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Keywords = AgroShadow tool

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18 pages, 1817 KiB  
Article
Model-Based Valuation of Ecosystem Services Using Bio-Economic Farm Models: Insights for Designing Green Tax Policies and Payment for Ecosystem Services
by Seyed-Ali Hosseini-Yekani, Stefan Tomaczewski and Peter Zander
Agriculture 2025, 15(1), 60; https://doi.org/10.3390/agriculture15010060 - 29 Dec 2024
Viewed by 1087
Abstract
The integration of ecosystem services (ESs) valuation into agricultural policy frameworks is critical for fostering sustainable land management practices. This study leverages the redesigned version of the bio-economic farm model MODAM (Multi-Objective Decision Support Tool for Agro-Ecosystem Management) to estimate the shadow prices [...] Read more.
The integration of ecosystem services (ESs) valuation into agricultural policy frameworks is critical for fostering sustainable land management practices. This study leverages the redesigned version of the bio-economic farm model MODAM (Multi-Objective Decision Support Tool for Agro-Ecosystem Management) to estimate the shadow prices of ESs, enabling the derivation of demand and supply curves for nitrate leaching and soil erosion control, respectively. Two hypothetical farms in Brandenburg, Germany—a smaller, arable farm in Märkisch-Oderland and a larger, diversified farm with livestock in Oder-Spree—are analyzed to explore the heterogeneity in shadow prices and corresponding cropping patterns. The results reveal that larger farms exhibit greater elasticity in response to green taxes on nitrate use and lower costs for supplying erosion control compared to smaller farms. This study highlights the utility of shadow prices as proxies for setting green taxes and payments for ecosystem services (PESs), while emphasizing the need for differentiated policy designs to address disparities between farm types. This research underscores the potential of model-based ESs valuation to provide robust economic measures for policy design, fostering sustainable agricultural practices and ecosystem conservation. Full article
(This article belongs to the Special Issue Agricultural Policies toward Sustainable Farm Development)
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11 pages, 1494 KiB  
Communication
AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture
by Ramona Magno, Leandro Rocchi, Riccardo Dainelli, Alessandro Matese, Salvatore Filippo Di Gennaro, Chi-Farn Chen, Nguyen-Thanh Son and Piero Toscano
Remote Sens. 2021, 13(6), 1219; https://doi.org/10.3390/rs13061219 - 23 Mar 2021
Cited by 20 | Viewed by 5251
Abstract
Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as [...] Read more.
Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA. Full article
(This article belongs to the Special Issue Image Enhancement Techniques to Guarantee Sensors Interoperability)
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