Assessment of Durum Wheat Cultivars’ Adaptability to Mediterranean Environments Using G × E Interaction Analysis

Abstract

Aside from plant breeding and agricultural inputs, understanding and interpreting the Genotype × Environment (G × E) interaction has contributed significantly to the increase in wheat yield. In Central Macedonia, Greece, fifteen commercially important durum wheat cultivars and one landrace were tested in six cultivation environments classified into high- and low- productivity environments. This study aimed to identify the most productive and stable durum wheat genotypes across Mediterranean farming systems through a comparative examination of genotype plus genotype by environment (GGE) biplot alongside fifteen parametric and non-parametric stability models. In the organic (low productivity) environment, cultivar Zoi and the landrace Lemnos showed remarkable results, indicating a potential solution for biological agriculture. For the late-sowing (low productivity) environment, some widespread varieties such as Mexicali-81, Meridiano, and Maestrale had excellent performance, showing potential to overcome more adverse conditions during critical grain filling periods such as higher air temperature and deficient soil moisture, i.e., conditions that correlate with climate change. Evaluation of genotypes in all environments for a combination of high yield and stable production, showed that the best genotypes were G8 (Simeto), G2 (Canavaro), and G12 (Elpida). In the subgroup with the three high-productivity environments, G12 (Elpida), G8 (Simeto), and G6 (Mexicali-81) were the best genotypes, followed by G2 (Canavaro), while in the low-productivity subgroup, the G2 (Canavaro), G13 (Zoi) and G8 (Simeto) genotypes were the best.

1. Introduction

The global population is expected to grow approximately 1% in the coming years, and the global use of major agricultural commodities, including wheat, is expected to grow by 1.1% due to both population growth and per capita growth in demand [1]. Global wheat production in 2022/23 is estimated at 770 million tonnes and is expected to rise to 840 million tonnes [1], of which durum wheat (Triticum durum Desf.) has an average production of approximately 40 million tonnes [2]. The increase in global cereal production results from increased yield, considering the limited potential for the availability of more arable land. However, environmental concerns and policies (e.g., the EU Green Deal) could negatively affect the yield in some places [1]. This increases the need to sustain and even increase the yield of field crops more sustainably, exploiting available techniques and agricultural practices that contribute to an efficient increase in productivity.

Originating from the Levant, where it was first domesticated, durum wheat was an essential cultivar for Greeks, Phoenicians, Romans, and all Mediterranean countries, which until 1955 planted local landraces [2]. Durum wheat holds substantial economic significance due to its distinctive attributes and the food products, notably pasta. It serves as a crucial energy source, offering diverse vitamins, minerals, and other essential nutritional compounds vital for human dietary requirements [3]. Over the last few decades, researchers have conducted many breeding programs. New genotypes produced by these breeding programs have to be evaluated in multi-environmental trials; AMMI analysis then needs to be conducted to group the genotypes being assessed into the most suitable environments [4]. Although innovations in the field of genetics have had the ultimate effect of increasing the yield [5], classical breeding programs are still important and, along with the use of new techniques and technologies, could continue to provide new cultivars of durum wheat [6]. Multi-location trials are also necessary to identify the most suitable genotypes for the target locations.

G × E interaction involving genotype performance changes across different environments and can complicate the identification of superior genotypes. This phenomenon is known as the crossover concept [7]. The ultimate target remains to identify the genotype that produces high and stable yields in various environments; this can then be put forth as a superior selection with high adaptability [4,8]. Genotypes with confirmed high grain yields and stability under certain environmental conditions could be used as parental lines in breeding programs in locations with similar conditions [9]. Various statistical tools have been developed to address G × E interpretation and crop stability and are classified into two categories based on their statistical properties: parametric and non-parametric measures. Commonly used parametric models are the variance of deviations from regression ($s_{di}^2$) [10], the stability variance (${\sigma }_i^2$) [11], the coefficient of variability (${CV}_i$) [12], and the ${ASV}_i$ stability measure [13] from AMMI analysis [14]. Concerning the non-parametric statistics, many models have been suggested, including Si by Nassaar and Huehn [15] and NPi by Thennarasu [16], which are based on similar genotype ranks across environments (stable genotypes), with genotypes that display similar ranks across environments assumed to be stable. Parametric and non-parametric statistical methods may approach stability differently. These approaches introduce two distinct facets of stability: the “static” or “biological” and the “agronomic” or “dynamic” [17]. In terms of the static aspect, a genotype is considered stable when it maintains a consistent yield across various environments. However, none of these methods comprehensively explain genotype performance across diverse environments [18].

AMMI and GGE biplot analyses were used to visualize the results of the interactions between genotypes and environments. These analyses aim to identify varieties that perform above the average and are stable in multiple environments and, second, suggest using varieties with low stability in particular environments by exploiting their adaptability under specific environmental conditions [19]. The GGE biplot model [7,20] offers a comprehensive visual assessment of the entire genotype–environment interaction, presenting a biplot that encapsulates both average yield performance and stability. GGE effectively filters out the noise originating from the environmental main effect (E) and highlights the two crucial components: genotype effects (G) and G × E interactions. The genotype’s value is visually represented using scores obtained from principal component analysis. Genotypes positioned closer to the performance line are deemed more stable compared with those located farther away. Moreover, the model quantifies a genotype’s distance from the “ideal genotype”, which represents the one with the highest mean performance and absolute stability, effectively occupying the central position within a series of concentric cycles. Although such an ideal genotype is rarely found in practice, it is a reference point for evaluating genotypes. The concentric circles, centered on the ideal genotype, facilitate the visualization of the relative distances between all and ideal genotypes. The plot distance between any given genotype and the ideal genotype can be used to measure its desirability [7,20].

Abiotic components that describe an environment are the non-living factors that affect the growth of living organisms, like plants, which grow in the ecosystem. Climatic parameters like rain, precipitation, and temperature are significant factors that characterize an environment [21]. Additionally, wind conditions, moisture, and light are some characteristics of the environment that affect grain yield [22]. Furthermore, soil parameters, like pH [21] and salinity [22], affect the adaptability of specific cereal genotypes. In summary, an agricultural system incorporates all the parameters mentioned above, which can affect crop growth and biomass accumulation and turn a percentage of biomass into seed production. Similarly, the agronomical practices adopted affect productivity and the efficient use of resources, biomass accumulation, and eventually higher grain yield. As a result, agricultural systems where different farming practices are applied are considered different environments.

