@article{Conradt:195732,
      recid = {195732},
      author = {Conradt, Sarah and Bokusheva, Raushan and Finger, Robert  and Kussaiynov, Talgat},
      title = {Yield Trend Estimation in the Presence of Farm  Heterogeneity and Non-linear Technological Change},
      journal = {Quarterly Journal of International Agriculture},
      address = {2014-05},
      number = {892-2016-65230},
      series = {Quarterly Journal of International Agriculture 53 (2014)},
      pages = {20},
      year = {2014},
      abstract = {An adequate representation of the technological trend  component of yield time series
is of crucial importance for  the successful design of risk management  instruments.
However, for many transition and developing  countries, the estimation of the technological
trend is  complicated by the joint occurrence of three phenomena: (i)  a high
level of heterogeneity among different farms in a  region; (ii) non-linear development
of technological  change; and (iii) high yield variations as a consequence of  high
exposure of rainfed agriculture to extreme weather  events. Under these situations, the
usually applied  approach to detrend crop yield data using Ordinary Least  Squares is
known to be biased. Based on a unique data set  of 47 farm yield data from northern
Kazakhstan, we  evaluated different alternative approaches. First, we  consider the use
of the MM-estimator, a robust regression  technique for detrending. Second, we evaluate
the effect of  adding information on extreme climate events as an  additional regressor.
Finally, we consider combinations of  the two former approaches and compare the
implications of  the different aggregation level on trend estimations. The  results reveal
the importance of using single farm yield  data for detrending, because technical trends
in Kazakh  wheat yields are highly farm-specific. Furthermore, our  analysis shows that
the estimation of technological trends  can be improved by incorporating weather
information in the  regression model if time series of crop yield data contain  severe
fluctuations due to occurrence of climatic extreme  events. Thus, the presented analysis
contributes to an  improved crop yield analysis for many developing and  transition
countries facing similar conditions.},
      url = {http://ageconsearch.umn.edu/record/195732},
      doi = {https://doi.org/10.22004/ag.econ.195732},
}