Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce
Abstract
:1. Introduction
2. Materials and Methods
2.1. Irrigation Scheduling
2.1.1. Weather Conditions Based Scheduling
- Predictive weather-based models that use a probabilistic approach to generate a representative set from historical recorded data for the targeted period consider dry years re-occurrence with a certain predefined probability. In this case, the generated schedule should be adjusted while being implemented based on daily data, especially rain fall events [23,24,25,26].
- Near-real-time weather-based models that use short forecasting for daily estimations of ET0 to automate irrigation events accordingly [27].
2.1.2. Soil Moisture Based Scheduling
2.2. Experimental Layout
2.3. Design and Development of the IoT Soil Moisture Tensiometer Prototype
3. Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mishra, R.K. Fresh water availability and its global challenge. Br. J. Multidiscip. Adv. Stud. 2023, 4, 1–78. [Google Scholar] [CrossRef]
- Fouial, A.; Khadra, R.; Daccache, A.; Lamaddalena, N. Modelling the impact of climate change on pressurised irrigation distribution systems: Use of a new tool for adaptation strategy implementation. Biosyst. Eng. 2016, 150, 182–190. [Google Scholar] [CrossRef]
- FAO. Water for Sustainable Food and Agriculture a Report Produced for the G20 Presidency of Germany; FAO: Rome, Italy, 2017. [Google Scholar]
- Hardie, M. Review of novel and emerging proximal soil moisture sensors for use in agriculture. Sensors 2020, 20, 6934. [Google Scholar] [CrossRef] [PubMed]
- Anjum, M.N.; Cheema, M.J.M.; Hussain, F.; Wu, R.-S. Precision irrigation: Challenges and opportunities. Precis. Agric. 2023, 85–101. [Google Scholar] [CrossRef]
- Wu, H.; Zhang, L.; Lv, J.; Zhang, Y.; Zhang, Y.; Yu, N. Optimization of irrigation and N fertilization management profoundly increases soil N retention potential in a greenhouse tomato production agroecosystem of Northeast China. Agric. Ecosyst. Environ. 2022, 340, 108185. [Google Scholar] [CrossRef]
- Khadra, R.; Sagardoy, J.A.; Taha, S.; Lamaddalena, N. Participatory irrigation management and transfer: Setting the guiding principles for a sustaining monitoring & evaluation system–a focus on the mediterranean. Water Resour. Manag. 2017, 31, 4227–4238. [Google Scholar]
- Abdelmoneim, A.A.; Daccache, A.; Khadra, R.; Bhanot, M.; Dragonetti, G. Internet of Things (IoT) for double ring infiltrometer automation. Comput. Electron. Agric. 2021, 188, 106324. [Google Scholar] [CrossRef]
- Koech, R.; Langat, P. Improving irrigation water use efficiency: A review of advances, challenges and opportunities in the Australian context. Water 2018, 10, 1771. [Google Scholar] [CrossRef]
- García-Tejera, O.; López-Bernal, Á.; Orgaz, F.; Testi, L.; Villalobos, F.J. The pitfalls of water potential for irrigation scheduling. Agric. Water Manag. 2021, 243, 106522. [Google Scholar] [CrossRef]
- Khadra, R.; Lamaddalena, N.; Inoubli, N. Optimization of on demand pressurized irrigation networks and on-farm constraints. Procedia Environ. Sci. 2013, 19, 942–954. [Google Scholar] [CrossRef]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; Volume 300, p. D05109. [Google Scholar]
- Penman, H.L. Natural evaporation from open water, bare soil and grass. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 1948, 193, 120–145. [Google Scholar]
- Monteith, J. Evaporation and environment. In GE Fogg Symposium of the Society for Experimental Biology. The State and Movement of Water in Living Organisms; Cambridge University Press: Cambridge, UK, 1965; Volume 19, pp. 205–234. [Google Scholar]
- Huffman, R.L.; Fangmeier, D.D.; Elliot, W.J.; Workman, S.R.; Schwab, G. Soil and Water Conservation Engineering; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2013. [Google Scholar]
- Cabelguenne, M.; Debaeke, P.; Puech, J.; Bosc, N. Real time irrigation management using the EPIC-PHASE model and weather forecasts. Agric. Water Manag. 1997, 32, 227–238. [Google Scholar] [CrossRef]
- Carr, M. Crop Yield Response to Water; Steduto, P., Hsiao, T.C., Fereres, E., Raes, D., Eds.; FAO Irrigation and Drainage Paper 66; Food and Agriculture Organization of the United Nations: Rome, Italy, 2012; p. 500. ISBN 978-92-5-107274-5. Available online: http://www.fao.org/docrep/016/i2800e/i2800e00.htm (accessed on 15 February 2023).
