Geostatistical interpolation and aggregation of crop growth model outputs

Research output: Contribution to conferenceAbstractAcademic

Abstract

Many mechanistic crop growth models require daily meteorological data; consequently, model simulations can only be obtained for locations close to weather stations with long-term records. Those simulations deliver potential yields as point data.
A widely used approach for aggregating (estimating total production per country from the simulated yields at points) is based on agro-ecological Climate Zones (CZ), e.g. the Global Yield Gap Atlas (www.yieldgap.org). A geostatistical approach that exploits the spatial correlation of simulated yields at points and its correlation with external environmental factors offers additional features to the CZ approach: yield predictions adjusted to conditions on every single location, quantification of the uncertainty of the predictions, and quantification of uncertainty of aggregated country production. As a case study, we interpolate and aggregate potential yields of millet in West Africa. We compare the results of the geostatistical approach with those of the CZ approach.
Original languageEnglish
Publication statusPublished - 2016
EventESA 14 : Growing landscapes - Cultivating Innovation Agricultural Systems - Edinburgh
Duration: 5 Sep 20169 Sep 2016

Conference

ConferenceESA 14
Period5/09/169/09/16

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interpolation
crop
climate
millet
weather station
prediction
atlas
simulation
environmental factor

Cite this

@conference{16b893e9ed73420eb0052e84368922c4,
title = "Geostatistical interpolation and aggregation of crop growth model outputs",
abstract = "Many mechanistic crop growth models require daily meteorological data; consequently, model simulations can only be obtained for locations close to weather stations with long-term records. Those simulations deliver potential yields as point data.A widely used approach for aggregating (estimating total production per country from the simulated yields at points) is based on agro-ecological Climate Zones (CZ), e.g. the Global Yield Gap Atlas (www.yieldgap.org). A geostatistical approach that exploits the spatial correlation of simulated yields at points and its correlation with external environmental factors offers additional features to the CZ approach: yield predictions adjusted to conditions on every single location, quantification of the uncertainty of the predictions, and quantification of uncertainty of aggregated country production. As a case study, we interpolate and aggregate potential yields of millet in West Africa. We compare the results of the geostatistical approach with those of the CZ approach.",
author = "L. Steinbuch",
year = "2016",
language = "English",
note = "ESA 14 : Growing landscapes - Cultivating Innovation Agricultural Systems ; Conference date: 05-09-2016 Through 09-09-2016",

}

Geostatistical interpolation and aggregation of crop growth model outputs. / Steinbuch, L.

2016. Abstract from ESA 14 , .

Research output: Contribution to conferenceAbstractAcademic

TY - CONF

T1 - Geostatistical interpolation and aggregation of crop growth model outputs

AU - Steinbuch, L.

PY - 2016

Y1 - 2016

N2 - Many mechanistic crop growth models require daily meteorological data; consequently, model simulations can only be obtained for locations close to weather stations with long-term records. Those simulations deliver potential yields as point data.A widely used approach for aggregating (estimating total production per country from the simulated yields at points) is based on agro-ecological Climate Zones (CZ), e.g. the Global Yield Gap Atlas (www.yieldgap.org). A geostatistical approach that exploits the spatial correlation of simulated yields at points and its correlation with external environmental factors offers additional features to the CZ approach: yield predictions adjusted to conditions on every single location, quantification of the uncertainty of the predictions, and quantification of uncertainty of aggregated country production. As a case study, we interpolate and aggregate potential yields of millet in West Africa. We compare the results of the geostatistical approach with those of the CZ approach.

AB - Many mechanistic crop growth models require daily meteorological data; consequently, model simulations can only be obtained for locations close to weather stations with long-term records. Those simulations deliver potential yields as point data.A widely used approach for aggregating (estimating total production per country from the simulated yields at points) is based on agro-ecological Climate Zones (CZ), e.g. the Global Yield Gap Atlas (www.yieldgap.org). A geostatistical approach that exploits the spatial correlation of simulated yields at points and its correlation with external environmental factors offers additional features to the CZ approach: yield predictions adjusted to conditions on every single location, quantification of the uncertainty of the predictions, and quantification of uncertainty of aggregated country production. As a case study, we interpolate and aggregate potential yields of millet in West Africa. We compare the results of the geostatistical approach with those of the CZ approach.

M3 - Abstract

ER -