Climate change adaptation in Nigerian agricultural sector: A systematic review and resilience check of adaptation measures

Robert Ugochukwu Onyeneke; Chinyere Augusta Nwajiuba; Chukwuemeka Chinonso Emenekwe; Anurika Nwajiuba; Chinenye Judith Onyeneke; Precious Ohalete; Uwazie Iyke Uwazie; Robert Ugochukwu Onyeneke; Chinyere Augusta Nwajiuba; Chukwuemeka Chinonso Emenekwe; Anurika Nwajiuba; Chinenye Judith Onyeneke; Precious Ohalete; Uwazie Iyke Uwazie

doi:10.3934/agrfood.2019.4.967

AIMS Agriculture and Food

2019, Volume 4, Issue 4: 967-1006. doi: 10.3934/agrfood.2019.4.967

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Review

Climate change adaptation in Nigerian agricultural sector: A systematic review and resilience check of adaptation measures

1.
Faculty of Agriculture, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State, Nigeria
2.
Faculty of Education, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State, Nigeria
3.
Faculty of Management and Social Sciences, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State, Nigeria
4.
Faculty of Agriculture, Ebonyi State University, Abakaliki, Nigeria
5.
Department of Economics, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Received: 26 August 2019 Accepted: 03 November 2019 Published: 15 November 2019

The changing climate is adversely affecting the productivity and livelihoods of Nigerian smallholder rural farmers. Several studies predict worsening outcomes for future climate events, for example heat waves, drought and intense precipitation. Farmers are required to adopt several measures to thrive, given the observed or expected climate change events. Existing studies about the interaction between climate change the agriculture sector has focused evaluating the bi-directional causal relationships, and identifying adaptation measures, but research on the climate resilience aspect of these adaptation measures is missing, or at best, low. This study aims to fill this gap in knowledge by assessing resilience and contribution to sustainability of farmer-adopted measures aimed at addressing risks posed by climate change. We conduct a systematic review of 95 studies concentrating on climate adaptation by smallholder rural farmers in Nigeria in the period 2010 to 2019. We assess the climate resilience of adaptation measures using the Ifejika-Speranza Resilience Check Toolkit. Our findings show that farmers are using climate-resilient adaptation measures; however, we could not ascertain how these have led to sustainable agricultural systems, since it is not the focus of the current study. Our findings show that majority of the adaptation studies in Nigeria are focused on crop farming subsystem. The major agricultural ecosystems and the broad adaptation areas are: Crop farming (improved soil and land management, crop-specific Innovation, water management practices, climate information services and education, access to finance, and off-farm diversification), livestock farming (improved livestock management systems, improved breeding strategies, sustainable health improvement, proper feed formulation early maturing and heat-resistant bird varieties), and fish farming (water harvesting measures, organic material, quick-maturing varieties).

Keywords:

Citation: Robert Ugochukwu Onyeneke, Chinyere Augusta Nwajiuba, Chukwuemeka Chinonso Emenekwe, Anurika Nwajiuba, Chinenye Judith Onyeneke, Precious Ohalete, Uwazie Iyke Uwazie. Climate change adaptation in Nigerian agricultural sector: A systematic review and resilience check of adaptation measures[J]. AIMS Agriculture and Food, 2019, 4(4): 967-1006. doi: 10.3934/agrfood.2019.4.967

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Abstract

1. Introduction

Spinal pain has been a global public health problem for decades [1]. Depending on the segment of the spine in which the pain appears, spinal pain is divided into neck pain (NP), thoracic pain (TP) and low back pain (LBP). NP is defined as the pain localized in the anatomic region of the neck (from the superior nuchal line to the spine of the scapula, from the external occipital protuberance to the superior border clavicle and the suprasternal notch), with or without pain radiating to the head, trunk and upper limb (s). TP is defined as spinal pain with localization from the cervical–thoracic hinge (C7–T1) to the thoracic–lumbar junction (T12–L1). LBP is defined as pain, muscle tension and restricted mobility, typically between lower rib margins and inferior gluteal folds, with or without radiating pain to one or both legs [2]–[4]. LBP and NP are the more common spinal pains and the leading causes of population disability, regardless of sex and age [1],[5]. TP has been less studied, although this type of spinal pain is equally disabling and burdensome for the individual, community and work environment [6],[7]. Regardless of spinal pain type, the symptoms, etiology and consequences are generally the same. Potential risk factors that are associated with spinal pain are demographic, hereditary, lifestyle and occupational factors [8]–[10].

Global epidemiological studies have reported that point-prevalence for LBP ranged from 1.0% to 58.1%, for NP ranged from 0 to 41.5% and for TP ranged from 4.0% to 72.0% [6],[11],[12]. In 19 European countries, the estimated one-year prevalence of LBP/NP ranged from 16.08 to 54.05% [13]. However, it was pointed out that the exact prevalence of spinal pain is difficult to determine due to the heterogeneity of the study methods, various definitions of spinal pain and data collection methods used by researchers [11],[12]. Moreover, several studies reported that many countries and regions lack data on population-based prevalence, risk factors and burden estimates for all three types of spinal pain [1],[5],[7]. The lack of prevalence data is particularly pronounced for the countries of the former Balkans, the geographical location of Bosnia and Herzegovina (BiH) [14]. Moreover, BiH was not included in the spinal pain prevalence European study, and we were unable to find such data in the published literature [13].

Determining the exact prevalence of LBP and other types of spinal pain is important for many reasons for experts in this field. Pain prevalence studies enable the assessment of burden and are important for developing effective public health policies, as well as changing existing public health policies. Prevalence data may foster health experts in this field and research aimed at improving diagnosis and treatments. Additionally, knowledge of the exact disease prevalence can lead to changes in curricula for educating medical experts such as physiotherapists and nurses in terms of providing more study hours for the most prevalent conditions in this profession [15]. This study aimed to determine the prevalence and risk factors for spinal pain in BiH among the general population via an online questionnaire.

2. Materials and methods

2.1. Study design

This was a cross-sectional observational web-based survey. The design of the study was based on previous studies [16],[17]. An online survey using Google Forms (https://docs.google.com/forms/u/0/) was created and sent by multiple channels to contacts and groups of various organizations located across BiH (both health and non-health organizations). The invitation to participate in the study was published two weeks before the start of the study via the social networks Facebook, email, WhatsApp and Viber; the same call with a link to the survey questionnaire was repeated at the beginning of the study.

We used a non-probabilistic sample stratified based on the geopolitical regions of BIH and demographic characteristics. The data were collected in November and December 2018, and the study was completed in January 2019.

The study was approved by the Ethics Committee of the Faculty of Health Studies, University of Mostar, Mostar, BIH (approval number: 01-994/18 from October 19, 2018). In the introductory part of the questionnaire and the cover letter, the participants were informed about the study, their rights and that the completion of the survey will be considered their informed consent.

2.2. Participants

Inclusion criteria were as follows: residents of BIH, aged ≥16 years of both sexes, having an email, Facebook, WhatsApp or Viber account for survey receipt.

Exclusion criteria were as follows: age ≤15 years, persons not residing in BIH (if participants did not provide information about BIH canton of residence, they were excluded), missing data (we excluded participants with more than half missing answers in the questionnaire), discrepant data on the presence of spinal pain (in one part of the questionnaire the participants indicated that they had spinal pain, and in the other part of the questionnaire that they did not have it) and duplicates (multiple submissions with identical time zone data and personal data of participants).

2.3. Questionnaire

For the study, we designed a questionnaire based on a literature review [16],[18]. Pilot testing of the questionnaire for understanding, clarity and relevance was conducted before the start of the survey. It consisted of 29 questions about demographic characteristics and spinal pain. A detailed description and the full version of the questionnaire are in Appendix 1. To avoid recall bias, we asked participants about their present pain and its duration. The point prevalence was measured.

2.4. Data collection

Potential participants received a personal message (cover letter) with a written invitation about the study and a link to the study. Details are described in Appendix 1. Only the principal investigator (AH) had access to the stored data (Appendix 1).

