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Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

ABSTRACT: This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public repository managed by INRA (https://data.inra.fr/; https://doi.org/10.15454/WB51LL).

SUBMITTER: Gesan-Guiziou G

PROVIDER: S-EPMC6685674 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

Gésan-Guiziou Geneviève G Alaphilippe Aude A Andro Mathieu M Aubin Joël J Bockstaller Christian C Botreau Raphaëlle R Buche Patrice P Collet Catherine C Darmon Nicole N Delabuis Monique M Girard Agnès A Grateau Régis R Kansou Kamal K Martinet Vincent V Membré Jeanne-Marie JM Sabbadin Régis R Soler Louis-Georges LG Thiollet-Scholtus Marie M Tonda Alberto A Van-Der-Werf Hayo H

Data in brief 20190722

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public re ...[more]

PMID: 31406900

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Project description:Fungal communities in agricultural soils are assumed to be affected by climate, weather, and anthropogenic activities, and magnitude of their effect depends on the agricultural activities. Therefore, a study was conducted to investigate the impact of the portfolio of management practices on fungal communities and soil physical-chemical properties. The study comprised different climate-smart agriculture (CSA)-based management scenarios (Sc) established on the principles of conservation agriculture (CA), namely, ScI is conventional tillage-based rice-wheat rotation, ScII is partial CA-based rice-wheat-mungbean, ScIII is partial CSA-based rice-wheat-mungbean, ScIV is partial CSA-based maize-wheat-mungbean, and ScV and ScVI are CSA-based scenarios and similar to ScIII and ScIV, respectively, except for fertigation method. All the scenarios were flood irrigated except the ScV and ScVI where water and nitrogen were given through subsurface drip irrigation. Soils of these scenarios were collected from 0 to 15 cm depth and analyzed by Illumina paired-end sequencing of Internal Transcribed Spacer regions (ITS1 and ITS2) for the study of fungal community composition. Analysis of 5 million processed sequences showed a higher Shannon diversity index of 1.47 times and a Simpson index of 1.12 times in maize-based CSA scenarios (ScIV and ScVI) compared with rice-based CSA scenarios (ScIII and ScV). Seven phyla were present in all the scenarios, where Ascomycota was the most abundant phyla and it was followed by Basidiomycota and Zygomycota. Ascomycota was found more abundant in rice-based CSA scenarios as compared to maize-based CSA scenarios. Soil organic carbon and nitrogen were found to be 1.62 and 1.25 times higher in CSA scenarios compared with other scenarios. Bulk density was found highest in farmers' practice (Sc1); however, mean weight diameter and water-stable aggregates were found lowest in ScI. Soil physical, chemical, and biological properties were found better under CSA-based practices, which also increased the wheat grain yield by 12.5% and system yield by 18.8%. These results indicate that bundling/layering of smart agricultural practices over farmers' practices has tremendous effects on soil properties, and hence play an important role in sustaining soil quality/health.

Project description:ObjectivesHealthcare budgets are limited, and therefore, research funds should be wisely allocated to ensure high-quality, useful and cost-effective research. We aimed to critically review the criteria considered by major Australian organisations in prioritising and selecting health research projects for funding.MethodsWe reviewed all grant schemes listed on the Australian Competitive Grants Register that were health-related, active in 2017 and with publicly available selection criteria on the funders' websites. Data extracted included scheme name, funding organisation, selection criteria and the relative weight assigned to each criterion. Selection criteria were grouped into five representative domains: relevance, appropriateness, significance, feasibility (including team quality) and cost-effectiveness (ie, value for money).ResultsThirty-six schemes were included from 158 identified. One-half of the schemes were under the National Health and Medical Research Council. The most commonly used criteria were research team quality and capability (94%), research plan clarity (94%), scientific quality (92%) and research impact (92%). Criteria considered less commonly were existing knowledge (22%), fostering collaboration (22%), research environment (19%), value for money (14%), disease burden (8%) and ethical/moral considerations (3%). In terms of representative domains, relevance was considered in 72% of the schemes, appropriateness in 92%, significance in 94%, feasibility in 100% and cost-effectiveness in 17%. The relative weights for the selection criteria varied across schemes with 5%-30% for relevance, 20%-60% for each appropriateness and significance, 20%-75% for feasibility and 15%-33% for cost-effectiveness.ConclusionsIn selecting research projects for funding, Australian research organisations focus largely on research appropriateness, significance and feasibility; however, value for money is most often overlooked. Research funding decisions should include an assessment of value for money in order to maximise return on research investment.

Dataset Information

Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

Publications

Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

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