Modeling of Quantiles for Probability Distribution of Crop Yield Under Climate Change (On the Example of Corn)

Abstract

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 is to conduct field trials of their response to climate change, is time consuming. An alternative to this should be the methods of mathematical modeling of crop yields in new climatic conditions. The article proposes to use a more flexible approach, the quantile regression method, for modeling yield dependence on climatic parameters, which allows to determine any quantile of the distribution function of yield, and not just one value (average), as in the case of standard regression. The crop yield model based on quantile regression was developed based on the VP-Dmitrenko model "Weather-harvest" [8], [9]. The following inputs were used as input: (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 were obtained by experts of the Ukrainian Hydrometeorological Institute [10] - [12]. The purpose of the paper: to develop a mathematical model for estimating crop yields that would take 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 were 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 be more likely to produce good corn yields.

Keywords

• adaptation to climate change
• crop yield modeling
• quantile regression
• interphase periods