Dataset Information

Automatic Irrigation Scheduling on a Hedgerow Olive Orchard Using an Algorithm of Water Balance Readjusted with Soil Moisture Sensors.

ABSTRACT: Recent technological advances have made possible automated irrigation scheduling using decision-support tools. These tools help farmers to make better decisions in the management of their irrigation system, thus increasing yields while preserving water resources. The aim of this study is to evaluate in a commercial plot an automated irrigation system combined with remote-sensing techniques and soil mapping that allows the establishment of regulated deficit irrigation (RDI) strategies. The study was carried out over 3 years (2015-2017) in a commercial hedgerow olive orchard of the variety 'Arbequina' located in Alvarado (Extremadura, Spain). An apparent electrical conductivity (ECa) map and a normalized difference vegetation index (NDVI) map were generated to characterize the spatial variability of the plot and classify the zones in homogeneous areas. Then, reference points were selected to monitor the different irrigation sectors. In 2015, the plot was irrigated according to the farmer's technical criteria throughout the plot. In 2016 and 2017, two different areas of the plot were irrigated applying an RDI strategy, one under expert supervision and the other automatically. The results show that in a heterogeneous plot the use of new technologies can be useful to establish the ideal location for an automatic irrigation system. Furthermore, automatic irrigation scheduling made it possible to establish an RDI strategy recommended by an expert, resulting in the homogenization of production throughout the plot without the need for human intervention.

SUBMITTER: Millan S

PROVIDER: S-EPMC7249181 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Automatic Irrigation Scheduling on a Hedgerow Olive Orchard Using an Algorithm of Water Balance Readjusted with Soil Moisture Sensors.

Millán Sandra S Campillo Carlos C Casadesús Jaume J Pérez-Rodríguez Juan Manuel JM Prieto Maria Henar MH

Sensors (Basel, Switzerland) 20200429 9

Recent technological advances have made possible automated irrigation scheduling using decision-support tools. These tools help farmers to make better decisions in the management of their irrigation system, thus increasing yields while preserving water resources. The aim of this study is to evaluate in a commercial plot an automated irrigation system combined with remote-sensing techniques and soil mapping that allows the establishment of regulated deficit irrigation (RDI) strategies. The study ...[more]

PMID: 32365621

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Project description:An increase in the land area dedicated to super-high density olive orchards has occurred in Chile in recent years. Such modern orchards have high irrigation requirements, and optimizing water use is a priority. Moreover, this region presents low water availability, which makes necessary to establish irrigation strategies to improve water productivity. An experiment was conducted during four consecutive growing seasons (2010-2011 to 2013-2014) to evaluate the responses of yield and water productivity to irrigation cut-off strategies. These strategies were applied after fruit set using midday stem water potential (Ψstem) thresholds in a super-high density olive orchard (cv. Arbequina), located in the Pencahue Valley, Maule Region, Chile. The experimental design was completely randomized with four irrigation cut-off treatments based on the Ψstem thresholds and four replicate plots per treatment (five trees per plot). Similar to commercial growing conditions in our region, the Ψstem in the T1 treatment was maintained between -1.4 and -2.2 MPa (100% of actual evapotranspiration), while T2, T3 and T4 treatments did not receive irrigation from fruit set until they reached a Ψstem threshold of approximately -3.5, -5.0, and -6.0 MPa, respectively. Once the specific thresholds were reached, irrigation was restored and maintained as T1 in all treatments until fruits were harvested. Yield and its components were not significantly different between T1 and T2, but fruit yield and total oil yield, fruit weight, and fruit diameter were decreased by the T3 and T4 treatments. Moreover, yield showed a linear response with water stress integral (SΨ), which was strongly influenced by fruit load. Total oil content (%) and pulp/stone ratio were not affected by the different irrigation strategies. Also, fruit and oil water productivities were significantly greater in T1 and T2 than in the T3 and T4. Moreover, the T2, T3, and T4 treatments averaged 37, 51, and 72 days without irrigation which represented 75-83, 62-76, and 56-70% of applied water compared with T1, respectively. These results suggest that using the T2 irrigation cut-off strategy could be applied in a super-high density olive orchard (cv. Arbequina) because it maintained yields, saving 20% of the applied water.

