Bastiaansen, J.

Dessie, Tadelle

Komen, H.

Lozano-Jaramillo, M.

Lozano Jaramillo, Maria

Komen, Hans

CGSpace

Proceedings of the World Congress on Genetics Applied to Livestock Production, 11. 394Understanding the Effect of the Environmental Conditions on theSuitability of a Breed for Different Agro-Ecological ZonesMaria Lozano-Jaramillo1*, John Bastiaansen1, Tadelle Dessie2, Hans Komen11Wageningen University & Research Animal Breeding and Genomics, PO Box 338, 6700 AHWageningen, The Netherlands*maria.lozanojaramillo@wur.nl (Corresponding Author)2International Livestock Research Institute, P. O. Box 5689 Addis Ababa, EthiopiaSummaryPredicting suitability of breeds for a production system can be challenging in livestock. Mostattempts to introduce exotic breeds in low input systems were unsuccessful mainly due to theantagonistic environmental conditions. Knowledge of the environmental conditions that areshaping the breed would be needed to elucidate their suitability to different locations.Predictive habitat distribution models use the current climatic conditions of a breed to makepredictions of the potential distribution of the breed. A methodology was developed topredict breed suitability for different agro-ecological zones based on GIS tools and PHDmodels. This methodology was tested on distribution data of two introduced chicken breedsin Ethiopia: the Koekoek, originally from South Africa, and the Fayoumi, originally fromEgypt. Results from cross-validation based on the current distribution of the breeds showedthis methodology to be effective in predicting breed specific environmental suitability.Furthermore, for both breeds the significant climatic factors that shape the breeds distributionwere similar between the suggested distribution area, and the environment from which thebreeds originated in South Africa and Egypt. This novel methodology applied to livestockresearch, allows for better decisions in introduction programs and the design of testingschemes, and increases our understanding of the role of the environment in livestockproductivity.Keywords: agro-ecology, breeding programs, distribution models, local adaptationIntroductionIndigenous breeds are exposed to natural selection process, which allow them to acquire traitsthat shape them to be better suited to their environment when compared to introduced breeds.They are known to be locally adapted to specific environmental conditions, as well astolerant to different parasites and diseases (Mirkena et al., 2010). In terms of productivity,however, exotic breeds show an advantage over the local breeds (Kosgey et al., 2006). Betterprediction of the suitability of a breed to environmental circumstances would be beneficial.The application of habitat distribution models is a tool that can be applied to makeinferences on the potential distribution of a breed taking into account different climaticvariables (Thomas et al., 2004; Keith et al., 2008; Phillips and Dudík, 2008; Loarie et al.,2009). Application of predictive habitat distribution models (PHD) to predict suitability ofbreeds in livestock production systems has not been done.Here we test the predictive habitat distribution models on two different introducedpoultry breeds in Ethiopia. Ethiopia is an ecologically highly diverse country with a broadrange of contrasting agro-ecologies (Fig.1) defined by altitude, temperature and rainfallProceedings of the World Congress on Genetics Applied to Livestock Production, 11. 394(Mengistu, 2003). The methodology was used to 1) make predictions for two breeds abouttheir potential suitable habitat range in Ethiopia, and to 2) indicate which bio-ecologicalvariables explain the differences in the areas where the different chicken breeds of chickenoccur. Having a better understanding of the environments’ role will improve the design ofintroduction schemes, and will help to better understand the factors that may cause adifference in breed productivity between environments.Material and methodsEnvironmental and breed dataConsidering all of Ethiopia, we used a total of 161 breed locations, 62 for the Fayoumi breed,and 99 for the Koekoek breed (Fig. 1b), obtained from the Ethiopian Institute of AgriculturalResearch (EIAR). From WorldClim (Hijmans et al., 2005), and the Harmonized World SoilDatabase v 1.2), we collected a total of 21 variables available at a 1km by 1km resolution.The environmental data included 19 bioclimatic variables and an elevation layer