Institutional Factors of the Rice Farmers
The arrays of these variables include; cooperative membership, years of cooperative membership, farm labour, source of labour, access to credit facilities, source of credit facilities, volume of credit obtain, access to extension service and number of extension contact. These variables are having great power to influence, to a large extent, the mitigation and adaptation strategies to climate change effects.
Cooperative membership: Results in Table 2 revealed that majorities (90.7%), (71.1%) and (92.5%) of the rice farmers in Bauchi, Borno and Taraba states, belong to one form of cooperative or another. The overall result indicates that majority (86.0%) of the rice farmers were cooperative members. This implies that majority of the rice farmers were cooperative members and this could influence the use of mitigation strategies. This finding agrees with position held by Yakubu and Oladele (2021) who affirmed that cooperative membership could make rice farmers adapt to changes in climate.
Years of cooperative membership: Distribution of rice farmers based on years of cooperative membership as presented in Table 2 shows that most (62.5%) and more than half (56.8%) of the rice farmers in Bauchi and Taraba states were members of cooperative for 6-10 years, while most (67.8%) of the rice farmers in Borno state were members of cooperative for 1-5 years. The overall result show that about (49.2%) of the rice farmers were members of cooperative for 6-7 years with a mean of 6.7 years. This indicates that farmers had a fair duration in a given cooperative in the study areas. This could likely promote the use of mitigation strategies. This finding is in tandem with opinion held by Onoh (2017) that reasonable years of cooperative membership influence farmers’ decision to practice responses to climate change.
Farm labour: Distribution of the rice farmers based on type of labour engaged as presented in Table 2 shows that all (100%) of the rice farmers in Bauchi state engaged labour on farm activities. Similarly, all (100%) of the rice farmers in Borno state engaged labour on farm activities. Also, in Taraba state all the rice farmers also engaged labour on farm activities. The implication of this result is that all the rice farmers engaged labour on farm activities in the study area. This finding corroborate the result reported of Mele (2021) who reported that all rural farmers engaged in labour in their farming activities.
Source of labour: Table 2 further shows that majorities (87.6%), (94.0%) and (89.2%) of the rice farmers in Bauchi, Borno and Taraba states respectively engaged family labour on farm activities. About 5.2%, 3.6% and 7.5% of the rice farmers in Bauchi, Borno and Taraba states engaged hired labour on farm activities, while 7.2%, 2.4% and 3.3% engaged both family and hired labour on farm activities. This finding implies that majority of the rice farmers engaged family labour in their farming activities. The possible explanation could be lack of financial capacity to hire laborers on faming activities and this finding is in line with Omotesho et al. (2016) who reported that farmers with high income engaged labour in their farming activities.
Access to credit facility: Table 2 disclosed that majority (92.8%) of the rice farmers in Bauchi had no access to credit, more than half (50.6%) of the rice farmers in Borno state had no access to credit facilities. Similarly, in Taraba state majority (95.8%) of the rice farmers had no access to credit facility. About 7.2%, 49.4% and 4.2% in Bauchi, Borno and Taraba states respectively, had access to credit. The pooled result show that majority (82.3%) of the rice farmers in the study areas had no access to credit facilities. This implies that majority of rice farmers in the study areas had no access to credit facilities and this could limits farmers’ investment on climate change mitigation strategies. This finding corroborates with Alawode (2021) who reported that access to credit increase farmers investment in agricultural activities.
Volume of credit received: Table 2 shows that all the farmers in Bauchi and Taraba states obtained less than ₦150,000 from their respective sources, majority( 80.5%) of the farmers in Borno states obtained credit facilities between ₦150,000 to ₦200,000, while few (19.5%) of the rice farmers received ₦300,000 and above. The overall result indicates that 62.3% of the rice farmers obtained between ₦150,000 to ₦200,000 of credit facilities. The implication of this result is that majority of the farmers obtained low volume of credit facilities for farming activities and this could render farmers’ financial stand insufficient to execute the use of climate change mitigation strategies. This result justify the findings of Oluwaseu et al. (2019) who reported that low credit facility could be responsible for poor responses to adverse effects of climate change.
