A calibration protocol for soil-crop models

Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Nitrous oxide emissions from grain production systems across a wide range of environmental conditions in eastern Australia

Nitrous oxide (N2O) emissions from Australian grain cropping systems are highly variable due to the large variations in soil and climate conditions and management practices under which crops are grown. Agricultural soils contribute 55% of national N2O emissions, and therefore mitigation of these emissions is important. In the present study, we explored N2O emissions, yield and emissions intensity in a range of management practices in grain crops across eastern Australia with the Agricultural Production Systems sIMulator (APSIM). The model was initially evaluated against experiments conducted at six field sites across major grain-growing regions in eastern Australia. Measured yields for all crops used in the experiments (wheat, barley, sorghum, maize, cotton, canola and chickpea) and seasonal N2O emissions were satisfactorily predicted with R2=0.93 and R2=0.91 respectively. As expected, N2O emissions and emissions intensity increased with increasing nitrogen (N) fertiliser input, whereas crop yields increased until a yield plateau was reached at a site- and crop-specific N rate. The mitigation potential of splitting N fertiliser application depended on the climate conditions and was found to be relevant only in the southern grain-growing region, where most rainfall occurs during the cropping season. Growing grain legumes in rotation with cereal crops has great potential to reduce mineral N fertiliser requirements and so N2O emissions. In general, N management strategies that maximise yields and increase N use efficiency showed the greatest promise for N2O mitigation.</jats:p

Systems of innovation, knowledge and networks: Latin America and its capability to capture benefits

23% of global agricultural land are situated in the subtropics. Nitrous oxide (N₂O) emissions were estimated to be higher under subtropical than under temperate climates. So mitigation of N₂O emissions from subtropical farming systems can make an important contribution to reducing global warming. Accordingly, in this study we explored long-term N₂O emissions and possible mitigation options for representative subtropical cropping systems (e.g., summer versus winter crops, inclusion of a legume in the rotation) and management practices (nitrogen fertilizer, irrigation) by calculating scenarios with the agricultural systems model APSIM. The model was tested against high temporal frequency data from experiments conducted on an oxisol and a vertisol in subtropical Australia, which comprised a number of fertilization and irrigation treatments. The threshold of water filled pore space above which denitrification starts was calibrated on a subset of the data while the rest of the large number of parameters controlling the carbon and nitrogen cycles were kept to default values. The validity of the model was confirmed with 11 validation data sets for yields of four different crops (<em>R</em>²=0.92) and 16 validation data sets for seasonal N₂O emissions during crop and fallow periods (<em>R</em>²=0.77). While these results show that the model performs well in sub-tropical environments, this modeling skill might not translate to other environments and the model would benefit from wider testing. In the scenario analyses, long-term average N₂O emissions from wheat, cotton, maize and sorghum were predicted to vary between 0.2 and 6.1;ha^-1;yr^-1 and showed large interannual variability of N₂O emissions. This highlights the risk that results from short-term experiments may not be representative for the long-term behavior of these agro-ecosystems, and thus the value simulation studies add to experiments. The scenario analysis revealed that long-term average yields and N₂O emissions increased in response to the same management practices (e.g., increase in nitrogen rate), leading to a trade-off between maximizing yield and minimizing N₂O emissions. When crop yields were limited due to water stress either by low seasonal rainfall or by lack of irrigation, average N₂O emissions increased. Given the annual variability in climate and soil nitrogen stocks, mitigating N₂O emissions without compromizing in yield is not a simple task but requires an optimal nitrogen management considering other limiting factors such as water supply

A calibration protocol for soil-crop models

International audienceProcess-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations

Proposal and extensive test of a calibration protocol for crop phenology models

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%