Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa

Journal of Environmental Management 196 (2017) 411e442

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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

Research article

Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa Janina Kleemann a, b, *, Gülendam Baysal b, Henry N.N. Bulley c, Christine Fürst d a

Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany b University of Bonn, Center for Development Research (ZEF), Walter-Flex-Str. 3, 53113 Bonn, Germany c (Geography & GIScience), Department of Social Sciences, Human Services & CRJ, BMCC, City University of New York, New York, NY 10007, United States d Martin Luther University Halle-Wittenberg, Institute for Geosciences and Geography, Sustainable Landscape Development, Von-Seckendorff-Platz 4, 06120 Halle, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 November 2016 Received in revised form 4 January 2017 Accepted 23 January 2017

Land use and land cover change (LULCC) is the result of complex human-environmental interactions. The high interdependencies in social-ecological systems make it difficult to identify the main drivers. However, knowledge of key drivers of LULCC, including indirect (underlying) drivers which cannot be easily determined by spatial or economic analyses, is essential for land use planning and especially important in developing countries. We used a mixed-method approach in order to detect drivers of LULCC in the Upper East Region of northern Ghana by different qualitative and quantitative methods which were compared in a confidence level analysis. Viewpoints from experts help to answer why the land use is changing, since many triggering effects, especially non-spatial and indirect drivers of LULCC, are not measurable by other methodological approaches. Geo-statistical or economic analyses add to validate the relevance of the expert-based results. First, we conducted in-depth interviews and developed a list of 34 direct and indirect drivers of LULCC. Subsequently, a group of experts was asked in a questionnaire to select the most important drivers by using a Likert scale. This information was complemented by remote sensing analysis. Finally, the driver analysis was compared to information from literature. Based on these analyses there is a very high confidence that population growth, especially in rural areas, is a major driver of LULCC. Further, current farming practice, bush fires, livestock, the road network and climate variability were the main direct drivers while the financial capital of farmers and customary norms regarding land tenure were listed as important indirect drivers with high confidence. Many of these driving forces, such as labour shortage and migration, are furthermore interdependent. Governmental laws, credits, the service by extension officers, conservational agriculture and foreign agricultural medium-scale investments are currently not driving land use changes. We conclude that the mixed-method approach improves the confidence of findings and the selection of most important drivers for modelling LULCC, especially in developing countries. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Land degradation Population pressure Remote sensing Expert interviews Agriculture Upper East Region

1. Introduction 1.1. The context of land use and land cover change Land use and land cover change (LULCC) is an emerging threat to the resilience of socio-ecological systems, since it is often related to

land degradation (Lambin and Meyfroidt, 2010). In the context of this study, land cover refers to the biophysical (e.g. soil, and water) land surface while land use is related to any human management activity affecting land. We define therefore land use change as either a shift into another land use or the intensification of the current land use (Turner and Meyer, 1994).

* Corresponding author. Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany. E-mail addresses: [email protected] (J. Kleemann), [email protected] (G. Baysal), [email protected] (H.N.N. Bulley), [email protected] (C. Fürst). http://dx.doi.org/10.1016/j.jenvman.2017.01.053 0301-4797/© 2017 Elsevier Ltd. All rights reserved.

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Today, it is acknowledged that driving forces of LULCC are often a mix between anthropogenic (social, political, economic, demographic, technological, cultural) and biophysical factors with direct or indirect impacts. Direct drivers of land use change exert obvious impact on the land surface, while indirect drivers are the underlying causes of direct drivers and are channelled through direct anthropogenic drivers, for example governance systems (Díaz et al., 2015; Lambin et al., 2003). Anthropogenic drivers of LULCC, such as population growth and dry season gardening, have mostly a short-term and often more perceivable impact than biophysical drivers. Conversely, climate change as one of the emerging drivers of LULCC is difficult to detect and quantify in the short term. Consequently, long-term studies are necessary to provide evidence of climate change. Biophysical drivers of LULCC, such as increasing inter-annual rainfall variability, have severe consequences especially in rural areas with a low financial and physical capacity and a high dependency on natural resources (Adger et al., 2003; Lambin et al., 2003). Examples for the eminent relevance of such climatic parameters for LULCC in marginalised rural regions can be found world-wide, but are most prominent in the Global South (Ahmed et al., 2009; IPCC, 2014), including countries like Ghana in West Africa. The role of climate change for environmental and socioeconomic changes in West Africa is still critically discussed in the scientific community (Antwi-Agyei et al., 2016; Mertz et al., 2010; Reenberg, 2001; Tschakert, 2007). Mertz et al. (2010) evaluated 1249 household questionnaires and held focus group discussions in 15 sites in Senegal, Mali, Burkina Faso, Niger, and Nigeria on driving forces for decreasing livestock, crop and pasture production. Climate factors were perceived as a driving force by 30e50% of the households, while 50e70% stated that decreasing production is based on other factors not related to climate. Furthermore, it depends on the climate models whether rainfall in West Africa will increase or decrease and, therefore, cannot be predicted (Mertz et al., 2010; Müller, 2009; Thornton et al., 2006). 1.2. The objectives of this study The complex and diverse interactions among social-ecological systems make it difficult to identify and quantify the main drivers of LULCC (Ostrom, 2009). We conducted this study against the background that driving forces of LULCC are often analysed from a disciplinary perspective, which lends significance either to socially related drivers or driving forces detected by natural science (Rindfuss and Stern, 1998). A holistic approach across disciplines with a focus on direct and indirect influences causing LULCC is needed for a comprehensive driver analysis which is still rarely conducted in land system science (Van Vliet et al., 2015). Further, an assessment of LULCC should include, as much as possible, the impact of the change on natural resources availability or disaster risks (Bulley,1996). Knowledge of relevant driving forces and their impact on gains or losses in ecosystem services contributes to provide consultations for sustainable development by delivering improved decision criteria and policy advice (Larigauderie and Mooney, 2010). The objectives of our study are to identify and characterise the most relevant driving forces of LULCC in the Upper East Region located in the Sudanian and Guinean Savannah Zone of northern Ghana. We selected this study site due to the particularly vital role of LULCC in land degradation and its negative impact on agriculturally dominated socio-ecological systems in developing countries. Specific research questions are:
How and why are land use and land cover changing in the Upper East Region? What are the land use types that increase or decrease?


What are the parameters that drive LULCC? Which ones are the most relevant direct and indirect driving forces of LULCC?
How reliable are our findings? What are the advantages and disadvantages of a mixed-method approach using our study as test case? Common methods for the identification of driving forces of LULCC in the Sudanian Savannah Zone are remote sensing (Braimoh, 2006; Mortimore et al., 2005), statistical analysis (Fischer et al., 2002; Zaal et al., 2004), local actor interviews (Mertz et al., 2009; Tschakert, 2007; West et al., 2008) or a combination of the above-mentioned methods (Antwi-Agyei et al., 2012; Dietz et al., 2004; Owusu et al., 2013; Wardell et al., 2003; Yiran et al., 2011). In this paper, we suggest and present a mixed-method approach which allows for the analysis of one key aspect e driving forces of LULCC e from different methodological angles and provides information on reliability by comparing the findings. 2. Methods 2.1. Study area Our study was conducted in the Upper East Region (UER) of Ghana, an agro-ecological zone of the Sudanian and Guinean Savannah close to the borders of Burkina Faso and Togo (Fig. 1). The climate is usually hot (mean annual temperatures: 28.9  C; FAO, 2005) with a unimodal rainfall regime between May and October, during which time all rain-fed crops have to be grown and harvested. However, the area is characterised by high rainfall variability (Hulme, 2001; Herrmann et al., 2005), which makes food crop production increasingly insecure (Roncoli et al., 2001). Conditions in the UER are typical of rural areas in West Africa with low socio-ecological resilience to climate and ecosystem changes (Hjelm and Dasori, 2012; IPCC, 2014). It is one of the poorest areas in Ghana and characterised by low educational status and a high rate of illiteracy. The UER covers 3.7% (8842 km2) of the total land area (238,535 km2) of Ghana and has one of the highest population densities in the country, with more than one million people (1,034,704; GSS, 2012). About 80% of the population is engaged in small-scale rain-fed subsistence farming (Birner et al., 2005). Most important crops are maize, sorghum, and millet; these are intercropped mainly with groundnuts or beans. Vegetables and rice are grown in irrigated areas or rain-fed lowlands. The main farm types are compound farms and bush farms. Compound farms grow primarily food for subsistence in immediate vicinity to the houses, while bush farms are located in remote areas where mostly cash crops like maize are produced. Compound farms receive direct manure from livestock (mainly goat, sheep and few cows) which are kept close to the homesteads. Bush farms normally receive less fertilizer and regenerate under a bush-fallow system, but shortened fallow periods have decreased soil fertility (Braimoh and Vlek, 2005; Kpongor, 2007). Due to the low capacities for food provision, especially in the dry season, up to 100,000 people (in 2000; Van der Geest et al., 2010) migrate to southern Ghana in the dry season to find work. 2.2. Mixed-method approach and confidence reporting Many LULCC analyses originating from remote sensing add social information through quantitative data (Rindfuss et al., 2004). Less common is the integration of narratives and other qualitative information. Appropriate methodological linkages were missing to compare qualitative and quantitative data from social and natural science. Consequently, we developed a mixed-method approach (Tashakkori and Teddlie, 2003) that is based on four different LULCC

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Fig. 1. Location of the Upper East Region in northern Ghana.

driver analysis methods, whose outcomes are subsequently compared in a confidence level analysis (Tables 1a and 1b). We started with the literature analysis to receive preliminary insight into LULCC in northern Ghana and used it as a basis for the expert interviews. The expert interviews were used to collect expert opinion on the relevant driving forces of LULCC and subsequently developed a questionnaire addressing the relevance of the most prominent driving forces. Geospatial (remote sensing and GIS) analyses and apriori information from the literature analysis were used as supportive tools to corroborate expert knowledge (Fig. 2). To evaluate the reliability of our results, we followed the approach of a confidence level analysis suggested by Jacobs et al. (2015) which was based on Mastrandrea et al. (2011) for the IPCC Fifth Assessment Report and the Millennium Ecosystem Assessment (MA, 2005). They used a combination of agreement and evidence levels to evaluate confidence in the validity of a finding. Our approach differs from Jacobs et al. (2015) as we evaluated findings from different methods instead of whole models and provided a standardised classification instead of using a descriptive classification based on expert judgements. The level of evidence is defined by the number of methods which can provide information. Thus, we have robust evidence if all four methods, medium evidence if two or three methods and limited evidence if only one method can provide conclusive information (Table 1a). The level of agreement is based on the consistency of the findings by the amount of statements found in literature, interviews and questionnaires or the level of significance in the remote sensing analysis (Table 1a). For instance, we define high agreement if 50% or more of the respondents/authors agreed in literature, interviews and questionnaires to the finding or if the analysed parameter impacts at least two land cover types with a p-

value  0.001 in the geospatial and statistical analysis. For the reliability analysis of the results we used very high, high, medium, low and very low confidence levels (Table 1b). For example, very high confidence is given if we have enough data and consistent results with high agreement from all four methods. The catalogue of combinations between agreement and evidence levels can be found in Annex 1. 2.3. Geospatial analysis (remote sensing and GIS) Remote sensing and geospatial data are reliable data sources for understanding LULCC and determining the drivers of change (Hansen et al., 2000). In this study, we used moderate resolution remote sensing images (500  500 m) and spatially referenced demographic and biophysical data. Further, spatially referenced data represented population change, distance to roads, distance to irrigation (dams and rivers), distance to settlements and average elevation (digital elevation model). The data sets were used to analyse spatio-temporal changes in land use and land cover (LULC) and their dependence on specific drivers of change. We used population change as a proxy for rural population growth in the confidence level analysis (Table 9). Temperature and rainfall data as drivers of LULCC were not available in sufficient spatio-temporal resolution for analysis. The LULC data were taken from MODIS land cover product (MCD12Q1) acquired in 2001 and 2013 (500  500 m resolution). MODIS data is a product of NASA, which provides global land use/ cover information (Friedl et al., 2010). GIS data on roads, rivers, dams and settlements were provided by the GLOWA Volta Project (GLOWA Volta, 2010) and population grids were acquired from a gridded population of the world database from the Socio-Economic

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Table 1a Agreement and evidence levels of the respective methods (remote sensing, expert interviews and literature review) for the table of confidence (Table 9).

Table 1b Methodological approach for the table of confidence of findings from interviews, questionnaires, remote sensing/spatio-temporal analysis and literature (Table 9). Adapted from Jacobs et al. (2015) which was based on Mastrandrea et al. (2011) and MA (2005).

Level of confidence

Limited evidence

Medium evidence

Robust evidence

High Agreement

Medium

High

Very high

Medium Agreement

Low

Medium

High

Low Agreement

Very low

Low

Medium

Data and Applications Center (CIESIN, 2005; SEDAC, 2016). Fig. 3 summarises the analytical steps of the different data sets. Pre-processing mainly included data standardisation processes, mostly data cleaning and projecting. We started with projecting the data to the same coordinate system (WGS_1984_UTM_Zone_30N). Second, data were reclassified (Table 2) to support LULCC analyses at regional scale (Friedl et al., 2010). Main classes were grassland, mixed vegetation, cropland and tree cover (with >30% tree cover). Urban areas were not included because changes could not be identified due to the coarse resolution of available data.

After pre-processing the preliminary data, a transformation process was performed. First, changes from 2001 to 2013 were detected by using the raster calculator function in ArcGIS 10.1: the Spatial Analyst extension was used to calculate cell values between selected raster layers by map algebra statements to observe the cell to cell changes. The next step was to process the initial data to acquire necessary drivers using geospatial analyses. Understanding LULCC requires multiple sets of spatially and/or temporally compatible data that can integrate historical LULC patterns (Foresman et al., 1997). For

Fig. 2. Structure of the study; LULCC ¼ Land use and land cover change.

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Fig. 3. Analytical steps; LULC ¼ Land use and land cover; LULCC ¼ Land use and land cover change.

this purpose, spatial and spatio-temporal drivers have been selected based on their capacity to reflect the variance. The spatial drivers represent the variance through space and the spatiotemporal drivers represent both spatial and temporal variance. For instance, the population distribution is considered as a spatial driver, but the change in the population density is a spatiotemporal driver and can be easier related to LULCC than the population distribution. In the geospatial analyses, changes in population density were again calculated through the raster calculator function in ArcGIS 10.1 by means of subtraction of the situations 2001 and 2013. Road networks, irrigation sources and settlement areas not only showed variance in space and/or time, the distance to them showed variance in space as well. Therefore, it was necessary to transform the preliminary spatial data into information using geospatial processes. Continuous raster surfaces were created using Euclidian distance function in ArcGIS 10.1 where each cell holds the distance value to observe the spatial variance of distance. Elevation as driver with spatial variance was not further transformed. After processing the data, drivers were standardised to 5  5 km cell units to perform a cell to cell analysis. LULCC studies are scale sensitive and it is important to decide the suitable scale based on the purpose of the work. As it has been indicated in previous studies, LULCC data over large regions are mainly acquired from moderate to coarse level data (Lambin and Geist, 2008). Therefore, a 5  5 km unit has been selected since the study was at regional level and a finer spatial resolution was not necessary (see Fig. 4). Multiple regression (Equation (1)) was applied to understand the relationships between LULCC (e.g. from grassland to cropland) and drivers of LULCC (also see Hersperger et al., 2010) as a means to examine how multiple independent variables are correlated with a dependent variable (Higgins, 2003; Tabachnick and Fidell, 1989). Y ¼ b1X1 þ b2X2 þ … þ A

statistical tests were performed using a R script, and the code can be found in Annex 6. 2.4. A priori information For the literature review, we used scientific data bases such as Science Direct and Google Scholar. We considered journal articles, book chapters, and in two cases also master and doctoral theses due to the fact that published research on driving forces of LULCC in the Upper East Region (UER) is rarely available. We focused on documents with data in our research time span from 2001 to 2013. However, some studies also include data sets before 2001. In total,

(1)

Y: dependent variable; X1, X2,..: independent variables; b1, b2, …: model parameters; A: model error. In this study, the dependent variable was the amount of LULCC and the independent variables were population change, elevation variation, and distance to roads, dams, rivers and settlements. The

Fig. 4. Steps for data standardization to a 5  5km cell unit of observation.

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Table 2 MODIS (USGS, 2014) land cover classes; the main class was used for our purpose. Main class

Sub-class

Tree cover >30%

Closed shrub lands, Deciduous broadleaf forest, Mixed forest, Woody savannah Barren or sparsely vegetated savannahs, Open shrub lands Grasslands Croplands

Mixed vegetation Grasslands Croplands

12 documents were identified as relevant in the context of LULCC in the UER (Table 3). The authors of these studies analysed the driving

forces of LULCC in the UER by using either methods from social science (e.g. Rapid Rural Appraisal) or natural science (e.g. remote sensing analysis) or both, mainly at local or district level (see also the map in Table 3). 2.5. Expert interviews In order to acquire professional knowledge regarding the most important drivers for LULCC and their interactions, we consulted exclusively scientists originating from Ghana. They were selected based on published papers, recommendations by other scientists

Table 3 Selected literature for analysis with research on land use and land cover changes in the Upper East Region (UER) with its location. Author(s)

Published Year

Time of investigation

Level of invest-igation (no. in map)

Methods

Agyemang, I.

2012

1990, 2000 and 2004 rainfall data; field work not specified

Local and district (no.1)

Aniah, P., Wedam, E., Pukunyiem, M., Yinimi, G.

2013

not specified but between 1990 and 2004

Local and district (no.2)

Armah, F. A., Odoi, J. O., Yengoh, G. T., Obiri, S., Yawson, D. O., Afrifa, E. K. A. Awen-Naam, M.B.

2011

Regional (UER)

2011

1961e2008 meteorological data 1990e2009 meteorological data, 2010 field work

Bugri, J. T.

2008

2003 and 2004

Local and district (no.4)

Dietz, T., Geest, K. Van der, Obeng, F. Laube, W., Schraven, B., Awo, M.

2013 2012

Not specified 2005e2007

Local (no.5) Local (no.6)

Owusu, A. B., Cervone G., Luzzadder-Beach, S.

2013

Regional (UER) and local (no.7)

Schindler, J.

2009

Wardell, D. A., Reenberg, A., Tottrup, C.

2003

Yaro, J. A.

2007

2007e2008 ground-truth; remote sensing data 1982 e2007 1961e2001 meteorological data, 1969 soil data, 2004 groundwater data, 2000 population data, 2006 field work 1968, 1986 and 2001 satellite images; 1901e2001 historical documents; 1931e1990 rainfall data; 1891e2000 population data; 2002 field work 1999 and 2002

Interviews, focus group discussions, field observation, literature review Interviews, questionnaires, literature review, remote sensing Statistical analysis (Markov chain and Fuzzy modelling) Household surveys, oral histories, group interviews, dietary recalls, observation Interviews, questionnaires, field observation Focus-group discussions Focus-group discussions, interviews, questionnaires, field observation Remote sensing analysis/GIS; interviews, focus-group discussions, questionnaires Statistical analysis, remote sensing/GIS, Agent-based modelling, field observation, questionnaires, interviews

Yiran, G. A. B., Kusimi, J. M., Kufogbe, S. K.

2011

1989, 1999 and 2006 satellite images; 1989e2006 rainfall data, field work not specified

Local and district (no.3)

Local (no. 8)

Regional (UER) and local (no.9)

Remote sensing, statistical analysis, interviews, literature review

Local (no.10)

Focus-group discussions, interviews, questionnaires, field observation Remote sensing, interviews, questionnaires, literature review, statistical analysis

Local and district (no.11)

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and/or consultations with directors of scientific institutions such as university departments and CSIR-institutes. A prerequisite was that they were affiliated with scientific institutions in Ghana and had a longstanding and comprehensive overview of causal relations between driving forces and LULCC in the UER. In order to ensure a professional background in research, only scientists with a doctoral degree were selected. During a field trip in Ghana, 29 institutions and university departments were visited and 20 interviews were conducted to identify experts in LULCC in the UER. Finally, 13 experts were selected based on the above mentioned selection

417

criteria and their willingness to contribute to our survey (the first 13 experts in Table 4). The majority (62%) was affiliated with university departments and 38% with other research institutions. The interviews lasted between 30 and 150 min, and our text analysis was based on the Grounded Theory (Glaser and Strauss, 1967) where text segments are combined to conceptual categories (codes) to develop a theoretic or schematic overview of a phenomenon based on the knowledge and perceptions of the interviewees (Strauss and Corbin, 1998). The codes were further refined after first reading. Codes, memos and quotations were

Table 4 Affiliation, scientific background, research focus and research level of the 29 experts for interviews (I) and questionnaires (Q); WASCAL ¼ West African Science Service Center on Climate Change and Adapted Land Use; CSIR ¼ Council for Scientific and Industrial Research, KNUST ¼ Kwame Nkrumah University of Science and Technology; LK ¼ Rather local knowledge (village level); RK ¼ Rather regional knowledge (research across districts). ID

Affiliation

Research focus

LK

RK

I

P1

CSIR- Forestry Research Institute, Kumasi, Ghana

X

X

X

P2

WASCAL Competence Center, Ouagadougou, Burkina Faso Department of Land Economy, KNUST, Kumasi, Ghana

Impact of anthropogenic activities on forests, forest management Agricultural engineering, water resource management

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28 P29

Department Ghana Department Ghana Department Ghana Department

of Geography, University of Ghana, Accra, of Geography, University of Cape Coast, of Geography, University of Ghana, Accra, of Agriculture, KNUST, Kumasi, Ghana

WASCAL Competence Center, Ouagadougou, Burkina Faso Department of Food Security and Climate Change, University of Development Studies, Nyankpala, Ghana Department of Geography, University of Ghana, Accra, Ghana CSIR-Water Research Institute, Accra, Ghana CSIR-Savannah Agricultural Research Institute, Tamale, Ghana Department of Crop and Soil Science, KNUST, Kumasi, Ghana University of Bonn, Center for Development Research, Political and cultural change, Germany University of Bonn, Center for Development Research, Political and Cultural Change, Germany University of Bonn, Center for Development Research, Economic and Technological Change, Germany University of Würzburg, Institute for Geography and Geology, Remote Sensing, Germany University of Bonn, Faculty of Agriculture, Institute of Plant Production, Germany University of Bonn, Faculty of Agriculture, Institute of Plant Production, Germany University of Bonn, Faculty of Agriculture, Institute of Plant Production, Germany Department of Agricultural Engineering, KNUST, Kumasi, Ghana Department of Agricultural Engineering, KNUST, Kumasi, Ghana University of Augsburg, Institute of Geography, Germany University of Bonn, Center for Remote Sensing of Land Surfaces, Germany University of Bonn, Center for Development Research, Ecology and Natural Resources Management University of Bonn, Center for Development Research, Ecology and Natural Resources Management University of Bonn, Center for Development Research, Ecology and Natural Resources Management University of Bonn, Center for Development Research, Economic and Technological Change United Nations University, Institute for Environment and Human Security, Bonn, Germany

Land tenure, land policy and land management and the implications for sustainable livelihoods Food security, sustainable livelihoods, migration studies, climate change adaptation Water management, regional and local land use planning Remote sensing, land use change and natural resources management Climate change and agriculture, soil and water engineering Agriculture and climate change, soil physics

X

Food and nutrition security, agricultural economics

X

X

Remote sensing and land use change Water resources management, especially surface water and irrigation Crop varieties and soil management for food security, impact of climate change Soil management, agriculture and soil conservation

X X

X

X

X

X

Q

X

X

X

X

X

X

X

X

X

Migration, population studies, climate change adaptation, rural development Rural markets, agriculture, irrigation schemes, rural livelihoods Agricultural economy, macro-quantitative policy analysis Land use change, GIS, land use type classification

X

X

X

X

Interactions between crop and soil, influences by climate change or crop and soil management Climate resilience of cropping systems

X

X

X

X

X

X

X

X

X

X

Interactions between crop and soil, influences by crop and soil management on yield Adaptation strategies of crop production system to climate change Impacts and adaptation strategies of climate change on land use/cover change Meteorology, Climatology Agroforestry, rural livelihoods, climate change and biodiversity, food security, rural development Ecosystem service assessment, agricultural land use scenarios Land use change analysis and land use decisions, Human-Landscape Dynamic Systems Landscape patterns and structures, interactions of agricultural Land uses and ecosystem services Agricultural economy and innovation Agricultural development, GIS, natural resources, disaster management

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

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analysed in the network view or query tool of ATLAS.ti (2014) to identify similarities and differences between the primary documents and for examining relationships between the codes, quotations and memos for theory development. A query tool was applied to retrieve quotations in the primary documents by using its connected codes. The network view in ATLAS.ti allowed the creation of graphical concept maps by connecting codes, quotations, and memos in order to analyse the data and develop theories. 2.6. Questionnaire Based on the interviews, we developed a questionnaire which comprised a list of 34 current and potential or future drivers of LULCC. The driving forces were grouped into two main categories; direct drivers of LULCC (anthropogenic drivers and biophysical drivers) and indirect drivers of LULCC (demographic, cultural, economic, political and technological drivers) (see Annex 2). The clustering of indirect drivers was performed according to Geist and Lambin (2002). The ranking of the driving forces was based on a Likert scale. This scale ranged from 0 (¼ no influence) to 5 (¼ strong influence). In addition, two pages of definitions were provided to the experts (see Annex 2). The questionnaire was sent by e-mail to junior and senior researchers from WASCAL working in the UER and to the experts in Ghana. In total, 21 out of 29 experts that were contacted answered the questionnaire. In order to improve the reliability of the answers given in the questionnaire, a Delphi-approach was applied (Okoli and Pawlowski, 2004). The questionnaire was returned to each expert together with the synthesised information of the expert group. By review and adoption of results of the first Delphi round, the variance in the driver ranks should be reduced and consensus should be approached. 3. Results 3.1. Observations of land use and land cover change by the mixedmethod approach The loss of natural vegetation has been the most visible evidence of land use and land cover change in the Upper East Region for the last 10 years reported by experts and literature with broad consensus that land degradation is likely to increase. The main argument for this assertion was the ongoing deforestation (70% of the experts confirmed) and agricultural expansion (77% of the experts confirmed) which is also verified by the remote sensing analysis (see Tables 5a and 5b). Three experts pointed out that agricultural expansion has reached its limits due to settlement growth and the increase in bare land. This opinion is also supported in literature by Yiran et al. (2011) and Owusu et al. (2013). Conversely, the remote sensing analysis showed that 16.55% of the natural vegetation was converted to cropland between 2001 and 2013 (Table 5b, see also Fig. 5). The change of 8% from cropland to mixed vegetation (Table 5a) could be either related to the still existent crop-fallow practice in agriculture or the abandonment of marginal cropland for livestock grazing according to the experts. The expert interviews revealed that the crop-fallow system is rarely practiced today and all agricultural land is under permanent use. Experts underpinned that, currently, every nonagricultural plot of land is bare or marginal land which cannot be used for agriculture. Even though the geo-statistical analysis cannot prove a change in urban area, 62% of the experts stated that there was an increase

Table 5a Percentage share (%) and amount (km2) of land use and land cover changes in the Upper East Region of different land cover classes. Land use and land cover changes Change of land cover class

2001e2013 (km2)

% of change

Croplands to grasslands Croplands to mixed vegetation Croplands to tree cover (>30%) Grasslands to croplands Grasslands to mixed vegetation Grasslands to tree cover (>30%) Mixed vegetation to croplands Mixed vegetation to grasslands Mixed vegetation to tree cover (>30%) Tree cover (>30%) to Croplands Tree cover (>30%) to grasslands Tree cover (>30%) to mixed vegetation

188.75 647.50 88.25 522.50 172.50 22.25 1440.75 93.00 194.00 369.25 39.25 229.75

2% 8% 1% 6% 2% 0% 17% 1% 2% 4% 0% 3%

Table 5b Total change of land use type between 2001 and 2013. Land Use Type

Change between 2001 and 2013

Cropland Grassland Mixed vegetation Tree cover (>30%)

þ16.55% 4.65% 7.87% 3.93%

in urban area (conversion of cropland to urban, see also Kleemann et al., 2017). 3.2. Identified drivers of LULCC by single methods 3.2.1. Identified drivers of LULCC by geospatial analysis The multiple regression analysis of remote sensing data showed that each of the drivers is interrelated to one or more different LULCC with a different significance level (Table 6; Annex 5). For example, expansion of agricultural land is related to the distance to irrigation by dams, to settlements, to roads and population change (see Table 6). Especially, areas closer to dam irrigation are more likely to be converted to cropland. Irrigation from rivers is regarded as minor driver of LULCC. Areas closer to settlements, dam irrigation and roads are more likely to be converted from tree cover to other classes, and from grasslands to croplands. Additionally, deforestation is positively related to population growth (pvalue  0.001). Multiple regression results from other land use classes which are also considered for the confidence table can be found in Annex 5. 3.2.2. Identified drivers of LULCC by expert interviews Interviews confirmed that urban and rural population growth is an important LULCC driver (in Table 7: mentioned by all experts). It has direct and indirect influences on LULCC: the direct influence is urban and rural settlement sprawl and conversion of cropland. Cropland is becoming fragmented on compound farms due to housing construction. The process is aggravated by culturally inherited agreements with regard to customary land tenure because of inheritance rights. Compound land owned by the father has to be shared between the male descendants after the father's death and land fragmentation is accelerated. In addition, such inheritance systems bear a high risk of losing land because tenure is based on oral agreements rather than official documents on land

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Fig. 5. Land use and land cover (LULC) change between 2001 and 2013 for the different land cover types in the Upper East Region.

