Modeling food price dynamics
To understand the impact of local weather and production shocks on food prices, models can
be used that incorporate local food price time series, international price time series and
regional NDVI time series data as inputs and generates local food price forecasts as outputs.
Here, a price-NDVI model developed in collaboration with Varun Kshirsagar is presented
that seeks to develop market-level predictions of commodity prices in food insecure markets
using NDVI and the international price as its inputs. This model is an extension of previous
work done by the author and her collaborators, but has a few novel elements, in particular the
much more extensive use of satellite data from beyond the immediate area around the market
(Brown et al ., 2009; Brown et al ., 2006; Brown et al ., 2008).
A successful prediction model will largely be a consequence of our ability to quantify and
measure a major source of price variability - local weather shocks - whose influence on local
food prices has not been previously quantified and analyzed in a systematic manner across a
large set of countries. The approach presented here is novel in the following ways:
agricultural growing area, not just the local area around a market;
If implemented in near-real time using current remote sensing and the latest price information,
the model can be used to understand the impact of large droughts on surrounding markets
through empirically connecting food price changes with vegetation signals. This approach can
also be used to understand seasonal variation in local food prices, and provide an assessment of
the vulnerability of these places to environmental change and to global commodity price
changes. The work can provide a predictive element to early warning organizations' efforts to
monitor food prices, and can add an analytical component to the necessarily qualitative analysis
of the impact of drought on livelihoods in specific markets. There is usually a delay of a month
or more between the time of the commodity price observation in a market and when it is incor-
porated into a database for access. Thus projections provide information about current food
prices, and can be integrated into food security outlooks, which are focused on providing assess-
ments of the food security situation in the coming three to six months.
Although true price “predictions” have limited use and present a thorny problem for
organizations such as the US government funded FEWS NET whose influence in the region
is extensive, projections that capture the likely future dynamics of prices could be of great use
to humanitarian organizations. To capture a significant change in direction, from increasing
to decreasing prices across an area for example, due to external influences from the weather
or of production that cannot be observed either locally or from afar, would be of great use.
In addition, if a model can estimate which markets and which commodities are likely to be
affected by an observed weather anomaly, then a much more accurate analysis of the liveli-
hood impacts of the event can be conducted. The objective of this model is to quantitatively
assess the impact of spatially extensive weather anomalies on specific markets in a way that can
be replicated, and can be regularly provided to decision makers.
Food price analysis demonstrates the influence of local weather shocks on local food price
dynamics across a large set of developing countries on four continents. The results suggest that