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Article

Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission

by
Amare Assefa Bogale
1,2,*,
Zoltan Kende
1,
Akos Tarnawa
1,
Peter Miko
1,
Marta Birkás
1,
Gergő Péter Kovács
1 and
Attila Percze
1
1
Institute of Agronomy, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
2
Department of Horticulture, College of Agriculture and Natural Resource, Mekdela Amba University, Tulu Awuliya P.O. Box 32, Ethiopia
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(1), 2; https://doi.org/10.3390/agronomy15010002
Submission received: 30 September 2024 / Revised: 17 November 2024 / Accepted: 20 December 2024 / Published: 24 December 2024
(This article belongs to the Special Issue Effects of Soil Tillage and Fertilizer Management on Production of Cereal Crops: 2nd Edition)
Browse Figures
Figure 1
<p>Meteorological data recorded during the 2023 and 2024 cropping season at Godollo (<a href="https://www.meteoblue.com" target="_blank">https://www.meteoblue.com</a>, accessed on 2 November 2024).</p> "> Figure 2
<p>Environmental Gas Monitor 5 (EGM-5), a portable gas analyzer instrument (<a href="https://images.app.goo.gl/KYdJtY5VsNaxdrwj9" target="_blank">https://images.app.goo.gl/KYdJtY5VsNaxdrwj9</a>, accessed on 2 November 2024).</p> "> Figure 3
<p>Response of soil CO<sub>2</sub> at different growth stages of winter barley in different soil tillage treatments. BBCH19-29 = leaf development–tillering stage; BBCH30-49 = stem elongation–booting stage; BBCH51-73 = beginning to heading–early milky stage.</p> "> Figure 4
<p>Mean of relative soil–plant analysis development (SPAD values) of winter barley at different time points across different tillage types.</p> "> Figure 5
<p>Mean of leaf area index (LAI) values of winter barley at various recording time points across different tillage types.</p> "> Figure 6
<p>Interaction effects of nutrient and tillage on grain yield of winter barley in 2023 and 2024 growing seasons.</p> "> Figure 7
<p>Interaction effects of nutrient and tillage treatment on thousand kernel weight of winter barley in 2023 and 2024 growing seasons.</p> ">
Review Reports Versions Notes

Abstract

:
Precision sustainable agronomic practices are crucial for achieving global food security as well as mitigating climate change. A field experiment was conducted at the Hungarian University of Agriculture and Life Sciences in Gödöllő from 2023 to 2024. The study aimed to evaluate the effects of soil tillage and foliar nutrient supplementation on winter barley yield, associated characteristics, and soil CO2 emissions. Employing a split-plot design with three replications, the experiment included four nutrient treatments (control, bio-cereal, bio-algae, and MgSMnZn blend) and two soil tillage type (i.e., plowing and cultivator). The study found that soil CO2 emissions were influenced by the crop growth stage across both tillage treatments throughout the growing seasons, but the tillage system itself did not have an effect. Similarly, the leaf chlorophyll content was not affected by tillage and nutrient treatments. Plant height, the leaf area index (LAI), and thousand kernel weights (TKW) were significantly affected by nutrient treatments across the growing seasons. Both nutrient and tillage treatments also had a notable effect on the number of productive tillers in winter barley. Moreover, nutrient and tillage treatments consistently influenced grain yield across the two growing seasons, and their interaction significantly impacted both grain yield and thousand kernel weights. The bio-cereal nutrient treatment combined with plowing tillage yielded the highest values for most parameters throughout the growing seasons. Therefore, it can be concluded that the combination of bio-cereal nutrient treatments and plowing tillage can boost winter barley yields. Notably, soil CO2 emissions peak during the crops’ reproductive stage, surpassing levels from early growth.

1. Introduction

Barley (Hordeum vulgare L.) holds significant importance globally, ranking as the fourth-largest cereal crop [1]. With a history spanning thousands of years, it has been a staple in human diets [2]. Its appeal to farmers lies in its adaptability to diverse environmental conditions, ensuring robust yields economically [3,4]. Its resilience allows for cultivation during main and short rainy seasons, as well as in regions with residual available moisture [5]. Its importance extends beyond mere sustenance, with applications in beer production and animal feed. Nutritionally, barley grains are rich in dietary fiber, dominantly β-glucan and tocols. These components offer numerous health benefits, including protection against circulatory diseases, hypertension, and diabetes [6]. These health benefits, coupled with its high fiber content and beneficial compounds, have propelled barley to recognition as a valuable functional food with significant contributions to both food and nutritional security [7]. However, despite its economic, nutritional, and industrial significance, barley productivity remains influenced by numerous agronomic factors that directly impact its potential to contribute to food and nutritional security in the face of a rapidly growing global population [8]. This implies that addressing these production challenges is essential to fully realize barley’s role in sustainable agriculture and global health.
The anticipated rise in global population by two billion by 2050, alongside nearly one billion people already grappling with hunger and malnutrition, is intensifying the global food security crisis [9,10]. This rapid population growth signals an urgent need to achieve both higher and more consistent yields in cereal crops to meet increasing grain demands. However, optimizing yields in crops like winter barley is not straightforward, as it depends on a range of production factors including nutrient availability, genetic variation, climate conditions, and tillage practices [11]. In Hungary, these agricultural challenges are further compounded by climate change, with rising temperatures and extreme weather events making the region’s agricultural sector especially vulnerable to yield fluctuations [12]. Such volatile conditions contribute to yield instability, placing both the agricultural sector and food supply chains at risk. Addressing these obstacles requires a commitment to sustainable farming practices that strengthen crop resilience. Advanced sustainable approaches, such as developing climate-resilient crop varieties, precision irrigation, targeted nutrient management, enhanced plant protection, and effective tillage and watershed management, are essential to navigating these pressing challenges [13]. These integrative approaches provide a path to steady, increased yields, helping secure food supplies in a world facing both environmental and demographic pressures.
In the face of these challenges, improving the productivity and quality of winter barley requires particular attention to the soil tillage system and nutrient supplies [14]. This emphasis is warranted because soil is one of the most important natural resources and medium for plant growth [15]. The attainment of necessary crop yield and quality hinges on implementing an appropriate soil tillage strategy and ensuring an adequate supply of nutrients based on the soil analysis indices [16]. Indeed, soil tillage practices can have a profound impact on a variety of soil-mediated processes, including soil carbon sequestration (SCS) or depletion, water pollution, and greenhouse gas emissions such as CO2, CH4, and N2O [17]. This indicates the critical role of tillage in agricultural systems and practices worldwide, aimed at mitigating climatic and soil constraints while sustaining several ecosystem services. Addressing these factors through appropriate management practices, integrated pest management strategies, crop breeding programs, and research efforts aimed at improving resilience to environmental stress can help optimize barley productivity and quality. Additionally, adopting sustainable agronomic practices can contribute to long-term productivity while minimizing negative impacts on the environment [18].
Agronomic approaches, particularly nutrient management, have been proposed to enhance winter barley grain yield and improve its nutritional composition. The decline in soil fertility due to factors such as insufficient fertilizer use, ongoing nutrient depletion by crops, and limited organic matter application poses a significant threat to crop productivity [19,20]. These approaches further encompass various agronomic strategies aimed at optimizing crop management practices and maximizing the genetic potential of barley varieties. Precise nutrient management fosters not just bountiful harvests and increased profitability but also promotes the responsible use of nutrients and water, all while minimizing greenhouse gas emissions. Therefore, the adoption of precision agriculture technologies is essential for reducing greenhouse gas emissions while improving farm productivity and economics, emphasizing the importance of sustainable practices in agriculture [21]. An integral aspect of this sustainable approach is the use of natural bio-resources to enhance crop nutrient status and enable biological nutrient transfer. Biological nutrients are pivotal in the regulated mineralization and fertilization process, offering an eco-friendly, renewable, and cost-effective alternative to traditional chemical fertilizers [22]. Additionally, studies have shown that blending algae with synthetic nitrogen fertilizer synchronizes nitrogen release with crop uptake, thus improving both crop yield and environmental sustainability [23]. In light of these considerations, this study aims to provide valuable insights into the complex relationship between agronomic practices, specifically soil tillage and nutrient management, and winter barley yield and related traits, as well as soil caron dioxide emissions. Thus, the objective of this investigation was to assess the impact of soil tillage methods and foliar nutrient supplementation on the yield and yield-related traits of winter barley and soil carbon dioxide emissions.

