Report on final version on decision support tool for soil health business models

2 NOVASOIL INNOVATIVE BUSINESS MODELS FOR SOIL HEALTH Grant agreement ID: 101091268 Report on final version on decision support tool for soil health business models Deliverable D 3.9 Project NOVASOIL Project title INNOVATIVE BUSINESS MODELS FOR SOIL HEALTH Work Package 3 Testing and development of a toolbox for incentives Deliverable 3.6 Period covered 01/11/2022-31/10/2025 Publication date 30/10/2025 Dissemination level PU Organisation name of lead beneficiary for this report New Bulgarian University Authors Dimitre Nikolov, Ekatherina Tzvetanova-Georgieva, Ivan Boevsky, Martin Banov, Krasimir Kostenarov, Vania Bankova Contributors Ref. Ares(2025)9374265 - 31/10/2025 3 4 QUALITY ASSURANCE PROCEDURES This document has been reviewed according to the NOVASOIL Project Management Plan. TABLE REVISION HISTORY DELIVERABLE Row Version Date Reviewers Description 1 V1.0 27/10/2025 NBU Draft version circulated by email 2 V2.0 30/10/2025 EVENOR Final version with minor changes 3 4 5 Project Consortium Nº Participant organisation name Countr y 1 EVENOR TECH SLU ES 2 LEIBNIZ-ZENTRUM FUER AGRARLANDSCHAFTSFORSCHUNG GE 3 ZEMNIEKU SAEIMA LV 4 NEW BULGARIAN UNIVERSITY BU 5 CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR 6 KOBENHAVNS UNIVERSITET DK 7 TECHNISCHE UNIVERSITAET MUENCHEN GE 8 ASSEMBLEE DES REGIONS EUROPEENNES FRUITIERES LEGUMIERES ET HORTICOLES FR 9 ISTITUTO DELTA ECOLOGIA APPLICATA SRL IT 10 UNIVERSITA DEGLI STUDI DI FERRARA IT 11 WAGENINGEN UNIVERSITY NL 12 CENTRE OF ESTONIAN RURAL RESEARCH AND KNOWLEDGEEESTI EE 13 UNIVERSIDAD POLITECNICA DE MADRID ES 14 UNIVERSITA DI PISA IT 15 ASOCIACION AGRARIA JOVENES AGRICULTORES DE SEVILLA ES 16 UNIVERSITY OF LEEDS GB 6 Table of contents List of figures .............................................................................................................................................. 7 List of tables ................................................................................................................................................ 7 List of abbreviations ............................................................................................................................... 8 Summary ...................................................................................................................................................... 9 1 Introduction ....................................................................................................................................... 9 1.1 Context of WP 3 objectives ............................................................................................. 9 1.2 Connection with the other WPs .................................................................................. 9 1.3 Purpose and scope of the document ..................................................................... 10 1.4 Process of development ................................................................................................. 10 2 Conceptual framework ............................................................................................................. 14 2.1 DST framework and connection between the tools ............................................ 14 2.2 Improvement of digital support tool ............................................................................. 15 2.3 NOVASOIL DST description .................................................................................................. 15 2.3.1 Risk assessment module (RAM) ................................................................................ 16 2.3.2 Optimisation module ....................................................................................................... 21 3. Decision support tool Design ...............................................................................................26 3.1 Risk assessment module (RAM) ........................................................................................26 3.2 Optimisation module (OM) ................................................................................................ 28 3.3 The registration ............................................................................................................................ 31 4 Conclusions ........................................................................................................................................... 32 Resources ................................................................................................................................................... 33 Acknowledgment ................................................................................................................................. 34 7 List of figures Figure 1. NOVASOIL digital toolbox ............................................................................................ 10 Figure 2 The testing and validation activities related to toolbox .............................. 11 Figure 3 Connection between the tools.................................................................................. 14 Figure 4 DST architecture ................................................................................................................. 