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Sustainable intensification schemes such as integrated soil fertility management ISFM are a proposed strategy to close yield gaps, increase soil fertility, and achieve food security in sub-Saharan Africa. Biogeochemical models such as DayCent can assess their potential at larger scales, but these models need to be calibrated to new environments and rigorously tested for accuracy. Here, we present a Bayesian calibration of DayCent, using data from four long-term field experiments in Kenya in a leave-one-site-out cross-validation approach. The experimental treatments consisted of the addition of low- to high-quality organic resources, with and without mineral nitrogen fertilizer. We assessed the potential of DayCent to accurately simulate the key elements of sustainable intensification, including 1 yield, 2 the changes in soil organic carbon SOC , and 3 the greenhouse gas GHG balance of CO 2 and N 2 O combined. Compared to the initial parameters, the cross-validation showed improved DayCent simulations of maize grain yield with the Nash—Sutcliffe model efficiency EF increasing from 0. The simulations of maize yield and those of SOC stock changes also improved by site with site-specific EF ranging between 0. The four cross-validation-derived posterior parameter distributions leaving out one site each were similar in all but one parameter. Together with the model performance for the different sites in cross-validation, this indicated the robustness of the DayCent model parameterization and its reliability for the conditions in Kenya. The simulated yield-scaled GHG balance was highest in control treatments without N addition between 0. They also indicate that the trade-off between maize yield and GHG balance is stronger in low-fertility sites and that preventing SOC losses, while difficult to achieve through the addition of external organic resources, is a priority for the sustainable intensification of maize production in Kenya. Laub, M. In Kenya, as in many other countries in sub-Saharan Africa SSA , maize yields have remained low; on average they have been 1. If yields are not improved, increased population growth will further deteriorate food self-sufficiency and food security in general in the coming decades Zhai et al. One of the key limitations to sustainable maize production in SSA is the insufficient use of mineral fertilizer and organic inputs Vanlauwe et al. Integrated soil fertility management ISFM is a sustainable intensification practice that can alleviate these limitations by combining the use of mineral fertilizers with organic inputs Vanlauwe et al. Several studies have reported that ISFM has the potential to more than double maize yields in Kenya, especially on infertile soils, due to its positive impact on soil fertility, including soil organic matter SOM content Chivenge et al. Furthermore, increasing SOM can help mitigate adverse effects of climate change, offering considerable potential in carbon-depleted soils across SSA Corbeels et al. However, the effectiveness of ISFM in increasing yields strongly depends on local site conditions, such as soil and climate Chivenge et al. To close yield gaps in a resource-efficient way and to assess the climate change mitigation potential of ISFM, we need to understand the long-term effects of ISFM practices at a larger scale. Ideally, this would be facilitated by implementing a large number of long-term experiments across a representative range of soil and climatic conditions. However, the significant costs, labor, and time required to maintain long-term experiments limit the number of sites for evaluating the variable effects of ISFM practices under site-specific conditions. In addition, relying on statistical predictive techniques to upscale results from a limited number of sites may lead to low predictive power and large errors because it is unlikely that the effects of soils and climate on yield and SOM would be fully captured in the statistical models. Biogeochemical process-based ecosystem models, such as DayCent Parton et al. Because they partly embed our current understanding of the complex ecosystem processes, they are more robust for scaling up the yield potential Saito et al. Because SOM is coupled to nitrogen N mineralization in biogeochemical models, there is the potential that this translates into biased crop responses to N addition and biased crop productivity predictions in any upscaling exercise. A potential solution to this issue is the simultaneous calibration of soil and crop parameters in DayCent using data from local long-term experiments. Ideally, this calibration would include the uncertainty in the model parameters and model outputs Clifford et al. This is especially relevant given a recent study showing considerable uncertainty in DayCent's SOM turnover rates, even when calibrated using a range of long-term experiments Gurung et al. With robust, we mean that the model evaluation statistics are representative of applying the model to new sites with the same climate and soils. Of these, two sites were in humid western Kenya and two were in subhumid-to-semiarid central Kenya. The first objective of our study was to evaluate to what extent DayCent can reproduce the differences in yields and SOM development in response to the addition of different qualities and rates of organic resources combined with different rates of N fertilizer for a number of contrasting sites. The second objective was to evaluate the greenhouse gas GHG balance of different addition rates of organic material in ISFM to find the optimal balance between limiting GHG emissions from the soil and optimizing crop yield that is, sustainable intensification. The specific steps to reach the objectives of this study were i to test the capability of an uncalibrated version of DayCent to simulate yield and SOC development