An Empirical Application and Evaluation of Discrete Stochastic Programming in Farm Management

1971 ◽  
Vol 53 (4) ◽  
pp. 625-638 ◽  
Author(s):  
Allan N. Rae
Keyword(s):  
Stochastic Programming ◽  
Farm Management ◽  
Empirical Application ◽  
Discrete Stochastic Programming

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

1987 ◽  
Vol 19 (2) ◽  
pp. 53-60 ◽  
Author(s):  
L. Garoian ◽  
J. R. Conner ◽  
C. J. Scifres
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Economic Feasibility ◽  
Range Management ◽  
Management Problem ◽  
Treatment Sequence ◽  
Net Returns ◽  
Discrete Stochastic Programming ◽  
Stochastic Programming Model

AbstractMacartney 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.

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

1994 ◽  
Vol 26 (2) ◽  
pp. 565-579
Author(s):  
Eustacius N. Betubiza ◽  
David J. Leatham
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Past Research ◽  
Income Level ◽  
Farm Income ◽  
Financial Assets ◽  
Discrete Stochastic Programming ◽  
Stochastic Programming Model ◽  
Highly Correlated

AbstractA 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.

scholarly journals Multiyear Discrete Stochastic Programming with a Fuzzy Semi-Markov Process

2016 ◽  
Vol 07 (06) ◽  
pp. 482-495
Author(s):  
C. S. Kim ◽  
Richard M. Adams ◽  
Dannele E. Peck
Keyword(s):  
Markov Process ◽  
Stochastic Programming ◽  
Discrete Stochastic Programming ◽  
Semi Markov Process

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

2011 ◽  
pp. 303-326 ◽  
Author(s):  
Tim B. Williamson ◽  
Grant K. Hauer ◽  
M. K.(Marty) Luckert
Keyword(s):  
Climate Change ◽  
Stochastic Programming ◽  
Risk Analysis ◽  
Objective Function ◽  
Programming Model ◽  
Expected Value ◽  
Chance Constraint ◽  
Expected Return ◽  
Different Types ◽  
Discrete Stochastic Programming

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.

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