| Livestock Research for Rural Development 32 (5) 2020 | LRRD Search | LRRD Misssion | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
This study aimed at assessing the impact of the African Chicken Genetic
Gains project on the livelihoods, and food security of smallholder poultry
farmers in Nigeria. A total of 2,100 households were selected from 60
villages located in five states representing different agro-ecological
zones: Kebbi (Sudan Savanna), Kwara (Southern Guinea Savanna), Nasarawa
(Derived Savanna), Imo (Humid Forest) and Rivers (Forest Lowlands and
Mangrove Swamp). Each household was randomly allocated an average of 30
birds from any one of the six improved chicken breeds (Fulani, FUNAAB Alpha,
Kuroiler, Noiler, Sasso and ShikaBrown) tested on-farm. Baseline survey was
conducted to provide a benchmark for both the on-farm test and post-on farm
survey. For each of the surveys, structured questionnaires were developed,
tested and administered using the Open Data Kit data collection tool
pre-installed on Lenovo tablets (Model: Lenovo TAB 2 A7-30H). Data were
subjected to inferential statistics (Chi-square test and Analysis of
Variance). During the on-farm study, supplementary feed and vaccination
services were provided for the birds, while the households received
trainings on poultry management practices. Average household size was 7.
Overall, the number of households consuming eggs increased by 50% (54% -
84%) while there was a 60% (47.7% - 76.5%) increase in the number of
households eating chicken meat. The number of chickens
consumed/household/month increased from 1 to 2, while the number of eggs
consumed/household/week increased from 1 to 3. The results showed that
average household monthly income from egg sales increased by 231% from
N
3,020 ($ 14) to
N
7,750 ($ 36) and when cocks were sold,
N
8,400 ($ 39) was added to household income. Conclusively, daily monthly
household income increased from
N
475 ($ 2.2) to
N
750 ($ 3.5) while egg and meat consumption increased by 200% and 100%,
respectively. The impact on food security, and livelihoods was a result of
the performance, and productivity of the improved, high producing chicken
breeds introduced by African Chicken Genetic Gains in Nigeria.
Keywords: African chicken, food-consumption, income, productivity
Smallholder poultry (SHP) production is practiced by most rural households (HHs) throughout the developing world. It is considered as a productive asset and makes important contribution to food security for the HH (Sonaiya 2007; Akinola and Essien 2011). Poultry meat and egg increase HH consumption of animal-sourced food. Moreover, the contribution of poultry to food security (Haselow and Stormer 2016; Marie et al 2018) can be related to income from sales of poultry and poultry products, which are often used for purchasing additional food and other necessary items needed by the HHs (Alabi et al 2007). Poultry production is the fastest growing sub-sector of agriculture (Mottet and Tempio 2017) particularly in the developing countries; this sub-sector is quick in terms of short production cycles (Magothe et al 2012; Alabi et al 2019). Developing this sector, especially the SHP production can play a vital role in the rapidly growing economy (Mahendra 2016).
Chicken is the most popular type of poultry reared in the rural areas for eggs and meat (Ogunlade and Adebayo 2009). However, productivity of village chicken is low and is hampered by problems of feed shortage, low chick survival rate, transportation, weather change, poor extension services, locally unimproved birds, high prevalence of poultry diseases, inadequate supply of vaccines and drugs and lack of good housing management (Billah et al 2013; Kuldeep et al 2015). The resultant effect of the challenges faced by SHP producers in the rural areas is reduction in the amount of poultry products available for sales and consumption (Chandrakumarmangalam and Vetrivel 2012; Yusuf et al 2014; Nordhagen and Klemm 2018; Rocio et al 2018). In Nigeria, the low productivity of local, unimproved chickens for eggs (30eggs/hen) and meat (680g/adult live-weight) has contributed to the poor nutritional, health and income status of SHP farmers (Alabi et al 2007; Ajayi 2010; Nwosu and Asuquo 1984). In order to improve the livelihoods, food security and socio-economic status of SHP farmers in Nigeria, the African Chicken Genetic Gains (ACGG) project ( https://africacgg.net/) introduced improved, high producing chicken breeds to farmers in 5 agro-ecologies of the country. Hence, the objective of this study was to assess the impact of the introduction of improved genetics on the livelihoods, food security and production systems of SHP farmers in Nigeria.
The baseline and post on-farm surveys included 2,100 HHs from 60 villages located in five states representing different agro-ecological zones: Kebbi (Sudan Savanna), Kwara (Southern Guinea Savanna), Nasarawa (Derived Savanna), Imo (Humid Forest), and Rivers (Forest Lowlands and Mangrove Swamp). In each of the 3 senatorial districts of each state, 2 Local Government Areas (LGA) were randomly selected (i.e. 6 LGAs) and two villages were randomly selected per LGA giving 12 villages per state and 60 villages in all. However, data for final analysis were only available for 2,063 HHs. The allocation of the different breeds within each state to the participating farmers was random. Majority of the farmers were in the rural areas across the five agro-ecological zones and their production methods across the zones are largely traditional with poor adoption of technologies.
The baseline survey provided the benchmark (control) for measuring the impact of the introduction of 6 improved tropically adapted breeds on HH income generation, poultry meat and eggs utilization and consumption in the participating HHs. The baseline study was conducted in three stages between August – November 2015 as pre-sensitization, sensitization and actual Survey. The third stage, which was the actual survey, was carried out in 39 days. A structured questionnaire was developed, tested and administered using the Open Data Kit (ODK) as a data collection application tool. The instrument used was Lenovo tablet (Model: Lenovo TAB 2 A7-30H). The post-on-farm study survey was carried out with exactly the same procedure as for the baseline survey.
The ethical guidelines provided by International Livestock Research Institute (ILRI), Ethiopia were observed strictly (ILRI-IREC2015-08/1). The consent of each farmer involved in the project was sought in line with global standard.
