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African Journal of Pig Farming ISSN: 2375-0731 Vol. 9 (3), pp. 001-009, March, 2021. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
  

 
 

 

Full Length Research Paper 
 

Factors influencing success of artificial insemination 
of pigs using extended fresh semen in rural 

smallholder pig farms of Rwanda 

 
Munyaneza Celestin1*, Nyiramuhire Valentine1, Mubashankwaya Isaac1,2, 

Munyandamutsa Fabrice1, Ndisanze Oscar3, Bagaragaza François1 and Mujyambere 
Jean Marie Vianey1

 
 

1
Department of Agriculture, Faculty of Agriculture, Environmental Management and Renewable Energy (FAEMRE), 

University of Technology and Arts of Byumba (UTAB), P. O. Box 25, Byumba, Rwanda. 
2
Uganda Martyrs University 

(UMU), Faculty of Agriculture, P. O. Box 5498 Kampala, Uganda. 
3
Animal Resources Office, Rulindo District, P. O. Box 

6892, Kigali, Rwanda. *Corresponding author. E-mail: munezacel@yahoo.fr. Tel: +250784643802. 
 

Accepted 8 February, 2020  
 

The present study was conducted to analyse the factors influencing the success of artificial 
insemination (AI) in pigs using extended fresh semen in rural smallholder pig farms. One hundred 
female pigs from Rulindo and Gicumbi districts in Northern Region of Rwanda were artificially 
inseminated using locally collected and extended fresh semen. The pregnancy was confirmed by using 
both no returns to estrus and abdomen palpation at 21 and two and half months post insemination, 
respectively. The factors influencing the success of AI were analysed using one way analysis of 
variance (ANOVA) test. The results showed that the number of inseminations per conception (IC) and 
litter size were 1.89 ± 0.83 and 7.94 ± 2.24, respectively. The number of AI per conception was 
significantly influenced (p < 0.05) by intrinsic factors (age and parity of the female pig) and extrinsic 
factors (sex, age and education of household head, time of AI and distance between the farm and 
semen collection center). The litter size was significantly influenced (p < 0.05) by parity. From the 
results, we recommend establishing more semen production centers and providing more training on 
pig management and AI. 

 
Key words: Inseminations per conception, litter size, swine, fresh semen, smallholder farms, natural oestrus.  

 
 
INTRODUCTION 

 
The world population continues to depend on pig meat as 
an important source of food and income. Pig meat 
contributes around 36% (118.7 million tonnes) to the 
world meat output comprising bovine, pig, poultry and 
ovine meat (FAO, 2018). Indeed, pig meat is the second,  

 
 
 
 

 
after poultry meat, among the most consumed types of 
meat worldwide (on average, 12.3 kg retail weight 
equivalent/capita/year) (OECD-FAO, 2018).  

The demand for meat including pig meat, is expected to 
increase due to the growing world population. The  



2 

 

 
 
 

 

projections have shown that the world population is 
expected to rise to 9.1 billion by 2050 (Godfray et al., 
2010). Simultaneously, global pig meat consumption on a 
per capita basis remains stable: from the average of 12.3 
to 12.2 kg retail weight equivalent/capita/year in 2015 to 
2017 and 2027, respectively (OECD-FAO, 2018). 
Therefore, there is a need for increasing pig production in 
order to meet the growing demand in pig meat. The 
increase in pig production will be made possible through 
the use of improved technology in key areas such as 
genetics, nutrition, management, sanity and reproduction. 
Among these areas, genetics play a primordial role in 
improving pig meat production and artificial insemination 
(AI) technology is among the best alternatives for its 
improvement.  

The use of AI in pig breeding presents great 
advantages, compared to natural mating, such as 
improving fertility, genetics, labour use efficiency and 
herd health which result in increased farm productivity 
and profitability (Knox, 2016). Artificial insemination 
enables to increase sire selection intensity by using the 
best sires available within individual breeds (Ronald et 
al., 2013). In fact, the use of AI greatly increases the 
selection differential, where the genetic potential of the 
best sires can be extensively transferred to a large 
number of pigs using diluted semen (Shimada et al., 
2016). Artificial insemination technique solves problems 
of infections due to the contact between boar and female 
pigs during mating (Shimada et al., 2016). Some 
infections caused mainly by viruses and bacteria can 
result in problems associated with sperm damage, 
pregnancy failure, abortion, embryo loss and endometritis 
(Knox, 2016). Artificial insemination reduces the use of 
the same male by a group of farmers, particularly within a 
village, which can lead to inbreeding (Kumar et al., 2014). 
A standard farm boar to sow ratio is 1:20 using natural 
mating (Kyriazakis and Whittemore, 2006). Hence, AI can 
reduce the ratio to 1:150 (Roelofse, 2013). The reduced 
number of boars in a farm results in optimum use of the 
facilities and labour as well as reduction of feed supply 
and medicines; thus, monetary benefit (Ronald et al., 
2013; Kadirvel et al., 2013).  

