4.2. Climatic conditions, and shocks
In NBR, CRR, and URR most of the respondents said temperature changes and rainfall changes affect their livelihoods as depicted in the Table 1.The percentage of peoples that sea-level rises affected is higher in CRR, follow by NBR and lower in URR.However, percentages of peoples affected in by salt intrusion are equally in NBR and CRR and lower in URR. This analysis indicated that slow onset climate changes affected rural Gambia communities through crops failures, infertility of the soil, migration through sea level rises, livelihood decreases through impacts on bushfire and changes in rainfall, animals and plant decline through the changes pattern of temperature, the author noted. The indicators we selected for sensitivity indicators are; lack of access to water during drought, floods and droughts. According to the Table 1 below, in NBR, 75 percent affected by floods and 97 percent affected by drought. We can see that drought is a major problems that causes food insecure in this region of the rural Gambia. In URR, affected least by drought and CRR is affected highest by flood in the rural Gambia. Due to climate condition differential, expenditure on crops are different across the three regions selected. Due to highest temperature exist, URR during drought only 42 percent has access to water compare to 78 percent in NBR and 75 percent in CRR respectively. As described earlier in this study, the survey regions are in the rural Gambia, which differ in many ways in term of changing in rainfall and temperature. As expected, average change in temperature in NBR and URR are higher than CRR. In CRR, both average change in temperature and rainfall are lower compared to the other regions.
Table 1
Climate condition in the study area
Regions | Average change in Temperature | Average change in rainfall | flood | Drought |
CRR | .8928571 | .9357143 | 62 | 74 |
NBR | 1 | 1 | 75 | 97 |
URR | .9692308 | .9923077 | 76 | 48 |
Own evaluation using stata 16 |
In general, most of the surveyed farmers who reported to have experienced shocks over the past 10 to 20 years. The extremes climate events and slow onset climate change affect the rural areas of the Gambia. Moreover, climate variability affects agriculture and that in turn causes food insecurity and livelihoods loss especially the rural communities as displays in Table 2 below.
Table 2
Major shocks encountered by surveyed farmers
Shock | Number of farmers | Percentage of farmers | Descriptive |
Floods | 278 | 70.74 | Average | Std |
Drought | 288 | 73.28 | | |
Salinization | 149 | 40.05 | | |
Shifting pattern of rainfall | 315 | 79.35 | | |
Shifting pattern of temperature | 381 | 95.25 | | |
Access to water at the time of drought | 232 | 64.80 | | |
Animals/livestock decline | | | 1.40069 | .8985611 |
Table 3
Region | Bushfire % | Sea-level rises % | Total expenditure on crops $ | Total expenditure per capita per months $ | Poultry Farming % | Food expenditure per capita per month $ |
NBR | 16 | 41 | 53 | 184 | 2 | 164 |
CRR | 35 | 23 | 235 | 149 | 25 | 145 |
URR | 40 | 44 | 137 | 175 | 45 | 170 |
ALL | 29 | 36 | 144 | 169 | 23 | 159 |
Own computation used Stata/MP 16.0 |
Furthermore, bushfire affects URR region most (about 44%) and less in NRB whereas sea-level affect more in URR and least in CRR. Total expenditure monthly is higher in NBR and lowest in CRR as depicted in Table 3 above. However, only 2 percent of the respondent said they do poultry farming in NBR and 45 percent in URR. Food expenditure monthly is higher in URR (about $170) and lowest in CRR (about $145).
4.3. Results
a) Classification of vulnerability components and their details.
Figure 4 and Table 6, in the appendix: illustrates the scree plot of eigenvalues after PCA for all the variables as indicated in the dot above and Eigenvector/Factor scores/loading/scoring coefficients for the third Principal Components (PCA) respectively. For instance, the first 8 components have eigenvalues above 1.Combining they explains almost 67% of the variation of the data from the original variables. Our target is the first 5 components and they explain 52% of the variation of the variables from the original variables. If you observed closely here “elbow” amid 5 and 8 components. Thus, 5 components will be used for this analysis but it also possible to use all the 8 components that has eigenvalues above one. In order to constructs the vulnerability components and vulnerability indices(VI), we selected the variables to be included in the calculation of the vulnerability components and VI for the rural Gambia that has negative associated with exposure and sensitivity and positive with adaptive capacity. In this case out of 23 variables or component, 14 variables are finally selected because they associated with correct signs. Note you can select any components to constructs the vulnerability components index and vulnerability index e.g. pca 1, pca 2, pca 3 etc. Subsequently, the coefficient for the components carried the correct signs. Correct signs means that adaptive capacity is positive, sensitivity and exposure carried negative signs. However, after normalization of each components by multiplied the weight or eigenvector with Z-score, we established that the coefficient for most of the variables or components carried a negative signs and vulnerability to change in climate rises in the rural Gambia ( See details in Table 4).