The on-farm study was carried out between August, 2016 and August, 2018. HHs were given one breed each among Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso, and ShikaBrown. Twenty five six-weeks-old pre-vaccinated and brooded chickens of the six breeds were managed under the traditional poultry scavenging system as shown in Table 1. Training on management of the breeds was given through an Innovation Platform. Vaccination and deworming services were provided through community animal health worker (CAHW) that were trained, supplied and supervised by veterinary officers. The cocks were raised till 20 weeks old for meat purpose, while hens were raised for eggs up till 72 weeks. At 20 weeks, the farmers were free to slaughter the cocks for meat consumption, or sell for income, while eggs produced by the hens, over the 52 week laying period, served as a source of nutrition and income.
The baseline data was subjected to inferential statistics (Chi-square test and Analysis of Variance (ANOVA). Associations between variables and mean difference for both non-parametric and parametric test were analyzed. The on-farm data was collected using electronic method (ODK and Survey Monkey) that generated CSV data files. Qualitative data was transcribed to quantitative to generate possible numeric coded data sets for easy cross tabulation and test of hypothesis. The CSV data was edited free from possible errors and imported to Statistical Package for Social Sciences (SPSS) Version 20 (Chicago, Illinois, USA) for statistical analysis to generate descriptive statistics, measure of central tendency and frequency tables using percentages, as well as inferential statistics – Chi square test and Analysis of Variance (ANOVA) for further inference on significant associations between variables and mean differences for both non-parametric and parametric tests, respectively. The dollar to naira exchange rate (215.73) was obtained from the World Bank open data repository, after adjusting for the inflation rate (12.1%), within the study period (World Bank Data 2019).
The gender distribution of the study had previously been reported by Yakubu et al. (2019). Table 1 shows that 68.4% of the farmers selected were women, and Imo (73.8%) and Rivers (70.4%) states had higher proportion of female farmers compared to the other states (Kebbi 68%, Nasarawa 68.8%, and Kwara 61.1%). The modal age range of the farmers, and their HHs was 20-39 years (61%), while those between ages 40-59 and 60-89 years accounted for 15.4% and 4.4%, respectively.
|
Table 1. Percentage distribution of farmers’ gender and breed received during on-farm test according to location |
||||||||
|
IMO |
RIVERS |
KEBBI |
KWARA |
NASARAWA |
TOTAL |
No. of |
||
|
Gender |
||||||||
|
Male |
26.2 |
29.6 |
32.0 |
38.9 |
31.2 |
31.6 |
652 |
|
|
Female |
73.8 |
70.4 |
68.0 |
61.1 |
68.8 |
68.4 |
1411 |
|
|
Breed |
||||||||
|
Noiler |
20 |
20 |
20 |
20 |
20 |
20.0 |
413 |
|
|
FUNAAB Alpha |
11.4 |
11.4 |
11.5 |
11.5 |
11.4 |
11.4 |
236 |
|
|
Kuroiler |
20 |
20 |
20 |
20 |
20 |
20 |
413 |
|
|
Sasso |
20 |
20 |
19.8 |
20 |
20 |
20 |
412 |
|
|
Shika Brown |
20 |
20 |
20 |
19.8 |
20 |
20 |
412 |
|
|
Fulani |
8.6 |
8.6 |
8.6 |
8.6 |
8.6 |
8.6 |
177 |
|
|
No. of households |
420 |
385 |
419 |
419 |
420 |
100 |
2063 |
|
Table 2 shows the gender disaggregated data for poultry activities in respondent HHs. Labour activities such as feeding, egg collection, egg sales, and sales of live chicken were observed during the project. Women were significantly (p<0.05) more involved in all the activities. Egg collection activity was done more by female respondents (54.4%) than male respondents. Majority of who performed egg sales were respondents. Egg sales activity was largely done by female respondents (55.1%). For sales of live chicken activity more female respondents (63.2%) performed the activity than male respondents. Table 3 shows the performance of the improved chicken breeds compared with the local chickens. Average live weights of the improved chickens at 18 weeks were significantly ( p<0.05) higher in Noiler (1461g), Sasso (1398g) and Kuroiler (1391g), followed by FUNAAB Alpha (1203g), Shika Brown (979g) and Fulani (814g). These values, though not statistically tested, appeared higher when compared with the average value of 680g reported for the local chickens. However, Shika Brown laid more eggs (56.9; p<0.05), while the least was recorded for Sasso (23.3). Egg weight was significantly ( p<0.05) higher in Sasso (55.9) and Kuroiler (55.4g) while the least was recorded for Fulani (42.8g). Egg number and egg weight of the improved breeds were also higher than the values of 30 and 35g reported for local chickens.
