Articles
Use of the AgroNIT smart-farming IoT ecosystem to support irrigation management and assess its impact on fruit trees’ economy and nutrition in Greece
Article number
1409_53
Pages
417 – 426
Language
English
Abstract
Raising temperatures and increased drought events due to climate change have increased the need for efficient use of natural resources and energy.
Thus, it is essential to develop adaptation and mitigation strategies to increase the sustainability of Mediterranean agricultural systems.
AgroNIT is an integrated IoT framework designed to foster and evaluate innovative, field-based solutions that aim to improve the efficiency and sustainability of agricultural resource management.
AgroNIT features energy-autonomous, mesh wireless sensor networks, cloud computing, and decision-support systems to provide real-time artificial intelligence-based consultancy to farmers leveraging on distributed field data collection.
This paper describes the in situ deployment of the AgroNIT smart-farming platform to support the irrigation management of two main tree crops in the plain of Thessaly, Greece: pear (Pyrus communis ‘Krystali’) and cherry (Prunus avium ‘Regina’). This is accomplished by employing a tree-adapted smart irrigation model that utilizes key climatic, crop and soil parameters measured in-field as inputs.
To this end, two use cases (UC) were carried out during a 2-year period to demonstrate how a typical pear and cherry tree grower is benefited by leveraging the AgroNIT to facilitate the irrigation decision-making process or assess alternative irrigation treatments.
Specifically, in each UC two irrigation treatments were applied: i) model-based irrigation (SI), and ii) rational irrigation with growth stage-based deficit irrigation (DI). These treatments were compared to the conventional method (i.e., grower’s method) regarding their impact on crop water productivity (CWP), irrigation and N-fertilization cost, and crop nutrition.
Both alternative irrigation treatments can generate significant water savings (10.5-46.2%), irrigation cost-savings (158.3-767.3 € ha‑1), as well as increased CWP (1.6-1.7 kg m‑3 tree‑1) compared to conventional irrigation.
The SI showed great results on water-savings and irrigation cost, while the DI scored better on N-fertilization cost savings.
Overall, SI was found to be the best method cost-wise.
Thus, it is essential to develop adaptation and mitigation strategies to increase the sustainability of Mediterranean agricultural systems.
AgroNIT is an integrated IoT framework designed to foster and evaluate innovative, field-based solutions that aim to improve the efficiency and sustainability of agricultural resource management.
AgroNIT features energy-autonomous, mesh wireless sensor networks, cloud computing, and decision-support systems to provide real-time artificial intelligence-based consultancy to farmers leveraging on distributed field data collection.
This paper describes the in situ deployment of the AgroNIT smart-farming platform to support the irrigation management of two main tree crops in the plain of Thessaly, Greece: pear (Pyrus communis ‘Krystali’) and cherry (Prunus avium ‘Regina’). This is accomplished by employing a tree-adapted smart irrigation model that utilizes key climatic, crop and soil parameters measured in-field as inputs.
To this end, two use cases (UC) were carried out during a 2-year period to demonstrate how a typical pear and cherry tree grower is benefited by leveraging the AgroNIT to facilitate the irrigation decision-making process or assess alternative irrigation treatments.
Specifically, in each UC two irrigation treatments were applied: i) model-based irrigation (SI), and ii) rational irrigation with growth stage-based deficit irrigation (DI). These treatments were compared to the conventional method (i.e., grower’s method) regarding their impact on crop water productivity (CWP), irrigation and N-fertilization cost, and crop nutrition.
Both alternative irrigation treatments can generate significant water savings (10.5-46.2%), irrigation cost-savings (158.3-767.3 € ha‑1), as well as increased CWP (1.6-1.7 kg m‑3 tree‑1) compared to conventional irrigation.
The SI showed great results on water-savings and irrigation cost, while the DI scored better on N-fertilization cost savings.
Overall, SI was found to be the best method cost-wise.
Authors
I. Moutsinas, P. Maletsika, V. Zafeiris, G. Tziokas, A. Serafeim, A. Apostolaras, V. Giouvanis, T. Korakis, G.D. Nanos
Keywords
Pyrus communis, Prunus avium, smart irrigation, deficit irrigation, crop water productivity, wireless sensor networks, sustainable production
Online Articles (64)