D2.2: HarvRESt monitoring KPIs for use cases

15/1/2025 Page 1 D2.2 HarvRESt monitoring KPIs for use cases www.harvrest.eu D2.2 HarvRESt monitoring KPIs for use cases 15 / 01 / 2025 15/1/2025 Page 2 D2.2 HarvRESt monitoring KPIs for use cases PROJECT INFORMATION ACRONYM HarvRESt PROJECT NAME Harnessing the vast potential of RES for sustainable farming PROGRAMME Horizon Europe TOPIC HORIZON-CL6-2023-CLIMATE-01-7 TYPE OF ACTION HORIZON Research and Innovation Actions PROJECT NUMBER 101136904 START DAY 1 January 2024 DURATION 36 months DOCUMENT INFORMATION TITLE D2.2 – HarvRESt monitoring KPIs for use case WORK PACKAGE WP2 TASK T2.4 LEAD PARTNER Suite5 CONTRIBUTORS CIRCE, BETA (UVic), NORCE, WR, EnG, CT DATE 15/01/2025 DISSEMINATION LEVEL Public 15/1/2025 Page 3 D2.2 HarvRESt monitoring KPIs for use cases DOCUMENT HISTORY VERSION DATE CHANGES RESPONSIBLE PARTNER 0.1 01/10/2024 Definition of ToC Suite5 0.4 28/11/2024 Review of known initiatives, addition of section 3 Suite5 0.5 06/12/2024 Initial definition of HarvRESt KPIs and mapping to Use Cases, addition of section 4 Suite5, CIRCE, BETA (UVic), NORCE, WR, EnG, CT 0.6 13/12/2024 Refinement of HarvRESt KPIs and mapping to Use Cases, addition of section 2 Suite5, CIRCE, BETA (UVic), NORCE, WR, EnG, CT 0.7 14/12/2024 Addition of Executive Summary & section 5 Suite5 0.8 16/12/2024 Draft version submitted for internal peer review Suite5 0.9 15/01/2025 Addition of KPIs conflict matrix (section 4.6), implementation of internal peer review comments Suite5, CIRCE, BETA (UVic), NORCE, WR, EnG, CT 1.0 15/01/2025 Final format changes CIRCE Disclaimer The project is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. 15/1/2025 Page 4 D2.2 HarvRESt monitoring KPIs for use cases TABLE OF CONTENTS EXECUTIVE SUMMARY ........................................................................................ 8 INTRODUCTION .................................................................................................. 9 Purpose and structure of the document ................................................... 9 Connection with other tasks ..................................................................... 9 FROM METRICS TO COMPREHENSIVE KPIS ........................................................ 10 Definition of KPIs .................................................................................... 10 KPI considerations for Agro Communities ............................................... 12 Review of known initiatives with KPIs for Agro Communities .................. 13 HARVREST USE CASES KEY PERFORMANCE INDICATORS ...................................... 23 Use Cases outcomes considerations ....................................................... 23 Initial definition of HarvRESt KPIs ............................................................ 25 Mapping of HarvRESt KPIs ....................................................................... 44 Monitoring & assessment approach ....................................................... 46 Data collection & processing ................................................................... 47 KPI conflicts ............................................................................................ 48 CONCLUSIONS .................................................................................................. 51 15/1/2025 Page 5 D2.2 HarvRESt monitoring KPIs for use cases REFERENCES ..................................................................................................... 52 15/1/2025 Page 6 D2.2 HarvRESt monitoring KPIs for use cases ABBREVIATIONS BaU Business as Usual BESS Battery Energy Storage System C Carbon CAP Common Agricultural Policy CAPEX Capital Expenditures CFA Climate Farm Advisors CFD ClimateFarm Demo CSF Climate Smart Farming D Deliverable DO Denominación Origen (Spanish, denomination of origin in English) DoA Description of Action DSS Decision Support System DST Decision Support Tool EC European Commission ETo Evapotranspiration EU European Union FAOSTAT Food and Agriculture Organization Statistics FEFTS Fossil-Energy-Free Technologies and Strategies GHG Greenhouse Gas ICT Information and Communication Technology IRR Internal Return Rate KPI Key Performance Indicator LAI Leaf Area Index LCOS Levelized Cost of Storage M Month NPK Nitrogen, Phosphorus, Potassium NPV Net Present Value OECD Organisation for Economic Co-operation and Development OPEX Operational and Maintenance Costs PDF Pilot Demo Farmers PV Photovoltaic 15/1/2025 Page 7 D2.2 HarvRESt monitoring KPIs for use cases RES Renewable Energy Sources RH Relative Humidity R&I Research and Innovation SA Surface Area SMART Specific, Measurable, Attainable, Relevant, and Timely SOC Solid Organic Carbon TBD To Be Defined TPAM True Price Assessment Method UC Use Case 15/1/2025 Page 8 D2.2 HarvRESt monitoring KPIs for use cases EXECUTIVE SUMMARY This deliverable reports on the project’s work related to the defini�on of KPIs and the analysis of their interac�on. As a first part, it provides a founda�onal understanding of KPIs and outlines key considera�ons specific to agro communi�es for selec�ng appropriate indicators. These considera�ons emphasize the unique characteris�cs of agricultural opera�ons, including environmental dependencies, social aspects and economic constraints. Furthermore, it conducts an extensive review of exis�ng projects and ini�a�ves in the same domain, leveraging insights to iden�fy established KPIs and associated best prac�ces. This groundwork ensures that the followed approach aligns with current advancements and addresses gaps in the measurement of RES integra�on in agricultural contexts. Building upon this founda�on, the second part commences by integra�ng findings from prior work inside HarvRESt which iden�fied preliminary considera�ons related to Use Case outcomes. It expands on these insights to propose a comprehensive set of KPIs under five categories: agricultural, economic, energy, environmental, and social. These KPIs aim to holis�cally evaluate the performance and sustainability of the project’s Use Cases, which include diverse farming opera�ons. Each KPI is me�culously defined with a clear descrip�on, detailed measurement guidelines, including data requirements, calcula�on formulas, and applicable units, ensuring their prac�cal applicability and reliability. To further facilitate the assessment of the project's Use Cases, a traceability matrix is laid out, linking KPIs to specific Use Cases. This mapping iden�fies the relevance of each KPI to individual Use Cases, offering clarity on performance measurement at both granular and overarching levels. The deliverable seeks, as much as possible at this stage of the project, to ensure that the KPIs could yield insights beyond the specifici�es of each Use Case’s context. To this end, considera�ons related to benchmarking the proposed KPIs against exis�ng agro community data, is provided wherever possible, to provide a reference point for evalua�ng RES integra�on while maintaining comparability with external contexts. Then, the deliverable outlines preliminary steps for data collec�on and pre-processing. These steps aim to standardise data gathering and enhance the robustness of KPI measurement across the five project Use Cases. By adop�ng this structured approach, we aim to develop a reliable ground for evalua�ng the integra�on of RES in agro communi�es; the work will be further evaluated in upcoming Work Packages, for example in order to understand trade-offs and to establish weigh�ng criteria. Finally, a conflict matrix has been developed in order to iden�fy which KPIs have a cri�cal effect in others. 