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Marco Acutis
Department of Agricultural and Environmental Sciences, University of Milan, via Celoria 2, 20133 Milano, Italy

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Short Biography

Marco Acutis is a full professor of Agronomy and Field crops at the University of Milan since 2005. He has coordinated research units of several international and national projects. His research work is the analysis of agricultural systems and their environmental impacts, through the study, implementation and development of simulation models and advanced techniques of data analyses.

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Journal article
Published: 19 July 2021 in Sustainability
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Proximal sensing represents a growing avenue for precision fertilization and crop growth monitoring. In the last decade, precision agriculture technology has become affordable in many countries; Global Positioning Systems for automatic guidance instruments and proximal sensors can be used to guide the distribution of nutrients such as nitrogen (N) fertilization using real-time applications. A two-year field experiment (2017–2018) was carried out to quantify maize yield in response to variable rate (VR) N distribution, which was determined with a proximal vigour sensor, as an alternative to a fixed rate (FR) in a cereal-livestock farm located in the Po valley (northern Italy). The amount of N distributed for the FR (140 kg N ha−1) was calculated according to the crop requirement and the regional regulation: ±30% of the FR rate was applied in the VR treatment according to the Vigour S-index calculated on-the-go from the CropSpec sensor. The two treatments of N fertilization did not result in a significant difference in yield in both years. The findings suggest that the application of VR is more economically profitable than the FR application rate, especially under the hypothesis of VR application at a farm scale. The outcome of the experiment suggests that VR is a viable and profitable technique that can be easily applied at the farm level by adopting proximal sensors to detect the actual crop N requirement prior to stem elongation. Besides the economic benefits, the VR approach can be regarded as a sustainable practice that meets the current European Common Agricultural Policy.

ACS Style

Calogero Schillaci; Tommaso Tadiello; Marco Acutis; Alessia Perego. Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield. Sustainability 2021, 13, 8059 .

AMA Style

Calogero Schillaci, Tommaso Tadiello, Marco Acutis, Alessia Perego. Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield. Sustainability. 2021; 13 (14):8059.

Chicago/Turabian Style

Calogero Schillaci; Tommaso Tadiello; Marco Acutis; Alessia Perego. 2021. "Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield." Sustainability 13, no. 14: 8059.

Journal article
Published: 07 June 2021 in Carbon Balance and Management
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Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0–30 cm soil depth and tested. Results The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = − 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017. Conclusions This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = − 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area.

ACS Style

Calogero Schillaci; Sergio Saia; Aldo Lipani; Alessia Perego; Claudio Zaccone; Marco Acutis. Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands. Carbon Balance and Management 2021, 16, 1 -15.

AMA Style

Calogero Schillaci, Sergio Saia, Aldo Lipani, Alessia Perego, Claudio Zaccone, Marco Acutis. Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands. Carbon Balance and Management. 2021; 16 (1):1-15.

Chicago/Turabian Style

Calogero Schillaci; Sergio Saia; Aldo Lipani; Alessia Perego; Claudio Zaccone; Marco Acutis. 2021. "Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands." Carbon Balance and Management 16, no. 1: 1-15.

Preprint content
Published: 03 March 2021
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Reviews and meta-analyses generally support the perception that organic farming systems are more environmentally friendly than conventional farming systems. Organic agriculture results in more soil organic matter and higher microbiological activity, thus, providing better water holding capabilities, decreased both runoff and concentration of nitrate in soil, leading to fewer risks of nitrate leaching loss from the soil to water bodies. However, environmental quality parameters can differ between organic plant and animal production farms, moreover, they can be higher calculated per unit product.

We used the ARMOSA process-based crop model (Valkama et al., 2020) to evaluate contribution of plant and animal organic farming to soil organic carbon (SOC) sequestration and N leaching loss reduction compare to conventional systems in South Savo (Finland). Since organic systems often produce about 30% less yields compared to conventional systems, we calculated SOC changes per total gross energy in harvested yields. For model inputs we used daily meteorological data, statistical annual crop yields, statistical data for sales of nitrogen fertilizers in the region during the last 20 years (1999-2018). Five-year crop rotations were simulated on loamy sand soil (C 3.5 %, C/N ratio 17, pH 6.2). On plant production farms, rotations consisted of cereals (with addition of pea in organic), oilseed rape and grass. Conventional crops were fertilized with mineral fertilizer, and residues were removed (PC-R) or retained (PC+R). Organic crops were fertilized with green manure only (POg+R) or also with commercial organic fertilizer (POf+R). On animal production farms, conventional (AC-R) and organic (AO-R) rotations consisted of 2 years of cereals and 3 years of grass, sown with clover in organic system. Conventional animal system was fertilized with mineral fertilizer and slurry, while organic system with slurry only, and residues were removed in both systems.

Simulations showed that both conventional plant production systems (PC-R and PC+R) led to SOC decline of 650 kg ha-1yr-1 at 0-30 cm soil depth. Organic systems showed either less SOC decline (120 kg ha-1yr-1) as in POg+R, or slight SOC increase (55 kg ha-1yr-1) as in POf+R. In contrast, organic animal production system did not differ from conventional system in terms of SOC change, showing a slight decreasing trend of about 150 kg ha-1yr-1. Estimates of SOC per gross energy in harvested yields showed the highest value (1.3 kg GJ-1) for organic plant production fertilized with commercial organic fertilizer (POf+R), while the lowest value (-18 and -13 kg GJ-1) for conventional plant production systems (PC-R and PC+R, respectively). In contrast, the estimates did not differ much between organic (-2.2 kg GJ-1) and conventional (-1.8 kg GJ-1) animal production systems. Simulated N leaching loss varied between 6 and 9 kg ha-1 yr-1 for all systems, except for organic plant rotation with green manure (POg+R), which N leaching loss was only 3 kg ha-1 yr-1

The modelling results suggest that organic plant production farms can be more environmentally friendly per unit area as well as per unit product compared to conventional farms, while organic animal production farms seem to cause similar environmental impact as conventional farms.

ACS Style

Elena Valkama; Marco Acutis. Is organic farming environmentally more friendly? 2021, 1 .

AMA Style

Elena Valkama, Marco Acutis. Is organic farming environmentally more friendly? . 2021; ():1.

Chicago/Turabian Style

Elena Valkama; Marco Acutis. 2021. "Is organic farming environmentally more friendly?" , no. : 1.

