Advertisement

Land Tenure and Communities’ Vulnerability to Climate Shocks: Insights from the Niger Basin of Benin

  • Boris Odilon Kounagbè Lokonon
Open Access
Chapter

Abstract

This chapter aims to assess the vulnerability of communities to climate shocks and to analyze the extent to which land tenure affects vulnerability in the Niger basin of Benin. The data used are relative to 14 villages tracked between 1998 and 2012, and 14 additional in 2012. The results reveal that the situation of many villages has improved between 1998 and 2012. Half of the communities tracked lacked adaptive capacity. On average, communities of agro-ecological zone (AEZ) II were the less vulnerable to climate shocks, followed by AEZs I, III, and IV in 2012. The econometric results suggest that farmers’ labor-sharing groups, farmers’ organizations, and access to primary education lessen vulnerability to climate shocks. Tenure security appears to non-significantly strengthen vulnerability to climate shocks.

8.1 Introduction

In sub-Saharan African (SSA) countries , the agricultural sector is expected to face serious difficulties due to climate change and variability (Fofana 2011). In these countries, agriculture is predominantly rain-fed, and consequently this sector is highly sensitive to climate change and variability. However, agriculture is the mainstay of the economy in most African countries, accounting for around 60% of Africa’s employment and about one-quarter of the gross domestic product (GDP) (AfDB et al. 2015).1 Farmers in these countries are mostly engaged in subsistence agriculture. Thus, the impacts of climate shocks and stresses are expected to translate into vulnerability, food and livelihood insecurity, and losses in human capital and in welfare (Davies et al. 2009).

It should be noted that climate-related shocks and stresses are not necessarily expected to lead to negative impacts on agriculture, because they are embedded in the practice of agriculture, and some farmers may develop coping and risk management strategies (Davies et al. 2008). Moreover, the frequency of occurrence of climate shocks are expected to increase with climate change (IPCC 2013), and actions in terms of reducing the vulnerability and boosting the resilience of the population are needed. In addition, agriculture is recognized to play an important role in the structural transformation of Africa and in poverty reduction (AfDB et al. 2015).

Vulnerability has negative connotation. Thus, owing to that, resilience which originated in ecology (Holling 1973) is becoming influential in development economics. Resilience is not a pro-poor concept, and therefore it should be used with caution when trying to implement development actions (Béné et al. 2012). In addition, social protection is considered as an important factor in reducing poverty and vulnerability and in boosting resilience (Stern 2008; Davies et al. 2008, 2009; Solórzano 2016). Land tenure security is considered as part of social protection (Mahadevia 2011). Land tenure is relative to the conditions under which farmers hold and occupy the land (Schickele 1952). Therefore, agricultural productivity can be influenced by land tenure through the security (or investment) effect (Gavian and Fafchamps 1996; Yegbemey et al. 2013). For instance, Gavian and Fafchamps (1996) found that land tenure status is determinant in manure application between borrowed and owned fields in Niger; farmers have diverted manure toward the latter. Therefore, secure land tenure is increasingly considered as having an appropriate role in reducing the vulnerability of poor people to climate shocks (Jayne et al. 2003; Callo-Concha et al. 2013; Chagutah 2013). However, some factors such as lack of financial capital and access to technology can impede the potential of land tenure security in lessening vulnerability.

This chapter aims to assess the vulnerability of communities to climate shocks in the Niger basin of Benin and to analyze the extent to which land tenure influences vulnerability using the integrated approach and an econometric regression by taking advantage of two-period pseudo panel data. To date, there is limited understanding of the potential role of land tenure in reducing vulnerability of rural communities to climate shocks.

The remainder of the chapter is organized as follow. Section 8.2 presents the background and the conceptual framework. The specification of the vulnerability and resilience approach is presented in Sect. 8.3. Variables used and data sources are presented in Sect. 8.4. Section 8.5 presents the empirical results and discussion and Sect. 8.6 concludes.

8.2 Background and Conceptual Framework

Assessing the vulnerability of communities to climate shocks is important in identifying and characterizing actions toward strengthening resilience (Kelly and Adger 2000; Islam et al. 2014). Yet, existing literature suggests that individuals and communities that depend on highly climate-sensitive sector such as agriculture are vulnerable and less resilient to climate shocks. The existing literature is related to fishery systems (e.g., Islam et al. 2014), agricultural livelihoods (e.g., Brooks et al. 2005; Vincent 2007; Shewmake 2008; Deressa et al. 2008, 2009; Tesso et al. 2012; Etwire et al. 2013; Simane et al. 2016), and many sectors of the economy (e.g., Dixon et al. 2003; Dunford et al. 2015). However, to date none of them investigated quantitatively the extent to which land tenure affects vulnerability to climate shocks.

Vulnerability of communities to climate shocks is the propensity or predisposition they are to be adversely affected (adapted from IPCC 2014, p. 1775). The three components of vulnerability are exposure, sensitivity, and adaptive capacity of the communities. Exposure has an external dimension, while sensitivity, and adaptive capacity have an internal dimension (Füssel 2007). Exposure is the presence of communities in places and settings that could be adversely affected (adapted from IPCC 2014, p. 1765). Sensitivity refers to the degree to which communities are affected, either adversely or beneficially, by climate shocks (adapted from IPCC 2014, p. 1772). As for adaptive capacity, it is the ability of communities to adjust to climate shocks, to take advantage of opportunities, or to respond to consequences (adapted from IPCC 2014, p. 1758). Adaptive capacity encompasses five types of capital: physical, financial, human, natural, and social capital (Scoones 1998).

As mentioned above, resilience is becoming influential in development economics. Resilience is the capacity of communities to cope with climate shocks, responding or reorganizing in ways that maintain its essential function, identity, and structure , while also maintaining the capacity for adaptation, learning, and transformation (adapted from IPCC 2014, p. 1772). Vulnerability and resilience are related concepts (Turner 2010). Resilience influences adaptive capacity (Klein et al. 2003; Adger 2006). Both vulnerability and resilience recognize adaptive capacity, so they overlap through adaptive capacity (Berman et al. 2012). Some scholars view resilience as an integral part of adaptive capacity, while others consider adaptive capacity as a main component of vulnerability (Cutter et al. 2008). Moreover, there are scholars that see resilience and adaptive capacity as nested concepts within an overall vulnerability structure (Cutter et al. 2008). Cutter et al. (2008) viewed resilience and vulnerability as separate but often linked concepts. As for Turner (2010), vulnerability and resilience constitute different but complementary framings. Some researchers employ the term “resilience” to the coping capacity component of its framework, whereas others view vulnerability as an antonym of resilience (Turner 2010). Resilience can be considered as adaptive capacity in the case that it is used with an emphasis on society while also integrating environmental characteristics (Malone 2009). In this chapter, resilience is investigated through adaptive capacity, although resilience is not assumed as a synonym of adaptive capacity.

