Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F18%3A78946" target="_blank" >RIV/60460709:41320/18:78946 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.agrformet.2018.06.009" target="_blank" >http://dx.doi.org/10.1016/j.agrformet.2018.06.009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.agrformet.2018.06.009" target="_blank" >10.1016/j.agrformet.2018.06.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Popis výsledku v původním jazyce

In the present study, multiple linear regression models were constructed to simulate the yield of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000-2016 time period. We used meteorological data and soil water content from meteorological reanalysis as predictors of the models in monthly resolution. We included annual fertilizer amount in the analysis to remove trend from the census data. We also used remote sensing based vegetation index to extend the approach for early crop yield forecast purposes and to study the added value of proxy data on the predictive power of the statistical models. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. We provided simple equations with well interpretable coefficients that can estimate crop yield with high accuracy. Cross-validated explained variance were 67% for winter wheat, 76% for rapeseed, 81% for maize and 68,5% for sunflower. The modelling exerc

Název v anglickém jazyce

Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Popis výsledku anglicky

In the present study, multiple linear regression models were constructed to simulate the yield of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000-2016 time period. We used meteorological data and soil water content from meteorological reanalysis as predictors of the models in monthly resolution. We included annual fertilizer amount in the analysis to remove trend from the census data. We also used remote sensing based vegetation index to extend the approach for early crop yield forecast purposes and to study the added value of proxy data on the predictive power of the statistical models. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. We provided simple equations with well interpretable coefficients that can estimate crop yield with high accuracy. Cross-validated explained variance were 67% for winter wheat, 76% for rapeseed, 81% for maize and 68,5% for sunflower. The modelling exerc

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

<a href="/cs/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Excelentní Výzkum jako podpora Adaptace lesnictví a dřevařství na globální změnu a 4. průmyslovou revoluci</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

AGRICULTURAL AND FOREST METEOROLOGY

ISSN

0168-1923

e-ISSN

—

Svazek periodika

260

Číslo periodika v rámci svazku

OCT2018

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

21

Strana od-do

300-320

Kód UT WoS článku

000445306700028

EID výsledku v databázi Scopus

2-s2.0-85049311759