Effect of Reduced Tillage and Weather Conditions on the Yield Formation of Selected Ancient and Modern Wheat Species

Abstract

A sustainable approach to agricultural production and increasing interest in alternative wheat species have intensified research on simplified soil management systems under changing climatic conditions. A three-year field experiment (2018–2020) was conducted to evaluate the effects of tillage methods (plowing, shallow tillage, and strip-till) and hydrothermal conditions on yield formation and yield components in three wheat species: Triticum sphaerococcum, Triticum persicum, and Triticum aestivum ssp. vulgare. The results showed that weather conditions during the growing season strongly modulated species responses to tillage systems. Multivariate analyses confirmed that grain yield was mainly determined by fertile generative tiller density and grain number per spike, whereas thousand-grain weight played a secondary or compensatory role. In T. sphaerococcum, clear tillage effects occurred only in the most favorable year, when shallow tillage enhanced yield. T. persicum consistently responded positively to strip-till across all years, increasing grain yield by 35.5% compared with plowing. In T. aestivum, the direction of tillage effects depended on weather conditions, with shallow tillage being most beneficial under favorable moisture and plowing under drier conditions. Overall, simplified tillage systems can enhance the productivity of ancient wheat species without reducing the performance of common wheat, provided that soil management is aligned with prevailing hydrothermal conditions.

1. Introduction

Ancient wheat varieties exhibit several beneficial quality characteristics that contribute to their nutritional value and suitability for food production [1,2]. The species like Triticum sphaerococcum (Indian dwarf wheat) and Triticum persicum (Persian wheat) are rich in dietary fiber, minerals, and vitamins. They have been shown to outperform common wheat in amino acid profiles, especially in essential amino acids [3,4,5,6]. The protein content in ancient wheat can be higher than in common wheat, contributing positively to its overall quality. For instance, Persian wheat has been distinguished for its elevated essential amino acid content, which is vital for human health [5,7]. Ancient wheat varieties tend to have more favorable technological parameters for baking and food processing. They exhibit higher values for protein complex and water absorption characteristics, making them suitable for producing high-quality bread and baked goods [4]. Moreover, ancient wheats contain a variety of low-molecular phytochemicals, including phenolic acids and alkylresorcinols, which are linked to biological and health benefits, such as antioxidant properties [6]. Unlike modern wheat, ancient wheats generally require fewer agricultural inputs. Their adaptability to diverse soil and climate conditions enhances their potential for sustainable agriculture [8,9]. The genetic diversity found in ancient wheat species contributes to their resilience against environmental stresses and changing climate conditions, making them a valuable option for future food security [1]. The characteristics mentioned above make ancient wheat varieties promising candidates for both enhancing human health and promoting sustainable farming practices.

Tillage practices are a key agronomic factor influencing wheat growth, soil condition, and the sustainability of crop production. Conventional tillage based on deep plowing remains widely used and is often treated as a reference system due to its effectiveness in seedbed preparation, mechanical weed control, and incorporation of fertilizers into the soil profile, which can support high yields under favorable conditions. However, intensive soil disturbance associated with plowing can lead to soil erosion, degradation of soil structure, reduced biological activity, high energy consumption, and increased environmental costs, including biodiversity loss and greenhouse gas emissions [10,11,12,13]. Reduced tillage systems, including shallow tillage and strip-till, aim to limit soil disturbance while maintaining suitable conditions for crop establishment. These systems are increasingly promoted due to their potential to improve soil moisture conservation, reduce fuel use, enhance soil biological activity, and increase resilience to climatic variability [14,15,16,17,18,19,20]. Nevertheless, reduced tillage may also involve challenges such as increased weed pressure, variable yield responses during the transition period, and limited fertilizer incorporation into deeper soil layers, which require site-specific management strategies [21]. The effectiveness of both conventional and reduced tillage systems depends strongly on local soil conditions, climate, and management intensity. Numerous studies indicate that weather variability may exert a stronger influence on wheat yield formation than the tillage system itself, particularly under conditions of water or temperature stress [10,22,23]. These interactions are especially relevant for wheat species characterized by distinct morphological and developmental traits.

Ancient wheat species, such as Indian dwarf wheat and Persian wheat, differ markedly from modern bread wheat in terms of their ecological adaptation and growth requirements, which have direct implications for their agronomic performance. Indian dwarf wheat is a hexaploid species historically cultivated in the Indian subcontinent, primarily under semi-arid conditions with limited water availability. It is characterized by short, rigid culms, compact spikes, high tillering capacity with a high proportion of non-productive tillers, and spherical grains, traits that are associated with adaptation to drought- and heat-prone environments and low-input cropping systems [8,9,24]. Persian wheat, a tetraploid species originating from the Caucasus region, is an early-maturing spring wheat adapted to less intensive cultivation, but it shows lower lodging resistance and greater sensitivity to environmental stresses compared with Indian dwarf wheat [25]. Previous studies indicate that the yield formation of ancient wheat species is strongly influenced by hydrothermal conditions and that interannual weather variability may outweigh the effects of individual agronomic practices [25]. Therefore, assessing the suitability of ancient wheat requires careful consideration of its specific ecological adaptations to cultivation under different tillage methods.

The aim of this study was to quantify the effects of different tillage systems (plowing, shallow tillage, and strip-till) on yield formation, yield components, and spike traits of selected ancient and modern spring wheat species under contrasting hydrothermal conditions. It was assumed that: (1) tillage methods will have a significant impact on yield components such as generative tiller density, number of grains per spike, 1000-grain weight, and grain yield; (2) the response of plant biometric traits and grain yield to tillage methods will depend on weather conditions; and (3) the interaction between the study year and tillage methods would be different for ancient wheat species and common wheat.

