Introduction

The earlier choruses of conflict, dictatorship, and poverty as the main obstacles to Africa's development  are gradually waning. Perhaps, progress in consolidating the rule of law and good governance, albeit with some hitches, accounts for this changing perspective on Africa (Henri 2019; Kalabamu 2019). However, at the center stage of the continent’s struggle today are the destructive effects of climate chang (Makuvaro et al. 2018; IPCC 2014; Tsegaye et al. 2013). The region has experienced a significant increase in the intensity and frequency of extreme climate events such as floods, storms, and droughts, with the number of events surging from 85 in the 1970s to over 540 between 2010 and 2019 with attendant high socio-economic cost (Kamer 2022). While the exact the total cost associated with these extreme events on the continent remains inconclusive, there is a broad consensus that there exists a substantial “adaptation finance gap” (Bahadir et al. 2018; Randazzo and Piracha 2019). Studies have consistently shown that public adaptation finance flows to the continent are considerably below the estimated needs and this gap continues to widen (OECD 2023; UNEP 2022).

The adaptation finance gap refers to the gap between the costs of meeting a given adaptation target and the amount of finance available (UNEP 2015). Amidst poor commitment from developed countries and massive resource rent coupled with poor gross domestic product (GDP) in some African countries, the adaptation finance gap is expected to grow exponentially (Watkiss et al. 2010; Stern 2008). Traditional public financing sources projected under the United Nations Framework Convention on Climate Change (UNFCCC) financial mechanism have arguably performed abysmally in their efforts to support Africa’s adaptation needs. Indeed, the seemingly poor commitment of developed countries to fulfil their pledges makes many scholars and practitioners doubt the USD100 billion climate finance pledge made in Paris in 2015 (Musah-Surugu et al. 2018; Bendandi and Pauw 2016). Describing the failure of international public finance for climate change, Buchner and Wilkinson (2015) stated: “since its emergence, the USD 100 billion goal has been both a touchstone of good faith and a hallmark of mistrust.”

Identifying new and additional private sources of finance to complement the ailing public sources is therefore topical in the climate finance discourse (Fridahl and Linnér 2016; Bodansky 2010). Climate finance scholars contend that private sector finance would be more sustainable, predictable, and stable to complement public finance (Pauw 2015). Emerging studies that focus on private sources of financing adaptation have identified remittances as showing prominence in this subset (Musah-Surugu et al. 2018; Pauw 2015). They indicate that remittances reach the core of the poor and could be a significant source in closing the adaptation financing gap, specifically at the household level. For instance, using propensity score matching and multinomial treatment methods, Randazzo and Piracha (2019) found an overall productive use of remittances at the household level in Senegal. Also, Bui et al. (2015) established that rural households in Vietnam increased investment in education and productive businesses, which augmented their human capital and financial gains for livelihood improvement.

Given the volume of remittance flows into developing countries and their potential to reach the poorest of the poor, many recent climate change financing studies have underscored their significance to support climate change adaptation (Kissinger et al. 2019; Marke and Sylvester 2018). Since 2007, remittance flows to developing countries have increasingly outstripped Official Development Assistance (ODA), net export, portfolio equity, tourism receipt, as well as foreign direct investment (FDI) (World Bank 2019). As a result of their predictability, lower volatility, counter-cyclical nature, and ability to bypass aid bureaucracies in reaching the poor, remittances have been tipped as a significant complementary source of financing adaptation (OECD 2016; Pauw 2015).

Previous studies in economics and development show that remittances have been a shock absorber for many vulnerable households in poorer countries (Couharde and Generoso 2015). Remittances contribute to financing adaptation-related investments ranging from short-term priorities, such as irrigation equipment, to longer term goals related to health and education (see Bayala in this volume; Couharde and Generoso 2015). Remittances are a form of self-insurance in developing countries. The insurance function is reflected in the tendency of migrants to send more remittances to their countries of origin following downturns in economic crises, natural disasters, and/or political/civil conflicts (Ratha 2007).

