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

Anikó Kern, Z. Barcza, Hrvoje Marjanović, Tamás Árendás, Nándor Fodor, Péter Bónis, Péter Bognár, J. Lichtenberger

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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 exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10%, 2% and 4% for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.

Original languageEnglish
Pages (from-to)300-320
Number of pages21
JournalAgricultural and Forest Meteorology
Volume260-261
DOIs
Publication statusPublished - Oct 15 2018

Fingerprint

vegetation index
Central European region
crop yield
remote sensing
climate
winter wheat
wheat
modeling
rapeseed
maize
winter
Helianthus annuus
corn
soil water content
soil water
water content
corn soils
census data
Europe
value added

Keywords

  • Climate variability
  • Crop yield
  • MODIS NDVI
  • Remote sensing
  • Statistical modelling
  • Yield forecast

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

Cite this

Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. / Kern, Anikó; Barcza, Z.; Marjanović, Hrvoje; Árendás, Tamás; Fodor, Nándor; Bónis, Péter; Bognár, Péter; Lichtenberger, J.

In: Agricultural and Forest Meteorology, Vol. 260-261, 15.10.2018, p. 300-320.

Research output: Contribution to journalArticle

Kern, Anikó ; Barcza, Z. ; Marjanović, Hrvoje ; Árendás, Tamás ; Fodor, Nándor ; Bónis, Péter ; Bognár, Péter ; Lichtenberger, J. / Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. In: Agricultural and Forest Meteorology. 2018 ; Vol. 260-261. pp. 300-320.
@article{d4edf6f5e6f94998abab352c3e4fe6b4,
title = "Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices",
abstract = "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 exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10{\%}, 2{\%} and 4{\%} for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.",
keywords = "Climate variability, Crop yield, MODIS NDVI, Remote sensing, Statistical modelling, Yield forecast",
author = "Anik{\'o} Kern and Z. Barcza and Hrvoje Marjanović and Tam{\'a}s {\'A}rend{\'a}s and N{\'a}ndor Fodor and P{\'e}ter B{\'o}nis and P{\'e}ter Bogn{\'a}r and J. Lichtenberger",
year = "2018",
month = "10",
day = "15",
doi = "10.1016/j.agrformet.2018.06.009",
language = "English",
volume = "260-261",
pages = "300--320",
journal = "Agricultural and Forest Meteorology",
issn = "0168-1923",
publisher = "Elsevier",

}

TY - JOUR

T1 - Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

AU - Kern, Anikó

AU - Barcza, Z.

AU - Marjanović, Hrvoje

AU - Árendás, Tamás

AU - Fodor, Nándor

AU - Bónis, Péter

AU - Bognár, Péter

AU - Lichtenberger, J.

PY - 2018/10/15

Y1 - 2018/10/15

N2 - 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 exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10%, 2% and 4% for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.

AB - 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 exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10%, 2% and 4% for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.

KW - Climate variability

KW - Crop yield

KW - MODIS NDVI

KW - Remote sensing

KW - Statistical modelling

KW - Yield forecast

UR - http://www.scopus.com/inward/record.url?scp=85049311759&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049311759&partnerID=8YFLogxK

U2 - 10.1016/j.agrformet.2018.06.009

DO - 10.1016/j.agrformet.2018.06.009

M3 - Article

AN - SCOPUS:85049311759

VL - 260-261

SP - 300

EP - 320

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

ER -