The International Journal of
Interdisciplinary Environmental Studies
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VOLUME 8
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Examining Agricultural Land-Use/Cover Change Options in Rural Northern Ghana
BIOLA K. BADMOS, GRACE B. VILLAMOR, SAMPSON K. AGODZO, SAMUEL N. ODAI, AND SAMUEL S. GUUG
A Participatory Scenario Exploration Exercise Approach
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Examining Agricultural Land-Use/Cover Change
Options in Rural Northern Ghana: A
Participatory Scenario Exploration Exercise
Approach
Biola K. Badmos, Kwame Nkrumah University of Science and Technology, Ghana
Grace B. Villamor, University of Bonn, Germany
Sampson K. Agodzo, Kwame Nkrumah University of Science and Technology, Ghana
Samuel N. Odai, Kwame Nkrumah University of Science and Technology, Ghana
Samuel S. Guug, West Africa Science Service Center on Climate Change and Adapted Land Use,
Ghana
Abstract: In sub-Saharan Africa, studies on land-use/cover change have focused more on transformation from forest or
woodland to other types of land-use/cover. Few studies have addressed the dynamics in agricultural landscapes which
could accompany impacts on the environment. This paper identified potential drivers of agricultural land-use/cover via household surveys and interviews of stakeholders, as well as information gathered from literature. Identified drivers were
used to develop descriptive scenarios (what-if questions) that were presented to farmers during participatory scenario
exploration exercises. The scenarios were used to view the farmers' agricultural land-use/cover options under changing climatic and socio-economic conditions. The parameters used include climate change resulting from rainfall, and
socioeconomic variables like availability of finance, access to fertiliser and change in the price of seeds. The agricultural land-use change options identified includes maize, soy-bean, potatoes, etc. Rainfall availability, tradition and land
suitability influences farmers’ agricultural land-use change options. We observed that climate change (rainfall)-related
scenarios may influence the replacement of crop(s) from agricultural land (agricultural-land-conversion) while socio-economic scenarios may influence the incorporation of crop(s) to an existing agricultural land (agricultural-land-
modification). Farmers may change their agricultural land-use/cover under certain conditions/stressors and this might
have accompanied environmental impact. In this part of the world, in-depth and well documented impacts of agricultural land-use/cover on the environment are scant. Therefore, to effectively relate policy in this direction, it will be necessary
to investigate more on the likely environmental trade-offs associated with diverse agricultural land-use/covers. In
addition, minimising the associated trade-offs should be our priority.
Keywords: Agricultural Land-use/Cover Change and Drivers, Agricultural Land-use change options, Participatory
Scenario Exploration Exercise, Northern Ghana
Introduction
and-cover is defined by the characteristics of the earth's land surface and immediate
subsurface, including biota, soil, topography, surface and groundwater and human
structures (Lambin et al., 2003). The purposes for which humans exploit the land-cover
define the land-use (Lambin et al., 2010), and the change between land-use and cover is known
as land-use/cover change (Lambin et al., 2006). Land is critical to human survival because it
provides several ecosystem services. The demand for land ecosystem services (food, fibre,
freshwater and shelter) continues to rise, along with growing human population and wealth
(Foley et al., 2005). Land-use/cover change processes are the main impacts of human activities
on the earth (Vitousek et al., 1997). The most important driver of ecological modifications and
biodiversity loss at the global scale involves long-term human-induced changes in land-use
(Foley et al., 2005). Land-use change has contributed to the loss of carbon from the terrestrial
ecosystems (Lal, 2004).
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THE INTERNATIONAL JOURNAL OF INTERDISCIPLINARY ENVIRONMENTAL STUDIES
In sub-Sahara Africa, studies on land-use/cover change have focused more on change from
forest or woodland to other types of land-use/cover. The environmental impacts of changing
from forest land to other land-use/cover have been documented in several literatures. On the
other hand, very few studies have looked at change from one agricultural land-use/cover to
another agricultural land-use/cover, and this change may have accompanied impact on the
environment. For example, CO2 flux varies across different agricultural land-use/cover, as well
as under different management practices (Fu et al., 2002; Frank et al., 2006). Also, crop cover
factor which has influence on soil loss varies for different crops (Roose, 1977).
