Interplay: A game for the participatory design of locally adapted cereal–legume intercrops

CONTEXT Although cereal–legume intercropping is a recognized approach to improve crop production sustainability, its uptake on European commercial farms remains slow, due to numerous questions raised by the introduction of intercrops in a cropping system. Co-design workshops allow multiple scenarios to be explored without risks. They favor identification of consistent answers to complex problems, considering local conditions and constraints. OBJECTIVE We present Interplay, the serious game we created to support players' exploration of intercrops by designing a wide-range of cereal–legume intercropping scenarios in given cropping system contexts and assessing eight ecosystem services provided by intercrops, i.e., intercropped cereal and legume yields, cereal protein content, nitrogen supply to the following crop, impact on soil structure and weed, insect and disease control. METHODS Interplay aims at being used with groups of farmers and their advisor or students and their agronomy teacher. The game includes a game board and cards to design intercropping scenarios defined by: (i) the cropping system and field context;(ii) the farmers' objectives when introducing an intercrop;(iii) the species to associate and (iv) the crop management. A computer model assesses the ecosystem services provided by the intercropping scenario designed on the game board. The players compare these results to their objectives and to sole crop performances and, if necessary, adjust the scenarios. The players are guided through the design process by a facilitator. RESULTS AND CONCLUSIONS In the context of the Covid-19 pandemic, we used the game with 70 French agriculture students divided into six groups to design intercropping scenarios improving nitrogen supply to the following crop in a rotation. Students designed scenarios that improved nitrogen supply compared to the initial sole crop, yet cereal yield decreased more than the farmer desired. Guided by the facilitator, students reconsidered the cropping system to improve nitrogen supply at the crop rotation level. Interplay is an interactive tool used to stimulate players' creativity by exploring intercropping scenarios and providing salient, credible and legitimate assessment of the ecosystem services provided. It also promotes knowledge sharing on intercropping and allows redesigning the cropping system completely. Students and teachers who used it declared that it helped enhance their knowledge on intercrops. SIGNIFICANCE Interplay is the first serious game suited to develop the practice of intercropping. It is currently available for cereal–grain legume intercrops sown simultaneously in a random pattern under French soil-climate conditions, but could be adapted to other countries and intercrops.

Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.

INTRODUCTION: Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports. OBJECTIVES: We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal. METHODS: Reports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period. RESULTS: Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results. CONCLUSIONS: Our artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.

The immediate impact of the first waves of the global COVID-19 pandemic on agricultural systems worldwide: Reflections on the COVID-19 special issue for agricultural systems.

CONTEXT: In May 2020, approximately four months into the COVID-19 pandemic, the journal's editorial team realized there was an opportunity to collect information from a diverse range of agricultural systems on how the pandemic was playing out and affecting the functioning of agricultural systems worldwide. OBJECTIVE: The objective of the special issue was to rapidly collect information, analysis and perspectives from as many regions as possible on the initial impacts of the pandemic on global agricultural systems, The overall goal for the special issue was to develop a useful repository for this information as well as to use the journal's international reach to share this information with the agricultural systems research community and journal readership. METHODS: The editorial team put out a call for a special issue to capture the initial effects of the pandemic on the agricultural sector. We also recruited teams from eight global regions to write papers summarizing the impacts of the first waves of the pandemic in their area. RESULTS AND CONCLUSIONS: The work of the regional teams and the broader research community resulted in eight regional summary papers, as well as thirty targeted research articles. In these papers, we find that COVID-19 and global pandemic mitigation measures have had significant and sometimes unexpected impacts on our agricultural systems via shocks to agricultural labour markets, trade and value chains. And, given the high degree of overlap between low income populations and subsistence agricultural production in many regions, we also document significant shocks to food security for these populations, and the high potential for long term losses in terms of human, natural, institutional and economic capital. While we also documented instances of agricultural system resilience capacities, they were not universally accessible. We see particular need to shore up vulnerable agricultural systems and populations most negatively affected by the pandemic and to mitigate pandemic-related losses to preserve other agricultural systems policy objectives, such as improving food security, or addressing climate change. SIGNIFICANCE: Despite rapid development of vaccines, the pandemic continues to roll on as of the time of writing (early 2022). Only time will tell how the dynamics described in this Special Issue will play out in the coming years. Evidence of agricultural system resilience capacities provides some hopeful perspectives, but also highlights the need to boost these capacities across a wider cross section of agricultural systems and encourage agri-food systems transformation to prepare for more challenges ahead.

