From field to atlas: Upscaling of location-specific yield gap estimates

L.G.J. van Bussel, P. Grassini, J. van Wart, J. Wolf, L. Claessens, H. Yang, H.L. Boogaard, H.L.E. de Groot, K. Saito, K.G. Cassman, M.K. van Ittersum

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Abstract

Accurate estimation of yield gaps is only possible for locations where high quality local data are available,which are, however, lacking in many regions of the world. The challenge is how yield gap estimates basedon location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence,insight about the minimum number of locations required to achieve robust estimates of yield gaps atlarger spatial scales is essential because data collection at a large number of locations is expensive andtime consuming. In this paper we describe an approach that consists of a climate zonation scheme supple-mented by agronomical and locally relevant weather, soil and cropping system data. Two elements of thismethodology are evaluated here: the effects on simulated national crop yield potentials attributable tomissing and/or poor quality data and the error that might be introduced in scaled up yield gap estimatesdue to the selected climate zonation scheme. Variation in simulated yield potentials among weatherstations located within the same climate zone, represented by the coefficient of variation, served as ameasure of the performance of the climate zonation scheme for upscaling of yield potentials.We found that our approach was most appropriate for countries with homogeneous topography andlarge climate zones, and that local up-to-date knowledge of crop area distribution is required for selectingrelevant locations for data collection. Estimated national water-limited yield potentials were found to berobust if data could be collected that are representative for approximately 50% of the national harvestedarea of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverageof the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimateswith the recommended crop area coverage of =50%. It was shown that the variation of simulated yieldpotentials within the same climate zone is small. Water-limited potentials in semi-arid areas are anexception, because the climate zones in these semi-arid areas represent aridity limits of crop productionfor the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimatesfrom field, i.e. weather station data supplemented by local soil and cropping system data, to regional andnational levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countrieswith more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.
Original languageEnglish
Pages (from-to)98-108
JournalField Crops Research
Volume177
DOIs
Publication statusPublished - 2015

Fingerprint

upscaling
atlas
climate
crop
crops
zonation
water yield
topography
cropping systems
cropping practice
weather stations
water
dry environmental conditions
weather station
aridity
data quality
crop yield
sensitivity analysis
soil
weather

Keywords

  • climate-change
  • weather data
  • crop yields
  • input data
  • resolution
  • model
  • scale
  • impact
  • maize
  • systems

Cite this

@article{141eaea2581a4491a68071b773503d96,
title = "From field to atlas: Upscaling of location-specific yield gap estimates",
abstract = "Accurate estimation of yield gaps is only possible for locations where high quality local data are available,which are, however, lacking in many regions of the world. The challenge is how yield gap estimates basedon location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence,insight about the minimum number of locations required to achieve robust estimates of yield gaps atlarger spatial scales is essential because data collection at a large number of locations is expensive andtime consuming. In this paper we describe an approach that consists of a climate zonation scheme supple-mented by agronomical and locally relevant weather, soil and cropping system data. Two elements of thismethodology are evaluated here: the effects on simulated national crop yield potentials attributable tomissing and/or poor quality data and the error that might be introduced in scaled up yield gap estimatesdue to the selected climate zonation scheme. Variation in simulated yield potentials among weatherstations located within the same climate zone, represented by the coefficient of variation, served as ameasure of the performance of the climate zonation scheme for upscaling of yield potentials.We found that our approach was most appropriate for countries with homogeneous topography andlarge climate zones, and that local up-to-date knowledge of crop area distribution is required for selectingrelevant locations for data collection. Estimated national water-limited yield potentials were found to berobust if data could be collected that are representative for approximately 50{\%} of the national harvestedarea of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25{\%} coverageof the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27{\%} compared to estimateswith the recommended crop area coverage of =50{\%}. It was shown that the variation of simulated yieldpotentials within the same climate zone is small. Water-limited potentials in semi-arid areas are anexception, because the climate zones in these semi-arid areas represent aridity limits of crop productionfor the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimatesfrom field, i.e. weather station data supplemented by local soil and cropping system data, to regional andnational levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countrieswith more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.",
keywords = "climate-change, weather data, crop yields, input data, resolution, model, scale, impact, maize, systems",
author = "{van Bussel}, L.G.J. and P. Grassini and {van Wart}, J. and J. Wolf and L. Claessens and H. Yang and H.L. Boogaard and {de Groot}, H.L.E. and K. Saito and K.G. Cassman and {van Ittersum}, M.K.",
year = "2015",
doi = "10.1016/j.fcr.2015.03.005",
language = "English",
volume = "177",
pages = "98--108",
journal = "Field Crops Research",
issn = "0378-4290",
publisher = "Elsevier",

}

From field to atlas: Upscaling of location-specific yield gap estimates. / van Bussel, L.G.J.; Grassini, P.; van Wart, J.; Wolf, J.; Claessens, L.; Yang, H.; Boogaard, H.L.; de Groot, H.L.E.; Saito, K.; Cassman, K.G.; van Ittersum, M.K.

