INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

Initial Estimates of Variance Components and Genetic Parameters

for Reproductive Traits in Large White Sows in Kenya.

Milka A. Kadenyi¹, Portas O. Olwande¹, Sophie Miyumo²and Chrilukovian B. Wasike^1,3*

¹Livestock Efficiency Enhancement Group (LEEG), Department of Animal and Fisheries Sciences,

Maseno University, P.O. Box Private Bag 40105, Maseno, Kenya

²Department of Animal Breeding and Husbandry in the Tropics and Sub-tropics, University of

Hohenheim, Garbenstr. 17, 70599 Stuttgart-Germany

³School of Agriculture, Food Security and Environmental Science, Maseno University, P.O. Box Private

Bag 40105, Maseno, Kenya

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1411000104

Received: 27 November 2025; Accepted: 05 December 2025; Published: 23 December 2025

ABSTRACT

This study aimed at estimating variance components and genetic parameters for reproductive traits of Large

White sows in Kenya, in order to facilitate genetic improvement of reproductive efficiency in sows through

selective breeding. 1145 records comprising 1129 records of litter size at birth (LSB), 1101 records of number

of piglets born alive (NPBA), 1114 records of litter size at weaning (LSW) and 681 records of inter-farrowing

interval (IFI) were obtained from 4 farms in western Kenya. After editing, a total of 1138 records of at least 2

traits were available for analysis. Genetic variance components were 0.10±0.03 for LSB, 1.33±1.80 for NPBA,

0.01±0.21for LSW and 23.28±22.71 for IFI. Phenotypic variance components were 7.63±0.33 for LSB,

6.67±0.29 for NPBA, 6.03±0.26 for LSW and 589.40±25.48 for IFI. Heritability estimates for reproductive traits

were generally low. The estimates were 0.014±0.040 for LSB, 0.011±0.039 for NPBA, (0.001±0.035 for LSW

and 0.039±0.038 for IFI. The standard errors for estimates were high. This is because, reproductive traits are

strongly influenced by environmental factors. High environmental variability relative to genetic variability can

make it difficult to accurately estimate heritability. The genetic correlation coefficients were 0.227.for LSB and

NPBA, -0.555 for LSB and LSW, 0.865 for LSB and IFI, -0.924 for NPBA and LSW, -0.283 for NPBA and IFI

as well as -0.079 for LSW and IFI. Phenotypic correlation coefficients were 0.810±0.011 for LSB and NPBA,

0.655±0.018 for LSB and LSW, 0.019±0.031 for LSB and IFI, 0.821±0.010 for NPBA and LSW, 0.004±0.031

for NPBA and IFI as well as 0.034±0.031 for LSW and IFI, The study concluded that, the low genetic variance

compared to phenotypic variance across traits indicates a strong influence of environmental factors on

reproductive traits, limiting the genetic contribution to variability. The very low heritability values with high

standard errors highlight the limited potential for genetic improvement through selection and emphasize the use

of other sources of information like progeny and ancestors. The genetic correlations with both positive and

negative relationships, suggest the need for careful, balanced selection to avoid unfavorable genetic trade-offs

between traits. Strong positive phenotypic correlations between traits like LSB and NPBA suggest shared

environmental influences, underscoring the importance of improved management for overall reproductive

performance.

Keywords: Pigs, fertility traits, heritability.

INTRODUCTION

Pork consumption in Kenya has seen steady growth over the past decade, largely attributed to urbanization,

rising incomes, and shifting dietary preferences among the population (Bosire et al., 2017). Pork is increasingly

recognized as an affordable and nutritious source of animal protein, making it an essential part of food security

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strategies. However, the swine industry in Kenya faces significant challenges in meeting the growing demand

for pork while maintaining sustainability and competitiveness (Murungi et al., 2021). Key barriers include low

productivity, high disease prevalence, and inadequate infrastructure for pig farming. Western Kenya stands out

as a region with great potential for pig farming, thanks to its conducive climate, abundant feed resources, and a

growing population of farmers eager to adopt modern agricultural practices (Rudel et al., 2015). Despite these

advantages, farmers in the region often struggle with limited access to quality breeding stock, poor disease

control measures, and fragmented value chains, which hinder their ability to scale up operations. Addressing

these issues is crucial for the growth of the swine industry and the country's efforts to meet the rising demand

for animal protein.