Scholars recognize the potential of social protection in reducing poverty and moving people into productive livelihoods (Davies et al. 2008). Social protection refers to all initiatives (public and private) which have the potential to reduce the economic and social vulnerability of poor, vulnerable, and marginalized groups, and social protection interventions can be classified as protective, preventive, promotive, and transformative measures (Devereux and Sabates-Wheeler 2004). Consequently, social protection can reduce poverty and move people into productive livelihoods, and is of paramount importance in helping the poorest to reduce their exposure to current and future climate shocks (Davies et al. 2008). Land tenure security is integral part of social protection (Mahadevia 2011).

In SSA , the livelihood of rural communities depends on land as key natural capital (Scoones 1998). Therefore, land tenure appears to be central to vulnerability and resilience research, although it is often overlooked (Berman et al. 2012; Chagutah 2013). Land tenure varies across households, communities, and individuals’ characteristics such as gender and social groups. Higher levels of tenure security are considered to be associated with higher living conditions, human development achievements, economic status, and access to entitlements (Mahadevia 2011). Through land tenure security people have protection against their involuntary removal from their land without process of law (Mahadevia 2011). Secure land tenure militates for diversified livelihoods and favor investment in appropriate technologies and uptake of sound environment management practices (Economic Commission for Africa 2003; Chagutah 2013). However, land tenure security can lead to environmental degradation in rural areas where farmers operate under customary tenure (Chagutah 2013), and therefore exacerbate vulnerability to climate shocks.

Traditional common property systems constitute the basis of land tenure in rural Northern Benin (Callo-Concha et al. 2013). In Benin, ownership of property is acquired and transmitted by succession, donation, purchase, will, and exchange. Property can also be acquired by accession, incorporation, prescription, and other effects of obligations. In this chapter, the focus is on the institutional arrangements on land: the ways and arrangements through which farmers have access to land (Yegbemey et al. 2013).2

8.3 Specification of the Vulnerability and Resilience Approach

Following Lokonon (2017) conceptual framework and based on Sect. 8.2, vulnerability and resilience to climate shocks is assessed through an integrated approach using the indicator method. Vulnerability index is calculated as the net effect of adaptive capacity, sensitivity, and exposure.
$$ Vulnerability= adaptive\kern0.17em capacity-\kern0.62em \left( exposure\kern0.62em +\kern0.62em sensitivity\right). $$
(8.1)

Weights are assigned to each indicator using the different weighting approach. Therefore, principal component analysis (PCA ) (Pearson 1901) is used to attribute weight to the different indicators of the three dimensions of vulnerability to climate shocks. Factor scores from PCA are employed as weights to construct vulnerability indices for each village based on Eq. (8.1). Moreover, each indicator is normalized using the z-score standardization, and all the extracted factors from PCA are used due to the multidimensionality nature of vulnerability (Vincent and Cull 2014). Therefore, each factor is weighted by the explained variance.

The extent to which land tenure affects vulnerability is investigated through an econometric analysis. The vulnerability equation is specified as follows:
$$ {\upsilon}_{it}={\beta}_0+{Y}_{it}{\beta}_1+{X}_{it}{\beta}_2+{\vartheta}_i+{\gamma}_{it} $$
(8.2)
where Xi is the set of variables belonging to the three dimensions of vulnerability apart from land tenure variables, Yi is a variable reflecting land tenure security (the percentage of crop land which is owned by the farmers themselves), β0, β1, and β2 are the vectors of the coefficients to be estimated, and ϑi + γit is the error term. All the variables used cannot be included in the regression for the sake of degree of freedom. Therefore, relevant regressors are chosen among the variables used to build vulnerability index through stepwise analyses. Panel specification tests are run to select the appropriate model (Baltagi 2008). Land tenure security is expected to negatively and significantly influence vulnerability to climate shocks. It should be noted that the variable capturing land tenure may be endogenous. Therefore, this chapter accounts for this likely endogeneity and use as instruments the departments in which the communities belong. Indeed, land tenure may vary with respect to the geographic settings.

Moreover, every model has to be tested for sensitivity and uncertainty. A Monte Carlo analysis (Metropolis and Ulam 1949) is performed to assess the uncertainty within the vulnerability index calculation model. Monte Carlo method calculates new results by relying on repetitive random sampling (Metropolis and Ulam 1949). The sensitivity of the vulnerability indicator to any variability in the input dataset is investigated through the change and omission of certain indicators.

8.4 Description of the Variables and Data

Variables that are used for the analysis capture the three aspects of the Intergovernmental Panel on Climate Change (IPCC) definition of vulnerability (exposure, sensitivity, and adaptive capacity). Adaptive capacity reflects the five capitals: physical, financial, human, natural, and social capital (Scoones 1998). According to Deressa et al. (2008), exactly how climate shocks affect income or any proxy of livelihood could be the best measure of sensitivity. However, it was not possible to find relevant data, so this research relies on the assumption that areas that experience climate shocks are subject to sensitivity due to loss in yield and thus in income. Exposure variables capture changes in temperature and in rainfall from long-term mean (1952–2012), under the assumption that areas with higher changes in temperature and precipitation are most exposed to climate shocks. Table 8.1 presents the indicators used to assess vulnerability and resilience to climate shocks.
Table 8.1

Indicators used to assess communities’ vulnerability and resilience to climate shocks

Vulnerability components

Indicators

Units

Hypothesized functional relationship

Exposure

Change in rainfall from the long-term mean

Percentage

Higher change = higher exposure

Change in temperature from the long-term mean

Degree Celsius

Sensitivity

Proportion of households that experienced flood over the last 20 yearsa

Proportion

Higher proportion = higher sensitivity

Proportion of households that experienced droughts over the last 20 yearsa

Proportion

Proportion of households that experienced strong winds over the last 20 yearsa

Proportion

Proportion of households that experienced heat waves over the last 20 yearsa

Proportion

Proportion of households that experienced erratic rainfall over the last 20 yearsa

