Roberto Ferrise

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.

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.

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.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley ( Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Energy and Water Use Related to the Cultivation of Energy Crops: a Case Study in the Tuscany Region

The contribution of agrobiomasses, as a source of energy, to the reduction of greenhouse gas emissions was confirmed by several studies. Biomass from agriculture represents one of the larger and more diverse sources to exploit and in particular ethanol and diesel have the potential to be a sustainable replacement for fossil fuels, mainly for transport purposes. However, the cultivation of energy crops dedicated to the production of biofuels presents some potential problems, e.g., competitiveness with food crops, water needs, use of fertilizers, etc., and the economic, energy, and environmental convenience of such activity depends on accurate evaluations about the global efficiency of the production system. In this study, the processes related to the cultivation of energy crops were analyzed from an energy and water cost perspective. The crops studied, maize (Zea mais) and sunflower (Helianthus annuus), were identified for their different water requirements and cultivation management, which in turns induces different energy costs. A 50-year climatic series of meteorological data from 19 weather stations scattered in the Tuscany region was used to feed the crop model CropSyst for the simulation of crop production, water requirement, and cultivation techniques. Obtained results were analyzed to define the real costs of energy crop cultivation, depending on energy and water balances. In the energy crop cultivation, the only positive energy balance was obtained with the more efficient system of irrigation whereas all the other cases provided negative balances. Concerning water, the results demonstrated that more than 1.000 liters of water are required for producing 1 liter of bioethanol. As a consequence, the cultivation of energy crops in the reserved areas of the region will almost double the actual water requirement of the agricultural sector in Tuscany.

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

Climate Change Impacts on Typical Mediterranean Crops and Evaluation of Adaptation Strategies to Cope With

Climatic change is expected to have important impact on different economic sectors (e.g. agriculture, forestry, energy consumptions, tourism, etc.). Among human activities, agricultural sector is likely to be particularly exposed to climate change hazard, since animal and crop growth are largely determined by the weather conditions during their life cycles. As a consequence, understanding the potential impacts of climate change on the agriculture has become increasingly important and is of a main concern especially for the sustainability of agricultural system and for policy-making purposes. Climate change is likely to affect agricultural systems very differently in various parts of the world. In the Mediterranean area particular attention should be devoted to climate change impact and adaptation assessments on typical Mediterranean crops like grapevine (Vitis vinifera L.), durum wheat (Triticum turgidum subs. durum Desf.) and olive (Olea europaea L.), since the projected global warming may seriously compromise the fragile equilibrium between climate and crops. In this study the impacts on durum wheat and grapevine yields, and olive suitable cultivation area were investigated for two time slices under A1B SRES scenario, at first. Then, some adaptation strategies to cope with these impacts were explored. The results indicated that projected higher temperatures resulted in a general advance of phenological stages with respect to the baseline and in a shorter inter-phase time for both durum wheat and grapevine. Despite the general decrease of time for biomass accumulation, durum wheat took advantage of the positive effect of higher CO2 concentration, while grapevine resulted more vulnerable to warmer and drier future climate. Adaptation options, aiming at avoiding extremely high temperatures during sensible phases and prolonging the duration of the reproductive stage, resulted as positive strategies to alleviate negative impacts or exploit possible beneficial effects of a changing climate. Finally, the rising temperature will cause a northward and eastward shift of the olive tree suitable area.

Climate Impact Assessments

This chapter highlights key climate impacts, hazards and vulnerabilities and associated indicators that have been used to assess current (recent) climate impacts at each of the case-study sites. The aim is to illustrate some of the wide range of information available from individual case studies and highlight common themes that are evident across multiple case-study locations. This is used to demonstrate linkages and sensitivities between the specific climate impacts of relevance for each case-study type (urban, rural and coastal) and the key climate hazards and biogeophysical and social vulnerabilities representing the underlying drivers and site conditions. For some impacts, there are clear, direct links with climate events, such as heat stress and flooding, while for others, such as energy supply and demand, the causal relationships are more indirect, via a cascade of climate, social and economic influences. Water availability and extreme temperatures are common drivers of current climate impacts across all case studies, including, for example, freshwater supply and heat stress for urban populations; irrigation capacity and growing season length for agricultural regions; and saltwater intrusion of aquifers and tourist visitor numbers at coastal locations. At some individual case-study locations, specific impacts, hazards and/or vulnerabilities are observed, such as peri-urban fires in Greater Athens, infrastructure vulnerability to coastal flooding in Alexandria, groundwater levels in Tel Hadya and vector-borne diseases in the Gulf of Oran. Throughout this chapter, evidence of current climate impacts, hazards and vulnerabilities from each of the case studies is detailed and assessed relative to other case studies. This provides a foundation for considering the wider perspective of the Mediterranean region as a whole, and for providing a context from which to assess consequences of future climate projections and consider suitable adaptation options.