MULTIFUNCTIONAL EVALUATION AND VALIDATION OF PRACTICES, TOOLS AND INNOVATIVE MODELS FOR THE LANDSCAPE AND AGROECOLOGICAL MANAGEMENT OF AGRICULTURAL SYSTEMS: TRADE-OFFS AND SYNERGIES IN THE ECOSYSTEM SERVICES FRAMEWORK

BACKGROUND. Severe agrobiodiversity loss trends are ongoing, altering the agroecosystem regulating processes. Biodiversity loss is both a consequence and driver of the current ecological crisis. Among rural systems, it is a pivotal driver of agroecosystems vulnerability and lack of resilience. Multi-spectrum solutions are needed, and this is being more and more recognised among all the different levels of agricultural and environmental studies, guidelines, policies, programs and strategic actions. Several agricultural management options exist: low-intensity and diversified agricultural land use can contribute to reversing these negative trends. Within this context, the agroecological approach specifically seeks the reintegration, within the agricultural systems, of their undermined ecosystem functions. Among the agroecological practices, agroforestry is a recognised, viable, multi-purpose rehabilitating strategy to reduce current environmental externalities of agriculture. To properly address these issues, the monitoring of the effectiveness of mitigation and rehabilitation strategies is needed, and result-based and mixed result-practice based approaches are recommended for securing biodiversity gains. AIMS. Within this framework, this research project focuses on the ecological and agri-environmental sides of farm models validation, based on mixed practice-result-based approaches. The project aims at developing a methodological framework for a multi-functional evaluation and validation of models for the landscape and agroecological management of agroecosystems among specific agri-environmental contexts. It starts from the experience of the farmers network of Polycolturae association, partner of the research project. The Polyculturae farms networking activity is aimed at promoting the agroecological approach through agroforestry-based and nature-based agricultural practices; this aim is also sought through the Biodiversitas label, a tool for ensuring the achieving of high environmental performances of farms, with a specific focus on agrobiodiversity. The Biodiversitas label is currently on a pilot stage and this research project addresses the steps propaedeutic to its final design and implementation. Research activities are directly addressed to two main objectives: Objective 1. Polyculturae model validation: - 1.a. The scientific validation of the agroecological and agroforestry-based model promoted by Polyculturae association - 1.b. The delivering of an analytical methodology to orient farm strategic ecological design and management, optimising the ecological effectiveness of interventions. Objective 2. To orient the further development of the Biodiversitas label, by delivering a proposal on its re-structuring and indicators system integration. METHODS. Research activities are led among the four farms currently member of the Polyculturae association (POLY farms), representing agroecology and agroforestry-based farm models in the western alluvial Po Plain district, Piedmont and Lombardy regions. Objective 1.a is sought through a multi-scale and multi-disciplinary analytical approach based on landscape ecology, floristic and vegetational (phytosociological) analyses and soil organic carbon analyses, coupled to the assessment of agronomic performances (yields and economic performance indicators). Extra-local and local landscape featuring is performed to set context-specific baselines, vulnerabilities and resilience drivers, against which each Polyculturae farm contributions are assessed. From the validation side, POLY farms are compared with local conventional baselines (farm models based on annual crop monocultures with no natural and semi-natural components active management). The Polyculturae model contributions to agrobiodiversity and agroecosystem ecological quality are also assessed from the Ecosystem services perspective, through a land-use based approach, to complement the assessment from a multi-functional point of view. Objective 1.b is sought through a design-oriented multi-scale analytical approach: current state is assessed, in comparison to other farm/landscape management models, and tools are provided for orienting the farm/landscape ecological improvement and for assessing the impacts of different farm/landscape management scenarios. Objective 2 is sought by interlinking the analytical approaches and wide set of indicators under assessment (Objective 1) with the Biodiversitas label current assessment scheme composition, and by comparing and screening the indicators on their suitability and/or limits to the label needs. RESULTS. Output 1.a. An analytical methodological framework to assess the ecological quality and agrobiodiversity contributions of farms was set up and tuned, fitting the peculiarities of those farms whose management model is inspired by the Polyculturae model. The multi-scale landscape, floristic-vegetational and soil analyses highlighted the multi-faceted positive contributions to agrobiodiversity and on-farm ecological quality of POLY farms, in relation to their local and extra-local landscape contexts and to their neighbouring conventional farms. Landscape ecology indices were tested and screened to reduce their redundancy and optimise their informative