Abstract: The most important factors limiting the agriculture in Puglia region in Southern Italy are typically  linked to high temperatures and low water availability. In expected future scenarios, increased challenges about such factors could further limit the crop productivity. We adopted an approach based on the simulation analysis carried out through the DSSAT implemented into AEGIS/WIN. This tool has proved to be an useful tool to manage the analysis results about the potential future impact of two regionalized climatic scenarios within the SRES scenario A2. Anomaly2 and Anomaly5, based on a target increase of global temperature of 2° and 5°C.  The winter durum wheat and tomato were simulated on the basis of the interaction climate-soil on a regional scale framework interesting the whole area of Puglia (about 20000 km2) subdivided in about 200 units of simulation. The wheat yield has proved to be mainly affected by the variability of precipitation. Conversely, the largest increment of temperature of spring-summer period caused a tomato yield reduction. As second step, in order to individuate the optimal adaptation strategies for both crops, a spatial analysis focused on sowing/transplanting times, nitrogen fertilization and tomato-irrigation has been carried out. The results have clearly indicated the different sensitivity of crops to climate change as influenced by the specific interaction soil-climate and an high degree of uncertainty, especially for the sowing date, depending even on small differences related to the climatic differences characterizing the areas of the Puglia territory.

Abstract: 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. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract: The Mediterranean region has always shown a marked inter-annual variability in seasonal weather, creating uncertainty in decisional processes of cultivation and livestock breeding that should not be neglected when modeling farmers’ adaptive responses. This is especially relevant when assessing the impact of climate change (CC), which modifies the atmospheric variability and generates new uncertainty conditions, and the possibility of adaptation of agriculture. Our analysis examines this aspect reconstructing the effects of inter-annual climate variability in a diversified farming district that well represents a wide range of rainfed and irrigated agricultural systems in the Mediterranean area. We used a Regional Atmospheric Modelling System and a weather generator to generate 150 stochastic years of the present and near future climate. Then, we implemented calibrated crop and livestock models to estimate the corresponding productive responses in the form of probability distribution functions (PDFs) under the two climatic conditions. We assumed these PDFs able to represent the expectations of farmers in a discrete stochastic programming (DSP) model that reproduced their economic behaviour under uncertainty conditions. The comparison of the results in the two scenarios provided an assessment of the impact of CC, also taking into account the possibility of adjustment allowed by present technologies and price regimes. The DSP model is built in blocks that represent the farm typologies operating in the study area, each one with its own resource endowment, decisional constraints and economic response. Under this latter aspect, major differences emerged among farm typologies and sub-zones of the study area. A crucial element of differentiation was water availability, since only irrigated C3 crops took full advantage from the fertilization effect of increasing atmospheric CO2 concentration. Rainfed crop production was depressed by the expected reduction of spring rainfall associated to the higher temperatures. So, a dualism emerges between the smaller impact on crop production in the irrigated plain sub-zone, equipped with collective water networks and abundant irrigation resources, and the major negative impact in the hilly area, where these facilities and resources are absent. However intensive dairy farming was also negatively affected in terms of milk production and quality, and cattle mortality because of the increasing summer temperatures. This provides explicit guidance for addressing strategic adaptation policies and for framing farmers’ perception of CC, in order to help them to develop an awareness of the phenomena that are already in progress, which is a prerequisite for effective adaptation responses.