Preprint content
Published: 23 March 2020
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Conservation agriculture (CA) is a farming system that promotes maintenance of (1) minimum soil disturbance avoiding soil inversion (i.e. no-tillage or minimum tillage), (2) a permanent soil cover with crop residues and/or cover crops, and (3) diversification of plant species. The adoption of CA is promoted by FAO as a response to sustainable land management, environmental protection and climate change adaptation and mitigation. According to FAO, implementation of CA in Europe would reduce emissions by about 200 Mt CO2 per year. The carbon credit system (1 credit = 1t CO2 reduced) allows the compensation of the release of GHG generated by industries by means of funding emission reduction projects. Despite its potential for emission reduction, agricultural systems, however, are nearly beyond of carbon market.

The objectives of this study were (1) to assess the potential of CA for soil organic carbon (SOC) sequestration for the current climate conditions and for a future climate scenario; (2) to estimates carbon balance and possibility to obtain carbon credits in Southern Finland.

Five cropping systems were simulated by using the ARMOSA process-based crop model: conventional systems under ploughing with monoculture and residues removed (Conv–R) or residues retained (Conv+R); no-tillage; 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 CA and CA+CC crop rotations were spring barley - oilseed rape - oats - spring wheat. Simulations were carried out for the current (1998-2017) and future climatic scenarios (period 2020-2040, scenario Representative Concentration Pathway 6.0).

We evaluated carbon balance by using SALM method (Verified Carbon Standard, VM0017), which is a method to quantify in terms of carbon credits the Sustainable Agricultural Land Management projects. The method takes into account the dynamics of carbon stored in soil and the direct emission of N2O due to use of fertilizers (organic and mineral) and CO2 emission due to chemical fertilizer production, the amount of fuel used in tillage and other field operations. For estimation, we used the value of carbon credit of 21€.

Under current climate conditions, Conv–R and Conv+R emitted totally about 4.7 and 2.0 t CO2e ha-1yr-1, respectively, mainly due to SOC loss (1 and 0.34 t ha-1 yr-1, respectively). No-tillage emitted 0.4 t CO2e ha-1yr-1, mainly, due to N2O from fertilizers and chemical fertilizer production. In contrast, CA and CA+CC allowed to SOC sequestration of 0.315 and 0.650 t ha-1yr-1, resulting in emissions reduction of 0.420 and 1.62 t CO2e ha-1 yr-1, respectively. By adopting CA and CA+CC in Finland, there is a potential to obtain 2.5 and 3.7 carbon credits with the value of 52 and 77 € ha-1yr-1 respect to baseline (Conv+R).

Under future climate scenario (+0.6 °C; –120 mm y-1), SOC decline for conventional systems will be more pronounced compared to that under actual climate, and SOC sequestration will be possible to accomplish only for CA+CC.

ACS Style

Elena Valkama; Marco Acutis. Modelling of soil organic carbon changes and carbon balance under Conservation Agriculture and conventional cropping systems in Southern Finland. 2020, 1 .

AMA Style

Elena Valkama, Marco Acutis. Modelling of soil organic carbon changes and carbon balance under Conservation Agriculture and conventional cropping systems in Southern Finland. . 2020; ():1.

Chicago/Turabian Style

Elena Valkama; Marco Acutis. 2020. "Modelling of soil organic carbon changes and carbon balance under Conservation Agriculture and conventional cropping systems in Southern Finland." , no. : 1.

Preprint content
Published: 23 March 2020
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To collate all the prior information about modelling the soil bulk density (BD) in Mediterranean climate agro-ecosystems at a world-scale, a systematic map was carried out. The strength of the systematic map approach is the collection of all the international peer review publications available in different archives that allows for the historical track of the topic developments.

To estimate BD, the most common approach is the use of Pedotransfer functions (PTFs). In this study, a search query was developed to find out all the already published PTFs for BD estimation and the search was carried out on the two most used citation database of peer-reviewed literature, namely SCOPUS and Web of Science (WoS).

The Bibliometrix package developed by Aria and Cuccurullo (2017) was used to map the main bibliometric information, extracted from Scopus and WoS. Following the systematic map procedure, we carried out a search on title, abstract and keywords using the following query: (bulk  AND density  AND  pedotransfer)  OR  (bulk  AND density  AND Mediterranean)) that yielded 750 results in Scopus and 889 in WoS.

Alternatively, ((bulk density  AND  pedotransfer)  OR  (bulk density AND Mediterranean)  AND NOT  (forest  OR  amazon*  OR  petrol*))  AND  (LIMIT-TO (DOCTYPE , article)  OR LIMIT-TO (DOCTYPE, review)), which have yielded 717 and 567 records in WoS and Scopus respectively, of which the 30% were found in both database.

The researches were published between 1989 and 2020. The final database consists of 889 articles coming from 243 different journals. The average annual publication growth rate was 4%, but in 2019 it was the 10%. United States was the most productive country with more than 90 articles published, as it was confirmed by the number of publications found in Geoderma and the American Soil Science journal with 20 and 15 % respectively. We found that less than 5% of the records were relevant to our target objective.

This search provided a background in terms of variables used to build the PFT, methodologies used (e.g. multiple linear, nonlinear regression, machine learning), and detailed land use. Given the importance of SOC stock for carbon sequestration and soil fertility, a PTFs is a valid tool to estimate the BD and therefore the amount of SOC in Mediterranean agro-ecosystems.

ACS Style

Marco Acutis; Sdae 2019 Team. Pedotransfer function to predict soil bulk density in Mediterranean agro-ecosystems, a systematic map. 2020, 1 .

AMA Style

Marco Acutis, Sdae 2019 Team. Pedotransfer function to predict soil bulk density in Mediterranean agro-ecosystems, a systematic map. . 2020; ():1.

Chicago/Turabian Style

Marco Acutis; Sdae 2019 Team. 2020. "Pedotransfer function to predict soil bulk density in Mediterranean agro-ecosystems, a systematic map." , no. : 1.