15/1/2025 Page 9 D2.2 HarvRESt monitoring KPIs for use cases INTRODUCTION Purpose and structure of the document The deliverable at hand is the output of Task 4.2 at month 12 of the project. In the deliverable, the consor�um partners iden�fy and comprise a list of KPIs to characterise and describe the performance of the project’s Use Cases (i.e., the farms) from a produc�on and sustainability point of view. The KPIs comprise energy, agricultural, environmental, economic and social aspects. The KPIs have been devised using informa�on from several sources: (a) the nature of the project’s Use Cases, (b) other known projects and ini�a�ves (past or running), (c) exis�ng EC Monitoring Frameworks, (d) the HarvRESt Pillars as described in the project’s DoA. In the deliverable at hand, all proposed KPIs have been included and are currently considered as a preliminary list of KPIs to be calculated, monitored and assessed along the project dura�on. Each KPI has been mapped to one or more Use Cases, for which it is deemed relevant. In future work, ]the ini�al list of KPIs could be reduced, if deemed necessary. For each selected KPI, a solid descrip�on has been provided, followed by detailed guidelines for their measurement, i.e., what data is needed, which is the calcula�on formula, and what is the unit in which the KPI is expressed. In sec�on 3, the methodology for selec�ng the right indicators is given and the review of known projects and ini�a�ves (past or running) is presented. In sec�on 4, the Use Cases considera�ons from deliverable D2.1 are presented again, then enriched in order to define the list of KPIs; following this step, the mapping among KPIs and Use Cases is given in the form of a traceability matrix, and further considera�ons related to data collec�on, pre-processing and monitoring are laid out. Sec�on 5 concludes the present deliverable. Connection with other tasks The input for this task is the work done in Task 2.3, documented in deliverable D2.1 of HarvRESt project. From this deliverable, the expected outcomes of the project’s Use Cases were considered and enriched. As men�oned in sec�on 2.1 above, the list of KPIs which is laid down in this deliverable is considered preliminary. The results of Task 2.4 and deliverable D2.4 in par�cular will feed into Task 4.2 “Developing KPIs to monitor the RES system”, as well as Task 6.1 “Development of the Agricultural Virtual Power Plant”. In those two tasks, the list of KPIs will be further evaluated, for example in order to understand trade-offs and to establish weigh�ng criteria, and ul�mately used. 15/1/2025 Page 16 D2.2 HarvRESt monitoring KPIs for use cases In order to build the “AgEnergy Pla�orm”, domain experts enumerated the parameters that affect the decision to be made, across five contexts: Legal/ regula�ve/ administra�ve context (P1), Financial context (P2), Technical context (P3), Social context (P4), Environmental and climate ac�on context (P5). In order to evaluate the parameters, indicators were used. These were iden�fied in literature or decided on a per case basis by the experts. The indicators are used as inputs of the DST. In order to actually run the DST, user input is also required for calcula�ng the relevant indicators depending on applica�on category. A list of ques�ons was developed with the aim of finding the op�mal balance between minimiza�on of ques�ons and collect enough data to provide meaningful results. The outputs are calculated based on the user input and the weights assigned to each indicator. The approach undertaken in AgroFossilFree Horizon2020 project can be replicated to increase the func�onality of other comparable pla�orms in the Agricultural sector and beyond. True Price Assessment Method The True Price Assessment Method (TPAM) for agri-food products is a framework for revealing the hidden social and environmental costs, or externali�es, of producing and consuming agri-food products. These costs, which include GHG emissions, water pollu�on, and unsafe labour condi�ons, are not reflected in market prices but significantly affect the planet and society. True pricing assigns a monetary value to these externali�es, combining them with the product’s market price to reflect its “true price.” This approach provides transparency, helps iden�fy sustainability gaps, and encourages stakeholders—consumers, producers, and policymakers—to make informed and responsible decisions. A methodology like TPAM is cri�cal because agri-food value chains are complex, o�en spanning countries with differing regula�ons. Many sustainability challenges, such as carbon emissions and labour rights viola�ons, require consistent measurement and management. TPAM fills this gap by quan�fying externali�es and providing a clear monetary framework to measure their impacts. This allows governments to design more effec�ve policies, helps companies priori�ze and compare sustainability interven�ons, and guides consumers toward making more sustainable choices. For agri-food producers, the benefits of TPAM are significant. It not only highlights areas for improvement but also enables the communica�on of posi�ve impacts, such as reduc�ons in emissions or enhanced labour condi�ons, fostering trust with consumers and investors. The transparency provided by true pricing can also incen�vize beter prac�ces and innova�on, paving the way for long-term sustainability and economic resilience in the agri-food sector. Through structured assessments, TPAM supports alignment with sustainability goals and creates value for all stakeholders in the food system. In more detail, TPAM iden�fies and quan�fies key environmental and social impacts to calculate the "true price" of agri-food products. As shown in Figure 4, this approach is structured into three components: the valua�on framework, assessment methods, and impact-specific modules. These modules provide tailored methodologies for measuring and valuing six environmental (natural) and five social/human capital impacts associated with food produc�on and consump�on. 15/1/2025 Page 17 D2.2 HarvRESt monitoring KPIs for use cases Figure 4. Components of TPAM for agri-food products [11] The natural impacts addressed include: • Contribu�on to climate change (e.g., GHG emissions). • Land use, biodiversity, and ecosystem service impacts. • Soil degrada�on. • Scarce water use. • Air, soil, and water pollu�on. • Deple�on of fossil fuels and other non-renewable materials. The social and human capital impacts consist of: • Occupa�onal health and safety. • Living income for producers. • Child labour. • Consumer health. • Animal welfare. TPAM offers a standardized methodology for incorpora�ng the above-men�oned into decision-making processes, enabling informed choices by producers, consumers, and policymakers. BECoop Project The BECoop project, funded by European Union’s Horizon 2020 programme, aimed at unleashing the untapped poten�al of community bioenergy by crea�ng favourable condi�ons and offering technical and business support tools. It aspired to make bioenergy ini�a�ves