Canadian Chicken Industry: Consumer Preferences, Industry Structure and Producer Benefits from Investment in Research and Advertising

The Canadian chicken industry has operated under supply management since the mid-1970s. Canadian consumer preferences for chicken have grown dramatically since then possibly in response to concerns about health and the levels of fat and cholesterol in red meats. However Canadian consumers are also looking for convenience with their food purchases. Canadians are buying their chicken in frozen further processed forms, fresh by cut without skin and bone and in a variety of other different ways reflecting their unique willingness to pay for various attributes. There is also an increasing trend for retailers and processors to brand the fresh chicken product sold through grocery stores (for example, Maple Leaf Prime). The preferences Canadian consumer have for various chicken products, the prices they are comfortable paying and the strategies followed by processors/retailers can directly affect the outcomes of industry wide strategies such as investment in generic advertising and research or the impact of international market changes such as border closures. This research is an initial attempt to quantify Canadian consumer preferences – for fresh product by type – for product by level of processing – for chicken product by cut - for fresh chicken by brand - to examine the impact of substitutability on a variety of market shocks. The various different disaggregations of Canadian chicken consumption are used in a number of simulation models to illustrate how important preferences are to producer returns when there are market shocks. If Canadians found all chicken products available in the grocery store to be perfectly substitutable then previous policy analysis assuming chicken is one homogeneous product would be sufficient for industry policy analysis purposes. If Canadians view all the different chicken products as imperfectly substitutable and given that various chicken products are produced in relatively fixed proportions (white and dark meat, for example) further understanding of how consumers make their purchase decisions could enhance the industries ability to predict outcomes. For example, border closing to Canadian exports ( as a result of an Avian influenza outbreak, for example) would result in a significant increase in the dark meat products available for sale through Canadian grocery stores. The results presented in this research could provide a clue as to how much dark meat prices might decline while white meat prices might remain unaffected. The results reported suggest that at the consumer level, chicken fresh and frozen products are not perceived to be perfect substitutes, within a narrow category such as fresh chicken breasts, they are not perceived as even close substitutes, within the fresh category branded products such as those developed by Lilydale and Maple Leaf are not perceived as perfect substitutes. As well, an initial look at the demand for individual chicken products by household suggests that there is far from a common buying pattern across Canadian households, even within a single province. The results also suggest that health and convenience attributes are driving Canadian consumer preferences. Simulation results highlight the fact that pricing strategies followed by major processors/retailers within Canada can influence the returns to generic advertising and research. Further research could provide additional robust estimates of the chicken product substitutability existing in the Canadian market and an increased understanding of the market characteristics currently operating. The results presented here suggest that further work in this area is important for the chicken industry to pursue.consumer behaviour, chicken consumption, differentiated products, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Marketing, D12, Q11, Q18,

Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

Producing high-quality forecasts of key climate variables such as temperature and precipitation on subseasonal time scales has long been a gap in operational forecasting. Recent studies have shown promising results using machine learning (ML) models to advance subseasonal forecasting (SSF), but several open questions remain. First, several past approaches use the average of an ensemble of physics-based forecasts as an input feature of these models. However, ensemble forecasts contain information that can aid prediction beyond only the ensemble mean. Second, past methods have focused on average performance, whereas forecasts of extreme events are far more important for planning and mitigation purposes. Third, climate forecasts correspond to a spatially-varying collection of forecasts, and different methods account for spatial variability in the response differently. Trade-offs between different approaches may be mitigated with model stacking. This paper describes the application of a variety of ML methods used to predict monthly average precipitation and two meter temperature using physics-based predictions (ensemble forecasts) and observational data such as relative humidity, pressure at sea level, or geopotential height, two weeks in advance for the whole continental United States. Regression, quantile regression, and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models are considered. The proposed models outperform common baselines such as historical averages (or quantiles) and ensemble averages (or quantiles). This paper further includes an investigation of feature importance, trade-offs between using the full ensemble or only the ensemble average, and different modes of accounting for spatial variability