Preprint content
Published: 23 March 2020
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Conservation agriculture (CA) involves complex and interactive processes that ultimately determine soil C storage, making it difficult to identify clear patterns, particularly, when the results originate from many experimental studies. 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 (SOC) sequestration at 0-30 cm soil depth and to compare it with SOC evolution under conventional agricultural practices. We simulated SOC changes in two sites located in Central Asia (Almalybak, Kazakhstan) and Southern Europe (Lombriasco, Italy), which have contrasting soils, organic carbon contents, climates, crops and management intensity.  Simulations were carried out for the current (1998-2017) and future climatic scenarios (period 2020-2040, scenario Representative Concentration Pathway 6.0).

Five cropping systems were simulated: conventional systems under ploughing at 25-30 cm with monoculture and  residues removed (Conv–R) or residues retained (Conv+R); no-tillage (NT) with residue retained and crop monocultures; 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 maize in Lombriasco. In CA and CA+CC, crop rotations were winter wheat - winter wheat - spring barley - chickpea in Almalybak; maize - winter wheat - soybean in Lombriasco, together with Italian ryegrass in the +CC options.

In Lombriasco, conventional systems led to SOC decline of 170-350 kg ha-1 yr-1, whereas, NT and CA prevented the decline and kept it on the slightly positive level under both climate scenarios. A low rate of SOC increase most likely stems from, in addition to climates, the low silt-clay fraction (34%), and thus, more vulnerable to mineralization and decay.

In Almalybak, SOC loss in conventional systems was 480-560 kg ha-1 yr-1 under current climate, and NT prevented the loss only under current climate, but not under the future climate scenario. In contrast, CA allowed for the annual C sequestration of 300 kg ha-1 and up to 620 kg ha-1 with cover crops. Under the future climate scenario, the model predicted somewhat less C sequestration under CA, probably, due to the reduction of residue biomass. Particularly, in Southern Kazakhstan, CA has the largest potential for C sequestration under both climate scenarios, twice exceeding the objectives of the “4 per 1000” initiative. This initiative claims that an annual growth rate of 0.4% in the soil carbon stocks, or 4‰ per year, in the first 30-40 cm of soil, would significantly reduce the CO2 concentration in the atmosphere related to human activities.

ACS Style

Marco Acutis; Elena Valkama; Gulya Kunypiyaeva; Muratbek Karabayev; Rauan Zhapayev; Erbol Zhusupbekov; Alessia Perego; Calogero Schillaci; Dario Sacco; Barbara Moretti; Carlo Grignani. SOC modelling and cropping system managements in contrasting climatic conditions. 2020, 1 .

AMA Style

Marco Acutis, Elena Valkama, Gulya Kunypiyaeva, Muratbek Karabayev, Rauan Zhapayev, Erbol Zhusupbekov, Alessia Perego, Calogero Schillaci, Dario Sacco, Barbara Moretti, Carlo Grignani. SOC modelling and cropping system managements in contrasting climatic conditions. . 2020; ():1.

Chicago/Turabian Style

Marco Acutis; Elena Valkama; Gulya Kunypiyaeva; Muratbek Karabayev; Rauan Zhapayev; Erbol Zhusupbekov; Alessia Perego; Calogero Schillaci; Dario Sacco; Barbara Moretti; Carlo Grignani. 2020. "SOC modelling and cropping system managements in contrasting climatic conditions." , no. : 1.

Journal article
Published: 20 March 2020 in Geoderma
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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.

ACS Style

Elena Valkama; Gulya Kunypiyaeva; Rauan Zhapayev; Muratbek Karabayev; Erbol Zhusupbekov; Alessia Perego; Calogero Schillaci; Dario Sacco; Barbara Moretti; Carlo Grignani; Marco Acutis. Can conservation agriculture increase soil carbon sequestration? A modelling approach. Geoderma 2020, 369, 114298 .

AMA Style

Elena Valkama, Gulya Kunypiyaeva, Rauan Zhapayev, Muratbek Karabayev, Erbol Zhusupbekov, Alessia Perego, Calogero Schillaci, Dario Sacco, Barbara Moretti, Carlo Grignani, Marco Acutis. Can conservation agriculture increase soil carbon sequestration? A modelling approach. Geoderma. 2020; 369 ():114298.

Chicago/Turabian Style

Elena Valkama; Gulya Kunypiyaeva; Rauan Zhapayev; Muratbek Karabayev; Erbol Zhusupbekov; Alessia Perego; Calogero Schillaci; Dario Sacco; Barbara Moretti; Carlo Grignani; Marco Acutis. 2020. "Can conservation agriculture increase soil carbon sequestration? A modelling approach." Geoderma 369, no. : 114298.

Journal article
Published: 05 August 2019 in Agronomy
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The agricultural area in the Po Valley is prone to high nitrous oxide (N2O) emissions as it is characterized by irrigated maize-based cropping systems, high amounts of nitrogen supplied, and elevated air temperature in summer. Here, two monitoring campaigns were carried out in maize fertilized with raw digestate in a randomized block design in 2016 and 2017 to test the effectiveness of the 3, 4 DMPP inhibitor Vizura® on reducing N2O-N emissions. Digestate was injected into 0.15 m soil depth at side-dressing (2016) and before sowing (2017). Non-steady state chambers were used to collect N2O-N air samples under zero N fertilization (N0), digestate (D), and digestate + Vizura® (V). Overall, emissions were significantly higher in the D treatment than in the V treatment in both 2016 and 2017. The emission factor (EF, %) of V was two and four times lower than the EF in D in 2016 and 2017, respectively. Peaks of NO3-N generally resulted in N2O-N emissions peaks, especially during rainfall or irrigation events. The water-filled pore space (WFPS, %) did not differ between treatments and was generally below 60%, suggesting that N2O-N emissions were mainly due to nitrification rather than denitrification.

ACS Style

Marcello Ermido Chiodini; Alessia Perego; Marco Carozzi; Marco Acutis. The Nitrification Inhibitor Vizura® Reduces N2O Emissions When Added to Digestate before Injection under Irrigated Maize in the Po Valley (Northern Italy). Agronomy 2019, 9, 431 .

AMA Style

Marcello Ermido Chiodini, Alessia Perego, Marco Carozzi, Marco Acutis. The Nitrification Inhibitor Vizura® Reduces N2O Emissions When Added to Digestate before Injection under Irrigated Maize in the Po Valley (Northern Italy). Agronomy. 2019; 9 (8):431.