A large number of people in northern Ghana earn their livelihood from agricultural
activities. Hence, the decisions that affect their agricultural land-use may also affect their
livelihood. The objectives of this paper therefore are (i) to apply participatory scenario
exploration exercise in viewing farmer’s agricultural land-use/cover change option in response to
climate change and socio-economic factors, (ii) to describe and analyse underlying factors
influencing their options and (iii) to discuss the implication of our findings on agricultural and
natural resources policy.
Scenario Analysis
The process of developing scenarios, comparing the outcomes and the analysis of impact is
referred to as scenario analysis. Scenario analysis (SA) creates a multidisciplinary and inventive
structure for analysing multifaceted environmental challenges, present solutions to these
challenges while providing future alternate environmental states in the presence or absence of
policy and revealing the effectiveness and robustness of policy pathways on ecosystem
management (Alcamo and Henrichs, 2008). The natural system is complex and uncertain, it drive
several processes and many processes feedback into it. An adequate understanding of the natural
system is very important in order to project how the system may behave in the future. Human
influence on the natural system increases its complexity and uncertainty. As human impact the
system, the system also impact on human. In order to capture the complexity and uncertainty of
the natural system, there is need to create plausible situations the system might present.
Scenario simplifies how the future may develop as a result of coherent and internally steady
set of assumptions about main driving forces and relationships (MA, 2005). Scenarios recognise
chances and risks that could be faced in relation to planned interventions and propose
management strategies to react to the situation (Enfors et al., 2008). Scenarios provide one means
to manage with many facets of uncertainty, and they help to investigate the unpredictable and
uncontrollable characteristics of change in ecosystem services and a number of socioeconomic
factors (MA, 2005). Differing effects that might result if basic assumptions are changed are
explored using scenarios (UNEP, 2002). And for policy makers, scenario can picture the
probable ways the future may present itself, thus providing options for solving future problems.
Nearly all of the techniques that have been applied to derive quantitative estimation of future
are not suitable for regional development because of the uncertainties, but SA is able to explicitly
take into account these uncertainties (Walz, 2007). Involvement of stakeholders in SA makes it
participatory, hence participatory scenario analysis. Alcamo and Teresa (2001), classify scenario
into three main categories based on numerical components (qualitative or quantitative),
speculation level (exploratory or anticipatory) and intervention level (baseline or policy).
Additional information on different categories of scenarios is described in (Alcamo and Teresa,
2001; Alcamo and Henrichs, 2008).
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Methodology
Study Area
This study was conducted within Vea catchment (Fig. 1) in the Upper East Region (UER) of
Ghana. UER is located on the North-East corner of Ghana between latitudes 10º 30ʹ to 11º 8ʹ
north and longitudes 1º 15ʹ west and 0º 5ʹ east. The region is directly bordered by Burkina Faso
in the North and by Togo to the East. The major part of Northern Ghana including UER belongs
to the West-African semi-arid Guinea Savannah belt (Adu, 1972). UER covers a total land area
of 8,842 km2, which represents 3.7% of the total area of Ghana (GSS, 2000). The region has a
population of 1,046,545 (Males- 48.4%; Female- 51.6%), which represents 4.2% of the total
population of Ghana and a growth rate of 1.2% between 2000 and 2010. The region has an
average household size of 5.8 and a 79% rural locality (GSS, 2012). Population density is
between 91-104 persons km-2
in 2000, which by far exceeds the country’s average population
density of 79 persons km-2
(GSS, 2002).