of the different ISFM practices; ii to calibrate DayCent to represent ISFM under Kenyan conditions using experimental data from four long-term experiments, displaying the uncertainty in model parameters by Bayesian calibration; and iii to use the calibrated model to gain understanding of the GHG balance of the different ISFM treatments. The present study used data from four long-term field experiments in Kenya, in which the effect of the addition of different organic resources at different rates was tested, either alone or in combination with the application of mineral nitrogen fertilizer, in the context of ISFM. The sites are located in agriculturally important areas in central and western Kenya Supplement Fig. The experiments at Embu and Machanga began in early , while those at Aludeka and Sidada began in early The sites cover a range of altitudes, temperatures, and precipitations. There are two rainy seasons at each site, corresponding to two maize growing seasons per year. The long rainy season occurs from March to August or September, while the short rainy season occurs from October until January or February. All experiments were set up as a split-plot design with three replicates, with different qualities and quantities of organic resources as main plots and the presence or absence of mineral N fertilizer as subplots. Maize was grown continuously in all experiments, with two crops per year, one in the long rainy season and one in the short rainy season. The experimental design was identical at all four sites and has been described in detail in earlier publications Chivenge et al. Organic-resource treatments consisted of high-quality Tithonia diversifolia TD green manure, high-quality Calliandra calothyrsus CC prunings, low-quality stover of Zea mays MS , low-quality sawdust from Grevillea robusta trees SD , locally available farmyard manure FYM , and a control treatment CT without organic-resource additions. Organic resources differed in quality by the contents of N, lignin, and polyphenols Supplement Table S2. Each organic resource was applied once a year at two rates, 1. Organic resources were applied only once a year, prior to planting in the long rainy season, i. It simulates daily C and N fluxes within the soil—plant—atmosphere continuum and has been parameterized for several crops and ecosystems Necpalova et al. It has submodules to simulate plant growth and organic-resource and soil organic matter SOM decomposition, including mineralization of N, soil water and temperature, N gas fluxes, and CH 4 oxidation. The net primary productivity NPP of plants is a function of their genetic potential, a simplified phenology, solar radiation, temperature, and stresses such as reduced water or N availability. Here, we used the non-growing degree day version of the DayCent crop module that does not simulate phenology but has a seedling stage with reduced growth until a certain biomass full canopy is reached. SOC and soil N in the topsoil are represented by an active, slow, and passive SOM pool, while litter and organic resources are represented by a structural- and metabolic-litter pool Parton et al. All SOM pools are conceptual and have no measurable counterparts, whereas the litter pools are semi-quantitative. Their division is based on the measurable ratio of lignin to N in the organic resources and plant litter. To provide an overall assessment of the performance of DayCent for its use in Kenya, a leave-one-site-out cross-validation approach was applied to evaluate the model performance. Specifically, this involved using a data subset from three of the four sites for model calibration, with evaluation performed using the data from the fourth site. This process was repeated four times, every time with another site serving as the evaluation site. Different data were used for this: maize grain yield and the aboveground biomass, both on a dry matter basis, were available for each cropping season between and further details in Laub et al. At Embu and Machanga, soil samples were taken every 2 to 3 years since the start of the experiment in until , while at Sidada and Aludeka, soil sampling occurred only in , , and further details in Laub et al. Data on N 2 O emissions were used in the model evaluation phase but not for model calibration, due to their scarcity and high uncertainty. The N 2 O measurements were conducted after N fertilization in weekly measurements from March to June at Embu and Machanga and daily measurements at Machanga in November , in and weekly measurements from March to the beginning of May at Sidada and Aludeka , and in weekly measurements from mid-March to mid-May at Sidada. The measurements applied the static-chamber method Hutchinson and Mosier , with two measuring frames per plot permanently installed for a whole rainy season one within, one between maize rows. The sampling chambers 0. Gas samples from within and between maize rows were combined per time point in the same syringe Arias-Navarro et al. Fluxes per surface area were determined using the linear slope of gas concentration over time Pelster et al. Simulated N 2 O emissions were evaluated against measured daily and cumulative N 2 O emissions. To determine the cumulative emissions at the plot scale, we used the trapezoid method Levy et al. Treatment-scale means and variances of the daily and cumulative N 2 O emissions were then computed in a similar way as for the other measurements. These soil moisture data were used to initially determine the optimal pedotransfer functions for soil hydraulic conductivity but not used in the model calibration. The site-specific crop management data were obtained from season- and site-specific records of field management operations. These included dates of organic-resource application, manual plowing before planting, maize planting, split application of mineral N, weeding, and harvest. Dates