The use of AI in pigs has presented an enormous 
progress, particularly in Europe and North America 
(Gerrits et al., 2005; Ronald et al., 2013). In some 
European countries, such as Belgium, Italy, the 
Netherlands, Norway and Spain, more than 80% of the 
female pigs are bred using AI. In North America (USA, 
Canada and Mexico) and Brazil, 75% of female pigs in 
large farm units are inseminated using AI (Feitsma, 2009; 
Roca et al., 2006). Moreover, the conception rates in AI 
and natural mating in pigs are the same (Kadirvel et al., 
2013). Indeed, the fertility rates of 80 to 90%, using AI, 
are common in many pig farms (Roca et al., 2006) and 
artificial insemination can lead to better results in 
farrowing rate and litter size than natural mating 
(Visalvethaya et al., 2011; Am-in et al., 2010; Kadirvel et 

 
 
 
 

 

al., 2013).  
Although AI in pigs has more advantages compared to 

natural mating, it can have some limitations. For example, 
AI in pigs can result in low number of piglets per 
farrowing compared to natural mating; and the limited 
storage period of fresh semen are the main problems to 
its expansion in the field all over the world (Shimada et 
al., 2016). Therefore, Maes et al. (2011) suggest that the 
success of AI in pigs is largely determined by the semen 
quality and the insemination procedure. Some of critical 
issues for AI procedure involve oestrus detection in the 
gilt/sow, timing of insemination, applying strict hygiene 
measures, semen age and age of boar (Maes et al., 
2011; Feitsma, 2009). Practically, the success of AI can 
be gauged using specific parameters including fertility 
rate, number of inseminations per conception, farrowing 
rate, litter size, piglets born alive and stillborn piglets 
(Vargas et al., 2009; Niyiragira et al., 2018).  

Despite its progress worldwide, the use of AI in pigs in 
Rwanda is still limited. Mbuza et al. (2016) reported that 
some pig farmers in Rwanda are not aware of the 
existence of AI in pigs. Moreover, Niyiragira et al. (2018) 
suggested that reproduction performances such as litter 
size of weaned sows bred by natural service and AI are 
similar; and some factors like sow breeds and parity can 
significantly influence the outcomes of AI namely litter 
size. However, the study was conducted in an organized 
farm, where a large number of factors, particularly socio-
economic and management factors were controlled 
compared to smallholder pig farms in rural areas. 
Currently, the literature shows that there is no study 
related to factors influencing the success of AI in 
smallholder pig farms in rural areas of Rwanda. The 
present study aims to assess the factors influencing the 
success of AI using locally collected extended fresh 
semen in smallholder pig farms in rural areas of Rwanda. 

 
MATERIALS AND METHODS 
 
Study area 
 
The study was conducted in Rulindo and Gicumbi districts in 
Northern Region of Rwanda during the rainy and dry seasons from 
January to September 2018. The districts are mostly characterized 
by hills interspersed by valleys and swamps that border rivers. In 
the two districts, agricultural sector provides the main jobs and 
source of revenues; where 76 and 77% of working population in 
Gicumbi and Rulindo districts, respectively, are involved in this 
sector. Around 14.7 and 14.9% of all households in Gicumbi and 
Rulindo districts, respectively, raise pigs. In this study, the semen 
were collected from Centre de Perfectionnement et de Promotion 
Agricole de Kisaro (CPPA) (1°37'41.99''S; 30°01'39.11''E), a private 
semen collection centre located in Kisaro Sector of Rulindo district. 
Figure 1 shows the location of Gicumbi and Rulindo districts and 
CPPA de Kisaro. 

 
Semen collection, dilution and storage 
 
The semen was collected by the trained technician of the semen 
collection centre of Centre de Perfectionnement et de Promotion 



3 

 

   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

Figure 1. Location of Gicumbi and Rulindo districts and Centre de Perfectionnement et de 
Promotion Agricole de Kisaro (CPPA). 

