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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 0.36 to 0.50) and of SOC stock changes (with EF increasing from 0.36 to 0.55). The simulations of maize yield and those of SOC stock changes also improved by site (with site-specific EF ranging between 0.15 and 0.38 for maize yield and between - 0.9 and 0.58 for SOC stock changes). 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. While DayCent poorly reproduced daily N 2 O emissions (with EF ranging between - 0.44 and - 0.03 by site), cumulative seasonal N 2 O emissions were simulated more accurately (EF ranging between 0.06 and 0.69 by site). The simulated yield-scaled GHG balance was highest in control treatments without N addition (between 0.8 and 1.8 kg CO 2 equivalent per kg grain yield across sites) and was about 30 % to 40 % lower in the treatment that combined the application of mineral N and of manure at a rate of 1.2 t C ha -1 yr -1. In conclusion, our results indicate that DayCent is well suited for estimating the impact of ISFM on maize yield and SOC changes. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The frequency and intensity of extreme weather years, characterized by abnormal precipitation and temperature, are increasing. In isolation, these years have disproportionately large effects on environmental N losses. However, the sequence of extreme weather years (e.g., wet-dry vs. dry-wet) may affect cumulative N losses. We calibrated and validated the DAYCENT ecosystem process model with a comprehensive set of biogeophysical measurements from a corn-soybean rotation managed at three N fertilizer inputs with and without a winter cover crop in Iowa, USA. Our objectives were to determine: (i) how 2-year sequences of extreme weather affect 2-year cumulative N losses across the crop rotation, and (ii) if N fertilizer management and the inclusion of a winter cover crop between corn and soybean mitigate the effect of extreme weather on N losses. Using historical weather (1951-2013), we created nine 2-year scenarios with all possible combinations of the driest ('dry'), wettest ('wet'), and average ('normal') weather years. We analyzed the effects of these scenarios following several consecutive years of relatively normal weather. Compared with the normal-normal 2-year weather scenario, 2-year extreme weather scenarios affected 2-year cumulative NO3− leaching (range: −93 to +290%) more than N2O emissions (range: −49 to +18%). The 2-year weather scenarios had nonadditive effects on N losses: compared with the normal-normal scenario, the dry-wet sequence decreased 2-year cumulative N2O emissions while the wet-dry sequence increased 2-year cumulative N2O emissions. Although dry weather decreased NO3− leaching and N2O emissions in isolation, 2-year cumulative N losses from the wet-dry scenario were greater than the dry-wet scenario. Cover crops reduced the effects of extreme weather on NO3− leaching but had a lesser effect on N2O emissions. As the frequency of extreme weather is expected to increase, these data suggest that the sequence of interannual weather patterns can be used to develop short-term mitigation strategies that manipulate N fertilizer and crop rotation to maximize crop N uptake while reducing environmental N losses. [ABSTRACT FROM AUTHOR]