In the last years, the Genotype (G) × Environment (E) interaction has been escalated to a triple interaction that includes the Management (M) factor, which is considered a third factor [5], or in some cases, is incorporated into the Environment factor. Identifying the ideal sowing window for durum wheat is now an essential management practice with a principal role in high yield, which is highly associated with the grain-filling period [23]. Similarly, the agronomical practices adopted affect productivity and the efficient use of resources, biomass accumulation, and, eventually, grain yield. As a result, agricultural systems where different farming practices are applied can represent different environments [24]. Nitrogen fertilization is another management practice crucial for grain yield and quality. The interaction between genotype and N availability should be exploited to produce durum wheat, a crop with quality requirements [25]. Unlike other cereal crops, durum wheat cultivation is not currently investigated for plant density, so this question is solved. Regarding irrigation, more and more countries in the Mediterranean region and in other areas where durum wheat is cultivated with supplemental irrigation focus their research and the strategy of their breeding programs on drought-tolerant genotypes in a period where intense climatic events are escalating [26,27].

This study aimed to identify the most productive and stable genotypes across Mediterranean farming systems, including biological and low/high productivity farming systems, through a comparative examination of the yield and analyses of the GGE biplot alongside fifteen parametric and non-parametric stability models.

2. Materials and Methods

2.1. Plant Material and Environments

Experiments were performed during the 2021–2022 growing season. Fifteen commercially available cultivars and one traditional landrace (shown in

Table 1. The present study used a list of genotypes (name, origin, genealogy, and release date).

Genotype	Name of Variety	Country of Origin	Genealogy	Year Released
G1	Limnos	Greece	Selection from the landrace Akbasak (Xynias et al., 2020 [6])	1932
G2	Canavaro	Italy	Coloseo/Simeto	2008
G3	Maestrale	Italy	Iride/Svevo	2004
G4	M. Aurelio	Italy	D95241/Arcobaleno/Svevo	(NA)
G5	Meridiano	Italy	Simeto/WB881/Duilio/F21	1999
G6	Mexicali-81	Greece	Selection from Mexicali 75	1981
G7	Monastir	France	Not Available (NA)	(NA)
G8	Simeto	Italy	Capeiti 8/Valnova	1988
G9	Svevo	Italy	Linea Cimmyt/Zenith	1996
G10	Vendeta	Italy	Creso/Ofanto)	2003
G11	Anna	Greece	Mexixali 85/Santa	2000
G12	Elpida	Greece	Sifnos/Mexicali-81	2010
G13	Zoi	Greece	Simeto/Mexicali-81	2011
G14	Thraki	Greece	Simeto/Mexicali-81	2014
G15	Pisti	Greece	ZEROUD 4/Selas	2008
G16	Sifnos	Greece	Lemnos/Mexicali 75	1991

) were evaluated in six environments (three low and three high-productivity environments;

Table 2. Details of the six environments used to study G × E interactions of durum wheat genotypes.

Environment	Location	Climate Type 1	Productivity 2	Fertilization 3	Planting Date
E1	Thermi	BSk	LP	Typical	Middle of November
E2	Thermi-Organic	BSk	LP	Organic	Middle of November
E3	Thermi-Late sowing	BSk	LP	Typical	End of January
E4	Thermi-Splitting N fertilization	BSk	HP	Splitting N in top-dressing application	Middle of November
E5	Nea Gonia	BSk	HP	Typical	Middle of November
E6	Sindos	BSk	HP	Typical	Middle of November

¹ Köppen-Geiger climate type BSk = cold semi-arid [28,29]; ² General classification as Low Productivity (LP) or High Productivity (HP); ³ Fertilization: In typical fertilization (total N amount of 180 kg ha⁻¹), one-third of which was applied (ammonium phosphate 20-10-0) before sowing and two-thirds (ammonium nitrate 33.5-0-0) was applied at full tillering (Zadok 29), in splitting N in top-dress application, one-third of which was applied (ammonium phosphate 20-10-0) before sowing, one-third (ammonium nitrate 33.5-0-0) was applied at full tillering (Zadok 29), and one-third was applied during formation of the first node (Zadok 31). All the other agronomic treatments were identically applied to all study plots.

). These genotypes were selected based on their popularity among growers, potential for high yields, and commercial value.

The six environments that were evaluated were:

• E1: Thermi-Typical fertilization/Typical date of sowing (middle of November). In the typical fertilization (total N amount of 150 kg ha⁻¹), one-third was applied before sowing (ammonium phosphate 20-10-0) and two-thirds at full tillering stage (Zadok 29) (ammonium nitrate 33.5-0-0). It is characterized as a low-productivity environment.
• E2: Thermi-organic field with no fertilization/Typical date of sowing (middle of November). It is characterized as a low-productivity environment.
• E3: Thermi-late sowing. Typical fertilization (described above) and late sowing i.e., end of January. It is characterized as a low-productivity environment.
• E4: Splitting N in top-dress application: splitting one-third (ammonium phosphate 20-10-0) before sowing, one-third (ammonium nitrate 33.5-0-0) at full tillering (Zadok 29), and one-third during the first node (Zadok 31).
• E5: Nea Gonia (Typical fertilization and date of sowing as described above).
• E6: Sindos (Typical fertilization and date of sowing as described above).

2.2. Experimental Design

Experimental trials were conducted in all environments using Randomized Complete Block Design (RCBD) of plot size, with four replicates. Each genotype was sowed in an area of 12 square meters (8 m × 1.5 m) comprising six rows with 0.25 m row-to-row distance. Details on soil/climatic data and agronomical practices, like the dates of sowing and fertilizer application, are presented in Table 2 and Table S1. Weedcide (6% pinoxaden and 1.55% clonquintocet-mexyl) was applied except for the biological field. The grain yield from each plot, reported in metric tonnes per hectare (t/ha), was standardized to a moisture content of 14%.

2.3. Statistical Analysis

Genotypes (G) and environments (E) were fixed treatment effects. Two-way ANOVA (

Table 3. Analysis of variance (ANOVA) between grain yields of sixteen genotypes of durum wheat in all environments (6), high-yielding environments (3), and low-yielding environments (3).