15 Figure 5 RAM first page .....................................................................................................................26 Figure 6 DST mandatory information regarding business model ......................... 27 Figure 7 Average daily temperature ......................................................................................... 27 Figure 8 RAM - result .......................................................................................................................... 28 Figure 9 RAM recommendations ............................................................................................... 28 Figure 10 OM – first screen of optimisation module ........................................................29 Figure 11 OM – before mitigation .................................................................................................29 Figure 12 OM – after mitigation.................................................................................................... 30 Figure 13 OM – the overview .......................................................................................................... 30 Figure 14 OM – limitations ............................................................................................................... 30 Figure 15 OM – the result .................................................................................................................... 31 List of tables Table 1 Digital tools for analysis evaluation ............................................................................. 11 Table 2 Case study data collection............................................................................................... 12 Table 3 Current temperature sum >100C ............................................................................... 16 Table 4. Assessment of natural atmospheric humidification .................................... 16 Table 5 Prevailing slope gradient ................................................................................................. 17 Table 6 Presence of stones and gravel in the topsoil ....................................................... 17 Table 7 Value of the texture factor characteristic of the soil ....................................... 17 Table 8 Groundwater level ............................................................................................................... 18 Table 9 Mechanical composition (physical clay - particles < 0.01 mm %)........... 18 Table 10 Depth to root barrier ........................................................................................................ 18 Table 11 Soil reaction value ............................................................................................................... 19 Table 12 Humus content of the surface layer ....................................................................... 19 Table 13 Soil Health Risk Assessment ........................................................................................ 19 Table 14 Recommendations for soil health improvement ......................................... 20 Table 15 Gross margin calculation for current practices ................................................ 21 Table 16 Gross margin calculation after risk mitigation practices .......................... 22 Table 17 Crop rotation scenario .................................................................................................... 24 8 List of abbreviations WP Working package EU European Union DHK Digital Hub of Knowledge SHBM Soil Health Business Models FAQ Frequently Asked Questions DST Decision support tool DTA Decision tool for analysis CoP Community of practice 9 Summary This document reports on final version of Decision Support Tool (DST) for soil health business models developed under T 3.5: “Decision support tool for soil health business models“ in the framework of WP3 "Testing and development of a toolbox for incentives" under the NOVASOIL project. In task 3.5, it is developed DST which supports the strategic decisions regarding to the soil health risks on two steps: 1) support evaluation of soil health in 12 short steps; 2) support risk mitigation optimizing gross margin data given specific limitations. The report presents structure and modules contained in the NOVASOIL DST for soil health business models. Also, it is presented the applied user-friendly architecture of the NOVASOIL DST and its design. 1 Introduction 1.1 Context of WP 3 objectives The objective of the task T 3.5: “Decision support tool for soil health business models” is build a DST for soil health business models. The tool is developed as a web-based digital tool and free-open access. The adopted approach provides web-based tools that allow live updates, eliminate the need for client-server infrastructure, and ensure direct user access without intermediary marketplaces. The NOVASOIL toolbox interface is responsive for mobile devices. 