Scientific Articles

Marialaura Bancheri, Rossella Albrizio, Marco Botta, Alessia Perego, Fabio Terribile, Angelo Basile, (2025)

Geoderma, Volume 462, 117494, ISSN 0016-7061

Globally, soil’s contribution to ecosystem services is integral to achieving several Sustainable Development Goals (SDGs), including food security (SDG 2), water quality (SDG 6), and climate action (SDG 13). At EU level, EU Mission “A Soil Deal for Europe” seeks to restore soil health by 2030. This study, in view of assessing soil health, introduces an integrated assessment of soil ecosystem services (SESs) using the ARMOSA process-based model. ARMOSA evaluates SESs, such as Food provisioning, Water regulation, Carbon Sequestration & Climate Regulation, and Nutrient cycling, through simulations that account for diverse pedoclimatic conditions and management practices. By emphasizing multiple integrated indicators over single-point measurements, this approach captures dynamic soil-plant-atmosphere interactions, offering a nuanced framework for soil health assessment. The results demonstrate the potential of comprehensive SES evaluations to support sustainable soil management and inform strategies to meet local and global soil health goals.

Mara Gabbrielli, Marco Perfetto, Marco Botta, Iride Volpi, Alessia Castellucci, Matteo Ruggeri, Marina Allegrezza, Nicola Alessi, Leonardo Vario, Marco Acutis, Alessia Perego, Giorgio Ragaglini, (2025)

European Journal of Agronomy, Volume 168, 127668, ISSN 1161-0301

This study assesses the greenhouse gases (GHG) mitigation potential of two cropping systems under the diverse pedoclimatic conditions of two sites in Northern and Southern Italy, belonging to moist and dry climate zones, respectively. The two cropping systems, implemented at field scale in silty-clay-loam soils, were an optimized cropping system (ECS), designed and managed to be more efficient in the use of nitrogen and in the conservation of soil organic carbon (SOC), and a conventional system (CCS). N2O and CO2 soil fluxes were measured daily over three to four years using automatic stations comprising eight non-steady-state chambers per site. The ARMOSA model, calibrated and validated with measured data, provided reliable simulations of GHG fluxes and crop yields, aiding environmental impact assessments. In the Northern moist site, the ECS showed a significant GHG mitigation effect, serving as a GHG sink due to reduced N2O emissions (N input-scaled emission: 0.0030 kg N-N2O kg N−1). Conversely, the CCS, despite its higher productivity and SOC storage, emitted more N2O (N input-scaled emissions 0.0051 kg N-N2O kg N−1), making it a GHG source. In the moist site ECS had an effective mitigation potential compared with CCS, while in the Southern dry site both systems had lower GHG emissions than at the moist site, due to the reduced N rates (-27 % in CCS, −33 % in ECS), thus resulting as GHG sinks. This study underscores the relevance of agronomic mitigation practices, like leguminous crops integration and optimized nitrogen management where GHG emission are fostered by site-specific pedoclimatic conditions.

Marialaura Bancheri, Angelo Basile, Fabio Terribile, Giuliano Langella, Marco Botta, Daniele Lezzi, Federica Cavaliere, Marco Colandrea, Luigi Marotta, Roberto De Mascellis, Piero Manna, Antonietta Agrillo, Florindo Antonio Mileti, Marco Acutis, & Alessia Perego, (2024)

Land Degradation & Development, 35(13), 3965–3980

Under the same perspective of the Sustainable Development Goal (SDG) 15.3 aiming to restore degraded land and soil, one of the current priorities of the new Common Agriculture Policy (CAP) is to overcome the serious environmental problems raised by intensive agriculture. Despite the steps forward guaranteed by new technologies and innovations (e.g., IoT, precision agriculture), the availability of real operational tools, which could help the member states fulfil the high requirements and expectations of the new CAP and SDGs, is still lacking. To fill this gap, in the H2020 LandSupport project, the web-based best practice tool was developed to identify, on-the-fly, optimized agronomic solutions to help achieve land-degradation neutrality. The tool’s core is the ARMOSA process-based model, which dynamically simulates the continuum soil–plant–atmosphere, combining several cropping systems, crops, nitrogen fertilization rates, tillage solutions, and crop residue management for specific regions of interest. It provides a synthetic “Best Practice index” to identify the optimized local solutions, which combines the production, nitrate leaching, and SOC_change, according to the end-user dynamic requests. The tool was implemented for three case studies: Marchfeld Region in Austria, Zala County in Hungary, and Campania Region in Italy, which are representative of a variety of different pedoclimatic conditions. In the present work, we report three possible cases of use in supporting best practices aiming toward soil and water conservation: (i) crop production optimization; (ii) impact of management practices (i.e., cover crops) over soil carbon; (iii) lowering the impact of nitrate leaching.