Timing of subsurface heat magnitude for the growth of El Niño events

The subsurface heat buildup in the western tropical Pacific and the recharge phase in equatorial heat content are intrinsic elements of El Niño–Southern Oscillation, leading to changes in zonal wind stress, sea surface temperature, and thermocline tilt that characterize the growing and mature phases of El Niño (EN) events. Here we use numerical simulations to study the impact on subsequent EN episodes of a sudden increase or decrease in ocean heat content during the recharge phase and compare results with previous studies in which this perturbation is prescribed earlier during the tilting mode. We found that while not substantially affected by the phase at which a sudden rise in heat content is prescribed, the timing and magnitude of the events are very sensitive to the phase at which a major decrease is imposed. The different response to the phase of increases and decreases substantiates the importance of nonlinear subsurface ocean dynamics to the onset and growth of EN episodes and provides insight into the irreversibility of the events at different stages of the oscillation

In vivo hypothalamic regional volumetry across the frontotemporal dementia spectrum

BACKGROUND: Frontotemporal dementia (FTD) is a spectrum of diseases characterised by language, behavioural and motor symptoms. Among the different subcortical regions implicated in the FTD symptomatology, the hypothalamus regulates various bodily functions, including eating behaviours which are commonly present across the FTD spectrum. The pattern of specific hypothalamic involvement across the clinical, pathological, and genetic forms of FTD has yet to be fully investigated, and its possible associations with abnormal eating behaviours have yet to be fully explored. METHODS: Using an automated segmentation tool for volumetric T1-weighted MR images, we measured hypothalamic regional volumes in a cohort of 439 patients with FTD (197 behavioural variant FTD [bvFTD]; 7 FTD with associated motor neurone disease [FTD-MND]; 99 semantic variant primary progressive aphasia [svPPA]; 117 non-fluent variant PPA [nfvPPA]; 19 PPA not otherwise specified [PPA-NOS]) and 118 age-matched controls. We compared volumes across the clinical, genetic (29 MAPT, 32 C9orf72, 23 GRN), and pathological diagnoses (61 tauopathy, 40 TDP-43opathy, 4 FUSopathy). We correlated the volumes with presence of abnormal eating behaviours assessed with the revised version of the Cambridge Behavioural Inventory (CBI-R). RESULTS: On average, FTD patients showed 14% smaller hypothalamic volumes than controls. The groups with the smallest hypothalamic regions were FTD-MND (20%), MAPT (25%) and FUS (33%), with differences mainly localised in the anterior and posterior regions. The inferior tuberal region was only significantly smaller in tauopathies (MAPT and Pick’s disease) and in TDP-43 type C compared to controls and was the only regions that did not correlate with eating symptoms. PPA-NOS and nfvPPA were the groups with the least frequent eating behaviours and the least hypothalamic involvement. CONCLUSIONS: Abnormal hypothalamic volumes are present in all the FTD forms, but different hypothalamic regions might play a different role in the development of abnormal eating behavioural and metabolic symptoms. These findings might therefore help in the identification of different underlying pathological mechanisms, suggesting the potential use of hypothalamic imaging biomarkers and the research of potential therapeutic targets within the hypothalamic neuropeptides

Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Nino: Lessons learned

A substantial body of work supports a teleconnection between the El Nino-Southern Oscillation (ENSO) and cholera incidence in Bangladesh. In particular, high positive anomalies during the winter (Dec-Feb) in sea surface temperatures (SST) in the tropical Pacific have been shown to exacerbate the seasonal outbreak of cholera following the monsoons from August to November. Climate studies have indicated a role of regional precipitation over Bangladesh in mediating this long-distance effect. Motivated by this previous evidence, we took advantage of the strong 2015-2016 El Nino event to evaluate the predictability of cholera dynamics for the city in recent times based on two transmission models that incorporate SST anomalies and are fitted to the earlier surveillance records starting in 1995. We implemented a mechanistic temporal model that incorporates both epidemiological processes and the effect of ENSO, as well as a previously published statistical model that resolves space at the level of districts (thanas). Prediction accuracy was evaluated with "out-of-fit" data from the same surveillance efforts (post 2008 and 2010 for the two models respectively), by comparing the total number of cholera cases observed for the season to those predicted by model simulations eight to twelve months ahead, starting in January each year. Although forecasts were accurate for the low cholera risk observed for the years preceding the 2015-2016 El Nino, the models also predicted a high probability of observing a large outbreak in fall 2016. Observed cholera cases up to Oct 2016 did not show evidence of an anomalous season. We discuss these predictions in the context of regional and local climate conditions, which show that despite positive regional rainfall anomalies, rainfall and inundation in Dhaka remained low. Possible explanations for these patterns are given together with future implications for cholera dynamics and directions to improve their prediction for the city

