6.1. Activities that contribute to climate variability

The various activities of the small scale maize farmers that contribute to climate variability are shown in Table 1.

Result in Table 1, reveals that the majority of the small scale maize farmers (88.28%) indicated that bush burning contribute to climate variability while (82.03%), (60.16%), (56.25%) and (50.78%) indicated that intensive agricultural land use, use of inorganic fertilizers, use of fossil fuels and deforestation as factors that contribute to climate variability. The implication of this finding is that many of the farming activities in the area contribute to climate change. This finding agrees with the study of Oladipo [41], who noted that most agricultural activities are the major factors of climate variability.

#### 6.2. Level of awareness of climate variability

The result of mean responses of the level of awareness of climate variability by small scale maize farmers is shown in Table 2.

The result here, reveals that the smallholder maize farmers were significantly aware of the following climate variability in the study area: decreased rainfall days ( = 2.05; SD = 0.914), early onset of rainfall and early cessation ( = 2.08; SD = 0.929), late onset of rainfall and early cessation ( = 2.02; SD = 0.816), shorter than normal rainfall ( = 2.14; SD = 1.132), low


Table 1. Percentage response of farmers according to the activities that contribute to climate variability.


intensity rainfall ( = 2.02; SD = 0.872), flash flooding ( = 2.02; SD = 1.166), unusual patterns of precipitation ( = 2.02; SD = 0.904) and high sunshine intensity ( = 2.0; SD = 0.886). The farmers indicated that they were aware of the following climate variability: erratic/unusual rainfall with ( = 1.55; SD = 0.914), longer period of dry spell ( = 1.82; SD = 1.132), unusual flooding ( = 1.53; SD = 0.904), longer hour of sunshine ( = 1.95; SD = 1.173), decrease in crop yield ( = 1.59; SD = 0.896), loss in soil fertility ( = 1.55; SD = 0.954), increased erosion ( = 1.50; SD = 0.854) and rainstorms ( = 1.62; SD = 0.896). They also indicated awareness of erosion/flooding ( = 1.61; SD = 0.796), presence of unfamiliar diseases ( = 1.95; SD = 1.149), presence of unfamiliar pest ( = 1.57; SD = 0.986), high incidence of pests ( = 1.56; SD = 0.970).

Table 2. Mean responses of the level of awareness of climate variability by small scale maize farmers.

= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

Source: Field Survey, 2017.

S/N Climate variability SD Decision 37. Increased in frequency of flooding 1.55 1.160 NS 38. Low sunshine intensity 1.23 0.846 NS 39. Early onset and early cessation of Hamattan 1.09 1.193 NS 40. Late onset and late cessation of Hamattan 1.38 0.887 NS 41. Early onset and late cessation of Hamattan 1.20 0.861 NS 42. Late onset and early cessation of Hamattan 1.91 1.184 NS 43. Typhoon wind 1.11 1.205 NS 44. Erratic wind 1.69 1.092 NS 45. High wind speed 1.88 1.136 NS 46. Low wind speed 1.48 0.913 NS 47. Frequency of cloudiness 1.05 1.179 NS 48. Frequency of clement weather 1.03 1.048 NS 49. Constant fog 1.08 1.188 NS 50. Constant drought 1.01 1.187 NS 51. Rising temperature 1.52 0.905 NS 52. Presence of frost 1.14 1.202 NS 53. Presence of hailstones 1.11 1.199 NS 54. Constant waves 1.08 1.164 NS 55. High humidity 1.39 0.889 NS 56. Low humidity 1.73 1.008 NS 57. Presence of unfamiliar diseases 1.95 1.149 NS 58. Presence of unfamiliar pests 1.57 0.986 NS 59. High incidence of pests 1.56 0.970 NS 60. High incidence of diseases 1.41 0.910 NS

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= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

