DOI: 10.5281/zenodo.18700796
title: Carbon Sequestration Potential in Model Systems of Conventional and Organic Farming
title: Dataset Generated Using the EPIC-IIASA CZ Simulation Model

description: The dataset presents the results of simulations assessing the carbon sequestration potential of arable soils in the Czech Republic, using the EPIC-IIASA CZ simulation platform. It comprises outputs from modelling various agronomic scenarios at the district (LAU1) level, incorporating three climate scenarios: RCP 2.6, 4.5, and 8.5. The data capture the effects of conventional farming (CF) and organic farming (OF) model systems on changes in average soil organic carbon content in the top 30 cm of soil during the periods 2040–2060 and 2080–2100, relative to the reference period 2000–2020. Changes are expressed in t C ha⁻¹. Management practices within the OF supersystems included a diverse crop structure, deeper tillage, and nitrogen inputs supplied exclusively through farmyard manure. In contrast, CF supersystems applied 30% of nitrogen in manure form. Total nitrogen input from fertilizers was approximately double in the CF supersystems compared to the OF systems, reflecting the substantially higher proportion of nitrogen-fixing crops in OF. For each cropping system, the reported values indicate the potential for carbon sequestration over 40- and 80-year time horizons.

Structure of the database (C_simulations_CF_and_OF_districts_Czechia_EPIC-IIASA_CZ_simulations_2026 v1.csv):






Column name



Meaning and description of values (categories)



ValuesExample





scenario



Climate scenario identifier



rcp26 / rcp45 / rcp85





timescale



Length of the period for which the value is calculated (years)



40 / 80





supersystem



Code of the model cropping system–see "Additional Description"



KZCR / KZH / KZB / KZR / KZK / EZCR / EZH / EZB / EZR / EZK 





poly_id



Unique geographical code of the region (LAU 1 code).



CZ0100





value



Numeric value of the metric. Average increase (+) or decrease (-) of organic C content in the top 30 cm of soil (t ha-1)



1.144678493





The dataset contains 5,112 records.

Two additional files are up-loaded:

Crop_shares_in_supersystems.csv defines input values for generating crop rotations in supersystems–see "Additional Description"

Crop_rotation_shares_in_supersystems.csv reports generated crop rotations and their shares in supersystems–see "Additional Description" 
description: Introduction

The presented result maps the carbon (C) sequestration potential in arable soils of the Czech Republic. It is based on the thesis that both organic and conventional farming can be practiced with a primary focus on maintaining soil quality and mitigating climate change. From the perspective of the soil C balance, formal certification of the farm is not the decisive factor; rather, it is the specific agrotechnical measures that support the formation and stability of soil organic matter.

While organic systems naturally benefit from more diverse crop rotations with a higher proportion of N-fixing crops and, potentially, a higher share of manure, conventional agriculture may possess more effective tools in the form of soil-conservation technologies that minimize mechanical soil disturbance. The presented dataset does not evaluate conventional and organic systems antagonistically; instead, it quantifies their capacity to function as a significant carbon sink when principles of good agricultural practice are followed. This approach demonstrates that the path toward carbon farming and increasing fertility is achievable in both management systems.

To estimate sequestration potential, the EPIC-IIASA CZ crop model was developed. This model was created by transferring the knowledge and processes of the EPIC-IIASA model and tailoring them to the conditions of the Czech Republic. The creation of this simulation platform was motivated by the research needs of the Czech Ministry of Agriculture to quantify the C sequestration potential in arable soil under various management conditions and climate change scenarios.

Map sets were created for model—simplified—systems of conventional and organic farming. The definition of these systems is based on the actual (current) structure of cultivated crops, with each system having its specific settings derived from real-world practice. The same N fertilization intensity is considered for both systems. Unlike the previous simulation output (https://zenodo.org/records/17826824), fewer system variants are considered here due to the high computational demand caused by the complexity of the generated crop rotations.

Despite the advantages of a simulation-based approach, it must be noted that the faithful replication of real agrotechnical procedures in a virtual environment represents a highly complex task. When applying the EPIC model to real systems, it was therefore necessary to resort to simplification in several aspects. Like any simulation, the implementation of the EPIC model for Czech conditions carries a certain degree of uncertainty. This stems from the nature of the input data, the configuration of process parameters, and the model itself, which is under continuous development. For reasons of technical feasibility and compatibility, an older version (EPIC v. 0810 from 2013) was used, which is also implemented within the global EPIC-IIASA system.