Access to extension contact: Access to extension services by farmers’ accord them the opportunity to know more about climate related issues. This would provide an idea about effective responses to climate change, in order to reduce its adverse effects and hence could increase farmers output. Result in Table 2 shows that majorities (90.7%), (90.4%) and (91.7%) of the rice farmers in Bauchi, Borno and Taraba states had no contact with extension agents. This implies that virtually all the rice farmers in the study areas had no extension contact and the possible explanation for this could be that most of the farmers are not enlightened about the climate change challenges on their farming activities. This finding is similar to the pattern of extension contacts of Zongoma (2016) in a study on Impact of the assessment of improved cowpea varieties on Women farmers in southern part of Borno State, Nigeria that majority of the farmers had no access to extension contacts.
Number of contacts with extension workers: Distribution of respondents based on number of contacts with extension worker as presented in Table 2 shows that majority (88.9%), (75.0%) and 90.0% in Bauchi, Borno and Taraba states respectively, had contact with extension worker once, while few (11.0%), (25.0%) and (10.0%) of the respondents in Bauchi, Borno and Taraba states had extension contacts twice. Overall result shows that majority (85.2%) of the rice farmers had no extension contact. This implies inadequate number of contacts with extension agents by majority of the farmers in the study areas. This could be due to insufficient number of extension agents and farmers may not access vital information on the use of climate change mitigation strategies. This agrees with Tajudeen (2022) who reported that access to adequate faming knowledge and skills rely on frequency of extension visits. Mele (2021) also reported that who stated that, frequency of extension contacts to farmers play an important role in the level accuracy with respect to appropriate climate change mitigation strategies.
Extension agent is responsible providing the knowledge and information that will enable a farmer to understand and make a decision about a particular technology. The provision of agricultural extension service is made to enhance farmers’ knowledge and skills toward improved yield as such inadequate extension services accounted for reluctance of farmers to adopt improved technologies.
Table 2: Distribution of the Respondents based on Institutional Factors (n=300)
Variables
|
BAUCHI STATE
(n=97)
|
BORNO STATE
(n=83)
|
TARABA STATE
(n=120)
|
POOLED SAMPLE
(n=300)
|
|
Freq.
|
%
|
Freq.
|
%
|
Freq.
|
%
|
Freq.
|
%
|
Co-op. Membership
|
|
|
|
|
|
|
|
|
Non-member
|
9
|
9.3
|
24
|
28.9
|
9
|
7.5
|
42
|
14.0
|
Member
|
88
|
90.7
|
59
|
71.1
|
111
|
92.5
|
258
|
86.0
|
Yrs of Co-op. mhsip
|
|
|
|
|
|
|
|
|
1-5
|
18
|
20.5
|
40
|
67.8
|
30
|
27.0
|
88
|
34.1
|
6-10
|
55
|
62.5
|
9
|
15.3
|
63
|
56.8
|
127
|
49.2
|
11-15
|
9
|
10.2
|
7
|
11.9
|
11
|
9.9
|
27
|
10.5
|
16-20
|
6
|
6.8
|
3
|
5.1
|
7
|
6.3
|
16
|
6.2
|
Mean
|
|
8.02
|
|
4.60
|
|
7.21
|
|
6.70
|
Farm labour
|
|
|
|
|
|
|
|
|
Engaged
|
97
|
100.0
|
83
|
100.0
|
120
|
100.0
|
300
|
100.0
|
Not- engaged
|
0
|
0.0
|
0
|
0.0
|
0
|
0.0
|
0
|
0.0
|
Access to credit facilities
|
|
|
|
|
|
|
|
|
Accessed
|
7
|
7.2
|
41
|
49.4
|
5
|
4.2
|
53
|
17.7
|
No accessed
|
90
|
92.8
|
42
|
50.6
|
115
|
95.8
|
247
|
82.3
|
Source of credit
|
|
|
|
|
|
|
|
|
Commercial bank
|
0
|
0.0
|
0
|
0.0
|
0
|
0.0
|
0
|
0.0
|
NGO
|
0
|
0.0
|
38
|
92.7
|
0
|
0.0
|
38
|
71.7
|
Informal Sources
|
6
|
85.7
|
0
|
0.0
|
5
|
100
|
11
|
20.8
|
Govt. loan scheme for farmers
|
1
|
14.3
|
3
|
7.3
|
0
|
0.0
|
4
|
7.5
|
Volume of credit received
|
|
|
|
|
|
|
|
|
<150,000
|
7
|
100
|
0
|
0.0
|
5
|
100
|
12
|
22.6
|
!50,000-200,000
|
0
|
0.0
|
33
|
80.5
|
0
|
0.0
|
33
|
62.3
|
>300,000
|
0
|
0.0
|
8
|
19.5
|
0
|
0.0
|
8
|
15.1
|
Accesses to Ext.
