A A P P P P L L Y Y I I N N G G D D P P S S I I R R T T O O L L A A N N D D D D E E G G R R A A D D A A T T I I O O N N A A N N A A L L Y Y S S I I S S : : T T H H E E C C A A S S

International Journal of Emerging Trends in Engineering and Development Issue 4, Vol.2 (March 2014)

Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149

R S. Publication (rspublication.com), [email protected] Page 712

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ABSTRACT

This paper assesses cause and effect linkages between Driving Forces-Pressures-State-Impacts-

Responses (DPSIR) analytical framework with regard to local farmers’ perspective on land

degradation. About 87.5% cause-effect relationship is supported by bivariate correlation and

linear regression analyses. The only exceptional case is the linkage between response and

pressure indicators which was not statistically correlated. Driving forces accounts for only 4.7%

of pressure; pressures explain 12.1% of state; state is responsible for 7.2% of impact; impact

accounts for 3.3% of response; response remedies 3.2% of driving forces; response addresses

6.3% of state of land degradation and response stems 3.3% of the impacts. Land degradation is

mainly driven by desire to get rich/money which exerts the principal pressure (timber extraction)

resulting in severe state of land degradation impacting primarily on farmer poverty in addition to

food shortage/hunger and low crop yield. Farmers respond through farm maintenance and also

resort to government interventions.

INTRODUCTION

The application of Driving forces-Pressures-State-Impacts-Responses (DPSIR) analytical

framework reported here concerns the analysis of land degradation in agrarian forest ecosystem

at Asunafo Districts. It represents a creative way of redefining an environmental problem and

engendering solutions from appropriate responses (Carr et al., 2007). Figure 1 portrays the causal

chains of the DPSIR framework. Driving forces lead to pressures followed by state of the natural

environment which is trailed by impacts as well as responses which shows forward link with




driving forces and backward links with pressures, state and impacts (Nachtergaele, 2003). The

origin of DPSIR is traced to 1970s in a work by Anthony Friend, a Canadian statistician who

developed PSR (Pressure – State – Response) framework and to the state of environment (SOE)

group of Organization for Economic Cooperation and Development (OECD) who converted PSR

to DPSIR (Weizsäcker and Jesinghaus, 1992). Carr et al. (2007) recognizes improvement upon

PSR to DSR (Driving forces-State-Response) by UN Commission on sustainable development

and the further refinement of DPSIR by the European Environmental Agency.

Figure 1: DPSIR framework

Source: Nachtergaele (2003)

Driving forces generate pressures; pressures influence or modify states; states provoke or

cause impacts; impacts stimulate or ask for responses; responses modify, substitute or remove

driving forces; responses eliminate, reduce or prevent pressures; responses restore or influence

states; and, responses compensate or mitigate impacts (Odermatt, 2004:337). In this context,

DPSIR provides a holistic and comprehensive approach to managing environmental problems

(Karageorgis et al., 2006).

Specific application of DPSIR framework in land degradation analysis will increase the

eco-efficiency of the natural environmental system and provide supporting data for land

managers and policy makers to make appropriate decisions (Porta and Poch, 2011). Gisladottir

State

Pressure Impact

Driving

forces Responses




and Stocking (2005) emphasize on the particular importance of DPSIR for land degradation as

identification of human activities that drives and pressurizes environmental systems and state of

natural resources, impacts and societal responses and the feedbacks in ways of controlling land

degradation. However, Ghana is not taking advantage of the use of DPSIR to benefit from its

problem solving ability and capacity to generate environmental policies (Agyemang et al., 2007).

MATERIALS AND METHODS

The study relied on perceptions of local farmers. Data was solicited through community

meeting, key informant interviews, farm visits and questionnaire survey based on a sample size

of 264 drawn from 774 farmers (Israel, 2009). Questionnaire was administered by fifteen Level

400 students who hailed from Asunafo and who have gained 3 years of field practical experience

from the University for Development Studies’ third trimester field practical training. Data was

analyzed via Statistical Package for Social Scientists (SPSS 18.0).

