the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A robust DayCent model calibration to assess the potential impact of integrated soil fertility management on maize yields, soil carbon stocks and greenhouse gas emissions in Kenya
Magdalena Necpalova
Marijn Van de Broek
Marc Corbeels
Samuel Mathu Ndungu
Monicah Wanjiku Mucheru-Muna
Daniel Mugendi
Rebecca Yegon
Wycliffe Waswa
Bernard Vanlauwe
Johan Six
Abstract. Sustainable intensification schemes that increase crop production and soil fertility, such as integrated soil fertility management (ISFM), are a proposed strategy to close yield gaps and achieve food security in sub-Saharan Africa while maintaining soil fertility. However, field trials are insufficient to estimate the potential impact of such technologies at the regional or national scale. Upscaling via biogeochemical models, such as DayCent, from the field-scale to a larger region can be a suitable and powerful way to assess the potential of such agricultural management practices at scale, but they need to be calibrated to new environments and their reliability needs to be assured. Here, we present a robust calibration of DayCent to simulate maize productivity under ISFM, using data from four long-term field experiments. The experimental treatments consisted of the addition of low- to high-quality organic resources to the soil, with and without mineral N fertilizer. We assess the potential of DayCent to represent the key aspects of sustainable intensification, including 1) yield, 2) changes in soil carbon, and 3) global warming potential. The model was calibrated and cross-evaluated with the probabilistic Bayesian calibration technique.
The standard parameters of DayCent led to poor simulations of maize yield (Nash-Sutcliffe modeling efficiency; EF 0.33) and changes in SOC (EF -1.3) for different ISFM treatments. After calibration of the model, both the simulation of maize yield (EF 0.51) and the change in SOC (EF 0.54) improved significantly compared to the model with the standard parameter values. A leave-one-site-out cross-evaluation indicated the robustness of the approach for spatial upscaling (i.e., the significant improvement, described before, was achieved by calibrating with data from 3 sites and evaluating with the remaining site). The SOC decomposition parameters were altered most severely by the calibration. They were an order of magnitude higher compared to the default parameter set. This confirms that the decomposition of SOC in tropical maize cropping systems is much faster than in temperate systems and that the DayCent temperature function is not suitable to capture this with a single parameter set. Finally, the global warming potential simulated by DayCent was highest in control -N treatments (0.5–2.5 kg CO2 equivalent per kg grain yield, depending on the site) and could be reduced by 14 to 72 % by combined application of mineral N and manure at a medium rate. In three of the four sites, the global warming potential was largely (> 75 %) dominated by SOC losses. In summary, our results indicate that DayCent is suitable for estimating the impact of ISFM from the site to the regional level, that trade-offs between yields and global warming potential are stronger in low-fertility sites, and that the reduction of SOC losses is a priority for the sustainable intensification of maize production in Kenya.
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Moritz Laub et al.
Status: open (until 28 Oct 2023)
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RC1: 'Comment on egusphere-2023-1738', Anonymous Referee #1, 26 Sep 2023
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1738/egusphere-2023-1738-RC1-supplement.pdf
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CC1: 'Comment on egusphere-2023-1738', David Pelster, 29 Sep 2023
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General comments: Very nice paper. Generally well written. The M&M in particular are very thorough. I have not done much modeling, but I found that the M&M did a good job of explaining the model parameters and their calibration along with how sensitive they were. Apart from a bunch of small issues (see below), I found that the discussion around objective iii. was lacking a bit. What I was really looking forward to was more discussion around the trade-offs between yield and SOM / increases along with the global warming potential of the different ISFM treatments.
Specific comments:
Lines 85-90: A map with the site locations would be helpful here as well.
Line 118 : should be “CH4 oxidation”.
Lines 150-155: How many samples per chamber? How long was the deployment time? How did you calculate the change in mixing ratios over time (linear or non-linear?), how were gas samples analyzed? (on a GC? What kind?). You need a bit more detail here.
Line 367: Is “langley” an SI unit? I had to do an internet search to find out what it is. Would it be possible to explain what this is? Or convert to SI units?
Figure 2: shouldn’t there be some label on the X and Y axes?
Line 395: perhaps I don’t quite understand, but isn’t the systemic underestimation at high yields (and AGB) a “bias”?
Line 446: wouldn’t a negative reduction be an increase?
Line 447: I would say “led to” rather than “could lead to”. Since there was a reduction noted.