S/N Climate variability SD Decision 1. Decreased rainfall days 2.05 0.914 S 2. Early onset of rainfall and early cessation 2.08 0.929 S 3. Late onset of rainfall and early cessation 2.02 0.816 S 4. Shorter than normal rainfall 2.14 1.132 S 5. Low intensity rainfall 2.02 0.872 S 6. Flash flooding 2.02 1.166 S 7. Unusual patterns of precipitation 2.02 0.904 S 8. High sunshine intensity 2.01 0.886 S 9. Increase in earth surface temperature 1.50 0.627 NS 10. Longer hours of sunshine 1.95 1.173 NS 11. Short-lived Hamattan 1.48 0.869 NS 12. Increase in crop yield 1.04 1.193 NS 13. Decrease in crop yield 1.39 0.896 NS 14. Loss in soil fertility 1.55 0.954 NS 15. Increased erosion 1.50 0.854 NS 16. Erratic/unusual rain 1.55 1.175 NS 17. Early onset of rain and late cessation 1.03 1.131 NS 18. Late onset of rain and late cessation 1.46 0.904 NS 19. Delay in the onset of rainfall 1.56 1.194 NS 20. Above normal rainfall 1.53 0.893 NS 21. Below normal rainfall 1.40 0.964 NS 22. Longer than normal rainfall 1.41 0.918 NS 23. Longer period of dry spell 1.82 1.141 NS 24. High intensity rainfall 1.52 0.947 NS 25. Increase in rainfall 1.59 1.157 NS 26. Erratic/torrential rainfall 1.48 0.930 NS 27. Increase rainfall days 1.26 1.170 NS 28. Rainstorms 1.62 0.896 NS 29. Coastal flooding 1.48 0.957 NS 30. Gustiness 1.09 1.191 NS 31. Erosion/flooding 1.61 0.796 NS 32. Rivers and stream overflowing their banks 1.41 0.910 NS 33. Constant waves 1.98 1.153 NS 34. Unusual flooding 1.53 1.170 NS 35. Wet spells 1.24 0.867 NS 36. Land slides 1.08 1.201 NS

124 Corn - Production and Human Health in Changing Climate

Table 2. Mean responses of the level of awareness of climate variability by small scale maize farmers.

intensity rainfall ( = 2.02; SD = 0.872), flash flooding ( = 2.02; SD = 1.166), unusual patterns of precipitation ( = 2.02; SD = 0.904) and high sunshine intensity ( = 2.0; SD = 0.886). The farmers indicated that they were aware of the following climate variability: erratic/unusual rainfall with ( = 1.55; SD = 0.914), longer period of dry spell ( = 1.82; SD = 1.132), unusual flooding ( = 1.53; SD = 0.904), longer hour of sunshine ( = 1.95; SD = 1.173), decrease in crop yield ( = 1.59; SD = 0.896), loss in soil fertility ( = 1.55; SD = 0.954), increased erosion ( = 1.50; SD = 0.854) and rainstorms ( = 1.62; SD = 0.896). They also indicated awareness of erosion/flooding ( = 1.61; SD = 0.796), presence of unfamiliar diseases ( = 1.95; SD = 1.149), presence of unfamiliar pest ( = 1.57; SD = 0.986), high incidence of pests ( = 1.56; SD = 0.970). However, they were not aware of the following climate variability: short-lived Hamattan ( = 1.48; SD = 0.869), presence of frost ( = 1.14; SD = 1.202), low wind speed ( = 1.48; SD = 0.913). The standard deviations show the means variability. By implication, the lower the standard deviation the more the respondents are aware of the climate variability; the higher the standard deviation the lesser the respondents are aware of climate variability. These findings were in line with the result from trend analysis on such climate change variables conducted by the studies of Nwaiwu [55], which show that climate change effect is disastrous to agricultural production and requires mitigation. Also, it supports the findings of FAO [17] that there has been spatial increase in climatic variables from 1905 to 2010, and this is expected to continue over time.

