• Energy Research

Bioenergy crops are well known for their ability to reduce greenhouse gas emissions and increase the soil carbon stock. Although such crops are often held to be in competition with food crops and thus raise the question of current and future food security, at the same time mitigation measures are required to tackle climate change and sustain local farming communities and crop production. However, in some cases the actions envisaged for specific pedo-climatic conditions are not always economically sustainable by farmers. In this frame, energy crops with high environmental adaptability and yields, such as giant reed (Arundo donax L.), may represent an opportunity to improve farm incomes, making marginal areas not suitable for food production once again productive. In so doing, three of the 17 Sustainable Development Goals (SDGs) of the United Nations would be met, namely SDG 2 on food security and sustainable agriculture, SDG 7 on reliable, sustainable and modern energy, and SDG 13 on action to combat climate change and its impacts. In this work, the response of giant reed in the marginal areas of an agricultural district of southern Italy (Destra Sele) and expected farm incomes under climate change (2021-2050) are evaluated. The normalized water productivity index of giant reed was determined (WP; 30.1gm-2) by means of a SWAP agro-hydrological model, calibrated and validated on two years of a long-term field experiment. The model was used to estimate giant reed response (biomass yield) in marginal areas under climate change, and economic evaluation was performed to determine expected farm incomes (woodchips and chopped forage). The results show that woodchip production represents the most profitable option for farmers, yielding a gross margin 50% lower than ordinary high-input maize cultivation across the study area.

Bioenergy crops are well known for their ability to reduce greenhouse gas emissions and increase the soil carbon stock. Although such crops are often held to be in competition with food crops and thus raise the question of current and future food security, at the same time mitigation measures are required to tackle climate change and sustain local farming communities and crop production. However, in some cases the actions envisaged for specific pedo-climatic conditions are not always economically sustainable by farmers. In this frame, energy crops with high environmental adaptability and yields, such as giant reed (Arundo donax L.), may represent an opportunity to improve farm incomes, making marginal areas not suitable for food production once again productive. In so doing, three of the 17 Sustainable Development Goals (SDGs) of the United Nations would be met, namely SDG 2 on food security and sustainable agriculture, SDG 7 on reliable, sustainable and modern energy, and SDG 13 on action to combat climate change and its impacts. In this work, the response of giant reed in the marginal areas of an agricultural district of southern Italy (Destra Sele) and expected farm incomes under climate change (2021-2050) are evaluated. The normalized water productivity index of giant reed was determined (WP; 30.1gm-2) by means of a SWAP agro-hydrological model, calibrated and validated on two years of a long-term field experiment. The model was used to estimate giant reed response (biomass yield) in marginal areas under climate change, and economic evaluation was performed to determine expected farm incomes (woodchips and chopped forage). The results show that woodchip production represents the most profitable option for farmers, yielding a gross margin 50% lower than ordinary high-input maize cultivation across the study area.

A methodological approach based on detailed land-use map, high-resolution LiDAR data and field surveys was developed to categorize productive and non-productive mixed forests, both in term of stand attributes and structural diversity. In 2011, leaf-off dedicated airborne LiDAR data were collected in a 20,000 ha inland patchy area which was representative of soil land use in the Apennines mountains of southern Italy. By combining field and LiDAR data in 5574 ha of forests with coexisting evergreen and deciduous species, wemodelled common forest stand variables (height, diameter, volumeand biomass)with high accuracy (0.60 <= Adj.R2 <= 0.89).Moreover, a moderate correlation (0.425 <= ? <= 0.462) between field- and LiDAR-derived diversity indices was found. About 3393 ha of forests are enclosed in protected areas of the Natura 2000 network, which in turn possesses 77% (~ 576,286 Mg) of total aboveground dry biomass. Overall, eight forest types were identified, one of which, the European beech, is only found in the Natura 2000 sites,while other forest types are also found elsewhere. This is the first study to undertake a LiDAR analysis ofMediterranean forests in the Campania Region and might help better evaluate trade-off, especially in protected areas, in order to enhance multiple benefits and support sustainable management of forests.