of pesticide applications and gap filling or maize thinning were also available, but these operations are not part of standard DayCent management and were therefore not included in modeling. Therefore, our model runs assumed no occurrence of pests or diseases and an optimal plant density at emergence, which, in practice, was ensured by manual thinning and gap filling. Recorded weather data existed for all sites, but filling in data gaps was necessary due to the unavailability and loss of recorded data. At Embu and Machanga, manual recordings of daily minimum and maximum temperature and precipitation were available from until the end of , but from until , only measured precipitation was available. At Aludeka and Sidada, manual recordings of daily minimum and maximum temperature and precipitation were available for all years from to In our specific case, the slopes were not significantly different from 1, but intercepts b were significantly different from 0. For precipitation, no bias correction was done. The data on the soil hydraulic properties needed in DayCent volumetric soil water content at field capacity, wilting point, and saturated hydraulic conductivity K S were calculated based on the soil texture measured at each site. The pedotransfer functions of Hodnett and Tomasella were used because they were specifically designed for tropical soils. Their soil hydraulic properties also showed better agreement between the measured and simulated soil moisture contents than when soil hydraulic properties of Saxton and Rawls were used. The equations can be found in Supplement Sect. It was assumed that the organic-resource inputs had the same properties across all sites i. The C content of maize grain was assumed to be This was the mean value of measured grain C content across sites standard deviation of 1. The DayCent simulations were conducted at the treatment scale using average values across all three replicate plots for soil parameters i. This aggregation was done to reduce the computation time of the simulations and because initial tests showed similar model performance as compared to applying the model to each experimental replicate individually. The site-specific standard deviation for each type of measurement was used as a measure of uncertainty in the measured data computed from the three replicates at each time point for each treatment at each site. This choice was based on the statistical models of Laub et al. The standard parameter values of the DayCent version were taken as initial model parameters, with three exceptions. First, we used the adjusted decomposition parameter values of the SOM pools from Gurung et al. The default value for this parameter is 0. Newer studies have, however, clearly shown that minimal structural litter is conserved in the long term, while metabolic litter forms SOC more efficiently Cotrufo et al. Thus, we opted for a more realistic prior value of 0. Third, for the parameters determining the minimum and maximum proportion of nitrified N lost as N 2 O, we used values that fell between the DayCent default values and recent values from Gurung et al. This choice was motivated by the fact that the DayCent default parameter values led to excessively high emissions, while the Gurung et al. Finally, we assumed that the maize growth parameters of the second highest production level C5 in DayCent represent best the production levels observed in the experiment. To identify which model parameters to include in the global sensitivity analysis see Sect. Additionally, we consulted the DayCent manual to identify and add further parameters of potential importance for the processes considered in our study i. This resulted in a selection of 66 parameters Table 1 and Supplement Table S3. Some of these parameters belong to the same category but can be individually calibrated in DayCent. Thus, we decided to have the same tillage multiplier value for all SOM and litter pools. Some parameters can have different values between the surface and soil SOM pools e. This simplified parameter sensitivity analysis and calibration with regard to surface and soil SOM pools. Finally, the parameters governing the minimum and maximum values were reformulated. Instead of calibrating them as a maximum and a minimum value, we considered the maximum value and the difference between the minimum and maximum values i. Instead of relying on spin-up simulation based on uncertain historical land use and management of the simulated sites, we used measured mineral-associated organic carbon MAOC fractions as a proxy for the initialization of the passive SOM pool Zimmermann et al. Replacing SOM initialization assumptions with measured proxies can enhance model performance Laub et al. It was derived by density fractionation using a sodium polytungstate solution 1. Additionally, the fractionation data were from , when the experiments were already 19 and 16 years old. To address these issues, two new parameters were introduced in the simulations: 1 an intercept IC MAOC to account for the passive SOM pool being smaller than the MAOC fraction and 2 a slope for the time since the start of the experiment SL t to account for SOM changes mostly losses since the start of the experiments, with the passive SOM pool typically changing at the slowest rate. Given that all sites were converted to agriculture only a few decades ago Laub et al. The uncertainty related to this initialization approach was accounted for in the model calibration by allowing large ranges for these parameters. To reduce the number of optimized parameters during the calibration, we performed a parameter screening van Oijen , For this purpose, a global sensitivity analysis was conducted to quantify the relative importance of different model parameters to the relevant model outputs regarding our study's focus on maize yield and the greenhouse gas mitigation potential