Journal of ISSN: 2377-4312JDVAR

Dairy, Veterinary & Animal Research
Research Article
Volume 1 Issue 2 - 2014
Characterization and Typology of Small-Scale Dairy Farmers Using Artificial Insemination in Senegal
Habimana R1*, Mouiche MMM2, Sow A1, Miguiri K1, Umutoni C1 and Sawadogo G J1
1Inter - State School of Science and Veterinary Medicine, Senegal 2School of Science and Veterinary Medicine, University of Ngaoundere, Cameroon
Received: April 20, 2014 | Published: September 23, 2014
*Corresponding author: Habimana R, Inter - State School of Science and Veterinary Medicine, Po Box 5077, Dakar Fann, Senegal, Email: @
Citation: Habimana R, Mouiche MMM, Sow A, Miguiri K, Umutoni C, et al. (2014) Characterization and Typology of Small-Scale Dairy Farmers Using Artificial Insemination in Senegal. J Dairy Vet Anim Res 1(2): 00007. DOI: 10.15406/jdvar.2014.01.00007

Abstract

This study is aimed to characterize and establish a typology of small-scale dairy farmers using artificial insemination in Senegal. It has allowed us to propose a typology of small-scale dairy farmers using AI. Three groups have been highlighted; type I, II and III respectively 48%, 15% and 37% of all farmers surveyed. These groups are distinguished by the geographical location, farming system, the mode of herd management and practice of AI. Typology setting up has enabled us to understand the logic of these operations, problems and limiting factors of their own. It also allowed us to identify the most sensitive small-scale dairy farmers and more receptive to possible AI programs.
Keywords: Characterization, Typology, Dairy farmers, Artificial insemination, Senegal

Abbreviations

GDP: Gross Domestic Product; AI: Artificial Insemination; DIRFEL: Database of the Regional Executive Women In Livestock; MCA: Multiple Correspondence Analysis

Introduction

Livestock plays an important role in Senegal’s economy, accounting for about 35% of the value added in agriculture and 7.5% that it participates in the formation of national Gross Domestic Product (GDP). Nine out of ten households are involved in this activity, employing labor alongside the agriculture. Milk production is insufficient to meet demand, resulting in a heavy reliance on imports represent more than half of the milk supply. Thus, an important breeding program using artificial insemination (AI) has been initiated to improve the national milk production. Because of expected improvements and opportunities for development of the dairy sector, many development initiatives and research are implemented. However, this government commitment stirs debate raises controversy because of past failures in scaling. This raises the debate on the relevance of the broad guidelines of the program and increases its chances of success. In the planning phase, managers need information on producers, their working conditions, their practices in crop and livestock production, the constraints they face and their potential for development. To this end, the approach should collect and analyze information, identify workable solutions and set priorities for research and development. And for sustainable development of traditional breeding, there is an urgent first answer the question of what is the characterization of different small-scale dairy farmers using artificial insemination in Senegal?
This study therefore aims to characterize and establish a typology of small-scale dairy farmers using AI in Senegal.