more atrac�ve to stakeholders while fostering collabora�on within the global bioenergy community. As part of Europe's transi�on to sustainable energy, BECoop promoted bioenergy as a clean, renewable, and locally sourced alterna�ve that reduces carbon emissions and strengthens local economies. The project underscored the mul�faceted impact of bioenergy, addressing not only environmental benefits but also societal, economic, and ecological dimensions. By driving 15/1/2025 Page 18 D2.2 HarvRESt monitoring KPIs for use cases tangible ac�ons and partnerships, BECoop exemplified the transforma�ve power of community-driven renewable energy solu�ons. Through the establishment of four Renewable Energy Communi�es (RESCoops) in Spain, Poland, Italy, and Greece, BECoop demonstrated the poten�al of bioenergy to accelerate a fair and inclusive clean-energy transi�on. These use cases served as tes�ng grounds for innova�ve prac�ces, focusing on sustainable biomass sourcing and GHG reduc�on. The project evaluated its impact across mul�ple dimensions, including selfassessment by RESCoops, socioeconomic advancements, and environmental benefits. A comprehensive analysis revealed cri�cal insights into market uptake, offering lessons and iden�fying risks to guide future bioenergy community ini�a�ves. This evidence-based approach further underscored BECoop’s contribu�on to fostering resilient and sustainable local energy systems (Figure 5). Figure 5. BECoop Poster [12] A cornerstone of the BECoop project has been its self-assessment tool, designed to evaluate and enhance the viability of community bioenergy projects. The tool featured a methodology and indicators that helped stakeholders assess the current status and poten�al of their ini�a�ves. By using self-evalua�on forms, users could iden�fy the technical, procedural, and business strategies required for success. This roadmap ensured that cri�cal considera�ons were addressed, providing tailored recommenda�ons and links to relevant resources. Outputs included a visual "spider-net" representa�on of strengths and weaknesses, a clear status 15/1/2025 Page 19 D2.2 HarvRESt monitoring KPIs for use cases overview, and ac�onable guidance for developing robust business models. This innova�ve tool empowered communi�es to navigate the complexi�es of bioenergy projects and achieve their goals effec�vely. SPARCS Project The SPARCS project, funded by the Horizon 2020 program, aimed to transform urban areas into sustainable, posi�ve energy, and zero-carbon communi�es. It focused on crea�ng ci�zen-centred, environmentally friendly ecosystems through innova�ve energy systems and governance models. The project was run by a consor�um of 31 en��es, including the Lighthouse ci�es of Espoo (Finland) and Leipzig (Germany), which led large-scale demonstra�ons. Fellow ci�es like Maia (Portugal) and Reykjavik (Iceland) worked on replica�ng these solu�ons. The project emphasized technologies such as district hea�ng and cooling, renewable energy integra�on, and ac�ve ci�zen engagement to advance urban energy transforma�on. To con�nuously monitor and evaluate the impact achieved by the implementa�on of SPARCS interven�ons in both the Lighthouse and the Fellow ci�es, an assessment framework was needed. In order to define the SPARCS Holis�c Impact Assessment Methodology and the related KPIs, a seven-step approach was introduced as presented in Figure 6 below. Figure 6. SPARCS seven-step Holistic Impact Assessment Methodology [14] 15/1/2025 Page 20 D2.2 HarvRESt monitoring KPIs for use cases In Step 1, the detailed analysis of the “Morgenstadt assessment framework” was introduced as well as the evalua�on of four Smart City projects related methodologies, as a basis for the subsequent ac�ons, providing guidance and best prac�ces. In Step 2, a top-down approach was adopted to iden�fy the main list of KPIs, drilling into the core of the SPARCS project as a Smart City ini�a�ve. In Step 3, a complemen�ng botom-up method was followed; working with the city stakeholders to co-produce and enhance the list of KPIs, by analysing in detail all planned city interven�ons and iden�fying the resul�ng impacts. Step 4 of the methodology elaborated on the final list of indicators which were used for the needs of the SPARCS project, from the SPARCS technical partners as well as from the city representa�ves of Leipzig and Espoo; the indicators evaluated the project's technical, socioeconomic, and environmental outcomes, with metrics for energy efficiency, carbon footprint reduc�on, social inclusion, and economic viability. They were designed to track progress, guide replica�on, and assess ini�a�ves such as posi�ve energy districts, ci�zen engagement, and sustainable urban planning. With a complete set of KPIs available, a detailed data requirements analysis to calculate the indicators was performed, followed by a verifica�on of the availability of that data with the city partners, consis�ng of the Step 5 of the methodology. The normaliza�on methodology in Step 6 dealt with the introduc�on of a tool for the compara�ve assessment of the KPIs, towards the objec�ve evalua�on of the SPARCS interven�ons and the easy cross-city adop�on. Finally, under Step 7, the SPARCS process evalua�on approach and its corresponding ac�vi�es were introduced, allowing for a complete impact assessment verifica�on, regarding efficiency and effec�veness of the result achieved [14]. The SPARCS Visualisa�on Dashboard (Figure 7) u�lises current and historical city data to enable performance monitoring of the project’s Lighthouse ci�es and their respec�ve Posi�ve Energy Districts/Blocks; also enabling tracking of their urban transforma�on progress towards mee�ng the city vision. Figure 7. SPARCS Visualisation Dashboard [13] 15/1/2025 Page 21 D2.2 HarvRESt monitoring KPIs for use cases The project has yielded significant results, including pioneering business models for posi�ve energy districts, frameworks for replica�on across Europe, and enhanced mechanisms for ci�zen par�cipa�on. By suppor�ng low-carbon transport and renewable energy adop�on, SPARCS contributes to achieving carbon neutrality by 2050, highligh�ng the cri�cal role of communi�es in sustainable urban development. SYNERGY Project The SYNERGY project, funded under the Horizon 2020 program, focused on addressing the challenges of fragmented and siloed electricity data by enabling collabora�ve, data-driven innova�on across the energy sector. It aimed to transform the energy data landscape through a big data pla�orm that facilitated real-�me, secure, and privacy-preserving data sharing among stakeholders. This pla�orm supported holis�c op�miza�on of electricity networks and energy performance, fostering synergies across the value chain. SYNERGY’s objec�ves included delivering added-value services for actors like Distribu�on System Operators, Transmission System Operators, RES operators, and aggregators, introducing innova�ve business models driven by data analy�cs, and valida�ng its solu�ons through large-scale demonstrators. To assess its outcomes, SYNERGY developed a comprehensive set of KPIs. These indicators evaluated technical aspects like grid stability and energy efficiency, economic factors such as cost-effec�veness and revenue genera�on, and environmental benefits, including carbon emission reduc�ons. Social metrics assessed