https://researchdata.se/en/catalogue Search results en The aim of this study is firstly to systematically explore, analyse, report on and synthesise the scientific studies on the topic of diffusion and adoption of innovations from the agri-food firms’ perspective, and secondly, to frame the results in a conceptual overview comprising key elements in the research over the period. The specific objectives are 1. to determine variables important in the diffusion and adoption of innovations from the agri-food firms’ perspective, 2. based on the results, to suggest development and improvements of capabilities in the supporting knowledge systems, in order to increase the sectors’ adoption of innovations 3. to discuss the implications for research. The study is based on systematic mapping of the literature (academic papers published in scientific journals) in the period 1997-2017. In total, abstracts for 202 papers were included in the mapping and analysed with an exploratory analytical approach. A delimitation of the study is to cover research in OECD-countries. Nine broad categories of key elements studied have been derived from the mapping: 1) Farm/firm characteristics; 2) Farmers’/managers’ characteristics; 3) Firm management practices; 4)  Adoption-diffusion behaviours and learning; 5) Sources of information and innovation; 6) Innovation characteristics; 7) Networks and relations; 8) Contextual variables, and 9) The agricultural knowledge and innovation system, AKIS. The open access data material consists of a spreadsheet containing included mapped studies, coding of abstracts, a meta-table with categorised keywords from the 202 papers, and unique reference numbers attached to each mapped item to allow tracing the themes from the meta-table to the connected excel sheet. All the mapped items have received a reference number, which is used in both the meta-tables of categorised keywords and the adhered excel spread sheet (data file). The data file shows the list of items included in the mapping and items removed in the later stages of the screening process. The data contains of: Meta-Table with categorised keywords Excel sheet with extracted information Notes on procedure (the procedure of the mapping process and analysis) Tue, 17 Dec 2019 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/snd1133-1 https://researchdata.se/en/catalogue/dataset/snd1133-1 Swedish University of Agricultural Sciences Fredrik Fernqvist Lisa Blix Germundsson Annie Drottberger Sara Spendrup This study systematically explores, analyses, reports on and synthesises research on the topic of sectoral innovation systems related to agriculture and agri-food in OECD countries. It is based on systematic mapping of the literature (academic papers published in scientific journals) in the period 1997-2017. The aim is to show the state of current knowledge on sectoral innovation systems in agri-food, in order to identify knowledge gaps and future areas for research and provide methodological and theoretical perspectives. Abstracts for a total of 320 papers were analysed, using a qualitative approach. Key elements of agricultural innovation systems identified were organised into 8 main themes/topics: agents, basic technologies, knowledge and learning processes, mechanisms of interaction, institutions, end-users, system transition and contextual variables. Areas identified as requiring research included making the sector more consumer- and market-oriented, increasing interactions outside conventional system boundaries, including the consumer perspective and societal changes, and determining the role of gender in innovation in agri-food systems. All the items included in the mapping were given a unique identification number. The data file (excel) shows the list of items included in the mapping and items removed in the latest stage of the screening process, together with a note on the reason for exclusion. Items only pertaining to innovation at firm level, without special attention to sectoral systems or AKIS, were excluded from the mapping in the screening process and not given an identification number. The items selected for the mapping were entered into the Excel sheet that constitutes the final database and analysed following a protocol. Based on availability of information in the abstracts, the following characteristics were noted: Period of study; country or countries of study; data type (qualitative, quantitative, mixed); data source (qualitative or quantitative); firm size; number of firms in the study; food product/category (e.g. agriculture, organic food, vegetables, wheat, dairy; food industry); research method (e.g. case study, econometric, survey); key elements and outcomes studied; theory/framework applied; and additional notes (not always applied). Key elements in the items included were recorded, based