Table 4
Vulnerability Components | Adaptive capacity | Exposure | Sensitivity |
NBR | -6.4529 | 0 | -0.0839 |
CRR | -2.8007 | -1.1804 | -0.0616 |
URR | -1.5055 | -3.0272 | -0.0381 |
All | -5.5512 | -1.8695 | -0.0754 |
Own Evaluation using pen, paper and calculator after Own computation used Stata/MP 16.0
Vulnerability components is classified into socioeconomic indicators and bio-physical indicators. They measure risks level of the region, country or continents towards climate change. As indicated in the table above, the vulnerability components in the study areas are interprets as follows; In the NBR region, adaptive capacity very low and associated with negative sign. Meaning the socio-economic indicators of vulnerability to climate changes are negligence and farmers have less education, large household size, feeble lands, lower knowledge of Ownership of agriculture machinery, lower irrigation facility and land is affected by flood and land is infertile, water scarcity, agriculture yields declines and cost of fertilizer may also be another costs and old age affected farmers because young peoples migrated either international or internally. Though they are highest receiver region of the remittance in the rural Gambia. This those not translate for farmers to solve the problems associated with climate change. The article demonstrated that in NBR regions, floods affects farmers seriously and that eroded the soil in the farmlands and leave the soil barren and infertile for crop production and even livestock rearing. In that rain-fed agriculture and less yields from agriculture production follows and that causes the region to be food insufficient. In addition, floods damages farmland, destroy households assets and income and can causes diseases and eventually food insecurity and high mortality rate in this region in the rural Gambia. Thus, practicing livestock is common in this region but due to the influences of change in climate, there is mass decline in animals. This is due to water scarcity and less grass to graze throughout the raining season and in the dry season no water, no food to eat or provide for animals and these eventually reduces their population (Malone, E. L. (2009).). Especially we can notice that in the feasts most of the animals bought are from the neighboring countries such as Senegal and Mali. An exposure to slow onset climate change is zero in NBR region of the Gambia and vulnerability to slow onset to climate change is slightly high in this region such as variability in temperature and variation in rainfall. This confirmed in the study by ten Have & Patrão Neves, 2021, studied vulnerability of households to climatic disasters in the low-lying atoll nation of Tuvalu and confirmed that poor households are exposure and vulnerable and less resources to response to climate shocks. Likewise, sensitivity as vulnerability indicators in NBR regions is medium high meaning floods as the variable used to measure sensitivity will negatively significant affect the region. In CRR, and URR adaptive capacity is very low as in the NBR and weakness to climate change is very high. The government should support and increases investment in adaptation strategies according (Maja et al., 2022). Likewise exposure and sensitivity has correct sign, negative and vulnerability to climate changes is very high.
b) Calculation of Vulnerability components
The vulnerability components (VC) across the three regions is calculate as follows (see details Eq. 1–5).
Vulnerability components for rural regions was calculated as follows;
$${\text{A}\text{c}}_{\text{J}=\text{A}\text{L}\text{L}}^{ }=\left[ \left(0.1077\text{*}-3.8263\right)+\left(0.0402\text{*}-1.1641\right)+\left(0.1097\text{*}-0.5034\right)+\left(0.4141\text{*}-1.4299\right)+\left(0.1983\text{*}-16.7973\right)+\left(0.2120\text{*}-1.1259\right)+\left(0.0621\text{*}-2.3636\right)+\left(0.1296\text{*}-0.8442\right)+\left(0.1521\text{*}-2.6879\right)+\left(0.0686\text{*}-0.4487\right)+\left(0.3480\text{*}-0.5160\right)\right]=-5.5512 {\text{E}\text{x}}_{\text{J}=\text{A}\text{L}\text{L}}^{ }=\left[(-0.2003\text{*}-6.4383)+\left(-0.1261\text{*}-4.5986\right)\right]=-1.8695$$
$${\text{S}}_{\text{J}=\text{A}\text{L}\text{L}}^{ }=\left[ (-0.0471\text{*}-1.6001)\right]=-0.0754$$
c) Calculation of the Vulnerability Index.
