Adoption of Irrigation Technology: The Effects of Personal, Structural, and Environmental Variables

During the past decade, there has been a growing interest in expanding the list of factors affecting the adoption and diffusion of agricultural technology. It has been suggested that most previous research efforts have been insensitive to contextual variables and institutional constraints. The physical environment has been suggested as one contextual variable that has been largely ignored in past adoption-diffusion research. The present study tested for the relative effects of a site-specific indicator of the physical environment (saturated thickness), as well as personal attributes and farm structural characteristics for the adoption of irrigation innovations in the Texas High Plains. The results revealed that saturated thickness is an important variable in understanding the adoption of the irrigation innovations considered. While a multivariate analysis revealed that farm size overall was the most important variable, the environmental factor was more important than traditional research variables selected for use in this study. It is concluded that the use of environmental variables does contribute to our knowledge of adoption of technology and should be included in studies of the diffusion of innovations

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases