Oat is an important cereal crop ranking seventh in the world and fourth in Europe regarding cultivated area, with more than 10 million Ha worldwide (FAO, 2017). It has been used as food and fodder since ancient times. As food, oat is being increasingly appreciated as part of different products including porridge, breakfast cereals, breads, muffins, cookies, snacks, meat extenders, beverages, and baby foods for its benefits on human health (Londono et al., 2015; Gilissen et al., 2016; Martinez-Villaluenga and Peñas, 2017). Regarding livestock, oat makes good hay and silage and it is used as feed, fodder, and straw. Avena sativa, the main cultivated oat species includes white oats, preferred for milling and used for human food and fodder, and the red oats (formerly known as A. byzantina K. Koch) preferred for hay (Stevens et al., 2004). Although oat center of origin is in the fertile crescent, it is argued that the cultivated hexaploid species, evolved during the migration of its ancestors northward, where it was established as a primary crop (Thomas, 1995). Thus, A. sativa is mostly grown as spring-sown crop in cool-temperate regions, under moist and cool conditions.

Surprisingly, the oat-cultivated area in the Mediterranean rim has steadily increased during the last 20 years reaching similar harvested area to that of the Northern European countries (FAO, 2017). This is in part due to its good adaptation to a wide range of soil types and because on marginal soils oat can outperform other small-grain cereals (Stevens et al., 2004). However, oat water requirements are among the highest of small grain cereals due to its high transpiration rate (Ehlers, 1989). This, together with the fact that most of the current cultivars used as winter crop in the Mediterranean rim are spring cultivars bred in Northern countries might explain the poor oat adaptation observed in this area. Under Mediterranean cultivation conditions, oat has to face high temperatures and drought that cause grain abortion (Sánchez-Martín et al., 2017). All the above could explain the almost three-fold lower yield observed in Mediterranean rim when compared with Northern regions.

In contrast to the low adaptation observed in many oat cultivars growing under Mediterranean conditions, this area is rich in locally well-adapted oat landraces. Plant landraces are regional and locally adapted ecotypes that have adapted to their natural environment over time. Thus, landrace populations have evolved in response to specific environmental conditions following a pattern of local adaptation (Williams, 1966). They are recognized to present a tangible crop genetic resource of actual or potential economic benefit for human kind and provide a large gene pool for future genetic breeding programs and food security (Ceccarelli, 1994; Chalak et al., 2015). Landrace performance may exceed those of the varieties derived from them in terms of adaptation to changes in environmental conditions and agricultural system (Kneupffer et al., 2003). However, during the last century, landraces have often been replaced by modern cultivars reducing the diversity of several crops including wheat, barley, and maize (revised by Newton et al., 2010). The diversity loss is becoming even more important nowadays as we face the need to adapt crops to climate change (Frison et al., 2011; Ceccarelli, 2012; Dwivedi et al., 2016). After the adoption of modern cultivars, landraces moved into unfavorable and stress-prone environments (Li et al., 2012). Thus, their assessment could reveal key aspects about adaptation to low-input environments, such as the Mediterranean oat crop system. While under optimal conditions modern cultivars may outyield landraces, under low input systems, landraces may show higher or similar yield than modern cultivars, as confirmed in barley (Yahiaoui et al., 2014) and oat (Rispail et al., 2018).

Although landraces constitute one of the most valuable sources of genetic diversity, their potential value for environmental adaptation has not been fully appreciated or appropriately documented. Further studies are thus necessary regarding the assessment of landraces of particular crops for their adaptation to environment. From the phenotyping point of view, there is a need to determine their genotype × environment interaction (GEI) since it attenuates the association between phenotype and genotype, reducing genetic progress in plant breeding programs. Gaining knowledge on how GEI affects the performance of genotypes may aid selecting the best genotypes and defining their optimum environments to maximize yield. This can be implemented through the additive main effects and multiplicative interaction (AMMI) models and GGE Biplots analyses (Gauch et al., 2008). Both, AMMI and GGE biplots are based on fixed-effects models with (additive) main effects for genotypes and environments and multiplicative effects for the interaction, all being fixed (Yang et al., 2009). However, it is frequently assumed that environments are random because the environment included in the multienvironmental trials is only a sample of a large environment. The use of mixed models using restricted maximum likelihood methodology (REML) algorithm, which considers the environment effects as random, and the Best Linear Unbiased Prediction (BLUP), which predicts the outcome of random variables, solve this problem (Parsad et al., 2009). Thus, GGE analyses have been previously proven useful to characterize disease resistance and to select breeding material for yield stability and other agronomic traits in field trials of oat (Sánchez-Martín et al., 2014, 2017; Rispail et al., 2018) and other species (Villegas-Fernandez et al., 2009; Fernandez-Aparicio et al., 2012; Flores et al., 2012, 2013; Rubiales et al., 2012, 2014).