representingcurrent climatic conditions. These 20 layers are commonly used as indicators of annualtrends in seasonality, temperature and precipitation. In addition, a land cover layer, totalcultivated land, was included as a proxy to anthropogenic intervention and agriculturalsystems, as smallholders presence and poultry density are closely linked in Ethiopia (Tadelle,2003; Mwacharo et al., 2013).Model building and predicting breed occurrenceTo implement the PHD models, we used the maximum entropy algorithm in Maxent(Phillips et al., 2006). The probability of suitability was presented on a map. We validatedthe model using the receiver operating characteristic (ROC) curve (known areas of presencepredicted absent, Phillips et al., 2006).For each breed independently, we generated a map of the potential distribution. Foreach breed we identified the environmental variables that had the highest predictivecontribution to the model and evaluated these variables against the environmentalbackground of the breed.Cross-validationTo determine if the model predictions could predict breed suitability, we divided thecountry in a total of 110 cells. For each breed independently, the localities within each cellwhere the breed was present were removed from the training data set, and the model wasfitted to predict a probability of occurrence for that same cell. This was done cell by cell forall of the cells that included the occurrence data.For the cells where the breed was not present, a set of random localities, defined asabsent localities was developed and the predicted probability for each of the cells wasestimated as the cells with occurrence data.To evaluate the ability of the model to identify suitable environmental conditions, wefirst selected the cells with reported poultry density greater than 10 individuals per squarekilometer (Robinson et al., 2014). Then the predicted probabilities for the cells withoccurrences and the cells with absences were compared and visualized with a density plot. At-test was applied to test whether both groups were significantly different.Data handling and Maxent analyses were conducted with R version 3.2.2 (RProceedings of the World Congress on Genetics Applied to Livestock Production, 11. 394Development Core Team, 2016) running on RStudio version 0.99.902 (RStudio Team,2015).ResultsPrediction of breed suitability and cross-validationPredicted suitability for cells where the breed is present was greater than the predictedsuitability for cells where the breeds is absent (P < 0.05). For both breeds, the modelpredicted that suitable environmental conditions exist beyond the current boundaries of thebreed distribution (Fig. 2). The area under the ROC curve for the model predicting thepotential distributions of the Fayoumi and Koekoek breeds were close to one (0.981 and0.975 respectively), indicating that the model performed well.Most important climatic conditionsThe variable explaining most of the variation in suitability for the Fayoumi breed(43.7%; Table 1) was associated with total cultivated land. The next two axes jointlyaccounted for 26.3% of the environmental variation (Table 1) and were associated withprecipitation. The variable explaining most of the variation for the Koekoek breed (PC1;18.1%; Table 1) was associated with minimum temperature of the coldest month. The nexttwo axes jointly accounted for 21.6% of the variation (Table 1) and were associated withmean temperature of the warmest quarter, and the range of mean monthly temperature.DiscussionOur results suggest that each breed occupies different climatic environments; the Fayoumibreed is kept in areas where there is a higher percentage of land used in agriculture, andwhere there is higher precipitation, whereas the Koekoek breed is kept in colderenvironments with larger temperature fluctuation. Even though both breeds are kept inoverlapping areas of the country, it should be noted that there are some areas where they donot occur together. The Koekoek breed is kept in some localities associated with tepid to coolmoist and sub-moist mid-highlands. The Fayoumi breed is kept in tepid to cool humid mid-highlands, and hot to warm moist lowlands. The distribution models also suggest that thereare more areas of the country where the Fayoumi and the Koekoek breeds could be suitablefor poultry production. This result can be informative in deciding which breed to keepdepending on the region.Differences on the current