Bio-based and Applied Economics BAE Copyright: © 2023 C.S. Onyenekwe, P.I. Opata, C.O. Ume, D.B. Sarpong, I.S. Egyir. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-13436 Citation: C.S. Onyenekwe, P.I. Opata, C.O. Ume, D.B. Sarpong, I.S. Egyir (2023). Heterogeneity of adaptation strate- gies to climate shocks: Evidence from the Niger Delta region of Nigeria. Bio- based and Applied Economics 12(1): 17-35. doi: 10.36253/bae-13436 Received: July 26, 2022 Accepted: January 30, 2023 Published: June 24, 2023 Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Davide Menozzi, Linda Arata. ORCID CSO:0000-0003-2625-1775 PIO: 0000-0001-6829-6125 COU: 0000-0003-2033-0560 DBS: 0000-0001-8288-2999 ISE: 0000-0003-2067-8946 Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Chinasa Sylvia Onyenekwe1, Patience Ifeyinwa Opata1, Chukwuma Otum Ume1,*, Daniel Bruce Sarpong2, Irene Susana Egyir2 1 Department of Agricultural Economics, Faculty of Agriculture, University of Nigeria, Nsukka, Nsukka, Enugu State, Nigeria 2 Department of Agricultural Economics and Agribusiness, University of Ghana, Legon, Ghana *Corresponding author. E-mail: chukwuma.ume@unn.edu.ng Abstract. There is overwhelming evidence to suggest that climate shocks undermine food security and livelihood well-being of the climate-impacted Niger Delta region of Nigeria. Employing survey data collected from farming and fishing households in the Niger Delta region of Nigeria, the study investigated the range of adaptation practices prevalent in the region, as well as factors influencing the adoption of these adaptation strategies. Five hundred and three (503) households (252 fishing households and 251 farming households) were selected using multi-stage sampling techniques. Multinomial logit model was used to determine factors affecting the household choice of adaptation strategies. The results show that adaptation strategies adopted by farming households were livelihood diversification (78.5%), crop management (77.7%), and soil and water management (64.5%). Factors influencing their choice of adaptation strategies were age, gender, household size, education, extension, and farm size. The adaptation strategies employed by the fishing households were livelihood diversification (83.61%) and intensi- fication [which include the use of improved fishing gears (80.33%), varying fishing loca- tions (67.21%), and expanding area of fishing (40.98%)]. Uncovering the heterogeneity in adaptation and resilience aspects to climate shocks has immense practical significance, particularly in providing targeted assistance for the two livelihood groups’ adoption. Keywords: Climate shocks, crop farmers, Fish farming, Adaptation strategies, Devel- oping nations. JEL codes: Q13, Q22, Q54. 1. INTRODUCTION Crop farming and fishing constitute the main economic activity of rural people especially in Sub-Saharan Africa (Giller, 2020). It is a source of liveli- hood for about 70-80% of the population and accounts for 30% of the GDP and 40% of the foreign exchange earnings of most nations in Sub-Saharan Africa (Bezner Kerr et al., 2019). As climate conditions change all over the http://creativecommons.org/licenses/by/4.0/legalcode 18 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. world, there are increasingly multiple and uneven risks to societies (Arfini, 2021). In Nigeria for instance, farm- ing and fishing constitute the main livelihood strategy for over 70% of the teaming rural population. In the Niger Delta region of Nigeria, despite the abundance of oil in the region, about 60% of the population of these rural people depend on farming for their life sustenance and livelihood (Fund World Life, 2018). Studies have also shown that the shock and impact are more on farm- ers in the Niger Delta region (Fund World Life, 2018; PEDI, 2020), as most of the areas in the Niger Delta region are coastal areas and as such are bedevilled with a number of environmental challenges and flood-relat- ed disasters. Also, like in most African countries, there is an over-dependence on rain-fed agriculture in the Niger Delta region of Nigeria, as well as limited adap- tive capacity among the farmers (Ume, 2017). According to Akpoti et al. (2021), over-dependence on the natural environment in the face of climate change, without an adequate safety net, exposes these farmers to climate shocks, which negatively affect productivity and sus- tainable development. The future sustainability of the agricultural sector and food security in the region will depend on the adaptation strategies adopted by farming and fishing households (Bandara & Cai, 2014; Kahsay & Hansen, 2016). This study, therefore, seeks to investigate the range of adaptation practices prevalent in the region, as well as factors influencing the adoption of these adap- tation strategies. As developing countries have been projected to be more impacted by climate change, adaptation has been increasingly identified as the policy option to help cope with the negative impact of climate change (Ford et al., 2011; Lamonaca et al., 2021). According to the IPCC (2001), adaptation is the ability of a system to adjust in response to actual or expected climatic stimuli to reduce harm and cope with the resulting condition. The impor- tance of mainstreaming climate change adaptation into farming activities for sustainable development is evident, and considerable research has investigated the determi- nants of adoption of climate change adaptation strategies among farmers in the global south, although reviews reveal mixed evidence thus far (Bezner Kerr et al., 2018; Fosu-Mensah et al., 2012; Ume et al., 2021; Zazu & Man- derson, 2020). For instance, Ume et al. (2021) concluded from 14 studies in Southeast Nigeria that the gender of the farmer has a significant effect on adaptation, while Enete & Amusa (2010) found an indeterminate influ- ence of socioeconomic factors such as age, education, and gender on adaptation. In Ghana Fosu-Mensah, Vlek, & MacCarthy (2012) found access to extension services, credit, soil fertility, and land tenure to be the major fac- tors that influenced farmers’ perception and adaptation. The authors suggested a need for more empirical investi- gations to establish coherence in the literature. In contrast to the large literature on determinants of adaptation among crop farmers in the developing nations, research documenting the range of adaptation practices prevalent in the region is sparse: our litera- ture search identified only three studies. Wetende et al., (2018) documented the different climate change adapta- tion strategies employed by smallholder dairy farmers in the Siaya Sub-County of Western Kenya. Sinharoy et al. (2018) assessed the determinants of crop farmers’ choice of coping methods to climate change and vari- ability in Ethiopia and usefully documented the adapta- tion method employed by highlands farmers. Onyeneke et al. (2018) presented the status of climate-smart agri- culture in Nigeria, and categorized them into mobility and social networks, adjusting agricultural production systems, diversification on and beyond the farm, farm financial management, and knowledge management and regulations. We expand these available adaptation options in literature by documenting additional adap- tation strategies and innovative agroecological farm- ing and fishing methods that farmers in the Niger Delta region of Nigeria employ. This paper contributes to the existing literature in three key ways. First, as highlighted above, very few studies have systematically examined the different adaptation options employed by farmers in developing nations, and these studies did not consider the pecu- liar vulnerabilities of riverine dwellers and fish farmers. According to the IPCC (2014), vulnerability describes a set of conditions derive from the prevailing cultural, historical, social, political, environmental, and eco- nomic contexts. For a long time, the Niger Delta region has been exposed to various degrees of environmental degradation and conflicts, hence can be referred to as a vulnerable region not only because they are exposed to climate hazards but because of everyday patterns of marginality and neglects experienced by farmers in this region. Second, the determinants of adaptation have been extensively covered in the literature. However, empiri- cal evidence in the context of the Niger Delta region is largely scarce. More so, the underlying drivers of adap- tation are complex, and have not been fully understood (Bezner Kerr et al., 2018). Recent studies suggest that they differ from place to place according to location- specific factors (Komba & Muchapondwa, 2015; Mead- ows, 2008). Furthermore, there is variation in the level of influence of different determinants of adaptation, which makes it difficult to generalize findings (Ume et al., 19Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 2020). As stated by Fosu-Mensah et al. (2012) the deter- minants of adaptation to climate change among small- holder farmers in the developing economies are still contentious issues, thus, making further empirical study necessary to clarify uncertainties and establish a coher- ent scholarship. Finally, we are not aware of any previous study that examined the different adaptation options for fish farm- ers in West Africa, though the fishery sector is widely acknowledged to have the potential of improving the nutritional status of the rural population. Previous research on climate change adaptation among farm- ers has mostly concerned with crop farmers (Amare & Simane, 2017; Onyeneke et al., 2018; Ume et al., 2022), with a few recent studies on dairy and livestock inno- vations (Apata, 2011; Wetende et al., 2018). We add another empirical point to this expanding literature with evidence on adaptation options for fish farmers. Importantly, the findings from this study can help guide development interventions, on the best way to frame an approach that will engender better climate change adap- tation among farmers in the coastal regions in the devel- oping nations and beyond. The rest of the paper is structured as follows: Sec- tion 2 presents the theories underlying determinants of adaptation strategies, Section 3 describes the methodol- ogy used in the study, Section 4 presents the empirical results, followed by section 5 which details our conclu- sions and policy implications. 2. THEORY UNDERLYING DETERMINANTS OF ADAPTATION STRATEGIES: UTILITY MAXIMIZATION AND PROTECTION MOTIVATION For explaining the choice of adaptation strate- gies adopted by households, the utility maximization theory is used. Households are assumed to be rational beings; hence they choose adaptation options that maxi- mize their expected utility among the available options (Amare & Simane, 2017; Gebrehiwot & van der Veen, 2013; Menozzi et al., 2015). The limitation of this theo- ry is that in the real world this may not always apply as there are other factors that may affect the behaviour of households. If Ui and Uj represent the household’s utility for any two adaptation options. Following Greene (2000) the random utility model can be stated thus: Uit=Vit+εit, Ujt=Vjt+εjt 2.1 where Uit and Ujt are the perceived utility from choosing adaptation options i and j at time t respectively; Vit and Vjt are the deterministic component and εit and εjt are the error terms of the utility function which are inde- pendently and identically distributed. Utility cannot be directly observed, it is rather indirectly observed from the choices that households make. Choice experiments assume that a household m chooses an option i at time period t, only if this adaptation option generates at least as much utility as any other option for example j, repre- sented as: Umit>Umjt, j≠i 2.2 The probability of a household m choosing adapta- tion option i among the available adaptation strategies at time t can then be specified as: Pmit=P(Umit)>Umjt), j≠i 2.3 The second theory, which has been found to be valu- able in explaining adaptive behaviours of individuals to climate change is the protection motivation theory (Cismaru et al., 2011). The theory of protection moti- vation was originally postulated by Rogers (1975) and applied in the field of health to explain how individu- als are motivated to act in a protective manner toward a perceived health risk. However, it has since been adapted and applied in other contexts such as environmental risk and natural hazards. For instance, it has been applied to the studies of natural hazards such as earthquakes in the United States (Mulilis & Lippa, 1990), and flood in Germany and the Netherlands (Grothmann & Reuss- wig, 2006 and Bubeck, Botzen, Kreibich, & Aerts, 2013) and even studies on climate change adaptation (Groth- mann & Patt, 2005; Keshavarz & Karami, 2016; Koerth, Vafeidis, Hinkel, & Sterr, 2013; Bockarjova & Steg, 2014). This theory postulates that individuals will act to protect themselves against a perceived risk if they perceive that the threat of that hazard, they are exposed to is severe (threat appraisal) and if the coping appraisal is high. Threat appraisal is composed of two main components: ‘perceived vulnerability’ (probability) and ‘perceived severity’ (consequences). Coping appraisal, on the other hand, consists of three components namely: ‘response efficacy’, ‘self-efficacy’ and ‘response cost’. The coping appraisal is considered high if individuals perceive the protective measures available to be effective i.e., able to mitigate the threat (high ‘response efficacy’), easy i.e., the individuals perception of their ability to implement the required actions (high ‘self-efficacy’) and inexpen- sive (low ‘response costs’) (Floyd et al., 2000). The two appraisal processes inf luence an individual’s protec- tion motivation (Maddux & Rogers, 1983; Opata et al., 20 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. 2021). However, Poussin et al. (2014) found that cop- ing appraisal has a far-reaching effect on self-protective bahaviours by individuals than threat appraisal. Groth- mann & Reusswig (2006) in their study concluded that it is just not enough to communicate the threat or risk individuals are exposed to (threat appraisal) but the ben- efits and cost of precautionary measures (coping apprais- al) should also be included. In this study, this theory can be adapted to explain the behaviour of households to act in a protective man- ner towards the perceived threat to their livelihoods occasioned by environmental and social factors (climate shock, environmental degradation, and conflict). There are two processes. In the first process, ‘threat appraisal’ the household assesses the threat probability for exam- ple climate shocks and the severity of the damage that will be done say to their food security or income should they choose not to act. The second process is the ‘adap- tation appraisal’ that has three components. The first is the ‘perceived adaptation efficacy’, which is the percep- tion of the effectiveness of the adaptive action in protect- ing one from the threat (e.g., a judgment that changing crop variety can protect one from climate shocks). The second component is the ‘perceived self-efficacy which refers to the household’s perceived ability to implement the adaptive action (e.g., a household might perceive that they lack the technical skills to implement a particular innovation). The third component is the ‘perceived adap- tation cost’, which refers to the cost of taking the adap- tive action (such as monetary, time, effort). Based on the outcome of these two processes the household responds to the threat. Two responses are possible: adaptation and maladaptation, while the former reduces the damage from the threat, the latter increases the damage. Some examples of maladaptive responses are denial of the threat and wishful thinking (Grothmann & Patt, 2005). One major limitation of this theory is that it does not take into account all of the cognitive and contextual fac- tors, including the influence of social norms. 