Authors
Keywords
Abstract
The vicissitudes of climatic conditions in Nigeria negatively impact agricultural production. Sustainability of agricultural production depends largely on farmers’ ability to make decisions based on their level of knowledge and information available to them. This paper reports farmers’ knowledge and perception of climate change on crop production in Akinyele Local Government Area, Southwestern Nigeria. Stratified random sampling method was employed for the study. Data obtained through administration of structured questionnaire on local residents were analyzed using descriptive and chi-square (χ2) statistics at α0.05. Secondary data were also obtained on some climatic variables and crop production in the study area. Modal age among the respondents’ (31.1%) was between 50-60years, 78.9% were males and 95.6% were married. Although 83.3% of them perceived their knowledge level on climate change as good, only 42.2% perceived reduced rainfall as impact of climate change. However, 70.0% perceived change in seasonal rainfall pattern as indicator of climate change while 97.8% believed that humans are not responsible for the observed climate change. But, respondents’ fingered deforestation (41.1%), bush burning (27.8%) and vehicular emissions’ (11.1%) as agents of climate change. Further, respondents’ age impacted their knowledge on climate change (χ2 = 33.85; df = 18) and their perceptions of climate change (χ2 = 27.77; df = 12) and its effect (χ2 = 46.69; df = 24). Secondary information corroborated famers’ perception of climate vagaries, most noticeably, the rainfall pattern. Therefore, farmers’ knowledge and perception of micro climate indices are important inputs in the formulation of sustainable food production policy.
Abstract
The vicissitudes of climatic conditions in Nigeria negatively impact agricultural production. Sustainability of agricultural production depends largely on farmers’ ability to make decisions based on their level of knowledge and information available to them. This paper reports farmers’ knowledge and perception of climate change on crop production in Akinyele Local Government Area, Southwestern Nigeria. Stratified random sampling method was employed for the study. Data obtained through administration of structured questionnaire on local residents were analyzed using descriptive and chi-square (χ2) statistics at α0.05. Secondary data were also obtained on some climatic variables and crop production in the study area. Modal age among the respondents’ (31.1%) was between 50-60years, 78.9% were males and 95.6% were married. Although 83.3% of them perceived their knowledge level on climate change as good, only 42.2% perceived reduced rainfall as impact of climate change. However, 70.0% perceived change in seasonal rainfall pattern as indicator of climate change while 97.8% believed that humans are not responsible for the observed climate change. But, respondents’ fingered deforestation (41.1%), bush burning (27.8%) and vehicular emissions’ (11.1%) as agents of climate change. Further, respondents’ age impacted their knowledge on climate change (χ2 = 33.85; df = 18) and their perceptions of climate change (χ2 = 27.77; df = 12) and its effect (χ2 = 46.69; df = 24). Secondary information corroborated famers’ perception of climate vagaries, most noticeably, the rainfall pattern. Therefore, farmers’ knowledge and perception of micro climate indices are important inputs in the formulation of sustainable food production policy.
Introduction
Climate change is one of the most serious environmental threats facing mankind worldwide and by extension, Nigeria. As supported by MOEFRN(2003) and Folke, et al.(2005) climate change has become a global issue, manifesting in variations of different climate parameters including cloud cover, precipitation, temperature ranges, sea levels and vapour pressure. According to IPCC (2007), it can be directly or indirectly attributed to human activities, which alter the composition of the global atmosphere in addition to natural variability observed over comparable periods of time. Nyong (2005) also predicted the possibilities of climate changes effects accumulating until thresholds are crossed, which could cause the entire thresholds to collapse. This envisaged risk is greatest where much of the livelihoods and socio economic systems depend on natural resources, one of which is the forests. According to World Development Report(2010), the impacts of climate change aggravate desertification and erosion processes, result in reversible changes in ecosystems and biodiversity loss and finally, affect human life and activities. This was reposed by UNEP (2008) that clearing of forests for agricultural production replaces forests with arable crops thereby reducing the rate at which carbon (IV) oxide gas trapping and absorbing occurs.