 

 
Agricole de Kisaro (CPPA)from the trained boars in a sterile beaker 
using a dummy sow. The motility and concentration of spermatozoa 
were assessed by using a microscope and spectrophotometer 
respectively. After estimating the concentration, the semen was 
immediately extended with the diluent to the final concentration of 

3×10
9
 spermatozoa per dose of 100 ml. Then, the extended semen 

was stored in squeeze bottles at a temperature between 15 and 
20°C. The storage period of the extended semen varied depending 
on appearance of heat in female pigs to be artificially inseminated, 
but it did not exceed five days before their utilization. 

 

Artificial insemination and pregnancy detection 
 
A total of 100 female pigs from 100 farms were randomly selected 
on the basis of insemination service provider records. Practically, 
an exhaustive list of the artificially inseminated female pigs during 
the study period was provided and the sample pigs were randomly 
selected. Once a sow/gilt showed heat signs, the farmer informed 
the inseminator. Then the inseminator transported the extended 
semen to the farmer’s place and inseminated the sow/gilt. A single 
AI per oestrus was performed for all sows/gilts using intra-cervical 
insemination (intra-CAI) procedure, as described by Kaysen (2013), 
12 h after the pig had expressed oestrus signs. The pregnancy was 
confirmed by either no return to estrus after 21 days or abdomen 
palpation two and half months post AI. The inseminations were 
carried out by trained inseminators from CARITAS, a non-
governmental organization that was providing artificial insemination 
service in the study area during the study period. 

 

Data collection and analysis 
 
The household and farm management data were collected using a 
structured questionnaire through interview with the farmers and 

 
 

 
personal observations. The farmers included 16 males and 84 
females. The data related to AI were collected alongside the AI 
procedure using a predesigned form. The factors which were 
hypothesized to influence conception and litter size were 
categorized into those which are related to the female pig (intrinsic) 
and those which are not related to the female pig (extrinsic). The 
intrinsic factors were the age, parity and breed of female pig. The 
extrinsic factors were the age, the sex and experience in pig 
keeping of the household head, distance between AI centre and pig 
farm, time of AI, district and floor material of the pig house. A one 
way ANOVA test was used to analyse the difference between 
means of number of inseminations per conception and litter size 
among groups. Statistically significant difference between means 
was considered to exist if the p-value is less than 0.05. The data 
analysis was performed using SPSS-IBM 20 version software (IBM 
Corp. Released, 2011). 
 

 

RESULTS AND DISCUSSION 

 

Number of inseminations per conception 

 

The overall number of artificial inseminations per 
conception (IC) was 1.89 ± 0.83. Moreover, the pigs that 
became pregnant after one, two, three and four AIs were 
37, 77, 97 and 100% of the total sample, respectively 
(Figure 2). The average number of AIs per conception 
was higher than the ones reported in the literature. For 
example, Niyiragira et al. (2018) reported 1.17 AIs per 
conception in Rwanda while Ronald et al. (2013) reported 
1.00 AI per conception in India. This superiority could be 
explained by the fact that those studies were conducted 



4 

 

    
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 2. Number of artificial inseminations per conception using extended fresh semen in smallholder 
rural pig farms. 

 
 

 

in organized farms using two AIs per oestrus contrary to 
one AI per oestrus in field conditions in this study. 
Several authors including Lamberson and Safranski 
(2000), Kaysen (2013), Bortolozzo et al. (2005) and 
Kumar et al. (2014) suggest that return to oestrus is 
higher in pigs artificially inseminated once than the pigs 
artificially inseminated twice per oestrus. The lower 
performance of AI in field condition compared to 
organized farm is due to the factors related to semen 
handling and farm management (Kumar et al., 2014). 
 

 

Extrinsic factors influencing the number of 
inseminations per conception 

 

The effects of extrinsic factors on the number of IC are 
shown in Table 1. The number of IC was significantly 
lower in male household headed farms than in female 
household headed farms (1.38 ± 0.62 and 2.00 ± 0.83; p  
< 0.05, respectively). The difference could be explained 
by better farm management among male farmers 
compared to female farmers. In fact, Mbuza et al. (2016) 
reported that, in Rwanda, a large proportion of males is 
more involved in pig management activities such as 
feeding, cleaning, treating and record keeping compared 
to females; and good farm management is among the 
factors influencing fertility of pigs (Merks et al., 2000). 
Moreover, Visalvethaya et al. (2011) argue that the male 
farmers have better management skills than female ones, 
especially in oestrus detection, where the males detect 
heat better than their female counterparts. Similarly, 
Visalvethaya et al. (2011) reported that male managed 

 
 
 

 

farms present better fertility than female headed ones in 
Thailand.  