2. Materials and Methods

2.1. Site Description and Crop Management

Field experiments were located in Poland (53°13′ N; 17°51′ E), in the Kuyavian–Pomeranian voivodeship. The soils at experimental sites were characterized as Alfisol (USDA). The abundance of available macronutrients, pH, and C-org content is presented in

Table 1. Chemical properties of soil before starting the experiment.

N-NO3	N-NH4	P	K	Mg	pH KCl	C-Org. %
mg kg−1 of Soil		mg 100 g−1 of Soil
4.18	4.36	11.21	15.31	5.58	7.01	1.11

. The organic carbon content was determined using a CN elemental analyzer (Variomax CN, ELEMENTAR, Lagesenbold, Germany), following the method described by Piotrowska-Długosz et al. [26]. The soil pH was determined in a 1 M KCL solution potentiometrically on a pH meter [27]. The content of P and K in the soil was determined by the Egner–Riehm method [28,29], and Mg by the Schachtschabel method [30]. The content of K and Mg was determined by atomic absorption spectrometry, and P by spectrophotometry. The content of N-NO₃ and N-NH₄ was determined using the flow colorimetry method [31].

2.2. Weather Conditions

Meteorological conditions during the growing seasons of 2018–2020 were characterized using daily records of precipitation and mean air temperature, aggregated into 10-day periods (decades) from 1 April to 30 June. Weather data were obtained from the local meteorological station located at the experimental site.

Moisture conditions were assessed using the Sielianinov hydrothermal coefficient (k), calculated and classified according to Radzka et al. [32]. The values of the Sielianinov index were related to phenological stages of wheat development, including emergence, leaf development, tillering, stem elongation, booting, heading/flowering, and beginning of grain development.

Meteorological conditions from 1 April to 30 June differed considerably among the years 2018–2020 (Figure 1,

Table 2. Sielianinov index in growing stages of T. sphaerococcum, T. persicum, and T. aestivum.

Year	April			May			June
	1–10	11–20	20–30	1–10	11–20	20–31	1–10	11–20	20–30
2018	1.98	0.93	0.71	0.51	0.28	0.10	0.36	0.00	1.16
2019	0.00	0.00	0.11	1.06	4.59	1.42	0.00	0.71	0.07
2020	0.00	0.00	0.07	1.46	1.15	0.55	4.23	1.72	2.96
	Emergence	Leaf development	Tillering		Stem elongation		Booting	Heading/ flowering	Grain development

Classification of hydrothermal conditions: extremely dry k ≤ 0.4; very dry 0.4 < k ≤ 0.7; dry 0.7 < k ≤ 1.0; quite dry 1.0 < k ≤ 1.3; optimal 1.3 < k ≤ 1.6; moderately wet 1.6 < k ≤ 2.0; wet 2.0 < k ≤ 2.5; very wet 2.5 < k ≤ 3.0; extremely wet k > 3.0.

). In 2018, precipitation was relatively evenly distributed in April, with Sielianinov index values indicating moderately wet to optimal conditions during early growth stages. However, from late April through May, a progressive decrease in rainfall was observed, accompanied by rising temperatures. This resulted in dry to extremely dry conditions during stem elongation, booting, and heading. June was characterized by very low rainfall, indicating severe drought stress during grain development.

In 2019, April was extremely dry, with no recorded rainfall throughout the month, coinciding with the emergence and early leaf development stages. A marked change occurred in May, when substantial rainfall combined with moderate temperatures led to optimal to extremely wet conditions, especially during booting. June again showed unfavorable moisture conditions, with drought prevailing during early and late grain development, interrupted only by short-term rainfall events.

In 2020, dry conditions dominated April. Moisture availability improved in May, when rainfall and temperature conditions resulted in quite dry to optimal moisture status during stem elongation and booting. The most favorable conditions occurred in June, particularly in the first decade, when high rainfall combined with moderate temperatures produced extremely wet conditions during early grain development. Subsequent decades of June remained moderately wet to very wet, ensuring an adequate water supply during grain filling.

2.3. Experimental Treatments and Crop Management

The three tillage methods—plowing, shallow tillage, and strip-till (main plots)—and three spring wheat species—common, Indian, and Persian wheat (subplots)—were tested. The experiment was established in a split-plot design in four replications. The width of the plots was 3 m, and the length was 7 m; hence, the total plot area was 21 m².

During the harvest of the previous crop (triticale), the straw was removed from the field. The soil was then shallowly tilled (5–7 cm) using a cultivator, and a catch crop (Pisum sativum) was sown. In late autumn (mid-November), on the field part designated for plowing treatment (PL), pre-winter plowing was carried out to a depth of 22–23 cm. At the same time, shallow disking (approx. 10 cm) was carried out in the part of the field with shallow surface tillage (SH). In the field designated for strip-till (ST), no pre-winter tillage was carried out.

In mid-March, pre-sowing fertilization was performed at the following rates: 30 P₂O₅ (superphosphate), 50 K₂O (potassium salt), and 30 N kg ha⁻¹ (ammonium sulphate). On the plowing and shallow tillage plots, fertilizers were broadcast applied. Next, for the plowing plots, a tillage unit consisting of a cultivator and string roller was used. In the shallow tillage plots, after the spring fertilizer application, shallow discing was performed to a depth of approximately 10 cm. At the beginning of the shooting stage, nitrogen fertilization (ammonium sulphate) was additionally applied by broadcast at a rate of 30 N kg ha⁻¹ on all plots.

Under strip-till technology, a Czajkowski ST cultivation and seeding was used. The detailed description of the cultivation and sowing unit used in our research was presented in an earlier article [33]. In this technology, fertilizers are applied deeply (to a depth of 25 cm) into the loosened strip of soil prepared by a cultivation knife (furrow opener). In our experiment, the nutrient doses and fertilizer forms were the same as in the other treatments (PL and SH). The sowing of wheat into the pea plant mulch was performed by the Czajkowski PS seeding attachment aggregated with the part for tillage and fertilizer application. In the attachment, seeds are sown in a strip prepared by a chisel opener sweep. The width of the sowing strip is 25.0 cm, and the unsown and uncultivated inter-row width is 12.5 cm. In the plow-tillage and shallow-tillage systems, sowing was performed using a Polonez D 3/550 disc seeder (Unia, Grudziądz, Poland) with a 12 cm row spacing.

