NEUTRALISE acronym of (laNd usE adaptation to cope with droUghT and heat waves in the Italian agRiculturAL sector: Impacts, riSks and synErgies for achieving the environmental targets) aims to create an interdisciplinary research group integrating knowledge and methods to obtain an operational instrument and know-how regarding the issue “climate – adaptation strategies in agriculture – indicators/risk maps”.
The realization of the project allows to overcome some current gaps and then provide useful information to stakeholders and policymakers regarding the Climate Change (CC) impacts on water resources and farms using indicators and risk maps. Specifically, considering the entity and the growing frequency of drought phenomena, different adaptation strategies are necessary to reduce the negative impacts, including land use change. Indeed, the National Strategic Plan of 2023-2027 CAP emphasizes the importance of land use adaptation to improve the resilience of farms to climatic events such as drought.
Furthermore, synergies should be exploited to achieve the environmental targets of the European Green Deal and the Farm to Fork strategy. The cultivation of more resilient and environmentally sustainable crops, also thanks to the support of new CAP, can maintain the farms’ profitability. Not to be overlooked are the negative impacts of drought on some typical production of the Italian agri-food system, which must be supported and maintained in order to safeguard food sovereignty.
NEUTRALISE considers all these aspects, through the collaboration between three research groups with different skills but which can be integrated with each other in the perspective of interdisciplinarity. This feature is essential for dealing in a complete and integrated way with a complex issue such as that of CC which affects, among other things, natural resources and economic and social aspects.
NEUTRALISE in this context has a national perspective, based on the analysis of the entire sample of Italian agricultural enterprises from the Agricultural Accountancy Data Network (FADN). FADN is a European data collection system providing detailed information on the economic activities of agricultural enterprises. Used for monitoring and analyzing profitability and productive processes, it offers data on costs, revenues, income, and agricultural production. The Italian sample includes about 11,000 farms selected to be representative of various Types of Farm (TF) and geographic regions, ensuring comprehensive coverage of the Country’s various agricultural realities. These insights are crucial for formulating and evaluating national and EU agricultural policies. NEUTRALISE‘s peculiarity lies in leveraging this detailed information considering various territorial and farm-specific characteristics. Within the project, these data will be used as inputs in a Mathematical Programming (MP) model capable of reconstructing the production structure of each farm and able to assess the impact of future climate scenarios on the economic and productive results of Italian farms. Specifically, the PM model identifies changes in land use resulting from hypothesized scenarios, guided by the principle of income maximization and constrained by resource availability.
Hence, NEUTRALISE‘s strengths lie in its interdisciplinary nature and the granularity of its analysis, leveraging (i) climatic data at the agricultural region level, (ii) an economic PM model operating at the individual farm level, differentiated by geographic zone and altitude level, and (iii) risk indicators and maps derived from the PM model results target specific territorial areas identified as key for analysis. NEUTRALISE will lead to an advance of knowledge and positive impacts on society. The progress will concern the individual research groups, and the whole integrated group as a whole, stimulating the international scientific community to operate and research on the topic. A positive impact on society will be to contribute to the achievement of various 2030 Sustainable Development Goals, in particular GOALS 1, 2, 6, 12, and 13.