Jednoduchý statistický model pro prognózu výnosu na trvalých travních porostech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F06%3A00102904" target="_blank" >RIV/62156489:43210/06:00102904 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A simple statistical model for predicting herbage production from permanent grassland

Popis výsledku v původním jazyce

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 causedby 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

Název v anglickém jazyce

A simple statistical model for predicting herbage production from permanent grassland

Popis výsledku anglicky

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 causedby 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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GC - Pěstování rostlin, osevní postupy

OECD FORD obor

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Projekt

<a href="/cs/project/GA205%2F05%2F2265" target="_blank" >GA205/05/2265: Kalibrace generátoru meteorologických dat pro místa s žádnými či neúplnými pozorováními</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2006

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Grass and Forage Science.

ISSN

1365-2494

e-ISSN

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Svazek periodika

61

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

19

Strana od-do

253-271

Kód UT WoS článku

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EID výsledku v databázi Scopus

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