Sowing of the wheat was performed at the beginning of the growing period (1 April 2018; 26 April 2019; 23 March 2020), at a density of 600 no m⁻². Ancient wheats: Indian dwarf wheat (Triticum sphaerococcum Perc.), cv. ‘Trispa’, and Persian wheat (Triticum persicum Vav.), cv. ‘Persa’, were used in our study. The characteristics of these cultivars are provided in previous work [34]. Common wheat (Triticum aestivum ssp. vulgare) cv. ‘Torridon’ was used. It is characterized by high grain yield and a medium TGW.

Pesticide treatments were applied using the same technology for wheat species, adjusting the selection of preparations to the current threats from pests and weeds. All details are contained in an earlier publication [25]. T. persicum was harvested at the end of July; however, T. sphaerococcum and T. aestivum ssp. vulgare were harvested 7–10 days later, using a Wintersteiger Classic Plus (Ried im Innkreis, Austria) plot harvester.

2.4. Measurements of Biometric Traits and Yield

At full maturity, the number of fertile generative tillers (bearing spikes with grains) and sterile generative tillers (bearing spikes without grains) was assessed within representative 1 m² sections of each plot. At the same growth stage, 50 spikes were randomly collected from every plot to determine the numbers of fertile and sterile spikelets as well as the number of grains per spike.

During harvest, grain and straw yields and their moisture content were measured, and the recorded weights were recalculated to a standard moisture level of 15%. The 1000-grain weight was determined from grain samples adjusted to a constant moisture of 12%, using 500 grains in four replications for each treatment.

2.5. Statistical Analyses

As part of the descriptive statistics, mean values ($\overline{x}$) and standard deviation (±SD) were determined. The results were verified by conducting statistical analysis in accordance with the methods of statistical inference [35]. Equality of variance was tested with Levene test, and normality of the distribution was tested with Kolmogorov–Smirnov test. ANOVA was used to find significant differences between the means, and in the case of significant differences, Tukey’s post hoc test was employed. The statistics were computed using the Jamovi software (version 2.6.44) [36]. The significance level for all analyses was assumed to be minimal, α = 0.05. Principal component analysis (PCA) was applied to quantitative variables describing plant morphological traits and yield, as well as weather conditions, to assess the response of genotypes to soil cultivation systems in variable years of research. Before the analysis, observations with missing data were removed, and all variables were standardized to ensure a comparable contribution of individual traits to the variance structure. The cultivation system and genotype were treated as grouping factors, visualized on PCA biplots, which allowed for the interpretation of relationships between yield, weather, and agrotechnical practices. The influence of experimental factors and weather conditions on wheat yield was additionally analyzed using generalized additive models (GAM). Genotype and cropping systems were considered as fixed effects, while year was considered a random effect. Temperature and precipitation variability were analyzed on a decadal basis, modeling them as two-dimensional functions of time and meteorological variable values. PCA and GAM analyses and visualization of results were performed using the R 4.5.2 statistical package integrated with RStudio 2025.09.2 Build 418.

3. Results

3.1. Triticum Sphaerococcum

In the three-year study period, significant differences were observed in the response of T. sphaerococcum to the soil tillage method, although the direction of these effects was strongly modulated by weather conditions (

Table 3. Grain yield and yield components of Triticum sphaerococcum under different tillage methods.

Year	Tillage Method	Grain Yield (Mg ha−1)	Straw Yield (Mg ha−1)	Grain Per Spike (No)	TGW (g)
2018	PL	2.01 ± 0.129 a	1.95 ± 0.144 a	31.5 ± 0.432 a	35.6 ± 3.99 a
	SH	2.29 ± 0.192 a	2.01 ± 0.123 a	28.1 ± 0.903 a	36.1 ± 3.89 a
	ST	2.32 ± 0.136 a	2.56 ± 0.196 a	27.4 ± 0.802 a	37.1 ± 2.94 a
2019	PL	2.59 ± 0.140 a	4.14 ± 0.106 a	34.5 ± 3.34 a	29.0 ± 0.63 a
	SH	2.63 ± 0.025 a	3.99 ± 0.116 a	30.4 ± 5.64 a	27.3 ± 1.31 a
	ST	2.71 ± 0.054 a	3.94 ± 0.114 a	31.8 ± 1.96 a	26.5 ± 2.06 a
2020	PL	3.72 ± 0.054 b	6.45 ± 0.358 ab	26.4 ± 1.64 a	31.7 ± 1.75 a
	SH	4.28 ± 0.084 a	6.63 ± 0.855 a	28.1 ± 1.12 a	31.0 ± 1.04 a
	ST	3.63 ± 0.231 b	5.63 ± 0.314 b	29.9 ± 1.49 a	29.0 ± 0.68 a

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

and

Table 4. Tillering, spike structure, and root weight of T. sphaerococcum under different tillage methods.

Year	Tillage Method	Fertile Generative Tillers (No m−2)	Sterile Generative Tillers (No m−2)	Fertile Spikelets (No Spike−1)	Sterile Spikelets (No Spike−1)	Root Weight (DM of 30 Plants, g)
2018	PL	211 ± 31.3 a	96.0 ± 15.7 a	13.5 ± 0.86 a	5.53 ± 0.18 a	3.06 ± 0.62 a
	SH	226 ± 14.1 a	94.0 ± 11.4 a	12.2 ± 0.28 a	5.93 ± 0.37a	4.21 ± 0.58 a
	ST	230 ± 14.3 a	103.3 ± 17 a	12.8 ± 0.96 a	5.70 ± 0.67 a	4.96 ± 0.86 a
2019	PL	375 ± 24.2 b	9.5 ± 0.58 a	13.9 ± 0.48 a	5.99 ± 0.19 a	4.23 ± 0.17 b
	SH	437 ± 28.1 b	8.7 ± 5.25 a	14.4 ± 1.81 a	5.28 ± 1.41 a	4.71 ± 0.46 ab
	ST	621 ± 56.2 a	8.0 ± 3.27 a	16.5 ± 0.29 a	2.76 ± 0.25 a	5.05 ± 0.64 a
2020	PL	604 ± 38.1 b	1.5 ± 1.91 a	15.0 ± 1.41 a	4.50 ± 1.00 a	9.63 ± 0.56 ab
	SH	725 ± 48.5 a	1.5 ± 1.00 a	12.3 ± 2.75 ab	6.50 ± 1.00 a	10.37 ± 0.46 a
	ST	601 ± 67.5 b	4.0 ± 1.41 a	10.8 ± 3.59 b	6.00 ± 1.41 a	8.93 ± 0.45 b

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

). The most pronounced differences concerned grain yield, the number of fertile generative tillers, and the number of fertile spikelets per spike. In the year with the most favorable environmental conditions (2020), T. sphaerococcum grown under SH had significantly larger grain yield compared with PL and ST (Table 3). In the years characterized by less favorable weather patterns (2018 and 2019), the differences between soil tillage methods were small and showed no consistent direction. Differences in straw yield were significant only in 2020, when it was significantly higher under SH compared to ST.

The number of fertile generative tillers varied substantially in 2019 and 2020. In 2019, the highest density of fertile generative tillers was recorded under ST (Table 4). In 2020, the highest number of fertile generative tillers occurred under SH, whereas no differences were observed between PL and ST. In terms of spike traits, significant differences between soil tillage methods were recorded for the number of fertile spikelets in 2020. A higher number of fertile spikelets was found in T. sphaerococcum under PL compared with ST.

Correlation analysis revealed that the grain yield of T. sphaerococcum was most strongly associated with the number of fertile generative tillers (

Table 5. Pearson’s correlation coefficients between grain yield and yield components in T. sphaerococcum.

Traits	Grain Yield	Fertile Generative Tillers	Grain Per Spike
Fertile generative tillers	0.864 ***
Grain per spike	−0.291	−0.119
TGW	−0.297	−0.547 ***	−0.297

*** p < 0.001.

). In contrast, there was no significant relationship between grain yield and the number of grains per spike, suggesting a limited contribution of this trait to yield formation under the studied conditions. Similarly, there was no correlation of grain yield with TGW. However, TGW was significantly and negatively correlated with the number of fertile generative tillers.

3.2. Triticum Persicum

In the three-year study, significant differences in morphological and agronomical traits were observed in the response of T. persicum to the soil tillage systems (

Table 6. Grain yield and yield components of Triticum persicum under different tillage methods.

Year	Tillage Method	Grain Yield (Mg ha−1)	Straw Yield (Mg ha−1)	Grain Per Spike (No)	TGW (g)
2018	PL	0.77 ± 0.090 b	1.76 ± 0.088 a	15.0 ± 1.68 a	32.0 ± 0.02 a
	SH	1.12 ± 0.139 a	1.87 ± 0.147 a	13.3 ± 1.47 a	33.6 ± 0.99 a
	ST	1.01 ± 0.073 a	2.05 ± 0.085 a	11.9 ± 0.71 a	32.4 ± 0.12 a
2019	PL	1.28 ± 0.034 c	3.51 ± 0.186 a	19.3 ± 2.29 a	27.2 ± 0.51 a
	SH	1.51 ± 0.014 b	3.48 ± 0.265 a	18.5 ± 1.59 a	26.5 ± 1.88 a
	ST	1.93 ± 0.082 a	3.51 ± 0.162 a	20.1 ± 2.45 a	23.7 ± 1.55 b
2020	PL	2.36 ± 0.015 c	5.53 ± 0.364 a	27.3 ± 4.41 a	26.1 ± 0.10 a
	SH	2.79 ± 0.091 b	5.69 ± 0.413 a	29.4 ± 2.35 a	26.5 ± 0.81 a
	ST	3.04 ± 0.187 a	3.99 ± 0.325 b	29.3 ± 2.82 a	25.2 ± 0.92 a

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

and

Table 7. Tillering, spike structure, and root weight of T. persicum under different tillage methods.

Year	Tillage Method	Fertile Generative Tillers (No m−2)	Sterile Generative Tillers (No m−2)	Fertile Spikelets (No Spike−1)	Sterile Spikelets (No Spike−1)	Root Weight (DM of 30 Plants, g)
2018	PL	65 ± 5.19 b	50.7 ± 0.47 b	10.4 ± 0.53 a	4.63 ± 0.43 a	2.98 ± 0.36 a
	SH	162 ± 1.41 a	65.3 ± 0.47 ab	10.1 ± 0.38 a	4.23 ± 0.32 a	4.15 ± 0.56 a
	ST	97 ± 6.60 ab	66.0 ± 2.83 a	9.6 ± 0.73 a	4.30 ± 0.45 a	3.55 ± 0.36 a
2019	PL	292 ± 8.35 c	42.0 ± 7.07 a	10.8 ± 1.34 a	2.48 ± 0.55 a	2.99 ± 0.47 b
	SH	310 ± 14.7 b	37.5 ± 9.29 a	10.0 ± 0.46 a	2.36 ± 0.17 a	3.74 ± 0.58 ab
	ST	333 ± 26.8 a	8.5 ± 1.00 b	10.9 ± 1.03 a	2.20 ± 0.39 a	4.61 ± 0.54 a
2020	PL	670 ± 35.3 b	52.0 ± 10.1 a	12.0 ± 1.15 a	1.50 ± 0.58 a	5.76 ± 0.91 b
	SH	713 ± 48.6 ab	31.0 ± 2.31 b	13.3 ± 2.87 a	1.75 ± 0.96 a	5.33 ± 0.97 b
	ST	753 ± 26.1 a	22.5 ± 9.81 b	12.3 ± 0.96 a	2.00 ± 0.82 a	9.93 ± 0.81 a