|
Table 2. Contingency table showing percentage distribution of poultry activity by gender of respondent farmer and household members |
||||||||||
|
Activity |
Gender |
Respondent |
Spouse |
Male |
Female |
All household |
Others |
N |
X2(df) |
p |
|
Feeding |
Male |
52.1 |
19.6 |
3.4 |
1.5 |
22.2 |
1.1 |
652 |
83.8(5) |
<0.005 |
|
Female |
64.4 |
6.7 |
3.5 |
2.7 |
20.0 |
0.8 |
1411 |
|||
|
Egg |
Male |
40.9 |
19.8 |
3.8 |
0.2 |
19.8 |
15.7 |
607 |
102.2(5) |
<0.005 |
|
Female |
54.4 |
6.3 |
3.4 |
2.8 |
19.6 |
13.5 |
174 |
|||
|
Egg sale |
Male |
41.4 |
20.5 |
1.2 |
0.2 |
8.0 |
28.8 |
601 |
113.7(5) |
<0.005 |
|
Female |
55.1 |
5.6 |
1.5 |
1.8 |
10.3 |
25.6 |
1260 |
|||
|
Sale of |
Male |
56.8 |
15.6 |
1.1 |
0.2 |
6.6 |
19.7 |
634 |
63.2(5) |
<0.005 |
|
Female |
63.2 |
7.3 |
4.3 |
1.2 |
9.5 |
14.4 |
1335 |
|||
|
N: Number of respondents Ha: There is no significant association between poultry activities renered by household members and gender of the farmers |
||||||||||
|
Table 3. Performance (LSM±SD) of the six improved chicken breeds introduced by ACGG in Nigeria |
||||||||
|
Performance Parameters |
Local |
Fulani |
Shika |
FUNAAB |
Kuroiler |
Sasso |
Noiler |
|
|
Average live-weight at 18 weeks |
680 |
814±29.6 c |
979±32.4 c |
1203±54.3 b |
1391±33.8 a |
1398±32.4 a |
1461±63.2a |
|
|
Average egg number/hen |
30 |
34.5±18.5 c |
56.9±43.5 a |
34.5±23.9 c |
35.4±24.4 c |
23.3±17.2 d |
50.7±27.4 b |
|
|
Average egg weight |
35 |
42.8±4.7 c |
51.8±4.9 b |
51.9±3.4 b |
55.4±4.4 a |
55.9±3.6 a |
50.9±5.3 b |
|
|
LSM: least square means, SD: standard deviation, Means
with different superscripts across the rows were
significantly different (p<0.05), |
||||||||
Tables 4 – 9 show the results on egg and meat consumption before and after ACGG interventions in Nigeria. The baseline survey indicated that the gender of the HHs did not significantly (p>0.05) influence egg consumption before (Table 4) and after (Table 5) ACGG interventions in Nigeria. However, based on location, percentage of HHs that did not consume eggs before interventions was significantly (p<0.05) higher in Kebbi (61.5), Nasarawa (55.2) and Kwara (44.6) and lowest in Rivers (27.2). After ACGG interventions, Kwara had the highest (p<0.05) percentage (27) of HHs that did not consume eggs while the least was recorded for Nasarawa (7.1). Before interventions, egg consumption once a week was significantly (p<0.05) low in Kebbi (32) and Nasarawa (43.1) whereas after interventions, Rivers (46.2) and Kebbi (49.3) recorded less than 50%. Before the introduction of ACGG birds, the consumption of eggs three or four times a week was low for majority of the HHs. After interventions, in Nasarawa, HHs increased their consumption of eggs three times a week from 1.7% to 18.1% and that of four times a week also increased to 10.7% from 0% (Table 5). Daily consumption of eggs was low for all the HHs before the introduction of ACCG birds but after the introduction of the birds, daily consumption for HHs in Rivers increased to 9.4% from 2.9% and that of Nasarawa increased to 10.7% from 0%. Percentage distribution of egg consumption by chicken breed was not significant ( p>0.05) (Table 5). The effect of breed on egg consumption was only significant (p<0.05) in Kebbi, with FUNAAB Alpha and Shika-Brown eggs having the highest consumption (Table 6). Obviously, there were farmers who did not eat chicken products before the introduction of ACGG birds, but this was not the case anymore after the introduction of the project both by gender and location (Figure 1). Gender did not also affect chicken consumption before (Table 7) and after (Table 8) ACGG interventions in Nigeria. However, based on location, Imo State farmers consumed more ( p>0.05) of chickens once a week after introduction of ACGG birds than before (66.1% vs. 61.9%). Similar results were obtained for other locations – Rivers (69.1% vs.59.3%), Kebbi (67.5% vs. 31.8%), Kwara (45 % vs. 28.5%) and Nasarawa (77.6% vs.40.5%). Percentage distribution of chicken consumption by breed was not significant (p>0.05) (Table 8). Table 9 shows the within location effect of breed on chicken consumption after ACGG interventions. There was no significant (p >0.05) relationship between breeds within location of farmers, with the exception of Kwara where Sasso was less consumed compared to the other five breeds.
|
Table 4. Contingency table showing percentage distribution of egg consumption by gender and location before ACGG interventions in Nigeria |
||||||||
|
Don’t |
Once |
Three times |
Four times |
Daily |
N |
X2(df) |
p |
|
|
Gendera |
||||||||
|
Male |
48.2 |
45.8 |
4.8 |
0.6 |
0.6 |
651 |
5.34(4) |
0.26 |
|
Female |
43.9 |
49.3 |
4.6 |
1.4 |
0.8 |
1407 |
||
|
Locationb |
||||||||
|
Imo |
36.4 |
59.5 |
3.3 |
0.5 |
0.2 |
420 |
221.7(16) |
<0.005 |
|
Rivers |
27.2 |
56.7 |
12.5 |
0.8 |
2.9 |
383 |
||
|
Kebbi |
61.5 |
32 |
4.1 |
1.9 |
0.5 |
416 |
||
|
Kwara |
44.6 |
50.4 |
2.4 |
2.4 |
0.2 |
419 |
||
|
Nasarawa |
55.2 |