Vea catchment is a sub-catchment of the White Volta. It lies between Burkina Faso and
Ghana, with a total area of about 8 and 293 km2 respectively. This study was conducted within
the Ghanaian section of the catchment which lies in Bongo and Bolgatanga district in the UER of
Ghana. The Vea dam is an important structure within the catchment which provides opportunities
for farmers to practice all year round agriculture. On the lower part of the catchment is a forest
reserve.
Figure.1: Map of study area (Land-cover map is an output of Gerald Forkuor; WASCAL WP 2.1)
The top pink boundary represents Burkina Faso section of the catchment
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Rainfall in UER is mono-modal and the peak of rainy season is around July - September
(Fig. 2a). Rainfall over the past 40years has averaged 1044 mm/annum and this is suitable for a
single wet season crop (IFAD, 2007). About 60% of the annual rain falls between July and
September. Wet period in UER is relatively short and is further marked by variations in arrival
time, duration and intensity of rainfall. This creates inter-year variations in agricultural
production potential (IFAD, 2007). Temperatures in this region are high, with the hottest month
being around March and April (Fig. 2b).
Upper east region of Ghana has experienced series of changing climate impacts, such as shift
in season and irregular climatic conditions. The real problem for the farmers in this part of Ghana
is the unreliability of rainfall caused by inter-annual variability of both the total amounts and
distribution (van de Geest and Dietz, 2004). Erratic rainfall makes agricultural planning very
difficult, and it is one of the principal sources of risk for rain-fed agriculturalists in the Sahel (van
de Geest and Dietz, 2004). The onset of rainfall is always unpredictable, and the first rains are
usually torrential with only a small amount percolating into the soils (Braimoh, 2004). In a study
by Dietz et al. (2004), residents of some communities in the UER reported a shorter and more
unpredictable rainy season from the stand point of both amount and timing. Traditional land
preparation has shifted as a result of unreliable and erratic climatic condition, and this has
reduced the planting season and it has affected the traditional cultivation practice (Laube et al.,
2008).
About 70% of economic active population (ages 15years and above) are involved in
agricultural practices (GSS, 2012). Crop production is carried out during the rainy season (rain-
fed) and in the dry season (irrigation farming), but larger part is done during the rainy season.
Millet (Pennisetum spp.) and guinea corn (Sorghum spp.) are the most important grain staples
grown in the upper east and upper west regions of Ghana while maize (Zea mays), millet and
sorghum are important staples in the northern region (Dietz et al., 2004; Gyasi et al., 2008).
Millet is the basis of the cropping system throughout UER. There are two groups of millet
cultivars, a short-season millet (naara) harvested in July and a long-season millet (zia), harvested
in November or December. Early millet is inter-planted either with late millet or guinea corn
(kimulga) in fields close to the compound where fertility is highest. The further fields are planted
with sorghum intercropped with legumes especially cowpeas (tier) and occasionally groundnuts
(suka) (Blench, 2006). According to Stanturf et al. (2011), millet is the least risky with regard to
climate-induced fluctuations in yield, followed closely by sorghum and maize (kayene), making
all of them important for food security.
Figure 2 (a): Monthly variation in rainfall of Bolgatanga (1985 - 2010), (b) Monthly variation in temperature of Bolgatanga (1985 – 2010). Source: Ghana metrological service department.
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Scenario Development
Previous studies in the study area and locations with similar climatic condition have indicated
rainfall as the main element of climate that influences agricultural activities compared to other
climatic parameters. Rainfall patterns experienced includes delay, fluctuation, periodic heavy,
and rainfall stop before the end of growing season. Farm inputs also influence agricultural land-
use change in the study area (Badmos et al., 2013). Scenarios/what if questions were developed
from drivers (rainfall, finance availability, fertiliser access, seed prices) of agricultural land-
use/cover change in the study area.
Using 5 months as rainy season period, five descriptive climate (rainfall) related scenarios
were created (Table 1). The rainfall scenarios addressed delay in rainfall, rainfall stops before the
end of growing season and intermittent fluctuation of rainfall within the growing season. Two
categories of rainfall regime were identified in the rainfall scenarios, poor rainfall and good
rainfall regime. The poor rainfall regime refers to rainfall period that cannot adequately support
crop growth while good rainfall regime can adequately support crop growth. The socio-economic
scenarios addressed fertiliser subsidies, change in market price and credits (Table 2).
