A Web-based spatial decision supporting system for land management and soil conservation

Terribile, Fabio; Agrillo, Antonietta; Bonfante, Antonello; Buscemi, Gilda; Colandrea, M.; D'Antonio, Amedeo; Mascellis, Roberto De; Michele, Carlo De; Langella, Giuliano; Manna, Piero; Marotta, L.; Mileti, Florindo Antonio; Minieri, Luciana; Orefice, Nadia; Valentini, Stefano; Vingiani, Simona; Basile, Angelo

doi:10.5194/se-6-903-2015

Cited by 62 publications

(34 citation statements)

References 44 publications

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“…SOILCONSWEB is a Geospatial Cyberinfrastructure based on the use of free open-source geospatial libraries and programmes and enables users to interact directly with the geospatial data on the map via the Web. Details of system features are provided in Terribile et al (2015); basically, it includes a 3-tier logical architecture in which the presentation, application processing and data management are logically separate processes. The presentation tier displays information relating to the services, the business logic tier controls the functionality of the application by performing detailed processing and, finally, the data tier consists of a database where information is stored and retrieved in such a way as to keep data neutral and independent of application servers or business logic.…”

Section: The Geospatial Cyberinfrastructure: Methodological and Technmentioning

confidence: 99%

“…Before being integrated into the database, uneven data (e.g. different spatial scales and data gaps) were processed in order to be homogenous and comparable (Terribile et al, 2015). Quality control assessments are required in order to ensure that the database accurately reflected data types, field survey data, etc.…”

Section: Data Set and Modelling Criteriamentioning

confidence: 99%

“…While, in Terribile et al (2015), the global description of the SOILCONSWEB Geospatial Cyberinfrastructure is reported, almost nothing is said about soil sealing as problem, as impact on ES, as procedures and obtained results to support planning.…”

Section: Introduction: From the Problem To Possible Solutionsmentioning

confidence: 99%

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Soil Sealing: Quantifying Impacts on Soil Functions by a Geospatial Decision Support System

et al. 2017

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Soil sealing is considered among the most dangerous of land degradation processes on global, European and national scales. Despite important policy documents aiming to mitigate this soil threat, it currently shows no signs of abating, and current efforts often do not result in appropriate implementation of soil sealing mitigation in spatial planning, which represents the subject area governing soil sealing. In this paper, we show a spatial decision support system – based on a Geospatial Cyberinfrastructure – with the aim of applying it as an operational instrument aiming towards soil sealing mitigation. The system has the ambition to impact on those who take decision over soil sealing; typically, these are not agriculture experts but rather spatial planners. This tool, focusing on mitigating such crucial land degradation, allows the users – via the Web – to produce ‘what‐if’ land planning scenarios thanks to the ‘on‐the‐fly’ modelling engines. Therefore, integrated geospatial quantitative data and procedures may be directly and freely used by planners. The tool has been applied to and tested in an area in the South of Italy. Results from two applications are reported: one addressing municipal planning and the other on a more detailed spatial scale. Furthermore, results include quantification of rural fragmentation, loss of soil ecosystem services and an estimate of soil sealing evolution over time. The tool was developed with the help of end users and indirectly explores a change of paradigm where soil science and landscape/urban planning work together to provide operational instruments that may be adopted by local communities in addressing soil sealing issues with a proactive approach. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: The Geospatial Cyberinfrastructure: Methodological and Technmentioning

confidence: 99%

Section: Data Set and Modelling Criteriamentioning

confidence: 99%

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Soil Sealing: Quantifying Impacts on Soil Functions by a Geospatial Decision Support System

et al. 2017

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“…so as to properly deliver concrete solutions for achieving a large set of goals, such as the Sustainable Development Goals (e.g., SDG2 in [1]). Moreover, the estimate of fluxes and storage of water in the SPA system is fundamental, especially for those applications-from field to landscape scale-which promise to deliver operational answers, as in the case of Decision Support Systems [2].…”