on the abstracts. The key elements, subject or main findings were then summarised in a qualitative interpretation based on the abstracts, which is presented in the second dataset. Thu, 13 Feb 2025 09:35:10 GMT https://researchdata.se/en/catalogue/dataset/snd1116-1 https://researchdata.se/en/catalogue/dataset/snd1116-1 Swedish University of Agricultural Sciences Fredrik Fernqvist Sara Spendrup Aim to characterize differentially expressed miRNAS between healthy Norwegian Forest cats and healthy Domestic Shorthair cats, and to compare with cats with hypertropic cardiomyopathy (HCM) Six neutered healthy cats, three male domestic cats (DOM) and one male and two female Norwegian Forest (NF) cats were included. Each healthy cat was matched (breed, sex, age, body-weight and body condition score) with a cat with HCM. The dataset includes the miRDeep2 report, rawcounts miRNAs, differentially expressed miRNAs for contrasts compared using DESeq2, human and feline target genes producing messenger RNA (mRNA) and gene ontology analysis (GO) for these 12 cats. Whole blood was collected in PAXgene blood RNA System frozen and stored in -20 °C until date of RNA-extraction for a median storage time of 177 days. Total RNA was extracted. Samples with an RNA integrity number (RIN)-value of 7.7 or higher were included in the study. Libraries were prepared and quantified and normalized prior sequencing. Paired-end sequencing data was generated. Bioformatic data processing and count genertion of known and novel miRNAs in cats were identified using miRDeep2. Mature miRNA and hairpin sequences were downloaded from miRBase with human as main reference, an mouse and dog assigned as close relatives. Main reference miRNAs identified in the dataset were classified as predicted known miRNAs, and miRNAs previously not described in the main reference were classified as novel miRNAs by miRDeep2. Only novel miRNAs with a miRDeep2 score of >5.0 was included in the count-file generated for subsequent differently expressed (DE)-analyses. Identification of DE miRNAs were performed in DESeq2. Number of variables 6: breed, age, sex, body-weight, body condition score, healthy or hypertrophic cardiomyopathy. The compiled files gives an overview of the data set. Compiled Excel file “miRDeep2_counts_DEsignmiRNAcontrasts_compiled” is a compiled file with the miRDeep2 report, raw counts of miRNAs and the differentially expressed miRNAs found in the dataset. The following nine csv-files are named miR_ and the name of the file in the compiled Excel file. Compiled Excel file “Target_HumanGOmiRNA_Feline_GOmiRNA_compiled” is a compiled file with the human and feline target genes producing messenger RNA for the differentially expressed miRNAs found in the dataset, gene ontology analysis of these miRNAS both for human and feline genes. The following nineteen csv-files are named Target_ and the name of the file in the compiled Excel file. Fri, 07 Jan 2022 13:18:19 GMT https://researchdata.se/en/catalogue/dataset/2021-334-1 https://researchdata.se/en/catalogue/dataset/2021-334-1 Swedish University of Agricultural Sciences Sofia Hanås Julie Lorent Åsa Ohlsson This study makes part of the research project GINFRA – green rights-of-way infrastructure for biodiversity and ecosystem services. The aim of the project was to quantify whether linear infrastructure habitats (road verges and power-line corridors) support biodiversity by assessing the influence of the area of these habitats in the landscape, their contribution to landscape connectivity and population persistence. The linked data was collected by surveying butterflies, bumblebees, and vascular plants in five types of prevalent grasslands (pastures, road verges along small gravel roads, road verges along big paved roads, power line corridors, and field borders). These grasslands were embedded in 32 landscapes with area 4 km² each, that differed in the area of linear infrastructure habitats (road verges and power line corridors) and semi-natural grasslands of high nature value, while other land-use types were kept constant. The landscapes were dominated by forest. Within each grassland habitat, the surveyor established a 200 m transect and then identified all butterflies and bumblebees along it. For plants, a 1 x 1 m quadrat was established at the centre of a 50 m section in each transect, i.e. each transect had four plots in which all plant species were identified. Denna studie är en del av projektet GINFRA – green rights-of-way infrastructure for biodiversity and ecosystem services. Projektets huvudsyfte var att kvantifiera om linjära infrastrukturmiljöer (vägkanter och kraftledningsgator) bidrar till mångfalden av växter och insekter i olika rumsliga skalor. Detta gjordes genom att uppskatta hur linjära infrastrukturmiljöers mängd i landskapet bidrar till mångfalden samt hur mycket