For example, the vulnerability index for NBR is calculated as follows using the factor scores from the first components and the rest of the regions follow :( see details on index formula above)
\({\text{V}\text{I}}_{\text{i}=\text{A}\text{L}\text{L}}=\left[ \left(0.1077\text{*}-3.8263\right)+\left(0.0402\text{*}-1.1641\right)+\left(0.1097\text{*}-0.5034\right)+\left(0.4141\text{*}-1.4299\right)+\left(0.1983\text{*}-16.7973\right)+\left(0.2120\text{*}-1.1259\right)+\left(0.0621\text{*}-2.3636\right)+\left(0.1296\text{*}-0.8442\right)+\left(0.1521\text{*}-2.6879\right)+\left(0.0686\text{*}-0.4487\right)+\left(0.3480\text{*}-0.5160\right)\right]-\left[(-0.2003\text{*}-6.4383)+(-0.1261\text{*}-4.5986)+(-0.0471\text{*}-1.6001)\right]=\) -3.6063
Table 5
Vulnerability index (VI) in the rural Gambia
| NBR | CRR | URR | All |
Vulnerability index(VI) | -6.3690 | -1.5587 | 1.5598 | -3.6063 |
Own evaluation using pen, paper and calculator after Own computation used Stata/MP 16.0
The definition of vulnerability index is to measure the shocks the regions, nations or households or continents faces at the time of risks and whether the indicators measures categorized as adaptive capacity, sensitivity and exposure gives a correct signs( See details in Table 5).. According to IPPC, 2001, 2007 Vulnerability is classified into three indicators i.e. adaptive capacity, exposure and sensitivity. Adaptive capacity must have positive sign for less vulnerable and exposure and sensitivity must have negative signs for less vulnerable. If this signs are in order, it means that region is not vulnerable to that shocks/calamities. As we finally calculated the VI from VC above. The vulnerability index (VI) was constructed to see the extend which region is relatively more and less vulnerable in the rural Gambia. So as illustrated in the Fig. 3 below, NBR region has vulnerability index of -6.369 - approximately and the region is relatively high vulnerability to climate change. This is due to the fact that in the NBR of the study areas variations in rainfall and fluctuations in temperature are negatively loaded and other socio-economic characteristics are positively loaded. In the URR, the only region that has positive VI, meaning that they are not vulnerable to climate changes and other household’s shocks based on VI but when we calculated the components above they are vulnerable to change in climate. Thus, we concur that this attributed to high temperature-extreme heat wave, water scarcity, river flood, and bushfire. In CRR even due to share of irrigated land (proxy for potential irrigation), the VI is negative and vulnerability to change in climates are high. The region with high food secure is CRR but vulnerability to adaptive capacity is negative in this region and that is why overall VI is negative and vulnerability to climate change is high. Finally, overall, in the rural Gambia according to the vulnerability indicators selected from vulnerability components in constructing the vulnerability index, we revealed that the regions’ are vulnerable to change in climate and other indicators listed in Fig. 3 below with VI is equal to -3.6063.The vulnerability of the rural Gambia to change in climate is due to many factors include less adaptive capacity, strange dependence on ecosystem for living, less Ownership of agriculture machinery, poor drainage system, changes in rainfall, water scarcity, bush fire, large family size, lower tertiary completion rate, lower life expectancy, poor market and transportation system, insufficient food availability, high changes temperature, and rainfall changes causes damages to agriculture. Finally, this will have certain risk and consequences in the Gambia such as agriculture failures included crop and livestock declines, water scarcity especially most neighborhoods and schools, livelihoods, food insecurity, public health, high migration-internal and international, commodity prices rises, energy problems-most villages still did not have electricity and causes problems to have irrigation facilities, market problems-to sell their goods and bad weather spoil their harvest, the author recommended.
Validation of PCA results
On Table 7 below indicated the validation of PCA: Firstly, I selected the indices and classified them into adaptive capacity, sensitivity and exposure. After, I constructed the indices based on weight and z-score by normalization and calculated each vulnerability component. In addition, after vulnerability components are calculated, we finally calculated vulnerability index. We found that based on the selected indices for vulnerability study, adaptive capacity is low, exposure and sensitivity is very high and vulnerability to climate change risk is very high in the rural Gambia After vulnerability calculation, we now selected different variables to validate that actually vulnerability to climate change is really affecting across the rural Gambia. What we do here is that we calculated the vulnerability index above and we now find the average values of indicator selected below and find the correlation between vulnerability index and average values of the selected variables in rural Gambia.NGO support reduces vulnerability to changes in climate by 82 percent. The international organization provides different support to rural Gambia population in terms of providing skills and training, bilateral supports, provides health facilities for them, provides seedling and fertilizer, provides taps to access water, sometimes provides bicycle for children with poor background and the distance to school is far, they provides books and other learn materials for children and they provides some training and inputs for women especially those working in vegetable garden in the rural Gambia, the author noted( see table below. Diverting and mismanagement in government sectors cost the subsistence farmers in the rural Gambia to depend on NGO support that assist. As government support increases, vulnerability to climate increases by 79 percent.