As observed by Adefolalu (2007) and Ikhile (2007) Nigeria is already being plagued with diverse ecological problems, which have been directly linked to climate change. In Oyo State, environmental problems that are termed degradation collectively, such as erosion, flooding and drought have strong links with deforestation.In Akinyele Local Government Area of Oyo State, climate change is perceived as a potential threat to sustainable development. Incidence of climate change include changes in soil moisture, soil quality, crop resilience, timing of growing seasons, yield in crops and animal production, atmospheric temperatures, weed insurgence, flooding, unprecedented droughts, sea level increment, and many more (Spore 2008; Nicholas and Nnaji 2011). The southern ecological zone of Oyo State largely known for high rainfall is currently confronted by irregularity in the rainfall pattern, while Derived Savannah to the North is experiencing gradually increasing temperature (MOEFRN 2009; Obioha 2008).
As submitted by Cotchinget al. (2009), the natural environment is changing as forests are been depleted when farmers clear bushes for farming; settlements, charcoal production and building materials without commensurate replacement. Increased intensity and frequency of drought and flooding, altered hydrological cycles as well as precipitation variance have implications for future food availability.Changes in the frequency and severity of droughts and floods pose challenges for farmers. These could make it more difficult to grow crops, in the same way and same places as they have done in the past. Climate change thus worsening the working conditions for farmers in several ways due to frequent crop failure, that farmers become more impoverished as frequent droughts also discourage farmers to invest more in farming (Kiteme 2009).
West Africa was submitted by IPCC (2007) as one of the most vulnerable to the vagaries of the climate, based on the scope of the impacts of climate variability spanning three to four earlier decades. The recent food crises in Nigeria is a reminder of the continuing vulnerability of the region to the vicissitudes of climatic conditions. This was largely attributed to weak institutional capacity, limited engagement in environmental and adaptation issues, and a lack of standard and practical local knowledge validation method by Spore (2008), NEST (2008), Royal Society (2005) as well as Adams et al. (1995).
Accordingly, there is the need to gain as much information as possible, and learn the positions of rural farmers and their needs, about what they know on climate change, in order to offer adaptation practices that meet these needs.Consequently, this paper reports farmers’ knowledge and perception of adaptation to changes and variation in climate change. This is important because sustainability of agricultural production depends largely on actions of farmers and their ability to make decisions given the level of knowledge and information available to them.
Figure 1: Map of Oyo State, Southwestern Nigeria indicating the study area.
Materials and Methods
The Study Area
The study was carried out in Akinyele Local Government Area (LGA) of Oyo state, Southwestern Nigeria. The LGA (Figure 1) is very close to Ibadan, the capital of Oyo State, which is lies in the southwestern part of Nigeria; on longitude 3o54' of the Greenwich meridian and latitude 7o54' north of equator. Ibadan city is elevated at about 234 meters above the sea level and is situated on gently rolling hills running in a northwest or southeast direction. Akinyele LGA was established in 1976 with the administrative headquarters situated at Moniya. The major farming activity practiced in the area is crop faming with the main crops being cassava, maize, cocoyam, vegetables, rice, groundnut and beans while maize, cassava, and cocoyam are the most important food crop grown. This is because apart from sales these important crops are also consumed by the household. The sales or prices of agricultural produce in this area are based on the season, market and the location of the individual farm.
Data Collection and Sampling Procedure
Akinyele LGA was stratified into wards. The Local Government Area consists of 12 wards. Six wards were randomly picked within the LGA. Fifteen respondents were randomly selected from each ward making a total of ninety respondents which constituted the sample size for the study. Primary data were obtained through oral interviews with the aid of structured questionnaire. The questionnaire sought for farmer’s perception of climate change effects,while the secondary data (Climatological and agricultural data for ten years) were also obtained. The climatological data included rainfall, temperature and relative humidity.Focus group discussion was also organized with the farmers in the local government area to assess their opinions about changes in some climatic variables.
Data Analysis
Data collected were subjected to descriptive (simple percentages; frequency counts, tables and line graph) and inferential (Cross-Tab and Chi-Square at α0.05)statistical analyses.