Section: Introductionmentioning

confidence: 99%

How does PTF Interpret Soil Heterogeneity? A Stochastic Approach Applied to a Case Study on Maize in Northern Italy

Basile

Bonfante

Coppola

et al. 2019

Water

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Soil water balance on a local scale is generally achieved by applying the classical nonlinear Richards equation that requires hydraulic properties, namely, water retention and hydraulic conductivity functions, to be known. Its application in agricultural systems on field or larger scales involves three major problems being solved, related to (i) the assessment of spatial variability of soil hydraulic properties, (ii) accounting for this spatial variability in modelling large-scale soil water flow, and (iii) measuring the effects of such variability on real field variables (e.g., soil water storage, biomass, etc.). To deal with the first issue, soil hydraulic characterization is frequently performed by using the so-called pedotransfer functions (PTFs), whose effectiveness in providing the actual information on spatial variability has been questioned. With regard to the second problem, the variability of hydraulic properties at the field scale has often been dealt with using a relatively simple approach of considering soils in the field as an ensemble of parallel and statistically independent tubes, assuming only vertical flow. This approach in dealing with spatial variability has been popular in the framework of a Monte Carlo technique. As for the last issue, remote sensing seems to be the only viable solution to verify the pattern of variability, going by several modelling outputs which have considered the soil spatial variability. Based on these premises, the goals of this work concerning the issues discussed above are the following: (1) analyzing the sensitivity of a Richards-based model to the measured variability of θ(h) and k(θ) parameters; (2) establishing the predictive capability of PTF in terms of a simple comparison with measured data; and (3) establishing the effectiveness of use of PTF by employing as data quality control an independent and spatially distributed estimation of the Above Ground Biomass (AGB). The study area of approximately 2000 hectares mainly devoted to maize forage cultivation is located in the Po plain (Lodi), in northern Italy. Sample sites throughout the study area were identified for hydropedological analysis (texture, bulk density, organic matter content, and other chemical properties on all the samples, and water retention curve and saturated hydraulic conductivity on a sub-set). Several pedotransfer functions were tested; the PTF‒Vereckeen proved to be the best one to derive hydraulic properties of the entire soil database. The Monte Carlo approach was used to analyze model sensitivity to two measured input parameters: the slope of water retention curve (n) and the saturated hydraulic conductivity (k0). The analysis showed sensitivity of the simulated process to the parameter n being significantly higher than to k0, although the former was much less variable. The PTFs showed a smoothing effect of the output variability, even though they were previously validated on a set of measured data. Interesting positive and significant correlations were found between the n parameter, from measured water retention curves, and the NDVI (Normalized Difference Vegetation Index), when using multi-temporal (2004–2018) high resolution remotely sensed data on maize cultivation. No correlation was detected when the n parameter derived from PTF was used. These results from our case study mainly suggest that: (i) despite the good performance of PTFs calculated via error indexes, their use in the simulation of hydrological processes should be carefully evaluated for real field-scale applications; and (ii) the NDVI index may be used successfully as a proxy to evaluate PTF reliability in the field.

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“…Nowadays there are several simulation models and decision support systems (DSS) for agricultural needs [21]. They differ in their mathematical formulations, structures and complexities and consist of a unique combination of empirical and mechanistic concepts [22,23]. Among the well-known models for irrigation planning, there is SWAP [24], WOFOST [25] and AquaCrop [26].…”

Section: Introductionmentioning

confidence: 99%

Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

et al. 2019

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The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

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A Web-based spatial decision supporting system for land management and soil conservation

Cited by 62 publications

References 44 publications

Soil Sealing: Quantifying Impacts on Soil Functions by a Geospatial Decision Support System

Soil Sealing: Quantifying Impacts on Soil Functions by a Geospatial Decision Support System

How does PTF Interpret Soil Heterogeneity? A Stochastic Approach Applied to a Case Study on Maize in Northern Italy

Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

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