Annachiara Colombi, Marialaura Bancheri, Marco Acutis, Angelo Basile, Marco Botta, Alessia Perego, (2024)

Environmental Modelling & Software, Volume 173, 105932, ISSN 1364-8152

The capability of cropping system models of depicting the crop and soil-related processes implies a high number of parameters. The aim of this work was to detect the key parameters, and the associated processes, of the ARMOSA cropping system model, considering two target outputs, crop yield and nitrogen leaching. A global sensitivity analysis (SA) was carried out in two steps: (1) the Morris method considering the whole set of parameters; (2) Sobol analysis was applied to the Morris outcome. The simulation was run on winter wheat in four soil types in Marchfeld (Austria, 2010–2018). Parameters affecting crop yield was the critical nitrogen concentration, the potential CO2 assimilation rate, and the drought sensitivity parameter. Nitrogen leaching was mainly affected by the decomposition of litter and the early aboveground biomass growth. The parameters ranking did not appreciably change across soil types. This study offers a quick and replicable methodology for model calibration.

Fabio Terribile, Marco Acutis, Antonella Agrillo, Erlisiana Anzalone, Sayed Azam-Ali, Marialaura Bancheri, Peter Baumann, Barbara Birli, Antonello Bonfante, Marco Botta, Federica Cavaliere, Marco Colandrea, Amedeo D’Antonio, Roberto De Mascellis, Carlo De Michele, Gloria De Paoli, Camilla Della Monica, Marco Di Leginio, Mitja Ferlan, … Angelo Basile, (2024)

Land Degradation & Development, 35(2), 813–834

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling ‘on-the-fly’ in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform’s functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.

Gianni Bellocchi, Zoltán Barcza, Roland Hollós, Marco Acutis, Emese Bottyán, Luca Doro, Dóra Hidy, Eszter Lellei-Kovács, Shaoxiu Ma, Julien Minet, Vivien Pacskó, Alessia Perego, Françoise Ruget, Giovanna Seddaiu, Lianhai Wu, Renáta Sándor, (2023)

Agricultural and Forest Meteorology, Volume 343, 109778, ISSN 0168-1923

Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with single-factor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1–2 °C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning.

Bertuzzi, Julie Constantin, Massimiliano De Antoni Migliorati, Benjamin Dumont, Jean-Louis Durand, Nándor Fodor, Thomas Gaiser, Pasquale Garofalo, Sebastian Gayler, Luisa Giglio, Robert Grant, Kaiyu Guan, Gerrit Hoogenboom, Qianjing Jiang, Soo-Hyung Kim, Isaya Kisekka, Jon Lizaso, Sara Masia, Huimin Meng, Valentina Mereu, Ahmed Mukhtar, Alessia Perego, Bin Peng, Eckart Priesack, Zhiming Qi, Vakhtang Shelia, Richard Snyder, Afshin Soltani, Donatella Spano, Amit Srivastava, Aimee Thomson, Dennis Timlin, Antonio Trabucco, Heidi Webber, Tobias Weber, Magali Willaume, Karina Williams, Michael van der Laan, Domenico Ventrella, Michelle Viswanathan, Xu Xu, Wang Zhou, (2023)

Agricultural and Forest Meteorology, Volume 333, 109396, ISSN 0168-1923

Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today’s growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites – Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET.

Tommaso Tadiello, Mara Gabbrielli, Marco Botta, Marco Acutis, Luca Bechini, Giorgio Ragaglini, Andrea Fiorini, Vincenzo Tabaglio, Alessia Perego, (2023)

Ecological Modelling, Volume 480, 110327, ISSN 0304-3800

In the agroecosystem, surface crop residues are widely recognized as affecting many processes such as soil water dynamics, crop growth, nitrogen and carbon cycling. For this reason, developing models that simulate the effect of surface residues and their decomposition is crucial, especially while modeling conservation agriculture. To date, even though many cropping systems and C-oriented models differently simulate the evolution of surface residue biomass, a comprehensive approach is still missing. In this study, we developed a new simulation module that explicitly simulates the decomposition of surface residues, by including all the variables and processes that are relevant for agroecosystem’s simulation. This module has been later integrated into the ARMOSA cropping system model. To quantify the contribution of each parameter to the simulated outputs (i.e., decomposed biomass), a sensitivity analysis (SA) was conducted, comparing the result with the APSIM model used as a benchmark. The SA was conducted on four different crop residues (maize, rye, soybean and wheat) over three different years. In addition, for each crop residue, we verified whether parameters changed their relevance depending on the considered time period. The most critical parameters of the new module reflected the importance of air temperature, soil water content and residue biomass in the decomposition process. The potential decomposition rate had minor importance, highlighting that, when setting crop-specific values, other environment-related parameters are more relevant for the actual decomposition rate. In the case of APSIM model, the potential decomposition rate and the optimum temperature for this process resulted in the first two ranks. Finally, concordance coefficients were used to compare SA outputs: compared to APSIM, the new model showed higher concordance passing from one crop residue to another, even when comparing the different simulation periods within the same crop. In summary, this work presented a novelty in surface crop residue representation and provided a deep survey of the module behavior and characteristics.