2.5. Sample size

The sample size was determined by using computer software. According to the latest census data about the population in BIH from 2013, BIH had 2,987,440 inhabitants over 14 years of age in BIH. Based on the 95% confidence level, the population proportion of 50% and absolute precision of 5%, it was determined that 385 participants were needed. Assuming that the difference in the established prevalence between persons with spinal pain and those who do not have spinal pain is 10%, with an alpha value of 0.05%, a beta value of 0.2% and the power of study of 0.8, we calculated that a total of 782 participants were needed. However, to satisfy the study criteria, the representation of participants by geographical and demographic strata, and to improve the quality of the study, we decided to increase the sample size to meet as closely as possible the percentage of the BIH population according to the data of the 2013 Census.

2.6. Statistical methods

Sample weighting was performed before the start of data analysis by the variables of region, sex and age group. Study weight was ensured by stratification of participants based on geopolitical regions of BiH and demographic characteristics (sex, age). The required percentages for the specified variables were calculated by data from the 2013 BIH Census. The determined frequencies were converted into percentages; based on them, sample balancing was performed. Analyses of the results were performed through weighted and unweighted estimates, and due to the absence of significant differences, they were presented without weighted variables. The normality of the data distribution was tested by visual inspection of the histogram. Descriptive categorical variables were presented as frequencies and percentages, and quantitative variables were presented as mean and standard deviation. The significance of the difference between the categorical variables was analyzed by the Chi-square test. Quantitative variables' differences between the two groups were tested by the Independent Student t-test, and differences between the three groups were tested by one-way ANOVA. The association of factors with the presence of spinal pain was conducted by logistic regression through bivariate and multivariate analysis. Multivariate analysis was conducted through three models: Model I consisted of demographic and occupation factors; Model II consisted of demographic, occupational and hereditary factors; and Model III consisted of demographic, occupational, hereditary and behavioral factors. The significance of the differences in the regression analyses was tested by Wald's Chi-square test, and the results were presented as the prevalence ratio and 95% confidence interval of the prevalence ratio. The statistical significance was p < 0.05 in all analyses. Statistical software IBM SPSS Statistics 23 (Armonk, NY: IBM Corp.) was used for data analysis.

3. Results

3.1. The sample

The total number of sent invitations was 1682 (email n = 705, text messages n = 774, Facebook n = 203), and the total number of collected responses was 1048. From 1048 study responses, we excluded 3% of surveys (n = 31) due to inadequate age (n = 10), missing data (n = 7), false responses (n = 3), discrepant data (n = 5) and duplicates (n = 6).

The final analysis included 1017 participants, of both sexes, aged from 16–78 years. The majority of participants were women (57%), aged 30–50 years, with a high-school education. Detailed socio-demographic characteristics of the participants are shown in Table 1.

Table 1. Socio-demographic characteristics of the surveyed population of Bosnia and Herzegovina.

Variables		Men (N = 434)	Women (N = 583)	Total (N = 1017)
Age^†		45.6 (15.0)	38.1 (11.4)	41.3 (13.5)
Age group
	16–29 years	62 (14.3)	142 (24.4)	204 (20.1)
	30–50 years	196 (45.2)	358 (61.4)	554 (54.5)
	≥51years	176 (40.6)	83 (14.2)	259 (25.5)
Height^†		182.6 (6.1)	170.4 (5.8)	175.6 (8.5)
Weight^†		90.1 (12.7)	70.0 (11.2)	78.6 (15.5)
BMI^†		27.0 (3.4)	24.1 (3.7)	25.3 (3.9)
BMI classification
	Underweight	- (-)	12 (2.1)	12 (1.2)
	Normal range	134 (30.9)	378 (64.8)	512 (50.3)
	Overweight	216 (49.8)	149 (25.6)	365 (35.9)
	Obese class I	84 (19.3)	44 (7.5)	128 (12.6)
	Obese class II	- (-)	- (-)	- (-)
	Obese class III	- (-)	- (-)	- (-)
Educational level
	Students	10 (2.3)	29 (5)	39 (3.8)
	Elementary school	1 (0.2)	3 (0.5)	4 (0.4)
	High school	218 (50.2)	310 (53.2)	528 (51.)
	College	205 (47.2)	241 (41.3)	446 (43.9)
Employment status (N = 978)^‡
	Employed	356 (36.3)	486 (49.6)	842 (85.9)
	Unemployed	19 (2)	54 (5.5)	71 (7.5)
	Retired	51 (5.1)	14 (1.5)	65 (6.6)

*Note: BMI: Body mass index; †mean (standard deviation); n (%); ‡ Without students.

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A comparison of the sample with the BiH population based on the 2013 BiH Census demonstrated that they were similar (data not shown) for sex and regional residence. Age was also similar, although the 30 to 50 age group was slightly overrepresented, and the ≥51 age group was slightly underrepresented in the sample.

3.2. Prevalence of spinal pain syndrome

The presence of global spinal pain was reported by 721 participants (70.9%): 440 (75.5%) women and 281 (64.7%) men (Table 2). Of the total number of reported spinal pain, 484 (67.1%) participants had LBP, 169 (23.4%) participants had NP, and 68 (9.4%) participants had TP. Women reported more NP and TP, and men reported more LBP. The prevalence of spinal pain, depending on the anatomical location, is shown in Figure 1.

Table 2. Point prevalence of spinal pain in surveyed population in Bosnia and Herzegovina.

Variables		Total N = 1017	Spinal pain N = 721 N (%)	PR (95% CI of PR)
Sex	Men	434	281 (64.7)	1
	Women	583	440 (75.5)	1.67 (1.27–2.20)*
Age	16–29	204	123 (60.3)	1
	30–50	554	423 (76.4)	2.13 (1.51–3.0)*
	≥51	259	175 (67.6)	1.37 (0.94–2.01)
BMI	Underweight	12	4 (33.3)	1
	Normal range	512	355 (69.3)	4.52 (1.34–15.24)***
	Overweight	365	271 (74.2)	5.77 (1.70–19.6)**
	Obese class I	128	91 (71.1)	4.92 (1.40–17.3)***
Education	Student	39	22 (56.4)	1
	Elementary school	4	4 (100)	- (-)
	High school	532	396 (74.4)	2.23 (1.15–4.32)***
	College	446	303 (67.9)	1.64 (0.84–3.18)
Work	Student	39	38 (97.4)	- (-)
	Unemployed	71	34 (47.9)	1
	Employed	842	605 (71.9)	2.78 (1.70–4.53)*
	Retired	65	44 (67.7)	2.28 (1.14–4.58)***
Type of work (N=844)	Sedentary	553	395 (71.4)	1
	Physical	291	228 (78.4)	1.45 (1.04–2.02)***
Hereditary (SP in parents)	No	444	287 (64.6)	1
	Yes	573	434 (75.7)	1.71 (1.30–2.24)*
Smoking	No	712	491 (69)	1
	Yes	305	230 (75.4)	1.38 (1.02–1.87)***
Physical activity (PA)	No	299	210 (70.2)	1
	Yes	718	511 (71.2)	1.05 (0.78–1.41)
Frequency of PA (day/week) (N = 718)	1–2	217	146 (67.3)	1
	3–4	191	123 (64.4)	0.88 (0.58–1.51)
	5–6	117	77 (65.8)	0.94 (0.52–1.51)
	Irregular	193	165 (85.5)	2.87 (1.75–4.68)*
TV (Hours/day)	1–2	632	429 (67.9)	1
	2–3	297	226 (76.1)	1.51 (1.10–2.06)***
	4–5	88	292 (75.8)	1.42 (0.85–2.37)
Computer/Mobile phone (Hours/day)	1–2	333	252 (75.7)	1
	3–4	273	189 (69.2)	0.72 (0.50–1.03)
	5–6	411	280 (68.1)	0.69 (0.50–0.95)***

*Note: PR (95% CI): Prevalence ratio (95% confidence interval of prevalence ratio); BMI: Body mass index; *Statistical significance p < 0.001; **Statistical significance p < 0.01; ***Statistical significance p < 0.05; Excluded from analysis.

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Figure 1. Prevalence of spinal pain with regard to anatomic locations by sex and age group in the surveyed population of Bosnia and Herzegovina.

