Spacing strategies for enhancing drought resilience and yield in maize agriculture

2.1 Maize variety selection

The selection of maize varieties was guided by their earliness, a parameter often referred to as the FAO number. Optimal choices for field experiments tend to favour varieties with lower FAO numbers for early maturation and medium-to-late or late varieties with higher FAO numbers. Two specific maize varieties (F1 hybrids, single cross) were selected for the experiment: Alombo, originating from Oseva company (CZE), and SY Ignis, developed by Syngenta (CHE). As previous testing shows, these chosen varieties have demonstrated exceptional adaptability to the prevailing field conditions. Alombo, characterized by an FAO number of 240, stands out with its grain type, falling between flint and dent maize. Conversely, SY Ignis, with an FAO number of 320, belongs to the dent maize category. Particularly, both varieties can be grown for either silage or grain, making them ideal for this field experiment.

2.2 Examination area and field trial

In this experiment, maize plants were cultivated in the South Moravian Region, Czech Republic (49.023 N,16.618 E). The climatic conditions at this site are characterized by an average temperature of 10.3°C and an annual precipitation total of 491.1 mm [15]. Meteorological data, including precipitation measurements obtained through a Met One 370 rain gauge from Met One Instruments (USA) and temperature readings taken at a height of 2 m above ground using a Vaisala HMP155A Merici sensor manufactured by Vaisala (Finland), were recorded throughout the experiment.

2.3 Extreme weather events

For the optimal growth of maize, early May is crucial for the favourable conditions necessary for the germination of maize seeds. The period extending from late May to August significantly influences both vegetative and reproductive growth and the ripening stage. Despite meteorological data indicating sufficient rainfall in May (Figure 1), only a few days exhibited precipitation levels beneficial to growth. June and July presented challenging conditions for maize growth, characterized by above-normal temperatures in July and a notable lack of precipitation in both months, leading to drought stress on maize plants. August is represented by increased precipitation; however, it is essential to highlight that this heightened precipitation results from intense storms with substantial rainfall.

Figure 1

Weather conditions at the Žabčice locality, such as precipitation totals and average air temperatures.

2.4 Field trial

Regarding the agricultural practices used, the maize field was fertilized with 40 kg/ha of manure in November 2022 and with 120 kg/ha of urea before sowing in April. Prior to sowing, a total herbicide, Roundup Flex containing 450 g/L glyphosate, was applied at a rate of 3.5 L/ha. During growth, the herbicide Lumax, combining mesotrione (37.5 g/L), S-metolachlor (375 g/L), and terbuthylazine (125 g/L), was used at 3.5  L/ha, and the plants were treated against the European corn borer with the insecticide Voliam, which contains 200 g/L chlorantraniliprole, at a rate of 0.1 L/ha. The seeds were treated with Force 20 CS containing 200 g/L tefluthrin, recommended at 50 mL per 50,000 seeds. Throughout their growth, the maize plants were irrigated only by rainfall.

The design of experiment featured randomized plots of maize for cultivation, each covering an area of 21 m². Each maize variety was cultivated in two independent replications. Sowing took place in May. Two distinct sowing spacings were selected, namely, 0.75 and 0.375 m (Figure 2), which were utilized to achieve an equal seed density of 80,000 seeds per hectare, which resulted in plants inside the row being further from each other when sown at a distance of 0.375 m. The proximate distance between plants within the row was 0.16 m for 0.75 m interrow spacing, and 0.33 m for 0.375 m interrow spacing. The sowing depth was maintained at 0.06–0.07 m to ensure uniformity and consistency in the planting process. The preceding crop species cultivated on the same site was sunflower.

Figure 2

Row spacing scheme of the experiment for two maize varieties.