Kenya’s pig breeding programs are in their infancy, with limited coordination and inconsistent implementation

across farming regions. While some medium and large-scale farms have adopted structured breeding programs,

smallholder farmers—who form the majority—lack access to the resources and knowledge needed to improve

their herds. The absence of systematic genetic improvement strategies has resulted in low reproductive

performance and overall productivity. For instance, farmers often rely on unverified breeding stock with

unknown genetic potential, leading to suboptimal litter sizes, growth rates, and fertility (Levit & Verchick, 2016).

Furthermore, technical support services such as veterinary care, extension services, and genetic evaluation

programs remain underdeveloped, leaving farmers to rely on traditional practices that do not maximize genetic

potential. The collection and analysis of reproductive performance data, such as litter size, inter-farrowing

intervals, and the number of piglets born alive, are critical steps toward addressing these gaps. Enhanced

breeding programs should focus on integrating local and imported genetics, supported by robust technical

support systems, to improve production efficiency and profitability. Genetic improvement is essential to boosting

productivity in Kenya’s pig industry, particularly in smallholder and tropical farming systems. Estimating

variance components and genetic parameters provides critical insights into the heritability of traits such as litter

size, growth rate, and disease resistance, enabling breeders to make informed decisions (Dumont et al., 2014).

By identifying traits with high heritability, breeders can focus on those with the greatest potential for genetic

gain, optimizing the use of limited resources. Genetic variation plays a pivotal role in enhancing adaptability,

resilience, and disease resistance in pigs—qualities that are indispensable in tropical environments where heat

stress and endemic diseases pose significant challenges (Phocas et al., 2016). Moreover, understanding

genotype-environment interactions helps breeders tailor selection strategies to specific ecological conditions,

ensuring the sustainability of genetic progress (Rose et al., 2016).

Accurate estimates of genetic parameters also mitigate the risks of inbreeding, preserving genetic diversity and

ensuring long-term population viability. For smallholder farmers, the benefits are particularly pronounced.

Genetic improvement enhances productivity, reduces production costs, and ultimately increases profitability.

Beyond economic benefits, genetic improvement contributes to food security by ensuring a consistent supply of

high-quality pork. However, achieving these outcomes requires investments in data collection, research, and the

establishment of breeding programs that prioritize the unique needs of smallholder systems. Through a

combination of science-based breeding and capacity building, Kenya’s swine industry can realize its full

potential while improving livelihoods and advancing national food security goals.

MATERIALS AND METHODS

Data source

Data for this study was collected from medium-scale pig farms located in Kisumu and Trans-Nzoia counties in

the Western region of Kenya. Farm 1 in Kisumu County was located within longitudes 33⁰20’E and 35⁰20’E

and latitudes 0⁰20’S and 0⁰50’S, at an altitude of 1131 m above Sea level, 72 km west of Kisumu city along

0

Kisumu-Bondo road. Temperatures on this farm were high and ranged between 16 C to 31 ⁰C with an annual

rainfall of 1311 mm. Farms 2, 3, and 4 were located in Trans-Nzoia county within longitudes 34.97⁰58^’E and

34⁰58’ E and latitude 1⁰45’N and 1⁰2’N, at an altitude of 1864.59 m above sea level, East of Kitale airstrip. The

farms received an annual rainfall that ranged from 1100 mm to 2700 mm per year with an annual average rainfall

of 1172 mm (Blanford et al., 2013; Okayo et al., 2015). Temperatures on these farms were mild and generally

warm throughout the year and ranged between 10⁰C- 28⁰C (Blanford et al., 2013). The study site experienced a

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bimodal rainfall pattern with four seasons; long rains from March to May, short rains from September to

November, long dry season from December to February, and short dry season from June to August (Mugalavai

et al., 2008).