distribution of breeds can be attributed to both natural andnon-natural processes, where anthropogenic influence, as well as the environment where thebreeds are kept can play an important role in shaping local adaptation (Mariante and Egito,2002). Part of this biological variation can be explained by the breeds’ origin, and whereenvironmental selective pressures have shaped their adaptation to specific environments. TheFayoumi is a breed of Egyptian origin (Hossaryl and Galal, 1994), while the Koekoekoriginated in South Africa (Grobbelaar et al., 2010). A study that assessed the geneticdiversity of chicken populations in Africa, Asia and Europe revealed that the Fayoumi breedwas grouped with chickens from the Mediterranean, while the Koekoek shared a cluster witheastern European breeds and broiler chickens (Lyimo et al., 2014). This genetic variabilityalso suggests that breeds might respond differently in different climates.In conclusion, this work showed the utility of habitat distribution models in predictingProceedings of the World Congress on Genetics Applied to Livestock Production, 11. 394livestock suitability to different agro-ecologies. Understanding the environmentalrequirements of different breeds, including the knowledge of which environmental variablesare determining the difference in performance, is an important tool to support higherproductivity in particular regions (Arthur and Albers, 2003). The PHD models allows forbetter decisions in introduction programs and in the design of testing schemes, and increasesour understanding of the role of the environment in livestock productivity.FiguresTable 1. Selected environmental variables with their percent contributions to the predictionmodel.Breed Environmental variablePercentagecontributionMin Temperature of Coldest Month 18.1Koekoek Mean Temperature of Warmest Quarter 11.9Mean Diurnal Range 9.7Total cultivated land 43.7Fayoumi Precipitation of Driest Quarter 16.9Precipitation of Coldest Quarter 9.4Figure 1. Map of Ethiopia showing the 18 major agro-ecological zones based ontemperature and precipitation.Proceedings of the World Congress on Genetics Applied to Livestock Production, 11. 394Figure 2. Suitability predictions for (a) Koekoek, and (b) Fayoumi breeds in Ethiopia.Predicted areas are shaded; darker colors denote areas of higher climatic suitability.Observed localities used to build the model are shown in black dots.List of ReferencesArthur, J. A., and G. A. Albers. 2003. Industrial perspective on problems and issuesassociated with poultry breeding. Poultry genetics, breeding and biotechnology:1-12.FAO/IIASA/ISRIC/ISSCAS/JRC. 2012. Harmonized World Soil Database (version 1.2),FAO, Rome, Italy and IIASA, Laxenburg, Austria. Available at:http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (accessed February 2017).Grobbelaar, J., B. Sutherland, and N. Molalakgotla. 2010. Egg production potentials ofcertain indigenous chicken breeds from South Africa. Animal GeneticResources/Ressources génétiques animales/Recursos genéticos animales 46:25-32.Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very highresolution interpolated climate surfaces for global land areas. International Journal ofClimatology 25(15):1965-1978.Hossaryl, M., and E. Galal. 1994. Improvement and adaptation of the Fayoumi chicken.Animal Genetic Resources Information 14:33-39.Keith, D. A., H. R. Akçakaya, W. Thuiller, G. F. Midgley, R. G. Pearson, S. J. Phillips, H. M.Regan, M. B. Araújo, and T. G. Rebelo. 2008. Predicting extinction risks underclimate change: coupling stochastic population models with dynamic bioclimatichabitat models. Biology Letters 4(5):560-563.Kosgey, I., R. Baker, H. Udo, and J. Van Arendonk. 2006. Successes and failures of smallruminant breeding programmes in the tropics: a review. Small Ruminant Research61(1):13-28.Loarie, S. R., P. B. Duffy, H. Hamilton, G. P. Asner, C. B. Field, and D. D. Ackerly. 2009.The velocity of climate change. Nature 462(7276):1052-1055.Lyimo, C., A. Weigend, P. Msoffe, H. Eding, H. Simianer, and S. Weigend. 2014. 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Understanding the effect of the environmental conditions on the suitability of a breed for different agro-ecological zones

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Understanding the effect of the environmental conditions on the suitability of a breed for different agro-ecological zones

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