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| Nivens, Heather D.; Kastens, Terry L.; Dhuyvetter, Kevin C.. |
| In production agriculture, good management is demonstrated by profits that are persistenly greater than those of similar neighboring farms. This research examined the effects of management practices on risk-adjusted profit per acre for Kansas farms over 1990-1999. The management practices were price, cost, yield, planting intensity, and technology adoption (less-tillage). Cost management, planting intensity, and technology adoption had the greatest effect on profit per acre, and cash price management was found to have the smallest impact. If producers wish to have continuously high profits, their efforts are best spent in management practices over which they have the most control. |
| Tipo: Journal Article |
Palavras-chave: Farm management; Marketing; Risk; Technology adoption; Farm Management; Marketing. |
| Ano: 2002 |
URL: http://purl.umn.edu/15507 |
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| Nivens, Heather D.; Kastens, Terry L.; Dhuyvetter, Kevin C.; Featherstone, Allen M.. |
| Can remotely sensed imagery improve hedonic land price models? A remotely sensed variable was added to a hedonic farmland value model as a proxy for land productivity. Land cover data were used to obtain urban and recreational effects as well. The urban and recreational effects were statistically significant but economically small. The remotely sensed productivity variable was statistically significant and economically large, indicating that knowing the "greenness" of the land increased the explanatory power of the hedonic price model. Thus, depending upon the cost of this information, including remotely sensed imagery in traditional hedonic land price models is economically beneficial. |
| Tipo: Journal Article |
Palavras-chave: Land Economics/Use. |
| Ano: 2002 |
URL: http://purl.umn.edu/31122 |
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| Nivens, Heather D.; Kastens, Terry L.; Dhuyvetter, Kevin C.. |
| Remotely sensed data have been used in the past to predict crop yields. This research attempts to incorporate remotely sensed data into a net farm income projection model. Using in-sample regressions, satellite imagery appears to increase prediction accuracy in the time periods prior to USDA's first crop production estimate for wheat and corn. Remotely sensed data improved model performance more in the western regions of the state than in the eastern regions. However, in a jackknife out-of-sample framework, the satellite imagery appeared to statistically improve only 8 of the 81 models (9 crop reporting districts by 9 forecasting horizons) estimated. Moreover, 41 of the 81 models were statistically better without the satellite imagery data. This indicates... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Net farm income; Remote sensing; Satellite imagery; Crop Production/Industries. |
| Ano: 2000 |
URL: http://purl.umn.edu/18943 |
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