2.12. Food Security Implications of Climate Variability and Climate Change

Published Date
Reference Module in Earth Systems and Environmental Sciences
Climate Vulnerability
Volume 2, 2013, Pages 117–128, doi:10.1016/B978-0-12-384703-4.00223-9
Current as of 4 August 2015. 

• Author 

• V. Mishra

Abstract

Understanding the impacts of plausible future climate on US corn and soybean production warrants better knowledge about the retrospective climate. This chapter offers an analysis of long-term (1915–2009) climate variability and trends in the US Corn Belt. Results indicate significant increases in mean annual precipitation (2.8 mm per decade, p < .05) during the grain-filling season (JJA). Between 1915 and 2009, temperature trends show warming in mean annual minimum temperature, while cooling in mean annual maximum temperature by 0.07 (p < .05) and 0.06 °C per decade (p > .05), respectively. Droughts have become less severe during recent decades. Cumulative heat during the grain-filling season declined (1.1 °C per decade, p < .05) significantly during the period 1915–2009. Consistent warming in minimum temperature resulted in increases in growing degree-days prominently in the northern part of the Corn Belt. Statistical analysis for crop yields and climate indices show that cumulative heat during the grain-filling season was the most suitable climate variable for the corn and soybean yield predictions in the US Corn Belt. Changes in cumulative heat during the last three decades indicate that climate has been supportive to corn and soybean production in the US Corn Belt.

Keywords

Corn belt

Crop yields

Drought

Growing degree-days

Heat waves

Wavelet analysis

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Vitae

Vimal Mishra is an assistant professor in Civil Engineering at Indian Institute of Technology (www.iitgn.ac.in), Gandhinagar. He completed PhD from Purdue University in 2010 and worked as a post-doctoral research associate at University of Washington, Seattle. His research interests include climate variability, climate change, food security, hydrologic modeling, and climate extremes.

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