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Statements

Subject Item
n2:RIV%2F62156489%3A43210%2F06%3A00102904%21RIV07-GA0-43210___
rdf:type
skos:Concept n12:Vysledek
dcterms:description
The considerable year-to-year and seasonal variation in grassland production is of major importance to dairy farmers in Europe, as production systems must allow for the risk of unfavourable weather conditions. A large portion of the variability is caused by weather and its interaction with soil conditions and grassland management. The present study takes advantage of the interactions between weather, soil conditions and grassland management to derive a reliable grassland statistical model (GRAM) for grasslands under various management regimes using polynomial regressions (GRAM-R) and neural networks (GRAM-N). The model performance was tested with a focus on predicting its capability during unusually dry or wet years using long-term experimental data from Austrian sites. The GRAM model was then coupled with the Met&Roll stochastic weather generator to provide estimates of harvestable herbage dry matter (DM) production early in the season. It was found that, with the GRAM-N or GRAM-R Podrobný popis viz. anglický abstrakt. The considerable year-to-year and seasonal variation in grassland production is of major importance to dairy farmers in Europe, as production systems must allow for the risk of unfavourable weather conditions. A large portion of the variability is caused by weather and its interaction with soil conditions and grassland management. The present study takes advantage of the interactions between weather, soil conditions and grassland management to derive a reliable grassland statistical model (GRAM) for grasslands under various management regimes using polynomial regressions (GRAM-R) and neural networks (GRAM-N). The model performance was tested with a focus on predicting its capability during unusually dry or wet years using long-term experimental data from Austrian sites. The GRAM model was then coupled with the Met&Roll stochastic weather generator to provide estimates of harvestable herbage dry matter (DM) production early in the season. It was found that, with the GRAM-N or GRAM-R
dcterms:title
Jednoduchý statistický model pro prognózu výnosu na trvalých travních porostech A simple statistical model for predicting herbage production from permanent grassland A simple statistical model for predicting herbage production from permanent grassland
skos:prefLabel
A simple statistical model for predicting herbage production from permanent grassland Jednoduchý statistický model pro prognózu výnosu na trvalých travních porostech A simple statistical model for predicting herbage production from permanent grassland
skos:notation
RIV/62156489:43210/06:00102904!RIV07-GA0-43210___
n3:strany
253;271
n3:aktivita
n11:P
n3:aktivity
P(GA205/05/2265)
n3:cisloPeriodika
3
n3:dodaniDat
n14:2007
n3:domaciTvurceVysledku
n16:5539498
n3:druhVysledku
n13:J
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
463829
n3:idVysledku
RIV/62156489:43210/06:00102904
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Austria; climate change; grassland
n3:klicoveSlovo
n9:Austria n9:climate%20change n9:grassland
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[2DA437608AFA]
n3:nazevZdroje
Grass and Forage Science.
n3:obor
n7:GC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
6
n3:projekt
n10:GA205%2F05%2F2265
n3:rokUplatneniVysledku
n14:2006
n3:svazekPeriodika
61
n3:tvurceVysledku
Schaumberger, Andreas Trnka, Miroslav Buchgraber, Karl Eitzinger, Josef Resch, K. Gruszczynski, Gregorz
s:issn
1365-2494
s:numberOfPages
19
n15:organizacniJednotka
43210