2,0.3,0.4,0.5,
0.6,0.7,0.8,0.9,1.0,
1.1,1.2,1.3,1.4,1.5,1.6
Taqpolymerase 0.5,1.0,1.5, 2.0, 2.5
concentration (5U/mL)
Optimized
conditions found
12.5 ng
3.0-3.5 mM
0.1-0.2 mM
0.2-0.3 mM
2.0U-2.5U
Remarks
Highest no. of amplified products observed at 12.5 ng
concentration of DNA. Absence of bands from higher
concentrations was observed.
Lower no. of bands observed at lower (2.0-2.5mM) and
higher (4.0-4.5mM) concentrations.
Increased concentration reduced intensity and
number of amplified products.
Intensity of amplified bands was same at 0.1-0.5mM
concentration. Intensity of individual band,
number of amplified products and high molecular
weight products decreased from 0.6 mM- 1.6 mM
concentrations.
Faint bands observed at lower concentrations (0.5U-
1.5U).
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
37
Research paper
_■_
1
1
1
M
1
1
2
I
3 4 5 6 7 8 9 10 11 12 13 14 15
55
8
:
-
Plate 2. RAPD-PCR optimization for the selection
of best MgCL2 concentration for S. chirayita
using UBC primer 1 and 12.5 ng of DNA. Lane
marked M is 100 bp ladder molecular weight
marker. Lanes 1-2, 3-4, 5-6, 7-8, 9-10,11-12,
13-15 represent 1.5 mM, 2.0 mM, 2.5mM, 3.0mM,
3.5mm, 4.0 mM, 4.5 mM concentration of MgCl2,
respectively. Lane 16 represents negative control.
Plate 3. RAPD-PCR optimization for the selection
of best dNTPs concentration for S. chirayita
using UBC primer 1 and 12.5 ng of DNA. Lane
marked M is 100 bp ladder molecular weight
marker. Lanes 1-2, 3-4, 5-6, 7-8, 9-10 represent
0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM and 0.5 mM
concentration of dNTPs, respectively. Lane 11
represents negative control.
Plate 4. RAPD-PCR optimization for the selection
of best Taq polymerase concentration for S.
chirayita using UBC primer 1 and 12.5 ng of
DNA. Lane marked M is 100 bp ladder molecular
weight marker. Lanes, 1-2, 4-5, 7-8,10-11,
13-14 represent Taq polymerase concentration
ranging from 1.0 U, 1.5 U, 2.0 U, 2.5 U and 3.0 U,
respectively. Lane 15 represents negative control.
Plate 5. RAPD-PCR optimization for the selection
of best DNA concentration for S. chirayita using
UBC primer 1. Lane marked M is 100 bp ladder
molecular weight marker. Lanes 1-2, 3-4, 5-6,
7-8, 9-10,11-12 represent 12.5ng, 25 ng, 50 ng,
75 ng, 100 ng, 125 ng concentration of template
DNA, respectively. Lane 13 represents negative
control.
38
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
Research paper
template DNA, 0.1-0.2 mM each of dNTPs, 0.2-0.3 mM each
of primers, and 2.0-2.5 units of Taq DNA polymerase (MBI
Fermentas Company) (Table 2). The optimized reaction concentrations were then used in subsequent primer screening
and RAPD profiling experiments (Plates 2, 3, 4 and 5).
Optimization of reaction parameters in RAPD is crucial
in order to maintain reproducibility of RAPD phenotypes
among laboratories. In order to generate reproducible
RAPD fingerprint profiles, two parameters, viz., quality
and quantity of template DNA, have been considered the
primary factors affecting reproducibility. Hence controlling
these two factors is essential in order to ensure reproducible
RAPD patterns (Weeden et al. 1992, Micheli et al. 1997).
Apart from DNA, other parameters to be optimized are the
concentrations of MgCl2, dNTPs and primers. In the present
study, MgCl2 concentration of 3.0-3.5, dNTPs concentration
of 0.1-0.2 mM each, primer concentration of 0.2-0.3 mM
and Taq polymerase concentration of 2.0-2.5 U had no
noticeable difference in banding patterns, while template
DNA concentration of 12.5 ng was found to be optimal for
PCR amplification at all these reagent concentrations.
