@article{Bauer:232323,
      recid = {232323},
      author = {Bauer, Leonard and Novak, Frank and Armstrong, Glen and  Staples, Blaine},
      title = {An Economic Analysis of Alternative Cropping Decisions  Under Uncertainty},
      address = {1992},
      number = {1528-2016-131850},
      series = {Project Report},
      pages = {42},
      year = {1992},
      abstract = {This project has examined after tax gross margin net  present values accruing to Albertawheatf armers
under three  fertilizer and crop rotation systems; a fixed rotation  traditional fertilizer system, a static economic
fertilizer  decision system within a fixed rotation, and a static  economic fertilizer decision system within a
dynamic  flex-cropping framework. Decision rules appropriate to each  system were developed for case farms
in three Alberta  agro-climatic regions; Medicine Hat, Lethbridge and  Olds.
The flex-cropping issue is expressed in a dynamic  programming framework and incorporates
elements not fully  explored in previous studies; income taxation, variable  input level decisions and
stochastically determined  moisture conditions and crop prices. Decisions are compared  by simulating net
present values of after tax gross margins  for each system. The traditional system generatedthe lowest  net
present value, approximately 5 to 17 per cent below the  static economic system. Greater improvements,
on the order  of 14 to 31 per cent above the static economic system, were  observed by following dynamic
flex-cropping decision rules.  Not only did the dynamic flex-cropping decision rules  generate superior
decision rules regarding mean net present  values, the rules were also risk efficient. The probability  of low
gross margins was minimized in all cases by  following the dynamic flex-cropping decision rules.
The  results of this and related studies indicate that dynamic  flex-cropping models are viable for solving
crop scheduling  problems. The prescriptive power of the model is limited by  available data, limitations which
reside primarily in the  agronomic components. The relationship between spring soil  moisture, soil nutrients,
and yield must be more clearly  defined. This may be accomplished through  extensive and  long term field
trials or through use of emerging  biophysical models. Standardization of soil moisture  classifications,
including method of sampling and depth of  measurement, would make field data more adaptable for  making
fertilizer and recropping decisions. The production  functions defining the relationship between spring  soil
moisture levels and yields are particularly important.  These require continued empirical attention.
The model  developed lends itself readily to extensions such as  additional crops,fertilizer inputs,
erosion costs and soil  degradation issues, financial structure of the farm, and  evaluating the influence of
government programs. Modern  computers with large computational and storage capabilities  make the
implementation of stochastic dynamic programming  methodology a viable farm management tool.},
      url = {http://ageconsearch.umn.edu/record/232323},
      doi = {https://doi.org/10.22004/ag.econ.232323},
}