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

). The magnitude and direction of changes in these traits were strongly dependent on weather conditions, which was particularly evident in grain yield and the number of fertile generative tillers. In 2018, a year characterized by less favorable weather conditions, the highest grain yield was obtained under SH and ST (Table 6). In 2019, the advantage of the ST system became even more pronounced. In 2020, which provided the most favorable environmental conditions, T. persicum produced larger grain yields than in the previous years, with ST again achieving the highest values. In all three years of the study, the lowest grain yield of T. persicum was consistently recorded under PL. The response of T. persicum straw yield to the tillage method was recorded only in 2020, in which it was the lowest under ST. TGW was most dependent on the year of study and only slightly responded to tillage methods. Only in 2019 was there a significant reduction in TGW under ST. The number of grains per spike and TGW showed moderate differences between soil tillage methods, and their variability was more strongly driven by year-to-year weather conditions than by tillage treatment.

A similar pattern to that observed in grain yield was noted for the number of fertile generative tillers (Table 7). In 2018, the highest fertile tiller density was recorded under SH; in 2019, ST dominated; and in 2020, all tillage systems showed very high values, with ST maintaining its advantage. The number of fertile spikelets varied within relatively narrow ranges, although slightly higher values were observed under SH in 2020. The highest number of sterile spikelets per spike was observed in 2018; however, there was no significant effect of tillage methods on this spike trait in any of the study years.

Correlation analysis revealed that the grain yield of Triticum persicum was very strongly and positively associated with the number of fertile generative tillers (

Table 8. Pearson’s correlation coefficients between grain yield and yield components in T. persicum.

Traits	Grain Yield	Fertile Generative Tillers	Grain Per Spike
Fertile generative tillers	0.968 ***
Grain per spike	0.898 ***	0.931 ***
TGW	−0.706 ***	−0.706 ***	−0.692 ***

*** p < 0.001.

). A highly significant positive correlation was also observed between grain yield and the number of grains per spike. In contrast, grain yield showed a strong and significant negative correlation with TGW. Moreover, TGW was significantly and negatively correlated with both the number of fertile generative tillers and the number of grains per spike.

3.3. Triticum aestivum ssp. Vulgare

In the three-year study period, significant variation was observed in the response of T. aestivum to the tillage systems, with the direction and magnitude of changes in individual traits being strongly dependent on weather conditions (

Table 9. Grain yield and yield components of Triticum aestivum ssp. vulgare under different tillage methods.

Year	Tillage Method	Grain Yield (Mg ha−1)	Straw Yield (Mg ha−1)	Grain Per Spike (no)	TGW (g)
2018	PL	2.95 ± 0.172 a	1.73 ± 0.126 a	25.1 ± 1.06 a	41.7 ± 4.40 b
	SH	3.03 ± 0.161 a	1.71 ± 0.128 a	26.1 ± 1.76 a	41.3 ± 3.38 b
	ST	2.51 ± 0.232 b	1.45 ± 0.152 a	22.8 ± 2.52 a	47.9 ± 0.40 a
2019	PL	3.56 ± 0.066 a	1.50 ± 0.043 a	28.7 ± 4.78 a	23.5 ± 2.47 a
	SH	3.03 ± 0.051 b	1.35 ± 0.112 a	29.5 ± 4.00 a	24.4 ± 1.36 a
	ST	2.60 ± 0.115 c	1.26 ± 0.185 a	32.0 ± 1.29 a	26.4 ± 1.92 a
2020	PL	5.84 ± 0.135 b	4.34 ± 0.456 a	32.4 ± 3.09 a	39.5 ± 2.01 a
	SH	6.20 ± 0.061 a	4.07 ± 0.419 a	34.7 ± 1.99 a	38.5 ± 0.998 a
	ST	5.38 ± 0.172 c	3.83 ± 0.486 a	30.2 ± 4.01 a	41.8 ± 1.94 a

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

and

Table 10. Tillering, spike structure, and root weight of T. aestivum ssp. vulgare under different tillage methods.

Year	Tillage Method	Fertile Generative Tillers (No m−2)	Sterile Generative Tillers (No m−2)	Fertile Spikelets (No Spike−1)	Sterile Spikelets (No Spike−1)	Root Weight (DM of 30 Plants, g)
2018	PL	304 ± 7.14 a	57.33 ± 3.40 ab	13.3 ± 1.16 a	3.56 ± 0.56 a	5.03 ± 0.16 a
	SH	306 ± 8.16 a	34.00 ± 3.27 b	12.7 ± 0.77 a	3.43 ± 0.59 a	5.97 ± 1.05 a
	ST	268 ± 19.5 a	79.33 ± 8.96 a	12.2 ± 0.49 a	3.93 ± 0.43 a	5.53 ± 0.50 a
2019	PL	377 ± 12.4 a	8.67 ± 0.94 a	13.2 ± 1.37 a	3.48 ± 0.71 a	10.81 ± 1.34 a
	SH	369 ± 20.8 ab	6.00 ± 1.63 a	13.6 ± 1.04 a	3.18 ± 0.74 a	8.18 ± 1.47 b
	ST	332 ± 8.64 b	17.33 ± 5.19 a	15.1 ± 0.35 a	2.20 ± 0.14 a	7.33 ± 0.94 b
2020	PL	523 ± 25.2 a	1.00 ± 0.52 a	13.0 ± 1.15 a	2.00 ± 0.62 a	13.80 ± 1.14 a
	SH	548 ± 27.0 a	0.50 ± 0.38 a	12.8 ± 1.71 a	3.50 ± 0.91 a	14.87 ± 1.51 a
	ST	466 ± 27.2 b	3.50 ± 0.99 a	12.5 ± 2.08 a	2.25 ± 0.66 a	13.81 ± 1.15 a

Mean values ± standard deviation (SD) in column followed by different letters indicate significant differences between treatments within a year at p ≤ 0.05.