43.1 |
1.7 |
0 |
0 |
420 |
||
|
N |
932 |
992 |
96 |
15 |
15 |
2058 |
||
|
*p<0.05.N: Number of respondents, ACGG: African Chicken Genetic Gains Ha: There is no significant association between farmer’s gender and egg consumption before ACGG interventionin Nigeria Hb: There is no significant association between farmer’s location and egg consumption before ACGG intervention in Nigeria |
||||||||
|
Table 5. Contingency table showing percentage distribution of egg consumption by gender, location and breed after ACGG interventions in Nigeria |
||||||||
|
Don’t |
Once |
Three times |
Four times |
Daily |
N |
X2(df) |
p |
|
|
Gendera |
||||||||
|
Male |
18.1 |
48.1 |
20.4 |
7.8 |
5.5 |
651 |
5.57(4) |
0.23 |
|
Female |
16.3 |
53.4 |
17.5 |
7.2 |
5.6 |
1409 |
||
|
Locationb |
||||||||
|
Imo |
15 |
56 |
18.8 |
4.3 |
6.0 |
420 |
149.9(16) |
<0.005 |
|
Rivers |
18 |
46.2 |
16.4 |
9.9 |
9.4 |
383 |
||
|
Kebbi |
17.2 |
49.3 |
22.2 |
9.6 |
1.7 |
418 |
||
|
Kwara |
27 |
53.2 |
16.5 |
2.9 |
0.5 |
419 |
||
|
Nasarawa |
7.1 |
53.2 |
18.1 |
10.7 |
10.7 |
420 |
||
|
Breedc |
||||||||
|
Noiler |
17.9 |
55.2 |
16.2 |
5.8 |
4.8 |
413 |
28.08(20) |
0.10 |
|
FUNAAB Alpha |
16.1 |
50 |
19.9 |
7.2 |
6.8 |
236 |
||
|
Kuroiler |
17 |
54.3 |
16.3 |
8 |
4.4 |
411 |
||
|
Sasso |
18.7 |
51.5 |
16.3 |
6.8 |
6.8 |
412 |
||
|
Shika Brown |
15.6 |
45.5 |
24.8 |
8 |
6.1 |
411 |
||
|
Fulani |
13.6 |
54.8 |
16.9 |
10.2 |
4.5 |
177 |
||
|
347 |
1065 |
380 |
153 |
115 |
2060 |
|||
|
N: Number of respondents ACGG: African Chicken Genetic Gains - Ha: There is no significant association between farmer’s gender and egg consumption after ACGG intervention in Nigeria - Hb: There is no significant association between farmer’s location and egg consumption after ACGG intervention in Nigeria - Hc: There is no significant association between breeds that farmer received and egg consumption after ACGG intervention in Nigeria |
||||||||
|
Table 6. Mean difference (±SEM) of household egg consumption per breed within the five locations after ACGG interventions in Nigeria |
|||||
|
Breed |
Imo |
Rivers |
Kebbi |
Kwara |
Nasarawa |
|
Noiler |
2.30±0.11a |
2.40±0.11a |
1.96±0.11c |
1.80±0.11a |
2.77±0.11a |
|
FUNAAB Alpha |
2.08±0.14a |
2.64±0.15a |
2.58±0.14a |
2.06±0.14a |
2.58±0.14a |
|
Kuroiler |
2.35±0.11a |
2.29±0.11a |
2.24±0.11b |
1.91±0.11a |
2.64±0.11a |
|
Sasso |
2.17±0.11a |
2.44±0.11a |
2.37±0.11b |
1.98±0.11a |
2.63±0.11a |
|
Shika-Brown |
2.54±0.11a |
2.57±0.11a |
2.41±0.11a |
2.10±0.11a |
2.58±0.11a |
|
Fulani |
2.28±0.17a |
2.61±0.17a |
2.33±0.17b |
2.07±0.17a |
2.61±0.17a |
|
Means with different superscripts across the columns are significantly different (p<0.05), SEM= standard error of the mean |
|||||
|
Table 7. Contingency table showing percentage distribution of chicken consumption by gender and location before ACGG interventions in Nigeria |
||||||||
|
Don’t |
Once |
Three times |
Four times |
Daily |
N |
X2(df) |
p
|
|
|
Gendera |
||||||||
|
Male |
53.2 |
42.6 |
1.9 |
1.9 |
0.5 |
632 |
3.17(4) |
0.53 |
|
Female |
51.8 |
45.4 |
1.9 |
0.7 |
0.1 |
1379 |
||
|
Locationb |
||||||||
|
Imo |
36.7 |
61.9 |
0.7 |
0.2 |
0.5 |
420 |
146.9(16) |
<0.005 |
|
Rivers |
37.8 |
59.3 |
2.6 |
0.0 |
0.3 |
388 |
||
|
Kebbi |
63.4 |
31.8 |
4.1 |
0.2 |
0.5 |
418 |
||
|
Kwara |
65.1 |
28.5 |
1.1 |
5.4 |
0.0 |
372 |
||
|
Nasarawa |
58.6 |
40.5 |
1.0 |
0.0 |
0.0 |
420 |
||
|
N |
1051 |
895 |
38 |
22 |
5 |
2011 |
||
|
N: Number of respondents, ACGG: African Chicken Genetic Gains, Ha: There is no significant association between farmer’s gender and chicken consumption before ACGG intervention in Nigeria, Hb: There is no significant association between farmer’s location and chicken consumption before ACGG interventionin Nigeria |
||||||||
|
Table 8. Contingency table showing percentage distribution of chicken consumption by gender, breed and location after ACGG interventions in Nigeria |
||||||||
|
Don’t |
Once |
Three times |
Four times |
Daily |
N |
X2(df) |
p |
|
|
Gendera |
||||||||
|
Male |
23.9 |
64.0 |
10.3 |
0.8 |
1.1 |
633 |
3.17(4) |
0.53 |
|
Female |
23.3 |
66.1 |
9.2 |
0.8 |
0.5 |
1376 |
||
|
Locationb |
||||||||
|
Imo |
19.6 |
66.1 |
13.4 |
0.5 |
0.5 |
419 |
146.9(16) |
<0.005 |
|
Rivers |
22.0 |
69.1 |
8.1 |
0.3 |
0.5 |
382 |
||
|
Kebbi |
19.3 |
67.5 |
10.8 |
1.4 |
1.0 |
415 |
||
|
Kwara |
44.2 |
45.0 |
8.8 |
1.6 |
0.3 |
373 |
||
|
Nasarawa |
14.5 |
77.6 |
6.2 |
0.5 |
1.2 |
420 |
||
|
Breedc |
||||||||
|
Noiler |
28.1 |
63.6 |
6.6 |
1.0 |
0.7 |
409 |
25.11(20) |
0.19 |
|
FUNAAB Alpha |
23.0 |
63.0 |
11.7 |
0.9 |
1.3 |
230 |
||
|
Kuroiler |
21.4 |
64.3 |
13.0 |
0.5 |
0.8 |
392 |
||
|
Sasso |
23.3 |
68.0 |
8.2 |
0.2 |
0.2 |
403 |
||
|
Shika Brown |
20.7 |
68.1 |
9.2 |
1.2 |
0.7 |
401 |
||
|
Fulani |
24.7 |
63.8 |
9.2 |
1.7 |
0.6 |
174 |
||
|
347 |
1065 |
380 |
153 |
115 |
2060 |
|||
|