Proportion

Proportion of households that experienced heavy rainfall over the last 20 yearsa

Proportion

Proportion of households that experienced a change in planting date over the last 20 yearsa

Proportion

Proportion of households that experienced a decrease in yield over the last 20 yearsa

Proportion

Adaptive capacity

Financial capital

Fertilizer-use value per household

Franc de la Communauté Financière Africaine (CFA F)Fb

More financial capital = greater adaptive capacity

Herbicide use value per household

CFA F

Insecticide use value per household

CFA F

Yearly income from agricultural off-farm activities per household

CFA F

Yearly income from non-agricultural off-farm activities per household

CFA F

Yearly income from cropping per household

CFA F

Yearly income from livestock per household

CFA F

Physical, institutional capital, and technology

Proportion of households within the communities that use plow

Percentage

Higher values = greater adaptive capacity

Livestock value per household

CFA F

Amount of credit obtained per household

CFA F

Frequency of access to extension services per household

Frequency

Distance from dwelling to food market per household

Km

Higher distance = lower adaptive capacity

Distance from dwelling to paved or tarred road per household

Km

Proportion of households within the communities that have access to electricity

Percentage

Higher values = greater adaptive capacity

Household asset value per household

CFA F

Higher asset value = greater adaptive capacity

Density of primary schools within the community

Quantitative

Higher values=greater adaptive capacity

Density of secondary schools within the community

Quantitative

Density of high schools within the community

Quantitative

Density of maternities within the community

Quantitative

Density of municipality hospitals within the community

Quantitative

Density of district hospitals within the community

Quantitative

Human capital

Average household head formal education

Years

More human capital = greater adaptive capacity

Natural capital

Bush and valley bottom land-use size per household

Ha

More natural capital = greater adaptive capacity

Irrigated land-use size per household

Ha

Social capital

Proportion of households within the communities that belong to labor sharing groups

Proportion

Higher proportion = greater adaptive capacity

Proportion of households within the communities that belong to farmers’ organizations

Proportion

Amount of financial assistance per household

CFA F

Higher financial assistance and higher in-kind assistance = greater adaptive capacity

Value of assistance in nature per household

CFA F

Source: Author

aProportion of households within the communities

bCurrency of African Financial Community. In 2012, $1 = CFA F 510.53

Two datasets are used for the analysis: 1998 small farmer survey data from the International Food Policy Research Institute and the Laboratoire d’Analyse Régionale et d’Expertise Sociale (IFPRI and LARES 1998) and the data of the survey which was implemented within the Niger basin of Benin in the 2012–2013 agricultural year. Regarding the later survey, three-stage sampling is used. First, communes were randomly chosen within each agro-ecological zone (AEZ), based on their number of agricultural households. Second, 28 villages were randomly selected within selected communes and last, random farm households within selected villages. AEZ V was disregarded, because only one of its communes is located within the Niger basin. The sample size is 545 agricultural households. The questionnaire used is composed of eight sections ranging from demographic information to household assets and basic services.

As for the 1998 small farmer survey, the households were selected using a two-stage stratified random sample procedure based on the 1997 Pre-Census of Agriculture. First, villages were randomly selected in each department, with the number of villages proportional to the volume of agricultural production. Second, in each village, nine households were randomly selected using the list prepared for the Pre-Census. The final sample size was 899 farm households in the country (153 farm households from 14 villages within the Niger basin of Benin). Regarding each data set, aggregation is done at village level using the weights attributed to each farm household. Moreover, additional information on the socio-economic infrastructures has been collected through an informal discussion with the village chiefs (number of primary, secondary and high schools, number of maternities, communal hospitals, district hospitals, dispensaries, clinics, and drinking water sources). In addition to the primary data, the research benefited socio-economic data from the Institut National de la Statistique et de l’Analyse Economique du Bénin (INSAE) and climatic data from the Agence pour la Sécurité de la Navigation Aérienne en Afrique et à Madagascar (ASECNA). The econometric analysis is on the 14 villages surveyed in 1998 and the 28 of 2012.

The Niger basin covers 37.74% of Benin, is located in the extreme north of the country between latitudes 11° and 12°30′ N and longitudes 2° and 3°20′40 E, and has an area of 43,313 km2. Five AEZs out of the eight of the country are covered by the basin (wholly and partially). It covers 17 communes, both wholly and partially (12 communes wholly, and 5 partially). These communes belong to 3 departments: Alibori, Atacora, and Borgou. The agricultural sector in Benin employs 70% of the active population, and contributes 35% to the GDP, 75% to export revenue (République du Bénin 2014). The agricultural production is extensive, relies on family labor combined with limited use of improved inputs, production methods, and farm equipment. The country’s agricultural trade is characterized by a weak performance, with a persistently negative agricultural trade balance.

8.5 Results and Discussion

8.5.1 Socio-economic Characteristics of the Communities and Environmental Attributes

The average percentage of farm households that used plows through animal traction within the communities amounted to 61% and 54% in 1998 and 2012, respectively. On average, the communities were poor in terms of income and asset ownership. The average yearly income per household within the communities from crop selling was CFA F 636,540.89 in 1998 and 1,423,760.69 in 2012. The value of the assets per household (except land) amounted to an average of CFA F 188,969.93 and 309,607.40 in 1998 and in 2012, respectively. Given that the subsistence and mixed crop-livestock production system was the dominant production system, livestock keeping was common among the surveyed communities. Livestock were used for consumption, traction, and manuring in farming, and as a means for cash income. The majority of cattle owned were for traction purposes. In terms of income from livestock, on average, a household within the communities earned CFA F 248,289.88 and 78,372.93 in 1998 and in 2012, respectively.

The farm households within the communities actively seemed to participate in off-farm activities to increase their livelihoods. The average yearly income per household from agricultural off-farm activities amounted to CFA F 11,063.49 and 30,596.35 in 1998 and in 2012, respectively. While the average yearly income from non-agricultural off-farm activities amounted to CFA F 96,856.92 and 293,713.73 in 1998 and in 2012, respectively.

