Characterization of Smallholder Beef Cattle Production System in Central Vietnam –Revealing Performance, Trends, Constraints, and Future Development

The objective of this study is to evaluate the characteristics of smallholder beef cattle production in Central Vietnam. A total of 360 households were interviewed by using semi-structured questionnaire; a total of 606 beef cows were investigated for evaluating calving interval (CI). Thirty-two fattening cattle were monitored for the estimation of diet structure. Results showed that the cattle herd size was 4.32-4.45 cattle/household. In North Central (NC), 55% of surveyed farmers kept local cattle, 45% kept crossbreeds, and none of surveyed farmers keeping exotic breeds. In South Central (SC), 63% of surveyed farmers kept cross cattle, 32% kept local cattle, and 5% kept exotic breeds. In the breeding method, 70% of surveyed farmers used artificial insemination (AI), 20% used natural mating (NM), and only 10% used both AI and NM in SC, whereas in NC 40% of farmers used AI, 40% used NM, and 20% used both AI and NM. The variety of feedstuffs fed to cattle including roughages and concentrate. The concentrate in the diet for fattening cattle was 25%-35% and protein level was 11%-13%, and the average daily gain of cattle was 0.51-0.63 kg/day. The CI of cows was 12-13 months in SC, whereas in NC it was 13-14 months. There were numerous constraints to cattle production in surveyed households including diseases, lack of good quality feed sources, breeds, knowledge, and lack of capital. In conclusion, cattle production in Central Vietnam is small scale and still largely extensive. There are constraints that must be solved to improve livestock systems in the near future, especially when shifting towards semi-intensive and/or intensive cattle production systems.

Forward-Backward smoothing for hidden markov models of point pattern data

© 2017 IEEE. This paper considers a discrete-time sequential latent model for point pattern data, specifically a hidden Markov model (HMM) where each observation is an instantiation of a random finite set (RFS). This so-called RFS-HMM is worthy of investigation since point pattern data are ubiquitous in artificial intelligence and data science. We address the three basic problems typically encountered in such a sequential latent model, namely likelihood computation, hidden state inference, and parameter estimation. Moreover, we develop algorithms for solving these problems including forward-backward smoothing for likelihood computation and hidden state inference, and expectation-maximisation for parameter estimation. Simulation studies are used to demonstrate key properties of RFS-HMM, whilst real data in the domain of human dynamics are used to demonstrate its applicability

Community metabolomics in environmental microbiology

Evidence from 16S rRNA gene sequences indicates that as yet uncultured microorganisms represent the vast majority of organisms in most environments on earth. However, since many species cannot be cultured within a laboratory setting, these communities are mostly unstudied and consequently there has been little insight into the genetics, physiology and biochemistry of their members. The new field of community metabolomics is about to change this scenario. In the same way as metagenomics indicates the analyses of all DNA from a given sample, community metabolomics looks at the entirety of the thousands of naturally occurring metabolites from the meta-population of a sample of a given environment such as soil or water, and perhaps even air. In this chapter we outline how this new field has recently been applied to generate new insights into these unexplored areas of the bacterial realm in the fields of environmental science and technology, within natural, laboratory and even industrial, settings. Potential future applications in this area are also discussed. Keyword