Climate resilience of the top ten wheat producers in the Mediterranean and the Middle East

Abstract

Wheat is the main staple crop and an important commodity in the Mediterranean and the Middle East. These are among the few areas in the world where the climate is suitable for growing durum wheat but also are among the most rapidly warming ones, according to the available scenarios of climate projections. How much food security and market stability in the Mediterranean and the Middle East, both depending on wheat production and its interannual variability, are going to be compromised by global warming is an overarching question. To contribute in addressing it, we use a recently established indicator to quantify crop production climate resilience. We present a methodological framework allowing to compute the annual production resilience indicator from nonstationary time series. We apply this approach on the wheat production of the 10 most important producers in the Mediterranean and the Middle East. Our findings shows that if no adaptation will take place, wheat production reliability in the Mediterranean and the Middle East will be threatened by climate change already at 1.5°C global warming. Average climaterelated wheat production losses will exceed the worst past event even if the 2°C mitigation target is met. These results call for urgent action on adaptation to climate change and support further efforts for mitigation, fully consistently with the Paris Agreement recommendations.

Estimating resilience of crop production systems: From theory to practice

“…This study provides a theoretical framework for addressing quantitative aspects of resilience and sustainability of real crop production systems using models and observed data for different world regions and at the global scale in the context of climate change ( Zampieri et al, 2019 , Zampieri et al, 2020 ). While most adaptation studies often neglect the importance of inter-annual crop production variability ( Challinor et al, 2014 ), our theoretical findings demonstrate how it can be accounted in order to assess resilience.…”

“…While most adaptation studies often neglect the importance of inter-annual crop production variability ( Challinor et al, 2014 ), our theoretical findings demonstrate how it can be accounted in order to assess resilience. If the stability of the production time series is measured by the reciprocal of the coefficient of variance of the production time series, we have demonstrated that the resilience indicator proposed is the square of the stability ( Zampieri et al, 2019 ) and we provided a definition that can be applied to non-stationary time series characterizing agricultural production ( Zampieri et al, 2020 ).…”

“…The sampling issue can be overcome by considering longer datasets, which can be generated by crop models, or by comparing and aggregating data over larger spatial units ( Zampieri et al, 2019 , Zampieri et al, 2020 ). Simulation model experiments and an example case study for France point to an important role of adaptation and management, and confirm the importance of crop diversification to enhance production resilience.…”

“…Since the crop production resilience indicator definition (Eq. (6) ) only depends on the ratio between mean production and variance, it is possible to normalize the production time series using the baseline trend defined as the smoothed time series, and to compute the resilience indicator for the normalized time series ( Zampieri et al, 2020 ). This procedure assumes that system resilience is not changing when the standard deviation of the crop production anomalies varies proportionally to the baseline mean values and allows computing the resilience indicator using non-stationary data.…”

“…More specifically, we first compute the smoothed production time series using the LOESS procedure ( Cleveland and Devlin, 1988 ) with span parameter set equal to 0.75 ( Zampieri et al, 2017a , Zampieri et al, 2017b , Zampieri et al, 2019a , Zampieri et al, 2020 ), then we compute the normalized production anomalies and we derive the standard deviation: where the p i represents the production values at year j of the time series under evaluation, π j are the normalized anomalies with respect to the baseline values (i.e. the smoothed time series p j ) and σ ′ is the standard deviation of the normalized anomalies.…”