The study area – Asunafo is divided between north and south for administrative purpose

and is located within latitudes 6o27’ and 7

o00’N and longitudes 2

o23’and 2

o52’W. Asunafo

shares administrative boundaries with Dormaa West and Asutifi Districts to the north and the

Ashanti and Western Regions to the south-east and west respectively. Together, the two districts

cover a land area of 2,187.5 km2 (Abagale et al., 2003). The land (forest dissected plateau) rises

from 550 ft to 800 ft above sea level with prominent elevations such as Small Juju Mountain

(1,250 ft), Big Juju Mountain (1,350 ft), South Aboum (1,750 ft), North Aboum (2,050 ft) and

Bosam Bepo (2,050 ft) (Survey Department, 1972). The vegetation is moist-semi deciduous

forest made up of 3 layers of trees: upper, middle and lower with or without undergrowth. The

forest has been modified by anthropogenic management such as farming, logging and bushfires.

Part of the forest is managed as protected areas (reserves) which host several economic trees. For

instance, Aboniyere forest reserve contains 37 timber species. The other forest reserves recorded:

Ayum 40, Bia-Tano 56, Bonkoni 43, Bosam Bepo 36 and Subim 40 timber species (Forestry

Commission, 2001).

RESULTS AND DISCUSSION




Figure 2 portrays the causal chains between DPSIR’s framework as applied in land

degradation analysis to the perspective of 264 local farmers. Regarding land use and exploitation

of natural resources, the principal driving force includes local people’s desire to make more

money or get rich (47%). As a result, there is increasing extraction of timber species from the

forest representing the largest pressure on land (40.5%). The state of natural environment is one

of degraded forest land which 39.8% of respondents consider to be severe. Consequently,

impacts manifest in the form of poverty (39.4%), food shortage or hunger (24.2%) and low crop

yield (20.8%). Major responses consist of farm maintenance (33.7%) as involving the use of

agrochemicals, chemical fertilizers and crop substitution, government intervention (21.6%) and

the use of fallow (11.4%). Government intervention involves provision of chemical fertilizers at

subsidized price, agricultural extension and creation of forest reserves.

In order to test the relationship between the five major indicators of DPSIR, the study

assumes that:

Ho (b = 0): there is no supported relationship between:

o Driving Forces and Pressures

o Pressures and State

o State and Impacts

o Impacts and Responses

o Responses and Driving forces

o Responses and Pressures

o Responses and State

o Responses and Impacts

If p > 0.01, Ho is retained (fail to reject the null hypothesis) and Ha is rejected

(alternative hypothesis fails to stand).

Ha (b ≠ 0): there is supported relationship between the variables, (accepted only when Ho

is rejected).




Driving forces Percent

No response 10.6

Poverty 12.9

Desire to get rich/ money 47.0

Demand for food 25.4

Natural 4.1

Total 100

Responses Percent

No response 18.6

Farm rehabilitation 2.3

Farm maintenance 33.7

Government intervention 21.6

Tree planting 6.4

Fallow 11.4

No action 4.1

Buy food 1.5

Migrate 0.4

Total 100

Pressures Percent

No response 21.6

Land fragmentation 8.0

Continuous cropping 24.2

Adverse effects of

agrochemical 1.9

Extraction of timber 40.5

Fires 3.8

Total 100

Impacts Percent

No response 9.5

Poverty 39.4

Food shortage/hunger 24.2

Low crop yield 20.8

Makes farming extra

difficult 6.1

Total 100

State

Percent

No response 16.2

Very severe 13.6

Severe 39.8

Moderate 16.7

Light 11.4

no degradation 2.3

Total 100

Figure 2: Cause-effect relationship between farmers indicators of land degradation captured

under DPSIR framework

Source: Author




Table 1 shows cross tabulation of driving forces and pressures. The main driving force

(desire to get rich or money) and the main pressure (extraction of timber) are reported by 56

respondents (21.2%). Driving forces and pressures give correlation coefficient of 0.217 (weak

positive relationship). The significance 2-tailed is 0.000 < 0.01, hence, Ho is rejected and Ha

accepted, that there is enough evidence to establish a statistically significant correlation between

driving forces and pressures although weak. Linear regression produces R value of 0.217 and R2

of 0.047 implying that driving forces explain only 4.7% of pressures. The remaining 95.3% is

accounted for by other factors. The regression equation is:

Y = a + b * X

Y = 1.706 + 0.364 * Driving Forces

Table 1: Relationship between driving forces and pressures of land degradation

Driving Forces of Land Degradation Total

No

Response

Poverty Desire to

get

rich/money

Demand

for

Food

Natural

Pressures

on Land

leading to

Degradation

No Response 24 2 25 5 1 57

Land

Fragmentation

1 2 6 10 2 21

Continuous

Cropping

1 6 29 25 3 64

Adverse

Effects of

Agrochemicals

0 0 2 1 2 5

Extraction of

Timber

2 24 56 22 3 107

Fires 0 0 6 4 0 10

Total 28 34 124 67 11 264

Source: Author

Table 2 indicates linkages between pressures and state of land degradation. The largest

pressure is exerted by timber extraction which corresponds to severe state of land degradation

(reported by 49 respondents representing 18.6%). Pressures and state provide correlation

coefficient of 0.348 (moderate positive relationship). The significance 2-tailed is 0.000 < 0.01,




hence, Ho is rejected. The regression indicates R value of 0.348 and R2 of 0.121 where Y =

1.351 + 0.267 * Pressure. Pressure indicators account for only 12.1% of the state of land

degradation.

Table 2: Linkages between pressures and state of land degradation

Pressure of Land Degradation Total

No

Response

Land

Fragmentation

Continuous

Cropping

Adverse

Effects of

Agrochemicals

Extraction

of Timber

Fires

State of

Land

Degradation

No

Response

35 1 3 2 2 0 43

Very

severe

1 5 7 0 22 1 36

Severe 13 10 29 1 49 3 105

Moderate 6 2 14 1 19 2 44

Light 1 1 10 0 15 3 30

No

degradation

1 2 1 1 0 1 6

Total 57 21 64 5 107 10 264

Source: Author

Table 3 shows the relationship between state and impact indicators of land degradation.

The largest indicator of state of land degradation is severe and the principal impact is poverty

reported by 46 respondents representing 17.4%. State and impact reveal correlation coefficient of

0.269 (weak positive relationship). The significance 2-tailed is 0.000 < 0.01, hence, Ho is

rejected. The regression gives R value of 0.269 and R2 of 0.072 where Y = 1.291 + 0.228 * State.

State of land degradation explains only 7.2% of the impacts suggesting that other factors

accounts for 92.8%.




Table 3: Cross tabulation of state and impact indicators of land degradation

State of Land Degradation Total

No

Response

Very

severe

Severe Moderate Light No

degradation

Impacts of

Land

Degradation

No Response 20 1 2 1 1 0 25

Poverty 16 12 46 13 15 2 104

Food

shortage/hunger

4 14 22 16 5 3 64

Low crop yield 2 9 25 12 6 1 55

Makes farming

extra difficult

1 0 10 2 3 0 16

Total 43 36 105 44 30 6 264

Source: Author

Table 4 reveals the linkages between impact and response indicators. The largest impact

is poverty and the largest response is farm maintenance reported by 31 farmers (11.7%). Impact

and Response reveal correlation coefficient of 0.181 (no or negligible relationship). The

significance 2-tailed is 0.003 < 0.01, hence, Ho is rejected. The regression model registers R

value of 0.181 and R2 of 0.033 where Y = 2.036 + 0.298 * Impact implying that impact

indicators are responsible for only 3.3% of the responses.

Table 4: Linkages between impacts and responses indicators of land degradation

Impacts of Land Degradation Total

No

responses

Poverty Food

shortage/hunger

Low

crop

yield

Makes

farming

extra

difficult

Responses

of Land

Degradation

No responses 22 11 9 6 1 49

Farm

rehabilitation

0 3 2 1 0 6

Farm

maintenance

1 31 29 26 2 89

Government

intervention

1 30 8 13 5 57

Tree planting 0 5 3 2 7 17

Fallow 1 17 7 5 0 30

No action 0 5 4 1 1 11

Buy food 0 2 1 1 0 4

Migrate 0 0 1 0 0 1

Total 25 104 64 55 16 264

Source: Author




Table 5 indicates the relationship between responses and driving forces of land

degradation. The largest response indicator is farm maintenance which corresponds to the desire

to get rich/money (driving force) reported by 37 farmers (14%) implying that farmers would not

integrate timber species in agroforestry farms because farmers have no control over such trees

and the fear that timber species would be exploited to destroy crops and further degrade the land.

