@article{Queiroz:311050,
      recid = {311050},
      author = {Queiroz,  Pedro Vertino de and Perrin, R.K. and Fulginiti,  L.E. and Bullock, D.},
      title = {An expected value of sample information (EVSI) approach  for estimating the payoff from a variable rate technology.},
      address = {2021-05-22},
      number = {1723-2021-1528},
      series = {Working Papers, 2021-3},
      pages = {34},
      month = {May},
      year = {2021},
      abstract = {This paper examines the payoff to variable rate technology  (VRT) using a Bayesian approach following literature on the  expected value of sample information (EVSI).  In  each cell  within a field, we compare the expected payoff from an  optimal variable rate conditioned on a signal from that  cell, with the expected payoff from a uniform rate  technology (URT) that is optimal for all cells in the  field. This comparison, when evaluated across the  theoretical distribution of signals, provides an estimate  of the expected gross benefit from VRT relative to URT.  Under plausible assumptions, a closed-form algebraic  solution relates this expected benefit to field and  nitrogen response characteristics. We apply our approach to  data from on-farm field-level experiments conducted by the  Data-Intensive Farm Management Project (DIFM) (Bullock, et  al. 2019), which examined nitrogen (N) response across  cells for which soil electroconductivity (EC) served as the  signal related to nitrogen response. We calculate the  expected gross benefits to be about $1.81/ac, insufficient  to support costs of VRT implementation. Our model provides  quantitative estimates of the extent to which this poor  outcome could be improved by a higher correlation between  the EC signal and the state of nature of interest, by  higher variability of the state of nature across cells, and  by a sharper curvature of yield response to N.},
      url = {http://ageconsearch.umn.edu/record/311050},
      doi = {https://doi.org/10.22004/ag.econ.311050},
}