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Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
Available online at www.elixirjournal.org
Agriculture
Elixir Agriculture 37 (2011) 3825-3831
Sustainable soil management practices and farmers livelihoods: A spatial
perspective
Gopal Datt Bhatta and Derek Lynch
Department of Plant Science, Nova Scotia Agricultural College, Truro, Nova Scotia, Canada.
ARTICLE INFO
ABSTRACT
Art i cl e h i st ory :
Received: 10 June 2011;
Received in revised form:
18 July 2011;
Accepted: 28 July 2011;
Diverse soil management practices exist even within a narrow transect of farming areas in
Nepal. This variation is principally due to location of farm households along the spatial
gradient, infrastructure availability, market demands and farmers’ awareness to on-farm
resource conservation. Over-exploitation of farm resources was negligible and disturbance to
agro-ecology was minimal in the past couple of decades. In the last decade, however, due to
a massive sprawl in the available farmlands along with a shift of subsistence farming
towards market-oriented conventional approach, prime agricultural lands have been overexploited. This led to negative repercussion on production base and farmers’ livelihoods.
This paper concerns with the simulation of farm income through spatial modeling
considering the strategy of sustainable soil management practices. Spatial modeling shows
higher farm income gains due to intervention in rural areas (low income zone) and periurban areas (high income zone) with existing unsustainable soil management practices.
Spatial explicit assessment shows that integration of micro-survey into spatial environment
and subsequently modeling of present and future situation would add more information on
the results from conventional surveys. Therefore spatial effects should be duly considered
while formulating agriculture and rural development policies.
Ke y w or d s
Future scenario,
Geographic information system,
Spatial modeling,
Peri-urban areas and Nepal.
© 2011 Elixir All rights reserved.
Introduction
Diverse soil management practices exist within a short
transect of farming zones in Nepal. This variation in farming
practices are basically due to location of the farm families along
the spatial gradient, access to infrastructures and farmers’
awareness to on-farm resource conservation (Bhatta and
Neupane, 2010).
Biophysical factors such as variation in weather, soil types
and resource availability (Verbung et al., 2004) as well as sociodemographic attributes such as family needs, market demands,
external influence and technological availability also lead to a
variation in farming practices (Briassoulis, 2000).
In Nepal, there was negligible encroachment on available
farm resources in the past couple of decades. Last decade,
however, showed a massive sprawl in the available farmlands
along with the shift of farming practices. This has led to overexploitation of prime agricultural lands and adoption of
conventional farming practices. Now, the problem of fertility
decline is reported in many parts of Nepal; however, the
intensity of fertility decline is higher in the peri-urban areas
(PUAs) where agro-chemicals are applied in injudicious manner
(Bhatta and Doppler, 2011; Bhatta, 2010a).
Although the fulfillment of subsistence requirements is the
primary objective of the majority of the farmers since centuries
(Brown, 1997; Carson, 1992), market-oriented production is a
key factor driving land-use intensification in the densely
populated farming areas of the Nepal (Brown and Shrestha,
2000). While cultivation of the sloping marginal hills leads to
severe soil erosion in the hilly areas, reduction in factor
productivity is realized in PUAs.
Intensive cultivation of crops depletes soil nutrients if
organic and inorganic fertilizers additions are insufficient
Tele: +1-902-893-3405
E-mail addresses: bhattagopal@gmail.com
© 2011 Elixir All rights reserved
(Brown and Shrestha, 2000). With growing food demands in the
cities along with farmers’ short term economic gain, family
farms are facing several challenges: traditional agricultural
systems are changing, landholding are getting steadily smaller in
size, farming is getting more sophisticated, focused and
intensive with the use of agro-chemicals (Bhatta and Doppler,
2011).
While farming towards rural areas is still subsistence which
is based on locally available resources with minimal or no
external market influence, shifting subsistence-based farming
towards market-oriented intensification is more pronounced
towards PUAs. This spatial effect is related to the road access
(Brown, 2003). Households with poor road access, for instance,
have relatively larger holdings, lower productivity and are more
reliant on the subsistence agriculture. Sustainability issues of
high external input use farming have widely been raised along
the spatial gradient (Bhatta et al., 2009), particularly in the areas
with market accessibility. Meanwhile, agriculture based on
balanced inputs use has shown a wide degree of resilience
(Sharma, 2006). Spatial explicit analyses are now getting more
importance in dealing with farmers’ livelihoods at the regional
level (Bhatta and Neupane, 2010; Bhatta et al., 2009; Codjoe,
2007; Evans and Moran, 2002; Schreier and Brown, 2001;
Bowers and Hirschfield, 1999).