). The most pronounced differences concerned grain yield and the number of fertile generative tillers. In 2018, the highest grain yields were obtained under SH and PL, whereas the lowest yields occurred under ST (Table 6). In 2019, significant differences were again recorded—PL produced the highest yields, SH significantly lower, and ST the lowest. In 2020, which was characterized by the most favorable weather conditions, the highest yields were achieved under SH, significantly lower under PL, and again the lowest under ST. Straw yield and the number of grains per spike did not differ significantly between the tillage systems in 2018 and 2019. Thousand-grain weight, however, was significantly differentiated only in 2018—the highest value was recorded under ST, while PL and SH produced significantly lower values. Significant differences were also noted in the number of fertile generative tillers. In 2019, the highest number of fertile tillers was found in PL and the lowest in ST. In 2020, T. aestivum produced more fertile tillers under PL and SH, whereas ST again showed the lowest values.

The number of sterile generative tillers differed significantly only in 2018—the fewest were recorded under SH, and the most under ST. In 2019 and 2020, no significant differences were observed between treatments (Table 9).

Spike structural traits (the number of fertile and sterile spikelets) showed no significant differences between tillage systems in any of the years studied. Both parameters varied within relatively narrow ranges and were more strongly influenced by the weather conditions of each season than by the tillage method.

Correlation analysis revealed that the grain yield of Triticum aestivum was strongly and positively associated with the number of fertile generative tillers (

Table 11. Pearson’s correlation coefficients between grain yield and yield components in T. aestivum ssp. vulgare.

Traits	Grain Yield	Fertile Generative Tillers	Grain Per Spike
Fertile generative tillers	0.946 ***
Grain per spike	0.575 ***	0.667 ***
TGW	0.197	−0.021	−0.301

*** p < 0.001.

). A significant positive correlation was also observed between grain yield and the number of grains per spike. In contrast, no significant relationship was found between grain yield and TGW, suggesting a limited direct contribution of grain weight to yield variability under the studied conditions. TGW showed no significant correlation with the number of fertile generative tillers and the number of grains per spike, indicating only a weak compensatory effect between grain size and grain number.

3.4. The Impact of Weather Conditions on Crop Yield

Principal component analysis (PCA) was performed on quantitative variables after their prior standardization (mean = 0, standard deviation = 1) in order to reduce the dimensionality of the data and identify the main axes of variability describing the studied morphological and yield-related traits and environmental indicators. The first two principal components explained a total of 74.2% of the total data variance, with PC1 accounting for 48.5% and PC2 for 25.7% of the variance (Figure 2). Such a high proportion of variance explained by the first two axes indicates a strong data structure and the validity of their further interpretation in a two-dimensional PCA space. The first principal component (PC1) was positively correlated primarily with characteristics such as fertile tillers, straw yield, root weight, and number of grains per spike, as well as precipitation in May and June. Negative PC1 values were associated with thousand-grain weight (TGW) and variables describing weather conditions in April and temperature in May.

The second principal component (PC2), explaining 25.7% of the total variance, is largely determined by meteorological variables, in particular precipitation in May (R V), temperature in June (T VI), and the SI index in May (SI V), which show strong negative loadings on the PC2 axis. The vectors of these variables are clearly elongated and located in the lower part of the graph, opposite to most biometric data, which indicates their high contribution to the variability described by this component.

The analysis indicates that precipitation in May and temperature in June, as well as the Sielianinow hydrothermal index in May, are positively correlated with fertile spikelets and negatively correlated with sterile spikelets and TGW. Grain yield is positively correlated with root weight and hydrothermal conditions in June and negatively correlated with precipitation in April and temperature in April and May, which are strongly related to the number of sterile tillers and sterile spikelets.

Generalized Additive Models (GAM) described yield variability very well (R² = 0.921), explaining 93.2% of the variability (

Table 12. Summary of the GAM model explaining yield variation.

Model Component	Term	Estimate/Edf	Test Statistic	Significance
Parametric	Intercept	1.47	t = 13.9	***
Parametric	Genotype	1.30–2.65	t = 12.7–25.8	***
Parametric	System	0.34–0.53	t = 3.27–5.13	**
Interaction	Genotype × System	(−0.04)–(−1.15)	t = −0.29–(−7.89)	*
Smooth	Period, Temperature	edf = 14.1	F = 9.99	***
Smooth	Period, Rainfall	edf = 11.7	F = 11.6	***
Random effect	Year	edf ≈ 0	—	***

The generalized additive model (GAM) explained 93.2% of deviance (adjusted R² = 0.921; n = 243; REML = 200.72). Smooth terms indicate strong nonlinear hydrothermal control of yield during specific developmental periods (10-day periods from April to June). Statistical significance levels are marked with the following symbols: * p < 0.05, ** p < 0.01, *** p < 0.001.

). Both the main effects of genotype and cultivation system and their interaction were statistically significant, as were the nonlinear effects of hydrothermal conditions as a function of period (p < 0.001). The analysis confirmed a significantly higher yield of T. sphaerococcum and T. aestivum compared to T. persicum. Shallow tillage and strip-till increased the yield compared to the plow system, but significant genotype × system interactions indicate that the increase in yield depended on a combination of factors. The strongest negative interaction effect was found for the T. aestivum × ST, indicating a reduction in the yield stability of this T. aestivum in the strip-till system.