Results
Respondents’ Background
The study (Table 1) revealed that modal age (31.1%) among the respondents’ was between 50-60years while those between ages 10 and 30 years were the least sampled. Also, the greater percentage of the respondents’ (78.9%) was found (Table 1) to be male and 95.6% of the respondents were married. Similarly, the larger groups (57.8%) of the respondents’ were followers of the Islamic faith 53.3% of the respondents have been a resident in the study area for more than 20years, majorly since birth.
Further, background information (Table 2) revealed the major occupation of 72.2% of the respondents to be farming, which is a secondary occupation to 41.1% of them and very next to trading (44.4%), the modal secondary occupation identified by the study. Also, the study (Table 2) found that the most subscribed (30.0%) household size was that holding between 9 – 10 people.
Also, educational status distribution in the study area (Table 2) revealed that 56.6% of the respondents’ had only primary education (the most subscribed).On farm size, the study(Table 2) showed that the most popular size accessed by the respondents’ was between 1 and 3 acres. Similarly on respondents’ monthly income, the study (Table 2) found that most of the farmers (31.1%) earn between ₦5000- ₦10,000 as net income/month.
Examining respondents’ level of knowledge about climate change, the study (Table 3) found that 83.3% of them perceived their knowledge of the subject as good (the majority). On their view about climate change, 70.0% of them submitted change in seasonal rainfall pattern as indicator of climate change while 24.4% attributed it to change in temperature (Table 3).Responding to the causes of climatic variability (Table 3) 97.8% were of the opinion that humans are not responsible for the observed climate change in the study area. However, they were able to identify with deforestation (41.1%), bush burning (27.8%) and vehicular emissions’ (11.1%) as agents of climate change (Table 3). Worthy of note also is that 20.0% attributed climate change as the work of God (Table 3).
On the impact of climate change, 42.2% of the respondents subscribed to reduced rainfall, 24.4%, rising temperature while 17.8% fingered the shift in growing season as an effect of climate change. The study (Table 3) also identified crop failure and low yield as perceived major effects (72.2%) of climate change. Further, the rainfall pattern was viewed as inconsistent and unpredictable in the last decade by 72.2% of the respondents’ while rainfall trend within the same timeframe was perceived as delayed and irregular by 57.8% and 37.8% of them, respectively (Table 3).
Impact of Farmers’ Background on their Knowledge and Perception
This was examined using chi-square statistics to test the dependence of farmers’ background on their Knowledge and perception of climate change using two null hypotheses viz:
Ho1Farmers’ socio-economic background has no impact on their Knowledge of Climate change in Akinyele LGA of Oyo State
Ho2Farmers’ socio-economic background has no impact on their perception of climate change
Further, where chi-square tests showed significant relationship, the cross-tab analyses were further used to explain the dependency.
Chi-square test of the dependence of knowledge on farmers’ age (Table 4) was significant (Asymp. Sig. = 0.013), hence the null hypothesis was rejected. Thus, there was a significant dependence of knowledge about climate change on age of farmers in the study area. Implicitly, farmers’ age impacted their knowledge on climate change in the study area. Further, even though respondents age is from 10 to >70 years, most respondents’ perceived their knowledge of climate change as good and this perception cuts across all age groups except those between 10 and 20 years of age.
Investigating the impact of farmers’ age on their perception of climate change, the study (Table 5) revealed a significant dependence (Asymp. Sig. = 0.006) of perception on age with Pearson’s chi-square (χ2) value of 27.771 at a degree of freedom of 12. Here, change in rainfall was the most favored climate change index among respondents and this was supported by thosein age groups of>30 to >70 years with the modal subscription being from the >50 – 60 years group.
Age was also found to impact farmers’ perception of the effect of climate change (Table 6). A chi-square value of 46.694 at a degree of freedom of 24 was gotten. The Asymptotic Significance value of 0.004 for the test revealed a highly significant dependence of farmers’ perception of the effect of climate change in the study area on age. All identified effects of climate change were subscribed to, but reduced rainfall was highest and this was most subscribed to by respondents’ in ages >50 – 60 years, the modal age group in the study.
