Human Impact Promotes Sustainable Corn Production in Hungary

Tibor András Marton,Anna Kis,Anikó Kern,Nándor Fodor,Anna Zubor-Nemes

doi:10.3390/su12176784

Tibor András Marton, Anna Kis + Show 3 more

Open Access

https://doi.org/10.3390/su12176784

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Abstract

We aim to predict Hungarian corn yields for the period of 2020–2100. The purpose of the study was to mutually consider the environmental impact of climate change and the potential human impact indicators towards sustaining corn yield development in the future. Panel data regression methods were elaborated on historic observations (1970–2018) to impose statistical inferences with simulated weather events (2020–2100) and to consider developing human impact for sustainable intensification. The within-between random effect model was performed with three generic specifications to address time constant indicators as well. Our analysis on a gridded Hungarian database confirms that rising temperature and decreasing precipitation will negatively affect corn yields unless human impact dissolves the climate-induced challenges. We addressed the effect of elevated carbon dioxide (CO2) as an important factor of diverse human impact. By superposing the human impact on the projected future yields, we confirm that the negative prospects of climate change can be defeated.

Highlights

Empirical studies put various climate, biophysical, and economic models in practice for assessing the impact of climate change on crop yields
As our research objective is built on revealing the factors of corn yield development, we used a stochastic approach with panel data to make more sufficient projections with the inclusion of progressive human impact
We address elevated CO2 and give 20% yield growth of maze as an upper estimate in case of a 200 ppm CO2 increase based on long-term Free Air CO2 Enrichment (FACE) experiments and crop model simulations [41]

Summary

Introduction

Empirical studies put various climate, biophysical, and economic models in practice for assessing the impact of climate change on crop yields. The results and inferences vary due to model singularities, differences in input data, and the heterogeneity of global food production areas [1,2,3,4,5]. Applications on a global scale have the potential to predict average global yields (under reported uncertainty) for the most important commodities with rather simple measures of highly aggregated growing season temperature and precipitation [6,7,8]. We argue to conceive studies with smaller spatial coverage, where essential local drivers may be considered and agronomic weather measures are executed at the potentially smallest (but realistic) gridded resolution to capture the most spatial heterogeneity and greatest data variability to decrease uncertainties

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