• Energy Research

ABSTRACTThis study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC‐ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios. Two spectral indices, NDVI (temperate area) and SAVI (arid area), served as vegetation proxies, classified into three classes using K‐means (NDVI: high = mainly associated with woodlands, medium = shrublands, continuous grasslands and crops, low = discontinuous grasslands, bare soil, and rocks; SAVI: high = mainly associated with woods, olive trees, and crops, medium = shrublands and sparse olive trees, low = bare soil and saline areas). Classes validated with ESA WorldCover 2020 data and sampled via 1390 presence‐only points. A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. Vegetation changes varied by area: in the temperate area, woodlands and shrublands were stable, but discontinuous grasslands expanded. In the arid area, woodlands gained suitable habitat, while bare soil declined under the pessimistic SSP 585 scenario. Both areas showed an upward shift for shrublands and grasslands. The models indicated significant shifts in areal distribution and environmental conditions, affecting habitat suitability and ecosystem dynamics. MaxEnt emerged as the most reliable algorithm for small presence‐only datasets, effectively predicting habitat suitability. The findings highlight significant vegetation redistribution and altered ecosystem dynamics due to climate change, underscoring the importance of these models in planning for future ecological challenges.

Biomass production helps address the worldwide energy demand. However, some controversial issues have been identified such as the possible conflict between the goal of increasing vegetable biomass and food production and the need to limit environmental impacts. In Mediterranean region, where the supply of some natural resources appears significantly limited (e.g., water) and the competition for land is higher than it was in the past, the objective of evaluating environmental burdens at a regional scale represents an important issue, especially if the assessment considers the farmer scope of increasing productivity. Using a Life Cycle Assessment (LCA) "from cradle to field gate" approach, this paper aims to evaluate land-based environmental sustainability related to four energy crop options. We carried out a LCA differentiating between annual and perennial species and between irrigated (giant reed and sorghum) and rainfed crops (cardoon and milk thistle) to determine their performances and impacts within the same context. The findings suggest that irrigated crops generate larger impacts on the environment than rainfed species and that annual crops (both irrigated and rainfed) are more damaging than the respective perennial crops. The damages were expressed in Ecopoints, where one Ecopoint corresponds to one thousandth of the annual overall environmental burden of an average European inhabitant. Ecopoints for sorghum, giant reed, milk thistle and cardoon are equal to 361, 288, 146, and 138, respectively. Except for irrigation, fertilizers were found to be the input with the largest effect, accounting for 37% (giant reed) to 75% (cardoon) of the environmental burden on the system. The results do not suggest the presence of a winning crop option - i.e., a crop that shows the best environmental performances everywhere and in all categories - since regional environmental burdens are simultaneously related to different factors (e.g., land allocation, crop productivity, and degree of practice intensification) that drive farmer choice. Finally, following a dynamic and innovative perspective, we evaluated the trade-off between productivity and environmental burden for each crop simulating an increasing product variation. We found that environmental burdens would increase more proportionally than crop yields done. Especially the latter finding provides interesting suggestions on energy cropping system integration within agricultural planning under stressed natural resource conditions.

The photovoltaic (PV) greenhouses are closed agrivoltaic (CA) systems that allow the production of energy and food on the same land, but may result in a yield reduction when the shading of the PV panels is excessive. Adopting innovative cropping systems can increase the yield of the CA area, generating a more productive and sustainable agrosystem. In this case study we quantified the increase of land productivity derived from the integration of an experimental vertical farm (VF) for baby leaf lettuce inside a pre-existing commercial CA. The mixed system increased the yield by 13 times compared to the CA and the average LER was 1.31, but only 12 % of the energy consumption was covered by the CA energy. To achieve the energy self-sufficiency and avoid the related CO2 emissions, the VF area should not exceed 7-18 % of the CA area, depending on the PV energy yield and the daily light integral (DLI) of the LED lighting, meaning a land consumption from 5 to 14 times higher than the VF area. The support of the PV energy was essential for the profitability of the VFCA. Design features and solutions were proposed to increase the agronomic and economic sustainability of the VFCA. The VFs can be considered a possible answer for the reconversion of the actual underutilized CAs with high PV cover ratios into productive and efficient cropping systems, but a trade-off between energy production and land consumption should be identified to ensure an acceptable environmental sustainability of the mixed system.

The oilseed rape conventional system can be moved to a more sustainable one by reducing herbicide application whilst ensuring at the same time effective weed control, maintaining oilseed rape yield, and quality and increasing profitability. Over three growing season periods, two field experiments at two different Southern Italy locations were carried out. In both sites, a conventional weed-control management system (recommended label dose), four alternative low-herbicide treatments, and an untreated control were compared. We monitored weeds and crop response to herbicide treatments, and calculated the net economic return, within site and year, for each treatment. In experiment 1, a half dose of herbicide did not show any significant difference in seed yield with respect to conventional treatment in two of three growing seasons. In experiment 2, compared with the conventional system, weedy control and the lowest applied herbicide dose treatment (25% of the recommended label dose) did not underline significant differences with regard to yield level. Net returns from the half dose of metazachlor herbicide were not significantly lower than net returns from conventional treatment in experiment 1 (on a three-year average 748 vs. 812 € ha−1, respectively). Our findings suggest that the herbicide dose might be cut by at least 50% in order not to jeopardize negative effects on production and economic performances.

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Abstract The development of biogas production exacerbated the competition for land availability between crops dedicated to human consumption and those intended for energy production. Residual biomasses have been often proposed for their positive outcomes in terms of reduced pressure on land use. However, literature did not assess optimization options for existing biogas plants feeding. This paper developed a bio-economic model for the optimization of agricultural biogas supply chains using artichoke byproducts in existing plants. A multiple goal linear programming approach was adopted, using two objective functions, calculating respectively net present value and land use from energy crops, associated to a regional biogas network. Three scenarios were defined using primary and secondary data on the residues of a specific artichoke variety - globe - and an Italian region - Sardinia. In the Business As Usual scenario, net present value is about 7 million € with a land use of about 2720 ha. When using artichoke residues, the economic impact increases by 28% and land use is reduced by 83% if net present value is optimized. When land use is optimized, the economic impact still grows by 25% and land use is reduced by 100%. Results from this study confirm that, under certain conditions, locally available residual biomasses can replace energy crops in existing biogas networks, coupling viability and sustainability.