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Dr. Calogero Schillaci
University of Milan, Department of Agricultural and Environmental Sciences

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Research Keywords & Expertise

0 GIS and Remote Sensing
0 Agronomy and Agricultural Research
0 soil organic carbon
0 soil erosion
0 Digital Soil Mapping

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soil organic carbon
soil erosion
Digital Soil Mapping

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Short Biography

Calogero received his PhD degree in Agricultural and Environmental Science at the University of Milan in 2018. His expertise include land degradation assessment, soil organic carbon mapping and modelling and machine learning.

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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.

Research article
Published: 25 April 2021 in International Journal of Remote Sensing
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Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management.

ACS Style

Thomas James; Calogero Schillaci; Aldo Lipani. Convolutional neural networks for water segmentation using sentinel-2 red, green, blue (RGB) composites and derived spectral indices. International Journal of Remote Sensing 2021, 42, 5338 -5365.

AMA Style

Thomas James, Calogero Schillaci, Aldo Lipani. Convolutional neural networks for water segmentation using sentinel-2 red, green, blue (RGB) composites and derived spectral indices. International Journal of Remote Sensing. 2021; 42 (14):5338-5365.

Chicago/Turabian Style

Thomas James; Calogero Schillaci; Aldo Lipani. 2021. "Convolutional neural networks for water segmentation using sentinel-2 red, green, blue (RGB) composites and derived spectral indices." International Journal of Remote Sensing 42, no. 14: 5338-5365.

Journal article
Published: 31 March 2021 in Environmental Research
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Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

ACS Style

Nejc Bezak; Matjaž Mikoš; Pasquale Borrelli; Christine Alewell; Pablo Alvarez; Jamil Alexandre Ayach Anache; Jantiene Baartman; Cristiano Ballabio; Marcella Biddoccu; Artemi Cerdà; Devraj Chalise; Songchao Chen; Walter Chen; Anna Maria De Girolamo; Gizaw Desta Gessesse; Detlef Deumlich; Nazzareno Diodato; Nikolaos Efthimiou; Gunay Erpul; Peter Fiener; Michele Freppaz; Francesco Gentile; Andreas Gericke; Nigussie Haregeweyn; Bifeng Hu; Amelie Jeanneau; Konstantinos Kaffas; Mahboobeh Kiani-Harchegani; Ivan Lizaga Villuendas; Changjia Li; Luigi Lombardo; Manuel López-Vicente; Manuel Esteban Lucas-Borja; Michael Maerker; Chiyuan Miao; Sirio Modugno; Markus Möller; Victoria Naipal; Mark Nearing; Stephen Owusu; Dinesh Panday; Edouard Patault; Cristian Valeriu Patriche; Laura Poggio; Raquel Portes; Laura Quijano; Mohammad Reza Rahdari; Mohammed Renima; Giovanni Francesco Ricci; Jesús Rodrigo-Comino; Sergio Saia; Aliakbar Nazari Samani; Calogero Schillaci; Vasileios Syrris; Hyuck Soo Kim; Diogo Noses Spinola; Paulo Tarso Oliveira; Hongfen Teng; Resham Thapa; Konstantinos Vantas; Diana Vieira; Jae E. Yang; Shuiqing Yin; Demetrio Antonio Zema; Guangju Zhao; Panos Panagos. Soil erosion modelling: A bibliometric analysis. Environmental Research 2021, 197, 111087 .