Another source of unknowns is long-term prediction, which involves uncertainties associated with climate scenarios, changes in crop composition, or the future development of cultivars and agrotechnology. Consequently, due to technical constraints and the scope of the processed data, absolute spatial and temporal accuracy of all information cannot be guaranteed. Simulation outputs should therefore be perceived primarily as an indication of trends in soil property development, rather than an exact quantification of stored carbon stocks.

Description of the simulation platform EPIC-IIASA CZ

The basis for simulating the sequestration potential of arable soils in the Czech Republic is the regional crop model EPIC-IIASA, which was developed to simulate the growth and production of agricultural crops at global, continental, and subcontinental scales. This simulation platform utilizes the already existing and extensively validated process-based crop model EPIC.

The dynamics of crop growth during the growing season are simulated by the EPIC-IIASA model on a daily basis simultaneously for a large number of separate, spatially localized sites, usually over a time horizon of several years to decades. EPIC-IIASA produces output data on the temporal and spatial changes in crop production in response to recorded (historical) or projected (future, alternative) factors that influence crop production, such as weather, topography, soil and its management, agronomic practices, the crop itself, and its cultivar characteristics.

The core of the platform is the EPIC model, which includes modules for simulating crop growth, soil water balance, and nutrient balance (cycling and transformation of C, N, and P in soil and plants). The EPIC model incorporates the validated and widely used CENTURY model of soil organic matter dynamics. The model is further supplemented by a wide range of agronomic management options.

The second component is the geospatial data infrastructure, which provides spatially represented inputs for the EPIC model (weather, location, geomorphology, soil, crop, nutrient inputs, and soil management), either in the form of a grid or spatial simulation units (SimU).

The third component consists of scenarios, which represent the formulation of specific research questions regarding the intended purpose. Scenarios affect the choice of outputs and are usually defined using alternative input data on climate (historical climate, climate projections) and land management (adaptation and mitigation measures, such as carbon farming).

The EPIC-IIASA model has been validated as a tool for assessing the impacts of climate change, both independently and as part of model ensembles. Its reliability as part of integrated modelling systems has also been demonstrated.

Thanks to the highly scalable process-based EPIC model, the project task was to modify the EPIC-IIASA platform for regional studies of agroecosystems in the Czech Republic, mainly to assess carbon sequestration potential and to propose and test the effectiveness of model carbon sequestration systems in agricultural soil.

For the needs of regional simulations of agricultural systems in the Czech Republic, a national geospatial data infrastructure, EPIC-IIASA CZ, was created and implemented in a LINUX environment. It consists of 977 spatial SimU, created by dividing districts according to climate region and prevailing soil properties (depth, soil type, and humus content class). Each SimU was assigned a typical soil profile and daily weather data from 1989–2019 from a 10 × 10 km grid, as well as estimated sowing and harvest dates of main crops based on the respective production area. The design allows for aggregation of outputs at the LAU1 (district) and NUTS2 (region) levels, which corresponds to the state administration's requirements for the level of quantification and interpretation of results.

Design of crop rotations

For the design of crop rotations, statistical data on the areas of cultivated crops obtained from LPIS for the years 2020–2022 were used. Only crops with the highest area representation were included in the list of crops selected for simulations, up to a total share of approximately 75–85% of the area of the respective production region (mountain/potato/sugar beet/maize production regions) or of the entire Czech Republic. For the given period, their average area share was calculated and subsequently proportionally adjusted so that the sum of shares equalled 100%. This approach was chosen due to technical limitations of the modelling process— a higher number of crop species would have resulted in a disproportionately large number of generated crop rotations and an extension of the simulation time.

The starting point of the workflow are crop lists with assigned area shares calculated for arable land of the Czech Republic and for 4 production areas. Each partial list represents a so-called supersystem. Five supersystems fall under conventional farming (KZ), and five under organic farming (EZ). Within each group of five, one supersystem represents the entire Czech Republic (KZCR and EZCR), while the remaining four correspond to individual production regions (KZM and EZM for mountain production region, KZB and EZB for potato production region, KZR and EZR for sugar beet production region and KZK and EZK for maize production region; see file Crop_shares_in_supersystems.csv).