Elena Valkama, Gulya Kunypiyaeva, Rauan Zhapayev, Muratbek Karabayev, Erbol Zhusupbekov, Alessia Perego, Calogero Schillaci, Dario Sacco, Barbara Moretti, Carlo Grignani, Marco Acutis, (2020)

Geoderma, Volume 369, 114298, ISSN 0016-7061

Conservation agriculture (CA) involves complex and interactive processes that ultimately determine soil carbon (C) storage, making it difficult to identify clear patterns. To solve these problems, we used the ARMOSA process-based crop model to simulate the contribution of different CA components (minimum soil disturbance, permanent soil cover with crop residues and/or cover crops, and diversification of plant species) to soil organic carbon stock (SOC) sequestration at 0–30 cm soil depth and to compare it with SOC evolution under conventional agricultural practices. We simulated SOC changes in three sites located in Central Asia (Almalybak, Kazakhstan), Northern Europe (Jokioinen, Finland) and Southern Europe (Lombriasco, Italy), which have contrasting soils, organic carbon contents, climates, crops and management intensity. Simulations were carried out for the current climate conditions (1998–2017) and future climatic scenario (period 2020–2040, scenario Representative Concentration Pathway RCP 6.0). Five cropping systems were simulated: conventional systems under ploughing with monoculture and residues removed (Conv − R) or residues retained (Conv + R); no-tillage (NT); CA and CA with a cover crop, Italian ryegrass (CA + CC). In Conv − R, Conv + R and NT, the simulated monocultures were spring barley in Almalybak and Jokioinen, and maize in Lombriasco. In all sites, conventional systems led to SOC decline of 170–1000 kg ha−1 yr−1, whereas NT can slightly increase the SOC. CA and CA + CC have the potential for a C sequestration rate of 0.4% yr−1 or higher in Almalybak and Jokioinen, and thus, the objective of the “4 per 1000” initiative can be achieved. Cover crops (in CA + CC) have a potential for a C sequestration rate of 0.36–0.5% yr−1 in Southern Finland and in Southern Kazakhstan under the current climate conditions, and their role will grow in importance in the future. Even if in Lombriasco it was not possible to meet the “4 per 1000”, there was a SOC increase under CA and CA + CC. In conclusion, the simultaneous adoption of all the three CA principles becomes more and more relevant in order to accomplish soil C sequestration as an urgent action to combat climate change and to ensure food security.

Renáta Sándor, Zoltán Barcza, Marco Acutis, Luca Doro, Dóra Hidy, Martin Köchy, Julien Minet, Eszter Lellei-Kovács, S. Ma, Alessia Perego, Susanne Rolinski, Françoise Ruget, Mattia Sanna, Giovanna Seddaiu, Lianhai Wu, Gianni Bellocchi, (2017)

European Journal of Agronomy, Volume 88, Pages 22-40, ISSN 1161-0301

This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R2>0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R2<0.6 with individual models and <∼0.5 with MMM) and biomass (R2<∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5°C for ST, −0.3m3m−3 for SWC and 360gDMm−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed.

Pirttioja N, Carter TR, Fronzek S, Bindi M, Hoffmann H, Palosuo T, Ruiz-Ramos M, Tao F, Trnka M, Acutis M, Cammarano D, Baranowski P, Basso B, Bodin P, Buis S, Wallach D, Deligios P, Destain MF, Dumont B, Gaiser T, Ferrise R, François L, Siebert S, Hlavinka P, Jacquemin I, Kersebaum KC, Kollas C, Krzyszczak J, Lorite IJ, Minet J, Mínguez MI, Montesino M, Moriondo M, Müller C, Wang E, Öztürk I, Perego A, Rodríguez A, Ruane AC, Ruget F, Sanna M, Semenov MA, Slawinski C, Stratonovitch P, Supit I, Waha K, Zhao Z, Wu L, Doro L, Rötter RP, (2015)

Clim Res 65:87-105

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (−2 to +9°C) and precipitation (−50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Piet Groenendijk, Marius Heinen, Gernot Klammler, Johann Fank, Hans Kupfersberger, Vassilios Pisinaras, Alexandra Gemitzi, Salvador Peña-Haro, Alberto García-Prats, Manuel Pulido-Velazquez, Alessia Perego, Marco Acutis, Marco Trevisan, (2014)

Clim Res 65:87-105

The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test for 2005–2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009–2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement, model efficiency, root relative squared error, Pearson’s linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates.

Alessia Perego, Andrea Giussani, Mattia Sanna, Mattia Fumagalli, Marco Carozzi, Lodovico Alfieri, Stefano Brenna, Marco Acutis, (2013)

Italian Journal of Agrometeorology. 18. 23-38

ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69,r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98).