AMA Style

Nejc Bezak, Matjaž Mikoš, Pasquale Borrelli, Christine Alewell, Pablo Alvarez, Jamil Alexandre Ayach Anache, Jantiene Baartman, Cristiano Ballabio, Marcella Biddoccu, Artemi Cerdà, Devraj Chalise, Songchao Chen, Walter Chen, Anna Maria De Girolamo, Gizaw Desta Gessesse, Detlef Deumlich, Nazzareno Diodato, Nikolaos Efthimiou, Gunay Erpul, Peter Fiener, Michele Freppaz, Francesco Gentile, Andreas Gericke, Nigussie Haregeweyn, Bifeng Hu, Amelie Jeanneau, Konstantinos Kaffas, Mahboobeh Kiani-Harchegani, Ivan Lizaga Villuendas, Changjia Li, Luigi Lombardo, Manuel López-Vicente, Manuel Esteban Lucas-Borja, Michael Maerker, Chiyuan Miao, Sirio Modugno, Markus Möller, Victoria Naipal, Mark Nearing, Stephen Owusu, Dinesh Panday, Edouard Patault, Cristian Valeriu Patriche, Laura Poggio, Raquel Portes, Laura Quijano, Mohammad Reza Rahdari, Mohammed Renima, Giovanni Francesco Ricci, Jesús Rodrigo-Comino, Sergio Saia, Aliakbar Nazari Samani, Calogero Schillaci, Vasileios Syrris, Hyuck Soo Kim, Diogo Noses Spinola, Paulo Tarso Oliveira, Hongfen Teng, Resham Thapa, Konstantinos Vantas, Diana Vieira, Jae E. Yang, Shuiqing Yin, Demetrio Antonio Zema, Guangju Zhao, Panos Panagos. Soil erosion modelling: A bibliometric analysis. Environmental Research. 2021; 197 ():111087.

Chicago/Turabian Style

Nejc Bezak; Matjaž Mikoš; Pasquale Borrelli; Christine Alewell; Pablo Alvarez; Jamil Alexandre Ayach Anache; Jantiene Baartman; Cristiano Ballabio; Marcella Biddoccu; Artemi Cerdà; Devraj Chalise; Songchao Chen; Walter Chen; Anna Maria De Girolamo; Gizaw Desta Gessesse; Detlef Deumlich; Nazzareno Diodato; Nikolaos Efthimiou; Gunay Erpul; Peter Fiener; Michele Freppaz; Francesco Gentile; Andreas Gericke; Nigussie Haregeweyn; Bifeng Hu; Amelie Jeanneau; Konstantinos Kaffas; Mahboobeh Kiani-Harchegani; Ivan Lizaga Villuendas; Changjia Li; Luigi Lombardo; Manuel López-Vicente; Manuel Esteban Lucas-Borja; Michael Maerker; Chiyuan Miao; Sirio Modugno; Markus Möller; Victoria Naipal; Mark Nearing; Stephen Owusu; Dinesh Panday; Edouard Patault; Cristian Valeriu Patriche; Laura Poggio; Raquel Portes; Laura Quijano; Mohammad Reza Rahdari; Mohammed Renima; Giovanni Francesco Ricci; Jesús Rodrigo-Comino; Sergio Saia; Aliakbar Nazari Samani; Calogero Schillaci; Vasileios Syrris; Hyuck Soo Kim; Diogo Noses Spinola; Paulo Tarso Oliveira; Hongfen Teng; Resham Thapa; Konstantinos Vantas; Diana Vieira; Jae E. Yang; Shuiqing Yin; Demetrio Antonio Zema; Guangju Zhao; Panos Panagos. 2021. "Soil erosion modelling: A bibliometric analysis." Environmental Research 197, no. : 111087.