Materials and Methods

Study area
This study was conducted in the regions of Kaolack and Kolda, respectively in the departments of Kaolack and Vélingara. Kaolack is located between 14°30’ and 16°30’ west longitude and 13°30’ and 14°30’ north latitude while Kolda is located in Upper Casamance, in the south-central part of the country.
Data collection and processing
A structured direct questionnaire was designed for interviews. The survey was conducted from May to June 2012 in 73 small-scale dairy farmers using AI, 41 of the Kaolack region and 32 of the Kolda region mainly in Vélingara. They are composed of 59 men and 14 women.
The questions were properly selected to obtain a general description of small-scale dairy farmers (using AI) characteristics and overall management practices. From Area of the study mentioned, a number of villages were chosen according to reasoned criteria such as available databases on farmers, presence grouping farmers and presence of dairy group. Thus, in the department of Kaolack, they are famers using AI that are listed in the database of the Regional Executive Women in Livestock (DIRFEL). In the Kolda region, farmers surveyed mainly come from Vélingara department; they belong to dairies LAROUGAL and BURDOUGAL.
The survey was conducted by a team of Inter - State School of Science and Veterinary Medicine (EISMV) in collaboration with livestock technicians who have good experience in the methodology of data collection. These technicians have served as interpreters and guides throughout the investigation period. The interview was conducted in Wolof, Pulaar and French in some cases following a direct mode (without an interpreter) or semi-direct (with an interpreter) and took an average of 30 minutes per person.
Data analysis
The data collected were entered into the software Sphinx plus2 V 4.0 and subjected to descriptive analysis. Subsequently, these data were reprocessed by the SPAD software V4.02 for cluster analysis.
Descriptive analysis: It has allowed us to obtain different proportions, means and standard deviations. The results are shown in tabular form.
Cluster analysis: Cluster analysis is a method for grouping aggregative individuals segments on the basis of similarities. It offers the advantage of simplifying the information while generating the main features. The following steps were monitored.
Choice of variables: The choice of variables is done based on the objectives of the typology. These objectives have been identified and classified in terms dummies active, additional nominal variables and continuous variables illustrative. Analysis of the histogram of eigenvalues and choice of axes from the analysis of the histogram of eigenvalues, we have chosen the most important routes for the factor analysis. These are interpreted in terms of axes and variables that have the maximum information.
Description of the factorial axes: This description was made using the methods introduced in the analysis and we have retained terms whose contribution to the establishment of the axis is high. Hierarchical Cluster and cluster identification.
The determination of clusters was performed by analysis of the classification tree or dendrogram. The classification is obtained by cutting the dendrogram. The level of the cut, meanwhile, is determined after consideration of the level diagram which shows the existence of early levels. The number of clusters is determined by the level of the cut. We then validated the number of classes selected by performing several classifications and observing the stability of the results.
Cluster analysis and cluster descriptions: Once the classification obtained performing a multiple correspondence analysis (MCA), and assigning colors (or symbols) to individuals depending on the cluster to which they belong. This gives an overall view of the positioning of clusters and variables from the variables representing the factorial axes, we characterized the classes.
The Chi-square test was used for comparison of different proportions. The significance level was set at 5%, this threshold represents the probability of making a mistake or the maximum risk. The application of this methodology yielded results which will be presented in the next chapter.

Results

Characterization of small-scale dairy farmers using artificial insemination
Sample composition: The proportion of women (19%) in the study population was significantly lower (p <0.05) compared to men (81%). The survey shows that the majority of small-scale dairy farmers using AI are Fulani ethnic group (92%) and many of them (71%) have no level of study (Table 1).
General characterization of small-scale dairy farmers using AI: The majority of small-scale dairy farmers using AI (92%) practiced a semi-extensive where animals are taken to pasture during the day and receive a complementation in the morning and / or evening after returning from pasture. However, the study shows that 62% of these farmers have no fodder reserves (Table 2). Analysis of this table shows also that 97% of small-scale farmers using AI make concentrate supplementation; however, this supplementation is reserved especially for lactating cows (51%). A significant difference in the frequency of concentrate supplementation is indicated between the two regions (Kaolack and Kolda). Indeed, supplementation with concentrate is done once a day (85%) and twice daily (75%) respectively in the regions of Kaolack and Kolda.
Typology of small-scale dairy farmers using artificial insemination
Identification of variables used in the analysis: 26 variables were selected: 13 of them are dummy variables active (Table 3); 10 variables are projected additional dummy variables are illustrative (Table 4); 2 of them are continuous variables illustrative (Table 5).

Variables

Terms

Kaolack

Kolda

Total

Gender

Male

28(68%)

31(97%)

59(81%)

Female

13(32%)

1(3%)

14(19%)

Principal Activity

Agro-breeder

41(100%)

32(100%)

73(100%)

Ethnic Group

Fulani

39(95%)

28(88%)

67(92%)

Serer

2(5%)

0(0%)

2(3%)

Soninke

0(0%)

4(12%)

4(5%)

Age (Year)

mean±standard deviation

48±13

46±14

46±14

 

Education Level

Illiterate

29(71%)

28(88%)

57(78%)

Primary

3(7%)

3(9%)

6(8%)

Secondary

6(15%)

1(3%)

7(10%)

University

3(7%)

0(0%)

3(4%)

Total

 

41(56%)

32(44%)

73(100%)

Table 1: Composition of survey sample.

