Genotype by Environment interactions for egg number and egg weight of five dual-purpose chicken breeds in different zones of Oromia region in Ethiopia

Kinderen, Maud Allegonda Johanna de; Sölkner, Johann; Mészáros, Gábor; Alemu, Setegn Worku; Esatu, Wondmeneh; Bastiaansen, J.W.M.; Komen, Hans; Dessie, Tadelle

doi:10.15414/afz.2020.23.mi-fpap.205-213

Cited by 4 publications

(8 citation statements)

References 25 publications

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“…Some of the breeds introduced into smallholder systems of the tropics were developed for temperate production systems (i.e., assuming reasonable level of environmental uniformity in their management) and did not exhibit optimum performance across heterogenous environments. The lack of proper matching between the right genetics and the right environment is a major factor for low productivity of improved chicken breeds in the tropics (Lozano-Jaramillo et al, 2019b;de Kinderen et al, 2020). Studies conducted in Ethiopia have shown that breeding objectives of chicken producers and levels of production performance of breeds in terms of total number of eggs and growth vary across agroecologies (Lozano Jaramillo et al, 2018;Tilahun et al, 2022) indicating the need for a more tailored genetic improvement and livestock technology scaling approach.…”

Section: Introductionmentioning

confidence: 99%

“…Current approaches of evaluating GxE for chicken breeds introduced into smallholder systems have significant drawbacks. Firstly, breed comparison studies are based on agroecologies defined on suitability of the environment for major crops (Lozano-Jaramillo et al, 2019b;de Kinderen et al, 2020). Agroecological zonation refers to the spatial classification of the geographic and environmental landscape into units, within each of them agricultural and ecological characteristics are similar (Hurni, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

Kebede,

Komen,

Dessie

et al. 2023

Front. Sustain. Food Syst.

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Animal performance is an outcome of genetic effects, environmental influences, and their interaction. Understanding the influences of the environment on performance is important to identify the right breeds for a given environment. Agroecological zonation is commonly used to classify environments and compare the performance of breeds before their wider introduction into a new environment. Environmental classes, also referred to as agroecologies, are traditionally defined based on agronomically important environmental predictors. We hypothesized that our own classification of agroecologies for livestock at a species level and incorporating the most important environmental predictors may improve genotype by environment interactions (GxE) estimations over conventional methodology. We collected growth performance data on improved chicken breeds distributed to multiple environments in Ethiopia. We applied species distribution models (SDMs) to identify the most relevant environmental predictors and to group chicken performance testing sites into agroecologies. We fitted linear mixed-effects models (LMM) to make model comparisons between conventional and SDM-defined agroecologies. Then we used Generalized Additive Models (GAMs) to visualize the influences of SDM-identified environmental predictors on the live body weight of chickens at species level. The model fit in LMM for GxE prediction improved when agroecologies were defined based on SDM-identified environmental predictors. Partial dependence plots (PDPs) produced by GAMs showed complex relationships between environmental predictors and body weight. Our findings suggest that multi-environment performance evaluations of candidate breeds should be based on SDM-defined environmental classes or agroecologies. Moreover, our study shows that GAMs are well-suited to visualizing the influences of bioclimatic factors on livestock performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

Kebede,

Komen,

Dessie

et al. 2023

Front. Sustain. Food Syst.

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“…Multi-environment performance evaluation of candidate breeds developed elsewhere requires proper definition of livestock production agroecologies to improve GxE estimations. Knowledge on GxE helps to select breeds adapted to, and productive under specific or wider environmental conditions (Birhanu et al, 2021;de Kinderen et al, 2020;Lozano-Jaramillo et al, 2019a;Lozano-Jaramillo et al, 2019b). Existing agroecological zone definitions in Ethiopia (Deressa et al, 2010;Dove, 1890;MoA, 1998;Tadesse Mulugeta, 2006) are based on environmental predictors that are primarily of agronomic importance.…”

Section: Discussionmentioning

confidence: 99%

“…Analytical frameworks (experimental designs and evaluation methods) are not available to compare candidate breeds and identify those with optimal productive performance and adaptability for wider use by smallholder farmers. Previous researches that focused on estimating GxE in smallholder poultry systems de Kinderen et al, 2020;Lozano-Jaramillo et al, 2019a;Lozano-Jaramillo, 2019) did not consider evaluation of yield stability in livestock performance analyses. Yield stability refers to the ability of a breed to be less affected by environmental influences and to provide reasonable yield.…”

Section: Discussionmentioning

confidence: 99%

“…I hypothesized that model fit in GxE predictions could be improved if I redefine agroecologies for chicken production based on environmental predictors influencing habitat suitability for chickens. This is unlike previous GxE studies conducted on introduced chickens (de Kinderen et al, 2020;Lozano-Jaramillo et al, 2019a) which relied on conventional agroecological classifications to evaluate performance. My findings suggest that this grouping of environments into environmentally homogenous units based on the most contributing environmental predictors is useful to improve model fit in GxE estimations.…”