In spite of the evidence presented for the case of remittance flows as a strong complementary source of adaptation financing, the existing literature in the emerging field of climate change focused mainly on sources such as central governments, donors, FDIs, solidarity and philanthropic with only cursory attention given to the role of remittance flows (Musah-Surugu et al. 2018). Thus, this chapter seeks to fill the gap by examining the role of remittances as a finance adaptation tool among smallholder farmers in Ghana. This is done by answering the questions of the extent of influence remittances has on climate change adaptation and the share of income spent on adaptation. The findings of the chapter provide an informed basis for designing strategies that support existing private adaptation measures of local farmers through appropriate public policy, investment, and collective actions so as to reduce the adverse impact of climate change. Therefore, the findings are useful to public policy makers, NGOs, the private sector, and philanthropists with an interest in climate governance.

Materials and Methods

This chapter employs both quantitative and qualitative approaches to data-gathering and analysis. Data was sourced from smallholder farmers in the Bole-Bamboi and Sawla-Tuna-Kalba districts of the Northern Region of Ghana (Fig. 15.1). The specific communities involved were Gbogdaa, Nahari, Bali, Mankuma, Bole, Nyanoa, Belma, Jilinkon, Sawla, and Kalba. These communities were selected due to their perceived climate change vulnerability (MoFA 2014). Maize, millet, and yam farmers were selected due to the large production of these food crops in the communities. For this study, the Food and Agriculture Organization of the United Nations (FAO 2010) definition of smallholder farmers was adopted: Thus, smallholder farmers are farmers who farm plots of two hectares or less and rely exclusively on family labor. The data collection was done by the researchers and assisted by two carefully chosen and well-trained field assistants. A convenience sampling technique was used in selecting 400 farmers, comprising 40 farmers from each community. The convenience sampling approach was appropriate because no lists of farmers (sample frame) in the communities existed and hence did not offer an opportunity for probability sampling.

Fig. 15.1
2 maps. A map of Ghana with Bole Bambio and Sawla Tuna Kalba districts highlighted. A map of the Bole Bambio and Sawla Tuna Kalba districts marks Bole and Sawla as the district capitals. The adjoining districts and the regional boundary are also marked.

Map of the study area (Bole-Bambio and Sawla-Tuna-Kalba districts). (Source: University of Ghana Geographic Information Systems Lab (UGGIS), 2017)

A disproportionate allocation was made to the districts to ensure representativeness. A semi-structured questionnaire was used in soliciting quantitative information. Section A of the questionnaire collected information on farmers’ perceptions and knowledge of climate change, with Section B focusing on adaptation strategies. While Section C looked at the flow of remittances, Section D focused on remittances and climate change adaptation. Section E collected socio-demographic information. Twenty Focus Group Discussions (FGDs) were conducted, two per community. These included male and female farmers with an average of nine participants per group. In-depth interviews were also conducted with the district assemblies and representatives from the MoFA (Ministry of Food and Agriculture).

Adaptation Strategy Index (ASI) was used to rank the adaptation practices in order of importance on the farm. Questions in that section of the questionnaire provided response options placed on a continuum (Likert scale): high, medium, low, and not at all. The scores assigned to the responses were 3, 2, 1, and 0 respectively. Farmers were asked to assess the different strategies by using the four-point rating scale and subsequent ranking was done using the weighted means. This approach has been established as efficient in ranking elements (Uddin et al. 2014; Devkota 2014; Ndamani and Watanabe 2016).