While on a large spatial scale, climate is one of the main sources of environmental variation (Santamaria et al., 2003), on small scale, different edaphic or biotic conditions have a key role on environmental variation and local plant adaptation (Bell et al., 2000; van der Putten et al., 2004). Particular climatic factors such as precipitation and temperature may determine the composition of the vegetation in a particular environment (Box, 1996). On the other hand, soil abiotic variables, such as nutrient content, structure, and composition, but also biotic soil properties may act as selective agents influencing plant performance and adaptation (Wardle, 2002; Macel et al., 2007). The influence of these different variables on local adaptation can be separated by regression-based methods between others. However, the use of partial regression is questionable for intercorrelated variables, which is common in nature because of the interaction among explanatory variables (Zar, 1984). Canonical ordination and more specifically variation partitioning allow the partition of the observed variation among different variables, such as climate and edaphic variables, and is commonly used to explore the relationships between biological phenomena and environmental influences (Legendre, 2008).

To assess the contribution of environmental and genetic variation and their interaction to phenotypic variation and agronomic performance in oat, we carried out multiyear and multisite trials involving a collection of 709 white and red A. sativa accessions, including mainly landraces and 65 cultivars for comparison. The main aim is to provide the theoretical basis to test oat performance in Mediterranean environments and to explain the local adaptation of the best performing landraces according to the influence exerted on their performance by different climate and edaphic factors.

During last decades, “elite” cultivars have replaced local varieties of many crops reducing their genetic diversity (revised by Newton et al., 2010). These modern cultivars are developed for high yield (Lasa et al., 2001; Gepts and Papa, 2002). However, most oat cultivars assessed in this work showed lower yield and biomass when compared with several landraces. One explanation might be that although modern cultivars are seldom targeted for a specific location, landraces were developed during long-term traditional cultivation at the same location and exposed to both human selection and ecogeographic pressure. Hence they are adapted to fit that environment (Chalak et al., 2015). Thus, under mild environmental cultivation conditions or high input systems, modern cultivars may offer very high yields. However, in low input systems and harsher environments, i.e., marginal soils, drought, or cold prone areas, landraces may outyield cultivars, as we observed.

This is the case of the oat crop in Mediterranean area. Although oat originated from the Fertile Crescent, it developed as a primary crop in the northern regions and consequently is better adapted to cool-temperate environments than to the warmer and drier Mediterranean climate (Thomas, 1995). Despite this, during the last 20 years oat has been increasingly cultivated in the Mediterranean area for its good adaptation to a wide range of soils, in particular in poor soils, because under a low input system oat may perform better than wheat or barley. However, the low attention paid by breeders and policy makers in this area resulted in a relatively low yearly genetic gain in yield in comparison to wheat or barley for which significant breeding investments have been made (Prats et al., 2014). Most oat cultivars used in the Mediterranean area are spring cultivars that are bred in northern countries and used in the Mediterranean rim in autumn sowings (Sánchez-Martín et al., 2017; Rispail et al., 2018). Interestingly, our data highlighted four cultivars that performed relatively well under Mediterranean conditions such as, cv. Cassandra, Alcudia, Rapidena, and Precoce Maroc. Not surprisingly, most of them were among the low number of oat cultivars developed in Mediterranean countries. Thus, Rapidena and Precoce Maroc were developed in Spain and Morocco, respectively. As their names suggest, these cultivars are characterized by early heading date, which could facilitate their escape from the terminal drought characteristic of Mediterranean environments, improving their adaptation (Shavrukov et al., 2017). Alcudia is a cultivar breed in France. It is highly resistant to crown rust (Sánchez-Martín et al., 2012), which might have contributed to the high yield observed. Cassandra is a red oat (subsp. byzantine) developed in Greece. Red oats are considered more adapted to Mediterranean area as they entered cultivation from the Western part of the Mediterranean (Loskutov, 2008). This might explain the slight but significant higher performance of red oats for all agronomic traits assessed in the present work. Interestingly, all cultivars that showed good field performance showed early heading date, whereas it was not always true for the landraces, suggesting that landraces possess further local adaptation capabilities beyond drought escape through early flowering.

