Trend, Weather Variables, and the Distribution of U.S. Corn Yields

Kaylen, Michael S.; Koroma, S.

doi:10.2307/1349641

Cited by 31 publications

(22 citation statements)

References 16 publications

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“…The key aspect for quantifying production risk is how the frequency distribution of yield responds to changes in the climatic settings (Kaylen and Korom 1991;Park and Sinclair 1993). There are several ways to infer a distribution of yield from climatic data.…”

Section: Introductionmentioning

confidence: 99%

Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland

et al. 2007

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A simple model of yield was used along with climate scenarios to assess the impact of climate change on grain maize productivity and associated economic risk in Switzerland. In a first application, changes in the precipitation regime alone were shown to affect the distribution of yield considerably, with shifts not only in the mean but also in the standard deviation and the skewness. Production risk was found to respond more markedly to changes in the long-term mean than in the inter-annual variability of seasonal precipitation amounts. In a further application, yield projections were generated with respect to a full climate scenario, with the emission pathway as specified in the IPCC A2 scenario. Anticipation of the sowing date was found to reduce the negative impact of climate change on yield stability, but was not sufficient to ensure average productivity levels comparable to those observed at present. We argued that this was caused by the reduction in the duration of the growing season, which had a stronger impact than suggested by previous studies. Assuming no change in price relations, the results also revealed a strong increase in production risk with climate change, with more than a doubling in the probability of yield falling short of a critical threshold as compared to today's situation.

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Section: Introductionmentioning

confidence: 99%

Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland

et al. 2007

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“…In general, the literature has pointed out that crop yield PDF are skewed and non-normal (e.g. Day, 1965;Buccola, 1986;Gallagher, 1986Gallagher, , 1987Nelson and Preckel, 1989;Nelson, 1990;Taylor, 1990;Kaylen and Koroma, 1991;Moss and Shonkwiler, 1993;Teigen and Thomas, 1995;Kaufmann and Snell, 1997;Ramírez, 1997;Goodwin and Ker, 1998). However, Just and Weninger (1999) have argued in favour of not rejecting normal distributions of crop yield.…”

Section: Resumen Elicitación De Fdp Subjetivas De Rendimientos De Culmentioning

confidence: 99%

Elicitation of subjective crop yield PDF [probability density functions]

Clop-Gallart

Juárez-Rubio

2007

Span. j. agric. res.

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The aim of this research is to investigate the coherence and reliability of subjective crop yield probability density functions (PDF) elicited from a series of interviews carried out on a wide group of farmers. Three different elicitation techniques were used: the Two-Step PDF estimation method, the Triangular distribution and the Beta distribution. Subjects who were interviewed gave both estimates for point crop yield (mean, highest possible, most frequent and lowest possible) and for the PDF based on interval estimates. To evaluate persistence, two concepts were used: time persistence and methodological persistence. The results are deemed valuable in order to determine the level of trust in the techniques applied in obtaining data, and in their effectiveness in designing a farm decision support system (DSS).

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“…In the stochastic trend models of Kaylen andKoroma (1991), andMoss andShonkwiler (1993), the drift term m is also allowed to change according to:…”

Section: Stochastic Trend Modelsmentioning

confidence: 99%

“…Positive shocks indicate an above average level of technological innovation while negative shocks indicate a below average level. Stochastic trend models imply that future yield growth paths are much more di¤cult to predict than in the deterministic trend case, and have been used successfully by a number of researchers to characterise trend and stationary components of US corn yields (Fackler 1989;Kaylen and Koroma 1991;Moss and Shonkwiler 1993). In particular, Moss and Shonkwiler (1993) get a very good ¢t to historical US corn yields by incorporating a stochastic trend and using an inverse hyperbolic sine transformation to model nonnormality in the stationary deviations around trend.…”

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confidence: 99%

Regime shifts and technology diffusion in crop yield growth paths with an application to maize yields in Zimbabwe

Myers

Jayne

1997

Aus J Agri & Res Econ

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An alternative speci¢cation for the trend component of crop yield growth is developed and applied to maize yield data for Zimbabwe's large-scale farming sector. This accounts for permanent regime shifts as new technologies are discovered but allows gradual absorption as adoption follows a di¡usion path. Econometric methods are used to estimate the timing and importance of innovations, as well as the length of the di¡usion path. Results from an application to Zimbabwe commercial maize yields indicate two major regime shifts that can be associated with the introduction of hybrid seed varieties, and a di¡usion path that extends over a decade.Crop yield time series are often modelled as the sum of two componentsâ trend process representing improvements in seed varieties, increased fertiliser use, better management practices etc. and a stationary process representing transitory deviations around trend caused primarily by weather. Both components are of interest to economists and policy-makers. The trend component can be interpreted broadly as improvements in technology, so that modelling the trend characterises the rate of technological advance. The stationary component accommodates much of the short-term risk inherent in crop yield distributions, and so can be a useful input into farm decision analysis, crop insurance rating and the pricing of yield futures and options.Early models of crop yield growth used deterministic trends in the form of linear or higher-order polynomial functions of time (e.g. Thompson 1969;Menz and Pardey 1983;Gallagher 1986). It soon became apparent, however, that polynomial trends are not adequate for capturing trend yield growth. Polynomial trends assume the trend component of yield grows at a very

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Trend, Weather Variables, and the Distribution of U.S. Corn Yields

Cited by 31 publications

References 16 publications

Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland

Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland

Elicitation of subjective crop yield PDF [probability density functions]

Regime shifts and technology diffusion in crop yield growth paths with an application to maize yields in Zimbabwe

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