Table 7 shows the summary of the chi-square statistics of the impacts of some respondents’ background information apart from age, which has significant impact on most tested perception variables apart from “Effects of climate change”. The study (Table 7) found that apart from income of farmers’, which impacted the level of knowledge of farmers about climate change (Pearson’s χ2 = 34.31; Asymp. Sig. = 0.01; df = 18; see Table 8 for the crosstab analysis) as well as farm size that impacted farmers’ perception of the effects of climate change (Pearson’s χ2 = 88.09; Asymp. Sig. = 0.000; df = 44; See Table 9 for the crosstab analysis), farmers’ socio economic background was found not have significant impact on farmers’ knowledge and perception of climate change in the study area.
A cross-tab analyses of the dependence of farmers’ income on their perception of climate change knowledge (Table 8) although most of the respondents’ were of the opinion that their knowledge of climate change was good in the study area, the bulk (26) fall under those earning between >N5000 and N10,000/Month followed by those earning between >N10,000 and N20,000/Month (15) and very closely (14) by those earning between >N20,000 and N30,000/Month. From the table, it can be concluded that reactions on perception about climate change knowledge was highest from farmers’ earning the modal income on the income distribution table from the study.
Examining how respondents’ farm size impacted their perception of the effect of climate change, the study (Table 9) revealed that though about five effects were identified with twelve different farm sizes in the study area, reduction in rainfall and increase in temperature were the most popular identified effects. Also while 38 respondents favoured reduction in the amount of rainfall, 18 of them own between 1 and 3 hectares of farmland, which is the modal farm size on the farm size distribution platform.
Analyses of the trend in some climate variables in the study area (Table 10) revealed inconsistencies in the distribution of rainfall, temperature and relative humidity from 2003 and 2013. For example, although annual rainfall increase sharply in 2004, it nose-dived in 2005 and sharply picked up again in 2006. This inconsistency was observed throughout the period under review for rainfall, temperature and relative humidity distribution in the study area.
Table 1: Frequency Distributions of Respondents General Background
| Frequency | Percentages | Mode | |
| Age (Years) | |||
| 10 -20 | 1 | 1.1 | 50 – 60 years |
| >20-30 | 1 | 1.1 | |
| >30-40 | 1 | 1.1 | |
| >40-50 | 22 | 24.4 | |
| >50-60 | 28 | 31.1 | |
| >60-70 | 21 | 23.3 | |
| >70 | 16 | 17.8 | |
| Sex | |||
| Male | 71 | 78.9 | Male |
| Female | 19 | 21.1 | |
| Marital Status | |||
| Married | 86 | 95.6 | Married |
| Single | 2 | 2.2 | |
| Divorced | 2 | 2.2 | |
| Religion | |||
| Christian | 34 | 37.8 | Islam |
| Islam | 52 | 57.8 | |
| Traditional | 4 | 4.4 | |
| Duration Residence in the Study Area (Years) | |||
| >0<5 | 2 | 2.2 | >20 |
| >5<10 | 3 | 3.3 | |
| >10<15 | 20 | 22.2 | |
