College of Agriculture

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Author: search.filters.author.ADDISU JIMMA FALITASubject: search.filters.subject.Offtake-rate

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    PERFORMANCE EVALUATION AND SCALING OPTIONS FOR DOYOGENA SHEEP COMMUNITY-BASED BREEDING PROGRAM IN CENTRAL ETHIOPIA
    (Hawassa University College of Agriculture, 2025) ADDISU JIMMA FALITA
    ABSTRACT The studies were carried out to: (1) explore genetic parameters for growth, reproductive, and survival traits; (2) asses morphological changes in the animals and socioeconomic benefits to the community, and describe the flock dynamics of Doyogena sheep; and (3) develop and compare effective scaling options for Doyogena Community Based Breeding Program (CBBP). Data were collected over ten years (2013–2022) on the following traits: birth weight (BWT), weaning weight (WWT), six months weight (SMW), annual reproductive rate (ARR), lambing interval (LI), lambing interval (LI), litter weights and sizes at birth (LWB, LSB) and weaning (LWW, LSW), ewe post-partum weight (EPPW), pre-weaning lamb survival (PWLS), and lamb survival up to six months (LSSM). Variance components and genetic parameters were investigated using the average information-restricted maximum likelihood algorithm were applied in a multivariate mixed model. The WOMBAT and DMU software was used for analyses. A survey of 260 farmers (130 CBBP members, 130 non-members) captured perceptions and flock data. Five scaling strategies were modeled using gene flow method, assuming a nucleus and production unit structure. Results indicated that the Additive heritability estimates were 0.37 (BWT), 0.26 (WWT), 0.21 (SMW), 0.10 (LSB), 0.24 (LWB), 0.03 (LSW), and 0.22 (LWW). For LI, ARR, EPPW, PWLS, and LSSM, estimates were 0.15, 0.12, 0.14, 0.10, and 0.08, respectively. Most performance traits showed positive and significant (P < 0.001) annual genetic trends. Positive and low to moderate genetic correlations were observed between growth traits (WWT, SMW) and most reproduction and lamb survival traits. CBBP members had significantly (p<0.05) larger flock size, with more lambs under 3 months, male lambs (3-6 months), intact males (6-12 months), breeding rams, and mature ewes. The primary routes of sheep entry into flocks were birth (81%), and purchase (17%). The total number of entries (284 vs. 240) and births (148 vs. 112) was higher (p<0.05) for CBBP members than non-members. The offtake-rate, representing the proportion of sheep exiting the flock was also significantly higher (p<0.05) for CBBP members (36.45%) than non-members (17.35%). The CBBP was attributed to performance improvements in traits such as growth, coat color, litter size, survival, and lambing interval. Among scaling options, strategy 3 showed the highest return on investment (ROI) at the xvii production unit level ($122), while Strategy 5 showed the highest nucleus ROI ($8.5) and the greatest accumulated discounted benefits (nucleus: $62,847; production: $353,757). The highest genetic progress at the nucleus level was achieved with strategy 4 (27.88 kg), where a proportion of sires were produced through AI (Artificial Insemination). Strategy 2 and 5 also outperformed the baseline strategy 1 at SMW trait levels, indicating that increasing the number of breeding females and retaining all candidates has a positive effect. Although strategy 3 did not achieve the highest genetic gains, it delivered a competitive nucleus trait level (SMW=26.00 kg) while maximizing dissemination, leading to exceptional economic outcomes. The AI-based strategies (strategies 4 and 5) produced higher SMW, but their economic returns varied. The medium heritability and moderate correlations between growth and litter weight traits (LWB and LWW) suggest the importance of considering litter weight in addition to growth traits in selection processes. The CBBP was effective and well-accepted by participants. Scaling up is recommended to benefit more farmers. The AI integration scaling strategy can accelerate genetic progress, optimal economic outcomes depend on effective candidate retention, dissemination strategies, and aligning breeding strategies with operational capacity. Policymakers and practitioners should tailor adoption strategies to their specific genetic improvement targets, financial constraints, and infrastructural readiness to ensure that both genetic and economic goals are met sustainably.