Variables

Terms

Kaolack

Kolda

Total

Type of farming

Intensive

1(2%)

1(3%)

2(3%)

Extensive

4(10%)

0(0%)

4(5%)

Semi-intensive

36(88%)

31(97%)

67(92%)

Number of present cows

Mean±standard deviation

28±20

68±46

45±39

Breeds used

Ndama

24(28%)

32(49%)

56(37%)

Gobra

35(40%)*

9(14%)*

44(29%)

Morish Zebu

1(1%)

0(0%)

1(1%)

Djakole

8(9%)

1(1%)

9(6%)

Crossbreed

19(22%)

23(35%)

42(28%)

Fooder reserves

Yes

17(42%)

28(87%)

45(62%)

Non

24(59%)*

4(13%)*

28(38%)

Reserved fooders

Maize straw

4(12%)

10(16%)

14(15%)

Rice straw

0(0%)

20(32%)

20(21%)

Millet straw

4(12%)

1(2%)

5(5%)

Bush straw

11(33%)*

1(2%)*

12(13%)

groundnut haulms

6(18%)

28(44%)

34(35%)

Cowpea haulms

3(9%)

3(5%)

6(6%)

Mowed grasses

4(12%)

0(0%)

4(4%)

Concentrate Supplementation

Yes

39(95%)

32(100%)

71(97%)

Animals receiving supplementation with concentrate

Lactating cows

23(35%)*

32(72%)*

55(51%)

Cows candidates for IA

2(3%)

1(2%)

3(3%)

Inseminated cows

2(3%)

0(0%)

2(2%)

Crossbreed

1(2%)

3(7%)

4(4%)

Weakned cows

27(41%)

7(16%)

34(31%)

Bull(s)

0(0%)

1(2%)

1(1%)

The whole herd

10(15%)*

0(0%)*

10(9%)

 

Concentrates used

Peanut meal

32(33%)

4(8%)

36(24%)

Cotton seed meal

0(0%)*

12(23%)*

12((8%)

Soyabean meal

0(0%)

1(2%)

1(1%)

Sesame oil cake

0(0%)

2(4%)

2(1%)

Cotton seed

4(4%)*

23(45%)*

27(18%)

Cereal bran

35(36%)

9(18%)

44(30%)

Commercial feed

26(27%)

0(0%)

26(18%)

Concentrate supplementation frequency

Once a day

33(85%)

8(25%)

41(57,7%)

Twice a day

6(15%)*

24(75%)*

30(42,3%)

Water source

Well

29(55%)

32(100%)

61(72%)

Pond

2(4%)

0(0%)

2(2%)

Drilling

4(7%)

0(0%)

4(5%)

Tap

18(34%)*

0(0%)*

18(21%)

Common diseases in livestock

Parasitic diseases

7(6%)*

24(38%)*

31(17%)

Lumpy skin

34(29%)

6(10%)

40(22%)

Foot and mouth disease

35(30%)

0(0%)

35(19%)

Pasteurellosis

39(33%)

18(29%)

57(32%)

Blackleg

1(1%)*

15(24%)*

16(9%)

Brucellosis

1(1%)

0(0%)

1(1%)

Bronchopneumonia

1(1%)

0(0%)

1(1%)

Total

 

41(56%)

32(44%)

73(100%)

Table 2: General characterization of small-scale dairy farmers using artificial insemination. * Significant difference between the regions of Kaolack and Kolda.
Analysis of the histogram of eigenvalues and choice of axes: The analysis of the histogram of eigenvalues allowed us to select the most relevant axes factor analysis. Thus, we selected the first two factorial axes that bring at least 34% of all information.
Hierarchical cluster and cluster identification: Hierarchical Cluster allowed us to obtain the histogram of levels index and the classification tree or dendrogram. The histogram of levels index has a drop in the levels 3-4. This level indicates that the stall cutoff level of the dendrogram must be at level 3. After cutting the dendrogram at level 3, then we got three types of famers.