The number of IC significantly varied (p < 0.05) with 
household head age, particularly between middle age 
(from 35 to 49 years) and old farmers (50 years and 
above). The lowest number of IC (1.59 ± 0.67) was 
observed in the farms managed by a group of 35 to 49 
years old farmers while the highest number of IC (2.00 ± 
0.90 and 2.04 ± 0.87) was observed in the farms 
managed by young and older farmers (Below 35 and 
above 50 years, respectively). This difference within age 
groups could be justified by the fact that age of the 
household head has a negative effect on the adoption of 
new technologies including AI (Dhraief et al., 2018; Bayei 
and Nache, 2014; Zanu et al., 2012) and innovations 
(Howley et al., 2012). The old farmers tend to stick to 
what they used to and hardly venture into new ways of 
doing things including AI (Bayei and Nache, 2014). The 
poor performances in young farmers could be explained 
by the fact that the young have fewer financial resources 
and are more interested in other activities compared to 
the other groups of age. Contrary, Visalvethaya et al. 
(2011) in Thailand reported that old farmers have better 
performances than young farmers. This difference 
between findings could be explained by the context 
specificity of the two areas.  

The highest number of IC (2.02 ± 0.78) was observed 
in farms managed by single household heads, followed 
by the farms managed by widows with 1.82 ± 0.89 IC. 
The lowest number of IC (1.82 ± 0.70) was observed in 
married household headed farms. Although the difference 
was not significant (p > 0.05), the better fertility in married 



5 

 

  
 
 

 
Table 1. Effects of extrinsic factors on the number of inseminations per conception using extended fresh semen in rural smallholder 
pig farms.  

 
Extrinsic factors N Mean Std. Deviation   
Sex of the household head  
P = 0.01 

 
 

Age of the household head  
P = 0.04 

 

 

Marital status of the household head 

P = 0.26 

 

Experience of the household head 

P = 0.12 

 

 

Education level of the household head 

p = 0.03 

 
 
 

Time of AI  
P < 0.01 

 
 

Distance from semen collection center to 

farm P < 0.001 

 
Floor material of pig house  
P = 0.08 

 
District  
P < 0.001 

 
 

Male 16 1.38
b
 0.62 

Female 84 1.99
a
 0.83 

Young (less than 35) 11 2.00
ab

 0.90 

Middle (35 to 49) 32 1.59
b
 0.67 

Old (50 years and above) 57 2.04
a
 0.87 

Single 45 2.02
a
 0.78 

Married 10 1.60
a
 0.70 

Widow 45 1.82
a
 0.87 

Five years and below 82 1.95
a
 0.83 

More than five years 18 1.61
a
 0.78 

No formal education 3 1.33
ab

 0.58 

Primary 27 2.26
a
 0.86 

Secondary 41 1.85
ab

 0.88 

University 29 1.66
b
 0.61 

07:00 to 12:00 am 37 1.97
a
 0.87 

12:00 to 16:30 31 2.19
a
 0.87 

After 16:30 32 1.50
b
 0.57 

Within 30 km 47 1.64
a
 0.67 

More than 30 km 53 2.11
b
 0.89 

Concrete 76 1.97
a
 0.88 

Timber 24 1.63
a
 0.58 

Gicumbi 62 2.11
a
 0.87 

Rulindo 38 1.53
b
 0.60 

  
Total 100 1.89 0.83 

 
a,b

Means in the same row with different superscripts significantly differ (P < 0.05). 
 
 

 

household compared to widow and single household 
headed farms could be explained by the fact that the 
married have more financial means and are more 
responsible, compared to the widows and single, which 
enable them to improve pig farm management and result 
in better reproduction performances.  

Although, in the present study, the difference was not 
significant (p > 0.05), the number of IC decreased with 
the experience of the household head in pig 
management. The farmers with less than five years of 
experience presented higher number IC per conception 
(1.95 ± 0.83) than the farmers with five years of 
experience and more (1.61 ± 0.78). Zanu et al. (2012) in 
Ghana reported that adoptions of improved technologies 

 
 
 

 

are associated with farming experience. Indeed, 
Visalvethaya et al. (2011) suggest that experience is 
crucial for pig reproduction particularly in heat detection.  

