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Intensive agricultural management significantly affects soil chemical properties. Such impacts, depending on the intensity of agronomic practices, might persist for several decades. We tested how current soil properties, especially heavy metal concentrations, reflect the land-use history over a 24,000 ha area dominated by intensive apple orchards and viticulture (South Tyrol, ITA). We combined georeferenced soil analyses with land-use maps from 1850 to 2010 in a space-for-time approach to detect the accumulation rates of copper and zinc and understand how present-day soil heavy metal concentrations reflect land-use history. Soils under vineyards since the 1850s showed the highest available copper concentration (median of 314.0 mg kg-1, accumulation rate between 19.4 and 41.3 mg kg-1·10 y-1). Zinc reached the highest concentration in the same land-use type (median of 32.5 mg kg-1, accumulation rate between 1.8 and 4.4 mg kg-1·10 y-1). Using a random forest approach on 44,132 soil samples, we extrapolated land-use history on the permanent crop area of the region, reaching an accuracy of 0.72. This suggests that combining current soil analysis, historical management information, and machine learning models provides a valuable tool to predict land-use history and understand management legacies.
G. Genova; S. Della Chiesa; T. Mimmo; L. Borruso; S. Cesco; E. Tasser; A. Matteazzi; G. Niedrist. Copper and zinc as a window to past agricultural land-use. Journal of Hazardous Materials 2021, 126631 .
AMA StyleG. Genova, S. Della Chiesa, T. Mimmo, L. Borruso, S. Cesco, E. Tasser, A. Matteazzi, G. Niedrist. Copper and zinc as a window to past agricultural land-use. Journal of Hazardous Materials. 2021; ():126631.
Chicago/Turabian StyleG. Genova; S. Della Chiesa; T. Mimmo; L. Borruso; S. Cesco; E. Tasser; A. Matteazzi; G. Niedrist. 2021. "Copper and zinc as a window to past agricultural land-use." Journal of Hazardous Materials , no. : 126631.
Easily accessible data is an essential requirement for scientific data analysis. The Data Browser Matsch | Mazia was designed to provide a fast and comprehensible solution to access, visualize and download the microclimatic measurements of the IT 25 LT(S)ER Match | Mazia research site in South Tyrol, Northern Italy, with the overall aim to provide straightforward data accessibility and enhance dissemination. Data Browser Matsch | Mazia is a user-friendly web-based application to visualize and download micrometeorological and biophysical time series of the Long-Term Socio-Ecological Research site Matsch | Mazia in South Tyrol, Italy. It is designed both for the general public and researchers. The Data Browser Matsch | Mazia drop-down menus allow the user to query the InfluxDB database in the backend by selecting the measurements, time range, land use and elevation. Interactive Grafana dashboards show dynamic graphs of the time series.
Martin Palma; Alessandro Zandonai; Luca Cattani; Johannes Klotz; Giulio Genova; Christian Brida; Norbert Andreatta; Georg Niedrist; Stefano Della Chiesa. Data Browser Matsch | Mazia: Web Application to access microclimatic time series of an ecological research site. Research Ideas and Outcomes 2021, 7, e63748 .
AMA StyleMartin Palma, Alessandro Zandonai, Luca Cattani, Johannes Klotz, Giulio Genova, Christian Brida, Norbert Andreatta, Georg Niedrist, Stefano Della Chiesa. Data Browser Matsch | Mazia: Web Application to access microclimatic time series of an ecological research site. Research Ideas and Outcomes. 2021; 7 ():e63748.
Chicago/Turabian StyleMartin Palma; Alessandro Zandonai; Luca Cattani; Johannes Klotz; Giulio Genova; Christian Brida; Norbert Andreatta; Georg Niedrist; Stefano Della Chiesa. 2021. "Data Browser Matsch | Mazia: Web Application to access microclimatic time series of an ecological research site." Research Ideas and Outcomes 7, no. : e63748.