1.2 Connection with the other WPs Connection with WP 1 and WP2: Information is provided based on the lessons learned from the key actors and stakeholders from Discrete Choice Experiments and interaction with NOVASOIL case studies. Connection with WP3: All tools under NOVASOIL toolbox, including the DST, were tested and it was made valorization based on the data and feedback from NOVASOIL Community of Practice (CoP) and case studies under T3.4. Connection with WP4: Lessons learned from Choice Experiments especially the one related the supply side. Connection with WP5: Task 3.5 is shared with the audience, including the Communities of Practice (CoP) in cooperation with WP 5. DST were verified by stakeholders in both preliminary and final versions. 10 1.3 Purpose and scope of the document The WP3 focuses on a toolbox development. It contains three major components (fig. 1). The final version of the first tool – Digital hub of knowledge (DHK) – was presented in Deliverable 3.5 Report on final version of the knowledge hub for soil health business models (Nikolov et al. 2024b). The final version of the second tool – Digital tool for analysis (DTA) - was presented in D 3.8 Report on final version of tools for analysis of the soil health business models (Nikolov et al. 2025b). The focus of this deliverable is the final framework of the third component – Decision support tool (DST). It presents the improvements of the tool based on stakeholders’ feedback compared to the one presented in the preliminary report (Nikolov et al. 2024c) submitted July 2024. DST ensures soil health risk evaluation and mitigation taking into consideration the profit margin of the farm. Figure 1. NOVASOIL digital toolbox 1.4 Process of development The development process of NOVASOIL toolbox incorporates feedback from stakeholders, members of NOVASOIL Community of Practice, and farmers from NOVASOIL’s Case Study community. Considering, their needs and demands, and the comments received, the toolbox tries to meet all their demands. The figure 2 presents the process of cooperation with the Community of Practice. These evaluations were made after the tool updates based on the comments received presenting the preliminary version (Nikolov et al., 2024c). 17 Table 5 Prevailing slope gradient Prevailing slopes % Levels of limitations LSL < 2 or terraced areas LSL0 2  8 LSL1 8  16 LSL2 16  30 LSL3 > 30 LSL4 Source: own calculations The farmer is asked to indicate the prevailing slope gradient for his farm, which is then to be estimated according to the second column in the table. Q4. Topsoil stoniness (%) Table 6 Presence of stones and gravel in the topsoil Stones and gravel in % volume Levels of limitations LST < 5 LST0 5  15 LST1 15  30 LST2 30  40 LST3 > 40 LST4 Source: own calculations The farmer is required to indicate the presence of stones and gravel in the topsoil, which indicator is then to be assessed according to the second column in the table. Q5. Texture coefficient Table 7 Value of the texture factor characteristic of the soil Texture coefficient Levels of limitations LTD < 1.3 LTD0 1.3  1.5 LTD1 1.5  2.0 LTD2 2.0  2.5 LTD3 > 2.5 LTD4 Source: own calculations The farmer is required to indicate the value of the texture factor characteristic of the soil on his farm, which is determined by the following approach. The farmer enters data on the content of physical clay - particles < 0.01 mm (%) in the lightest layer of the surface horizons (layers) (IB) and the content of physical clay - particles < 0.01 mm (%) in the lightest layer of the surface horizons (IB) horizons (layers) (IA). Based on this data, the texture factor is calculated using the formula below and the restriction levels are determined. Kt = IB/ IA 18 IB - physical clay content – particles < 0.01 mm (%) in the heaviest layer of the subsurface horizons (layers); IA - physical clay content – particles < 0.01 mm (%) in the lightest layer of surface horizons (layers). Q6. Groundwater level (cm) Table 8 Groundwater level Groundwater level cm Levels of limitations LGWT > 300 LGWT0 300  200 LGWT1 200  100 LGWT2 100  50 LGWT3 < 50 LGWT4 Source: own calculations The farmer is required to indicate the groundwater level, which is then assessed according to the second column in the table. Q7. Soil fertility data Table 9 Mechanical composition (physical clay - particles < 0.01 mm %) Physical clay % Levels of limitations LTX < 10 LTX4 10  20 LTX3 20  30 LTX2 30  45 LTX1 45  60 LTX0 60  75 LTX1 > 75 LTX2 Source: own calculations The farmer is asked to indicate the physical clay content of the soil profile, which is then estimated according to the second column in the table. Q8. Root space (depth to root barrier cm) Table 10 Depth to root barrier Depth to root barrier cm Levels of limitations LRS > 130 LRS0 130  100 LRS1 100  80 LRS2 80  50 LRS3 < 50 LRS4 Source: own calculations The farmer is asked to indicate the depth at which a barrier to the development of the plant root system occurs, which indicator is to be assessed according to the second column in the table. 