University of Buenos Aires Analyzes Agricultural Portfolios with @RISK

With the advent of corporate farming, large agricultural companies have begun to apply the same kinds of risk analysis techniques to the powerful uncertainties of the natural world and market prices that their counterparts in the manufacturing and finance sectors use. And Prof. Ariadna Berger, professor of Farm Management at the University of Buenos Aires, has gone so far as to introduce a portfolio approach to assessing and balancing the risks and opportunities of large cash cropping operations in Argentina. She began using @RISK when she was a graduate student at Cornell University and reports that it is the perfect tool to show her clients how to manage the risks in their large-scale farming operations.

@RISK and a Different Kind of Investment Portfolio
In her consulting for farming corporations, Prof. Berger creates portfolios analogous to the portfolios of stocks and bonds used by financial analysts. Just like investment portfolios, the idea is to spread risk via diversification, except that in agricultural portfolios she is creating a mix of climate regions, soils, crops, and cultivation practices. By planting different crops, both market and yield risks are reduced; by planting in different regions and with different cultivation practices, yield risk is reduced even more. Like so many of her counterparts in finance, Prof. Berger uses @RISK to simulate risks and rewards throughout her clients’ potential agricultural “holdings” and to help her clients compare those possible outcomes with potential results from other possible portfolios with different diversification schemes.

Farming operations are conditioned by extreme fluctuations of weather and market price. Fortunately, Argentina has a number of climate zones, and farmers can choose from a number of crops to plant: mainly wheat, soybeans, corn or sunflowers. This situation allows farmers to spread the risk strategically.

Agronomic Simulation Models
Provide Data for @RISK Yield Distributions
According to Prof. Berger, the big challenge in creating her simulations is that, due to continually changing agricultural practices, it has been difficult to get data based on similar practices and technology over a long enough period of time to generate distributions.  The way she compensates for the lack of historical data is by using agronomic simulation models to generate yield data to enter as @RISK distributions.  These models are based on approximately 30 years of data on soil, water, nutrients, plant variety, and planting methods, and generate simulated yields that can be used to create a distribution.  However, agronomic simulation models predict yields based only on restrictions for water and nutrients, while in the field there are other factors reducing yields, such as hail, frost, pests or diseases.  Prof. Berger accommodates this limitation by complementing the yield data generated by the agronomic simulation models with other distributions for weather and weather-related random variables, which are based on historical data.  In this way, yields simulated in each @RISK iteration are a combination of the distribution generated by the agronomic simulation models and of other random variables that may affect yield.

Large-Scale Farming = Large-Scale Uncertainty
Finally, she rolls land and crop costs into this complex mix, and @RISK suggests answers to such questions as: How much land to assign to each crop?  How much land  to lease?  And how many climate zones to include in the portfolio?  This information is crucial to her clients because farming has always been a chancy business, and large-scale farming involves large-scale downturns and upswings with large-scale investments at stake.  Investment in crop costs and land rent depends on crops and regions, but on average it can be estimated around US $125 per hectare. Some portfolios can be as big as 100,000 hectares, and more.

Because of the unpredictable nature of agriculture, serious planning can only happen if yield and market risks are considered. “Decisions made exclusively on the grounds of expected returns can be misleading and generate an unbearable risk exposure,” says Prof. Berger.  “By accounting for a nearly limitless variety of uncertainties in our work, @RISK adds tremendous value.  It easily integrates in the Excel programs with which we evaluate portfolios and helps us to make far better decisions on how to structure our agricultural portfolios.”

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