Table 6 Multiple regression analysis with spatial-temporal data with a focus on agricultural expansion. To cropland

From tree cover (30%)

Drivers

From mixed vegetation

From tree cover (30%)

From grassland

Drivers

To cropland

To grassland

To mixed vegetation

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

*** *

***

**

***

**

***

**

** ***

*** *** **

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

** ***

**

*** . .

Significance levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

entitlement. Such a lack of security to maintain landownership impedes efficient use of available resources and hinders investments, for example for purchasing fertilizer. Population growth in urban centres in the regions (e.g. Bolgatanga and Bawku) was considered by the experts to be mainly driven by migrants from Burkina Faso and Nigeria, and also those from surrounding villages looking for jobs mainly in administration, education and trading. Indirectly, population growth leads to changes in agricultural

land use practices due to land pressure. In the past, missing or low fertilizer input was compensated by the crop-fallow practice, but land pressure due to population growth has led to permanent and recurrent use of agricultural parcels and recurrent cultivation of the same crops. The effect is particularly critical when shallow rooting plants such as cereals are sowed every year, which exhaust nutrients in the upper soil layer. Even in compound farms (i.e. farms that are around housing), where small amounts of manure are regularly

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Table 7 Summary table of the number of experts who mentioned the parameters as driver of land use and land cover change in the interviews and statements by authors in literature. The results can be found in Annex 3 and Annex 4. Classification of driver

Current drivers of LULCC

Interviews: % (no.) of experts (n ¼ 13) who mentioned the driver

Literature: % (no.) of authors (n ¼ 12) who mentioned the driver

Direct driver: Anthropogenic

Population growth rural Population growth urban Agricultural intensification: modern Agricultural intensification: traditional Agricultural extensification Conservation agriculture

100% (13) 100% (13) 31% (4) 77% (10) 69% (9) 0% (0) - only as future driver 46% (6) 39% (5) 39% (5) 62% (8) 23% (3) 62% (8) 77% (10) 23% (3) 39% (5) 46% (6) 77% (10) 15% (2) 31% (4) 77% (10) 31% (4) 39% (5) 46% (6) 69% (9) 23% (3) 77% (10) 15% (2) 15% (2) 0% (0) 31% (4) 15% (2) 18% (1) 31% (4) 0% (0)

67% (8) 33% (4) 33% (4) 83% (10) 67% (8) 0% (0)

Direct driver: Biophysical

Indirect driver: Demographic Indirect driver: Cultural

Indirect driver: Economic

Indirect driver: Technological Indirect driver: Political

Irrigation (dams, rivers) Dry season gardening Improved crop varieties Use of wood Mining Bushfire Livestock Road network Soil type and fertility Topography Rainfall variability Temperature variability Wind intensity Migration Labour shortage Change in religious patterns Level of education Customary land tenure system Rising living standard Financial capital of rural farmers (poverty) Foreign agricultural medium-scale investments International funding/development aid Credits by family, bank, government or NGO Science and research Service by extension officers Governmental laws National agricultural programmes Fertilizer subsidies

8% (1) 25% (3) 17% (2) 75% (9) 33% (4) 75% (9) 67% (8) 25% (3) 58% (7) 0% (0) 92% (11) 42% (5) 8% (1) 42% (5) 33% (4) 17% (2) 25% (3) 58% (7) 8% (1) 50% (6) 0% (0) 25% (3) 8% (1) 0% (0) 17% (2) 8% (1) 8% (1) 8% (1)

Bold indicates the highest rank in the respective method.

applied, increasing degradation was expected. The intensification of agricultural land use without fertilizer input was the main agricultural process mentioned for land use changes and land degradation (in Table 7: 77% of the experts). Deforestation takes place as a consequence of agricultural intensification and extensification. The removal of single trees in a cropping system is the result of agricultural intensification due to the use of machinery, while agricultural extensification particularly takes place close to river beds where virgin forest still exists. Riverine forests have been indirectly protected for a long time since they have been avoided due to river-related diseases such as river blindness (Onchocerciasis; see also Boatin et al., 1997). Today, these areas are free from diseases and are, therefore, threatened by agriculture. Local beer production was blamed for the high demand for firewood but comparable numbers could not be given by the experts. Another cause of deforestation is charcoal production, but some experts stated that much more is going on in large parts of the wooded areas around, for example, Tamale (the capital of the Northern Region). Livestock contributes to tree cover degradation (mentioned by 77%) since pastures are poor and tree leaves are used as fodder. Especially goats were mentioned as destructive to the environment.

In the comparative analysis of the expert interviews, the main processes and relations between drivers and land use types were similarly explained by the different experts. However, diverging expert statements were made on bush fires as driver of LULCC (mentioned by 62%). For example, crop residues could be used as fodder in the dry season but eight experts reported that farmers prefer burning crop residues at the beginning of the season, which could initiate bush fires. Also slash and burn at the end of the dry season can result in bush fires. Many local actors were blamed by the experts for causing bush fires e these included farmers, herders, gatherers, hunters or sometimes young people just setting fires for fun. However, the dominant cause of bush fires could not be clarified by the experts. Biophysical factors as drivers of LULCC were mentioned most often in relation to anthropogenic drivers. For example, land degradation was most often linked to anthropogenic overuse and poor natural conditions, such as rocky landscapes with shallow and infertile soils, rather than to climate variability. Harsh conditions for people due to rainfall variability were mentioned by 10 experts, and low soil fertility was mentioned by five experts. Climate variability as an anthropogenic driver was less exhaustively explained, but briefly mentioned, often in relation to weather extremes.

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Important indirect anthropogenic drivers were migration and poverty (77% of experts). Most of the youth migrate to southern Ghana due to increasing population and, therefore, increasing seasonal food shortage. In addition, they use it as opportunity for additional livelihood to work as cheap labour in big cities like Accra and Kumasi. Some of them do not return to the Upper East Region (UER) but send remittances to their families in the north. Remittances are often used to buy important things like cloths or food, so that nothing is left over to cover agricultural investments. The consequence of mainly youth migration as a demographic change in the UER is that older generation farmers have to stay on the farms to do heavy work which they cannot handle. As a second consequence, renting to other farmers and labour saving practices are applied, for example, burning of refuse to prepare the land. Additionally, two experts mentioned that some have lost hope in farming because land has become very impoverished, and the younger generation in particular is not keen to continue with farming. The majority of experts were rather pessimistic about future development in the UER without political or economic incentives. They shared the opinion that law enforcement and properly placed financial support could lead to a change in the future but currently, government agencies are not driving the system. For example, fertilizer subsidies do not reach the small-scale farmers, and this money is lost to middle men who are often used to link wholesalers to consumers. Even if fertilizer subsidies do reach the farmers, only a few large-scale farmers are able to benefit due to their stronger network and better access to such governmental programmes. The experts suggested improved farm technology, efficient

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governmental support, better market access and purchasing power to improve livelihoods and ensure food security. In addition, four experts envisioned an increase in the impact of science and research. 3.2.3. Identified drivers of LULCC by literature With regard to population growth, the literature analysis revealed that only rural population growth can be considered as driver of LULCC with high agreement, while urban population growth was considered with medium agreement (33%, see Table 7). Literature analyses showed high agreement (83%) for traditional agricultural intensification as driver of LULCC. However, unsustainable traditional agricultural practices were not always related to population growth. For instance, Wardell et al. (2003) and Dietz et al. (2004) linked increasing agricultural activities to economic driving forces. Other important direct anthropogenic drivers were livestock husbandry, bush fires and use of wood. The most relevant biophysical driver of LULCC was rainfall variability confirmed by 11 authors (92%). In addition, the impact of the customary land tenure system as indirect driver was mentioned by seven authors (58%). As in the expert interviews, Yaro (2007), Yiran et al. (2011), and Dietz et al. (2013) see a correlation between population growth and customary land tenure causing land fragmentation. Poverty was only seen by half of the authors (50%) as driver of LULCC. Yaro (2007) rejected the hypothesis that poverty directly leads to land degradation. He concluded that macro level policies and natural processes influence LULCC more than farmer income, since relatively rich farmers show similar ecologically destructive

Table 8 Results of the questionnaire which show the number (no.) of experts who answered the question, the share of experts (%) who ranked the driver with a Likert-value of 3 (0 ¼ no influence; 5 ¼ strong influence), the median, mean and standard deviation (Std. Dev.). Classification of driver

Current drivers of LULCC

No. of experts

% who ranked 3

Median

Mean

Std. Dev.

Direct driver: Anthropogenic

Population growth rural Population growth urban Agricultural intensification: modern Agricultural intensification: traditional Conservation agriculture Irrigation (dams, rivers) Dry season gardening Improved crop varieties Use of wood Mining Bushfire Livestock Road network Soil type and fertility Topography Rainfall variability Temperature variability Wind intensity Migration Labour shortage Change in religious patterns Level of education Customary land tenure system Rising living standard Financial capital of rural farmers (poverty) Foreign agricultural medium-scale investments International funding/development aid Credits by family, bank, government or NGO Science and research Service by extension officers Governmental laws National agricultural programmes Fertilizer subsidies

20 19 21 21 20 18 21 21 21 21 18 19 19 18 18 20 19 19 21 16 20 21 20 19 21 19 19 14 20 19 19 18 20

90 68 38 81 20 44 38 19 76 38 56 42 32 67 39 85 53 21 43 38 30 33 55 58 33 11 32 21 5 21 21 50 40

4 3 2 3 1 2 2 2 3 2 3 3 2 3 2 4 3 1 2 2 1 2 3 3 2 1 2 1 1 2 2 2 2

3.6 2.9 2.2 3.2 1.5 2.3 2.0 1.7 3.3 2.4 2.9 2.7 2.1 3.0 2.2 3.5 2.7 1.6 2.5 2.0 1.6 2.3 2.4 2.3 2.2 1.2 1.8 1.6 1.4 1.8 1.7 2.3 2.2

1.0 1.2 1.3 1.5 1.1 1.3 1.3 1.2 1.3 1.7 1.7 1.5 1.0 1.3 1.2 1.1 1.4 1.3 1.4 1.5 1.7 0.9 1.0 1.2 1.1 1.0 1.1 0.9 0.9 1.3 1.1 0.9 1.2

Direct driver: Biophysical

Indirect driver: Demographic Indirect driver: Cultural

Indirect driver: Economic

Indirect driver: Technological Indirect driver: Political

Bold indicates the highest rank.

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Table 9 Confidence levels for the different drivers of land use and land cover change; RS ¼ remote sensing/GIS/spatio-temporal analysis (quantitative); I¼ Interviews (qualitative); L ¼ Literature (qualitative); Q ¼ Questionnaires (quantitative); detailed results in Annex 2, Annex 3, Annex 4 and Annex 5.

Classification Current drivers of LULCC of driver Direct driver: Population growth rural Anthropogenic Population growth urban (urbanisation) Agricultural intensification: modern Agricultural intensification: traditional Agricultural extensification Conservation agriculture Irrigation (dams, rivers) Dry season gardening Improved crop varieties Use of wood (especially fuel wood) Mining Bushfire Livestock Road network Direct driver: Soil type and fertility Biophysical Topography Rainfall variability Temperature variability Wind intensity Migration Indirect driver: Labour shortage Demographic Change in religious patterns Indirect driver: Level of education Cultural Customary land tenure system Indirect driver: Economic

Rising living standard Financial capital of rural farmers (poverty) Foreign agricultural medium-scale investments International funding/ development aid Credits by family, bank, government or NGO Science and research Service by extension officers

RS

L

I

Q

XX -

XX X

XX XX

XX XX

Level of Confidence Very High High

-

X

X

X

Medium

-

XX

XX

XX*

High

X° X X ? -

XX ? ? X ? XX X XX XX X XX XX X ? X X

XX ? X X X XX X XX XX X X X XX ? X XX X

XX* ? X X ? XX X XX X X XX X XX XX ? X X

High Low Medium Medium Low Very High Medium High High High High Low High Low Low Medium Medium

-

? X XX

X X XX

X X XX

Low Medium High

-

? XX

X XX

XX X

Low High

-

-

?

?

Very Low

-

X

?

X

Low

-

?

?

?

Low

?

X ?

? ?

Very Low Low

? ? ?

? X ?

? XX X

Low Low Low

Indirect driver: Technological Governmental laws Indirect driver: National agricultural programmes Political Fertilizer subsidies * Merged as “traditional agriculture” in the questionnaire °

In geospatial analysis is distance to dams and distance to rivers differentiated: distance to dams was with high agreement and distance to rivers with low agreement and we used the middle (X) in the table of confidence

practices compared to very poor farmers. The relatively rich farmers had the capacity for agricultural extensification due to access to labour and mechanical tools while the very poor farmers were forced to abandon fields due to a lack of operational capacity. Fertilizer subsidies, agricultural programmes, credit and rising living standards are not considered to be relevant indirect drivers of LULCC. No information could be obtained for potential drivers such as topography, foreign agricultural investments or science and

research in the literature analysis. 3.2.4. Identified drivers of LULCC according to the questionnaire The questionnaire results also showed that particularly rural population growth is an important anthropogenic driver of LULCC, and rainfall variability was identified as the most important biophysical driver with a median value of 4 (see Table 8). Furthermore, important driving forces that were ranked by the majority of the

J. Kleemann et al. / Journal of Environmental Management 196 (2017) 411e442

participants (>50%) with a Likert-value of 3 are urban population growth, traditional agricultural intensification, bush fires, land tenure, rising living standards, agricultural programmes and the use of wood for fuel, feed and construction as anthropogenic drivers, while soil fertility and temperature variability are listed as biophysical drivers. The lowest importance was given to foreign agricultural medium-scale investments, where only two experts ranked the driver with 3 as highest value on the Likert scale. Similarly, conservation agriculture as driver with positive influence on food security, is not a relevant current driver, but was stated as potential future driver by six experts (importance level of 3). Lowest standard deviation with 0.9 can be perceived for credits, research, educational level and agricultural programmes. Highest standard deviation with 1.7 can be found for mining, bush fires, and changes in religious patterns.

