Development and evaluation of a Canadian agricultural ecodistrict climate database

Xu, Hanqing; Jong, R. De; Gameda, S.; Qian, Budong

doi:10.4141/cjss09064

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…Our climate data was based on Xu et al () which consisted of daily meteorological data from 1951 to 2006 that were interpolated to the centroid of each ecodistrict from neighbouring climate stations. These climate variables were maximum daily temperature ( T max , °C), minimum daily temperature ( T min , °C), daily precipitation (mm) and daily solar radiation ( R d , KJ m −2 ).…”

Section: Methodsmentioning

confidence: 99%

“…Owing to the interpolated nature of the data, there was some uncertainty associated with it. Through cross‐validation analysis and correlation analysis between observed and interpolated, Xu et al () showed that T max and T min had the lowest uncertainty amongst the set of climate variables (between T max and T min , T max had the least uncertainty). The analyses also indicated that their uncertainty was least over the growing season and greatest in January and February.…”

Section: Methodsmentioning

confidence: 99%

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Determining the influence of agricultural land use on climate variables for the Canadian Prairies

et al. 2014

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ABSTRACT:Since the 1970s the Canadian Prairies have experienced a steady replacement of summer fallow with continuous cropping practices. It has been suggested that such regional scale changes in agricultural land use result in the modification of the regional climate and its variability. Our study explores this influence using linear discriminant analysis (LDA) to determine whether agricultural land use variables can discriminate the classes of exceedance (positive/negative beyond a given small scale variability range) found in the maximum and minimum daily temperature, diurnal temperature range (DTR), daily precipitation and daily incoming solar radiation. These classes of exceedance describe whether the climate was warmer/cooler, drier/wetter and sunnier/cloudier relative to the small scale variability range. The land use data consisted of summer fallow, annuals and perennials for the 127 ecodistricts in the Prairies ecozone. The climate data were neighbouring station data interpolated to the ecodistrict's centroid. Both data sets covered the period 1951-2006 and the analysis covered the entire data period with focus on 1971-2006 that coincided with the decline in summer fallow practice. It considered the primary growing season months of June, July and August and used the winter months of January and February as a contrast. Our approach showed that where large scale changes in agricultural land cover occurs we can establish a statistical relationship between such changes and the modification of the climate's variability. June coincided with the period of maximum positive growth rate and hence land cover change. The greatest discriminating power was therefore found to occur in June for the exceedance classes of maximum and minimum temperature and solar radiation over the 1971-2006 period with success rates ranging from 85 to 93%. The greatest success in discriminating the DTR classes was at 82% in July.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Determining the influence of agricultural land use on climate variables for the Canadian Prairies

et al. 2014

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“…These daily climate data were extracted from the Canadian Agricultural Ecodistrict Climate Dataset (Xu et al 2010), which provides an interpolated data value assigned to the centroid of each agricultural ecodistrict in the country. Values for an ecodistrict were used in the yield simulation of all soils within the ecodistrict, which meant that differences in the simulated crop yields within an ecodistrict were associated with soil characteristics, rather than climate.…”

Section: Climate Datamentioning

confidence: 99%

“…As a base for broad-scale productivity modeling, the Soil Landscapes of Canada (SLC; Soil Landscapes of Canada Working Group 2010) database contains detailed soil property data for all agricultural regions of the country, while a daily climate dataset at the scale of ecodistricts (Xu et al 2010) contains climate inputs applicable to all soils within each polygon. Annual crop yield estimates derived from producer surveys (Statistics Canada 2013a) and released at the spatial level of 82 Census Agricultural Regions (CAR) ( Fig.…”

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

et al. 2015

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. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49Á61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERESÁWheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha(1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.Key words: CERES-Wheat crop model, spring wheat, yield aggregation, regional application (Canada) Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. et Yang, J. 2015. Mise a`l'e´chelle re´gionale des mode`les du rendement des cultures : e´tude de cas avec le DSSAT pour le ble´de printemps cultive´dans les Prairies canadiennes. Can. J. Soil Sci. 95: 49Á61. On recourt souvent aux mode`les agricoles dynamiques a`l'e´chelle de la parcelle ou du champ. Toutefois, une mise a`l'e´chelle s'impose quand on utilise les mode`les articule´s sur les processus pour des applications re´gionales comme la pre´vision du rendement des cultures ou l'e´valuation de l'impact du changement climatique sur la productivite´des cultures dans une re´gion. Les mode`les agricoles dynamiques ont souvent besoin de donne´es de´taille´es sur le climat, le sol et les pratiques agronomiques. C'est pourquoi leur fiabilite´diminue a`l'e´chelon re´gional, l'incertitude des re´sultats de la simulation pouvant s'accroıˆtre avec les incertitudes associe´es aux donne´es saisies. Dans le cadre de cette e´tude, les auteurs ont mode´lise´le rendement du ble´de printemps au niveau de nombreux sols individuels en recourant au mode`le CERES du ble´dans le syste`me d'aide a`la prise de de´cisions pour le transfert des technologies agricoles (...

