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

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 Remote Sens. 2014, 6 10194 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.

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotelysensed 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 and comparing 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 forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were underestimated by 1-4% in mid-season and overestimated 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.

SUMMARYFindings from multi-year, multi-site field trial experiments measuring maize yield response to inoculation with the phosphorus-solubilizing fungus, Penicillium bilaiae Chalabuda are presented. The main objective was to evaluate representative data on crop response to the inoculant across a broad set of different soil, agronomic management and climate conditions. A statistical analysis of crop yield response and its variability was conducted to guide further implementation of a stratified trial and sampling plan. Field trials, analysed in the present study, were conducted across the major maize producing agricultural cropland of the United States (2005–11) comprising 92 small (with sampling replication) and 369 large (without replication) trials. The multi-plot design enabled both a determination of how sampling area affects the estimation of maize yield and yield variance and an estimation of the ability of inoculation with P. bilaiae to increase maize yield. Inoculation increased maize yield in 66 of the 92 small and 295 of the 369 large field trials (within the small plots, yield increased significantly at the 95% confidence level, by 0·17 ± 0·044 t/ha or 1·8%, while in the larger plots, yield increases were higher and less variable (i.e., 0·33 ± 0·026 t/ha or 3·5%). There was considerable inter-annual variability in maize yield response attributed to inoculation compared to the un-inoculated control, with yield increases varying from 0·7 ± 0·75 up to 3·7 ± 0·73%. No significant correlation between yield response and soil acidity (i.e., pH) was detected, and it appears that pH reduction (through organic acid or proton efflux) was unlikely to be the primary pathway for better phosphorus availability measured as increased yield. Seed treatment and granular or dribble band formulations of the inoculant were found to be equally effective. Inoculation was most effective at increasing maize yield in fields that had low or very low soil phosphorus status for both small and large plots. At higher levels of soil phosphorus, yield in the large plots increased more with inoculation than in the small plots, which could be explained by phosphorus fertilization histories for the different field locations, as well as transient (e.g., rainfall) and topographic effects.

. 2006. A proposed approach to estimate and reduce net greenhouse gas emissions from whole farms. Can. J. Soil Sci. 86: 401-418. Greenhouse gas emissions from farms can be suppressed in two ways: by curtailing the release of these gases (especially N 2 O and CH 4 ), and by storing more carbon in soils, thereby removing atmospheric CO 2 . But most practices have multiple interactive effects on emissions throughout a farm. We describe an approach for identifying practices that best reduce net, wholefarm 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. [401][402][403][404][405][406][407][408][409][410][411][412][413][414][415][416][417][418]. La réduction des émissions de gaz à effet de serre (GES) des exploitations agricoles peut être obtenue de deux façons : en restreignant la libération des GES (en particulier le N20 et le CH4) et en stockant dans le sol plus de carbone provenant du CO 2 atmosphérique. Malheureusement, bon nombre de pratiques agricoles ont des interactions multiples qui agissent simultanément sur l'ensemble des émissions de l'exploitation agricole. Les auteurs décrivent une approche pour identifier les pratiques agricoles permettant d'atteindre la meilleure réduction nette possible des émissions de GES de l'exploitation. Ils proposent la création d'une « ferme virtuelle », qui sera en fait une série d'algorithmes reliés entre eux qui prédiront les émissions nettes de flux de carbone, d'azote et d'énergie. A priori, la Ferme virtuelle comprendra trois éléments : des descripteurs de l'exploitation agricole, des algorithmes calculant les émissions des composantes de l'exploitation et un intégrateur reliant les algorithmes entre eux ainsi qu'aux descripteurs qui produira des estimations de GES pour l'exploitation entière. Idéalement, la Ferme virtuelle devrait inclure des limites explicites, son unité fondamentale étant l'exploitation individuelle; elle devrait pouvoir décrire divers types d'exploitation, être modulaire, simple et transparente, ne requérir qu'un nombre limité de données d'entrée facilement accessibles, être cohérente en soi, compatible avec les modèles ...

We investigate the application of quantitative techniques for distinguishing adaptive search behaviour in Atlantic bluefin tuna (Thunnus thynnus). The analysis demonstrates the application of a novel spectral analysis technique for resolving and measuring periodicity in animal behaviour patterns. Two different search strategies are identified that include regulation of turning (klinokinesis) and speed (orthokinesis). Our results provide evidence that bluefin tuna attempt to optimize their searching efficiency through adjustments in the duration and timing of switching between these two searching strategies. Repetitive, diurnal deep dives were also found to coincide with switching of search behaviour. Additional tracking experiments with larger sample sizes are needed to better identify how individuals switch between the two search strategies and how such decisions may collectively improve the searching and foraging efficiency of their schools (synchrokinesis, social taxis) in response to changes in the size or composition of prey aggregations.

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.

. 2007. Energy balances of biodiesel production from soybean and canola in Canada. Can. J. Plant Sci. 87: 793-801. Biodiesel is currently produced in Canada mostly from recycled oils and animal fats. If biodiesel is to supply 5% of diesel usage, a government objective, first-time vegetable, likely from canola and soybean, oil will also be required to provide adequate feedstocks. In this review, we estimate the life cycle energy balances for biodiesel produced from soybean and canola oil under Canadian conditions. The three broad areas of energy inputs were crop production, oil extraction, and transesterification of the vegetable oil into biodiesel. Per unit seed yield, farm production energy inputs for canola were about three times higher than for soybean, mostly because of higher nitrogen fertilizer requirements for canola. Energy required for processing and oil extraction, per unit oil, was higher for soybean. Energy allocation for co-products was allocated using a system expansion approach. Protein meal was assigned about 12% of the energy expended for canola to grow the crop and extract the oil, and about 37% for soybean. Glycerine produced during the transesterification process was allocated energy on a weight basis (11.4%). The ratio of biodiesel energy produced per energy input ranged from 2.08 to 2.41. The energy ratio was similar for soybean and canola; soybean required less energy inputs, but also produced less oil than canola, for a given weight of seed.