The Order Rank Logit (ORL) and multinomial logit structural decomposition models were used to examine the effect of remittances on climate change adaptation strategies and the effect of remittances on farmers’ share of income spent on adaptation respectively. The ORL was employed to examine the influence of remittances on adaptation strategies. Remittance (explanatory variable) data was continuous as respondents were allowed to indicate their average amount of remittance per year. Adaptation strategies (outcome variable) by the ASI provided ranking options (Likert scale), offering ample possibilities for ORL analysis (Hill and Jones 2014; Stock and Watson 2012; Doherty and Clayton 2011). In this study, adaptation strategies are conceptualized as the responsive mechanism adopted (directly and indirectly) by farmers to offset the effects of climate change and reduce vulnerability (Huang et al. 2018; Descheemaeker et al. 2017; IPCC 2015; Doherty and Clayton 2011). In testing for the influence of remittances on the share of income spent on adaptation, the multinomial logit regression was appropriate. In this case, the independent variable remained the same (remittances as a continuous variable) and the share of income spent on adaptation was categorized in sums of USD100–300, 301–600, and 601–900. According to Hilbe (2011) and Allison (2014), the multinomial logistic regression is used to predict categorical placement or the probability of category membership in a dependent variable based on multiple independent variables, hence making it an appropriate tool for the analysis.

The models enabled an independent testing of the direct paths of the variables involved. The resulting R2s for both models indicate that the exogenous variables are significant and fitted predictors of variance in adaptation and share of income. These were further confirmed by all post-estimations tests (such as margins) carried out. All estimations were made using STATA 15.

The major limitation of the study is that it did not disaggregate farmers who received national (internal) and international (external) remittances. This was because the number of recipients of international remittances was insignificant for statistical analysis. The authors propose that future studies with enough financial capacity expand the geographical dispensation in order to attract more international remittance recipients for a disaggregate analysis.

Results of the Study

Information on Sampled Farmers

The mean age of respondents was 46 years and more than half (59 percent) were males. Fifty percent had no formal education and 89.5 percent were married. The average household size was seven people, the average farm consisted of four acres, and the average number of years spent farming was 35 (Table 15.1). Eighty-five percent owned a bicycle and 82 percent a mobile phone. Many had a radio and motor bicycle (78.8 percent and 65 percent respectively). Water pumps (28 percent), televisions (33 percent), and motor kings/tricycles (35 percent) were not owned by many (Table 15.2). Eighty-two percent held the view that the climate has changed over the past fifteen years (Fig. 15.2). According to the distribution of perception on climate change, all respondents (100 percent) thought rainfall had decreased while 98 percent perceived temperatures as having increased. There was also a perceived increase in the occurrence of storm surges (74 percent) and a 100 percent increase in the occurrence of prolonged drought (Table 15.3). Exactly half (50 percent) of respondents thought the change in climatic conditions in the area is caused by human activities, 20 percent saw it is a punishment from ancestors, 15 percent saw it as a punishment from a natural God, and 15 percent thought it is a natural cycle (Fig. 15.3). Figure 15.4 shows that the major effect of climate change as stated by respondents is low productivity (97 percent).

Table 15.1 Socio-demographic characteristics of respondents (n = 400)
Table 15.2 Property ownership
Table 15.3 Distribution of responses to perceived changes in specific climatic events (n = 400)

Farmers’ Perception and Knowledge of Climate Change

Fig. 15.2
An illustration of two horizontal bars. The first bar reads, no, 18%. The second bar reads, yes, 82%.

The climate has changed over the past fifteen years

Fig. 15.3
An area graph depicts the following values. Supernatural God, 15%, punishment from ancestors, 20%, human activities, 50%, and natural cycle, 15%.

Perceived causes of climate change

Fig. 15.4
A geometric pattern depicts the perceived effects of climate change. Low productivity, 97%, abandoning farmlands, 58.5%, exertion of more energy, 48.8%, stunted growth of crops, 76.3%, and inadequate feed and water for livestock, 84.0%.