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In bivariate regression, significant associations with the presence of spinal pain were found for the following groups: women, aged 30 to 50 years, with high school education, employed and retired persons, physical work, smoking, irregular physical activity, watching TV 2 to 3 hours per day and use of a computer or cell phone 5 to 6 hours on a day. Likewise, the bivariate analysis found a statistically significant difference between normal and elevated body mass index, but due to the excessive range of the confidence interval, this result is questionable. No statistically significant difference was found in the repeated analysis without underweight body mass index (Table 2). Multivariate analysis confirmed statistically significant associations with the presence of spinal pain for the following groups: women, aged 30 to 50 years, employed and retired persons, spinal pain in parents and irregular physical activity (Table 3).

3.3. Characteristics of pain

The average intensity of current spinal pain on a scale from 0 to 10 was 5, without difference by sex, but a significant difference was found in the pain intensity score in different age groups (F (2) = 4.562; p = 0.011). Characteristics of spinal pain by sex are shown in Table 4. The mean pain intensity in the age group from 16 to 29 years was 4.6 (SD 2.0), in the age group from 30 to 50 years was 5.2 (SD 1.8) and in the age group ≥51 years was 5.2 (SD 1.2). More than half of the participants in the age group 30 to 50 years, as well as 51 years, reported moderate pain intensity (53.2% vs. 53.1%), while participants in the age group 16 to 29 years reported a higher percentage of mild pain (47.4%) (Figure 2).

Table 3. Multivariate analysis for the independent demographics, occupational, hereditary and behavioral factors associated in bivariate analysis (p < 0.02) with spinal pain in a surveyed population of Bosnia and Herzegovina.

Variables		Model I PR (95% CI)	Model II PR (95% CI)	Model III PR (95% CI)
Sex
	Men	1	1	1
	Women	1.64 (1.21–2.21)**	1.60 (1.18–2.16)**	1.32 (0.89–1.96)
Age
	16–29	1	1	1
	30–50	2.01 (1.36–2.99)*	2.17 (1.45–3.25)*	2.39 (1.45–3.92)**
	≥51	1.45 (0.90–2.33)	1.70 (1.04–2.78)^c	1.18 (0.98–1.44)
Education
	Students	1	1	1
	High school	1.54 (0.74–3.20)	1.62 (0.78–3.39)	1.48 (0.62–3.52)
	College	1.08 (0.51–2.83)	1.09 (0.51–2.33)	0.99 (0.41–2.43)
	Employment status
	Unemployed	1	1	1
	Employed	2.85 (1.71–4.75)*	3.15 (1.87–5.31)*	3.53 (1.84–6.76)*
	Retired	2.91 (1.32–6.42)**	2.98 (1.34–6.65)**	1.60 (1.09–2.35)***
Spinal pain in parents
	No		1	1
	Yes		1.86 (1.39–2.49)*	1.71 (1.19–2.45)**
Frequency of physical activity
	1–2 days/week			1
	3–4 day/week			1.04 (0.67–1.61)
	5–6 days/week			1.19 (0.70–2.02)
	Irregular			2.88 (1.70–4.87)*
	Time watching TV
	1–2 hours/day			1
	3–4 hours/day			1.48 (0.99–2.23)
	4–5 hours/day			2.01 (0.92–4.38)

*Note: PR: Prevalence ratio; 95% CI: 95% confidence interval; *Statistical significance p < 0.001; **Statistical significance p < 0.01; ***Statistical significance p < 0.05.

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Figure 2. Distribution of pain intensity expressed with visual analog scale (VAS) by age group in the surveyed population of Bosnia and Herzegovina.

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The mean pain intensity in participants who reported LBP and NP was 5.1 (SD 1.8 vs. 1.9), and in TP it was 4.8 (SD 1.5). The presence of pain up to 6 weeks was reported by 78 (46.2%) participants with NP, 36 (52.9%) with TP and 194 (40.1%) participants with LBP. Pain present for more than 12 weeks was reported by 74 (43.8%) participants with NP, 30 (44.1%) participants with TP and 257 (53.1%) participants with LBP.

3.4. Consequences of global spinal pain syndrome

Diagnosed changes in the spine were confirmed by 40% of the participants; women had such a diagnosis more commonly than men (44% vs. 34%; p < 0.05). About 54% of participants sought the help of health professionals to relieve spinal pain; most commonly, they visited physiotherapists (52%). Medication use for the alleviation of spinal pain was confirmed by 34% of participants (Table 4).

Knowledge of good posture during various daily activities and its importance in the prevention and treatment of spinal pain was reported by 71% of participants; men's affirmative answers were significantly higher than women's (77% vs. 66%). However, the use of good posture in everyday life was reported by only 25% of the participants (Table 4).

Men were significantly longer on sick leave because of spinal pain compared to women (p < 0.001); absence from work or school for 3 ≥ days was confirmed by 72% of men, and 54% of women confirmed absence from work or school lasting from 1 to 2 days (Table 4).

Table 4. Characteristics and consequences of the presence of spinal pain by sex among a surveyed population of Bosnia and Herzegovina.

Variables		Total (N = 721) N (%)	Men (N = 281) N (%)	Women (N = 440) N (%)	p
Pain intensity (0–10 cm)^†		5.1 (1.8)	4.9 (1.7)	5.2 (1.8)	0.066^‡
	Mild (2–4)	272 (38.2)	118 (42.6)	154 (35.4)
	Moderate (5–7)	366 (51.4)	136 (49.1)	230 (52.9)	0.097
	Strong (8–10)	74 (10.4)	23 (8.3)	51 (11.7)
Duration of pain
	Up to 6 weeks	308 (42.5)	127 (45.2)	181 (41.1)
	6–12 weeks	52 (7.2)	23 (8.2)	29 (6.6)	0.307
	≥12 weeks	361 (49.8)	131 (46.6)	230 (52.3)
Localization of pain
	Stays in the region back	521 (72.3)	203(72.2)	318 (72.3)	0.993
	Spreads down the limbs	200 (27.7)	78(27.8)	122 (27.7)
Frequency of pain
	Daily	268 (37.1)	82 (29.2)	186 (42.3)
	Once a week	104 (14.4)	42 (14.9)	62 (14.1)	0.001
	Once a month	73 (10.1)	27 (9.6)	46 (10.5)
	After severe activity	276 (38.3)	130 (46.3)	146 (33.2)
Diagnostic (Herniated IVD)		289 (40.1)	96 (34.2)	193 (43.9)	0.010
Medical help		387 (53.7)	144 (51.2)	243 (55.2)	0.296
Type of medical help
	Family medicine	47 (12.1)	12 (8.3)	35 (14.3)	0.126
	Neurologist/Neurosurgeon	122 (31.4)	54 (37.5)	68 (27.9)
	Physiotherapy	203 (52.3)	73 (50.7)	130 (53.3)
	Someone else	16 (4.1)	5 (3.5)	11 (4.5)
Medication use		247 (34.3)	86 (30.6)	161 (36.6)	0.099
Medication frequency
	Daily	50 (20.2)	13 (15.1)	37 (23)
	Once a week	53 (21.5)	14 (16.3)	39 (24.2)	0.084
	Once a month	40 (16.2)	14 (16.3)	26 (16.1)
	Rarely	104 (42.1)	45 (52.3)	59 (36.6)
Knowledge of proper posture* (YES)		508 (70.5)	216 (76.9)	292 (66.4)	0.003
Use of proper posture (YES)		183 (25.4)	97 (34.5)	86 (19.5)	<0.001
Absence from work/school		246 (34.1)	95 (33.5)	151 (34.6)	0.760
Frequency of absence
	1–2 day	110 (44)	26 (28)	84 (53.5)
	3–4 day	47 (18.8)	18 (19.4)	29 (18.5)	<0.001
	5–7 day	26 (10.4)	14 (15.1)	12 (7.6)
	More than a week	67 (26.8)	35 (37.6)	32 (20.4)

*Note: IVD: Intervertebral disk; ^†Mean (standard deviation); Chi-Square test; ^‡Independent student t-test; *Proper posture in daily activities (standing, walking, sitting, lifting and walking).

