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There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Linear variable differential transformer (LVDT) sensors were used to detect continuous diameter growth responses ofPinus pinea(stone pine) seedlings inoculated with the ectomycorrhizal fungusRhizopogon roseolus. Colonised and non-colonised seedlings provided with sensors were submitted to different water regimes in two consecutive experiments established in a controlled-temperature greenhouse module (cycle 1), and in an adjacent module without temperature control (cycle 2). Under regular irrigation, colonised seedlings showed significantly higher growth than non-colonised seedlings. Water-stressed seedlings showed no benefit from inoculation in terms of growth. Also, seedlings with a high colonisation level recovered more slowly from water stress than control seedlings. A significant positive relationship between maximum daily shrinkage (amplitude of the daily stem contraction) and global radiation was observed only in the first water-stress period in cycle 1 and in regularly irrigated seedlings in both cycles. However, no differential responses due to inoculation were observed. The mycorrhizal colonisation of the seedlings at the end of the experiment was related with the initial colonisation level. Mycorrhizal colonisation byR. roseolusin old roots was maintained at significantly higher levels in seedlings which had an initial colonisation level >50% than in seedlings with <50% initial colonisation. Also, more newly formed roots became colonised in seedlings which had an initial colonisation level >50% than in seedlings with an initial colonisation <50%, which had almost no new root colonisation. From the results obtained, it can be concluded that LVDT sensors can be used to detect a differential response of plants according to water supply, mycorrhizal status and, in some cases, to their colonisation level. The results are discussed in relation to the predictive possibilities of the method for the selection of efficient mycorrhizal fungi for the promotion of plant growth.

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

This corrects the article DOI: 10.1038/nplants.2017.102

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.