Review
Published: 04 March 2021
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Mediterranean and humid subtropical climate is characterized by hot summer and cold to mild winter with a medium-low soil organic carbon (SOC) content and high risk of land desertification. Recent EU policies pointed out the need to preserve the SOC stock and to enhance its accumulation by promoting the adoption of conservation agriculture (CA) as an efficient action for climate change adaptation and mitigation. The meta-analysis is a powerful data analysis tool, which can be useful to evaluate the effectiveness of CA in increase SOC in comparison with conventional agriculture. In fact, this topic has been addressed by several published articles even though the methodology shortcomings make sometimes difficult to draw reliable conclusions about the contribution of CA. In our work, we applied a robust methodology to comply with the meta-analytic assumptions, such as an independence of effect sizes and weighting, as well as the requirement to use no predictive functions like pedotransfer. Therefore, the present meta-analysis defines a conservative and replicable approach to deal with soil carbon data, explaining the differences between conventional (control) and CA management (treatment) in terms of SOC stock accumulation in the first 0-0.3 m layer. A defined methodology was developed to summarize carbon data within a unique soil layer taking into account the real variance and correlation between different initial soil carbon layers. A final database of 49 studies has been used to summarize the effect and to explain the heterogeneity across studies, including also several pedoclimatic moderators in the analysis. An overall positive effect of about 13 % change in SOC accumulation was found due to CA practices compared to control. To better explain the data variability, we created two different groups of studies ("low carbon in control, LC" and "high carbon in control", HC) base on the amount of SOC in control (with 40 Mg ha-1 as a threshold). This method leads to more reliable conclusions that it is more likely to find a response to CA management in soil with low carbon content rather than in soil that have more than 40 t C stock ha-1 . A positive correlation was also found between clay soils with high carbon content in control and carbon sequestration event though the texture classification did not explain data variability. Agronomic management plays an essential role in inducing C accumulation under CA in both LC and HC groups, especially with high residue retention during long-term experiments (0.21 Mg C ha-1 yr-1 for the whole database). We also found that climatic and geographical moderators can explain the variability among the effect sizes, like the absolute value of latitude or the precipitation during the year, even though the different continent or climate Köppen classification did not give significant results.

ACS Style

Tommaso Tadiello; Marco Acutis; Alessia Perego; Calogero Schillaci; Elena Valkama. Can Conservation Agriculture Enhance Soil Organic Carbon Sequestration In Mediterranean And Humid Subtropical Climates? A Meta-Analysis. 2021, 1 .

AMA Style

Tommaso Tadiello, Marco Acutis, Alessia Perego, Calogero Schillaci, Elena Valkama. Can Conservation Agriculture Enhance Soil Organic Carbon Sequestration In Mediterranean And Humid Subtropical Climates? A Meta-Analysis. . 2021; ():1.

Chicago/Turabian Style

Tommaso Tadiello; Marco Acutis; Alessia Perego; Calogero Schillaci; Elena Valkama. 2021. "Can Conservation Agriculture Enhance Soil Organic Carbon Sequestration In Mediterranean And Humid Subtropical Climates? A Meta-Analysis." , no. : 1.

Preprint content
Published: 04 March 2021
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Legacy data are frequently unique sources of data for the estimation of past soil properties. With the rising concerns about greenhouse gases (GHG) emission and soil degradation due to intensive agriculture and climate change effects, soil organic carbon (SOC) concentration might change heavily over time.

When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-aligned data) may lead to biased results. The sampling schemes adopted to capture SOC variation usually involve the resampling of the original sample using a so called paired-site approach.

In the present work, a regional (Sicily, south of Italy) soil database, consisting of N=302 georeferenced soil samples from arable land collected in 1993 [1], was used to select coinciding sites to test a former temporal variation (1993-2008) obtained by a comparison of models built with data sampled in non-coinciding locations [2]. A specific sampling strategy was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested.

To spot SOC changes the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years has been estimated. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an a=0.05.

After the collection of the 30 samples, SOC concentration in the newly collected samples was determined in lab using the same method

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 (not always significant) SOC concentration than in 2017.

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-aligned data) [2], when compared to 1994 observed data (Z = -9.119; 2-tailed asymptotic significance < 0.001).

Such a result implies that the use of legacy data to estimate SOC concentration changes need 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.

Bibliography

[1]Schillaci C, et al.,2019. A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area. CATENA.

[2]Schillaci C, et al., 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. Sci Total Environ. 

ACS Style

Calogero Schillaci; Sergio Saia; Aldo Lipani; Alessia Perego; Claudio Zaccone; Marco Acutis. Matching legacy estimation of soil organic carbon changes from non-paired data with measured values in paired soil samples after two decades: a case study. 2021, 1 .

AMA Style

Calogero Schillaci, Sergio Saia, Aldo Lipani, Alessia Perego, Claudio Zaccone, Marco Acutis. Matching legacy estimation of soil organic carbon changes from non-paired data with measured values in paired soil samples after two decades: a case study. . 2021; ():1.