user engagement and stakeholder collabora�on, ensuring the solu�ons address diverse needs [15]. The SYNERGY Evalua�on Framework was mainly based on the European Electricity Grids Ini�a�ve (EEGI) framework, which proposes to compare the benefits of applying Research and Innova�on (R&I solu�ons) with the expected benefits of applying Business as Usual (BaU) solu�ons (Figure 8). Figure 8. European Electricity Grids Initiative (EEGI) framework Evaluation Proposal [15] However, considering the data orienta�on of the SYNERGY project and the func�onal capabili�es of the SYNERGY Big Data Pla�orm, it was also deemed important to evaluate the technical data-related aspects. Therefore, apart from SYNERGY KPIs, a list of Technical KPIs was also defined, introducing a set of Quan�ta�ve Technical Evalua�on KPIs, Data Asset Quality Evalua�on KPIs and User Experience/Acceptance Evalua�on KPIs. Another significant component of the SYNERGY Evalua�on Framework was the consolida�on of valida�on scenarios per demo case; each valida�on scenario involving a dis�nct set of use cases, a dis�nct set of energy applica�ons relevant to the demo cases, as well as a descrip�ve narra�ve of the workflow, data exchange, triggering events, interac�ons between stakeholders as they interweave within each demo case. 15/1/2025 Page 22 D2.2 HarvRESt monitoring KPIs for use cases Figure 9. SYNERGY Project Evaluation Framework [15] The project’s impact has been evident in its large-scale demonstrators, which tested the pla�orm across diverse energy scenarios. These demonstra�ons showcased improved energy system efficiency, enhanced decisionmaking through real-�me analy�cs, and the integra�on of RES. The SYNERGY pla�orm has also promoted collabora�on between stakeholders, driving the crea�on of new energy-as-a-service (EaaS) applica�ons. Outcomes included increased grid flexibility, reduced opera�onal costs, and a measurable reduc�on in carbon footprints across pilot sites. The solid Evalua�on Framework facilitated the replica�on of SYNERGY’s innova�ons across different contexts, suppor�ng the EU’s broader sustainability goals. 15/1/2025 Page 23 D2.2 HarvRESt monitoring KPIs for use cases HarvRESt USE CASES KEY PERFORMANCE INDICATORS Use Cases outcomes considerations The Italian Use Case centres on Fatoria Solidale del Circeo, an organic farm in the Circeo region dedicated to social inclusion and sustainable agriculture. The farm integrates individuals with disabili�es into its workforce, fostering personal and professional growth. Located in a Mediterranean climate near the Tyrrhenian Sea, the farm benefits from diverse soil types and water resources, suppor�ng the cul�va�on of crops like olives, tomatoes, zucchinis, and watermelons. It employs organic farming prac�ces, automated equipment, and advanced irriga�on techniques. The farm is also expanding its renewable energy infrastructure with an agroPV plant and aims to enhance its digital and monitoring systems for improved opera�ons. As discussed in deliverable D2.1 [16], the expected outcomes of this Use Case encompass the following aspects which should be considered. These include metrics related to the performance of assets, economic impact improvements in agricultural produc�on, enhanced social impact, and increased sustainability of farming prac�ces. These outcomes align with efforts to create new business models that value reduced carbon footprints and social impacts, leveraging renewable energy and exploring opportuni�es such as carbon credits and ESG-compliant cer�fica�ons. The Danish Use Case explores opportuni�es within Denmark’s highly advanced agricultural sector, characterized by extensive livestock farming, advanced agronomic prac�ces, and a robust biogas industry. With 62% of its land dedicated to agriculture, the country emphasizes sustainability, leveraging resources like sandy loam and clay soils for diverse crop cul�va�on and efficient irriga�on supported by groundwater. Danish farms integrate precision farming technologies, promote renewable energy use such as wind and biogas, and implement sustainable manure management to enhance produc�vity and environmental conserva�on. The Use Case focuses on leveraging these strengths to op�mize manure-based biogas produc�on and improve energy self-sufficiency. As discussed in deliverable D2.1 [16], the expected outcomes of this Use Case encompass the following aspects which should be considered. These include metrics related to the performance of biogas assets, op�miza�on of biogas produc�on processes, and improvements in the economic and environmental impacts of agricultural prac�ces. Specific metrics also address reduc�ons in GHG emissions, advancements in nutrient recovery and management, and scalability poten�al for innova�ve business models. This framework supports the development of a biogas planning tool and policy recommenda�ons, aligning with Denmark’s sustainability goals and promo�ng replicable strategies across the EU. The first Spanish Use Case focuses on two dis�nct loca�ons: Viñas del Vero in Somontano, Huesca, and Viñedos del Río Tajo in Guadamur, Toledo. These sites represent complementary approaches to integra�ng agricultural prac�ces and renewable energy. Viñas del Vero operates within the Somontano DO, cul�va�ng 15 grape varie�es across 515 hectares of vineyards and u�lizing advanced energy management systems to op�mize winery opera�ons. The area benefits from stony, limestone-rich soils and a Mediterranean climate with con�nental influences. Conversely, Viñedos del Río Tajo specializes in mechanized grape cul�va�on for highquality brandy produc�on on 430 hectares. This region leverages agrivoltaic systems to monitor the effects of par�al shading on vine health and grape quality, supported by IoT technologies and precise agricultural methods like vigour mapping and automated irriga�on. 15/1/2025 Page 24 D2.2 HarvRESt monitoring KPIs for use cases As discussed in deliverable D2.1 [16], the expected outcomes of this Use Case encompass the following aspects which should be considered. At VdV, the focus is on energy efficiency and renewable energy integra�on, with metrics such as solar genera�on performance, self-consump�on ra�os, batery storage efficiency, and reduc�ons in grid energy dependence and GHG emissions. Addi�onally, the opera�onal efficiency of the electric tractor is a key metric. At VRT, metrics include crop yield and quality, such as grape size, sugar content, and leaf area index, alongside vine physiology indicators like trunk diameter varia�on and photosynthesis rates. Furthermore, the influence of agrivoltaic systems on the microclimate and irriga�on water consump�on is also monitored to op�mize sustainable vineyard management. The second Spanish Use Case is centred on Torre Santamaria, a dairy farm and biogas plant in the Noguera region of Catalonia, operated by ACSA-Sorigué. This region, characterized by its con�nental Mediterranean climate, supports extensive agriculture and livestock farming, including over 25,000 cows. Torre Santamaria has pioneered waste-to-energy prac�ces, processing 30,000 tons of livestock waste and 20,000 tons of agrifood waste annually to produce biomethane, injected directly into the natural gas grid. The surrounding farmland cul�vates essen�al feed crops like alfalfa, corn, and straw, irrigated by the