2. Materials and Methods

2.1. Description of Experimental Site

The field experiment was conducted during the winter cropping season of 2022/2023 and 2023/2024 at the Department of Agronomy field experimental plot within the Hungarian University of Agriculture and Life Sciences, located in Godollo, northeast of Budapest, Hungary. The experimental site’s geographical coordinates are approximately 47°36′0″ N latitude and 19°22′0.12″ E longitude, situated at an elevation of 207 m (690 ft) meters above sea level. The soil at the experimental site is classified as sandy loam. The average temperature, precipitation, and average relative humidity throughout the experiment were recorded (Figure 1).

2.2. Treatments, Experimental Design, and Procedure

The field experiment was conducted using a split-plot design, organized within a randomized complete block arrangement. Each treatment was replicated three times to ensure the robustness of the results. The experimental plots were structured as follows: each sub-plot measured 5 m × 6.2 m, resulting in a total plot size of 31 square meters. To maintain proper spacing and minimize interference between plots, a distance of 1 m was set between them. Additionally, blocks were positioned 2 m apart to maintain uniformity within the experimental setup.
The experiment consisted of two main factors, namely soil tillage treatment (plowing and cultivator) and nutrient treatment (control, bio-cereal, bio-algae, and MgSMnZn blend). Plowing was conducted at a depth of 30 cm, while cultivator was performed at a depth of 28 cm. Both tillage methods were followed by surface consolidation using rollers at the same time. The compositions of the applied nutrient treatments are described in Table 1. Soil tillage treatment was assigned to the main plots, while nutrient treatments were assigned to the sub-plots. The initial phase of the cultivation process involved land preparation, which was conducted in accordance with the selected soil tillage treatments facilitated by a tractor. Before planting, the crop received comprehensive basic fertilization. The soil provided phosphorus (111.1 mg/kg and 207.4 mg/kg) and potassium (119.9 mg/kg and 206.79 mg/kg) during the first and second growing seasons, respectively. Following this, two row winter barley seeds (KH TARNA variety) were sowed at a recommended rate of 190 kg ha−1 at a row distance of 12 cm. Nitrogen fertilizer was used in the springtime as a top dressing at a rate of 200 kg/ha. The nutrient treatment was applied at the stem elongation stage of the crop by a foliar application method based on the production company recommendation rate (Table 1). Weed management was efficiently carried out through granstar super 50 SX herbicide with the recommended rate of 50 g/ha to ensure a weed-free environment conducive to optimal crop growth. Harvesting operations were executed at the physiological maturity of the crop, completing a well-structured and meticulous agricultural process.

2.3. Data Collection and Measurements

Soil carbon dioxide (CO2) emissions were measured using an Environmental Gas Monitor 5 (EGM-5), a portable gas analyzer (Figure 2). According to the description in [24], the instrument employs an infrared gas analyzer (IRGA) as its core component to detect CO2. The IRGA measures the absorption of infrared light at specific wavelengths characteristic of CO2, leveraging the principle of infrared absorption. The EGM-5 is also equipped with a soil respiration chamber (SRC-2), which directly attaches to the instrument for direct CO2 measurements from the soil surface. The system is closed, meaning that the air within the chamber is completely isolated from ambient air. It is also dynamic, as it continuously circulates sample gas between the chamber and the infrared gas analyzer (IRGA). In addition, the SRC-2 chamber encloses a specific soil area, enabling the IRGA to monitor the increase in CO2 concentration within the chamber as CO2 is emitted from the soil. In our experiment, soil CO2 flux (g/m2/h) was measured every two weeks between 9:00 and 13:00 on non-rainy days, following the sowing of a winter barley crop. The measurements were taken from two replicated tillage treatments, namely two plowings and two cultivators until the crop was changed at the maturity stage. During each measurement session, six repetitions of CO2 flux from each replication across treatment levels were recorded at the center of each designated plot. The CO2 flux values across the recording period were then averaged for each treatment, allowing for a comparison of CO2 emissions between the two tillage treatments. Additionally, the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und Chemical Industry scale) was used to describe the growth stages of winter barley, enabling comparisons of CO2 flux across different plant development stages. The measured CO2 flux is automatically calculated by the EGM-5 instrument by following the formula which is found in the EGM-5 operational manual [24].
Leaf chlorophyll content was assessed using a handheld chlorophyll meter (SPAD) at regular intervals every week. Measurements were taken from the ten middle, fully expanded, and intact leaves of plants. This sampling protocol was consistently applied across all treatments and replications, ensuring uniformity in data collection. The leaf area index (LAI) was determined using a PAR (photosynthetically active radiation) LP-80 ceptometer on a weekly basis. This method involved evaluating the light intercepted by the canopy and comparing it with the incident light above the canopy. At each sampling plot, the measurement was conducted three times. Initially, the ceptometer was positioned above the canopy to record the incoming PAR (above canopy PAR). Subsequently, the ceptometer was placed below the canopy to record the transmitted PAR (below canopy PAR). The LAI was automatically calculated by the device using the following formula, as described by [25,26]:
L A I = − l o g c   ( b e l o w   c a n o p y   P A R / a b o v e   c a n o p y   P A R ) K
where
  • LAI = leaf area index;
  • below canopy PAR = PAR measured below the canopy;
  • above canopy PAR = PAR measured above the canopy;
  • K = extinction coefficient, which depends on the canopy structure and properties of the vegetation being measured. This coefficient is often determined empirically for specific vegetation types or conditions.
Plant height measurements were conducted at the physiological maturity stage of the plants, whereby ten randomly selected plant samples from the central rows of each plot were assessed. The height was measured in centimeters from ground level to the tip of the spike, excluding the crowns. This meticulous approach ensured accuracy and consistency across the treatment levels. Additionally, main spike measurements were taken from the same ten sampled plants in each plot, also in centimeters.
The number of productive (fertile) tillers bearing spikes was counted from ten individual plants from each plot at the physiological maturity stage of the crop. After harvesting the crop from each plot by a combine harvester, the weight of a thousand grains was determined using a sensitive balance, measured in grams. This measurement provided insights into the individual grain size and weight variability within the sampled population. Furthermore, the grain yield was assessed by weighing the grains obtained from a defined plot area of 6.25 square meters. Subsequently, to account for the natural moisture content of the harvested grains, which was approximately 12.5%, adjustments were made to the recorded weight. This correction ensured that the reported grain yield accurately reflected the dry weight of the grains. Finally, the grain yield was converted to tons per hectare (t/ha), providing a standardized unit for comparison across different plots and treatments.