Planning for electronic data interchange

A model of strategic implementation is developed as a possible solution to the inability of numerous US business organizations to effectively plan their electronic data interchange (EDI) systems. The model is developed based on an extensive analysis of the EDI systems of numerous US firms and illustrates the dynamics of such systems in reference to supplier-customer relationship, as influenced by corporate structure, marketing and distribution channels, and buyer power. Five generic business strategies are formulated using the EDI model. These are the strategy in which retail firms follow the lead by suppliers and customers, the strategy in which EDI's functions in the supply chain are expanded, the new products and services strategy, the tie-in strategy, and the time-based competition strategy

Improvements in the X-ray luminosity function and constraints on the Cosmological parameters from X-ray luminous clusters

We show how to improve constraints on \Omega_m, \sigma_8, and the dark-energy equation-of-state parameter, w, obtained by Mantz et al. (2008) from measurements of the X-ray luminosity function of galaxy clusters, namely MACS, the local BCS and the REFLEX galaxy cluster samples with luminosities L> 3 \times 10^{44} erg/s in the 0.1--2.4 keV band. To this aim, we use Tinker et al. (2008) mass function instead of Jenkins et al. (2001) and the M-L relationship obtained from Del Popolo (2002) and Del Popolo et al. (2005). Using the same methods and priors of Mantz et al. (2008), we find, for a \Lambda$CDM universe, \Omega_m=0.28^{+0.05}_{-0.04} and \sigma_8=0.78^{+0.04}_{-0.05}$ while the result of Mantz et al. (2008) gives less tight constraints $\Omega_m=0.28^{+0.11}_{-0.07}$ and \sigma_8=0.78^{+0.11}_{-0.13}. In the case of a wCDM model, we find \Omega_m=0.27^{+0.07}_{-0.06}, $\sigma_8=0.81^{+0.05}_{-0.06}$ and $w=-1.3^{+0.3}_{-0.4}$, while in Mantz et al. (2008) they are again less tight \Omega_m=0.24^{+0.15}_{-0.07}, \sigma_8=0.85^{+0.13}_{-0.20} and w=-1.4^{+0.4}_{-0.7}. Combining the XLF analysis with the f_{gas}+CMB+SNIa data set results in the constraint \Omega_m=0.269 \pm 0.012, \sigma_8=0.81 \pm 0.021 and w=-1.02 \pm 0.04, to be compared with Mantz et al. (2008), \Omega_m=0.269 \pm 0.016, \sigma_8=0.82 \pm 0.03 and w=-1.02 \pm 0.06. The tightness of the last constraints obtained by Mantz et al. (2008), are fundamentally due to the tightness of the $f_{gas}$+CMB+SNIa constraints and not to their XLF analysis. Our findings, consistent with w=-1, lend additional support to the cosmological-constant model.Comment: 9 pages, 4 Figures. A&A accepted. Paper Subitted Previously To Mantz et al 2009, arXiv:0909.3098 and Mantz et al 2009b, arXiv:0909.309

Verification of Land-Atmosphere Coupling in Forecast Models, Reanalyses and Land Surface Models Using Flux Site Observations

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring

A Brief Facial Morphing Intervention to Reduce Skin Cancer Risk Behaviors: Results from a Randomized Controlled Trial

The current study was designed to test the efficacy of an appearance-based facial morphing program to reduce intentional UV exposure among individuals at risk for skin cancer. A three-arm randomized controlled trial was employed (N = 219) comparing facial morphing + health information to: (1) mindfulness + health information; and (2) health information only. Participants were young adults with a history of recent intentional tanning and future intentions to tan. Primary outcomes were indoor and outdoor tanning frequency and tanning intentions, with secondary outcomes of tanning attitudes, body image, and affect. Facial morphing participants reported less frequent tanning, compared to mindfulness and control participants at 1-month follow-up. Facial morphing participants also generally reported lower intentions to tan at immediate follow-up, although the magnitude of these effects weakened at 1-month follow-up. Facial morphing programs may offer a brief, efficacious, and scalable augmentation to standard of care in reducing intentional UV exposure