Chicago/Turabian Style

Marcello Ermido Chiodini; Alessia Perego; Marco Carozzi; Marco Acutis. 2019. "The Nitrification Inhibitor Vizura® Reduces N2O Emissions When Added to Digestate before Injection under Irrigated Maize in the Po Valley (Northern Italy)." Agronomy 9, no. 8: 431.

Journal article
Published: 13 December 2018 in CATENA
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Legacy databases provide unique information on soil properties and act as a guide for the setup of monitoring processes. However, their use requires an evaluation of their drawbacks, especially when aiming to model the soil traits by depth. We set up a procedure for the integration and error correction of a soil legacy database. This database consisted of 6994 records in its original form and 6674 records after correction. These records were collected from 2886 locations in the south of Italy on a 25,711-km2 island (Sicily, Italy). Samples were taken in arable lands (5471 records), orchards, vineyards and seminatural lands (3010 records), and woodland and natural areas (1203 records). The procedure for the integration and error highlighting improved the prediction of soil organic carbon (SOC), and a general linear model with covariate selection by Least Absolute Shrinkage and Selection Operator (LASSO) tested the procedure. We focussed on exploring the amount of legacy information as georeferenced soil properties. SOC and fine earth fractions were analysed for each sample. Bulk density was provided for only 20% of the samples. These results will help to account for the legacy data available and propose an analysis to harmonize an SOC dataset; highlight missing or incorrect data; summarize data; and offer synthesis criteria for benchmarking SOC in different land uses and pedological areas. In addition, the results may stimulate funding bodies to support research in an open data frame, which can be turned into more sustainable use of resources, improved communication between governments and farmers, and the production of standard datasets that meet and facilitate the requirements for regional agro-environmental modelling.

ACS Style

Calogero Schillaci; Marco Acutis; Fosco Vesely; Sergio Saia. A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area. CATENA 2018, 175, 110 -122.

AMA Style

Calogero Schillaci, Marco Acutis, Fosco Vesely, Sergio Saia. A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area. CATENA. 2018; 175 ():110-122.

Chicago/Turabian Style

Calogero Schillaci; Marco Acutis; Fosco Vesely; Sergio Saia. 2018. "A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area." CATENA 175, no. : 110-122.

Research article
Published: 03 December 2018 in Agronomy for Sustainable Development
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Herbicide resistance is a major weed control issue that threatens the sustainability of rice cropping systems. Its epidemiology at large scale is largely unknown. Several rice weed species have evolved resistant populations in Italy, including multiple resistant ones. The study objectives were to analyze the impact in Italian rice fields of major agronomic factors on the epidemiology of herbicide resistance and to generate a large-scale resistance risk map. The Italian Herbicide Resistance Working Group database was used to generate herbicide resistance maps. The distribution of resistant weed populations resulted as not homogeneous in the area studied, with two pockets where resistance had not been detected. To verify the situation, random sampling was done in the pockets where resistance had never been reported. Based on data from 230 Italian municipalities, three different statistics, stepwise discriminant analysis, stepwise logistic regression, and neural network, were used to correlate resistance distribution in the main Italian rice growing area with seeding type, rotation rate, and soil texture. Through the integration of complaint monitoring, mapping, and neural network analyses, we prove that a high risk of resistance evolution is associated with traditional rice cropping systems with intense monoculture rates and where water-seeding is widespread. This is the first study that determines the degree of association between herbicide resistance and a few important predictors at large scale. It also demonstrates that resistance is present in areas where it had never been reported through extensive complaint monitoring. However, these resistant populations cause medium-low density infestations, likely not alarming rice farmers. This highlights the importance of integrated agronomic techniques at cropping system level to prevent the diffusion and impact of herbicide resistance or limit it to an acceptable level. The identification of concise, yet informative, agronomic predictors of herbicide resistance diffusion can significantly facilitate effective management and improve sustainability.

ACS Style

Elisa Mascanzoni; Alessia Perego; Niccolò Marchi; Laura Scarabel; Silvia Panozzo; Aldo Ferrero; Marco Acutis; Maurizio Sattin. Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale. Agronomy for Sustainable Development 2018, 38, 68 .

AMA Style

Elisa Mascanzoni, Alessia Perego, Niccolò Marchi, Laura Scarabel, Silvia Panozzo, Aldo Ferrero, Marco Acutis, Maurizio Sattin. Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale. Agronomy for Sustainable Development. 2018; 38 (6):68.

Chicago/Turabian Style

Elisa Mascanzoni; Alessia Perego; Niccolò Marchi; Laura Scarabel; Silvia Panozzo; Aldo Ferrero; Marco Acutis; Maurizio Sattin. 2018. "Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale." Agronomy for Sustainable Development 38, no. 6: 68.

Journal article
Published: 09 November 2018 in Agricultural Systems
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An evaluation of the effect of the conservation agriculture (CA) on agro-environmental aspects is needed at the farm scale in intensive production systems, which are likely prone to reduce soil fertility. Here, as part of the HelpSoil LIFE+ Project and involving 20 farms in the Po valley (Northern Italy), we have estimated the soil organic carbon (SOC) content, SOC stock, crop yield, biological fertility, soil biodiversity, and economic efficiency under different agricultural systems (CA and conventional, CvtA) at the beginning (March 2014) and end (October 2016) of the experimental period. CA was mostly represented by no-till practice (NT) coupled with the cultivation of winter cover crops. Minimum tillage (MT) was considered as CA or CvtA practice according to the farm design. The CA practices have been implemented on the monitored farms at different times (Long-term = before 2006, Medium-term = between 2006 and 2013, Short-term = after 2013). A direct comparison between CA and CvtA of soil-related variables, yields, and costs was performed on 14 out of the 20 farms; data were statistically treated with a linear mixed model. Overall, CA resulted in significantly higher SOC content, SOC stock, biological fertility, QBS-ar, and earthworms for the Medium-term group. Considering the effect of tillage practices observed on the 20 farms, SOC content was the highest in NT for the Long-term group. The biological fertility index was higher in NT and MT compared to CvtA within the Long-term and Medium-term groups in 2016. QBS-ar was the higher in MT and NT than CvtA for the Long-term and Medium-Term groups. The number of earthworms was the highest under NT for the Long-term group. Maize, winter wheat, and soybeans yields were generally 1 t ha−1 higher in CvtA than in CA, but this did not reach statistical significance. The cost for herbicides was 18% more expensive in NT, whereas the fuel consumption and total costs for weeding operations did not differ between NT and CvtA. The overall outcome of the analysis was that CA is a viable solution for intensive farms in the monitored area, but further skills need still to be acquired in to enhance its economic feasibility.