2.4. Statistical Data Analysis

Before proceeding with statistical analysis, the normality assumption of the data was assessed using the Shapiro–Wilk test [27], along with an examination of skewness and kurtosis [28]. It was found that the studied traits met this assumption, ensuring the validity of subsequent analyses. Additionally, the homogeneity of variance was examined using Levene’s test. By following these assumptions, independent samples T-tests were used to analyze the soil CO2 data to differentiate the means between tillage treatments. The CO2 emission data at different growth stages, the soil–plant analysis development (SPAD) value data at different recording time points, and leaf area index (LAI) data at different recording time points were analyzed by using general linear model repeated measures analysis of variance (RMANOVA). Conversely, to differentiate the significant effects between groups for dependent variables, such as the SPAD value, leaf area index, plant height, spike length, effective tiller number, thousand kernel weight, and grain yield, they were analyzed by using the general linear model multivariate analysis of variance (MANOVA) method. In the same way, to see the synergetic effects of tillage and nutrient treatments, the general linear model multivariate analysis of variance (MANOVA) method was used. The fixed factors are tillage and nutrient treatment. By following these model methods, statistical analyses were performed using IBM SPSS version 29 software. Significant values of each parameter have been checked by the Bonferroni correction. To identify significant differences among treatment means, Tukey’s test was utilized with a significance level set at 0.05. For visual representation of the data, graphs were prepared using Microsoft Excel, facilitating clear and concise presentation of the findings.

3. Results

3.1. Effect of Soil Tillage on Soil Carbon Dioxide (CO2) Emissions Under Winter Barley Cultivation

Analysis of soil CO2 emissions across the two growing seasons revealed no significant difference between tillage treatments (Table 2). Graphical representations of soil CO2 data distribution (Figures S1 and S2) further support this finding, showing similar trends and frequencies for both tillage types.

3.2. Emissions of Soil CO2 at Different Growth Stages of Winter Barley Within Different Tillage Methods

In this study, we observed significant differences in soil CO2 emissions at different growth stages of winter barley across the two growing seasons, using different tillage methods (Figure 3). During the first growing season, the highest soil CO2 emissions were recorded at the growth stages corresponding to BBCH 51-73, with values of 0.394 g/m2/h for the cultivator treatment and 0.388 g/m2/h for the plowing treatment. In contrast, the growth stages BBCH 19-29 and BBCH 30-40 exhibited the lowest CO2 emissions; however, these differences were not statistically significant.
In the subsequent growing season, we noted a peak soil CO2 emission during the growth stages of BBCH 30-49, where emissions reached 0.504 g/m2/h under the cultivating treatment and 0.43 g/m2/h under the plowing treatment. Additionally, the growth stages of BBCH 51-73 also demonstrated substantial emissions, with values of 0.401 g/m2/h for the cultivator and 0.55 g/m2/h for the plowing treatment. Conversely, the minimum soil CO2 emissions recorded during this season occurred at the growth stage of BBCH 19-29, yielding 0.249 g/m2/h and 0.222 g/m2/h for the cultivator and plowing treatments, respectively.

3.3. Effect of Nutrient and Tillage on Leaf Chlorophyll Content (SPAD Value) of Winter Barley

MANOVA results indicated no significant differences in the multivariate means of leaf chlorophyll content across nutrient and tillage treatments during the first and second growing seasons. However, a significant interaction effect between nutrient and tillage treatments emerged during the second growing season (F (3, 856) = 3.197, p = 0.023, Table 3). Moreover, significant differences in mean leaf chlorophyll content were observed across recording time points within nutrient and tillage treatments during both growing seasons (Figure 4 and Figure S3).
In the first growing season, all nutrient treatments, except for the control, recorded the highest SPAD values at 196 days after sowing (DAS), whereas the lowest SPAD values were observed at 203 DAS. In the second growing season, the maximum SPAD value, including that of the control nutrient treatment, was again recorded at 196 DAS, and the minimum SPAD value remained consistent with that of the first growing season across all nutrient treatments (Figure S3).
Regarding tillage treatments, the minimum SPAD value was recorded at 203 DAS, while the other three recording points showed maximum values that were statistically similar for both tillage treatments during the first growing season (Figure 4). In the second growing season, the minimum SPAD value was still recorded at 203 DAS, with the remaining recording points exhibiting maximum and statistically similar results for the plowing tillage treatment. In contrast, for the cultivator tillage treatment, the maximum SPAD value occurred at 196 DAS, while the minimum was recorded at 203 DAS.

3.4. Effect of Nutrient and Tillage on Leaf Area Index (LAI) of Winter Barley

Statistically significant differences in the leaf area index (LAI) were observed among nutrient treatments in both the first (F (3, 232) = 4.879, p = 0.003) and second (F (3, 856) = 8.635, p < 0.001) growing seasons. No significant differences were found between tillage treatments in the first season, but it became significant in the second growing season (F (1, 856) = 30.060, p < 0.001). The interaction between nutrient and tillage treatments was not significant in the first season but became significant in the second season (F (3, 856) = 5.842, p < 0.001) (Table 3).
Mean LAI values (Table 4) revealed that bio-cereal nutrient treatment exhibited the highest LAI (2.4403) in the first growing season, significantly differing from the comparable values of the control, bio-algae, and MgSMnZn blend treatments. During the second growing season, both bio-cereal (3.3372) and bio-algae (3.2939) nutrient treatments yielded the maximum LAI, while the control and MgSMnZn blend treatments showed similar minimal values.
Regarding tillage, no significant LAI differences were observed in the first growing season, although plowing numerically produced the highest LAI. However, in the second growing season, plowing (3.3385) and cultivating (3.0767) treatments exhibited significantly different LAI values, with plowing resulting in the highest LAI (Table 5).
The LAI varied significantly across different recording time points within both growing seasons for both nutrient and tillage treatments (Figure S4 and Figure 5). In the first season, control, bio-algae, and MgSMnZn blend treatments recorded the highest LAI at 196 and 203 days after sowing (DAS), while the lowest LAI was observed at 189 DAS. The bio-cereal treatment showed no significant differences across recording points. In the second season, the control treatment exhibited the maximum and minimum LAI at 196 DAS and 182 DAS, respectively. For the bio-cereal treatment, the maximum LAI occurred at 182 and 196 DAS, and the minimum at 189 and 203 DAS. Bio-algae and MgSMnZn blend treatments did not show significant LAI differences across recording times (Figure S4).
For tillage treatments in the first season, plowing showed the highest LAI at 203 DAS, while the remaining time points showed similar low values. Cultivator treatment exhibited the maximum LAI at 196 and 203 DAS and the minimum at 182 and 189 DAS. In the second season, the maximum LAI at 196 DAS and the minimum LAI at 189 and 203 DAS were recorded from plowing treatment, whereas the cultivator treatment showed maximum and minimum values at 189 and 196 DAS, respectively (Figure 5).

3.5. Effect of Nutrient and Tillage on Plant Height and Spike Length of Winter Barley

Multivariate analysis of variance (MANOVA) in Table 6 indicated significant differences in plant height among nutrient treatments in both the first (F (3, 232) = 6.072, p < 0.001) and second (F (3, 856) = 17.550, p < 0.001) growing seasons. However, tillage treatments and their interactions with nutrients did not significantly affect plant height in the first growing season. In contrast, both tillage (F (1, 856) = 71.959, p < 0.001) and the interaction between nutrients and tillage (F (3, 856) = 45.533, p < 0.001) significantly influenced plant height in the second growing season. Bio-cereal nutrient treatment resulted in the highest mean plant height (82.7333 cm) in the first growing season (Table 7). In the second growing season, both bio-algae (78.583 cm) and bio-cereal (77.528 cm) treatments produced taller plants compared to the control and MgSMnZn blend treatments (Table 7). For tillage treatments, plowing produced the longest plant height (78.315 cm), and cultivator resulted in the shortest (76.134 cm) in the second growing season (Table 8).
No significant differences in spike length were observed among nutrient treatments, tillage treatments, or their interactions in the first growing season. In contrast, in the second growing season, significant differences among nutrient treatments (F (3, 856) = 7.881, p < 0.001), between tillage treatments (F (1, 856) = 31.101, p < 0.001), and their interactions (F (3, 856) = 3.432, p < 0.017) on spike length were found (Table 6). Bio-algae and MgSMnZn blend treatments yielded the maximum spike length (8.630 cm and 8.657 cm), respectively, in the second growing season, while the control and bio-cereal treatments had the minimum spike length (8.333 cm) (Table 7). For tillage treatments, the cultivator produced the longest spikes (8.667 cm), and plowing resulted in the shortest (8.310 cm) in the second growing season (Table 8).

