Between the winter of 2006 and the summer of 2008 prices of the world's four most importat staple food commodities (rice, wheat, corn and soybeans) more than tripled. These four staple crops comprise about 75% of the world's caloric base. This project develops a new methodology to use detailed data about weather and agricultural output to examine how climate change affects food commodity prices and particularly commodity price variability.
The project begins with using econometric techniques and detailed data on weather and agricultural output. The data are used to estimate and test a statistical model of a non-linear relationship between weather and output; the results of that model are predictions about the yield variability of the four major staple crops in all major global production regions. The next step uses existing climate change projections to develop sensible predictions for future specific changes in weather patterns, including changes in the mean/average outcome, changes in the variability of climate, and correlation between the different production regions. The results of the first two steps are then combined to give a prediction of how climate change will change the distribution of agricultural yields.
The next step is to consider how past weather shocks have affected supply and demand for these staple crops. This information is used to estimate a structural model of supply and demand for each of the four staple crops. Combining this market model with the predicted changes in the distribution of agricultural yields allows the PIs to make predictions about how future climate change will affect commodity prices. The models will also be used to examine the likely effects of changes in government policy and changes in how farmers and commodity traders store crops from year to year.
The specifics of the research plan include
A: Linking weather and yield variability. The PIs extend their earlier work to examine yield variablility of the four major staple crops throughout the world.
B: Climate change projections. The PIs summarize the predictions of various General Circulation Models on (i) mean outcomes; (ii) changes in variability, and (iii) correlation between production regions. Each one of these three changes in weather patterns would directly influence the distribution of yields.
C: Predict yield distributions Combining parts A and B gives revised yield distributions under climate change. The non linear temperature-yield relationship from part A implies that even a mean increase in temperatures with constant variance could impact yield variability, as would a change in weather variability. Finally, if weather becomes more (or less) correlated or if the geography of agricultural production becomes more (or less) concentrated, idiosyncratic productions shocks will no longer average out and total world production could become more (or less) variable.
D: Yield variability and commodity prices. Using weather-induced yield shocks as instruments allows the researchers to estimate supply and demand elasticities for the four staple crops. While randome yield shocks have previously been used to estimate demand elasticities, lagged yield shocks can also be used to identify supply elasticities, as past production shocks are linked to the current period's effort through storage. With the elasticity estimates the researchers can translate their yield distribution into a distribution of commodity prices.
E: Simulate the effects of competitive storage and government policies. Farmers and commodity traders are likely to resond to changes in production and price variability by adjusting inventory holdings. Inventories help to attenuate price variability in the face of year-t0-year productions variability. Inventory adjustments will therefore partly buffer changes in production variability, as could continued expansion of irrigated agriculuture. On the other hand, export restrictions and other government policies might exaggerate price variability.
This interdisciplinary project brings economists, agronomists, and environmental scientists together. The results will be useful for guiding both policymakers and farmers as they adjust to future climate change.