Redundancy and canonical correspondence analysis together with variance partitioning allowed determining the relative contribution of climate and edaphic factors to the agronomic traits assessed (Legendre, 2008). These canonical ordination methods offer the possibility to incorporate different environmental variables in the analysis as constrains for the ordination (Legendre, 2008). In particular, variation partitioning is used when two or more complementary sets of hypotheses, in this case the effect of climate and soil, can be invoked to explain the variation of an ecological or agronomic variable such as, in our case, yield or biomass. The total variation was partitioned in different fractions among the sets of environmental variables, and these fractions were tested for significance to fully support the conclusions. As far as we know, there are no other works combining climate and soil variables to partition their effects on main agronomic traits. We observed that most agronomic traits, such as yield and heading date showed significant variation partitioning. The largest fraction was explained by climate variables, whereas the variation explained by soil variables was relatively constant for all agronomic traits. It is known that climate factors largely affect agronomic traits, with precipitation being one of the most important factors determining the final performance, albeit the combination of all-weather elements occurring simultaneously can have additive effects (i.e., Vining, 1990). Climate variables greatly determined the distribution of the environments, as observed in Figure 2. The variation explained by climate and soil factors jointly (named “interaction” in Table 1) represented a relatively large fraction in the case of yield. Surprisingly, soil variables alone, without the interaction of climate factors, were not significant for any of the agronomic traits assessed. This could be due to a lower effect of soil as compared with climate. Alternatively, it may reflect the good adaptation of oat to a wide range of soils (Stevens et al., 2004). Nevertheless, the adjusted multivariate redundancy statistic Ra2 (Miller and Farr, 1971; Legendre, 2008), which estimates the contributions of the climate and edaphic variables to the explanation of the agronomic response, was 0.40 in average, indicating that a large fraction of the observed variation was not explained by the considered climate or soil factors. In addition to these abiotic factors, biotic factors, such as crown rust or powdery mildew infection, largely constrain oat yield and are responsible for yield instability (Sánchez-Martín et al., 2011, 2012). In addition to plant pathogens, other biotic, i.e., beneficial interactions with microorganisms, may also affect oat performance. This highlights the complexity of foreseen agronomic traits in changing environments and the need to include different variables that account as much as possible for the variation observed. As expected, grain abortion was the only agronomic trait exclusively affected by climate variables, in particular, high rain preflowering, high temperature, and low rain post-flowering. The high levels of rain pre-flowering would induce a high number of flowers, whereas high temperatures and water deficit during post-flowering might promote the failure in the filling of the grains (Sánchez-Martín et al., 2017).

Graphical representation of climatic and edaphic variables (in relation to the different agronomic traits) allowed the intuitive determination of collinear traits. Some correlations among variables might be expected, for instance the positive collinearity between cation exchange capacity, nutrient, and organic matter content and their negative correlation with phosphorous content and pH. Cation exchange capacity influences soil structure stability, nutrient availability, soil pH, and the soil’s reaction to fertilizers and other ameliorants (Hazelton and Murphy, 2007). Thus, it influences the soil’s ability to hold onto essential nutrients and provides a buffer against soil acidification. In particular, organic matter has a very high cation exchange capacity, so the correlation observed in Figure 4 is not surprising. Interestingly, although temperature post-flowering was highly correlated with radiation, it was not so during the preflowering period. This might be due to the fact that variation of the atmospheric constituents affecting the relationship between radiation and temperature, i.e., cloudiness, relative humidity, rain, and concentration of atmospheric particles, is smaller during the post-flowering periods in the Mediterranean springs characterized by clear skies than during the preflowering periods characterized by winter-covered skies (Atwater and Ball, 1981; Bristow and Campbell, 1984). Surprisingly, several accessions showed differential adaptation to rain levels during pre- or post-flowering period. This differential adaptation has also been reported in sorghum between others, where QTLs for water deficit tolerance during preflowering periods differed from those identified for coping with post-flowering drought (Tuinstra et al., 1996, 1997). Obviously, the different developmental periods involve different metabolic/physiological processes leading to growth and the differential response might indicate local adaptation to particular microclimates and an evolutionary advantage.