High quality global soil maps are crucial to face several challenges such as reducing soil erosion, climate change adaptation and mitigation, ensuring food and water security, and biodiversity conservation planning. To obtain accurate and robust soil properties maps, research and development are necessary to identify the most appropriate prediction models and to develop efficient and robust workflows. A few recent studies used Artificial Neural Networks (ANN) in Digital Soil Mapping, in some cases improving the accuracy of the predicted maps compared to other methods like Random Forest (RF). In this study we tested different ANN architectures on a global top-soil dataset of ca. 110 000 samples, comparing the results for the different architectures with the more traditional approach of RF. The target variables considered are pH, Soil Organic Carbon, Sand, Silt, and Clay. We selected 40 environmental covariates from a pool of over 400 to represent the most important soil forming factors. We tried simpler architectures (single input – single target) using point observations for one target variable with corresponding raster cell values for spatially explicit environmental covariates. We also used more complex architectures (multi input - multi target) incorporating contextual information surrounding an observation (convolutional) and with multiple target variables. Preliminary results show that increasing the number of hidden layers in the neural network does not significantly influence the results, while changing the type of architecture can play a bigger role in the overall accuracy of the model. The overall prediction accuracy of the ANN was comparable with the RF model. We conclude that ANN are a promising, relatively new, approach for Global Digital Soil Mapping and that further research is needed to improve performance.
Giulio Genova; Luis de Sousa; Tanja Mimmo; Luigi Borruso; Laura Poggio. Global soil mapping with Neural Networks . 2021, 1 .
AMA StyleGiulio Genova, Luis de Sousa, Tanja Mimmo, Luigi Borruso, Laura Poggio. Global soil mapping with Neural Networks . . 2021; ():1.
Chicago/Turabian StyleGiulio Genova; Luis de Sousa; Tanja Mimmo; Luigi Borruso; Laura Poggio. 2021. "Global soil mapping with Neural Networks ." , no. : 1.
In recent decades, agriculture has faced the fundamental challenge of needing to increase food production and quality in order to meet the requirements of a growing global population. Similarly, viticulture has also been undergoing change. Several countries are reducing their vineyard areas, and several others are increasing them. In addition, viticulture is moving towards higher altitudes and latitudes due to climate change. Furthermore, global warming is also exacerbating the incidence of fungal diseases in vineyards, forcing farmers to apply agrochemicals to preserve production yields and quality. The repeated application of copper (Cu)-based fungicides in conventional and organic farming has caused a stepwise accumulation of Cu in vineyard soils, posing environmental and toxicological threats. High Cu concentrations in soils can have multiple impacts on agricultural systems. In fact, it can (i) alter the chemical-physical properties of soils, thus compromising their fertility; (ii) induce toxicity phenomena in plants, producing detrimental effects on growth and productivity; and (iii) affect the microbial biodiversity of soils, thereby influencing some microbial-driven soil processes. However, several indirect (e.g., management of rhizosphere processes through intercropping and/or fertilization strategies) and direct (e.g., exploitation of vine resistant genotypes) strategies have been proposed to restrain Cu accumulation in soils. Furthermore, the application of precision and smart viticulture paradigms and their related technologies could allow a timely, localized and balanced distribution of agrochemicals to achieve the required goals. The present review highlights the necessity of applying multidisciplinary approaches to meet the requisites of sustainability demanded of modern viticulture.
Stefano Cesco; Youry Pii; Luigimaria Borruso; Guido Orzes; Paolo Lugli; Fabrizio Mazzetto; Giulio Genova; Marco Signorini; Gustavo Brunetto; Roberto Terzano; Gianpiero Vigani; Tanja Mimmo. A Smart and Sustainable Future for Viticulture Is Rooted in Soil: How to Face Cu Toxicity. Applied Sciences 2021, 11, 907 .
AMA StyleStefano Cesco, Youry Pii, Luigimaria Borruso, Guido Orzes, Paolo Lugli, Fabrizio Mazzetto, Giulio Genova, Marco Signorini, Gustavo Brunetto, Roberto Terzano, Gianpiero Vigani, Tanja Mimmo. A Smart and Sustainable Future for Viticulture Is Rooted in Soil: How to Face Cu Toxicity. Applied Sciences. 2021; 11 (3):907.
Chicago/Turabian StyleStefano Cesco; Youry Pii; Luigimaria Borruso; Guido Orzes; Paolo Lugli; Fabrizio Mazzetto; Giulio Genova; Marco Signorini; Gustavo Brunetto; Roberto Terzano; Gianpiero Vigani; Tanja Mimmo. 2021. "A Smart and Sustainable Future for Viticulture Is Rooted in Soil: How to Face Cu Toxicity." Applied Sciences 11, no. 3: 907.
