Simulating regional winter wheat yields using input data of different spatial resolution

Nendel, Claas; Wieland, Ralf; Mirschel, Wilfried; Specka, Xenia; Guddat, Christian; Kersebaum, Kurt Christian

doi:10.1016/j.fcr.2013.02.014

Cited by 32 publications

(18 citation statements)

References 94 publications

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“…Although the relevance of scale (Hansen & Jones 2000, Ewert et al 2011, Nendel et al 2013 and spatial data aggregation (Gardner et al 1982, Cale et al 1983, Cale & O'Neill 1988, Rastetter et al 1992, Pierce & Running 1995, Nungesser et al 1999, Gong et al 2003, Syphard & Franklin 2004, Lorite et al 2005, Ershadi et al 2013 is well known and data aggre gation has been addressed, for instance, in soil or hydrological process modelling (Heuvelink & Pebesma 1999, Haverkamp et al 2005, Leopold et al 2006, Bormann et al 2009, few studies have characterized the effect in application of crop models with spatially aggregated climate input data on simulated regional yields, hereafter called the aggregation effect. For example, De Wit et al (2005) used precipitation and radiation aggregated from 10 to 50 km resolution as model input to simulate winter wheat and grain maize yields in Germany and France.…”

Section: Variability Of Effects Of Spatial Climate Data Aggregation Omentioning

confidence: 99%

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Hoffmann¹,

Zhao²,

Bussel³

et al. 2015

Clim. Res.

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Co-author addresses are provided in the Supplement (www. int-res. com/ articles/ suppl/ c065 p053 _ supp. pdf) ABSTRACT: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, waterlimited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha −1 , whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.

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Section: Variability Of Effects Of Spatial Climate Data Aggregation Omentioning

confidence: 99%

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Hoffmann¹,

Zhao²,

Bussel³

et al. 2015

Clim. Res.

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“…The LPIS data have a high spatial resolution that allows for a precise intersection with other spatial information and yields precise answers to field scale questions. Area-wide crop information on field scale could also be useful for the validation of crop growth models especially for areas with a large diversity of cropping systems [53,54] and for modeling procedures when information concerning cropping practices is needed [52,55,56]. The scientific use of LPIS data, e.g., for the prediction of the crop yield or for projecting changes in agricultural land use practice, is increasingly becoming important [55,[57][58][59][60].…”

Section: Reflections On the Methods Usedmentioning

confidence: 99%

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Stein

Steinmann

Isselstein

2019

Land

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Agricultural land use is influenced in different ways by local factors such as soil conditions, water supply, and socioeconomic structure. We investigated at regional and field scale how strong the relationship of arable crop patterns and specific local site conditions is. At field scale, a logistic regression analysis for the main crops and selected site variables detected, for each of the analyzed crops, its own specific character of crop–site relationship. Some crops have diverging site relations such as maize and wheat, while other crops show similar probabilities under comparable site conditions, e.g., oilseed rape and winter barley. At the regional scale, the spatial comparison of clustered variables and clustered crop pattern showed a slightly stronger relationship of crop combination and specific combinations of site variables compared to the view of the single crop–site relationship.

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“…Consistent with our findings, however, Zhao et al (2015) concluded that weather data with high resolution should be used in regions with large spatial heterogeneity in weather data, which is a characteristic of the semi-arid climate zones. Likewise, Nendel et al (2013) concluded that crop yields for a given region could be considerably underestimated if spatial distribution of available weather data is poor for the area under investigation.…”

Section: Tablementioning

confidence: 97%

“…To our knowledge no other studies exist that evaluated the performance of a climate zonation scheme as the basis for scaling up location-specific crop growth simulation results. Yet recent studies, such as Nendel et al (2013) and Zhao et al (2015), have noted the errors introduced when crop growth models are used with a top-down approach that applies using input data at large spatial scales. Due to differences in the studied regions with respect to climatic conditions and applied methodologies, the value of direct comparisons with our study is limited.…”

Section: Tablementioning

confidence: 99%

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

Bussel

Grassini

Wart

et al. 2015

Field Crops Research

174

151

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a b s t r a c tAccurate 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 based on 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 at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure 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 and large climate zones, and that local up-to-date knowledge of crop area distribution is required for selecting relevant locations for data collection. Estimated national water-limited yield potentials were found to be robust if data could be collected that are representative for approximately 50% of the national harvested area of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national waterlimited yield potentials were found to be over-or underestimated by 3 to 27% compared to estimates with the recommended crop area coverage of ≥50%. It was shown that the variation of simulated yield potentials within the same climate zone is small. Water-limited potentials in semi-arid areas are an exception, because the climate zones in these semi-arid areas represent aridity limits of crop production for the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimates from field, i.e. weather station data supplemented by local soil and cropping system data, to regional and national levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countries with more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.

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Simulating regional winter wheat yields using input data of different spatial resolution

Cited by 32 publications

References 94 publications

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

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

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