of ISFM. The aim was to identify and fix less influential model parameters to their initial values, reducing the computational cost for performing the consecutive Bayesian model calibration see Sect. The global sensitivity analysis was performed using the Sobol method Saltelli , a , b , which allows for the estimation of the proportion of variance in the model outputs that is explained by each model parameter while considering the first-order and higher-order interaction terms Gurung et al. This function implements a simultaneous Monte Carlo estimation of first-order and total-effect Sobol indices. The preselected model parameters to include are described above and in Table 1 and Supplement Table S3. The ranges used for the global sensitivity analysis were centered around the initial parameter value obtained as described above Sect. The upper and lower parameter boundaries were based on previous sensitivity analyses e. The parameters were then grouped according to the magnitude of their ranges. For parameters with large and very large ranges, the upper boundaries were the initial parameter values multiplied by 3 and 10, respectively, and the lower boundaries were the initial parameter values divided by 3 and 10, respectively. The parameter sensitivity was independently determined for the mean maize grain yield and aboveground biomass, averaged over all seasons at all sites, as well as for the SOC and soil N stocks at the end of the simulation period equations presented in the Supplement Sect. Table 1 DayCent model parameters and the coefficient of variation used in the calibration. The presented calibrated parameter values correspond to the single parameter set with the highest likelihood, which was derived using the data from all four sites combined. The posterior was also derived using the data from all four sites combined. In practice it is derived for a given set of parameters sampled from the prior, by running and evaluating the model using the measured data, the simulated counterpart, and the variance—covariance matrix of the model residuals. Following Gurung et al. The likelihood was a function of the following form:. By using the inverse of the standard deviation of each type of measurement as weight of the zero-intercept model, it is possible to include different types of measurements into the same likelihood function. The logLik function is then used to extract the log likelihood, which is transformed to the likelihood by raising e to the power of the log likelihood. The sampling importance resampling method, which was used in this study, is a direct form of Bayesian calibration, which has recently been used by Gurung et al. Combined Bayesian calibration of the sensitive DayCent parameters was performed using all available data on maize grain yield, harvest index calculated from aboveground biomass , and SOC stocks. A notable exception was that SOC stocks from the Machanga site were not used in the calibration process because this site was severely affected by soil erosion Laub et al. The main reason for only using grain yield, the harvest index, and SOC data was that the yields, SOC stocks, and their trade-offs were the focus of this study. Technical constraints also influenced the decision; the creation and readout of daily simulation outputs to match simulated and measured soil moisture content, mineral nitrogen content, and N 2 O fluxes would slow down the whole Bayesian calibration process by a factor of 5. The Bayesian calibration would have taken more than 3 months on the virtual machine with 64 cores. Additionally, the new parameters that were associated with the initialization, IC MAOC and SL t , had to be calibrated, resulting in a total of 13 parameters for calibration Table 1. It was assured that this number of simulations was sufficient by splitting the simulations into two halves and visually assessing the similarity of derived posteriors for these subsets. In our experience, the sampling importance resampling algorithm is highly suitable for DayCent, which is prone to crashing with inappropriate parameter combinations. Unlike chain-dependent methods such as Markov chain Monte Carlo, this method relies on model runs that are independent of each other, ensuring that an erroneous run does not stop the algorithm. In addition, this method allows for an efficient cross-validation of the posterior parameter set, such as the leave-one-site-out cross-validation employed in this study. Notably, the sampling importance resampling algorithm's advantage lies in its ability to store model results for each parameter set by site, allowing for straightforward cross-validation by site, without the need for rerunning the model for each iteration. The posterior parameter distributions of this study are displayed for both the leave-one-site-out cross-validation and the combined dataset from all four sites Fig. The former shows the importance of individual sites in the calibration process, while the latter provides the most representative posterior distribution for model upscaling, making efficient use of all available data. To ensure computational efficiency, we used informed Gaussian priors that were centered around the standard parameter values of DayCent, with different coefficients of variation based on different observed ranges in previous studies. To make optimal use of existing knowledge about the parameters, the selected coefficients of variation per range were initially based on previous studies that had performed Bayesian calibration of the DayCent model. The coefficients of variation were chosen in a way that the prior from our study covered the whole range of the posterior from previous studies and then was multiplied by a factor of 1. The studies of Gurung et al. The study of Yang et al. For himax and prdx 1 , we looked into the default parameters of annual crops in DayCent to assure that