Elsevier

Agricultural Water Management

Volume 204, 31 May 2018, Pages 247-261
Agricultural Water Management

Evaluation of olive response and adaptation strategies to climate change under semi-arid conditions

https://doi.org/10.1016/j.agwat.2018.04.008Get rights and content

Highlights

  • An ensemble of climate models is required for assessing climate change impacts.

  • Positive and negative effects resulted in a limited impact of climate change on olive.

  • Flowering is a critical stage to evaluate the impacts of climate change on olive.

  • Olive cultivars with earlier flowering dates are less impacted by heat/water stresses.

  • Regulated deficit irrigation is an excellent adaptation strategy for olive.

Abstract

AdaptaOlive is a simplified physically-based model that has been developed to assess the behavior of olive under future climate conditions in Andalusia, southern Spain. The integration of different approaches based on experimental data from previous studies, combined with weather data from 11 climate models, is aimed at overcoming the high degree of uncertainty in the simulation of the response of agricultural systems under predicted climate conditions.

The AdaptaOlive model was applied in a representative olive orchard in the Baeza area, one of the main producer zone in Spain, with the cultivar ‘Picual’. Simulations for the end of the 21st century showed olive oil yield increases of 7.1 and 28.9% under rainfed and full irrigated conditions, respectively, while irrigation requirements decreased between 0.5 and 6.2% for full irrigation and regulated deficit irrigation, respectively. These effects were caused by the positive impact of the increase in atmospheric CO2 that counterbalanced the negative impacts of the reduction in rainfall. The high degree of uncertainty associated with climate projections translated into a high range of yield and irrigation requirement projections, confirming the need for an ensemble of climate models in climate change impact assessment.

The AdaptaOlive model also was applied for evaluating adaptation strategies related to cultivars, irrigation strategies and locations. The best performance was registered for cultivars with early flowering dates and regulated deficit irrigation. Thus, in the Baeza area full irrigation requirements were reduced by 12% and the yield in rainfed conditions increased by 7% compared with late flowering cultivars. Similarly, regulated deficit irrigation requirements and yield were reduced by 46% and 18%, respectively, compared with full irrigation. The results confirm the promise offered by these strategies as adaptation measures for managing an olive crop under semi-arid conditions in a changing climate.

Introduction

Olive constitutes the main economic and social crop in numerous areas of the region of Andalusia (southern Spain). Currently, Andalusia is the main olive oil producer in the world and olive cultivation is almost the sole economic driver in many areas of this region. Recently, there has been growing concern about the future sustainability of this agricultural system (Gómez-Limón and Riesgo, 2010; Fernández-Escobar et al., 2013). In this regard, the installation of irrigation systems in traditional rainfed olive orchards in Andalusia during the 1990s resulted in a significant increase in yields and inter-annual yield stability (Sanchez-Martinez and Paniza, 2015). However, despite the huge effort made by farmers and public institutions, vast olive growing areas of Andalusia still have deficient irrigation water management. This, together with other factors such as high energy costs, result in a low profitability (Lanzas and Moral, 2008; Parras, 2013), which is threatening the sustainability of this agricultural system.

In this context, the impact of climate change on olive orchards located in semi-arid regions such as Andalusia could generate severe economic losses, and even lead to the disappearance of this crop in many areas (Areal and Riesgo, 2014). To identify adaptation strategies for olive orchards, the first step is the development of a robust simulation approach for evaluating olive crop behavior under baseline and future climate conditions. To date, however, olive modeling has been limited. Three clear methodologies have been used to assess crop development, yield and irrigation requirements: the first is based on complex physiological approaches (Villalobos et al., 2006; Morales et al., 2016), the second applies a simplified focus based on crop coefficients (Allen et al., 1998; Clarke et al., 1998), and the third employs statistical models such as the one described by Quiroga and Iglesias (2009). However, despite the advanced methodologies that have been developed for the characterization and evaluation of Mediterranean olive orchards (Santos et al., 2012), numerous uncertainties undermine the quality of the simulation results.

Regardless of the methodology applied, phenology is a crucial component of any study related to olive and climate change. Thus, Ayerza and Sibbett (2001) and Rapoport et al. (2012) considered flowering as a key phenological stage for final oil yield. Therefore, the likely occurrence of high temperatures and/or water stress (due to normal or extreme events) during this period would have a strong impact on irrigation scheduling and olive oil production. However, the existing uncertainties about the impact of climate change on olive phenology (Osborne et al., 2000; De Melo-Abreu et al., 2004; Oteros et al., 2013) require the development of new phenological models able to simulate the crop’s response under future climate conditions (Gabaldón-Leal et al., 2017).