Basic services and infrastructure were generally poor in the surveyed villages as is the case with the rest of the country. The communities had generally access to extension services through cotton production. However, in Malanville, a commune located at the vicinity of the Niger River, they had access to extension services through rice production. On average, the farm households had access 0.71 time to extension services in 1998 and 1.18 times in 2012. In fact, cotton production is organized in Benin through the farmers’ organizations and 79% and 36.3% of the households were members of these organizations in 1998 and 2012, respectively. Access to health care was relatively low (low density of health infrastructures). The average amount of credit received per household within the communities amounted to CFA F 14,952.38 and 19,357.33 in 1998 and in 2012, respectively. Only 10% of the farm households within the communities had access to electricity in 1998 and 23% in 2012. Therefore, the percentage of households within the communities that have access to electricity had at least double between 1998 and 2012. However, they were too far away from paved or tarred roads, which meant that they did not have access to adequate roads even though the situation has been improved between the two periods.

Apart from the farmers’ organizations, the farm households within the communities worked together through labor-sharing groups. Through labor-sharing groups, they alternated working on the farms of each member of the group. About one-third and a quarter of the farm households (31% and 24%) within the communities belonged to at least one labor-sharing group in 1998 and in 2012, respectively. The data reveal the existence of social capital in the basin. Indeed, the value of in-kind assistance per household amounted to CFA F 1902.95 in 2012, whereas the financial assistance per household within the communities amounted to 3178.57 and 3364.58 in 1998 and in 2012, respectively.

As for land ownership, in 1998, two types of tenure were found in the basin: owned land and others (use without paying any fee, and commune property). Land tenure security appeared to be high (at least 50% of crop land) within the communities except for one village (Donwari) which had 40.75% of owned land. The situation in 2012 differs relatively from that of 1998. Indeed, in 2012, there were lease and rented land in some communities (e.g., Bodjecali, Garou 1, Tintinmou Peulh, and Perma), although their level is low compared with owned land (at least 75% of crop land is under tenure security). During the last 20 years, the communities faced many climate shocks. Strong winds were the major climate shock that the communities faced over the last 20 years, followed by erratic rainfall, heavy rainfall, heat waves, floods, and finally droughts. The distribution of the shocks differs across villages.

8.5.2 Vulnerability and Resilience Levels of the Communities

Factor scores from the extracted components are employed to construct indices for adaptive capacity (financial capital, physical capital, institutional capital and technology, human capital, natural capital, and social capital), sensitivity, and exposure. The analyses help to understand the situation of 14 villages over time (in 1998 and in 2012) and 28 villages in 2012 (Tables 8.2 and 8.3). Higher values of the vulnerability indices depict less vulnerability, whereas lower values show more vulnerability. It is worth mentioning that on average both 1998 and 2012 were wet years. However, water excess was higher in 1998 than in 2012 (Figs. 8.1 and 8.2). The 1998 survey did not include the sensitivity to climate shocks and therefore, it was not possible to build the sensitivity index for this year. The situation of the villages has been improved except for Kossou, Kpbébéra, Gantiéco, Kota Monongou, and Moupémou.
Table 8.2

Vulnerability index and its components across villages

Villages

Exposure

Sensitivity

Adaptive capacity

Vulnerability without sensitivity

Vulnerability

1998

2012

2012

1998

2012

1998

2012

2012

Bodjecali

 

−0.123

−0.937

 

−0.490

 

−0.367

0.570

Garou 1

 

−0.123

0.769

 

−1.123

 

−1.00

−1.769

Kassa

0.121

−0.123

0.451

−0.462

−0.597

−0.583

−0.474

−0.925

Toumboutou

 

−0.123

−0.863

 

0.538

 

0.661

1.524

Angaradebou

 

−0.230

0.275

 

1.791

 

2.021

1.746

Sonsoro Bariba

 

−0.230

−0.501

 

1.424

 

1.654

2.155

Donwari

−0.123

−0.230

0.362

0.719

1.379

0.842

1.609

1.247

Tankongou

−0.123

−0.230

−0.425

0.143

0.509

0.266

0.739

1.164

Kandifo Peulh

−0.123

−0.230

0.575

−0.119

1.107

0.004

1.337

0.762

Bouhanrou

−0.133

−0.069

−0.453

0.737

0.851

0.869

0.920

1.373

Tintinmou Bariba

 

−0.069

0.655

 

1.269

 

1.338

0.683

Tintinmou Peulh

−0.133

−0.069

0.004

−0.303

0.577

−0.170

0.646

0.642

Sirikou

−0.133

−0.069

0.585

2.101

3.066

2.234

3.135

2.550

Tepa (Gan Maro)

 

−0.008

−0.762

 

1.120

 

1.128

1.890

Kali

 

−0.008

−1.131

 

−0.624

 

−0.615

0.516

Serekale Centre

 

−0.008

−0.417

 

−0.358

 

−0.349

0.068

Kassakpere

 

−0.008

0.115

 

−0.816

 

−0.807

−0.922

Bembereke Ouest

 

−0.064

−0.605

 

−1.010

 

−0.946

−0.341

Kossou

−0.126

−0.064

0.416

0.934

−2.322

1.060

−2.259

−2.674

Kpebera

−0.126

−0.064

−0.156

0.573

−0.652

0.699

−0.588

−0.432

Kabanou

−0.126

−0.064

0.025

0.142

0.842

0.268

0.906

0.881

Makrou-Gourou

 

0.285

0.338

 

0.326

 

0.040

−0.297

Beket Peulh

0.185

0.285

0.718

−0.681

−0.292

−0.866

−0.577

−1.295

Gantieco

0.185

0.285

−0.081

−0.835

−1.257

−1.021

−.543

−1.462

Chabi Couma

 

0.285

0.220

 

−1.120

 

−1.405

−1.624

Kota Monongou

0.327

0.356

0.547

−1.972

−2.125

−2.299

−2.481

−3.028

Moupemou

0.327

0.356

−0.137

−0.976

−1.657

−1.303

−2.013

−1.877

Perma

 

0.356

0.415

 

−0.354

 

−0.710

−1.125

Table 8.3

Adaptive capacity components across villages

Villages

Financial capital

Physical, institutional capital, and technology

Human capital

Natural capital

Social capital

1998

2012

1998

2012

1998

2012

1998

2012

1998

2012

Bodjecali

 

−0.288

 

0.266

 

0.086

 

−0.299

 

−0.255

Garou 1

 

−0.086

 

−0.264

 

0.029

 

−0.771

 