3. METHODOLOGY 3.1 Description of the study area and sampling The study area is Niger Delta region. It is located at latitudes 4°25’N to 6°00’N and longitudes 5°00’E to 7°5’E (PEDI, 2020). It is situated on the Atlantic Coast of south- ern Nigeria where the River Niger divides into many branches (Uyigue and Agho 2007). It is the second big- gest delta in the world having a coastline covering around 450 kilometers which ends at the mouth of Imo River (Awosika 1995). The region is divided into four ecological zones namely coastal inland zone, mangrove swamp zone, freshwater zone, and lowland rain forest zone. The Niger Delta region officially comprises nine states namely, Abia, Akwa Ibom, Bayelsa, Cross River, Delta, Edo, Imo, Ondo, and River States. It has about 185 local government areas (LGAs) and over 40 ethnic groups in an estimated 3000 communities (PEDI, 2020). The region has an estimated population of about 36 mil- lion (World meter 2020), and the large majority depend on fishing and farming as a means of livelihood. Figure 1 below shows the map of Nigeria showing the two states in the Niger Delta region where data for the study was collected. A multi-stage sampling technique was used in selecting the households used in the study. In the first stage, 2 states were purposively selected out of the nine states due to their dependence on farming and fish- ing, and the coastal nature of the states which predis- poses them to frequent flooding and coastal erosion. In the second stage 13 local government areas (LGAs) out of 23 LGAs were selected from Rivers State purposively due to the predominance of agricultural activities and 4 LGAs out of 8 LGAs were selected from Bayelsa state. In the third stage proportional random sampling was used to select 18 and 8 communities from the selected LGAs in Rivers and Bayelsa states respectively. In the fourth stage, proportional random sampling was used in select- ing the 251 farming households and 252 fishing house- holds. A total of 503 household heads were interviewed and where the household head was not available the next available adult was interviewed. We are aware that the choice to interview the next available adult where the household head was not available could have impacted the results in some ways, but we cannot comment on the magnitude of any potential selection bias. Figure 1. Map of Nigeria showing the study area. 21Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 The United Nations (2005 p. 44-45) sample size for- mula (see equation 3.1) was used to determine the num- ber of households selected for the study. Using a confi- dence interval (Z) of 95%, 50% default value of preva- lence of indicators (r), a sample size of 430 households was required. However, to account for possible missing values and outliers, the sample size was increased to 510. In the end, only 503 of the questionnaires were valid and were used for the analysis. 3.1 Where: N= sample size, Z = confidence interval (95% level is 1.96), r = estimate of key indicators being measured (default value is 0.5), f = sample design effect (has a default value of 2), k = multiplier accounting for non-response (1.1), p= proportion of the total population accounted for by the target population (0.4), n = mean of household size (5), e = precision level (10% precision level equals 0.01r) 3.2 Data collection Both secondary and primary data were collected for the study. The secondary data on temperature and rainfall were collected from the Nigerian Meteorological Agency (NIMET). The primary data on quantitative information was obtained from households of farmers and fishermen. Structured questionnaires were employed. To ensure the reliability and validity of the survey instrument, the survey instrument was given to 3 experts for validation. Ques- tionnaires were pre-tested on 10 respondents and modifi- cations were done where necessary before actual data col- lection (e.g., we modified the framing of some questions that appeared ambiguous to better target the goal of the study). The questionnaires were administered between March and April 2018. The questionnaires had sections on household socio-demographic and institutional character- istics, perceptions of climate shocks and impact, and adap- tation strategies (for a detailed description of the type of questions asked see supplementary materials). The second- ary data comprises annual temperature and rainfall data for the region for the period between 1982 and 2018. Rain- fall was measured in millimeters (mm) and temperature in degrees Celsius (°C). For ethical considerations, we includ- ed an informed consent form to the introductory note on the purpose of the survey and the survey team used it to obtain verbal consent of each respondent’s willingness to participate in the survey. 3.3 Econometric estimation To identify adaptation strategies employed by the two livelihood groups descriptive statistics such as percent- ages were employed. First, the respondents were asked if they’ve experienced any changes in the temperature and rainfall pattern in the last 30 years. Where the answer is yes, a follow question is asked on the strategies used to adapt to these changes. Some of the respondents report- ed having been using some of the management practices before the changes but had to intensify their use with the recent changes in climate, while some reported that they only started using the management practices in response to the climate change. In this study the adaptation strate- gies employed by farmers have been grouped into three namely: soil and water management, crop management and livelihood diversification while adaptation strate- gies employed by fishermen have been grouped into two: intensification and livelihood diversification. To determine factors influencing choice of adapta- tion strategies by the two livelihood groups the multi- nomial logit model was used. The multinomial logit and multinomial probit models are usually used to analyse adoption decisions involving multiple choices such as adaptation decisions that are made jointly (Wooldridge, 2002 Madalla, 1983). Given the myriads of possible driv- ers of climate change adaptation, Zucaro et al., (2021) propose the need for applying multi-criteria analy- sis to select the most effective climate change adapta- tion measures. However, the choice of the multinomial logit model over the multinomial probit is because it is computationally easier to calculate the choice prob- abilities which are expressible in analytical form (Tse, 1987). It provides a suitable closed form for underlying choice probabilities, ruling out the need for multivari- ate integration and this makes it easy to compute choice situations with several alternatives. The computation is also made easier as a result of its likelihood func- tion which is globally concave (Hausman & McFadden, 1984). The limitation of the model is the independence of irrelevant alternatives (IIA) property. This assump- tion states that the ratio of the probabilities of choos- ing any two alternatives is independent of the attributes of any other alternative in the choice set (Hausman & McFadden, 1984; Tse, 1987). Specifically, this assump- tion means that the probability of using a particular adaptation strategy by a household should be independ- ent of the probability of choosing another adaptation strategy. Hausman test was used to judge the validity of the assumption. The test is based on the fact that if an alternative is irrelevant, removing an alternative or sev- eral alternatives from the model should not change the 22 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. coefficients systematically. The result of the Hausman tests of IIA assumption (Appendix 1 and 2) showed that null hypothesis: Odds (Outcome-J vs Outcome-K) are independent of other alternatives (P>chi2 =.), hence does not violate the assumption that the probability of using a particular adaptation strategy by a household should be independent of the probability of choosing another adaptation strategy. To describe the multinomial logit model let Ai denote a random variable representing the adaptation strategy adopted by any household (already identified). We assume that each household faces a set of discrete, mutually exclusive options for adaptation strategies. These strategies are assumed to depend on a number of households, institutional, environmental and other attributes X. The multinomial logit model specifies the relationship between the probability of choosing alterna- tive Ai and the set of explanatory variables X as seen in equation 3.2 (Greene, 2003): 3.2 In this study the adaptation strategies employed by farmers have been grouped into three namely: soil and water management, crop management and livelihood diversification while adaptation strategies employed by fishermen have been grouped into two: intensification and livelihood diversification. The independent variables used in the model are listed in Table 3.1. Estimating equation 3.2 gives the J log-odds ratio in equation 3.3. 