Perceived effects of climate change

Climate Impacts and Adaptation

Due to the rising effects of climate change, many (85.5 percent) of the farmers were implementing various types of adaptation strategies as responsive mechanisms (Fig. 15.5). For strategic policy toward remittance-based adaptation, the study identified the adaptation strategies employed by farmers. The response options were placed on a continuum (Likert scale): “high, medium, low, and not at all,” and as above were assigned scores of 3, 2, 1, and 0 respectively. Backyard gardening was the foremost activity implemented by farners as an adaptation strategy (ASI, 318.5), with cultivation of drought-tolerant crops (ASI, 300.0) ranking second and mixed cropping ranking third (ASI, 282.5). Off-farm jobs, residue management, and alternating planting dates were ranked fourth, fifth, and sixth respectively. Index-Based Insurance (ASI, 174.9), use of compost/animal manure (ASI, 198.3), and land fallowing (ASI, 192.4) were all given lower ranks of importance (Table 15.4).

$$ \mathrm{ASI}={\mathrm{AS}}_n\times 0+{\mathrm{AS}}_{\mathrm{l}}\times 1+{\mathrm{AS}}_m\times 2+{\mathrm{AS}}_h\times 3 $$
  • ASI = Adaptation Strategy Index

  • ASn = Frequency of farmers rating adaptation strategy as having no importance

  • ASl = Frequency of farmers rating adaptation strategy as having low importance

  • ASm = Frequency of farmers rating adaptation strategy as having moderate importance

  • ASh = Frequency of farmers rating adaptation strategy as having high importance

Fig. 15.5
A shaded graph conveys the following information. Yes, 85.5%, and no, 14.5%

Implemented adaptation practices

Table 15.4 Ranked order of adaptation strategies to climate change

Flow of Remittances and Use of Remittances for Adaptation

On average, our respondents receive USD369.00 of remittances annually. Half (50 percent) receive USD100–500 per year, 31.8 percent USD501–1000, and only 18.2 percent receive above USD1000. The main medium of receiving remittance was through friends or relatives (72.8 percent) (Table 15.5). Only 27 percent received wage-based income. Table 15.6 indicates that remittances enabled respondents to engage in some practices that could potentially become adaptation strategies. Buying chemicals or fertilizer (21.6 percent), acquiring extra food to supplement family needs (17.8 percent), and buying drought-tolerant maize seeds (14 percent) were indicated as activities remittances are used for. Other practices outlined were investing in children’s education (13.4 percent), buying irrigation equipment (10.3 percent), and putting up a storage facility (7.2 percent) (Table 15.6). Major challenges associated with receiving remittances were mobile network problems (29.2 percent), traveling longer distances to redraw remittances (22.5 percent), and remitters being too busy to send money (20 percent).

Table 15.5 Amount of remittances received by respondents per year (USD)
Table 15.6 Practices smallholder farmers use remittances for that can potentially become adaptation practices

Influence of Remittances on Adaptation

For Table 15.4, respondents were asked to indicate in order of importance the adaptation strategies implemented on their farms. Consequently, provided a general overview of the adaptation strategies respondents deemed important without remittance. The study also  examined the influence of remittance on adaptation. A Rank Order Logit (ORL) regression was employed in examining the influence remittance on adaptation. The ORL model was developed based on the adaptation practices ranked by the ASI. The ASI offered an opportunity for respondents to rank their most preferred adaptation practices in the order of importance. Adequate information can be obtained if respondents are asked to rank the set of alternatives (Doherty and Clayton 2011; Stock and Watson 2012). In statistical terms, the preferences can then be estimated more efficiently. In a situation where respondents are asked to select their preferred option out of a set of presented alternatives, appropriate estimation choice would have been a standard discrete model, like the multinomial logit model. However, it is empirically established that more information can be obtained from a respondent when asked to give a complete ranking of all presented alternatives. Moreover, Hill and Jones (2014) indicate that when a dependent variable has more than two categories and the values have a meaningful sequential order where a value is indeed “higher” than the previous one, then an ORL model is the most appropriate.

