LEAF AREA INDEX IN WINTER WHEAT: RESPONSE ON SEED RATE AND NITROGEN APPLICATION BY DIFFERENT VARIETIES

The most important photosynthesis acceptor – leaf area vary among cultivation measures and it is limited factor for creating exact growth models in common winter wheat. The objective of this study was to investigate changes of leaf area index (LAI) affected by agricultural treatments – 4 sowing rates and 9 nitrogen treatments based on fertilising rates, target values based on soil mineral nitrogen and plant sap tests target values including different varieties. Increasing sowing rates from 350 to 800 viable seeds m-2 increased LAI at EC 75 stage from 2.9 to 5.5, where LAI 4.1 at 500 seeds m-2 did not vary between lower and higher rates; also at EC 85 stage LAIs did not differ significantly. At EC 75 stage LAI differed among control and nitrogen treatments from 1.0 to 6.5 and at EC 85 stage from 0.1 to 2.4, with differences in interaction among varieties. Higher nitrogen rates for first and second top dressing increased LAI in both stages compared without dressing treatments. Due to significant differences among LAI as consequence of production system, we suggest to take this into account in every prediction and modelling of growth in winter wheat.Listna površina, kot najpomembnejši fotosintetski akceptor je odvisna od pridelovalnih ukrepov in je omejitveni dejavnik za izdelavo natančnih rastnih modelov navadne ozimne pšenice. Cilj te študije je preveriti spremembe indeksa listne površine (LAI) pod vplivom agrotehničnih ukrepov – 4 gostot setve, 9 odmerkov dušika temelječih na odmerkih gnojil in temelječih na ciljnih vrednostih Nmin-a ter hitrih nitratnih rastlinskih testov vključujoč različne sorte. Povečevanje setvene norme od 350 do 800 kalivih semen m-2 povečuje LAI v fazi EC 75 od 2.9 do 5.5, medtem ko med njima in 500 kalivimi zrni m-2 značilnih razlik med LAI ni bilo; tudi v fazi EC 85 med LAI nismo ugotovili značilnih razlik. V fazi EC 75 je LAI variral od 1.0 v kontrolnem obravnavanju do 6.5 v gnojilnih obravnavanjih, v fazi EC 85 pa od 0.1 do 2.4, s tem da so bile značilne razlike tudi v interakciji s sortami. Višji odmerki dušika za prvo in drugo dognojevanje povečujejo LAI v obeh fazah v primerjavi z obravnavanji brez dognojevanja. Zaradi značilnih razlik med LAI kot posledica agrotehnike, priporočamo upoštevati razlike med LAI pri vsakem načrtovanju ali modeliranju rasti ozimne pšenice

Bilan et perspectives de la Recherche en Agriculture Bio-dynamique

L’Agriculture Biodynamique (BD) a été l’objet de nombreux efforts de recherches durant les dernières décennies, bien qu’une partie de la communauté scientifique regarde les méthodes biodynamiques avec scepticisme et les considère comme dogmatiques. Néanmoins, comme cela est montré dans cet article de synthèse, une part non négligeable des résultats présentés dans des revues scientifiques à comité de lecture et issus d’expérimentations contrôlées de plein champ, ou d’étude de cas, montrent des effets des préparations biodynamiques sur le rendement, la qualité du sol et la biodiversité. De plus, les préparations biodynamiques ont un impact environnemental positif en termes d’utilisation et d’efficacité énergétique. Cependant, le mode d’action mécanique des préparations biodynamique est toujours en cours d’investigation en sciences naturelles. Par ailleurs, les méthodes d’évaluations de la qualité basées sur des approches globales (holistiques) sont de plus en plus étudiées et reconnues. L’agriculture BD s’efforce également, comme cela est montré dans plusieurs publications, d’influencer positivement le paysage culturel. La synthèse des données montre le besoin de poursuivre les recherches dans le domaine de la qualité des aliments, de la sécurité alimentaire, des performances environnementales (par ex. l’empreinte écologique), et sur l’influences des pratiques BD sur les animaux d’élevage

Search and Mining Entity-relationship Data

This paper summarizes the details of the first international workshop on search and mining entity-relationship data. This workshop will bridge between IR, DB, and KM researchers to seek novel solutions for search and data mining of rich entity-relationship data and their applications in various domains. We first provide an overview about the workshop. We then briefly discuss the workshop program

StreamStory: Exploring Multivariate Time Series on Multiple Scales.

This paper presents an approach for the interactive visualization, exploration and interpretation of large multivariate time series. Interesting patterns in such datasets usually appear as periodic or recurrent behavior often caused by the interaction between variables. To identify such patterns, we summarize the data as conceptual states, modeling temporal dynamics as transitions between the states. This representation can visualize large datasets with potentially billions of examples. We extend the representation to multiple spatial granularities allowing the user to find patterns on multiple scales. The result is an interactive web-based tool called StreamStory. StreamStory couples the abstraction with several tools that map the abstractions back to domain-specific concepts using techniques from statistics and machine learning. It is aimed at users who are not experts in data analytics, minimizing the number of parameters to configure out-of-the-box. We use three real-world datasets to demonstrate how StreamStory can be used to perform three main visual analytics tasks: identify the main states of a complex system and map them back to data-specific concepts, find high-level and long-term periodic behavior and traverse the scales to identify which scales exhibit interesting phenomena. We find and interpret several known, as well as previously unknown patterns in these datasets.This work was supported by the Slovenian Research Agency and by the EC projects ProaSense (FP7 612329) and OPTIMUM (H2020- MG-636160)