Intensive agricultural management can have significant impacts on soil properties. Such effects and their degree are often related to the history of land use and to the agronomic practices. When legacy soil data are missing, historical land use maps can help to describe how crop management might have changed the concentration of certain elements in soils. In this study, we prove how permanent crop management (vineyards and apple orchards) influenced heavy metal concentration in agricultural soils in South Tyrol, Italy. We selected areas where land-use change was unidirectional going from forests, grasslands and arable lands to apple orchards or vineyards. We hypothesize that the heavy metal accumulation in the soil starts when a parcel is converted to intensive permanent crops. This hypothesis allows us to see if there are any significant differences between parcels with a longer or shorter intensive agriculture history. We used approx. 6000 soil samples analyzed between 2006 and 2016 and coupled them with historical land use maps dating from the 1850s until today. Soils that have been cultivated as apple orchards or vineyards since the 1850s are characterized by higher concentrations of Cu. The oldest vineyards have much higher soil Cu concentrations than apple orchards of the same age with a median content of 342 mg kg–1 and 212 mg kg–1 of Cu respectively. Similar patterns, but with smaller extent can be described also for Zn concentration. Comparing the age of vineyards with today’s concentration we estimate an accumulation rate of 2.4 mg kg–1 year–1 of Cu. We conclude that historical land use maps are extremely helpful in understanding today’s soil characteristics especially with not degradable pollutants such as heavy metals. High concentrations of Cu in vineyards reveal the widespread and abundant use of this metal in viticulture for plant defense programs through time. The accumulation trend proves that further research and monitoring is needed to understand spatial and temporal pattern of Cu and Zn pollution in intensively managed permanent crops and to estimate their impact on taxonomical and functional fungal and bacterial diversity. These aspects are of pivotal role in determining the soil fertility levels of our cultivated soils.
Giulio Genova; Georg Niedrist; Stefano Della Chiesa; Erich Tasser; Luigimaria Borruso; Stefano Cesco; Tanja Mimmo. Effects of land use history on heavy metals concentration in agricultural soils. 2020, 1 .
AMA StyleGiulio Genova, Georg Niedrist, Stefano Della Chiesa, Erich Tasser, Luigimaria Borruso, Stefano Cesco, Tanja Mimmo. Effects of land use history on heavy metals concentration in agricultural soils. . 2020; ():1.
Chicago/Turabian StyleGiulio Genova; Georg Niedrist; Stefano Della Chiesa; Erich Tasser; Luigimaria Borruso; Stefano Cesco; Tanja Mimmo. 2020. "Effects of land use history on heavy metals concentration in agricultural soils." , no. : 1.
Stefano Della Chiesa; Giulio Genova; Daniele La Cecilia; Georg Niedrist. Phytoavailable phosphorus (P2O5) and potassium (K2O) in topsoil for apple orchards and vineyards, South Tyrol, Italy. Journal of Maps 2019, 15, 555 -562.
AMA StyleStefano Della Chiesa, Giulio Genova, Daniele La Cecilia, Georg Niedrist. Phytoavailable phosphorus (P2O5) and potassium (K2O) in topsoil for apple orchards and vineyards, South Tyrol, Italy. Journal of Maps. 2019; 15 (2):555-562.
Chicago/Turabian StyleStefano Della Chiesa; Giulio Genova; Daniele La Cecilia; Georg Niedrist. 2019. "Phytoavailable phosphorus (P2O5) and potassium (K2O) in topsoil for apple orchards and vineyards, South Tyrol, Italy." Journal of Maps 15, no. 2: 555-562.
Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon.
Giulio Genova; Mattia Rossi; Georg Niedrist; Stefano Della Chiesa. Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy. Research Ideas and Outcomes 2019, 5, e35894 .
AMA StyleGiulio Genova, Mattia Rossi, Georg Niedrist, Stefano Della Chiesa. Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy. Research Ideas and Outcomes. 2019; 5 ():e35894.
Chicago/Turabian StyleGiulio Genova; Mattia Rossi; Georg Niedrist; Stefano Della Chiesa. 2019. "Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy." Research Ideas and Outcomes 5, no. : e35894.