- Piccinni, G.; Ko, J.; Wentz, A.; Leskovar, D.; Marek, T.; Howell, T. Determination of crop coefficients (Kc) for irrigation management of crops. In Proceedings of the 28th Annual International Irrigation Show, San Diego, CA, USA, 9–12 December 2007. [Google Scholar]
- Guerra, E.; Ventura, F.; Snyder, R. Crop coefficients: A literature review. J. Irrig. Drain. Eng. 2016, 142, 06015006. [Google Scholar] [CrossRef]
- Grattan, S.; Bowers, W.; Dong, A.; Snyder, R.; Carroll, J.; George, W. New crop coefficients estimate water use of vegetables, row crops. Calif. Agric. 1998, 52, 16–21. [Google Scholar] [CrossRef]
- Cahn, M.D.; Johnson, L.F. New approaches to irrigation scheduling of vegetables. Horticulturae 2017, 3, 28. [Google Scholar] [CrossRef]
- Lea-Cox, J.D. Using wireless sensor networks for precision irrigation scheduling. In Problems, Perspectives and Challenges of Agricultural Water Management; InTech Press: Rijeka, Croatia, 2012; pp. 233–258. [Google Scholar]
- Gowing, J.; Ejieji, C. Real-time scheduling of supplemental irrigation for potatoes using a decision model and short-term weather forecasts. Agric. Water Manag. 2001, 47, 137–153. [Google Scholar] [CrossRef]
- Wang, D.; Cai, X. Irrigation scheduling—Role of weather forecasting and farmers’ behavior. J. Water Resour. Plan. Manag. 2009, 135, 364–372. [Google Scholar] [CrossRef]
- Lorite, I.; Ramírez-Cuesta, J.; Cruz-Blanco, M.; Santos, C. Using weather forecast data for irrigation scheduling under semi-arid conditions. Irrig. Sci. 2015, 33, 411–427. [Google Scholar] [CrossRef]
- Mahan, J.R.; Lascano, R.J. Irrigation analysis based on long-term weather data. Agriculture 2016, 6, 42. [Google Scholar] [CrossRef]
- Yang, Y.; Cui, Y.; Bai, K.; Luo, T.; Dai, J.; Wang, W.; Luo, Y. Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts. Agric. Water Manag. 2019, 211, 70–80. [Google Scholar] [CrossRef]
- Yoder, R.; Ley, T.; Elliott, R. Measurement and reporting practices for automatic agricultural weather stations. In Proceedings of the 4th Decennial Symposium, Phoenix, AZ, USA, 14–16 November 2000; pp. 260–265. [Google Scholar]
- Delgoda, D.; Saleem, S.K.; Malano, H.; Halgamuge, M.N. Root zone soil moisture prediction models based on system identification: Formulation of the theory and validation using field and AQUACROP data. Agric. Water Manag. 2016, 163, 344–353. [Google Scholar] [CrossRef]
- Bianchi, A.; Masseroni, D.; Thalheimer, M.; Medici, L.; Facchi, A. Field irrigation management through soil water potential measurements: A review. Ital. J. Agrometeorol. 2017, 22, 25–38. [Google Scholar]
- Yu, L.; Gao, W.; R Shamshiri, R.; Tao, S.; Ren, Y.; Zhang, Y.; Su, G. Review of research progress on soil moisture sensor technology. Int. J. Agric. Biol. Eng. 2021, 14, 32–42. [Google Scholar] [CrossRef]
- Kassaye, K.T.; Boulange, J.; Saito, H.; Watanabe, H. Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for Andosol in the temperate monsoon climate. Agric. Water Manag. 2020, 229, 105930. [Google Scholar] [CrossRef]
- Abdelmoneim, A.A.; Khadra, R.; Derardja, B.; Dragonetti, G. Internet of Things (IoT) for Soil Moisture Tensiometer Automation. Micromachines 2023, 14, 263. [Google Scholar] [CrossRef] [PubMed]
- Lieth, J.H.; Oki, L.R. Irrigation in soilless production. In Soilless Culture; Elsevier: Amsterdam, The Netherlands, 2019; pp. 381–423. [Google Scholar]
- Campbell, G.S. Soil water potential measurement: An overview. Irrig. Sci. 1988, 9, 265–273. [Google Scholar] [CrossRef]
- Richards, S. Soil suction measurements with tensiometers. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling; The American Society of Agronomy: Madison, WI, USA, 1965; Volume 9, pp. 153–163. [Google Scholar]