The analysis of smoothing tensor functions revealed a significant, nonlinear impact of temperature and precipitation depending on the development period (Figure 3). Critical phases for yield were identified in the middle of the analyzed time interval, covering 11 May–31 May, as the period with the strongest negative impact of high temperature on yield, especially when combined with a shortage of precipitation. The analysis showed that during the period from 1 June to 20 June, the yield remained significantly sensitive to hydrothermal conditions, but this sensitivity was much lower than in May. In later periods (after around 20 June), the impact of temperature and rainfall on the value of the linear yield predictor weakened significantly, indicating reduced susceptibility of the yield to environmental stress after the completion of key stages of yield formation. Shallow tillage contributed to greater yield stability under unfavorable conditions, suggesting its potential to mitigate the negative effects of water and heat stress during critical periods. In contrast, despite its positive main effect, the strip-till system exacerbated the negative effects of rainfall deficiency and high temperatures in interaction with the high-yielding genotype (Triticum aestivum), especially during key developmental periods.

4. Discussion

Analysis of the three wheat species revealed that their responses to tillage systems were strongly shaped by annual weather variability, confirming the role of hydrothermal conditions in determining growth dynamics and yield formation. This pattern aligns with earlier findings indicating that the effect of tillage on wheat productivity is not fixed but depends on year-specific climatic constraints [37]. Many authors similarly conclude that the impact of reducing soil disturbance interacts strongly with both environmental conditions and genetic traits of a species or cultivar [15,38,39]. The combined influence of genotype and tillage system on wheat productivity has also been confirmed elsewhere [40].

4.1. Triticum Sphaerococcum

In our study, T. sphaerococcum showed strong sensitivity to precipitation patterns during the spring months. In the wettest year (2020, 215 mm from March to June), total generative tiller density reached 646 m⁻², nearly double that recorded in the driest year (2018, 98 mm). Water deficit in 2018 not only reduced tillering but also increased the proportion of sterile generative tillers to 31%, demonstrating premature death of shoots under drought stress. Under moderate water deficit in 2019 (137 mm), total generative tillers decreased by 26% relative to 2020, but most shoots (98%) remained fertile.

These tillering responses corresponded closely to grain and straw yield patterns. Severe drought in 2018 resulted in weak performance (2.2 Mg ha⁻¹), whereas yield in 2020 increased by 76%, with 2019 showing intermediate productivity. Only in the most favorable year (2020) was a clear differentiation among tillage systems observed, with shallow tillage (SH) consistently resulting in the highest grain and straw yields. The same system also promoted the formation of fertile generative tillers. The correlation structure indicates that the grain yield of T. sphaerococcum was mainly determined by the number of fertile generative tillers (r = 0.864). In contrast, TGW and grain number per spike played a secondary or compensatory role in yield formation. Similar benefits of reduced tillage on tillering and wheat yield have been documented previously [10]. Wesołowska et al. [41], in a three-year study on spelt, found no effect of tillage system on grain yield, suggesting that reducing tillage disturbance can be effective in some species. Kulig et al. [42] likewise attributed the higher yield in simplified systems to adequate spring rainfall.

4.2. Triticum Persicum

T. persicum proved even more sensitive to drought stress than T. sphaerococcum. In 2018, the species produced only 169 generative tillers, of which merely 62% developed fertile spikelets. In the favorable year 2020, the total number of generative tillers increased more than fourfold (712 m⁻²), and nearly all (95%) formed spikes with grain. Weather conditions had a strong influence on grains per spike, which doubled in 2020 compared with 2018. Grain yield showed a parallel response: lowest in 2018 (0.97 Mg ha⁻¹), intermediate in 2019 (1.57 Mg ha⁻¹), and highest in 2020 (3.04 Mg ha⁻¹). The correlation structure indicates that the grain yield of T. persicum was determined primarily by the number of fertile generative tillers (r = 0.968) and the number of grains per spike (r = 0.898). In contrast, TGW played a compensatory role in yield formation. Previous research on this species also demonstrated a strong correlation between fertile generative tillers, grain number per spike, and yield (r = 0.95 and r = 0.94, respectively) [25].

Reduced tillage, especially SH and particularly ST, generally increased the number of fertile generative tillers and improved grain yield relative to plowing (PL). Across all years, PL consistently resulted in the lowest grain yield. Notably, a reduction in straw yield was observed in ST in 2020, and TGW was reduced in 2019. Although strip-till consistently increased grain yield, the accompanying reductions in straw biomass and, in some years, thousand-grain weight indicate the presence of yield trade-offs that merit biological interpretation. The observed pattern suggests a shift in assimilate allocation towards grain production at the expense of vegetative biomass, particularly under favorable hydrothermal conditions. This interpretation is supported by the strong association between grain yield and fertile tiller density, as well as by the increased root biomass observed under strip-till, which likely enhanced early nutrient and water uptake. Reduced straw yield under strip-till may therefore reflect more efficient resource partitioning rather than a limitation of crop growth. Similarly, the occasional reduction in thousand-grain weight appears to result from compensatory mechanisms between grain number and grain size, especially in years with high yield potential. These trade-offs indicate that the yield advantage of strip-till was primarily driven by increased sink capacity (number of fertile tillers and grains). In contrast, individual grain mass and vegetative biomass played a secondary role. Such responses are consistent with strong climatic control of yield formation and further emphasize that the benefits of strip-till are conditional on weather conditions during key developmental stages. The positive effects of reduced tillage in wheat cultivation shown in our study were previously described by Mirzavand and Moradi-Talebbeigi [43]. Conversely, results from other studies are mixed: Armengot [39] found similar grain yields under reduced and conventional tillage, while Khorami et al. [10] reported no significant differences in tiller number or grain yield. Kayan et al. [37] further support the idea that simplified tillage can enhance grain yield when soil moisture is preserved.