Variables

Terms

Informations sources

Regional veterinary service

Private veterinary cabinet

Famers association

NGOs

Other famers

School

Mean of informing

Regional Council

Meeting

Methodology to inform

Very satisfactory

Modelately satisfactory

Unsatisfactory

Knowledge of the difference between two types of IA

Yes

No

Choice between two types of AI

AI on natural heart

AI on heart-induced

Reason for the choice

Simple

Cheaper

Easy

Importance of AI

Milk

Milk and meat

AI campaigns organizers

State programs

Private programs

Motivation for AI campaigns

free service

Quality of service

AI campaigns seasons

Dry season

Rainy season

willing to pay for AI

For

Against

Housing of cows candidates for AI and inseminated cows

Yes

No

Fooder reserves

Yes

Non

Table 3: Dummy variables active.
Cluster analysis and cluster descriptions: This partition is coupled to the description of the factors provided by ACM to describe famers using AI, and the distribution of classes in the factorial 1.2. (Table 6)
Description of the different types’ small-scale dairy farmers using artificial insemination in Kaolack and Kolda
Cluster analysis revealed three types of famers using AI whose characteristics are shown in Table 7. Based on the different characteristics of small-scale dairy farmers using AI, three types of farmers have been identified (Type I, II and III):
Type I: Farmers of type I represent 48% of all surveyed farmers. A large majority (97%) of them comes from Kaolack. These farmers have experienced AI through veterinary practices and the association of other famers. All know very well a difference between the two types of AI, but 97% of them prefer the AI on natural heat. These are farmers interested in AI because they are willing to pay for AI. The majority of farmers (86%) practice AI in order to have milk and meat, but 51% practice stalling of cow’s candidates for AI and inseminated cows. All these farmers have participated at least once in the AI campaigns, especially during the rainy season.

Variables

Terms

Region

Kaolack

Kolda

Gender

Male

Female

Ethnic group

Fulani

Serer

Soninké

Study level

Illiterate

Primary

Secondary

University

Used breeds

Ndama

Gobra

Morish Zébu

Djakolé

Crossbreed

Reserved fooder

Maize straw

Rice straw

Millet straw

Bush straw

groundnut haulms

Cowpea haulms

Mowed grasses

Used concentrate

Peanut meal

Cottonseed meal

Soybean meal

Sesame oil cake

Cotton seed

Cereal bran

Commercial feed

Concentrate supplementation frequency

Once a day

Twice a day

Animals receiving concentrate supplémentation

Lactating cows

Cows candidates for IA

Inseminated cows

Crossbreed

Weakned cows

Bull(s)

The whole herd

Common deseases in livestock

Parasitic diseases

Lumpy skin

Foot and mouth disease

Pasteurellosis

Blackleg

Brucellosis

Table 4: Dummy variables illustrative.

Variables

Terms

Age (year)

mean±standard deviation

Number of participation in AI campaigns

Private program

State programs

On heat-induced

On natural heat

Table 5: Continuous variables illustrative.

Factorial Axe

Positive

Negative

 

Region of Kaolack

Region of Kolda

1

Knowledge of two types of AI and prefer AI on natural heat

No difference between two types of AI

Willing to pay for AI

No stalling of cows

stalling of cows, supplementation of cows candidates to AI & inseminated cows: once day

Supplementation of lactiting cows twice a day

Number of cows

2

AI campaigns participation: at least once, in rainy season

Region of Kaolack and region of Kolda

AI importance : milk & meat

No willing to pay for AI

Fooder reserves

No fooder reserves

Ndama & Gobra breeders

No supplementation

Motivation for AI campaigns :free service and quality of service

No motivation for AI campaigns

Veterinary cabinet, farmers association

Other farmers, veterinary services

Table 6: Definition of the factorial axes.

Variables

Type I

Type Ii

Type Iii

Pourcentage

48%

15%

38%

Concerned regions

-Kaolack : 97%
-Kolda : 3%

-Kaolack : 48%
- Kolda : 52%

Kaolack : 16%

Kolda : 84%

Gender

Male : 80%

Female : 20%

Male : 71%

Female : 29%

Male : 95%

Female : 5%

Study level

illiterate : 71%

illiterate : 41%

illiterate : 88%

Information sources on AI

Veterinary cabinet

Famers associations

Other famers

Veterinary services

Importance of IA

Meat and milk : 86%

Milk (100%)

Milk : 63%

Knowledge of the difference between two types of IA

100%

0%

0%

Choice between two types of AI

AI on natural heat: 97%

-

-

Number of participation in AI campaigns

>1

>1

>4

Motivation for AI campaigns

free service : 41%

Importance of AI : 42%

Free service : 90%

free service :44%

Importance of AI : 48%

AI campaigns seasons

Rainy season : 69%

Dry season : 31%

Rainy season : 38%

Dry season : 62%

Rainy season : 4%

Dry season: 96%

Willing to pay for AI

90%

1%

89%

concentrate Supplementation

91%

0%

44%

Animals receiving concentrate supplementation

Lactating cows 40%

The whole herd: 29%

 

Lactating cows : 96%

Concentrate supplementation frequency

Once a day : 71%
Twice a day : 29%

 