19 Q9. Soil reaction (pH measured in aqueous suspension) Table 11 Soil reaction value Soil reaction – рН in Н2О Levels of limitations LРН < 5.0 LPH4 5.0  6.0 LPH3 6.0  6.5 LPH2 6.5  7.3 LPH0 7.3  8.6 LPH1 > 8.6 not evaluated Source: own calculations The farmer is asked to indicate the soil reaction value to be estimated according to the second column in the table. Q10. Humus content Table 12 Humus content of the surface layer Humus content % Levels of limitations LHC > 3.0 LHC0 3.0  2.5 LHC1 2.5  2.0 LHC2 2.0  1.0 LHC3 < 1.0 LHC4 Source: own calculations The farmer is asked to indicate the humus content of the surface layer of the soil profile, which is estimated according to the second column in the table. SOIL RISK ASSESMENT OUTPUT After the farmers goes through all 10 questions it is collected L estimation for all questions. That can be summarized as: Table 13 Soil Health Risk Assessment Soil class Units Criteria S1 Very good soil Land units that: (1) have no restrictions (2) or have up to 5 L1 restrictions S2 Good soil Land units that: (1) have up to 5 L1 restrictions (2) or have up to 4 restrictions L2 S3 Average good soil Land units that: (1) have up to 4 restrictions L2 (2) or have up to 3 restrictions L3 N1 Poor soil Land units that: (1) have up to 3 restrictions L3 (2) or have up to 1 restrictions L4 20 N2 Unsuitable soil Land units that: (1) have up to 4 restrictions L3 (2) or have up to 1 restrictions L4 Source: own calculations Placing the lands in the above-mentioned classes allows making recommendations for improving soil health (table 14). Table 14 Recommendations for soil health improvement Land classes Leading limiting factor Use Improvement measures S1 Without limitations For all crops in the given climate Fertilization and irrigation, bioprotective belts S2 Some have high groundwater levels For all crops for which there are climatic conditions - cereals, root crops and tubers, perennial fodder, essential oil and medicinal, technical, fruit, berry crops Fertilization, drainage (if necessary), amelioration S3 Stony, erosion, light mechanical composition, high level of groundwater, unfavourable soil reaction Cereals, vegetable crops, fodder, lavender, walnuts, cherries, vineyards, pastures, forests Stone removal, fertilization, land reclamation, antierosion measures, weeding, afforestation N1 Shallow hard rock, erosion, poor in nutrients, acidic reaction or high carbonate content Potatoes, tobacco, lavender, raspberries, herbs, pastures, forests Anti-erosion measures, fertilizing, weeding, afforestation, terracing, horizontal processing N2 Shallow hard rock, erosion, poor in nutrients, acidic reaction or high carbonate content Grasslands, protected areas around settlements, forests Anti-erosion measures, preservation of forests, afforestation, terracing 21 2.3.2 Optimization module Selection of incentives and practices for soil health The result of the process in RAM module is the definition of soil health, which have to be visible for the farmer. After the estimation of the soil health risk a list with incentives and practices that he can use to mitigate the soil health risk. - The farmer can see a list with incentives from D3.1. - The farmer can see list with practices. Risk mitigation The Gross Margin (GM) is used to estimate the risk mitigation. To calculate the GM the farmer must be asked a list of questions for every crop on the farm. Every row from table 2 must be answered by the farmer. Table 15 Gross margin calculation for current practices Crop type: Income Measure (kg, l, …) per ha Yield Price Total EUR EUR Main product Side product 1 kg Side product 2 kg Subsidy EUR Total income Vatable Costs Quantity Price Value EUR EUR Seeds kg kg Fertilizers kg kg kg Plant protection l l l Labor 22 Manual labour Person days Harvesting Person days Marketing EUR Packing EUR Contracts Ploughing EUR Disking EUR Harvest EUR Sowing EUR Fertilizing and spraying EUR Other Equipment Rental Irrigation M3 Other costs Fuel l Total Variable Costs Source: own calculations GM= Total income – Total variable costs Application of risk mitigation brings some change in income and costs related to GM calculation. The farmer must be asked the same set of questions after the application of risk mitigation practices for every crop. Calculation of GM after risk mitigation practices is presented in table 16. Table 16 Gross margin calculation after risk mitigation practices Crop type: Income Measure (kg, l, …) per ha Yield Price Total EUR EUR Main product Side product 1 kg Side product 2 kg Subsidy EUR Total income Vatable Costs Quantity Price Value 23 EUR EUR Seeds kg kg Fertilizers kg kg kg Plant protection l l l Labor Manual labor Person days Harvesting Person days Marketing EUR Packing EUR Contracts Plowing EUR Disking EUR Harvest EUR Sowing EUR Fertilizing and spraying EUR Other Equipment Rental Irrigation M3 Other costs Fuel l Total Variable Costs Source: own calculations Linear optimization Linear optimization plays a crucial role in economics, offering powerful tools for solving complex problems. Linear programming, a branch of applied mathematics, focuses on minimizing or maximizing linear cost functions subject to linear constraints. This approach is particularly useful in production planning (Schulze, 1998). For the optimization, it is necessary to derive a function to find a minimum or a maximum. It is also necessary to determine the quantitative constraints of the model. If the problem has m variables and n constraints, the standard form of the linear optimization problem may be represented as: Maximize ∑𝑐𝑖𝑥𝑖 𝑛 𝑖=1 Subject to 24 ∑𝑎𝑗𝑖𝑥𝑖 𝑛 𝑖=𝑛 ≤ 𝑏𝑗, i=1…m 𝑥𝑖≥ 0, j=1…n The problem may have three possible categories: Infeasible, Unbounded and Optimal Solution. Optimal solution means the function achieves a unique minimum (or maximum) value. The simplex method, along with its variants like