2.5 Relative water content (RWC)

Leaf RWC represents the percentage of water content at the sampling time relative to the water content observed at full turgor. A prerequisite for each sampling event was the absence of rainfall for a minimum of 3–4 days. Samples, consistent in size (approximately 1 cm × 5 cm), were collected from the third youngest and third oldest leaf on three specific dates: 20th June (48 days after sowing, BBCH 15), 12th July (70 days after sowing, BBCH 59), and 16th August (105 days after sowing, BBCH 77), and securely placed in sealed plastic tubes. Subsequently, these field samples were transported to the field station within cool boxes. Each sample was individually weighed to determine its fresh weight (on the scale with at least four decimal places). The leaves were then cut into approximately 1 cm × 1 cm pieces and returned to their tubes, into which water was added until complete submersion of all sample components was achieved. Following a 3-h interval, the tubes were emptied of water, and the samples were subjected to drying using filter paper. The saturated weight was measured, after which the samples were further dried in an oven (BMT Venticell ECO) set at 80°C for 1 h, then 103°C for 2 h (noting that both the drying time and temperature were modified from the original protocol) [16]. The samples were weighed again to determine their dry weight after this drying process. The RWC was subsequently calculated using the following formula:

2.6 Tiller abundance and maize yield quantification

The number of tillers was counted for all plants per plot prior to a harvest. The maize plants were harvested through a systematic procedure involving four replicates, guided by the dry matter content of the aboveground biomass, which typically accounts for approximately 33% of maize’s total biomass. Continuous sampling methods were employed to monitor and determine the optimal harvest timing. The plants were cut at a height of approximately 0.1 m above the ground. Harvesting was conducted by cutting ten consecutively growing plants for both row spacings in four replicates. After the harvest, the biomass was weighed, followed by crushing and homogenization to ensure uniformity. The collected material underwent a two-stage drying process within the drying oven. The initial drying stage spanned 24 h at a temperature of 65°C, followed by a subsequent 4-h drying stage at 105°C. Ultimately, the dry aboveground biomass was quantified in tonnes per hectare.

Harvesting of maize grain from the main stem for each replication was performed using a combine harvester. Manual harvesting of maize ears from the main stem depended on the ripening stage. Comprehensive analyses of ear and grain traits were conducted, covering parameters such as the weight of ears with and without grains, the number of rows on each ear, ear size (length and diameter at the middle), as well as grain count and weight, including the measurement of the thousand kernel weight (TKW). The ear yield was calculated as follows:

The calculation of the percentage of tillering plants was performed using the following formula:

2.7 Statistical analysis

Statistical analyses were conducted using XLSTAT, a software program that functions as an add-on to Microsoft Excel (Lumivero, USA). The observed data underwent analysis employing the Kruskal–Wallis test, a nonparametric statistical method. For subsequent post hoc testing involving multiple pairwise comparisons, the Steel–Dwass–Critchlow–Fligner test was employed. A significance level of p < 0.05 was chosen, indicating that results achieving this threshold were considered statistically significant. Additionally, principal component analysis (PCA) was performed to explore the variability within the dataset and to identify patterns among the variables related to maize tillering. This PCA was executed using Python and Matplotlib for visualizing the PCA results.

Furthermore, data values were depicted through violin plots, which are enhancements of box plots that show not only the summary statistics but also the distribution of the data. The broadest segment of a violin plot indicates the density of data points, offering a visual cue to the most common value ranges and making the data’s distribution and density immediately apparent. For these visualizations, Matplotlib was used, while Seaborn extended these with improved aesthetics and additional analytical features, thereby enhancing the overall representation of data. PCA findings were illustrated using box plots segmented into quadrants, effectively showcasing the variations and concentrations of data points across different principal components.

3.1 Water content variability in maize foliage under different row spacing conditions

Our investigation into the effects of row spacing on the RWC of maize revealed significant findings, highlighting the intricate interplay between agricultural practices and plant physiological responses. The study differentiated the impacts by row spacing (0.75 m vs 0.375 m), maize variety (Alombo vs SY Ignis), and plant part (bottom vs top), providing a comprehensive understanding of RWC variations (Figure 3).

Figure 3

Distribution of RWC by maize variety and row spacing, indicated with median lines (dashed) and units (%).

The analysis revealed a clear distinction in RWC attributed to row spacing (Table 1). Plants cultivated at a closer row spacing of 0.375 m exhibited a higher mean RWC of 95.0%, compared to those at a wider spacing of 0.75 m, which showed a lower mean RWC of 92.5%. This difference was statistically significant (p = 0.044), underscoring the role of row spacing in optimizing maize hydration status. The narrower row spacing facilitated a more conducive microenvironment for moisture retention, as evidenced by the reduced standard deviation (SD) in RWC measurements (1.9 for 0.375 m spacing vs 5.3 for 0.75 m spacing), indicating a more uniform water content across samples.