The ability of geographic information system (GIS) to
integrate maps and databases, using the geography as the
common feature has been extremely effective in the context of
agriculture development and resource management. The
Collecting socio-economic data in a geographic realm and
maintaining the original location information could reveal
patterns in the data, which would otherwise be missed (Brown,
2003).
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Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
Socio-economic data integration in the GIS environment
has implications for policy development, particularly
infrastructure development policies that require the socioeconomic assessment in the spatial context (Brown, 2003). It is
with this background information that this research is based on
the concept of spatial differentiation on fertility management
practices and it simulates farm income at regional scale by
integrating socio-economic and biophysical information.
Methodology and data integration
Study area
The study was conducted in the peri-urban areas of
Kathmandu Valley, Nepal. This covers Lalitpur and Bhaktapur
districts (Figure 1). These districts represent the typical
biophysical and socio-economic characteristics of the rural and
peri-urban farm families in Nepal (Bhatta, 2010b). The study
area is composed of vivid altitudinal gradients ranging from 900
to 2500 meters above sea level (Table 1, Figure 2). While a
sizeable portion of the area possesses elevation that ranges from
1500 to 1800 meter above sea level, area with less than 1000
meters of elevation is negligible. Almost 49% of the study area
possesses flat or nearly flat (0 to 5%) lands while the remaining
part has steep to vey steep slope (10% to >30%) (Table 2). The
rural hills in Nepal have relatively higher slopes than that of the
PUAs. Slope along with the fragile landscape leads to a severe
soil erosion in the hill farming systems throughout the country
(Brown and Shrestha, 2000).
In order to facilitate comparison in spatial explicit analysis,
the study area was divided into two zones viz., high income and
low income zones. The underlying assumption was that farmers
living towards rural areas have less access to infrastructures and
their production is lower while opposite is true towards PUAs.
Figure 1: Map of Nepal showing study districts (Bhaktpur and
Lalitpur districts)
Figure 2: Elevation (meters) ranges in the study area derived
from digital elevation model
Sampling and the data
This research was based on cross-sectional study of 130
farm households selected through spatial and random sampling
procedures. Using spatial sampling and simple random
sampling, 95 and 35 farm households were selected respectively
from within the study area. Spatial sampling was adopted
because information on the number of households that had
settled down was not available and the settlement was scattered
throughout the region with wider distance between each
household. Furthermore, as the study focuses on spatial
simulation of farm income, the conventional sampling design
would not justify their use. The spatial sampling method is based
on the concept of spatial dependency which relies on the
principle of proximity of locations to one another (Tobler,
1970). The selection of this method is based on the principle that
all households settled down in the study area were surveyed.
Spatial buffers were prepared and an attempt was made to select
centrally located household from each buffer.
Data related to farm income were collected using structured
questionnaire administered through personal interview. Different
analogue maps were purchased from the Nepal Department of
Survey and baseline GIS data for the study area was prepared
using such maps. These maps cover roads, rivers and streams,
settlements, administrative boundary, contour lines (100-m
spacing) and elevations.
Spatial data integration
The strength of the GIS lies in its ability to integrate socioeconomic data into a common spatial platform. Geographic
locations of the sampled households were taken using
geographic positioning system (GPS) and after linking GPS
receiver to a computer, the recorded data were exported into
ArcView 3.3. Farm income was finally integrated into GIS after
testing for spatial autocorrelation, which measures two things
within the geo-space: the proximity of the locations and the
similarity of the location attribute (Lee and Wong, 2001). It was
then interpolated using inverse distance weighted (IDW) method
which is one of the commonly available methods (Longley et
al., 2004). This method assumes that each point has a local
influence which is inversely proportional to a selected power of
the distance. Therefore, the variable being mapped decreases in
influence with the distance from its sampled location. With
IDW, farm income throughout the region (more precisely, in
each pixel) was calculated.
Cost distance analysis
The basic principle of the cost distance analysis is that farm
activities have a close link with market. Production practices,
farm-family income and living standard follow spatial tendency.
Therefore, it is based on the J. H. von Thünen model which
incorporates agricultural market to illustrate the importance of
spatial location and the resulting transport costs to a central
market and its effect on production at various locations (Nelson,
2002).
