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.
2023
Sammendrag
Nutrient uptake and transport depend on the root system of a tree. Various apple rootstock genotypes may interact fruit tree nutrition. In 2017, two multi-location apple rootstock trials were established at 16 sites in 12 European countries. The evaluations are performed by members of the EUFRIN (European Fruit Research Institute Network) Apple & Pear Variety & Rootstock Testing Working Group. Following rootstocks are included in the tests: G.11, G.41, G.202 and G.935 (US), EM_01, EM_02, EM_03, EM_04, EM_05 and EM_06 (UK), 62-396-B10® (Russia), P 67 (Poland), NZ-A, NZ-B, NZ-C and NZ-D (New Zealand) and Cepiland-Pajam®2 as control. The effect of rootstocks on the mineral content of leaf and fruit was studied at the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry in 2019-2020. The leaf and fruit mineral concentration of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and leaf mineral content of copper (Cu), zinc (Zn), iron (Fe), manganese (Mn) and boron (B) were measured. Significant rootstock effect was established on leaf P, Mg, Zn, Mn, B, and fruit Ca and Mg content. Rootstocks EM_01 and G.41 were the most efficient in leaf mineral uptake, while G.935 had the lowest content of all leaf macro nutrients. Rootstocks EM_06 and P 67 were the most efficient in fruit mineral uptake, while EM_02 had the lowest content of three nutrients. Current research reveals differences among rootstocks and their capacity to absorb separate minerals and enables creation of rootstock specific nutrition management.
Forfattere
Synnøve GrenneSammendrag
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Forfattere
Rui Dong Yuxin Miao Pete Berry Xinbing Wang Fei Yuan Krzysztof Kusnierek Chris Baker Mark SterlingSammendrag
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications.
Sammendrag
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Forfattere
Nhat Strøm-AndersenSammendrag
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Forfattere
Nhat Strøm-AndersenSammendrag
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Sammendrag
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Sammendrag
The fungus Neonectria ditissima causes Fruit Tree Canker on apple and pear. In the past years the disease has become a threat for Swedish and Northern European apple production since devastating outbreaks destroy large numbers of trees. To date, no complete genetic resistance to N. ditissima is known in apple but genotypes (scion cultivars and rootstocks) differ greatly in their level of partial resistance. Furthermore, the degree of susceptibility of a scion cultivar may be influenced by the rootstock it is grafted to. Thus, we aimed to improve our understanding of genetically determined differences in resistance among rootstocks and clarify cultivar/rootstock interactions with regards to canker resistance. For that, we evaluated differences in resistance to fruit tree canker in 24 rootstocks (including two M9 clones). We also evaluated differences in resistance of four most widely grown in Sweden scion cultivars grafted to four common rootstocks differing in vigour. The new knowledge will be useful for growers and breeders to minimize canker damages, prevent loss of the fruit-bearing surface in the orchards, save time and money for the growers.
Forfattere
Ola FlatenSammendrag
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Forfattere
Ola FlatenSammendrag
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