The model predicted a 64 percent variation of the explanatory variable (remittance) on the outcome variable (adaptation practices). This implies that holding all other things constant, the probability of farmers engaging in climate change adaptation given remittance is 64 percent. More specifically, remittance positively predicted off-farm jobs (ק = 0.19), implying that respondents are 19 percent likely to engage in off-farm jobs with an increase in remittance. Remittance also positively correlated  irrigation farming (ק = 0.18), cultivation of improved crop varieties (ק = 0.17), and use of compost/animal manure (ק = 0.14). An inverse relationship was established for Indexed-Based Insurance (ק = −0.12) and no association observed for alternating planting dates and mixed cropping (Table 15.7).

Table 15.7 Influence of remittance on adaptation practices

As shown on Table 15.8, using multinomial logit regression, the Nagelkerke R2, Cox, Snell, and McFadden R-square values show that the multinomial logit model was fitted and remittance significantly predicted respondents’ share of income spent on adaptation. Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables (Allison 2014; Hilbe 2011). The independent variables can either be dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). Multinomial logistic regression is a simple extension of binary logistic regression that allows for more than two categories of the dependent or outcome variable. Unlike binary logistic regression, multinomial logistic regression uses maximum likelihood estimation to evaluate the probability of categorical membership (Al-Mudhafer 2014). In testing for the influence of remittance on the share of income spent on adaptation, the multinomial logit was appropriate as the independent variable (remittance) was continuous whereas share of income spent on adaptation was categorized (USD100–300, 301–600, and 601–900). The basic assumption developed indicates that receiving remittances would be a determining factor in the amount of money farmers are willing to spend on adaptation. The model showed that respondents are 28 percent likely to spend USD100–300 of their share of income on adaptation given that there is an increase in remittance (β = 2.85). Similarly, the model predicted a 26 percent and a 19 percent likelihood of spending between USD301–600 and USD601–900 respectively of the share of income on adaptation given an increase in remittance.

Table 15.8 Amount of remittance received influences the share of income spent on adaptation

Discussion

Climate change risks are profound and evidential rather than perceptual (Luís et al. 2018; Hu et al. 2017; IPCC 2015). Literature shows  that climate change is associated with negative impacts on the natural environment, society, and the physical and psychological health of individuals (Huang et al. 2018; Descheemaeker et al. 2017; IPCC 2015; Doherty and Clayton 2011). The majority of respondents in this study ascribed a change in the climate as manifested in decreased precipitation, increased temperatures, and prolonged drought. This climate cognition is not out of the ordinary because the mercurial nature of the Sudan Savanna and the Guinea Savanna zones have made farmers conversant and knowledgeable about the vagaries of weather conditions in the area (Antwi-Agyei et al. 2012). The finding does not depart substantially from Ndamani and Watanabe (2016), who found that 87 percent of respondents perceived a decrease in rainfall, with 82 percent perceiving an increase in temperature in parts of the study area. Climate change is undoubtedly affecting agriculture and the entire agricultural value chain. Smallholder farmers in developing countries are at the serious core of this extreme change due to their weak adaptive capacity (Liu et al. 2016; Mulatu Debalke 2011). The concomitant effect is low productivity and impoverishment of more households. Empirical studies (Descheemaeker et al. 2017; IPCC 2015; Laube et al. 2012) further show that higher temperatures and less rainfall have resulted in reduced yield in Ghana and other African countries. The findings of this study point to a similar direction as it was established that the major effect of climate change was low productivity. Smallholder farmers were further devastated by inadequate feed and water for livestock and in critical situations abandoned their farms. Indeed, climate-induced water stress has caught up with many farmers due to the drying up of rivers, streams, and underground aquifers (Kundzewicz et al. 2018; Smerdon 2017). Consequently, livestock, which is a valuable household livelihood asset and supplements the needs of many, is at a crossroads as farmers can no longer engage in this lucrative venture with ardent interest (Descheemaeker et al. 2017).