Although the lowest number of IC (1.33 ± 0.58) was 
observed in the farmers who do not have any formal 
education, it significantly increased (p < 0.05) with 
education level of the household head. The household 
head with primary, secondary and university levels of 
education presented on average 2.26 ± 0.86, 1.85 ± 0.89 
and 1.66 ± 0.61 AIs per conception. The present study 
results are consistent with the report by Bayei and Nache 
(2014) and Dhraief et al. (2018) who suggest that the 
adoptions of new technologies are associated with 
education of the household head. Indeed, Ajala (2007) in 



6 

 

 
 
 

 
Table 2. Effect of intrinsic factors on the number of inseminations per conception using extended fresh semen in rural 
smallholder pig farms.  

 
Intrinsic factors N Mean Standard deviation  

 
Age of the female pig (years)  
p = 0.01 

 
 

 
Parity of the female 

pig p = 0.01 

 
 

 
Breed of the female 

pig P = 0.50 

  

1 14 1.21 
b
 0.43 

2 66 1.95
a
 0.82 

3 16 2.13
a
 0.89 

More than 3 4 2.25 0.96 

1 12 2.42 
a
 0.79 

2 64 1.92
ab

 0.80 

3 and more 24
b
 1.54

b
 0.78 

Landrace 21 2.00 
a
 0.90 

Large white 13 2.15 
a
 0.99 

Pietrain 5 1.80 
a
 0.84 

Cross 61 1.80 
a
 0.77 

  
Total - 100 1.89 0.83   

a,b
Means in the same row with different superscripts differ (p < 0.05). 

 
 

 

Nigeria revealed that the level of education is positively 
correlated with good farm management, thus, better 
fertilization or conception rate.  

The number of IC significantly increased (p < 0.05) with 
the distance between the semen collection center and pig 
farm. The farmers located within 30 km from semen 
collection center presented the lower IC (1.64 ± 0.67) 
than the farms located at 30 km and more with 2.11 ± 
0.90 IC. Moreover, the average number of insemination 
per conception was significantly higher (p < 0.05) in 
Gicumbi district than in Rulindo district (2.11 ± 87 and 
1.89 ± 60, respectively). The negative influence of 
distance to insemination center on conception rate was 
reported during AI in cows (Nishimwe et al., 2015). On 
the contrary, Am-in et al. (2010) revealed that distance to 
AI center does not have negative effect on farrowing rate 
and non-return to oestrus. This difference could be due to 
the fact that return to oestrus after AI depends on the 
timing of insemination relative to the time of ovulation 
(Lamberson and Safranski, 2000). Hence, the long 
distance could compromise the precision in timing and 
risk to deterioration of semen due to shaking and 
increase in temperature, particularly when the 
inseminators use public transport. Eventually, difference 
within the two districts could be explained by the fact that 
the semen collection center of Kisaro is located in 
Rulindo district. According to Chanapiwat et al. (2014), 
the main limitation of the extended fresh semen is the 
short timing of its storage; therefore, the transport of the 
semen for long distances becomes difficult.  

Although the difference was not significant (p > 0.05), 
the number IC varied with the type of floor material of pig 
house. The farms with floor in timber presented the lower 

 
 
 

 

average number of AIs per conception (1.97 ± 0.88) than 
the farm with concrete floor (1.63 ± 0.58). In the present 
case, slatted timber floor is easier to clean than concrete 
floor which justify lower number of inseminations per 
conception. According to Oravainen et al. (2006), better 
housing, particularly in terms of cleanness has a positive 
effect on fertility in female pigs. Dee (1992) suggests that 
poor environmental hygiene is responsible of urogenital 
track diseases which result in poor farrowing rates.  

The number of IC significantly varied (p < 0.05) with the 
time of insemination. The pigs inseminated in the morning 
and evening (before 12:00 and after 16:30) showed the 
lowest number IC (1.97 ± 0.87 and 1.50 ± 0.57, 
respectively) while the pigs inseminated in the afternoon 
(from 12:00 to 16:00) presented the highest number of IC 
(2.19 ± 0.873). This difference could be explained by the 
cool temperatures observed during the morning and 
evening compared to the high temperature in the 
afternoon which can damage the fresh semen. 
 

 

Intrinsic factors influencing the number of 
inseminations per conception 

 

The intrinsic factors influencing the number of IC are 
shown in Table 2. The number of IC significantly 
increased (p < 0.05) with age of female pigs. The lowest 
number of inseminations per conception (1.21 ± 0.43) 
was observed in one-year old pigs while the largest 
number of IC (2.25 ± 0.83) was observed in female pigs 
aged of four years and more. This poor fertility in aged 
pigs could be associated with embryonic mortality. 
According to Vanroose et al. (2000), older animals have 



7 

 

   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 3. Litter size per female pig after artificial insemination (AI) using extended fresh semen in smallholder 
rural pig farms of Rwanda. 