Cost distances from different parts of the study areas to the
market was measured using a GIS-based cost weighted distance
model (ESRI, 1997) and distance grid cells to travel from
different locations of the study area to the main market were
prepared (KC, 2005). This technique is based on the idea that
each cell in a map can be given a relative “cost” associated with
moving across that cell (ESRI, 1992). 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.
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Scoring landforms
Regional spatial model considers the cost distance to the
market, dominant landforms and existing soil management
practices along the spatial gradient. The study area is composed
of four dominant landforms (Figure 3) each with differing soil
quality and production potential.
Soils with dark color and alluvial deposits, for instance,
have better water holding and nutrient supplying capacity, thick
soil layer, well-drained soil and almost neutral in reaction (Singh
et al., 2007) and these are the essential requirements of the
majority of the crops such as rice and wheat (Rajbhandari and
Bhatta, 2008).
Lands rich in this type of soil were given a higher score
because of the higher potency to produce crops. The second
group of land quality is composed of the soils around the ancient
lakes and river terraces which have a higher rate of erosion than
the former class. Lands dominated by this type of soils grow
food crops successfully but comparable yields could not be
achieved as of the former landform and hence it is weighted
lesser than the former class.
Figure 3: Dominant landforms available in the study
area
The third group of the landform is composed of mountain
terrains with moderate slope, generally suitable for the
subsistence farming and has higher cost of land management
than the alluvial lands. This landform was given lower value
than former classes.
The fourth landform class is the mountain terraces with
steep to very steep slope, thin soil layer, stony subsoil and is
subjected to severe erosion caused both by wind and water
(Müller-Böker, 1991). This group of lands was allocated the
lowest score.
The difference in the score between two classes of landform
(alluvial flat lands and mountain terrains) was calculated using
gross margin of rice (Bhatta, 2010b). The ratio of the gross
margin of rice in both classes is almost equivalent to 1.5. The
differences in the productive potential of two landforms
composed of the alluvial soils are very narrow.
They were, therefore, given higher values with a narrow
difference. Similarly, for giving weight to steep slope and very
steep slope, gross margin of maize was considered and the ratio
was equivalent to 1.2. Therefore, 1.70 was given to steep land
while 1.40 was assigned for highly steep land.
Scoring existing soil management practices (current
scenario)
Four existing soil management practices namely sustainable
management, conventional, unbalanced application and farm
manure application were considered for preparing a
comprehensive soil quality weighting of the study area. These
existing soil management practices are considered as current
scenario for modeling purpose.
Sustainable soil management is the production practice
followed by the limited number of organic growers around the
peri-urban areas. Farmers with this practice give due attention
towards the use of organic manure and other locally available
resources to meet the plant needs to nutrients and indigenous
knowledge in controlling pests and diseases. In contrast to this,
use of agro-chemicals is intensive, particularly with commercial
conventional farming.
Nearness of the family farms to the market also motivates
farmers to follow this practice (Bhatta and Doppler, 2010). Use
of farm manure is the dominant practice of supplying nutrients
to plants in the rural areas. Even if some farmers apply inorganic
inputs, the amount applied is negligible to be considered as the
conventional farming. As such, this farm production is
frequently referred to as ‘organic by default’ or ‘organic by
neglect’ (Scialabba, 2000).
Under this practice, nutrient supplied is far below than
requirements and also the organic manure applied in the field is
not enough to hold the soil against soil erosion. Therefore, this
practice of soil management is not considered sustainable. There
is an intermediate practice that embraces the unbalanced use of
manure and fertilizers. Farmers give credence to organic manure
and they also apply chemical fertilizers. However, application of
chemical fertilizers is higher than the buffering ability of the
applied organic manure. Farmers with this practice notice the
problem of fertility decline.
Sustainable soil management practice is considered very
important for getting good yield and hence one of the key
components of sustainable agriculture (Bhatta, 2010b). It was, in
this realm, given a higher value (2.00) followed by the soil
managed intensively using inorganic inputs mainly through urea
fertilizer (1.90). Application of high amount of inorganic
fertilizer is enough to get good yield, however, application of
farm manure is not enough to maintain good structure of soil.
Therefore, this land received lower weight (1.80) than former
practices of soil management. The last practice of soil
management is based on application of farm manure only and
the amount applied is not enough to supply nutrients to the
plants. Such lands were given the lowest value (1.50) among all
existing practices.