−0.031

Kassa

−0.375

−0.489

0.073

−0.146

0.031

−0.052

−0.043

0.237

−0.148

−0.147

Toumboutou

 

0.360

 

0.230

 

0.007

 

−0.230

 

0.171

Angaradebou

 

0.669

 

0.045

 

−0.040

 

0.225

 

0.891

Sonsoro Bariba

 

0.562

 

0.455

 

0.013

 

0.301

 

0.093

Donwari

0.381

0.495

0.487

0.065

−0.219

0.164

0.124

0.191

−0.054

0.464

Tankongou

0.409

0.143

0.094

0.367

−0.005

0.102

−0.301

0.246

−0.123

−0.350

Kandifo Peulh

0.089

0.212

−0.138

−0.439

0.080

0.125

−0.097

0.229

−0.054

0.981

Bouhanrou

0.787

0.753

0.192

0.149

0.095

−0.083

−0.364

0.181

0.027

−0.149

Tintinmou Bariba

 

0.665

 

0.037

 

−0.032

 

0.174

 

0.425

Tintinmou Peulh

0.196

0.601

−0.079

0.154

−0.148

0.108

−0.161

−0.104

−0.111

−0.183

Sirikou

0.966

1.637

0.668

0.523

0.095

0.024

0.426

0.420

−0.054

0.463

Tepa (Gan Maro)

 

0.257

 

0.583

 

0.122

 

−0.026

 

0.184

Kali

 

−0.298

 

−0.103

 

−0.021

 

−0.017

 

−0.185

Serekale Centre

 

−0.294

 

0.109

 

−0.018

 

0.142

 

−0.297

Kassakpere

 

−0.201

 

−0.133

 

−0.116

 

−0.013

 

−0.353

Bembereke Ouest

 

−0.171

 

−0.232

 

−0.178

 

−0.038

 

−0.392

Kossou

−0.167

−1.180

0.367

−0.578

−0.091

−0.066

0.389

−0.089

0.436

−0.410

Kpebera

−0.065

0.098

−0.010

−0.364

0.095

−0.094

0.124

0.052

0.429

−0.344

Kabanou

0.055

−0.210

−0.082

0.165

0.031

0.187

0.221

0.056

−0.083

0.645

Makrou-Gourou

 

0.308

 

−0.146

 

0.102

 

0.008

 

0.053

Beket Peulh

−0.433

−0.339

−0.158

−0.347

0.095

0.119

−0.170

−0.088

−0.013

0.363

Gantieco

−0.329

−0.270

−0.409

−0.382

0.023

−0.038

−0.115

−0.234

−0.005

−0.334

Chabi Couma

 

−0.532

 

0.098

 

−0.212

 

−0.044

 

−0.430

Kota Monongou

−1.002

−1.120

−0.553

−0.600

0.052

−0.010

−0.030

−0.090

−0.439

−0.305

Moupemou

−0.512

−0.652

−0.451

−0.443

−0.133

−0.060

−0.002

−0.207

0.122

−0.296

Perma

 

−0.630

 

0.930

 

−0.167

 

−0.213

 

−0.274

Fig. 8.1

Rainfall index evolution between 1954 and 2012 in Kandi. Note: Rainfall index is calculated using this equation: Rainfall indext = Rainfallt − Mean/Standard deviation

Fig. 8.2

Rainfall index evolution between 1954 and 2012 in Natitingou

Sirikou is the less vulnerable community in 2012, whereas the most vulnerable is Kota Monongou. Indeed, Sirikou is in the AEZ II and has a vulnerability index of 3.14. Kota Monongou is in the AEZ IV and has −2.48 as vulnerability index. In 2012, the range between sensitivity, exposure, and adaptive capacity of the two communities is 1.90, 0.59, and 5.39, respectively. Even in 1998, Kota Monongou was the most vulnerable and Sirikou the less vulnerable to climate shocks. Indeed, Sirikou has the highest adaptive capacity in 1998 and in 2012. Kota Monongou and Moupémou were the most exposed villages to climate shocks in 1998, while in 2012, the exposure level was similar for all the villages. The villages are classified in terms of vulnerability for the two periods. In 1998, 42.86% of the communities were vulnerable (without accounting for sensitivity), while 53.57% were vulnerable in 2012. In terms of overall vulnerability to climate shocks, 46.43% of the communities were vulnerable in 2012. Among the communities that were vulnerable in 1998, 83.33% were still vulnerable in 2012.

As for adaptive capacity, 50% of the communities lacked it in 1998 and 2012, out of the 14 villages tracked. When considering all the 28 villages in 2012, 53.57% lacked adaptive capacity. The situation of some villages in terms of adaptive capacity has been improved between 1998 and 2012 (e.g., Kandifo Peulh and Tintinmou Peulh), while some have seen their situation worsened (e.g., Gantieco and Kota Monongou). Therefore, at least half of the communities in the basin appear to be not resilient to climate shocks. The situation differs across the five capitals. Lack in financial capital is relatively common among surveyed communities: 50% and 53.57% of the villages lacked financial capital in 1998 and 2012, respectively. Regarding physical, institutional capital, and technology, 57.14% of the surveyed villages lacked it in 1998, while a decrease in this percentage is noted in 2012 (46.43%). Therefore, this capital has been improved among the surveyed communities in the basin during the two periods. Although 35.71% of the communities lacked human capital in 1998, the situation has worsened in 2012; this percentage amounted to 53.57% in 2012. On average, the situation in terms of natural capital has been improved among surveyed communities, although it needs improvements. In 1998, 64.29% lacked natural capital, while in 2012 they were 53.57% lacking this capital. In 1998, on average, social capital level was quite low, as reflected by the 71.43% of surveyed villages lacking this capital. Social capital level has been improved in 2012 compared with the situation in 1998, although a large number of communities still lacked this capital (60.71%). Overall, resilience level is low in the basin.