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“…Figure 1, modified from Ecological Stratification Working Group (1995), provides an example of the spatial relationships amongst ecozones, ecoregions, and ecodistricts. The eco-stratification framework provides the spatial segmentation for much of the biophysical and environmental research in Canada (Xu et al 2010), and by providing a crop yield database at one of the smaller (ecodistrict) spatial units, much research work related to productivity, such as estimating biomass supply for biofuel production (Kumarappan et al 2009) or developing more refined nitrogen balances such as that outlined by Yang et al (2007) could be improved (Dyer et al 2010). Such an improved database could also provide a useful temporal base for calibration of regional crop yield forecasting models.…”

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confidence: 99%

Improving the spatial resolution and ecostratification of crop yield estimates in Canada

Huffman

Daneshfar

et al. 2015

Can. J. Soil. Sci.

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2015. Improving the spatial resolution and ecostratification of crop yield estimates in Canada. Can. J. Soil Sci. 95: 287Á297. Canada's terrestrial ecostratification framework provides nested spatial units for organizing national data related to soils, landforms and land use. In the agricultural domain, the lack of national, uniform crop yield data on the ecostratification framework severely hinders our ability to evaluate the biophysical data with respect to economic and climatic conditions. We developed a national crop yield database at the regional (ecodistrict) level by aggregating individual records of an existing but very broad-level samplederived yield database according to the ecostratification hierarchy. Issues related to the different sampling frameworks and the need for confidentiality of individual records were resolved in order to generate an ecostratified crop yield dataset at a reasonably detailed spatial scale. Sixty crops were first statistically arranged into 37 agronomically similar crop groups in order to increase class size, and these crop groups were aggregated into increasingly large spatial units until confidentiality was assured. The methodology maintained data quality and confidentiality while producing crop yield estimates at the ecodistrict level. Comparison to independent crop insurance data confirmed that the resulting crop yield data are valid where estimates were derived from data released at the level of an ecodistrict or an ecoregion, but not at the ecoprovince level. Our crop yield estimates offer a reasonably high level of spatial precision while remaining within standard confidentiality constraints.Key words: Eco-stratification, crop yields, yield database, agriculture, Canada Du, Y., Huffman, T., Daneshfar, B., Green, M., Feng, F., Liu, J., Liu, T. et Liu, H. 2015. Ame´lioration de la re´solution spatiale et de l'e´costratification des estimations du rendement agricole au Canada. Can. J. Soil Sci. 95: 287Á297. L'e´costratification des terres canadiennes fournit un cadre de re´fe´rence compose´d'unite´s spatiales imbrique´es les unes dans les autres avec lequel il est possible de structurer les donne´es nationales concernant le sol, le relief et la vocation des terres. En agriculture, le manque de donne´es nationales uniformes sur le rendement dans le syste`me d'e´costratification nuit conside´rablement a`l'e´valuation des donne´es biophysiques en fonction des conditions e´conomiques et climatiques. Les auteurs ont e´labore´une base de donne´es nationale sur le rendement des cultures au niveau re´gional (district) en regroupant les rapports individuels d'une base de donne´es sur le rendement existante, mais tre`s grossie`re, reposant sur l'e´chantillonnage d'apre`s la hie´rarchie du syste`me d'e´costratification. Les proble`mes relatifs a`la variabilite´des me´thodes d'e´chantillonnage et al a confidentialite´des rapports individuels ont e´te´re´solus de manie`re a`produire un jeu de donne´es sur le rendement agricole e´costratifie´d'une e´chelle spatiale raisonnablement pre´...

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Development and evaluation of a Canadian agricultural ecodistrict climate database

Cited by 9 publications

References 26 publications

Determining the influence of agricultural land use on climate variables for the Canadian Prairies

Determining the influence of agricultural land use on climate variables for the Canadian Prairies

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

Improving the spatial resolution and ecostratification of crop yield estimates in Canada

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