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4. Discussion

The overall prevalence of spinal pain in the surveyed population of Bosnia and Herzegovina at the end of the year 2018, in a sample of the population aged 16–78 years, was 70.9%. LBP was the most common, with a prevalence of 67.2%, followed by NP (23.4%) and TP (9.4%). Women had a larger prevalence of NP and TP compared to men, while men had a larger prevalence of LBP (80.8% versus 58.4%). The largest prevalence of NP was found in participants aged 30 and 50 years, and LBP in those aged ≥51 years, while the prevalence of TP was found to be the same at age 50 and slightly lower at age ≥51 but with no statistical significance.

A systematic review from 2019 (Fatoye and colleagues; n = 13 studies) reported that real-world prevalence for LBP ranged from 1.4% to 20.0%. However, these data were determined based on studies from different regions/countries, which are geographically sufficiently distant from our region [19]. In our study, the determined point-prevalence for NP and TP is in line with the estimated ranges of global point prevalence, while the determined point-prevalence for LBP is higher than the global point-prevalence range [6],[11],[12]. The high prevalence of LBP and NP among our population is congruent with the results of a study published in 2019, which processed data on pain syndromes from several European countries, geographically closer to our country [13].

A lower incidence of TP compared to other spinal pain was also found in a study by Fouquet et al., which was published in 2015 and conducted in France between 2002 and 2005 among 3710 workers aged 20 to 59 years, as well as a higher susceptibility of women to TP [3]. In a systematic review by Briggs et al., which was published in 2009 and included 33 studies, the authors reported that the established prevalence point for TP ranges from 4–72% and that the higher prevalence of TP was found in childhood and adolescence, and especially in women [6]. These results are consistent with the findings of our study regarding the established prevalence and higher prevalence in women, except for the difference in prevalence based on age group, because we did not find differences in the prevalence of TP across age groups. However, in our study, we did not include participants younger than 16 years.

Sex at the risk factor associated with spinal pain in our study is in line with the evidence of previously published studies [8],[10]–[12]. In our study, women were more susceptible to NP and TP, and men to LBP. A systematic review published in 2012 reported that the average prevalence of LBP in one year was 37%, the highest in middle age and more common in women. The median overall prevalence of LBP was higher among women in all age groups; women had a significantly higher point and a one-month prevalence, while in the one-year and lifetime prevalence, there was no significant difference between the sexes [20]. On the other hand, the systematic review by Fatoye et al., which was published in 2019, reported that the male sex had a significant association with LBP compared with women [19]. The 2010 Global Report also reports greater susceptibility of men to LBP compared to women [21]. Therefore, it appears that the research literature is still ambivalent regarding the susceptibility of LBP between sexes.

In a cross-sectional study conducted in Spain between June 2006 to June 2007, which included 29,478 individuals aged ≥16, a higher prevalence of NP and LBP was found in women compared to men; the prevalence of NP was higher in individuals aged 51–70 years, and LBP in those aged >70 years [22]. The findings of that Spanish study were partially consistent with those of our study. Our findings on the association of women with the onset of NP, as well as the peak of NP prevalence in middle age, were also reported by other studies [23],[24].

In addition to sex, significant associations with spinal pain in our study were identified for occupational factors, hereditary factors, irregular physical activity, watching TV ≥3 hours per day and using a computer or mobile phone ≥5 hours per day. The hereditary factor we studied, the presence of spinal pain in participants' parents, was significantly associated with the presence of spinal pain in participants and the anatomic location of the pain. LBP and TP were more prevalent in participants with this hereditary factor present, whereas NP was more prevalent in participants without this specific hereditary factor.

According to a global study about occupational factors, published in 2005, 37% of LBP in the world is a result of the work environment as a risk factor, with a higher percentage in countries with lower health status [25]. NP was more common in participants with office (sedentary) jobs, and LBP in those with physical jobs [11]. In our study, there was no difference in the occurrences of NP, TP and LBP concerning the type of work, sedentary or physical, but employed persons and retired participants had a statistically significant risk of spinal pain compared to unemployed people. Considering the anatomical location of spinal pain, it is noticeable that employed persons had a higher prevalence of NP, compared to unemployed and retired people, while retired persons had a higher prevalence of LBP compared to employed and unemployed persons. In this study we did not study the type of occupation, so we could not stratify results based on different types of working environment.

Insufficient or excessive physical activity may be an important factor for functional spinal pain, especially in the growing period [26]. We found that all types of spinal pain together had a significant association with irregular physical activity but not with physical inactivity. Irregular physical activity was not a significant factor for NP, TP and LBP when this association was analyzed for these conditions individually.

This study had several limitations. We analyzed point prevalence and not lifetime prevalence, which is not necessarily comparable to other studies analyzing prevalence of spinal pain. The difficulty in comparing estimated prevalence due to research heterogeneity is a global problem because there is no uniform research methodology for such studies [11],[12]. We used online data collection as the simplest and most economically acceptable way to collect data. The limitation of this methodology may be reduced representativeness of the sample, as online data collection excludes access to individuals without internet access and potentially older individuals. However, to mitigate these issues as much as possible, we used a design weighted based on the number of inhabitants in the regions of BiH and their demographic characteristics (sex and age group). Moreover, a systematic review from 2016 reported similar representativeness of samples recruited on Facebook and known traditional methods [17].

Despite limitations, we believe that our study is an important contribution due to reported lack of data on the prevalence of spinal pain syndrome in our geographical region [14]. We were unable to find any spinal pain prevalence data in BiH, so this could be the first study on this topic in BiH and may serve as a comparison for future spinal pain prevalence studies that will be conducted in BiH. Moreover, this study can serve as a “wake-up call” and the beginning of changes in health policy and health approaches in our country, which need to be in line with scientific recommendations for painful conditions [27]–[30].

Future research in this area should aim to homogenize ways of determining the prevalence of spinal pain syndrome and associated risk factors, to enable comparison of data between different studies on pain prevalence. The problem of data heterogeneity in epidemiological research has been a problem for decades, which requires a global consensus about methodology recommendations, validated questionnaires and preferred statistical processing and interpretation of data.

5. Conclusions

To our knowledge, this is the first study of prevalence of spinal pain in Bosnia and Herzegovina. The prevalence of spinal pain in the surveyed adult BiH population in 2018 was very high. Demographic factors associated with spinal pain were sex and age 30 to 50 years. Behavioral factors associated with spinal pain include work status, irregular physical activity and length of watching TV per day. Some of the factors associated with spinal pain are modifiable, and therefore interventions for their elimination should be considered as a public health measure.