N: Number of respondents, ACGG: African Chicken Genetic Gains, Ha: There is no significant association between farmer’s gender and chicken consumption after ACGG intervention in Nigeria, Hb: There is no significant association between farmer’s location and chicken consumption after ACGG intervention in Nigeria, Hc: There is no significant association between breeds that farmer received and chicken consumption after ACGG intervention in Nigeria |
||||||||
|
Table 9. Mean difference (±SEM) of household chicken consumption per breed within the five locations after ACGG interventions in Nigeria |
||||||
|
Breed |
Imo |
Rivers |
Kebbi |
Kwara |
Nasarawa |
|
|
Noiler |
1.99±0.07a |
1.81±0.07a |
1.75±0.07a |
1.84±0.07a |
1.74±0.07a |
|
|
FUNAAB Alpha |
1.85±0.09a |
1.93±0.10a |
2.11±0.09a |
1.72±0.10a |
2.08±0.09a |
|
|
Kuroiler |
2.00±0.07a |
1.88±0.07a |
1.88±0.07a |
1.70±0.08a |
2.10±0.07a |
|
|
Sasso |
1.89±0.07a |
1.88±0.07a |
2.08±0.07a |
1.49±0.07b |
1.93±0.07a |
|
|
Shika-Brown |
2.08±0.07a |
1.87±0.07a |
2.00±0.07a |
1.67±0.07a |
2.00±0.07a |
|
|
Fulani |
1.83±0.11a |
2.03±0.11a |
1.89±0.11a |
1.73±0.11a |
2.00±0.11a |
|
|
Means with similar superscripts across the columns are not significantly different (p>0.05), SEM = standard error of the mean, ACGG: African Chicken Genetic Gains |
||||||
|
| Figure 1. Percentage distribution of consumption of chicken products before and after ACGG interventions in Nigeria |
Tables 10 to 12 show the income generated from monthly sales of eggs and
live birds (cocks) by farmers that participated in ACGG. Gender, location
and breed significantly (p< 0.05) influenced sale of eggs (Table
10) while only location and breed significantly (p< 0.05) affect
sale of cocks (Table 12). The average number of eggs sold per month ranged
from 198 (Kwara) to 302 (Imo) at 40 Naira (N) per egg, while the number of
live birds sold ranged from 3 (Rivers) to 5 (Imo) at an average price of
(N) 2,100 per cock. Women received higher price per unit sale of egg/cock. Table
11 shows a significant (p< 0.05) breed effect within all the
locations, except Kwara for HH income realized from sale of eggs. There was
a significant effect of breed on HH income from sale of cocks and live-birds
in all the states except, Kebbi and Kwara.
|
Table 10. Analysis of variance of percentage distribution of amount made from monthly sale of eggs during the on-farm project by gender, breed and location |
|||||||||
|
Av. eggs |
Av. Cost/ |
<1,000 |
1,000-5,000 |
5,001-10,000 |
>10,000 |
N |
F(df)F(a,b) |
p |
|
|
Gendera |
|||||||||
|
Male |
162 |
40 |
19.5 |
59.7 |
12.7 |
8.1 |
236 |
9.68(1,726) |
0.02 |
|
Female |
339 |
40 |
30.7 |
56.9 |
10.8 |
1.6 |
492 |
||
|
Locationb |
|||||||||
|
Imo |
302 |
40 |
5.2 |
77.8 |
16.1 |
0.9 |
230 |
32.0(4,723) |
0.05 |
|
Rivers |
235 |
40 |
14.1 |
64.6 |
15.2 |
6.1 |
99 |
||
|
Kebbi |
245 |
40 |
28.0 |
55.2 |
16.0 |
0.8 |
125 |
||
|
Kwara |
198 |
40 |
12.1 |
55.2 |
6.9 |
25.9 |
58 |
||
|
Nasarawa |
272 |
40 |
59.7 |
35.6 |
3.2 |
1.4 |
216 |
||
|
Breedc |
|||||||||
|
Noiler |
140 |
40 |
30.0 |
60.7 |
7.9 |
1.4 |
140 |
3.11(5,722) |
0.01 |
|
FUNAAB Alpha |
89 |
40 |
27.0 |
59.6 |
9.0 |
4.5 |
89 |
||
|
Kuroiler |
138 |
40 |
29.7 |
56.5 |
10.9 |
2.9 |
138 |
||
|
Sasso |
133 |
40 |
21.8 |
57.1 |
15.0 |
6.0 |
133 |
||
|
Shika- Brown |
173 |
40 |
23.7 |
59.5 |
12.1 |
4.6 |
173 |
||
|
Fulani |
55 |
40 |
36.4 |
47.3 |
14.5 |
1.8 |
55 |
||
|
197 |
421 |
83 |
27 |
728 |
|||||
|
+ data obtained during focus group discussion at community
innovation platforms. N: Number of respondents Yij=
µ+ɑi + Ꜫ i ( j )
, where ɑi is a fixed effect (gender, location,
breed); i=1,……,n; j=1,…..,p
|
|||||||||
|
Table 11. Mean difference (±SEM) of monthly income made from sale of eggs per breed within the five locations after ACGG interventions in Nigeria |
|||||
|
Breed |
Imo |
Rivers |
Kebbi |
Kwara |
Nasarawa |
|
Noiler |
1446.43±204.98c |
936.99±308.27a |
292.77±212.00c |
274.41±256.73a |
662.89±216.89a |
|
FUNAAB Alpha |
916.67±191.72cd |
981.82±170.17a |
1072.92±105.11a |
302.08±178.85a |
718.51±213.01a |
|
Kuroiler |
1360.48±174.58c |
486.30±170.86c |
704.76±70.32b |
203.57±134.89a |
669.20±124.42a |
|
Sasso |
1816.07±178.35b |
566.23±97.64bc |
654.22±97.68b |
400.83±105.75a |
800.12±176.80a |
|
Shika-Brown |
2282.14±221.67a |
626.10±162.55b |
1033.33±112.16a |
345.78±91.76a |
507.02±151.97b |
|
Fulani |
711.11±204.45d |
730.30±153.79b |
1213.89±102.75a |
458.33±99.21a |
369.31±144.03b |
|
Means with different superscripts across the columns are significantly different (p< 0.05), SEM= standard error of the mean, ACGG: African Chicken Genetic Gains |
|||||
|
Table 12. Analysis of variance showing percentage distribution of average cost of a cock by gender, location and breed after ACGG interventions |
||||||||
|
Average No. |
Average price |
Less than |
1,000 - 5,000 |
Over |
N |
F(df) F(a,b) |
p |
|
|
Gendera |
||||||||
|
Male |
3 |
2050 |
2.6 |
89.4 |
8.0 |
426 |
1.61(1,1339) |
0.21ns |
|
Female |
4 |
2150 |
4.8 |
92.9 |
2.3 |
915 |
||
|
Locationb |
||||||||
|
Imo |
5 |
2350 |
4.5 |
95.5 |
0.0 |
313 |
3.27(4,1336) |
0.01* |
|
Rivers |
3 |
2200 |
0.0 |
98.4 |
