Crop productivity is commonly assumed as a deterministic function when developing
agricultural plans. Actual data prove however that, even for the same soil at the same location, crop
productivity can be better interpreted as a random variable due to the meteorological conditions of the
specific year. For the production of biodiesel, crops are easily substitutable and the farmer can chose
every year between various alternatives. Without information on the seasonal meteorology, the
farmers select the crop to cultivate mainly on the basis of the expected productivity. However,
changes in the meteorological situation may result in a reduction in crop profitability. As a result,
a crop, that on average is less interesting, may become the best choice in a specific year. Given that
seasonal forecasts based on long range climatic variables, such as ENSO, are becoming available,
the paper examines their effectiveness in biodiesel production plans, with reference to an area in Mato Grosso, Brazil. We formulate and solve a mathematical programming problem to determine the most efficient crop plan under different scenarios: (i) no information about the seasonal meteorology, (ii) perfect information and (iii) meteorological forecasts with different precision. This allows us to
quantitatively evaluate how important the availability of seasonal productivity forecasting might be and
shows that even a rough seasonal forecast, if systematically applied, may improve the average
production and reduce the risk of traditional agricultural decisions
