2020, issue 1, p. 53-61
Received 06.02.2020; Revised 24.02.2020; Accepted 10.03.2020
Published 31.03.2020; First Online 26.04.2020
MODELING OF QUANTILES FOR PROBABILITY DISTRIBUTION OF CROP YIELD UNDER CLIMATE CHANGE (ON THE EXAMPLE OF CORN)
Volodymyr Pepelyaev 1 *, Olexandr Golodnikov 1, Nina Golodnikova 1
1 V.M. Glushkov Institute of Cybernetics, Kyiv, Ukraine
Introduction. In the context of global warming, there is an urgent need to adapt the agrarian sector to climate change, which, in particular, provides for an adequate choice of crop structure. For this purpose it is necessary to determine which crops are most adapted to the new climatic conditions and to scientifically substantiate their placement in the territory of Ukraine. The traditional approach to crop selection, which consists in conducting field trials of crop response to climate change, is time consuming. An alternative to this approach is application of the methods of mathematical modeling of crop yields in new climatic conditions. The article proposes to use a more flexible approach, namely, the quantile regression method, for modeling yield dependence on climatic parameters, which allows to determine any quantile of the yield distribution function, rather than only one value (average), as in the case of standard regression. The crop yield model based on quantile regression is developed on the grounds of V.P. Dmitrenko model "Weather-harvest" [8, 9]. The following data are used as inputs: 1) corn yields in the context of several areas of the Ukrainian Forest-Steppe in recent years; 2) information on average monthly temperatures and rainfall in these areas in recent years; forecasts of average monthly air temperatures and rainfall in Ukraine for the nearest (by 2030) and more distant (2031 – 2050) perspectives, which are obtained by experts of the Ukrainian Hydrometeorological Institute [10–12].
The purpose of the paper is to develop a mathematical model for estimating crop yields that takes into account the uncertainty, associated with climate change in the near and distant perspectives.
Results. Using the developed model, estimates of the quantiles of the corn yield distribution function for the nearest (up to 2030) and for the more distant (2031 - 2050) perspectives are obtained both at the level of the individual (Central) region of Ukraine and at the level of the individual (Ternopil) region. The simulation results indicate that weather conditions forecast in [10–12] over the next 30 years will more likely produce good corn yields.
Keywords: adaptation to climate change, crop yield modeling, quantile regression, interphase periods.
Cite as: Pepelyaev V., Golodnikov O., Golodnikova N. Modeling of Quantiles for Probability Distribution of Crop Yield Under Climate Change (On the Example of Corn). Cybernetics and Computer Technologies. 2020. 1. 53–61. (in Ukrainian) https://doi.org/10.34229/2707-451X.20.1.6
1. Polevoy А.N. Modeling of hydrometeorological regime and productivity of agroecosystems. Odessa State Ecological University. Odessa: Ekologiya, 2013. 430 p. (in Ukrainian)
2. Wallach D., Makowski D., Jones J.W., Brun F. Working with Dynamic Crop Models. Methods, Tools and Examples for Agriculture and Environment. 2nd Edition. Academic Press, 2014. 504 p.
3. Zubov O.R., Zubova L.G., Slavgorodska Yu.V. Estimation of the impact of meteorological factors on the productivity of winter crops in conditions of north part of the Lugansk region. Visnik Poltav.Derg.Agr.Acad. 2012. 2. P. 14–20. (in Ukrainian)
4. Holod S.G. The dependence of the yield of millet and its elements on the agro-climatic conditions of the growing zone. Visnik Centru naukovogo zabezpechennya APV Harkivskoy oblasty. 2016. 20. P. 75–83. (in Ukrainian) http://nbuv.gov.ua/UJRN/Vcnzapv_2016_20_13
5. Shastry A., Sanjay H.A., Bhanusree E. Prediction of Crop Yield Using Regression Techniques. International Journal of Soft Computing. 2017. 12 (2). P. 96–102. https://medwelljournals.com/abstract/?doi=ijscomp.2017.96.102
6. Sellam V., Poovammal E. Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology. 2016. 9 (38). P. 1–5. https://doi.org/10.17485/ijst/2016/v9i38/91714
7. Koenker R., Bassett G. Regression Quantiles. Econometrica. 1978. 46 (1). P. 33–50. https://doi.org/10.2307/1913643
8. Dmytrenko V.P. Weather, climate and harvest of field crops. Kyiv: Nika-Centr, 2010. 620 p. (in Ukrainian)
9. Dmytrenko V.P., Odnoletok L.P., Krivosheyn O.O., Krukivska A.V. Development of a methodology for assessing the yield potential of crops, taking into account the impact of climate and agro-phytotechnology. Ukrainsky hidrometeorologichniy gurnal. 2017. 20. P. 52–60. (in Ukrainian)
Krakovska S.V., Gnatyuk N.V., Shpital T.M. Possible scenarios of climatic conditions in the Ternopil region during the XXI century. Naukovy zapisky Ternopylskogo Nacionalnogo pedagogichnogo universytetu imeny Volodimyra gnatyuka. seriya : geographiya. 2014. 1. P. 55–67. (in Ukrainian)
Krakovska S.V., Gnatyuk N.V., Shpital T.M Palamarchuk L.V. Projections of changes in surface air temperature according to the ensemble of regional climate models in the regions of Ukraine in the 21st century. Naukovy praci Ukrainskogo naukovo-doslidnoigo hydrometeorologichnogo institutu. 2016. 268. P. 33–44. (in Ukrainian)
12. Final report “On the research work of developing climate change scenarios in Ukraine for the medium and long term using data from global and regional models”. UkrGMI. (in Ukrainian) https://uhmi.org.ua/project/rvndr/climate.pdf
13. Tank Klein A.M.G. and Coauthors. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology. 2002. 22. P. 1441–1453. https://doi.org/10.1002/joc.773
ISSN 2707-451X (Online)
ISSN 2707-4501 (Print)