Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2018
Forfattere
Zelalem Bekeko Chemeda Fininsa Shimelis Hussien Temam Hussien Dagne Wegari Belachew Asalf TadesseSammendrag
Fourteen advanced maize inbred lines and locally adapted hybrid maize (BH-540) as a check were used to investigate their reaction to GLS disease. Field experiments were conducted at Bako National Maize Research Centre in 2015 and 2016 main plan ng seasons arranged in a randomized complete block design (RCBD) with three replications. Artificial inoculation with Cercospora zeae-maydis was conducted by applying dry, ground, infected maize leaves into the whorls of younger maize plants. Data on agronomic and disease parameters (latent period, disease severity, disease incidence and lesion type) were recorded from the middle two rows. From the combined analysis of variance, maize genotypes showed significant differences with reaction to GLS indicating the existence of genetic variability among the selected genotypes. Highly significant differences were also observed among entries for all agronomic parameters in both seasons. Gray leaf spot incidence and severity varied among genotypes and between years. The mean GLS incidence and severity were higher in 2016 than 2015. GLS disease incidence in two years ranged from 35% on Sc22 to 95% on CML-387 and severity ranged from 15% on A-7016 to 75% on CKL05003. Significant differences in epidemic variability were also observed among genotypes and seasons. From the analysis of disease progress curves Logistic model (R2=94.55) better described the disease progress curves than the Gompertz model (R2=91.50). Parents; P6 and P8 had the most desirable quality for the most of agronomic traits whereas P2, P7 and P9 were the best parents for grain yield. Among all inbred lines, P6, P7 and P14 were iden fied as the most desirable sources of genes for GLS disease resistance. But P6, P7, P8 and P14 were iden fied as the best genotypes in yield, yield related traits and GLS disease parameters. Thus, these parents were recommended to be used in breeding programs with a purpose of developing high yielder and GLS disease resistant open pollinated varieties. In conclusion this study identified potential and promising high yielding and GLS resistant open pollinated genotypes (CKL05017-B-B, CML-395, CML-387, A-7016, Gu o and Sc22). Therefore, it is recommended that these OPVs can be used by resource poor farmers for direct production where this disease is the most prevalent and/or for further breeding programs in generating novel hybrids for future use.
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Redaktører
Heidi KnutsenSammendrag
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Forfattere
Arne Verstraeten Elena Gottardini Nicolas Bruffaerts Bruno de Vos Elena Vanguelova Fabiana Cristofolini Sue Benham Pasi Rautio Liisa Ukonmaanaho Päivi Merilä Peter Waldner Marijke Hendrickx Gerrit Genouw Peter Roskams Nathalie Cools J Neirynck Anita Nussbaumer Mathias Neumann Nicholas Clarke Volkmar Timmermann Karin Hansen Hans-Peter Diettrich Manuel Nicolas Maria Schmitt Anne Thimonier Katrin Meusburger Silvio Schueler Anna KowalskaSammendrag
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Forfattere
Cecilie Marie Mejdell Grete H. Meisfjord Jørgensen Knut Egil BøeSammendrag
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Forfattere
Monika Suškevičs Sebastian Eiter Stanislav Martinat Dina Stober Elis Vollmer Cheryl L. de Boer Matthias BucheckerSammendrag
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The present work focuses on an assessment of the applicability of groundwater table (GWT) measures in the modelling of soil water retention characteristics (SWRC) using artificial neural network (ANN) methods. Model development, testing, validation and verification were performed using data collected across two decades from soil profiles at full-scale research objects located in Southwest Poland. A positive effect was observed between the initial GWT position data and the accuracy of soil water reserve estimation. On the other hand, no significant effects were observed following the implementation of GWT fluctuation data over the entire growing season. The ANN tests that used data of either soil water content or GWT position gave analogous results. This revealed that the easily obtained data (temperature, precipitation and GWT position) are the most accurate modelling parameters. These outcomes can be used to simplify modelling input data/parameters/variables in the practical implementation of the proposed SWRC modelling variants.