3.3. Identified drivers of LULCC by the mixed-method approach (including the qualitative part from interviews and literature) By using the mixed-method approach, we noted with very high confidence that population growth, especially in rural areas, is an important driver of LULCC in the UER (Table 9). Expert interviews explained the magnitude and consequence of population growth. Specifically for the UER, the unfavourable combination with an originally high population density of 94 people/km2 in rural areas (GSS, 2012) increases the pressure on land and exacerbates food insecurity and poverty. If there had been an originally low population density, population growth and food shortage could have been buffered by expanding agricultural area as shown in a LULCC analysis in Senegal by Wood et al. (2004). Through the expert interviews, an interrelationship between population growth and customary land tenure could be revealed. In the mixed-method approach, customary land tenure and poverty together has the highest confidence level as indirect driver, which, at least, does not negate the assumption that population growth and customary land tenure could be related. Also, literature showed a correlation. The use of wood seems to be a driver of LULCC with very high confidence in the mixed-method approach. The remote sensing analysis showed that it is linked to population change and the interviews provided a relation between agricultural activities and deforestation. Furthermore, intensive and extensive traditional agricultural practices have a high confidence score in the mixed-method approach, which is related to poverty and population growth according to the expert interviews. But literature showed that this phenomenon cannot always be directly linked to population growth. On the contrary, economic drivers were not highlighted for the current situation in expert interviews and questionnaires since commercial activities are limited due to the high dependence on subsistence agriculture, high population densities, low presence of farmer cooperatives, low soil fertility, lack of road network and absence of irrigation systems. The interviewed experts emphasised that cash crops would be a strong driver only if markets for such crops were available in the UER. The discordance of literature compared to interviews and questionnaires for irrigation (medium confidence) and topography (low confidence) could be due to the fact that it was not a subject of research in literature. Also, geospatial analysis could not provide a clear trend because dam irrigation was considered as strong driver while river irrigation was seen as minor driver. (In the mixed-method approach, dam and river irrigation were classified as one driver of change). Comparing our study with other regions in the Sudanian and

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Guinean Savannah Zone, population growth, deforestation and agricultural activity are prominent drivers of LULCC. Agricultural expansion and increasing population are often considered to be interlinked, but agricultural activity does not need to be directly related to population growth. For example, Braimoh (2004) identified - apart from population growth - agricultural commercialisation and wood cutting as main driving forces of LULCC in the Northern Region, an area close to our study area. There, population densities of 25 people/km2 (2010) are low. Therefore, large-scale agriculture with a focus on commercialisation could be better implemented (Chapoto et al., 2013). Regarding large-scale commercial agriculture, land grabbing is a driver of LULCC in the Northern Region (Ahwoi, 2010; Tsikata and Yaro, 2013) but was not mentioned by the experts for the UER where modernised commercial farming is hindered by small-scale land tenure and high population density. Among the indirect anthropogenic drivers, migration has a medium confidence level in the mixed-method approach because it cannot be identified in our remote sensing analysis, and literature as well as questionnaires show medium agreement. From the expert interviews, we learned that migration is a driver and a consequence of LULCC and population pressure to which the driver “labour shortage” is related. It should be stated that, despite massive out-migration, the population in northern Ghana is still increasing (Van der Geest et al., 2010). The mixed-method approach also revealed that poverty contributes indirectly but with high confidence to LULCC because farmers cannot buy the required inputs such as fertilizer to maintain soil fertility. However, some authors from the literature review showed a diverging picture of poverty as a driver of LULCC. With high confidence, rainfall variability is a driving force of LULCC. Interviews and literature review showed that biophysical factors as drivers of LULCC were frequently mentioned in relation to anthropogenic drivers. For example, land degradation was most often associated with intensive anthropogenic activities rather than with climate variability. Often, a distinction between climate change and naturally high climatic variability was not possible in the interviews and literature. Further, experts might have assigned a high significance to rainfall variability due to communication in their scientific network. But literature analyses apart from the selected studies showed that rainfall variability is considered to be only one driver among many others with same relevance (AntwiAgyei et al., 2016; Mertz et al., 2010; Nielsen and Reenberg, 2010; Nyantakyi-Frimpong and Bezner-Kerr, 2015). Authors also avoided giving a classification of drivers because of its interwoven character (Lambin et al., 2003). In addition, it was challenging to differentiate between described characteristics which have been present in the UER for a long time and factors which drive a system to change. Therefore, it was difficult to differentiate between states or patterns and processes. We should also consider those drivers which were not seen as relevant for LULCC. One explanation could be the indistinct influence of indirect drivers on LULCC, for example, changes in religious patterns. However, it also becomes evident that the majority of parameters with low or very low confidence potentially contribute to food and livelihood security, for example, improved crop varieties, agricultural programmes, irrigation, fertilizer subsidies, and credits. Highest discordance between the methods was generated by agricultural programmes and rising living standard, among others. These parameters might be important for changing the system in the future, as countermeasures against threats to food provision (e.g. droughts), but they are currently considerably

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Table 10 Strengths and weaknesses of individual methods used in our study; LULCC ¼ Land use and land cover change; UER¼ Upper East Region. Method

Strengths

Weaknesses

- Limited analysis of indirect drivers of LUCC (Veldkamp and Lambin, 2001) - The need of high-quality data to give information on many drivers of change; e.g. changes in urban area were not possible to detect - Studies on drivers of LULCC in the UER were rare and not at Gave a first idea about the topic to develop hypotheses the same temporal and spatial level; also, the research focus Can support or reject hypotheses considering a study and methods differed which could lead to bias of the same local and temporal dimension (Geist und Lambin, 2002; Veldkamp et al., 2001) - Maybe, not exhaustively all relevant literature was found even though an extensive review of potential scientific literature was conducted - Interviews also dependent on the level of familiarity with the Can fill data gaps which were not provided by other interviewees, and cultural differences (Brook and McLachlan, 2005) methods (flexible information source) - Results are influenced by the approach how data are interpreted Can provide an overview on a thematic issue and a professional by the analyst (Huntington and Fernandez-Gimenez, 1999). view on the topic without self-interest in the study (Thompson, 1967) - The identification of expert knowledge also depends on its Already a small number has the potential to deliver relevant definition and its distinction from other knowledgeable groups, input because they can filter thematically irrelevant information for example farmers or local officers (Raymond et al., 2010) A similar understanding of the world (“Western knowledge”; - Open question design was preferred to cover the full range of Agrawal, 1995) as scientific community possible drivers and therefore, questions were not standardised, same language (English) where the risk of translation for example, by asking about the importance as a closed misinterpretation is reduced question to everybody - Still, not all drivers could have been covered, since we focused only on scientists as experts and not on other knowledge groups - The huge list (34 potential driving forces) could have reduced Co-design with the expert interviews to focus on relevant driving forces and to improve the acceptance of the survey (Driscoll et al., 2007) the equal consideration of all parameters by the respondents (Crawford, 1997) Standardised format - Some aspects were not filled by the respondents Uncertainties can be quantified - Low feedback from scientists in Ghana - The Delphi-approach did not improve the consensus but rather increased the variance - Classification of the driving forces of LULCC for the questionnaires was difficult as some issues overlap

Geo-statistical analysis - Provision of information about the spatial distribution of the driver, the relation of the driver to other parameters, the rate of change, and its relevance and uncertainty through statistical tests (Rindfuss and Stern, 1998) Literature review

-

Expert interviews

-

Questionnaire

-

undeveloped (Mbow et al., 2008). Supporting this argument, direct drivers with positive influence on food security (dry season gardening, improved seeds and fertilizer input as part of the “modern” agricultural intensification) have low confidence levels while indirect drivers with negative impact on food and livelihood security (poverty and traditional land tenure, i.e. land insecurity) have high confidence levels. Especially laws have been regarded as very important by the experts, but their implementation and enforcement are lacking which lead to low confidence as current driver of change in the mixed-method approach. For example, the law of the riverine buffer zone does not hinder people from clearing riverine forest for crop cultivation if there is no control or intervention (mentioned by two experts). 4. Discussion In a mixed-method approach, all advantages and disadvantages of each method are accumulated (Table 10; Amaratunga et al., 2002; Todd, 1979) which could have had an influence on the degree of evidence. However, the consideration of four different methods for analysing drivers of LULCC has the advantage that information can be incorporated into another method and that their outcomes can be compared cross-tabulated. In our study, initial literature review provided an idea about the research context and research needs which were then used to formulate questions for the interviews. The interviews helped to develop the

questionnaire and to fill data gaps from literature. The geospatial analysis provided an objective analysis of changes on the ground (Rindfuss and Stern, 1998). This advantage reduces the weakness of subjectivity in interviews, questionnaires and literature analysis. In addition, studies from social science are often locally specific which can bias an analysis at the regional level. Qualitative methods (e.g. expert interviews and literature analysis) are often seen as less reliable than quantitative methods (e.g. remote sensing or Monte Carlo Simulation) because objective quality criteria are provided rather for quantitative than qualitative methods (Amaratunga et al., 2002; Malina et al., 2011; Todd, 1979). For example, in Johnson (2008), expert opinion and peer review are ranked as low confidence methods. He gives a high confidence score to modelling methods based on testing and historical data analysis. If we had given remote sensing a higher intrinsic “reliability”, and therefore more weight than the other three methods in our analysis, irrigation could have been given a “high confidence” score instead of a medium level. However, the geospatial analysis is weak in detecting indirect drivers of LULCC (Veldkamp and Lambin, 2001). Easily measurable proxies for land-cover conversions are needed, which refers mainly to direct drivers of LULCC (ibid.). Additionally, in our case, the spatio-temporal resolution was insufficient to adequately detect some of the driver proxies. For example, the analysis of income and education level as proxies was technically possible but its spatiotemporal coverage was insufficient. The stagnation of urban area

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in the geo-statistical analysis can be explained by the coarse resolution and lower classification accuracy of MODIS compared to Landsat or other finer resolution images and therefore, changes in urban area could not be detected. But the geospatial analysis of a peri-urban area in the Upper East Region between 2007 and 2013 in Kleemann et al. (2017) could show that urban development is taking place. For the analysis of indirect drivers, methods from social science perspectives provide more information. Therefore, a combination of qualitative and quantitative data analyses from natural and social science is necessary to cover all of the driving forces. In our confidence table, the majority of parameters were indirect driving forces of LULCC and the most comprehensive information came from expert interviews. Experts are a reliable information source where other methods cannot provide information, especially regarding reasons for LULCC and interlinkages between the driving forces because they can see patterns which novices cannot see (Shanteau, 1992). Furthermore, there are to date no scientific studies that focus on scientists as experts in LULCC in our WestAfrican research context. More often, household surveys or farmer interviews are conducted to identify drivers of LULCC in the Sudanian Savannah Zone (e.g. Antwi-Agyei et al., 2016; Mertz et al., 2010; Nielsen and Reenberg, 2010). The experts had regional as well as local knowledge. The consideration of local representatives in our study might have resulted in rather locally relevant driving forces because they are much more concerned with issues on site (Raymond et al., 2010; Yearley, 2000). However, we also engaged scientists with local knowledge as questionnaire participants. But those participants were able to embed their knowledge into the regional context because of their access to publications and their extensive scientific network. Furthermore, experts from social science might have been underrepresented but also our experts from natural science had predominantly an interdisciplinary background and could speculate on socially driven drivers of LULCC. Another advantage of using scientists as experts is that a few of them have the potential to deliver relevant input because they can filter out thematically irrelevant information (Shanteau, 1992; Shanteau and Gaeth, 1981). The potential criticism of smaller sample sizes compared to household surveys when consulting experts is refuted by earlier studies where the knowledge of a few experts was the basis of research, for example see Celio et al. (2014), Chalmers and Fabricius (2007), Geneletti (2010), Gossens et al. (2001), and Lamond and Farnell (1998). One of the disadvantages of interviews in our study was the open question design. Even though open questions were intentionally used to explore the range of potential drivers, it could have been improved by using standardised questions to crosscheck on the consistency of major and minor drivers of LULCC with the questionnaires. However, the mixed-method approach improved the expert statements by using a standardised format for the questionnaires. Further, social information can be quantified in the questionnaires. For example, mining, bush fires, and change in religious patterns had the highest standard deviation, which could hint at discordance regarding the opinions of experts. In addition, Galamsey (small-scale surface mining, e.g. see Schueler et al., 2011) is a very local driver and it depends on the expert who knows the types of mining activities in the Upper East Region. Summing up the cases described above, the mixed-method approach should ideally compensate the methodological weaknesses of one method through the strengths of another method (Brewer and Hunter, 1989). The insights from different scientific

425

approaches increase the probability that important variables of a multifaceted phenomenon are discovered because attention is also given to contradictive findings or surprises (Mollinga, 2009; Rossman and Wilson, 1984). In any case, evidence provided by multiple rather than single sources of information should be preferred (Yin, 1994). Our mixed-method approach allows the reader to follow the scientific steps of comparing and combining data from different methods and to get their own idea about the discordance and accordance of findings by different methods. Our framework enhances the confidence table from Jacobs et al. (2015) and Mastrandrea et al. (2011) in providing a standardised and consistent classification across methods to assess the level of reliability of findings for LULCC in the Upper East Region of Ghana.

5. Conclusions The mixed-method approach provides a condensed analysis of qualitative and quantitative data of direct and indirect driving forces of LULCC in the Upper East Region and offers a transparent and objective framework for assessing the reliability of the results using a confidence table. Its particular value is that it reveals synergies and contradictions in drivers identified by single methods, so that political advice for sustainable land development could be based on more solid information. For instance, our mixed-method approach revealed that particularly rural population growth is an important driver of LULCC in the Upper East Region where countermeasures should be taken to ensure sustainable livelihoods without threatening ecosystem service provision. Drivers with medium, low or very low confidence are questioned and should be further analysed before taking action to address them.

6. Outlook The results of this study are used as input for a modelling approach with a Bayesian Belief Network on land use changes and their impacts on food and water provision in the rural agricultural socio-ecological system of the Upper East Region. The data described here provide information on the importance (to be considered in the modelling approach), interlinkages and the impact of drivers of land use and land cover change on land use types and selected ecosystem services.

Acknowledgements This work was funded by the German Federal Ministry of Education and Research (BMBF) through the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) [grant number 00100218]. The WASCAL-initiative is a West AfricanGerman scientific collaboration with the focus on enhancing the resilience of coupled human-environmental systems regarding climate variability and other environmental changes (WASCAL, 2016). We would like to express our sincere gratitude to all the experts who spent their valuable time and shared their experience.

Annex 1. Catalogue of combinations for evidence levels Possible combinations between agreement and evidence levels in arbitrary order (important is the amount of occurring symbols).

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Method A XX XX XX XX X XX XX XX XX XX X XX XX X XX X XX X XX XX X XX XX X XX X ? XX X X ? ?

