Propagation of positional measurement errors to agricultural field boundaries and associated costs

It has been argued that the upcoming targeted approach to managing field operations, or precision farming, requires that field boundaries are measured with cm level accuracy, thus avoiding losses such as wasted inputs, unharvested crops and inefficient use of the land. This paper demonstrates a method for verification of such claims, based on a statistical model that accounts for temporal correlation in positional measurement errors. Our implementation employs the Data Uncertainty Engine (DUE), which is free software that aids the user in defining probability distributions for uncertain spatial objects, and draws random samples from these distributions. A case study concerning the financial consequences of uncertain geometry for a farmer who uses a digital map to optimise field operations for 15 ha of a potato crop is presented. The error model was parameterised on measurement scenarios representing (1) the Dutch registry of agricultural fields; (2) differential GPS-based field checks for verification of area declarations; and (3) special purpose Real Time Kinematic (RTK)-GPS surveys. We found that a farmer who has a manually digitised map of the study area would benefit from a RTK-GPS survey in a single crop year if the survey would cost less than € 442. An independent test case showed that the results of the error model were consistent with field data.