1.6 |
186 |
||
|
Kebbi |
4 |
2000 |
6.3 |
86.7 |
7.0 |
315 |
||
|
Kwara |
3 |
1850 |
11.2 |
72.6 |
16.2 |
179 |
||
|
Nasarawa |
4 |
2100 |
0.3 |
99.4 |
0.3 |
348 |
||
|
Breedc |
||||||||
|
Noiler |
15 |
2800 |
3.1 |
91.7 |
5.1 |
254 |
4.61(5,1335) |
0.00* |
|
FUNAAB Alpha |
5 |
3900 |
2.5 |
93.2 |
4.3 |
161 |
||
|
Kuroiler |
10 |
2600 |
1.8 |
96.5 |
1.8 |
282 |
||
|
Sasso |
11 |
2800 |
3.0 |
92.5 |
4.5 |
266 |
||
|
Shika Brown |
6 |
2200 |
5.1 |
90.9 |
4.0 |
275 |
||
|
Fulani |
5 |
1900 |
15.5 |
77.7 |
6.8 |
103 |
||
|
55 |
1231 |
55 |
1341 |
|||||
|
*p<0.05; nsnot significant; N: Number of
respondents, ACGG: African Chicken Genetic Gains Y
ij= µ+ɑi + Ꜫ i ( j ) , where ɑi
is a fixed effect (gender, location, breed) ; i=1,……,n ;
j=1,…..,p
|
||||||||
Access to Newcastle disease vaccine increased from 47.4% to 71.3% while farmers’ access to trainings and education on SHP management practices increased from 36% - 64% (Figure 2)
 |
| Figure 2. Farmers’ accessibility to Newcastle disease vaccine and training in SHP |
This study agrees with other findings on the significant role of women in the production and income generating activities of SHP (Gučye 2000; FAO 1998; FAO 2010). However, there was no significant influence of gender on HH consumption of eggs and meat in this study. Previously, Alemayehu et al. (2018) had reported the decision-making role of women in the sale of eggs and live birds in Nigeria. This according to FAO (2011) and Wong et al. (2016) positively impacts HH food security, and empowers women to take control of the marketing, and income generated from SHP production.
Small scale poultry system is an important source of genetic biodiversity, and equally plays important roles in food-insecure resource-poor areas (Wong et al. 2016). The theory of change of the African Chicken Genetics Gains project highlights the importance of improved, high producing chicken genetics to the socio-economic transformation of poor SHP farmers (Alemayehu et al. 2018, p. 47). Central to this theory of change is the availability and accessibility of poultry products for HH nutrition and income. The introduction of improved chickens by ACGG, contributed to the increased supply of animal protein for participating HH. Compared with the local chickens, the average live-weight, egg number, and egg weight of the improved chickens were higher by 120% - 215%, 115% - 190%, and 122% - 160%, respectively. Consequently, this resulted in increased consumption, and sale of eggs, and meat by the HH. Noiler chickens had the highest in average live-weight (1461g) compared with other breeds of chickens. Sasso chickens and Kuroiler chickens had similar average egg weight of 55.9g and 55.4g respectively. The performance trait of the different breed can guide in the choice of any of the breed (Yakubu et al 2020).
In addition to the increase in the number of HH consuming eggs and chicken, the number of chickens consumed/HH/month increased from 1 to 2, while the number of eggs consumed/HH/week increased from 1 to 3. This represents a 100%, and 200% increase in the number of chickens and eggs consumed, respectively. ACGG interventions increased the availability of poultry products in SHP HH in Nigeria, which as a result, contributed to the overall health status, and intake of animal protein (Vizard 2000; Dessie 2017).
Using the Helen Keller International's model for nutrition-sensitive poultry production, Nordhagen and Klemm (2018) reported that children whose mothers were exposed to project messages on nutrition were more likely to eat eggs, and consumption was consistently higher among HHs with chickens. This is consistent with the findings of Gelli et al (2017) on the use of poultry to promote diets and feeding. Such SHP interventions may improve food security and physical health of the entire community (Dumas et al 2016).
Before ACGG interventions, the average monthly income of participating
farmers was N
15,100 ($ 70) and sales from eggs/chicken represented about 20% (N 3,020) of
that total income. However, within the project period, overall, average HH
monthly income from egg sales increased by 231% from N 3,020 ($ 14) to
N 7,750 ($ 36). Increase in HH income was as a result of the higher egg
number, and bigger egg weights produced by the improved breeds compared with
the local chickens. This agrees with the findings of FAO (Dumas et al.
2016), that market price of eggs is influenced by both egg number and size.
Overall, the average number of live-birds (cocks) sold per HH was 4, and the
average selling price was N 2,100 ($ 10) per bird. This added
N 8,400 ($ 39) to the total HH income. Compared with other states, farmers in
Rivers (70%), and Imo (46%) states sold cocks at a higher price range of
3,000 – 5,500 Naira per adult live bird. The high market price of the
improved breeds compared with the local chickens N 400 ($ 1.9) -
N 800 ($ 3.7) was due to the bigger live-weights (size) of the cocks (FAO
2004; Orajaka 2009).