As in our previous studies conducted in organic farming [44], ancient wheat species (T. sphaerococcum and T. persicum) grown conventionally generally responded favorably to reduced tillage, likely due to lower nutrient requirements associated with lower natural productivity than in T. aestivum. Zikeli and Gruber [20] argued that reduced nutrient mineralization under shallow disturbance conditions limits productivity mainly in high-demand crops.

4.3. Triticum aestivum ssp. Vulgare

T. aestivum showed high reproductive stability, with 84–100% of generative tillers producing fertile spikes across years. Nevertheless, both tillering and grain yield were markedly constrained by drought in 2018, when grain number per spike dropped to 22.8–26.1 and grain yield to 2.51–3.03 Mg ha⁻¹. The most favorable year was 2020, when grain yields reached 5.38–6.20 Mg ha⁻¹ (approximately 50% higher than in 2018–2019), reflecting both higher tiller density and improved spike fertility. The correlation structure indicates that the grain yield of T. aestivum was mainly determined by the number of fertile generative tillers (r = 0.946), with the number of grains per spike playing a supportive role. At the same time, TGW had a minor influence on yield formation.

Tillage effects varied by year. In 2018 and 2019, PL produced the highest yields, and SH performed similarly or slightly lower, while ST consistently resulted in the lowest yields and the fewest tillers (despite producing the highest TGW in 2018). The contrasting response of modern and ancient wheat species to strip-till reflects differences in their physiological and agronomic adaptation to soil management and hydrothermal variability. The negative response of Triticum aestivum to strip-till, particularly under dry or thermally stressful conditions, may be linked to limited early-season access to water and nutrients resulting from row spacing and localized fertilizer placement, which can restrict tiller establishment in modern wheat. In contrast, ancient wheat species showed greater plasticity under strip-till, as indicated by higher root biomass and a stronger dependence of yield formation on fertile tiller density rather than grain size. Multivariate analyses further demonstrated that yield responses across species were primarily constrained by hydrothermal conditions during critical developmental stages, especially from mid-May to early June. Under these conditions, tillage effects became secondary, whereas under more favorable moisture regimes, simplified tillage systems enhanced yield formation. This confirms that the effectiveness of strip-till is species-dependent and strongly conditioned by weather variability.

In our study, in 2020, SH was most productive, suggesting that the benefits of reduced disturbance are expressed primarily under adequate moisture. The pronounced year effect on TGW, especially the reduction in 2019, when July rainfall was minimal, emphasizes the sensitivity of grain filling to late-season moisture supply.

The advantage of plowing for bread wheat observed in several years here is consistent with numerous studies demonstrating its positive effect on nutrient distribution and root access to deeper resources [45,46,47]. Negative responses to reduced tillage reported by Yildirim et al. [47] and Sans et al. [48] also align with our findings for ST. Nevertheless, as emphasized in earlier studies [14,15,49], the yield response to tillage simplification remains highly dependent on climatic conditions.

The multivariate analyses provided additional insight into the mechanisms underlying yield formation across tillage systems and years. The PCA separated variables related to yield and biomass production from those associated with grain size, indicating that grain yield was primarily driven by fertile tiller density, root biomass, and grain number per spike rather than by TGW. These yield-forming traits were closely aligned with precipitation and hydrothermal conditions in May and June, whereas unfavorable early-season conditions were associated with increased numbers of sterile tillers and spikelets. This confirms that hydrothermal stress during tillering and stem elongation constrains the establishment of productive shoots, which is consistent with earlier findings on the decisive role of weather during these stages [10,37]. The GAM analysis further showed that yield responses to temperature and precipitation were nonlinear and strongly dependent on the timing of stress. The most critical period occurred in mid-May, when high temperatures combined with low rainfall exerted the strongest negative effect on yield, while crop sensitivity to weather conditions declined after mid-June. Shallow tillage contributed to greater yield stability under unfavorable hydrothermal conditions, suggesting improved soil water availability during key developmental stages, as also reported in previous studies [14,15]. These multivariate patterns provide a common framework for interpreting the species-specific responses described in Section 4.1, Section 4.2 and Section 4.3, where differences among T. sphaerococcum, T. persicum, and T. aestivum reflect contrasting sensitivities of tillering and grain development to tillage practices under variable weather conditions.

5. Conclusions

This three-year field study demonstrated that wheat yield formation is primarily governed by the interaction between tillage system and hydrothermal conditions during the growing season, with apparent differences among wheat species. Multivariate analyses confirmed that grain yield was mainly determined by fertile generative tiller density and grain number per spike, whereas thousand-grain weight played a secondary or compensatory role. Weather conditions during critical developmental stages, particularly tillering and stem elongation in May and early June, exerted a more substantial influence on yield formation than tillage alone.

Ancient wheat species showed a favorable response to simplified tillage systems under suitable hydrothermal conditions. In Triticum sphaerococcum, shallow tillage enhanced tiller fertility and grain yield in the most favorable year, while under less favorable conditions, differences between tillage systems were limited. Triticum persicum exhibited the most consistent response to strip-till, which increased fertile tiller density and grain yield across all study years, whereas conventional plowing repeatedly resulted in the lowest productivity. These results indicate that reduced soil disturbance can improve the performance of ancient wheat species, particularly when moisture availability during early growth is sufficient.

In contrast, Triticum aestivum showed greater yield stability across years but a more variable response to tillage systems. Shallow tillage was the most beneficial option under favorable moisture conditions, while plowing provided higher yields in drier years. Strip-till remained the least advantageous system for common wheat, despite occasionally increasing thousand-grain weight. This confirms that the effectiveness of reduced tillage in modern wheat is strongly dependent on prevailing weather conditions.

Overall, the findings highlight that simplified tillage systems can enhance the productivity of ancient wheat species without compromising the performance of common wheat, provided that tillage practices are aligned with expected hydrothermal conditions. Species-specific traits and seasonal weather patterns should therefore be considered key criteria when selecting soil management strategies to improve yield stability and sustainability of wheat production under changing climatic conditions.