 
 

 

lower follicular activity and oocyte quality resulting in a 
decrease in embryos development. Indeed, the older 
animals are, the more the quality of the endometrium 
gets deteriorated.  

The number of IC significantly (p < 0.05) decreased 
with parity of the female pig. The lowest number of IC 
(1.54 ± 0.78) was observed in the sows which had 
farrowed at least three times while the highest (2.42 ± 
0.79) was observed in primiparous. The present findings 
are in line with the report by Kaysen (2013) and Vargas et 
al. (2009) who found the significant increase in fertility 
with parity. However, Kaysen (2013) argues that the 
conception rate decreases when the parity goes beyond 
seven.  

Large white breed presented the highest number of IC 
(2.15 ± 0.99), followed by Landrace breed with 2.00 ± 
0.89 IC. The lowest number of IC per conception (1.80 ±  
0.837 and 1.80 ± 0.771) was observed in Pietrain and 
cross breeds, respectively. However, there was no 
significant difference (p>0.05) between the number of IC 
among the breeds. Similarly, Kaysen (2013) and 
Niyiragira et al. (2018) confirm that the breed of the 
female pig has no influence on the number of IC. 
 

 

Litter size 

 

The overall litter size was 7.94 ± 2.24 piglets per sow and 
33% of all pigs gave birth to 8 piglets par sow (Figure 3). 
The litter size was comparable with 8.06 piglets per sow 
reported by Niyiragira et al. (2018) in Rwanda using 
imported fresh semen. The litter size was slightly higher 
than 7.2 piglets reported by Mbuza et al. (2016) in 

 
 
 

 

Rwanda using natural service. Similarly, Kumar et al. 
(2014) in India argue that litter size in higher when using 
AI than using natural service. 
 

 

Effects of intrinsic factor on litter size 

 

Table 3 shows the effect of intrinsic factors on litter size 
per female pig. The number of piglets per female pig 
significantly increased (p < 0.05) with parity of the female 
pig. Female with 1, 2 and 3 and more parities presented 
the litter size of 6.75 ± 3.17, 7.81 ± 1.94 and 8.88 ± 2.19, 
respectively. Lower ovulation rate and smaller uterine 
capacity than older sows are the possible reasons for 
smaller litter size in the first few parities (Foxcroft et al., 
2006). These results corroborate with the report by 
Niyiragira et al. (2018) who observed that the litter size 
increased with parity. After the first parity, it increases 
gradually to a maximum in the third to fifth parity and 
slowly decreases through higher parities (Tummaruk et 
al., 2000). However, the age of the female pig did not 
show any influence on litter size (p > 0.05).  
Pietrain breed presented the highest average litter size 
(8.80 ± 1.92). However, the difference was not significant. 
On the other hand, Niyiragira (2018) reported that 
Pietrain breed has significantly the largest litter size than 
landrace and cross between landrace and Pietrain breed. 
 

 

Conclusions 

 

The present study shows that the conception rate using 
AI of pigs in rural smallholder farms of Rwanda is lower 



8 

 

 
 
 

 
Table 3. Effect of intrinsic factors on litter size per female pig using extended fresh semen in rural smallholder pig 
farms.  

 
 Intrinsic factors  N Mean Std. Deviation 

 

  Landrace 21 7.86
a
 2.06 

 

 Female breed Largewhite 13 7.92
a
 2.36 

 

 P = 0.86 Pietrain 5 8.80
a
 1.92 

 

  Cross 61 7.90
a
 2.34 

 

 
Parity 

1 12 6.75
b
 3.17 

 

 

2 64 7.81
ab

 1.94 
 

 

P = 0.02 

 

 3 24 8.88
a
 2.19 

 

 Age Between 1 and 2 years 80 7.93
a
 2.29 

 

 P = 0.89 Above 2 years 20 8.00
a
 2.08 

 

 Total  100 7.94 2.24 
  

a,b
Means in the same row with different superscripts significantly differ (P < 0.05). 

 
 

 

compared to the other studies done elsewhere and can 
be influenced by intrinsic and extrinsic factors. Among the 
intrinsic factors influencing pregnancy rate, parity had a 
positive effect on pregnancy rate. For the extrinsic 
factors, the male managed farms presented better 
conception rates than female managed farms. Farms 
managed by middle aged household heads presented 
better conception rates than the farms managed by 
young and old farmers. Education level of the household 
head had positive effect on conception rate. Inseminating 
in morning and evening results in better pregnancy rate 
than inseminating in the afternoon. Reducing distance 
between insemination center and the farm had positive 
effect on conception rate.  