3.6. Effect of Nutrient and Tillage on Yield and Related Traits of Winter Barley

The analysis conducted using MANOVA revealed significant differences in the effective tiller number, thousand grain weight, and grain yield due to the main effects of tillage and nutrient treatments, as well as their interactions across the two growing seasons (Table 9).
The effective tiller number was significantly influenced by both tillage and nutrient treatments in each of the growing seasons. In the first growing season, both nutrient (F (3, 232) = 9.86, p < 0.001) and tillage treatments (F (1, 232) = 33.356, p < 0.001) had significant effects. However, the interaction between nutrient and tillage treatments was not significant. In the second growing season, significant differences were again noted for both nutrient (F (3, 856) = 8.848, p < 0.001) and tillage treatments (F (1, 856) = 6.820, p < 0.009), as well as their interaction.
The mean effective tiller numbers (Table 10) indicated that the bio-cereal and bio-algae nutrient treatments consistently produced more tillers than the control and MgSMnZn blend treatments in both seasons. While distinct differences among nutrient treatments were observed in the first season, these discrepancies were less pronounced in the second season, with all treatments, except the control, displaying similarly high tiller numbers. Plowing consistently produced higher effective tiller numbers compared to cultivator in both growing seasons, with average values of 6.5083 in the first season and 4.94 in the second season (Table 11).
Regarding thousand kernel weight (TKW), the MANOVA results showed significant differences for nutrient treatments (F (3, 232) = 52.809, p < 0.001) and the interaction of nutrient and soil tillage treatments in the first growing season (F (3, 232) = 14.165, p < 0.001). This trend continued in the second growing season, with significant differences observed for nutrient treatments (F (3, 856) = 14.065, p < 0.001) and the interaction with tillage (F (3, 856) = 42.680, p < 0.001). However, no significant difference was noted for the main effect of tillage treatments in the first growing season, though it became significant in the second growing season (F (1, 856) = 232.368, p < 0.001). The mean values presented in Table 10 highlight this finding, showing the highest and lowest mean TKW values of 44.2383 g and 39.1633 g, respectively, from the bio-cereal and control nutrient treatments. The other two treatments (bio-Algae and MgSMnZn blend) showed statistically similar results in the first growing season. In the second growing season, both bio-cereal and the MgSMnZn blend exhibited the highest TKW values of 50.833 g and 51.283 g, respectively. In the case of tillage, cultivator treatment showed the maximum (51.517 g) TKW (Table 11).
Grain yield showed significant differences in the main effects of nutrient (F (3, 232) = 6.720, p < 0.001) and tillage treatments (F (1, 232) = 8.191, p = 0.0413) in the first growing season and again in the second season with values of (F (3, 856) = 9.335, p < 0.001) and (F (1, 856) = 459.406, p < 0.001) (Table 9). Additionally, significant interaction effects between nutrient and tillage treatments were observed in both growing seasons (F (3, 232) = 7.215, p < 0.001 for the first; F (3, 856) = 19.246, p < 0.001 for the second).
The bio-cereal nutrient treatment achieved the highest mean grain yield of 4.20933 t/ha, followed by bio-algae with 3.90267 t/ha. The control and MgSMnZn blend yielded the lowest, at 3.476 t/ha and 3.39467 t/ha, respectively, in the first growing season. In the second growing season, both bio-cereal and the MgSMnZn blend nutrient treatments recorded maximum grain yields of 5.53493 t/ha and 5.55157 t/ha, respectively (Table 10). Notably, the plowing soil tillage treatment resulted in the highest grain yields of 3.956 t/ha in the first growing season and 5.77 t/ha in the second growing season (Table 11).

3.7. Interaction Effects of Nutrient and Tillage on Grain Yield and Thousand Kernel Weight of Winter Barley

The interaction effects of nutrient and tillage treatments on grain yield and thousand kernel weights (TKWs) are illustrated in Figure 6 and Figure 7, respectively. The analysis revealed that the highest grain yield of 4.41 t/ha in the first growing season and 5.996 t/ha in the second season was achieved through the combined application of the bio-cereal nutrient treatment with plowing. Following closely, the combination of bio-algae nutrient treatment and plowing resulted in grain yields of 4.101 t/ha and 5.798 t/ha for the first and second growing seasons, respectively. In contrast, the lowest grain yield of 2.691 t/ha was recorded when using the MgSMnZn blend with cultivator tillage treatment. Additionally, the combination of the control treatment with plowing resulted in a grain yield of 3.213 t/ha in the first growing season, while in the second growing season, the cultivator combined with bio-algae nutrient treatment yielded 5.064 t/ha.
Regarding the thousand kernel weight, the highest value of 44.7 g was observed from the interaction of the bio-cereal nutrient treatment with cultivator tillage. Conversely, the lowest TKW values of 37.97 g and 39.33 g were recorded from the interaction of the control treatment with plowing and the MgSMnZn blend treatment with cultivator, respectively, in the first growing season. In the second growing season, the maximum TKW of 51.867 g was achieved with the combination of the control and the MgSMnZn blend with cultivator, while the minimum TKW of 49.1 g was recorded from the combination of the control with plowing tillage treatment.

4. Discussion

4.1. Response of Soil CO2 Emissions for Different Soil Tillage Treatments at Different Phases of Winter Barley Growth

Soil carbon dioxide (CO2) emissions refer to the release of CO2 from the soil into the atmosphere, primarily resulting from biological processes such as microbial respiration and root respiration. This process is a critical component of the global carbon cycle and significantly contributes to atmospheric CO2 levels, which are closely linked to climate change [29]. The emissions are influenced by various factors, including soil temperature, moisture content, organic matter availability, and land management practices [30,31]. In our study, it has been observed that there was no significant difference in emissions between the cultivator and plowing tillage treatments. This finding is consistent with previous studies that have shown similar results under varying tillage practices, suggesting that the type of tillage may not significantly influence soil CO2 emissions in certain contexts [32,33].
However, in terms of the growth stage of the crop, the early growth stage of the crop showed minimum soil CO2 emissions, while the crop development stage and reproductive stages showed maximum soil CO2 emission across the growing seasons in both tillage treatments. This consistency across growing seasons highlights the potential influence of crop growth stages on soil CO2 emissions, which may be more pronounced than the effects of the tillage type [34]. This indicated that the physiological processes associated with barley growth stages, particularly during later stages, and temperature may enhance microbial activity and root respiration, leading to increased CO2 emissions [35]. The minimum emissions recorded during the early growth stages (BBCH 19-29) further emphasize the relationship between plant development and soil respiration dynamics, as lower root and microbial activity during these stages likely contribute to reduced CO2 emissions [29]. Moreover, the result was also consistent with [36], which suggested that the CO2 emissions during the early growth stage of the crops are generally lower due to reduced root respiration and microbial activity associated with lower root exudation rates. Additionally, the physiological processes that drive soil respiration are closely linked to the plants phenological stages, with significant peaks in respiration often coinciding with reproductive growth stages [37,38].