References

[1]	IPCC (2014) Climate Change 2014: Impacts, adaptation, and vulnerability-Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom: Cambridge University Press.
[2]	IPCC (2014) Climate Change 2014: Synthesis Report. Geneva, Switzerland: Intergovernmental Panel on Climate Change. Retrieved July 1, 2019, Available from: https://www.ipcc.ch/site/assets/uploads/2018/05/SYR_AR5_FINAL_full_wcover.pdf.
[3]	Abidoye BO, Kurukulasuriya P, Mendelsohn R (2017) South-East Asian farmer perceptions of climate change. Clim Change Econ 8: 1740006. doi: 10.1142/S2010007817400061
[4]	Konchar KM, Staver B, Salick J, et al. (2015) Adapting in the shadow of Annapurna: a climate tipping point. J Ethnobiol 35: 449-471. doi: 10.2993/0278-0771-35.3.449
[5]	Aldunce P, Handmer J, Beilin R, et al. (2016) Is climate change framed as 'business as usual' or as a challenging issue? The practitioners' dilemma. Environ Plann 34: 999-1019.
[6]	Scheffers BR, Meester L, Bridge TC, et al. (2016) The broad footprint of climate change from genes to biomes to people. Science 354: 6313.
[7]	Voccia A (2012) Climate change: What future for small, vulnerable states? Int J Sust Dev World Ecol 19: 101-115. doi: 10.1080/13504509.2011.634032
[8]	Spires M, Shackleton S, Cundill G (2014) Barriers to implementing planned community-based adaptation in developing countries: A systematic literature review. Clim Dev 6: 277-287. doi: 10.1080/17565529.2014.886995
[9]	Bockel L, Vian L, Torre C (2016) Towards sustainable impact monitoring of green agriculture and forestry investments by NDBs: Adapting MRV methodology. Rome, Italy: Food and Agriculture Organization.
[10]	Glenn A, James WE, Fuller RA (2016) Global mismatch between greenhouse gas emissions and the burden of climate change. Sci Rep 6: 20281. doi: 10.1038/srep20281
[11]	Jackson G, McNamara K, Witt B (2017) A framework for disaster vulnerability in a small island in the Southwest Pacific: A case study of Emae Island, Vanuatu. Int J Disaster Risk Sci 8: 358-373. doi: 10.1007/s13753-017-0145-6
[12]	Easterling WE (1996) Adapting North American agriculture to climate change in review. Agric For Meteorol 80: 1-53. doi: 10.1016/0168-1923(95)02315-1
[13]	FAO (2007) Adaptation to climate change in agriculture, forestry and fisheries: Perspective, framework and priorities. Rome: Food and Agriculture Organization.
[14]	Berrang-Ford L, Pearce T, Ford JD (2015) Systematic review approaches for climate change adaptation research. Reg Environ Chang 15: 755-769. doi: 10.1007/s10113-014-0708-7
[15]	Ifejika-Speranza C (2010) Resilient adaptation to climate change in African Agriculture. Bonn: Deutsches Institut für Entwicklungspolitik (DIE).
[16]	Abiodun BJ, Salami AT, Tadross M (2011) Climate change scenarios for Nigeria: Understanding biophysical impacts. Ibadan, Nigeria: Building Nigeria's Response to Climate Change (BNRCC) Project.
[17]	IPCC (2018) Global warming of 1.5 ℃ Geneva, Switzerland: Intergovernmental Panel on climate change.
[18]	NBS (2017) Nigerian Gross Domestic Product Report, Abuja, Nigeria: National Bureau of Statistics.
[19]	Yakubu MM, Akanegbu BN (2015) The Impact of international trade on economic growth in Nigeria: 1981-2012. Eur J Bus Econ Account 3: 26-36.
[20]	Berg A, de Noblet-Ducoudre N, Benjamin S, et al. (2013) Projections of climate change impacts on potential C4 crop productivity over tropical regions. Agric For Meteorol 170: 89-102. doi: 10.1016/j.agrformet.2011.12.003
[21]	Mereu V, Santini M, Cervigni R, et al (2018) Robust decision making for a climate-resilient development of the agricultural sector in Nigeria. In: Lipper L, McCarthy N, Zilberman D, et al., Eds., Climate Smart Agriculture: Building Resilience to Climate Change, Rome, Italy: Food and Agriculture Organization of the United Nations (FAO), 277-306.
[22]	Adejuwon JO (2005) Food crop production in Nigeria. Present effects of climate variability. Clim Res 30: 53-60.
[23]	Odekunle TO (2004) Rainfall and the length of the growing season in Nigeria. Int J Climatol 24: 467-479. doi: 10.1002/joc.1012
[24]	Remling E, Veitayaki J (2016) Community-based action in Fiji's Gau Island: A model for the Pacific? Int J Clim Chang Str Manage 8: 375-398. doi: 10.1108/IJCCSM-07-2015-0101
[25]	Ogbo A, Lauretta NE, Ukpere W (2013) Risk management and challenges of climate change in Nigeria. J Hum Ecol 41: 221-235.
[26]	Obioha EE (2008) Climate change, population drift and violent conflict over land resources in Northeastern Nigeria. J Hum Ecol 23: 311-324. doi: 10.1080/09709274.2008.11906084
[27]	Mburu BM, Kung'u JB, Muriuku JN (2015) Climate change adaptation strategies by small-scale farmers in Yatta District, Kenya. Afr J Environ Sci Technol 9: 712-722. doi: 10.5897/AJEST2015.1926
[28]	Cooper C, Booth A, Varley-Campbell J, et al. (2018) Defining the process to literature searching in systematic reviews: A literature review of guidance and supporting studies. BMC Med Res Methodol 85: 1-14.
[29]	Babatunde KA, Begum RA, Said FF (2017) Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review. Renew Sust Energ Rev 78: 61-71. doi: 10.1016/j.rser.2017.04.064
[30]	Escarcha JF, Lassa JA, Zander KK (2018) Livestock under climate change: A systematic review of impacts and adaptation. Climate 6: 54. doi: 10.3390/cli6030054
[31]	Folke C (2006) Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environ Chang 16: 253-267. doi: 10.1016/j.gloenvcha.2006.04.002
[32]	Ifejika-Speranza C (2013) Buffer capacity: Capturing a dimension of resilience to climate change in African smallholder agriculture. Reg Environ Chang 13: 521-535. doi: 10.1007/s10113-012-0391-5
[33]	Pretty J (2008) Agricultural sustainability: Concepts, principles and evidence. Philos Trans R Soc Lond B Biol Sci 363: 447-465. doi: 10.1098/rstb.2007.2163
[34]	Dorward A, Anderson S, Clark S (2001) Asset functions and livelihood strategies: A framework for pro-poor analysis, policy and practice. Imperial College at Wye, Department of Agricultural Sciences: ADU Working Papers 10918.
[35]	Shaffril HA, Krauss SE, Samsuddin SF (2018) A systematic review on Asian's farmers' adaptation practices towards climate change. Sci Total Environ 644: 683-695. doi: 10.1016/j.scitotenv.2018.06.349
[36]	Moher D, Liberati A, Tetzlaff J, et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 6: e1000097. doi: 10.1371/journal.pmed.1000097
[37]	Singh C, Deshpande T, Basu R (2017) How do we assess vulnerability to climate change in India? A systematic review of literature. Reg Environ Chang 17: 527-538.
[38]	Rusinamhodzi L, Corbeels M, Nyamangara J, et al. (2012) Maize-grain legume intercropping is an attractive option for ecological intensification that reduces climatic risk for smallholder farmers in central Mozambique. Field Crops Res 136: 12-22. doi: 10.1016/j.fcr.2012.07.014
[39]	Challinor A, Wheeler T, Garfoth C, et al. (2007) The vulnerability of food crop systems in Africa to climate change. Clim Chang 83: 381-399. doi: 10.1007/s10584-007-9249-0
[40]	Morton JF (2007) The impact of climate change on smallholder and subsistence agriculture. Proc Natl Acad Sci USA 104: 19680-19685. doi: 10.1073/pnas.0701855104
[41]	Armbrecht I, Gallego-Ropero MC (2007) Testing ant predation on the coffee berry borer in shaded and sun coffee plantations in Colombia. Entomol Exp Appl 124: 261-267. doi: 10.1111/j.1570-7458.2007.00574.x
[42]	Lin BB (2011) Resilience in agriculture through crop diversification: Adaptive management for environmental change. BioScience 61: 183-193. doi: 10.1525/bio.2011.61.3.4
[43]	Grubben G, Klaver W, Nono-Womdim R, et al. (2014) Vegetables to combat the hidden hunger in Africa. Chronica Hort 54: 24-32.