Chicago/Turabian Style

Calogero Schillaci; Sergio Saia; Aldo Lipani; Alessia Perego; Claudio Zaccone; Marco Acutis. 2021. "Matching legacy estimation of soil organic carbon changes from non-paired data with measured values in paired soil samples after two decades: a case study." , no. : 1.

Preprint content
Published: 21 January 2021
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Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use can pose 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-aligned 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 a=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-aligned data), when compared to 1994 observed data (Z = -9.119; 2-tailed asymptotic significance < 0.001). Such a result implies 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. Determination of minimum number of samples allowing to detect long term soil organic carbon changes in Mediterranean arable lands using paired-sites. 2021, 1 .

AMA Style

Calogero Schillaci, Sergio Saia, Aldo Lipani, Alessia Perego, Claudio Zaccone, Marco Acutis. Determination of minimum number of samples allowing to detect long term soil organic carbon changes in Mediterranean arable lands using paired-sites. . 2021; ():1.

Chicago/Turabian Style

Calogero Schillaci; Sergio Saia; Aldo Lipani; Alessia Perego; Claudio Zaccone; Marco Acutis. 2021. "Determination of minimum number of samples allowing to detect long term soil organic carbon changes in Mediterranean arable lands using paired-sites." , no. : 1.

Journal article
Published: 21 January 2021 in Remote Sensing Applications: Society and Environment
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Building fire risk prediction is crucial for allocation of building inspection resources and prevention of fire incidents. Existing research of building fire prediction makes use of data relating to local demography, crime, building use and physical building characteristics, yet few studies have analysed the relative importance of predictive features. Furthermore, image features relating to buildings, such as aerial imagery and digital surface models (DSM), have not been explored. This research presents a multi-modal hybrid neural network for the prediction of fire risk at the building level using the London Fire Brigade dataset. The inclusion of traditional and novel image features is assessed using Shapley values and an ablation study. The ablation study found that while building use is the most effective contributor of classification performance, demographic features, apart from social class, are detrimental. Moreover, while the DSM did not lead to any notable improvement in classification performance, the inclusion of the aerial imagery feature lead to a 4% increase in median validation ROC AUC. The final model presented achieved an ROC AUC of 0.8195 on the test set.

ACS Style

Jake Anderson-Bell; Calogero Schillaci; Aldo Lipani. Predicting non-residential building fire risk using geospatial information and convolutional neural networks. Remote Sensing Applications: Society and Environment 2021, 21, 100470 .

AMA Style

Jake Anderson-Bell, Calogero Schillaci, Aldo Lipani. Predicting non-residential building fire risk using geospatial information and convolutional neural networks. Remote Sensing Applications: Society and Environment. 2021; 21 ():100470.

Chicago/Turabian Style

Jake Anderson-Bell; Calogero Schillaci; Aldo Lipani. 2021. "Predicting non-residential building fire risk using geospatial information and convolutional neural networks." Remote Sensing Applications: Society and Environment 21, no. : 100470.

Journal article
Published: 27 June 2020 in Geosciences
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Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes.

ACS Style

Mariaelena Cama; Calogero Schillaci; Jan Kropáček; Volker Hochschild; Alberto Bosino; Michael Märker. A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands. Geosciences 2020, 10, 248 .

AMA Style

Mariaelena Cama, Calogero Schillaci, Jan Kropáček, Volker Hochschild, Alberto Bosino, Michael Märker. A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands. Geosciences. 2020; 10 (7):248.

Chicago/Turabian Style

Mariaelena Cama; Calogero Schillaci; Jan Kropáček; Volker Hochschild; Alberto Bosino; Michael Märker. 2020. "A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands." Geosciences 10, no. 7: 248.

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.

