Optimal Information Acquisition under a Geostatistical Model
| dc.creator | Pautsch, Gregory R. | |
| dc.creator | Babcock, Bruce A. | |
| dc.creator | Breidt, F. Jay | |
| dc.date | 2017-04-01T18:33:24Z | |
| dc.date.accessioned | 2026-07-09T03:24:00Z | |
| dc.description | Studies examining the value of switching to a variable rate technologies (VRT) fertilizer program assume producers possess perfect soil nitrate information. In reality, producers estimate soil nitrate levels with soil sampling. The value of switching to a VRT program depends on the quality of the estimates and on how the estimates are used. Larger sample sizes, increased spatial correlation, and decreased variability improve the estimates and increase returns. Fertilizing strictly to the estimated field map fails to account for estimation risk. Returns increase if the soil sample information is used in a Bayesian fashion to update the soil nitrate beliefs in non-sampled sites. | |
| dc.identifier | doi:10.22004/ag.econ.18358 | |
| dc.identifier | https://ageconsearch.umn.edu/record/18358/files/wp990217.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/18358 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/531891 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/18358 | |
| dc.title | Optimal Information Acquisition under a Geostatistical Model | |
| dc.type | Text |