We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heat-waves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.

A possible alternative to minimize the effects of salt and drought stress is the introduction of species tolerating these conditions with a good adaptability in terms of quantitative and qualitative yield. So quinoa (Chenopodium quinoa Willd.) cultivar Titicaca was grown in an open field trial in 2009 and 2010 to investigate the effects of salt and drought stress on quantitative and qualitative aspects of the yield. Treatments irrigated with well water (Q100, Q50 and Q25) and corresponding treatments irrigated with saline water (Q100S, Q50S and Q25S) with an electrical conductivity (ECw) of 22 dS m−1 were compared. Salt and drought stress in both years did not cause significant yield reduction, while the highest level of saline water resulted in higher mean seed weight and as a consequence the increase in fibre and total saponin content in quinoa seeds.

Chenopodium quinoa Willd. or ‘quinoa’ is a plant having many uses as a food. Importantly, it offers an alternative to normal cereals in coeliac diets because its seeds are gluten‐free. For this reason, it is worthwhile to determine the properties of quinoa and to evaluate the suitability of this crop for the south of Italy. At the CNR‐ISAFoM’s experimental station in Vitulazio (CE), a 2‐year (2006–2007) field trial under rain‐fed conditions was carried out to compare the two quinoa genotypes: KVLQ520Y (KV) and Regalona Baer (RB). Comparison was also made between two sowing dates for KV. The results showed that April was the best sowing time for quinoa in our typical Mediterranean region. Of the two genotypes, RB recorded better growth and productivity, apparently being more tolerant to abiotic stress (high temperatures associated with water stress). Chemical analyses reveal the potential of quinoa seed as a valuable ingredient in the preparation of cereal foods having improved nutritional characteristics.

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.