[44]	Luoh JW, Begg CB, Symonds RC, et al. (2014) Nutritional yield of African indigenous vegetables in water-deficient and water-sufficient conditions. Food Nutri Sci 5: 812-822.
[45]	Lunduka RW, Mateva KL, Magoroshoko C, et al. (2019) Impact of adoption of drought-tolerant maize varieties on total maize production in south Eastern Zimbabwe. Clim Dev 11: 35-46. doi: 10.1080/17565529.2017.1372269
[46]	Akinnagbe OM, Irohibe IJ (2014) Agricultural adaptation strategies to climate change impacts in Africa: A review. Bangladesh J Agric Res 39: 407-418.
[47]	Waha K, Müller C, Bondeau A, et al. (2013) Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Global Environ Chang 23: 130-143. doi: 10.1016/j.gloenvcha.2012.11.001
[48]	Atedhor GO (2015) Strategies for agricultural adaptation to climate change in Kogi state, Nigeria. Ghana J Geogr 7: 20-37.
[49]	Westengen OT, Brysting AK (2014) Crop adaptation to climate change in the semi-arid zone in Tanzania: The role of genetic resources and seed systems. Agri Food Secur 3: 1-12. doi: 10.1186/2048-7010-3-1
[50]	Sanz MJ, de Vente J, Chotte JL, et al. (2017) Sustainable land management contribution to successful land-based climate change adaptation and mitigation: A report of the science-policy interface. Bonn, Germany: United Nations Convention to Combat Desertification (UNCCD).
[51]	FAO (2017) Voluntary guidelines for sustainable soil management. Rome, Italy: Food and Agriculture Organization.
[52]	Stavi I (2013) Biochar use in forestry and tree-based agro-ecosystems for increasing climate change mitigation and adaptation. Int J Sust DevWorld Ecol 20: 166-181. doi: 10.1080/13504509.2013.773466
[53]	Lal R (2015) Sequestering carbon and increasing productivity by conservation agriculture. J Soil Water Conserv 70: 55-62. doi: 10.2489/jswc.70.3.55A
[54]	Stavi I, Bel G, Zaady E (2016) Soil functions and ecosystem services in conventional, conservation, and integrated agricultural systems. A review. Agron Sustain Dev 36: 1-12. doi: 10.1007/s13593-015-0343-9
[55]	Agbonlahor MU, Aromolaran AB, Aiboni VI (2003) Sustainable soil management practices in small farms of southern Nigeria: A poultry-food crop integrated farming approach. J Sustain Agric 22: 51-62. doi: 10.1300/J064v22n04_05
[56]	Thierfelder C, Matemba-Mutasa R, Rusinamhodzi L (2015) Yield response of maize (Zea mays L.) to conservation agriculture cropping system in Southern Africa. Soil Till Res 146: 230-242.
[57]	Oyekale AS, Oladele OI (2012) Determinants of climate change adaptation among cocoa farmers in Southwest Nigeria. ARPN J Sci Technol 2: 154-168.
[58]	Merrey DJ, Sally H (2008) Micro-AWM Technologies for food security in Southern Africa: Part of the solution or a red herring? Water Policy 10: 515-530. doi: 10.2166/wp.2008.025
[59]	CGIAR (2016) Agricultural practices and technologies to enhance food security, resilience and productivity in a sustainable manner: Messages to SBSTA 44 agriculture workshops, CCAFS Working Paper no. 146, Copenhagen, Denmark, 2016.
[60]	Abraham TW, Fonta WM (2018) Climate change and financing adaptation by farmers in northern Nigeria. Financ Innov 4: 11. Available from: https://doi.org/10.1186/s40854-018-0094-0. doi: 10.1186/s40854-018-0094-0
[61]	BNRCC (2011) Reports of pilot projects in community-based adaptation to climate change in Nigeria. Ibadan, Nigeria: Building Nigeria's Response to Climate Change (BNRCC) Project.
[62]	Asfaw A, Simane B, Hassen A, et al. (2017) Determinants of non-farm livelihood diversification: Evidence from rainfed-dependent smallholder farmers in Northcentral Ethiopia (Woleka sub-basin). Dev Stud Res 4: 22-36. doi: 10.1080/21665095.2017.1413411
[63]	Nzegbule EC, Nwajiuba C, Ujor G, et al. (2019) Sustainability and the effectiveness of BNRCC community-based adaptation (CBA) to address climate change impact in Nigeria. In: Leal FW Eds., Handbook of Climate Change Resilience, Cham: Springer, 1-22.
[64]	Akrofi-Atitianti F, Ifejika-Speranza C, Bockel L, et al. (2018) Assessing climate smart agriculture and its determinants of practice in Ghana: A case of the cocoa production system. Land 7: 30. doi: 10.3390/land7010030
[65]	Adepoju AO, Osunbor PP (2018) Small scale poultry farmers' choice of adaption strategies to climate change in Ogun State, Nigeria. Rural Sustain Res 40: 32-40.
[66]	Salem BH, López-Francos A (2012) Feeding and management strategies to improve livestock productivity, welfare and product quality under climate change. 14th International Seminar of the Sub-Network on Nutrition of the FAO-CIHEAM Inter-Regional Cooperative Research and Development Network on Sheep and Goats. Hammamet, Tunisia.
[67]	IAEA (2010) Improving livestock production using indigenous resources and conserving the environment. Vienna, Austria: International Atomic Energy Agency.
[68]	Lamy E, van Harten S, Sales-Baptista E, et al. (2012) Factors influencing livestock productivity. In: Sejian V, Naqvi SM, Ezeji T, et al. Eds., Environmental Stress and Amelioration in Livestock Production, Berlin, Germany: Springer, 19-51.
[69]	Gebremedhin B, Hoekstra D, Jemaneh S (2007) Heading towards commercialization? The case of live animal marketing in Ethiopia. Nairobi, Kenya: Improving Productivity and Market Success (IPMS) of Ethiopian Farmers. Working Paper 5. ILRI (International Livestock Research Institute).
[70]	Batima P (2006) Climate change vulnerability and adaptation in the livestock sector of Mongolia. Washington, DC: Assessments of Impacts and Adaptations to Climate Change (AIACC), Project No. AS 06.
[71]	Okonkwo WI, Akubuo CO (2001) Thermal analysis and evaluation of heat requirement of a passive solar energy poultry chick brooder. Nig J Renew Energ 9: 83-87.
[72]	Nyoni NM, Grab S, Archer ER (2019) Heat stress and chickens: Climate risk effects on rural poultry farming in low-income countries. Clim Dev 11: 83-90. doi: 10.1080/17565529.2018.1442792
[73]	Elijah OA, Adedapo A (2006) The effect of climate on poultry productivity in Ilorin Kwara State, Nigeria. Int J Poult Sci 5: 1061-1068. doi: 10.3923/ijps.2006.1061.1068
[74]	Ampaire A, Rothschild MF (2010) Effects of training and facilitation of farmers in Uganda on livestock development. Livest Res Rural Dev 22: 1-7.
[75]	Shelton C (2014) Climate change adaptation in fisheries and aquaculture: Compilation of initial examples. FAO Fisheries and Agriculture Circular No. 1088. Rome: Food and Agriculture Organization.
[76]	Ficke AD, Myrick CA, Hansen LJ (2007) Potential impacts of global climate change on freshwater fisheries. Rev Fish Biol Fisher 17: 581-613. doi: 10.1007/s11160-007-9059-5
[77]	Nwabeze GO, Erie AP, Erie GO (2012) Fishers' adaptation to climate change in the Jebba Lake Basin, Nigeria. J Agric Ext 16: 68-78.
[78]	Adebayo OO (2012) Climate change perception and adaptation strategies on catfish farming in Oyo State, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7.
[79]	Huq S, Reid H (2007) Community-based adaptation: A vital approach to the threat climate change poses to the poor. London: International Institute for Environment and Development.
[80]	Achoja FO, Oguh VO (2018) Income effect of climate change adaptation technologies among crop farmers in Delta State, Nigeria. Int J Agric Rural Dev 21: 3611-3616.
[81]	Agomuo CI, Asiabaka CC, Nnadi FN, et al. (2015) Rural women farmers' use of adaptation strategies to climate change in Imo State. Nigeria Int J Agric Rural Dev 18: 2305-2310.
[82]	Ajayi JO (2015) Adaptation strategies to climate change by farmers in Ekiti State, Nigeria. Appl Trop Agric 20: 01-07.
[83]	Ajieh PC, Okoh RN (2012) Constraints to the implementation of climate change adaptation measures by farmers in delta state, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7.