Method B XX XX XX X X XX XX X XX XX X XX X X ? ? X X XX XX XX X X ? ? ? ? ? ? -

Method C XX XX X X X XX X X XX X X ? ? ? ? ? X X ? ? ? ? ? ? -

Method D XX X X X X ? ? ? ? ? ? ? ? ? -

Confidence Level Very High Very High Very High Very High High High High High High High Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Low Low Low Low Low Low Very Low Very Low Very Low Very Low

Annex 2. Definitions of drivers of land use and land cover change (LULCC)

Classification of driver

Drivers of LULCC

Definition

Direct anthropogenic drivers

Population growth rural

Increase of household size and/or number of households in rural communities mainly by reproduction Population reproduction (newborn) and in-migration from other national areas, from rural areas and from other countries to urban areas; expansion of urban area Land preparation and land cultivation with regular or occasional use of tractors; regular fertilizers and pesticides input; “cash crops”; mono-cropping is dominating Land preparation and land cultivation with low or no fertilizer input and rarely fallow periods; occasional bullock ploughing; usually subsistence farming (compound farming) Only frequently used agricultural area (mainly bush farming) with long fallow periods; also including slash and burn Differs from traditional and modern agriculture by reducing soil disturbance, including crop rotation, zaii method, longer fallow periods than in traditional and modern agriculture Using water from rivers, lakes or dams with hydraulic equipment, e.g. canals or pumps; construction of dams; including lowland irrigation Horticulture in the dry season; special form of irrigation with wells and buckets (small-scale irrigation) from groundwater or from surface water; with fertilizer input

Population growth urban (urbanisation)

Agricultural intensification: modern

Agricultural intensification: traditional

Agricultural extensification Conservation agriculture

Irrigation (dams, rivers) Dry season gardening

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Drivers of LULCC

Definition

Improved crop varieties

Use of hybrids and/or genetically modified plants e.g. drought-adapted plant species using less water; plants with short maturing periods Cutting and collecting branches and other tree parts for household purposes, livestock feeding or for selling Extraction of sand, gravel, stones and other resources for construction or digging for gold, e.g. Galamsay or professional mining Bush fires as result from slash and burn; intentional bush fires for hunting included Any activities (especially grazing) by cows, goats, sheep, etc. including poultry leading to changes in the landscape; livestock which belongs to families, communities or Fulani for keeping, eating and selling Paved roads, gravel roads and small streets in UER; focus is on sufficient connections to markets, centers, factories, public facilities etc., for rural as well as for urban people; on the other hand, the density of roads increase land fragmentation The soil type determines the natural humus and mineral content, the capacity of water and nutrient storage; e.g. weathered soil type and soil with thin humus layer have a lower capacity for nutrient storage and they are more prone to soil degradation The topography creates a micro-climate and determines the rate and direction of runoff, the surface exposure to wind, sun and rain; prescribes (in combination with the soil type) the water and nutrient storage capacities, e.g. clay soil is more water-logging in valleys The onset of rain and the intensity of rainfall is changing in the UER in long term which could have an effect on land use/cover; increase or decrease in precipitation Seasonal change in temperature in UER belongs to the Sudanian Savannah Zone but variation in temperature and extreme temperatures increase in the long run Very hot and dry winds are predominant in JanuaryeMarch in the Upper East Region but changes in wind intensity (increase or decrease) could affect the land cover Movement of people; also residents who leave the UER for longer than one farming seasons to the transition zone and southern Ghana and/or to other countries and people who come to the UER from other areas inside and outside the country Access to strong and healthy labour force to work on the field and support the farmer; there is an increasing lack of young farmers which impacts current farming practices Due to the advent of Christianity and Islam, traditional believes and local knowledge in natural features could be affected, e.g. the appreciation of sacred be groves and holy places Attending school and get the chance go to university for people in the UER Customary land tenure with informal agreements in traditional household structures is still predominant in the UER and differs to statutory land tenure; problems occur mainly concerning land security Ghana's economy is growing and is becoming a middle income country; the standards in living are increasing, e.g. the provision of electricity, education, sanitation and health facilities The income for rural farmers and the potential to buy fertilizer, chemicals, tools and other things for crop input Foreign investors providing capital for agricultural areas in the UER, e.g. cash crops for biofuel; “land grabbing” International programmes and projects related to development in UER (projects related to agriculture, education, health and economy) Credits provided by the family, the community, the government, the bank or non-governmental organizations to the smallholder farmer for agricultural activities, e.g. buying seeds, fertilizer, etc. Goes beyond “improved crop types”: development of new strategies of land use practice, new land use systems and possible value chains The support by extension officers for rural farmers, e.g. training programmes on sustainable land use practices The importance of laws for land use-related issues, e.g. laws against bush fires, laws for forest protection Projects and programmes for developing agriculture in the UER, provided by government, e.g. projects by SADA (Savannah Accelerated Development Authority); outgrower schemes etc. Governmental financial support in purchasing fertilizer

Use of wood Mining Bushfire Livestock

Road network

Biophysical drivers

Soil type and fertility

Topography

Rainfall variability

Temperature variability Wind intensity

Demographic drivers

Migration

Labour shortage

Cultural drivers

Change in religious patterns

Level of education Customary land tenure system

Economic drivers

Rising living standard

Financial capital of rural farmers (poverty) Foreign agricultural medium-scale investments International funding/development aid Credits by family, bank, government or NGO

Technological drivers

Science and research Service by extension officers

Political drivers

Governmental laws National agricultural programmes

Fertilizer subsidies

Annex 3. Drivers of land use and land cover change (LULCC) found in literature. The table shows the literature where the driver of LULCC have been mentioned. Statements as “no driver”, “would help”, “as

consequence” and “not a current driver” were not counted.

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Classification of driver

Current drivers of LULCC

Literature

Direct anthropogenic drivers

Population growth rural

Aniah et al. (2013), Bugri (2008), Wardell et al. (2003) contested, Yaro (2007), Owusu et al. (2013), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012), Schindler (2009) Aniah et al. (2013), Wardell et al. (2003) contested, Yaro (2007), Yiran et al. (2011), Agyemang (2012) Bugri (2008), Yaro (2007), Owusu et al. (2013), Dietz et al. (2013), Awen-Naam (2011) would help Aniah et al. (2013), Bugri (2008), Wardell et al. (2003), Yaro (2007), Owusu et al. (2013), Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012), Schindler (2009) Aniah et al. (2013), Wardell et al. (2003), Yaro (2007), Owusu et al. (2013), Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Awen-Naam (2011) Yaro (2007) would avoid soil erosion, Dietz et al. (2013) would avoid soil and water erosion, Schindler (2009) no driver Yaro (2007), Laube et al. (2012) would help but not a current driver, Dietz et al. (2013), Schindler (2009) no driver and would not help, Awen-Naam (2011) would help Yaro (2007) as negative driver, Laube et al. (2012), Dietz et al. (2013) Yaro (2007) as negative driver, Laube et al. (2012) would help, Dietz et al. (2013), Awen-Naam (2011) would help Aniah et al. (2013), Bugri (2008), Wardell et al. (2003), Yaro (2007), Owusu et al. (2013), Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012) Aniah et al. (2013), Bugri (2008) minor driver, Wardell et al. (2003), Agyemang (2012) Aniah et al. (2013), Bugri (2008) minor driver, Wardell et al. (2003), Yaro (2007), Owusu et al. (2013) major, Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012) Aniah et al. (2013), Bugri (2008) minor driver, Yaro (2007) as positive; overgrazing contested, Owusu et al. (2013), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012), Schindler (2009) Wardell et al. (2003), Dietz et al. (2013), Agyemang (2012), Schindler (2009) no driver Aniah et al. (2013), Bugri (2008), Yaro (2007), Owusu et al. (2013), Yiran et al. (2011) as consequence, Laube et al. (2012) as initiator, Dietz et al. (2013), AwenNaam (2011) e Aniah et al. (2013), Armah et al. (2011), Bugri (2008), Yaro (2007), Owusu et al. (2013), Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012), Schindler (2009), Awen-Naam (2011) Laube et al. (2012), Dietz et al. (2013), Agyemang (2012), Awen-Naam (2011), Armah et al. (2011) Yiran et al. (2011) Aniah et al. (2013) as consequence, Wardell et al. (2003), Yaro (2007), Owusu et al. (2013), Laube et al. (2012) as consequence, Dietz et al. (2013), Awen-Naam (2011) Bugri (2008), Wardell et al. (2003), Yaro (2007), Awen-Naam (2011) Dietz et al. (2013), Agyemang (2012) Bugri (2008), Yaro (2007), Dietz et al. (2013) Bugri (2008), Wardell et al. (2003), Yaro (2007), Yiran et al. (2011), Laube et al. (2012), Dietz et al. (2013), Agyemang (2012) Agyemang (2012) Aniah et al. (2013), Bugri (2008), Yaro (2007), Agyemang (2012), Schindler (2009), Owusu et al. (2013) e Bugri (2008) minor driver, Laube et al. (2012) would help, Dietz et al. (2013), Awen-Naam (2011) minor driver Bugri (2008) no driver, Schindler (2009), Awen-Naam (2011) no driver but would help e Bugri (2008) minor driver, Laube et al. (2012) no service, Schindler (2009) no driver, Awen-Naam (2011) Agyemang (2012) as negative for mining and conflict with Fulani Schindler (2009) Aniah et al. (2013) minor driver, Laube et al. (2012) would help, Awen-Naam (2011) would help

Population growth urban (urbanisation) Agricultural intensification: modern Agricultural intensification: traditional

Agricultural extensification Conservation agriculture Irrigation (dams, rivers)

Dry season gardening Improved crop varieties Use of wood (especially fuel wood)

Mining Bushfire

Livestock

Road network Biophysical drivers

Soil type and fertility loss

Topography Rainfall variability

Temperature variability

Demographic drivers

Cultural drivers

Economic drivers

Wind intensity Migration

Labour shortage Change in religious patterns Level of education Customary land tenure system Rising living standard Financial capital of rural farmers (poverty) Foreign agricultural medium-scale investments International funding/development aid Credits by family, bank, government or NGOs

Technological drivers

Political drivers

Science and research Service by extension officers (environmental education) Governmental laws National agricultural programmes Fertilizer subsidies

Annex 4. Drivers of land use and land cover change (LULCC) found by expert interviews The table shows the experts (quotations) where the driver of LULCC have been mentioned. Statements as “no driver” and “future” were not counted.

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Classification of driver

Drivers of LULCC

Interviews

Direct anthropogenic drivers

Population growth rural

P1 (minor): So, population is one factor but I don't think that population is the very big factor. P2: If you do a survey you will find that most of the guys come from the other side of the border. I tell you maybe they will not be able to secure a piece of land to do agriculture or whatever. They move down south a little bit by crossing the border and where they think that rainfall are better e basically that it is. […] Today, it is anthropic because of the pressure on land, because you have more people. P3 (major): Population growth, for me, is the most important factor. […] … there is a very small land area available per person and the population is also increasing very fast. P4: So apart from villages that are far away. That are mainly agriculture. And for those areas, population growths also becomes an important thing. P5: … moving from the farmlands to urban settlements. P6: But there is also the population pressure, especially in the UER. You know, there is so much population. P7: Yes. Population growth can be a driver. […] If you want to share the land per person, with population growth, you are going to have very small … P8: Population is increasing because, as I said, because it is on the way: this is the first stop for many travelers, they come from Niger, from Burkina and they are stopping here for some time before heading towards Kumasi and later to Accra. They come, crop for some time. P9 (major): But more importantly, because of the constant population increase, the population increase is a … because the land is virtually limited. You can't have the expand. P10: … the North is becoming more and more urbanised. P11 (major): That is the main influence. Population. P12 (major): (Interviewer: So you have said that population growth is the important driver.) Yes. Is a very important driver. […] In the rural communities, there will be expansion of the agricultural land due to population increase. P13: … because they are ever increasing the population within the family. So, within a household, you may be getting more and more kids and so on. They are all growing up and they don't have more land, so you get fractionation of the land, and the lands will become smaller and smaller for each of the members of the group. P1: I expect urban areas to expand. People will still migrate from villages to the main cities. P2: I assume as most of the African cities people are coming from the rural areas towards the cities trying to find a job or whatever e that could be one of the reasons why, I mean, people are quitting completely agriculture because of the fact that it is risky most of the time because we have some droughts and people are not able to harvest anything and they move to other jobs in the city. P3: And decided to build like ministries and educational institutions in the past. The question is now, the land which is adjoining the building is still part of the public land but because it was taken from family A or family B and they have no land to farm. This could be a school and the back of the school which is still school ground, a family member is saying “I have to cultivate maize or groundnuts here because I don't have land”. […] Urbanisation, too, is a factor. […] As the population is increasing, you realize that there is the need for housing and in that context people who are able to even just to do the rural housing can only find land in the fringe of the town. P4: Yes, urban sprawl. So peri-urbanisation. Now, Bolgatanga is growing, the villages along roadsides are growing. P5: … increasing urban settlements … [..] They don't have so much shea. Because of the level of expansion of urbanisation. P6: They move to the urban areas. Some move to the down south. In the North, the 3 urban areas are: Tamale, Bolgatanga, Wa, and of course Bawku. So they also move from the villages around the big towns. P7: [Interviewer: So Bolgatanga is also increasing as a town? So there is urbanisation?] Yes, because maybe the township itself. And you know, there is a university in Navrongo. P8: Let's say there is a lot of expansion in terms of buildings and the rest and they have moved the communities out of this place [Bolgatanga]. So all this was used to be farmlands. […] You got a high population density, the number of people per square … and I'm sure it's still increasing. P9: We know that urbanisation is coming, it's growing. P10: The urbanisation process like in Bongo or Bolga is driven by activities of people from the south who also establish businesses in the North. Like banks are also going in there, then for housing … […] If better jobs are established, even more people are coming and the urbanisation process will go on. P11: Like urbanisation or housing. Not only the villages. P12: … along the cities, the most significant land use change has been conversion of agricultural land to urban areas and settlements. P13: I mean, here's like population growth, which leads to urbanisation … P3 (minor): Yes. It is happening in a very limited way. The best cash crop which is doing well in the area is cotton. Cotton is the cash crops that they grow there, in Northern Region and Upper East Region. And this is going for the means of having to get money to send their kids to school but in the recent past, the cotton company of Ghana has not been able to drive that process to its maximal possible advantage. P5: That depends on the individual. You can have intensification, addition of chemicals, and high yield in variety. […] … they use the bullock plough and the tractor plough and for the tractor plough to …. plough effectively they have to cut the trees, and also the tree stumps. Because the tree stumps … if you cut the tree, the tree stumps will regenerate. But now there is no regeneration. P6 (no driver): When you say intensification e yes. It also means fertilizers but that is part of the innovation: improved seeds and all that. But here, they haven't … they are doing just their

Population growth urban (urbanisation)