Climate variability Coefficient Standard

Rivers/streams Overflow their

banks

Erratic/unusual rain 1.017 0.411 0.002 1 0.002 Delay rainfall onset 0.476 0.492 0.938 1 0.333 Longer dry season period 0.041 0.45 0.008 1 0.928 Increased rainfall days 0.184 0.424 0.188 1 0.002 Decreased rainfall days 0.038 0.422 0.008 1 0.004 Unusual flooding 0.338 0.445 0.575 1 0.448 Increased flooding freq 0.829 0.441 3.542 1 0.060 Increased earth surface temp 1.429 0.703 4.130 1 0.042 Longer sunshine hours 0.463 0.486 0.906 1 0.341 Short-lived Harmattan 0.403 0.585 0.474 1 0.491 Increased crop yield 0.397 0.609 0.425 1 0.514 Decreased crop yield 1.105 0.388 8.105 1 0.004 Loss of soil fertility 1.166 0.482 0.118 1 0.001 Increased erosion 0.263 0.443 0.352 1 0.553 Early rainfall and early cessation 1.108 0.424 0.065 1 0.004 Early rainfall and late cessation 0.105 0.409 0.066 1 0.798 Late rainfall and late cessation 0.493 0.537 0.846 1 0.358 Late rainfall and early cessation 1.225 0.453 0.248 1 0.000 Above normal rainfall 0.157 0.476 0.109 1 0.741 Below normal rainfall 0.332 0.509 0.425 1 0.514 Longer than normal rainfall 0.149 0.428 0.121 1 0.728 Shorter than norm rain 0.186 0.581 0.102 1 0.749 High rainfall intensity 0.5 0.427 1.368 1 0.242 How rainfall intensity 0.007 0.360 0.000 1 0.985 Erratic/torrential rain 0.533 0.490 1.181 1 0.277 Flash flooding 0.636 0.501 1.612 1 0.204 Rainstorms 0.323 0.569 0.322 1 0.57 Coastal flooding 0.534 0.476 1.257 1 0.262 Gustiness 0.250 0.434 0.333 1 0.564 Erosion/flooding 2.230 4.017 0.308 1 0.002

error

Increased rainfall 0.044 0.369 0.014 1 0.003 0.572 78.688\*

0.381 0.600 0.402 1 0.526

Constant waves 0.240 0.390 0.378 1 0.538 Unusual precipitate pattern 0.322 0.453 0.504 1 0.478 Wet spells 0.146 0.440 0.11 1 0.741

Wald df Sig. Cox & Snell (R<sup>2</sup> )

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## 6.3. Effects of climate variability on maize production

The ordinal regression on the effects of climate variability on maize production in Anambra State is shown in Table 3.

The R-square value of 0.572 explains about 57.2% of the level of climate variability affecting maize production in the study area. The chi-square value of 78.688 with the p-value less than 0.05 shows that the model prediction is good. Maize production is affected by increased rainfall (0.003), decreased rainfall days (0.004), increased rainfall days (0.002), erratic/ unusual rainfall (0.002), increased earth surface temperature (0.042), decreased crop yield (0.004), loss in soil fertility (0.001), early rainfall and cessation (0.004), late rainfall and early cassation (0.000), erosion/flooding (0.002) and presence of unfamiliar diseases because they have significant coefficients (p < 0.05). This means maize production is affected by climate variability in Anambra State. This research finding justifies why, between 2015 and 2017, there was some worrying fluctuations regarding corn production as against its supply and demand trend in Nigeria (Table 4). Consequently, it is hereby expected that the Anambra state maize production index could further be constrained mainly by lack of climate smart improve measures that can contribute to reversing the current national export capacities at an average of minus-forty-percent (40%) for Nigeria (Table 4) as against import of the maize commodity. Worse-still, the lack of government financial support to smallholder maize farmers and insecurity resulting from incessant herdsmen killings of farmers are expected to reduce maize production in the study area.

A high percentage of smallholder maize farmers in Anambra State do recycle their own maize seed from crops from their harvest and only a fraction of farmers purchase these seeds from other sources.

Detail results of the mean responses of the level of use of indigenous and improved adaptation strategies by small scale maize farmers in Anambra State are shown in Tables 5 and 6.

Table 5 shows that, planting of cover crops ( = 2.96; SD = 1.30) is largely adopted by the farmers to mitigate climate change impacts. Also, mixed farming ( = 2.59; SD = 1.25), change in tillage methods ( = 2.62; SD = 1.25), diversification from non-farming to farming activities ( = 2.70; SD = 1.31), use of organic/farmyard/mulch material ( = 2.80; SD = 1.19) were used by maize farmers as indigenous adaptation strategies. On the other hand, mixed