Preprint content
Published: 23 March 2020
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Barley is a widespread crop in the Mediterranean area and in temperate climates. Barley impact in the food chain is very important for its value as food and feed. The societal demand is for more productive varieties, which can be able to cope with the current and future climate scenarios. Change in climate is expected to result in more adverse conditions for the barley growth and alter land suitability in its growing regions, such as the Mediterranean basin. In this context, laboratory and modelling activities for the so-called “in silico ideotyping” can be effectively carried out to design new germplasms and to define optimal field management practices. As a first step to reach this objective, we collate the available scientific research about the identification of optimal phenotypic traits for the adaptation to harsh environments. In the framework of the GENDIBAR project (Utilization of local genetic diversity for studying barley adaptation to harsh environments and for pre-breeding; PRIMA European Funding Programme), a bibliometric analysis was carried out in the SCOPUS database with the aim to find published papers about barley adaptation in relation to changing climate. The initial query was (barley AND climate AND adaptation); it contained few keywords and resulted in less than 200 publications. By adding (barley AND ideotyping OR barley AND phenotyping), the search reached 450 records. The most comprehensive search was achieved by adding another OR condition (Barley AND future climate OR climate change) that yielded more than 1000 results. Although these records seemed relevant, a deeper analysis showed that less than 5% of these studies are of real interest and moreover the manual screening of the abstracts of all records will require around a month of work. The second query represents a compromise between the simplest query (barley AND climate AND adaptation) and the last query made by three conditions bonded together. This literature search approach highlighted the results of manipulative experiments and modelling studies for deriving phenotyping and agronomic traits to address in-silico ideotyping design. However, the search outcome suggests that there is a gap of knowledge about the barley phenotypic traits needed to cope with climate change in the semi-arid and arid regions of the Mediterranean basin. This approach is expected to further provide useful information for the development of land suitability models, as well as for barley breeding.

ACS Style

Agostino Fricano; Erica Mica; Raffaella Battaglia; Alessandro Tondelli; Calogero Schillaci; Alessia Perego. Barley ideotyping for the adaptation to heat stress in the Mediterranean basin. A bibliometric search approach. 2020, 1 .

AMA Style

Agostino Fricano, Erica Mica, Raffaella Battaglia, Alessandro Tondelli, Calogero Schillaci, Alessia Perego. Barley ideotyping for the adaptation to heat stress in the Mediterranean basin. A bibliometric search approach. . 2020; ():1.

Chicago/Turabian Style

Agostino Fricano; Erica Mica; Raffaella Battaglia; Alessandro Tondelli; Calogero Schillaci; Alessia Perego. 2020. "Barley ideotyping for the adaptation to heat stress in the Mediterranean basin. A bibliometric search approach." , no. : 1.

Preprint content
Published: 10 March 2020
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Conservative Agriculture (CA) practices are recognized to enhance soil organic carbon stock and in turn to mitigate the effect of climate change. One of the CA principles is to integrate cover crops (CC) into the cropping systems. The termination of CC before the cash crop sowing and the weeds control are the most critical aspects to manage in the CA. The technique currently adopted by farmers for the termination of CC implies the use of Glyphosate. However, the European Commission is currently discussing the possibility of banning the use of this herbicide due to the negative effects on human health and the agro-environment. The disk harrow (DH) or the roller-crimper (RC) can be adopted in CA as an alternative to the use of Glyphosate for the devitalization of CC, their incorporation into the soil (in the case of the disk harrow), and the reduction of weed pressure on the subsequent cash crop.

From November 2017 to October 2019, soil organic carbon (SOC, g kg-1) and crop biomass production were observed in a 2-year field experiment located in Lodi (northern Italy), in which minimum tillage (MT) has been applied for the last 5 years. The soil was loamy and SOC was 16.2 g kg-1 at the beginning of the experiment. The winter CC was barley (from November to May) and the cash crop was soybean (from June to October). The experiment consisted in three treatments replied for two consecutive years in a randomized block design: Glyphosate spray + DH + sowing + hoeing (MT-GLY); DH + sowing + hoeing (MT-ORG); RC + sod seeding (NT-ORG).

At the end of 2019, SOC resulted in a higher increase in MT-GLY (+15%) and in MT-ORG (+14%) than in NT-ORG (+6%; p<0.01). This was due to the fact that CC litter in NT-ORG was not in direct contact with soil particles and the process of immobilization was lower than in the other treatments.