Agricultural intensification: modern

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Drivers of LULCC

Agricultural intensification: traditional

Agricultural extensification

Interviews continuous cropping, annual cropping. They farm on the same land but they apply little technology to it. P7: … the introduction and use of tractors, the aim of increasing food productivity … if not properly managed can bring an adverse effect. […] For example, if you use a tractor to plough and you don't do it properly, it can expose the land to erosion, serious erosion. Two: it can also result in soil compaction. And then, it also results in removal of trees. […] You see, over here, the land units are very small. So if you want to practice agroforestry, then tractor movement can be restricted. But in other places where land holdings can be very big, we can create permanent tree stands. Permanent. But over here, it will be difficult. Because somebody will have 2 ha, 1 ha … P9 (no driver): Because there's no intensification in terms of … we don't intensify but fragmentation. […]. The possibilities is to equip the rural areas a bit better so that the young ones will want to go back. If there is some incentive for them or they can get some means of living, better than selling or doing odd jobs in town they would definitely go back. P10 (no driver): (Interviewer: But did you see modern intensification in the Upper East Region with fertilizers, tractors … ?) We are not seeing much intensification yet. P11 (future): (Interviewer: But do you think also that the land which is available will be - like agriculture land - crops will be more intensified in the future? That there is more fertilizer … ?) That is the trend. I think so. That should be the case. There's a likelihood that it will be more commercial. P12: (Interviewer: For fertilizer input, is also the case that the young people...) … are more likely to buy fertilizer and use it? Yes. (Interviewer: Because they want to have more yield?) They grow more for commercial purposes. […] And they grow them only during the dry season. And then they make a lot of investments in terms of manure because without fertile land the vegetables will not grow. So they will manure, the field, they put a lot of fertilizer and soil improves. P2: They come, crop for some time. If you even look at the soils e they are heavily cropped. It is just nutrients mining almost. They don't use fertilizer. P3: … very, very critical when it comes to the ease of land use because they are intensively used the land and become much degraded. […] … intensively means that one land area being continuously used year in year out.. P5: Well, they have the fallow system also. But because of the need to sustain once life fallowing is … it fallows not for quite a long time, it fallows for some few months. P6: Because if you talk to the people they say that when you use fire to clear the land it burns the vegetation and the ash is added back to the soil which, basically, you add nutrients back to the soil. You see? The time when they burn the land is dry season. So it does not rain. If it rains the ash would be added back to the soil but it does not rain. So wind blows and blow all the ash away. […] Rather weed the land and clear the land or use a plough to plough the land. That would have been better thing to do. […] Because it is high population there. As a result you have to cultivate the same land every year. […] If you think of other land use types like intensive cultivation without fertilizers and others e that's certainly not a good one. P8: But for the typical farmers, it's virtually input free. […] (Interviewer: And it's more single trees. Is it because of slash and burn in the past?) Yes, it think, this is also driven by agriculture. P9: That means you are cropping the same land over and over again. Meaning you are taking nutrients and probably putting very little back. Therefore, it means that the crops that need a lot of nutrients will naturally not be doing well after some time. P10: the exposure of land cover. […] People are cropping year in year out and there are also bush fires. You don't have regeneration. P11: Fallow? No, their land is under heavy use. P12: In fact, in the Upper East Region, you hardly find any land being left to fallow. P13: … intensification in terms of less fallow. Intensification in terms of overworking, overuse of the land, and without the necessary inputs. P4: … you need to move out to get land to farm. […] But if you take the communities that are on the other side - from Zuarungu, they have the possibility. Because towards Naga and Biu - there is a lot of land. P5: … areas with trees will be converted to cropland. P6: And in addition, more people are coming in there and population is increasing. So you still have to convert more land into that. P8: Those we call marginal lands. They are not supposed to be crops but they are crops. So you see, they dig shallow, they plant on it. They don't get much but they just keep scratching that shallow land. Yes, those grasslands are going to be threatened. Or are threatened. And I suspect, there will be a reduction in the grassland area. P9 (minor): That area is still virgin in a sense, virgin in the sense that it's not been cultivated. So people are moving out there. To go and farm and come back. That's where you can have extensification. But within the households, there's no extensification. P10: Also from Burkina Faso people are coming, people from Togo are coming and end to Upper East area. They also want more land. So traditionally, every year there is struggle over land in rainy season. […] Huge parts of the forest cover of the savannah ecosystem within riverine and forest groves have been degraded. P11: What may come to mind, or what is farm on the field is … almost everything is going. Vegetation is being cleared in a disorderly manner. P12: see “Population growth rural” P13 (minor): So, there is a likelihood that people may want to move into these particular areas. Particularly, if they can identify areas where they think that they have not been cultivated, and they think the fertility there is likely to be better. […] Because already they're struggling to even get more land.

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(continued ) Classification of driver

Drivers of LULCC

Interviews

Conservation agriculture

P6 (future): If you are doing erosion control e it is part of land conservation. P7 (future): … if you move away from tractor use and we think of conservation agriculture. That can also favour agroforestry. P2 (minor): Bolgatanga bust very quickly, the city was expanding heavily and they is a need for domestic water and it why they used the tab water from the reservoir and by doing so and having an increase in the need of water; that reduces the irrigated land e that was one of the major factor. […] The problem is that irrigation in Ghana is very weak developed. P3 (no driver): So, with available water for irrigation purposes, the farmer could do all year farming and that would be an important boost to the agricultural production potential of the area. […] The Upper East Region is not included, again, because of the terrain, limited land size, land is not good for agricultural purposes. P5: [Interviewer: But it's not so easy to get access to more fertile land.] It's not so easy. So those, some people would go for land or rent land along the river banks. P6 (no driver): But if you can invest into dams and provide small irrigation dams, climate variability may not be a big issue. Because the factor of climate that we need is the water. And if you can store the water, and pump it and get water from underground and feed the farms e that should be fine. P8 (minor): … from what I saw there … the last time I visited there [Vea dam], the whole system has collapsed. […] There is not so much production there. Again, I think, there is even a reduction, to me. To expand it in future e it's possible if there are … some of the problems are solved. And then, the irrigation canals are put in place again. P9 (no driver): One of them is actually irrigation. If somehow the irrigation system works you can get more money from irrigation. Somehow this is also not working. P11: In fact, all the White Volta … rivers have been farmed very close by, because of the drought. Dry season. To assure them water, they are very close to … P12 (minor): They will grow some rice in the rainy season. But during the dry season, it's the vegetable that are mainly irrigated. A few people will still grow some rice but the vegetables are that much more important. Only problem now is they have a lot of nematode problem which is affecting the tomatoes. P13: And then again, the people also, because they want water, have gone verydin fact, in some casesdinto the riverbed, so that now the people are doing this. P2: It is mainly increase of dry season gardening using shallow water is the most striking element in northern Ghana, in all the lowland. Its bucket irrigation most of the time; meaning you fetch water. P4 (minor): … the garden culture dry season gardening. […] That's a huge culture. But that culture is limited. Mainly to Navrongo. In the Kassena-Nankana, only a few villages are lucky to have water table … P5: Now, we have much more of irrigated farming during the dry season … P9: In fact, the dry season is a very big problem for livestock. Because those valleys that they could graze in e definitively there is the shallow well irrigation. So they just depend on the very dry grass. P12: And then, there is also the increased dry season gardening. P5: … high yield in variety. That means you would have tomatoes that will yield so many fruits. P7: You know, crop breeding, they are coming out with suitable varieties that would make it suitable … […] And people are talking about the GM crops that may not even use so much nutrients, so much fertilizer needs. Drought resistant. P8: There are now new varieties. Early maturing maize and people are going for maize and the cultivation of millet is rather reducing. P9: Gradually, maize is becoming very popular also there. […] But what I think is the main reason for that is: first, it is short duration … that they are able to get a variety that is short duration. P12 (future): They have another reason, crop research institute in Kumasi to develop these crop varieties and these two are specific for this area because they are short duration varieties. P13: … because some of the varieties they have developed, the people still feel that they would rather depend on their own old varieties they know because there they know that at least even when the climate or the rainfall fails, they can get something. The new varieties that they are bringing, it will … yes, high yielding and everything, but they are not very much aware as to how it will respond to let's say, a drought. P3: The tree cover is going because they would have to cut the tree to make charcoal. That is impacting on climate variability. P4: … And where you have all this forest and cutting this forest … P5: … most of the trees have been cut. P6 (minor): You don't cut them down much e that is not the major problem. Because of course in the past, they've cut most of the trees and that is about 20, 30 years back. But these days, nobody is involved in cutting. In the past, they've used it to burn charcoal. P8: It's from cutting off trees. But like here, it is virtually, most of the trees are gone. So most if it is coming from the hinterland, from further off and people carry it to … P11: But they also need the wood for fire wood, for power, for fuel. P12: … there are 3 or 4 communities close to Bolga where they produce a lot of charcoal. P13 (minor): … at the moment, we have a problem of getting enough wood for fuel, let's say fuelwood, and so on. P8: Yes, there was a conflict. A community complained because they were devastating the area. But they have moved. Maybe they have gone to other areas now. There are many more, bigger companies. The first one was just people, individual efforts, all the Galamsay digging. But now you have people, I mean companies, are coming with machines coming in and they are able to establish a proper mining system. P11: They prospecting a mining for gold. They do this surface and pit mining up to a shallow depth.

Irrigation (dams, rivers)

Dry season gardening

Improved crop varieties

Use of wood (especially fuel wood)

Mining

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Drivers of LULCC

Bushfires

Livestock

Road network

Biophysical drivers

Soil type

Topography

Interviews Associated with this mining, illegal mining, they clear the land. I mean, they use bull dozers or whatever instrument they can get. P12: Those migrating there maybe just those coming for … well there is increasing illegal mining activity in the Upper East Region. P3: Ensuring that bush burning - which is a very critical element in this degradation process - is minimized. P5: So, the burning is also a result of hunting. P6: From my perspective, the land use aspect that is degrading the land is including the fire … P7: But that aspect where people don't want to plant trees is because of the bush fires. People think if you plant a tree. The fire would consume everything. P9: Yes sometimes they just burn it off. Maybe it's really a very funny thing that you don't have something to feed your animals and yet you don't think that there's a need to harvest those things to use to feed the animals rather than burn it all. P10: see “Agricultural intensification: traditional” P11: Yes. Bush fires. It's a big problem here. You may have a small property that you are burning. There's a lot of bush burning. P12: Just go on a rampage burning any vegetation you see around. P2: … the fact of the matter also, they have these small animals like goats and whatever that can just destroy the whole system. Goats eat everything. P3: There is a conflict, because sometimes these cattle can destroy the food crops in Ghana. P3 (no driver): What happens is that, when you go and you don't see the cattle in the area. Because there is not so much grassland, they are grazing the cattle in other areas around the Northern Region where there is a lot of grassland but at the end of the grazing period, they would send their cattle back to the Upper East Region. If they want to do anything again, like grazing or feeding, they travel into the interior and feed them in other areas. P4 (minor): Yes, the livestock roams around. But in the rainy season, not possible because not much land. Than it means that kids have to go far away with their cattle. And that is why cattle population has gone down because we don't have kids to take the cows out. P5: The Fulanis. Because they come in herds. And their cattle when they are browsing, when they are eating the grass they eat to the core. They eat it very short. […] So, maybe one of the landscape drivers would be the invasion of cattle herders from the Nord. P6: It's easier for them to burn the land for land preparation because it reduces your costs. In addition, people also burn it because you see a lot of people who rear livestock like cattle and other things. And when grass is old, grass is not that juicy for the cattle. So they burn it to clear the land so that new ones will grow and their cattle can feed on it. P7: … animal rearing, overgrazing … […] When animal population is high, we are going to have intensive grazing. And that can result … you know, the land will be less protected … and that can result in land degradation. P8: … that are the critical issues surrounding intensive agriculture. But the way things are right now: all the residues are collected. So legumes have no benefit. [..] They taking it and go and feed it to the animals. P9: … livestock is very, very prominent. Very, very prominent. But you would also probably realize that grass that is feed for the animals is also a problem …. overgrazed. […] I mean you have to fence it in also that goats and sheep don't feed on them. P12: … you may find a few land fallowed but used a graze land because it's too shallow to support crop growth so they leave it as range land, for communal graze land, they send their animals there. P13: … the animal pressure is also there because the land is limited. You are producing a lot of animals and therefore they are overgrazing. If there should be any grass, then they might as well chop it. P3 (no driver): And then the road networks are not very developed, too … P4: … all along roadsides. So you have the road Bolga-Navrongo and you see the villages all along the road. P5: It is along the road. Mostly it is along the road … P7: (Interviewer: So also the road network contributes to urbanisation?) … then also the road network can also be added. P3: The Bongo area is a very classical example where the ground is very rocky and you cannot do much in terms of agriculture. P4: One of the reasons could be the nature of the soils. It's poor and it contains too many rocks. [..] Because soils out there have been cultivated for more than 60 years. These have never been touched. So even if they have been poor before, they are now better than the normal farms. P5: … the soils in the Upper East Region are very thin, not too fertile, it's only valleys that are fertile. P6: It's not that fertile, yes. Most of the savannah lands … has the fertility issues … P13: And given the fact that a lot of the soils in most of these areas are also shallow. P3: Apart from that, this is nothing something where plantation agriculture could capture on because it is rocky area. P5: see “Soil type” P9: (Interviewer: …. the shallow soils, the rocky stones? Like bare soil?) Exactly, can hardly do anything on it. Just waste land I would say. P11: (Interviewer: But if I'm thinking of the Vea catchment, mainly here it's rice.) Cause there is a valley. P12: Also rice, but rice … rice will do only in the valleys, in those areas. There are not so many valleys there. P13: … given the fact that most of the lands over there have restrictions in terms of iron concretions, in terms of boulders, particularly in the Bongo area. […] Exactly, the rocky sides. You see these big granite boulders.

