Validation and field application of a low-cost device to measure CO2 and ET fluxes
Abstract. Mitigating the global climate crisis and its consequences, such as more frequent and severe droughts, is one of the major challenges for future agriculture. Therefore, identifying land use systems and management practices that reduce greenhouse gas emissions (GHG) and promote water use efficiency (WUE) is crucial. This, however, requires accurate and precise measurements of carbon dioxide (CO2) fluxes and evapotranspiration (ET). Despite that, commercial systems to measure CO2 and ET fluxes are expensive and thus, often exclude research in ecosystems within the Global South. This is especially true for research and data of agroecosystems in these areas, which are to date still widely underrepresented. Here, we present a newly developed, low-cost, non-dispersive infrared (NDIR)-based, CO2 and ET flux measurement device (~200 Euro) that provides reliable, accurate and precise CO2 and ET flux measurements in conjunction with manual closed chambers. To validate the system, laboratory and field validation experiments were performed, testing multiple different low-cost sensors. We demonstrate that the system delivers accurate and precise CO2 and ET flux measurements using the K30 FR NDIR (CO2) and SHT31 (RH) sensor. An additional field trial application demonstrated its longer-term stability (> 3 months) and ability to obtain valid net ecosystem C balances (NECB) and WUE. This was the case, even though environmental conditions at the field trial application site in Sub-Saharan Africa were rather challenging (e.g., extremely high temperatures, humidity and intense rainfall). Consequently, the developed low-cost CO2 and ET flux measurement device not only provides reasonable results but might also help to democratize science and close current data gaps.
Reena Macagga et al.
Status: open (until 25 Jun 2023)
- RC1: 'Comment on egusphere-2023-553', Anonymous Referee #1, 17 May 2023 reply
Reena Macagga et al.
Reena Macagga et al.
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This paper from Reena et al. does a good job by testing the use of low-cost devices to measure CO2 and ET fluxes in agricultural soils in order to estimate, NEE, GPP, NECB and WAE. Not only the application is interesting but also the approach they have used, with a preliminary laboratory test, a field validation and finally the field trial application.
Nevertheless, I would suggest the authors to modify some parts of the manuscripts in order to make it clearer and more robust.
-In section 2.5.3. the author says that Error calculation for CO2 and ET fluxes were quantified using a comprehensive error prediction algorithm described in detail by Hoffmann et al (2015). However, the reader would appreciate an understandable error analysis. For example, in figures 6 and 7 you say “error bars indicate calculated flux error (α =0.9). What does this α means? The only α I know in statistics is the level of significance, and is never higher than 0.05. Moreover, when you compared the fluxes from K30-SCD-30 with the Li-850 ones, you are talking about the r^2 (linearity) but it will be also interesting to know something about the error (RMSE, RSE, ...). Also, has the uncertainty of the measurement been taken in consideration when calculating the error of the fluxes? Finaly n section 3.3 you enumerate all the parameters without citing any kind of deviation, error, variability or confidence interval. Please revise. Also indicate what the error bars means in figure 7.
-In your manuscript you say that the LI-850 CO2 values were corrected for H2O. But you did not correct the K30 CO2 values for H2O or Temperature. As you restricted the temperature increase to 1.5 ºK, maybe the temperature increase won’t affect so much the readings. However, what about the influence of H2O increase? In some of the literature you cited (C.R. Martin et al., Curcoll et al. and others) there is an evaluation of how T and RH influences the measurement. Therefore, you can:
Figure 1c: which are the different elements represented the figure? They should be indicated (e.g. K30 sensor, Arduino board, etc…)
Paragraph beginning in line 117: please re-write in order to make it easier to read and understand. Make it shorter and enumerate the sensors at the end of the phrase.
Line 160: What do you mean for “changes in the chamber headspace”?
Line 163: “derived temperature (Reco)”. Is this correct?
Line 196: “death band of 10%”. What do you mean for a “death band”? Correct or specify.
Line 222: ”steepest slope and closest to chamber deployment”. Why the steepest slope? Justify, with reference. What you meant by “closest to chamber deployment”??
Figure 6: As in the text you talk about Reco and NEE fluxes, you could differentiate it by using different colours or point shapes.
Lines 415 to the end of the section: this paragraph may be split. Some of the information you write must go to methods section, and some other in the conclusions.