Of the two randomly selected RAPD-PCR programs
(Yu and Pauls 1992, Edwards 1998), the cycling conditions
described by Edwards (1998) produced the best profiles for
S. chirayita. The program consisted of an initial denaturation
step at 95°C for 2 min followed by 45 cycles of 95°C for 20 s,
37°C for 60 s and 72°C for 60 s and a final extension step of
72° C for 10 min. The maximum ramp rate available for the
PCR machine (Eppendorf, Germany) was used. In some
cases, the faster PCR cycle described by Yu and Pauls (1992)
would be more applicable. With alfalfa genomic DNA, Yu and
Pauls (1992) optimized the denaturing time; they tested 5s,
30s and 60s and found that 5s gave the best PCR products.
They also showed that there is a relationship between the
time required for primer annealing and the GC content ofthe
primer. For primers having a GC content of 50-80%, a primer
annealing time of 30 seconds appeared to be appropriate. It
was also shown that strand elongation time affects the size of
amplified fragments in the PCR reaction.
For the optimization of RAPD cycling parameters of
a number of medicinal and aromatic plants, Padmalatha
and Prasad (2006) tested initial denaturation times of 2, 3, 4
and 5 min at 94°C and found 3 min most effective. A range
of annealing temperatures (20 to 70° C) and exposure times
(30s, 60s, 90s and 120s) was also tested; 37° C for 60 s was
found to be the best. In RAPD, random primers should have
a minimum of 40% GC content, although 50-80% is generally
used (Micheli 1997). While it is generally claimed that RAPD
is very sensitive to reaction and cycling parameters, Weeden
et al. (1992) concluded that the amplification process is not
so sensitive to one or more of the parameters as to seriously
affect reproducibility of the technique. Therefore standard
reaction conditions and cycling parameters appear to be
appropriate for a wide range of plant materials.
Conclusion
Swertia chirayita is one of Nepal's most highly prized
medicinal plants both at home and abroad. However, due to
the availability of multiple species of Swertia in Nepal, not
only S. chirayita but eight other allied species are also being
highly exploited in trade. It is anticipated that the present
study aimed at studying genetic diversity within S. chirayita
populations of Nepal using molecular marker techniques
such as RAPDs will furnish valuable information for the
sustainable utilization and conservation of these natural
resources. Furthermore, DNA samples collected during
this investigation and the information thus generated will
be highly valuable for planning future molecular projects
focusing on S. chrayita as well as other Swertia species found
in Nepal.
Acknowledgements
The authors would like to thank Nepal's Ministry of Science and
Technology for providing the necessary funds to carry out this
project. The authors would also like to thank the Nepal Academy of
Science and Technology (NAST) for supporting this project.
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HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE
2010
Brief communication
Malaxis biaurita (Lindley) Kuntze (Orchidaceae):
a new record for Nepal
Bhakta B Raskoti* and Rita Ale
Pokharathok 9, Arghakhanchi, NEPAL
* For correspondence, email: bbraskoti@gmail.com
Malaxis biaurita (Lindley) Kuntze (Orchidaceae) has been reported for the first time in Nepal. The species occurs
in the subtropical forest at an elevation of 1400 meters. The identifying characters are purple-red flowers, apically
entire, obtuse labellum. Detailed description, illustration and relevant notes are provided.
Keywords: orchids, taxonomy, distribution
The genus Malaxis was established by O Swartze in 1788.
The genus comprises about 300 species worldwide, found
primarily in tropical and subtropical regions of South East
Asia with few species in temperate regions (Pearce and Cribb
2002). Malaxis biaurita (Lindley) Kuntze is distributed in the
subtropical region of the Andaman Islands (Pradhan 1979).
It is also reported in the north-west Himalaya of India (Deva
1982).
Genus Malaxis is represented by ten species in Nepal
(Rajbhandari and Dahal 2004). Malaxis biaurita had not been
previously reported from Nepal (Hara et al. 1978; Banerji and
Pradhan 1984; Press et al. 2000; DPR 2001; Rajbhandari and
Dahal 2004; Rajbhandari and Baral 2010). There is no record
of this species in the National Herbarium, Kathmandu
(KATH) or Tribhuvan University Central Herbarium (TUCH).
Here we report a new record of Malaxis biuarita (Lindley)
Kuntze for Nepal. The first author collected it in Lamidanda,
Makwanpur District, Narayani Zone (Central Nepal) at an
altitude of 1400 meter and deposited it at KATH.