[84]	Akinbile LA, Oluwafunmilayo AO, Kolade RI (2018) Perceived effect of climate change on forest dependent livelihoods in Oyo State, Nigeria. J Agric Ext 22: 169-179.
[85]	Akinwalere BO (2017) Determinants of adoption of agroforestry practices among farmers in Southwest Nigeria. Appl Trop Agric 22: 67-72.
[86]	Anyoha NO, Nnadi FN, Chikaire J, et al. (2013) Socio-economic factors influencing climate change adaptation among crop farmers in Umuahia South Area of Abia State, Nigeria. Net J Agric Sci 1: 42-47.
[87]	Apata TG (2011) Factors influencing the perception and choice of adaptation measures to climate change among farmers in Nigeria: Evidence from farm households in Southwest Nigeria. Environ Econ 2: 74-83.
[88]	Arimi K (2014) Determinants of climate change adaptation strategies used by rice farmers in Southwestern, Nigeria. J Agr Rural Dev Trop 115: 91-99.
[89]	Asadu AN, Ozioko RI, Dimelu MU (2018) Climate change information source and indigenous adaptation strategies of cucumber farmers in Enugu State, Nigeria. J Agric Ext 22: 136-146.
[90]	Ayanlade A, Radeny M, Morton JF (2017) Comparing smallholder farmers' perception of climate change with meteorological data: A case study from southwestern Nigeria. Weather Clim Extremes 15: 24-33. doi: 10.1016/j.wace.2016.12.001
[91]	Ayoade AR (2012) Determinants of climate change on cassava production in Oyo State, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7.
[92]	Chukwuone N (2015) Analysis of impact of climate change on growth and yield of yam and cassava and adaptation strategies by farmers in Southern Nigeria. African Growth and Development Policy Modelling Consortium Working Paper 0012. Dakar-Almadies, Senegal: African Growth and Development Policy Modelling Consortium.
[93]	Chukwuone NA, Chukwuone C, Amaechina EC (2018) Sustainable land management practices used by farm households for climate change adaptation in South East Nigeria. J Agric Ext 22: 185-194.
[94]	Emodi AI, Bonjoru FH (2013) Effects of climate change on rice farming in Ardo Kola Local Government Area of Taraba State, Nigeria. Agric J 8: 17-21.
[95]	Enete AA, Madu II, Mojekwu JC, et al. (2011) Indigenous agricultural adaptation to climate change: Study of Imo and Enugu States in Southeast Nigeria. African Technology Policy Studies Network Working Paper No. 53. Nairobi: African Technology Policy Studies Network.
[96]	Enete AA, Otitoju MA, Ihemezie EJ (2015) The choice of climate change adaptation strategies among food crop farmers in Southwest Nigeria. Nig J Agric Econ 5: 72-80.
[97]	Eregha PB, Babatolu JS, Akinnubi RT (2014) Climate change and crop production in Nigeria: An error correction modelling approach. Int J Energ Econ Policy 4: 297-311.
[98]	Esan VI, Lawi MB, Okedigba I (2018) Analysis of cashew farmers adaptation to climate change in South-Western Nigeria. Asian J Agric Ext Econ Sociol 23: 1-12.
[99]	Ezeh AN, Eze AV (2016) Farm-level adaptation measures to climate change and constraints among arable crop farmers in Ebonyi State of Nigeria. Agric Res J 53: 492-500. doi: 10.5958/2395-146X.2016.00098.3
[100]	Ezike KN (2019) Implications for mitigation and adaptation measures: Rice farmers' response and constraints to climate change in Ivo Local Government Area of Ebonyi State. In: Leal FW Eds., Handbook of Climate Change Resilience, Cham: Springer, 1787-1799.
[101]	Falola A, Achem BA (2017) Perceptions on climate change and adaptation strategies among sweet potato farming households in Kwara State, Northcentral Nigeria. Ceylon J Sci 46: 55-63.
[102]	Farauta BK, Egbule CL, Idrisa YL, et al. (2011) Farmers' perceptions of climate change and adaptation strategies in Northern Nigeria: An empirical assessment. African Technology Policy Studies Network Research Paper No 15. Nairobi, Kenya: African Technology Policy Studies Network.
[103]	Henri-Ukoha A, Adesope OM (2019) Sustainability of climate change adaptation measures in Rivers State, South-South, Nigeria. In: Leal FW, Eds., Handbook of Climate Change Resilience, Cham: Springer, 675-683.
[104]	Ifeanyi-Obi CC, Asiabaka CC, Matthews-Njoku E, et al. (2012) Effects of climate change on fluted pumpkin production and adaptation measures used among farmers in Rivers State. J Agric Ext 16: 50-58.
[105]	Ifeanyi-Obi CC, Asiabaka CC, Adesope OM (2014) Determinants of climate change adaptation measures used by crop and livestock farmers in Southeast Nigeria. J Human Soc Sci 19: 61-70.
[106]	Igwe AA (2018) Effect of livelihood factors on climate change adaptation of rural farmers in Ebonyi State. J Biol Agric Healthc 8: 10-15.
[107]	Iheke OR, Agodike WC (2016) Analysis of factors influencing the adoption of climate change mitigating measures by smallholder farmers in Imo State, Nigeria. Sci Papers Ser Manag Econ Eng Agric Rural Dev 16: 213-220.
[108]	Ihenacho RA, Orusha JO, Onogu B (2019) Rural farmers use of indigenous knowledge systems in agriculture for climate change adaptation and mitigation in Southeast Nigeria. Ann Ecol Environ Sci 3: 1-11.
[109]	Ikehi ME, Onu FM, Ifeanyieze FO, et al. (2014) Farming families and climate change issues in Niger Delta Region of Nigeria: Extent of impact and adaptation strategies. Agric Sci 5: 1140-1151.
[110]	Kim I, Elisha I, Lawrence E, et al. (2017) Farmers adaptation strategies to the effect of climate variation on rice production: Insight from Benue State, Nigeria. Environ Ecol Res 5: 289-301.
[111]	Koyenikan MJ. Anozie O (2017) Climate change adaptation needs of male and female oil palm entrepreneurs in Edo State, Nigeria. J Agric Ext 21: 162-175.
[112]	Mbah EN, Ezeano CI, Saror SF (2016) Analysis of climate change effects among rice farmers in Benue State, Nigeria. Curr Res Agric Sci 3: 7-15.
[113]	Mustapha SB, Undiandeye UC, Gwary MM (2012) The role of extension in agricultural adaptation to climate change in the Sahelian Zone of Nigeria. J Environ Earth Sci 2: 48-58.
[114]	Mustapha SB, Alkali A, Zongoma BA, et al. (2017) Effects of climatic factors on preference for climate change adaptation strategies among food crop farmers in Borno State, Nigeria. Int Acad Inst Sci Technol 4: 23-31.
[115]	Nnadi FN, Chikaire J, Nnadi CD, et al. (2012) Sustainable land management practices for climate change adaptation in Imo State, Nigeria. J Emerg Trends Eng Appl Sci 3: 801-805.
[116]	Nwaiwu IU, Ohajianya DO, Orebiyi JS, et al. (2014) Climate change trend and appropriate mitigation and adaptation strategies in Southeast Nigeria. Glob J Biol Agric Health Sci 3: 120-125.
[117]	Nwalieji HU, Onwubuya EA (2012) Adaptation practices to climate change among rice farmers in Anambra State of Nigeria. J Agric Ext 16: 42-49.
[118]	Nwankwo GC, Nwaobiala UC, Ekumankama OO, et al. (2017) Analysis of perceived effect of climate change and adaptation among cocoa farmers in Ikwuano Local Government Area of Abia State, Nigeria. ARPN J Sci Technol 7: 1-7.
[119]	Nzeadibe TC, Egbule CL, Chukwuone NA, et al. (2011) Climate change awareness and adaptation in the Niger Delta Region of Nigeria. Nairobi, Kenya: African Technology Policy Studies Network Working Paper Series No.57. Nairobi, Kenya: African Technology Policy Studies Network.
[120]	Obayelu OA, Adepoju AO, Idowu T (2014) Factors influencing farmers' choices of adaptation to climate change in Ekiti State, Nigeria. J Agric Environ Int Dev 108: 3-16.
[121]	Ofuoku AU (2011) Rural farmers' perception of climate change in central agricultural zone of Delta State, Nigeria. Indones J Agric Sci 12: 63-69. doi: 10.21082/ijas.v12n2.2011.p63-69
[122]	Ogbodo JA, Anarah SE, Abubakar SM (2018) GIS-based assessment of smallholder farmers' perception of climate change impacts and their adaptation strategies for maize production in Anambra State, Nigeria. In: Amanullah, & S. Fahad (Eds.), Corn production and human health in changing climate, 115-138.