Once a day: 30%
Twice a day :70%

stalling of cows candidates for AI and inseminated cows

51%

19%

100%

Fooder reserves

29%

0%

100%

Used breeds

Ndama : 54%

Gobra : 42%

-Ndama : 34
-Gobra : 64%

-Ndama: 100%
- crossbreed: 70%

Number of cows

30±21

< >

73 ± 47

Table 7: Description of different types of small-scale dairy farmers using artificial insemination in Kaolack and Kolda regions. Based on the different characteristics of small-scale dairy farmers using AI, three types of farmers have been identified (Type I, II and III).
Type II: Famers of this type represent 15% of all surveyed farmers. Some of these farmers come from the region of Kaolack (48%) and others from the Kolda region (52%). These farmers have experienced AI through other famers.
All famers of type II
i. Fail to distinguish the natural heat of AI and AI on heat-induced;
ii. Are motivated by AI as a free service and also they do not agree with the payment of the benefit of AI;
iii. Do not make concentrate supplementation of their cows;
Although they participated at least once in the AI campaigns, especially in the dry season, the majority of them (81%) do not practice stalling of cows candidates for AI and inseminated cows.
Type III: Farmers of type III represent 37% of all surveyed farmers. 84% of them come from the region of Kolda. Despite the fact that farmers have participated in at least 4 times in the AI campaigns (96% in the dry season), nobody knows the difference between the AI on natural heat and AI on heat-induced. More than half of them (63%) practice AI for milk. Stalling of cows candidates for AI and inseminated cows is provided in 100% of cases.
All famers have fodder reserves. However, 44% of them make concentrate supplementation of their cows. This supplement is for lactating cows in 96% of cases. In most cases (59%), the used concentrate is cottonseed. Concentrate supplementation is executed twice a day in 70% of cases. Furthermore parasitic diseases in 59% of cases, blackleg is found in 26% of cases. Most farmers (59%) had AI through veterinary services.