Canal d’Urgell. The site’s energy demand, exceeding 5.6 GWh annually, is monitored and op�mized through a SCADA system, with both automated and manual data collec�on processes enhancing energy and nutrient management. As discussed in deliverable D2.1 [16], the expected outcomes of this Use Case encompass the following aspects which should be considered. These include the op�miza�on of biogas produc�on from agro-residues and the enhancement of nutrient recovery processes from digestate, contribu�ng to improved soil quality and farm circularity. Key metrics to monitor include biogas produc�on efficiency, nutrient recovery rates, and improvements in soil health parameters such as water reten�on and fer�lity. Addi�onally, the assessment of methane produc�on from recycled CO2 sources offers theore�cal insights into advancing renewable energy pathways. These metrics align closely with cri�cal exploitable results, such as biogas planning tools and soil quality methodologies, ensuring sustainability and opera�onal efficiency. The Norwegian Use Case focuses on Røysland Gaard, a farm in southwestern Norway managed by Grønn Gardsenergi. The farm spans 2.2 million square meters, featuring grasslands, forests, and two lakes that offer hydropower poten�al. Dedicated to livestock produc�on, it supports 20 Wagyu catle and 175 pigs, producing premium meat for high-end establishments. Renewable energy ini�a�ves include PV panels, batery storage, and plans for hydropower installa�ons. The energy demand of 400,000 kWh/year primarily serves the integrated butchery and farm opera�ons. Advanced energy management and monitoring systems aim to achieve energy independence, leveraging local resources and automa�on to op�mize energy use. As discussed in deliverable D2.1 [16], the expected outcomes of this Use Case encompass the following aspects which should be considered. These include op�mizing energy produc�on and reducing costs through the integra�on of renewable sources, such as PV and hydropower, and leveraging advanced energy management tools. Addi�onally, metrics focus on reducing environmental impacts and improving sustainability in agricultural prac�ces, aligning with broader goals of economic and environmental benefits. Key exploitable results include smart energy system algorithms, decision support systems (DSS), and innova�ve business models, suppor�ng scalability and policy recommenda�ons for sustainable energy and agricultural prac�ces across Norway. 15/1/2025 Page 25 D2.2 HarvRESt monitoring KPIs for use cases Initial definition of HarvRESt KPIs In the following sec�ons 4.2.1 through to 4.2.5. a total of 53 preliminary HarvRESt KPIs have been iden�fied (Figure 10). The HarvRESt KPIs are cover five categories related to agro communi�es, which have been already iden�fied in sec�on 3.2 above. The following pie chart shows the number of KPIs per category. Figure 10. Preliminary HarvRESt KPIs Agricultural KPI Name KPI.AG.01 Crop Yield per Hectare and per Plant Description Measures the crop production per plant or tree and per hectare of land Data Crop Yield: number of fruits /ha, average weight of fruit, fruit production (kg) /ha Total size of land in hectare Total number of plants or trees per hectare Calculation Crop yield based on the total production (number of fruits and weight production (kg) obtained per hectare of land Units [kg/ha], [kg/plant], [nb fruits/ha], [nb fruits/plant] Category Agricultural 15/1/2025 Page 32 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EC.10 Investment ROI Description Measures the return on investment for the farm Data Net revenue (total revenue minus expenses) per year Total investment cost Calculation Net revenue per year / Total investment cost Units [%] Category Economic KPI Name KPI.EC.11 Levelized Cost of Storage (LCOS) Description Measures the average cost per unit of energy stored and delivered over the entire lifetime of the system Data CAPEX = Capital Expenditures (initial investment costs) OPEX = Operational and Maintenance Costs FUEL = Price of electricity input Energy Throughput = Total energy stored and delivered over the lifetime of the project, typically measured in kWh or MWh Calculation LCOS = ∑Total Costs (CAPEX+OPEX+FUEL) / ∑Energy Throughput Units [€/MWh] Category Economic Energy KPI Name KPI.EN.01 Self-consumption Ratio Description Measures the percentage difference in energy self-consumption ratio during a specific timeframe Self-consumption ratio refers to energy that is produced within the farm itself divided by the total amount of energy used by the farm Data Energy self-consumption ratio initial Energy self-consumption ratio final Calculation (Self-consumption final – Self-consumption initial) / Self-consumption initial 15/1/2025 Page 33 D2.2 HarvRESt monitoring KPIs for use cases Units [%] Category Energy KPI Name KPI.EN.03 Operational Flexibility Description Measures the percentage difference of the flexibility capacity of demand assets during a specific timeframe Data Flexibility Capacity initial Flexibility Capacity final Calculation (Flexibility Capacity final – Flexibility Capacity initial) / Flexibility Capacity initial Units [%] Category Energy KPI Name KPI.EN.04 Energy Storage Capacity Description Measures the percentage difference of the energy storage capacity during a specific timeframe Data Energy Storage Capacity initial Energy Storage Capacity final Calculation (Energy Storage Capacity final – Energy Storage Capacity initial) / Energy Storage Capacity initial Units [%] Category Energy 15/1/2025 Page 34 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.05 Energy Export Ratio Description Measures the percentage difference in energy export share during a specific timeframe Energy export share refers to energy that is sold to the grid divided by the total amount of energy produced within the farm Data Energy Export Share initial Energy Export Share final Calculation (Energy Export Share final – Energy Export Share initial) / Energy Export Share initial Units [%] Category Energy KPI Name KPI.EN.06 Energy Import Ratio Description Measures the percentage difference in energy import share during a specific timeframe Energy import share refers to energy that is bought from the grid divided by the total amount of energy consumed by the farm Data Energy Import Share initial Energy Import Share final Calculation (Energy Import Share final – Energy Import Share initial) / Energy Import Share initial Units [%] Category Energy KPI Name KPI.EN.07 Effective Renewable Generation Description Measures the amount of renewable energy that was produced within the farm Data Renewable Energy Produced Calculation Renewable Energy Produced Units [MWh] Category Energy 15/1/2025 Page 35 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.08 Renewable Energy Surplus Description Measures the amount of renewable energy that was produced within the farm which was not used by the farm Data Renewable Energy Produced Renewable Energy Consumed Calculation Renewable Energy Produced – Renewable Energy Consumed Units [MWh] Category Energy KPI Name KPI.EN.09 Effective Consumption Description Measures the amount of energy consumed within the farm during a specific period Data Energy consumed Calculation Energy consumed Units [MWh] Category Energy KPI Name KPI.EN.10 BESS Cycles (net capacity) Description Equivalent