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The objective of this study was to assess genetic diversity among 5 Brazilian (155 animals) and 5 U.S. goat (120 animals) breeds using 23 microsatellite markers. Samples from the United States represented a broad geographic distribution whereas Brazilian samples were from the northeast region. Samples from Boer were common to each country's breed count. Expected and observed heterozygosity among breeds ranged from 0.55 to 0.72, suggesting ample genetic diversity in the breeds evaluated. United States Angora, U.S. Spanish, and Brazilian Nambi ranked highest for allelic richness, averaging 6.1, 7.1, and 6.5 alleles per locus, respectively. Angora and Spanish also ranked highest in private alleles (7 and 9, respectively). Using STRUCTURE, the U.S. Spanish were also found to share a common cluster assignment with Brazilian Nambi, suggesting that progenitor breeds may have been the same and passed through the Canary Islands or Cape Verde in route to the New World. When non-Boer breeds were pooled by country, the effect of the subpopulation compared with total population (Fst) = 0.05, suggesting minor genetic differences exist between countries. The lack of genetic structure among goat breeds when compared with other species (e.g., Bos taurus vs. Bos indicus) suggests goat breeds may exhibit a plasticity that facilitates productivity across a wide range of countries and environments. Taken a step further, the concept of breed for meat goats may not be as relevant for goat production. [ABSTRACT FROM AUTHOR]

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Genetic improvement of animals plays an important role in improving the economic and environmental sustainability of livestock production systems. This paper proposes a method to incorporate mitigation of environmental impacts and risk preferences of producers into a breeding objective via economic values (EVs). The paper assesses the effects of using these alternative EVs of breeding goal traits on discounted economic response to selection and on environmental impacts at commercial farm level. The application focuses on a Brazilian pig production system. Separate dam- and sire-line breeding programs that supply parents in a 3-tier production system for producing crossbreds (fattening pigs) at commercial level were assumed. Using EVs that are derived from utility functions by incorporating risk aversion increases the cumulative discounted economic response to selection in sire-line selection (6%) while reducing response in dam-line selection (12%) compared with the use of traditional EVs. The use of EVs that include environmental costs increases the cumulative discounted social response to selection in both dam-line (5%) and sire-line (10%) selections. Emission of greenhouse gases, and excretion of nitrogen and phosphorus can be reduced more with genetic improvements of production traits than reproduction traits for the typical Brazilian farrow-to-finish pig farm. Reductions in environmental impacts do not, however, depend on the use of the different EVs (i.e., with and without taking into account environmental costs and risk). Both environmental costs and risk preferences of producers need to be considered in sire-line selection, and only environmental costs in dam-line selection to improve, at the same time, the economic and environmental sustainability of the Brazilian pig production system.