	All Environments				Low-Yielding Environments				High-Yielding Environments
Source	df	SS	MS	SS%	df	SS	MS	SS%	df	SS	MS	SS%
Environment	5	137.21	27.44 ***	41.7	2	29.12	14.56 ***	39.5	2	2.25	1.13 *	1.5
Genotype	15	50.28	3.35 ***	15.3	15	17.15	1.14 ***	23.3	15	69.71	4.65 ***	46.6
G × E	15	141.52	1.89 ***	43.0	15	27.43	0.91 ***	37.2	15	77.51	2.58 ***	51.9
Blocks	285	13.29	4.43 ***		141	5.05	1.68 ***		141	8.74	2.91 ***
Error	75	75.68	0.27		30	30.92	0.22		30	44.25	0.31
CV	16.57				18.09				15.41
H2	0.92				0.81				0.93

*, *** = significance at 0.05 and 0.001.

) was performed for all environments (6), high-yielding environments (3), and low-yielding environments (3). The mean performances of grain yield (ton/ha) for all the genotypes in the environments (3 for high-yielding and 3 for low-yielding) are presented in

Table 4. Effect of genotype on yield (ton.ha⁻¹) in six different environments (3 low-yielding environments, three high-yielding environments, and over fields).

Low-Productivity						High-Productivity						Over Fields
G	Ε1	G	E2	G	Ε3	G	Ε4	G	Ε5	G	Ε6	G	All E
8	4.31 a*	13	2.75 a	6	3.08 a	14	5.27 a	5	5.73 a	5	5.73 a	5	3.77 a
13	4.13 a	1	2.64 ab	3	2.71 b	2	4.56 b	4	4.62 b	6	5.24 a	6	3.61 ab
3	4.13 a	5	2.62 ab	5	2.67 bc	6	4.46 b	3	4.43 bc	12	4.30 ab	8	3.47 ab
15	3.95 ab	2	2.46 abc	7	2.61 bc	11	4.41 b	12	4.30 bc	10	4.01 ab	12	3.37 abc
9	3.30 bc	4	2.40 abcd	13	2.60 bc	16	4.31 b	6	4.10 bcd	4	3.96 ab	2	3.33 abcd
11	3.29 bcd	14	2.25 cde	9	2.60 bc	12	4.19 bc	2	4.06 bcd	8	3.86 bcd	13	3.28 bcd
12	3.20 cd	16	2.21 cde	8	2.56 bc	8	4.04 bc	7	4.00 bcd	13	3.65 bcd	4	3.22 bcde
2	3.17 cd	8	2.20 cde	2	2.51 bc	15	3.79 c	10	3.92 bcd	9	3.26 cde	14	3.21 bcde
6	3.06 cd	3	2.16 cde	14	2.46 bcd	7	3.23 d	8	3.86 bcd	2	3.19 cde	3	3.20 bcde
4	2.88 cde	11	2.04 def	15	2.43 bcd	5	3.11 de	13	3.78 bcde	14	3.19 cde	15	2.94 cde
7	2.75 cde	15	1.97 ef	4	2.39 bcd	4	3.07 def	14	3.59 cdef	15	3.07 de	7	2.93 cde
16	2.73 cde	7	1.95 ef	12	2.36 bcd	10	2.92 def	1	3.24 defg	3	3.06 de	10	2.86 de
5	2.73 cde	9	1.93 ef	10	2.30 cde	13	2.76 defg	9	2.89 efg	7	3.05 de	11	2.78 e
14	2.51 de	12	1.84 ef	11	2.11 de	3	2.70 efg	16	2.86 efg	16	2.49 ef	16	2.76 e
10	2.15 ef	10	1.84 ef	16	1.98 e	9	2.58 fg	11	2.70 fg	1	2.19 f	9	2.76 e
1	1.55 f	6	1.73 f	1	1.98 e	1	2.27 g	15	2.43 g	11	2.12 f	1	2.31 f

* Different letters in a column indicate significant differences between genotypes in each environment based on the Duncan test (p < 0.05).

. Two-way ANOVA (Table 3) was performed for all environments (6), high-yielding environments (3), and low-yielding environments (3), and the effects of the interaction between genotype and environment (6 for all, 3 for high-yielding, and 3 for low-yielding) on the yield are presented in Table 4. The Shapiro–Wilk test was used to check the normal distribution of variables, whereas Levene’s test was used to check the ANOVA assumptions for equality of the error variances and residual normality [30]. Differences between genotypes were identified using a post hoc Duncan test. To assess the contribution of each source of variation on the variability in yield, the ANOVA treatment sum of squares (SS) was partitioned into three components (SS of G, SS of E, and SS of G × E), and the broad sense heritability coefficient (H²) was calculated [31]. Spearman’s rank correlation coefficients were also computed and assessed for their significance at three probability levels: 0.001 (indicating a strong correlation), 0.01 (indicating a moderate correlation), and 0.05 (indicating a weak correlation). The above-mentioned statistical analyses were conducted using IBM’s SPSS package v. 23 (IBM Corp., Armonk, NY, USA, 2015).

2.3.1. Parametric

Wricke’s ecovalence, W_i²

Wricke [32] introduced the concept of ecovalence, which represents the contribution of each genotype to the sum of squares of genotype–environment (GE) interactions. The ecovalence (Wi) of the ith genotype is calculated by squaring its interaction with the environments and then summing this value across all environments.

Shukla’s stability variance, σ²_i

In 1972, Shukla [11] proposed the stability variance of genotype i, defined as its variance across environments after accounting for the main effects of environmental means. Based on this statistic, genotypes with the lowest values are the most stable.

Deviation from regression, S²_di

In addition to slope regression, the variance of deviations from the regression (S²_di) has been suggested as one of the most used parameters for selecting stable genotypes. Genotypes with S²_di = 0 would be the most stable, while S²_di > 0 would indicate lower stability across all environments [10].

Coefficient of variance, CV_i

Francis and Kannenberg (1987) [12] suggested the coefficient of variation, which serves as a stability statistic. It combines the coefficient of variation (CV), mean yield, and environmental variance (EV).

$${CV}_i=\left(\frac{\sqrt{{\delta }_i^2}}{\overline{x_i}}\right)\times 100$$

Kang’s rank-sum KR

Kang’s rank-sum method [33] employs yield and σ²_i (variance) as selection criteria. This parameter accords equal weight to yield and stability statistics in identifying high-yielding and stable genotypes. The genotype with the highest yield and the lowest σ²_i is assigned a rank of one. Next, the yield and stability variance ranks for each genotype are combined.

