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

Research output: Contribution to conferenceAbstractAcademic

Abstract

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
https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/

Conference

Conference2018 ASA and CSSA Meeting
CountryUnited States
CityBaltimore, MD
Period4/11/187/11/18
Internet address

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remote sensing
growers
monitoring
crops
profitability
soil
precision agriculture
soil heterogeneity
mechanistic models
application rate
spatial variation
farming systems
potatoes

Cite this

@conference{c9782cf7f6de4418b3241e12b36cc145,
title = "Improving the Simulation of Crop Growth in Commercial Growers' Fields Using Soil Scans, Remote Sensing Imagery and Yield Monitor Data",
abstract = "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.",
author = "{van Evert}, F.K. and Frenk-Jan Baron and J.A. Booij and E.J.J. Meurs and {van Oort}, P.A.J. and C. Kempenaar",
year = "2018",
language = "English",
note = "2018 ASA and CSSA Meeting ; Conference date: 04-11-2018 Through 07-11-2018",
url = "https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/",

}

Improving the Simulation of Crop Growth in Commercial Growers' Fields Using Soil Scans, Remote Sensing Imagery and Yield Monitor Data. / van Evert, F.K.; Baron, Frenk-Jan; Booij, J.A.; Meurs, E.J.J.; van Oort, P.A.J.; Kempenaar, C.

2018. Abstract from 2018 ASA and CSSA Meeting, Baltimore, MD, United States.

Research output: Contribution to conferenceAbstractAcademic

TY - CONF

T1 - Improving the Simulation of Crop Growth in Commercial Growers' Fields Using Soil Scans, Remote Sensing Imagery and Yield Monitor Data

AU - van Evert, F.K.

AU - Baron, Frenk-Jan

AU - Booij, J.A.

AU - Meurs, E.J.J.

AU - van Oort, P.A.J.

AU - Kempenaar, C.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

M3 - Abstract

ER -