cycles of BESS based on the effective battery capacity (max charge-min charge) Data Energy Throughput = Total energy stored and delivered over the lifetime of the project, typically measured in kWh or MWh Effective BESS Capacity = Real capacity considering the max charge-min charge BESS boundaries Calculation BESS net Cycles = ∑Total Energy Throughput / Effective BESS Capacity Units [Cycles] Category Energy 15/1/2025 Page 36 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.11 BESS Cycles (total capacity) Description Equivalent cycles of BESS based on the total (nominal) battery capacity Data Energy Throughput = Total energy stored and delivered over the lifetime of the project, typically measured in kWh or MWh Nominal BESS Capacity = Total capacity not considering max charge-min charge BESS boundaries Calculation BESS total Cycles = ∑Total Energy Throughput / Nominal BESS Capacity Units [Cycles] Category Energy KPI Name KPI.EN.12 Electric Tractor Operational Efficiency Description Measures the energy consumption per hour of electric tractor operation Data Energy Consumed: The total energy consumed during charging (e.g., in MWh or kWh) Operating Hours: The total hours the tractor has been operated Calculation 𝐿𝐿𝑂𝑂𝐸𝐸𝐸𝐸𝐶𝐶𝑡𝑡𝐼𝐼𝑃𝑃𝐸𝐸𝐶𝐶𝐹𝐹 𝐿𝐿𝐸𝐸𝐸𝐸𝐼𝐼𝑃𝑃𝐼𝐼𝐸𝐸𝐸𝐸𝑃𝑃𝐸𝐸 =𝐿𝐿𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐿𝐿𝑃𝑃𝐸𝐸𝐶𝐶𝑃𝑃𝐼𝐼𝐸𝐸𝑃𝑃 𝐿𝐿𝑂𝑂𝐸𝐸𝐸𝐸𝐶𝐶𝑡𝑡𝐼𝐼𝐸𝐸𝐸𝐸 ℎ𝑃𝑃𝑃𝑃𝐸𝐸𝐶𝐶 Units [MWh/hour] or [kWh/hour] Category Energy KPI Name KPI.EN.13 Energy Use Intensity Description Measures the amount of energy used per unit of production output (e.g., per kilogram of crop produced, per unit of livestock, or per hectare of land) Data Total Energy Consumed Unit of Production Calculation Energy Consumed Units [MWh/unit of production output] Category Energy 15/1/2025 Page 37 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.14 PV Performance in an Agrivoltaic System Description Measures the efficiency of a solar Agri PV system. It represents the ratio of actual energy generated in a one axe tracker installation tracking the sun with a modified algorithm that benefits the crop to the theoretical energy expected in the same installation using the standard astronomical sun tracking algorithm Data PV Energy Produced with a modified algorithm that benefits the crop PV Potential Energy Expected with the standard astronomical algorithm Calculation PV Energy Produced with a crop under panels (modified sun tracking algorithm) / PV Energy produced without crop (standard astronomical sun tracking algorithm) Units [%] Category Energy KPI Name KPI.EN.15 Renewable Energy Share Description Measures the percentage of renewable energy that is used by the farm divided by the total amount of energy used by the farm Data Renewable Energy Consumed Total Energy Consumed Calculation Renewable Energy Consumed / Total Energy Consumed Units [%] Category Energy KPI Name KPI.EN.16 Energy Consumption Reduction Description Measures the percentage in total energy consumption during a specific timeframe Data Total Energy Consumed initial Total Energy Consumed final Calculation (Total Energy Consumed final – Total Energy Consumed initial) / Total Energy Consumed initial Units [%] Category Energy 15/1/2025 Page 38 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.17 Energy Recovery Ratio Description Measures the percentage of energy that is recovered and reused within the farm system Data Recycled Energy Total Energy Consumed Calculation Recycled Energy / Total Energy Consumed Units [%] Category Energy KPI Name KPI.EN.18 PV Performance Ratio Description Measures the overall efficiency of a solar photovoltaic system. It represents the ratio of actual energy generated to the theoretical energy expected under ideal conditions Data PV Energy Produced PV Energy Expected Calculation PV Energy Produced / PV Energy Expected Units [%] Category Energy KPI Name KPI.EN.19 Effective energy charged in BESS Description Measures the total amount of energy charged into the BESS during a specific period Data Energy input to BESS (measured in MWh) Calculation Sum of all energy charged into the BESS over the evaluation period. Units [MWh] Category Energy 15/1/2025 Page 39 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EN.20 Effective energy discharged from BESS Description Measures the total amount of energy discharged from the BESS during a specific period. Data Energy output from BESS (measured in MWh). Calculation Sum of all energy discharged from the BESS over the evaluation period. Units [MWh] Category Energy Environmental KPI Name KPI.EV.01 Soil Quality Impact Description Measures the RES impact on soil quality / soil degradation based on soil organic carbon loss texture, fertility, acidity and microbial activity. Data Soil organic C (SOC), organic matter Soil texture (% of clay, sand and silts) Soil fertility (N, P, K content) Soil pH Soil biological activity Calculation Determination of soil organic C, nutrient content (N, P, K), texture, pH and microbial activity Units [%], [µg/g], [mg/kg], [kg/ha] Category Environmental KPI Name KPI.EV.02 Reduction in GHG Emissions Description Measures the percentage difference in GHG emissions during a specific timeframe Data GHG Emissions initial GHG Emissions final Calculation (GHG final – GHG initial) / GHG initial Units [%] Category Environmental 15/1/2025 Page 40 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EV.03 Reduction in CO2 Emissions Description Measures the percentage difference in CO2 emissions during a specific timeframe Data CO2 Emissions initial CO2 Emissions final Calculation (CO2 final – CO2 initial) / CO2 initial Units [%] Category Environmental KPI Name KPI.EV.04 N & P Losses Description Measures the soil losses in Nitrate and Phosphorus (including N & P leaching and NH3 emissions) Data Available N (NH4+, NO3-), total N Available P (PO4), total P NH3 emissions Calculation Content of N and P at different soil depths to estimate leaching process, and analysis of NH3 emissions from the soil (when the product is applied). Units [%], [mg/kg], [kg/ha] Category Environmental KPI Name KPI.EV.05 Carbon Emissions Intensity Description Measures the amount of carbon emissions released per unit of production output (e.g., per kilogram of crop produced, per unit of livestock, or per hectare of land) Data Total CO2 Emissions Unit of Production Calculation CO2 Emissions Units [kTons] Category Environmental 15/1/2025 Page 41 D2.2 HarvRESt monitoring KPIs for use cases KPI Name KPI.EV.06 Changes in the crop micro-climate (ETo reduction) Description The potential Evapotranspiration (mm) measures the amount of water lost by plants transpiration and soil evaporation and it is a great calculation for expressing all the atmospheric parameters (temperature, RH, wind speed and solar radiation) The coexistence of solar panels with a crop in an Agri PV system may change this parameters, expecting the ETo to be reduced by the partial shadowing of the crop. Data ETo (mm) in shaded plants by PV panels ETo (mm) in not shaded plants Calculation (ETo in shaded plants by PV panels – ETo in not shaded plants ) / ETo in not shaded plants Units [%] Category Environmental KPI Name KPI.EV.07 Reduction of soil and air temperature Description In an Agri PV the crop is partially shaded by solar panels. This shadowing may result in the reduction of temperature (both air and