Simulating impacts of nitrogen fertilization using DAYCENT to optimize economic returns and environmental services from bioenergy sorghum production

Wang, Yong; Dou, Fugen; Grosso, Stephen J. Del; Storlien, Joseph O.; Wight, Jason P.; Hons, Frank M.

doi:10.1002/agj2.20390

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…A10) illustrates the difficulty of simulating the timing of microbial processes, in which nitrate (NO 3 -) is converted to N 2 and N 2 O gasses, with intermediate complexity models such as DayCent. Several recent studies show that poor simulation of N 2 O emissions by process models is common (Zhang and Yu, 2021;Wang et al, 2020), which is attributed, among other factors, to a poor representation of soil hydraulics.…”

Section: N 2 O Emissions and Global Warming Potentialmentioning

confidence: 99%

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

Laub,

Necpalova,

Van de Broek

et al. 2023

Preprint

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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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Section: N 2 O Emissions and Global Warming Potentialmentioning

confidence: 99%

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

Laub,

Necpalova,

Van de Broek

et al. 2023

Preprint

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Carbon savings from sugarcane straw-derived bioenergy: Insights from a life cycle perspective including soil carbon changes

Bordonal,

Tenelli,

da Silva Oliveira

et al. 2024

Science of The Total Environment

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Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model

Laub,

Necpalova,

Van de Broek

et al. 2024

Biogeosciences

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Abstract. 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 CO2 and N2O 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 N2O emissions (with EF ranging between −0.44 and −0.03 by site), cumulative seasonal N2O 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 CO2 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.

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Simulating impacts of nitrogen fertilization using DAYCENT to optimize economic returns and environmental services from bioenergy sorghum production

Cited by 3 publications

References 69 publications

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

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

Carbon savings from sugarcane straw-derived bioenergy: Insights from a life cycle perspective including soil carbon changes

Modeling integrated soil fertility management for maize production in Kenya using a Bayesian calibration of the DayCent model

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