Table 1: Rainfall Scenarios (R-Sc)
Rainfall scenarios (R-Sc) description Monthly Rainfall pattern
May Jun. Jul. Aug. Sep.
R-Sc 1 Two months delay in
rainfall
R-Sc 2 Three months delay in
rainfall
R-Sc 3 Three months rainfall and
stops
R-Sc 4 Four months rainfall and
stops
R-Sc 5 Fluctuation in rainfall
Poor rainfall regime
Good rainfall regime
Table 2: Socio-economic Scenarios (S-Sc)
Socio-economic scenarios (S-Sc) description
S-Sc 1 Fertiliser subsidy (50%)
S-Sc 2 Fertiliser subsidy (100%)
S-Sc 3 Price of groundnut seed increase by (50%)
S-Sc 4 Price of groundnut seed increase by (100%)
S-Sc 5 Credit from government for maize farming
Process and Analysis
The main rainfed crops cultivated by the farmers were summarised into five classes. The number
of households that cultivate a particular crop and the average land area occupied by each crop is
presented in (Fig. 3). Some crops are cultivated by the farm households, but the area cultivated
for such crops are quite small. With respect to the number of sampled households that cultivates
a crop and the area cultivated for the crop, three dominant agricultural land-uses/covers were
identified and they are traditional cereals (early millets, guinea corn and late millet), groundnut
and rice. These dominant agricultural land-uses/covers were included in the created agricultural
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landscape map - ALM (Fig. 4) that we used to stimulate the participatory scenario exploration
exercise (PSEE). PSEE is a form of exercise where group of people (e.g. stakeholders) are
presented with scenario(s)/what-if-question(s), and they are allowed to discuss before giving
their response to the presented scenario(s). The ALM was designed to mimick the typical
farmland arrangement in the study area. Traditional cereal farms are usually located around the
farmer’s compound, rice farm is located in low lying areas (valleys) because it is a water loving
crop and groundnut may be located on relatively higher areas. The PSEE was conducted in three
locations within the catchment (upper, middle and lower) with the assistance of trained natives
from the locality.
Figure 3a: Sampled household (%) involved in the
cultivation of crop
Figure 3b: Sampled household crop
area coverage (%)
Prior to the day of PSEE, all the selected locations were visited to alert the farmers about the
pending exercise. This was necessary to have a good number of farmers participating. Also, the
period of the exercise was in the rainy season, and this coincides with a time when farmers are
usually very busy with their farm activities. In each location, the process commenced with a
group discussion to allow some level of familiarity between researcher and farmers. Issues on the
general environment were discussed during the group discussion. PSEE was introduced with the
ALM to participants of the group discussion (Fig.5a), and about 4-6 farmers were asked to
volunteer and participate in the PSEE. Scenarios were presented to the group (Fig.5a), and they
were allowed to discuss and come up with land-use/cover change options.
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Figure 4: Agricultural landscape map (ALM) used in this study
Figure.5a: Introducing the scenario exploration exercise to farmers in one community
Figure.5b: Farmers participating during the scenario exploration exercise
Results
The descriptive statistics of some selected household variables are presented in Table 3. The
agricultural land-use/cover change options mentioned by farmers during the PSEE are
summarised and described in Table 4. The pattern of change in the agricultural land-use/cover
with respect to rainfall and socio-economic scenarios are presented in Tables 5 and 6
respectively. Across the three locations, the main underlying factors influencing the decision of
the farmers to go for an agricultural land-use/cover depend on rainfall availability and timing,
tradition and suitability of land. Across all locations (Tables 5a, b, c, R-Sc 5), it clearly shows
that farmers are not willing to alter their agricultural land-use/cover for the intermittent
fluctuation in the period of rainfall. Farmer’s group in the lower catchment indicated no response
to all socio-economic scenarios except scenarios addressing price increase in seed (Table 6).
