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Sessile intertidal organisms live in a harsh environment with challenging environmental conditions and increasing anthropogenic pressure such as microplastic (MP) pollution. This study focused on effects of environmentally relevant MP concentrations on the metabolism of intertidal Pacific oyster Crassostrea gigas, and its potential MP-induced vulnerability to warming during midday low tide. Oysters experienced a simulated semidiurnal tidal cycle based on their natural habitat, and were exposed to a mixture of polystyrene microbeads (4, 7.5 and 10 µm) at two environmentally relevant concentrations (0.025 µg L−1 and 25 µg L−1) for 16 days, with tissue samplings after 3 and 12 days to address dose-dependent effects over time. On the last day of exposure, the remaining oysters were additionally exposed to low tide warming (3 °C h−1) to investigate possible MP-induced susceptibility to aerial warming. Metabolites of digestive gland and gill tissues were analysed by using untargeted 1H nuclear magnetic resonance (NMR) based metabolomics. For the digestive gland metabolite profiles were comparable to each other independent of MP concentration, exposure time, or warming. In contrast, gill metabolites were significantly affected by high MP exposure and warming irrespective of MP, initiating the same cellular stress response to counteract induced oxidative stress. The activated cascade of antioxidant defence mechanisms required energy on top of the general energy turnover to keep up homeostasis, which in turn may lead to subtle, and likely sub-lethal, effects within intertidal oyster populations. Present results underline the importance of examining the effects of environmentally relevant MP concentrations not only alone but in combination with other environmental stressors.

The current agroecological transition of agriculture pushes to a diversification of cropping systems, which requires quantified data describing crop successions. “Crop successions indicators 2015-2021” dataset provides a set of twenty synthesis indicators to characterize crop diversity, crop seasonality, particular components of crop successions and duration of crop rotations. Indicators are computed for a seven-year period from 2015 to 2021. Data source are raw crop sequences open access dataset. Indicators are available for municipalities, departments, regions and the whole mainland France, for arable land. A group of experts in agronomy has been associated to this work, in order to define relevant themes, relevant indicators and relevant indicator definitions. This dataset could be useful to characterize agricultural practices on a given territory, for researchers, local actors as decentralized state services, water agencies, territorial collectivities, chambers of agriculture, or agricultural cooperatives. Proposed indicators could be useful for policy makers to monitor the evolution of cultivation practices, in order to design, implement or evaluate measures targeting cultivation practices.

Maize is the most-produced food crop in the world and is grown in intensive rotations or in monoculture (continuous maize) systems. As maize production has expanded massively across the world, many concerns have emerged about its local environmental and other global impacts. Agronomists have designed innovative cropping systems and assessed them using system experiments to make arable systems more sustainable. However, knowledge is still lacking on the sustainability of innovative cropping systems compared to highly intensive systems such as irrigated maize-based monoculture. Here, we present the assessment results of a nine-year system experiment in Alsace, France, developed to compare an innovative system based on a diversified rotation and innovative management practices (three-year rotation of maize/soybean/winter wheat (plus a cover crop) combined with reduced tillage) with a continuous maize reference system. The results cover a six-year assessment period following an initial three-year design period. Classic criteria, such as profitability, workload, pesticide use, fossil energy consumption and nitrate leaching, were assessed along with other less studied criteria, such as pesticide leaching risk, soil structure, soil chemical quality and soil biological activity. Sustainability – which includes environmental, social and economic dimensions – was assessed with the MASC 2.0 method. Overall sustainability was substantially enhanced in the innovative system (5 out of 7 sustainability classes) in comparison with the low level of the reference system (2 out of 7). This was due to a clear improvement in the environmental performance (from 2 out of 5 to 5 out of 5) while social performance was high in both systems (4 out of 5) and economic performance was low (2 out of 5) due to very low contribution to economic development. Nevertheless, the innovative system had a major drawback: lower profitability, especially when scenarios included high maize prices. Furthermore, herbicide use on maize was higher in the innovative system than in the reference one. Avenues for progress, such as encouraging stakeholder participation at the assessment stage or additional innovations such as multiple cropping, are suggested.

Biodiversity is a key component of agricultural ecosystems and belongs to one of the exceeded planetary boundaries. In the quest for innovation to mitigate impact, it is essential to have tools to assess the anthropogenic impact on biodiversity. In this study, we developed a new indicator, I-BIO that aimed to predict the impacts of management practices and the influence of the landscape on overall biodiversity at species group level. I-BIO encompassed four groups: microorganisms, vegetation, invertebrates and vertebrates. This predictive indicator was built using DEXi software that facilitates the design of hierarchical decision trees, based on input variables in the form of qualitative classes, aggregated using utility functions. I-BIO was constructed using data from a long-term platform in Scotland and then applied to two data sets in France and one in Scotland. The results showed that the lowest class is predominant for all branches especially for invertebrates and soil invertebrates. Monte Carlo sampling was used to test for sensitivity. Then, a validation step was carried out to compare I-BIO outputs with real data using data on the four groups from Scottish and French datasets. I-BIO predictions of weed and soil invertebrate abundance conformed to field data. Due to low numbers of samples, validation of microorganisms, flying invertebrates and vertebrates predictions require more data. Future developments of I-BIO will focus on predictions specifically to assess the impact of management on species richness. Avenues for improvement include a better integration of landscape features and the history of agricultural practices as they both have a great influence on biodiversity.

The general purpose of the primary and secondary data available in this article is to support an integrated assessment of scenarios of crop-livestock integration at the territorial level i.e. of exchanges between arable and livestock farms. The data is a result of a research collaboration between the scientist from INRAE, agricultural advisers from Chamber of Agriculture of Pays de la Loire (CRAPL) and a collective of five arable and two livestock farmers located in the district of Pays de Pouzauges (Vendée department, western France). All participants formed part of the DiverIMPACTS project (https://www.diverimpacts.net/) that aims to achieve the full potential of diversification of cropping systems for improved productivity, delivery of ecosystem services and resource-efficient and sustainable value chains in Europe. The first dataset corresponds to the inputs of MAELIA (http://maelia-platform.inra.fr/), a spatial agent-based simulation platform that was used to support an iterative design and assessment of scenarios to redesign cropping systems. The second dataset corresponds to the outputs of MAELIA simulations and the associated indicators at the farm, group and territory level. The data comprise multiple shape and csv files characterizing the edaphic-climatic heterogeneity of the territory and cropping systems, farmers’ crop management rules (IF-THEN rules) and general information about the farms (e.g. crops, agricultural equipment, average crop yields). Data is reported for the baseline situation and three exchange scenarios containing different innovative cropping systems co-designed by scientists, agricultural advisers and the farmers. The data presented here can be found in the Portail Data INRA repository (https://doi.org/10.15454/3ZTCF5) and were used in the research article “Fostering local crop-livestock integration via legume exchanges using an innovative integrated assessment and modelling approach: MAELIA” [1].

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public repository managed by INRA (https://data.inra.fr/; https://doi.org/10.15454/WB51LL). Keywords: Literature search query, INRA divisions, Trade-offs, Multicriteria decision, Multicriteria assessment