| >15<20 | 17 | 18.9 | |
| >20 | 48 | 53.3 | |
Table 2: Frequency Distribution of Respondents Other Background Information
| Frequency | Percentage | Mode | ||
| Primary Occupation | ||||
| Farming | 65 | 72.2 | Farming | |
| Trading | 21 | 23.3 | ||
| Civil servant | 2 | 2.2 | ||
| Artisan | 2 | 2.2 | ||
| Secondary Occupation | ||||
| Farming | 37 | 41.1 | Trading | |
| Trading | 40 | 44.4 | ||
| Civil servant | 2 | 2.2 | ||
| Artisan | 9 | 10.0 | ||
| Others | 2 | 2.2 | ||
| Household Size (Person) | ||||
| 1 – 4 | 5 | 5.5 | 9 - 10 | |
| 5– 8 | 26 | 28.9 | ||
| 9– 10 | 27 | 30.0 | ||
| 11– 12 | 12 | 13.3 | ||
| 13– 15 | 17 | 18.9 | ||
| >15 | 3 | 3.3 | ||
| Level of Education | ||||
| Primary Education | 50 | 55.6 | Primary Education | |
| Secondary Education | 19 | 21.1 | ||
| OND/NCE | 7 | 7.8 | ||
| HND/First Degree | 2 | 2.2 | ||
| Higher Degree | 1 | 1.1 | ||
| No formal education | 11 | 12.2 | ||
| Farm Size (Acre) | ||||
| 1– 3 | 44 | 48.9 | 1 - 3 | |
| >3 – 5 | 23 | 25.5 | ||
| >5 – 8 | 15 | 16.6 | ||
| >8 –10 | 5 | 5.6 | ||
| >10 –15 | 1 | 1.1 | ||
| >15 –20 | 1 | 1.1 | ||
| >20 | 1 | 1.1 | ||
| Monthly Income (₦) | ||||
| <5000 | 4 | 4.4 | >5000<10,000 | |
| >5000 - 10,000 | 28 | 31.1 | ||
| >10,000 - 20,000 | 19 | 21.1 | ||
| >20,000 - 30,000 | 15 | 16.7 | ||
| >30,000 - 40,000 | 13 | 14.4 | ||
| >40,000 - 50,000 | 4 | 4.4 | ||
| >50,000 | 7 | 7.8 | ||
Farmers’ Knowledge and Perception of Climate Change
Table 3: Frequency Distribution of Information on Farmers’ Perception of Climate Change
| Frequency | Percentage | Mode | |||
| Knowledge Level | |||||
| Very good | 3 | 3.3 | Good | ||
| Good | 75 | 83.3 | |||
| Don't know | 6 | 6.7 | |||
| Poor | 6 | 6.7 | |||
| Perceptionof Climate Change | |||||
| Change in seasonal rainfall pattern | 63 | 70.0 | Change in seasonal rainfall pattern | ||
| Change in temperature characteristics | 22 | 24.4 | |||
| Frequent flooding | 5 | 5.6 | |||
| Consent on Human as Agent of Climate Change | |||||
| Yes | 2 | 2.2 | No | ||
| No | 88 | 97.8 | |||
| Perceived Human Actions Promoting Climate Change | |||||
| Emissions of vehicular fumes | 10 | 11.1 | Deforestation | ||
| Deforestation | 37 | 41.1 | |||
| Bush burning | 25 | 27.8 | |||
| God | 18 | 20.0 | |||
| Perceived Effects | |||||
| Reduced rainfall | 38 | 42.2 | Reduced rainfall | ||
| Flooding | 11 | 12.2 | |||
| Rising temperature | 22 | 24.4 | |||
| Shifts in growing season | 16 | 17.8 | |||
| Drought | 3 | 3.3 | |||
| Perceived Impacts of Climate Change on Crop Production | |||||
| Loss of crops prematurely | 25 | 27.8 | Crop failure & low yield | ||
| Crop failure & low yield | 65 | 72.2 | |||
| Perception of Rainfall Pattern | |||||
| Consistent & predictable | 6 | 6.7 | Inconsistent & not predictable | ||
| Inconsistent & not predictable | 65 | 72.2 | |||
| Normal onset | 19 | 21.1 | |||
| Perception ofRainfall Trend | |||||
| Delayed rainfall | 52 | 57.8 | Delayed rainfall | ||
| Irregular pattern | 34 | 37.8 | |||
| Sometimes doesn’t come at all | 4 | 4.4 | |||
Table 4: Cross-Tab Analyses and Chi-Square Statistics of Impact of Farmers’ Age on their Knowledge about Climate Change
