@article{Zanini:21720,
      recid = {21720},
      author = {Zanini, Fabio C. and Irwin, Scott H. and Schnitkey, Gary  D. and Sherrick, Bruce J.},
      title = {ESTIMATING FARM-LEVEL YIELD DISTRIBUTIONS FOR CORN AND  SOYBEANS IN ILLINOIS},
      address = {2000},
      number = {372-2016-19409},
      series = {Selected Paper},
      pages = {29},
      year = {2000},
      abstract = {Many yield modeling approaches have been developed in  attempts to provide accurate characterizations of  farm-level yield distributions due to the importance of  yield uncertainty in crop insurance design and rating, and  for managing farm-level risk.  Competing existing models of  crop yields accommodate varying skewness, kurtosis, and  other departures from normality including features such as  multiple modes. Recently, the received view of crop yield  modeling has been challenged by Just and Weninger who  indicate that there is insufficient evidence to reject  normality given data limitations and potential  methodological shortcomings in controlling for  deterministic components (trend) in yields.  They point out  that past empirical efforts to estimate and validate  specific-farm distributional characterizations have been  severely hampered by data limitations.  As a result, they  argue in favor of normality as an appropriate  parameterization of crop yields.
This paper investigates  alternate representations of farm-level crop yield  distributions using a unique data set from the University  of Illinois Endowment farms, containing same-site yield  observations for a relatively long period of time, and  under conditions that closely mirror actual farm conditions  in Illinois.  Results from alternate econometric model  specifications controlling for trend effects suggest that a  linear trend provides an adequate representation of crop  yields at the farm level during the period covered by the  estimations.  Specification tests based on a linear-trend  model suggest significant heteroskedasticity is present in  only a few farms, and that the residuals are white  noise.
With these data, Jarque-Bera normality test results  indicate that normality of detrended yield residuals is  rejected by a far greater number of fields than would be  explained due to randomness.  Thus, to further clarify the  issue of yield distribution characterizations, more  complete goodness-of-fit measures are compared across a  larger set of candidate distributions.  The results  indicate that the Weibull distribution consistently ranks  better than the normal distribution both in fields where  normality is rejected and in fields where normality is not  rejected.  The results highlight the fact that failing to  reject normality is not the same as identifying normality  as a "best" parameterization, and provide guidance for  progressing toward better representations of farm-level  crop yields.},
      url = {http://ageconsearch.umn.edu/record/21720},
      doi = {https://doi.org/10.22004/ag.econ.21720},
}