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Assemblage thinking is an increasingly influential approach in critical studies of food and farming, and partic-ularly in research on new agri-food technology such as precision agriculture (PA). This research is important in highlighting the distributed forms of power and agency through which farming worlds are assembled, and what these engender for more sustainable and equitable farming futures. However, to date, there has been limited attention to assembling PA from the perspective of farmers, what Legun and Burch (2021) refer to as 'deliberative assembling'. This paper contributes to knowledge in this area by applying post actor network theoretical work to investigate across case studies in Australia, the Netherlands and France, the forms of tinkering by which farmers attempt to make PA workable, and what these engender for farmer agency. Through our analysis, we show that much of the tinkering by farmers is aimed at holding together their own priorities, routines, and experiences with practices inscribed in PA technology, such as dependence on commercial advice, data-driven knowledge, and commitment to a single technological platform/company. Integral to this tinkering work are support networks that include agronomists, advisors, machinery dealers and/or farmer discussion groups. We argue that whilst these support networks are critical to holding together different practices, and making PA workable, they also play a more diverse and nuanced role in PA implementation than what has previously been recognised. Our case studies provide insights into three key forms of tinkering used by farmers in navigating support networks to make PA workable - disconnection, experimentation and trial-and-error, and trade-offs and compromises - and the specific distributions of agency which these tinkering practices engender. In conclusion, we argue that a tinkering lens provides a valuable approach for enabling agri-food scholars to tease out in greater depth delib-erative assembling practices and how these variously open-up or foreclose options for farmers in making PA workable. International audience

Green synthesis of gold nanoparticles (AuNPs) using microorganisms has been generally studied aiming for high-yield production and morphologies appropriated for various applications, such as bioremediation, (bio)sensors, and (bio)catalysis. Numerous approaches showed the individual effect of factors influencing the synthesis of AuNPs with limited analysis of the governing factors enhancing the production and desired quality of the precipitates. This study proposes a fractional-factorial design to investigate the simultaneous influence of seven environmental factors (cell concentration, temperature, anoxic/oxic conditions, pH, gold concentration, electron donor type, and bacterial species) on the recovery yield and synthesis of targeted AuNPs. Various sizes and morphologies of the AuNPs were obtained by varying the environmental factors studied. The factors with significant effects (i.e., 0.2 mM Au and pH 5) were selected according to statistical analysis for optimal removal of 88.2 ± 3.5% of gold and with the production of valuable 50 nm AuNPs, which are known for their enhanced sensitivity. Implications of the cytochrome-C on the bacterial mechanisms and the provision of electron donors via an electrochemical system are further discussed. This study helps develop gold recovery and nanoparticle synthesis methods, focusing on the determining factor(s) for efficient, low-cost, green synthesis of valuable materials.

Atmospheric mercury (Hg) observations in the lower free troposphere (LFT) can give important insights into Hg redox chemistry and can help constrain Hg background concentrations on a regional level. Relatively continuous sampling of LFT air, inaccessible to most ground-based stations, can be achieved at highaltitude observatories. However, such high-altitude observatories are rare, especially in the Southern Hemisphere (SH), and atmospheric Hg in the SH LFT is unconstrained. To fill this gap, we continuously measured gaseous elemental mercury (GEM; hourly) and reactive mercury (RM; integrated over ∼ 6-14 d) for 9 months at Maïdo mountain observatory (2160 m a.s.l.) on remote Réunion Island (21.1 • S, 55.5 • E) in the tropical Indian Ocean. GEM exhibits a marked diurnal variation characterized by a midday peak (mean: 0.95 ng m −3 ; SD: 0.08 ng m −3) and a nighttime low (mean: 0.78 ng m −3 ; SD: 0.11 ng m −3). We find that this diurnal variation is likely driven by the interplay of important GEM photo-reemission from the islands' vegetated surfaces (i.e. vegetation + soil) during daylight hours (8-22 ng m −2 h −1), boundary layer influences during the day, and predominant LFT influences at night. We estimate GEM in the LFT based on nighttime observations in particularly dry air masses and find a notable seasonal variation, with LFT GEM being lowest from December to March (mean 0.66 ng m −3 ; SD: 0.07 ng m −3) and highest from September to November (mean: 0.79 ng m −3 ; SD: 0.09 ng m −3). Such a clear GEM seasonality contrasts with the weak seasonal variation reported for the SH marine boundary layer but is in line with modeling results, highlighting the added value of continuous Hg observations in the LFT. Maïdo RM is 10.6 pg m −3 (SD: 5.9 pg m −3) on average, but RM in the cloud-free LFT might be about twice as high, International audience

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models. "Article signat per 19 autors/es: Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté , Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie" This research has been supported by the Horizon 2020 (S2S4E; grant no. 776787), EUCP (grant no. 776613), ERA4CS (grant no. 690462), and the Ministerio de Ciencia e Innovación (grant no. FPI PRE2019-088646). Peer Reviewed

Tree species diversity promotes multiple ecosystem functions and services. However, little is known about how above- and belowground resource availability (light, nutrients, and water) and resource uptake capacity mediate tree species diversity effects on aboveground wood productivity and temporal stability of productivity in European forests and whether the effects differ between humid and arid regions. We used the data from six major European forest types along a latitudinal gradient to address those two questions. We found that neither leaf area index (a proxy for light uptake capacity), nor fine root biomass (a proxy for soil nutrient and water uptake capacity) was related to tree species richness. Leaf area index did, however, enhance productivity, but negatively affected stability. Productivity was further promoted by soil nutrient availability, while stability was enhanced by fine root biomass. We only found a positive effect of tree species richness on productivity in arid regions and a positive effect on stability in humid regions. This indicates a possible disconnection between productivity and stability regarding tree species richness effects. In other words, the mechanisms that drive the positive effects of tree species richness on productivity do not per se benefit stability simultaneously. Our findings therefore suggest that tree species richness effects are largely mediated by differences in climatic conditions rather than by differences in above- and belowground resource availability and uptake capacity at the regional scales. ispartof: SCIENCE OF THE TOTAL ENVIRONMENT vol:812 ispartof: location:Netherlands status: published

ispartof: FUNCTIONAL ECOLOGY vol:35 issue:8 pages:1756-1767 status: published