When examining the effect of maize variety, the differences in RWC between Alombo and SY Ignis varieties were influenced by row spacing. For the wider spacing of 0.75 m, SY Ignis variety showed a significantly lower mean RWC of 91.3% compared to Alombo, which was not statistically significant (p = 0.854), suggesting variety-specific responses to environmental conditions mediated by row spacing. However, at the narrower spacing of 0.375 m, both varieties achieved a comparable mean RWC of approximately 95.0%, highlighting the potential of row spacing optimization to mitigate variety-specific differences in water content retention.

The study further segmented the RWC analysis by plant parts, revealing a significant variation (p = 0.001) between the bottom (third oldest) and top (third youngest) leaf of the plants. The bottom leaf maintained a mean RWC of 92.6%, contrasting sharply with the top leaf’s mean of 95.0%. This delineation accentuates the physiological gradient within the plant structure, potentially reflective of water distribution efficiency and evapotranspirative demands.

3.2 Tillering responses to varied row spacing

The study investigated the influence of maize variety and row spacing on tillering dynamics to uncover patterns that could inform optimized agricultural practices. The analysis spanned several variables indicative of tillering vigour, such as the percentage of tillering plants, plants with one, two, or three tillers (Figure 4).

Figure 4

Distribution of maize plants with tillers per row: (a) one tiller, (b) two tillers, and (c) three tillers.

A significant effect of row spacing on the proportion of tillering plants was observed for both Alombo and SY Ignis varieties (Table 2). Specifically, the narrower row spacing of 0.375 m led to a higher mean percentage of tillering plants (67.8% for Alombo and 73.3% for SY Ignis) compared to the wider spacing of 0.75 m (47.4% for Alombo and 43.9% for SY Ignis), with the differences being statistically significant (p = 0.019 for Alombo and p = 0.002 for SY Ignis). This underscores the critical role of row spacing in promoting tillering, likely through altered microenvironmental conditions conducive to tiller initiation and development.

The analysis further delineated the effects on the number of tillers per plant. For plants with one tiller, while an increase in tiller count was observed at the narrower spacing, the differences were not statistically significant. However, for plants with two tillers, both varieties exhibited a significant increase in the mean percentage of plants at the narrower spacing (30.0% for Alombo and 34.0% for SY Ignis) compared to the wider spacing (16.2% for Alombo with p = 0.023 and 8.2% for SY Ignis with p = 0.001), highlighting row spacing as a determinant factor in tiller proliferation.

Intriguingly, the occurrence of plants with three tillers was relatively rare and showed no significant response to row spacing in Alombo. In contrast, SY Ignis showed a slight increase in the mean percentage of plants with three tillers at the narrower spacing (1.9%), which was statistically significant (p = 0.001), suggesting variety-specific genetic predispositions influencing tillering capacity under varying spatial arrangements.

The PCA indeed indicates that the first two principal components (variety and row spacing) together explain approximately 77.55% of the variance which includes measurements related to the tillering of maize plants (Figure 5). Plants with three and two tillers being positioned in Quadrant I suggest that these plants exhibit higher tillering capacity. The presence of plants with more tillers in this quadrant can imply that the combination of variety and row spacing factors here promotes better tillering. This could be due to optimal spacing allowing for sufficient resources and space for tiller development, and/or specific variety traits that support multiple tillering.

Figure 5

PCA of maize tillering.

On the other hand, plants with no tillers or only one tiller positioned in Quadrant IV indicate a lower tillering capacity. This quadrant is characterized by a positive score on one principal component and a negative score on the other, suggesting a negative correlation or an inverse relationship between the factors represented by these components and tillering capacity. The placement in Quadrant IV could imply that the specific combinations of variety and row spacing associated with this quadrant might restrict tillering, possibly due to competition for resources, less optimal genetic predisposition for tillering, or environmental factors not conducive to tiller development.

3.3 Row spacing’s dual impact on plant biomass (including tillers) and main stem grain yield

3.3.1 Dry biomass yield

Plants spaced at 0.375 m exhibited a higher mean value for dry biomass yield of 15.9 t/ha, compared to 13.1 t/ha for those at the wider spacing of 0.75 m (Figure 6). This difference was statistically significant (p = 0.036), suggesting that narrower row spacing favourably affects the dry biomass, possibly due to enhanced microclimatic conditions or resource availability that stimulates better performance (Table 3).