The farms under study were medium-scale pig farms, keeping large white breeds for commercial pork production

with an average herd size of 100 sows. In farm 1, sows were housed in specialized housing in group pens on the

basis of their age, physiological status, and climatic conditions. The houses were made of concrete floors and

walls and had large open windows for ventilation and free air circulation. Pigs were fed on concentrate diets

formulated on the farm. Feeding was done twice daily and the ration composition and quantity varied based on

the age and physiological status of sows. The farm kept both reproduction and production records. Like farm 1,

housing in farm 2 had concrete floors with open windows. Commercial concentrates were used in feeding pigs.

Feeding was done twice a day, with the sows being fed together with their young ones, weaners in a group, and

boars fed individually. This farm practiced hand-mating, with the sow being mated two to three times during

estrus. Primarily, disease management was by curative treatment with low-level prophylaxis. Housing in Farm

3 was similar to Farm 2. Pigs were fed once a day (at midday), but the young ones were fed twice daily on

commercial concentrates. Water was given adlib to cater for the rest of the hours without feed. Pregnant and

nursing sows were fed on sow and weaner meals. The farm leased boars from the neighborhood for mating sows

on the farm whenever one manifested signs of estrus. Like farms 1 and 2, disease management on the farm was

predominantly curative. Like the other farms, pigs were housed in group pens except for nursing and pregnant

sows. Farm 4 had a relatively high stocking rate compared to other farms. Feeding was done twice a day using

commercial concentrate feeds. Pregnant sows were given an additional snack of sow and weaner meal in the

afternoon. Disease management on the farm was largely curative with treatment given whenever a disease was

reported. There were no biosecurity measures, just like on the other farms. Other routine management procedures

on the farm including teeth clipping, notching for identification, administration of iron dextran, and tail docking

were done based on standard schedule.

Data collection

Records on reproductive performance of sows were extracted from farm record books targeting sows born

between the years 2010 to 2020 in the four farms. The information collected included sow identification, sire,

dam, dates of birth, farrowing dates, parity, litter size at birth, number of stillbirths, and number of litters weaned.

Information on litter size at birth and weaning formed two traits of interest namely; litter size at birth (LSB) and

litter size at weaning (LSW). Inter-farrowing interval (IFI) was determined as the difference between the date of

a farrowing and the subsequent one, while number of piglets born alive (NPBA) was the difference between

litter size at birth and number of stillbirth. Some individuals had only two parities while others had up to five

and therefore, parities were truncated to three resulting in a dataset that had two inter-farrowing intervals. Months

were clustered into four seasons namely: long rains from March to May, short rains from September to

November, long dry season from December to February, and short dry season from June to August. The data

were edited to remove individuals that had only one record, records of farrowing following abortions and

farrowings whose activity dates were missing. Further edits involved the removal of records with inconsistent

dates of birth and farrowing. 1138 records of at least 2 traits per animal were available for analysis.

Data analysis

Data on reproductive traits (LSB, NPBA, LSW, IFI, and AFF) was subjected to preliminary fixed model analysis

to determine fixed effects of significant influence on reproductive traits using GLM package of R software (R

Core Team 2023). The effects fitted included Herd, Yob, Sob, Sof, Yof. The effects that were significant were

used in animal model evaluation as fixed effects. A multivariate animal model fitting 4 traits at a time was used

for genetic analyses that were performed using restricted maximum likelihood methodology based on average

information algorithm (AI-REML) in WOMBAT (Meyer, 2007). Mixed model equations in the analyses were

solved iteratively and estimates at convergence of previous runs were used as starting values for the subsequent

runs until no differences were observed in variance components in at least two consecutive runs, a global

convergence was then assumed. The multivariate animal model in matrix notation is presented in equation 1:

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푦₁

⋮

푎₁

⋮

푒₁

푥₁

0

⋱

0

푧₁

0

⋱

0

푏₁

⋮

[

] = [

] [

] + [

] [

] + [ ]…......………………………….equation 1

푦_푛

푎_푛

푒_푛

0

푥_푛

푏_푛

0

푧_푛

where: 푦₁…푦_푛is the vector of observations on sow reproductive performance traits, 푏₁… b_nis the vector of

fixed effects (only effects that were significant in the preliminary analysis); 푎1 … a_nis the vector of random

animal additive genetic effects assumed to be a ~ N (Aσ²_a) in which the random vector a, follows a multivariate

normal distribution. A is the numerator relationship matrix, which describes the genetic relationships among

individuals based on their pedigree and σ²_ais the additive genetic variance, representing the variability in the

trait due to genetic factors. 푒1 − e_nis the vector of random residual effect assumed to be e ~N (0, Iσ²_e) in which

the random vector e follows a multivariate normal distribution. I is an identity matrix, ascertaining that residual

effects are independent across individuals and σ²_eis the residual variance, representing the variability in the trait

due to non-genetic, random factors; 푥1 … x_nand z1- z_nare incidence matrices relating records to fixed and

random animal effects, respectively.

The random effects were assumed to follow a normal distribution with mean zero and covariance structure as

presented in equation 2:

A_훔퐚ퟐ

0

⋱

0

a

⋮

var [ ] = [

]……………………………………………………………equation 2

e

0

I_훔퐞ퟐ

in which,a is a vector of additive genetic effects; e is a vector of random residual effects; A is the numerator

relationship matrix; I is the identity matrix; σa²is the direct additive genetic variance; σe²is the residual

s

variance.

The (co)variance components and variance ratios from the several analyses were pooled, weighting each estimate

by the inverse of its sampling variance (S.E²). This means that variances, heritability estimates and correlations

estimates or any other mean was pooled using this equation as below.

w x E





_E=

………………………………………… ……………………………..equation 3

w



Ē is the weighted mean, W is the reciprocal of the sampling variance (weight), E is the variance component and

ratio to be pooled.

RESULTS

Data structure and summary statistics for reproductive traits are presented in Table 1. Number of animals in the

dataset were 1169, while those with records were 1145. However, animals that showed no records were 24, with

518 sires showing known grandsires. Consequently, dams with progeny were 113. The overall mean of LSB was

10.83 piglets, NPBA was 9.41±2.76 piglets, LSW was 8.72 piglets, and IFI was 147.79 days, respectively.

Table 4.1 Data structure and summary statistics for studied reproductive traits.

Number of animals in

the dataset

Number of animals

with records

Number of animals

without records

Sires with

known

Dams with

progeny

grandsire

1169

1145

24

518

113

LSB

NPBA

LSW

IFI

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Mean

10.83

2.79

2

9.41

2.65

1

8.72

2.50

1

147.79

34.48

101

Standard deviation

Minimum

Maximum

19

15

282

^aLSB=Litter size at birth; LSW= Litter size at weaning; NPBA= Number of piglets born alive; IFI= Inter

farrowing interval; AFF= Age at first farrowing.

2

Estimates of genetic (σ_a) and phenotypic (σ_p) variances and heritability for reproductive traits in sows are

presented in Table 2. Genetic variances were LSB (0.10±0.30), NPBA (1.33±1.80), LSW (0.01±0.21) and IFI

(23.29±22.71). Phenotypic variances were LSB (7.63±0.33), NPBA (6.67±0.29), LSW (6.03±0.26) and IFI

(589.38±23.48). Heritability estimates for reproductive traits were low. The estimate for LSB was 0.014±0.040,

NPBA was 0.011±0.039, LSW was 0.001±0.035 and IFI was 0.039±0.038.

Table 2 Traits, variance components and heritability.

Traits

Variance components

σa²

Heritability

σp²

LSB

NPBA

LSW

IFI

0.10±0.03

7.63±0.33

6.67±0.29

6.03±0.26

589.40±25.48

0.014±0.040

0.011±0.039

0.001±0.035

0.039±0.038

1.33±1.80

0.01±0.21

23.28±22.71

^aLSB=litter size at birth, NPBA=number of piglets born alive, LSW=litter size at weaning, IFI= inter-farrowing

interval. σ²a= Genetic variance, σ²p= phenotypic variance.

