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Article

Some Agronomic Properties of Winter Wheat Genotypes Grown at Different Locations in Croatia

by
Valentina Spanic
1,*,
Goran Jukic
2,
Marina Zoric
2 and
Ivan Varnica
2
1
Agricultural Institute Osijek, Juzno Predgradje 17, 31000 Osijek, Croatia
2
Croatian Agency for Agriculture and Food, Vinkovacka Cesta 63c, 31000 Osijek, Croatia
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(1), 4; https://doi.org/10.3390/agriculture14010004
Submission received: 3 November 2023 / Revised: 1 December 2023 / Accepted: 18 December 2023 / Published: 19 December 2023
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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Abstract

:
A collection of fourteen winter wheat accessions was evaluated to describe agro-morphological traits over a two-year study at four locations. Changes in grain yield, test weight, and plant height were related to differences in growing seasons, locations, genotypes, and their interactions. Thus, some genotypes are suitable for one location but not for another. However, a PCA showed that genotypes 2, 26, 28, 32, and 31 were the most stable across environments. In the 2022/2023 season, a negative relationship was observed between septoria leaf blotch, septoria nodorum blotch, yellow rust, fusarium head blight, and grain yield. Grain yield decreased by 84.8, 72.3, 37.4, and 4.3% in Kutjevo, Osijek, Tovarnik, and Zagreb, respectively, compared to the 2021/2022 season. Additionally, in the 2022/2023 season, barley yellow dwarf virus weakened wheat plants, especially at the locations Kutjevo, Tovarnik, and Osijek, where a relationship was observed with septoria leaf blotch and powdery mildew. At the Zagreb location, wheat genotypes were planted at the latest sowing date, probably escaping the virus pressure after plants easily tolerated diseases, resulting in a significantly higher mean grain yield.

1. Introduction

Bread wheat (Triticum aestivum L.) is the third most important cereal after maize and rice, with a global production of 7.8 × 108 tons in 2023 [1]. Also, wheat alone supplies a fifth of global food calories and protein [2]. Climate change and population growth will influence food security and the self-sufficiency of food production at the global level, which will result in a decline in wheat supply capacity [3]. It is estimated that an increase in food production of 60% is required as the world’s population will reach more than 9 billion people [4]. Already in 2020, one-third of people were under insufficient nutrition [5], mostly coming from Asia and Sub-Saharan Africa. Further, unpredictable climate changes resulting in decreased crop yields are highly involved in food security questions in the future. This is also a challenge for human food security, where some models predict that it is expected to have 175 million more undernourished individuals by 2080 [6]. Therefore, continued enhancement of wheat productivity is needed to ensure global food security. According to Nutrition and Food Systems [7], the required increase in food production should be reached through increased grain yields and land-use intensities rather than from area expansion. Globally, wheat grain yield accounts for 3.5 t ha−1, whereas higher grain yields are obtained in East Asia and the European Union (4.3–5.3 t ha−1) [8]. For all the dominant crops in western and southern Europe, grain yield decreased from 6.3 to 21.2% due to climate change, which is partially explained by the stagnation of grain yields [9]. To a lesser extent, grain yield losses were observed in eastern and northern Europe for wheat by 2.1%. In that context, Croatia is an important region for the growth of winter wheat, located in the southeast of the European continent, with a cultivation area of approximately 161,000 hectares in the year 2022 [10]. There is more and more evidence that Croatia is under the influence of climate change, and its vulnerability to climate change is assessed as high [11]. In that manner, a climate change adaptation strategy was developed in synergy with the Sustainable Development Strategy of the Republic of Croatia and with relevant sectoral strategies [12]. For example, it has been reported that Croatian wheat production has decreased by 4.2% year over year since 1997 [13].
Globally speaking, climate change has a significant influence on wheat productivity through water deficits, the appearance of extreme events like floods and severe storms, drought and heat stress, and increased biotic stress with the more intense distribution of pests and diseases. For example, even small changes in mean annual precipitation can impact grain productivity [14]. Thus, extreme weather events, such as high rainfall, flooding, and drought, might affect wheat quality due to reduced soil fertility as a result of changes in the composition of mineral elements in the soil [15]. It was concluded that drought is one of the most important risks to food production compared to other abiotic stresses [16], as drought has become more frequent, longer, and severe in the last few years. Furthermore, alterations in temperature and precipitation may result in variations related to wheat pathogens in the context of geographical distribution, seasonal phenology, and population dynamics [17]. Different environmental conditions influence plants’ resistance genes to specific diseases and affect disease resistance in a positive or negative way [18]. Undoubtedly, taking into account biotic and abiotic stresses under diverse climatic conditions, they affect growth, development, and final crop production [19]. Plant height is also an important trait related to plant architecture and grain yield potential. It is known that weather conditions affect the timing and intensity of stem elongation. For example, Kronenber et al. [20] reported that temperature response was highly heritable and positively related to stem elongation as well as final plant height. On the other hand, severe soil water stress can lead to a reduction in wheat plant height [21].
The quantitative traits of wheat plants are influenced to a different extent and in different ways by environmental conditions [22]. Further, the selection of those traits is relatively difficult as many traits such as grain yield, flowering time, and canopy architecture are quantitatively inherited, displaying continuous variation due to the contribution of multiple genes with small effects called quantitative trait loci (QTL) [23]. The rate of phenological development is under the impact of environment and genetic backgrounds, thus allowing wheat genotypes to achieve optimum growth and development in the environment in which they were selected [24]. Significant grain yield differences among genotypes (Gs) and among environments (Es) have been observed, and their interaction is often found to contribute a great proportion to the total grain yield variation that could enhance breeding of wheat genotypes that are resistant/tolerant to stress [24]. Usually, environmental effects are a major source of variation, which capture approximately 72.2% of the total variation, whereas genotype and GE interactions explain 6.9 and 18.3%, respectively [25]. Many similar findings were also documented in other GE studies on wheat genotypes. Also, grain quality is a complex trait that depends on different other traits, where the contribution of each trait further depends on environmental conditions [26]. To cope with climatic changes, it is necessary to work on genetic improvement and agronomical management adaptation to increase wheat productivity [27].
Assessment of different traits in winter wheat genotypes across diverse locations in Croatia and growing seasons is needed in order to find stable wheat genotypes that could be recommended for release as new wheat varieties. The overall goal is to (i) demonstrate how differently wheat genotypes respond to eight different environmental conditions (2 years × four locations) and (ii) explore the significance of G × E interactions for traits that are primarily influenced by environmental factors.