Figure 6

Distribution of dry aboveground biomass yield data.

Table 3

Results of the Kruskal–Wallis and post hoc tests for biomass data

Factor	Variety	Row spacing	Min.	Max.	Mean value	SD	P value
Row spacing		0.75	11.0	16.7	13.1A	1.9	0.036*
		0.375	12.7	20.7	15.9B	2.5
Variety	Alombo		11.5	15.1	13.2A	1.4	0.027*
	SY Ignis		11.0	20.7	15.9B	2.9
Within-Variety	Alombo	0.75	11.5	13.0	12.2A	0.8	0.023*
		0.375	12.7	15.1	14.1A	1.1
	SY Ignis	0.75	11.0	16.7	14.0A	2.5
		0.375	15.8	20.7	17.7B	2.1

The values marked with an asterisk (*) are statistically significant at the p < 0.05 level, SD = standard deviation.

Groups marked with different letters (A, B) indicate statistically significant differences as determined by the Kruskal-Wallis test.

A further examination by variety exposed a differential response to the environmental conditions dictated by row spacing. The Alombo variety demonstrated a mean value of 13.2 t/ha, slightly lower than the SY Ignis variety, which achieved a mean value of 15.9 t/ha. This distinction was statistically supported (p = 0.027 for Alombo), indicating inherent varietal differences in reacting to the growing conditions and also their genetical predisposition to certain yield performance.

Looking more closely, the within-variety comparisons across row spacings showed important findings. For the Alombo variety, a shift from wider to narrower row spacing (from 0.75 to 0.375 m) led to an increase in the mean value from 12.2 to 14.1 t/ha, although this change was not statistically significant within this variety (p = 0.023, indicating a borderline significance due to the possibly small sample size or effect size). Conversely, the SY Ignis variety manifested a pronounced response to row spacing, with mean values escalating from 14.0 t/ha at the wider spacing to 17.7 t/ha at the narrower spacing, a difference that was statistically significant (p = 0.023), underscoring the critical role of genetic makeup in modulating the impact of cultivation practices.

3.4 Grain yield and grain yield components

Grain yield analysis revealed that SY Ignis significantly benefited from the narrower row spacing (0.375 m), exhibiting a marked increase in mean grain yield from 7.3 to 9.1 t/ha, a pattern not mirrored in Alombo (Figure 7). Specifically, the differences across spacing for Alombo did not reach statistical significance (p = 0.321), emphasizing the varietal sensitivity to cultivation spacing.

Figure 7

Distribution of maize grain yield data.

For ear weight and ear length, both varieties showed enhanced mean values at the narrower spacing, with SY Ignis displaying a particularly pronounced response in ear weight (Figure 8). The variation in ear length between row spacings was statistically significant for Alombo (p = 0.029), indicating that row spacing influences the physical dimensions of maize ears (Table 4). A significant finding was the increase in seed number and TKW for both varieties at the narrower spacing, with SY Ignis showcasing an exceptional rise in seed number from 525.3 to 641.5 (p = 0.021 for Alombo). This suggests that intra-row spacing may facilitate higher fertility in terms of seed production. Similarly, TKW improvements were observed at the narrower spacing, particularly for Alombo, which showed a statistically significant increase in TKW (p = 0.034), pointing to the potential for achieving grains of superior weight through strategic row spacing adjustments.

Figure 8

Distributions of maize grain yield components: (a) ear weight (g), (b) ear length (cm), (c) seed number per ear, (d) TKW (g), and (e) seed weight per ear (g).

The analyses of the perimeter at the middle of the ear, row number, and ear yield did not yield statistically significant differences that could be attributed directly to row spacing or variety, indicating a more complex interaction of these traits with environmental and genotype.

The post hoc testing, conducted a pairwise comparisons, did not reveal any statistically significant differences among the ear length, ear seed number, and TKW for the groups (Alombo 0.75, Alombo 0.375, SY Ignis 0.75, and SY Ignis 0.375). Although the Kruskal–Wallis test may indicate statistical significance overall, this significance may dissipate when the groups are analysed through pairwise comparisons.