However, they were not aware of the following climate variability: short-lived Hamattan ( = 1.48; SD = 0.869), presence of frost ( = 1.14; SD = 1.202), low wind speed ( = 1.48; SD = 0.913). The standard deviations show the means variability. By implication, the lower the standard deviation the more the respondents are aware of the climate variability; the higher the standard deviation the lesser the respondents are aware of climate variability. These findings were in line with the result from trend analysis on such climate change variables conducted by the studies of Nwaiwu [55], which show that climate change effect is disastrous to agricultural production and requires mitigation. Also, it supports the findings of FAO [17] that there has been spatial increase in climatic variables from 1905 to 2010, and this is expected to continue

The ordinal regression on the effects of climate variability on maize production in Anambra

The R-square value of 0.572 explains about 57.2% of the level of climate variability affecting maize production in the study area. The chi-square value of 78.688 with the p-value less than 0.05 shows that the model prediction is good. Maize production is affected by increased rainfall (0.003), decreased rainfall days (0.004), increased rainfall days (0.002), erratic/ unusual rainfall (0.002), increased earth surface temperature (0.042), decreased crop yield (0.004), loss in soil fertility (0.001), early rainfall and cessation (0.004), late rainfall and early cassation (0.000), erosion/flooding (0.002) and presence of unfamiliar diseases because they have significant coefficients (p < 0.05). This means maize production is affected by climate variability in Anambra State. This research finding justifies why, between 2015 and 2017, there was some worrying fluctuations regarding corn production as against its supply and demand trend in Nigeria (Table 4). Consequently, it is hereby expected that the Anambra state maize production index could further be constrained mainly by lack of climate smart improve measures that can contribute to reversing the current national export capacities at an average of minus-forty-percent (40%) for Nigeria (Table 4) as against import of the maize commodity. Worse-still, the lack of government financial support to smallholder maize farmers and insecurity resulting from incessant herdsmen killings of farmers are

A high percentage of smallholder maize farmers in Anambra State do recycle their own maize seed from crops from their harvest and only a fraction of farmers purchase these seeds from

Detail results of the mean responses of the level of use of indigenous and improved adaptation strategies by small scale maize farmers in Anambra State are shown in Tables 5 and 6.

Table 5 shows that, planting of cover crops ( = 2.96; SD = 1.30) is largely adopted by the farmers to mitigate climate change impacts. Also, mixed farming ( = 2.59; SD = 1.25), change in tillage methods ( = 2.62; SD = 1.25), diversification from non-farming to farming activities ( = 2.70; SD = 1.31), use of organic/farmyard/mulch material ( = 2.80; SD = 1.19) were used by maize farmers as indigenous adaptation strategies. On the other hand, mixed

over time.

other sources.

State is shown in Table 3.

6.3. Effects of climate variability on maize production

126 Corn - Production and Human Health in Changing Climate

expected to reduce maize production in the study area.


Corn market begin year in

1000 (Ha), 1000 (MT)

Source: Adapted from [53].

Source: Field survey, 2017.

USDA Other source

2015/2016 2016/2017 2017/2018 Percentage (%)

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USDA Other source

Oct 2015 Oct 2016 Oct 2017 2016–2017

USDA Other source

Harvested area 3800 3800 4000 4000 0 3800 5.13 Beginning stocks 361 361 161 161 0 161 0.00 Production 7000 7000 7200 7200 0 6900 4.26 MY imports 300 300 300 300 0 200 40.00 TY imports 300 300 300 300 0 200 40.00 TY imports (USA) 98 0 0 0 0 0 0.00 Total supply 7661 7661 7661 7661 0 7261 5.37 MY Exports 200 200 200 200 0 300 40.00 TY Exports 200 200 200 200 0 300 40.00 Feed and Residual 1800 1800 1800 1800 0 1800 0 FSI consumption 5500 5500 5500 5500 0 5000 9.52 Total demand 7300 7300 7300 7300 0 6800 7.09 Ending stocks 161 161 161 161 0 161 0

Table 4. Observable trend on corn production, supply and demand in Nigeria, 2015–2017.

= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

S/N Items SD Decision

1. Mixed cropping 2.05 1.297 S 2. Mixed farming 2.59 1.245 S 3. Changing planting dates 2.06 1.155 S 4. Changing tillage methods 2.62 1.255 S 5. Diversification from farming to non-farming activities 2.70 1.312 S 6. Planting of cover crops 2.96 1.376 S 7. Use fertilizers (organic/farmyard/mulch materials) 2.79 1.186 S 8. Change in fallow period 1.60 1.231 NS

Table 5. Mean responses of level of indigenous adaptation strategies used by small scale maize farmers.

difference

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Other source, only

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Nigeria

Table 3. Ordinal regression of the climate variability affecting maize production.

cropping ( = 2.05; SD = 1.30) and changing planting dates ( = 2.06; SD = 1.15) were moderately used by maize farmers as indigenous adaptation strategies while change in fallow period ( = 1.60; SD = 1.23) was used to a low extent by small scale maize farmers in Anambra State. This finding is in agreement with Okali [56], who found that the use of mulching materials (Figure 4) could prevent excessive soil moisture loss, and improve soil GIS-Based Assessment of Smallholder Farmers' Perception of Climate Change Impacts and Their… http://dx.doi.org/10.5772/intechopen.79009 129


Table 4. Observable trend on corn production, supply and demand in Nigeria, 2015–2017.


Source: Field survey, 2017.

cropping ( = 2.05; SD = 1.30) and changing planting dates ( = 2.06; SD = 1.15) were moderately used by maize farmers as indigenous adaptation strategies while change in fallow period ( = 1.60; SD = 1.23) was used to a low extent by small scale maize farmers in Anambra State. This finding is in agreement with Okali [56], who found that the use of mulching materials (Figure 4) could prevent excessive soil moisture loss, and improve soil

Climate variability Coefficient Standard

128 Corn - Production and Human Health in Changing Climate

Early onset of Harmattan and early

Late onset of Harmattan late and

Early onset of Harmattan and late

Late onset of Harmattan and early

Source: Field survey, 2017.

cessation

cessation

cessation

cessation

Landslides 0.283 0.358 0.624 1 0.43 High sun intensity 0.205 0.443 0.214 1 0.644 Low sun intensity 0.352 0.386 0.832 1 0.362

Typhoon wind 0.275 0.472 0.339 1 0.561 Erratic wind 0.371 0.345 1.156 1 0.282 High wind speed 0.208 0.374 0.310 1 0.578 Low wind speed 0.391 0.509 0.590 1 0.442 Freq cloudiness 0.451 0.399 1.278 1 0.258 Freq clement weather 0.379 0.503 0.566 1 0.452 Constant fog 0.445 0.601 0.549 1 0.459 Constant drought 0.012 0.372 0.001 1 0.975 Rising temp 0.345 0.454 0.577 1 0.447 Presence of frost 0.495 0.557 0.790 1 0.374 Presence of hailstones 0.022 0.398 0.003 1 0.956 Constant waves 0.010 0.392 0.001 1 0.979 High humidity 0.121 0.552 0.048 1 0.827 Low humidity 0.316 0.561 0.317 1 0.573 Presence of unfamiliar diseases 1.145 0.525 0.076 1 0.021 Presence of unfamiliar pests 0.294 0.368 0.639 1 0.424 High incidence of pests 0.197 0.46 0.184 1 0.668 High incidence of diseases 0.013 0.433 0.001 1 0.976

Table 3. Ordinal regression of the climate variability affecting maize production.

error

0.393 0.481 0.667 1 0.414

0.253 0.460 0.303 1 0.582

0.095 0.375 0.065 1 0.799

0.114 0.395 0.084 1 0.772

Wald df Sig. Cox & Snell (R<sup>2</sup> )

Chi-square (goodness-of-fit)

= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

Table 5. Mean responses of level of indigenous adaptation strategies used by small scale maize farmers.


Source: Field Survey, 2017.

= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

Table 6. Mean responses of the level of improved adaptation strategies used by small scale maize farmers.

Consequently, some smallholder maize farmers plant vetiver grass (Chrysopogon zizanioides) in

Figure 5. The type of vertiva grass (red circled) that is planted for controlling erosion on farm farms in Anambra State.

