Improving the Simulation of Crop Growth in Commercial Growers' Fields Using Soil Scans, Remote Sensing Imagery and Yield Monitor Data

Research output: Contribution to conferenceAbstract


Precision agriculture can in many cases increase the profitability and sustainability of farming. Current precision ag recommendations are often based on empirical models, such as linear or curvilinear relationships between measurement and recommended application rate. For further increases in profitability and sustainability, mechanistic models of crop growth are needed. We investigated whether crop and soil parameters in these models can be determined via inverse modelling using readily available “big data” obtained from soil scans, remote sensing and yield monitors. Moreover, we investigated whether soil parameters determined in this way express within-field spatial variation in these measurements. We used data from several commercial potato growers in the period 2014-2018 (approx. 30 site-years). Initial results show that variations between fields can be parameterized. Accounting for within-field spatial variability through the soil parameters of the fields in question is considerably more difficult.
Original languageEnglish
Publication statusPublished - 2018
Event2018 ASA and CSSA Meeting - Baltimore, MD, United States
Duration: 4 Nov 20187 Nov 2018


Conference2018 ASA and CSSA Meeting
CountryUnited States
CityBaltimore, MD
Internet address


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