Keywords
biodiesel; agricultural planning; mathematical programming.
Location
Session H8: New Challenges for Agricultural Systems Modeling and Software
Start Date
17-6-2014 9:00 AM
End Date
17-6-2014 10:20 AM
Abstract
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.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
The Value of Seasonal Productivity Forecasting in Biodiesel Plans
Session H8: New Challenges for Agricultural Systems Modeling and Software
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.