4.2. Effect of Nutrient Supply and Tillage on Morpho-Physiological Parameters of Winter Barley

Leaf chlorophyll content is a crucial indicator of plant nutritional status and plays a significant role in optimizing fertilizer management to improve crop quality and yield [34,35,39,40]. In this study, multivariate analysis of variance (MANOVA) showed no significant differences in chlorophyll content due to the main effects of various nutrient and tillage treatments across both growing seasons. However, leaf chlorophyll content varied significantly at different recording times within each treatment across both seasons. These results are consistent with previous research on nutrient and tillage practices. For example, ref. [41] found that the foliar application of micronutrients in okra led to variations in chlorophyll content, while ref. [42] demonstrated that different nutrient solutions significantly increased chlorophyll and other photosynthetic pigments in plantlets. These studies highlight the importance of nutrient management in affecting chlorophyll content and overall plant health. The study observations on the effects of tillage treatments on chlorophyll content are supported by research in [43,44]. They found that different tillage practices impact soil compaction, plant growth, and chlorophyll content in maize, highlighting the importance of tillage management in crop production. These findings emphasize that both nutrient and tillage practices must be carefully managed to optimize chlorophyll content and, ultimately, crop yield. The observed pattern, where chlorophyll content peaks before the reproductive phase and declines as plants mature, aligns with findings in [45]. They noted that chlorophyll levels typically decrease as plants shift resources towards grain filling or other reproductive activities. This pattern, seen across both nutrient and tillage treatments, suggests that it is driven more by plant lifecycle stages than by the treatments themselves. The study [46] also observed similar trends, with chlorophyll content peaking mid-season and declining as crops matured, regardless of tillage or fertilization practices.
The leaf area index (LAI) plays a crucial role as an indicator of a plant photosynthetic capacity [47]. Alterations in the LAI have been linked to modifications in canopy structure, thereby influencing light interception and ultimately affecting plant photosynthesis and yield [48]. The relationship between the LAI and light interception was highlighted in [49], which suggested that variations in the LAI can affect the amount of light that plants are able to absorb. For instance, the assessment of winter malting barley grain yield through fractional green canopy cover evaluation methods underscores the significance of monitoring the dynamics of plant canopy for the accurate prediction of grain yield potential [50]. However, the LAI has been found to be influenced by nutrient management and associated practices. In this study, it has been observed that the application of bio-cereal nutrient consistently improves the LAI of winter barley across seasons. This improvement could be due to the unique characteristics of the applied fertilizer, as it contains multiple (Fe, Mn, Cu, Zn, B, Mo) nutrients composed together, which may satisfy the seasonal nutrient requirements of the crop. The presence of these micronutrients’ composition potentially leads to significant alterations in plant growth dynamics. A substantial improvement in plant growth parameters due to nutrient application has been previously reported [51]. However, the profound effect of nutrient supply to improve the LAI can be detected by the source of nutrients, which indicates the significance of nutrient management in shaping plant development [52]. This has been confirmed that the specific application of nutrients can result in differences in the LAI [53,54], further emphasizing the pivotal role of nutrient management in governing plant growth. Additionally, significant differences in the mean leaf area index across recording time points within nutrient and tillage treatments during both growing seasons provides valuable insights into the impact of these factors on plant growth and development. In this study, it was also observed that the control, bio-algae, and MgSMnZn blend treatments exhibited varying LAI values at different time points, with some treatments showing significant differences in the LAI across recording times. This highlights the importance of nutrient and tillage management practices in influencing the leaf area and overall plant growth. These findings are consistent with previous studies that have explored the effects of nutrient management on plant growth. For example, ref. [55] reported that oat plants exhibited improved leaf nutrient concentrations and growth parameters under low-phosphorus treatments, emphasizing the role of nutrient availability in influencing plant growth. Similarly, ref. [56] found that leaf breadth increased significantly with nitrogen fertilization, indicating the positive impact of nutrient levels on leaf development. Furthermore, the study observations on tillage treatments align with research by [57], which noted that crop planting patterns can affect early growth and canopy shape in oats.

4.3. Effect of Nutrient Supply and Tillage on the Growth Parameters of Winter Barley

Plant height is a fundamental morphological trait in plants, defined as the perpendicular distance from the soil at the base of the plant to the highest point reached by the plant with all parts in their natural position [58]. It plays a significant role in determining the structure, biomass allocation, and competitive ability of plants within their environment [59]. However, it has been affected by different factors including nutrient management and tillage practice. In our study, it has been observed that nutrient treatments significantly influenced plant height across the two growing seasons, with notable differences in the impact of tillage practices. This aligns with previous research indicating that nutrient availability is a critical factor in plant growth, particularly when plants are establishing their root systems and overall biomass [60,61]. The highest mean plant height observed with the bio-cereal treatment (82.7333 cm) in the first season and the competitive heights of bio-algae and bio-cereal in the second season may indicate and further support the notion that specific nutrient formulations can optimize plant growth under varying conditions [62]. Likewise, the result was also consistent with [63], which emphasized the significant influence of specific and suitable nutrient supplies on winter barley plant height. While no significant differences in plant height were observed between tillage treatments during the first growing season, plowing tillage led to significantly greater plant height in the second growing season compared to cultivator tillage. This suggests that the long-term effects of tillage practices on soil structure and nutrient dynamics become more pronounced over time, influencing root development and nutrient uptake [60,64]. Plowing, a deep tillage method, inverts the soil and buries crop residues, breaking up compacted soil and creating larger pores [65]. This may improve soil structure and enhance aeration, drainage, and root penetration, leading to better nutrient and water uptake by plants and, ultimately, increased plant height.
Regarding spike length, the first growing season did not reveal significant differences among treatments, which may indicate that the plants were still in a phase of establishing their vegetative growth rather than reproductive structures. In contrast, the second growing season showed significant effects of nutrient treatment and tillage treatment on spike length, indicating that as the plants transitioned to reproductive growth, both nutrient availability and soil management practices became critical for optimizing yield components [66]. The maximum spike lengths achieved with bio-algae and MgSMnZn blend treatments highlight the importance of tailored nutrient applications in enhancing the reproductive traits of plants. The tillage practices also exhibited significant effects on spike length in the second growing season. Cultivator tillage treatment produced the longest spikes. This suggests that tillage methods can influence not only the physical properties of the soil but also the biological processes that govern nutrient availability and uptake, thus affecting overall plant performance [67]. Cultivator tillage, as a shallower tillage method compared to plowing, can maintain soil structure and minimize disturbances to soil microorganisms. This might create better nutrient cycling and plant health, particularly for reproductive strictures like spikes. Additionally, by reducing soil disturbances, cultivator tillage significantly mitigates moisture loss, contributing to improved soil moisture conservation [68]. These factors collectively might lead to improved plant vigor and reproductive development. The findings align with previous research [69,70], which pointed out the necessity of integrating nutrient management with appropriate tillage practices to optimize crop performance in agricultural systems.