the revised simplex method and network simplex method, has been the primary algorithm for solving linear programming problems efficiently over the past 50 years. The simplex method consists of two main phases. The first phase involves finding a feasible solution, which is often straightforward for small or certain larger problems, sometimes using a trivial solution like x=0. In the second phase, the simplex method iteratively improves the function by adjusting variables in a manner that increases one while decreasing another, leading to an overall enhancement in the function. A large advantage of the linear optimization problem is that in the last few decades it has been computerized and there exists software that can solve such problems. When it comes to linear optimization of GM, the function can be represented as (Schulze, 1998): Maximize GM=∑𝑄𝑖 𝑛 𝑖=1 𝐺𝑀𝑖, where: Qiis the quantity of the land with crop i; GMiis the gross margin per ha for crop i. This function is subject to several type of constraints: i. Constraints for the land ii. Constraints for the crop rotation iii. Constraints for the labor iv. Constraints for the irrigation Based on the collected information defined above a linear optimization can be done. The objective of optimization is GM of the farm. The quantity of land with a single crop is the optimization solution. For that purpose, the information provided in table 17 is needed: Table 17 Crop rotation scenario Type of crops Total Wheat Maize Sunflower Barley Rye Oats Vegetables Number of crops 1 1 1 1 1 1 1 Quantity hectares 100 50 0 0 0 0 0 50 Revenue per hectare Average yield per hectare 470 600 200 460 3500 25 Price per unit 0.3 0.3 0.6 0.29 0.6 Income per hectare of main production 141 180 120 133.4 0 0 2100 Incentive (Subsidy) per hectare 30 30 30 30 31 Quantity of by-product per hectare By-product price per unit Income from secondary production 0 0 0 0 0 0 0 Total revenue 8550 0 0 0 0 0 106550 Cost per hectare Seed quantity per hectare 30 2 0.5 22 0.1 Price per unit quantity 0.32 5.5 20 0.38 200 Seed costs 9.6 11 10 8.36 0 0 20 Fertiliser 1 quantity per hectare 1 1 1 1 2 Fertiliser 1 price per unit 33 35 17 32 12 Cost of fertiliser 1 33 35 17 32 0 0 24 Fertiliser 2 quantity per hectare 30 Fertiliser 2 price per unit 1.4 Cost of fertiliser 2 0 0 0 0 0 0 42 Fertiliser 3 quantity per hectare 3 Fertiliser 3 cost per unit 6.25 Cost of fertiliser 3 0 0 0 0 0 0 18.75 Labour 1 person/day per ha Labour 1 price per person day Total Labour 1 0 0 0 0 0 0 0 Labour 2 person/day per ha 1 1 1 1 0.8 Labour 2 price per person day 6 12 11 6.5 35 Total Labour 2 6 12 11 6.5 0 0 28 Labour 3 person/day per ha Labour 3 price per person day Total Labour 3 0 0 0 0 0 0 0 Fuel quantity per hectare 6.5 8 8 6.5 40 Fuel price per unit 2.66 2.66 2.66 2.66 2.66 Total fuel 17.29 21.28 21.28 17.29 0 0 106.4 Number of people in permanent labour Price per hectare of permanent labour 0 0 0 0 0 0 0 Seasonal labour in man-days 17 Price per day of seasonal labour 20 0 0 0 0 0 0 340 Irrigation quantity per hectare 90 Irrigation price per unit 1.6 0 0 0 0 0 0 144 Total variable costs 3 294.5 0 0 0 0 0 36 157.5 26 Source: own calculations Limitations’ definitions A set of limitations for linear optimization will apply. These limitations come in 2 ways: 1. They are preset in the software. 2. The farmer may set some of the limitations. 3. Decision support tool Design The NOVASOIL DST for soil health business models contains two modules: • Risk assessment. • Optimisation. The user needs to make registration to sign in and archive the analysis. 3.1 Risk assessment module (RAM) In this module the user could evaluate the soil in 9 easy steps. Figures 5 present the process for evaluation. Figure 5 RAM first page Before the start of the analysis, it is needed to define the business model. 33 Resources 1. Dimitre Nikolov, Ivan Boevsky, Martin Banov, Ekatherina Tzvetanova, Krasimir Kostenarov, Tsvetelina Marinova (2023) NOVASOIL D3.1 Report on mapping existing incentives for sustainable soil health business models 2. Nikolov D., Tzvetanova E., Boevsky I., Banov M., Kostenarov K., Todorova K. (2024a) NOVASOIL D 3.3 Preliminary version of tools for analysis of the soil health business models. 3. Nikolov D., Tzvetanova E., Boevsky I., Banov M., Kostenarov K., Todorova K. (2024b) Deliverable D 3.5 Report on final version of the knowledge hub for soil health business models 4. Nikolov D., Tzvetanova E., Boevsky I., Banov M., Kostenarov K., Todorova K. (2024c) NOVASOIL D 3.6 Preliminary version on decision support tool for soil health business models 5. Nikolov D., Tzvetanova E., Boevsky I., Banov M., Kostenarov K., Dragnev P. (2025a) NOVASOIL D 3.7 Testing and valorisation of toolbox instruments. 6. Nikolov D., Tzvetanova E., Boevsky I., Banov M., Kostenarov K., Bankova, V. (2025b) NOVASOL D 3.8 Report on final version of tools for analysis of the soil health business models 7. Schulze, M.A. (1998). Linear Programming for Optimization. 8. NOVASOIL DST is available at: https://novasoil.blueberry.bg 34 Acknowledgment The authors would like to thank the EU for funding, in the frame of the European Union’s Horizon Europe research and innovation programme under Grant Agreement GA 101091268. The document reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.