de bidrar till landskapets konnektivitet. Datan samlades genom att inventera dagfjärilar, humlor, och växter i fem typer av gräsmarker (betesmarker, vägrenar längs enskilda vägar, vägrenar längs allmänna vägar, kraftledningsgator, och åkerkanter). Alla dessa habitat typer fanns inom 32 landskap med area 4 km2 som skilde sig i areal av linjära infrastrukturmiljöer (vägrenar och kraftledningsgator) och ängs-och betesmarker. Arealen av andra markanvändningar var konstanta mellan landskap och alla landskap var skogsdominerade. Thu, 04 Apr 2024 09:16:47 GMT https://researchdata.se/en/catalogue/dataset/2023-23-1 https://researchdata.se/en/catalogue/dataset/2023-23-1 Swedish University of Agricultural Sciences Juliana Dániel-Ferreira Yoan Fourcade Riccardo Bommarco Jörgen Wissman Erik Öckinger Data contain standardised yields of several different cereals collected between 1958 and 2020 from 32 long-term agricultural trials across North America and Europe. Yields in tonnes per hectare were standardised against the overall mean yield per site across all treatments and years. Treatments include different levels of crop rotational diversity and nitrogen fertilisation. This data was used in the article: Smith et al., Increasing crop rotational diversity can enhance cereal yields, Communications Earth and Environment, 2023. See the attached documents for more information, including, ‘Metadata.txt’ for description of data codes, ‘Crop_rotation_information_desc.txt’ and ‘Crop_rotation_information.tsv’ detailing cropping sequence and mean yields of each rotation per site and ‘R_script_Smith_etal.Rmd’ to see how this data was used in the associated article. R markdown output of the script is provided in the form of R_script_Smith_etal.pdf. Tue, 07 Mar 2023 14:06:25 GMT https://researchdata.se/en/catalogue/dataset/2022-230-1 https://researchdata.se/en/catalogue/dataset/2022-230-1 Swedish University of Agricultural Sciences Monique Smith Riccardo Bommarco The International Cooperative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystems (ICP IM) presents a comprehensive long-term dataset of ongoing integrated ecosystem monitoring from European forested catchments. The dataset encompasses measurements from monitoring stations across 14 European countries, with temporal coverage extending for most sites from the 1990s to 2020. The dataset will be updated with new data once per year. The integrated monitoring approach applies over multiple monitoring subprogrammes to simultaneously measure physical, chemical, and biological properties across ecosystem compartments including atmosphere, precipitation, throughfall, soil, soil water, groundwater, runoff water, vegetation, and biota. All measurements follow standardised protocols detailed in the ICP IM Manual, ensuring data quality and comparability across sites and time periods. The dataset supports research on ecosystem responses to air pollution, climate change impacts, and biogeochemical cycling. Data is provided by sub-programme (all sites and all years with data in one file), geographic co-ordinates for sites are available in a separate file. Historical data from inactive sites in Belarus, Denmark, Iceland and the United Kingdom are currently available by request, as is data from Finland in sub-programmes TF,SF,SC,SW,FC,LF,FD,VG,EP and BV, and data from Poland. The monitoring is done under the framework of the UN Convention on Long-Range Transboundary Air Pollution (CLRTAP) and also has an important role in reporting under the EU national emissions ceiling directive (NECD). Users are strongly encouraged to refer to the ICP IM Monitoring Manual which describes in detail the methods used to make measurements in the field and the laboratory, the data formats used, explanations of column headers and flags used in all subprogrammes and example files. This is provided alongside the data. Thu, 19 Mar 2026 08:16:31 GMT https://researchdata.se/en/catalogue/dataset/2024-180 https://researchdata.se/en/catalogue/dataset/2024-180 Swedish University of Agricultural Sciences James Weldon Wenche Aas Barbara Albiniak Algirdas Augustaitis Ieva Baužienė Camilla Capelli Nicholas Clarke Thomas Cummins Heleen de Wit Thomas Dirnböck Ika Djukic Karin Eklöf Martin Forsius Martyn Futter Ulf Grandin Sergey Gromov Adéla Holubová Šmejkalová Ricardo Ibañez Iveta Indriksone Sara Jutterström Johannes Kobler Heidi Koger Angelika Kölbl Andrzej Kostrzewski Anna Koukhta Pavel Krám Robert Kruszyk Esther Lasheras Kairi Lõhmus Mikołaj Majewski Hampus Markensten Rafael Miranda Michael Mirtl Filip Moldan Giancarlo Papitto Johannes Peterseil Ainis Pivoras Gisela Pröll Pernilla Rönnback Carolina Santamaría Jesus Miguel Santamaría Thomas Plha Krzysztof Skotak David Elustondo Mercedes Valerio Sarah Venier Lieke Vlaar Jussi Vuorenmaa Nicole Wellbrock Liisa Ukonmaanaho Ulla Makkonen