Johannes Brenner; Giulio Genova; Giacomo Bertoldi; Georg Niedrist; Stefano Della Chiesa. SWCalibrateR: Interactive, Web – Based Calibration of Soil Moisture Sensors. Journal of Open Research Software 2019, 7, 1 .
AMA StyleJohannes Brenner, Giulio Genova, Giacomo Bertoldi, Georg Niedrist, Stefano Della Chiesa. SWCalibrateR: Interactive, Web – Based Calibration of Soil Moisture Sensors. Journal of Open Research Software. 2019; 7 (1):1.
Chicago/Turabian StyleJohannes Brenner; Giulio Genova; Giacomo Bertoldi; Georg Niedrist; Stefano Della Chiesa. 2019. "SWCalibrateR: Interactive, Web – Based Calibration of Soil Moisture Sensors." Journal of Open Research Software 7, no. 1: 1.
We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate dependencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000.
Gianluca Mastrantonio; Giovanna Jona Lasinio; Alessio Pollice; Giulia Capotorti; Lorenzo Teodonio; Giulio Genova; Carlo Blasi. A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles. The Annals of Applied Statistics 2019, 13, 797 -823.
AMA StyleGianluca Mastrantonio, Giovanna Jona Lasinio, Alessio Pollice, Giulia Capotorti, Lorenzo Teodonio, Giulio Genova, Carlo Blasi. A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles. The Annals of Applied Statistics. 2019; 13 (2):797-823.
Chicago/Turabian StyleGianluca Mastrantonio; Giovanna Jona Lasinio; Alessio Pollice; Giulia Capotorti; Lorenzo Teodonio; Giulio Genova; Carlo Blasi. 2019. "A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles." The Annals of Applied Statistics 13, no. 2: 797-823.
Detailed knowledge of agricultural soil properties is a key element for high-quality food production. However, high-resolution soil data covering a large agricultural region are generally unavailable. This study explores a demand-driven cooperative framework for soil data sourcing that connects individual farmers to several stakeholders by means of a centralised database containing more than 16,000 records of soil information collected within the framework of an integrated production program for intensively managed permanent crops in the Adige/Etsch and Venosta/Vinschgau valleys in South Tyrol, Italy. Data for soil pH, soil organic matter (SOM), and soil texture were used to produce digital soil maps with a RMSE of 0.21, 1.25% and a cross-validation of 43%, respectively. Spatialisation was conducted using either regression-kriging or multinomial logistic regression. Collaboration among farmers, public administrators, and researchers provided a successful cooperative framework for digital soil mapping. The maps highlight the complex interplay of the postglacial evolution of these valleys due to the presence of a cluster of large alluvial fans and the anthropogenic influences of intense farming on pH, SOM, and soil texture. This study regarded a subset of the available soil properties, which can be dealt with using the geostatistical approaches presented herein. Thus, a long-term soil monitoring program and the combination of all available variables will allow digital assessment of the spatial patterns of nutrient availability, ecological risk assessments, change detection studies, and an overall long-term plan for soil security at larger spatial scales.
Stefano Della Chiesa; Daniele la Cecilia; Giulio Genova; Andrea Balotti; Martin Thalheimer; Ulrike Tappeiner; Georg Niedrist. Farmers as data sources: Cooperative framework for mapping soil properties for permanent crops in South Tyrol (Northern Italy). Geoderma 2019, 342, 93 -105.
AMA StyleStefano Della Chiesa, Daniele la Cecilia, Giulio Genova, Andrea Balotti, Martin Thalheimer, Ulrike Tappeiner, Georg Niedrist. Farmers as data sources: Cooperative framework for mapping soil properties for permanent crops in South Tyrol (Northern Italy). Geoderma. 2019; 342 ():93-105.
Chicago/Turabian StyleStefano Della Chiesa; Daniele la Cecilia; Giulio Genova; Andrea Balotti; Martin Thalheimer; Ulrike Tappeiner; Georg Niedrist. 2019. "Farmers as data sources: Cooperative framework for mapping soil properties for permanent crops in South Tyrol (Northern Italy)." Geoderma 342, no. : 93-105.