ACS Style

A. Perego; A. Rocca; Valentina Cattivelli; Vincenzo Tabaglio; Andrea Fiorini; S. Barbieri; Calogero Schillaci; M.E. Chiodini; S. Brenna; Marco Acutis. Agro-environmental aspects of conservation agriculture compared to conventional systems: A 3-year experience on 20 farms in the Po valley (Northern Italy). Agricultural Systems 2018, 168, 73 -87.

AMA Style

A. Perego, A. Rocca, Valentina Cattivelli, Vincenzo Tabaglio, Andrea Fiorini, S. Barbieri, Calogero Schillaci, M.E. Chiodini, S. Brenna, Marco Acutis. Agro-environmental aspects of conservation agriculture compared to conventional systems: A 3-year experience on 20 farms in the Po valley (Northern Italy). Agricultural Systems. 2018; 168 ():73-87.

Chicago/Turabian Style

A. Perego; A. Rocca; Valentina Cattivelli; Vincenzo Tabaglio; Andrea Fiorini; S. Barbieri; Calogero Schillaci; M.E. Chiodini; S. Brenna; Marco Acutis. 2018. "Agro-environmental aspects of conservation agriculture compared to conventional systems: A 3-year experience on 20 farms in the Po valley (Northern Italy)." Agricultural Systems 168, no. : 73-87.

Journal article
Published: 01 November 2018 in Rendiconti Online della Società Geologica Italiana
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A general feature of soil health is the sustainment of soil organic carbon (SOC) concentration and its stock. Digital soil mapping (DSM) development allowed for the implementation of soil properties mapping at various spatial and time scales. However, many of these studies were made in temperate or cold environments from central and northern Europe or United States or in stably arid ecosystems ofAustralia. Geographical information on the SOC are often fragmented, and this does not allow for a comparison on SOC regional variability in contrasting areas. Here a systematic research of peer-reviewed papers in the Web of science (WoS) and Scopus databases was carried out to highlight knowledge gaps in SOC studies in the Mediterranean area. The systematic searches identified 500 articles in WoS and750 in Scopus, but only few of them were eligible as ad hoc studies. Regarding WoS, after screening, 150 studies were further analysed for inclusion in the map and only 128 included in the final map (1995-2018). From Scopus, only 104 studies were included in the map (1995-2017). Of all the countries around the Mediterranean Basin, report studies on SOC are available for 15 countries, only. Data gaps identified included the absence of long-term monitoring networks in the south of Europe, a scarcity of information from countries on the eastern coast of the Adriatic and Mediterranean sea and almost lack of detailed information on SOC models and maps from north Africa.Model exportation built in neighbourhood countries (e.g. from Sicily, Italy, to northern Tunisia, or Andalusia, Spain, to northern Morocco) are strongly needed.

ACS Style

Calogero Schillaci; Sergio Saia; Marco Acutis. Modelling of Soil Organic Carbon in the Mediterranean area: a systematic map. Rendiconti Online della Società Geologica Italiana 2018, 46, 161 -166.

AMA Style

Calogero Schillaci, Sergio Saia, Marco Acutis. Modelling of Soil Organic Carbon in the Mediterranean area: a systematic map. Rendiconti Online della Società Geologica Italiana. 2018; 46 ():161-166.

Chicago/Turabian Style

Calogero Schillaci; Sergio Saia; Marco Acutis. 2018. "Modelling of Soil Organic Carbon in the Mediterranean area: a systematic map." Rendiconti Online della Società Geologica Italiana 46, no. : 161-166.

Journal article
Published: 01 January 2018 in Agricultural Systems
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Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities

ACS Style

Stefan Fronzek; Nina Pirttioja; Timothy R. Carter; Marco Bindi; Holger Hoffmann; Taru Palosuo; Margarita Ruiz-Ramos; Fulu Tao; Miroslav Trnka; Marco Acutis; Senthold Asseng; Piotr Baranowski; Bruno Basso; Per Bodin; Samuel Buis; Davide Cammarano; Paola Deligios; Marie-France Destain; Benjamin Dumont; Frank Ewert; Roberto Ferrise; Louis François; Thomas Gaiser; Petr Hlavinka; Ingrid Jacquemin; Kurt Christian Kersebaum; Chris Kollas; Jaromir Krzyszczak; Ignacio Lorite; Julien Minet; M Ines Minguez; Manuel Montesino; Marco Moriondo; Christoph Müller; Claas Nendel; Isik Öztürk; Alessia Perego; Alfredo Rodríguez; Alex C. Ruane; Françoise Ruget; Mattia Sanna; Mikhail Semenov; Cezary Sławiński; Pierre Stratonovitch; Iwan Supit; Katharina Waha; Enli Wang; Lianhai Wu; Zhigan Zhao; Reimund Rötter. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. Agricultural Systems 2018, 159, 209 -224.

AMA Style

Stefan Fronzek, Nina Pirttioja, Timothy R. Carter, Marco Bindi, Holger Hoffmann, Taru Palosuo, Margarita Ruiz-Ramos, Fulu Tao, Miroslav Trnka, Marco Acutis, Senthold Asseng, Piotr Baranowski, Bruno Basso, Per Bodin, Samuel Buis, Davide Cammarano, Paola Deligios, Marie-France Destain, Benjamin Dumont, Frank Ewert, Roberto Ferrise, Louis François, Thomas Gaiser, Petr Hlavinka, Ingrid Jacquemin, Kurt Christian Kersebaum, Chris Kollas, Jaromir Krzyszczak, Ignacio Lorite, Julien Minet, M Ines Minguez, Manuel Montesino, Marco Moriondo, Christoph Müller, Claas Nendel, Isik Öztürk, Alessia Perego, Alfredo Rodríguez, Alex C. Ruane, Françoise Ruget, Mattia Sanna, Mikhail Semenov, Cezary Sławiński, Pierre Stratonovitch, Iwan Supit, Katharina Waha, Enli Wang, Lianhai Wu, Zhigan Zhao, Reimund Rötter. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. Agricultural Systems. 2018; 159 ():209-224.