|
|
|
|
|
|
|
|
|
Not accessed
|
88
|
90.7
|
75
|
90.4
|
110
|
91.7
|
273
|
91.0
|
Accessed
|
9
|
9.3
|
8
|
9.6
|
10
|
8.3
|
27
|
9.0
|
No. of Ext. contact
|
|
|
|
|
|
|
|
|
Once
|
8
|
88.9
|
6
|
75.0
|
9
|
90.0
|
23
|
85.2
|
Twice
|
1
|
11.1
|
2
|
25.0
|
1
|
10.0
|
4
|
14.8
|
Source: Field survey data, 2022; Coptive=cooperative, mhsip=membership
Level of Climate Change Mitigation Strategies amongst Rice Farmers
The level of the use of mitigation strategies to climate change is essential phenomenon in the response to changes in climate. Because, it determines the extent to which the adverse effects of climate change would be reduce.
Level of Climate Change Mitigation Strategies amongst Rice Farmers in Bauchi State
Table 3 shows that in Bauchi State the mitigation strategies mean value for mid-season drainage was (x̄=1.0), direct rice seeding was (x=1.01), use of rice cultivars with short root system was (x̄=1.04), Integrated management rice system was (x̄=1.03), water saving technology was (x̄=1.05), mulching was (x̄=1.14), reduce use of nitrogen for precision farming was (x=1.07), use organic fertilizer mixed with gypsum/inorganic fertilizer with sulphate was (x̄=1.06), minimum tillage/zero tillage was (x̄=1.12) and short term vegetation was (x̄=1.04). This implies that all the respondents in Bauchi State used mitigation strategies at low level. This could be that the respondents were poorly enlightened about significance of the use of climate change mitigation strategies. This finding agrees with Oba and Ahmad (2020) who reported that the use of climate change mitigation actions were below average among rural farmers in Nigeria.
Table 3: Distribution of respondents based on level of climate change mitigation strategies in Bauchi State (n=97)
Mitigation strategies
|
Bauchi state (n=97)
1 2 3 x̄ level
|
Mid-season drainage
|
97
|
0
|
0
|
1.0
|
L
|
Direct rice seeding
|
96
|
1
|
0
|
1.01
|
L
|
Use of rice cultivar with short root system
|
95
|
0
|
2
|
1.04
|
L
|
Integrated management rice system
|
95
|
1
|
1
|
1.03
|
L
|
Water saving technology
|
93
|
3
|
1
|
1.05
|
L
|
Mulching
|
90
|
0
|
7
|
1.14
|
L
|
Reduce use of nitrogen for precision farming
|
90
|
7
|
0
|
1.07
|
L
|
Use of org fert, with sulphate and in-org. fert. with gypsum
|
92
|
3
|
2
|
1.06
|
L
|
Minimum/zero tillage
|
87
|
8
|
2
|
1.12
|
L
|
Short term vegetation
|
94
|
2
|
1
|
1.04
|
L
|
Source: Field Survey, 2022; Weighted scores=1, 2 and 3; Lv=Level, x̄=mean
Level of Climate Change Mitigation Strategies amongst Rice Farmers in Borno State
Table 4 shows that in Borno State mitigation strategies mean value of mid-season drainage was (x̄=1.0), direct rice seeding was (x̄=1.04), use of rice cultivars with short root system was (x̄=1.08), integrated management rice system was (x̄=1.06), water saving technology was (x̄=1.07), mulching was (x̄=1.10), reduce use of nitrogen for precision farming was (x̄=1.01), use of organic fertilizer with gypsum/inorganic fertilizer with sulphate was (x̄=1.11), minimum tillage/zero tillage was (x̄=1.19) and short term vegetation was (x̄=1.04). This shows that all the respondents in Borno state used mitigation strategies low level. This could be that the respondents were not much more informed of climate change mitigation strategies. This finding is in agreement with Oladipo and Onuche (2020) who reported that limited use of climate change mitigation strategies was evident amidst rural farmers in Nigeria.