Soil quality weighting
After having weights assigned, a combined land quality
weighting map was produced using GIS overlay technique.
Current scenario considers present state of arts in soil
management along with dominant landforms while future
scenario takes into account the improvement in the soil quality
provided soil is managed sustainably.
Mathematically,
(SQpresent)i = (Wlf x Wmp)i
(1)
(2)
(SQfuture)i = {Wlf x (Wmp+ Wmp x % )}i
Where, SQi is the soil quality of the ith cell in the space, Wlf
is the weight given to the landform, Wmp is the land weight to the
soil management under different scenarios and each of the value
is associated to the ith cell.
Equation (1) represents the current scenario while equation
(2) represents soil quality in the future scenario (intervention)
after resorting sustainable soil management practices.
Following equation (1), altogether 16 classes are formed in
which the highest weight (4.00) goes to the alluvial plain lands
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Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
with sustainable fertility management practices while the lowest
weight goes to the mountain terrains with a steep slope (Table 3)
in which only farm manure is applied (2.10).
Results and discussion
Sustainable soil management and soil quality weighing
(future scenario)
Sustainable soil management strategy, an assumed scenario,
is intervention in existing fertility management practices to
simulate farm income along the spatial gradient. For simplicity,
this strategy is named as future scenario. The underlying
assumption is that farm income will be improved by resorting to
sustainable soil management practices that would enhance soil
fertility, prevent erosion, provide good yields and hence improve
farmers’ livelihoods. This practice encompasses adoption of
efficient crop rotation, intercropping, adequate use of better
quality farm manure, use of terracing and contouring in the hills,
agro-forestry system and application of inorganic fertilizers
considering the nutrient supplying capacity through other means.
Lands with balanced or sustainable practice at present were
assumed to have same land quality in the future too. The scope
of quality enhancement, therefore, lies on those lands where
only inorganic fertilizers or organic manures are applied.
Adopting sustainable soil management practices would assume
to increase quality by 3% in alluvial plains and 5% in other
landforms. Similarly, it is assumed that sustainable management
would increase land quality score associated to land
management by 5% under existing unbalanced application in
alluvial plains and by 10% in other landforms. With sustainable
management, 5% of the land management value is expected to
increase with existing manure application in alluvial plains, 10%
in river terraces and 20% in the rest. The higher percentage
increment in soil quality in the hills is principally owing to the
bigger scope of quality enhancement through sustainable soil
management practices. In the slope lands, more farm manure
application (2-3 tonne ha-1 more) than the present amount would
replace organic matter lost through soil erosion (Tiwari et al.,
2009; Weber, 2003; Subedi and Sapkota, 2001; Brown and
Shrestha, 2000). Existing practices of farm manure collection,
handling and overall management is inefficient (Jaishy et al.,
1999; Dahal, 1996) and there is big room for getting higher
yields with sustainable management practices (Bhatta, 2010b).
The comprehensive soil quality weighing under existing practice
and sustainable soil management practices is depicted in Table
3. The combined soil quality weighing after sustainable soil
management (future scenario) is derived using equation (2).
Values in the parentheses indicate the increase in the score by a
given percentage due to sustainable soil management practice
Combined soil quality weight follows the patterns of individual
weighting with some variations (Table 3). Most of the farmlands
situated in the higher altitude get a poor combined score as
compared to those which are situated on the valley bottom
(relatively plain lands). Alluvial plains and river terraces with
existing practice of farm manure application only got land
quality increment compared to the mountain terraces with
existing sustainable soil management practices. However, soil
quality weight in steep and very steep slopes of mountain
terrains has been increased through intervention. With some
increment in soil quality would increase farm income
appreciably in the hills and hence enhance livelihoods of the
families.
For the purpose of our calculations, the prices of inputs as
well as outputs were kept constant with the assumption that the
impact of future inflation will be approximately equal on both
sides of the ledger. It is also assumed that there is no
technological development in the short span of time.
Consequently, land management is the one largest factor
influencing the performance production and farm income in our
model.
Base model
GIS-based multiple regression model was employed to
estimate farm income using soil quality and cost distance to
main market as explanatory variables. The results show
significant effects of explanatory variables on farm income and
have expected direction of relationship (Equation 3). The model
has 61% of predictive power. A unit change in cost distance
affects farm income by NRs 2615 while that of land quality by
NRs 163200, ceteris paribus.