The introduction and integration of sound adaptive strategies has been identified as a necessary pathway to mitigating climate change effects (Ndamani and Watanabe 2016). Even though Mulatu Debalke (2011) is of the view that a minority of smallholder farmers are vehement implementers of adaptation strategies, the dividend of these strategies cannot be underestimated. Using ASI to assess the order of importance of adaptation practices implemented by farmers knowingly or unknowingly (autonomous and planned), backyard gardening, cultivation of drought-tolerant crops, and mixed cropping were ranked as the top three most important. Traditionally, backyard gardening has long been a practice associated with the people of the area (Yiridoe and Anchirinah 2005). Having larger and flourishing farmlands elsewhere did not discourage farmers from backyard gardening (Danso et al. 2004). Accordingly, this was used to usher farmers into the harvesting season as they start consuming the produce from their backyard gardens before venturing into their main farmlands (Yiridoe and Anchirinah 2005). In recent happennings, backyard gardening is used more as a risk management strategy as farmers who proclaimed they are confronted with climate change see the need for backyard gardening to support their main farmlands as confirmed in the FGDs. The study also found that vulnerable groups including women and migrants build their livelihood mostly from backyard gardening due to limited access to land. Mixed cropping was also perceived as a risk management strategy as farmers felt that having multiple crops on a piece of land could save them from disappointment. “When one crop fail the other will not.” Hence, farmers are unwilling to engage in only a single crop for a farming season. Simply, farmers adopted mixed cropping to reduce overall risk while expanding opportunities for farm profit, thus boosting their average income (Ndamani and Watanabe 2016; Uddin et al. 2014; Mulatu Debalke 2011). An interviewee stated:

It is not advisable to keep one farmland. Farmers engage in backyard gardening to get more yield to feed their families. Farmers get less produce  due to the harsh weather conditions, so it is better not to waste time and walk longer distances to bring home nothing. Mixed cropping helps to give alternative produce if other crops fail. 

Justifiably, the pervasive decline in precipitation, prolonged drought, and increased temperature could be reasons for farmers engaging in the cultivation of drought-tolerant crops, as is corroborated by the significant rating of the practice (Uddin et al. 2014). Weather Index-Based Insurance (WIBI) was ranked the least important adaptation strategy, most likely due to the significant lack of good management of financial institutions in the country underwriting agriculture and offering farm-based insurance products. Also, poor deployment of technical assistance and low levels of farmer awareness about the use of agricultural insurance explain its low adoption (Ndamani and Watanabe 2016; Uddin et al. 2014).

Studies have indicated a certain unanimity regarding the medium-to long-term impact of climate change (Fosu-Mensah et al. 2012; Apata 2011; Mertz et al. 2009), which has driven a plethora of adaptation strategies to militate against climate-induced effects (Nhemachena and Hassan 2007). The novelty and nuance related to this study is the integral role of remittance in adaptation. The twenty-first century has seen remittance represent one of the key issues in economic development (Inoue 2018; Acosta et al. 2007; Adams Jr 2004) by reducing the level, depth, and severity of poverty in most developing countries (Vacaflores 2017; Akobeng 2016), increasing incomes (Oduro and Boakye-Yiadom 2014; Adams and Cuecuecha 2013), and smoothening household consumption (Schiantarelli 2005). A study by Adams and Cuecuecha (2013) using a two-stage multinomial logit model revealed that increased remittance provides available working capital for small-scale entrepreneurs. Similarly, Bang et al. (2016) established that a rise in remittances increased self-employment and private sector investment in Kenya. The increase in remittance positively predicting engagement in off-farm jobs as found in this study relates to findings of (Inoue 2018; Bang et al. 2016; Akobeng 2016; Oduro and Boakye-Yiadom 2014; Adams and Cuecuecha 2013). It can be inferred that smallholder farmers are beginning to look beyond farming, which is seen as mundane, due to the extreme effects of climate change. Therefore, given available remittances, they are more likely to delve into other income-generating activities outside of the farm, which is contrary to what most of the farmers currently use remittances for (such as buying chemicals or fertilizer) as seen in Table 15.6. This could insinuate that their current state of remittance is low (USD369 on average per annum) as shown in Table 15.5, thereby committing some of it to agricultural output with the aim of sustaining their farms. The objective nonetheless is to divert into other economic sectors and/or activities should there be an increase in remittances. An interviewee stated:

If I (interviewee) get more money from my family outside [(remittance)], I want to buy a motor-king (tricycle) that will be used to fetch water, carry goods and passengers at a fee. I also want to open a provision shop where I will sell provisions to make more money. Investing money on the farm is useless these days because the rains will not come and everything will be destroyed.

