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Dibari, C., Argenti, G., Catolfi, F., Moriondo, M., Staglianò, N., & Bindi, M. (2014). Climate change impacts on natural pasturelands of Italian Apennines. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: As well as the entire Mediterranean area, the Italian Apennines have been affected by increasing temperatures, rainfall extreme events and decreases in annual precipitation due to climate change. Moreover, permanent grasslands, species-diverse ecosystems characterizing the marginal areas of the Apennines landscape, are acknowledged as very sensitive and vulnerable to climate variation. Building on these premises, statistical classification models coupled with data integration by GIS techniques, were used to territorially assess future climate change impacts on pastoral communities on the Italian Apennines chain. Specifically, a machine learning approach (Random Forest – RF), firstly calibrated for the present period and then applied to future conditions, as projected by HadCM3 General Circulation Model (GCM), was used to simulate potential expansion/reduction and/or altitudinal shifts of the Apennine pasturelands in two time slices, centred on 2050 and 2080, under A2 and B2 SRES scenarios. RF classification model proved to be robust and very efficient to predict lands suited to pastures with regards to present period (classification error: 12%). Furthermore, according to RF simulations, relevant reductions (46 and 34%) of areas potentially suitable for pastoral resource are expected under A2 at the middle and end of the century, respectively, as depicted by the GCM and SRES scenarios. Moreover, progressive upwards shifts are predicted by the model under both SRES scenarios. These reductions will likely interest the central area of the chain threatening the typical and unique herbaceous biodiversity characterizing the Apennine pasturelands.
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Brilli, L., Ferrise, R., Dibari, C., Bindi, M., & Bellocchi, G. (2017). Needs on model improvement (Vol. 10).
Abstract: The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.
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Bellocchi, G., B., Brilli, L., Ferrise, R., Dibari, C., & Bindi, M. (2017). Model comparison and improvement: Links established with other consortia (Vol. 10).
Abstract: XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org ) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org ). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls.
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Tao, F., Palosuo, T., Roetter, R. P., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2020). Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agricultural and Forest Meteorology, 281, 107851.
Abstract: Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.
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Yin, X., Kersebaum, K. - C., Beaudoin, N., Constantin, J., Chen, F., Louarn, G., et al. (2020). Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models. Field Crops Research, , 107863.
Abstract: Modelling N transformations within cropping systems is crucial for N management optimization in order to increase N use efficiency and reduce N losses. Such modelling remains challenging because of the complexity of N cycling in soil–plant systems. In the current study, the uncertainties of six widely used process-based models (PBMs), including APSIM, CROPSYST, DAISY, FASSET, HERMES and STICS, were tested in simulating different N managements (catch crops (CC) and different N fertilizer rates) in 12-year rotations in Western Europe. Winter wheat, sugar beet and pea were the main crops, and radish was the main CC in the tested systems. Our results showed that PBMs simulated yield, aboveground biomass, N export and N uptake well with low RMSE values, except for sugar beet, which was generally less well parameterized. Moreover, PBMs provided more accurate crop simulations (i.e. N export and N uptake) compared to simulations of soil (N mineralization and soil mineral N (SMN)) and environmental variables (N leaching). The use of multi-model ensemble mean or median of four PBMs significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15% for yield, aboveground biomass, N export and N uptake. Multi-model ensemble also significantly reduced the MAPE for net N mineralization and annual N leaching to around 15%, while it was larger than 20% for SMN. Generally, PBMs well simulated the CC effects on N fluxes, i.e. increasing N mineralization and reducing N leaching in both short-term and long-term, and all PBMs correctly predicted the effects of the reduced N rate on all measured variables in the study. The uncertainties of multi-model ensemble for N mineralization, SMN and N leaching were larger, mainly because these variables are influenced by plant-soil interactions and subject to cumulative long-term effects in crop rotations, which makes them more difficult to simulate. Large differences existed between individual PBMs due to the differences in formalisms for describing N processes in soil–plant systems, the skills of modelers and the model calibration level. In addition, the model performance also depended on the simulated variables, for instance, HERMES and FASSET performed better for yield and crop biomass, APSIM, DAISY and STICS performed better for N export and N uptake, STICS provided best simulation for SMN and N leaching among the six individual PBMs in the study, but all PBMs met difficulties to well predict either average or variance of soil N mineralization. Our results showed that better calibration for soil N variables is needed to improve model predictions of N cycling in order to optimize N management in crop rotations.
Keywords: multi-model ensemble; crop rotations; catch crops; N cycling; N export
Area: CropM
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