Application of the Σommit index to fruit orchards: background, methods and results of the application of the Σommit index on fruit tree orchards

Towards climate-smart sustainable management of agricultural soils SUstainable Management of soil Organic Matter to MItigate Tradeoffs between C sequestration and nitrous oxide, methane and nitrate losses Deliverable D5.4 Application of the ∑ommit index to fruit orchards: background, methods and results of the application of the ∑ommit index on fruit tree orchards. Due date of deliverable: M54 Actual submission date: 31.07.2024 2 Acronym: ∑OMMIT Start date of the project: February 1st, 2021 Project duration: 36 Months Topic: CM8 Project type: Medium-sized project Project coordinator: CREA (Italy) Alessandra Lagomarsino (alessandra.lagomars[email protected].it) DELIVERABLE NUMBER: D-WP 5.4 DELIVERABLE TITLE Application of the ∑ommit index to fruit orchards: background, methods and results of the application of the ∑ommit index on fruit tree orchards. DELIVERABLE TYPE: Technical report WORK PACKAGE N: WP5 WORK PACKAGE TITLE: Tradeoffs and synergies synthesis: best practices, critical thresholds, indicators DELIVERABLE LEADERS: CREA-AA (Italy) Simone Bregaglio ([email protected]) ISPRA (Italy) Angela Fiore (angela.fior[email protected]) CONTRIBUTING PARTNERS TO THIS REPORT CREA-AA (Italy): Roberta Calone ([email protected]) CREA-DC (Italy): Gabriele Mongiano (gabriele.mongian[email protected]) CREA-AA (Italy): Alessandra Lagomarsino (alessand[email protected].it) CREA- (Italy): Corrado Ciaccia ([email protected]) CREA-AA (Italy): Claudia Di Bene ([email protected]) CREA (Italy): Elena Testani (elena.[email protected]) CREA-AA (Italy): Filippo Rocchi (filippo.ro[email protected]) CREA-OFA (Italy): Danilo Ceccarelli ([email protected]) 3 Table of Contents List of Tables 4 List of Figures 5 List of Abbreviation 6 1. Introduction 7 2. Materials and methods 8 2.1. Environmental conditions 8 2.2. Experimental design 8 2.3. Management practices 10 2.4. Data collection and elaboration 10 3. Results and discussions 11 4. Conclusions 13 References 14 4 List of Tables Table 1. Description of the three agroecological strategies compared in the LTE Table 2. Description of the agroecological service crops sowed in the Innovative diversified system with Cover crops and Compost (ICC) management system. Table 3. trend of the mean weights assigned to the four T-Ocs in narratives V1 a group of young innovative farmers (Young farmers), V2 a multinational agrochemical company (Agrochem corporation), and V3 an EU national agency responsible for allocating Common Agricultural Policy subsidies to farmers (CAP paying agency), compared to the balanced version (V0), where T-Ocs were weighted 25 each. 5 List of Figures Figure 1. Pictures from the experimental site showing the apricot tree rows and inter-rows. Figure 2. Trade-off components trend across the three compared agronomic systems: BAU (Business As Usual), ICC (Innovative diversified system with Cover crops and Compost), INC (Innovative diversified system with Natural cover and Compost) Figure 3: Median and interquartile ranges of the Σommit index (Σi) value distributions for version 1 (young farmers), version 2 (agrochemical corporation), and version 3 (CAP paying agency) across three agronomic systems: BAU (Business As Usual), ICC (Innovative Diversified System with Cover Crops and Compost), and INC (Innovative Diversified System with Natural Cover and Compost). Black stars indicate the Σi in a balanced scenario (version 0) where equal weight is given to all trade-off components. 6 List of Abbreviation Abbreviation Description C Carbon N Nitrogen ∆SOC Soil organic carbon changes GHG Greenhouse gases N2O Nitrous oxide NO3-N Nitrate nitrogen CO2 Carbon dioxide ∑i ∑ommit index T-Ocs Tade-off components 7 1. Introduction In previous deliverables D5.2 and D5.3, we introduced the final version of the SOMMIT index, a composite indicator designed to identify optimal agricultural management strategies by balancing trade-offs among soil organic carbon changes (∆SOC), nitrous oxide (N2O) emissions, nitrate nitrogen (NO3-N ) leaching, and crop yield. This index utilizes fuzzy logic, a rigorous mathematical framework that, unlike the binary and rigid nature of Boolean logic, accommodates the concept of partial truth. This flexibility makes it well-suited for capturing the complexities of agricultural systems, which are characterized by intricate interdependencies among multiple interacting factors (Amini et al., 2020a; Zrobek et al., 2020), and for managing the uncertainties and subjectivities involved in interpreting and evaluating such complex systems, enabling a more holistic representation of sustainability's multiple dimensions. Additionally, fuzzy logic allows for dynamic adjustments in evaluation criteria to cater to the varying priorities and perspectives of users and permits the incorporation of expert knowledge, which may enhance the robustness and effectiveness of the evaluations. Four alternative versions of the SOMMIT index have been developed, reflecting the perspectives of different stakeholders with distinct economic, productivity, and sustainability priorities. So far, we have applied the SOMMIT index to evaluate annual crops. In this deliverable, we present the application of the fuzzy-based trade-off analysis index to evaluate and compare three different management strategies in an orchard setting. Fruit tree ecosystems are significant carbon pools, accounted for in annual national greenhouse gases (GHG) reports (IPCC, 2006), and their production is crucial for long-term food security on a global scale (FAO, 2018). The fruit sector is highly competitive, and yield is a significant concern for fruit growers. At the same time, the internal quality standards and consumer expectations, especially for those sold in the fresh market, are very high (Codron et al., 2005). These productive pressures have led to massive agricultural intensification in orchards over the last century, increasing production through high inputs of inorganic fertilizers, pesticides, and