Examining Agricultural Land-Use/Cover Change Options in Rural Northern Ghana: A Participatory...

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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).

L

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).

16

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

17


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.

18


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

19


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.

20


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).

21


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

22


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

23


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


Groundnut

Rice 100%

R-Sc 3


Groundnut

Rice 100%

R-Sc 4

Traditional cereals

Groundnut

Rice 100%

R-Sc 5

Traditional cereals

Groundnut

Rice

24


Table 5b: Agricultural Land-Use/Cover Option in Response to Rainfall Scenarios (Middle

Catchment)

Rainfall



M MTC OL V/P F

R-Sc 1 Traditional cereals 100%

Groundnut 100%

Rice 100%


Groundnut 100%

Rice 100%

R-Sc 3 Traditional cereals 67% 33%

Groundnut

Rice 100%

R-Sc 4 Traditional cereals

Groundnut

Rice


Groundnut

Rice

25


Table 5c: Agricultural Land-Use/Cover Option in Response to Rainfall Scenarios (Lower

Catchment)

Rainfall



M MTC OL V/P F


Groundnut

Rice


Groundnut

Rice


Groundnut 100%

Rice 100%

R-Sc 4 Traditional cereals 50% 50%

Groundnut

Rice


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.

26


Table 6a Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios

(Upper Catchment)

Socio-economic



M MTC OL V/P F

S-Sc 1 Traditional cereals 50%

Groundnut 50%

Rice


Groundnut 80%

Rice

S-Sc 3 Traditional cereals

Groundnut 33%

Rice


Groundnut 50%

Rice


Groundnut 60%

Rice

27


Table 6b Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios

(Middle Catchment)

Socio-economic



M MTC OL V/P F


Groundnut

Rice


Groundnut 80%

Rice


Groundnut 33%

Rice


Groundnut 50%

Rice


Groundnut

Rice

28


Table 6c Agricultural Land-Use/Cover Options in Response to Socio-Economic Scenarios

(Lower Catchment)

Socio-economic



M MTC OL V/P F


Groundnut

Rice


Groundnut

Rice


Groundnut 33%

Rice


Groundnut 50%

Rice


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.

29


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.

30


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.

31


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.

32


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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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