Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N O emissions. Yield-scaled N O emissions (N O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N O emissions at field scale is discussed.
Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.
The objective of this study was to measure enteric CH4 emissions using a new portable automated open-circuit gas quantification system (GQS) and the sulfur hexafluoride tracer technique (SF6) in midlactation Holstein cows housed in a tiestall barn. Sixteen cows averaging 176 ± 34 d in milk, 40.7 ± 6.1 kg of milk yield, and 685 ± 49 kg of body weight were randomly assigned to 1 out of 2 treatments according to a crossover design. Treatments were (1) ad libitum (adjusted daily to yield 10% orts) and (2) restricted feed intake [set to restrict feed by 10% of baseline dry matter intake (DMI)]. Each experimental period lasted 22d, with 14 d for treatment adaptation and 8d for data and sample collection. A common diet was fed to the cows as a total mixed ration and contained 40.4% corn silage, 11.2% grass-legume haylage, and 48.4% concentrate on a dry matter basis. Spot 5-min measurements using the GQS were taken twice daily with a 12-h interval between sampling and sampling times advanced 2h daily to account for diurnal variation in CH4 emissions. Canisters for the SF6 method were sampled twice daily before milking with 4 local background gas canisters inside the barn analyzed for background gas concentrations. Enteric CH4 emissions were not affected by treatments and averaged 472 and 458 g/d (standard error of the mean = 18 g/d) for ad libitum and restricted intake treatments, respectively (data not shown). The GQS appears to be a reliable method because of the relatively low coefficients of variation (ranging from 14.1 to 22.4%) for CH4 emissions and a moderate relationship (coefficient of determination = 0.42) between CH4 emissions and DMI. The SF6 resulted in large coefficients of variation (ranging from 16.0 to 111%) for CH4 emissions and a poor relationship (coefficient of determination = 0.17) between CH4 emissions and DMI, likely because of limited barn ventilation and high background gas concentration. Research with improved barn ventilation systems or outdoors is warranted to further assess the GQS and SF6 methodologies.
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