Spatial and temporal uncertainty of crop yield aggregations

Vera Porwollik*, Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Toshichika Iizumi, Deepak K. Ray, Alex C. Ruane, Almut Arneth, Juraj Balkovič, Philippe Ciais, Thomas A.M. Pugh, Allard de Wit

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)

Abstract

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r. =0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r. =0.56 (maize, India), r. =0.05 (wheat, Russia), r. =0.13 (rice, Vietnam), and r. =-0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.
Original languageEnglish
Pages (from-to)10-21
JournalEuropean Journal of Agronomy
Volume88
DOIs
Publication statusPublished - 2017

Fingerprint

crop yield
uncertainty
crop
crops
time series analysis
time series
soybean
wheat
soybeans
rice
maize
productivity
corn
Uruguay
crop models
Bolivia
food security
Vietnam
Pakistan
Russia

Keywords

  • Aggregation uncertainty
  • Crop yields
  • Global crop model
  • Gridded data
  • Harvested area

Cite this

Porwollik, V., Müller, C., Elliott, J., Chryssanthacopoulos, J., Iizumi, T., Ray, D. K., ... de Wit, A. (2017). Spatial and temporal uncertainty of crop yield aggregations. European Journal of Agronomy, 88, 10-21. https://doi.org/10.1016/j.eja.2016.08.006
Porwollik, Vera ; Müller, Christoph ; Elliott, Joshua ; Chryssanthacopoulos, James ; Iizumi, Toshichika ; Ray, Deepak K. ; Ruane, Alex C. ; Arneth, Almut ; Balkovič, Juraj ; Ciais, Philippe ; Pugh, Thomas A.M. ; de Wit, Allard. / Spatial and temporal uncertainty of crop yield aggregations. In: European Journal of Agronomy. 2017 ; Vol. 88. pp. 10-21.
@article{d9bfc2bce9c04c6cb2c740a4184b8503,
title = "Spatial and temporal uncertainty of crop yield aggregations",
abstract = "The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17{\%} relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r. =0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10{\%} or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67{\%} (maize, South Africa), 43{\%} (wheat, Pakistan), 51{\%} (rice, Japan), and 427{\%} (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r. =0.56 (maize, India), r. =0.05 (wheat, Russia), r. =0.13 (rice, Vietnam), and r. =-0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.",
keywords = "Aggregation uncertainty, Crop yields, Global crop model, Gridded data, Harvested area",
author = "Vera Porwollik and Christoph M{\"u}ller and Joshua Elliott and James Chryssanthacopoulos and Toshichika Iizumi and Ray, {Deepak K.} and Ruane, {Alex C.} and Almut Arneth and Juraj Balkovič and Philippe Ciais and Pugh, {Thomas A.M.} and {de Wit}, Allard",
year = "2017",
doi = "10.1016/j.eja.2016.08.006",
language = "English",
volume = "88",
pages = "10--21",
journal = "European Journal of Agronomy",
issn = "1161-0301",
publisher = "Elsevier",

}

Porwollik, V, Müller, C, Elliott, J, Chryssanthacopoulos, J, Iizumi, T, Ray, DK, Ruane, AC, Arneth, A, Balkovič, J, Ciais, P, Pugh, TAM & de Wit, A 2017, 'Spatial and temporal uncertainty of crop yield aggregations', European Journal of Agronomy, vol. 88, pp. 10-21. https://doi.org/10.1016/j.eja.2016.08.006

Spatial and temporal uncertainty of crop yield aggregations. / Porwollik, Vera; Müller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovič, Juraj; Ciais, Philippe; Pugh, Thomas A.M.; de Wit, Allard.

In: European Journal of Agronomy, Vol. 88, 2017, p. 10-21.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Spatial and temporal uncertainty of crop yield aggregations

AU - Porwollik, Vera

AU - Müller, Christoph

AU - Elliott, Joshua

AU - Chryssanthacopoulos, James

AU - Iizumi, Toshichika

AU - Ray, Deepak K.

AU - Ruane, Alex C.

AU - Arneth, Almut

AU - Balkovič, Juraj

AU - Ciais, Philippe

AU - Pugh, Thomas A.M.

AU - de Wit, Allard

PY - 2017

Y1 - 2017

N2 - The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r. =0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r. =0.56 (maize, India), r. =0.05 (wheat, Russia), r. =0.13 (rice, Vietnam), and r. =-0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

AB - The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r. =0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r. =0.56 (maize, India), r. =0.05 (wheat, Russia), r. =0.13 (rice, Vietnam), and r. =-0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

KW - Aggregation uncertainty

KW - Crop yields

KW - Global crop model

KW - Gridded data

KW - Harvested area

U2 - 10.1016/j.eja.2016.08.006

DO - 10.1016/j.eja.2016.08.006

M3 - Article

VL - 88

SP - 10

EP - 21

JO - European Journal of Agronomy

JF - European Journal of Agronomy

SN - 1161-0301

ER -

Porwollik V, Müller C, Elliott J, Chryssanthacopoulos J, Iizumi T, Ray DK et al. Spatial and temporal uncertainty of crop yield aggregations. European Journal of Agronomy. 2017;88:10-21. https://doi.org/10.1016/j.eja.2016.08.006