Moreover, the increase in SOC resulted positively correlated to the CC biomass (2018+2019), which was significantly lower in NT-ORG. In particular, no differences of soybean and CC between the three treatments were observed at the end of 2018, but MT-GLY resulted in significantly higher CC and soybean biomass at the end of the second year (+32%, p<0.01). MT-GLY allows to stock more carbon via photosynthesis that in turn results in higher SOC content.

However, if we consider the tractor fuel consumption (for Glyphosate spray, DH, RC, hoeing), along with the biomass production, the carbon sequestration did not vary between the three treatments.

Further studies are needed for the definition of optimized field management practices to reduce the passage of machinery while increasing crop production and SOC.

ACS Style

Alessia Perego; Marco Acutis; Calogero Schillaci. Alternatives to Glyphosate in conservation agriculture: effects on carbon sequestration in a field experiment in northern Italy. 2020, 1 .

AMA Style

Alessia Perego, Marco Acutis, Calogero Schillaci. Alternatives to Glyphosate in conservation agriculture: effects on carbon sequestration in a field experiment in northern Italy. . 2020; ():1.

Chicago/Turabian Style

Alessia Perego; Marco Acutis; Calogero Schillaci. 2020. "Alternatives to Glyphosate in conservation agriculture: effects on carbon sequestration in a field experiment in northern Italy." , no. : 1.

Preprint content
Published: 10 March 2020
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Mediterranean areas are vulnerable and at high risk of desertification, although harboring high fractions of the global biodiversity. Resilience of these (agro)ecosystem strongly relies on soil preservation, and thus the reduction of both the sediment and soil organic carbon (SOC) losses. However, SOC dynamic is understudied in the Mediterranean areas, especially in the arid and semiarid regions [1].

Here we are summarizing the known and unknown of the SOC modelling in a highly variable Mediterranean area, namely Sicily (southern Italy). In addition, we highlight main research needs to increase the reliability of the estimation of the SOC change in time.

A total of 6674 soil samples were taken in various sampling campaigns from the 1993 to the 2008 from various depths (of which only 20% with soil bulk density [SBD] information) from both agricultural and forest lands on a 25,711-km2 area [2]. Such database was used for SOC modelling through various procedures including classification and regression trees (CARTs) and Least Absolute Shrinkage and Selection Operator (LASSO) [3-5].

Modelling SOC stock estimated with an already developed pedotransfer (R2 = 0,3) function for SBD consisted in a high uncertainty, with a ratio between the model mean absolute error and the modelled 90th percentile higher than 26.9%, suggesting that SBD information or its reliable prediction is a prerequisite for SOC stock modelling in these areas, especially in agricultural land. In addition, taking into account the sampling campaign almost doubled the r squared of the CART models, which on average outcompeted the kriging and LASSO methods for the prediction certainty.

When modelling the time-variation of the SOC concentration through the use of non-paired samples [5], the closer of which was few km apart, a mean SOC variation was highlighted, and the model yielded high pseudo-R2 (0.63–0.69) and low uncertainty (s.d. < 0.76 g C kg−1). However, these s.d. can be used only to highlight strong variations at a relatively low resolution (i.e. 1-km), especially if data are not collected with the same sampling scheme. The variation found in the aforementioned work [5] likely depended on a change of both the sampling scheme and land use rather than an accumulation or loss of SOC in a given land use.

Thus, measuring SOC concentration and SBD in time-paired sites appears as a prerequisite to detect a SOC change in a given land use, especially if taking into account that the most important SOC predictors throughout the experiments were rainfall and temperatures and climate change is likely to differentially affect each site. To overcome such a lack, a time paired-sampling was performed in 2017 in 30 sites in the arable land, providing evidence that the increases estimated from the 1993 to 2008 were not evident when resampling the 10% of the 1993’s sites in field with continuous arable land use.

