a b s t r a c tEarly warning information on crop yield and production are very crucial for both farmers and decisionmakers. In this study, we assess the skill and the reliability of the Integrated Canadian Crop Yield Forecaster (ICCYF), a regional crop yield forecasting tool, at different temporal (i.e. 1-3 months before harvest) and spatial (i.e. census agricultural region -CAR, provincial and national) scales across Canada. A distinct feature of the ICCYF is that it generates in-season yield forecasts well before the end of the growing season and provides a probability distribution of the forecasted yields. The ICCYF integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms. The model was evaluated against yield survey data of spring wheat, barley and canola during the 1987-2012 period. Our results showed that the ICCYF performance exhibited a strong spatial pattern at both CAR and provincial scales. Model performance was better from regions with a good coverage of climate stations and a high percentage of cropped area. On average, the model coefficient of determination at CAR level was 66%, 51% and 67%, for spring wheat, barley and canola, respectively. Skilful forecasts (i.e. model efficiency index > 0) were achieved in 70% of the CARs for spring wheat and canola, and 43% for barley (low values observed in CAR with small harvested area). At the provincial scale, the mean absolute percentage errors (MAPE) of the September forecasts ranged from 7% to 16%, 7% to14%, and 6% to 14% for spring wheat, barley and canola, respectively. For forecasts at the national scale, MAPE values (i.e. 8%, 5% and 9% for the three respective crops) were considerably smaller than the corresponding historical coefficients of variation (i.e. 17%, 10% and 17% for the three crops). Overall, the ICCYF performed better for spring wheat than for canola and barley at all the three spatial scales. Skilful forecasts were achieved by mid-August, giving a lead time of about 1 month before harvest and about 3-4 months before the final release of official survey results. As such, the ICCYF could be used as a complementary tool for the traditional survey method, especially in areas where it is not practical to conduct such surveys.Crown
The National Drought Model (NDM) is an amalgamation of the atmospheric component of the original Palmer Drought and Versatile Soil Moisture Budget (VSMB) models. The NDM uses locally derived coefficients from the station or gridded climate data to calculate a calibration factor for comparing locations in time and space. A modular approach is used to model major processes such as evapotranspiration, biometeorological time, snowmelt, and the cascading of soil moisture down to the root zone. The modular approach allows modifications to be made to specific sections without making structural changes to the entire model or the data inputs. The NDM is an operational tool, integrating data from the climate, soil, and plant sciences to monitor agroclimatic risks such as drought and excess moisture. In this paper, the capacity of the NDM to monitor extreme agroclimatic risks, such as drought and flooding of agricultural soils, was assessed. Using the Palmer Drought Severity Index component of the NDM, the mapping of the spatial extent and severity of the 2001 and 2002 droughts across Canada and the excess moisture conditions on the Canadian Prairies in 2010 agreed with other assessments. The validation study of soil moisture at two Alberta locations (Lethbridge and Beaverlodge) showed that the VSMB tracked the soil moisture flux in the root zone successfully in response to changing environmental conditions. The VSMB explained about 70 and 60% of the variance in observed soil moisture at the two respective locations.RÉSUMÉ [Traduit par la rédaction] Le modèle national de sécheresse (NDM) est un amalgame de la composante atmosphérique du modèle original de Palmer et du modèle adaptatif de bilan d'humidité du sol (VSMB). Le NDM se sert de coefficients dérivés localement des données aux stations ou d'une grille de données climatiques pour calculer un facteur d'étalonnage servant à comparer des endroits dans le temps et dans l'espace. Une approche modulaire est utilisée pour modéliser les processus principaux, comme l'évapotranspiration, le temps biométéorologique, la fonte de la neige et la progression de l'humidité du sol vers le bas jusque dans la zone des racines. L'approche modulaire permet de faire des modifications dans des sections particulières sans faire de changements structuraux dans l'ensemble du modèle ou dans les données d'entrée. Le NDM est un outil opérationnel qui intègre les données des sciences climatologique, pédologique et botanique pour surveiller les risques agroclimatiques, comme la sécheresse ou l'excès d'humidité. Dans cet article, nous avons évalué la capacité du NDM de surveiller les risques agroclimatiques extrêmes comme la sécheresse ou l'inondation des sols agricoles. En utilisant la composante
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