Figure 7: It seems that you are unable to simulate the high emission days, which could be why the cumulative simulated emissions are typically lower than the 1:1 line. Also, in the Sidada site for simulate vs measured cumulative emissions, you have one data point that has a lot of leverage. I would consider seeing how the regression line looks without that point. And maybe investigate why that point is so different from the rest of the data at that site.
Lines 483-484: Mention here that DayCent overestimated SOC pre-calibration, but after the calibration the SOC concentrations (or stocks) were simulated much more accurately. This difference is clear when you look at the figure, but since the figure is in the appendix, it may not be readily apparent to the readers.
Line 513: why do you use 21 and 23 here? Why not just say 2 and 8? Or am I missing something?
Lines 526 to 529: Is there a reason why you switch between SOC and SOM? It seems like you are talking about the same thing.
Line 534: “vary” not “very”.
Line 543: Are you saying that DayCent does not capture yield increases above 100-150 kg N per ha per season in general? Or just specifically in Kenya. I have not used DayCent, but I would be very surprised if it does not capture yield increases above 150 kg N per ha in temperate regions.
Line 549-553: I wouldn’t worry too much about the poor match between simulated and measured daily fluxes. I would mention though that the timing of peak fluxes is related more to soil gas diffusivity and that soil hydraulics are more just a proxy of the diffusivity.
Line 553: Sommer et al. 2016 does not quite say this. What they say is that “As such, the overall model fit was exceptionally good, even though the visual impression would suggest a significant overestimation of emissions by CropSyst”. If you look at the figures in their study, the simulated line up very well with the measured emissions. It is just that there are a lot of peaks in the simulated that occur between samplings.
Line 569: I guess this is somewhat true, in that maize mono-cropping will still produce some GHG emissions. However what is the difference between the ISFM practices and the “typical” treatment (what is typical? No inputs? No N input and a small amount of FYM)? It seems like adding some inorganic N with 1.2 T C increased yields, without increasing yield scaled emissions compared with 0N 0C and compared with 0n 1.2T C. So even though it is not exactly “negative emission technology” it still seems to be an improvement.
Line 570: why say “positive absolute” in stead of just “positive”?
Lines 578-580: While I agree that N fertilizer should only be applied to responsive soils, I’m not sure that is a conclusion of the date that you have here. If you look at yields, all the sites respond to N fertilizer (either mineral or organic). It is just that they seem to respond a bit differently, particularly in the N2O emissions, to the fertilizer applications. Besides, the 0N control also has much higher yield scaled GWP in Embu and Machanga, mainly related to loss of SOC, so I don’t think the higher yield scaled emissions (compared with Sidada and Aludeka) with the +N treatments indicate that these shouldn’t be fertilized. In fact, the decrease in yield-scaled GWP when adding N is greater at the sites in Central Kenya than they are at Sidada, which almost contradicts what you are saying here.
Line 610: Just mention which treatment had the lowest yield-scaled emissions (the mix of FYM +N) as the preferred INMS for Kenya.
Table A1: can you add the sand content as well?
Figure A3: what depth are you using to calculate the stocks? You mention 15 cm depth in some locations, but you also mention that DayCent uses 20 cm depth. And, I am having a hard time seeing how the Machanga site lost so much of its C. at 20 cm depth a soil with a C content of 0.3 and a BD of 1.51 would have about 10 t C per ha. And you are saying here that it lost about 10 t per ha (or essentially all of its soil C). Is my math off (wouldn’t be the first time).
Figure A6: the figure caption needs to be re-done. For example, the second sentence is missing a word somewhere (perhaps “was” before “insensitive”?). And secondly, are you sure about the 50/50 split application? You were calibrating to data where the split application was 40 kg N at planting and 80 kg after ~ 6 weeks (see line 107-108; also line 165).
Figure A10, can you increase the font size in the figure please?
Citation: https://doi.org/10.5194/egusphere-2023-1738-CC1
Moritz Laub et al.
Data sets
ISFM/SOM long-term trials soil data Bernard Vanlauwe, Johan Six, Moritz Laub, Samuel Mathu, Daniel Mugendi https://doi.org/10.25502/wdh5-6c13/d
ISFM/SOM long-term trials maize Bernard Vanlauwe, Johan Six, Moritz Laub, Samuel Mathu, Daniel Mugendi https://doi.org/10.25502/be9y-xh75/d
Moritz Laub et al.
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