ABSTRACT Background Chicken production in the context of nutrition-sensitive agriculture may benefit child nutrition in low-income settings. Objectives This study evaluated effects of 1) a chicken production intervention [African Chicken Genetic Gains (ACGG)], and 2) the ACGG intervention with nutrition-sensitive behavior change communication (BCC) [ACGG + Agriculture to Nutrition (ATONU)], on child nutrition and health outcomes and hypothesized intermediaries. Methods Forty ACGG villages received 25 genetically improved chickens and basic husbandry guidance; of these, 20 ACGG + ATONU villages in addition received a nutrition-sensitive behavior change and homegardening intervention; 20 control clusters received no intervention. We assessed effects of the interventions on height-for-age z scores (HAZ), weight-for-age z scores (WAZ), and weight-for-height z scores (WHZ) at 9 (midline) and 18 mo (endline) through unadjusted and adjusted ordinary least squares (OLS) regressions. We examined the interventions’ effects on hypothesized intermediaries including egg production and consumption, dietary diversity, women's empowerment, income, child morbidities, anemia, and chicken management practices through OLS and log binomial models. Results Data included 829 children aged 0–36 mo at baseline. ACGG + ATONU children had higher midline HAZ [mean difference (MD): 0.28; 95% CI: 0.02, 0.54] than controls. The ACGG group had higher HAZ (MD: 0.28; 95% CI: 0.05, 0.50) and higher WAZ (MD: 0.18; 95% CI: 0.01, 0.36) at endline than controls; after adjusting for potential baseline imbalance, effects were similar but not statistically significant. At endline, differences in ACGG + ATONU children's HAZ and WAZ compared with controls were similar in magnitude to those of ACGG, but not statistically significant. There were no differences in anthropometry between the intervention groups. ACGG + ATONU children had higher dietary diversity and egg consumption than ACGG children at endline. Both interventions showed improvements in chicken management practices. The interventions did not increase anemia, diarrhea, fever, or vomiting, and the ACGG + ATONU group at midline showed reduced risk of fever. Conclusions A chicken production intervention with or without nutrition-sensitive BCC may have benefited child nutrition and did not increase morbidity. This trial was registered at clinicaltrials.gov as NCT03152227.

Genetic and environmental parameters were estimated for pre-and post-weaning average daily gain (ADG1, ADG2) and Kleiber ratio (KR1, KR2) using the ASREML program. Twelve models, formed with inclusion or exclusion of the maternal genetic, permanent environmental and common (litter) environmental variance components and the covariance between the direct and maternal additive effect on the basic direct additive genetic model, were used. The same models were applied to birth weight (BWT), weaning weight (WWT) and bi-monthly weights to 12 months of age (WT2 to WT12), and weight at 18 months of age (WT18). Two-trait analyses were done among all traits. Maternal genetic and common environmental components were found to be important for ADG1, KR1 and weights up to six-months of age, while the common environmental component was found to be important for ADG2 and KR2. The maternal permanent environmental component was important for WT2 and WWT. Total heritability estimates for ADG1, ADG2, KR1 and KR2 were 0.13, 0.04, 0.13, and 0.01, respectively. Direct genetic correlations of ADG1 with BWT, WWT and WT6 were 0.01, 0.96 and 0.84 while with KR1 they were -0.40, 0.75 and 0.66, respectively. The relatively higher heritability in weight traits and the presence of positive and high correlations of weight traits with daily gain and Kleiber ratio tend to suggest that it would be more practical to select on the weight traits to improve gain and efficiency.

Variance components and genetic parameters were estimated for growth traits: birth weight (BWT), weaning weight (WWT), 6‐month weight (6MWT) and yearling weight (YWT) in indigenous Ethiopian Horro sheep using the average information REML (AIREML). Four different models: sire model (model 1), direct animal model (model 2), direct and maternal animal model (model 3) and direct–maternal animal model including the covariance between direct and maternal effects (model 4) were used. Bivariate analysis by model 2 was also used to estimate genetic correlation between traits. Estimates of direct heritability obtained from models 1–4, respectively, were for BWT 0.25, 0.27, 0.18 and 0.32; for WWT, 0.16, 0.26, 0.1 and 0.14; for 6MWT 0.18, 0.26, 0.16 and 0.16; and for YWT 0.30, 0.28, 0.23, and 0.31. Maternal heritability estimates of 0.12 and 0.23 for BWT; 0.19 and 0.24 for WWT; 0.09 and 0.09 for 6MWT and 0.08 and 0.14 for YWT were obtained from models 3 and 4, respectively. The correlations between direct and maternal additive genetic effects for BWT, WWT, 6MWT and YWT were –0.64, –0.42, 0.002 and –0.46, respectively. On the other hand, the genetic correlations between BWT and the rest of growth traits (WWT, 6MWT and YWT, respectively) were 0.45, 0.33 and 0.31, whereas correlations between WWT and 6MWT, WWT and YWT and 6MWT and YWT were 0.98, 0.84 and 0.87, respectively. The medium to high direct and maternal heritability estimates obtained for BWT and YWT indicate that in Horro sheep faster genetic improvement through selection is possible for these traits and it should consider both (direct and maternal) h2 estimates. However, since the direct‐maternal genetic covariances were found to be negative, caution should be made in making selection decisions. The high genetic correlation among early growth traits imply that genetic improvement in any one of the traits could be made through indirect selection for correlated traits.

Abstract. Weight (kg)-age (days) data of 524 Horro sheep of Ethiopia were fitted to a Brody function to estimate parameters of growth curve and their genetic and phenotypic parameters. Genetic and phenotypic relationships were also estimated between growth curve parameters and weight at birth (BW), weaning (WW) six-month (WT6) and yearling (YW). For ewes Pearson correlations were also calculated between growth curve parameters and ewe productivity over first to fourth parities. Least squares means of growth curve parameters A (asymptotic mature weight, kg), B (proportion of mature weight attained after birth) and K (the rate of maturity, kg gain kg-1 body weight) were 37.6, 0.88, and 0.27∙10-2, respectively. Heritability estimates were 0.29, 0.18 and 0.14 for A, B, and K, respectively. Genetic correlations between A and B, A and K, and B and K were 0.39, −0.07, and 0.25 respectively. Genetic correlations of A and K with BW, WW, WT6, and YW were 0.27 and −0.13, 0.34 and 0.37, 0.44 and 0.61, and 0.67 and 0.66, respectively. The growth curve parameters have small but positive (r=0.05 to 0.28) relationship with indicators of lifetime productivity. Medium heritability estimates of A and K indicate that progress in improving these traits can be made through selection. WT6 and YW have medium genetic correlations with the growth curve parameters and these may allow the use of these weights as indirect early selection criteria for optimum growth curve.