To design crop rotations based on the relative share of crops, the CropRota model (Schönhart et al., 2011) was chosen. This linear optimization model was developed to generate agronomically optimal crop rotations based on observed land-use data. The model links agronomic criteria with historical data on crop composition (so-called crop mixes) at the field, farm, or regional level. Its main principle is the maximization of the overall agronomic value of the crop sequence within a rotation. The model uses an expert point-value matrix, which quantifies the suitability of successive crops, and applies constraints on the frequency of certain crops (e.g., no more than once every four years). The input to the model is the relative crop shares over a given time period. The model is used to bridge gaps in empirical data on rotations for biophysical and economic models.

The original author version of CropRota, programmed in GAMS software, was replaced for the purposes of this output by the program freecrot (Żelazny, 2025), which implements the crop rotation model in Python and optionally extends it to support two- and multi-year crops.

In the first step, crop rotations with a maximum period of six years were generated using freecrot (v1.0.1). Crops with a higher share in the input data appear more frequently in the generated rotations (and vice versa). The resulting rotations also favor pairs of consecutive crops whose more frequent occurrence reflects actual farming practices (suitability of successive crops). These two conditions can sometimes conflict; therefore, three sets of rotations were generated for each supersystem, with different weights assigned to these conditions. The resulting crop rotations were then merged, and duplicates were removed (see file Crop_rotations_in_supersystems.csv).

Definition of management in cropping systems

Cultivation within each crop rotation was then simulated using elements of the EPIC-IIASA CZ model. The simulated agronomic practices in the rotations of organic supersystems included deeper soil tillage compared to conventional supersystems, reflecting different strategies for weed control (and volunteer crops).

For crops in conventional supersystems, the total nitrogen (N) dose was set at a medium level (i.e., 60% of the maximum dose defined by the Nitrate Directive), with 30% of this dose applied in the form of organic fertilizers.

In organic supersystems, the N input from fertilizers was set at half this level (30% of the maximum dose according to the Nitrate Directive) and only in the form of organic fertilizers. This setting is based on the assumption that additional N enters the system through the higher share of N-fixing crops.

Simulation procedure

The simulations were designed to cover all simulation units for nationwide supersystems and the geographically corresponding simulation units for supersystems of individual production regions. The prepared simulation specifications are passed to the EPIC-IIASA CZ v0.32.32-hotfix1 workflow for execution of the simulations and subsequent processing of results. Among the various modules of the EPIC-IIASA CZ platform, modules of the 7 series were primarily used.

The 7pre module is intended for generating EPIC model input files and scripts to run simulations based on a provided task file. The specifications of crop sequences and climate scenarios are arranged into a list of simulations. Introducing a delay factor allows the simulation of variants with a shifted start of the crop rotation. Sowing and harvesting dates of crops are set based on average (typical) dates for conditions in the Czech Republic.

In the next step, EPIC operation templates corresponding to individual crops are identified. For each EPIC model run, a list of values to be filled in or replaced within the template is generated to match the simulation definition. The results are then passed to the frontend of the libepici workflow manager, which generates EPIC input files and scripts to run the simulations. It replaces values in operation templates, merges templates into operation calendars, applies delays to calendars, and resolves overlaps between crop timings.

The 7post module then processes the EPIC output files associated with the 7pre module. The main outputs of the workflow are generated. Data from EPIC output files are imported into CSV files. The monitored variable is the carbon (C) content in organic matter in the top 30 cm layer of soil (parameter OCPD).

For the purposes of this study, C sequestration was expressed as the difference in the average SOC content in the top 30 cm layer of soil for the periods 2040–2060 (and 2080–2100) compared to the average for 2000–2020 (expressed in t C/ha), while simulating the same cropping system (and its variants) throughout the period. In this way, the defined sequestration potential in arable soil reflects the retention/release of C in the topsoil over 40 or 80 years.

Based on this definition, the relevant datasets are subjected to a series of aggregations to obtain average values. First, temporal aggregation reduces the dataset along the axes of calendar delay and cultivation year. For the monitored output, minimum, maximum, mean, and median annual increment are calculated. Values at the level of crop rotations are then averaged to the level of supersystems.

Following this step, maps and tables are generated to describe the distribution of focys output values across simulation units.

Simulations were carried out for the period 2000–2100 using the regional circulation model CSC_REMO2009_MPI-ESM-LR. The climate scenarios considered were: RCP 2.6 – optimistic scenario, assuming rapid reduction of global CO₂ emissions; RCP 4.5 – medium scenario; RCP 8.5 – pessimistic scenario, assuming efforts to reduce CO₂ emissions will not succeed.