the whole range of values 0. The final coefficients of variation were 0. For the newly introduced parameters, we used large coefficients of variation, namely 0. Additionally, all parameters were constrained to remain within their physically sensible limits i. We used the following standard model evaluation statistics Loague and Green , :. We expressed them as a percentage of the MSE y :. This calculation was based changes in the SOC content and cumulative emissions of N 2 O using a year time horizon of global warming potentials Necpalova et al. The CH 4 oxidation capacity was not considered because it usually makes a very limited contribution to GHG balance in rainfed cropping systems Lee et al. The fact that the Sobol first-order and total sensitivity indexes were similar for most parameters suggested only a limited number of interactions between the parameters identified by the global sensitivity analysis. Figure 1 Results of the global sensitivity analysis of the most relevant DayCent model parameters. Parameter sensitivity was independently determined for the mean maize aboveground biomass, grain yield, and SOC and soil N stocks at the end of the simulation period. Figure 2 Prior compared to the posterior model parameter distribution resulting from the Bayesian model calibration of DayCent using a data from all sites combined top and b the leave-one-site-out cross-validation bottom. The uncertainty ranges of the priors were based on the range of parameter values found in the literature and increased by a factor of 1. Dashed vertical lines represent the values of the initially selected parameter set. The posterior distributions are based on all four study sites combined. For the description of the parameters, see Table 1. Following the global sensitivity analysis, 13 selected model parameters were calibrated using Gaussian priors which were centered around the initial parameter value, with standard deviations according to the uncertainty ranges Table 1. It should be noted that the presented calibrated parameter values in Table 1 correspond to the single best parameter set for all four sites combined i. The posterior parameter sets of the leave-one-site-out cross-validations were largely in agreement with each other and with the posterior parameter sets calibrated with data from all four sites. The parameter that changed most strongly in the parameter sets calibrated with data from all four sites was the scaling factor for potential evapotranspiration fwloss 4 ; from 0. The turnover rates increased for both the slow SOM pool dec5 2 ; from 0. The maximum harvest index slightly increased himax; from 0. Finally, the optimum temperature for maize growth decreased ppdf 1 ; from 30 to Overall, the parameter correlations in the posterior parameter set across the four sites were low for soil-carbon-related parameters around 0. Figure 3 Simulated compared to measured maize grain yields at the four study sites for a the initial DayCent parameter set versus b the calibrated parameter set by leave-one-site-out cross-validation. The data points correspond to the observations from the experimental treatments over 32 to 38 seasons, depending on the site. Symbols represent the different organic-resource and chemical-nitrogen-fertilizer treatments. Model statistics across all sites are the following. Before calibration — EF: 0. Figure 4 Simulated compared to measured maize aboveground biomass AGB at the four study sites for a the initial DayCent parameter set versus b the calibrated parameter set by leave-one-site-out cross-validation. While the overall variation in maize grain yields across sites and treatments could be captured to some extent with the initial model parameter set, a negative model efficiency was obtained for two sites Fig. With the leave-one-site-out cross-validation approach, the model efficiency for maize grain yields at the left-out site improved ubiquitously i. The same was true for the simulation of aboveground biomass e. Overall, biases in simulated grain yields were mostly eliminated through the model calibration, and biases in simulated aboveground biomass were eliminated at Sidada and reduced at Embu but increased at Machanga. Figure 5 Bar plots of mean simulated and mean measured a maize grain yield and b aboveground biomass AGB from cross-validation. Error bars represent standard deviation. While DayCent could not capture the full season-to-season variability in grain yields and aboveground biomass, the mean yields and aboveground biomass throughout the simulation period were simulated well for most treatments without the addition of mineral nitrogen Supplement Fig. Nonetheless, DayCent was able to acceptably simulate the variability in grain yields across sites by organic-resource and mineral-nitrogen-fertilizer treatment model efficiencies between 0. Interestingly, DayCent poorly distinguished the mean yields and aboveground biomass of treatments with high compared to very high rates of N inputs i. An additional test of the model sensitivity of mean yields to different levels of mineral nitrogen fertilizer in the control provided further insights into this Supplement Fig. Similar to the simulation of maize grain yields, the simulations of changes in SOC stocks following the application of organic resources at different rates 1. The improvement was even stronger when compared to DayCent simulations with the default CUE value for the structural pool these had a negative model efficiency at all four sites; Supplement Fig. Also across sites, the model efficiency computed without Machanga improved considerably from 0. DayCent performed well in simulating the variability in the changes in SOC stocks across sites, evaluated by organic-resource and mineral-nitrogen-fertilizer treatment also computed without Machanga. The other treatments still had positive model efficiencies 0. Figure 6 Simulated