In: Field Crops Research, Vol. 177, 2015, p. 98-108.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - From field to atlas: Upscaling of location-specific yield gap estimates

AU - van Bussel, L.G.J.

AU - Grassini, P.

AU - van Wart, J.

AU - Wolf, J.

AU - Claessens, L.

AU - Yang, H.

AU - Boogaard, H.L.

AU - de Groot, H.L.E.

AU - Saito, K.

AU - Cassman, K.G.

AU - van Ittersum, M.K.

PY - 2015

Y1 - 2015

N2 - Accurate estimation of yield gaps is only possible for locations where high quality local data are available,which are, however, lacking in many regions of the world. The challenge is how yield gap estimates basedon location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence,insight about the minimum number of locations required to achieve robust estimates of yield gaps atlarger spatial scales is essential because data collection at a large number of locations is expensive andtime consuming. In this paper we describe an approach that consists of a climate zonation scheme supple-mented by agronomical and locally relevant weather, soil and cropping system data. Two elements of thismethodology are evaluated here: the effects on simulated national crop yield potentials attributable tomissing and/or poor quality data and the error that might be introduced in scaled up yield gap estimatesdue to the selected climate zonation scheme. Variation in simulated yield potentials among weatherstations located within the same climate zone, represented by the coefficient of variation, served as ameasure of the performance of the climate zonation scheme for upscaling of yield potentials.We found that our approach was most appropriate for countries with homogeneous topography andlarge climate zones, and that local up-to-date knowledge of crop area distribution is required for selectingrelevant locations for data collection. Estimated national water-limited yield potentials were found to berobust if data could be collected that are representative for approximately 50% of the national harvestedarea of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverageof the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimateswith the recommended crop area coverage of =50%. It was shown that the variation of simulated yieldpotentials within the same climate zone is small. Water-limited potentials in semi-arid areas are anexception, because the climate zones in these semi-arid areas represent aridity limits of crop productionfor the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimatesfrom field, i.e. weather station data supplemented by local soil and cropping system data, to regional andnational levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countrieswith more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.

AB - Accurate estimation of yield gaps is only possible for locations where high quality local data are available,which are, however, lacking in many regions of the world. The challenge is how yield gap estimates basedon location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence,insight about the minimum number of locations required to achieve robust estimates of yield gaps atlarger spatial scales is essential because data collection at a large number of locations is expensive andtime consuming. In this paper we describe an approach that consists of a climate zonation scheme supple-mented by agronomical and locally relevant weather, soil and cropping system data. Two elements of thismethodology are evaluated here: the effects on simulated national crop yield potentials attributable tomissing and/or poor quality data and the error that might be introduced in scaled up yield gap estimatesdue to the selected climate zonation scheme. Variation in simulated yield potentials among weatherstations located within the same climate zone, represented by the coefficient of variation, served as ameasure of the performance of the climate zonation scheme for upscaling of yield potentials.We found that our approach was most appropriate for countries with homogeneous topography andlarge climate zones, and that local up-to-date knowledge of crop area distribution is required for selectingrelevant locations for data collection. Estimated national water-limited yield potentials were found to berobust if data could be collected that are representative for approximately 50% of the national harvestedarea of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverageof the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimateswith the recommended crop area coverage of =50%. It was shown that the variation of simulated yieldpotentials within the same climate zone is small. Water-limited potentials in semi-arid areas are anexception, because the climate zones in these semi-arid areas represent aridity limits of crop productionfor the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimatesfrom field, i.e. weather station data supplemented by local soil and cropping system data, to regional andnational levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countrieswith more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.

KW - climate-change

KW - weather data

KW - crop yields

KW - input data

KW - resolution

KW - model

KW - scale

KW - impact

KW - maize

KW - systems

U2 - 10.1016/j.fcr.2015.03.005

DO - 10.1016/j.fcr.2015.03.005

M3 - Article

VL - 177

SP - 98

EP - 108

JO - Field Crops Research

JF - Field Crops Research

SN - 0378-4290

ER -