- Freire, A.G.; de Alencar, T.L.; Chaves, A.F.; do Nascimento, Í.V.; de Assis Junior, R.N.; van Lier, Q.d.J.; Mota, J.C.A. Comparison of devices for measuring soil matric potential and effects on soil hydraulic functions and related parameters. Agric. Water Manag. 2018, 209, 134–141. [Google Scholar] [CrossRef]
- Thompson, R.; Gallardo, M.; Valdez, L.; Fernández, M. Using plant water status to define threshold values for irrigation management of vegetable crops using soil moisture sensors. Agric. Water Manag. 2007, 88, 147–158. [Google Scholar] [CrossRef]
- Dobriyal, P.; Qureshi, A.; Badola, R.; Hussain, S.A. A review of the methods available for estimating soil moisture and its implications for water resource management. J. Hydrol. 2012, 458, 110–117. [Google Scholar] [CrossRef]
- Hubbell, J.; Sisson, J. Soil water potential measurement by tensiometers. In The Encyclopedia of Water Science; Marcel Dekker: New York, NY, USA, 2003; pp. 904–907. [Google Scholar]
- Shock, C.C.; Wang, F.-X. Soil water tension, a powerful measurement for productivity and stewardship. HortScience 2011, 46, 178–185. [Google Scholar] [CrossRef]
- Pardossi, A.; Incrocci, L. Traditional and new approaches to irrigation scheduling in vegetable crops. HortTechnology 2011, 21, 309–313. [Google Scholar] [CrossRef]
- Thompson, T.L.; Doerge, T.A.; Godin, R.E. Subsurface drip irrigation and fertigation of broccoli: I. Yield, quality, and nitrogen uptake. Soil Sci. Soc. Am. J. 2002, 66, 186–192. [Google Scholar] [CrossRef]
- Smittle, D.A.; Dickens, W.L.; Stansell, J.R. Irrigation regimes affect cabbage water use and yield. J. Am. Soc. Hortic. Sci. 1994, 119, 20–23. [Google Scholar] [CrossRef]
- Thompson, T.L.; Doerge, T.A.; Godin, R.E. Nitrogen and water interactions in subsurface drip-irrigated cauliflower II. Agronomic, economic, and environmental outcomes. Soil Sci. Soc. Am. J. 2000, 64, 412–418. [Google Scholar] [CrossRef]
- Gallardo, M.; Jackson, L.; Schulbach, K.; Snyder, R.; Thompson, R.; Wyland, L. Production and water use in lettuces under variable water supply. Irrig. Sci. 1996, 16, 125–137. [Google Scholar] [CrossRef]
- Thompson, T.L.; Doerge, T.A. Nitrogen and water rates for subsurface trickle-irrigated romaine lettuce. HortScience 1995, 30, 1233–1237. [Google Scholar] [CrossRef]
- Kukal, S.; Hira, G.; Sidhu, A. Soil matric potential-based irrigation scheduling to rice (Oryza sativa). Irrig. Sci. 2005, 23, 153–159. [Google Scholar] [CrossRef]
- Gendron, L.; Letourneau, G.; Anderson, L.; Sauvageau, G.; Depardieu, C.; Paddock, E.; van den Hout, A.; Levallois, R.; Daugovish, O.; Solis, S.S. Real-time irrigation: Cost-effectiveness and benefits for water use and productivity of strawberries. Sci. Hortic. 2018, 240, 468–477. [Google Scholar] [CrossRef]
- Smajstrla, A.G.; Locascio, S.J. Irrigation scheduling of drip-irrigated tomato using tensiometers and pan evaporation. Proc. Fla. State Hortic. Soc. Am. Soc. Agric. Biol. Eng. 1990, 103, 88–91. [Google Scholar]
- Munoz-Carpena, R.; Bryan, H.; Klassen, W. Automatic soil moisture-based drip irrigation for improving tomato production. In Proceedings of the Florida State Horticultural Society, Sheraton World Resort, Orlando, FL, USA, 8–10 June 2003; pp. 80–85. [Google Scholar]
- Buttaro, D.; Santamaria, P.; Signore, A.; Cantore, V.; Boari, F.; Montesano, F.F.; Parente, A. Irrigation management of greenhouse tomato and cucumber using tensiometer: Effects on yield, quality and water use. Agric. Agric. Sci. Procedia 2015, 4, 440–444. [Google Scholar] [CrossRef]
- Yang, J.; Liu, K.; Wang, Z.; Du, Y.; Zhang, J. Water-saving and high-yielding irrigation for lowland rice by controlling limiting values of soil water potential. J. Integr. Plant Biol. 2007, 49, 1445–1454. [Google Scholar] [CrossRef]