Table 7
Variables | Correlation | Component |
NGO support | -0.82 | Adaptive Capacity |
government support | 0.79 | Adaptive capacity |
Lack of food consumption | 0.10 | Sensitivity |
Lack of food security | -0.86 | Sensitivity |
rising sea level affect your farm land | 0.00 | Exposure |
Use more labour power machines | 0.78 | Exposure |
Receiving infrastructural support | 0.74 | Adaptive capacity |
Migration of households members | 0.83 | Adaptive capacity |
Taken insurance | -0.94 | Adaptive capacity |
Total expenditure on crops | 0.55 | Adaptive capacity |
Changing remittances | 0.97 | Adaptive capacity |
Plant other crops or varieties | 0.91 | Adaptive capacity |
Access to credit | 0.97 | Adaptive capacity |
Access to extension | 0.92 | Adaptive capacity |
Own Evaluation using StataMP/16.0
4.3.1. Discussion the results of the study
First we found that in the description Statistic of Socio-economic, all the farmers used related similar proportion of fertilizer in their farms and that increases crop yields. This is parallel with study done by Aryal et al., 2021 and found that various socio-economic and geographical factors influence the use of organic and inorganic fertilizers in rice and wheat and across all locations, farmers reported a decline in manure application. In another separate study Musafiri et al., 2023 found that smallholder farmers applied a limited amount of inorganic fertilizer and they established that hired labor, agricultural training, and farmers’ perception of soil erosion were significant positive determinants of inorganic fertilizer and finally the use of inorganic fertilizer increased crop yields by 14%.
Second we revealed that from climate condition and shocks, the analysis indicated that slow onset climate changes affected rural Gambia communities through crops failures, infertility of the soil, and migration through sea level rises, livelihood decreases through impacts on bushfire and changes in rainfall, animals and plant decline through the changes pattern of temperature. This is confirmed by Jaiteh Malanding, S. (2010) and Self Help Africa. (2009).
Third, we also found that bushfire and sea-level rises affects farmers in the rural areas of the Gambia.This is in line with the finding from Jan Van Oldenborgh et al., 2021 about Australia’s bushfire and they found that with the agreement with previous analyses we find that heat extremes have become more likely by at least a factor two due to the long-term warming trend and the trend is mainly driven by the increase of temperature extremes and hence also likely underestimated.
Subsequently, we found that adaptive capacity, sensitivity and exposure had a correct sign. This was also revealed in the study done by Swami & Parthasarathy, 2021 revealed that a few districts are vulnerable despite being least exposed to climate variability suggesting the contribution of sensitivity and adaptive capacity parameters to their vulnerability
In addition, for vulnerability components, In the NBR region, adaptive capacity very low and associated with negative sign. Meaning the socio-economic indicators of vulnerability to climate changes are negligence and farmers have less education, large household size, feeble lands, lower knowledge of Ownership of agriculture machinery, lower irrigation facility and land is affected by flood and land is infertile, water scarcity, agriculture yields declines and cost of fertilizer may also be another costs and old age affected farmers because young peoples migrated either international or internally. This analysis, conducted by Swami and Parthasarathy in 2021, highlights the multifaceted nature of vulnerability trends across districts in Maharashtra, demonstrating how various factors such as adaptive capacity, exposure, and sensitivity influence the overall vulnerability profile over time.
Furthermore, for vulnerability index, NBR region has vulnerability index of -6.369 - approximately and the region is relatively high vulnerability to climate change. This is due to the fact that in the NBR of the study areas variations in rainfall and fluctuations in temperature are negatively loaded and other socio-economic characteristics are positively loaded. In the URR, the only region that has positive VI, meaning that they are not vulnerable to climate changes and other household’s shocks based on VI but when we calculated the components above they are vulnerable to change in climate. Guillaumont & Simonet, 2011 found that vulnerability index depending on the recurrent shocks (such as droughts), and the risks of progressive and irreversible shocks (floods).
Moreover, We found that based on the selected indices for vulnerability study, adaptive capacity is low, exposure and sensitivity is very high and vulnerability to climate change risk is very high in the rural Gambia. Gomez et al., 2020 found that in Coastal erosion is a major challenge along the coast of The Gambia and their study found high levels of exposure and susceptibility of the households to coastal erosion, given the limited adaptation capacity and that 74% of the households do not have sustainable adaptation strategies to the impacts of coastal erosion in the Gambia.
However, for the validation of vulnerability, after vulnerability calculation, we now selected different variables to validate that actually vulnerability to climate change is really affecting across the rural Gambia and the result found that .NGO support reduces vulnerability to changes in climate by 82 percent while government support increases, vulnerability to climate increases by 79 percent.