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Table3: Descriptive Statistics of Some Selected Agricultural Land-Use/Cover Related Household
Variables
Sample size: 186
Variables Mean Std. Error
of Mean
Std.
Deviation
Age of household head 56 1 17
Household size 8 0 3
Household member ≥15years 5 0 2
Land area cultivated for traditional cereals (ha) 0.73 0.04 0.53
Land area cultivated for maize ( ha ) 0.06 0.01 0.17
Land area cultivated for groundnut ( ha ) 0.34 0.02 0.30
Total land area cultivated for rice in a year (ha ) 0.15 0.02 0.22
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Table 4: Agricultural Land-Use/Cover Change Options
Land-use/cover Description
Maize (M) Maize culture(s)
Modified traditional cereal
(MTC)
One or two of the three crops that make up traditional cereals
is/are abandoned
Other legumes (OL) Cowpea or soy-bean
Vegetables / Potatoes (V/P) Leafy or fruit vegetables / Potatoes
Fallow (F) No cultivation
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Table 5a: Agricultural Land-Use/Cover Option in Response to Rainfall Scenarios (Upper
Catchment)
Rainfall
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
R-Sc1
Traditional cereals 100%
Groundnut
Rice
R-Sc 2
Traditional cereals 100%
Groundnut
Rice 100%
R-Sc 3
Traditional cereals 100%
Groundnut
Rice 100%
R-Sc 4
Traditional cereals
Groundnut
Rice 100%
R-Sc 5
Traditional cereals
Groundnut
Rice
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Table 5b: Agricultural Land-Use/Cover Option in Response to Rainfall Scenarios (Middle
Catchment)
Rainfall
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
R-Sc 1 Traditional cereals 100%
Groundnut 100%
Rice 100%
R-Sc 2 Traditional cereals 100%
Groundnut 100%
Rice 100%
R-Sc 3 Traditional cereals 67% 33%
Groundnut
Rice 100%
R-Sc 4 Traditional cereals
Groundnut
Rice
R-Sc 5 Traditional cereals
Groundnut
Rice
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Table 5c: Agricultural Land-Use/Cover Option in Response to Rainfall Scenarios (Lower
Catchment)
Rainfall
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
R-Sc 1 Traditional cereals 100%
Groundnut
Rice
R-Sc 2 Traditional cereals 100%
Groundnut
Rice
R-Sc 3 Traditional cereals 100%
Groundnut 100%
Rice 100%
R-Sc 4 Traditional cereals 50% 50%
Groundnut
Rice
R-Sc 5 Traditional cereals
Groundnut
Rice
Note: Shaded cells indicate farmer’s willingness to go for the corresponding agricultural land-use/cover change option.
Number with percentage sign indicates the area of initial land that will be covered by the agricultural land-use/cover
change option. M = maize culture, MTC = modified traditional cereals, OL = other legumes, V/P = vegetable/potato, F= fallow.
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Table 6a Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios
(Upper Catchment)
Socio-economic
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
S-Sc 1 Traditional cereals 50%
Groundnut 50%
Rice
S-Sc 2 Traditional cereals 30%
Groundnut 80%
Rice
S-Sc 3 Traditional cereals
Groundnut 33%
Rice
S-Sc 4 Traditional cereals
Groundnut 50%
Rice
S-Sc 5 Traditional cereals 70%
Groundnut 60%
Rice
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Table 6b Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios
(Middle Catchment)
Socio-economic
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
S-Sc 1 Traditional cereals 70%
Groundnut
Rice
S-Sc 2 Traditional cereals 80%
Groundnut 80%
Rice
S-Sc 3 Traditional cereals
Groundnut 33%
Rice
S-Sc 4 Traditional cereals
Groundnut 50%
Rice
S-Sc 5 Traditional cereals 80%
Groundnut
Rice
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Table 6c Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios
(Lower Catchment)
Socio-economic
scenarios Agricultural land-use/cover
Agricultural land-use/cover change option
M MTC OL V/P F
S-Sc 1 Traditional cereals
Groundnut
Rice
S-Sc 2 Traditional cereals
Groundnut
Rice
S-Sc 3 Traditional cereals
Groundnut 33%
Rice
S-Sc 4 Traditional cereals
Groundnut 50%
Rice
S-Sc 5 Traditional cereals
Groundnut
Rice
Note: Shaded cells indicate farmer’s willingness to go for the corresponding agricultural land-use/cover change option.
