Indigenous Knowledge of Farmers in Breeding Practice and Selection Criteria of Dairy Cows at Chora and Gechi Districts of Ethiopia: An Implication for Genetic Improvements

Gebisa, Gelaye; Dewo, Oda Gizaw; Desta, Mengistu Asrat; Nwogwugwu, Chiemela Peter

doi:10.1155/2022/3763724

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Most of the available knowledge is conventional. The younger generations are, therefore, more receptive to CK than IK (Ndlela et al 2021 ; Bulcha et al 2022 ). Most youths associate IK with backwardness.…”

Section: Discussionmentioning

confidence: 99%

Factors influencing the integration of indigenous and conventional knowledge of water security for livestock

Getyengana

Kamba

Mkwanazi

et al. 2023

Trop Anim Health Prod

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Farmers have developed indigenous knowledge (IK) on predictive and adaptation strategies to sustain water security. The objective of the study was to determine factors that influence the integration of IK and conventional knowledge (CK) to ensure water security for livestock. Focus group discussions and semi-structured interviews were used to gather data. Farmers in Musina and uMhlabuyalingana use IK indicators to predict rain for water security. Farmers in uMhlabuyalingana predicted rain using wind movement more than their counterparts in Musina (P < 0.05). Taboos were used (P > 0.05) in both Musina and uMhlabuyalingana to ensure water security. Cultural prohibitions were used more in uMhlabuyalingana to cope with water shortages than in Musina (P < 0.05). Boreholes, home taps and municipal water were not properly maintained (P > 0.05). Socio-economic factors had greater influence on the integration of IK and CK. Males were 2.24 times likely to support integration of IK and CK compared to women. Adults were 7.1 times likely to support integration of IK and CK compared to those that were younger. Farmers were open to the integration of conventional and indigenous knowledge to ensure water security. Promoting the integration of IK and CK enables stakeholders to gain access to valuable information which can, in turn, promote sustainable community development.

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Section: Discussionmentioning

confidence: 99%

Factors influencing the integration of indigenous and conventional knowledge of water security for livestock

Getyengana

Kamba

Mkwanazi

et al. 2023

Trop Anim Health Prod

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“…First of all, the majority of smallholder systems' dairy cows are either indigenous dairy cattle or crossbreds. Second, the conservation of genetic resources of local breeds that are adapted to specific agro-ecologies is crucial for the sustainability of the breed and biodiversity (Bulcha et al, 2022). Several countries, such as Kenya, Senegal, East Africa, Ethiopia, etc., have already implemented genomic technologies for indigenous breeds in Africa.…”

Section: Potential For ML Applications To Genomic Prediction In Anima...mentioning

confidence: 99%

A review of machine learning models applied to genomic prediction in animal breeding

Chafai,

Hayah,

Houaga

et al. 2023

Front. Genet.

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The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there’s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

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“…This top-down approach method usually backfired. In most of the cases, the respondents were not aware of the needs of the animals, the husbandry practices, and the feed-and nutrition-related issues, and in many cases, the animals could not adjust to the prevailing agroclimate and also the diseases endemic to a given area [2,3,5,13,14]. In many cases, the products obtained from such livestock were not accepted by the end users [15].…”

Section: Introductionmentioning

confidence: 99%

“…However, over the years, the important aspect of indigenous livestock breeding has been grossly overlooked, and very little systematic work has been done so far on the possibilities of improving native breeds of tropical livestock [16]. It has also been reported in several studies that traditional livestock keepers have a better understanding of their livestock than researchers, as they are able to understand the homeostasis between the genotype and the production system where they are raised [13,17].…”

Section: Introductionmentioning

confidence: 99%

“…The participatory approach takes into account the understanding of various stakeholders (local leaders, extension agents, traders, and cattle owners) at different levels, who frankly discuss with the livestock scientists the approaches to be taken for the further lifetime of the species [3,18]. In doing so, the researchers understand the needs and aspirations of the stakeholders and, therefore, together help improve the genotype in question [2,4,13].…”

Section: Introductionmentioning

confidence: 99%

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Participatory Identification of Breeding Objectives and Selection Criteria for Begaria Cattle Breed

Aseged

Getachew²,

Banerjee

2022

Sustainability

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The objectives of this study were to identify the breeding objectives of Begaria cattle keepers. A semi-structured questionnaire, live animal ranking, and focus group discussions were employed to identify the breeding objectives of the farmers. The study was conducted on a total of 75 conveniently sampled households. Among the total cattle population of the sampled households, 506 heads of cattle (252 male and 254 female animals) were ranked as first best, second best, third best, and/or the worst animals. Owners were asked to choose the first best, second best, third best, and worst animals from their herd for both sexes. Reasons for ranking, important morphometric measurements, and (re)production performance of the ranked animal were taken. The R Commander package version 2.6-0 was used to analyze qualitative data and analysis of variance in R software was used to analyze quantitative data. Reasons for ranking of cows in the study area were milk yield (39.14%), calving interval (15.14), temperament (12), and body size (10.86), whereas for bulls, reasons were body size (30.39%), growth (30.66), body condition score (16.85), and color (15.19). Survival rate (Sur), calving interval (CI), milk yield (MY), and lactation milk yield (LMY) for the cows and body condition score (BCS), weaning age (WA), yearling weight (YW), and weaning weight (WW) for the bulls were significantly different (p < 0.05) mainly between the first- and last-ranked animals. Farmers were willing to pay up to Ethiopian birr (ETB) 11,500, 10,700, 10,400, and 9500 on average for first-, second-, and third-ranked breeding females and up to ETB 6300, 5800, 5200, and 4500 for breeding males. Lactation milk yield, calving interval, mothering ability, growth rate, and age at first calving were determined as breeding objective traits of the community. with suggested selection criteria of lactation milk yield, calving interval, survival rate, yearling weight, and age at first calving, respectively.

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Indigenous Knowledge of Farmers in Breeding Practice and Selection Criteria of Dairy Cows at Chora and Gechi Districts of Ethiopia: An Implication for Genetic Improvements

Cited by 5 publications

References 7 publications

Factors influencing the integration of indigenous and conventional knowledge of water security for livestock

Factors influencing the integration of indigenous and conventional knowledge of water security for livestock

A review of machine learning models applied to genomic prediction in animal breeding

Participatory Identification of Breeding Objectives and Selection Criteria for Begaria Cattle Breed

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