- Raes, D.; Steduto, P.; Hsiao, T.C.; Fereres, E. AquaCrop—The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 2009, 101, 438–447. [Google Scholar] [CrossRef]
- Amirouche, M.; Smadhi, D.; Zella, L. Calibration and validation of the AquaCrop model for the culture lettuce (Lactuca sativa L.) under fertilization levels in pluvial condition. Agric. Water Manag. 2018, 208, 107–119. [Google Scholar] [CrossRef]
- Pasha, S. ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis. Int. J. New Technol. Res. 2016, 2, 263492. [Google Scholar]
- Peters, R.T.; Desta, K.G.; Nelson, L. Practical Use of Soil Moisture Sensors and Their Data for Irrigation Scheduling; Washington State University Extension: Washington, DC, USA, 2013. [Google Scholar]
- Michael, C.; Barry, F. Using Tensiometers for Scheduling Irrigations of Coastal Vegetables; University of California US Cooperative Extension: Los Angeles, CA, USA, 2012. [Google Scholar]
- Dessureault-Rompré, J.; Caron, J.; Plamondon, L.; Gaudreau, L.; Jutras, S.; Lafond, J.A. Growth and water-use characteristics of Romaine lettuce cultivated in Histosol as affected by irrigation management, compaction, and seeding type. Can. J. Soil Sci. 2020, 100, 278–288. [Google Scholar] [CrossRef]
Parameter | Reference/Source | |||
---|---|---|---|---|
Climate | Rainfall (mm) | Daily data for the past 3 years was provided from the weather station situated close to the field at CIHEAM Bari | ||
Evapotranspiration (mm) | ||||
Minimum and Maximum Temperature (°C) | ||||
Mean annual CO2 concentration (ppm) | MaunaLoa.CO2 file from Aquacrop data base | |||
Crop | Calendar | Growing period | From 21 April to 21 June 2023 | |
Crop | Description | Display crop parameters: Full set | ||
Mode | Mode in: Growing Degree Days | |||
Development |
| |||
Fertility stress | Not considered | |||
Salinity and cold stress | ||||
Temperature |
| |||
Water |
| |||
Type |
| From the FAO irrigation and drainage paper No. 56 “Crop evapotranspiration” | ||
Management | Irrigation | Mode | Generation of irrigation schedule | Chosen by user preferences |
Irrigation method |
| |||
Time and depth criteria |
| |||
Field | None | |||
Soil | Soil profile | Characteristic of soil horizons |
| Measured through a soil texture and structure analysis performed in the CIHEAM Bari soil lab |
Groundwater | None |
Reading of Soil Water Tension (kPa) | Water Amount Allocated (mm) |
---|---|
−26 to −27 | 1.76 |
−27 to −28 | 2.64 |
−28 to −29 | 3.52 |
−29 to −30 | 4.4 |
Item | Quantity | Cost ($) |
---|---|---|
ESP32 WROOM | 1 | 10 |
BMP 180 sensor | 1 | 2.5 |
MT3608 DC–DC | 1 | 2 |
Tensiometer plexiglass tube | 1 | 15 |
Permeable ceramic cup | 1 | 15 |
2 cm airtight rubber cap | 1 | 3.20 |
Li-ion batteries 3.7 volts | 2 | 11 |
BMS 2S 10A charging model | 1 | 4 |
1.1 W 6 V solar panel | 1 | 14 |
Miscellaneous (Wires, isolation tape, pins…) | 1 | 15 |
PTGE filament | 0.5 kg | 1.5 |
Total | 82.20 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Abdelmoneim, A.A.; Khadra, R.; Elkamouh, A.; Derardja, B.; Dragonetti, G. Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce. Sustainability 2024, 16, 306. https://doi.org/10.3390/su16010306
Abdelmoneim AA, Khadra R, Elkamouh A, Derardja B, Dragonetti G. Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce. Sustainability. 2024; 16(1):306. https://doi.org/10.3390/su16010306
Chicago/Turabian StyleAbdelmoneim, Ahmed A., Roula Khadra, Angela Elkamouh, Bilal Derardja, and Giovanna Dragonetti. 2024. "Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce" Sustainability 16, no. 1: 306. https://doi.org/10.3390/su16010306
APA StyleAbdelmoneim, A. A., Khadra, R., Elkamouh, A., Derardja, B., & Dragonetti, G. (2024). Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce. Sustainability, 16(1), 306. https://doi.org/10.3390/su16010306