The degree of vulnerability of the communities across AEZs is also investigated. The communities of AEZ II were the least vulnerable to climate shocks, followed by AEZs I, III, and IV in 2012. Indeed, communities of AEZ IV were the most exposed and the most sensitive to climate shocks, and also had the lowest adaptive capacity. Moreover, communities of AEZ IV had the lowest social capital, whereas communities of AEZ II have the highest. This means that farmers in communities of AEZ II helped one another in mitigating the effects of climate shocks, and this leads to their highest resilience level. Even in 1998, communities of AEZ II had the highest adaptive capacity and were the less exposed to climate shocks, whereas communities of AEZ IV had the lowest adaptive capacity and were the most exposed to these shocks. The highest adaptive capacity level of communities of AEZ II was due to their higher financial capital, and physical, institutional capital and technology in 1998. The social capital of communities of AEZ II has been improved over time. The analyses show that, in 1998, communities of AEZ IV lacked all kind of capital; the situation is alarming for financial, physical, and institutional capital, and technology.

8.5.3 Sensitivity and Uncertainty Analyses

Vulnerability indices for the two periods were computed 1000 times to map their probability distribution. For each dimension of vulnerability, random values were generated between its minimum and maximum values. The generated vulnerability indices for the two periods follow the normal distribution (Figs. 8.3 and 8.4). Moreover, the reliability of the original calculated vulnerability indices is estimated through determination of the range of the standard deviations around the means, and Student’s t-tests revealed that they lie within the respective range (p < 0.05). As for sensitivity, the values of some indicators have been changed or some indicators were simply disregarded to explore the influence on vulnerability indices. These analyses showed that the indices are sensitive to these changes.
Fig. 8.3

Kernel density of the generated vulnerability index (2012)

Fig. 8.4

Kernel density of the generated vulnerability index (1998)

8.5.4 Econometric Results

Panel specification tests have been run through Fisher test and Breusch and Pagan Lagrangian multiplier test. The Fisher test indicates that there are significant individual (village level) effects, implying that pooled ordinary least squared (OLS) would be inappropriate (Prob > F = 0.0.3). The Breusch and Pagan Lagrangian multiplier test indicates the presence of random effects (Prob > chibar2 = 0.06). Thus, both of these two specification tests indicate that pooled OLS would be inappropriate (at 5% and 10% significance level for the fixed effects and random effects, respectively). The Hausman specification test is used to choose among fixed effects and random effects. Prob > chi2 = 0.11 leads to the strong non-rejection of the null hypothesis that the difference in coefficients is not systematic. As the time period is short (two periods), random effects seem more appropriate than fixed effects. Consequently, the estimation procedure accounts for the likely endogeneity of land tenure in the random effects model. Table 8.4 presents the results of the estimations.
Table 8.4

Regression results of vulnerability

Dependent variable: vulnerability index

Variables

Coefficients

Standard errors

Z-statistics

Change in rainfall from long-term mean

0.026

0.021

1.26

Square of change in rainfall from long-term mean

−0.001

0.001

−1.64

Change in temperature from long-term mean

37.605**

19.118

1.97

Square of change in temperature from long-term mean

−54.043*

30.805

−1.75

Proportion of households that belong to farmers’ labor-sharing groups

1.012*

0.562

1.80

Proportion of households that belong to farmers’ organizations

2.776***

0.745

3.73

Density of primary schools within the community

96.583*

55.914

1.73

Percentage of households that have access to electricity

−0.011

0.921

−0.01

Land tenure security

−0.065

0.061

−1.07

Constant

2.012

4.354

0.46

R-squared

Overall = 0.522

Within = 0.536

Between = 0.485

Note: ***, **, * Significant at the 1%, 5%, and 10% levels, respectively. Lower values of the dependent variable (vulnerability) indicate improvement in vulnerability

The impact of the two climate variables is non-linear. A given change in temperature from long-term mean will lessen vulnerability to climate shocks up to 0.35 degree Celsius, and beyond this threshold, the impact will be positive, ceteris paribus. This could be explained by the fact that the crops will gain from carbon fertilization under a change less than 0.35 degree Celsius, ceteris paribus. Regarding precipitations, a given change in rainfall from long-term mean will lessen vulnerability to climate shocks up to 10.79%, and beyond this threshold, the impact will be positive, ceteris paribus. However, the coefficients associated to the change in percentage in rainfall from long-term mean and its square are not significant.

Land tenure security leads to strengthening vulnerability to climate shocks, with the impact being non-significant. This finding suggests that farmers within the communities may not be yet taken advantage of their land tenure status in terms of investments in appropriate technologies relative to farming. This chapter considers also off-farm activities in the analyses of vulnerability, therefore the findings need to be analyzed with respect to that. Lack of tenure security may push farmers within the communities to look for off-farm activities, which are less climate dependent, and therefore, they appear to be less vulnerable through diversifying income sources. However, the result needs to be taken with caution, and needs further investigation in terms of socio-cultural elements that may impede the advantage of having secure tenure.

Membership to farmers’ labor-sharing groups and to farmers’ organizations appears to be useful for farmers. Indeed, through these organizations, farmers receive relevant information regarding how to deal with farming, such as fertilizer requirements, when to use fertilizers and pesticides, and climatic information. However, the impact of membership to farmers’ organizations is higher than the impact of membership to farmers’ labor-sharing groups. This could be explained by the fact that farmers had access to extension services mainly through farmers’ organizations. These findings confirm the usefulness of social capital in improving welfare as suggested by the literature (Ostrom and Ahn 2007; Ostrom 1994). More primary schools will lessen vulnerability to climate shocks. Indeed, the more the populations are literate, the more they will be able to obtain appropriate information regarding adapting to climate shocks. The impact of the percentage of households that have access to electricity is negative. This finding means that access to electricity is costly to the communities, and it reduces the share of financial means that is invested in adaptation strategies. However, the percentage of households that have access to electricity does not have a significant impact on vulnerability level, ceteris paribus.

8.6 Conclusion

This chapter assesses the vulnerability of communities to climate shocks in the Niger basin of Benin (14 villages tracked between 1998 and 2012, and 14 additional ones in 2012), and analyzes the extent to which land tenure affects vulnerability. First, indices were built for each dimension of vulnerability: adaptive capacity (financial, human, natural, social, physical, and institutional capital, and technology), sensitivity, and exposure. Second, overall vulnerability indices were built. The findings reveal that between 1998 and 2012, the situation of the villages has been improved except for five villages (Kossou, Kpbébéra, Gantiéco, Kota Monongou and Moupémou). Sirikou was the less vulnerable community in 1998 and 2012, whereas the most vulnerable was Kota Monongou. Half of the communities tracked lacked adaptive capacity (through which resilience was analyzed) during the two periods. In 2012, 53.57% of the 28 communities appeared to have a lack in adaptive capacity. Thus, resilience level is low in the basin. On average, communities of AEZ II were the less vulnerable to climate shocks, followed by AEZs I, III, and IV in 2012. The econometric results suggest that farmers’ labor-sharing groups, farmers’ organizations, and access to primary education have the potential to lessen vulnerability to climate shocks. Tenure security appears to lead to strengthening non-significantly vulnerability to climate shocks.