Beyond the impact on HH nutrition and income, there was an increased capacity of farmers, and empowerment of women towards initiating several business ventures along the SHP value chain (SHP-VC). According to FAO (2004), farmer training, education and provision of extension services can boost SHP production. The community innovation platforms (CIP) provided specialized trainings, which resulted in over 70% increase in the number of farmers, trained on SHP management practices. The CIPs were transformed into SHP cooperatives after three years of regular quarterly CIP meetings, the transformation is done in order to ensure continuous farmer engagements, and access to micro-credits and inputs. The cooperatives were registered with both the local and state governments, and affiliated with the SHP Forum (RC:106425, https://spfnigeria.org/). Establishment of farmer organisations, groups and cooperatives has been identified as a sustainable pathway to the development of SHP (Dumas et al 2016).
The results of this study on income generation, nutrition, and empowerment agree with previous reports on the economic significance, and contribution of SHP to resource-poor farmers, if managed as a business venture (Vizard 2000; FAO 2004). This calls for rural chicken producers to put value in their production by shifting from subsistence to poultry production business. Alder et al. (2018) stressed the economic sustainability of family poultry production enterprises to guarantee improvement in the livelihoods of the populace, especially the rural poor farmers. This is in consonance with the report of Dumas et al. (2016) on the improved economic resilience of farmers as a result of poultry interventions. Mottet and Tempio (2017) equallyaffirmed that poultry is a major asset and key to poverty alleviation, as it provides income through market participation.
Disease control in SHP, especially Newcastle disease control, is associated with food security and improved livelihoods because of increased availability of eggs and meats (Vizard 2000; Alder et al. 2018). The adoption of the village vaccinator model increased access to Newcastle disease vaccination, and deworming services by farmers to 50%. This supports the findings of Alders et al. (2018) and Bagnol et al (2013), that the deployment of community vaccinators increases farmers’ access to Newcastle disease vaccination, which consequently reduces the outbreak of Newcastle disease.
This study was carried out under the auspices of International Livestock Research Institute (ILRI)-led African Chicken Genetic Gains project sponsored by Bill and Melinda Gates Foundation (Grant Agreement OPP1112198). The Nigerian Team appreciates Dr. TadelleDessie (ACGG International Program Leader) and his ILRI Team for their support.
This work was an International Livestock Research Institute (ILRI)-led African Chicken Genetic Gains project sponsored by Bill and Melinda Gates Foundation (Grant Agreement OPP1112198).
All raw data are available as open access at: http://data.ilri.org/portal/dataset/groups/accgngbaselinepublic
Adedokun S A and Sonaiya E B 2002 Crossbreeding Nigeria indigenous with the Dahlem Red chickens for improved productivity and adaptability.ArchivTierzuchtDummerstorf 45(3): 297–305. https://doi.org/10.5194/aab-45-297-2002
Ajayi F O 2010 Nigerian indigenous chicken: A valuable genetic resource for meat and egg production. Asian J. of Poultry Science 4:164-172. DOI: 10.3923/ajpsaj.2010.164.172
Akinola L and EssienA 2011 Relevance of rural poultry production in developing countries with special reference to Africa. World's Poultry Science Journal.67(4): 697-705. · DOI: 10.1017/S0043933911000778
Alabi O O, Shoyombo A J, Ajala A O and Ogunjimi S I 2019 Animal agriculture: a viable tool for rural women empowerment and redemption from poverty. International Journal of Civil Engineering and Technology 10: 2365–2373.
Alabi R A, Esobhawan A O and Aruna M B 2007 Econometric determination of contribution of family poultry to women's income in Niger-delta, Nigeria. Journal of Central European Agriculture 7 (4): 753-760
Alders R G, Dumas S E, Rukambile E, Magoke G, Maulaga W, Jong J and Costa R 2018 Family poultry: Multiple roles, systems, challenges, and options for sustainable contributions to household nutrition security through a planetary health lens. Maternal & child nutrition; Available from: doi:10.1111/mcn.12668
Alemayehu T, Bruno J, GetachewF,andDessie T 2018 Socio-economic, marketing and gender aspects of village chicken production in the tropics: A review of literature. Nairobi, Kenya: International Livestock Research Institute (ILRI) Project Report 2018.
Alemayehu T, Bruno J, Poole J, Getachew F, Sonaiya EB, Bamidele O, Adebambo O, Adeyinka I A and Dessie T 2018 Household baseline data in Nigeria: Monitoring delivery of chicken genetic gains. ILRI Research Report 2018; 47. Nairobi, Kenya: International Livestock Research Institute (ILRI).
Bagnol B, Alders R G, Costa R, Lauchande C, Monteiro J, Msami H, Mgomezulu R, Zandamela A and Young M 2013 Contributing factors for successful vaccination campaigns against Newcastle disease. Livestock Research for Rural Development. Volume 25, Article #95. Retrieved April 6, 2020, from http://www.lrrd.org/lrrd25/6/bagn25095.htm
Billah S M, Nargis F, Hossan M E, Hoelider M A, Lees S H 2013 Family poultry production and consumption patterns in selected households of Bangladesh. Journal of Agricultural Extension and Rural Development. 2013; 5 (4):62 – 69. Available from: doi: 10.5897/JAERD12.113. ISSN 2141 – 2170
Chandrakumarmangalam S and Vetrivel S C 2012 A Study on Production and Marketing of Poultry Eggs in Tamilnadu - India (With Reference to Namakkal District) International Journal of Poultry Science. 11(3): 237-242. DOI: 10.3923/ijps.2012.237.242
Dessie T 2017 Challenges and opportunities for improved tropical poultry productivity and resilience. Centre for Tropical Livestock Genetics and Health (CTLGH) Annual Meeting, Edinburgh. https://www.slideshare.net/ILRI/ctlgh-sep2017-p2tadelle [Accessed 30th September 2017]
Dumas S E, Lungu L, Mulambya N, Daka W, McDonald E, Steubing E, Lewis T, Backel K, Jange J, Lucio-Martinez B, Lewis D and Travis A J 2016 Sustainable smallholder poultry interventions to promote food security and social, agricultural, and ecological resilience in the Luangwa Valley, Zambia. Food security.8(3), 507–520. DOI: 10.1007/s12571-016-0579-5
FAO 1998 Village Chicken Production Systems in Rural Africa. Household Food Security and Gender Issues, by A.J. Kitalyi. The Food and Agriculture Organization 1998, Rome.