3.5 Tillering variability affects yield outcomes

For Alombo, moving to a narrower inter-row and wider intra-row spacing increased the mean percentage of plants with two tillers significantly (from 16.2% at 0.75 m to 30.0% at 0.375 m, p = 0.023), and a similar pattern was observed for SY Ignis. Increased tillering might indicate a greater leaf area, possibly enhancing photosynthetic capacity and, consequently, biomass production. The grain yield for SY Ignis significantly increased at the narrower intra-row and wider intra-row spacing (from 7.3 to 9.1), indicating that the enhanced tillering associated with more spacing could be contributing to higher grain production. This effect was not as pronounced for Alombo, which may suggest varietal differences in how tillering translates to grain yield. Significant improvements in ear weight, ear length, and seed number at 0.375 m spacing for both varieties, especially for SY Ignis, suggest a positive correlation between tillering and these yield components. The increased ear weight and seed number at narrower spacing could be indicative of the positive impact of tillering on reproductive success and biomass accumulation.

4.1 Dual perspective on plant hydration of inter-row and intra-row spacing

The distinction between inter-row and intra-row spacing emerges as a critical factor in optimizing maize hydration. The closer inter-row spacing of 0.375 m, paired with wider intra-row spacing (0.33 m), facilitated a higher mean RWC of 95.0%, compared to the wider inter-row spacing of 0.75 m with narrower intra-row spacing (about 0.16 m), which yielded a lower mean RWC of 92.5%. This configuration likely influences microenvironmental conditions favourably, enhancing soil moisture conservation [17,18,19] and may reduce evaporative stress, thereby optimizing plant hydration, as spacing of plants affects evapotranspiration [20]. The statistical significance of this observation (p = 0.044) not only validates the role of spatial arrangement in water retention but also indicates a strategic balance between inter-row and intra-row spacing as important for maintaining optimal moisture levels within maize foliage.

The study’s insights into varietal responses – Alombo and SY Ignis – to these spacing configurations further illuminate the potential of row spacing optimization to reconcile inherent genetic predispositions with environmental management practices. The comparable mean RWC observed at the narrower inter-row spacing underscores the capacity of strategic spacing decisions to equilibrate varietal discrepancies in water retention [21], suggesting a harmonization of genotype with agronomic input to foster plant resilience against hydration stress.

Significantly, the vertical stratification of RWC across plant parts – from the bottom (third oldest) to the top (third youngest) leaves – reveals a physiological gradient within the maize plant, likely reflective of innate water allocation strategies and differential transpirational demands [22]. This internal modulation of water use underscores the adaptability of maize to varying water availabilities, highlighting the importance of considering both spatial arrangement and plant architectural characteristics in water management strategies.

Incorporating the interaction between inter-row and intra-row spacing into maize cultivation practices offers a promising avenue for enhancing RWC and, by extension, plant health and productivity. This study supports the use of a comprehensive approach to farming management that combines precise spacing with the characteristics of different crops to improve water use and promote sustainable farming. The aim is to provide practical advice that helps tailor farming methods to meet the unique needs of maize varieties, especially under varying climatic challenges.

4.2 Interpreting tillering responses to diverse row spacing

The observed increase in the proportion of tillering plants under narrower inter-row spacing yet wider intra-row spacing (0.375 m inter-row and 0.33 m intra-row) for both Alombo and SY Ignis varieties emphasizes the critical role of precise spatial arrangement in promoting tillering [23,24]. This configuration appears to offer an optimal balance between minimizing competition for light [14], nutrients [25], and water [13]. The statistical significance of these findings (p = 0.019 for Alombo and p = 0.002 for SY Ignis) not only highlights the importance of row spacing but also suggests that the interplay between inter-row and intra-row spacing is a determinant factor in enhancing plant hydration and tillering vigour.

The variance in tillering responses between the two maize varieties highlights the genetic predisposition’s role in environmental adaptability. Particularly, the variety SY Ignis exhibited a notable adaptability to the narrower inter-row but wider intra-row spacing, as evidenced by the significant increase in plants with three tillers (p = 0.001). This suggests that certain genetic traits may enhance responsiveness to optimized spatial configurations, presenting an opportunity for breeding programs to select for varieties with heightened adaptability to specific agronomic practices. These genetic traits include genes promoting tillering such as well studied tb1 [26], tin1 [27], and gt1 [28].

It is important to mention other factors affecting tillering in maize such as cumulative growing degree days, photothermal quotient, extremes in daily temperatures, cumulative vapour pressure deficit, soil nitrate and phosphorus [29], and shading [28].