Chicago/Turabian Style

Stefan Fronzek; Nina Pirttioja; Timothy R. Carter; Marco Bindi; Holger Hoffmann; Taru Palosuo; Margarita Ruiz-Ramos; Fulu Tao; Miroslav Trnka; Marco Acutis; Senthold Asseng; Piotr Baranowski; Bruno Basso; Per Bodin; Samuel Buis; Davide Cammarano; Paola Deligios; Marie-France Destain; Benjamin Dumont; Frank Ewert; Roberto Ferrise; Louis François; Thomas Gaiser; Petr Hlavinka; Ingrid Jacquemin; Kurt Christian Kersebaum; Chris Kollas; Jaromir Krzyszczak; Ignacio Lorite; Julien Minet; M Ines Minguez; Manuel Montesino; Marco Moriondo; Christoph Müller; Claas Nendel; Isik Öztürk; Alessia Perego; Alfredo Rodríguez; Alex C. Ruane; Françoise Ruget; Mattia Sanna; Mikhail Semenov; Cezary Sławiński; Pierre Stratonovitch; Iwan Supit; Katharina Waha; Enli Wang; Lianhai Wu; Zhigan Zhao; Reimund Rötter. 2018. "Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change." Agricultural Systems 159, no. : 209-224.

Journal article
Published: 01 December 2017 in Science of The Total Environment
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SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R (0.63-0.69) and low uncertainty (s.d.<0.76gCkg with RS, and <1.25gCkg without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

ACS Style

Calogero Schillaci; Marco Acutis; Luigi Lombardo; Aldo Lipani; Maria Fantappiè; Michael Maerker; Sergio Saia. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling. Science of The Total Environment 2017, 601-602, 821 -832.

AMA Style

Calogero Schillaci, Marco Acutis, Luigi Lombardo, Aldo Lipani, Maria Fantappiè, Michael Maerker, Sergio Saia. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling. Science of The Total Environment. 2017; 601-602 ():821-832.

Chicago/Turabian Style

Calogero Schillaci; Marco Acutis; Luigi Lombardo; Aldo Lipani; Maria Fantappiè; Michael Maerker; Sergio Saia. 2017. "Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling." Science of The Total Environment 601-602, no. : 821-832.

Journal article
Published: 01 August 2017 in European Journal of Agronomy
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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, −0.3 m3 m−3 for SWC and 360 g DM m−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 grassland systems

ACS Style

Renáta Sándor; Z. Barcza; Marco Acutis; Luca Doro; D. Hidy; Martin Köchy; J. Minet; E. Lellei-Kovács; S. Ma; Alessia Perego; S. Rolinski; F. Ruget; Mattia Sanna; G. Seddaiu; L. Wu; G. Bellocchi. Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance. European Journal of Agronomy 2017, 88, 22 -40.

AMA Style

Renáta Sándor, Z. Barcza, Marco Acutis, Luca Doro, D. Hidy, Martin Köchy, J. Minet, E. Lellei-Kovács, S. Ma, Alessia Perego, S. Rolinski, F. Ruget, Mattia Sanna, G. Seddaiu, L. Wu, G. Bellocchi. Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance. European Journal of Agronomy. 2017; 88 ():22-40.

Chicago/Turabian Style

Renáta Sándor; Z. Barcza; Marco Acutis; Luca Doro; D. Hidy; Martin Köchy; J. Minet; E. Lellei-Kovács; S. Ma; Alessia Perego; S. Rolinski; F. Ruget; Mattia Sanna; G. Seddaiu; L. Wu; G. Bellocchi. 2017. "Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance." European Journal of Agronomy 88, no. : 22-40.

Journal article
Published: 01 June 2017 in Agricultural Systems
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Timely crop yield forecasts at regional and national level are crucial to manage trade and industry planning and to mitigate price speculations. Sugarcane is responsible for 70% of global sugar supplies, thus making yield forecasts essential to regulate the global commodity market. In this study, a sugarcane forecasting system was developed and successfully applied to São Paulo State, the largest cane producer in Brazil. The system is based on multiple linear regressions relating agro-climatic indicators and outputs of the sugarcane model Canegro to historical yield records. The resulting equations are then used to forecast the yield of the current season using 10-day period updated values of indicators and model outputs as the season progresses. We quantified the reliability of the forecasting system in different stages of the sugarcane cycle by performing cross-validations using the 2000-2013 time series of official stalk yields. Agro-climatic indicators alone explained from 38% of inter-annual yield variability (at State level) during the boom growth phase (i.e., January-April) to 73% during the second half of the harvesting period (i.e., September-October). When Canegro outputs were added to the regressor set, the variability explained increased to 63% for the boom growth phase and 90% after mid harvesting, with the best performances achieved while approaching the end of the harvesting window (i.e. at the beginning of October, SDEP = 0.8 t ha-1, R2cv = 0.93). It is concluded that the overall performances of the system are satisfactory, considering that it was the first attempt based on information exclusively retrieved from the literature. Further improvements to operationalize the system could be possibly achieved by the use of more accurate inputs possibly supplied by the collaboration with local authorities.JRC.D.5-Food Securit

ACS Style

Valentina Pagani; Tommaso Stella; Tommaso Guarneri; Giacomo Finotto; Maurits Van Den Berg; Fabio Ricardo Marin; Marco Acutis; Roberto Confalonieri. Forecasting sugarcane yields using agro-climatic indicators and Canegro model: A case study in the main production region in Brazil. Agricultural Systems 2017, 154, 45 -52.

AMA Style

Valentina Pagani, Tommaso Stella, Tommaso Guarneri, Giacomo Finotto, Maurits Van Den Berg, Fabio Ricardo Marin, Marco Acutis, Roberto Confalonieri. Forecasting sugarcane yields using agro-climatic indicators and Canegro model: A case study in the main production region in Brazil. Agricultural Systems. 2017; 154 ():45-52.

Chicago/Turabian Style

Valentina Pagani; Tommaso Stella; Tommaso Guarneri; Giacomo Finotto; Maurits Van Den Berg; Fabio Ricardo Marin; Marco Acutis; Roberto Confalonieri. 2017. "Forecasting sugarcane yields using agro-climatic indicators and Canegro model: A case study in the main production region in Brazil." Agricultural Systems 154, no. : 45-52.