| Age (Years) | Level of Knowledge about Climate Change | Total | Chi-Square Statistics | |||
| Very good | Good | Don’t know | Poor | |||
| 10 – 20 | 1 | 0 | 0 | 0 | 1 | Pearson’sχ2 = 33.853;Asymp. Sig. = 0.013;df = 18. |
| >20 – 30 | 0 | 1 | 0 | 0 | 1 | |
| >30 – 40 | 0 | 1 | 0 | 0 | 1 | |
| >40 – 50 | 1 | 17 | 2 | 2 | 22 | |
| >50 – 60 | 1 | 24 | 2 | 1 | 28 | |
| >60 – 70 | 0 | 18 | 2 | 1 | 21 | |
| >70 | 0 | 14 | 0 | 2 | 16 | |
| Total | 3 | 75 | 6 | 6 | 90 | |
Table 5: Cross-Tab Analyses and Chi-Square Statistics of Impact of Farmers’ Age on their Perception of Climate Change
| Age (Years) | Perception of climate change | Total | Chi-Square Statistics | ||
| Change in Rainfall | Change in Temperature | Frequent Flooding | |||
| 10 – 20 | 0 | 0 | 1 | 1 | Pearson’sχ2 = 27.771;Asymp. Sig. = 0.006;df = 12. |
| >20 – 30 | 0 | 1 | 0 | 1 | |
| >30 – 40 | 1 | 0 | 0 | 1 | |
| >40 – 50 | 13 | 6 | 3 | 22 | |
| >50 – 60 | 19 | 8 | 1 | 28 | |
| >60 – 70 | 16 | 5 | 0 | 21 | |
| >70 | 14 | 2 | 0 | 16 | |
| Total | 63 | 22 | 5 | 90 | |
Table 6: Cross-Tab Analyses and Chi-Square Statistics of Impact of Farmers’ Age on their Perception about
Identified Effects of Climate Change
| Age (Years) | Perceived Effects of Climate Change | Total | Chi-Square Statistics | ||||
| Reduced rainfall | Flooding | Rising temperature | Shift in growing season | Drought | |||
| 10 – 20 | 0 | 0 | 0 | 0 | 1 | 1 | Pearson’sχ2 = 46.694;Asymp. Sig. = 0.004;df = 24. |
| >20 – 30 | 1 | 0 | 0 | 0 | 0 | 1 | |
| >30 – 40 | 1 | 0 | 0 | 0 | 0 | 1 | |
| >40 – 50 | 8 | 6 | 5 | 3 | 0 | 22 | |
| >50 – 60 | 11 | 2 | 6 | 8 | 1 | 28 | |
| >60 – 70 | 8 | 1 | 9 | 3 | 0 | 21 | |
| >70 | 9 | 2 | 2 | 2 | 1 | 16 | |
| Total | 38 | 11 | 22 | 16 | 3 | 90 | |
Table 7: Chi-Square Statistics of Impact of Farmers’ Background on their Perception
| Respondents’ Perception of.. | Background | χ 2 Value | Df. | Asymp. Sig | Decisions |
| Climate change | Age | 6.668 | 6 | 0.353 | NS |
| Climate change | Sex | 0.047 | 2 | 0.977 | NS |
| Marital status | 1.628 | 4 | 0.804 | NS | |
| Education | 6.826 | 10 | 0.742 | NS | |
| Farm size | 21.465 | 22 | 0.492 | NS | |
| Income/month | 9.519 | 12 | 0.658 | NS | |
| Level of knowledge of climate change | Sex | 7.459 | 3 | 0.059 | NS |
| Marital status | 12.419 | 6 | 0.053 | NS | |
| Education | 7.252 | 15 | 0.950 | NS | |
| Farm size | 30.864 | 33 | 0.574 | NS | |
| Income/month | 34.305 | 18 | 0.012 | S | |
| Effects of climate change | Sex | 9.090 | 4 | 0.059 | NS |
| Marital status | 8.005 | 8 | 0.757 | NS | |
| Education | 21.306 | 20 | 0.379 | NS | |
| Farm size | 88.091 | 44 | 0.000 | HS | |
| Income/month | 19.402 | 24 | 0.730 | NS | |
| Impact of climate change on Crop Production | Sex | 2.464 | 1 | 0.116 | NS |
| Marital status | 1.030 | 2 | 0.597 | NS | |
| Education | 2.875 | 5 | 0.719 | NS | |
| Farm size | 8.947 | 11 | 0.627 | NS | |
| Income/month | 11.961 | 6 | 0.063 | NS |
NB* NS = Not Significant; S = Significant; HS = Highly Significant (All tests are at α0.05)
Table 8: Cross-Tab Analyses onthe Dependence of Farmers’ Income/Month on their Perception of their Knowledge of Climate Change