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Table 6 reveals that to a low extent precision agriculture ( = 1.50; SD = 1.11), climate predictions ( = 1.56; SD = 1.05), were used by maize farmers as improved adaptation strategies. Improved crop variety ( = 2.93; SD = 1.11), drought resistant varieties ( = 2.53; SD = 1.07) and drought tolerant varieties ( = 2.60; SD = 1.06), were used by maize farmers in high extent as improved adaptation strategies while resistant to temperature stresses varieties ( = 2.16; SD = 1.11), high yield water sensitive varieties ( = 2.06; SD = 1.10), mixed-crop-livestock farming system ( =2.14; SD =1.07), crop diversification ( = 2.14; SD =1.06), changing in harvesting date (=2.03; SD =1.06) and rain making ( = 2.06; SD =1.12) were moderately used by maize farmers as improved adaptation strategies to climate variability. This finding concurs with the work of [57], who concluded that farmers can adapt to climate changes through

(Figure 5) to control erosion menace on their maize farms.

improved adaptation strategies relevant to them.

Figure 4. Cross section of mulched maize farms available in the study area (photo credit: Mr. Samuel Anarah).

aeration and moisture holding capacity of the soil. Types of grasses usually used for mulching purposes in the study area include: spear grass (Heteropogon contortus), and guinea grass (Panicum maximum). [57] observed that growing of varieties of crops on the same plot of land is an appropriate adaptation strategy for farmers because it helps to avoid complete crop failure as different crops may be affected differently by climate variability and may also require different soil nutrients.

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Consequently, some smallholder maize farmers plant vetiver grass (Chrysopogon zizanioides) in (Figure 5) to control erosion menace on their maize farms.

Table 6 reveals that to a low extent precision agriculture ( = 1.50; SD = 1.11), climate predictions ( = 1.56; SD = 1.05), were used by maize farmers as improved adaptation strategies. Improved crop variety ( = 2.93; SD = 1.11), drought resistant varieties ( = 2.53; SD = 1.07) and drought tolerant varieties ( = 2.60; SD = 1.06), were used by maize farmers in high extent as improved adaptation strategies while resistant to temperature stresses varieties ( = 2.16; SD = 1.11), high yield water sensitive varieties ( = 2.06; SD = 1.10), mixed-crop-livestock farming system ( =2.14; SD =1.07), crop diversification ( = 2.14; SD =1.06), changing in harvesting date (=2.03; SD =1.06) and rain making ( = 2.06; SD =1.12) were moderately used by maize farmers as improved adaptation strategies to climate variability. This finding concurs with the work of [57], who concluded that farmers can adapt to climate changes through improved adaptation strategies relevant to them.

aeration and moisture holding capacity of the soil. Types of grasses usually used for mulching purposes in the study area include: spear grass (Heteropogon contortus), and guinea grass (Panicum maximum). [57] observed that growing of varieties of crops on the same plot of land is an appropriate adaptation strategy for farmers because it helps to avoid complete crop failure as different crops may be affected differently by climate variability and may also

Figure 4. Cross section of mulched maize farms available in the study area (photo credit: Mr. Samuel Anarah).

S/N Items SD Decision

1. Improved crop variety 2.93 1.112 S 2. Climate predictions 1.56 1.048 NS 3. Precision agriculture 1.50 1.089 NS 4. Drought resistant varieties 2.53 1.065 S 5. Drought tolerant varieties 2.60 1.056 S 6. Resistant to temperature stresses varieties 2.16 1.114 S 7. High yield water sensitive varieties 2.06 1.978 S 8. Mixed crop-livestock farming system 2.14 1.070 S 9. Crop diversification 2.14 1.055 S 10. Changing harvesting date 2.03 1.059 S 11. Rain making 2.06 1.121 S

= mean; SD = standard deviation; mean ≥ 2 = significant; mean ≤ 2 = not significant.

Table 6. Mean responses of the level of improved adaptation strategies used by small scale maize farmers.

require different soil nutrients.

Source: Field Survey, 2017.