From the results, it is recommended to improve AI 
procedure in order to reduce the number of artificial 
inseminations per conception. This could be possible, for 
example, through increasing the number of AI centers in 
order to reduce the distance between the households and 
semen collection center, thus, optimize the success of AI 
in Rwanda. Indeed, more attention should be made 
regarding the pig farms managed by old, young, female 
farmers and farmers with only primary education to 
optimize the success of artificial insemination in pigs. 
 

 

CONFLICT OF INTERESTS 

 

The author has not declared any conflict of interests. 

 
REFERENCES 
 
Ajala MK (2007). Analyses of factors affecting the management of pigs 

in Kaduna State, Nigeria. Agricultural Journal 2(2):343-347.  
Am-in N, Wichai T and Mongkol T (2010). Comparison of artificial 

insemination with natural mating on smallholder farms in Thailand, 

 
 
 

 
and the effects of boar stimulation and distance of semen delivery on 
sow reproductive performance. Tropical Animal Health and 
Production 42 (5):921-924.  

Bayei JD, Nache AI (2014). The effect of socio-economic characteristics 
of cattle farmers on the adoption of artificial insemination technology 
in Kaduna State of Nigeria. International Organization of Scientific 
Reearch Journal of Agriculture and Veterinary Science 7(9):2319-
2372.  

Bortolozzo FP, Daniela AU, Paulo EB, Mauro CP, Cezar DC, Carlos 
HP, Waldomiro B, Ivo W (2005). Influence of time of insemination 
relative to ovulation and frequency of insemination on gilt fertility. 
Theriogenology 64(9):1956-1962.  

Chanapiwat P, Olanratmanee E, Kaeoket K, Tummaruk P (2014). 
Conception rate and litter size in multiparous sows after intrauterine 
insemination using frozen-thawed boar semen in a commercial swine 
herd in Thailand. Journal of Veterinary Medical Science 76(10):1347-
1351.  

Dee SA (1992). Porcine urogenital disease. Veterinary Clinics of North 
America: Food Animal Practice 8(3):641-660.  

Dhraief MZ, Bedhiaf-romdhani S, Dhehibi B, Oueslati-zlaoui M, Jebali 
O, Ben S (2018). Factors affecting the adoption of innovative 
technologies by livestock farmers in arid area of Tunisia. Research 
Report, Forum for Agricultural Research in Africa, Accra, Ghana.  

Food and Agriculture Organization (FAO) (2018). Meat Market Review.  
Food and Agriculture Organization, Rome.  

Feitsma H (2009). Artificial insemination in pigs, research and 
developments in The Netherlands, a review. Acta Scientiae 
Veterinariae 37(1):61-72.  

Foxcroft GR, Dixon WT, Novak S, Putman CT, Town SC, Vinsky MD 
(2006). The biological basis for prenatal programming of postnatal 
performance in pigs. Journal of Animal Science 84(E. Suppl.):E105-
E112.  

Gerrits RJ, Joan KL, Lawrence AJ, Vernon GP, Robert RK, Gary AR, 
John RD (2005). Perspectives for artificial insemination and genomics 
to improve global swine populations. Theriogenology 63(2):283-299. 

 
Godfray H, Charles J, John RB, Ian RC, Lawrence H, David L, James 

FM, Jules P, Sherman R, Sandy MT, Camilla T (2010). Food 
security : the challenge of feeding 9 billion people. Science 327:812-
818.  

Howley P, Cathal OD, Kevin H (2012). Factors affecting farmers’ 
adoption of agricultural innovations: a panel data analysis of the use 
of artificial insemination among dairy farmers in Ireland. Journal of 
Agricultural Science 4(6):171-179. 



9 

 

 
 
 

 
IBM Corp. Released (2011). IBM SPSS Statistics for Windows, Version 

20.0. Armonk, NY: IBM Corp. Available at http://www-  
01.ibm.com/support/docview.wss?uid=swg24029274  

Kadirvel G, Kumaresan A, Das A, Bujarbaruah KM, 
Venkatasubramanian V, Ngachan SV (2013). Artificial insemination of 
pigs reared under smallholder production system in northeastern 
india : success rate , genetic improvement and monetary benefit. 
Tropical Animal Health Production 45:679-686.  

Kaysen BL (2013). Factors that impacto probability of pregnanc when 
using ai boars. A Thesis Submitted to the Department of Animal 
Sciences. Colorado State University, United States.  

Knox RV (2016). Artificial insemination in pigs today. Theriogenology 
85(1):83-93.  