soil temperature) in shaded areas. This temperature reduction can be important in the event of a heat wave, making plants have a bigger physiological activity and lower thermal stress. Data Air temperature (ºC) and soil temperature (ºC) at different depths in plants shaded by solar panels; continuously measured by T sensors Air temperature (ºC) and soil temperature (ºC) at different depths in not shaded plants; continuously measured by T sensors Calculation (Air and Soil Temperature in shaded plants by PV panels – Air and Soil Temperature in not shaded plants) / Air and Soil Temperature in not shaded plants Units [%] Category Environmental KPI Name KPI.EV.08 Reduction of Solar Radiation received by the crop Description In an Agri PV the crop is partially shaded by solar panels. It is important to know and measure the solar radiation received by the crop, and it is going to be reduced compared to the same crop never shaded Data Solar radiation (W/m2 ) received by plants partially shaded by solar panels; continuously measured with a pyranometer 15/1/2025 Page 48 D2.2 HarvRESt monitoring KPIs for use cases percep�ons and socio-economic condi�ons, allowing for the calcula�on of qualita�ve KPIs, more prominently in the “Social” category, but also in the “Economic” category. IoT sensors, installed in the farms, or in the systems used by the farms for monitoring reasons, shall provide real-�me monitoring of metrics like PV systems energy genera�on, or energy usage of the electric tractor. Addi�onally, publicly available datasets, such as those from FAOSTAT [20] and na�onal agricultural databases, could be used to enrich the analysis with historical and compara�ve data. The Use Cases of the HarvRESt project are vital for gathering context-specific informa�on, such as local PV installa�ons and their par�culari�es, energy expenditure and energy availability, ensuring data relevance and granularity. To ensure comparability, collected datasets shall be normalized, in order to account for variability in agroecological condi�ons, enabling fair comparisons across different regions. Sta�s�cal models and machine learning techniques shall be employed to isolate and adjust for these confounding factors, ensuring the data reflects true performance differences rather than external influences. Benchmarking shall also involve aligning the data with interna�onal standards, as described in sec�on 4.4 above. Before feeding them to the analy�cs pipelines, the collected datasets shall undergo rigorous pre-processing to enhance their quality and usability. Handling missing or incomplete data is cri�cal; techniques like imputa�on (mean, median, or machine learning-based) and sensi�vity analysis shall be used to fill gaps without compromising accuracy. Seman�c enrichment shall standardize and contextualize the data, such as unifying units of measurement, annota�ng data with metadata, and harmonizing terminologies to ensure consistency. These steps should establish clean, enriched datasets ready for analysis. With this founda�on, con�nuous monitoring and real-�me KPI assessment become feasible, enabling dynamic decision-making and adap�ve management in the context of the HarvRESt project’s goals. KPI conflicts A�er the defini�on of preliminary HarvRESt KPIs (sec�on 4.2) and the subsequent assignment of those KPIs to the project’s Use Cases (sec�on 4.3), an ini�al iden�fica�on of conflic�ng KPIs was performed. Ini�ally, the iden�fica�on of possible conflicts at this point has been at a high-level, purely based on the defini�on and objec�ve of each KPI, according also to the exper�se of those partners who proposed each KPI. As a further step, the degree of conflict was assessed (Low or High). Building upon this preliminary work, further analysis of the conflict extent shall be performed in WP4 and WP6 where the weigh�ng criteria (and respec�ve trade-offs) will be assessed. The following Table 3 presents the possible conflicts between HarvRESt KPIs. 15/1/2025 Page 49 D2.2 HarvRESt monitoring KPIs for use cases Table 3. Conflicting HarvRESt KPIs KPI ID KPI ID Conflict Level KPI.AG.01 Crop Yield per Hectare and per Plant KPI.EC.03 Energy Market Revenue High KPI.EN.01 Self-consumption Ratio High KPI.EN.04 Energy Storage Capacity High KPI.EN.05 Energy Export Ratio High KPI.EN.07 Effective Renewable Generation High KPI.EN.14 PV Performance in an Agrivoltaic System High KPI.EN.18 PV Performance Ratio High KPI.EN.19 Effective energy charged in BESS High KPI.EV.02 Reduction in GHG Emissions High KPI.EV.03 Reduction in CO2 Emissions High KPI.AG.05 Grape Quality KPI.EC.03 Energy Market Revenue Low KPI.EN.07 Effective Renewable Generation High KPI.EN.14 PV Performance in an Agrivoltaic System High KPI.EN.18 PV Performance Ratio High KPI.AG.06 Leaf Area Index and SA KPI.EC.03 Energy Market Revenue High KPI.EN.07 Effective Renewable Generation High KPI.EN.18 PV Performance Ratio Low KPI.AG.07 Irrigation Water Consumption KPI.EN.07 Effective Renewable Generation Low KPI.EN.14 PV Performance in an Agrivoltaic System Low KPI.EN.18 PV Performance Ratio Low KPI.EC.01 Profit per Hectare KPI.EC.03 Energy Market Revenue High KPI.EC.04 Levelized Cost of Energy Low KPI.EC.09 OPEX Reduction Low KPI.EN.01 Self-consumption Ratio High KPI.EN.04 Energy Storage Capacity High KPI.EN.05 Energy Export Ratio High KPI.EN.19 Effective energy charged in BESS High KPI.EV.02 Reduction in GHG Emissions High KPI.EV.03 Reduction in CO2 Emissions High 15/1/2025 Page 50 D2.2 HarvRESt monitoring KPIs for use cases KPI ID KPI ID Conflict Level KPI.EC.03 Energy Market Revenue KPI.EN.01 Self-consumption Ratio High KPI.EN.04 Energy Storage Capacity High KPI.EN.06 Energy Import Ratio Low KPI.EN.08 Renewable Energy Surplus High KPI.EN.19 Effective energy charged in BESS Low KPI.EV.06 Changes in the crop micro-climate (ETo reduction) Low KPI.EV.07 Reduction of soil and air temperature Low KPI.EV.08 Reduction of Solar Radiation received by the crop Low KPI.EC.06 Internal Return Rate KPI.EV.03 Reduction in CO2 Emissions Low KPI.EC.08 Energy Purchase Expenditure KPI.EC.10 Investment ROI Low KPI.EC.10 Investment ROI KPI.EN.06 Energy Import Ratio Low KPI.EC.10 Investment ROI KPI.EN.19 Effective energy charged in BESS Low KPI.EN.01 Self-consumption Ratio KPI.EN.19 Effective energy charged in BESS Low KPI.EV.08 Reduction of Solar Radiation received by the crop Low KPI.EN.04 Energy Storage Capacity KPI.EV.08 Reduction of Solar Radiation received by the crop Low KPI.EN.07 Effective Renewable Generation KPI.EV.06 Changes in the crop micro-climate (ETo reduction) Low KPI.EV.07 Reduction of soil and air temperature Low KPI.EV.08 Reduction of Solar Radiation received by the crop Low KPI.EN.14 PV Performance in an Agrivoltaic System KPI.EV.06 Changes in the crop micro-climate (ETo reduction) Low KPI.EV.07 Reduction of soil and air temperature Low KPI.EV.08 Reduction of Solar Radiation received by the crop Low KPI.EN.18 PV Performance Ratio KPI.EV.06 Changes in the crop micro-climate (ETo reduction) Low KPI.EV.07 Reduction of soil and air temperature Low KPI.EV.08 Reduction of Solar Radiation received by the crop Low 15/1/2025 Page 51 D2.2 HarvRESt monitoring KPIs for use cases CONCLUSIONS This deliverable presented the outcomes of the project’s efforts to define KPIs and analyse their interac�on within the context of RES integra�on in