2. Materials and Methods

2.1. Experimental Layout and Materials

A total of 11 recently developed wheat genotypes were registered with the Commission of Plant Variety Recognition in Croatia, and three local checks (codes 1, 2, and 3) were planted to compare differences in agro-morphological traits. The eleven wheat genotypes originated from different private companies and public institutions that perform winter wheat breeding. In further text, codes will be used due to the different origins of the wheat genotypes. Fourteen winter wheat genotypes were sown in field experiments in October 2021 and 2022 using a sowing machine (Wintersteiger Toolcarier 2700, Wintersteiger Seedmech GmbH, Ried im Innkreis, Austria). Field experiments were conducted at the experimental stations of the Croatian Agency for Agriculture and Food in Osijek, Agro-Tovarnik d.o.o. in Tovarnik, Kutjevo d.d. in Kutjevo, and at the Faculty of Agriculture (Šašinovec location) in Zagreb (Table 1). At the site in Osijek, maize was the pre-crop in the first year of investigation, and it was soybean in the second. At the sites in Kutjevo and Zagreb, the pre-crop was soybean for each year of testing. At the site in Tovarnik, in the first year of testing, the pre-crop was soybean, and it was sunflower in the second year.
To control seedborne diseases, the seeds were treated with sedaxane 25 g L−1 and fludioxonil 25 g L−1 at a rate of 2 mL per kg. The sowing dates at the locations Osijek, Tovarnik, Zagreb, and Kutjevo were on 19 October, 25 October, 28 October, and 29 October, respectively, in 2021, and on 13 October, 18 October, 28 October, and 20 October, respectively, in 2022.
The field experiments were laid out in a randomized complete block design with four replications. The experimental field plot consisted of 10 rows, 8 m long, with a 12.5 cm inter-row spacing. The annual precipitation and average temperatures during the growing seasons of 2021/2022 and 2022/2023 at each location are shown in Table 1, while exact information about rainfall and temperatures for each month at each location can be found in Table S1. Fertilizer application and other crop management practices were performed as per recommendations for each test location. During the vegetative season, insecticides and herbicides were applied as needed for weed and aphid protection in the field experiments. The total amount of applied nitrogen (N), phosphorus (P), and potassium (K) in 2021/2022 was 137:80:120, 132:80:120, 123:80:120, and 134:90:135 kg ha−1 at the Osijek, Tovarnik, Kutjevo, and Zagreb locations, respectively. At the same locations, in 2022/2023, NPK fertilization was as follows: 136:90:135, 132:80:120, 128:80:120, and 123:80:120 kg ha−1, respectively. The harvest was performed in all locations and growing seasons in the first part of July.

2.2. Disease and Virus Assessment

Each disease and virus attack was screened through two replications at each location. The barley yellow dwarf virus was screened in the early tillering stage. Septoria leaf blotch and powdery mildew were evaluated in the stem elongation stage (0–100%), while yellow rust was evaluated in the late stem elongation stage (0–100%). The percentage of fusarium head blight-bleached spikelets and septoria nodorum blotch per plot was estimated according to a linear scale (0–100%) 22 days after flowering. The mean value of each disease assessment at the four locations separately was used for principal component (PC) purposes. All diseases and viruses were scored according to the United Kingdom Variety List Trials: Trial Procedures for Official Examination of Value for Cultivation and Use (VCU) Harvest 2022 Cereals—Wheat, Barley, Oats, Triticale, Rye, and Spelt Wheat (https://assets.publishing.service.gov.uk/media/62972586e90e070397a00fc2/vcu-procedures-cereal-22.pdf, accessed on 2 November 2023).

2.3. Agro-Morphological Traits

The heading date was the main parameter to divide wheat genotypes into three maturity groups (7–9 May = 1st maturity group, 10–12 May = 2nd maturity group, and 13–15 May = 3rd maturity group) (Table S2). Plant height was measured from the base of the plant to the end of the spike (cm) prior to harvest. After adjusting for moisture content at 14%, grain yield (t ha−1) and test weight (kg hL−1) were calculated.

2.4. Statistical Analysis

Data on grain yield, test weight, and plant height for fourteen winter wheat genotypes from four locations and two growing seasons were used to combine analyses of variance (ANOVA) to determine the effects of year, location, and genotype and their interactions utilizing the Statistica statistical software package (Statistica 14.0). Those three traits were expressed as the average value of four replications at each location in both years. Other traits, such as disease and virus pressure and maturity group, were taken as an average of two replications at each location and growing season for a principal component analysis (PCA). The PCA was plotted using https://www.bioinformatics.com.cn/en, (accessed on 2 November 2023), a free online platform for data analysis and visualization.

3. Results

The ANOVA results for the recorded agro-morphological traits are summarized in Table 2. Significant differences due to all sources of variation were observed for grain yield, test weight, and plant height. Further, all interactions were highly significant.
By combining two growing seasons and four locations, the PCA biplot showed that 54.5% of the total variability for grain yield was explained by the first principal component (PC1) and 18.8% by the second principal component (PC2) (Figure 1). The first two principal components (PCs) together explained 73.3% of the total variability. As can be seen from the biplot, locations in the 2022/2023 growing season were grouped on the upper right side, while locations in 2021/2022 were grouped on the lower right side of the PCA plot. At the same time, the positioning of the genotypes showed that genotype 29 was grouped near location vectors in 2022/2023 (Osijek, Tovarnik, and Kutjevo) and genotype 30 near Zagreb, which could be classified as high yielding in most of the locations. Genotype 21 was shifted toward the location vectors Kutjevo and Osijek in 2021/2022. Furthermore, genotype 34 was near Zagreb in 2021/2022 and was positioned further to the lower right side of the PCA plot. Accordingly, the highest contribution to factor 1 was from genotypes 3, 1, 30, 29, and 33 (Figure 1; Table S3). The very high contribution to factor 2 was from genotypes 35, 1, 27, 34, and 3. The lowest contribution to both factors 1 and 2 was from genotypes 2, 26, 28, 32, and 31 (Figure 1; Table S3).
In both growing seasons, a significant effect of the location was observed (Table 3). All wheat genotypes had higher yields and larger test weights in 2021/2022 compared to 2022/2023. On the other hand, plant height was lower in 2021/2022 compared to the growing season of 2022/2023.
The average grain yield in Osijek and Kutjevo was at the same significance level in 2021/2022, which was significantly higher than the grain yield in Zagreb and Tovarnik (Table 3). The average test weight was significantly higher in Tovarnik, followed by the test weight in Zagreb, while in Osijek and Kutjevo, the test weight was at the same significance level. On the other hand, the average plant height was significantly lower in Tovarnik compared to the other three locations. In 2022/2023, the highest average grain yield was obtained in Zagreb, while the lowest grain yield was recorded in Tovarnik and Kutjevo (Table 3). However, the average test weight was the lowest at the location in Zagreb, as was the plant height, which had the same significance as the plant height in Tovarnik. As shown in Figure 2A,B, there seemed to be significant differences in the grain yield of each wheat genotype between the four locations, mostly reaching a significance level of α = 0.05, indicating that there existed a true yield difference. Overall, the average grain yield of the fourteen genotypes in the two years was 7.96 t ha−1, of which the average yield in the whole test in 2021/2022 was 9.47 t ha−1, and the average yield in 2022/2023 was 6.46 t ha−1.

3.1. Grain Yield Performance of Wheat Genotypes in 2021/2022

Genotype 2 performed best at one location (Zagreb) in 2021/2022, which was below the mean grain yield of all locations together for that genotype (Figure 2A). Almost all genotypes had grain yields at the same significance level for Tovarnik and Zagreb, except genotype 35, which showed a significantly decreased grain yield in Zagreb compared to Tovarnik. Further, genotypes 29, 1, 25, 31, 35, 30, 34, and 27 had significantly increased grain yields in Kutjevo compared to Zagreb. Genotypes 33 and 28 had significantly higher grain yields in Osijek compared to Kutjevo, while other genotypes remained at the same level at those two locations. All genotypes had grain yields in Tovarnik and Zagreb below the mean grain yield of all locations, while in Kutjevo, only genotype 3 had a lower grain yield than the mean of all locations. Considering the location in Osijek, all genotypes out yielded the mean grain yield of the four locations.

3.2. Grain Yield Performance of Wheat Genotypes in 2022/2023

Genotype 1 performed similarly for grain yield at all locations, with the lowest mean grain yield among the fourteen genotypes in 2022/2023 (Figure 2B). The grain yield difference between the four locations was significant (p < 0.05) for this genotype, where the grain yield in Kutjevo was the lowest, followed by Osijek and Tovarnik having grain yields of the same significance. Significantly, the highest grain yield for genotype 1 was obtained in Zagreb. That was the only location where grain yield was above the mean grain yield obtained at the four locations. The yields of genotypes 3, 33, 25, 27, and 26 were at the same significance level at three locations (Kutjevo, Tovarnik, and Osijek), which was below the mean grain yield at the four locations for specific genotypes with values of 4.9 t ha−1, 5.7 t ha−1, 5.8 t ha−1, 6.6 t ha−1, and 6.8 t ha−1, respectively. The yields of genotypes 34, 32, 2, 28, 31, and 30 were significantly the same in Kutjevo and Tovarnik and significantly lower in Zagreb. Only genotype 32 significantly decreased the grain yield in Osijek compared to Tovarnik, while genotypes 2 and 28 significantly increased it. The best yielding genotype 29 on average for the four locations (8.0 t ha−1) had grain yield at the same significance level at three locations (Kutjevo, Osijek, and Zagreb), while a significant decrease was obtained in Tovarnik. All genotypes had grain yields in Zagreb above average, while in Osijek, only genotypes 2, 31, 35, 30, and 29 showed grain yields higher than the mean of all locations for a specific genotype. In Tovarnik, only genotype 32 had grain yields above the mean of all locations for a specific genotype, while in Zagreb, it was genotype 29.

3.3. Relationship Analysis between Investigated Traits and Locations in Two Growing Seasons Separately

In the growing season of 2021/2022 in Osijek, the grain yield showed a significant negative correlation with plant height, which was also evident from the PCA, where grain yield and plant height were on opposite sides (Figure 3A). Test weights at all locations significantly positively correlated with each other, although the vector of test weight in the PCA was closely correlated with genotypes in Tovarnik (Figure 3A). Also, plant height and maturity groups had vectors on opposite sides of the PCA. At all four locations, grain yields were positively correlated with each other in the growing season of 2022/2023, as most values of the groups overlapped, but the closest vector of grain yield was with genotypes at the location in Zagreb (Figure 3B). Septoria leaf blotch, septoria nodorum blotch, yellow rust, and fusarium head blight in the PCA biplot were positively correlated, having a negative correlation with grain yield (Figure 3B). Further, test weight and plant height were positively correlated and closely associated with genotypes from Osijek. Also, powdery mildew and barley yellow dwarf virus were in a positive relationship.

4. Discussion

The main priority in bread wheat breeding is the creation of high-yielding varieties with improved grain quality. Winter wheat should be primarily enhanced for agro-morphological traits such as grain yield, heading date, plant height, and resistance to biotic stress [28]. A total of 11 recently developed wheat genotypes were registered with the Commission of Plant Variety Recognition in Croatia, and together with three local checks were evaluated to compare differences in agro-morphological traits. The eight trials (2 years × four locations) did not suffer winter damage and were retained for the measurement of grain yield, test weight, plant height, maturity group, disease pressure from yellow rust, septoria leaf blotch and septoria nodorum blotch, powdery mildew, and fusarium head blight, and attack from barley yellow dwarf virus. Most of those traits are yield-related, as wheat yield is a complex, polygenic trait and the result of yield-related components such as plant height, number of productive tillers, number of grains per spike, grain weight per spike, thousand-kernel weight, etc. [29]. It is very often and common that wheat breeders perform a selection of yield-related traits such as kernel weight, plant height, and other related traits [30]. In the current research, the evaluation of two-year field data from a winter wheat collection indicated a wide range of phenotypic variations in the western and eastern regions of the country. According to the highly significant differences among genotypes, it is obvious that high genetic variation among genotypes was observed. The introduction of new high-yielding wheat varieties and the intensification of high year-to-year or location variability demands additional information about environmental effects for future-released wheat varieties in Croatia. It was observed that most of the mean squares were assigned to environmental effects (year and location), with the highest effect of the year, indicating that weather conditions were diverse in the two investigated vegetative seasons, causing most of the variation in grain yield and related traits. Similar observations were obtained from the research of Bocianowski et al. [31], who reported that environmental effects were most significant on the grain yield of wheat hybrids.
In the current research, however, test weight and plant height were more affected by genotypes and environments (years and locations) and their interactions than grain yield. Test weight is an important physical characteristic of grain as an indicator of potential processing quality. It was previously reported that test weight varied among wheat genotypes under similar growing conditions because of G × E interactions [32]. In the current research, the interaction of year × location × genotype (Y × L × G) was highly significant for the tested traits (grain yield, test weight, and plant height), indicating different performances of the wheat genotypes at the locations in each year of the experiment and referring to differences in the climatic conditions of each year and soil properties at each location. The significant interaction between genotypes and environments suggested that genotypes reacted differently to changes in the environment [33]. Nevertheless, it is challenging to perform a selection of superior genotypes with high grain yield due to GE interaction, which is defined as the variation in relative performance of genotypes in different environments [34]. Quantitative trait loci (QTL) mapping analyses suggested that the length of the third, fourth, and fifth internodes, which are dependent on growth stages, had a high genetic association with plant height [35]. Further, each growth stage period of wheat varies somewhat, depending on the environment.

4.1. PCA of Fourteen Genotypes for Grain Yield in Four Locations and Two Growing Seasons

Further, in each growing season separately, given the average grain yield of different genotypes, the fourteen genotypes performed differently at different locations. It is important to stress that, in the case of an effect of the GE interaction, the selection of genotypes based on the mean grain yield is inadequate [36]. A principal component analysis (PCA) can fully explore multi-environment trials by partitioning G + GE into principal components through the singular value decomposition of environmentally centered yield data [37]. The combined PCA for the two investigated seasons indicated considerable genotypic variation for grain yield, implying that there is a high potential for grouping among the studied locations. The PCA biplot showed the relationship between the different locations, years, and genotypes with respect to the first two principal components. Genotypes at specific locations excelling in grain yield were plotted closer to the vector of growing season, like the proximity of all vectors with locations of the 2022/2023 growing season, plotted on the positive side of the biplot. The vector of the 2021/2022 growing season was on the lower side of the biplot. Along with this grouping of locations in a specific growing season comes the fact that the mean grain yield of all varieties in the 2021/2022 growing season was higher than the one in 2022/2023.
To organize the locations into first- and second-order predictors based on genotype contribution to the variation in grain yield, they corresponded differently at the four locations. The best wheat genotype for high grain yield or for any other desirable trait needs to express genetic potential with a low value of variance in different environments or years. Genotypes 2, 26, 28, 32, and 31 contributed the lowest values to the correlations. It was reported that the stability of genotypes is an important indicator in light of climate change in the Mediterranean region [38]. Thus, except for the selection of high-yielding genotypes, the goal is to identify wheat genotypes showing stability under different environmental conditions. In that manner, the interaction of genotype with the environment should be calculated [37]. Genotypes should possess stability in different environments that can be evaluated by this interaction of genotype × environment before the release of new wheat varieties. Also, the interaction between genotype × environment × management (G × E × M) regulates wheat plasticity and attainable grain yield [39]. Nevertheless, the economic value of common wheat is determined by grain yield, crop quality, and yield stability [29]. In the current research, it is difficult to identify stable genotypes that would fit all locations and growing seasons. Thus, it would be practically easier to select a few genotypes that are specifically targeted at two to three locations.

4.2. Results of Grain Yield in the 2021/2022 Growing Season

The mean grain yields in Kutjevo and Osijek were significantly higher than those in Zagreb and Tovarnik; there was no significant grain yield difference between Zagreb and Tovarnik. In the first growing season, the grain yield of 12 genotypes was at the same significance level in Kutjevo and Osijek, while 13 genotypes had the same significance level for grain yield in Zagreb and Tovarnik, five of them in Tovarnik and Kutjevo and one in Tovarnik and Osijek.
From October to December 2021, the amount of precipitation at all locations increased, but from January to March, a rainfall deficit was recorded, thus declaring this period as dry. This period coincided with the growth stage of tillering, which is very important in determining both the tiller number and the development of reproductive primordia (spikes, spikelets, and florets) [40,41]. In the current research, the accumulation of water in the soil in previous months was satisfactory for plants to use in the stage of tillering. Several studies have shown that the extreme depletion of soil moisture and plant carbohydrate reserves under drought conditions leads to a major challenge for the yield and quality of any crop, which is water supply throughout the growing period [42]. Drought not only reduces grain yield but also changes the grain protein content [43], especially during the anthesis stage. Thus, in the current research of the first growing season, drought was not pronounced before and during the reproductive period (April–June). According to previous research by Wan et al. [44], drought had significant negative effects on the grain yield of winter wheat, especially during the flowering and grain filling stages. Temperature and rainfall patterns were more or less similar at all locations over the investigated months, thus proving that soil properties under optimal precipitation and temperature may have the most important influence on final grain yield.

4.3. Results of Grain Yield in the 2022/2023 Growing Season

In the growing season of 2022/2023, the mean grain yield of all genotypes in Zagreb was significantly higher than that in Osijek, Tovarnik, and Kutjevo. Only five genotypes had grain yields at the same significance level at three locations (Kutjevo, Osijek, and Tovarnik), twelve in Osijek and Tovarnik, four in Zagreb and Osijek, and one in Zagreb and Kutjevo (genotype 29).
Besides abiotic stresses, biotic stress can damage wheat yields very much, even up to 100% in the case of severe disease attacks [45]. Under unfavorable climatic conditions, the response of wheat plants to different malnutrition and biotic stress factors may be even more pronounced [46]. It was previously reported that early-fall seeding might increase the risk of diseases such as wheat streak mosaic virus, barley yellow dwarf virus, and soil-borne wheat mosaic [47]. Thus, grain yield in Zagreb at the sowing date of 28 October in 2022 was far better than earlier sowing dates (13, 18 and 20 October in 2022 in Osijek, Tovarnik, and Kutjevo, respectively). At the earlier sowing dates, rising temperatures and rainfall during November and December 2022 increased accumulated temperatures and promoted the tillering of plants, which resulted in an overgrowth of seedlings before winter that were attacked by aphids, which are vectors of the barley yellow dwarf virus [48]. Further, more than 50% of precipitation fell from January to May 2023 at all locations, which was critical for the development of different wheat diseases, such as rusts (Puccinia spp.), Septoria spp., and Fusarium spp., especially as plants were already weakened by the barley yellow dwarf virus in autumn. It was reported that among the viral diseases affecting wheat, yellow dwarf disease and wheat dwarf disease can induce grain yield losses of up to 80 and 90%, respectively [49].

4.4. Relationship between Investigated Traits

Year-to-year variation in weather changes has a strong effect on the degree of stress experienced by crops, prompting the use of testing environments to represent target stress environments. In the current research, the interactions for year × location were highly expressed for grain yield, test weight, and plant height, followed by year × genotype interactions. Van Oosterom et al. [50] reported that grain yield at locations was not repeatable in different years as the same locations fell into different groups in different years according to clustering. We performed a PCA analysis in separate years for grain yield, test weight, plant height, maturity group, disease, and virus evaluation, as a PCA biplot is a very powerful tool for the analysis and interpretation of GE, which effectively detects the interaction pattern graphically. This is also important because a correlation analysis may not provide a clear picture of the importance of each secondary trait in determining grain yield [51].
In 2021/2022, grain yield was correlated in Osijek and Zagreb, while in 2022/2023, grain yield was correlated at all locations with each other, which could be seen as overlapping clusters. The PCA showed the closest grouping of genotypes in Tovarnik for test weight. Nehe et al. [52] reported that location contributed 97.6% of the total variance for test weight, but grain yield and test weight had a positive connection. In the research of Mecha et al. [53], grain yield had a positive correlation with the grain filling period, the number of productive tillers per plant, spike length, number of spikelets per spike, number of kernels per spike, thousand-kernel weight, biomass yield per plot, test weight, and harvest index at both the phenotypic and genotypic levels. Further, grain yield was significantly associated with spike number per unit area, grain number per spike, thousand-kernel weight, and harvest index [54]. According to those reports, and taking into account our investigation, we can see that grain yield and test weight were in a stronger relationship when there was a severe occurrence of some environmental stress. Our results showed different behavior of the investigated traits in different years, as was previously obtained by the research of Van Oosterom et al. [50]. The differential response of genotypes across environments could be due to differences in the expression of different sets or the same set of genes in different environments [55]. The grouping of traits is associated with genotype performance [56], and grain yield was close to the maturity group in both growing seasons.
Grain yield and traits related to two diseases (powdery mildew and septoria leaf blotch) in 2021/2022 were grouped on the first principal axis, showing a positive association between them and a negative association with the test weight. Septoria leaf blotch and powdery mildew, caused by Septoria tritici blotch and Erysiphe graminis f. sp. tritici, are the most common foliar diseases of wheat that reduce green leaf area for photosynthesis early in the spring. Both diseases may cause grain yield losses of 50% in epidemic conditions for susceptible genotypes [57,58]. High disease infection levels were expected as fungicides were not applied. Further, it was observed that plant height and maturity group were in a negative association, while plant height had a positive relationship with fusarium head blight and septoria nodorum blotch. It is known that plant height and heading date are adaptive traits, which means that the optimum plant height and heading date depend on the target environment. However, plant height and heading date might affect disease progression by determining the likelihood of a plant escaping the disease [59]. Due to the low disease pressure of spike diseases (fusarium head blight and septoria nodorum blotch) in the observed growing season, it was obvious that diseases that occurred in the earlier stage of a plant’s growth, such as tillering, affected grain yield. This influence was minor, as grain yield was above average in the previous few years.
In 2022/2023, grain yield was on the opposite side of the PCA biplot of septoria leaf blotch, septoria nodorum blotch, and fusarium head blight, which were positively associated. Further, very near those diseases were powdery mildew and the barley yellow dwarf virus, which were in a positive correlation with each other. Test weight and plant height are also positively correlated. During stress, a positive and significant correlation between stress tolerance index and awn length, spike length, and plant height was obtained [60]. In the current research, in taller plants, disease pressure might have been reduced, resulting in increased grain yield. In Kutjevo, grain yield was decreased due to the occurrence of yellow rust, septoria leaf blotch, and septoria nodorum blotch, caused by S. tritici and S. nodorum, when wheat plants were already weakened by the barley yellow dwarf virus in autumn, as already mentioned. Altogether, biotic stresses resulted in the lowest average grain yield in Kutjevo compared to other locations. Grain yield in Tovarnik had the same significance level as in Kutjevo and was under the influence of yellow rust and septoria nodorum blotch. It could be observed that grain yield was severely reduced in 2022/2023 compared to 2021/2022 in Kutjevo, which was relatively more than 80%. It was reported that septoria leaf blotch can cause grain yield losses of up to 50% [61], while the infection of yellow spots or septoria nodorum blotch causes losses in grain yield ranging from 18 to 31% [62]. In Osijek, grain yield was also affected by yellow rust, septoria nodorum blotch, and septoria leaf blotch, as in Kutjevo, which was in connection with the virus and resulted in a decrease in grain yield of more than 70% compared to the previous year. Only grain yield in Zagreb was under the influence of one disease, thus expressing the highest values of grain yield in that growing season among the other locations. However, those results might be explained by the later sowing date in Zagreb among the other locations, which helped the plants escape virus infection and better tolerate disease pressure due to increased rainfall.

5. Conclusions

A winter wheat collection was tested at four stations in Croatian regions during the 2021/2022 and 2022/2023 growing seasons and comparatively analyzed by examining agro-morphological traits. During a two-year study across multiple locations, it was found that grain yield response to the effect of the location was evident in both investigated seasons. Due to the strong regional effect, some genotypes suitable for one location may not be suitable for other locations. A PCA biplot indicated that genotypes 2, 26, 28, 32, and 31 were the most stable across the environments, as they did not exert strong interactive forces. However, genotype 30 performed very well at a few locations. In the vegetative season of 2022/2023, when the barley yellow dwarf virus weakened plants early in the autumn and rainfall increased disease pressure later, plant height was elevated in an inverse proportion to the reduced grain yield. Also, the sowing date influenced the virus attack, where wheat genotypes at a later sowing date escaped the virus attack and were better able to bear diseases. These findings suggest the need to be able to produce tolerant wheat varieties to different stress conditions and manage and adjust fungicide and insect protection according to weather, particularly when spring is moist.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture14010004/s1; Table S1: Rainfall amount (mm) and average temperature (°C) during two vegetative seasons (2021/2022 and 2022/2023); Table S2: Maturity group of 14 winter wheat genotypes; Table S3: Contributions of genotypes based on correlations.

Author Contributions

Conceptualization, V.S.; methodology, V.S., G.J., M.Z. and I.V.; formal analysis, G.J., M.Z. and I.V.; investigation, V.S., G.J., M.Z. and I.V.; writing—original draft, V.S.; writing—review and editing, G.J., M.Z. and I.V.; visualization, V.S.; supervision, G.J.; project administration, I.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Agriculture of the Republic of Croatia.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO. Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/en/#home (accessed on 25 July 2023).
  2. Erenstein, O.; Jaleta, M.; Mottaleb, K.A.; Sonder, K.; Donovan, J.; Braun, H.J. Global trends in wheat production, consumption and trade. In Wheat Improvement; Reynolds, M.P., Braun, H.J., Eds.; Springer: Cham, Switzerland, 2022; pp. 47–56. [Google Scholar] [CrossRef]
  3. Sun, H.; Ma, J.; Wang, L. Changes in per capita wheat production in China in the context of climate change and population growth. Food Sec. 2023, 15, 597–612. [Google Scholar] [CrossRef] [PubMed]
  4. UN. Growing at a Slower Pace, World Population Is Expected to Reach 9.7 Billion in 2050 and Could Peak at Nearly 11 Billion around 2100|UN DESA. United Nations Department of Economic and Social Affairs. 2019. Available online: https://www.un.org/development/desa/en/news/population/world-population-prospects-2019.html (accessed on 14 July 2023).
  5. UN. The Sustainable Development Goals Report. 2022. Available online: https://unstats.un.org/sdgs/report/2022/The-Sustainable-Development-Goals-Report-2022.pdf (accessed on 20 July 2023).
  6. Fischer, G.; Shah, M.; Tubiello, F.N.; Van Velhuizen, H. Socio-economic and climate change impacts on agriculture: An integrated assessment, 1990–2080. Philos. Trans. Royal Soc. B Biol. Sci. 2005, 360, 2067–2083. [Google Scholar] [CrossRef]
  7. HLPE. Nutrition and Food Systems. Committee of World Food Security (CFS). Available online: http://www.fao.org/3/a-i7846e.pdf (accessed on 18 June 2023).
  8. Grote, U.; Fasse, A.; Nguyen, T.T.; Erenstein, O. Food security and the dynamics of wheat and maize value chains in Africa and Asia. Front. Sustain. Food Syst. 2021, 4, 617009. [Google Scholar] [CrossRef]
  9. Ray, D.K.; West, P.C.; Clark, M.; Gerber, J.S.; Prishchepov, A.V.; Chatterjee, S. Climate change has likely already affected global food production. PLoS ONE 2019, 1, e0217148. [Google Scholar] [CrossRef] [PubMed]
  10. Croatian Bureau of Statistics. Available online: https://podaci.dzs.hr/2022/hr/29384 (accessed on 23 July 2023).
  11. Oplanić, M.; Marić, A.Č.; Goreta Ban, S.; Čop, T.; Njavro, M. Horticultural farmers’ perceived risk of climate change in Adriatic Croatia. Sustainability 2023, 15, 539. [Google Scholar] [CrossRef]
  12. NN 46/2020. Available online: https://narodne-novine.nn.hr/clanci/sluzbeni/2020_04_46_921.html (accessed on 2 June 2023).
  13. Croatia Wheat Industry Outlook 2022–2026. Available online: https://www.reportlinker.com/clp/country/4757/726378 (accessed on 10 June 2023).
  14. Lobell, D.B.; Burke, M.B. Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environ. Res. Lett. 2008, 3, 034007. [Google Scholar] [CrossRef]
  15. Berhanu, M.; Wolde, A.O. Review on climate change impacts and its adaptation strategies on food security in Sub-Saharan Africa. Agric. Soc. Econ. J. 2019, 19, 145–154. [Google Scholar] [CrossRef]
  16. Seleiman, M.F.; Al-Suhaibani, N.; Ali, N.; Akmal, M.; Alotaibi, M.; Refay, Y.; Dindaroglu, T.; Abdul-Wajid, H.H.; Battaglia, M.L. Plants drought stress impacts on plants and different approaches to alleviate its adverse effects. Plants 2021, 10, 259. [Google Scholar] [CrossRef]
  17. Miedaner, T.; Juroszek, P. Climate change will influence disease resistance breeding in wheat in Northwestern Europe. Theor. Appl. Genet. 2021, 134, 1771–1785. [Google Scholar] [CrossRef]
  18. Duveiller, E.; Singh, R.P.; Nicol, J.M. The challenges of maintaining wheat productivity: Pests, diseases, and potential epidemics. Euphytica 2007, 157, 417–430. [Google Scholar] [CrossRef]
  19. Mérida-García, R.; Liu, G.; He, S.; Gonzalez-Dugo, V.; Dorado, G.; Gálvez, S.; Solís, I.; Zarco-Tejada, P.J.; Reif, J.C.; Hernandez, P. Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain. PLoS ONE 2019, 14, e0211718. [Google Scholar] [CrossRef] [PubMed]
  20. Kronenberg, L.; Yates, S.; Boer, M.P.; Kirchgessner, N.; Walter, A.; Hund, A. Temperature response of wheat affects final height and the timing of stem elongation under field conditions. J. Exp. Bot. 2021, 72, 700–717. [Google Scholar] [CrossRef] [PubMed]
  21. Jiang, T.; Liu, J.; Gao, Y.; Sun, Z.; Chen, S.; Yao, N.; Ma, H.; Feng, H.; Yu, Q.; He, J. Simulation of plant height of winter wheat under soil Water stress using modified growth functions. Agric. Water Manag. 2020, 232, 106066. [Google Scholar] [CrossRef]
  22. Spanic, V.; Cosic, J.; Zdunic, Z.; Drezner, G. Characterization of agronomical and quality traits of winter wheat (Triticum aestivum L.) for Fusarium head blight pressure in different environments. Agronomy 2021, 11, 213. [Google Scholar] [CrossRef]
  23. Farré, A.; Sayers, L.; Leverington-Waite, M.; Goram, R.; Orford, S.; Wingen, L.; Mumford, C.; Griffiths, S. Application of a library of near isogenic lines to understand context dependent expression of QTL for grain yield and adaptive traits in bread wheat. BMC Plant Biol. 2016, 16, 161. [Google Scholar] [CrossRef] [PubMed]
  24. Fowler, D.B.; N’Diaye, A.; Laudencia-Chingcuanco, D.; Pozniak, C.J. Quantitative trait loci associated with phenological development, low-temperature tolerance, grain quality, and agronomic characters in wheat (Triticum aestivum L.). PLoS ONE 2016, 11, e0152185. [Google Scholar] [CrossRef]
  25. Saeidnia, F.; Taherian, M.; Nazeri, S.M. Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates. BMC Plant Biol. 2023, 23, 198. [Google Scholar] [CrossRef] [PubMed]
  26. Peterson, C.J.; Graybosch, R.A.; Shelton, D.R.; Baenziger, P.S. Baking quality of hard red winter wheat: Response of cultivars to environments in the Great Plains. Euphytica 1998, 100, 157–162. [Google Scholar] [CrossRef]
  27. Dueri, S.; Brown, H.; Asseng, S.; Ewert, F.; Webber, H.; George, M.; Craigie, R.; Guarin, J.R.; Pequeno, D.N.L.; Stella, T.; et al. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment. J. Exp. Bot. 2022, 73, 5715–5729. [Google Scholar] [CrossRef]
  28. Ward, B.P.; Brown-Guedira, G.; Tyagi, P.; Kolb, F.L.; Van Sanford, D.A.; Sneller, C.H.; Griffey, C.A. Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Sci. 2019, 59, 491–507. [Google Scholar] [CrossRef]
  29. Popović, V.; Ljubičić, N.; Kostić, M.; Radulović, M.; Blagojević, D.; Ugrenović, V.; Popović, D.; Ivošević, B. Genotype × environment interaction for wheat yield traits suitable for selection in different seed priming conditions. Plants 2020, 9, 1804. [Google Scholar] [CrossRef] [PubMed]
  30. Mohammadi, R.; Armion, M.; Sadeghzadeh, D.; Amri, A.; Nachit, M. Analysis of genotype-by-environment interaction for agronomic traits of durum wheat in Iran. Plant Prod. Sci. 2011, 14, 15–21. [Google Scholar] [CrossRef]
  31. Bocianowski, J.; Prażak, R. Genotype by year interaction for selected quantitative traits in hybrid lines of Triticum aestivum L. with Aegilops kotschyi Boiss. and Ae. variabilis Eig. using the additive main effects and multiplicative interaction model. Euphytica 2022, 218, 11. [Google Scholar] [CrossRef]
  32. Bordes, J.; Branlard, G.; Oury, F.X.; Charmet, G.; Balfourier, F. Agronomic characteristics, grain quality and flour rheology of 372 bread wheats in a worldwide core collection. J. Cereal Sci. 2008, 48, 569–579. [Google Scholar] [CrossRef]
  33. Eltaher, S.; Baenziger, P.S.; Belamkar, V.; Emara, H.A.; Nower, A.A.; Salem, K.F.; Alqudah, A.M.; Sallam, A. GWAS revealed effect of genotype× environment interactions for grain yield of Nebraska winter wheat. BMC Genom. 2021, 22, 2. [Google Scholar] [CrossRef] [PubMed]
  34. Mohammadi, R.; Farshadfar, F.; Amri, A. Interpreting genotype × environment interactions for grain yield of rainfed durum wheat in Iran. Crop J. 2015, 3, 526–535. [Google Scholar] [CrossRef]
  35. Yu, M.; Liu, Z.H.; Yang, B.; Chen, H.; Zhang, H.; Hou, D.-B. The contribution of photosynthesis traits and plant height components to plant height in wheat at the individual quantitative trait locus level. Sci. Rep. 2020, 10, 12261. [Google Scholar] [CrossRef]
  36. Singh, C.; Gupta, A.; Gupta, V.; Kumar, P.; Sendhil, R.; Tyagi, B.S.; Singh, G.; Chatrath, R.; Singh, G.P. Genotype × environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models. Crop Breed. Appl. Biotechnol. 2019, 19, 309–318. [Google Scholar] [CrossRef]
  37. Gupta, V.; Kumar, M.; Singh, V.; Chaudhary, L.; Yashveer, S.; Sheoran, R.; Dalal, M.S.; Nain, A.; Lamba, K.; Gangadharaiah, N.; et al. Genotype by environment interaction analysis for grain yield of wheat (Triticum aestivum (L.) em.Thell) genotypes. Agriculture 2022, 12, 1002. [Google Scholar] [CrossRef]
  38. Farag, F.M.; Awaad, H.A.; Abdel-Hameed, I.M.; Abdul-Hamid, M.I.E.; Morsy, A.M. Stability of grain yield in bread wheat genotypes under different environments. Zagazig J. Agric. Res. 2019, 46, 1797–1807. [Google Scholar] [CrossRef]
  39. Valério, I.P.; de Carvalho, F.I.F.; Benin, G.; da Silveira, G.; da Silva, J.A.G.; Nornberg, R.; Hagemann, T.; de Souza Luche, H.; de Oliveira, A.C. Seeding density in wheat: The more, the merrier? Sci. Agric. 2013, 70, 176–184. [Google Scholar] [CrossRef]
  40. Khadka, K.; Earl, H.J.; Raizada, M.N.; Navabi, A. A Physio-morphological trait-based approach for breeding drought tolerant wheat. Front. Plant Sci. 2020, 11, 715. [Google Scholar] [CrossRef] [PubMed]
  41. Duvnjak, J.; Lončarić, A.; Brkljačić, L.; Šamec, D.; Šarčević, H.; Salopek-Sondi, B.; Španić, V. Morpho-physiological and hormonal response of winter wheat varieties to drought stress at stem elongation and anthesis stages. Plants 2023, 12, 418. [Google Scholar] [CrossRef] [PubMed]
  42. Selim, D.A.-F.H.; Nassar, R.M.A.; Boghdady, M.S.; Bonfill, M. Physiological and anatomical studies of two wheat cultivars irrigated with magnetic water under drought stress conditions. Plant Physiol. Biochem. 2019, 135, 480–488. [Google Scholar] [CrossRef] [PubMed]
  43. Farooq, M.; Hussain, M.; Siddique, K.H. Drought stress in wheat during flowering and grain-filling periods. CRC Crit. Rev. Plant Sci. 2014, 33, 331–349. [Google Scholar] [CrossRef]
  44. Wan, C.; Dang, P.; Gao, L.; Wang, J.; Tao, J.; Qin, X.; Feng, B.; Gao, J. How does the environment affect wheat yield and protein content response to drought? A Meta-Analysis. Front. Plant. Sci. 2022, 13, 896985. [Google Scholar] [CrossRef] [PubMed]
  45. Bhatta, M.; Morgounov, A.; Belamkar, V.; Wegulo, S.N.; Dababat, A.A.; Erginbas-Orakci, G.; Bouhssini, M.E.; Gautam, P.; Poland, J.; Akci, N.; et al. Genome-wide association study for multiple biotic stress resistance in synthetic hexaploid wheat. Int. J. Mol. Sci. 2019, 20, 3667. [Google Scholar] [CrossRef]
  46. Atkinson, N.J.; Urwin, P.E. The interaction of plant biotic and abiotic stresses: From genes to the field. J. Exp. Bot. 2012, 63, 3523–3543. [Google Scholar] [CrossRef]
  47. Tapley, M.; Ortiz, B.V.; van Santen, E.; Balkcom, K.S.; Mask, P.; Weaver, D.B. Location, seeding date, and variety interactions on winter wheat yield in southeastern United States. Agron. J. 2013, 105, 510–518. [Google Scholar] [CrossRef]
  48. Aradottir, G.I.; Crespo-Herrera, L. Host plant resistance in wheat to barley yellow dwarf viruses and their aphid vectors: A review. Curr. Opin. Insect. Sci. 2021, 45, 59–68. [Google Scholar] [CrossRef]
  49. Armand, T.; Souquet, M.; Korn, L.; Gauthier, K.; Jacquot, E. Asymmetric interactions between barley yellow dwarf virus-PAV and wheat dwarf virus in wheat. Front. Plant Sci. 2023, 14, 1194622. [Google Scholar] [CrossRef] [PubMed]
  50. van Oosterom, E.J.; Kleijn, D.; Ceccarelli, S.; Nachit, M.M. Genotype-by-environment interactions of barley in the Mediterranean region. Crop Sci. 1993, 33, 669–674. [Google Scholar] [CrossRef]
  51. Kozak, M.; Azevedo, R.A. Sequential path analysis: What does “sequential” mean? Sci. Agric. 2014, 71, 525–527. [Google Scholar] [CrossRef]
  52. Nehe, A.; Akin, B.; Sanal, T.; Evlice, A.K.; Ünsal, R.; Dinçer, N.; Demir, L.; Geren, H.; Sevim, I.; Orhan, Ş.; et al. Genotype × environment interaction and genetic gain for grain yield and grain quality traits in Turkish spring wheat released between 1964 and 2010. PLoS ONE 2019, 14, e0219432. [Google Scholar] [CrossRef]
  53. Mecha, B.; Alamerew, S.; Assefa, A.; Assefa, E.; Dutamo, D. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.) genotypes. Adv. Plants Agric. Res. 2017, 6, 128–136. [Google Scholar]
  54. Li, Y.; Tao, F. Interactions of genotype, environment and management on wheat traits and grain yield variations in different climate zones across China. Agric. Syst. 2022, 203, 103521. [Google Scholar] [CrossRef]
  55. Yang, R.C.; Baker, R.J. Genotype-environment interactions in two wheat crosses. Crop Sci. 1991, 31, 83–87. [Google Scholar] [CrossRef]
  56. Kaya, Y.; Akcura, M. Effects of genotype and environment on grain yield and quality traits in bread wheat (T. aestivum L.). Food Sci. Technol. 2014, 34, 386–393. [Google Scholar] [CrossRef]
  57. Fones, H.; Gurr, S. The impact of Septoria tritici Blotch disease on wheat: An EU perspective. Fungal Genet. Biol. 2015, 79, 3–7. [Google Scholar] [CrossRef]
  58. Mapuranga, J.; Chang, J.; Yang, W. Combating powdery mildew: Advances in molecular interactions between Blumeria graminis f. sp. tritici and wheat. Front. Plant Sci. 2022, 13, 1102908. [Google Scholar] [CrossRef]
  59. Vidal, T.; Saint-Jean, S.; Lusley, P.; Leconte, M.M.; Krima, S.B.; Boixel, A.L.; Consortium, W.; de Vallavieille-Pope, C. Cultivar mixture effects on disease and yield remain despite diversity in wheat height and earliness. Plant Pathol. 2020, 69, 1148–1160. [Google Scholar] [CrossRef]
  60. Mohammadi, M.; Sharifi, P.; Karimizadeh, R.; Shefazadeh, M.K. Relationships between grain yield and yield components in bread wheat under different water availability (Dryland and supplemental irrigation conditions). Not. Bot. Horti Agrobot. Cluj-Napoca 2012, 40, 195–200. [Google Scholar] [CrossRef]
  61. Berraies, S.; Salah, G.; Mohamed, R.S.; Yahyaoui, A. Estimating grain yield losses caused by septoria leaf blotch on durum wheat in Tunisia. Chil. J. Agric. Res. 2014, 74, 432–437. [Google Scholar] [CrossRef]
  62. Bhathal, J.; Loughman, R.; Speijers, J. Yield reduction in wheat in relation to leaf disease from yellow (tan) spot and Septoria nodorum Blotch. Eur. J. Plant Pathol. 2003, 109, 435–443. [Google Scholar] [CrossRef]
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Figure 1. Principal component analysis (PCA) showing the relationship between grain yield in two growing seasons (2021/2022 and 2022/2023) at four locations (Kutjevo, Osijek, Tovarnik, and Zagreb) for fourteen winter wheat genotypes (G). The red ellipse represents 68% of the confidence intervals for the core region. The green ellipse represents the correlation circle. Arrows represent growing seasons at different locations; the directions of the arrows represent correlation between growing seasons at different locations and principle components; and lengths represent devotion of original data to principle components.
Figure 1. Principal component analysis (PCA) showing the relationship between grain yield in two growing seasons (2021/2022 and 2022/2023) at four locations (Kutjevo, Osijek, Tovarnik, and Zagreb) for fourteen winter wheat genotypes (G). The red ellipse represents 68% of the confidence intervals for the core region. The green ellipse represents the correlation circle. Arrows represent growing seasons at different locations; the directions of the arrows represent correlation between growing seasons at different locations and principle components; and lengths represent devotion of original data to principle components.
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Figure 2. Grain yield of fourteen wheat genotypes at four locations in the growing seasons of 2021/2022 (A) and 2022/2023 (B) Different letters mean different statistical significance of grain yield for each genotype separately at four locations.
Figure 2. Grain yield of fourteen wheat genotypes at four locations in the growing seasons of 2021/2022 (A) and 2022/2023 (B) Different letters mean different statistical significance of grain yield for each genotype separately at four locations.
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Figure 3. Principal component analysis (PCA) showing the relationship between grain yield (GY), test weight (TW), plant height (PH), maturity group (MG), powdery mildew (PM), septoria leaf blotch (SLB), septoria nodorum blotch (SNB), fusarium head blight (FHB), and barley yellow dwarf virus (BYDW) for 2021/2022 (A) and 2022/2023 (B) growing seasons at four locations (Kutjevo, Osijek, Tovarnik, and Zagreb) for fourteen winter wheat genotypes.
Figure 3. Principal component analysis (PCA) showing the relationship between grain yield (GY), test weight (TW), plant height (PH), maturity group (MG), powdery mildew (PM), septoria leaf blotch (SLB), septoria nodorum blotch (SNB), fusarium head blight (FHB), and barley yellow dwarf virus (BYDW) for 2021/2022 (A) and 2022/2023 (B) growing seasons at four locations (Kutjevo, Osijek, Tovarnik, and Zagreb) for fourteen winter wheat genotypes.
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Table 1. Climatic data, soil properties, and locations of the experimental areas.
Table 1. Climatic data, soil properties, and locations of the experimental areas.
LocationAverage Temperature (°C)Precipitation
(mm)		Soil TypeAltitude
(m a.s.l.)		Geographical Position
2021/20222022/20232021/20222022/2023LatitudeLongitude
Osijek10.911.7459.5729.0eutric cambisol9445°32′ N18°44′ E
Tovarnik11.512.0517.3680.4chernozemic soil8845°10′ N19°09′ E
Kutjevo11.411.9526.1745.9loamic soil22645°25′ N17°52′ E
Zagreb11.111.7585.9912.3eutric fluvic cambisols15845°48′ N15°58′ E
Table 2. ANOVA for grain yield, test weight, and plant height of fourteen winter wheat genotypes at four locations for two years.
Table 2. ANOVA for grain yield, test weight, and plant height of fourteen winter wheat genotypes at four locations for two years.
Source
of Variability		DFMean Square
Grain YieldTest WeightPlant Height
Year (Y)11000.98 ***9436 ***16623.75 ***
Location (L)356.34 ***524 ***919.83 ***
Genotype (G)1315.68 ***234 ***275.31 ***
Y × L3127.84 ***652 ***551.57 ***
Y × G136.21 ***47 ***128.91 ***
L × G391.81 ***31 ***31.61 ***
Y × L × G391.64 ***32 ***22.97 ***
Error3360.591211.81
*** = significant at p < 0.001, DF—degrees of freedom.
Table 3. Significant differences between four locations in mean values for grain yield, test weight, and plant height during two growing seasons.
Table 3. Significant differences between four locations in mean values for grain yield, test weight, and plant height during two growing seasons.
LocationGrain Yield (t ha−1)Test Weight (kg hL−1)Plant Height (cm)
2021/20222022/20232021/20222022/20232021/20222022/2023
Tovarnik8.05 b5.86 c77.14 a66.17 b72.05 b85.37 bc
Zagreb8.35 b8.01 a75.92 b61.67 c76.38 a85.20 c
Kutjevo10.61 a5.74 c74.84 c66.13 b77.77 a86.38 b
Osijek10.87 a6.31 b75.34 bc72.55 a76.54 a94.53 a
Different letters mean different statistical significance between grain yields at four locations in one growing season.
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Spanic, V.; Jukic, G.; Zoric, M.; Varnica, I. Some Agronomic Properties of Winter Wheat Genotypes Grown at Different Locations in Croatia. Agriculture 2024, 14, 4. https://doi.org/10.3390/agriculture14010004

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Spanic V, Jukic G, Zoric M, Varnica I. Some Agronomic Properties of Winter Wheat Genotypes Grown at Different Locations in Croatia. Agriculture. 2024; 14(1):4. https://doi.org/10.3390/agriculture14010004

Chicago/Turabian Style

Spanic, Valentina, Goran Jukic, Marina Zoric, and Ivan Varnica. 2024. "Some Agronomic Properties of Winter Wheat Genotypes Grown at Different Locations in Croatia" Agriculture 14, no. 1: 4. https://doi.org/10.3390/agriculture14010004

APA Style

Spanic, V., Jukic, G., Zoric, M., & Varnica, I. (2024). Some Agronomic Properties of Winter Wheat Genotypes Grown at Different Locations in Croatia. Agriculture, 14(1), 4. https://doi.org/10.3390/agriculture14010004

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