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Evaluating the Influence of Climate Change on Food Security and Nutritional Status among People in Monguno Local Government Area of Borno State, Northeast Nigeria

2024; Volume: XI; Issue: XI Linguagem: Inglês

10.51244/ijrsi.2024.11110055

2321-2705

Abdulrahman Ahmed, Fatima Abacha Ali, Hadiza Yahaya, Abba Jidda, Mairo Bukar Nbahi,

Agriculture Sustainability and Environmental Impact

One aspect that poses a threat to the health and wellbeing of human race today is climate change. The impacts of climate change can never be over emphasized, climate change and health are fundamental elements that are surrounded by countless indicators. Climates change without doubt or fear of contradictions remain one of the determinants of health. The human body as a machine must be kept within a narrow physiological limit so also climate are indices that must be observed within an atmospherically friendly manner. This study on Evaluating the influence of climate change on food security and nutritional status among people in Monguno Local Government Area of Borno State, northeast Nigeria is a step to provide durable solutions to the ever-increasing negative influence of climate change on the continual survival of the people in Monguno local government area of Borno state northeast Nigeria. This study is explored to investigate the food security indicators of the people in Monguno local government, nutritional status of children under-five, climate change perceptions of the people in Monguno LGA and determination of regression analysis of predicting food insecurity respectively. The research was a mixed method research design. It was conducted in Monguno local government area of Borno state northeast Nigeria. The targeted population for the study is 125,000, emanating the population of people in Monguno LGA according to the United Nations office of humanitarian coordination (UNOCHA). A stratified random sampling method was used to determine the sample size. Sample size was obtained using Cochran’s formula of sample size determination. And a total of 384 samples was arrived at. By defining the strata, the population (125,000) was divided into 2 distinct groups based on the key features they possessed. The first group being the farmers/Fishermen and the second group being the vulnerable groups largely residing within internally displaced person camps which includes women, children and elderly persons. About 25,000 farmers and fishermen while 100,000 is for the vulnerable group. The sample size for each group was obtained as 77 for farmers/fishermen while 307 for vulnerable group respectively. Random sampling within each stratum was done by creating a list of individuals and household in each stratum or sampling frame. Simple random sampling was employed to select participants, numbers were assigned to the individuals in the sampling frame and excel was utilized to select participants. An adjustment for non-response was put into account by increasing the sample size proportionally for 10% non-response rate predicted. The adjusted sample size is 427 and divided proportionally to the 2 strata as 85 sample size to the farmers/fishermen and 342 to the vulnerable groups accordingly. This approach clears out that non-response rates have been put into account while maintaining proportional representation across the strata. For clear data analysis, food security, household hunger scale (HHS) food consumption score (FCS), and coping strategies index (CSI) will be calculated. These will aid in having baseline data to determine frequency of hunger, the diversity and frequency of food groups and strategies used to manage food shortages. a discussion of the anthropometric measurements emanating prevalence of malnutrition vis a vis stunting, wasting, underweight. This data will be presented in tables histograms respectively. Comparative analysis between farmers/fishermen and Vulnerable groups will be don’t thoroughly via T-test i.e. comparing food consumption scores between strata. Chi-Square Tests examine categorical variables such as food insecurity levels across groups. And comparatively, analyze subgroup i.e. women, children and elderly. Also, with regards to regression analysis, linear regression will be used to predict food security scores based on the factors like income, access to food and climate variabilities. Findings related to the household size. Based on this study Farmers/Fishermen have a higher mean age (39.2 ± 9.8 years) compared to the vulnerable groups (34.1 ± 10.6 years), with an overall mean age of 35.6 ± 10.3 years. Findings on the gender/sex distributions revealed that among farmers/fishermen, the majority are male (82%), with only 18% female. In comparison to the vulnerable groups are predominantly female (71%), with 29% male. On the household size findings, Farmers/Fishermen have smaller households on average (6.2 ± 2.1 members) compared to the vulnerable groups (7.5 ± 3.0 members), with an overall mean household size of 7.2 ± 2.8 members. The findings on household hunger scale, Farmers/Fishermen: 59% experience little to no hunger, Vulnerable Groups: Only 23% experience little to no hunger. The p-value < 0.01. A much higher 33% face severe hunger. The findings postulate FCS assess dietary diversity and frequency, categorizing households into poor, borderline, or acceptable consumption levels. The mean FCS for the Farmers/Fishermen is 48.2 ± 12.5, and for the Vulnerable Groups is 36.7 ± 11.2. Additionally, the p-value < 0.001: Indicates a significant difference between the groups. The findings postulate FCS assess dietary diversity and frequency, categorizing households into poor, borderline, or acceptable consumption levels. The mean FCS for the Farmers/Fishermen is 48.2 ± 12.5, and for the Vulnerable Groups is 36.7 ± 11.2. Additionally, the p-value < 0.001: Indicates a significant difference between the groups. Borderline food consumption findings shows that 35% of the farmers/fishermen are within borderline food consumption. Vulnerable groups fell between 44% to the borderline food consumptions. On acceptable food consumption, 53% for fishermen/farmers, and 27% for the vulnerable groups. Farmers/Fishermen 30% of children are stunted. While Vulnerable Groups 40% of children are stunted. The p-value = 0.05 Indicates a statistically significant difference at a borderline level. On the level of wasting of children under-five, Farmers/Fishermen 15% of children are wasted. Vulnerable Groups 25% of children are wasted. And the p-value < 0.05 Indicates a statistically significant difference. The findings on underweight composite of malnutrition suggest that Farmers/Fishermen 20% of children are underweight. Vulnerable Groups 30% of children are underweight. The p-value = 0.05 Indicates a statistically significant difference at a borderline level. Farmers/Fishermen 82% reported changes in rainfall patterns. Vulnerable Groups 80% reported changes in rainfall patterns. vast majority of both groups perceive altered rainfall patterns, which align with climate variability in the region. Findings on the reported brought in the last 5 year shows that Farmers/Fishermen 71% reported experiencing droughts. Vulnerable Groups 64% reported droughts. Farmers/fishermen report higher drought awareness (71%) due to the direct impact of water scarcity on crop and fish production. On attributed reduced food access to climate, Farmers/Fishermen 76% attribute reduced food access to climate change. Vulnerable Groups 85% attribute reduced food access to climate change. The regression analysis explores the relationship between various factors and food insecurity. Based on the findings on climate variability, coefficient (β) 0.45 indicates that as climate variability increases, food insecurity also tends to increase. Standard Error 0.1 shows relatively small standard error suggests a precise estimate of the coefficient. The p-value: < 0.01 indicates that this relationship is statistically significant at the 1% level, meaning there is strong evidence that climate variability influences food insecurity. Findings on household income suggest that Coefficient (β) -0.3 means negative coefficient implies that higher household income is associated with lower food insecurity. Standard Error 0.08 connotes small standard error suggests a precise estimate of the coefficient. And p-value < 0.05 shows that the relationship is statistically significant at the 5% level, supporting the conclusion that income plays an important role in mitigating food insecurity. The study dive in to provide conclusions and offer recommendations on the basis of immediate or short-term, medium-term, and long-term including policy and structural recommendations.