compared to measured changes in SOC stocks since the start of the experiment at the four study sites for a the initial DayCent parameter set versus b the calibrated parameter set by leave-one-site-out cross-validation. Model statistics across all sites except Machanga from which SOC data was excluded in the calibration process due to strong erosion. Figure 7 Measured dots versus simulated SOC stocks over time at the four study sites for the different organic-resource and chemical-nitrogen-fertilizer treatments. Note that due to intense soil erosion, data from Machanga were not used in the calibration process. Figure 8 Simulated compared to measured N 2 O emissions at the four study sites for the different organic-resource and chemical-nitrogen-fertilizer treatments, based on the calibrated parameter set using leave-one-site-out cross-validation. Displayed are the measured versus modeled values per treatment for a the days where measurements were conducted and b the mean of cumulative flux measurements per season using the trapezoid method. The data points a correspond to the daily measurements from the experimental treatments over one to two seasons, depending on the site. Note that the credibility intervals are only informed by yield, SOC, and harvest index data and therefore do not represent the full uncertainty in N 2 O emissions. The negative model efficiencies and the absence of correlation between observed and simulated daily N 2 O values indicated that model performance for daily N 2 O emissions was poor Fig. While treatments with higher N loads had both higher simulated and measured N 2 O fluxes compared to those with lower loads, the peaks of N 2 O emissions were often simulated on different dates than the measurements. Conversely, the simulated cumulative N 2 O emissions per season were in a better agreement with the measured values. All sites, except Machanga, showed positive model efficiencies highest at Embu, 0. Additionally, the correlation between simulated and measured N 2 O emissions was notably higher for the cumulative emission fluxes than for daily fluxes R 2 of 0. Furthermore, despite some bias at Aludeka and Sidada, most of the error in seasonal N 2 O emissions was not systematic i. Figure 9 Cumulative simulated greenhouse gas GHG balance of N 2 O emissions and CO 2 emissions due to the loss of SOC at the four study sites for different organic-resource and chemical-nitrogen-fertilizer treatments combined throughout the simulated period 16 years for Aludeka and Sidada, 19 years for Embu and Machanga. Displayed are the a GHG balance per area of land and year, b difference in GHG balance per area of land and year to a no-input treatment, and c yield-scaled GHG balance. Yet, the magnitude of emissions, as well as the relative contributions of N 2 O and CO 2 , differed strongly between sites and treatments. The relative contribution of N 2 O also differed strongly by site. Finally, there were site- and treatment-specific differences in the yield-scaled GHG balance. In contrast, the farmyard manure, Calliandra , and Tithonia treatments at inputs of 1. As shown by the leave-one-site-out cross-validation Figs. The model evaluation statistics from this calibration were comparable to those reported in recent publications that also combined the predictions of crop yield and SOC Necpalova et al. However, while these studies generally showed a better simulation of crop yield than SOC, our study diverged. We found that while better yield simulations compared to SOC simulations were evident at the Aludeka and Machanga sites with soils of low clay content, the results were different at the Embu and Sidada sites with clay-rich soils. Here, SOC stock changes were more accurately simulated than maize grain yield. This, together with the fact that the simulation of aboveground biomass worsened at two sites as a result of the calibration Fig. In that regard, the discrepancy between the sites with clay-rich and clay-poor soils could indicate that DayCent insufficiently includes soil textures effects on nutrient availability and SOC formation. Yet, drawing definitive conclusions from just four sites is probably not warranted. In the absence of data from more sites, it is preferable to apply the full range of possible parameter sets that are supported by the available data Mathers et al. Because our calibration shows a good model fit with observed mean yields and changes in SOC stocks across sites, with no overall major bias positive EF and errors mostly consisting of LC , the parameter set, especially the full posterior, appears suitable for upscaling of model simulations. Specifically, the yields of the ISFM treatments applying farmyard manure, Calliandra , and Tithonia were simulated well, both with and without the addition of mineral nitrogen fertilizer Supplement Fig. However, one should keep in mind that the season-to-season yield variability is captured less accurately than the mean yields lower RMSE and that changes in SOC are better represented at sites with clay-rich soils than those with clay-poor soils. Because the model calibration and evaluation were performed at sites with diverse characteristics, it is reasonable to assume that DayCent, when applied to sites with similar climate and soil conditions, will provide satisfactory results with similar model uncertainties and errors. In that respect, while the leave-one-site-out cross-validation made efficient use of data for model evaluation, further model upscaling should apply the full posterior model parameter set including all sites Fig. In that case, a computationally inexpensive exercise would use only the single best parameter set Table 1 , while the full posterior parameter set should be used to get estimates of the posterior credibility intervals for changes in SOC stocks. To estimate the potential yield and long-term sustainability of