Journal article
Published: 01 January 2017 in Geoderma
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Efficient modelling methods to assess soil organic carbon (SOC) stocks have a pivotal importance as inputs for global carbon cycle studies and decision-making processes. However, laboratory analyses of SOC field samples are costly and time consuming. Global-scale estimates of SOC were recently made according to categorical variables, including land use and soil texture. Remote sensing (RS) data can contribute to the better modelling of the spatial distribution of SOC stock at a regional scale. In the present study, we used Stochastic Gradient Treeboost (SGT) to estimate the topsoil (0–30 cm) SOC stock of a Mediterranean semiarid area (Sicily, Italy, 25,286 km2). In particular, our study examined agricultural lands, which represent approximately 64% of the entire region. An extensive soil dataset (2202 samples, 1 profile/7.31 km2 on average) was acquired from the soil database of Sicily. The georeferenced field observations were intersected with remotely sensed environmental data and other spatial data, including climatic data from WORLDCLIM, land cover from CORINE, soil texture, topography and derived indices. Finally, the SGT was compared to published global estimates (GSOC) and data from the International Soil Reference and Information Centre (ISRIC) Soil Grids by comparing the pseudo-regressions of the SGT, GSOC and ISRIC with soil observations. The mean SOC stock across the entire region that was estimated by GSOC and ISRIC was 3.9% lower and 46.2% higher compared to the SGT. The SGT efficiently predicted SOC stocks that were < 70 t ha− 1 (corresponding to the 90th percentile of the observed values). On average, the coefficient of variation of the SGT model was 3.6% when computed on the whole dataset and remained lower than 23% when computed on a distribution basis. The SGT mean absolute error was 14.84 t ha− 1, 18.4% and 36.3% lower than GSOC and ISRIC, respectively. The mean annual rainfall, soil texture, land use, mean annual temperature and Landsat 7 ETM+ panchromatic Band 8 were the most important predictors of SOC stock. Finally, SOC stocks were estimated for each land cover class. SGT predicted SOC stock better than GSOC and ISRIC for most data. This resulted in a percentage of data in the prediction confidence interval ± 50% compared to the observed values of 71.4%, 65.8%, and 50.7% for SGT, GSOC, and SGT, respectively. This consisted of a higher R2 and a slope (β) that was closer to 1 for the pseudo-regression constructed with SGT compared to GSOC and ISRIC. In conclusion, the results of the present study showed that the integration of RS with climatic and soil texture spatial data could strongly improve SOC prediction in a semi-arid Mediterranean region. In addition, the panchromatic band of Landsat 7 ETM + was more predictive compared to the conventionally used NDVI. This information is crucial to guiding decision-making processes, especially at a regional scale and/or in semi-arid Mediterranean areas. The model performance of the SGT could be further improved by adopting predictors with greater spatial resolutions. The results of the present experiment yield valuable information, especially for assessing climate change or land use change scenarios for SOC stocks and their spatial distribution

ACS Style

Calogero Schillaci; Luigi Lombardo; Sergio Saia; Maria Fantappiè; Michael Maerker; Marco Acutis. Modelling the topsoil carbon stock of agricultural lands with the Stochastic Gradient Treeboost in a semi-arid Mediterranean region. Geoderma 2017, 286, 35 -45.

AMA Style

Calogero Schillaci, Luigi Lombardo, Sergio Saia, Maria Fantappiè, Michael Maerker, Marco Acutis. Modelling the topsoil carbon stock of agricultural lands with the Stochastic Gradient Treeboost in a semi-arid Mediterranean region. Geoderma. 2017; 286 ():35-45.

Chicago/Turabian Style

Calogero Schillaci; Luigi Lombardo; Sergio Saia; Maria Fantappiè; Michael Maerker; Marco Acutis. 2017. "Modelling the topsoil carbon stock of agricultural lands with the Stochastic Gradient Treeboost in a semi-arid Mediterranean region." Geoderma 286, no. : 35-45.

Journal article
Published: 01 November 2016 in Environmental Modelling & Software
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For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. A taxonomy-based approach was used to classify AgMIP rice simulation models.Different model structures often resulted in similar outputs.Similar structures often led to large differences in outputs.User subjectivity likely hides relationships between model structure and behaviour.Shared protocols are still needed to limit the risks during calibration.

ACS Style

Roberto Confalonieri; Simone Bregaglio; Myriam Adam; Françoise Ruget; Tao Li; Toshihiro Hasegawa; Xinyou Yin; Yan Zhu; Kenneth Boote; Samuel Buis; Tamon Fumoto; Donald Gaydon; Tanguy Lafarge; Manuel Marcaida; Hiroshi Nakagawa; Alex C. Ruane; Balwinder Singh; Upendra Singh; Liang Tang; Fulu Tao; Job Fugice; Hiroe Yoshida; Zhao Zhang; Lloyd T. Wilson; Jeff Baker; Yubin Yang; Yuji Masutomi; Daniel Wallach; Marco Acutis; Bas Bouman. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation. Environmental Modelling & Software 2016, 85, 332 -341.

AMA Style

Roberto Confalonieri, Simone Bregaglio, Myriam Adam, Françoise Ruget, Tao Li, Toshihiro Hasegawa, Xinyou Yin, Yan Zhu, Kenneth Boote, Samuel Buis, Tamon Fumoto, Donald Gaydon, Tanguy Lafarge, Manuel Marcaida, Hiroshi Nakagawa, Alex C. Ruane, Balwinder Singh, Upendra Singh, Liang Tang, Fulu Tao, Job Fugice, Hiroe Yoshida, Zhao Zhang, Lloyd T. Wilson, Jeff Baker, Yubin Yang, Yuji Masutomi, Daniel Wallach, Marco Acutis, Bas Bouman. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation. Environmental Modelling & Software. 2016; 85 ():332-341.

Chicago/Turabian Style

Roberto Confalonieri; Simone Bregaglio; Myriam Adam; Françoise Ruget; Tao Li; Toshihiro Hasegawa; Xinyou Yin; Yan Zhu; Kenneth Boote; Samuel Buis; Tamon Fumoto; Donald Gaydon; Tanguy Lafarge; Manuel Marcaida; Hiroshi Nakagawa; Alex C. Ruane; Balwinder Singh; Upendra Singh; Liang Tang; Fulu Tao; Job Fugice; Hiroe Yoshida; Zhao Zhang; Lloyd T. Wilson; Jeff Baker; Yubin Yang; Yuji Masutomi; Daniel Wallach; Marco Acutis; Bas Bouman. 2016. "A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation." Environmental Modelling & Software 85, no. : 332-341.