The high genetic variance observed in IFI suggests that direct selection for this trait could yield significant

genetic responses, as genetic variation is the foundation of evolutionary progress (Meryer, 2005; Briggs &

Walters, 2016; Dobrzański et al., 2020). However, the small dataset may have inflated these estimates,

necessitating cautious interpretation to avoid genetic bias in improvement programs. On the other hand, the low

genetic variance in LSB, NPBA, and LSW indicates limited genetic diversity and low response to direct selection

for these traits, which reduces the resilience and persistence of the population. Therefore, genetic improvement

for these traits may require leveraging information from progeny and ancestors, rather than relying solely on

direct selection (Meuwissen et al., 2016; Rauw & Gomez-Raya, 2015; Walsh & Lynch, 2018). Furthermore,

higher phenotypic variance compared to genetic variance across traits suggests a substantial influence of

environmental factors. Sows should be selected to perform in environments similar to the study region (western

region) to mitigate genotype-by-environment interactions, emphasizing the role of management in trait

improvement.

The generally low heritability estimates across the traits indicate slow genetic progress for these traits if genetic

selection is applied (Reproto, 2020; Samorè & Fontanesi, 2016; Rydhmer, 2000). The heritability of LSW, as

low as 1%, underscores that most variability in this trait is environmentally driven, necessitating management-

based improvement strategies. Interestingly, heritability estimates appeared to increase from LSB to IFI, except

for LSW, highlighting a shift in the influence of genetic factors toward traits associated with farrowing intervals.

These findings contrast with reports for other breeds, which often show higher heritability due to the exclusion

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of permanent environmental effects (Hermesch et al., 2001; Ajayi & Akinokun, 2013; Freyer, 2018). High

heritability values, such as 0.5, indicate a stronger genetic influence, whereas low values around 0.1 imply

environmental dominance (Kavlak & Uimari, 2019; Homma et al., 2021; Poulsen et al., 2020). For traits with

low heritability, genetic improvement can be achieved by employing advanced selective breeding methods that

integrate multiple sources of information, such as pedigree data, genomic information, and repeated

measurements, rather than relying solely on mass selection. By leveraging these combined strategies, the

accuracy of selection can be enhanced, even for traits with substantial environmental variability.

Table 3 Genetic (below the diagonal) and phenotypic (above the diagonal) correlations of reproductive

traits studied.

Traits^d

LSB

1

NPBA

0.810±0.011

1

LSW

IFI

0.655±0.018

0.821±0.010

1

0.019±0.031

0.004±0.031

0.034±0.031

1

NPBA

LSW

IFI

0.227

-0.555

0.865

-0.924

-0.283

-0.079

^cStandard errors after the ± sign ^dLSB=litter size at birth; NPBA=number of piglets born alive; LSW= litter size

at weaning; IFI=, inter-farrowing interval

There was a positive genetic correlation between NPBA and LSB (0.227) and between IFI and LSB (0.865).

Conversely, negative genetic correlations were observed between LSW and LSB (-0.555), LSW and NPBA (-

0.924), and IFI and NPBA (-0.283).Phenotypic correlation estimates showed strong positive relationships

between LSB and NPBA (0.810 ± 0.011), LSB and LSW (0.655 ± 0.018), and NPBA and LSW (0.821 ± 0.010).

However, low positive phenotypic correlations were noted between LSB and IFI (0.019 ± 0.031), NPBA and IFI

(0.004 ± 0.031), and LSW and IFI (0.034 ± 0.031).