Y = -110504 (-57**) – 2615 X1 (-135**) + 163200 X2 (301**)
(3)
R2= 0.61, F stat (2, 282212) = 212500 (p<0.01)
Where, Y is the farm income (NRs ha-1), X1 is the accumulated
cost distance to the market (minute), X2 is the land quality
weight.
** highly significant at 0.01 level of probability
Values in the parentheses indicate t statistic
Note: 1 US $ = 73 NRs
Estimated farm income along the spatial gradient using
regression equation (3) shows that it is higher towards PUAs and
it declines towards rural areas (Figure 4). This proves that the
assumption of regional stratification based on income seems
correct. Although several classes within the region could be
noticed, broadly there are two regions: upper half region towards
the north (towards PUAs) show higher income (>186474 NRs)
and lower half region towards the south (towards rural hills)
show lower income. It is further clear that the farming areas with
road access have higher estimated income as compared to those
without road access. It is because farmers with road access do
have easy access to other infrastructures, particularly market and
hence they could buy the inputs and sell outputs very easily with
lower cost distance to the market. In contrary, farmers without
road access have to spend much time to reach to the market and
hence farming is subsistence-based with less dependency to the
market. This leads to poor livelihoods of the rural farmers.
Figure 4: Estimated farm income (NRs ha-1) along the
spatial gradient
Simulated model under sustainable soil management
scenario Farm income under sustainable soil management
scenario was estimated using spatial regression model and the
resulting functional form is presented in equation (4). The
impact on farm income due to intervention (sustainable soil
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Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
management practices) was calculated by deducting the
estimated income through future scenario (Figure 5) and present
scenario (Figure 4) and expressed in percentage increment
(Figure 6).
Empirical model shows that both cost distance and land
quality weighing after sustainable soil management practices
have significant effect on predicting farm income. The degree of
prediction is 58%. With one unit increase in cost distance in
terms of travelling time in minutes, there is consequent decrease
in farm income by NRs 3632, ceteris paribus, while with a unit
increment in land quality, farm income will be improved by NRs
160200.
Y = -110338 (-44**) – 3632 X1 (-186**)+ 160200 X2 (238**)
(4)
R2= 0.58, F stat (2, 282212) = 180200 (p<0.01)
Where, Y is the farm income (NRs ha-1), X1 is the
accumulated cost distance to the market (minute), X2 is the land
quality weight
Note: Values in the parentheses indicate t-statistics and **
indicates highly significant (p<0.01)
Figure 5: Simulated farm income (NRs ha-1) under
sustainable soil management strategy
Figure 6: Impact of sustainable soil management on farm
income (% increment)
Estimated farm income under future scenario still reflects
similar tendency as of current scenario (Figure 5). However,
there has been a substantial increment in farm income in the
future as compared to the existing situation. Farm income
increment due to soil quality improvement lies between no
increments to as high as more than 60%. The improvement in
farm income in the PUAs, particularly in the areas with existing
sustainable soil management practices is almost negligible
(<2%) while areas with the intensive commercial inorganic
farming have higher improvement that goes as high as 40%
(Figure 6). This is basically due to increment in soil quality by
employing sustainable soil management practices instead of
intensive conventional farming. Similarly, increment in the farm
income in the poor income zone (rural hills) is very high
(>60%). Since rural farm families depend heavily on local
resources, especially on land, this increment in the farm income
due to sustainable land management practices in the rural areas
would have substantial impact on the local livelihoods.
Conclusion
The future scenario illustrates how land quality weighing
could be improved by employing sustainable soil management
practices. The baseline model shows spatial effects on farm
income: rural hills with relative inaccessibility have lower farm
income while it is higher towards PUAs. The same is true in the
future scenario too. There is substantial increment in farm
income in the rural areas after intervention. As farm-families
living in the higher altitudes have the lower standard of living
and they depend much on farming for their subsistence,
sustainable soil management practices provide more economic
incentives to them. Similarly, peri-urban areas with existing
unsustainable soil management practices should also be replaced
by sustainable practices for getting farm income improved and
fertility restored. As spatial location of the farm family plays
crucial role in livelihoods, any projects for rural development
should take spatial effects into account.
References
[1]Bhatta GD and Doppler W. Farming differentiation in the
rural-urban interface of the Middle Mountains, Nepal:
Application of analytic hierarchy process modeling. Journal of
Agricultural Science.2010; 2(4), 37-51.
[2]Bhatta GD and Doppler W. Smallholder peri-urban organic
farming in Nepal: a comparative analysis of farming systems.