herbicides (Reganold et al., 2001). However, this intensification has also led to increased production costs and environmental damage within and around the orchards (Reganold et al., 2001). These concerns have spurred interest in environmentally friendly production methods, such as organic management, conservation practices, and integrated pest management techniques (Dib et al., 2016; Simon et al., 2010). Agricultural practices like minimized soil disturbance and permanent soil cover may positively impact biodiversity and ecosystem services (Palm et al., 2014). In particular, there is a growing body of research exploring the introduction of selected weeds as cover plants with specific functional ecological traits and agronomical characteristics as one possible strategy for managing weeds in orchards with minimal herbicide use. Reintroducing biological diversity in orchard systems through cover crops could enhance biological regulation, contribute to controlling bio-aggressors in the agroecosystem, reduce the use of chemicals, and provide additional services such as benefiting pollinators, reducing runoff and soil erosion, and supporting natural enemies of crop pests (Barberi et al., 2018). The challenge lies in promoting a weed community that supports biodiversity while minimizing competition with crops (Mézière et al., 2015). In this study, the ∑OMMIT index was applied to data from a newly planted experimental organic apricot orchard in Central Italy to compare three agrobiodiversity management strategies: soil ripping versus reduced tillage at orchard planting, soil tillage versus minimum/no tillage, and the introduction of different weed communities as cover crops. The experiment compared three management systems: Business as Usual (BAU), a tilled organic management system; 8 Innovative Tilled Diversified System with Cover Crops and Compost Use (ICC); and Innovative Diversified System with Minimum Tillage, Natural Cover, and Compost Use (INC). The application of the ∑I provided a comprehensive evaluation of the three management strategies, highlighting the trade-offs between agroecological intensification practices and their impact on GHG emission and productivity. 2. Materials and methods 2.1. Environmental conditions The data analysed in this study derive from the “MAIntenance of Organic oRchards” long-term experiment (MAIOR LTE) conducted by CREA - Research Centre for Olive, Fruit and Citrus Crops - located in Rome, Italy (latitude 41.8000 N, longitude 12.5690 E, altitude 86 m a.s.l.). The site climate is classified as hot-summer Mediterranean (Köppen classification), characterized by mild winters and warm to hot summers. Long-term weather data (1971–2000) identify January as the coldest month (7.5 °C), and July and August as the warmest (25.1 and 25.4°C, respectively) and driest months. November and December are the wettest months (average rainfall of 110–120 mm month-1). Monthly weather data during the experiment were collected using an automatic weather station (Campbell Scientific, Inc.) located within the experimental site. From 2016 to 2018, temperatures during winter were close to 0°C, peaking around 35°C in summer. The period from 2019 to 2021 showed warmer winters and milder summers. Summer rainfall was very low in 2016, 2017, and 2019, while autumn 2016, winter 2018, and 2020 experienced higher precipitation than the 5-years average, with an extreme event in November 2019 (270 mm). The soil at the experimental site is classified as Eutric Phaeozem (IUSS Working Group WRB, 2015), with a sandy clay loam texture (22% clay and 25% silt). It has a mean bulk density of 1.4 g cm-3, an electrical conductivity of 0.140 mS cm1, and a pH of 7.0. It contains 1.22 g kg-1 of N and 20.1 g kg-1 of organic matter (0–40 cm layer). 2.2. Experimental design The MAIOR LTE started in winter 2017, following a participatory process that engaged farmers in identifying key factors to include in the research activities (Ciaccia et al., 2019b). The area designated for the LTE had not been cultivated for over 10 years and was regularly mowed twice per year, in autumn and spring. The design of the MAIOR LTE is a splitplot arrangement with three blocks. The main plot was allocated to organic agronomic management, comparing three systems with varying levels of agroecological intensification, as detailed in Table 1. Table 1. Description of the three agroecological strategies compared in the LTE Experiment ID Description BAU - Business As Usual Local practices commonly used for organic fruit orchards: soil tillage and organic commercial fertilizer (24% of organic C and 5% of organic N on dry basis). ICC - Innovative diversified system with Cover crops and Compost Soil tillage and Municipal Waste Compost (MWC - 37% of organic Carbon and 2.8% of organic N content on dry basis) supplied by the “AMA Roma Spa” company, deriving from organic wastes collected in the metropolitan area of Rome. Cover crops (mixture of Phacelia sp. and Coriandrum sativum in tree inter-rows + mixture of Trisetum sp., Vicia sp. and Trifolium sp. on tree rows). INC - Innovative diversified system with Natural cover and Compost Soil tillage limited to transplanting furrow and soil ripping in central inter-row space, natural cover, MCW. 9 Two self-compatible cultivars of apricot (Prunus armeniaca L.) with varying vigour levels were selected for the study: Kioto* (KM), a medium-ripening cultivar (3rd week of June in Central Italy) characterized by moderate vigour and high productivity, and Pieve* (PM), a medium-late ripening cultivar (4th week of June in Central Italy) known for its high vigor and good productivity. Both cultivars were grafted onto the Myrobalan 29 C rootstock, a clonal selection of Prunus cerasifera, which is known for its ability to moderate plant vigour and suitability for a wide range of soil types. The trees were planted at a spacing of 4.4 by 3 m, with each tree occupying an area of 13.2 m2. Each sub-plot consisted of 3 rows of 4 trees, covering an area of 132 m2. Figure 1. Pictures from the experimental site showing the apricot tree rows and inter-rows.