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International Theoretical Science Journal
Volume 59 >>> № 5 SEPTEMBER — OCTOBER 2023
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UDC 330.115
T. Ermolieva1, P. Havlik2, A. Lessa-Derci-Augustynczik3, E. Boere4,
S. Frank5, T. Kahil6, G. Wang7, J. Balkovic8, R. Skalsky9,
C. Folberth10, N. Komendantova11, P.S. Knopov12


1 International Institute for Applied Systems Analysis, Laxenburg, Austria

 ermol@iiasa.ac.at

2 International Institute for Applied Systems Analysis, Laxenburg, Austria

 havlik.petr@gmail.com

3 International Institute for Applied Systems Analysis, Laxenburg, Austria

 augustynczik@iiasa.ac.at

4 International Institute for Applied Systems Analysis, Laxenburg, Austria

 boere@iiasa.ac.at

5 International Institute for Applied Systems Analysis, Laxenburg, Austria

 frank@iiasa.ac.at

6 International Institute for Applied Systems Analysis, Laxenburg, Austria

 kahil@iiasa.ac.at

7 China Agricultural University (CAU), Beijing, China

 gangwang@cau.edu.cn

8 International Institute for Applied Systems Analysis, Laxenburg, Austria

 balkovic@iiasa.ac.at

9 International Institute for Applied Systems Analysis, Laxenburg, Austria

 skalsky@iiasa.ac.at

10 International Institute for Applied Systems Analysis, Laxenburg, Austria

 folberth@iiasa.ac.at

11 International Institute for Applied Systems Analysis, Laxenburg, Austria

 komendan@iiasa.ac.at

12 V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv, Ukraine

 knopov1@yahoo.com, knopov1@gmail.com

A NOVEL ROBUST META-MODEL FRAMEWORK
FOR PREDICTING CROP YIELD PROBABILITY
DISTRIBUTIONS USING MULTISOURCE DATA

Abstract. There is an urgent need to better understand and predict crop yield responses to weather disturbances, in particular, of extreme nature, such as heavy precipitation events, droughts, and heat waves, to improve future crop production projections under weather variability, extreme events, and climate change. In this paper, we develop quantile regression models for estimating crop yield probability distributions depending on monthly temperature and precipitation values and soil quality characteristics, which can be made available for different climate change projections. Crop yields, historical and those simulated by the EPIC model, are analyzed and distinguished according to their levels, i.e., mean and critical quantiles. Then, the crop yield quantiles are approximated by fitting separate quantile-based regression models. The developed statistical crop yield meta-model enables the analysis of crop yields and respective probabilities of their occurrence as a function of the exogenous parameters such as temperature and precipitation and endogenous, in general, decision-dependent parameters (such as soil characteristics), which can be altered by land use practices. Statistical and machine learning models can be used as reduced form scenario generators (meta-models) of stochastic events (scenarios), as a submodel of more complex models, e.g., Integrated Assessment model (IAM) GLOBIOM.

Keywords: extreme events, climate change, food security, crop yields projections, probability distributions, quantile regressions, robust estimation and machine learning, two-stage STO.


full text

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