Malaxis biaurita (Lindley) Kuntze, Gen. PL 2: 673. 1891.
Microstylis biaurita Lindley, Gen. Sp. Orchid. PL 20. 1830.
Herb, terrestrial, up to 40 cm tall. Stem: fleshy, cylindrical,
2-2.5 cm, enclosed in leaf sheaths. Leaves: ascending; petiole
sheath-like, 1.5-3 cm, clasping; leaf blade ovate, oblong-ovate,
or sub-elliptic, 6-12x1.8-4.5 cm, base contracted into a stalk,
apex acute or acuminate. Scape: erect, 14-21 cm, wingless;
raceme 8-10 cm, 20-30 laxly flowered. Floral bracts: reflexed,
4.5-6 mm, narrowly lanceolate, apex acuminate. Pedicel and
ovary 4-5 mm. Flowers: purplish red, 8 mm across. Abaxial
sepal: oblong-lanceolate, 5-5.5x1.5-2 mm, both margins
revolute, apex obtuse; lateral sepals: narrowly oblong-ovate,
oblique, 6x1.5-2 mm, apex obtuse. Petals: narrowly linear,
5x0.2 mm, apex blunt. Labellum: ovate-lanceolate in outline,
7 mm, both ends tapering, base with two falcate auricles,
apex obtuse. Column: 1 mm thick.
Flowering: luly
^^^*^r ^B^^S
■ *_______{
i li ! r
fnm " ^fc Tmt _____________* _
\\\\\________\\\\\\\\\ \
_Y"- . ii^^^^ ^^^^
Figure 1. Malaxis biaurita habit
Himalayan lournal ofSciences 6(8): 41-42, 2010
doi: 10.3126/hjs.v6i8.1804
Copyright©2010 by Himalayan Association for the
Advancement of Science
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
41
Brief communication
Habitat: Cool growing terrestrial on humus rich
sandy slopes, likes partial shade, occurs in the
subtropical forest margins at an altitude of 1400
meters.
Occurrence: Central Nepal, Narayani Zone,
Makwanpur District, Lamidanda, 25 luly 2007,
Raskoti 204 (KATH).
Acknowledgements
We are grateful to the Curators ofthe National Herbarium
and Plant Laboratories, Godawari, Lalitpur (KATH)
and Tribhuvan University Central Herbarium, Kirtipur,
Kathmandu (TUCH) for allowing us to study herbarium
specimens for comparison.
References
Banerji ML and Pradhan P. 1984. The orchids of Nepal
Himalaya. Vaduz, Germany: I. Cramer. 292p
Deva S. 1982. Malaxis biaurita (Lindl.) - A new record for
North-West Himalaya. Indian Journal of Forestry 5(3):
242-244
DPR 2001. Flowering plants of Nepal (Phanerogams).
Bulletin No. 18. Kathmandu, Nepal: Department of
Plant Resources, His Majesty Government of Nepal.
215 p
Hara H, WT Stearn and LHJ Williams (eds). 1978. An
enumeration of the flowering plants of Nepal, Volume-
I. London: British Museum (Natural History). 49 p
Pearce N and P Cribb. 2002. The orchids of Bhutan.
Edinburgh: Royal Botanic Garden. 212 p
Pradhan UC. 1979. Indian orchids: Guide to identification
and culture. Volume II. Kalimpong, West Bengal, India.
209 p
Press IR, KK Shrestha and DA Sutton. 2000. Annotated
checklist of the flowering plants of Nepal. London: The
Natural History Museum. 220 p
Rajbhandari KR and SR Baral. 2010. Catalogue of Nepalese
flowering plants - Gymnosperms and Monocotyledons-
1. Kathmandu: Department of Plant Resources,
National Herbarium and Plant Laboratories/
Government of Nepal
Rajbhandari KR and S Dahal. 2004. Orchids of Nepal: A
checklist. Botanica Orientalis 4(1): 89-106
Figure 2 (top on the left). A. Habit ofthe plant; B.
Front view of flower; C. Back view of flower; D. Side
view of flower; E. Labellum; E Column; G. Dorsal
sepal; H. Lateral sepal; I. Petal; I. Anther. Sections
ofthe figure (A to I) are not in the same scale.
Figure 3 (left). Close up view of the flower with
sepals, petals, lip and column visible.
42
HIMALAYAN IOURNAL OF SCIENCES VOL 6 ISSUE 8 2010
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