Number with percentage sign indicates the area of initial land that will be covered by the agricultural land-use/cover
change option. M = maize culture, MTC = modified traditional cereals, OL = other legumes, V/P = vegetable/potato, F= fallow.
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Discussion
The impact of land-use/cover change on the global environmental change cannot be
overemphasised. It is second to fossil fuel burning with respect to CO2 emission, and it is the
major source of CO2 emission by developing countries. In this study, traditional cereals (early
millets, guinea corn and late millet), groundnut and rice represent default agricultural landscape
map (ALM) used to stimulate the participatory scenario exploration exercise (PSEE). Similar
agricultural land-use cuts across the study area, hence some similarities in the agricultural land-
use/cover change option observed.
Farmers that participated in this exercise are very sensitive to rainfall availability in their
cropping decision making. Participating farmers took into consideration, water availability
during the life time of a crop before deciding whether to cultivate that crop, another variety of the
crop, or another crop entirely. Tradition and timing also interact with rainfall pattern, thus
influencing the cropping decision of the farmers. Cultural practices vary in time for different
crops, for example, the first sets of crops cultivated by the farmers are the traditional cereals
(early millet, guinea corn and late millet). When the first sets of crops have established, the
second sets mainly rice and groundnut, are planted, and usually about one month interval
provided the rains are available. If the timing of rainfall elapsed a certain period, the farmer may
abandon some crops. Land suitability also play significant role in influencing agricultural land-
use decision of farmers in the study area. There are particular types of land a farmer is willing to
put particular type of crop(s). For example, in most of the cases maize is preferred on the
compound farm used for the traditional cereals, though, some still put it on the groundnut land.
On the other hand, not all crops can grow effectively on rice lands because of the wet nature of
the land. Across the three locations, farmers were not willing to alter their agricultural land-use
for scenario that addressed the intermittent fluctuation in rainfall distribution. They preferred
regular rainfall which comes for short period and shorter period of dryness is good for their crops
because the soil will be able to absorb the water during the period of dryness. Hence, their
unwillingness to alter their agricultural land-use/cover for scenario which addressed the
intermittent fluctuation in rainfall distribution.
Crop modification and changing agricultural practices have been reported amongst the
adaptation measures in northern Ghana and neigbouring Burkina Faso (Stanturf, 2011; Barbier et
al., 2009). Farmers’ group in the lower catchment indicated no response to all socio-economic
scenarios except scenarios addressing price increase in seed. According to the farmers in the
lower catchment, they have already used some portion of their compound farm to cultivate
maize. Therefore, they were not willing to add more because they still want to grow their
traditional cereal (millets and guinea corn). The ratio of sampled households that cultivated
maize to total number of sampled household within the position on the catchment was higher
(29.8%) for lower catchment compared to mid (20%) and top (7.4%) catchment. Furthermore,
average land area cultivated for maize was higher in the lower part of the catchment compared to
middle and upper part (Fig. 6). Omnibus test of model coefficients based on Chi-Square test for
the surveyed household shows that the Bi-logit model is significant (p<0.008) in explaining the
effect of household’s catchment location on cultivation of maize. Maize frequent occurrence as
an agricultural land-use/cover option may be an indication of the adoption of maize in the study
area. This finding is in accordance with Ministry of Food and Agriculture (MoFA, 2012) report
which also indicate an increasing trend in the area of land cultivated for maize in the region.