Discussion

Characterization of small-scale dairy farmers using artificial insemination
The majority of surveyed farmers were male (81%). The preponderance of men (68%) in the Kaolack region is consistent with the results found by Asseu [1] in the same area. However, these results are lower than Nkolo [2] with a relatively high proportion in Thies where 98.1% of men hold farms. Results in the Kolda region (91%) are higher than the Kaolack region but are in agreement with those obtained by Nkolo [2] in the region of Thies.
All surveyed farmers are agro-pastoralists. A study by Nkolo [2] showed that the type of agro-pastoralists was ideal for the success of the AI. According to Ba (2001) cited by Diadhiou [3], this form accompanies sedentary recent advances in livestock intensification and contributes to the stabilization of pastoral migration. In general, the combination of agriculture and livestock results in the use of animal traction, use of animal manure to fertilize the fields and use of crop residues for animal feed [1].
Our study shows that farmers using AI in the region of Kaolack and Kolda Fulani are mainly (92%), which is in agreement with the data ethnological Ansd [4]. The Fulani are traditionally pastoralists for centuries, what explains the first place in the possession of the farms. The work of Somda et al. [5] made in Gambia, in Guinea and in Guinea Bissau shows respectively that 43.3%, 98.9% and 96.2% were Fulani herders. These results are also consistent with those of Ba (1992) which showed that agro-pastoral farming is practiced in the Casamance region by the Fulani (90%).
The average age of farmers is 46 ± 14 years in the two regions thus relatively able to carry out the activities of herd. This is an asset in the intensification policy. This result is similar to Alary [6] in Reunion Island. However, these results differ to those of Somda et al. [5], who reported the famers’ average age of 54±17 years in Gambia, 58±11 years in Guinea and 53±14 years in Guinea Bissau.
Many of them (78%) are illiterates. This result is in agreement with the results of Somda et al. [5] who reported that 52.2% of famers in Gambia and 54.6% of famers in Guinea Bissau are illiterates. Likewise, Beyene et al. [7] related that in Ethiopia, 55% of famers are illiterates. However in Bangladesh, Major percentage of farmers study level is Higher Secondary level (60%) [8].
The majority (92%) of farmers in the region of Kaolack and Kolda practice semi-extensive. With this mode of conduct, the animals are taken to pasture during the day and receive a complementation in the morning and/or evening after returning from pasture. This is consistent with the work of Sere et al. [9] which showed a predominance of semi-intensive system in sub-Saharan Africa. By cons Badji [10] in Louga, Kaolack and Fatick showed that inseminations were made mainly in extensive farms (72.2%). These results differ from those obtained by Asseu [1] in the region of Kaolack, where 87.2% of farmers practiced extensive system. This difference can be explained partly by the fact that farmers abandon more traditional extensive systems and transhumant and another part by the balance of agro-pastoralists in our sample.
The main breed exploited in the Kaolack region is the zebu Gobra (40%). This is in agreement with the results obtained by Asseu in 2010 (82%) in the same region and Nkolo (86.4%) in 2009 at Thies and by Sery (68%) in 2003 at Thies and in Dakar [11].
On the contrary in the Kolda region, the bullfighting Ndama (49%) is the most exploited. This is explained by the fact that this region is enzootic bovine trypanosome and is trypanotolerant Ndama and it fits well in the region. This is in agreement with the results of Diop [12] where he showed that most of the cattle in the southern region consist of the Ndama.
The study shows that most farmers (62%) have fodder reserves and 97% make complementation, which is a favorable aspect to the practice of AI. For, indeed food is a major factor of success or failure in reproduction according to Chicoteau [13] and Moudi [14]. Food provides the cow all elements energy, protein and minerals it needs to meet upkeep, gestation and production Bofia [15]. These results are similar to those obtained by Nkolo (2009) [2] at Thies where 97.4% of farmers have fodder reserves and 72.9% make concentrate complementation.
The reserve consists mainly of groundnut haulms (35%) and rice straw (21%), this in agreement with the data of agricultural activity in two regions studied (groundnut in Kaolack and rice in Kolda) [4] According to Bouyer [16], proper nutrition is a prerequisite for any attempt to AI, otherwise the results will be very disappointing fertility. Therefore one hand develop fodder reserves and in forage programs, especially when they occur in the dry season and on the other have an idea of the amount of fodder reserves according to the size of his herd .
Food supplements are used in most cases groundnut cake (24%) and cereal bran (30%). Animals receiving concentrate supplementation are lactating cows (51%) and weak cows (31%) in agreement with the results obtained by Asseu [1] in Kaolack. This supplement is 85% once a day in Kaolack as farmers in this region have not the means to be able to concentrate twice a day. By cons in the Kolda region concentrate supplementation is done twice a day. These results are similar to those of Nkolo [2] at Thies where most of the surveyed farmers who make complementation, 65% distributed concentrate twice daily (morning and evening).
The source of water is essentially the wells (55%). This result could be explained by a large number of wells constructed in Senegal during the past two years. These results different from those obtained by Beyene et al. [7] in Ethiopia, where the main sources of water during the dry season were rivers, springs and pond (River is about 92%).
The dominant pathology in all operations is pasteurellosis (32%) that strongly affect the fertility of the herd. These results could be explained by the fact that our study was performed in the favorable season to this disease. These results differ to those obtained by Tebug et al. [17] in Malawi where the most common diseases is mastis (39, 3%). Furthermore, mastitis was the most reported cause of morbidity in 65 studies carried out in smallholder dairy farms in East and South African (ESA) countries Lema et al. [18]; Phiri et al. [19].
Typology of small-scale dairy farmers using artificial insemination
Our results show that the different types of famers are formed according to geographical location, farming system, the mode of herd and practice of AI. This is in agreement with the results obtained by Nkolo [2] at Thies on the typology of farms using IA where geography seems crucial factor. The same results were found in Mali by Faly [20] on the typology of farmers. Faly [20] found that farmers are divided into groups according to areas. Famers’type II correspond to those practicing extensive pastoral system is a typical production system where one or more inputs are limiting in terms of quantity, which imposes severe pressure continues or variable on animals, resulting in low survival, reproduction or production level Isra [21]. According to Bouyer [16], this group makes it very difficult to achieve AI. Indeed, it is very difficult to meet the dates of appointment for insemination plans since the farmer and his herd to move sandstone seasons and available pasture. In addition, the presence of males “vagrants” sometimes disturbs the results of artificial insemination. Farmers’ type I and III have similar characteristics to those described in the agro-pastoral system by Tache [22]. According to Diop [23], the system evolves towards sedentary animals with the use of agricultural by-products. These two classes are more suitable to the achievement of AI, since there are no transhumance animals, and they are often better fed than pastoral. However, this study shows that there is a significant difference (p <0.05) between the famers’type I and those of type III in fodder reserves and practice of AI on natural heat. This difference is the result of their geographical location.

Acknowledgement

We thank the Agence Universitaire de la francophonie (AUF) for financial support, field extension staff for assisting in collecting data and farmers as well as inseminators for volunteering information.
This research is supported by AMPROLAIT (project which is funded by the World Bank through competitive funds of CORAF / WECARD project).

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