Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

HuffmanTed,; Qian, Budong; JongReinder, De; Liu, Jiangui; WangHong,; McConkeyBrian,; BrierleyTony,; YangJingyi,

doi:10.4141/cjss-2014-076

Cited by 16 publications

(10 citation statements)

References 43 publications

(41 reference statements)

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“…For wheat, the AgMIP (Agricultural Model Intercomparison and Improvement Project) listed 27 representative wheat models around the world (Asseng et al, 2013). The decision support system for agrotechnology transfer (DSSAT) including the CERES-Wheat model has been widely used over the last 25 years by many researchers for many different applications, and it has been shown to be capable of robust yield predictions across various environmental, soil, and management strategy conditions (Landau et al, 1998;Jones et al, 2003;Nain et al, 2004;Fang et al, 2008;Thorp et al, 2010;Liu et al, 2011;Huffman et al, 2015). When applied over a large-scale area, however, the model has shown low prediction performance because the boundary conditions (soil, management) are often poorly known and model parameters are uncertain (Moulin et al, 1998;Dorigo et al, 2007).…”

Section: Introductionmentioning

confidence: 98%

Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

Jin

Zhao

et al. 2015

European Journal of Agronomy

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Section: Introductionmentioning

confidence: 98%

Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

Jin

Zhao

et al. 2015

European Journal of Agronomy

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“…Its cultivar coefficients used in DSSAT v4.0 were determined by Wang et al (). A recent study (Huffman et al , ) showed that spring wheat yields aggregated from the DSSAT simulations with AC Barrie on different soils reasonably matched reported spring wheat yields at the census agricultural region level on the Canadian Prairies.…”

Section: Methodsmentioning

confidence: 81%

Projecting spring wheat yield changes on the Canadian Prairies: effects of resolutions of a regional climate model and statistical processing

Qian

Wang

et al. 2015

Intl Journal of Climatology

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In addition to the uncertainty associated with crop models, climate scenarios are still a major source of uncertainty in projecting crop yield changes under climate change. Regional climate models (RCMs) are used as tools for dynamic downscaling of climate scenarios from global climate models (GCMs) to regional scales for climate change impact studies. It is known that running an RCM is more expensive and time consuming at a higher resolution than at a lower resolution. Therefore, it is interesting to investigate how resolutions of an RCM and statistical processing of RCM data may result in differences in projected crop yield changes. We used the decision support system for agrotechnology transfer (DSSAT)-CERES-Wheat model to simulate yield changes of spring wheat at 13 locations across the Canadian Prairies, with climate scenarios from a Canadian RCM (CanRCM4) driven by a Canadian earth system model (CanESM2) with the forcing scenarios RCP4.5 and RCP8.5 at 25 and 50 km resolutions. Bias correction and a stochastic weather generator referred to as AAFC-WG were used as statistical processing tools to develop future climate scenarios as input to the crop model. The results showed that when changes were averaged across the locations, whether 25-or 50-km resolution CanRCM4 data were used, the projected yield changes were fairly consistent with those based on its driving GCM-CanESM2, especially if AAFC-WG was used to develop future climate scenarios. The spatial patterns of the projected yield changes were also similar for the two resolutions of CanRCM4, although the magnitude of the projected changes had a relatively large range among the scenarios at some locations. These results indicated that using future climate scenarios based on climate change simulations by GCMs might be sufficient for projecting regional crop yield changes on the Canadian Prairies.

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“…The soil properties from the 1998 to 2001 experiments in Ottawa were used to simulate the site yield. Soil data for the ecodistrict were obtained from the Canadian Soil Information System (CanSIS) (MacDonald & Kloosterman 1984) and Soil Landscapes of Canada (SLC), version 3.2 (Soil Landscapes of Canada Working Group 2010), as described in Huffman et al (2015). A total of 19 types of soils were retrieved from CanSIS for the selected ecodistrict.…”

Section: Methodsmentioning

confidence: 99%

Using the CSM–CERES–Maize model to assess the gap between actual and potential yields of grain maize

et al. 2016

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SUMMARYMaize in Canada is grown mainly in the south-eastern part of the country. No comprehensive studies on Canadian maize yield levels have been done so far to analyse the barriers of obtaining optimal yields associated with cultivar, environmental stress and agronomic management practices. The objective of the current study was to use a modelling approach to analyse the gaps between actual and potential (determined by cultivar, solar radiation and temperature without any other stresses) maize yields in Eastern Canada. The CSM–CERES–Maize model in DSSAT v4·6 was calibrated and evaluated with measured data of seven cultivars under different nitrogen (N) rates across four sites. The model was then used to simulate grain yield levels defined as: yield potential (YP), water-limited (YW, rainfed), and water- and N-limited yields with N rates 80 kg/ha (YW, N-80N) and 160 kg/ha (YW, N-160N). The options were assessed to further increase grain yield by analysing the yield gaps related to water and N deficiencies. The CSM–CERES–Maize model simulated the grain yields in the experiments well with normalized root-mean-squared errors <0·20. The model was able to capture yield variations associated with varying N rates, cultivar, soil type and inter-annual climate variability. The seven calibrated cultivars used in the experiments were divided into three grades according to their simulated YP: low, medium and high. The simulation results for the 30-year period from 1981 to 2010 showed that the average YP was 15 000 kg/ha for cultivars with high yield potential. The YP is generally about 6000 kg/ha greater than the actual yield (YA) at each experimental site in Eastern Canada. Two-thirds of this gap between YP and YA is probably associated with water stress, as a gap of approximately 4000 kg/ha between the YW and the YP was simulated. This gap may be reduced through crop management, such as introducing irrigation to improve the distribution of available water during the growing season. The simulated yields indicated a gap of about 3000 and 1000 kg/ha between YW and YW,N-80N for cultivars with high YP and low YP, respectively. The gap between YW and YW,N-160N decreased to <2000 kg/ha for high Yp cultivars with little difference for the low Yp cultivars. The different yield gaps among cultivars suggest that cultivars with high YP require high N rates but cultivars with low YP may need only low N rates.

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Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Cited by 16 publications

References 43 publications

Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

Projecting spring wheat yield changes on the Canadian Prairies: effects of resolutions of a regional climate model and statistical processing

Using the CSM–CERES–Maize model to assess the gap between actual and potential yields of grain maize

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