Journal of Agriculture, Food Systems and Community
Development. 2011; 1(3), Advance Online Publication,
doi:10.5304/jafscd.2011.013.002.
[3]Bhatta GD and Neupane N. Simulating farm income under
current soil management regime in mid-hills of Nepal.
Himalayan Journal of Sciences 2010; 6(8), 27-34.
[4]Bhatta GD, Doppler W and KC KB. Spatial differentiation in
farming practices and their impact on rural livelihood: A case
from hills of Nepal. Proceedings of International Research on
Food Security, Natural Resource Management and Rural
Development, 2009, Hamburg, Germany; 2009.
[5]Bhatta GD. Socio-economic and spatial assessment of
smallholder peri-urban farming in Middle Mountains of Nepal.
Margraf Publishers, Weikersheim, Germany; 2010b.
[6]Bhatta GD. Stakeholder & spatial perspectives of ecofarming in Nepal: Economic and farm-family resource
assessment of farming systems in the Middle Mountains.
LAMBERT Academic Publications, Saarbrücken, Germany;
2010a.
[7]Bowers K and Hirschfield R. Exploring links between crime
and disadvantage in northwest England: an analysis using
geographic information systems. International Journal of
Geographic Information Science. 1999; 13: 159–184.
[8]Briassoulis H. Analysis of land use change, theoretical and
modeling approaches. In: W.R. Jackson, editor. The web-book
of regional science, USA. Regional Research Institute, West
Virginia University; 2000.
3830
Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
[9]Brown S. and Shrestha B. Market driven land use dynamics
in the Middle Mountains of Nepal. Journal of Environmental
Management. 2000; 59: 217-225.
[10]Brown S. Soil fertility, nutrient dynamics and
socioeconomic interactions in the Middle Mountains of Nepal.
Ph. D. dissertation, Interdisciplinary Studies in Resource
Management Science, University of British Columbia, Canada.
1997.
[11]Brown S. Spatial analysis of socio-economic issues: gender
and GIS in Nepal. Mountain Research and Development. 2003;
23(4): 338-344.
[12]Buckley DJ. The GIS Primer: An Introduction to
Geographic Information Systems. Pacific Meridian Resources,
Inc., 1997.
[13]Carson B. The land, the farmer and the future: A soil
fertility management strategy for Nepal. ICIMOD Occasional
Paper no. 21, Kathmandu, Nepal; 1992.
[14]Codjoe SNA. Integrating remote sensing, GIS, census and
socioeconomic data in studying the population-land use/cover
nexus in Ghana: A literature update. Africa Development. 2007;
32(2): 197–212.
[15]Dahal H. Consultative Report on Soil Fertility and Crop
Nutrient Management System in Nepal. Soil Science Division
(NARC) and Department of Agriculture. 1996; 15-17.
[16]ESRI. Cell-based modeling with grid. Inc. Redlands CA:
Environmental Systems Research Institute; 1992.
[17]ESRI. Understanding GIS the Arc/Info method. ESRI, USA:
Environmental System Research Institute. ESRI Inc. Redlands;
1997.
[18]Evans TP and Morans EF. Spatial integration of social and
biophysical factors related to landscape change. Population and
Development Review. 2002; Supplement to Vol 28: 165–186.
[19]Jaishy SN, Mandal SN, Manadhar R, Karki TB and Maskey
KH. Production and Utilization of Compost by the Farmers in
Four Selected Districts of Nepal. Nepal Journal of Science and
Technology. 1999; 57-62.
[20]KC KB. Combining socio-economic and spatial
methodologies in rural resources and livelihood development: A
case from Mountains of Nepal. Wekersheim: Universität
Hohenheim, Margraf Verlag; 2005.
[21]Lee J and Wong DS. Statistical Analysis with ARCVIEW
GIS. John Wiley & Sons Inc, New York; 2001.
[22]Longley PA, Goodchild MF, Maguire DJ, Rind DW.
Geographic Information Systems and Science. John Wiley &
Sons Ltd., 2004.
[23]Müller-Böker U. Knowledge and evaluation of the
environment in traditional societies of Nepal. Mountain
Research and Development. 1991; 11: 101-114.
[24]Nelson GC. Introduction to the Special Issue on Spatial
Analysis for Agricultural Economists. Agricultural Economics.
2002; 27:197-200.
