In cranberry production systems, stands are covered by 1–5 cm of sand every 2–5 years to stimulate plant growth, resulting in alternate layers of sand and litter in soil upper layers. However, almost intact twigs and leaves remain in subsurface layers, indicating a slow decomposition rate. The Tea Bag Index (TBI) provides an internationally standardized methodology to compare litter decomposition rates (k) and stabilization (S) among terrestrial ecosystems. However, TBI parameters may be altered by time-dependent changes in the contact between litter and their immediate environment. The aims of this study were to determine the TBI of cranberry agroecosystems and compare it to the TBI of other terrestrial ecosystems. Litters were standardized green tea, standardized rooibos tea, and cranberry residues collected on the plantation floor. Litter decomposition was monitored during two consecutive years. Added N did not affect TBI parameters (k and S) due to possible N leaching and strong acidic soil condition. Decomposition rates (k) averaged (mean ± SD) 9.7 × 10−3 day−1 ± 1.6 × 10−3 for green tea, 3.3 × 10−3 day−1 ± 0.8 × 10−5 for rooibos tea, and 0.4 × 10−3 day−1 ± 0.86 × 10−3 for cranberry residues due to large differences in biochemical composition and tissue structure. The TBI decomposition rate (k) was 0.006 day−1 ± 0.002 in the low range among terrestrial ecosystems, and the stabilization factor (S) was 0.28 ± 0.08, indicating high potential for carbon accumulation in cranberry agroecosystems. Decomposition rates of tea litters were reduced by fractal coefficients of 0.6 for green tea and 0.4 for rooibos tea, indicating protection mechanisms building up with time in the tea bags. While the computation of the TBI stabilization factor may be biased because the green tea was not fully decomposed, fractal kinetics could be used as additional index to compare agroecosystems.

Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.

Background Herd immunity or community immunity refers to the reduced risk of infection among susceptible individuals in a population through the presence and proximity of immune individuals. Recent studies suggest that improving the understanding of community immunity may increase intentions to get vaccinated. Objective This study aims to design a web application about community immunity and optimize it based on users’ cognitive and emotional responses. Methods Our multidisciplinary team developed a web application about community immunity to communicate epidemiological evidence in a personalized way. In our application, people build their own community by creating an avatar representing themselves and 8 other avatars representing people around them, for example, their family or coworkers. The application integrates these avatars in a 2-min visualization showing how different parameters (eg, vaccine coverage, and contact within communities) influence community immunity. We predefined communication goals, created prototype visualizations, and tested four iterative versions of our visualization in a university-based human-computer interaction laboratory and community-based settings (a cafeteria, two shopping malls, and a public library). Data included psychophysiological measures (eye tracking, galvanic skin response, facial emotion recognition, and electroencephalogram) to assess participants’ cognitive and affective responses to the visualization and verbal feedback to assess their interpretations of the visualization’s content and messaging. Results Among 110 participants across all four cycles, 68 (61.8%) were women and 38 (34.5%) were men (4/110, 3.6%; not reported), with a mean age of 38 (SD 17) years. More than half (65/110, 59.0%) of participants reported having a university-level education. Iterative changes across the cycles included adding the ability for users to create their own avatars, specific signals about who was represented by the different avatars, using color and movement to indicate protection or lack of protection from infectious disease, and changes to terminology to ensure clarity for people with varying educational backgrounds. Overall, we observed 3 generalizable findings. First, visualization does indeed appear to be a promising medium for conveying what community immunity is and how it works. Second, by involving multiple users in an iterative design process, it is possible to create a short and simple visualization that clearly conveys a complex topic. Finally, evaluating users’ emotional responses during the design process, in addition to their cognitive responses, offers insights that help inform the final design of an intervention. Conclusions Visualization with personalized avatars may help people understand their individual roles in population health. Our app showed promise as a method of communicating the relationship between individual behavior and community health. The next steps will include assessing the effects of the application on risk perception, knowledge, and vaccination intentions in a randomized controlled trial. This study offers a potential road map for designing health communication materials for complex topics such as community immunity.

No abstract

Background To reduce the transmission of SARS-CoV-2 and the associated spread of COVID-19, many jurisdictions around the world imposed mandatory or recommended social or physical distancing. As a result, at the beginning of the pandemic, various communication materials appeared online to promote distancing. Explanations of the science underlying these mandates or recommendations were either highly technical or highly simplified. Objective This study aimed to understand the effects of a dynamic visualization on distancing. Our overall aim was to help people understand the dynamics of the spread of COVID-19 in their community and the implications of their own behavior for themselves, those around them, the health care system, and society. Methods Using Scrum, which is an agile framework; JavaScript (Vue.js framework); and code already developed for risk communication in another context of infectious disease transmission, we rapidly developed a new personalized web application. In our application, people make avatars that represent themselves and the people around them. These avatars are integrated into a 3-minute animation illustrating an epidemiological model for COVID-19 transmission, showing the differences in transmission with and without distancing. During the animation, the narration explains the science of how distancing reduces the transmission of COVID-19 in plain language in English or French. The application offers full captions to complement the narration and a descriptive transcript for people using screen readers. We used Google Analytics to collect standard usage statistics. A brief, anonymous, optional survey also collected self-reported distancing behaviors and intentions in the previous and coming weeks, respectively. We launched and disseminated the application on Twitter and Facebook on April 8, 2020, and April 9, 2020. Results After 26 days, the application received 3588 unique hits from 82 countries. The optional survey at the end of the application collected 182 responses. Among this small subsample of users, survey respondents were nearly (170/177, 96%) already practicing distancing and indicated that they intended to practice distancing in the coming week (172/177, 97.2%). Among the small minority of people (n=7) who indicated that they had not been previously practicing distancing, 2 (29%) reported that they would practice distancing in the week to come. Conclusions We developed a web application to help people understand the relationship between individual-level behavior and population-level effects in the context of an infectious disease spread. This study also demonstrates how agile development can be used to quickly create personalized risk messages for public health issues like a pandemic. The nonrandomized design of this rapid study prevents us from concluding the application’s effectiveness; however, results thus far suggest that avatar-based visualizations may help people understand their role in infectious disease transmission.