Table 4: Distribution of Respondents based on Level of Climate Change Mitigation Strategies in Borno State (n=83)
Mitigation strategies
|
Borno State (n=83)
1 2 3 x̄ level
|
Mid-season drainage
|
83
|
0
|
0
|
1.0
|
L
|
Direct rice seeding
|
80
|
3
|
0
|
1.04
|
L
|
Use of rice cultivar with short root system
|
77
|
5
|
1
|
1.08
|
L
|
Integrated management rice system
|
79
|
3
|
1
|
1.06
|
L
|
Water saving technology
|
77
|
6
|
0
|
1.07
|
L
|
Mulching
|
75
|
8
|
0
|
1.10
|
L
|
Reduce use of nitrogen for precision farming
|
82
|
1
|
0
|
1.01
|
L
|
Use of org fert, with sulphate and in-org. fert. with gypsum
|
76
|
5
|
2
|
1.11
|
L
|
Minimum/zero tillage
|
70
|
10
|
3
|
1.19
|
L
|
Short term vegetation
|
81
|
1
|
1
|
1.04
|
L
|
Source: Field Survey, 2022; High=2.31-3.0; Moderate=1.71-2.3; and Low=1.0-1.70
Weighted scores=1, 2 and 3; Lv=Level, x̄ = mean
Level of Climate Change Mitigation Strategies amongst Rice Farmers in Taraba State
Table 5 shows that in Taraba State the mitigation strategies mean value, mid-season drainage was (x̄=1.0), direct rice seeding was (x̄=1.11), rice cultivars with short root system was (x̄=1.09), Integrated management rice system was (x̄=1.19), water saving technology was (x̄=1.23), mulching was (x̄=1.03), reduce use of nitrogen for precision farming was (x̄=1.05), organic fertilizer with gypsum/inorganic fertilizer with sulphate was (x̄=1.34), minimum tillage/zero tillage was (x̄=1.08) and short term vegetation was (x̄=1.18). This indicates that all the respondents in Taraba state used mitigation strategies at low level. This could be that the respondents were rarely properly aware of the importance of use climate change mitigation strategies. This finding corroborate with Obot (2022) who reported that minimum practice of mitigation strategies to climate change by small holder farmers.
Table 5: Distribution of Respondents based on Level of Climate Change Mitigation Strategies in Taraba State (n=120)
Mitigation strategies
|
Taraba State (n=120)
1 2 3 x̄ level
|
Mid-season drainage
|
120
|
0
|
0
|
1.0
|
L
|
Direct rice seeding
|
111
|
5
|
4
|
1.11
|
L
|
Use of rice cultivar with short root system
|
110
|
9
|
1
|
1.09
|
L
|
Integrated management rice system
|
99
|
19
|
2
|
1.19
|
L
|
Water saving technology
|
93
|
26
|
1
|
1.23
|
L
|
Mulching
|
118
|
1
|
1
|
1.03
|
L
|
Reduce use of nitrogen for precision farming
|
115
|
4
|
1
|
1.05
|
L
|
Use of org fert, with sulphate and in-org. fert. with gypsum
|
89
|
21
|
10
|
1.34
|
L
|
Minimum/zero tillage
|
112
|
7
|
1
|
1.08
|
L
|
Short term vegetation
|
100
|
18
|
2
|
1.18
|
L
|
Source: Field Survey, 2022; High=2.31-3.0; Moderate=1.71-2.3; and Low=1.0-1.70
Weighted scores=1, 2 and 3; Lv=Level, x̄ = mean
Level of Climate Change Mitigation Strategies amongst Rice Farmers Pooled Result
Table 6 shows the pooled result that the mitigation strategies mean value for mid-season drainage was (x̄=1.0), direct rice seeding was (x̄=1.06), rice cultivars with short root system was (x̄=1.07), Integrated management rice system was (x̄=1.10), water saving technology was (x̄=1.13), mulching was (x̄=1.08), reduce use of nitrogen for precision farming was (x̄=1.05), organic fertilizer with gypsum/inorganic fertilizer with sulphate was (x̄=1.19), minimum tillage/zero tillage was (x̄=1.12) and short term vegetation was (x̄=1.09) were all having low mean value. This implies that all the respondents in the study areas used mitigation strategies at low level and this could be that the respondents used of mitigation strategies poorly in the zone. This agrees with the findings of Nasiru (2018) who reported low level of climate change mitigation strategies utilization among rice farmers.