agro communi�es. At first, it established a founda�onal understanding of KPIs and highlighted key considera�ons specific to agro communi�es for selec�ng appropriate indicators. It then conducted desk research of exis�ng projects and ini�a�ves in the same domain, in order to ensure alignment with current advancements and to iden�fy any possible gaps in the measurement of RES integra�on in agricultural contexts. Building on this founda�on, insights from prior work conducted within HarvRESt, related to Use Case outcomes, was taken on board in order to act as a stepping stone for the proposal of a comprehensive, yet preliminary, set of KPIs across five categories: agricultural, economic, energy, environmental, and social. Each KPI has been carefully defined to ensure prac�cal applica�on and reliability, with detailed measurement guidelines provided. These guidelines include the necessary data inputs, calcula�on formulas, and units of measurement, ensuring consistency and applicability across different Use Cases. Addi�onally, a traceability matrix was developed to map the relevance of each KPI to specific Use Cases, providing clarity for performance assessment both at the granular level of individual Use Cases and across the project as a whole. Addi�onally, the deliverable took on the challenge of ensuring that KPI results are broadly applicable beyond the specifici�es of the project’s Use Cases. Where feasible, benchmarking considera�ons were provided to align the proposed KPIs with data from exis�ng agro communi�es, thereby enabling compara�ve analysis and enhancing the generalizability of findings. Moreover, the deliverable discussed the ini�al considera�ons on data collec�on and pre-processing to standardize data gathering and improve the robustness of KPI measurement across the five project’s Use Cases. This structured approach lays the groundwork for evalua�ng the integra�on of RES in agricultural communi�es. Future work in upcoming Work Packages will build on this founda�on to explore trade-offs, refine weigh�ng criteria, and further analyze the interac�on between KPIs. This itera�ve process aims to support the development of sustainable, scalable frameworks for RES integra�on in agro communi�es. Finally, an assessment of the conflic�ng KPIs was performed based on the defini�on of each KPI and the exper�se Consor�um. Further analysis of the conflict extent shall be performed in WP4 and WP6 where the weigh�ng criteria (and respec�ve trade-offs) will be assessed. 15/1/2025 Page 52 D2.2 HarvRESt monitoring KPIs for use cases REFERENCES [1] Artley, W., Stroh, S. (2001). The performance-based management handbook. Volume Two. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:The+PerformanceBased+Management+Handbook#2 [2] Rooijen, T. van, Nesterova, N. (2013). Applied framework for evaluation in CIVITAS PLUS II. CIVITAS WIKI, Deliverable 4.10. Retrieved from https://civitas.eu/sites/default/files/Results and Publications/civitas_wiki_d4_10_evaluation_framework.pdf [3] European Commission, European Green Deal, The common agricultural policy: 2023-27. Retrieved from https://agriculture.ec.europa.eu/common-agricultural-policy/cap-overview/cap-2023-27_en [4] EU CAP Network, Climate Neutral Farms – ClieNFarms. Retrieved from https://eu-capnetwork.ec.europa.eu/projects/climate-neutral-farmsclienfarms_ennetwork.ec.europa.eu/projects/climate-neutral-farms-clienfarms_en [5] Cesbio, ClieNFarms. Retrieved from https://www.cesbio.cnrs.fr/agricarboneo/projects/clienfarms/ [6] Open Access Government, Supporting Europe’s transition to climate-neutral farming. 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True price assessment method for agri-food products, Version 1, January 2023. Retrieved from https://www.truepricefoundation.org/wp-content/uploads/2023/04/230206_True-Price-AssessmentMethod_published.pdf [12] BECoop Project Dissemination Material. Retrieved from https://www.becoopproject.eu/resources/dissemination-material/project.eu/resources/dissemination-material/ [13] SPARCS Project Visualisation Dashboard. Retreived from https://sparcsvf.s5labs.eu/PUBLIC/LEIPZIGvf.s5labs.eu/PUBLIC/LEIPZIG [14] SPARCS Project, Deliverable D2.2 Definition of SPARCS Holistic Impact Assessment Methodology and Key Performance Indicators (updated version). Retrieved from https://sparcs.info/en/deliverables/d2-02definition-of-sparcs-holistic-impact-assessment-methodology-and-key-performance-indicators-updatedversion/ 15/1/2025 Page 53 D2.2 HarvRESt monitoring KPIs for use cases [15] SYNERGY Project, Deliverable D8.4 SYNERGY Evaluation Framework and Respective Validation Scenarios. Retrieved from https://synergyh2020.eu/wp-content/uploads/sites/19/2024/02/8.4.pdf [16] HarvRESt Project, Deliverable D2.1 Mapping of RES integration in farms at EU level. Retrieved from https://harvrest.eu/media/gqkpfwmt/harvrest_d21_mapping-of-res-integration_ckic_web.pdf [17] GHG Protocol, Standards & Guidance. Retrieved from https://ghgprotocol.org/standardsguidanceguidance [18] Social Accountability International, SA8000 Resource Center. Retrieved from https://saintl.org/resources/sa8000-resource-center/intl.org/resources/sa8000-resource-center/ [19] OECD, Agriculture & Fisheries. Retrieved from https://www.oecd.org/en/topics/agriculture-andfisheries.htmland-fisheries.html [20] Food and Agriculture Organization of the United Nations, FAOSTAT Food and Agriculture Data. Retrieved from https://www.fao.org/faostat/en/?/data/QC#home 15/1/2025 Page 54 D2.2 HarvRESt monitoring KPIs for use cases PARTNER SHORT NAME CIRCE Research Centre CIRCE BETA Technological Centre BETA (UVic) NORCE NORCE Tecnoalimenti TCA WHITE WR Suite5 Data Intelligence Solutions Ltd. Suite5 EnGreen EnG ConTerra CT Confagricoltura CONFAGRI The project The HarvRESt project aims to enhance the sustainable produc�on of renewable energy at farm-level. This approach not only makes farms climate-neutral but also op�mizes produc�on, reduces their impact on natural resources and biodiversity, and provides energy services to communi�es, thereby diversifying economic income. However, deciding how best to integrate renewable energy sources (RES) on a farm is not without its challenges. The decision is a complex one, with many factors to consider. Due to this, HarvRESt seeks to iden�fy, understand, and overcome the exis�ng barriers hindering the widespread adop�on of this innova�ve approach. Current ini�a�ves o�en overlook the complex interac�ons and factors within the farming and RES context, resul�ng in ineffec�ve support for decision-making based on accurate projec�ons, es�ma�ons, and forecasts. HarvRESt will therefore consolidate and enhance exis�ng knowledge, crea�ng an Agricultural Virtual Power Plant capable of running diverse scenarios and farm configura�ons. This tool will determine the best opera�onal procedures for a given RES solu�on, providing valuable data to a decision support system. This system will weigh trade-offs and key indicators, offering tailor-made recommenda�ons to farmers and policymakers. 15/1/2025 Page 55 D2.2 HarvRESt monitoring KPIs for use cases Contact us www.harvrest.eu htps://linkedin.com/harvRESt htps://twiter.com/HarvRESt_eu Fattoria Solidale del Circeo FSDC Viñas del Vero VdV Viñedos del Rio Tajo VRT Sorigué ACSA-Sorigué Grønn Gårdsenergi AS GGE Food & Bio Cluster Denmark FBCD EIT Climate-KIC CKIC