cropping systems without major bias using biogeochemical models, region-specific model calibrations are needed Rattalino Edreira et al. Therefore, while previous studies have simulated crop productivity under ISFM and similar practices with the default parameter values e. On the one hand, the similar ranges of the prior and posterior model parameter sets for SOC-decomposition-related parameters i. This indicates the difficulty in stabilizing the organic-resource additions into SOM at the tropical soils of these four long-term experiments. However, because these values had reached their predefined upper boundary limit in the study by Mathers et al. In general, including prior knowledge about model parameter values from similar studies substantially improves model performance compared to using default parameter values e. In fact, the aligning turnover rates of the slow and passive SOM pools with those derived for temperate conditions Gurung et al. It is important to note that our sites were under natural vegetation i. Consequently, upon the start of cultivation, erosion and potentially accelerated decomposition due to soil disturbance occurred, and SOC had likely not yet reached a new equilibrium with C inputs from maize cultivation. Therefore, C loss is the dominant process occurring at the sites. Such conceptual pools require many assumptions about the initial vegetation and soil conditions e. In fact, the high uncertainty about initial vegetation, as well as time and management since site conversion, was a major reason to move away from the model spin-up and site history run usually typically done with DayCent. Nevertheless, soil property maps, which would be needed to initialize measurable SOM pools at scale, are also subject to uncertainty. For example, differences between different SOC maps used in model initialization propagate into differences in the changes in SOC stocks Zhou et al. It was shown that uncertainty in the simulated effect of a soil management practice on the difference in SOC stocks compared to a counterfactual is lower than the uncertainty in the simulated temporal development of SOC stocks Zhou et al. Therefore, it may be best practice to work with a baseline and an improved scenario. Both spin-up and SOC map initialization have their shortcomings, and in the end the model user must make an informed decision on which initialization method they consider subject to less uncertainty, based on which data are locally available. The similarity of our DayCent model calibration with that of Gurung et al. It suggests that the SOM turnover and maize traits in DayCent are representative of temperate to tropical conditions. The adjustments made to the values of optimal and maximum temperature for maize growth ppdf 1 and ppdf 2 could be attributed to the local maize varieties that are adapted to the higher temperatures in Kenya. For example, Yang et al. However, the differences in model performance by site shows that the broad representativeness of DayCent comes at the cost of model simplification and site-specific model performance. A main reason for this may be that DayCent model formalisms do not include the latest mechanistic understandings of the role of microbes in SOM decomposition Laub et al. Additionally, DayCent does not fully consider that a lot of stabilized SOC is formed by microbes from metabolic and not structural litter Cotrufo et al. While model calibration can compensate for deficiencies in mechanistic accuracy at a single site Laub et al. An interesting observation is that while the model bias for the mean maize yield was treatment specific i. A potential explanation for this site-specific bias for SOC is the fact that DayCent was developed under the paradigm of SOM formation occurring mainly from recalcitrant humic compounds in the soil. Alternatively, it might indicate that soil texture alone is insufficient to explain the mineralogy-driven storage potential of SOC e. Here, it should be noted that DayCent does not include other potential beneficial effects of organic-resource treatments, such as increased pH from farmyard manure application Xiao et al. In general, the poor match between observed and measured daily N 2 O emissions Supplement Fig. However, the fact that cumulative N 2 O emissions were better simulated than daily emissions, that there was no systematic under- or overprediction of cumulative N 2 O emissions, and that simulated N 2 O emissions were within the uncertainty range of measured N 2 O emissions demonstrates the suitability of DayCent to represent average N 2 O emissions with the current calibration. Nonetheless, the fact that the uncertainty around predicted cumulative N 2 O emissions was lower than the uncertainties in the measurements indicates that the posterior, which was only calibrated with yield, SOC, and harvest index data, underestimates the uncertainty around N 2 O emission predictions. Thus, although DayCent's simulations of N 2 O emissions are superior to using emission factor approaches dos Reis Martins et al. The findings also support the postulate that closing yield gaps in SSA will increase N 2 O emissions per area of land Leitner et al. Consequently, sustainable intensification and mitigation of greenhouse gases can go hand in hand. Because mean maize yields across sites were reasonably well represented by the calibrated version of DayCent, it can be used for upscaling to predict the potential impact of ISFM in lowering yield gaps at national levels. However, the plateauing of mean yields at high nitrogen loads Supplement Fig. S5 indicates that DayCent may not be suitable for estimating maximum achievable yields e. At all sites, the prediction of mean maize yields was reasonably well for Calliandra , Tithonia , and farmyard manure treatments at 1. S7 and S8. While this shows the general capability of DayCent to simulate differences in yields