Marco Moriondo

Projected shifts of wine regions in response to climate change

This research simulates the impact of climate change on the distribution of the most important European wine regions using a comprehensive suite of spatially informative layers, including bioclimatic indices and water deficit, as predictor variables. More specifically, a machine learning approach (Random Forest, RF) was first calibrated for the present period and applied to future climate conditions as simulated by HadCM3 General Circulation Model (GCM) to predict the possible spatial expansion and/or shift in potential grapevine cultivated area in 2020 and 2050 under A2 and B2 SRES scenarios. Projected changes in climate depicted by the GCM and SRES scenarios results in a progressive warming in all bioclimatic indices as well as increasing water deficit over the European domain, altering the climatic profile of each of the grapevine cultivated areas. The two main responses to these warmer and drier conditions are 1) progressive shifts of existing grapevine cultivated area to the north–northwest of their original ranges, and 2) expansion or contraction of the wine regions due to changes in within region suitability for grapevine cultivation. Wine regions with climatic conditions from the Mediterranean basin today (e.g., the Languedoc, Provence, Côtes Rhône Méridionales, etc.) were shown to potentially shift the most over time. Overall the results show the potential for a dramatic change in the landscape for winegrape production in Europe due to changes in climate.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

The meteorological conditions associated with extreme fire risk in Italy and Greece: relevance to climate model studies

The meteorological conditions associated with elevated and extreme long- and short-timescale forest fire risk are investigated by validating and diagnosing the Canadian Fire Weather Index (FWI) in the context of Tuscany in Italy, and Thessaloniki, Athens and Heraklion in Greece. The aim is to provide information to assist diagnosing experiments that use output from climate models to calculate FWI values. Links are made from fire risk to the widely used FWI, and then to the underlying meteorology, complementing other more complex fire risk model studies. First, the information about Mediterranean fire risk provided by the FWI is assessed by comparing the observed number of fires per day with FWI values based on the locally observed meteorology. This shows that the FWI provides some relatively consistent information for different locations, and suggests useful FWI thresholds indicating elevated and extreme fire risk. Then, the FWI system is split according to contributions from long- and short-timescale components, in a different way than usually adopted in the literature. Using the FWI thresholds suggested above, the long- and short-timescale meteorological conditions causing elevated and extreme FWI values are diagnosed. The results may help studies that investigate what aspects of projected climate change drive changes in fire weather risk, compare fire risk calculations from different climate models, or assess how climate models can be improved to provide better fire risk projections.

Erratum to: Agronomic adaptation strategies under climate change for winter durum wheat and tomato in southern Italy: irrigation and nitrogen fertilization

Agricultural crops are affected by climate change due to the relationship between crop development, growth, yield, CO2 atmospheric concentration and climate conditions. In particular, the further reduction in existing limited water resources combined with an increase in temperature may result in higher impacts on agricultural crops in the Mediterranean area than in other regions. In this study, the cropping system models CERES-Wheat and CROPGRO-Tomato of the Decision Support System for Agrotechnology Transfer (DSSAT) were used to analyse the response of winter durum wheat (Triticum aestivum L.) and tomato (Lycopersicon esculentum Mill.) crops to climate change, irrigation and nitrogen fertilizer managements in one of most productive areas of Italy (i.e. Capitanata, Puglia). For this analysis, three climatic datasets were used: (1) a single dataset (50 km × 50 km) provided by the JRC European centre for the period 1975–2005; two datasets from HadCM3 for the IPCC A2 GHG scenario for time slices with +2°C (centred over 2030–2060) and +5°C (centred over 2070–2099), respectively. All three datasets were used to generate synthetic climate series using a weather simulator (model LARS-WG). Adaptation strategies, such as irrigation and N fertilizer managements, have been investigated to either avoid or at least reduce the negative impacts induced by climate change impacts for both crops. Warmer temperatures were primarily shown to accelerate wheat and tomato phenology, thereby resulting in decreased total dry matter accumulation for both tomato and wheat under the +5°C future climate scenario. Under the +2°C scenario, dry matter accumulation and resulting yield were also reduced for tomato, whereas no negative yield effects were observed for winter durum wheat. In general, limiting the global mean temperature change of 2°C, the application of adaptation strategies (irrigation and nitrogen fertilization) showed a positive effect in minimizing the negative impacts of climate change on productivity of tomato cultivated in southern Italy.