The genetic correlations observed among reproductive traits highlight complex relationships. Negative genetic

correlations between traits such as LSB and LSW, and NPBA and LSW, indicate that improving one trait could

adversely affect the other, presenting challenges for breeding programs (Serão et al., 2014; Yu et al., 2022; Lee

et al., 2015). In contrast, positive correlations, such as between LSB and NPBA, suggest opportunities for multi-

trait breeding strategies. The low negative genetic correlation between LSW and IFI implies that improving litter

size at weaning could slightly reduce inter-farrowing intervals. However, the strong negative correlation between

NPBA and IFI suggests that increasing the number of piglets born alive may significantly shorten the inter-

farrowing interval, offering potential for reproductive efficiency. NPBA emerges as a promising reproductive

trait for breeding improvement due to its favorable correlations with other traits.

CONCLUSION

The low genetic variance compared to phenotypic variance across traits indicates a strong influence of

environmental factors on reproductive traits, limiting the genetic contribution to variability. The very low

heritability values with high standard errors highlight the limited potential for genetic improvement through

selection and emphasize the use of other sources of information like progeny and ancestors. The genetic

correlations with both positive and negative relationships, suggesting the need for careful, balanced selection to

avoid unfavorable genetic trade-offs between traits. Strong positive phenotypic correlations between traits like

LSB and NPBA suggest shared environmental influences, underscoring the importance of improved

management for overall reproductive performance.

The low genetic variance compared to phenotypic variance across traits indicates a strong influence of

environmental factors on reproductive traits, limiting the genetic contribution to variability. The very low

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heritability values with high standard errors highlight the limited potential for genetic improvement through

selection and emphasize the use of other sources of information like progeny and ancestors.. The genetic

correlations with both positive and negative relationships, suggesting the need for careful, balanced selection to

avoid unfavorable genetic trade-offs between traits. Strong positive phenotypic correlations between traits like

LSB and NPBA (0.810) suggest shared environmental influences, underscoring the importance of improved

management for overall reproductive performancethe low genetic variance compared to phenotypic variance

across traits indicates a strong influence of environmental factors on reproductive traits, limiting the genetic

contribution to variability. The very low heritability values with high standard errors highlight the limited

potential for genetic improvement through selection and emphasize the use of other sources of information like

progeny and ancestors.. The genetic correlations with both positive and negative relationships, suggesting the

need for careful, balanced selection to avoid unfavorable genetic trade-offs between traits. Strong positive

phenotypic correlations between traits like LSB and NPBA (0.810) suggest shared environmental influences,

underscoring the importance of improved management for overall reproductive performanceThe strong direct

relationship between litter size at birth (LSB) and litter size at weaning (LSW) is highly significant. Larger litter

sizes at birth in all the herds generally resulted in a larger number of piglets at weaning. Effective management

of larger litters requires proper resource allocation, including adequate nutrition, space, and healthcare (Maes et

al., 2020), to support the survival and growth of all piglets. This was observed in herd 1, where feeding was

conducted twice daily and records were kept up to date. Thekkoot et al. (2016), in agreement with this study, in

their research on parameter estimation of reproductive traits, suggest that traits associated with lactation in sows

have a sizable genetic component and show potential for genetic improvement.The strong direct relationship

between litter size at birth (LSB) and litter size at weaning (LSW) is highly significant. Larger litter sizes at birth

in all the herds generally resulted in a larger number of piglets at weaning. Effective management of larger litters

requires proper resource allocation, including adequate nutrition, space, and healthcare (Maes et al., 2020), to

support the survival and growth of all piglets. This was observed in herd 1, where feeding was conducted twice

daily and records were kept up to date. Thekkoot et al. (2016), in agreement with this study, in their research on

parameter estimation of reproductive traits, suggest that traits associated with lactation in sows have a sizable

genetic component and show potential for genetic improvement.

ACKNOWLEDGMENT

The authors acknowledge Maseno University for availing facilities for this research and the pig producers in

western Kenya who volunteered their data for this study.

Conflict of Interest

The authors declare no conflict of interest.

Data availability statement

The data that support the findings of this study is available from the corresponding author upon reasonable

request

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Figure 1. Heritability Estimates

Figure 2. Phenotypic Correlation Heatmap

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