Project description:Soil volumetric water content ( V W C ) is a vital parameter to understand several ecohydrological and environmental processes. Its cost-effective measurement can potentially drive various technological tools to promote data-driven sustainable agriculture through supplemental irrigation solutions, the lack of which has contributed to severe agricultural distress, particularly for smallholder farmers. The cost of commercially available V W C sensors varies over four orders of magnitude. A laboratory study characterizing and testing sensors from this wide range of cost categories, which is a prerequisite to explore their applicability for irrigation management, has not been conducted. Within this context, two low-cost capacitive sensors-SMEC300 and SM100-manufactured by Spectrum Technologies Inc. (Aurora, IL, USA), and two very low-cost resistive sensors-the Soil Hygrometer Detection Module Soil Moisture Sensor (YL100) by Electronicfans and the Generic Soil Moisture Sensor Module (YL69) by KitsGuru-were tested for performance in laboratory conditions. Each sensor was calibrated in different repacked soils, and tested to evaluate accuracy, precision and sensitivity to variations in temperature and salinity. The capacitive sensors were additionally tested for their performance in liquids of known dielectric constants, and a comparative analysis of the calibration equations developed in-house and provided by the manufacturer was carried out. The value for money of the sensors is reflected in their precision performance, i.e., the precision performance largely follows sensor costs. The other aspects of sensor performance do not necessarily follow sensor costs. The low-cost capacitive sensors were more accurate than manufacturer specifications, and could match the performance of the secondary standard sensor, after soil specific calibration. SMEC300 is accurate ( M A E , R M S E , and R A E of 2.12%, 2.88% and 0.28 respectively), precise, and performed well considering its price as well as multi-purpose sensing capabilities. The less-expensive SM100 sensor had a better accuracy ( M A E , R M S E , and R A E of 1.67%, 2.36% and 0.21 respectively) but poorer precision than the SMEC300. However, it was established as a robust, field ready, low-cost sensor due to its more consistent performance in soils (particularly the field soil) and superior performance in fluids. Both the capacitive sensors responded reasonably to variations in temperature and salinity conditions. Though the resistive sensors were less accurate and precise compared to the capacitive sensors, they performed well considering their cost category. The YL100 was more accurate ( M A E , R M S E , and R A E of 3.51%, 5.21% and 0.37 respectively) than YL69 ( M A E , R M S E , and R A E of 4.13%, 5.54%, and 0.41, respectively). However, YL69 outperformed YL100 in terms of precision, and response to temperature and salinity variations, to emerge as a more robust resistive sensor. These very low-cost sensors may be used in combination with more accurate sensors to better characterize the spatiotemporal variability of field scale soil moisture. The laboratory characterization conducted in this study is a prerequisite to estimate the effect of low- and very low-cost sensor measurements on the efficiency of soil moisture based irrigation scheduling systems.

Project description:The electrical response of plants to environmental stimuli can be measured and quantitatively related to the intensity of several stimulating sources, like temperature, solar radiation, soil water content, evapotranspiration rates, sap flow and dendrometric cycles. These relations can be used to assess the influence of different environmental situations on soil water availability to plants, defined as a steady state condition between leaf transpirative flow and soil water flow to plant roots. A restricted soil water flow due to soil dryness can trigger water stress in plants, if the atmospheric evaporative demand is high, causing partial stomata closure as a physiological response to avoid plant dehydration; water stressed and unstressed plants manifest a differential electrical response. Real time plant electrical response measurements can anticipate actions that prevent the plant reaching actual stress conditions, optimizing stomata gas exchange and photosynthetic rates. An electrophysiological sensor developed in this work, allows remote real-time recording information on plant electrical potential (EP) in the field, which is highly related to EP measurements obtained with a laboratory Keithley voltmeter sensor used in an highly controlled experimental setup. Our electrophysiological sensor is a wireless, autonomous devise, which transmits EP information via Internet to a data server. Using both types of sensors (EP electrodes with a Keithley voltmeter and the electrophysiological sensor), we measured in real time the electrical responses of Persea americana and Prunus domestica plants, to induced water deficits. The differential response for 2 scenarios: irrigation and water restriction is identified by a progressive change in slope on the daily maximal and minimal electric signal values in stressed plants, and a zero-slope for similar signals for well-watered plants. Results show a correspondence between measured signals obtained by our electrophysiological sensor and the EP electrodes connected to the Keithley voltmeter in each irrigation stage. Also, both sensors show a daily cyclical signal (circadian cycle).

Dataset Information

Automatic Irrigation Scheduling on a Hedgerow Olive Orchard Using an Algorithm of Water Balance Readjusted with Soil Moisture Sensors.

Publications

Automatic Irrigation Scheduling on a Hedgerow Olive Orchard Using an Algorithm of Water Balance Readjusted with Soil Moisture Sensors.

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