GE variance component θ_(i)

This statistic represents a modified measure of stability. It involves removing the ith genotype from the entire dataset and calculating the variance of the remaining subset’s genotype–environment interaction (GEI). This GEI variance serves as the stability index for the ith genotype. This statistic shows that genotypes with higher values are more stable.

AMMI’s stability value, ${ASV}_i$,

The AMMI models [14] were implemented using the GenStat (13th edition) statistical package [34], which is reliant on the first and second principal component (PC) scores—namely PC1 (representing the interaction of the first PCA) and PC2 (representing the interaction of the second PCA)—and calculated using the following equation, according to Purchase [13].

$${ASV}_i=\sqrt{\frac{SSPC1}{SSPC2}({PC1)}^2+({PC2)}^2}$$

where SS is the sum of squares. The genotype with the smallest ${ASV}_i$ value was regarded as the most stable.

Genotype superiority, $P_i$,

The calculation of genotype superiority [35], denoted as $P_i$, involved determining the mean square distance between the genotype and the maximum response:

$$P_i={\sum }_i{\left({\overline{y}}_{ij}-{max}_j\right)}^2/2e$$

Here, Y_max represents the maximum response observed among all genotypes in the given environment (j).

Y_ij: the yield of the ith cultivar grown in the jth location.

e: the number of locations.

The key takeaway is that the smallest $P_i$ value indicates the better genotype.

2.3.2. Non-Parametric Statistics

Nassar and Huhn’s statistics S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁶⁾

Huhn [36] and Nassar and Huhn [15] proposed 4 non-parametric statistics for assessing genotype stability:

• S⁽¹⁾: Represents the mean of the absolute rank differences of genotypes across all tested environments.
• S⁽²⁾: Represents the variance among the ranks across all tested environments.
• S⁽³⁾: Represents the sum of the absolute deviation for each genotype relative to the mean of ranks.
• S⁽⁶⁾: Represents the sum of squares of rank for each genotype relative to the mean of ranks.

$$S_i^{\left(1\right)}=2\sum _J^{m-1}\sum _{\overline{J}=J+1}^m\frac{\left|r_{ij}-r_{i_J^{-}}\right|}{m(m-1)}$$

$$S_i^{\left(2\right)}=\sum _{J=1}^m\frac{{(r_{ij}-\overline{r_{i.}})}^2}{(m-1)}$$

$$S_i^{\left(3\right)}=\sum _{J=1}^m\frac{{(r_{ij}-\overline{r_{i.}})}^2}{\overline{r_{i.}}}$$

$$six{S}_i^{\left(6\right)}=\sum _{J=1}^m\frac{\left|r_{ij}-\overline{r_{i.}}\right|}{\overline{r_{i.}}}$$

where J represents genotypes, M represents environments, r_ij denotes the ranking of the ith genotype in the jth environment, and $\overline{r_{i.}}$ represents the ith genotype rank across the environments.

The mean yield data must be transformed into ranks for each genotype and environment to calculate these stability measures. Genotypes with similar ranks across environments are deemed to be stable. A lower value for these statistics indicates higher stability for a particular genotype.

Thennarasu’s statistics NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾

These four stability statistics were introduced by Thennarasu [16]. They are derived from the ranks of adjusted means of genotypes in each environment. When these statistics give low values, it indicates high stability.

$${NP}^{\left(1\right)}=\frac{1}{N}\sum _{j=1}^n\left|r_{ij}^{\ast }-M_{di}^{\ast }\right|$$

$${NP}^{\left(2\right)}=\frac{1}{N}\left[\sum _{j=1}^n\left|r_{ij}^{\ast }-M_{di}^{\ast }\right|/M_{di}\right]$$

$${NP}^{\left(3\right)}=\frac{\sqrt{\frac{\sum {(r_{ij}^{\ast }-{\overline{r}}_{i.}^{\ast })}^2}{N}}}{ri.}$$

$${NP}^{\left(4\right)}=\frac{2}{N(N-1)}\left[\sum _{j=1}^{n-1}\sum _{\left[j’=j+1\right]}^n\left|r_{ij}^{\ast }-r_{ij’}^{\ast }\right|/\overline{r_i}\right]$$

where r_ij^* is the rank of the ith genotype in the jth environment based on adjusted data, $\overline{r_{ij}^{\ast }}$ and $M_{di}^{\ast }$ are mean and median ranks, respectively, for adjusted values, $\overline{r_i}$ and M_di are the mean and median ranks of the ith genotype in the jth environment, respectively. $\overline{r_i}$ and $M_{di}^{\ast }$ are the mean and median ranks obtained from the unadjusted data.

The GGE biplot model is based on singular value decomposition of the first two principal components:

$$y_{ij}-\mu -{\beta }_j={\lambda }_1{\xi }_{i1}{\eta }_{j1}+{\lambda }_2{\xi }_{i2}{\eta }_{j2}+{\epsilon }_{ij}$$

where $y_{ij}$ is the measured mean of genotype i in environment j, μ is the grand mean, ${\beta }_j$ is the main effect of environment j, ${\lambda }_1$ and ${\lambda }_2$ are the singular values for the first and second principal components (PC1 and PC2, respectively), ${\xi }_1$ and ${\xi }_2$ are eigenvectors of genotype i for PC1 and PC2, ${\eta }_1$ and ${\eta }_2$ are eigenvectors of environment j for PC1 and PC2, ${\epsilon }_{ij}$ is the residual associated with genotype i in environment j.

3. Results

3.1. Analysis of Variance and Mean Performance

Based on the ANOVA (Table 3) for all the environments, the grain yield of durum wheat was significantly affected by environment, genotype, and their interaction (p < 0.001 for all effects). Even the partial ANOVA of high-yielding environments (E4–E6) and low-yielding environments (E1–E3) indicated that both G, E, and their interaction significantly affected the yield. Another interesting finding from this analysis is that for all environments, the highest percentage of variation was explained by G × E (43%), followed by E (41.7%) and G (15.3%). However, in low-yielding environments, E, G × E, and G were more similar, explaining 39%, 37.2%, and 23.3% of the variation, respectively. In the case of high-yielding environments, the G × E interaction explained 51.9% of the variation, G explained 46.6%, and E explained 1.5%.

The studied durum wheat genotypes across the environments showed significant imprints of productivity (Table 4). Based on the results from all the examined environments, G5 had the highest grain yield (3.77 ton.ha⁻¹), followed by G6 (3.61 ton.ha⁻¹), G8 (3.47 ton.ha⁻¹), G12 (3.37 ton.ha⁻¹), and G2 (3.33 ton.ha⁻¹); however, there were no statistically significant differences between these best-performing genotypes. The genotypes with the lowest overall productivity were G1 (2.31 ton.ha⁻¹), G9 (2.76 ton.ha⁻¹), G16 (2.76 ton.ha⁻¹), and G11 (2.78 ton.ha⁻¹).

The results of the productivity of the genotypes in each environment are particularly interesting. In the case of E1 (low-yielding environment), G8 (4.31 ton.ha⁻¹), G13 (4.13 ton.ha⁻¹), and G3 (4.13 ton.ha⁻¹) had the highest yields, but with no significant differences between them, while genotypes G1 (1.55 ton.ha⁻¹) and G10 (2.15 ton.ha⁻¹) recorded the lowest yields. In E2, an organic low-yielding environment, G13 (2.75 ton.ha⁻¹), G1 (2.64 ton.ha⁻¹), G5 (2.62 ton.ha⁻¹), G2 (2.46 ton.ha⁻¹), and G4 (4.40 ton.ha⁻¹) gave the highest yields, with no statistically significant differences between them. The lowest grain yields were recorded for G6 (1.73 ton.ha⁻¹) and G10 (1.84 ton.ha⁻¹). In E3, a late-sowing low-yielding environment, G6 (3.08 ton.ha⁻¹) had the highest grain production, with significant differences compared with all the other genotypes, followed by G3 (2.71 ton.ha⁻¹) and G5 (2.67 ton.ha⁻¹). Genotypes G1 (1.98 ton.ha) and G16 (1.98 ton.ha⁻¹) recorded the lowest grain yields. In E4, a high-yielding environment, G14 had the highest grain production (5.27 ton.ha⁻¹), with statistically significant differences compared with all the other genotypes, i.e., G2 (4.56 ton.ha⁻¹), G6 (4.46 ton.ha⁻¹), G11 (4.41 ton.ha⁻¹), and G16 (4.31 ton.ha⁻¹), followed by a very good performance and no statistically significant differences between them. In this environment, the lowest grain yields were recorded for G1 (2.27 ton.ha⁻¹) and G9 (2.58 ton.ha⁻¹). In E5, a high-yielding environment, G5 had the highest grain production (5.73 ton.ha⁻¹), with statistically significant differences compared with all the other genotypes, i.e., G4 (4.62 ton.ha⁻¹), G3 (4.43 ton.ha⁻¹), G12 (4.30 ton.ha⁻¹), G6 (4.10 ton.ha⁻¹), and G2 (4.06 ton.ha⁻¹), which had no statistically significant differences between them, while genotype G15 (2.43 ton.ha⁻¹) had the lowest grain yield. Finally, in E6, a high-yielding environment, genotypes G5 (5.73 ton.ha⁻¹), G6 (5.24 ton.ha⁻¹), G12 (4.30 ton.ha⁻¹), G10 (4.01 ton.ha⁻¹), and G4 (3.96 ton.ha⁻¹) recorded high grain yields, while genotypes G11 (2.12 ton.ha⁻¹) and G1 (2.19 ton.ha⁻¹) had low grain yields.

3.2. AMMI and GGE Biplot Analyses

Based on AMMI data analysis, two mega-environments, including all six environments (three low-productivity and three high-productivity fields), were identified, as depicted in Figure 1. E1, E2, and E3 form the first mega-environment, representing the low-productivity environments, while E4, E5, and E6 form the second mega-environment, representing the high-productivity environments. Based on the position of the two environment subgroups, genotypes such as G1 and G9 are better suited to low-productivity environments, while genotypes such as G14, G6, G5, and G12 are better adapted to high-productivity environments.

Combined and partial GGE biplot analyses explained 73.06% (Figure 2), 92.42% (Figure 3), and 92.48% (Figure 4) of the total variability in all, low-, and high-productivity environments, respectively. Genotypes G6 and G12 were very close to the stability line in the combined analysis of all the environments.

The GGE biplot identified G8 as the best-performing genotype for the low-productivity fields, which also showed high stability. G3 and G15 followed, with inferior productivity and stability. As for the high-productivity environments, GGE biplot analysis indicated that G6 was the genotype closest to the ideal, as it had the best performance and high stability. This was followed by G12 and G5, with the latter showing comparatively low stability.

3.3. Prominent Genotypes Based on Multiple Stability Parameters

Several parametric and non-parametric indices were calculated to evaluate the genotypes based on their adoption in different environments.

Table 5. Genotypes were categorized into the top 5 and bottom five groups based on both mean yield and stability measures of genotype superiority using the GGE biplot. The classification considered parametric measures (ASVi, Pi, σ²_i, CVi, s²d_i, W_i², KR and θ_(i)) as well as non-parametric measures (S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁴⁾, NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾).

All Fields	Yield	GGE	ASVi	Pi	σ2i	CVi	s2di	Wi2	KR	θ(i)	S(¹)	S(2)	S(³)	S(⁶)	NP(¹)	NP(2)	NP(³)	NP(⁴)
Top 5	G5	G6	G713	G6	G7	G9	G12	G7	G8	G7	G8	G2	G2	G8	G2	G2	G2	G8
	G6	G12	G1	G5	G2	G13	G7	G2	G2	G2	G2	G8	G8	G2	G7	G8	G8	G2
	G818 *	G8	G8	G12	G8	G7	G9	G8	G12	G8	G7	G7	G7	G13	G8	G4	G7	G4
	G1210	G5	G12	G8	G12	G1	G2	G12	G4	G12	G4	G4	G4	G4	G9	G12	G13	G5
	G216	G2	G9	G2	G410	G8	G8	G4	G7	G4	G15	G15	G13	G7	G13	G13	G6	G13
Bottom 5	G107	G15	G15	G15	G67	G10	G15	G6	G15	G6	G13	G11	G6	G9	G11	G5	G15	G9
	G1118	G9	G16	G9	G1	G11	G3	G1	G14	G1	G5	G5	G16	G16	G16	G10	G10	G16
	G16	G16	G147	G16	G14	G6	G14	G14	G16	G14	G11	G3	G10	G10	G1	G11	G16	G10
	G9	G11	G11	G11	G11	G14	G11	G11	G11	G11	G3	G1	G11	G11	G3	G16	G11	G11
	G114	G1	G510	G1	G5	G5	G5	G5	G1	G5	G6	G6	G1	G1	G5	G1	G1	G1
High	Yield	GGE	ASVi	Pi	σ2i	CVi	s2di	Wi2	KR	θ(i)	S(¹)	S(2)	S(³)	S(⁶)	NP(¹)	NP(2)	NP(³)	NP(⁴)
Top 5	G5	G816	G5	G6	G12	G12	G12	G12	G12	G12	G6	G6	G6	G6	G12	G6	G12	G6
	G612	G3	G11	G5	G8	G8	G7	G8	G8	G8	G12	G12	G12	G12	G13	G8	G8	G12
	G1216	G15	G14	G12	G7	G9	G8	G7	G7	G7	G8	G8	G8	G8	G8	G2	G2	G8
	G14	G13	G16	G8	G9	G6	G1	G9	G6	G9	G1	G1	G2	G2	G7	G5	G6	G2
	G28	G11	G15	G4	G1	G7	G6	G1	G2	G1	G13	G13	G7	G13	G2	G12	G7	G5
Bottom 5	G1616	G14	G1	G16	G312	G3	G4	G3	G1	G3	G5	G16	G15	G15	G16	G16	G3	G3
	G158	G7	G9	G15	G16	G14	G16	G16	G3	G16	G16	G5	G14	G1	G6	G11	G16	G16
	G1116	G6	G7	G9	G14	G16	G14	G14	G15	G14	G14	G14	G16	G3	G3	G15	G15	G1
	G9	G10	G8	G11	G11	G5	G11	G11	G16	G11	G3	G3	G3	G16	G14	G9	G1	G15
	G1	G1	G12	G1	G57	G11	G5	G5	G11	G5	G11	G11	G11	G11	G11	G1	G11	G11
Low	Yield	GGE	ASVi	Pi	σ2i	CVi	s2di	Wi2	KR	θ(i)	S(¹)	S(2)	S(³)	S(⁶)	NP(¹)	NP(2)	NP(³)	NP(⁴)
Top 5	G6	G6	G2	G13	G2	G7	G7	G2	G2	G2	G2	G2	G2	G2	G9	G9	G9	G2
	G5	G12	G7	G3	G914	G9	G2	G9	G9	G9	G1	G1	G9	G9	G2	G13	G2	G9
	G87	G5	G12	G8	G14	G2	G9	G14	G13	G14	G16	G9	G1	G13	G16	G8	G13	G13
	G1310	G8	G16	G15	G7	G14	G12	G7	G4	G7	G9	G16	G13	G8	G4	G6	G4	G8
	G3	G217	G47	G2	G4	G16	G14	G4	G12	G4	G13	G13	G16	G6	G14	G2	G8	G6
Bottom 5	G7	G15	G514	G7	G10	G11	G11	G10	G5	G10	G7	G7	G3	G3	G1	G10	G5	G12
	G14	G16	G311	G14	G3	G3	G3	G3	G16	G3	G3	G11	G7	G5	G3	G11	G10	G5
	G1115	G9	G15	G16	G11	G6	G10	G11	G10	G11	G11	G3	G5	G7	G15	G14	G16	G7
	G16	G11	G8	G1014	G6	G10	G6	G6	G1	G6	G10	G10	G10	G10	G6	G16	G11	G10
	G1	G1	G1	G1	G5	G5	G5	G5	G11	G5	G5	G5	G11	G11	G5	G1	G1	G11

* Denotes the frequency of the genotype in the group with ≥7.

displays the genotypes found in each index’s first five and last five positions based on the ranking for each respective statistic. In the total evaluation of all environments, G8, G2, and G7 dominated the top 5 ranking, with frequencies of 18/18, 16/18, and 13/18, respectively, identifying them as the most stable genotypes. For the bottom five rankings, G11 and G1 had the worst positions, with frequencies of 18/18 and 14/18, respectively. In the subgroup with the three high-productivity environments, G12, G8, and G6 were the genotypes with the highest presence in the top 5 ranking, with frequencies of 16/18, 16/18, and 12/16, respectively. In the bottom five ranking, G16 and G11 had frequencies of 16/18 for all indices, followed by G3 with 12/18. For the low-productivity subgroup, G2, G9, and G13 were the genotypes with the highest presence in the top 5 rankings, with frequencies of 17/18, 14/18, and 10/18, respectively. Considering the bottom five ranking, genotypes G11 and G10 each had a high frequency of 15/18, followed by G5 (14/18) and G3 (11/18).

Table 6. Spearman’s rank correlation coefficients were computed between the statistics for mean yield, cultivar superiority on the GGE biplot, parametric measures (ASVi, Pi, σ²_i, CVi, s²d_i, W_i², KR and θ_(i)), and non-parametric measures (S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁴⁾, NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾).

	Yield	GGE	ASVi	Pi	σ2i	CVi	s2di	Wi2	KR	θ(i)	S(¹)	S(2)	S(³)	S(⁶)	NP(¹)	NP(2)	NP(³)
GGE	0.935 **
ASVi	−0.106	0.053
Pi	0.971 ***	0.974 ***	0.024
σ2i	0.082	0.215	0.744 ***	0.194
CVi	−0.350	−0.359	0.668 ***	−0.312	0.594 *
s2di	−0.029	0.156	0.862 ***	0.118	0.918 **	0.626 **
Wi2	0.082	0.215	0.744 ***	0.194	1.000 **	0.594 *	0.918 ***
KR	0.732 ***	0.760 ***	0.459	0.783 **	0.726 ***	0.180	0.593 *	0.726 ***
θ(i)	0.082	0.215	0.744 ***	0.194	1.000 **	0.594 *	0.918 **	1.000 **	0.726 ***
S(¹)	−0.109	−0.044	0.482	−0.085	0.697 ***	0.497	0.576 *	0.697 ***	0.375	0.697 ***
S(2)	−0.018	0.021	0.324	−0.009	0.726 ***	0.438	0.538 *	0.726 ***	0.453	0.726 ***	0.947 ***
S(³)	0.576 *	0.503 *	0.241	0.562 *	0.582 *	0.321	0.385	0.582 *	0.782 ***	0.582 *	0.600 *	0.688 ***
S(⁶)	0.694 ***	0.600 *	0.159	0.659 ***	0.479	0.244	0.271	0.479	0.792 ***	0.479	0.482	0.576 *	0.965 ***
NP(¹)	0.050	0.201	0.470	0.113	0.655 ***	0.461	0.618 *	0.655 ***	0.444	0.655 ***	0.621 **	0.673 ***	0.587 *	0.513 *
NP(2)	0.668 ***	0.691 ***	0.394	0.703 ***	0.679 ***	0.253	0.526 *	0.679 ***	0.910 ***	0.679 ***	0.376	0.479	0.821 ***	0.838 ***	0.550 *
NP(³)	0.691 ***	0.706 ***	0.374	0.732 ***	0.632 ***	0.244	0.471	0.632 ***	0.892 ***	0.632 ***	0.347	0.447	0.844 ***	0.876 ***	0.612 *	0.935 ***
NP(⁴)	0.812 ***	0.735 ***	0.188	0.812 ***	0.479	0.118	0.288	0.479	0.883 ***	0.479	0.338	0.426	0.897 ***	0.938 ***	0.336	0.865 ***	0.897 ***

*, **, and *** represent significance at the p < 0.05, p < 0.01, and p < 0.001 levels of probabilities, respectively.

displays the rank correlations between the statistical measures assessed across the six environments. The mean grain yield demonstrates a strong positive correlation with measures of GGE, $P_i$, KR, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾. Among the statistical measurements that were estimated, GGE biplot, $P_i$, and ${ASV}_i$ consider yield and G × E interaction for cultivar evaluation, while the remaining statistical measures mainly consider stability. GGE analysis, $P_i$, and KR were strongly and positively correlated, but had no correlation found with ${ASV}_i$. Moreover, strong positive correlations were recorded for: (1) σ²_i with all except S⁽³⁾ and S⁽⁶⁾, (2) CVi with s²d_i, (3) W_i² with all except S⁽³⁾, S⁽⁶⁾, and NP⁽⁴⁾, (4) θ_(i) with all except S⁽³⁾, S⁽⁶⁾, and NP⁽⁴⁾, (5) S⁽¹⁾ with S⁽²⁾ and NP⁽⁴⁾, (6) S⁽²⁾ with S⁽³⁾ and NP⁽¹⁾, (7) S⁽³⁾ with S⁽⁶⁾, NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾, (8) S⁽⁶⁾ with NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾, and (9) between NP⁽²⁾, NP⁽³⁾, and NP⁽⁴⁾.

4. Discussion

Durum wheat stands as the predominant cereal crop in the Mediterranean basin. Environmental and climatic elements, with notable sensitivity to water availability and elevated temperatures during the grain-filling period, can significantly affect the productivity of durum wheat [6,37,38]. Limited water availability and adverse conditions underline the significance of identifying the most productive and stable genotypes across different Mediterranean farming systems. Selection of the most appropriate crop variety stands out as the most cost-effective and efficient method for enhancing the productivity of a Mediterranean agricultural system. This strategic choice enables increased and more consistent production outcomes without a parallel rise in production costs, thereby maximizing resource utilization. In this multi-environment trial, durum wheat varieties such as Meridiano (G5), Mexicali-81 (G6), Simeto (G8), Elpida (G12), and Canavaro (G2) had mean grain yield productivities ranging from 3.33 to 3.77 ton.ha⁻¹ based on the total evaluation of all environments, an outcome that was more or less expected. In the case of low-productivity environments, Mexicali-81 (G6) and Simeto (G8) were high-performing genotypes, although the relatively new varieties Maestrale (G3) and Zoi (G13), released in 2004 and 2011, respectively, also had high productivity. With tonnage above 5.00 tonnes per hectare, Meridiano (G5), Mexicali-81 (G6), and Thraki (G14) had high grain yield production in the high-productivity environments.

In parallel, the adoption of organic farming is on the rise globally. The European Commission (EC) has set an ambitious goal of converting a minimum of 25% of the European Union’s agricultural land to organic farming by 2030 [39]. However, the challenge is that modern commercial varieties are better suited for high-input farming, leaving a gap in cultivars adaptable to organic or low-input agriculture. One potential solution to this issue in wheat farming could be found in landraces, as they have demonstrated remarkable adaptability and productivity in low-input farming systems or under organic conditions. In our research, the landrace Limnos G1 exhibited high productivity (2.64 ton.ha⁻¹) in the organic environment (E2) and ranked among the top three most productive cultivars; thus, proposing the cultivation of the Limnos landrace in organic farming systems could be a viable option. Furthermore, integrating landraces into agricultural systems has the added benefit of contributing to biodiversity expansion. This is particularly significant in wheat breeding, as landraces play a crucial role in reducing the genetic erosion of cultivated wheat. By broadening the genetic base in breeding programs, landraces provide essential traits that help crops cope with the challenges of changing environments, such as those influenced by climate change [40,41]. In the context of an autogamous crop like wheat, landraces represent a mix of pure lines. This characteristic can lead to the development of genetically stable genotypes or commercial cultivars relatively quickly, making them suitable for organic or low-input environments. Numerous successful examples exist where intensive breeding programs incorporating effective plant selection under ultra-low plant densities have harnessed the variations in landraces/cultivars in wheat [42,43] and other autogamous species [44,45].

The timing of sowing stands out as a critical factor influencing cereal production and quality, as emphasized by McLeod et al. [46]. The ideal sowing period typically falls around November in the Mediterranean Basin countries. Despite this, farmers frequently face challenges, leading to delayed sowing attributed to excessively wet or dry soil conditions. Notably, delaying sowing beyond the optimal agronomically recommended period often decreases yield [46] since it exposes durum wheat crops to potentially adverse conditions such as high air temperature and deficient soil moisture during the critical period of anthesis and grain filling. This situation becomes particularly relevant when considering climate change projections, as Porter and Gawith hypothesized [47]; therefore, evaluating cultivars in late-sowing environments becomes crucial. In Environment 3 (E3), characterized by late-sowing and low yields, Mexicali-81 (G6) exhibited the highest grain production, showing significant differences from all the other genotypes. Following closely were Meridiano (G5) and Maestrale (G3), with Meridiano (G5) also demonstrating notable performance.