[25]Rajbhandari BP and Bhatta GD. Food crops: Agro-ecology
and modern agro-techniques. Kathmandu, Nepal: HICAST
Publications; 2008.
[26]Schreier H and Brown S. Scaling issues in watershed
assessments. Water Policy. 2001; 3: 475–489.
[27]Scialabba N. Opportunities and Constraints of Organic
Agriculture: A Sociological Analysis. FAO, Rome; 2000.
[28]Sharma G. Organic agriculture in Nepal: An analysis in to
status, policy, technology and psychology. In: Sharma G and
Thapa PB (eds), Proceedings of National Workshop on Organic
Agriculture and Farming System in Nepal. Kathmandu, Nepal:
Nepal Permaculture Group. p 3–14; 2006.
[29]Singh PK, Singh G and Tiwari BK. Critical evaluation of
geo-environmental scenario of Damodar river basin. New Delhi,
Indial; 2007.
[30]Subedi K. and Sapkota GP. Integrated Plant Nutrient
Management in Maize: Pilot Testing the Extension of IPNS with
Farmers in Sindhupalchowk. In: Proceedings of Maize
Symposium, Sustainable Maize Production System for Nepal,
December 3-5, 2001, Kathmandu, Nepal, 2001.
[31]Tiwari K, Sitaula B, Bajracharya R and Børresen T. Effects
of soil and crop management practices on yields, income and
nutrients losses from upland farming systems in the Middle
Mountains Region of Nepal. Nutrient Cycling Agroecosystem.
2009; 12: 26-41.
[32]Tobler WR. A computer movie simulating urban growth in
the Detroit Region. Economic Geography. 1970; 234-240.
[33]Verbung PH, Eck Ritsema van J, De Nijs T, Schot P and
Dijst M. Determinants of land-use change patterns in the
Netherlands. Environment and Planning. 2004; 31: 125-150.
[34]Weber G. Compilation of Baseline Information for
Integrated Plant Nutrient Management in Mid-hill Farming
Systems of Nepal, Version 2, SSMP Document No: 89,
Sustainable Soil Management Program, Lalitpur, Nepal, 2003.
Table 1: Area distribution under different elevation ranges in the study area
Elevation range (meters)
Total area (ha)
Percentage of total
900-1200
251.69
1.41
1200-1300
493.63
2.76
1300-1500
8640.81
48.26
1500-1800
5462.38
30.51
1800-2400
3054.81
17.06
Total
17903.32
100
Table 2: Area distribution under different slopes in the study area
Slope range (percentage)
<5
5-10
10-20
20-30
>30
Total
Total area (ha)
8707.56
1338.16
1449.66
1945.25
4462.69
17903.32
Percentage of total
48.64
7.47
8.10
10.87
24.92
100
3831
Gopal Datt Bhatta et al./ Elixir Agriculture 37 (2011) 3825-3831
Table 3: Soil quality weighting based on landforms and farmers’ practices of soil fertility
management under current and the future scenarios (sustainable soil management practices)
Landform
Land
management
Current scenario
Integrated management
scenario
Landform
Management Combined Management Combined
Alluvial plains
Sustainable
2.00
2.00
4.00
2.00
4.00
and fans
Conventional
2.00
1.90
3.80
1.96(3)
3.92
(depositional)
Unbalanced
2.00
1.80
3.60
1.89(5)
3.78
Manure
2.00
1.50
3.00
1.58(5)
3.16
Sustainable
1.90
2.00
3.80
2.00
3.80
Lake and river
terraces (tars,
Conventional
1.90
1.90
3.61
2.00(5)
3.80
erosional)
Unbalanced
1.90
1.80
3.42
1.98(10)
3.76
Manure
1.90
1.50
2.85
1.65(10)
3.14
Mountain terrains
Sustainable
1.70
2.00
3.40
2.00
3.40
with moderate
Conventional
1.70
1.90
3.23
2.00(5)
3.40
slope
Unbalanced
1.70
1.80
3.06
1.98(10)
3.37
Manure
1.70
1.50
2.55
1.80(20)
3.06
Sustainable
1.40
2.00
2.80
2.00
2.80
Mountain terrains
with steep to very Conventional
1.40
1.90
2.66
2.00(5)
2.80
steep slope
Unbalanced
1.40
1.80
2.52
1.98(10)
2.77
Manure
1.40
1.50
2.10
1.80(20)
2.52
Values in the parentheses indicate the increase in the score by a given percentage due to sustainable soil management
practice