3.3 The coefficient βj of the multinomial logit model only shows the direction of the effect of the explanatory variable on the dependent variables (adaptation option) and does not provide the actual magnitude of the change or probability. Therefore, differentiating equation (3.2) above with respect to the independent variables gives the marginal effects of the independent variables and is stated in equation 3.4: 3.4 Marginal effects measure the expected change in the likelihood of a particular adaptation strategy being chosen with respect to a unit change in an explanatory variable from the mean (Greene, 2000). The signs of the marginal effects and respective parameter estimates may vary, this is because marginal effects depend on the sign and magnitude of all other parameter estimates. Some studies (e.g., Amare & Simane, 2017; Atinkut & Mebrat, 2016; Deressa, Hassan, Ringler, Alemu, & Yesuf, 2009; Gunathilaka, Smart, & Fleming, 2018) have adopted the multinomial logit model to assess the determinants of adaptation strategies employed. 3.4 Model specification Household socio-economic, institutional, farm level, environmental and location characteristics were hypoth- esized to influence the choice of adaptation strategies employed. The following explanatory variables were considered in the multinomial model: educational level, household size, age of household head, years of experi- ence in farming/fishing, sex of household head, house- hold income, access to extension services, membership of association, access to information on climate change, access to credit, farm size, perception of shift in tem- perature, perception of shift in rainfall and location. The empirical model is stated in equation 3.5. ADSi=B0+BnSn+BmIm+BzIz 3.5 Where ADSi denotes the adaptation strategies employed by farming or fishing households, S, B and I represent the sociodemographic, institutional, and cli- matic factors, respectively. B0 denotes the intercept; Bn, Bm and Bz denote the parameters estimates for each sociode- mographic (n), institutional (m) and climatic (z) factor. A description of the explanatory variables used in the model, the measurement and the apriori expectation has been presented in Table 1. 4. RESULTS AND DISCUSSION 4.1 Household characteristics and climatic patterns Descriptive results are presented in Table 2 and Fig- ures 2-5. Based on the results, about 62% of the sampled households were male-headed households, the major- ity (77.3%) of them were married and only a few (3%) of them had no formal education. This profile on marital status is higher than the national average, where about 58% of the population are married, but lower in terms of education where literacy rate reached 77.62% in 2021 (Statistica, 2022). Most (94%) of the households had no access to extension services, no access to credit (about 23Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 88%) and do not belong to any farmer/fisher-based asso- ciation (89%). This finding corresponded to the national average as reported in Emeana (2017) who reported a farmer to extension service ration of 1:10. About 79% of the households had access to health care which is low- er than the national average where over 90 percent of Nigerian households reported being able to access nec- essary healthcare (Statistica, 2022). About 51% of the households were engaged in off-farm work, this is close to the national average as reported in Ume, Nuppenau and Domptail (2022). On average, the sampled house- hold heads were aged 48 years, had 9 years of school- ing, a household size of 7, a farming/fishing experience of 25 years, and farm size (for farming households) of 0.3 hectares. There were no significant differences in the age, experience, household size, access to credit, and access to climate information by farming and fishing households which is evident from the two samples mean comparison test. However, there were significant differ- ences in the gender, years of schooling, membership in social networks, access to extension services, household income, and perception regarding changes in tempera- ture and rainfall by farming and fishing households. Responses on farmers’ perception about long-term temperature and rainfall changes (Figure 2) show that majority (84.46%) of the surveyed households perceived that the temperature has increased over the last 20 years, 12.75% perceived that it has decreased while the remain- ing 2.79% did not perceive any change. On the other hand, majority (63.49%) of the respondents perceived that precipitation has decreased, 31.75% perceived that there has been an increase in rainfall while the remain- ing 4.76% have not observed any change. The percep- tion of households regarding climate shocks has serious implications as to whether to adapt or not and the type of adaptation strategies to adopt. Households cannot adapt to what they do not perceive or experience. Some studies show that farmers who perceive or experience climate related risks are more likely to plan for adapta- tion (Al-Amin et al., 2019; Habtemariam et al., 2020; Mahmood et al., 2021) Furthermore, descriptive analysis presented the annual temperature and rainfall data for the region for the period between 1982 and 2018 as shown in Figures 3, 4, and 5. This also validated the local perception of the long-term change in temperature and rainfall. This aligns with the findings of Mahmood et al., (2021) who found that the farmers’ perception of the local climate was consistent with historical meteorological trends of temperature and rainfall from 1980 to 2017. The rainfall data showed a large negative deviation compared to their long-term means (dotted lines) for most years particularly between 1982-1983 and 1992- 1998 indicating high rainfall variability (Figure 3). The rainfall data revealed that the annual rainfall increased by 2.29 mm every decade. This result does not corrobo- rate the local perception of observed decrease in rainfall. However, the findings are consistent with Koomson et Table 1. Description of explanatory variable and hypothesized signs. Variable Description Measure Apriori expectation Sociodemographic factors Educ Years of education Continuous (years) + HHsize Size of household Continuous (number) +/- Age Age of household head Continuous (years) +/- Exp Farming/fishing experience Continuous (years) +/- Sex Sex of household head Dummy (1=male, 0=female) +/- HHincome Household income Continuous (naira) + Fsize Farm size Continuous (hectares) +/- Institutional factors Ext Access to extension services Dummy (1=yes, 0=no) + Asso Membership of association Dummy (1=yes, 0=no) + Info Information on climate change Dummy (1=yes, 0=no) + Cred Access to credit Dummy (1=yes, 0=no) + Climate factors Temp Perception of shift in temperature Dummy (1=yes, 0=no) + Rain Perception of shift in rainfall Dummy (1=yes, 0=no) + State Location Dummy (1=Bayelsa, 0=Rivers) +/- Source: Author. 24 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. al. (2020), who showed an overall increase in rainfall in the last decade in Eff utu Municipality, Ghana from 1989 to 2018. As expected, the minimum temperature data showed less dramatic variability over time with over- all warming being noticeable, particularly in the mid- dle of the temporal span (Figure 4). Th e period between 1989 and 2012 had lower temperatures than the annual mean minimum temperature of 22.4°C. Th e analysis of the descriptive results further showed that the mean annual minimum temperature increased by 0.01°C every decade. Th e annual mean maximum temperature (Fig- ure 5) shows a more dramatic variability over time than the annual minimum temperature and is increasing at a faster rate of 0.02°C per decade. Th e annual mean tem- perature shows a less dramatic variability over time than the annual maximum temperature and is increasing at a rate of 0.01°C per decade. Th is evidently shows that the days are warming over time. From the analysis of the temporal data, it can be inferred that the local percep- Table 2. Summary statistics of household characteristics. Variables Description Full sample Mean Farmers Mean Fishers Mean t-test t-value Age Age of HH head (years) 47.75 (12.60) 47.48 (13.48) 48.02 (11.68) -0.48 Gender Gender of HH head (1= male; 0 = female) 0.62 (0.49) 0.39 (0.49) 0.86 (0.35) -12.31*** Experience Farming/fi shing experience