Livestock Research for Rural Development 32 (5) 2020 LRRD Search LRRD Misssion Guide for preparation of papers LRRD Newsletter

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Impact assessment of improved chicken genetics on livelihoods and food security of smallholder poultry farmers in Nigeria

O O Alabi, F O Ajayi1, O Bamidele2, A Yakubu4, E U Ogundu5, E B Sonaiya3, M A Ojo6, W A Hassan7 and O A Adebambo8

Department of Animal Science, Landmark University, Omu-Aran, Kwara State, Nigeria
alabi.olayinka@lmu.edu.ng
1 Department of Animal Science, University of Port-Harcourt, Rivers State, Nigeria
2 African Chicken Genetic Gains Project National Secretariat, Department of Animal Science, ObafemiAwolowo University, Ile-Ife, Osun State, Nigeria
3 African Chicken Genetic Gains Project National Secretariat, Department of Animal Science, ObafemiAwolowo University, Ile-Ife, Osun State, Nigeria
4 Department of Animal Science, Faculty of Agriculture, Nasarawa State University, Keffi, Shabu-Lafia Campus, Lafia, Nasarawa State, Nigeria
5 Department of Animal Science, Federal University of Technology, Owerri, Imo State, Nigeria
6 Department of Demography and Social Statistics, Ile-Ife, Osun State, Nigeria
7 Department of Animal Science, UsmanuDanfodiyo University, Sokoto, Sokoto State, Nigeria
8 Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria

Abstract

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


Introduction

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.


Materials and Methods

Description of study area

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.

On-farm study

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.

Statistical analysis

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).


Results

Socio-demography of Households

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
households

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

Poultry Production and Breed Performance

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
farmer

Spouse

Male
children

Female
children

All household
members

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
collection

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
live chicken

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
Chickens*

Fulani

Shika
Brown

FUNAAB
Alpha

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),
* data obtained from Ajayi (2010), Nwosu and Asuquo (1984), Adedokun and Sonaiya (2002), Olori and Sonaiya (1992), and Nwosu, (1979), ACGG: African Chicken Genetic Gains

Household Nutrition

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
eat

Once
a week

Three times
a week

Four times
a week

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
eat

Once
a week

Three times
a week

Four times
a week

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
eat

Once
a week

Three times
a week

Four times
a week

Daily

N

X2(df)

p
value

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
eat

Once
a week

Three times
a week

Four times
a week

Daily

N

X2(df)

p
value

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
Household income and business interest

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
sold

Av. Cost/
egg+

<1,000
Naira

1,000-5,000
Naira

5,001-10,000
Naira

>10,000
Naira

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
Ha: There is no significant difference between monthly income made from egg sales and farmer’s gender during on-farm project in Nigeria
Hb: There is no significant difference between monthly income made from egg sales and farmer’s location during on-farm project in Nigeria
Hc: There is no significant difference between monthly income made from egg sales and breed received by farmer during the project in Nigeria



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.
of cocks sold

Average price
per cock

Less than
1,000 naira

1,000 - 5,000
naira

Over
5,000 naira

N

F(df) F(a,b)

p
value

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
Ha: There is no significant difference between income made from cock sales and farmer’s gender during on-farm project in Nigeria
Hb: There is no significant difference between income made from cock sales and farmer’s location during on-farm project in Nigeria
Hc: There is no significant difference between monthly income made from cock sales and breed received by farmer during the project in Nigeria

Extension services and training

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


Discussion

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.


Conclusions


Acknowledgments

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.


Funding details

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).


Data availability statement

All raw data are available as open access at: http://data.ilri.org/portal/dataset/groups/accgngbaselinepublic


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Received 27 March 2020; Accepted 8 April 2020; Published 1 May 2020

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