Based on Simmonds [48], genotypes contributed 25–40%, the environment contributed 30–60%, and G × E interaction contributed the remaining 15–25%; thus, multi-environment field evaluation is a necessary practice that has been followed for the selection of high-yield best-adapted genotypes for many types of cereals cultivated for their grains, including durum wheat [49] and other species such as barley [50] where kernel yield is significant. Moreover, considering the effect of G × E on wheat yield per ha increase [48], the study of this parameter is of high importance since genotype rank changes from one environment to another. However, the concept of crossover interaction hampers the discovery of superior genotypes [51]. Several statistical tools have been proposed for G × E interpretation and the study of crop stability. However, a complete visual evaluation of all G × E interaction aspects is possible with the GGE biplot model [7,52].

This study aimed to compare the utilization of the GGE biplot alongside fifteen parametric and non-parametric stability models in assessing durum wheat genotypes. Another target of the study was the identification of the most productive and stable genotypes across different Mediterranean farming systems, including biological and low/high productivity farming systems. The statistical metrics mentioned above can be categorized into two main groups. The first set considers the inherent genetic characteristics (G) and the interactions between genotypes and their respective environments (GE). Consequently, they offer a comprehensive assessment considering yield performance and genotype stability under varying conditions. Conversely, the second set of statistics emphasizes genotype–environment interactions (GE) while primarily focusing on stability as a critical factor in genotype evaluation. A supplementary but essential parameter for evaluating a genotype is its stability under different environmental conditions. The stability statistics indicated that, in general, in the overall evaluation, Simeto (G8), Canavaro (G2), Monastir (G7), and M. Aurelio (G4) were the most stable genotypes across different environmental conditions, while Anna (G11) and Limnos (G1) had significant fluctuations in their performance across the different environments. In the low-performing environments, Canavaro (G2) and Svevo (G9) were the most stable genotypes, while Anna (G11) and Vendeta (G10) had the lowest stability. In the high-yielding environments, Elpida (G12), Simeto (G8), and Mexicali-81 (G6) showed the highest stability. On the contrary, Sifnos (G16) and Anna (G11) were exceptionally unstable. Adapting specific genotypes to high- and low-yielding environments is a valuable tool for evaluating varieties [53]. Selection of the most suitable genotype and fertilizer application is now considered a precision farming component [54].

All statistical tools that consider both G and G × E, including GGE biplot, P_i, and KR, were strongly correlated with each other and yield, apart from ${ASV}_i$. Similar results in other environmental evaluations of wheat were found by Smutna et al. [53]. These findings align with prior research indicating that these tools contribute to the agronomic concept of stability [18,55]. ${ASV}_i$ was positively correlated with σ²_i, CVi, s²d_i, W_i², and θ_(i), parameters that do not correlate with yield, thus demoting the highest yielding genotypes. Similar results were found by other wheat researchers [53]. Moreover, the Si, Npi, and CVi statistics are influenced by both yield and stability. Consequently, they promote genotypes that display typical stability and may not excel under optimal conditions [18,56].

Extensive crossover genotype–environment (GE) interactions imply that the current genotype evaluation procedures may not accurately reflect the prevailing conditions. However, utilizing the GGE biplot has proven efficacious in facilitating the straightforward identification of superior genotypes. The GGE biplot model comprehensively considers both genotypic (G) and genotype–environment (GE) interactions, presenting a graphical representation that serves as a valuable methodology for assessing the adaptability and stability of genotypes across diverse environments. This approach offers a visual and integrated evaluation of performance and stability [7,20,24,52].

GGE biplot analysis of all environments, which evaluated both average yield performance and stability, indicated that Mexicali-81 (G6) and Elpida (G12) are the varieties closest to the ideal genotype. Simeto (G8) and Maestrale (G3) had good performance in the low-yielding environments, while Mexicali-81 (G6) and Elpida (G12) were the varieties closest to the ideal genotype in the high-yielding environments, validating the results of the evaluation of all the environments. GGE biplot analysis has been used to evaluate genotypes in wheat, with successful results [57] and adoption to heat stress and irrigation environments [58]. Considering both performance and stability through visual analysis constitutes a significant advantage, instilling confidence in the agronomists in the decision-making process of promoting superior genotypes. Consequently, this approach aids breeders in identifying genotypes that merit release for cultivation.

Evaluation of all environments for a combination of high yield and stable production showed that the best genotypes were G8 (Simeto), G2 (Canavaro), and G12 (Elpida). Similarly, G12 (Elpida), G8 (Simeto), and G6 (Mexicali-81) were the best genotypes in the subgroup with the three high-productivity environments, followed by G2 (Canavaro), while G2 (Canavaro), G13 (Zoi), and G8 (Simeto) were the best genotypes in the low-productivity environment subgroup.

5. Conclusions

Evaluation of sixteen durum wheat genotypes in six multi-environmental trials confirmed that a detailed study of the Genotype × Environment interaction could increase the yield of durum wheat farming systems. The stability models used in the study resulted in a diverse ranking of genotypes. A very interesting result was that many statistical tools that consider both G and G × E, such as GGE biplot, P_i, and KR, showed strong correlations with each other and with yield. This aided in the identification of the most suitable genotypes. This statistical set centered on the agronomic aspect of stability. On the other hand, statistical parameters focusing on the statical aspect of stability, such as ${ASV}_i$, σ²_i, Cvi, s²d_i, W_i², KR, and θ_(i), downplayed the significance of the highest yielding genotypes. Ultimately, the GGE biplot proved to be a highly effective statistical tool for assessing the worth of wheat genotypes in terms of both specific and general adaptation. It achieved this while preserving yield capacity and providing graphical representations. An interesting outcome emerged regarding the landrace Lemnos, suggesting its potential use in organic agriculture. Finally, our study revealed that some commercial cultivars exhibited better performance under late-sowing conditions, indicating their tolerance of more adverse conditions during the critical grain filling period, such as higher air temperature and deficient soil moisture, i.e., conditions correlated with climate change.