Abstract
Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).
Highlights
Since the turn of 21st century, global climate change debate has gained traction with many scientific studies undertaken to understand the ramifications of this phenomenon
Climate change, as defined by FAO, is the variation in climatic patterns derived from emission of greenhouse gases (GHGs) such as carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) through deforestation, combustion, industrialization and urbanization [10]
The specific aims were to model the variables using vector autoregressive model, and employ the structural tools to determine the dynamic relationship between the endogenous variables
Summary
Since the turn of 21st century, global climate change debate has gained traction with many scientific studies undertaken to understand the ramifications of this phenomenon. Climate change, a phenomenon caused by anthropogenic factors and natural processes, has proven to be a challenging environmental challenge that has potential significantly impact the world economy. The dependence of agriculture on phonological factors has augmented the susceptibility of the sector to climatic patterns especially in the developing countries. Climate change continues to pose a threat to majority of people across the globe due to its effects on agriculture, water resources, forests, snow cover and the resulting geological processes, such as desertification, flood and landslides. According to McPhail et al, climate change leads to rising sea levels, extreme rainfall and drought in areas that had hitherto experienced normal rainfall and drought, as well as unpredictability in weather conditions, affecting agricultural production the most [24]
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