Besides phenology, a key process for olive yield is water management. The assessment of future irrigation requirements of olive orchards has generally involved simplified and statistical approaches (Rodríguez-Díaz et al., 2007; Tanasijevic et al., 2014). However, due to the limited available knowledge of the physiological response of the olive crop under future climate conditions, these approaches fail to account for several important processes such as the impact of an increase in atmospheric CO2 or the interactions between phenology and heat and water stress.

The above mentioned limitations make climate change impact assessments prone to a high degree of uncertainty, primarily arising from the field data, the crop modeling and the climate projections. The quality and quantity of available field data means that there is limited existing knowledge about olive and, therefore, few crop models able to accurately reproduce olive development and growth. The crop and climate model-related uncertainty can be partially handled by ensemble modeling, which has proven to be an efficient alternative (Ruiz-Ramos et al., 2011; Pirttioja et al., 2015; Ruiz-Ramos et al., 2018). In this study, the lack of simulation models for olive able to simulate future climate conditions has been tackled by integrating simplified physically-based approaches based on experimental data to obtain a new simulation model named AdaptaOlive. Thus, data on key components such as phenology, atmospheric CO2 effects on transpiration efficiency or the impact of water stress during flowering on yield have been considered. In addition, the AdaptaOlive model with an ensemble of bias-corrected regional climate projections (Dosio et al., 2012) has been applied for the first time in order to assess future olive crop response to climate change, and the related uncertainty.

The objectives of this study were: 1) to assess the impact of climate change on yield, water requirements and economic components of traditional Mediterranean olive orchards by means of a simulation model based on experimental data from previous studies, and 2) to identify the most suitable strategies for climate change adaptation for the olive crop under semi-arid conditions by analyzing a number of climate projections, olive genotypes, irrigation management strategies and locations within Andalusia.

Section snippets

Study area and climate

Andalusia displays a high variability of climate conditions, ranging from rainy areas with moderate temperatures (lower section of the Guadalquivir Valley) to warm and arid areas (east of the region). To represent this spatial climate variability, eight Andalusian locations were selected (Fig. 1): Antequera, Baena, Baeza, Córdoba, Martos, Osuna and Seville, all located within the traditional Andalusian olive-growing area; and Jerez, which lies outside it but is a future alternative

Olive response to climate change

The results of the AdaptaOlive model applied in a representative olive orchard cultivated in the Baeza area with ‘Picual’ cultivar (Table 1), and under the DMIB-Pic-BA scenario indicate that olive crop transpiration (T) would be reduced in NF and FF periods, compared with the B period, with the most severe reductions under rainfed conditions (decreases of around 9 and 22% for NF and FF, respectively; Fig. 3a). The opposite was found for transpiration efficiency (TE), with increases in NF and FF

Discussion

The development of a specific simulation model for olive, named AdaptaOlive, based on experimental data from previous studies and considering key physiological components, enabled the evaluation of the response of olive yield, irrigation water requirements and economic projections to climate change under semi-arid conditions. The AdaptaOlive model constitutes an intermediate option between simple models (Rodríguez-Díaz et al., 2007; Quiroga and Iglesias, 2009; Tanasijevic et al., 2014) and full

Conclusions

A simulation model named AdaptaOlive, based on experimental data from previous studies, has been developed for evaluating key processes such as the response of transpiration, transpiration efficiency and yield to atmospheric CO2 and crop water stress. This mechanistic simulation has enabled an assessment of olive response to climate model predictions of changes in weather conditions and atmospheric CO2 in the Andalusia region during the 21st century.

Despite the predicted reduction in rainfall

Acknowledgements

This study has been financially supported by the project RTA2014-00030-00-00 funded by INIA, FEDER 2014–2020 “Programa Operativo de Crecimiento Inteligente”, project AVA201601.2 funded by the European Regional Development Fund (FEDER), FACCE MACSUR – Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub, and by MULCLIVAR, from the Spanish Ministerio de Economía y Competitividad (MINECO) CGL2012-38923-C02-02. The contributions of Dr. Orgaz and Dr.

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