Apart from the harsh weather conditions under which farmers work, the respondents acknowledged limited access to land, the unattractiveness of farming as a trade, the seasonality of farming, and poor farm income as the reasons why they would divert their remittance into other economic activities they consider attractive and economically rewarding and that can be operated all year round. One interviewee said:

Unlike the past, farmers currently engaged in farming have less resources. Those doing other businesses are better off economically and are able to complete their houses. However, those into only farming are seasonal workers, and unable to catch up with basic responsibilities required of every responsible person.

Remittances highly influenced irrigation farming and the cultivation of improved crop varieties. Farming cannot be alienated from the livelihood and culture of rural folks. Some hold it in high esteem and as a valuable inheritance passed on by generations which needs to be protected and enhanced (Al-Hassan and Poulton 2009; Braimoh 2009). Despite being challenged by current trends in weather events, which is motivating farmers to wanting to engage in off-farm jobs, this might be done concurrently as respondents are more likely to buy irrigation equipment and drought-tolerant seeds to augment their farms given increased remittances (Tables 15.6 and 15.7). An increase in remittances, however, inversely predicted Indexed-Based Insurance (IBI). IBI is an innovative approach to insurance provision that pays out benefits or compensates clients on the basis of a predetermined index (e.g., rainfall level or temperature variation) for the loss of assets and investments resulting from weather and catastrophic events (Conradt et al. 2015).

The expectation is that when farmers’ income increases, they will exhibit a higher potential to take up IBI (Carter et al. 2016; Conradt et al. 2015). However, the inverse was found, which could be attributed to the fact that remittance itself is a form of insurance against shocks. For instance, Adams and Cuecuecha (2013) stated that remittance is a strong insurance policy for rural and poorer households to mitigate weather-related risks. Remittances may lessen credit constraints because a stable stream of remittance income may make households more creditworthy in the eyes of formal sector financial institutions (Adams and Cuecuecha 2013; Schiantarelli 2005). Lucas and Stark (1985) found that remittances to Botswana increased with the extent of drought in the remitters’ place of origin and that the responsiveness of remittance levels to drought was greater for households with more drought-sensitive assets. As found in this study, remittance also significantly predicted respondents’ share of income spent on adaptation. Respondents were more likely to spend between USD100–300 of their income on adaptation, which arguably might be insufficient to enhance a farmer’s adaptive capacity considering the frequency and magnitude of current climatic events.

Conclusions

Several studies have pointed to the potentially valuable complement of remittances to broad-based development and economic growth. In a disaggregate milieu, this study examined the potential of remittances in financing climate change adaptation at the local level. Using ASI analysis to assess general adaptation strategies of smallholder farmers, it was discovered that farmers rated backyard gardening, cultivation of drought-tolerant crops, mixed cropping, off-farm jobs, and residue management as the most important adaptation strategies. On average, a farmer receives USD369 of remittance per annum, which is channeled into buying chemicals or fertilizer to use on farms, acquiring extra food to supplement family needs, buying drought-tolerant maize seeds, and investing in children’s education. Receiving remittances was cropped with challenges including mobile network problems, traveling longer distances to redraw remittance money, limited financial institutions, and poor communication from intermediaries. The ORL model predicted that an increase in remittances is significantly associated with off-farm jobs, irrigation farming, cultivation of improved crop varieties, use of compost/animal manure, and crop rotation. However, it inversely predicted IBI. Similarly, a multinomial logit model showed that farmers are highly likely to invest USD100–300 of their income in adaptation with increased remittances.