4.4. Impact of Nutrient Supply and Tillage on Winter Barley Yield and Yield-Related Traits

The productivity of crops is significantly influenced by yield component parameters, which play a crucial role in determining the overall yield potential. For instance, ref. [71] discussed the impact of the tiller number on the grain-to-straw ratio and grain yield in cereal crops, indicating that the tiller number is a critical parameter in small cereals such as barley, playing a pivotal role in determining plant density, light and nutrient capture, and subsequently growth, development, and yield potential [72]. Similarly, the thousand kernel weight (TKW) serves as a crucial yield component parameter directly affecting grain yield in small cereals like barley. It reflects individual seed size and density, influencing seedling vigor, grain quality, and overall productivity. High TKW correlates with superior grain filling, larger seed size, and enhanced yield potential in cereal crops [73]. Regardless of their importance in improving the productivity of the crop, they have been influenced by different agronomic factors like soil tillage and nutrient management. The findings of our study results, shown in Table 9 and Table 10, suggested significant effects of tillage and nutrient treatments on the tiller number, thousand grain weight, and grain yield across two growing seasons. The findings indicate that both tillage and nutrient treatments play crucial roles in influencing these agronomic traits, which are essential for optimizing crop production. In the first growing season, the tiller number was positively significantly affected by both tillage and nutrient treatments, while their interaction was not significant. This aligns with previous research indicating that an optimal number of productive tillers is critical for enhancing grain yield, as productive tillers compete effectively for resources such as sunlight and nutrients [74]. In the second growing season, the significance of both tillage and nutrient treatments persisted, with their interaction also being significant. This suggests that the effects of these treatments may vary over time, potentially due to changes in environmental conditions and plant responses [75]. The mean tiller numbers indicated that the bio-cereal and bio-algae treatments consistently produced more tillers than the control and MgSMnZn blend treatments. This indicates that these specific nutrient treatments may have multiple nutrient compositions, which may have a positive impact on the growth and development of barley plants, potentially leading to increased yield. The study [76] supports this finding, demonstrating that the application of algae extracts and bio-fertilizer positively influenced the vegetative growth and flowering of Freesia hybrid, resulting in higher average values for growth parameters of the crop. Similarly, the result also supports the idea that nutrient management is pivotal in maximizing tiller production [77].
Additionally, the association of higher tiller numbers with plowing as a tillage treatment suggests that this specific practice may enhance tiller development compared to cultivator tillage treatment. This effect may be attributed to the deeper soil disturbance associated with plowing, which enhances the root length density of the crop, thereby improving access to nutrients and water [78,79]. Consequently, this root growth enhancement stimulates tiller development and increases the tiller number. Furthermore, according to [80], plowing is more effective than cultivator tillage in incorporating crop residues into the soil, leading to improved soil fertility and nutrient availability, which further supports tiller initiation and development. This might have occurred because plowing, which turns the soil more deeply than cultivator tillage, is more successful in incorporating crop leftovers into the soil. The residues are buried beneath the soil’s surface by this deep tillage process, allowing them to break down more slowly and release nutrients over an extended period of time. Tiller development may benefit from this continuous flow of nutrients. The result is consistent with [81], which demonstrated that compared to reduced tillage and no tillage methods, the conventional tillage method caused substantial improvement in almost all the growth, yield, and yield component traits of bread wheat.
Regarding the thousand kernel weight (TKW), significant differences were noted for nutrient treatments in both growing seasons. The interaction between nutrient and tillage treatments was also significant, particularly in the second growing season. This finding is consistent with studies that emphasize the importance of nutrient availability in determining grain quality, as higher nutrient levels can enhance the TKW by promoting better plant health and resource allocation [82]. The mean TKW values further illustrated that the bio-cereal treatment yielded the highest TKW, which is indicative of its effectiveness in nutrient delivery compared to other treatments [83]. Moreover, refs. [84,85] reported that the application of micronutrients improved the inferior grain of maize and wheat thousand kernel weight and ensured the process of grain filling. Concerning tillage treatment, the higher TKW observed with cultivator tillage compared to plowing may be attributed to better soil structure, improved moisture retention, enhanced nutrient availability, and reduced plant stress. Cultivator tillage may create a more favorable environment for grain filling, leading to heavier and better developed kernels. This outcome suggests that under certain conditions, the less aggressive approach of cultivator tillage can optimize the conditions needed for producing high-quality grain, resulting in a higher TKW.
Grain yield, as a composite outcome of growth and yield-related traits, serves as a comprehensive measure of crop productivity [86]. It is a critical measure of agricultural productivity, also exhibiting significant differences due to the main effects of tillage and nutrient treatments in the first growing season, with similar trends observed in the second season. The interaction effects between tillage and nutrient treatments were significant in both seasons, indicating that the combination of these factors can synergistically influence yield outcomes [87]. The bio-cereal treatment achieved the highest mean grain yield in the first season, which aligns with findings that highlight the role of balanced nutrient application in maximizing crop yields [88]. In the second growing season, both bio-cereal and MgSMnZn blend treatments recorded maximum yields, further underscoring the importance of nutrient management in achieving optimal productivity [89]. These results underscore the effectiveness of targeted nutrient interventions in augmenting grain yield, highlighting their relevance in optimizing agricultural practices for enhanced productivity. In the case of tillage treatment, plowing tillage treatment improved the grain yield of winter barley compared to cultivator soil tillage treatment in both seasons. These findings shed light on the intricate relationship between nutrient treatments and soil tillage methods, providing valuable insights for optimizing crop productivity and resource management strategies. Significant enhancements in grain yields attributed to plowing soil tillage have been reported previously [86]. Our study supports the conclusions of [90], indicating that soil tillage practices and nutrient supply can influence winter wheat grain yield. However, our results contradict the findings of [91], who observed that soil tillage did not consistently affect grain yield across various crops.

5. Conclusions

The improper application of nutrients and soil tillage practices poses a significant threat to crop productivity and environmental health. To address these challenges, adopting precision agriculture technologies is essential for enhancing farm productivity while promoting sustainable agricultural practices and mitigating climate change. Sustainable agriculture involves producing enough food to satisfy today’s needs without compromising the ability of future generations to meet their own needs by protecting environmental health. Our study highlights the critical importance of nutrient management and tillage practices in the production of winter barley and soil carbon dioxide emissions. Commonly, the soil CO2 emissions are higher after plowing than in tillage systems without rotation. But in this experiment, surface consolidation performed simultaneously with primary tillage significantly reduced CO2 emissions during plowing. As a result, there was no significant difference in CO2 emissions between the tillage treatments. However, we found that soil carbon dioxide emissions were influenced by the growth stage of the crop across both tillage treatments throughout the growing season. Similarly, the SPAD value and leaf area index (LAI) were affected by the growth stages in relation to nutrient and tillage treatments during the same period.
Key crop characteristics, such as leaf area index, plant height, and thousand kernel weights, were significantly influenced by nutrient treatments across the growing seasons. Additionally, both nutrient and tillage treatments had a notable impact on the number of productive tillers produced in winter barley, underscoring the necessity for integrated management approaches to enhance crop development. Moreover, the main effects of nutrient and tillage treatments consistently influenced grain yield across the two growing seasons, emphasizing the need to optimize these practices to maximize winter barley productivity. Notably, the interaction between nutrient and tillage treatments also significantly affected grain yield and thousand kernel weights, indicating the synergistic effects of effective nutrient management and tillage practices on overall crop performance.
In conclusion, our results suggest that implementing plowing soil tillage along with bio-cereal nutrient treatments yields better results for winter barley production. While we observed that soil carbon dioxide emissions were primarily influenced by the crop’s growth stage rather than soil tillage treatments, this highlights the importance of adopting sustainable agricultural practices. Strategies such as precision nutrient management, appropriate soil tillage techniques, the selection of low-emission crop varieties, efficient water use, and the use of slow-release fertilizers can significantly improve winter barley productivity and help minimize soil carbon dioxide emissions at different growth stages. By integrating precision agriculture technologies and prioritizing sustainable practices, farmers can enhance profitability, promote environmental sustainability, and contribute to the long-term resilience of agricultural systems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15010002/s1, Figures S1 and S2: The distribution range and frequency of soil CO2 emission data for the 2023 and 2024 growing season, respectively; Figure S3: Mean of relative soil–plant analysis development (SPAD) values of winter barley at various time points across different foliar nutrient treatments; Figure S4: Mean of leaf area index (LAI) values of winter barley at various recording time points across different foliar nutrient treatments.

Author Contributions

Conceptualization, A.A.B. and A.P.; methodology, A.A.B.; software, Z.K.; validation, P.M., A.T. and Z.K.; formal analysis, A.A.B.; investigation, A.A.B.; resources, G.P.K.; data curation, A.P.; writing—original draft preparation, A.A.B.; writing—review and editing, A.P.; visualization, M.B.; supervision, A.P.; project administration, A.P.; funding acquisition, G.P.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Tempus Public Foundation (Hungary) under the Stipendium Hungaricum Scholarship Program at the Hungarian University of Agriculture and Life Sciences (MATE).

