Economic Concepts, Methods, and Tools for Risk Analysis in Forestry under Climate Change

Climate change will affect the expected values and distributions of key variables that influence forest management decisions. Risk analysis will likely play a more prominent role in forestry decision making. There are, however, different types of risk problems and different types of models and approaches to choose from. Three possible models that could have application in a climate change risk context are: (1) the Markowitz Portfolio Frontier Model; (2) Expected Value-variance/Chance Constraint Hybrid Model; (3) Discrete Stochastic Programming. These models are applicable in different contexts and answer different questions. For example, the Markowitz model looks for the asset mix that minimizes portfolio variance subject to a minimum expected return. The expected value-variance/chance constraint model accounts for risk preferences and uncertainty in both objective function and constraints variables. The objective function is to maximize certainty equivalent. The discrete stochastic programming model allows for learning to occur and for the decision maker to modify his/her decisions as new information becomes available over a planning horizon.

Value of Temperature-Activated Polymer-Coated Seed in the Northern Corn Belt

The value of an innovative seed technology is estimated in a discrete stochastic programming framework for a representative farm in the northern Corn Belt. Temperature-activated polymer-coated seed has the potential to increase net returns by increasing yields due to early planting and use of longer season varieties, as well as reducing yield loss due to delayed planting. A biophysical simulation model was used to estimate die impact of polymer-coated seed on corn and soybean yields and on field day availability for five planting periods, three crop varieties, and two tillage systems on two different soils under varying weather conditions.

The Effects of Holding Nonfarm Related Financial Assets On Risk-Adjusted Farm Income

A discrete stochastic programming model is formulated to study the gains from diversification when farming operations are augmented with off-farm financial assets that are not highly correlated with returns from farming. We extend past research by considering the dynamics of accumulating these financial assets and the farm's leverage and tenure position. Results show that farmers' income level and stability can be improved by including nonfarm financial assets in their portfolios.

A Discrete Stochastic Programming Model to Estimate Optimal Burning Schedules on Rangeland

Macartney rose is a range management problem on 500,000 acres of rangeland in Texas. Roller chopping followed by burning is an effective method of improving infested rangeland. However, uncertainty associated with implementing effective burns adversely affects economic feasibility of the treatment sequence. Discrete stochastic programming is used to determine optimal burning schedules under uncertainty. Optimal schedules and expected net returns vary with changes in the probability of a successful burn.