The situation of the communities in terms of food and nutrition security will be affected if any action is taken, as the farm households are mainly subsistence farmers. Indeed, this could lead to vulnerability to food insecurity. Therefore, public policies should encourage formal and informal social networks that enable group discussions and better information flows and improve adaptation to climate shocks. They should promote access to primary education and raise the awareness of the farmers within the communities on investment in relevant technology and environmental management practices which have the potential to lessen their vulnerability and strengthen their resilience to climate shocks. Moreover, diversification of income sources off the farm can be promoted. Furthermore, they should think about providing timely climate information to the communities. Results indicating differences among villages and AEZs suggest that adaptation technologies should be targeted to the various villages and AEZs to enhance their specific adaptation potential.

Footnotes

  1. 1.

    The AfDB through its Ten-Year strategy (called the ‘High 5s’), is committed to improving food security and rural livelihoods by tackling the most important constraints on agricultural productivity, and to building resilience to climate change (AfDB 2016).

  2. 2.

    Yegbemey et al. (2013) distinguished between inheritance, gifting, renting, and purchasing in Northern Benin.

References

  1. Adger, N. W. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281.CrossRefGoogle Scholar
  2. AfDB. (2016). Annual Development Effectiveness Review 2016: Accelerating the Pace of Change. Abidjan: African Development Bank Group.Google Scholar
  3. AfDB, OECD, & UNDP. (2015). African Economic Outlook 2015: Regional Development and Spatial Inclusion. African Development Bank, Organisation for Economic Co-operation and Development, United nations Development.Google Scholar
  4. Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). West Sussex: John Wiley & Sons.Google Scholar
  5. Béné, C., Wood, R. G., Newsham, A., & Davies, M. (2012). Resilience: New Utopia or New Tyranny? Reflection About the Potentials and Limits of the Concept of Resilience in Relation to Vulnerability Reduction Programs. Institute of Development Studies (IDS), Working Paper 2012(405) and Center for Social Protection (CSP), Working Paper 006, Brighton.Google Scholar
  6. Berman, R., Quinn, C., & Paavola, J. (2012). The Role of Institutions in the Transformation of Coping Capacity to Sustainable Adaptive Capacity. Environmental Development, 2, 86–100.CrossRefGoogle Scholar
  7. Brooks, N., Adger, N. W., & Kelly, M. (2005). The Determinants of Vulnerability and Adaptive Capacity at the National Level and Implications for Adaptation. Global Environmental Change, 15(2), 151–163.CrossRefGoogle Scholar
  8. Callo-Concha, D., Gaiser, T., Webber, H., Tischbein, B., Müller, M., & Ewert, F. (2013). Farming in the West African Sudan Savanna: Insights in the Context of Climate Change. African Journal of Agricultural Research, 8(38), 4693–4705.CrossRefGoogle Scholar
  9. Chagutah, T. (2013). Land Tenure Insecurity, Vulnerability to Climate-Induced Disaster and Opportunities for Redress in Southern Africa. Jàmbá: Journal of Disaster Risk Studies, 5(2), 1–8.CrossRefGoogle Scholar
  10. Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Jennifer, W. (2008). A Place-Based Model for Understanding Community Resilience to Natural Disasters. Global Environmental Change, 18(4), 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013.CrossRefGoogle Scholar
  11. Davies, M., Guenther, B., Leavy, J., Mitchell, T., & Tanner, T. (2008). ‘Adaptive Social Protection’: Synergies for Poverty Reduction. IDS Bulletin, 39(4), 105–112.CrossRefGoogle Scholar
  12. Davies, M., Oswald, K., & Mitchell, T. (2009). Climate Change Adaptation, Disaster Risk Reduction and Social Protection. In OECD, Promoting Pro-Poor Growth: Social Protection (pp. 201–217). Paris: Organisation for Economic Cooperation and DevelopmentGoogle Scholar
  13. Deressa, T. T., Hassan, R. M., & Ringler, C. (2009). Assessing Household Vulnerability to Climate Change: The Case of Farmers in the Nile Basin of Ethiopia. International Food Policy Research Institute, Discussion Paper 00935, Washington, DC.Google Scholar
  14. Deressa, T. T., Hassan, R. M., & Ringler, C. (2008). Measuring Ethiopian Farmers’ Vulnerability to Climate Change Across Regional States. International Food Policy Research Institute, Discussion Paper 00806, Washington, DC.Google Scholar
  15. Devereux, S., & Sabates-Wheeler, R. (2004). Transformative Social Protection. Working Paper 232, Institute of Development Studies, Brighton.Google Scholar
  16. Dixon, R. K., Smith, J., & Guill, S. (2003). Life on the Edge: Vulnerability and Adaptation of African Ecosystems to Global Climate Change. Mitigation and Adaptation Strategies for Global Change, 8(2), 93–113.CrossRefGoogle Scholar
  17. Dunford, R., Harrison, P. A., Jäger, J., Rounsevell, M. D., & Tinch, R. (2015). Exploring Climate Change Vulnerability Across Sectors and Scenarios Using Indicators of Impacts and Coping Capacity. Climatic Change, 128(3), 339–354.CrossRefGoogle Scholar
  18. Economic Commission for Africa. (2003). Land Tenure Systems and Sustainable Development in Southern Africa. ECA/SA/EGM. Land/2003/2, United Nations Economic Commission for Africa, Southern Africa Office, Lusaka.Google Scholar
  19. Etwire, P. M., Al-Hassan, R. M., Kuwornu, J. K., & Osei-Owusu, Y. (2013). Application of Livelihood Vulnerability Index in Assessing Vulnerability to Climate Change and Variability in Northern Ghana. Journal of Environment and Earth Science, 3(2), 157–170.Google Scholar