of HH head (years) 24.97 (13.81) 25.19 (14.74) 24.75 (12.86) 0.36 Household size Number of HH members 7.42 (2.55) 7.41 (2.74) 7.43 (2.36) -0.10 Education Formal education of HH head (years) 9.07 (4.50) 9.61 (4.63) 8.54 (4.30) 2.69*** Access to credit HH had access to credit services (1 = yes, 0 = no) 0.12 (0.33) 0.15 (0.36) 0.10 (0.30) 1.65 Social network HH had membership in local organization (1= yes, 0 = no) 0.11 (0.31) 0.15 (0.36) 0.07 (0.25) 2.92*** Extension HH had access to extension services (1= yes, 0 = no) 0.06 (0.24) 0.08 (0.28) 0.04 (0.19) 2.28** Access to climate information HH had access to information on climate (1=yes, 0=no) 0.49 (0.50) 0.49 (0.50) 0.50 (0.50) -0.13 Farm size Size of land cultivated (hectare) - 0.63 (0.54) - - Household income Total HH annual income (N) 821805.2 (718922) 610908.5 (529628) 1031865 (815801) -6.86*** Perception of shift in temperature HH perceived that temperature has changed over the last 30 years (1 = yes, 0 = no) 0.78 (0.42) 0.88 (0.33) 0.68 (0.47) 5.25*** Perception of shift in rainfall Perception of change in rainfall has changed over the last 30 years period (1 = yes, 0 = no) 0.64 (0.48) 0.74 (0.44) 0.53 (0.50) 4.99*** Location HH located in Bayelsa (1= Bayelsa, 0 = otherwise) 2.99 (1.00) 3.00 (1.00) 3.00 (1.00) 0.04 HH located in Rivers (1= Rivers, 0 = otherwise) 2.99 (1.00) 3.00 (1.00) 3.00 (1.00) - 0.04 Note: ***, ** and * indicate 1%, 5% and 10% level of signifi cance respectively; 1 USD = N380; Values in parenthesis are standard deviations. Source: Field survey, 2018. Figure 2. Local perception of long-term temperature and rainfall changes. Source: Field survey (2018). 25Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 tion of climate variability agreed with the historical data on temperature. 4.2 Adaptation strategies to climate shocks The adaptation strategies the farming households employed were grouped into three (3) categories for com- putational ease. They include soil and water management, crop management, livelihood diversification, and the ‘no adaptation’ option, which was used as the base catego- ry in the MNL. In this study, the following adaptation strategies (cover crops, deep tillage, hedging, mulching, ridge cultivation, and run-off harvesting) were grouped into the soil and water management component (SWM). Crop rotation, crop diversification, agroforestry, chang- ing of planting and harvesting dates, use of improved and drought resistant varieties were grouped under crop management component (CM). Engagement in off-farm and non-farm activity was grouped under livelihood diversification component (LD). Majority (78.5%) of the surveyed farming households used livelihood diversifi- cation as an adaptation option (Table 3). This is followed by crop management (77.7%) and soil and water man- agement options (64.5%). However, 10% of the farming households mentioned that they do not use any adapta- tion strategies. Similar studies which have found that farmers adopted some of the above-mentioned strategies are (Khanal et al., 2018; Mahmood et al., 2021; Owusu et al., 2021; Shikuku et al., 2017). Furthermore, the adaptation strategies the fishing households employed were categorized into two: inten- sification and livelihood diversification for the pur- pose of computational ease (Table 3). Use of improved gears, extension of working hours, varying fishing locations and fishing over large expanses were grouped as intensification. Engagement in off-fishing and non- fishing activities were grouped as livelihood diversifi- cation. The ‘no adaptation’ option was included in the Figure 3. Interannual variability in rainfall in the study area between 1982-2018. Source: Author’s creation from CRU climate data. y = 2.2863x + 2200.9 R² = 0.0272 0 500 1000 1500 2000 2500 3000 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018A nn ua l m ea n ra in fa ll (m m ) Year rainfall y = 0,0177x + 22,43 R² = 0,3586 21,4 21,6 21,8 22 22,2 22,4 22,6 22,8 23 23,2 23,4 23,6 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Te m pe ra tu re (o C) Year Tmin Figure 4. Interannual variability in minimum temperature in the study area between 1982 and 2018. Source: Author’s own creation from CRU climate data. y = 0,0202x + 30,577 R² = 0,511 29,6 29,8 30 30,2 30,4 30,6 30,8 31 31,2 31,4 31,6 31,8 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 Te m pe ra tu re (o C ) Year Tmax Figure 5. Inter-annual variability in maximum temperature in the study area between 1982 and 2018. Source: Author’s own creation from CRU climate data. y = 0.0191x + 26.477 R² = 0.5363 25,8 26 26,2 26,4 26,6 26,8 27 27,2 27,4 27,6 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 Te m pe ra tu re (o C ) Year Mean temp Figure 6. Inter annual variability in mean temperature in the study area between 1982 and 2018. Source: Author’s creation from CRU climate data. 26 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. computation of factors influencing choice of adapta- tion strategies. Majority (83.61%) of the surveyed fish- ing households used livelihood diversification as an adaptation option. This is followed by use of improved gears (80.33%) and varying fishing locations (67.21%) while the least used strategy was fishing over large expanse (40.98%). Similar studies which reported fish- ers using the afore mentioned adaptation strategies are (Deb & Haque, 2017; Galappaththi et al., 2019, 2021; Kabisa & Chibamba, 2017; Mabe & Asase, 2020; Yanda et al., 2018). 4.3 Determinants of choice of adaptation strategies 4.3.1 Determinants of choice of adaptation strategies by farming households The decision to choose a certain adaptation strategy is based on several socio-demographic, economic, insti- tutional, and biophysical factors, which are estimated using the multinomial logit model. The results of the multinomial logit model are presented in Table 4. The marginal effects of all the explanatory variables have been reported. The results indicate that age of household head posi- tively and significantly affected the probability of adopt- ing soil and water management practices as an adapta- tion strategy at probability level of 0.05. The magnitude of this effect is 0.003. This suggests that the likelihood of adopting soil and water management practices increases by 0.3% for every year of household head age. A plausible explanation for this result is that older farmers are more experienced and more likely to experience changes in cli- mate and therefore, adopt adaptation strategies to cope with the change. For instance, the study by Al-Amin et al. (2019) showed that older women were more likely to per- ceive climate change than younger women. Previous stud- ies that reported that age positively affected the adoption of adaptation strategies to climate change include Adi- massu & Kessler (2016); Opiyo et al., (2016); Alemayehu & Bewket (2017) and Belay & Fekadu (2021), while oth- ers like Kassim, Alhassan, & Appiah-Adjei (2021) and Ali & Erenstein (2017) contradicted the results by report- ing negative and significant relationship of age with early planting adaptation strategies in Ghana and crop manage- ment (i.e., adjustment in sowing time, drought-tolerant varieties and shift to new crops) in Pakistan. The result shows that gender of household head exerts a positive and significant (p<0.1) influence on the adoption of soil and water management practices. This means that male-headed households are 13% more likely to use soil and water management practices as an adaptation strategy than female headed households. This is probably because male-headed households have bet- ter access to resources and information as well as higher decision power to make decisions regarding adaptation. Previous studies that corroborate these findings include Asfaw & Admassie (2004), Deressa et al., (2014), Der- essa et al., (2009) Mahmood et al., (2021). On the other hand, gender was found to influence the adoption of livelihood diversification as an adaptation strategy nega- tively and significantly (p<0.1). The marginal effect of the variable is -0.1841. This means that female-headed households had an 18% higher chance of adopting liveli- hood diversification as an adaptation strategy. This result is in agreement with the findings of Amare & Simane (2017) and (Kassim et al., 2021) who found that female headed households diversified more