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| Zheng, Qiujie; Wang, H. Holly; Shi, Qinghua. |
| Modeling crop yield distributions has been an important topic in agricultural production and risk analysis, and nonparametric methods have gained attention for their flexibility in describing the shapes of yield density functions. In this article, we apply a nonparametric method to model joint yield distributions based on farm-level data for multiple crops, and also provide a way of simulation for univariate and bivariate distributions. The results show that the nonparametric models, both univariate and bivariate, are estimated quite well compared to the original samples, and the simulated empirical distributions also preserve the attributes of the original samples at a reasonable level. This article provides a feasible way of using multivariate... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Yield distribution; Multi-variate nonparametric; China; Farm-level; Risks; Farm Management; Risk and Uncertainty. |
| Ano: 2008 |
URL: http://purl.umn.edu/6509 |
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| Kobus, Pawel. |
| The paper deals with the problem of modelling yield risk measures for major crop plants in Poland. Hence, in some cases the gamma distribution offers a better fit to the data than normal distribution, and in addition to linear models, generalized linear models were also used. The research was based on data from Polish FADN, with sample sizes ranging from 416 up to 2300, depending on the crop plant. It was found that models based on the farm level data, can explain on average 20% of variation coefficient unevenness. The most important variables were average yield, type of farming, arable area and land quality. The elimination of the average yield from the models reduced the average determination coefficient to about 9%. |
| Tipo: Presentation |
Palavras-chave: Production risk; Risk measures; Yield distribution; Risk and Uncertainty; Q10; C46. |
| Ano: 2012 |
URL: http://purl.umn.edu/122535 |
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