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Wale, Edilegnaw; Virchow, Detlef. |
Crop genetic resources are the building blocks of sustainable agricultural development due to their relevance not only as inputs for variety development but also as indigenous crop insurance mechanisms through traditional variety portfolio management. Their continuous survival is, however, threatened by natural and human driven factors. This threat has induced the need for designing conservation measures. Among the in situ and ex situ conservation options available to conserve crop genetic resources, on-farm conservation has recently attracted enormous attention. To make this option operational, placing incentives (that link conservation with utilization) and removal of perverse incentives are believed to be crucial so that landraces of no immediate... |
Tipo: Conference Paper or Presentation |
Palavras-chave: On-farm conservation; Sorghum genetic resources; Incentives; Poisson regression; Ethiopia; Crop Production/Industries. |
Ano: 2003 |
URL: http://purl.umn.edu/25882 |
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Miranda, Alfonso; Rabe-Hesketh, Sophia. |
Studying behavior in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable—also known as a regime-switch variable—or in which the response variable is observed only if a particular selection condition is met. In either case, standard regression techniques deliver inconsistent estimators if unobserved factors that affect the response are correlated with unobserved factors that affect the switching or selection variable. Consistent estimators can be obtained by maximum likelihood estimation of a joint model of the outcome and switching or selection variable. This article describes a “wrapper” program, ssm, that calls gllamm (Rabe-Hesketh, Skrondal, and Pickles, GLLAMM Manual [University... |
Tipo: Journal Article |
Palavras-chave: Endogenous switching; Sample selection; Binary variable; Count data; Ordinal variable; Probit; Poisson regression; Adaptive quadrature; Gllamm; Wrapper; Ssm; Research Methods/ Statistical Methods. |
Ano: 2006 |
URL: http://purl.umn.edu/117582 |
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