130 Corn - Production and Human Health in Changing Climate


Table 8 shows multiple linear regressions of the socio-economic characteristics of small scale maize farmers and their production level. The R-square value of 0.176 indicates that the socio-economic variables explained 17.6% variability of maize production. Of all the socio-economic variables, age (0.028), household size (0.015), farming years (0.019), farm size (0.046) and labor source (0.037) have significant coefficients (p < 0.05). The coefficient value of 0.278 for age indicates that a unit increase in age increases level of maize production by 0.278 kg. The coefficient value of 0.370 for household size indicates that increase in household size increases level of maize production by 0.370 kg; that of farming years which is 0.428 indicates that increase in farming experience increases level of maize production by 0.428 kg; that of farm size which is 0.624 indicates that increase in farm size increases level of maize production by 0.624 kg while that of labor source which is 0.021 indicates that increase in labor source increases the level of maize production by 0.021 kg. The p-value at 0.048, indicate that there is a significant relationship between socio-economic characteristics and production level by the small scale maize farmers in the study area. This further means that as the age, household size, farming years, farm size and labor source of small scale maize farmers in Anambra State increase, their propensity to produce maize also increases. This finding is in agreement with the study of [41] who noted that household size and farm size increases farmers' food

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Better understanding and perception of climate variability and adaptions to climate change impacts in Anambra State, Nigeria, is crucial for increasing farmers adoption of improved maize seed varieties and practicing of climate-smart maize production. The ultimate objective of this study was to assess the smallholder maize farmers' perception on climate variability

The results of this study show that, approximately 57.2% of climate variability negatively impacts on maize production in the study area. Basically flooding ( = 2.02 1.166), erratic rainfall ( = 2.02 0.816), and decrease in crop yield by strange pests and diseases ( = 1.59 0.896) were identified as climate change effects on maize production. The smallholder maize farmers are significantly aware of the consequences of climate variability on their maize farms, reason for some of them, practicing climate change adaptations. 88.28% of the smallholder maize farmers perceived bush burning as a major contributor to climate variability in the study area. Whereas, other identified climate change drivers include: intensive agricultural land use (82.03%), use of inorganic fertilizers (60.16%), use of fossil fuels (56.25%) and deforestation (50.78%). Finally, from the statistical analysis in this study, we conclude that, the lower the standard deviation values, the more knowledgeable the farmers are about climate variability and on practice of climate change

and their use of climate change adaptation approaches in Anambra state.

production.

7. Conclusion

adaptations; and, vice-versa.

Source: Field Survey, 2017. NiMET = Nigerian Metrological Agency weather forecast.

Table 7. Percentage response of sources of information on climate variability by maize farmers in Anambra State.


Table 8. Multiple linear regressions of the socio-economic characteristics and production level of small scale maize farmers.

#### 6.4. Sources of information on climate variability

The percentage response of sources of information among small scale farmers on climate variability in Anambra State is shown in Table 6.

Result from Table 7 reveals that majority (77.34%) of the maize farmers source their information from fellow farmers, (61.72%) from extension agents, few (52.34%) from radio set, very few (48.44%) source from television set while (20.31%) source their information from the internet/ social media. The implication is that farmers that belong to agricultural groups are more likely to have access to farm information on climate variability adaptation strategies than those who do not belong to any. This finding is similar to that of [36, 57] whose studies showed that adequate information flow channel and extension contact with registered farmers have a positive relationship with the adoption of agricultural strategies since extension agents transfer modern agricultural technologies to farmers to help counteract the negative impact of climate change.

Table 8 shows multiple linear regressions of the socio-economic characteristics of small scale maize farmers and their production level. The R-square value of 0.176 indicates that the socio-economic variables explained 17.6% variability of maize production. Of all the socio-economic variables, age (0.028), household size (0.015), farming years (0.019), farm size (0.046) and labor source (0.037) have significant coefficients (p < 0.05). The coefficient value of 0.278 for age indicates that a unit increase in age increases level of maize production by 0.278 kg. The coefficient value of 0.370 for household size indicates that increase in household size increases level of maize production by 0.370 kg; that of farming years which is 0.428 indicates that increase in farming experience increases level of maize production by 0.428 kg; that of farm size which is 0.624 indicates that increase in farm size increases level of maize production by 0.624 kg while that of labor source which is 0.021 indicates that increase in labor source increases the level of maize production by 0.021 kg. The p-value at 0.048, indicate that there is a significant relationship between socio-economic characteristics and production level by the small scale maize farmers in the study area. This further means that as the age, household size, farming years, farm size and labor source of small scale maize farmers in Anambra State increase, their propensity to produce maize also increases. This finding is in agreement with the study of [41] who noted that household size and farm size increases farmers' food production.