and are more likely to engage in off-farm activities. However, it contradicts the findings of Asfaw et al., (2017) and Rahman & Akter (2014) who found that males adopted non-farm liveli- hood diversification more than females because of their involvement in household chores which leaves them with little or no time to engage in off-farm activities. Household size was found to influence the adop- tion of crop management practices positively and sig- Table 3. Adaptation strategies employed by farming and fishing households in the study area. Adaptation options Frequency Percentage (%) Farmers Soil and water management (SWM) Cover crops Deep tillage Hedging Mulching Ridge cultivation Run-off harvesting 162 106 130 58 33 69 12 64.54 42.23 51.79 23.11 13.15 27.49 4.78 Crop management (CM) Crop rotation Crop diversification Agroforestry Changing of planting and harvesting date Improved and drought resistant varieties Livelihood diversification (LD) 195 118 143 12 168 157 197 77.69 47.01 56.97 4.78 66.93 62.55 78.49 Fishing households Intensifying fishing efforts Using improved fishing gear Extending working hours Varying fishing location Fishing over large expanse Livelihood diversification 49 36 41 25 51 80.33 59.02 67.21 40.98 83.61 Note: multiple responses indicated. Source: Field Survey (2018). 27Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 nificantly (p<0.01). As household size increases by one the probability that the household will adopt crop management practices increases by 8.9%. This is prob- ably because activities involved in crop management are capital intensive and so only large households size who have household members engaged in other income gen- erating activities that generate extra income to invest in this adaptation option. In addition, it is understandable that large households would like to engage their work- force in different income generating activities and hence are more likely to diversify. Another reason could be that most agricultural activities in Nigeria are labour intensive due to low mechanization; large household size therefore constitutes a source of labour to enable house- holds engage in adaptation practices and other agricul- tural practices such as tree planting, soil conservation and other crop management practices. This result is in agreement with the findings of Shikuku et al., (2017), Ali & Erenstein (2017), Habtemariam et al., (2020) and Diallo, Donkor, & Owusu (2021) who found that large households are more likely to adopt adaptation strategies such as changing planting date, improved varieties and planting of trees. The variable education exerts a positive and signifi- cant effect on farming households’ decision to adopt soil and water management and crop management practices as an adaptation strategy to climate shocks albeit at the 10% and 5% levels. The marginal effects result thus indi- cates that an increase in the year of schooling by 1 year increases the probability that households will adopt soil and water management and crop management practices by 0.6% and 2.3% respectively. This is expected as edu- cation provides more understanding as to the impacts of climate change as well as adaptation methods to Table 4. Multinomial regression results for determinants of adaptation strategies by farming households. Explanatory variables Soil and water management Marginal effects Crop management Marginal effects Livelihood diversification Marginal effects Age 0.003** (0.061) -0.002 (0.015) 0.000 (0.019) Gender 0.127* (1.164) 0.023 (-0.326) -0.184* (-0.889) Household size -0.0135 (-0.048) 0.089*** (0.353) -0.061 (0.002) Education 0.006* (0.188) 0.023** (0.142) -0.021 (0.036) Access to credit -0.067*** (-0.226) 0.113 (1.672) 0.036 (1.532) Social network -0.559** (-0.583) -0.028 (0.619) 0.131 (0.985) Extension 0.113 (0.148) 0.037 (-0.849) -0.218*** (-1.983) Access to climate information 0.036 (0.689) -0.136* (-0.168) 0.112 (0.422) Farm size 0.091** (-0.433) -0.042 (0.867) 0.218*** (1.517) Perception of shift in temperature -0.018 (-0.507) 0.036 (-0.177) -0.039 (-0.355) Perception of shift in rainfall 0.015 (0.074) 0.181** (0.271) -0.211 -0.661 Constant -4.695** -3.684 -0.013 Diagnostics Number of observations LR(33) Prob > chi2 Log likelihood Pseudo R2 251 128.64 0.0000 -240.00978 0.2113 Note: Base category: no adaptation; ***, ** and * indicate significance at 1%, 5% and 10% respectively. Values in parentheses are the stand- ard errors. Source: Field survey (2018). 28 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. be adopted to be able to cope with these impacts. This result is in agreement with previous studies such as Alauddin & Sarker (2014), Alam et al., (2016), Khanal et al., (2018), Belay & Fekadu, (2021) Mahmood et al., (2021) and Kassim et al., (2021) which reported the posi- tive influence of education on adaptation. Interestingly, access to extension services was found to exert no significant influence on the adoption of soil and water conservation and crop management but was found to exert a significant (p<0.05) and negative effect on the adoption of livelihood diversification as an adap- tation strategy. This means that households with no access to extension services are 21.8% more likely to adopt livelihood diversification as an adaptation strat- egy. This is contrary to apriori expectations as extension agents are expected to be at the forefront of communi- cating climate information and innovations in agri- culture. A plausible explanation for the negative effect could be that households with no access to extension services are equipped with information on other adapta- tion strategies such as off-farm activities that they could choose. Another plausible explanation could be the weakness of the extension delivery system typically in most African countries as pointed out by Oladele & Sak- agami (2004) and Antwi-Agyei & Stringer (2021) which include poor financial decentralization, inadequate use of alternative extension methods, lack of knowledge on climate change by extension agents, high bureaucratic setting and inadequate cooperation and coordination with other agencies. Most previous studies such as (Al- Amin et al., 2019; Alemayehu & Bewket, 2017; Ali & Erenstein, 2017; Habtemariam et al., 2020; Kassim et al., 2021) have often reported positive effects of extension service on adoption of adaptation strategies such as use of improved varieties, soil and water conservation prac- tices. However, our finding is consistent with the find- ings of Owusu et al., (2021) and Shikuku et al., (2017) who reported a negative effect of extension services on adoption of adaptation strategies. Farm size was found to exert a positive and sig- nificant influence on farming household decisions to adopt soil and water management practices and liveli- hood diversification as an adaptation strategy to cli- mate shocks at 5% and 1% levels respectively. A unit increase in farm size increases the chances of adoption of livelihood diversification and soil and water manage- ment practice as an adaptation strategy by 22% and 9.1% respectively. This means that households with larger farm sizes were more likely to diversify more probably to generate additional income for adaptation and expand production. They are more likely to have capacity to invest in climate shock adaptation options. It could also be that farming households with large farm sizes are more worried about the impact of climate shocks since they are more likely to lose a larger proportion of their output compared to those with smaller farm sizes and so are not willing to take the risk. Hence, their eagerness to adopt livelihood diversification and soil and water management practices to off-set any adverse effects. This result contradicts the findings of Deressa et al., (2011); Bazezew et al., (2013) and Gebreyesus (2016) that reported that farm size negatively affects the probability of using livelihood diversification as an adaptation meas- ure. However, it agrees with the findings of (Al-Amin et al., 2019; Ali & Erenstein, 2017; Kassim et al., 2021) who reported positive effect of farm size on adaptation strat- egies such as upland planting, planting of horticultural crops and improved varieties. Another interesting finding is access to credit which was found to exert negative and significant effect on the probability to adopt soil and water