Again, farm maintenance as remedy to demand for food (driving force) as registered by 34

farmers (12.9%) suggesting that such maintenance in the farm would improve upon land quality

and crop production. Meanwhile, 39 farmers (14.8%) recorded government intervention

(response) and desire to get rich/money (driving force) suggesting that government could do

more to stem the desire to get rich/money by properly regulating timber extraction business, the

main pressure of land degradation. Responses and driving forces show correlation coefficient of

0.179 (no or negligible relationship). The significance 2-tailed is 0.004 < 0.01, hence, Ho is

rejected. The regression indicates R value of 0.179 and R2 of 0.032 where Y = 1.741 + 0.100 *

Response. The implication is that response indicators remedies only 3.2% of the driving forces.

Table 5: Relationship between responses and driving forces of land degradation

Responses of Land Degradation

Total No

Response

Farm

rehabilitation

Farm

maintenance

Government

intervention

Tree

planting Fallow

No

action

Buy

food Migrate

Driving

forces of

Land

Degradation

No

Response 21 0 3 1 0 1 2 0 0 28

Poverty 2 0 11 8 5 5 0 3 0 34 Desire to

get rich/

money 17 3 37 39 9 11 7 1 0 124

Demand

for food 9 3 34 6 3 11 1 0 0 67

Natural 0 0 4 3 0 2 1 0 1 11

Total 49 6 89 57 17 30 11 4 1 264

Source: Author

Table 6 shows the linkages between responses and pressures of land degradation. The largest

response is farm maintenance which corresponds to timber extraction (largest pressure) as

reported by 45 farmers (17%). A further emphasis is placed on the fact that farmers are likely not

to support timber species in-situ or plant timber species in agroforestry farms for the fear that

tree extraction someday will destroy crops. Responses and pressures provide correlation




coefficient of 0.101 (no or negligible relationship). The significance 2-tailed is 0.091 > 0.01,

hence, Ho is retained that there is not enough evidence to support the linkages between response

and pressure indicators. The regression gives R value of 0.101 and R2 of 0.011 where Y = 2.183

+0.097 * Response implying that responses could stem only 1.1% of the pressures if there were

any supported relationship.

Table 6: Linkages between response and pressure indicators of land degradation


Total No

Respon

se

Farm

rehabilitation

Farm

maintenance

Government

intervention

Tree

planting Fallow

No

action

Buy

food Migrate

Pressures of

Land

Degradation

No Response 32 0 6 6 0 8 5 0 0 57

Land

fragmentation 0 0 7 6 1 5 1 0 1 21

Continuous

cropping 7 0 28 13 3 9 1 3 0 64

Adverse effects

of agrochemicals

1 0 1 0 0 2 1 0 0 5

Extraction of

timber 8 5 45 26 13 6 3 1 0 107

Fires 1 1 2 6 0 0 0 0 0 10

Total 49 6 89 57 17 30 11 4 1 264

Source: Author

Table 7 shows the relationship between responses and state of land degradation. Farm

maintenance (largest response indicator) corresponds to severe state of land degradation as

reported by 40 farmers (15.2%).The implication is that farm maintenance could help remedy the

severe state of land degradation. Response and state indicators register correlation coefficient of

0.251 (weak positive relationship). The significance 2-tailed is 0.000 < 0.01, hence, Ho is

rejected. The regression shows R value of 0.251 and R2 of 0.063 where Y = 1.539 + 0.180 *

Response suggesting that response indicators could remedy only 6.3% of state of land

degradation whiles the remaining 93.7% could be stemmed by other factors.