and SOC changes between sites as a function of organic-resource composition, it also shows that DayCent cannot capture the better performance of farmyard manure compared to Calliandra and Tithonia treatments when only considering C, N, and lignin contents. Overall, simulated mean maize yields at medium nitrogen levels are likely representative of the achievable yield through ISFM. In summary, the model calibration seems suitable for assessing the long-term effects of relevant ISFM practices on soil fertility, maize yield, and GHG emissions as well as their trade-offs, given the good representation of mean yield potential and SOC changes by the model. Nevertheless, since year-to-year yield variations were not captured well by DayCent, it remains uncertain how effectively the current model calibration can simulate scenarios of climate change, where temperature and precipitation patterns will become more erratic. In the absence of major pests which in the experiments were controlled , the variations in seasonal precipitation and temperature are responsible for these differences, and if these are not well represented, the applicability of DayCent beyond the climatic range that it was calibrated for is questionable. Using a Bayesian calibration approach, our study shows the importance of using a local calibration and of choosing correct prior values for model parameters. Although the initial DayCent parameterization represented the tropical conditions in Kenya acceptably, the overall model performance for maize grain yield, aboveground biomass, and SOC stock changes was improved after calibration using local data. Furthermore, while parameters related to SOM turnover were comparable to previous studies, a lower carbon use efficiency of applied organic resources higher values of CO 2 -loss-related parameters compared to previous studies highlighted the difficulty in building new SOC stocks in the studied tropical soils. Our leave-one-site-out cross-validation showed that the calibration-derived parameter set is robust for upscaling the model simulations to larger areas in Kenya, particularly when applying the full posterior parameter set. At the same time, while mean maize grain yields were well simulated, the year-to-year yield variability raised concerns about the model's ability to capture the short-term effects of climate change adequately. Finally, while no ISFM treatment was predicted to act as a net sink of greenhouse gases, treatments with high and intermediate yields exhibited the lowest yield-scaled emissions. To get the latest version of DayCent, we suggest contacting the developers directly, who in our case kindly provided the latest DayCent version. ML summarized the data, conducted the modeling exercise, and prepared the original draft. All co-authors contributed to the writing and editing of the final submitted article. The contact author has declared that none of the authors has any competing interests. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. We want to thank Silas Kiragu, who is responsible for maintaining the experiments at the Embu and Machanga sites, and John Mukalama, who implemented and maintained the experiments at Aludeka and Sidada. Also, we want to thank John Waruingi for helping with sample processing over the years, Moses Thuita for coordinating the experiments for many years, and Britta Jahn-Humphrey for organizing and overseeing the measurement of most SOC and soil N instances in recent years. The AI language model Writefull for Overleaf was used to improve the grammar of an earlier version of this paper. This paper was edited by Lutz Merbold and reviewed by Kathrin Fuchs and two anonymous referees. Abramoff, R. Ahrens, B. Arias-Navarro, C. Barthel, M. Bates, D. Chivenge, P. Clark, M. Clifford, D. Corbeels, M. Cotrufo, M. Change Biol. Dangal, S. Earth Sy. Del Grosso, S. Denef, K. Frimmel, F. Gauch, H. Gentile, R. Gurung, R. Total Environ. Hartman, M. Hodnett, M. Hutchinson, G. Iooss, B. Ittersum, M. Kallenbach, C. Kamoni, P. Model Dev. Lee, J. Leitner, S. Lemma, B. Levavasseur, F. Levy, P. Soil Sci. Loague, K. Lobell, D. Mainka, M. Mathers, C. Mtangadura, T. Mueller, T. Mutuku, E. Necpalova, M. Nezomba, H. Nyawira, S. Parton, W. Pelster, D. Puttaso, A. Rattalino Edreira, J. Reichenbach, M. Saito, K. Saltelli, A. Saxton, K. Sommer, R. Stella, T. Tuszynski, J. Water Res. Vanlauwe, B. Wang, Q. Wang, Y. Wendt, J. Xiao, Q. Yang, Y. Zhai, R. Zhang, Y. Forest Meteorol. Zhou, W. Zimmermann, M. Articles Volume 21, issue Article Assets Peer review Metrics Related articles. This work is distributed under the Creative Commons Attribution 4. Research article. Research article 22 Aug Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model Modeling integrated soil fertility management for maize production in Kenya using a Bayesian Moritz Laub et al. Magdalena Necpalova. Marc Corbeels. Box , , Nairobi, Kenya. Samuel Mathu Ndungu. Monicah Wanjiku Mucheru-Muna. Daniel Mugendi. Box 6, , Embu, Kenya. Wycliffe Waswa. Bernard Vanlauwe. Supplement KB. How to cite. Code availability. Data availability. Author contributions. Competing interests. Financial support. Review statement. Short summary. We used the DayCent model to assess the potential impact of integrated soil fertility management ISFM on maize production, soil fertility, and greenhouse gas emission in Kenya. After adjustments, DayCent represented measured mean yields and soil carbon stock changes well and N 2 O emissions acceptably. Our results showed that soil fertility losses could be reduced but not completely eliminated with ISFM and that, while N 2 O emissions increased with ISFM, emissions per kilogram yield decreased. We used the DayCent model to assess the potential impact of integrated soil fertility management Read more. Final-revised paper. Turn MathJax on Sections.

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