Innovation Intensity: From IT Use and Innovative Culture to Organizational Performance

Drawing from past research on information technology (IT) use, organizational culture, and innovation, the present study tests a model exploring the effects of collaborative and experiential culture as well as internally and externally-focused use of IT on innovation intensity and organizational performance. As the innovation process is complex and uncertain, we try to open the black box of innovation intensity by exploring the roles played by the use of IT and the innovation culture. The research model was tested via a structural equation model using PLS with data collected from 395 top executives. Results indicate that intense use of internally and externally- focused IT positively affect innovation intensity. The results also show that collaborative culture increases innovation intensity. In turn, innovation intensity increases both operational and financial performance

AgroFIMS v.1.0 - User manual

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HIDAP (Highly Interactive Data Analysis Platform) created by CGIAR’s International Potato Center, CIP. AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology (AgrO)1. It consists of modules that represent the typical cycle of operations in agronomic trial management (seeding, weeding, fertilization, harvest, and more) and enables the creation of data collection sheets using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore enables a priori harmonization with metadata and data interoperability standards and adherence to the FAIR Data Principles essential for data reuse and increasingly, for compliance with funder mandates - without any extra work for researchers. AgroFIMS is therefore of value to anyone (scientist, researcher, agronomist, etc.) who wishes to easily design a standards-compliant agronomic research fieldbook following the FAIR Data Principles. AgroFIMS also allows users to collect data electronically in the field, thereby reducing errors. Currently this is restricted to the KDSmart Android platform, but we expect to enable this capability with other platforms such as the Open Data Kit (ODK) and Field Book in v.2.0. Once data is collected using KDSmart, the data can be uploaded back to AgroFIMS for data validation, statistical analysis, and the generation of statistical analysis reports. V.2.0 will allow easy upload of the data from AgroFIMS to an institutional or compliant repository of the user’s choice

Les choix organisationnels des propriétaires de chevaux de loisir dans les espaces ruraux

La possession d’équidés de loisir a récemment connu un essor important. Pour entretenir ces animaux, leurs propriétaires peuvent soit les prendre en charge eux-mêmes, soit faire appel à des prestataires de services marchands. Les assimilant à des producteurs de leur propre loisir, nous proposons d’étudier les déterminants de leur choix organisationnel grâce aux théories traitant habituellement des frontières de la firme. Les 251 enquêtes réalisées démontrent la pertinence de l’application de ces cadres d’analyse à la production domestique. Les propriétaires d’équidés poursuivent un objectif d’efficience. Ils cherchent à maximiser leur utilité, tout en minimisant leurs coûts de production et de transaction et en s’adaptant aux ressources auxquelles ils peuvent avoir accès.The number of recreational Equidae owners has recently increased sharply. These owners can choose between stabling and caring for their animals domestically, on their own property, or using professional service suppliers. By considering them as producers of their own leisure activity, we proposed to study the parameters involved in this choice using theories usually employed to analyze firm boundaries. Results from our 251 inquiries demonstrated the relevance of transposing these theoretical tools to domestic production. Equidae owners pursue an objective of efficiency. They aim at maximizing their utility while minimizing their production and transaction costs and adapting to resources they can access

Pluralisme juridique et sécurisation foncière dans une commune cadastrée Le cas de Miadanandriana

Sur la base du décret du 29 août 1929 relatif au droit foncier indigène, une procédure cadastrale a été engagée sur la commune de Miadanandriana en 1935. Depuis l'établissement du plan cadastral jusqu'à l'inscription des titres cadastraux à la matrice foncière, prés de 40 ans se sont écoulés. De gros investissements en moyens humains, financiers et techniques ont été justifiés par le fait que la maîtrise du foncier constitue pour l'administration centrale un enjeu stratégique majeur de gestion des territoires et de leur développement. Aujourd'hui, l'heure est à la décentralisation de la gestion du foncier. Or à Miadanandriana, force est de constater que la gestion du foncier par les citoyens fait déjà intervenir les autorités locales notamment lors d'une recherche accrue de sécurisation foncière

The Life Cycle of Toxoplasma gondii in the Natural Environment

Chapitre 1International audienc

AgroFIMS v.2.0 - User manual.

This documentation provides instructions to help you get familiarized with the Agronomy Field Information Management System (AgroFIMS) and to produce a fieldbook that you can use to collect well-described, standards-compliant data in the field. AgroFIMS allows users to create fieldbooks to collect agronomic data. The fieldbook is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the industry standard Dublin Core Metadata Schema and required by CGIAR and many other repositories). The data variables and protocol parameters in AgroFIMS fieldbooks align with semantic standards like the Agronomy Ontology (AgrO). This a priori compliance with data standards facilitates data to be Findable, Accessible, Interoperable, and Reusable (FAIR) at collection, making it easier to interpret and aggregate. Data collection is currently available via the Android-based KDSmart or Field Book applications, and the collected data can be imported back to AgroFIMS for statistical analysis and reports. By mid-2021 you will be able to easily upload this collected data through AgroFIMS to a Dublin Core or CG Core-compliant Dataverse repository. To enable access, exchange, and integration of agronomic data across systems and applications we have made available the Agronomy API or AgrAPI, which is a RESTful web service API specification. The AgrAPI blueprint can be implemented in different programming languages, but is currently implemented in the R statistical programming language, allowing you to analyze your data with the R packages and graphics of your choice

A dynamo driven by zonal jets at the upper surface: Applications to giant planets

We present a dynamo mechanism arising from the presence of barotropically unstable zonal jet currents in a rotating spherical shell. The shear instability of the zonal flow develops in the form of a global Rossby mode, whose azimuthal wavenumber depends on the width of the zonal jets. We obtain self-sustained magnetic fields at magnetic Reynolds numbers greater than 1000. We show that the propagation of the Rossby waves is crucial for dynamo action. The amplitude of the axisymmetric poloidal magnetic field depends on the wavenumber of the Rossby mode, and hence on the width of the zonal jets. We discuss the plausibility of this dynamo mechanism for generating the magnetic field of the giant planets. Our results suggest a possible link between the topology of the magnetic field and the profile of the zonal winds observed at the surface of the giant planets. For narrow Jupiter-like jets, the poloidal magnetic field is dominated by an axial dipole whereas for wide Neptune-like jets, the axisymmetric poloidal field is weak.Comment: published in Icaru

Translational Genomics in Legumes Allowed Placing In Silico 5460 Unigenes on the Pea Functional Map and Identified Candidate Genes in Pisum sativum L.

To identify genes involved in phenotypic traits, translational genomics from highly characterized model plants to poorly characterized crop plants provides a valuable source of markers to saturate a zone of interest as well as functionally characterized candidate genes. In this paper, an integrated view of the pea genetic map was developed. A series of gene markers were mapped and their best reciprocal homologs were identified on M. truncatula, L. japonicus, soybean, and poplar pseudomolecules. Based on the syntenic relationships uncovered between pea and M. truncatula, 5460 pea Unigenes were tentatively placed on the consensus map. A new bioinformatics tool, http://www.thelegumeportal.net/pea_mtr_translational_toolkit, was developed that allows, for any gene sequence, to search its putative position on the pea consensus map and hence to search for candidate genes among neighboring Unigenes. As an example, a promising candidate gene for the hypernodulation mutation nod3 in pea was proposed based on the map position of the likely homolog of Pub1, a M. truncatula gene involved in nodulation regulation. A broader view of pea genome evolution was obtained by revealing syntenic relationships between pea and sequenced genomes. Blocks of synteny were identified which gave new insights into the evolution of chromosome structure in Papillionoids and Eudicots. The power of the translational genomics approach was underlined

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing