Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard

Simone Priori,Riccardo Gucci,Giovanni Caruso,Giacomo Palai

doi:10.3390/app12031309

Simone Priori, Riccardo Gucci + Show 2 more

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https://doi.org/10.3390/app12031309

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Journal: Applied Sciences	Publication Date: Jan 26, 2022
Citations: 15	License type: CC BY 4.0

Affiliation: Tuscia University, University of Pisa

Abstract

The aim of this study was to evaluate the potential use of remote and proximal sensing techniques to identify homogeneous zones in a high density irrigated olive (Olea europaea L.) orchard subjected to three irrigation regimes (full irrigation, deficit irrigation and rainfed conditions). An unmanned aerial vehicle equipped with a multispectral camera was used to measure the canopy NDVI and two different proximal soil sensors to map soil spatial variability at high resolution. We identified two clusters of trees showing differences in fruit yield (17.259 and 14.003 kg per tree in Cluster 1 and 2, respectively) and annual TCSA increment (0.26 and 0.24 dm2, respectively). The higher tree productivity measured in Cluster 1 also resulted in a higher water use efficiency for fruit (WUEf of 0.90 g dry weight L−1 H2O) and oil (WUEo of 0.32 g oil L−1 H2O) compared to Cluster 2 (0.67 and 0.27 for WUEf and WUEo, respectively). Remote and proximal sensing technologies allowed to determine that: (i) the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard; (ii) tree vigour played a major role in determining the final fruit yield under optimal soil water availability, whereas soil features prevailed under rainfed conditions.

Highlights

Water availability is the main limiting factor for the growth and yield of crops in the Mediterranean region, which is expected to undergo dramatic changes in temperature and precipitation due to climate change [1,2]
Moral et al [13] developed a predictive model based on soil features to identify homogeneous zones within olive orchards, but the precision and the accuracy of these maps were strictly related to the number of soil samples analysed
The objectives of this study were: (i) to evaluate the effect of different irrigation regimes on tree productivity and growth in different management zones identified using remote and proximal sensing techniques; (ii) to discriminate the effect of tree vigour (NDVI) and soil features derived by proximal sensing on fruit yield and vegetative growth under different conditions of soil water availability

Summary

Introduction

Water availability is the main limiting factor for the growth and yield of crops in the Mediterranean region, which is expected to undergo dramatic changes in temperature and precipitation due to climate change [1,2]. An important aspect in irrigation management is to understand the multiple interactions between water availability and soil properties or canopy vigour, which may potentially cause variations in yield and vegetative growth. In this respect, site-specific management of inputs, such as fertilizers and irrigation water, requires the understanding of the spatial distribution of soil characteristics, tree vigour, and productivity. Homogenous zones within the orchard for tree vigour and soil properties can be identified by soil sampling protocols and by monitoring parameters of vegetative growth (e.g., canopy volume increment, trunk growth rate), albeit at a high cost of money and labor [11,12]. The use of remote and proximal sensing technologies for estimating field variability is becoming more and more common in precision agriculture due to their relatively lower cost and the non-invasive approach with respect to conventional methods [14,15,16]

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