[123]	Ogogo AU, Ekong MU, Ifebueme NM (2019) Climate change awareness and adaptation measures among farmers in Cross River and Akwa Ibom States of Nigeria. In: Leal FW (Ed), Handbook of Climate Change Resilience, 1983-2002, Cham: Springer.
[124]	Okpe B, Aye GC (2015) Adaptation to climate change by farmers in Makurdi, Nigeria. J Agric Ecol Res Int 2: 46-57.
[125]	Oluwatusin FM (2014) The perception of and adaptation to climate change among cocoa farm households in Ondo State, Nigeria. Acad J Interdiscipli Stud 3: 147-156.
[126]	Oluwole AJ, Shuaib L, Dasgupta P (2016) Assessment of level of use of climate change adaptation strategies among arable crop farmers in Oyo and Ekiti States, Nigeria. J Earth Sci Clim Chang 7: 369.
[127]	Onyeagocha SU, Nwaiwu IU, Obasi PC, et al. (2018) Encouraging climate smart agriculture as part solution to the negative effects of climate change on agricultural sustainability in Southeast Nigeria. Int J Agric Rural Dev 21: 3600-3610.
[128]	Onyegbula CB, Oladeji JO (2017) Utilization of climate change adaptation strategies among rice farmers in three states of Nigeria. J Agric Ext Rural Dev 9: 223-229. doi: 10.5897/JAERD2017.0895
[129]	Onyekuru NA (2017) Determinants of adaptation strategies to climate change in Nigerian forest communities. Nig Agric Policy Res J 3: 42-59.
[130]	Onyeneke RU (2016) Effects of livelihood strategies on sustainable land management practices among food crop farmers in Imo State, Nigeria. Nig J Agric Food Environ 12: 230-235.
[131]	Onyeneke RU (2018) Challenges of adaptation to climate change by farmers Anambra State, Nigeria. Int J BioSciences Agric Technol 9: 1-7.
[132]	Onyeneke RU, Madukwe DK (2010) Adaptation measures by crop farmers in the Southeast Rainforest Zone of Nigeria to climate change. Sci World J 5: 32-34.
[133]	Onyeneke RU, Iruo FA, Ogoko IM (2012) Micro-level analysis of determinants of farmers' adaptation measures to climate change in the Niger Delta Region of Nigeria: Lessons from Bayelsa State. Nig J Agric Econ 3: 9-18.
[134]	Tarfa PY, Ayuba HK, Onyeneke RU, et al. (2019) Climate change perception and adaptation in Nigeria's Guinea Savanna: Empirical evidence from farmers in Nasarawa State, Nigeria. Appl Ecol Environ Res 17: 7085-7112.
[135]	Onyeneke R, Mmagu CJ, Aligbe JO (2017) Crop farmers' understanding of climate change and adaptation practices in South-east Nigeria. World Rev Sci Technol Sust Dev 13: 299-318. doi: 10.1504/WRSTSD.2017.089544
[136]	Oriakhi LO, Ekunwe PA, Erie GO, et al. (2017) Socio-economic determinants of farmers' adoption of climate change adaptation strategies in Edo State, Nigeria. Nig J Agric Food Environ 13: 115-121.
[137]	Orowole PF, Okeowo TA, Obilaja OA (2015) Analysis of level of awareness and adaptation strategies to climate change among crop farmers in Lagos State, Nigeria. Int J Appl Res Technol 4: 8-15.
[138]	Oruonye ED (2014) An Assessment of the level of awareness of climate change and variability among rural farmers in Taraba State, Nigeria. Int J Sustain Agric Res 1: 70-84.
[139]	Oselebe HO, Nnamani CV, Efisue A, et al. (2016) Perceptions of climate change and variability, impacts and adaptation strategies by rice farmers in south east Nigeria. Our Nature 14: 54-63.
[140]	Oti OG, Enete AA, Nweze NJ (2019) Effectiveness of climate change adaptation practices of farmers in Southeast Nigeria: An empirical approach. Int J Agric Rural Dev 22: 4094-4099.
[141]	Owombo PT, Koledoye GF, Ogunjimi SI, et al. (2014) Farmers' adaptation to climate change in Ondo State, Nigeria: A gender analysis. J Geog Reg Plann 7: 30-35. doi: 10.5897/JGRP12.071
[142]	Ozor N, Madukwe MC, Enete AA, et al. (2012) A framework for agricultural adaptation to climate change in Southern Nigeria. Int J Agric 4: 243-251.
[143]	Sangotegbe NS, Odebode SO, Onikoyi MP (2012) Adaptation strategies to climate change by food crop farmers in Oke-Ogun Area of South Western Nigeria. J Agric Ext 16: 119-131.
[144]	Sanni DO (2018) Local knowledge of climate change among arable farmers in selected locations in Southwestern Nigeria. In: Leal FW, Eds., Handbook of Climate Change Resilience, Cham: Springer, 1-18.
[145]	Solomon E, Edet OG (2018) Determinants of climate change adaptation strategies among farm households in Delta State, Nigeria. Curr Invest Agric Curr Res 5: 615-620.
[146]	Tanko L, Muhsinat BS (2014) Arable crop farmers' adaptation to climate change in Abuja, Federal Capital Territory, Nigeria. J Agric Crop Res 2: 152-159.
[147]	Usman MN, Ibrahim FD, Tanko L (2016) Perception and adaptation of crop farmers to climate change to in Niger State, Nigeria. Nig J Agric Food Environ 12: 186-193.
[148]	Uzokwe UN, Okonkwo JC (2012) Survival strategies of women farmers against climate change in Delta State and implication for extension services. Banat J Biotechnol 3: 97-103.
[149]	Weli VE, Bajie S (2017) Adaptation of Root crop farming system to climate change in Ikwerre Local Government Area of Rivers State, Nigeria. Am J Clim Chang 6: 40-51. doi: 10.4236/ajcc.2017.61003
[150]	Chah JM, Odo E, Asadu AN, et al. (2013) Poultry farmers' adaptation to climate change in Enugu North Agricultural Zone of Enugu State, Nigeria. J Agric Ext 17: 100-114
[151]	Chah JM, Attamah CO, Odoh EM (2018) Differences in climate change effects and adaptation strategies between male and female livestock entrepreneurs in Nsukka Agricultural Zone of Enugu State, Nigeria. J Agric Ext 22: 105-115
[152]	Ibrahim FD, Azemheta T (2016) Climate change effects and perception on smallholder poultry farms in Lokoja Local Government Area of Kogi State: Implications for Policy Intervention. Nig J Agric Food Environ 12: 164-173.
[153]	Tologbonse EB, Iyiola-Tunji AO, Issa FO, et al. (2011) Assessment of climate change adaptive strategies in small ruminant production in rural Nigeria. J Agric Ext 15: 40-57.
[154]	Ume SI, Ezeano CI, Anozie R (2018) Climate change and adaptation coping strategies among sheep and goat farmers in Ivo Local Government Area of Ebonyi State, Nigeria. Sustain Agri Food Environ Res 6: 50-68.
[155]	Adeleke ML, Omoboyeje VO (2016) Effects of climate change on aquaculture production and management in Akure Metropolis, Ondo State, Nigeria. Nig J of Fish Aquacult 4: 50-58.
[156]	Aphunu A, Nwabeze GO (2012) Fish farmers' perception of climate change impact on fish production in Delta State, Nigeria. J Agric Ext 16: 1-13.
[157]	Owolabi ES, Olokor J (2016) Climate change and fish farmers adaptation: A case study of New Bussa fishing population. J Natur Sci Res 6: 123-141.
[158]	Amusa TA, Okoye CU, Enete AA (2015) Determinants of climate change adaptation among farm households in Southwest Nigeria: A heckman's double stage selection approach. Rev Agric Appl Econ 18: 3-11.
[159]	NEST, Woodley E (2012) Learning from experience: Community-based adaptation to climate change in Nigeria. Ibadan, Nigeria: Building Nigeria's response to climate change.
[160]	BNRCC, FederalMinistry of Environment (2011) National Adaptation Strategy and Plan of Action on Climate Change for Nigeria (NASPA-CCN). Abuja, Nigeria: Federal Ministry of Environment (Climate Change Department).
[161]	Oladipo E (2010) Towards enhancing the adaptive capacity of Nigeria: A Review of the Country's state of preparedness for climate change adaptation. Abuja, Nigeria: Report Submitted to Heinrich Böll Foundation Nigeria.
[162]	Tijjani AR, Chikaire JU (2016) Fish farmers perception of the effects of climate change on water resource use in Rivers State, Nigeria. J Sci Eng Res 3: 347-353.

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