Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling

In this study we defined smallholders’ sheep breeding objectives using farmers’ trait preferences through a proportional-piling tool and a bio-economic model to evaluate the congruence between the two breeding objectives. We took Menz sheep of Ethiopia as a case study. Multinomial logistic regression analysis showed that there were no statistically significant differences among weights allocated by farmers to growth rate, survival and lambing interval, which formed the high priority traits for Menz farmers. The likelihood of farmers attaching more weight to these three traits was significantly higher than they do to 6-month weight, mature weight, fleece weight and litter size (odds ratio = 1.16 to 2.32, P < 0.001). The percentage of farmers who allocated the highest weights to 6-month weight, mature weight, fleece weight, litter size, growth rate, lambing interval and survival were 0.0, 2.1, 0.0, 2.1, 37.5, 29.2 and 29.2%, respectively. Survival, lambing interval, growth rate and 6-month weight were found to be the most economically important traits using a bio-economic model. A genetic improvement by one σa in these traits resulted in a profit of Birr 31.80 to 58.68 per ewe per year. The average of the correlations between economic values of traits and individual farmers’ trait weights was 0.591 ± 0.231, indicating a fair congruence between farmers’ preferences for traits and economic values of traits. There is a fair congruence between farmers’ preferences for traits and economic values of traits, both in terms of the rankings of traits and their relative weights. This would indicate that weighting traits in selection indexes with farmers’ trait ratings using quantitative methods such as proportional-piling (a simple visual method suitable for illiterate smallholders) would direct genetic improvement towards desired profitability with reasonable accuracy. Conversely, the fair correspondence between the two methods suggests that bio-economic modelling, if designed properly considering farmers concerns, could fairly be used to reflect farmers’ breeding objectives. However, since both methods suffer from drawbacks, complementary use of farmers’ trait preferences and bio-economic modeling would enable the combination of farmers’ indigenous knowledge and choices and the genetic properties of traits and their accurate economic values. This combined approach would enable to arrive at breeding objectives that would result in rapid and profitable genetic progress while maintaining the adaptability of the local genetic resource. This would involve multiple engagements between experts and villagers unlike the usual one-off survey of farmers’ preferences. Further research on the modalities for complementary use of the two methods to define breeding objectives under smallholder conditions is warranted.

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Main Authors: Gizaw, Solomon, Abebe, A., Bisrat, A., Zewdie, T., Tegegne, Azage
Format: Journal Article biblioteca
Language:English
Published: Elsevier 2018-08
Subjects:sheep, animal breeding, small ruminants,
Online Access:https://hdl.handle.net/10568/93067
https://doi.org/10.1016/j.livsci.2018.05.021
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spelling dig-cgspace-10568-930672023-12-08T19:36:04Z Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling Gizaw, Solomon Abebe, A. Bisrat, A. Zewdie, T. Tegegne, Azage sheep animal breeding small ruminants In this study we defined smallholders’ sheep breeding objectives using farmers’ trait preferences through a proportional-piling tool and a bio-economic model to evaluate the congruence between the two breeding objectives. We took Menz sheep of Ethiopia as a case study. Multinomial logistic regression analysis showed that there were no statistically significant differences among weights allocated by farmers to growth rate, survival and lambing interval, which formed the high priority traits for Menz farmers. The likelihood of farmers attaching more weight to these three traits was significantly higher than they do to 6-month weight, mature weight, fleece weight and litter size (odds ratio = 1.16 to 2.32, P < 0.001). The percentage of farmers who allocated the highest weights to 6-month weight, mature weight, fleece weight, litter size, growth rate, lambing interval and survival were 0.0, 2.1, 0.0, 2.1, 37.5, 29.2 and 29.2%, respectively. Survival, lambing interval, growth rate and 6-month weight were found to be the most economically important traits using a bio-economic model. A genetic improvement by one σa in these traits resulted in a profit of Birr 31.80 to 58.68 per ewe per year. The average of the correlations between economic values of traits and individual farmers’ trait weights was 0.591 ± 0.231, indicating a fair congruence between farmers’ preferences for traits and economic values of traits. There is a fair congruence between farmers’ preferences for traits and economic values of traits, both in terms of the rankings of traits and their relative weights. This would indicate that weighting traits in selection indexes with farmers’ trait ratings using quantitative methods such as proportional-piling (a simple visual method suitable for illiterate smallholders) would direct genetic improvement towards desired profitability with reasonable accuracy. Conversely, the fair correspondence between the two methods suggests that