| Income/Month (N) | Perception of Knowledge of climate change | Total | |||
| Very good | Good | Don’t know | Poor | ||
| <5000 | 1 | 2 | 1 | 0 | 4 |
| >5000 - 10,000 | 0 | 26 | 2 | 0 | 28 |
| >10,000 - 20,000 | 0 | 15 | 1 | 3 | 19 |
| >20,000 - 30,000 | 0 | 14 | 0 | 1 | 15 |
| >30,000 - 40,000 | 0 | 11 | 1 | 1 | 13 |
| >40,000 - 50,000 | 0 | 3 | 1 | 0 | 4 |
| >50,000 | 2 | 4 | 0 | 1 | 7 |
| Total | 3 | 75 | 6 | 6 | 90 |
Table 9: Cross-Tab Analyses of the Dependence of Farmers’ Farm Size on their Perceived Effects of Climate Change
| Farm Size (Ha) | Perceived Effects of climate change | Total | ||||
| Reduced rainfall | Flooding | Rising temperature | Shift in growing season | Drought | ||
| 1 | 3 | 1 | 9 | 2 | 0 | 15 |
| 2 | 8 | 3 | 2 | 5 | 0 | 18 |
| 3 | 7 | 2 | 1 | 1 | 0 | 11 |
| 4 | 5 | 0 | 5 | 1 | 0 | 11 |
| 5 | 4 | 2 | 3 | 3 | 0 | 12 |
| 6 | 4 | 2 | 0 | 2 | 1 | 9 |
| 7 | 2 | 0 | 0 | 2 | 0 | 4 |
| 8 | 1 | 0 | 0 | 0 | 1 | 2 |
| 10 | 4 | 0 | 1 | 0 | 0 | 5 |
| 15 | 0 | 1 | 0 | 0 | 0 | 1 |
| 20 | 0 | 0 | 0 | 0 | 1 | 1 |
| 30 | 0 | 0 | 1 | 0 | 0 | 1 |
| Total | 38 | 11 | 22 | 16 | 3 | 90 |
Table 10: Annual Rainfall, Temperature and Relative Humidity Distribution in the Study Area
| Year | Rainfall (mm) | Temperature ( o C) | Relative Humidity (%) |
| 2003 | 1236.60 | 27.40 | 82.00 |
| 2004 | 1869.40 | 26.20 | 81.00 |
| 2005 | 1436.10 | 26.50 | 83.00 |
| 2006 | 1770.10 | 26.28 | 80.80 |
| 2007 | 1855.30 | 26.40 | 77.50 |
| 2008 | 1303.50 | 26.55 | 76.20 |
| 2009 | 958.70 | 26.73 | 82.00 |
| 2010 | 1504.30 | 27.06 | 80.00 |
| 2011 | 1222.60 | 26.59 | 80.00 |
| 2012 | 1176.40 | 26.39 | 79.00 |
| 2013 | 874.90 | 26.81 | 82.00 |
Table 11: Annual Crop Production (‘000 MT) – 2003 to 2013 in Akinyele Local Government Area, Oyo State, Nigeria
Source: Oyo State Agricultural Development Programme
(OYSADEP), 2015
Analyses of annual crop production in the study area (Table 11) also reveal variations in the yield of all identified crops. The variation was much demonstrated in Figure 3, which shows the trend in the yield of major crops in the study area. The production of Cassava, the leading crop in tonnage/year, was observed to increase up till 2005 before sharply dropping in 2006 and picking up again gradually up till 2010 before dropping sharply again in 2011. This inconsistency, which was more pictorially obvious in Figure 3, was observed in the annual production of all the crops produced in the study area just and it commensurate the inconsistencies observed in the climate variables in Table 10.
Discussion
Respondents’ Background
Findings on respondents’ age in this study is similar to that of Sangotegbeet al; (2012) who reported majority of farmers’ in their study to be within the age range of 41 and 60years of age. Ratsimbazafyet al. (2012) also reported 67.0% of the respondents from the Makira Forest Project in Madagascar were between 30 and 55 years of age, which they expressed as the most productive age group.Tesfaye (2017) also reposed that people within this age bracket germane to decision making