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

We are grateful to the Tempus Public Foundation (Hungary) for providing this opportunity and to the Institute of Agronomy and Plant Science Department at the Hungarian University of Agriculture and Life Sciences (MATE) for supplying experimental materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Meteorological data recorded during the 2023 and 2024 cropping season at Godollo (https://www.meteoblue.com, accessed on 2 November 2024).
Figure 1. Meteorological data recorded during the 2023 and 2024 cropping season at Godollo (https://www.meteoblue.com, accessed on 2 November 2024).
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Figure 2. Environmental Gas Monitor 5 (EGM-5), a portable gas analyzer instrument (https://images.app.goo.gl/KYdJtY5VsNaxdrwj9, accessed on 2 November 2024).
Figure 2. Environmental Gas Monitor 5 (EGM-5), a portable gas analyzer instrument (https://images.app.goo.gl/KYdJtY5VsNaxdrwj9, accessed on 2 November 2024).
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Figure 3. Response of soil CO2 at different growth stages of winter barley in different soil tillage treatments. BBCH19-29 = leaf development–tillering stage; BBCH30-49 = stem elongation–booting stage; BBCH51-73 = beginning to heading–early milky stage.
Figure 3. Response of soil CO2 at different growth stages of winter barley in different soil tillage treatments. BBCH19-29 = leaf development–tillering stage; BBCH30-49 = stem elongation–booting stage; BBCH51-73 = beginning to heading–early milky stage.
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Figure 4. Mean of relative soil–plant analysis development (SPAD values) of winter barley at different time points across different tillage types.
Figure 4. Mean of relative soil–plant analysis development (SPAD values) of winter barley at different time points across different tillage types.
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Figure 5. Mean of leaf area index (LAI) values of winter barley at various recording time points across different tillage types.
Figure 5. Mean of leaf area index (LAI) values of winter barley at various recording time points across different tillage types.
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Figure 6. Interaction effects of nutrient and tillage on grain yield of winter barley in 2023 and 2024 growing seasons.
Figure 6. Interaction effects of nutrient and tillage on grain yield of winter barley in 2023 and 2024 growing seasons.
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Figure 7. Interaction effects of nutrient and tillage treatment on thousand kernel weight of winter barley in 2023 and 2024 growing seasons.
Figure 7. Interaction effects of nutrient and tillage treatment on thousand kernel weight of winter barley in 2023 and 2024 growing seasons.
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Table 1. Overview of the applied nutrient regarding their compositions (production company data).
Table 1. Overview of the applied nutrient regarding their compositions (production company data).
No.Nutrient TypeCompositionsRecomandation RateRemarks
1Bio-cerealComposition (w/v%)5 L/ha
Fe 0.5 Have modern chelating agent (EDDHSA)
Mn 1
Cu 1.8
Zn 0.3
B 0.3
Mo 0.03
2Bio- algaeComposition30 L/ha
Total Chlorella vulgaris algae 2.5 × 107 db/cm3
N 600 mg/L
P2O5 2000 mg/L
K2O 5000 mg/L
Mg 300 mg/L
S 400 mg/L
B 1000 Mg/L
Zn 300 mg/L and Organic Matter content 0.4 m/m%
3Mix of MgSMnZnComposition3 L/ha for S, Mn, and Zn nutrients and 5 L/ha for Mg nutrient
6.6% of FitoHorm 24 Mg solution
60% of FitoHorm Turbo sulfur solution
4% of FitoHorm 54 Mn solution
10% of FitoHorm Turbo Zn solution
Table 2. Mean soil CO2 emissions under different tillage treatments during winter barley cultivation for the 2023 and 2024 growing seasons.
Table 2. Mean soil CO2 emissions under different tillage treatments during winter barley cultivation for the 2023 and 2024 growing seasons.
Tillage TreatmentSoil CO2 Emission (g/m2/h)
20232024
Mean and Standard DeviationMin/Max
Value		Mean and Standard DeviationMin/Max
Value
Plowing0.2621 ± 0.13713 a0.04/0.550.3114 ± 0.23232 a0.3/1.35
Cultivator0.2631 ± 0.15292 a0.05/0.670.3415 ± 0.23224 a0.05/1.30
SEM±0.01475 0.1499
Sig. at 0.05 ns ns
Similar letters indicate non-significant difference between treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; ns = non-significant difference; Min/Max = minimum and maximum value, respectively.
Table 3. Multivariate analysis of variance (MANOVA) for leaf chlorophyll (SPAD value) and leaf area index (LAI) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
Table 3. Multivariate analysis of variance (MANOVA) for leaf chlorophyll (SPAD value) and leaf area index (LAI) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
SourceDependent VariablesDfMean SquareF-ValueSig.
20232024202320242023202420232024
TillageSPAD1175.7139.9891.9060.8550.169 ns0.356 ns
LAI111.00414.8052.33430.0600.128 ns<001 **
NutrientSPAD3363.8669.0781.6080.7770.188 ns0.507 ns
LAI332.0984.2534.8798.6350.003 **<001 **
Tillage × NutrientSPAD3363.32137.3701.5943.1970.192 ns0.023 *
LAI331.3442.8773.1265.8420.27 ns<001 **
ErrorSPAD23285639.72911.689
LAI2328560.4300.493
TotalSPAD240864
LAI240864
* = p < 0.05, ** = p < 0.01, ns = non-significant, DF = degrees of freedom, SPAD = soil–plant analysis development.
Table 4. The mean values of leaf chlorophyll content (SPAD values) and the leaf area index (LAI) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Table 4. The mean values of leaf chlorophyll content (SPAD values) and the leaf area index (LAI) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Nutrient TreatmentStudied Parameters
Leaf Chlorophyll (SPAD Values)Leaf Area Index (LAI)
2023202420232024
Control39.93 ± 6.6719 a49.062 ± 3.3871 a2.1047 ± 0.6155 a3.0238 ± 0.6616 a
Bio-cereal42.1267 ± 5.9639 a48.782 ± 3.5149 a2.4403 ± 0.6597 b3.3372 ± 0.7887 b
Bio-algae40.6967 ± 6.0519 a48.863 ± 3.1402 a2.2832 ± 0.7651 a3.2939 ± 0.63267 b
MgSMnZn blend39.9467 ± 6.6342 a49.238 ± 3.6631 a2.0218 ± 0.6135 a3.1757 ± 0.78090 a
SEM±0.4100.1160.0440.024
CV15.67.030.822.7
Different letters indicate a significant difference between treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; CV = coefficient of variation.
Table 5. The mean values of leaf chlorophyll content (SPAD values) and the leaf area index (LAI) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Table 5. The mean values of leaf chlorophyll content (SPAD values) and the leaf area index (LAI) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Tillage TreatmentStudied Parameters
Leaf Chlorophyll (SPAD Values)Leaf Area Index (LAI)
2023202420232024
Plowing41.2367 ± 6.2212 a48.879 ± 3.4255 a2.2772 ± 0.5450 a3.3385 ± 0.81815 a
Cultivator40.1133 ± 6.4773 a49.094 ± 3.4357 a2.1478 ± 0.7924 a3.0767 ± 0.59869 b
SEM±0.4100.1160.0440.024
CV15.67.030.822.7
Different letters indicate a significant difference between treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; CV = coefficient of variation.
Table 6. Multivariate analysis of variance (MANOVA) for plant height (cm) and spike length (cm) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
Table 6. Multivariate analysis of variance (MANOVA) for plant height (cm) and spike length (cm) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
SourceDependent
Variables		DfMean SquareF-ValueSig.
20232024202320242023202420232024
TillagePlant Height (cm)11201.6671027.0423.81971.9590.52 ns<0.001 **