bio-economic modelling, if designed properly considering farmers concerns, could fairly be used to reflect farmers’ breeding objectives. However, since both methods suffer from drawbacks, complementary use of farmers’ trait preferences and bio-economic modeling would enable the combination of farmers’ indigenous knowledge and choices and the genetic properties of traits and their accurate economic values. This combined approach would enable to arrive at breeding objectives that would result in rapid and profitable genetic progress while maintaining the adaptability of the local genetic resource. This would involve multiple engagements between experts and villagers unlike the usual one-off survey of farmers’ preferences. Further research on the modalities for complementary use of the two methods to define breeding objectives under smallholder conditions is warranted. 2018-08 2018-06-06T08:35:27Z 2018-06-06T08:35:27Z Journal Article Gizaw, S., Abebe, A., Bisrat, A., Zewdie, T. and Tegegne, A. 2018. Defining smallholders’ sheep breeding objectives using farmers trait preferences versus bio-economic modelling. Livestock Science 1871-1413 https://hdl.handle.net/10568/93067 https://doi.org/10.1016/j.livsci.2018.05.021 en Copyrighted; all rights reserved Limited Access Elsevier Livestock Science
institution CGIAR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cgspace
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CGIAR
language English
topic sheep
animal breeding
small ruminants
sheep
animal breeding
small ruminants
spellingShingle sheep
animal breeding
small ruminants
sheep
animal breeding
small ruminants
Gizaw, Solomon
Abebe, A.
Bisrat, A.
Zewdie, T.
Tegegne, Azage
Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
description In this study we defined smallholders’ sheep breeding objectives using farmers’ trait preferences through a proportional-piling tool and a bio-economic model to evaluate the congruence between the two breeding objectives. We took Menz sheep of Ethiopia as a case study. Multinomial logistic regression analysis showed that there were no statistically significant differences among weights allocated by farmers to growth rate, survival and lambing interval, which formed the high priority traits for Menz farmers. The likelihood of farmers attaching more weight to these three traits was significantly higher than they do to 6-month weight, mature weight, fleece weight and litter size (odds ratio = 1.16 to 2.32, P < 0.001). The percentage of farmers who allocated the highest weights to 6-month weight, mature weight, fleece weight, litter size, growth rate, lambing interval and survival were 0.0, 2.1, 0.0, 2.1, 37.5, 29.2 and 29.2%, respectively. Survival, lambing interval, growth rate and 6-month weight were found to be the most economically important traits using a bio-economic model. A genetic improvement by one σa in these traits resulted in a profit of Birr 31.80 to 58.68 per ewe per year. The average of the correlations between economic values of traits and individual farmers’ trait weights was 0.591 ± 0.231, indicating a fair congruence between farmers’ preferences for traits and economic values of traits. There is a fair congruence between farmers’ preferences for traits and economic values of traits, both in terms of the rankings of traits and their relative weights. This would indicate that weighting traits in selection indexes with farmers’ trait ratings using quantitative methods such as proportional-piling (a simple visual method suitable for illiterate smallholders) would direct genetic improvement towards desired profitability with reasonable accuracy. Conversely, the fair correspondence between the two methods suggests that bio-economic modelling, if designed properly considering farmers concerns, could fairly be used to reflect farmers’ breeding objectives. However, since both methods suffer from drawbacks, complementary use of farmers’ trait preferences and bio-economic modeling would enable the combination of farmers’ indigenous knowledge and choices and the genetic properties of traits and their accurate economic values. This combined approach would enable to arrive at breeding objectives that would result in rapid and profitable genetic progress while maintaining the adaptability of the local genetic resource. This would involve multiple engagements between experts and villagers unlike the usual one-off survey of farmers’ preferences. Further research on the modalities for complementary use of the two methods to define breeding objectives under smallholder conditions is warranted.
format Journal Article
topic_facet sheep
animal breeding
small ruminants
author Gizaw, Solomon
Abebe, A.
Bisrat, A.
Zewdie, T.
Tegegne, Azage
author_facet Gizaw, Solomon
Abebe, A.
Bisrat, A.
Zewdie, T.
Tegegne, Azage
author_sort Gizaw, Solomon
title Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
title_short Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
title_full Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
title_fullStr Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
title_full_unstemmed Defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
title_sort defining smallholders' sheep breeding objectives using farmers trait preferences versus bio-economic modelling
publisher Elsevier
publishDate 2018-08
url https://hdl.handle.net/10568/93067
https://doi.org/10.1016/j.livsci.2018.05.021
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