conservation as an adaptation strategy at 1%. This means that credit con- strained households are 6.7% more likely to take up soil and water conservation as an adaptation strategy. This finding is consistent with the findings of Tekle- wold et al., (2019) who found that households with lack of access to credit are more likely to take up soil conser- vation practices. However, it contradicts studies such as Al-Amin et al., (2019), Diallo et al., (2021), Shikuku et al., (2017), Belay & Fekadu, (2021) which all argued that households with access to credit are more likely to take up adaptation strategies such as soil and water conser- vation and crop management practices since access to capital is a major deciding factor in the choice to adopt an innovation and hence required to facilitate adoption of adaptation strategies. Again, the variable social network also showed some interesting results. It was found to exert a negative and significant influence on the adoption of soil and water management practices as an adaptation strategy against climate shocks at a 5% level. This means that those who do not belong to any farmer-based organizations or groups are 55.9% more likely to adopt soil and water conservation as an adaptation strategy. This is contrary to apriori expectation since studies such as Teklewold et al., (2019), and Owusu et al., (2021) have shown that social capital networks positively influence adoption of adaptation strategies and innovation. The reason for the negative influence could be that the farmers belonged to several organizations and received conflicting climate change information from several sources. However, our finding is consistent with the findings of Belay & Fekadu (2021), Diallo et al., (2021) and Al-Amin et al., (2019) who found that social capital negatively influences farm- 29Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 ers’ adoption of climate change adaptation strategies such as fertilizer, short duration and drought tolerant varieties. Perhaps again somewhat surprisingly, access to information was found to exert negative and significant effect on the adoption of crop management as an adapta- tion strategy albeit at a 10% level. This means that those who do not have access to climate information are 13.6% more likely to employ crop management as an adapta- tion strategy. There are mixed findings about the effect of climate information on adaptation strategies. Access to climate information has been found by some studies such as Kassim et al., (2021), Khanal et al., (2018), Alam et al., (2016) to promote adoption of adaptation strate- gies. However, Owusu et al., (2021) found no significant impact of the use of climate information on the adoption of adaptation strategies in response to climate change. Our finding corroborates the findings of Teklewold et al., (2019) who found that climate information negatively influences the adoption of soil conservation as an adap- tation strategy. Finally, the variable perception of a shift in rainfall showed a positive and significant influence on the adop- tion of crop management as an adaptation strategy. This means that households who perceived that there has been a change in rainfall are 18.1% more likely to adopt crop management as an adaptation strategy. Our find- ing aligns with other studies like Khanal et al., (2018), Kassim et al., (2021) Al-Amin et al., (2019) Owusu et al., (2021) who all argue that households who perceive and experience climate change are more likely to adopt adap- tation strategies to respond to the adverse effect of cli- mate change. 4.3.2 Determinants of choice of adaptation strategies by fishing households Studies have shown that farmers’ attitudes perceived behavioral control and past behavior are very important in predicting intentions to adopt the private sustainabil- ity schemes (Menozzi et al., 2015). This means that the decision to choose a certain adaptation strategy is based on several socio-demographic, economic, institutional and biophysical factors. The results of the multinomial logit model are presented in Table 5. The results indi- cate that education of household heads positively and significantly affected the probability of adopting inten- sification of fishing efforts and livelihood diversification as an adaption strategy at 1% and 5% levels respectively. This means as years of schooling of household head is increased by one year the probability of adopting inten- sification increases by 1.5% and livelihood diversifica- tion by 0.9%. Higher education is associated with great- er access to information and skills to adopt adaptation strategies and innovation (Belay & Fekadu, 2021). This result agrees with previous studies such as Sereenonchai & Arunrat, (2019) and Alam et al., (2016) which reported that education positively influences adaptation choices. Access to climate information was found to have a significant negative influence on the choice of intensifi- cation as an adaptation strategy by fishing households at a 5% level. This means that fishing households who do not have access to climate information are 7% more like- ly to adopt intensification as an adaptation strategy. This result is contrary to some studies like Mabe & Asase, (2020) and Sereenonchai & Arunrat (2019) which asserts that fishing households with access to climate informa- tion are more likely to adopt adaptation strategies to avert the adverse effect of climate change. As expected, the results of the study showed that household income positively and significantly inf lu- ences the probability of adopting livelihood diversifi- cation as an adaptation strategy at a 5% significance level. This means that as income increases, the prob- ability of households diversifying their sources of live- lihood increases. This may be because of the availabil- ity of capital to invest in other non-fishing activities to reduce the risk that climate shock poses to their fish- ing livelihood. This result is supported by the findings of Sereenonchai & Arunrat, (2019) who showed that an increasing non-fishing income increases the probability of adopting adaptation strategies. Findings from Meressa & Navrud, (2020) also showed that farmers’ adoption of new varieties could be greatly increased by incorporat- ing traits that are in high demand, suggesting the need for increased income in increasing farmers’ adoption of new technology. The variable perception of shift in rainfall exerts a positive and significant effect on farming households’ decision to adopt both intensification and livelihood diversification as an adaptation strategy at 5% level. This means as fishing households who perceive that there have been changes in rainfall are 9.7% and 9.2% more likely to adopt intensification and livelihood diversifica- tion respectively as an adaptation strategy. Finally, location was found to exert a negative and significant effect on the adoption of intensification as an adaptation strategy at 1%. This means that fishing households who were located in Rivers State were 13.8% more likely to adopt intensification as an adaptation strategy. It is important to note that when compared to Bayelsa State, Rivers State is more developed and has a weather station. It is possible to fishers located there has more access to climate information than their counter- parts thereby making them adopt adaptation strategies. 30 Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 Chinasa Sylvia Onyenekwe et al. Location has been found by others studies such as Mabe & Asase (2020), Ali & Erenstein (2017) to be an impor- tant factor influencing adoption of adaptation strategies. 