Abstract
In many countries, water supplies are limited and must be managed for different uses. Providing additional resources for irrigation can be an expensive option, with an unsustainable impact on small farms and on the environment. Therefore, the main efforts should concentrate on the optimal management of existing water resources, paying particular attention to the adequate management of water demands. In Italy, the majority of scattered smallholder farmers are not provided with enough information on how to correctly manage water for irrigation. This paper presents the developing of a web-based irrigation decision support system adaptable and scalable to individual farms. The irrigation advisory service has been calibrated and validated for pomegranate trees, aubergine, and zucchini through 2 years of tests, in a farm in Southern Italy (Calabria). The results are very satisfactory, both in terms of water resources management for irrigation, and for the feedback provided by the farmers involved in the project. Future development regarding the application of wireless technology in smart irrigation is also evaluated.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
This research work was carried out with the support of COTRAPA 2000 s.a.c., Mongrassano Cosenza, Italy. Daniel Dunea and Nicolae Petrescu received support from a grant of the Romanian National Authority for Scientific Research, CNDI– UEFISCDI, project number PN-III-1.2-PCCDI-2017-0721 (https://inter-aspa.ro/).
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Conceptualization: SC, DD, NP. Data curation: SC, FP, FV, DD. Formal analysis: SC, NP, FP. Methodology: SC, FP, FV, DD. Project administration: SC, DD. Resources: SC, RT, NP. Validation: SC, FP, FV, NP. Writing—original draft: SC, DD, NP. Writing—review and editing: SC, FP, FV, RT, DD, NP. All authors read and approved the final manuscript.
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Casadei, S., Peppoloni, F., Ventura, F. et al. Application of smart irrigation systems for water conservation in Italian farms. Environ Sci Pollut Res 28, 26488–26499 (2021). https://doi.org/10.1007/s11356-021-12524-6
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DOI: https://doi.org/10.1007/s11356-021-12524-6