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(continued ) Classification of driver

Drivers of LULCC

Interviews

Rainfall variability

P3: Now, the critical thing about the land use dynamics in the Upper East Region is the changing rainfall pattern because the rainfall pattern has become very, very diminished in terms of duration or time. Only for 3 months or so, the rains have come and they stopped. P4: … creates the demand for land and the need to convert agricultural lands which comparatively now, therefore, have a lower value for farming than for housing. Because a few grow their crops … I mean, with unreliable rainfall, among others, you might end up just getting, say, half a ton. P5: If we have constant rainfall with the duration … same duration, then we still have regeneration. But now during some years, three years, you have very low rainfall, then the following year you have very torrential rainfall. P7: … climate variability. As a driver. […] You know, when we have flooding, extreme weather events of flooding can also … when there is flood water standing on the soil surface for longer time this can also degrade the soil. And when we have drought for longer period, this can also degrade the soil. P8: Climate variability? Yes, I think so. The usual observations of everybody, when we were kids, at least, this was a dry area compared to other places. But the rains used to be more predictable with the onset. But now, it is very unpredictable. P9 (major): You plant, the rain comes, you plant and for maybe three, four weeks … or maybe two, three weeks, there's no rain. And that is the time that is so critical. […] And if it doesn't do it at that time, that's the end. It won't do well. […] So if you're not sure of the rain or when it's coming, buy fertilizer, whether you will apply it. For some reason, the rain doesn't come, you've wasted the little money that you have. Survival money has been put in fertilizer, rain comes and destroys it completely. P10: The main reasons are, first, global climate change and changes in rainfall pattern. P11: Because of the weather, they dry the … the drought … Because of the drought, they know sorghum and millet are able to stand … […] But we think climate change has an impact. Climate change is having an effect. Impact on water resources … P12: Because the main climate variability effect is the instability in the onset of the rains and also there seems to be a reduction in the moisture availability period. The rain are now starting late. The rain is used to start in May but now in June, July before this onset. Then still ends in October. So the rainfall period is becoming shorter and becoming more variable, especially the onset. P13: You ask them and they will tell you that there is a change in the rainfall pattern in terms of normal rainy days and moisture availability. […] Because now, we are getting less rainy days, intense rains, and so on. Floods are coming. P3: So it has the potential to lead to desertification because the temperatures become very, very high. P13: But in terms of change, we will also see that given the fact that there is these evident changes in climate, in terms of rainfall, in terms of temperature, whatever it is. P4: The Harmattan comes early and the place look so … you can see across 3 km and … P5: … I still want to stick to also wind. P6: Winds are very important in the northern Savannah Region of Ghana. […] There is a lot of the land … the greater part of the land gets exposed. P7: So if the land is degraded so bad that wind erosion can carry a lot of the dust. It can create health problems to the people in the south of Ghana. So it can generate into bigger and bigger, and serious problems. And even to the point where, initially, it can become an environmental disaster, it will result in to social disaster and economic disaster. P3: What we realize, is that the majority of the youth, especially the young female and also men, they decide to migrate away from the area to the southern part of the country. […] In the dry season, they come. But some come and because they realize they make a lot of income, then they don't go back and stay longer than for one season. […] When they leave and their family members are left behind. As you know, fertilizer is very expensive. So they maybe ask the people sending money to buy fertilizer or to buy an animal. P5: [Interviewer: So what do you think are the main drivers of land use change in this area?] P5: I would say the population dynamics. […] You know, what happens is that some during the dry season, they move to the southern part of Ghana to work and get more money and come back in the wet, in the rainy season. So in the dry season, when his land is vacant, he can rent it out to you and he will move down south. P6: Yes, most of the northern regions, they rather move to the southern Ghana. Some move to the cacao areas, some move to urban areas … P7: Seriously, people migrate. […] So people start losing hope in their environment as supportive of their livelihood. Then they must look somewhere else for opportunity. P8: So I think, you call it migration? People have come in to work because there are opening up new institutions and government agencies. […] You have a lot of people even from this community or from Bolgatanga who are in the South, working in the South. But they are investing back here in things like gust houses and in businesses P9 (minor): Well, migration is probably also another … it's a driver in a sense but maybe not too much. Because if the young people move out, the old ones just stay with what they know before. They are not going to do anything new. If the young ones were there, they'd probably would do something new. But they're all going away. P10: About 70e80% of the youth coming to the south to work during dry season and leaving their farms in the Upper East Region. Traditionally, they work as cacao farmer in the forest zone. Most of them are cash crop labour. But now, they come also for other works in urban areas. Now, also females are coming to southern Ghana. Most of them sending money back but if those migrating people are getting poor, it will be difficult to send money back. And life is expensive in urban areas. P11: That one is counterbalanced by also a mass movement of people.

Temperature variability

Wind intensity

Demographic drivers

Migration

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(continued ) Classification of driver

Drivers of LULCC

Labour shortage

Cultural drivers

Change in religious patterns

Level of education

Customary land tenure system

Interviews P12: They are still young, a lot of young people in the villages but they migrate to Southern Ghana to work during the dry season if they can get a job. […] (Interviewer: So that they are coming here for teaching or like nurses?) Not so many of them. Most people will be people who come from the area. But you also have because if this … Bolga is quite cosmopolitan so you will have other people who are coming to stay there. P13: … you get a lot of the older people remaining there and then the younger people would want to migrate. So, this rural- urban migration might be intensified over time. P3: So the difficulty is that, if the young ladies are coming here to do the head porter thing, it is left with the elderly women who don't have enough energy to work in the area. P4: Today, the problem of labour … labour in northern Ghana is a huge problem. You know that? Even if northern Ghana supplies the rest of Ghana with labour. […] We have the biggest labour scarcities in northern Ghana. P9: If the old man or women are left in the villages and all the young men are gone out and all I need is to see how at least I can do something. The best thing is “well, if I don't have the labour why don't I just burn it?” I know it but it doesn't solve my problem. P13: But the old man has no option but to remain there. And that might be one of the main change that you may have in terms of, you know … P3: Like I said, this traditional thinking that you plant a tree and you would die is also changing. P4: Even the rural people themselves. But they are picking an urban mentality. They are absorbing the culture of the urban. The rural culture was based on the fact of respect for other people's property, respect for their own gods, are still not doing steeling. P7: Maybe my culture says that this a reserved area. No farming, no agricultural activity … so culture, religion, traditional believes can also affect land use change. P12: Yes, sacred forest are used for protection. But they are gradually breaking down because of changes in norms and increasing Christianity and other religious activities. P13: … historically, everywhere you go, there is this sacred grove where they leave, and it's a gallery … a forest with all the medicinal plants and so on. And now, people have abandoned all those things … P3: It is changing because of education. But traditionally, many people in the area belief that if you plant a tree and the tree survives - you, the one who planted, would die. P5: The other driver would be education. The level of illiteracy as related to the importance of vegetation and trees. P6: You are getting education to work, isn't it? Let's say, we have an agricultural institution which teaches people how to farm, for example. And people graduate from such institutions and they are resourced. They can do farming. P7 (future, minor): … farmers have worries and their worries can be addressed by proper education, scientific research and innovations. […] And also if you have a farmer who has formal education. Let's say, somebody graduates from university or some knowledge in agriculture, he becomes a farmer. […] But education can also change my way of understanding the way my father did this work. But if I don't have the right education I might want to continue the same way as my father did. P8: Those who are a bit … mostly they are literate farmers. The fellow has some official government work but he is still doing some small side farming. P9 (no driver): But I've realize that over time education has not changed it because it is not really … I don't know how to explain it. I don't know why it is going on. When it can really be changed. Ask all the NGO's. They have done a whole lot of things educating people. People know it, people know that this thing is not the best way to do to and they still do it. The question is: what is the real reason for them knowing that it is not good and still doing it.. P11 (no driver): They haven't been to formal school but with time … if they get fertilizers, they will apply it but then they don't have the power to purchase the fertilizer. P13: And especially those who are likely to really migrate would be those who have had the chance to have some education in the family. P2: The land tenure system is really one of those elements that really are not helping the development of agriculture and really not in West Africa. P3: So there is a lot of conflict when it comes to compulsory acquired land by government in the past. Now, there is some small land left - the people say they have to cultivate that land and it is important to look at it historically because their parents or grandparents were not even compensated by government. So government just took the land and then build a school because the school is going to serving the community. But the community now is saying “yes, we understand but we also need food to eat”. […] The land tenure system is a very much factor. P5: So it's the Tendana who gives out the land. […] So, it depends on, if they give you the land. […] They are going to rent, you can rent it. We have various rental system after cultivating you may give the landowner one third of the yield, of the harvest. P6: … let's say in the North, my dad has a land, my dad would be farming it and my dad has 3 kids. So when my dad dies the land would be shared between me and my siblings. P8: They can't go to the East […] Is tight there. It's not their land. P9: So there is a house, there's a household head. He has a number of people in the house. The compound farm is for all of them but headed by the household head. The outside farms can belong to the individuals within this farm. Therefore, because of … the younger ones … that they must produce what the household head thinks are important which is the basic food. P11: Most lands are in the hands of the family, so the lands that the current farmers are using are not necessarily where the commercial farmer may go in. Because in the North, their lands are just close by their houses. Their hamlets. Their hamlets and villages are there. P12: Yes, land security is a problem. So he has …. could be … because if people have permanent tenure of the land they probably manage it better because then they can make investments in their

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(continued ) Classification of driver

Economic drivers

Drivers of LULCC

Rising living standard

Financial capital of rural farmers (poverty)

Foreign agricultural medium-scale investments

International funding/development aid

Technological drivers

Credits by family, bank, government or NGOs Science and research

Interviews land that may refund immediately but in the future. P13: So, you may have a Tendana who is in charge of the land over here. And somebody who is over here, cultivating. If he needs land for cultivation, he may ask for a piece of land over here to cultivate, and he might be granted that opportunity to do that … P4: But beyond population, it also, I mean, has to do with the fact that the economy in a way in the whole of Ghana … the economy has kind of grown. And as a result, people are investing in this land and buying land at home to put up houses. So a major driver, beyond population … I mean, a major explanation for the peri-urbanisation has to do with the fact that the economy of Ghana as a whole is improving. […] So it means that if I am remote in a sense of distance, might not be remote by the sense of the word as used in literature. I am actually not remote if you take the mechanisms of the capitalist relations. I might not be remote at all because I have every single market information, I know very well the incentives, the rationality to increase my production, I know of some modern benefits of life and sources of happiness that I want to take part in … P5: Yeah, so they have phones. So this looking at the quality of life style. So, they move from the low degraded areas to areas where they can get access to fertile land or water, where they can cultivate almost all the time. P8: … you see, the standard of living has in way improved much more in the rural area. […] I see much of the agriculture is really shrinking. P3: … if they stay back home there, they can't sustain their lives. […] The farmers do not have the money to buy fertilizer. P4: If you don't want to go to Naga to go and farm, then you stay in the village and you have to steal. […] So you end up … you live an unhappy life or do something stupid. P5: It's not that everybody has the resources to do that. You need money to pump water, you need money to cultivate the land, and you need money to buy chemicals and fertilizers. So during the dry season, you have a lot of big groups of people during the dry season cultivating vegetables or water melon. P6: They don't apply so much fertilizer. Because fertilizer in this country is very expensive. Farmers don't have access to it. […] But the point is, let's assume you are a poor person, you need to clear the land, you don't have money e what you would do? P7: … people are unemployed, people are not engaged. […] The farmers know there is a problem but on their own, on their own resources, they cannot solve the problem alone. And they would need external support by several means. P8: But they'll do. They have to manage with that. That's the driver of the poverty. […] Yes, you have to pay for the water, maybe buy pumping equipment etc. Usually, it's the people who are in the urban areas who do this vegetable production. P9: You should probably be able to send your child to school and pay. It's after all that that you can think of where you will buy fertilizer and fertilizer is very expensive as it is. P11: Because those who can afford, for example, it doesn't matter the land. They do ploughing. If they can pay for the plough fee, plough, hoe this and that, that. […] But if you were talking of the small lands … like half this room and most of them are farming on acre size. […] … but then they don't have the power to purchase the fertilizer. P12: So they cannot relate to such an investment where they have to wait for so long. It's short term survival is what matters to them. P13: And those who didn't have, and you could see that for almost all the poor families who don't have animal traction animals, their farm sizes are very, very small. […] And if they want to remain like that, it will go on and they'll become poorer and poorer and poorer … P3 (no driver): They need large areas and in Upper East Region, land is limited. Apart from that, this is nothing something where plantation agriculture could capture on because it is rocky area. So when you look at the agri-investments which are coming, the global north companies, they are mostly in the Northern Region … P4 (no driver): Yes, basically, we are looking at only very limited patches in the Upper East Region. So it might take time as we see … what is it - foreign investors in the Upper East Region, right? P5: It's foreign. They have these out grower scheme were farmers are given the seedlings. They grow a plant, they grow plants for the company. The company helps them to irrigate and grow. So, that has changed the landscape because now you have a lot of trees in there. P7: … it can be foreign investments. P8: Not only NGOs but … other departments have come in. Other organizations. Governmental organizations have all come in. International organizations - I can see all the sign boards all around. So when they come … I mean, it attracts people. P9 (no driver): You don't depend on donor support to solve problems like this. Most of the donor support is on something completely outside the basic necessities of farmers. […] Well, what we have been seeing is that it looks like if government, NGOs, donors, everybody had kept away. Maybe farmers would have done better. Because they would have been more innovative. P13: So, what I know is that in those areas you have a lot of NGOs working all over the place, and so on. […] What Care International did was working through the district assembly. The district assembly acquired the land legally with a land title, and shared it among, let's say, 50 young people, and they provided them with boreholes to really do their farming. P2 (no driver): But here no bank would take that even if you have a title. I doubt that this is for agricultural land. P5: They have been conducted by SARI on the possible types of crops that … the farmer adapt. They have introduced soya beans but I don't know how well it is doing. They have introduced soy beans. Formerly they used to cultivate tomatoes, now they have a lot of water melon being cultivated during the dry season. P7 (current and future) (see “Level of education” and): But I know very well, very serious research (continued on next page)

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(continued ) Classification of driver

Drivers of LULCC

Service by extension officers

Political drivers

Governmental laws (lack of law enforcement)

National agricultural programmes

Fertilizer subsidies

Interviews is going on in reducing the gestation life of shea butter, shea tree. I think, it is now about 6 years. It's been reduced from 20 to about 6 years. And there is a possibly for people to start planting or growing the shea. […] I think it has started already because research results, when it is translated into information for famers, can also make a change. P9 (no driver): But extension and research is just exactly the way … extension is not working. So researcher is not doing anything. Because researchers are all over the place, they also get their salary but they don't get anything to do research except donor support. P12: SARI is working in developing crop varieties that are very short duration. For Maize we now have varieties that have 75 days. P13 (minor): So, in terms of changes, I think all the available technologies are there for practice but it's one thing having the technology, another thing transferring it for adoption. P7: Because in some cases, they can learn from extension officers. P9 (no driver): Extension is not working because there is actually no extension, proper extension taking place in the field. They don't, maybe when you go you can find out. The extension officer is supposed to have, at least, to be able to buy fuel for his motorbike to get to wherever he's supposed to get. For the past two years, no one gave him money for that. P13 (minor): … you have an agric extension agent who is in charge to really do some extension work with the farmers. […] So, we need to change extension in the way of doing things. P2 (no driver): It is feasible. It is just a matter of political will. There is a lack of will e it is nothing else. It can be fixed. P3 (no driver): In this country, we have a bush fire law that nobody should set a fire in the bush. But in the rural area, who is there to monitor? P4: And that means, as a driver state policy, government policy also comes to explain this new change. P4 (no driver): Government has too much work. Government actually isn't there. Government is in Accra. When you go to the districts, do you see government? Government is basically sitting behind the desk. He doesn't see anything. Even if he sees things, he cannot do anything. He has no power, beyond his salary - that's it … P5 (no driver): If government earmark this land as a forest reserve, nobody would go in. [Interviewer: Is it the case?] Well, this is not the case. P7 (no driver): Yes, you know, the government has not seen it as getting … becoming more serious because when there is flooding in Upper East Region and people are losing their lives, their property - government goes in to help through the national disaster management organizations. So government thinks it hasn't got to that level of seriousness. That is why nothing seems to happen. P9 (no driver): I don't think any of the government policy are actually driving anything. P11 (no driver): How to control? Also if it's already there, how you say “you have to go away”. P13 (no driver): … the laws are saying that for a river, I think you have to leave … is it 30 m or 20 m or something on both sides, and all that. We have all these buffer zones. […] (Interviewer: But I heard also that it’s not working because you cannot keep away the people from the water.) Yes. For some of us, we feel very strongly that the way we go about some of these things is another problem altogether. P4: Upper East Region didn't grow maize. But for the first time, everyone grow maize because you get fertilizer, you get some money from government and NGOs and so … […] SADA has a new program … […] They have 2 models: farmers come together, they support them with tractors and what. But now, they have started a new program where they look for individual investors … P5: Not giving up land but they want to increase the financial wellbeing, employment, and they say “well ok, now we would prefer to get more mangos than millet”. So, people now would go into cultivation of mangos and the support they would derive from the government. P6: But I know that EPA has some programs on that and there is also some new policy that is now going on in Ghana on bush fires. So some sort of control of bush fires. P7 (minor): Government builds a dam, here, to provide water for dry season cropping. People now start dry season cropping. Governmental policies also come in a form of provision of infrastructure, provision of market access. P8 (no driver): That's why the government started some program here of guinea fowl rearing for those people who are landless can go into rearing of guinea fowls and that kind of thing. But they just buckle the whole program politically. So it's useless. P9 (no driver): And nobody seems to think that it's a feasible program. It has been tried in many, many places. But somehow it hasn't been accepted. P13 (past, no driver): … because we have seen situations where ministry of agric went and bought a whole lot of sheep and goats and so on, and they were supplying the families. […] So, a lot of problems came in. P2 (no driver): I doubt it really doubt because if you go to the real figures - I really doubt. Because you have soils and they are getting poor, and poor and poor … and you want to meet food security. P3 (no driver): … here this year government decided to reduce subsidy on fertilizer. […] So it's becoming problematic because it makes it more expensive to buy fertilizer. P4 (no driver): First, subsidies were across borders. So you had subsidies of fertilizer. Everyone buys fertilizer - low prices. Now, we don't have that. Now, you have targeted subsidies. That means that targeted subsidies is targeted at cotton, targeted at rice, targeted at maize … also where and for whom. P6 (no driver): You know, these days, Ghana government has a program of subsidized fertilizers. […] … a lot of people don't have access to it. P7: The second point on governmental policy is the subsidies on agric impose like fertilizers. When fertilizers are free, people also want to use them. P9 (no driver): hey don't get it. So you are subsidizing, in other words, for unintended beneficiaries. Those who are supposed to benefit from your subsidy don't get it. Others get it. Those who don't

