A web-based operational tool for the identification of best practices in European agricultural systems

IF 3.6 2区 农林科学 Q2 ENVIRONMENTAL SCIENCES
Marialaura Bancheri, Angelo Basile, Fabio Terribile, Giuliano Langella, Marco Botta, Daniele Lezzi, Federica Cavaliere, Marco Colandrea, Luigi Marotta, Roberto De Mascellis, Piero Manna, Antonietta Agrillo, Florindo Antonio Mileti, Marco Acutis, Alessia Perego
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Abstract

Under the same perspective of the Sustainable Development Goal (SDG) 15.3 aiming to restore degraded land and soil, one of the current priorities of the new Common Agriculture Policy (CAP) is to overcome the serious environmental problems raised by intensive agriculture. Despite the steps forward guaranteed by new technologies and innovations (e.g., IoT, precision agriculture), the availability of real operational tools, which could help the member states fulfil the high requirements and expectations of the new CAP and SDGs, is still lacking. To fill this gap, in the H2020 LandSupport project, the web-based best practice tool was developed to identify, on-the-fly, optimized agronomic solutions to help achieve land-degradation neutrality. The tool's core is the ARMOSA process-based model, which dynamically simulates the continuum soil–plant–atmosphere, combining several cropping systems, crops, nitrogen fertilization rates, tillage solutions, and crop residue management for specific regions of interest. It provides a synthetic “Best Practice index” to identify the optimized local solutions, which combines the production, nitrate leaching, and SOC_change, according to the end-user dynamic requests. The tool was implemented for three case studies: Marchfeld Region in Austria, Zala County in Hungary, and Campania Region in Italy, which are representative of a variety of different pedoclimatic conditions. In the present work, we report three possible cases of use in supporting best practices aiming toward soil and water conservation: (i) crop production optimization; (ii) impact of management practices (i.e., cover crops) over soil carbon; (iii) lowering the impact of nitrate leaching.

Abstract Image

用于确定欧洲农业系统最佳做法的网络操作工具
在旨在恢复退化土地和土壤的可持续发展目标(SDG)15.3 的同一视角下,新的共同农业政策(CAP)当前的优先事项之一是克服集约农业带来的严重环境问题。尽管新技术和创新(如物联网、精准农业)保证了前进的步伐,但仍缺乏可帮助成员国实现新的共同农业政策和可持续发展目标的高要求和期望的实际操作工具。为了填补这一空白,在 H2020 LandSupport 项目中开发了基于网络的最佳实践工具,以即时确定优化的农艺解决方案,帮助实现土地退化中和。该工具的核心是基于过程的 ARMOSA 模型,可动态模拟土壤-植物-大气的连续过程,并结合特定相关区域的若干种植系统、作物、氮肥施用率、耕作解决方案和作物残留物管理。它提供了一个合成的 "最佳实践指数",以根据最终用户的动态要求,确定结合产量、硝酸盐沥滤和 SOC 变化的本地优化解决方案。该工具用于三个案例研究:这三个案例分别是奥地利的马奇费尔德地区、匈牙利的扎拉县和意大利的坎帕尼亚地区,它们代表了各种不同的生态气候条件。在目前的工作中,我们报告了支持水土保持最佳实践的三个可能的使用案例:(i) 农作物生产优化;(ii) 管理实践(即覆盖作物)对土壤碳的影响;(iii) 降低硝酸盐沥滤的影响。
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来源期刊
Land Degradation & Development
Land Degradation & Development 农林科学-环境科学
CiteScore
7.70
自引率
8.50%
发文量
379
审稿时长
5.5 months
期刊介绍: Land Degradation & Development is an international journal which seeks to promote rational study of the recognition, monitoring, control and rehabilitation of degradation in terrestrial environments. The journal focuses on: - what land degradation is; - what causes land degradation; - the impacts of land degradation - the scale of land degradation; - the history, current status or future trends of land degradation; - avoidance, mitigation and control of land degradation; - remedial actions to rehabilitate or restore degraded land; - sustainable land management.
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