5. CONCLUSION AND POLICY RECOMMENDATION The study examined the farmers’ and fishers’ per- ceptions of the changing climate. They perceived a decrease in rainfall and an increase in temperature which is consistent with the historical meteorological trend from 1982-2018. Furthermore, the study inves- tigated the various adaptation strategies employed by farmers and fishers to adapt to climate shocks and fac- tors that affect the adoption of these adaptation strate- gies. The main adaptation strategies employed by farm- ing households were soil and water conservation prac- tices, crop management practices, and livelihood diversi- fication while fishing households adopted intensification and livelihood diversification as adaptation strategies. Livelihood diversification was a common adaptation strategy for both livelihood groups. We used the MNL model to examine the factors inf luencing the adop- tion of the various adaptation strategies by both liveli- hood groups and the findings confirm that age, educa- tion, farm size, and being a male-headed household are among the important factors that increase the likeli- hood of farmers to adapt to climate shock using soil and water conservation practices whereas access to credit and social network discourages farmers from using this as an adaptation strategy. Our results further show that household size, education, and perception of changes in rainfall exert positive effects on the use of crop manage- ment as an adaptation strategy while access to climate information exerts a negative influence. We also find that farm size positively influences farmers to diversify their sources of livelihood whereas female headed house- holds and households who do not have access to exten- sion are more likely to adopt livelihood diversification as an adaptation strategy. On the other hand, factors such as education, household income, and perception of rain- fall change positively influence the adoption of liveli- hood diversification as an adaptation strategy by fishing households. Furthermore, the results show that educa- tion and perception of changes in rainfall exert posi- tive effects on the use of intensification as an adaptation strategy while fishers who do not have access to climate information and in Rivers State are more likely to use intensification. The findings of this study have strong implications for agricultural policy formulation. The heterogeneity in adaptation strategies and determinant suggest that “one size fits all” policies will not work to adapt to climate change. Institutional factors such as extension visits, access to credit, social networks, and access to climate information for the farmers should be further investigat- ed as such factors negatively influence the choice of cli- mate change adaptation strategies contrary to the find- ings of some studies. The empirical findings of this study reinforce the need for policymakers to intensify their efforts in improving the extension service in Nigeria. As can be seen from the results only 8% and 4% of the farmers and fishers respectively had access to extension. Table 5. Multinomial regression results for determinants of adapta- tion strategies by fishing households. Explanatory variables Intensification Coefficient Livelihood diversification Coefficient Age 0.001 (0.030) -0.001 (0.019) Gender 0.018 (0.529) 0.006 (0.139) Fishing experience -0.002 (-0.039) 0.001 (0.018) Household size -0.004 (-0.104) -0.001 (-0.032) Education 0.015*** (0.367) 0.009** (0.196) Access to credit 0.030 (0.622) 0.023 (0.416) Social network -0.017 (-0.518) -0.028 (-0.700) Extension 0.034 (1.077) 0.309 2.4239 Access to climate information -0.070** (-1.664) -0.042 (-0.886) Household income 2.61e-08 (6.83e-07) 3.71e-08** (7.38e-07) Perception of shift in temperature -0.037 (-0.859) -0.058 (-0.980) Perception of shift in rainfall 0.097** (2.346) 0.092** (1.867) Location -0.138*** (-2.738) 0.003 (-0.099) Constant -5.5811*** -4.5704** Diagnostics Number of observations LR(26) Prob > chi2 Log likelihood Pseudo R2 252 144.03 0.0000 -106.04623 0.4044 Note: Base category: no adaptation; ***, ** and * indicate signifi- cance at 1%, 5% and 10% respectively; Location base category: Riv- ers; Values in parentheses are the standard errors. Source: Field survey (2018). 31Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Bio-based and Applied Economics 12(1): 17-35, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13436 This would facilitate the free flow of information on cli- mate and agricultural innovations to farmers and fish- ers, especially to those who cannot afford information technology devices. Again, membership in associations is another important channel for climate information acquisition that facilitates the adoption of adaptation measures. As can be seen from the study only 15% and 7% of farmers and fishers had membership in any social group. Local opinion leaders and other stakeholders should encourage the establishment of farmer and fish- er-based organizations in the communities. This could facilitate efficient relay of climate information, and edu- cation on the use of climate information in the adoption of adaptation measures. Also, the limited access of the farmers (15%) and fishers (10%) to credit could be the reason why they are not adopting the crop management and livelihood diversification as an adaptation strategy. These adaptation strategies could be capital intensive, so policy makers and relevant stakeholders could help ease their liquidity constraints by providing them with affordable credit schemes. In addition, the meteorologi- cal services in the region should be improved so that they can educate and provide real-time weather infor- mation to enhance the households’ understanding of climatic changes to make strategic adaptation decisions. Investing in education is critical for overall development and may thus provide a policy instrument for enhancing their perception of climate change and promoting the use of climate shock adaptation strategies and thereby reducing the vulnerability of both farmers and fishers. Finally, since household size and farm size were found to positively influence adoption, the policy implication could be the provide access to farm machinery, which will minimize labour requirements and thereby enable farming households to implement adaptation measures. Given the increasing threat from climate change and increase in the demand for food resulting from increas- ing population, improving adaptation by addressing the aforementioned issues is a fundamental intervention in pursuit of reducing the vulnerability of farming and fish- ing households thereby improving their livelihoods. More so, since women are mainly responsible for food produc- tion in the area, as well as supply majority of the labour used in agriculture, further research could be conducted to examine how climate change might have a differen- tial impact based on gender as well as the determinants of adaptation through gender lenses. As gender has been found to play an important role in decision-making in households. Finally, this study was based on cross-sec- tional data and hence might not provide a robust mech- anism for establishing causality, as would have been the case with a time series or panel data. In addition, the data used in this study is not representative of the nation- al demography. We, therefore, recommend future studies using nationally representative panel data to better test addressed the research questions posed in this study. REFERENCES Adimassu, Z., & Kessler, A. (2016). Factors affecting farmers’ coping and adaptation strategies to per- ceived trends of declining rainfall and crop produc- tivity in the central Rift valley of Ethiopia. Environ- mental Systems Research, 5(13), 1–16. https://doi. org/10.1186/s40068-016-0065-2 Akpoti, K., Dossou-Yovo, E. R., Zwart, S. J., & Kiepe, P. (2021). The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso. 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Chi square df P>Chi square 0 -4.039 24 . 1 -1.943 23 . 2 4.638 24 1.000 3 -14.650 24 . Appendix 2. Hausman tests of IIA assumption for fishermen. Chi square df P>Chi square No adapt -6.131 13 . intensif -0.533 13 . diversif -7.049 13 . Farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities Linda Arata1, Davide Menozzi2 How do farmers’ pluriactivity projects evolve? How do farmers’ pluriactivity project evolve? Clarisse Ceriani*, Amar Djouak, Marine Chaillard Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria Chinasa Sylvia Onyenekwe1, Patience Ifeyinwa Opata1, Chukwuma Otum Ume1,*, Daniel Bruce Sarpong2, Irene Susana Egyir2 Organic cocoa farmer’s strategies and sustainability Ibrahim Prazeres1,*, Maria Raquel Lucas1, Ana Marta-Costa2, Pedro Damião Henriques3 A complex web of interactions: Personality traits and aspirations in the context of smallholder agriculture Luzia Deißler1,*, Kai Mausch2, Alice Karanja3, Stepha McMullin3, Ulrike Grote1 Exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural Rwanda Ronja Seegers*, Etti Winter, Ulrike Grote