• Energy Research

With an approach combining crop modelling and biotechnology to assess the performance of three durum wheat cultivars (Creso, Duilio, Simeto) in a climate change context, weather and agronomic datasets over the period 1973–2004 from two sites, Benatzu and Ussana (Southern Sardinia, Itay), were used and the model responses were interpreted considering the role of DREB genes in the genotype performance with a focus on drought conditions. The CERES-Wheat crop model was calibrated and validated for grain yield, earliness and kernel weight. Forty-eight synthetic scenarios were used: 6 scenarios with increasing maximum air temperature; 6 scenarios with decreasing rainfall; 36 scenarios combining increasing temperature and decreasing rainfall. The simulated effects on yields, anthesis and kernel weights resulted in yield reduction, increasing kernel weight, and shortened growth duration in both sites. Creso (late cultivar) was the most sensitive to simulated climate conditions. Simeto and Duilio (early cultivars) showed lower simulated yield reductions and a larger anticipation of anthesis date. Observed data showed the same responses for the three cultivars in both sites. The CERES-Wheat model proved to be effective in representing reality and can be used in crop breeding programs with a molecular approach aiming at developing molecular markers for the resistance to drought stress.

Along with the concept of improving reforestation efforts in Lebanon, this study aimed to provide a land suitability analysis for forest species in Lebanon while considering the effect of climate change. Herein, the soil evaluation criteria developed by FAO (The Food and Agriculture Organization) for land suitability classification were implemented through the weighted overlay method to produce suitability maps based on natural variables (soil, climate, and topography) influencing the presence of the species on the land. Cedrus libani, Quercus calliprinos, Ceratonia siliqua, Eucalyptus globulus, and Pinus halepensis are the species considered in this study. The results of this study provide useful information to inform reforestation activities in Lebanon, considering the expected climate change projections for medium- (2050) and long-term (2070) periods, according to two different scenarios (RCP4.5 and RCP8.5) and three General Circulation Models: CCSM4, GFDL-CM3, and HadGEM2-ES. The suitability maps showed a generally critical situation for the spatial distribution of forest species under future climate change compared to the current situation (1970–2000). The distribution of thermophilic species, which tolerate high temperatures (over 20 °C), was projected to expand compared to the current situation. In contrast, the expansion of cold-adapted species may be limited by future climate change conditions. It is crucial to consider the expected effects of climate change to better select species for reforestation and, therefore, to maintain forest cover in Lebanon.

Climate change is a concerning phenomenon worldwide. The reduction in greenhouse gas emissions is the core of the mitigation strategies that are effective countermeasures to address a changing climate in the long term. Nevertheless, the need for short-term solutions regarding adaptation to climate change (ACC) has gained growing interest in the scientific community and in European institutions. European and national ACC principles are being integrated in strategies and plans. In Italy, some regions have adopted ACC principles in strategic plans, which influence the whole local planning system and persuade local communities to become more climate-resilient. This study focuses on the mainstreaming of ACC into strategies, plans, programs, and projects (SPPPs) adopted by the following Italian cities: Bologna, Milan, Naples, Rome, Turin, and Venice. We scrutinize the contents of SPPPs with respect to four criteria: (i) references to strategies or plans for ACC; (ii) inclusion of ACC objectives and (iii) measures; and (iv) references to—or the inclusion of—climatic analysis on historical series and/or future projections. We found out that most SPPPs adopted by the cities have considered ACC in a promising way, i.e., all the cities are inclined to promote ACC, despite three of them lacking a municipal ACC strategy or plan and a National Adaptation Plan not being in force.

The Mediterranean basin has been identified as a prominent hotspot of climate change, with expected negative impacts on crop productivity, among others. Given the primary role that agriculture has to sustain cultural values, economic opportunities, and food security, it is crucial to identify specific risks in agriculture due to climate change, which can address more effective adaptation strategies and policies to cope with climate change. This study aims to evaluate the high-resolution impacts of climate change on the length of the growing cycle and yield of durum wheat, common wheat, and maize in Italy by using the CERES-Wheat and CERES-Maize crop models implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software. A digital platform (GIS-DSSAT) was developed to couple crop simulation models with dynamically downscaled climate projections at high resolution for Italy, which can better represent the Italian landscape complexity and the spatial distribution of different pedological and crop management features, providing more detailed information on the expected impacts on crops respect to previous studies at a coarser resolution. The projections have been extended for two climate change scenarios and accounting for uncertainty, either considering or not the potential direct effects of increasing atmospheric CO2 concentrations ([CO2]). Results show that climate change may affect Italian cereal production in the medium to long term periods. Maize is the main affected crop, with yield reductions homogeneously distributed from North to South Italy. Wheat yield is expected to decrease mainly in southern Italy, while northern Italy may benefit from higher precipitation regimes. Higher levels of atmospheric CO2 concentrations may partially offset the negative impact posed by climate change and increase the benefits in the northern regions, especially for common and durum wheat.

Several modelling tools reported the climate change impact on the coffee agrosystems. This article has adopted a systematic approach to searching out information from the literature about different modelling approaches to assess climate change impacts or/and adaptation on coffee crops worldwide. The review included all scientific publications from the date of the first relevant article until the end of 2022 and screened 60 relevant articles. Most results report research conducted in America, followed by Africa. The models assessed in the literature generally incorporate Intergovernmental Panel on Climate Change (IPCC) emission scenarios (80% of manuscripts), particularly Representative Concentration Pathways (RCP) and Special Report on Emission Scenarios (SRES), with the most common projection periods until 2050 (50% of documents). The selected manuscripts contain qualitative and quantitative modelling tools to simulate climate impact on crop suitability (55% of results), crop productivity (25% of studies), and pests and diseases (20% of the results). According to the analysed literature, MaxEnt is the leading machine learning model to assess the climate suitability of coffee agrosystems. The most authentic and reliable model in pest distribution is the Insect Life Cycle Modelling Software (ILCYM) (version 4.0). Scientific evidence shows a lack of adaptation modelling, especially in shading and irrigation practices, which crop models can assess. Therefore, it is recommended to fill this scientific gap by generating modelling tools to understand better coffee crop phenology and its adaptation under different climate scenarios to support adaptation strategies in coffee-producing countries, especially for the Robusta coffee species, where a lack of studies is reported (6% of the results), even though this species represents 40% of the total coffee production.