4.3 Tillering as a biomass indicator

The data of this study suggest that increased tillering, particularly at 0.375 m row spacings, is associated with improved yield components such as ear weight, seed number, and potentially grain yield, especially in SY Ignis. Tillers can positively affect yield [24,30] and they may reduce dependence on a seasonally variable optimum plant density [31,32]. Our findings also indicate that tillering could serve as a valuable indicator of biomass accumulation and reproductive success in maize. The differential impact of tillering on the yield components between Alombo and SY Ignis highlights the need for variety-specific management practices. While increased tillering positively affects SY Ignis in terms of grain yield and other yield components, Alombo’s response suggests a more complex relationship that may not directly translate to increased grain yield.

4.4 Row spacing effecting maize production dynamics of aboveground biomass yield

Plants cultivated at a closer inter-row spacing of 0.375 m, with wider intra-row spacing of approximately 0.33 m, exhibited a significantly higher mean dry biomass yield of 15.9 t/ha, compared to 13.1 t/ha for those at the wider spacing of 0.75 m, with narrower intra-row spacing of about 0.16 m (p = 0.036). This finding suggests that narrower inter-row and wider intra-row spacing making more space for each plant positively influences dry biomass yield, potentially due to improved microclimatic conditions [33] or enhanced resource availability [34], fostering better crop performance and biomass accumulation [35].

A response to row spacing was observed across maize varieties. The Alombo variety demonstrated a mean dry biomass yield of 13.2 t/ha, slightly lower than the SY Ignis variety, which achieved a mean value of 15.9 t/ha (p = 0.027 for Alombo). This variance is the inherent varietal differences in their ability to adapt to environmental conditions and utilize available resources, reflecting their genetic predisposition to yield performance. Within-variety comparisons provided further insights into the relationship between row spacing and dry biomass yield. For the Alombo variety, transitioning from wider to narrower inter-row spacing led to a modest increase in mean yield from 12.2 to 14.1 t/ha, although this change did not reach statistical significance within this variety (p = 0.023). In contrast, the SY Ignis variety exhibited a more pronounced response to row spacing, with mean yields escalating from 14.0 t/ha at wider spacing to 17.7 t/ha at narrower inter-row spacing (p = 0.023), pinpointing the pivotal role of genetic factors in modulating the impact of cultivation practices.

4.5 Interplay between spacing strategies and maize grain production efficiency

Our study reveals that SY Ignis significantly benefits from narrower row spacing (0.375 m) and wider intra-row spacing (0.33 m), with a remarkable increase in grain yield from 7.3 to 9.1 t/ha. This improvement shows the potential of optimized spacing strategies to enhance grain productivity, echoing findings from previous research [36]. In contrast, Alombo’s grain yield did not exhibit a statistically significant variation with changes in row spacing, highlighting an aspect of varietal sensitivity to cultivation practices [37]. Both varieties demonstrated enhanced ear weight and length at 0.375 m spacing, with SY Ignis showing a notably pronounced response in ear weight. This finding is consistent with theories that suggest wider planting can influence not only the yield but also the physical attributes of crops, potentially due to altered competition for resources [38]. The statistically significant variation in ear length for Alombo further indicates that row spacing is a critical factor in determining the physical dimensions of maize ears. However, there are studies that conflict with our findings, suggesting that narrower spacing leads to an increase in grain yield in maize [39,40].

The substantial increase in seed number and TKW for both varieties at the narrower spacing further substantiates the argument for precision in planting density. Specifically, SY Ignis exhibited an exceptional rise in seed number, which, alongside the TKW improvements, particularly for Alombo, suggests that closer intra-row spacing may facilitate enhanced seed production and grain weight. However, the lack of statistically significant differences in the perimeter at the middle of the ear, row number, and ear yield between different row spacings or varieties points to a more complex relationship among these traits, environmental conditions, and genetic factors.

Incorporating narrow inter-row spacing (0.375 m), which adjusts intra-row spacing accordingly, presents a strategic approach to enhancing maize biomass and grain yield. Such precision in agricultural management can play a crucial role in meeting the growing demands for food production. However, the results of this study are not only variety-specific but also year/weather-specific and, most crucially, site-specific, highlighting that each site may yield different outcomes due to the unique reactions of the varieties to the dominant conditions.