Mapping impaired olive tree development using electromagnetic induction surveys

Pedrera‐Parrilla, Aura; Martínez, Gonzalo; Espejo-Pérez, A. J.; Gómez, José A.; Giráldez, Juan Vicente; Vanderlinden, Karl

doi:10.1007/s11104-014-2207-5

Cited by 18 publications

(9 citation statements)

References 38 publications

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“…The coefficients in the exponential relationship between ECa and water content at each sampling location varied with the first PC value. These findings indicate that no unique relationship existed between ECa and SWC for the entire study field, as noted by Islam et al (2011), Pedrera-Parrilla et al (2014, and Robinson et al (2009).…”

Section: Discussionmentioning

confidence: 66%

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Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Section: Discussionmentioning

confidence: 66%

“…Peralta et al (2013), Pedrera-Parrilla et al (2014), and Bonfante et al (2015) have also used EMI to establish management zones. The delineation of management zones within a given field is one of the most common uses of EMI data (Vaudour et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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“…To the best of our knowledge Castro et al (2006) and Pedrera-Parrilla et al (2014) are some of the few authors who reported on the spatial patterns of runoff (Q, hereafter) and runon (Q in , hereafter) across an olive orchard. Although olive orchards have long been recognized as consisting of a mosaic pattern of infiltration and runoff (Gómez et al, 2001a;Romero et al, 2007), there are almost no studies comparing these factors under CC and CT treatments.…”

Section: Introductionmentioning

confidence: 99%

Temporal stability and patterns of runoff and runon with different cover crops in an olive orchard (SW Andalusia, Spain)

López‐Vicente

García‐Ruiz

Guzmán

et al. 2016

CATENA

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Conventional tillage (CT) and cover crops (CC) trigger different runoff (Q) and runon (Q in) magnitudes and patterns in woody crops. The spatial and temporal stability of these patterns is not well known yet. In this study, we run the uncalibrated DR2-2013 © SAGA v1.1 model (0.5 x 0.5 m of cell size) to simulate time to ponding (Tp), runoff duration (T Q), initial runoff per raster cell (q 0), Q sim and Qin in six olive plots (480 m 2 per plot) during two years (108 rainfall events and 648 simulations). Two plots were managed with a mixture of plant species (CC-I), two with one single plant species (CC-II) and two with CT. Runoff yield from each plot was collected (Q obs) in gaugingstations during 27 time-integrated samples and used for modelling validation (162 control points). On average, Q obs was 9% higher under CT than under CC-I, and 8% higher than under CC-II. Topsoil saturation was simulated for the entire plots during 29 events (test-period), and Q sim appeared in another 51 and 52 events in the plots with CC and CT. Tp with CT was 2.3 times higher (59 s) than the average duration with CC and the topsoil became saturated 3.3 times faster in the inter-rows than below the trees. Values of q 0 with CC were 2.3% lower than with CT and total Q sim with CC was 2% higher than with CT. However, the differences of Q sim between the different treatments were not statistically significant. The mean observed and simulated runoff coefficients were of 11 and 14%, with median values of 7 and 10%. Q sim correlated well with Q obs (Pearson ca. 0.861), and Q sim was overestimated ca. 10%. The model performed better CATENA 147: 125-137 (2016) when rainfall depth and intensity were high, and the range of variability of both Q sim and Q obs was similar. The average, best and worst Nash-Sutcliffe coefficients were 0.665, 0.791 (P6) and 0.512 (P3) and thus model simulations were satisfactory. The four plots with CC presented on average a worse performance (Kling-Gupta coefficient = 0.607) than the two plots with CT (KGE = 0.769). The lowest spatial variability of q 0 , Q obs , Q sim and actual available water (Waa, the sum of Qin and stored water in the soil surface) were found in the plots with CC. CT triggered higher spatial variability of runoff and higher temporal variability of runon than CC.

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“…Using data related to the electrical properties of the soil and multispectral images, easily available at high resolution, can be the best option to delineate different homogeneous zones. For instance, Pedrera-Parrilla et al [31] and Moral et al [32] used soil electrical data for zoning purposes, and Hall and Wilson [33] and Martínez-Casanovas et al [34] utilized vegetation indices computed from multispectral images. The use of satellite data to evaluate changes in vegetation properties aimed at applications in precision agriculture has also been investigated [35,36].…”

Section: Introductionmentioning

confidence: 99%

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

Millán

Campillo

Casadesús

et al. 2020

Sensors

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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.

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Mapping impaired olive tree development using electromagnetic induction surveys

Cited by 18 publications

References 38 publications

Temporal stability of electrical conductivity in a sandy soil

Temporal stability of electrical conductivity in a sandy soil

Temporal stability and patterns of runoff and runon with different cover crops in an olive orchard (SW Andalusia, Spain)

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

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