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
Figure 6: Mean area cultivated for maize within the catchment
Implication on Agricultural and Natural Resources Policy
The change from one agricultural land-use/cover to another may seem not to be of urgent
importance to us. Several land-use/cover classification have lumped crop covers under one
category as agricultural land. However, cultural practices and management for different crops
differ, and these may have varying impacts on the environment. For example, different croplands
have different impacts on soil loss, carbon flux (Roose, 1977; Frank et al., 2006). Agriculture has
been described as one way to close emission gap, and in this part of the world, not much work
has been done to know the impact of cropland types on the environment. Farmers may change
their agricultural land-use/cover under certain conditions/stressors, and this might have
accompanied environmental trade-off. In depth and well documented impacts of agricultural
land-use/cover on the environment are scanty. Hence to effectively relate policy in this direction,
it will be necessary to investigate more on the environmental impacts associated with various
agricultural land-use/covers. Furthermore, minimising the possible associated environmental
trade-offs should be of major concern.
Summary and Conclusion
This paper presented the application of participatory scenario exploration exercise (PSEE) to
examine farmers’ agricultural land-use change response under climate change and socio-
economic scenarios. This study was conducted within the Vea catchment in the upper east region
of the northern part of Ghana. Scenarios presented to the farmers were designed from drivers of
agricultural land-use change identified during household survey (186), interview of stakeholders
and information from locations with similar agro-ecological conditions. Participatory scenario
exploration exercise was an efficient tool in this study. Based on the farmer’s reflection, the tool
did not only facilitate interaction between the researcher and the farmers, it also afforded the
participants opportunity to learn in groups. Climate change and socio-economic factors are clear
drivers of agricultural land-use change within the study area. However, their impact on
agricultural land-use change differs to some extent from each other. In most of the cases, the
presented scenarios have impact on the agricultural land-use change decision of the farmers.
Climate change related scenarios (rainfall) tend to influence replacement of crop(s) from
agricultural land (agricultural land conversion) while socio-economic scenarios tend to influence
the incorporation of crop(s) to an existing agricultural land (agricultural land modification).
Agricultural land-use/cover classification is very challenging in the study area due to the
small scale farming that is usually practiced by the farmers. However, extra effort is necessary to
address this challenge, because different cropland may impact the environment in a different
way.
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Acknowledgement
We thank West African Science Service Center on Climate Change and Adapted Land Use
(WASCAL) Graduate Research Programme (GRP) for providing research fund. We also thank
Comfort, Mohammed, and Thomas for their assistance on the field. Appreciation also goes to Dr
Tinuke Adebanji of KNUST.
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BADMOS ET AL.: EXAMINING AGRICULTURAL LAND-USE/COVER CHANGE OPTIONS
ABOUT THE AUTHORS
Biola K. Badmos: Ph.D Candidate, West African Science Service Center on Climate Change and
Adapted Land Use (WASCAL), Civil Engineering, Kwame Nkrumah University of Science and
Technology, Kumasi, Ghana
Dr. Grace B. Villamor: Senior Researcher, West Africa Science Service Center on Climate
Change and Adapted Land Use (WASCAL) Center for Development Research, University of
Bonn, Germany
Prof. Sampson K. Agodzo: Professor of Agricultural Engineering, Department of Agricultural
Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Prof. Samuel N. Odai: Professor in Civil Engineering, West African Science Service Center on
Climate Change and Adapted Land Use (WASCAL), Civil Engineering, Kwame Nkrumah
University of Science and Technology, Kumasi, Ghana
Mr. Samuel S. Guug: Field Technician, West Africa Science Service Center on Climate Change
and Adapted Land Use (WASCAL), Bolgatanga, Ghana
35
The International Journal of Interdisciplinary Environmental Studies is one of eight thematically focused journals in the collection of journals that support the Interdisciplinary Social Sciences knowledge community—its journals, book series, conference and online community.
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