Table 7: Relationship between response and state indicators of land degradation


Total

No Response

Farm rehabilitation

Farm maintenance

Government intervention

Tree planting

Fallow No

action Buy food

Migrate

State of

Land

Degradation

No Response 29 0 4 7 0 1 2 0 0 43

Very severe 4 1

17 6 2 1 4 0 1 36

Severe 6 2 40 23 11 19 2 2 0 105

Moderate 5 3 19 6 4 5 1 1 0 44

Light 5 0 8 12 0 3 1 1 0 30

No

degradation 0 0 1 3 0 1 1 0 0 6

Total 49 6 89 57 17 30 11 4 1 264

Source: Author

Table 8 reveals the linkages between response and impact indicators. The largest

response indicator is farm maintenance which coincides with the largest impact indicator

(poverty) as reported by 31 farmers (11.7%). As land quality improves, there will be increase in

crop production as result of farm maintenance. This will help reduce poverty thereby reducing

land degradation. This is so because poverty reduces farmers’ ability to combat land degradation

as stated by Stiles (1997) and Boahen et al. (2007). Again, 30 respondents (11.4%) expect that

government intervention would help stem poverty occasioned by land degradation. Response and

impact indicators record correlation coefficient of 0.181 (no or negligible relationship). The

significance 2-tailed is 0.003 < 0.01, hence, Ho is rejected. The regression model provides R

value of 0.181 and R2 of 0.033 where Y = 1.464 + 0.110 * Response implying that responses

could help stem only 3.3% of the impact of land degradation in which the remaining 96.7%

could be remedied by other factors.




Table 8: Linkages between response and impact indicators of land degradation


Total No

Response

Farm

rehabilitation

Farm

maintenance

Government

intervention

Tree

planting Fallow

No

action

Buy

food Migrate

Impact of

Land

Degradation

No Response 22 0 1 1 0 1 0 0 0 25

Poverty 11 3 31 30 5 17 5 2 0 104 Food

shortage/hun

ger 9 2 29 8 3 7 4 1 1 64

Low crop yield

6 1 26 13 2 5 1 1 0 55

Makes

farming extra

difficult

1 0 2 5 7 0 1 0 0 16

Total 49 6 89 57 17 30 11 4 1 264

Source: Authors

CONCLUSION

The study examined local farmers’ perspective of land degradation under the DPSIR

analytical framework and found about 87.5% statistically supported cause-effect relationships in

seven out of the eight causal linkages within the DPSIR framework and 12.5% no supported

relationship between responses and pressures. The relationships were established by low

percentages which range between 3.2% and 12.1%. Translation of the farmers’ perspective on

land degradation into the DPSIR framework reveals that, drivers of land degradation include

poverty, desire to get rich or money, demand for food and natural factors. These exert land use

pressures such as land fragmentation, continuous cropping, adverse effects of agrochemicals,

timber extraction and fires. Consequently, the state of the environment is categorized into very

severe, severe, moderate, light and no land degradation. Subsequently, impacts are reflection of

poverty, shortage of food or hunger, low crop yield and difficulties involved in tilling such land.

Farmers respond through farm rehabilitation, farm maintenance, government intervention, tree

planting, fallow, buy food, simply migrate or failure to take any action.

In specific terms, driving force (desire to get rich/money) causes people to engage in

timber exploitation which exerts land use pressure on the forest. The state of land degradation is

described as severe mainly resulting from timber extraction. The consequence of the severe land

degradation is primarily poverty although there is perennial food shortage/hunger and low crop




yield. Farmers respond particularly through farm maintenance since farming is the main source

of farmer livelihood. In so doing, agroforestry farming would exclude the use of trees with

economic values but include adoption of practices that would improve land quality and crop

production as well as reliance on government policies to properly regulate timber extraction.

Again, farmers expect farm maintenance and government intervention (response indicators) to

help stem poverty (impact indicator) as success in this venture would remedy land degradation.

The DPSIR framework is useful in analyzing land degradation at local farmer level

amidst difficulties in translating the terminologies into local dialect.

ACKNOWLEDGEMENT

I appreciate the support of Prof. Edwin A. Gyasi, Prof. Michael A. Stocking, Prof. Seth

K. A. Danso, Prof. R. B. Bening, the farmers of Asunafo, Commonwealth Scholarship

Secretariat, The British Council and University for Development Studies, Tamale.

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Documents

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