DYNAMISM: a low-cost automatic system for measurements of gas exchange at canopy scale in dynamic 2 conditions 3

Obtaining instantaneous gas exchanges data is fundamental to gain information on photosynthesis. Leaf level 27 data are reliable, but their scaling up to canopy scale is difficult as they are acquired in standard and/or controlled 28 conditions, while natural environments are extremely dynamic. Responses to dynamic environmental conditions 29 need to be considered, as measurements at steady state and their related models may overestimate total carbon 30 (C) plant uptake. In this paper, we describe an automatic, low-cost measuring system composed of 12 open chambers (60 x 60 x 33 150 cm; around 400 euros per chamber) able to measure instantaneous CO 2 and H 2 O gas exchanges, as well as 34 environmental parameters, at canopy level. We tested the system’s performance by simulating different CO 2 35 uptake and respiration levels using a tube filled with soda lime or pure CO 2 , respectively, and quantified its 36 response time and measurement accuracy. We have been also able to evaluate the delayed response due to the 37 dimension of the chambers, proposing a method to correct the data by taking into account the response time ( 𝑡 0 ) 38 and the residence time (τ). Finally, we tested the system by growing a commercial soybean variety in fluctuating 39 and non-fluctuating light, showing the system to be fast enough to capture fast dynamic conditions. At the end 40 of the experiment, we compared cumulative fluxes with total plant dry biomass. 41 C 43 the overall CO 2 fluxes at the 44 the response determine steady state fluxes from unsteady state measured 45 several environmental

3 Background 52 Despite being the most important biological process on Earth, photosynthesis still presents mechanisms that are 53 not deeply understood and it is considered a matter of priority interest for new pioneering research fields 54 (Bahadur et al., 2015;Tanaka and Makino, 2009). By converting solar energy into chemical energy, plants 55 accumulate biomass by which several human activities depend on (Hall, 2013;Vitousek et al., 1986). Due to 56 the rise in food demands (Alexandratos and Bruinsma, 2012;South et al., 2019) and, more in general, in plant-57 derived products, the newest research is aiming to target those processes in photosynthesis that would improve 58 the overall crop yield (Kromdijk et al., 2016;Long et al., 2006;Ort et al., 2015). This can be achieved in 59 laboratories and tested in green houses, but translating these results into the field is highly demanding (Kaiser

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To facilitate the translation of information from the laboratory to the field, it is also necessary to mimic natural 64 environmental conditions within growth chambers (Kaiser et al., 2018b). For example, simulating dynamic light 65 conditions is necessary to retrieve canopy scale data that would reflect environmental variability. In fact, 66 whereas most of the past experiments and models considered photosynthesis at the steady state (Farquhar and  70 Tomimatsu and Tang, 2016). Plants are usually exposed to fluctuating irradiance due to the movements of 71 clouds, the effect of wind and the gaps within the canopy (Pearcy, 1990 Clearly, there is the need for adopting measuring systems able to take into account light dynamics and to obtain 78 reliable and repeatable measurements at canopy scale. Therefore, an accurate determination of canopy 79 photosynthesis is necessary to evaluate the observations and to test the results obtained in the laboratories.

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Nowadays, plant net primary production (NPP) can be measured destructively by harvesting the whole plant 81 and weighting its components (i.e. leaves, stem, roots) or can be assessed by measuring instantaneous CO2 82 exchanges (Hall, 2013). Following this last approach, measurements are generally rapid and can be taken from 83 leaf to canopy scale either in the laboratories or in the field. and energy dissipation) can be retrieved (Baker, 2008;Maxwell and Johnson, 2000). Leaf level data are reliable 91 and repeatable, but these data can be hardly scaled up at whole plant or whole canopy scale.

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Canopy gas exchange measurements can be based on the use of micro-meteorological techniques or of growth 93 chambers (Baldocchi, 2003;Matese et al., 2008;Wang et al., 2018). Open-field micro-meteorological 94 techniques, such as eddy covariance, are appealing, but can suffer of three main weaknesses: i) difficulties in 95 separating the target vegetation/canopy from the neighbor vegetation and different microclimates; ii) these 96 methods do not provide a direct measure of the canopy CO2 exchange per se (gross or net primary production), 97 but rather of the whole community (i.e. net ecosystem production, NEP) (Flexas et al., 2011;Hall, 2013; 98 Juszczak et al., 2012); iii) these methods usually need for particular environmental conditions in order to obtain 99 reliable measurements (i.e. flat terrain, large footprint areas, atmospheric stability) (Acosta et al., 2017).

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Growth chamber systems allow direct CO2 gas exchange measurements at plant or small canopy scales. In open 101 chambers, net carbon (C) exchange is estimated by measuring the inlet flux and the difference between inlet 102 and outlet CO2 concentrations; in the closed chambers, the change with time in CO2 concentration within the 103 chamber headspace is measured and the assimilation rate is then calculated (Hall, 2013;Wheeler, 1992). While 104 open chambers can measure gas exchange for long time periods, closed chambers can be used only for short 105 5 time periods in order to avoid increase in air temperature or water condensation . Several 106 growth chamber systems have been described in the literature (Andriolo et al., 1996;Hall, 2013;Mitchell, 107 1992), but some of them showed low ability to control environmental conditions (Miller et al., 1996; 108 al., 2019), are not adapted to long-term continuous measurements (Andriolo et al., 1996)  Transmitted radiation is measured using solar bars placed horizontally at the bottom of the canopy. Each bar is 187 made of eight photodiodes in parallel (model S1087-01, Hamamatsu Photonics, Japan) with a 100 Ω resistance 188 and was calibrated against a reference quantum sensor (Li-190R, Licor, USA) before setting up the system.

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Finally, in Table 1 we report the list of all the major parts of the system, their technical specification and prices.

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The overall system cost is 5.000 euro (only 417 euro per chamber), without considering the reference sensors 191 for calibrations, and the analyzers (LI-840 and LI-7000). One of the strengths of DYNAMISM is that any 192 number of chambers is possible in the multiplexer mode, thus allowing to have a high number of replicates with 193 a limited cost; nevertheless, if only a multiplexer is used, it will go in a repeated cycle.

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In Figure 4, we reported the total error (T) and the errors related to F and ∆ 2 only, as those due to 0 and 0 280 were smaller than 1% and thus negligible. According to our sensitivity analysis, the major source of error in the