FAO 2004 A Technology Review: Newcastle Disease - with special emphasis on its effects on village chickens. By Alexander D.J., Bell J.G. and Alders R.G. FAO 2004 Animal Production and Health Paper No. 161. Rome
FAO 2010 Smallholder poultry production: livelihoods, food security and sociocultural significance, by Kryger, K.N., Thomsen, K.A., Whyte, M.A., and Dissing, M. FAO 2010; Smallholder Poultry Production Paper No. 4. Rome, Italy
FAO 2011 The role of women in agriculture.By SOFA team and Cheryl Doss. FAO 2011; Agricultural Development Economics Division. March 2011. Rome. http://www.fao.org/publications/sofa/en/.Gelli, A., Becquey, E., Ganaba,
Gelli A, Becquey E, Ganaba R, Headey D, Hidrobo M, Huybregts L, Verhoef H, Kenfack R, Zongouri S and Guedenet H 2017 Improving diets and nutrition through an integrated poultry value chain and nutrition intervention (SELEVER) in Burkina Faso: study protocol for a randomized trial. Trials.18(1), 412. Available from:doi:10.1186/s13063-017-2156-4
Gučye E F 2000 The Role of Family Poultry in Poverty Alleviation, Food Security and the Promotion of Gender Equality in Rural Africa. Outlook on Agriculture Vol. 29(2): 129-136. DOI: 10.5367/000000000101293130
Haselow N J and Stormer A 2016 Evidence-based evolution of an integrated nutrition- focused agriculture approach to address the underlying determinants of stunting Matern. Child Nutr. 12:155-168. Available from:10.1111/mcn.12260
Kuldeep Dhama, Shyma K. Latheef, Saminathan Mani, Hari Abdul Samad, K. Karthik, RuchTiwari, RifatUllah Khan, Mahmoud Alagawany, Mayada R. Farag, GaziMahabubulAlam, Vito Laudadio and Vincenzo Tufarelli 2015 Multiple Beneficial Applications and Modes of Action of Herbs in Poultry Health and Production-A-Review. International Journal of Pharmacology. 11: 152-176. DOI: 10.3923/ijp.2015.152.176
Magothe T, Okeno T, Muhuyi W and Kahi, A 2012 Indigenous chicken production in Kenya: II. Prospects for research and development.World's Poultry Science Journal.68(1), 133-144. Available from: doi:10.1017/S004393391200013X
Mahendra K P 2016 Importance of Indigenous Breeds of Chicken for Rural Economy and Their Improvements for Higher Production Performance Scientifica. Available from: doi/10.1155/2016/2604685
Marie T, Ruel A, Agnes R QuisumbingMysbahBalagamwala 2018 Nutrition-sensitive agriculture: What have we learned so far? Global Food Security. 17:128-15. https://doi.org/10.1016/j.gfs.2018.01.002
Mottet A and Tempio G 2017 Global poultry production: Current state and future outlook and challenges. World's Poultry Science Journal. 73(2): 245-256. DOI: https://doi.org/10.1017/S0043933917000071
Nordhagen S and Klemm R 2018 Implementing small-scale poultry-for-nutrition projects: Successes and lessons learned. Matem Child Nutr.14 (3). Available from: doi: 10.1111/mcn.12676.
Nwosu, C C 1979 Characterization of the Local Chicken of Nigeria and its potential for egg and meat production. Proc. 1 st National Seminar Poultry Production in Nigeria.Ahmadu Bello University, Zaria (Olomu, Offiong, Buvanedran and Osinowo, Eds.) p. 187-210.
Ogunlade I and Adebayo S A 2009 Socio-economic status of women in rural poultry production in selected areas of Kwara State, Nigeria. International Journal of Poultry Science. 8; 55 – 59. DOI: 10.3923/ijps.2009.55.59
Olori, V E and Sonaiya E B 1992 Effect of length of lay of Nigeria Indigenous chickens on their egg composition and shell quality. Nig. Journal Anim. Prod. 19:95-100.
Orajaka L J E 2009 The role of locals chickens in poultry production in Nigeria. Nigerian veterinary journal 26(2):69-72
Rocio Abin, Amanda Laca, Adriana Laca and Mario Diaz 2018 Environmental assessment of intensive egg production: A Spanish case study. Journal of Cleaner Production. 179:160-168. Doi: 10.1016/j.jclepro.2018.01.067
Sonaiya E 2007 Family poultry, food security and the impact of HPAI.World's Poultry Science Journal .63(1), 132-138. https://doi.org/10.1017/S0043933907001353
VizardA L 2000 Animal contributions to human health and well-being.A sian- Australas. J. Anim. Sci., 13: 1-9
Wong J T, de Bruyn J, Bagnol B, Grieve H, Li M, Pym R and Alders R G 2016 Small-scale poultry and food security in resource-poor settings: A review. Global Food Security.15: 43-52. https://doi.org/10.1016/j.gfs.2017.04.003
World Bank Data 2019 Nigeria inflation rate. International Monetary Fund,International Financial Statistics and data files. https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?end=2018&locations=NG&most_recent_year_desc=true&start=2015. Accessed 5th September, 2019.
Yakubu A, Bamidele O, Hassan W A, Ajayi F O, Ogundu U E, Alabi O, Sonaiya E B and Adebambo O A 2020 Farmers’ choice of genotypes and trait preferences in tropically-adapted chickens in five agro-ecological zones in Nigeria. Tropical Animal Health and Production. 52(1) 95 (2020). https://doi.org/10.1007/s11250-019-01993-0
Yusuf S F G, Lategan F S and Masika P J 2014 Characterization of Indigenous Poultry Production Systems in the Nkonkobe Municipality, Eastern Cape Province South Africa, Journal of Agricultural Science 5:1-2, https://doi.org/10.1080/09766898.2014.11884710
Received 27 March 2020; Accepted 8 April 2020; Published 1 May 2020