potential. The POLY model showed to give relevant contributions to the local landscape ecological re-balancing (amelioration of landscape eco-mosaic, diversity and biological territorial capacity values), in response to local and extra-local agricultural landscapes vulnerability and resilience traits. POLY farms significantly differ from conventional ones on several landscape ecological traits (forest and semi-natural components ratio, farm landscape diversity, connectivity and circuitry, and mean farm biological territorial capacity). This reflects higher contributions of the POLY model to the agricultural landscape ecological balance, which were synthetized through a guiding tool on the ecological interpretation of landscape ecology indices values. The Ecosystem Services analysis allowed a spatial representation of such multi-faceted contributions. POLY farms floristic and vegetational traits were depicted, allowing to compare farms performances through species richness and α-diversity, ecological and chorological traits of their spontaneous flora and main phytocoenoses types. Positive synergies were highlighted between species richness and α-diversity values and medium-long life cycle species (hemicryptophytes and phanerophytes), whereas negative trade-offs were found between allochthonous degree and richness/diversity traits, and generally, allochthonous degree was related to higher therophytes and lower phanerophytes species ratio. Anthropic disturbance degree was hence identified as a major driver to species richness and α-diversity decrease. Focusing on rice fields weed flora, significantly higher α-diversity values were found in POLY rice paddies, compared to conventional ones. Concerning inter-scale patterns, trade-offs and synergies were described between extra-local and local landscape ecological traits and farm scale floristic-vegetational traits. Generally, higher local landscape natural components ratio, landscape diversity, biological territorial capacity and connectivity/circuitry tended to be related to higher farm scale species richness, α-diversity, hemicryptophytes and phanerophytes ratio and Eurasiatic species, and to lower therophytes ratio and allochthonous degree; on the opposite, therophytes and allochthonous degree tended to increase with higher local landscape eco-mosaic agricultural components ratio, to the detriment of the natural ones. Soil organic carbon analyses confirmed the relevant contribution of landscape features management on on-farm soil organic carbon storage and long-term turnover, with landscape features ecological quality being recognised as a significantly influencing factor. The analyses on the agronomic traits of POLY farms provided a report on the main components of POLY farms economic sustainability, standing for higher economic flows efficiency compared to conventional models and suggesting a lower dependency of POLY rice yields on climate instability, which should be further tested on wider datasets and timeframes. Output 1.b. A detailed methodology was delivered dealing with the assessment, design and monitor of on-farm land use choices. These tools are specifically intended to support the integration of new farms within the POLY model and Biodiversitas labelling, by supporting and guiding the progressive transition towards the POLY model, optimising the contributions to agrobiodiversity and the overall farm ecological quality. Output 2. The wide set of agri-environmental indicators tested on POLY farms and their surroundings, compared and screened, were interconnected with current Biodiversitas label indicators system. Each indicator was screened on its suitability and/or limits for the project purposes. A preliminary proposal was delivered on the labelling assessment scheme re-structuring, based on a hierarchical structure: two baseline mandatory levels, delivering a first, low time-demanding but sufficiently informative synthesis on the farm ecological status; and three complementary, optional levels, deepening the ecological investigation, demanding higher time, skills and implementation costs. The pre-existing Biodiversitas label indicators system was strengthened on its result-based components, deepening its multi-functional overview on the on-farm ecological functions and services status (Ecosystem Services perspective). CONCLUSIONS. Concerning the Polyculturae model validation and the science-driven support to its implementation, this research provides a first milestone. The further testing of the here-proposed methodology on new farms, belonging to the same or different territorial contexts and/or productive systems, is envisaged to complete the here-presented results, enhance their statistical significance and better fit the variety of case histories on which the POLY model might be implemented. Concerning the Biodiversitas label development, the here-presented preliminary contribution intends to orient the future steps of the Biodiversitas label design phase: the further testing of the proposed indicators set and analytical methodology on wider datasets including new farms; the integration and testing of additional indicators; the consequent tuning of the assessment scheme through thresholds setting and validation, the final setting up of the scoring system and monitoring protocols. Citizen-science-based approaches are also expected to be further developed by setting up participative monitoring protocols on the most easy-to-measure indicators.