 

Reference: [1] Schillaci et al. DOI: 10.3301/ROL.2018.68; [2] Schillaci et al. DOI: 10.1016/j.catena.2018.12.015; [3] Veronesi and Schillaci DOI: 10.1016/j.ecolind.2019.02.026; [4] Lombardo et al. DOI: 10.1016/j.geoderma.2017.12.011; [5] Schillaci et al. DOI: 10.1016/j.scitotenv.2017.05.239

ACS Style

Sergio Saia; Calogero Schillaci; Aldo Lipani; Alessia Perego; Marco Acutis. Achievements and challenges of the modelling of soil organic carbon in a highly variable Mediterranean area. 2020, 1 .

AMA Style

Sergio Saia, Calogero Schillaci, Aldo Lipani, Alessia Perego, Marco Acutis. Achievements and challenges of the modelling of soil organic carbon in a highly variable Mediterranean area. . 2020; ():1.

Chicago/Turabian Style

Sergio Saia; Calogero Schillaci; Aldo Lipani; Alessia Perego; Marco Acutis. 2020. "Achievements and challenges of the modelling of soil organic carbon in a highly variable Mediterranean area." , no. : 1.

Preprint content
Published: 10 March 2020
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To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R2=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.

ACS Style

Calogero Schillaci; Edoardo Tomasoni; Marco Acutis; Alessia Perego. Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy. 2020, 1 .

AMA Style

Calogero Schillaci, Edoardo Tomasoni, Marco Acutis, Alessia Perego. Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy. . 2020; ():1.

Chicago/Turabian Style

Calogero Schillaci; Edoardo Tomasoni; Marco Acutis; Alessia Perego. 2020. "Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy." , no. : 1.

Science
Published: 03 July 2019 in Journal of Maps
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The landscape of the surroundings of the Melka Kunture prehistoric site, Upper Awash Basin, Ethiopia, were studied intensively in the last decades. Nonetheless, the area was mainly characterized under a stratigraphic/geological and archaeological point of view. However, a detailed geomorphological map is still lacking. Hence, in this study, we identify, map and visualize geomorphological forms and processes. The morphology of the forms, as well as the related processes, were remotely sensed with available high-resolution airborne and satellite sources and calibrated and validated through extensive field work conducted in 2013 and 2014. Furthermore, we integrated multispectral satellite imagery to classify areas affected by intensive erosion processes and/or anthropic activities. The Main Map at 1:15,000 scale reveals structural landforms as well as intensive water-related degradation processes in the Upper Awash Basin. Moreover, the map is available as an interactive WebGIS application providing further information and detail (www.roceeh.net/ethiopia_geomorphological_map/).

ACS Style

Michael Maerker; Calogero Schillaci; Rita T. Melis; Jan Kropáček; Alberto Bosino; Vít Vilímek; Volker Hochschild; Christian Sommer; Flavio Altamura; Margherita Mussi. Geomorphological processes, forms and features in the surroundings of the Melka Kunture Palaeolithic site, Ethiopia. Journal of Maps 2019, 15, 797 -806.

AMA Style

Michael Maerker, Calogero Schillaci, Rita T. Melis, Jan Kropáček, Alberto Bosino, Vít Vilímek, Volker Hochschild, Christian Sommer, Flavio Altamura, Margherita Mussi. Geomorphological processes, forms and features in the surroundings of the Melka Kunture Palaeolithic site, Ethiopia. Journal of Maps. 2019; 15 (2):797-806.

Chicago/Turabian Style

Michael Maerker; Calogero Schillaci; Rita T. Melis; Jan Kropáček; Alberto Bosino; Vít Vilímek; Volker Hochschild; Christian Sommer; Flavio Altamura; Margherita Mussi. 2019. "Geomorphological processes, forms and features in the surroundings of the Melka Kunture Palaeolithic site, Ethiopia." Journal of Maps 15, no. 2: 797-806.