Resilience of French organic dairy cattle farms and supply chains to the Covid-19 pandemic.

Context: Identifying and developing resilient farming and food systems has emerged as a top priority during the Covid-19 pandemic. Many academics suggest that farming and food systems should move towards agroecological models to achieve better resilience. However, there was limited evidence to support this statement during the Covid-19 pandemic. Objective: Our objectives were to report evidence for the resilience of French organic dairy cattle farms and supply chains to the Covid-19 pandemic and to discuss the features of those farms and supply chains that promoted resilience. Methods: We combined online surveys with farmers, semi-structured interviews with supply chain actors and a review of the gray and technical literature, and whenever possible, we compared this qualitative data against quantitative industry data. We also asked farmers to rank 19 pre-identified risks according to their likelihood and potential impacts. Results and conclusions: We showed the pandemic had zero to moderate impacts on most farms. Among respondents, 38 farmers reported no impacts, another 43 experienced minor impacts on aspects such as their income and workload while only 5 faced major impacts, such as the closure of sales outlets. Most farms were family farms and were not greatly affected by worker availability issues. Moreover, the vast majority of these farms were nearly autonomous for livestock feeding and none reported input supply shortages or related impacts on farm functioning and productivity. The pandemic had moderate impacts on supply chains. Despite staff reductions, supply chains continued producing sufficient amounts of dairy products to meet consumer demand. To do so, they narrowed the scope of products manufactured to concentrate on a basic mix: milk, cream, butter and plain yogurt. Logistics were also adapted by hiring retired drivers to keep up with milk collection and reorganizing the delivery of products by shunting usual sub-level platforms that were saturated. Consequently, even after this pandemic, farmers remained more concerned with climate change-related risks on their farms than by sanitary risks. Several resilience factors were identified that promoted buffer and adaptive capacity at the farm level and that favored adaptive capacity at the supply chain level. Significance: These findings confirm the relevance of agroecological models in achieving resilience in farming and food systems against shocks such as the Covid-19 pandemic. This preliminary work carried out at the end of the first lock-down period needs to be pursued in order to understand the impacts of the Covid-19 pandemic over longer time horizons.

Psychological effects of remote-only communication among reference persons of ICU patients during COVID-19 pandemic.

BACKGROUND: During COVID-19 pandemic, visits have been prohibited in most French ICUs. Psychological effects, for reference persons (RPs), of remote-only communication have been assessed. METHODS: All RPs of patients referred to ICU for COVID-19 were included. HADS, IES-R, and satisfaction were evaluated at admission, discharge/death, and 3 months. At 3 months, a psychologist provided a qualitative description of RPs' psychological distress. RESULTS: Eighty-eight RPs were included. Prevalence of anxiety and depression was 83% and 73% respectively. At 3 months, lower HADS decrease was associated with patient death/continued hospitalization, and/or sleeping disorders in RPs (p < 0.01). Ninety-nine percent RPs felt the patient was safe (9 [7; 10]/10 points, Likert-type scale), confident with caregivers (10 [9; 10]/10 points), and satisfied with information provided (10 [9; 10]/10 points). All RPs stressed the specific-type of "responsibility" associated with being an RP in a remote-only context, leading RPs to develop narrow diffusion strategies (67%) and restrict the array of contacted relatives to a very few and/or only contacting them rarely. 10 RPs (30%) related the situation to a prior traumatic experience. CONCLUSION: RPs experienced psychological distress and reported that being an RP in a remote-only communication context was a specific responsibility and qualified it as an overall negative experience. TRIAL REGISTRATION: NCT04385121 . Registered 12 May 2020. https://clinicaltrials.gov/ .