Section: Distribution-model-defined-agroecologies Improve Model Fit I...mentioning

confidence: 92%

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Adaptive phenotypic and genetic variation in chickens: a landscape genomics approach

Kebede¹

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. Adaptive phenotypic and genetic variation in chickens: a landscape genomics approach Smallholder chicken production is an integral part of tropical farming systems and contributes significantly to sustainable livelihoods. Performance of chickens in these systems is too low to meet the growing demands for meat and eggs. Unavailability of productive and adaptive breeds is a major constraint. Knowledge on phenotypic and genetic variation among populations contributes to the design of sustainable chicken genetic improvement and development programmes. I follow a landscape genomics approach and integrate genetic, phenotypic, and environmental information in my study design. In the first part of this thesis, I aim to identify the environmental drivers of local adaptation and detect genomic footprints of natural selection in indigenous chickens. I use species distribution models (SDMs) to identify environmental predictors associated with habitat suitability. Based on higher level of matching between the presence of distinct phenotypes and availability of unique environmental niches, I classify the Ethiopian chicken populations into three ecotypes. I perform selection signatures analyses ( 𝐹 and XP-EHH) and redundancy analyses (RDA) at different analytical layers (considering gradient and agroecology) to identify candidate loci and genomic regions linked mainly with local adaptation. I show that Ethiopian chicken populations differentiated the most between gradients but selection pressures leading to adaptive variation are stronger between agroecologies. I indicate that the results from RDA match the outputs from signatures of selection analyses ( 𝐹 and XP-EHH). I show that RDA can be used as an alternative approach to GWAS in random mating, indigenous chicken populations which have sufficiently interacted with the environment. I demonstrate that signatures of selection analysis with the two methods ( 𝐹 and XP-EHH) can be used complementarily with RDA to shed light on the relationship between genomic, phenotypic, and environmental variation in local adaptation studies in indigenous chickens. I show that phenotypic distribution models (PDMs) such as boosted generalized additive models (GAMs) are valuable tools in animal breeding to integrate environmental and phenotypic information and to predict phenotypic values. In the second part of the thesis, I evaluate the performance of improved chicken breeds introduced into smallholder systems. I show that that agroecologies defined by SDMs improve model fit in GxE predictions. I utilize the concept of phenotypic plasticity to compare yield stability of improved chicken breeds. I show that two approaches of multi-environment breed performance analysis (MEPA), namely, additive main effects and multiplicative interaction models (AMMI) and linear mixed-effects models (LMM) are applicable in chicken to identify and recommend more productive and stable breeds. Together, I demonstrate how adaptive phenotypic and genetic variation can be exploited to enhance the performance of chick...

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Genotype by Environment Interactions (G*E) of Chickens Tested in Ethiopia Using Body Weight as a Performance Trait

de Kinderen,

Sölkner,

Mészáros

et al. 2023

Animals

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Ethiopia is a developing nation that could highly benefit from securing food via improved smallholder poultry farming. To support farmer and breeding decisions regarding which chicken strain to use in which Ethiopian environment, G*E analyses for body weight (BW) of growing male and female chickens were conducted. Research questions were (1) if a G*E is present for BW and (2) which strain performs best in which environment in terms of predicted BW. Analyses were performed using predicted BW at four different ages (90, 120, 150, and 180 days) of five strains (Horro, Koekoek, Kuroiler, Sasso-Rhode Island Red (S-RIR), and Sasso) tested in five Ethiopian regions (Addis Ababa, Amhara, Oromia, South Region, and Tigray) that are part of three Agro-Ecological Zones (AEZ) (cool humid, cool sub-humid, and warm semi-arid). The indigenous Horro strain was used as a control group to compare four other introduced tropically adapted strains. The dataset consisted of 999 female and 989 male farm-average BW measurements. G*E was strongly present (p < 0.001) for all combinations of strain and region analyzed. In line with previous research, Sasso was shown to have the highest predicted BW, especially at an early age, followed by Kuroiler. Horro had the lowest predicted BW at most ages and in most regions, potentially due to its young breeding program. The highest predicted BW were observed in Tigray, Oromia, and Amhara regions, which are in the main part of the cool sub-humid AEZ.

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Genotype by Environment interactions for egg number and egg weight of five dual-purpose chicken breeds in different zones of Oromia region in Ethiopia

Cited by 4 publications

References 25 publications

Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

Adaptive phenotypic and genetic variation in chickens: a landscape genomics approach

Genotype by Environment Interactions (G*E) of Chickens Tested in Ethiopia Using Body Weight as a Performance Trait

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