Effect of agrometeorological parameters on the phenology of pollen emission and production of olive trees (Olea europea L.)

The pollination period and pollen concentrationof olive trees (Oleaeuropea L.) and olive production were analysedfor Prato and Florenceusing a data set of 8 years (1991–1998).Meteorological data have been usedto obtain information about weather conditionsduring vegetative seasonsand correlations were found both for thephenology and yield.The results showed that air temperatureprevious to the onset of floweringis of great importance in determining thereproductive cycle of olive treebut the chill period in January and Februaryshould also be considered.Olive pollen collected during thepollination period was positivelycorrelated with the production level for bothsites. Weather conditionfollowing pollination were also taken intoaccount for a better assessmentof the final yield.

Modelling olive trees and grapevines in a changing climate

The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. Empirical models are generally unreliable for their possible application in a changing climate.Complex process-based models have already the potential to provide reliable simulations for a changing climate.There is a clear need to improve the simulation of crop processes in response to increased CO2 and higher temperatures.Process-based models should be improved to simulate soil biochemical processes.

Air temperature-related human health outcomes: current impact and estimations of future risks in Central Italy.

The association between air temperature and human health is described in detail in a large amount of literature. However, scientific publications estimating how climate change will affect the populations health are much less extensive. In this study current evaluations and future predictions of the impact of temperature on human health in different geographical areas have been carried out. Non-accidental mortality and hospitalizations, and daily average air temperatures have been obtained for the 1999-2008 period for the ten main cities in Tuscany (Central Italy). High-resolution city-specific climatologic A1B scenarios centered on 2020 and 2040 have been assessed. Generalized additive and distributed lag models have been used to identify the relationships between temperature and health outcomes stratified by age: general adults (<65), elderly (aged 65-74) and very elderly (≥75). The cumulative impact (over a lag-period of 30 days) of the effects of cold and especially heat, was mainly significant for mortality in the very elderly, with a higher impact on coastal plain than inland cities: 1 °C decrease/increase in temperature below/above the threshold was associated with a 2.27% (95% CI: 0.17-4.93) and 15.97% (95% CI: 7.43-24.51) change in mortality respectively in the coastal plain cities. A slight unexpected increase in short-term cold-related mortality in the very elderly, with respect to the baseline period, is predicted for the following years in half of the cities considered. Most cities also showed an extensive predicted increase in short-term heat-related mortality and a general increase in the annual temperature-related elderly mortality rate. These findings should encourage efforts to implement adaptation actions conducive to policy-making decisions, especially for planning short- and long-term health intervention strategies and mitigation aimed at preventing and minimizing the consequences of climate change on human health.

Integration of remote sensing and ecosystem modelling techniques to estimate forest net carbon uptake

Estimates of forest gross primary production (GPP) can be obtained using a parametric model (C‐Fix) that combines ground and remotely sensed data. A methodology is presented to convert these GPP estimates into values of net ecosystem exchange (NEE). The methodology is based on the use of a process model (BIOME‐BGC) that, after proper calibration, simulates all main functions of forest ecosystems at the climax condition. The estimated photosynthesis and respirations are transformed into net carbon fluxes of actual forests by using a simplified approach that relies on the difference between actual and potential stand biomass. The methodology was applied to eight forest sites in Italy where flux measurements were available and GPP estimates had been previously produced. The comparison of the obtained NEE estimates to the ground data indicates the potential of the approach and the prospects for future investigation.

Modelling compensatory effects of defoliation on leaf area growth and biomass of sunflower (Helianthus annuus L.)

Leaf area loss is a typical damage of fungal and insect attacks or hail. In this work growth responses to defoliation were analysed by removing 50% of leaf area at different phenological stages in sunflower (Helianthus annuus L.). Plants defoliated at head-visible stage showed an increased single leaf area which partly compensated the reduction in light interception during the season, while later treatments, during pre-anthesis and complete anthesis, did not show increased single leaf area but a delayed leaf senescence. A decrease in biomass accumulation was observed particularly in pre-anthesis treated plants. Results were implemented into the crop model OILCROP-SUN 6.0 to assess the impact of compensation on growth and yield. It was concluded that leaf area recovery should be incorporated into the growth models for a reliable simulation of yield after partial defoliation.