A methodological approach based on detailed land-use map, high-resolution LiDAR data and field surveys was developed to categorize productive and non-productive mixed forests, both in term of stand attributes and structural diversity. In 2011, leaf-off dedicated airborne LiDAR data were collected in a 20,000 ha inland patchy area which was representative of soil land use in the Apennines mountains of southern Italy. By combining field and LiDAR data in 5574 ha of forests with coexisting evergreen and deciduous species, wemodelled common forest stand variables (height, diameter, volumeand biomass)with high accuracy (0.60 <= Adj.R2 <= 0.89).Moreover, a moderate correlation (0.425 <= ? <= 0.462) between field- and LiDAR-derived diversity indices was found. About 3393 ha of forests are enclosed in protected areas of the Natura 2000 network, which in turn possesses 77% (~ 576,286 Mg) of total aboveground dry biomass. Overall, eight forest types were identified, one of which, the European beech, is only found in the Natura 2000 sites,while other forest types are also found elsewhere. This is the first study to undertake a LiDAR analysis ofMediterranean forests in the Campania Region and might help better evaluate trade-off, especially in protected areas, in order to enhance multiple benefits and support sustainable management of forests.

This article introduces a new web-based and freely accessible tool, the Nitrate Fate tool (NFt), for the assessment of groundwater vulnerability to nitrate pollution in a variety of pedoclimatic conditions. The contamination of water resources by nitrate, in fact, represents a growing and persistent global environmental problem, and the utilization of practical tools to assist personnel working in the agricultural sector is key for mitigating the impact on land use, while maintaining farmers’ incomes. The (NFt) has been developed and integrated into the geospatial decision support system, LandSupport, as a way to support multiple stakeholders in conducting the so-called what-if scenario analysis (e.g., what would happen to the crop production if I substitute a quote of inorganic fertilizer with the same quote of an organic one?). The tool couples a state-of-art crop-growth model—which simulates crop growth dynamics, the nitrogen and carbon cycles—with a novel transfer function model in order to assess the transport of nitrate through the unsaturated zone to the groundwater table. Within the LandSupport platform, the results are shown both as coloured maps and as cumulative charts representing the travel times and the concentrations of root leachate to groundwater table depths. This work details the tool’s rationale, the coupling of the models, and their implementation. Moreover, this article shows examples of applications supporting several public authorities and end-users, underlining that, by combining all of the information on soils, groundwater table depths, management and climates, it is possible to obtain a comprehensive understanding of nitrogen transport dynamics. Two case studies are presented: the Piana del Sele and the eastern plain of Naples, both located in the Campania region of Italy. The results of the tool’s applications reveal significant groundwater vulnerability in both plains, mainly due to the shallow groundwater table depths, resulting in remarkably fast mean nitrate travel times ranging from 0 to 6 years. Finally, the tool provides a reproducible and replicable solution, and future implementation is foreseen for additional case studies all over the world.

This article introduces a new web-based and freely accessible tool, the Nitrate Fate tool (NFt), for the assessment of groundwater vulnerability to nitrate pollution in a variety of pedoclimatic conditions. The contamination of water resources by nitrate, in fact, represents a growing and persistent global environmental problem, and the utilization of practical tools to assist personnel working in the agricultural sector is key for mitigating the impact on land use, while maintaining farmers’ incomes. The (NFt) has been developed and integrated into the geospatial decision support system, LandSupport, as a way to support multiple stakeholders in conducting the so-called what-if scenario analysis (e.g., what would happen to the crop production if I substitute a quote of inorganic fertilizer with the same quote of an organic one?). The tool couples a state-of-art crop-growth model—which simulates crop growth dynamics, the nitrogen and carbon cycles—with a novel transfer function model in order to assess the transport of nitrate through the unsaturated zone to the groundwater table. Within the LandSupport platform, the results are shown both as coloured maps and as cumulative charts representing the travel times and the concentrations of root leachate to groundwater table depths. This work details the tool’s rationale, the coupling of the models, and their implementation. Moreover, this article shows examples of applications supporting several public authorities and end-users, underlining that, by combining all of the information on soils, groundwater table depths, management and climates, it is possible to obtain a comprehensive understanding of nitrogen transport dynamics. Two case studies are presented: the Piana del Sele and the eastern plain of Naples, both located in the Campania region of Italy. The results of the tool’s applications reveal significant groundwater vulnerability in both plains, mainly due to the shallow groundwater table depths, resulting in remarkably fast mean nitrate travel times ranging from 0 to 6 years. Finally, the tool provides a reproducible and replicable solution, and future implementation is foreseen for additional case studies all over the world.