Data showed that landraces showing the best performance, in terms of yield and biomass, in the tested environments presented local adaptation to low or moderate levels of rain pre- and post-flowering, high temperature and radiation post-flowering, and poor nutrient content, which are the main characteristic of most oat cultivation fields in the Mediterranean area (Prats et al., 2014). However, although biomass was highly correlated with yield, landraces with the highest biomass showed adaptation to relatively higher levels of rain preflowering than those showing the highest yield. No lodging was observed in the field experiments. Although lodging is an important issue for oat cultivation in northern areas, it is not a usual limitation of the oat crop in Mediterranean environments. This may be at least in part because oat is not cultivated in the most fertile soils (Prats et al., 2014). Since heading date may largely influence yield in areas such as Mediterranean, (with gradual water depletion as plant mature) while being highly influenced by environmental variables, we grouped the accessions according to their flowering time to infer further the effect of environment on yield (removing the effect of climate on heading date). Interestingly, we observed that soil variables contributed similarly to the variation observed in the different groups. However, climate had a lower contribution to the final observed yield in the early flowering oats than in the mid or late flowering groups. This supports the hypothesis that early flowering accessions are less exposed to extreme environmental events, and in this case terminal drought and/or heat (Shavrukov et al., 2017) and hence, their yield were less influenced by variations in climatic variables. On the other hand, although the contribution of cationic exchange capacity on yield was significant in the general and partial models for the late flowering group, it was only significant for the partial model for the mid flowering group and was not significant in the early flowering group. This suggests that contribution of cation exchange capacity to final plant performance is proportional to heading date. Cation exchange capacity is directly correlated with clay content (Parfitt et al., 1995), and hence with water availability (Farrar and Coleman, 1967), and as stated above, they largely determine nutrient availability. Our data would support these claims since higher cation exchange capacity largely influences the performance of the latest flowering oats growing in the Mediterranean conditions, probably due to the capacity of these soils to retain higher water and nutrient levels.

Plant height of the best performing landraces was highly influenced by the level of phosphorous. Phosphorus is an essential element determining plant growth and productivity (Malhotra et al., 2018). As it plays an important role in cell division and cell enlargement (Assuero et al., 2004), its influence on plant height is not strange. Indeed, in maize, phosphorous has been directly correlated with plant height, in particular under low nitrogen levels (Khan et al., 2014) and several phosphorous-inducible QTLs for plant height were found, which in addition were sensitive to climatic conditions (Cai et al., 2012). Accessions with lower grain abortion grouped around the vector defining rain pre-flowering and cation exchange capacity. These data suggest that high level of rain during pre-flowering period (increasing water reservoir) together with soils characterized by high cation exchange capacity (increasing water availability longer) could reduce grain abortion, and that these accessions were particularly adapted to take advantage of these conditions.

Here, we show for the first time the adaptation strategy of a wide range of oat accessions, including cultivars and landraces to Mediterranean environments and decipher the main climate and edaphic factors driving oat performance under Mediterranean conditions. Taking this into account, plant breeding programs might be able to develop varieties adapted to the specific environments (local adaptation) in which they will be grown, and/or farmers could select those varieties better adapted to their particular growing conditions. This is particularly important where the environment itself cannot be improved by agricultural practices, for instance in low input systems such as the oat cultivation in the Mediterranean rim. Another strategy might be the development of new varieties with wider adaptive plasticity or low sensitivity to the different climatic and/or soil factors (Jinks and Pooni, 1984) by selecting the accessions with good performance clustering near the vector origins. This might be especially relevant for plants that have to adapt to changing scenarios such as in the current context of climate change. Local adaptation to particular environmental conditions or adaptive plasticity may be complementary rather than exclusive. In the current climatic change scenario in which episodes of drought and temporal flooding may alternate in Mediterranean environments, breeding for a wider adaptive plasticity may be more effective in terms of time and cost, although it should be combined with local adaptation in the harshest environments to allow a better plant performance.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

FC made most of the experimental work and data analysis. GM-B performed most of the experimental work. LMG-S contributed in part of the experimental field. FF made part of the data analysis. NR and EP steered the research, designed experiments, and contributed to the interpretation of results and writing of the manuscript. All authors contributed to critical reading and writing.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.