Kumar PM, Kent Y, Rungsung S, Ngullie L, Nakhro R, Deka BC (2014). 
Performance appraisal of artificial insemination technique in pig under 
organized farm and field condition in nagaland. Indian Research 
Journal of Extension Education 14(4):55-60.  

Kyriazakis I, Whittemore CT (2006). Whittemore’s science and practice 
of pig production. Blackwell Publishing, Oxford, UK.  

Lamberson WR, Safranski TJ (2000). A model for economic comparison 
of swine insemination programs. Theriogenology 54:799-807.  

Maes D, Alfonso LR., Tom R, Philip V, Ann VS (2011). Artificial 
Insemination in Pigs, Artificial Insemination in Farm Animals, Milad  
Manafi, IntechOpen, Available from: 
https://www.intechopen.com/books/artificial-insemination-in-farm-
animals/artificial-insemination-in-pigs.  

Mbuza F, Majyambere D, Ayabagabao JD, Dutuze MF (2016). Inventory 
of pig production systems in Rwanda. International Journal of 
Livestock Production 7(7):41-47.  

Merks J, Ducro-Steverink D, Feitsma H (2000). Management and 
genetic factors affecting fertility in sows. Reproduction in Domestic 
Animals 35(6):261-266.  

Nishimwe K, BizimanaJ, Manishimwe R, Ayabagabo J, Byukusenge M, 
Habimana R and Bareeba F (2015). Factors affecting the pregnancy 
rate in small scale dairy farms after the artificial insemination in rural 
area, Twanda. International Journal of Livestock Research 5(3):19-
25.  

Niyiragira V, Rugira KD, Hirwa CD (2018). Success drivers of pig 
artificial insemination based on imported fresh semen. International 
Journal of Livestock Production 9(6):102-107.  

OECD-FAO (2018). OECD-FAO Agricultural Outlook 2018-2027, OECD 
Publishing, Paris/Food and Agriculture Organization of the United 
Nations, Rome. http://www.fao.org/publications/oecd-fao-agricultural-
outlook/2018-2027/en/  

Oravainen J. Heinonen M, Seppä-Lassila L, Toomas O, Tast A, 
Virolainen JV, Peltoniemi OAT (2006). Factors affecting fertility in 
loosely housed sows and gilts: vulvar discharge syndrome, 
environment and acute-phase proteins. Reproduction in Domestic 
Animals 41(6):549-554. 
 

Roca J, Vázquez JM, Gil MA, Cuello C, Parrilla I, Martínez EA (2006). 
Challenges in pig artificial insemination. Reproduction in Domestic 
Animals 41(2):43-53.  

Roelofse JJ (2013). Economic feasibility study of the establishment of 
smallholder pig farmers for the commercial market : empolweni case 
study. Thesis presented in partial fulfilment of the requirements for 
the degree Master of Science in Engineering (Engineering 
management) at the University of Stellenbosch, South Africa.  

Ronald BSM, Jawahar TP, Gnanaraj PT, Sivakumar T (2013). artificial 
insemination in swine in an organized farm - a pilot study. Veterinary 
World 6(9):651-654. 

  
  

 
 

 
Shimada M, Yasuhisa Y and Tetsuji O (2016). Development of artificial 

insemination technique in pig production based on the evidence of 
physiology and molecular biology in reproductive organs. AGri-
Bioscience Monographs 6(2):59-83.  

Tummaruk P, Lundeheim N, Einarsson S, Dalin AM (2000). 
Reproductive performance of purebred swedish landrace and 
swedish yorkshire sows: i. Seasonal variation and parity influence. 
Acta Agriculturae Scandinavica, Section A - Animal Science 
50(3):205-216. 

 
Vanroose G, de Kruif A, Van Soom A (2000). Embryonic mortality and 

embryo–pathogen interactions. Animal Reproduction Science 60-
61:131-143. 

Vargas AJ, Bernardi ML, Bortolozzo FP, Mellagi APG, Wentz I (2009). 
Factors associated with return to estrus in first service swine females.  
Preventive Veterinary Medicine 89(1-2):75-80.  

Visalvethaya W, Wichai T, Mongkol T (2011). The development of a 
model for artificial insemination by backyard pig farmers in thailand. 
Tropical Animal Health and Production 43(4):787-793.  

Zanu HK, Antwiwaa A, Agyemang CT (2012). Factors influencing 
technology adoption among pig farmers in Ashanti Region of Ghana. 
Journal of Agricultural Technology 8(1):81-92. 