  20. Fofana, I. (2011). Simulating the Impact of Climate Change and Adaptation Strategies on Farm Productivity and Income: A Bioeconomic Analysis. Discussion Paper 01095, International Food Policy Research Institute, Washington, DC.Google Scholar
  21. Füssel, H.-M. (2007). Vulnerability: A Generally Applicable Conceptual Framework for Climate Change Research. Global Environmental Change, 17(2), 155–167.CrossRefGoogle Scholar
  22. Gavian, S., & Fafchamps, M. (1996). Land Tenure and Allocative Efficiency in Niger. American Journal of Agricultural Economics, 78(2), 460–471.CrossRefGoogle Scholar
  23. Holling, C. S. (1973). Resilience and Stability of Ecological Systems. Annual Review of Ecology and Systematics, 4(1973), 1–23.CrossRefGoogle Scholar
  24. IFPRI, & LARES. (1998). National Survey of Small Farmers in Benin. International Food Policy Research Institute, and Laboratoire d’Analyse Régionale et d’Expertise Sociale, Washington, DC.Google Scholar
  25. IPCC. (2013). Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Google Scholar
  26. IPCC. (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (edited by Barros, V. R., Field, C. B., Dokken, D. J., Mastrandrea, M. D., Mach, K. J., Bilir, T. E., Chatterjee, M., et al.). Cambridge: Cambridge University Press.Google Scholar
  27. Islam, M. M., Sallu, S., Hubacek, K., & Paavola, J. (2014). Vulnerability of Fishery-Based Livelihoods to the Impacts of Climate Variability and Change: Insights from Coastal Bangladesh. Regional Environmental Change, 14(1), 281–294.CrossRefGoogle Scholar
  28. Jayne, T. S., Yamano, T., Weber, T. M., Tschirley, D., Benfica, R., Chapoto, A., & Zulu, B. (2003). Smallholder Income and Land Distribution in Africa: Implications for Poverty Reduction Strategies. Food Policy, 28, 253–275.CrossRefGoogle Scholar
  29. Kelly, M., & Adger, W. N. (2000). Theory and Practice in Assessing Vulnerability to Climate Change and Facilitating Adaptation. Climatic Change, 47(4), 325–352.CrossRefGoogle Scholar
  30. Klein, R. J., Nicholls, R. J., & Thomalla, F. (2003). Resilience to Natural Hazards: How Useful Is This Concept? Environmental Hazards, 5(1–2), 35–45.CrossRefGoogle Scholar
  31. Lokonon, B. O. K. (2017). Farmers’ Vulnerability to Climate Shocks: Insights from the Niger Basin of Benin. Working Paper Series No. 248, African Development Bank, Abidjan.Google Scholar
  32. Mahadevia, D. (2011). Tenure Security and Urban Social Protection in India. Research Report 05, Institute of Development Studies, Centre for Social Protection, Sussex.Google Scholar
  33. Malone, E. L. (2009). Vulnerability and Resilience in the Face of Climate Change: Current Research and Needs for Population Information. PNWD-4087, Battelle Memorial Institute, Washington.Google Scholar
  34. Metropolis, N., & Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association, 44(247), 335–341.CrossRefGoogle Scholar
  35. Ostrom, E. (1994). Constituting Social Capital and Collective Action. Journal of Theoretical Politics, 6(4), 527–562.CrossRefGoogle Scholar
  36. Ostrom, E., & Ahn, T. K. (2007). The Meaning of Social Capital and Its Link to Collective Action. Bloomington: Indiana University, Workshop in Political and Policy Analysis.Google Scholar
  37. Pearson, K. (1901). On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, 2, 559–572.Google Scholar
  38. République du Bénin. (2014). Analyse Globale de la Vulnérabilité et de la Sécurité Alimentaire. Cotonou, Benin: INSAE.Google Scholar
  39. Schickele, R. (1952). Theories Concerning Land Tenure. Journal of Farm Economics, 34(5), 734–744.CrossRefGoogle Scholar
  40. Scoones, I. (1998). Sustainable Rural Livelihoods: A Framework for Analysis. Institute of Development Studies, Working Paper 72, Brighton.Google Scholar
  41. Shewmake, S. (2008). Vulnerability and the Impact of Climate Change in South Africa’s Limpopo River Basin. International Food Policy Research Institute, Discussion Paper 00804, Washington, DC.Google Scholar
  42. Simane, B., Zaitchik, B. F., & Foltz, J. D. (2016). Agroecosystem Specific Climate Vulnerability Analysis: Application to the Livelihood Vulnerability Index to a Tropical Highland Region. Mitigation and Adaptation Strategies for Global Change, 21(1), 39–65.CrossRefGoogle Scholar
  43. Solórzano, A. (2016). Can Social Protection Increase Resilience to Climate Change?: A Case Study of Oportunidades in Rural Yucatan, Mexico. IDS Working Paper 465, Center for Social Protection, Institute of Development Studies, Brighton.Google Scholar
  44. Stern, N. (2008). Key Elements of a Global Deal on Climate Change. London: London School of Economics and Political Science.Google Scholar
  45. Tesso, G., Emana, B., & Ketema, M. (2012). Analysis of Vulnerability and Resilience to Climate Change Induced Shocks in North Shewa, Ethiopia. Agricultural Sciences, 3(6), 871–888.CrossRefGoogle Scholar
  46. Turner, B. L. (2010). Vulnerability and Resilience: Coalescing or Paralleling Approaches for Sustainability Science? Global Environmental Change, 20(4), 570–576. https://doi.org/10.1016/j.gloenvcha.2010.07.003.CrossRefGoogle Scholar
  47. Vincent, K. (2007). Uncertainty in Adaptive Capacity and the Importance of Scale. Global Environmental Change, 17(1), 12–24.CrossRefGoogle Scholar
  48. Vincent, K., & Cull, T. (2014). Using Indicators to Assess Climate Change Vulnerabilities: Are There Lessons to Learn for Emerging Loss and Damage Debates. Geography Compass, 8(1), 1–12.CrossRefGoogle Scholar
  49. Yegbemey, R. N., Yabi, A. J., Tovignan, D. S., Gantoli, G., & Kokoye, H. S. (2013). Farmers’ Decisions to Adapt to Climate Change Under Various Property Rights: A Case Study of Maize Farming in Northern Benin (West Africa). Land Use Policy, 34, 168–175. https://doi.org/10.1016/j.landusepol.2013.03.001.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Boris Odilon Kounagbè Lokonon
    • 1
  1. 1.Université de ParakouParakouBenin

Personalised recommendations