Journal article
Published: 16 February 2019 in Ecological Indicators
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In recent years, the environmental modeling community has moved away from kriging as the main mapping algorithm and embraced machine learning (ML) as the go-to method for spatial prediction. The drawback of this shift has been a gradual decline in the number of papers in which uncertainty is presented and mapped alongside estimates of the target variables because in some ML algorithms, computing the local uncertainty can be challenging. This drawback has been recently identified in the literature as one of the key areas in DSM where progress is most needed. The main objective of this work is to compare geostatistical techniques, ML methods and hybrid methods, e.g., regression kriging, in terms of not only their overall accuracy but also their precision in providing useful confidence intervals at unsampled locations. We aim to provide clear application guidelines for future mapping exercises. For this experiment, we used a legacy soil dataset (n = 414) of topsoil observations from the semi-arid Mediterranean region of Sicily. This dataset was collected in a 2008 survey with a pedo-landscape sampling design; hence, it is ideal for comparing geostatistics and ML. In the comparison, we included algorithms that have been widely adopted in the literature: ordinary and universal kriging, linear regression, random forest (RF), quantile regression forest, boosted regression trees (BRT) and hybrid forms of kriging (e.g., regression kriging with RF and BRT used as regressors). To evaluate the accuracy of each algorithm, a validation test that was based on the random exclusion of 25% of the samples was repeated 100 times. In addition, we performed a test of the transferability, in which the locations with the largest nearest-neighbor distances were excluded from training and re-predicted. The validation results demonstrate that ordinary and universal kriging are the best performers, followed closely by random forest (RF) and quantile regression forest (QRF). In terms of local uncertainty, RF and QRF provide confidence intervals that most often include the observed values of SOC. However, they both provide very wide confidence intervals, which may be problematic in some studies. Other algorithms, such as boosted regression trees and boosted regression kriging, performed slightly worse (on this dataset), but produced narrower ranges of uncertainty. Hence, they may be more attractive since their estimates are very robust against changes and noise in the predictors.

ACS Style

Fabio Veronesi; Calogero Schillaci. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation. Ecological Indicators 2019, 101, 1032 -1044.

AMA Style

Fabio Veronesi, Calogero Schillaci. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation. Ecological Indicators. 2019; 101 ():1032-1044.

Chicago/Turabian Style

Fabio Veronesi; Calogero Schillaci. 2019. "Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation." Ecological Indicators 101, no. : 1032-1044.

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.

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: 18 May 2018 in AUC GEOGRAPHICA
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Morphometric Terrain Analysis was successfully applied in different sectors of environmental studies. However, other disciplines, such as archaeology, might also profit from spatially distributed high-resolution terrain information. In this paper, we show how detailed topographic analysis and simple hydrological modelling approaches help to explain complex terrain pattern and to assess geohazards affecting archaeological sites. We show that Melka Kunture, a cluster of Pleistocene sites in the Upper Awash valley of Ethiopia, is affected by flooding and erosion/sedimentation processes. Moreover, we identified paleo-landscape features, such as changes in drainage pattern and evidences of tectonic activity. The topographic indices indicate especially a different paleo-drainage pattern with a lake or palustrine environment in the upstream areas. Furthermore, a different drainage of the paleo-lake via the Atabella tributary is likely and might be also stressed by the dimensions of the lower Atabella valley with quite large cross sections not corresponding to the present-day drainage situation.

ACS Style

Michael Maerker; Calogero Schillaci; Jan Kropáček. Morphometric terrain analysis to explore present day geohazards and paleolandscape forms and features in the surroundings of the Melka Kunture prehistoric site, Upper Awash Valley, Central Ethiopia. AUC GEOGRAPHICA 2018, 53, 10 -19.

AMA Style

Michael Maerker, Calogero Schillaci, Jan Kropáček. Morphometric terrain analysis to explore present day geohazards and paleolandscape forms and features in the surroundings of the Melka Kunture prehistoric site, Upper Awash Valley, Central Ethiopia. AUC GEOGRAPHICA. 2018; 53 (1):10-19.

Chicago/Turabian Style

Michael Maerker; Calogero Schillaci; Jan Kropáček. 2018. "Morphometric terrain analysis to explore present day geohazards and paleolandscape forms and features in the surroundings of the Melka Kunture prehistoric site, Upper Awash Valley, Central Ethiopia." AUC GEOGRAPHICA 53, no. 1: 10-19.