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Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present pa...

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Autores principales: Lalić, B., Firanj Sremac, A., Eitzinger, J., Stričević, R., Thaler, S., Maksimović, I., Daničić, M., Perišić, D., Dekić, Lj.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199546/
https://www.ncbi.nlm.nih.gov/pubmed/30369629
http://dx.doi.org/10.1017/S0021859618000047
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author Lalić, B.
Firanj Sremac, A.
Eitzinger, J.
Stričević, R.
Thaler, S.
Maksimović, I.
Daničić, M.
Perišić, D.
Dekić, Lj.
author_facet Lalić, B.
Firanj Sremac, A.
Eitzinger, J.
Stričević, R.
Thaler, S.
Maksimović, I.
Daničić, M.
Perišić, D.
Dekić, Lj.
author_sort Lalić, B.
collection PubMed
description A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).
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spelling pubmed-61995462018-10-25 Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria Lalić, B. Firanj Sremac, A. Eitzinger, J. Stričević, R. Thaler, S. Maksimović, I. Daničić, M. Perišić, D. Dekić, Lj. J Agric Sci Crops and Soils Research Paper A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf). Cambridge University Press 2018-07 2018-02-14 /pmc/articles/PMC6199546/ /pubmed/30369629 http://dx.doi.org/10.1017/S0021859618000047 Text en © Cambridge University Press 2018 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Crops and Soils Research Paper
Lalić, B.
Firanj Sremac, A.
Eitzinger, J.
Stričević, R.
Thaler, S.
Maksimović, I.
Daničić, M.
Perišić, D.
Dekić, Lj.
Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title_full Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title_fullStr Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title_full_unstemmed Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title_short Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria
title_sort seasonal forecasting of green water components and crop yield of summer crops in serbia and austria
topic Crops and Soils Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199546/
https://www.ncbi.nlm.nih.gov/pubmed/30369629
http://dx.doi.org/10.1017/S0021859618000047
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