Table 6 Distribution of Respondents based on Mitigation Strategies Pooled Result (n=300)
Mitigation strategies
|
Pooled result (n=300)
1 2 3 Mean (x̄) Level
|
Mid-season drainage
|
300
|
0
|
0
|
1.00
|
Low
|
Direct rice seeding
|
287
|
9
|
4
|
1.06
|
Low
|
Use of rice cultivar with short root system
|
282
|
14
|
4
|
1.07
|
Low
|
Integrated management rice system
|
273
|
23
|
4
|
1.10
|
Low
|
Water saving technology
|
263
|
35
|
2
|
1.13
|
Low
|
Mulching
|
283
|
9
|
8
|
1.08
|
Low
|
Reduce use of nitrogen for precision farming
|
287
|
12
|
1
|
1.05
|
Low
|
Use of org fert, with sulphate and in-org. fert. with gypsum
|
257
|
29
|
14
|
1.19
|
Low
|
Minimum/zero tillage
|
267
|
25
|
6
|
1.12
|
Low
|
Short term vegetation
|
275
|
21
|
4
|
1.09
|
Low
|
Source: Field survey, 2022: Weighted scores=1, 2 and 3; High=2.31-3.0; Moderate=1.71-2.3; and Low=1.0-1.70
Relationship between Institutional Factors of the Respondents and their Climate Change Mitigation Strategies
Relationship between institutional factors of the respondents and their mitigation strategies to climate change shows the effects of independent variables (institutional factors) on the dependent variable (mitigation strategies) amongst rice farmers in the study areas.
Cooperative membership: Multiple linear regression analysis result in table 7 shows a significant (p<0.1) at 10% probability level between cooperative membership and mitigation strategies. This implies cooperative membership influenced use mitigation strategies to climate change and this could be due to technical guidance and more knowledge obtained on climate related issues from members of cooperative association. This finding is in line with Yakubu (2018) who reported that cooperative membership significantly influence responses to changes in climate. Also, agrees with Malabe et al. (2019) who reported that cooperative membership significantly influence the use of mitigation strategies to climate change.
Years of Cooperative membership: Multiple linear regression analysis result in table 7 indicates a significant relationship (p<0.05) at 5% probability level between cooperative membership and mitigation strategies. This implies more years of cooperative membership significantly influenced effective use mitigation strategies to climate change and this could be due to more information and guidance obtained on climate change from cooperative association.. This finding is similar with Josephine and Amaechi (2016) who reported that long duration of cooperative membership significant influence the practice of mitigation strategies to climate change.
Access to credit facilities: Multiple linear regression analysis result in table 7 shows a significant relationship (p<0.01) at 1% probability level between access to credit and mitigation strategies. The implication of the result is that access to credit facilities are more likely to influence and encourage farmers to practice mitigation strategies to climate change. This could be due to financial capability to cope with the adverse effects of climate change. This finding is in resonance with Daniel and Isaac (2018) who reported significant relationship between access to credit and response to climate change.
Access to extension Agents: Multiple linear regression analysis result in table 7 shows a significant (p<0.05) at 5% probability level between access to extension agents and mitigation strategies. This implies that access to extension influence the use of mitigation strategies to climate change and this could be due to knowledge and information obtained on climate change from extension agents. This finding agrees with Alawode (2021) who reported that access to extension contacts gets farmers well-informed about changes in climate.
Table 7: Relationship between institutional factors of the respondents and mitigation strategies
Variables
|
Coefficient
|
Standard Error
|
t-ratio
|
P-Value
|
Constant
|
1.231
|
0.077
|
15.945
|
0.000
|
Cooperative membership
|
0.007
|
0.005
|
1.436
|
0.06*
|
Years of cooperative membership
|
0.004
|
0.002
|
2.184
|
0.030**
|
Access to credit facilities
|
0.089
|
0.013
|
6.732
|
0.000***
|
Access to extension service
|
0.016
|
0.091
|
0.177
|
0.04**
|
R2
|
0.717
|
|
|
|
R2 Adjusted
|
0.713
|
|
|
|
F- Statistic
|
0.000***
|
|
|
|
Source: Field survey, 2022
*Significant at 10%, **Significant at 5% level, ***Significant at 1%level, NS =Not Significant.