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(continued ) Classification of driver

Drivers of LULCC

Interviews need it, actually get it. So in the long run, you haven't achieved anything. P10 (no driver): There is no pricing system in the North. The subsidies are more in the South with cacao. P13 (no driver): In fact, at that point there was a lot of corruption in the system. So it looks like they are now trying to either withdraw the subsidy because it was not get into the right kind of people.

Annex 5. Spatially explicit drivers of land use and land cover change by spatio-temporal analysis

To cropland

From cropland

Drivers

From mixed vegetation

From tree cover (30%)

From grassland

Drivers

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

*** *

***

**

**

** ***

*** *** **

From cropland

From tree cover (30%)

From grassland

To mixed vegetation Drivers Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads To grassland Drivers Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads To tree cover (30%) Drivers Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

*** ** ** ** .

*** . .

** .

From cropland

From mixed vegetation

From tree cover (30%)

**

*

**

* ** .

*

From cropland

From grassland

From mixed vegetation

.

***

**

* **

*

To mixed vegetation

To tree cover (30%)

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

** ** ** .

. . ** .

From mixed vegetation Drivers

To cropland

To tree cover (30%)

To grassland

*** *

***

*

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads From grassland Drivers

To grassland

* **

* * **

*

To cropland

To mixed vegetation

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

*** *** **

** .

From tree cover (30%) Drivers

To cropland

To grassland

To mixed vegetation

***

**

***

** ***

**

*** . .

Distance to Dams Distance to Rivers Average Elevation Population Change Distance to Settlements Distance to Roads

**

To tree cover (30%) .

Significance levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Annex 6. R code for multiple regression analysis. Call: lm(formula ¼ Croplands_to_Grasslands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

1.1345

0.4445

0.1915

0.093

6.0688

Coefficients:

438

J. Kleemann et al. / Journal of Environmental Management 196 (2017) 411e442

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

3.99E-01 6.15E-06 5.25E-05 2.89E-04 1.24E-03 5.66E-05 3.33E-05

3.52E-01 2.16E-06 6.56E-05 1.47E-03 5.78E-04 2.09E-05 1.72E-05

1.132 2.849 0.8 0.197 2.14 2.708 1.943

0.2583 0.00462 ** 0.42399 0.84355 0.03299 * 0.00707 ** 0.05268.

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

6.93E-01 0.000001412 3.77E-05 0.001074 4.21E-04 0.00002417 1.39E-05

1.55E-01 9.481E-07 2.88E-05 0.0006443 2.54E-04 0.000009185 7.54E-06

4.476 1.489 1.31 1.668 1.654 2.632 1.844

1e-05 *** 0.13726 0.19113 0.09620. 0.09884. 0.00883 ** 0.06590.

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 0.9391 on 389 degrees of freedom Multiple R-squared: 0.1018, Adjusted R-squared: 0.08796 F-statistic: 7.349 on 6 and 389 DF, p-value: 1.855e-07 > mod.lm_agg2 <- lm(Croplands_to_Mixed_veg ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg2, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Croplands_to_Mixed_veg ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Residual standard error: 0.4128 on 389 degrees of freedom Multiple R-squared: 0.05312, Adjusted R-squared: 0.03851 F-statistic: 3.637 on 6 and 389 DF, p-value: 0.001594 > mod.lm_agg4 <- lm(Grasslands_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg4, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Grasslands_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

2.7149

1.19Eþ00

4.57E-01

0.8418

7.7666

Min

1Q

Median

3Q

Max

2.6984

1.1912

0.4979

0.5429

10.1336

Coefficients: Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation

Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

4.46Eþ00 3.25E-06

6.13E-01 3.75E-06

7.284 0.867

1.82e-12 *** 0.38672

6.35E-05

1.14E-04

0.557

0.57788

8.13E-03

2.55E-03

3.188

0.00155 **

2.85E-03

1.01E-03

2.833

0.00485 **

1.08E-04

3.64E-05

2.974

0.00312 **

4.93E-05

2.98E-05

1.652

0.09941.

negative correlation meaning in lower elevation more Croplands_ to_Mixed_ veg

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 1.634 on 389 degrees of freedom Multiple R-squared: 0.07202, Adjusted R-squared: 0.0577 F-statistic: 5.031 on 6 and 389 DF, p-value: 5.52e-05 > mod.lm_agg3 <- lm(Croplands_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg3, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Croplands_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

0.4079

0.2351

0.1429

0.0706

3.2215

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

2.42Eþ00 0.00001418 1.92E-04 0.0004955 4.44E-03 0.00019 9.92E-05

0.7241 0.000004434 0.0001348 0.003013 0.001189 0.00004296 0.00003526

3.335 3.19 1.424 0.164 3.734 4.424 2.814

0.000935 *** 8 0.00149 ** 0.155132 0.869481 0.000217 *** 0.0000126 *** 0.005141 **

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 1.931 on 389 degrees of freedom Multiple R-squared: 0.191, Adjusted R-squared: 0.1785 F-statistic: 15.31 on 6 and 389 DF, p-value: 9.159e-16 > mod.lm_agg5 <- lm(Grasslands_to_Mixed_veg ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg5, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Grasslands_to_Mixed_veg ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

0.8403

0.4507

0.2138

0.1568

6.8616

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

6.76E-01 0.000001553 1.18E-05 0.0003551 8.23E-04 0.00006281 2.61E-05

0.3217 1.97 0.00005988 0.001339 0.0005282 0.00001909 0.00001566

2.102 0.788 0.196 0.265 1.558 3.291 1.663

0.03616 * 0.4309 0.84432 0.79097 0.12009 0.00109 ** 0.09715.

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

J. Kleemann et al. / Journal of Environmental Management 196 (2017) 411e442

Residual standard error: 0.8579 on 389 degrees of freedom Multiple R-squared: 0.07854, Adjusted R-squared: 0.06433 F-statistic: 5.526 on 6 and 389 DF, p-value: 1.645e-05 > mod.lm_agg6 <- lm(Grasslands_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg6, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Grasslands_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

0.11566

0.06814

0.04648

0.02334

1.95543

Coefficients:

439

Residuals:

Min

1Q

Median

3Q

Max

0.3985

0.2374

0.1413

0.1135

4.0823

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

2.23E-01 2.01Eþ00 4.55E-05 3.11E-04 6.03E-04 6.75E-07 9.69E-06

1.64E-01 6 1.002e-0 3.05E-05 6.81E-04 2.69E-04 9.71E-06 7.97E-06

1.366 6e2.002 1.494 0.456 2.244 0.07 1.217

0.1728 0.0460 * 0.1361 0.6486 0.0254 * 0.9446 0.2245

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

7.13E-02 8.11 7.60E-06 0.00009436 9.67E-05 0.000005973 9.36E-07

0.06851 7 4.196e-0 0.00001275 0.0002851 0.0001125 4.065E-06 3.336E-06

1.041 1.933 0.596 0.331 0.859 1.47 0.281

0.299 0.054 0.552 0.741 0.391 0.142 0.779

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 0.1827 on 389 degrees of freedom Multiple R-squared: 0.02213, Adjusted R-squared: 0.007045 F-statistic: 1.467 on 6 and 389 DF, p-value: 0.1882 > mod.lm_agg7 <- lm(Mixed_veg_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg7, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Mixed_veg_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

0.1157

0.06814

0.04648

0.023

1.95543

Coefficients:

Residual standard error: 0.4362 on 389 degrees of freedom Multiple R-squared: 0.03593, Adjusted R-squared: 0.02106 F-statistic: 2.417 on 6 and 389 DF, p-value: 0.02637 > mod.lm_agg9 <- lm(Mixed_veg_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg9, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Mixed_veg_to_Tree_30p ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

1.0838

0.4659

0.2257

0.1815

4.2765

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

2.87E-01 7.07E-06 2.57E-05 2.66E-03 5.52E-04 3.84E-05 1.98E-05

2.94E-01 1.80E-06 5.47E-05 1.22E-03 4.83E-04 1.74E-05 1.43E-05

0.977 3.92 0.469 2.17 1.14 2.204 1.386

0.32915 0.000101 *** 0.639324 0.030607 * 4 0.253271 0.028099 * 0.166551

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

6.68Eþ00 5.20E-05 4.51E-04 4.12E-03 2.59E-03 1.73E-04 2.95E-05

1.04Eþ00 6.34E-06 1.93E-04 4.31E-03 1.70E-03 6.14E-05 5.04E-05

6.452 8.197 2.342 0.955 1.524 2.813 0.586

3.29e-10 *** 3.61e-15 *** 0.01967 * 0.33999 0.12827 0.00516 ** 0.55815

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 2.761 on 389 degrees of freedom Multiple R-squared: 0.2138, Adjusted R-squared: 0.2017 F-statistic: 17.63 on 6 and 389 DF, p-value: < 2.2e-16 > mod.lm_agg8 <- lm(Mixed_veg_to_Grasslands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg8, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Mixed_veg_to_Grasslands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r)

Residual standard error: 0.7839 on 389 degrees of freedom Multiple R-squared: 0.07189, Adjusted R-squared: 0.05758 F-statistic: 5.022 on 6 and 389 DF, p-value: 5.648e-05 > mod.lm_agg10 <- lm(Tree_30p_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg10, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Tree_30p_to_Croplands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

1.964

0.74

0.3111

0.35

8.1335

Coefficients:

440

J. Kleemann et al. / Journal of Environmental Management 196 (2017) 411e442

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

2.474eþ00 1.876e-05 3.217e-05 4.418e-04 1.005e-03 8.238e-05 8.792e-05

4.572e-01 2.800e-06 8.509e-05 1.903e-03 7.505e-04 2.712e-05 2.226e-05

5.412 6.702 0.378 0.232 1.339 3.037 3.950

1.09e-07 *** 7.25e-11 *** 0.70563 0.81648 0.18151 0.00255 ** 9.29e-05 ***

Estimate

Std. Error

t value

Pr(>jtj)

2.47Eþ00 1.88E-05 3.22E-05 4.42E-04 1.01E-03 8.24E-05 8.79E-05

4.57E-01 2.80E-06 8.51E-05 1.90E-03 7.51E-04 2.71E-05 2.23E-05

5.412 6.702 0.378 0.232 1.339 3.037 3.95

1.09e-07 *** 7.25e-11 *** 0.70563 0.81648 0.18151 0.00255 ** 9.29e-05 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 1.219 on 389 degrees of freedom Multiple R-squared: 0.1983, Adjusted R-squared: 0.1859 F-statistic: 16.03 on 6 and 389 DF, p-value: < 2.2e-16 > mod.lm_agg11 <- lm(Tree_30p_to_Grasslands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg11, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Tree_30p_to_Grasslands ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

0.22467

0.12961

0.07181

0.01088

2.34258

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

3.389e-01 1.893e-06 4.601e-06 2.194e-04 1.160e-04 1.633e-05 4.094e-06

9.590e-02 5.873e-07 1.785e-05 3.991e-04 1.574e-04 5.689e-06 4.669e-06

3.534 3.223 0.258 0.550 0.737 2.870 0.877

0.000459 *** 0.001377 ** 0.796737 0.582779 0.461664 0.004329 ** 0.381139

Estimate Std.

Error

t value

Pr(>jtj)

3.39E-01 1.89E-06 4.60E-06 2.19E-04 1.16E-04 1.63E-05 4.09E-06

9.59E-02 5.87E-07 1.79E-05 3.99E-04 1.57E-04 5.69E-06 4.67E-06

3.534 3.223 0.258 0.55 0.737 2.87 0.877

0.000459 *** 0.001377 ** 0.796737 0.582779 0.461664 0.004329 ** 0.381139

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Residual standard error: 0.2557 on 389 degrees of freedom Multiple R-squared: 0.06377, Adjusted R-squared: 0.04933 F-statistic: 4.416 on 6 and 389 DF, p-value: 0.0002463 > mod.lm_agg12 <- lm(Tree_30p_to_Mixed_veg ~ dam þ river

þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) > summary(mod.lm_agg12, Nagelkerke ¼ TRUE) Call: lm(formula ¼ Tree_30p_to_Mixed_veg ~ dam þ river þ Avg_dem þ pop_diff þ towns þ road, data ¼ r) Residuals:

Min

1Q

Median

3Q

Max

1.0095

0.5148

0.2616

0.1986

4.4184

Coefficients:

(Intercept) Distance to DAMs Distance to Rivers Avg Elevation Population Change Distance to Settlements Distance to Roads

Estimate

Std. Error

t value

Pr(>jtj)

1.52Eþ00 8.34E-06 2.87E-05 1.75E-04 1.72E-03 3.29E-05 2.76E-05

3.15E-01 1.93E-06 5.87E-05 1.31E-03 5.18E-04 1.87E-05 1.54E-05

4.825 4.317 0.489 0.134 3.319 1.758 1.796

2.01e-06 *** 2.01e-05 *** 0.625231 0.893817 0.000987 *** 0.079510. 0.073241.

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

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