Early warning information on crop yield and production are very crucial for both farmers and decision-makers. 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

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions.

Greenhouse gas emissions from farms can be suppressed in two ways: by curtailing the release of these gases (especially N₂O and CH₄), and by storing more carbon in soils, thereby removing atmospheric CO₂. But most practices have multiple interactive effects on emissions throughout a farm. We describe an approach for identifying practices that best reduce net, whole-farm emissions. We propose to develop a “Virtual Farm”, a series of interconnected algorithms that predict net emissions from flows of carbon, nitrogen, and energy. The Virtual Farm would consist of three elements: descriptors, which characterize the farm; algorithms, which calculate emissions from components of the farm; and an integrator, which links the algorithms to each other and the descriptors, generating whole-farm estimates. Ideally, the Virtual Farm will be: boundary-explicit, with single farms as the fundamental unit; adaptable to diverse farm types; modular in design; simple and transparent; dependent on minimal, attainable inputs; internally consistent; compatible with models developed elsewhere; and dynamic (“seeing”into the past and the future). The Virtual Farm would be constructed via two parallel streams - measurement and modeling - conducted iteratively. The understanding built into the Virtual Farm may eventually be applied to issues beyond greenhouse gas mitigation. Key words: CO₂, N₂O, CH₄, agroecosystems, models, climate change

Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models—but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution—a reanalysis dataset (~55 km) and weather station network township-aggregated data (~10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats.

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.