Evaluation of an autonomous smart system for optimal management of fertigation with variable sources of irrigation water

Front Plant Sci. 2023 Apr 12:14:1149956. doi: 10.3389/fpls.2023.1149956. eCollection 2023.

Abstract

Modern irrigation technologies and tools can help boost fertigation efficiency and sustainability, particularly when using irrigation water of varying quality. In this study, a high-tech irrigation head using a new fertigation optimization tool called NutriBalance, which is designed to manage feed waters of different qualities, has been evaluated from technical and economic perspectives. NutriBalance computes the optimal fertigation dose based on specific data about the equipment, the crop, the irrigation water, and the fertilizers available, in order to enable autonomous and accurate water and fertilizer supply. The system was trialed in a grapefruit orchard irrigated with fresh and desalinated water for several values of crop nutritional requirements and considering different fertilizer price scenarios. The results showed the good interoperability between the tool and the irrigation head and the nearly flawless ability (error below 7% for most ions) of the system to provide the prescribed fertigation with different combinations of irrigation water. Fertilizer savings of up to 40% were achieved, which, for the lifespan of the equipment, were estimated to correspond to around 500 EUR/ha/year. The results of this study can encourage the adoption of novel technologies and tools by farmers.

Keywords: high-tech irrigation head; interoperability; non-conventional irrigation water; nutritional adjustment; precise fertigation.

Grants and funding

This research and the APC were funded by the project SEA4CROP (PID2020-118492RA-C22), funded by Ministry for Science and Innovation and the State Research Agency (MCIN/AEI/10.13039/501100011033, Spain). Additional support is acknowledged for the project SEARRISOST (RTC-2017-6192-2) funded by the State Research Agency and the European Regional Development Fund (ERDF, EU). AIM acknowledges the support for this PhD work from the project SEA4CROP and the predoctoral program of the Technical University of Cartagena (RV-484/21, UPCT, Spain). BGE acknowledges the support from the Spanish Ministry of Universities (“Beatriz Galindo” Fellowship BEAGAL18/00081).