Journal article
Published: 01 July 2016 in Environmental Modelling & Software
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Crop models are used to estimate crop productivity under future climate projections, and modellers manage uncertainty by considering different scenarios and GCMs, using a range of crop simulators. Five crop models and 20 users were arranged in a randomized block design with four replicates. Parameters for maize (well studied by modellers) and rapeseed (almost ignored) were calibrated. While all models were accurate for maize (RRMSE from 16.5% to 25.9%), they were, to some extent, unsuitable for rapeseed. Although differences between biomass simulated by the models were generally significant for rapeseed, they were significant only in 30% of the cases for maize. This could suggest that in case of models well suited to a crop, user subjectivity (which explained 14% of total variance in maize outputs) can hide differences in model algorithms and, consequently, the uncertainty due to parameterization should be better investigated.

ACS Style

Roberto Confalonieri; Francesca Orlando; Livia Paleari; Tommaso Stella; Carlo Gilardelli; Ermes Movedi; Valentina Pagani; Giovanni Alessandro Cappelli; Andrea Vertemara; Luigi Alberti; Paolo Alberti; Samuel Atanassiu; Matteo Bonaiti; Matteo Ceruti; Andrea Confalonieri; Gabriele Corgatelli; Paolo Corti; Michele Dell'Oro; Alessandro Ghidoni; Angelo Lamarta; Alberto Maghini; Martino Mambretti; Agnese Manchia; Gianluca Massoni; Pierangelo Mutti; Stefano Pariani; Davide Pasini; Andrea Pesenti; Giovanni Pizzamiglio; Adriano Ravasio; Alessandro Rea; David Santorsola; Giulia Serafini; Marco Slavazza; Marco Acutis. Uncertainty in crop model predictions: What is the role of users? Environmental Modelling & Software 2016, 81, 165 -173.

AMA Style

Roberto Confalonieri, Francesca Orlando, Livia Paleari, Tommaso Stella, Carlo Gilardelli, Ermes Movedi, Valentina Pagani, Giovanni Alessandro Cappelli, Andrea Vertemara, Luigi Alberti, Paolo Alberti, Samuel Atanassiu, Matteo Bonaiti, Matteo Ceruti, Andrea Confalonieri, Gabriele Corgatelli, Paolo Corti, Michele Dell'Oro, Alessandro Ghidoni, Angelo Lamarta, Alberto Maghini, Martino Mambretti, Agnese Manchia, Gianluca Massoni, Pierangelo Mutti, Stefano Pariani, Davide Pasini, Andrea Pesenti, Giovanni Pizzamiglio, Adriano Ravasio, Alessandro Rea, David Santorsola, Giulia Serafini, Marco Slavazza, Marco Acutis. Uncertainty in crop model predictions: What is the role of users? Environmental Modelling & Software. 2016; 81 ():165-173.

Chicago/Turabian Style

Roberto Confalonieri; Francesca Orlando; Livia Paleari; Tommaso Stella; Carlo Gilardelli; Ermes Movedi; Valentina Pagani; Giovanni Alessandro Cappelli; Andrea Vertemara; Luigi Alberti; Paolo Alberti; Samuel Atanassiu; Matteo Bonaiti; Matteo Ceruti; Andrea Confalonieri; Gabriele Corgatelli; Paolo Corti; Michele Dell'Oro; Alessandro Ghidoni; Angelo Lamarta; Alberto Maghini; Martino Mambretti; Agnese Manchia; Gianluca Massoni; Pierangelo Mutti; Stefano Pariani; Davide Pasini; Andrea Pesenti; Giovanni Pizzamiglio; Adriano Ravasio; Alessandro Rea; David Santorsola; Giulia Serafini; Marco Slavazza; Marco Acutis. 2016. "Uncertainty in crop model predictions: What is the role of users?" Environmental Modelling & Software 81, no. : 165-173.

Journal article
Published: 01 May 2016 in Ecological Modelling
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Despite modellers are paying increasing attention to analyse and manage the different sources of uncertainty affecting model predictions, the impact of the uncertainty in the observations used for calibration has been ignored. This study proposes a methodology for its quantification and provides an illustrative case study with data collected in two field experiments where rice was grown under flooded conditions in northern Italy in 2002 and 2004. Latin hypercube sampling was used to generate virtual series of observations from the mean and standard deviation of aboveground biomass values collected during the season in the two experiments. Each of the generated series was then used to calibrate the parameters maximum radiation use efficiency and optimum temperature for growth of the WARM model by means of the simplex optimization algorithm. The analysis of the distribution of key outputs (aboveground and panicle biomass at harvest) and of agreement metrics revealed that the impact of uncertainty in the observations used for calibration (explored here running calibration experiments for each of the generated series) can be large. The difference between maximum and minimum aboveground biomass at maturity was 2.79 t ha−1 and 3.78 t ha−1 for the datasets collected in 2004 and 2002, respectively. Corresponding values for panicle biomass were 0.97 t ha−1 and 2.36 t ha−1. In all cases, model outputs were normally distributed. Large differences were achieved also in the values of the agreement metrics, with RRMSE ranging from 13.64% to 36.22% and from 8.04% to 29.97% for the 2004 and 2002 datasets. The methodology proposed – although applicable to a variety of models and domains – deals only with the uncertainty due to random errors, which could derive, e.g. from non-representative sampling or from the repeatability of the method used to determine the variable of interest. Other sources of uncertainty, like those involved with systematic errors, need to be addressed in further studies. This study highlighted the need for conceptual and mathematical frameworks where the different sources of uncertainty affecting model predictions can be analysed in an integrated way.

ACS Style

Roberto Confalonieri; Simone Bregaglio; Marco Acutis. Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration. Ecological Modelling 2016, 328, 72 -77.

AMA Style

Roberto Confalonieri, Simone Bregaglio, Marco Acutis. Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration. Ecological Modelling. 2016; 328 ():72-77.

Chicago/Turabian Style

Roberto Confalonieri; Simone Bregaglio; Marco Acutis. 2016. "Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration." Ecological Modelling 328, no. : 72-77.