Spike Length (cm)114.167 × 10 −527.4490.00031.1010.995 ns<0.001 **
NutrientPlant Height (cm)33320.619250.4866.07217.550<0.001 **<0.001 **
Spike Length (cm)330.2826.9550.2447.8810.865 ns<0.001 **
Tillage * NutrientPlant Height (cm)33133.342649.8692.52545.5330.58 ns<0.001 **
Spike Length (cm)330.1293.0290.1123.4320.953 ns0.017 *
ErrorPlant Height (cm)23285652.80714.273
Spike Length (cm)2328561.1520.883
TotalPlant Height (cm)240864
Spike Length (cm)240864
Where * = p < 0.05, ** = p < 0.01, ns = non-significant, DF = degrees of freedom.
Table 7. The mean value of plant height (cm) and spike length (cm) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Table 7. The mean value of plant height (cm) and spike length (cm) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Nutrient TreatmentStudied Parameters
Plant Height (cm)Spike Length (cm)
2023202420232024
Control77.3583 ± 7.0729 a76.676 ± 5.0173 a7.4083 ± 1.1552 a8.333 ± 0.835 a
Bio-cereal82.7333 ± 6.7217 b77.528 ± 4.525 b7.4333 ± 0.9849 a8.333 ± 0.978 a
Bio-algae79.725 ± 8.0766 a78.583 ± 2.978 c7.5517 ± 0.9475 a8.630 ± 0.890 b
MgSMnZn blend78.5167 ± 7.5767 a76.111 ± 4.0182 a7.4083 ± 1.1552 a8.657 ± 1.111 b
SEM±0.4900.1460.0680.033
CV9.55.5614.211.43
Different letters indicate a significant difference among treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; CV = coefficient of variation.
Table 8. The mean value of plant height (cm) and spike length (cm) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Table 8. The mean value of plant height (cm) and spike length (cm) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Tillage TreatmentStudied Parameters
Plant Height (cm)Spike Length (cm)
2023202420232024
Plowing80.500 ± 6.2375 a78.315 ± 4.2533 a7.4500 ± 0.9860 a8.310 ± 0.950 a
Cultivator78.666 ± 8.6884 a76.134 ± 4.0648 b7.4508 ± 1.1333 a8.667 ± 0.958 b
SEM±0.4900.1460.0680.033
CV9.55.5614.211.43
Different letters indicate a significant difference between treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; CV = coefficient of variation.
Table 9. Multivariate analysis of variance (MANOVA) for effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
Table 9. Multivariate analysis of variance (MANOVA) for effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different nutrient and tillage treatments in 2023 and 2024 growing seasons.
SourceDependent VariableDfMean SquareF-ValueSig.
20232024202320242023202420232024
TillageETN1141.6677.22333.3566.820<0.001 **0.009 **
TKW1131.320388.8155.871232.3680.1454 ns<0.001 **
Grain Yield1110.61776.2748.191459.4060.0413 *<0.001 **
NutrientETN3312.3179.3729.8608.848<0.001 **<0.001 **
TKW33281.72723.53552.80914.065<0.001 **<0.001 **
Grain Yield338.7111.5506.72009.335<0.001 **<0.001 **
Tillage * NutrientETN334.6565.3043.7275.0070.108 ns0.002 **
TKW3375.56771.41514.16542.680<0.001 **<0.001 **
Grain Yield339.3533.1957.21519.246<0.001 **<0.001 **
ErrorETN2328561.2491.059
TKW2328565.3351.673
Grain Yield2328561.2960.166
TotalETN240864
TKW240864
Grain Yield240864
Where * = p < 0.05, ** = p < 0.01, ns = non-significant, DF = degrees of freedom.
Table 10. The mean value of the effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Table 10. The mean value of the effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different nutrient treatments in 2023 and 2024 growing seasons.
Nutrient TreatmentStudied Parameters
Effective Tiller NumberTKW (g)Grain Yield (t/ha)
202320242023202420232024
Control5.80 ± 0.7657 a4.56 ± 0.918 a39.1633 ± 2.2484 a50.483 ± 2.2107 a3.47600 ± 0.7937 a5.37429 ± 0.5073 a
Bio-cereal6.55± 1.2845 b4.92 ± 1.065 b44.2383 ± 2.1518 b50.833 ± 1.2806 b4.20933 ± 1.3792 b5.53493 ± 0.5954 b
Bio-algae6.40 ± 1.5092 b5.05 ± 1.215 b40.6150 ± 3.0770 c50.783 ± 0.4269 a3.90267 ±1.6029 a5.43088 ± 0.4529 a
MgSMnZn blend5.61 ± 1.1577 a4.85 ± 0.933 b40.5883 ±2.4892 c51.283 ± 1.6606 b3.39467± 0.8069 a5.55157 ± 0.4932 b
SEM±0.0810.0360.2020.0530.0790.017
CV20.721.77.63.07139.48
Different letters indicate a significant difference among treatments at p < 0.05; ± = standard deviation, SEM = standard error of total mean; CV = coefficient of variation.
Table 11. The mean value of the effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Table 11. The mean value of the effective tiller number, thousand kernel weight (g), and grain yield (t/ha) of winter barley by different tillage treatments in 2023 and 2024 growing seasons.
Tillage TreatmentStudied Parameters
Effective Tiller NumberTKW (g)Grain Yield (t/ha)
202320242023202420232024
Plowing6.508 ± 0.8285 a4.94 ± 0.994 a40.79 ± 2.64875 a50.175 ± 1.6196 a3.956 ± 0.5925 a5.77 ± 0.4793 a
Cultivator5.675 ± 1.4753 b4.75 ± 1.103 b41.5125 ± 3.52026 a51.517 ± 1.1667 b3.535 ± 1.6221 b5.17 ± 0.3646 b
SEM±0.0810.0360.2020.0530.0790.017
CV20.721.77.63.07139.48
Different letters indicate a significant difference between treatments at p < 0.05; ± = standard deviation; SEM = standard error of total mean; CV = coefficient of variation.
Table 12. Short summary of the different statistical tests that were run on all the various studied variables and the major conclusions from the test results.
Table 12. Short summary of the different statistical tests that were run on all the various studied variables and the major conclusions from the test results.
Studied VariablesStatistical TestPurpose of Statistical TestConclusions
Soil CO2 emission (g/m2/h)Independent samples T-testTo compare the means of the two tillage treatmentsNo significant difference found between treatment groups
RMANOVATo test within-subject effectSignificant difference found at different growth stages
Leaf chlorophyll content (SPAD) valuesMANOVATo test between-subject effectNo significant difference found between treatment groups
RMANOVATo test within-subject effectSignificant difference found at different recording time points
Leaf area index (LAI)MANOVATo test between-subject effectSignificant difference found between treatment groups
RMANOVATo compare within-subject effectSignificant difference found at different recording time points
Plant height (cm)MANOVATo test between-subject effectsSignificant differences found between treatments groups
Spike length (cm)
Effective tiller number
Thousand kernel weights (g)
Grain yield (t/ha)
Where RMANOVA = repeated measures analysis of variance, MANOVA = multivariate analysis of variance.
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Bogale, A.A.; Kende, Z.; Tarnawa, A.; Miko, P.; Birkás, M.; Kovács, G.P.; Percze, A. Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission. Agronomy 2025, 15, 2. https://doi.org/10.3390/agronomy15010002

AMA Style

Bogale AA, Kende Z, Tarnawa A, Miko P, Birkás M, Kovács GP, Percze A. Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission. Agronomy. 2025; 15(1):2. https://doi.org/10.3390/agronomy15010002

Chicago/Turabian Style

Bogale, Amare Assefa, Zoltan Kende, Akos Tarnawa, Peter Miko, Marta Birkás, Gergő Péter Kovács, and Attila Percze. 2025. "Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission" Agronomy 15, no. 1: 2. https://doi.org/10.3390/agronomy15010002

APA Style

Bogale, A. A., Kende, Z., Tarnawa, A., Miko, P., Birkás, M., Kovács, G. P., & Percze, A. (2025). Precision Nutrient and Soil Tillage Management for Sustainable Winter Barley Production (Hordeum vulgare L.) and Tillage Impact on Soil CO2 Emission. Agronomy, 15(1), 2. https://doi.org/10.3390/agronomy15010002

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