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Article

Determination of Feed Yield and Quality Parameters of Bermudagrass (Cynodon dactylon L. (Pers.)) Populations Collected from Natural Flora

by
Sedat Severoglu
* and
M. Kerim Gullap
Department of Field Crops, Faculty of Agriculture, Ataturk University, 25240 Erzurum, Turkey
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(6), 1471; https://doi.org/10.3390/agronomy13061471
Submission received: 18 May 2023 / Revised: 23 May 2023 / Accepted: 24 May 2023 / Published: 26 May 2023
(This article belongs to the Special Issue Advances in Genetics, Breeding, and Quality Traits in Forage and Turf Grass)
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Abstract

:
A two-year study (2020–2021) was conducted in the Erzurum province to investigate the feed quality parameters of 102 genotypes of Bermudagrass (Cynodon dactylon) collected from different locations in natural flora. Two control cultivars were also examined in this study, namely Coastal and Survivor. Parameters such as the fresh herbage weight, dry herbage weight, crude protein (CP) ratio, acid detergent fiber (ADF) ratio, neutral detergent fiber (NDF) ratio, and relative feed values (RFVs) were analyzed. In the first year of this study, Bermudagrass populations collected from natural flora were rooted in pots under greenhouse conditions. In the second year, the rooted plants were transplanted into experimental fields at the Atatürk University Plant Production Implementation and Research Center. The present genotypes generally had superior crude protein, ADF, NDF ratios, and relative feed values (RFVs) than the control cultivar. The fresh herbage weights ranged from 9.20 to 95.37 g per plant, while the dry herbage weights varied from 5.22 to 45.24 g per plant. The findings of this study showed that most of the genotypes collected from natural flora had superior feed quality parameters to the control genotypes.

1. Introduction

As the world’s population increases, the demand for food is growing, while the available agricultural land remains limited [1]. In this context, increasing crop production per unit area may play a vital role. For this purpose, in addition to revealing the right cultivation techniques, it is of great importance to develop new plant varieties with a superior yield and high quality. The natural flora is an important source of genes for plant breeding studies and forage crops play an important role in this respect.
Especially with regard to global warming, it is important to cultivate plants with high water use efficiency and high temperature resistance [2,3,4]. Indeed, as a result of global warming, daytime temperatures in the region during the summer months are recorded to be significantly higher than the optimal photosynthesis temperature range (20–25 °C) for cool-season plants [5,6]. In this regard, Bermudagrass (Cynodon dactylon L. Pers.), which is a typical C4 plant, is of significant importance. The most common photosynthetic system in plants is the three-carbon system, and plants that perform photosynthesis through this system are called C3 plants. Examples of such plants include wheat, barley, cotton, sugar beet, clover, and alfalfa. On the other hand, in hotter regions of the world, the photosynthetic system is the four-carbon system, and plants that utilize this system for photosynthesis are referred to as C4 plants. Examples of C4 plants include maize, sorghum, sugarcane, pearl millet, and Bermudagrass [7]. This plant, which is widely distributed in Turkey’s flora, can grow in a wide range from coastal areas up to an altitude of 3000 m [8,9]. Bermudagrass (Cynodon dactylon) is of great importance in terms of forage production, pasture improvement, soil conservation, and landscaping due to its ability to produce high-quality forage, suitability for mixed cropping, and its ability to cover a large area of the soil surface.
The primary objective in plant breeding is to obtain a more uniform plant population by disabling undesirable genes [10,11,12]. Due to the absence of selective breeding in natural ecotypes, a wide variation is observed in terms of yield and quality traits [13,14]. Indeed, a study conducted with button clover (Medicago orbicularis L.) collected from natural flora reported wide variation in terms of forage production per plant (ranging from a minimum of 134 g per plant to a maximum of 1655.0 g per plant) [15]. Similar results were obtained in the studies conducted by [16,17].
In C4 forage crops, it is expected that the quality of the forage would be lower compared to C3 plants due to their anatomical and biosynthetic structure [18]. However, while C3 plants experience a decline in forage quality during their summer dormancy period, reaching a value that cannot even meet the basic needs of animals for survival [19], C4 plants, due to their ability to stay green, can provide sufficient quality forage. In [20], it was reported that, under terrestrial climate conditions, due to the impact of global warming and the extension of the summer drought period, supplementary feeding costs would increase in pastures dominated by C3 plants during the summer months. The identification of suitable C4 plants that can remain green during the summer months and the development of genotypes with high yield and high forage quality among them can be effective in mitigating the severity of this problem. As a matter of fact, studies conducted by [21,22,23] reported wide variation in terms of quality within the plant materials collected from natural flora. The study of Bituminaria bituminosa (L.) C.H. Stirtion in [22] reported that the crude protein (CP) content ranged from 13.65% to 21.05%, the neutral detergent fiber (NDF) content ranged from 43.03% to 57.01%, and the acid detergent fiber (ADF) content ranged from 27.85% to 44.72%.
Commercial varieties generally do not adapt well to the eastern Anatolia region due to their low-altitude origin. However, the natural ecotypes of this plant, encountered in the region’s natural flora, are expected to be cold-tolerant. In fact, the collected natural material in this study was successfully grown in Erzurum, one of the coldest provinces in the region. In this study, the forage production and basic quality characteristics of the material discussed are emphasized. Furthermore, forage yield and quality characteristics of wild Bermudagrass genotypes collected from natural flora were tried to be determined.

2. Materials and Methods

This study was carried out during a period of two years with materials collected from the natural flora (mainly pastures, in addition to meadows, forests, and field lands) of Agri, Ardahan, Bayburt Bingol, Erzincan, Erzurum, Igdir, Kars, and Mus provinces in the eastern Anatolia and northeastern Anatolia regions (Figure 1). The materials collected from natural flora in 2020 were grown for 2 years in the experimental fields of Ataturk University Plant Production Implementation and Research Center in 2020 and 2021. The relative feed values were calculated using the NDF (Neutral Detergent Fiber) and ADF (Acid Detergent Fiber) ratios. For each genotype, 10 plants were harvested separately, and their individual CP (Crude Protein), ADF, and NDF values were calculated and averaged. The relative feed values were calculated using the NDF (Neutral Detergent Fiber) and ADF (Acid Detergent Fiber) ratios. For each genotype, 10 plants were harvested separately, and their individual CP (Crude Protein), ADF, and NDF values were calculated and averaged.
The texture classes of the soil samples taken from the experimental area were determined using the Bouyoucos hydrometer method [24]. The soil structure of the experimental area was recorded as clay-loam [25]. The soils of the experimental area were unsaline with an average electrical conductivity of 2.23 dS m−1 [26], slightly alkaline with a pH value of 7.64 [27], limey with a lime content of 2.24% [27], low in organic matter with an organic matter content of 1.05% [27], low in available phosphorus with a value of 0.53 kg P2O5 ha−1 [27], and excessive in extractable potassium with a value of 15.31 kg K2O ha−1 [28].
The average temperatures in 2020 (6.8 °C) and 2021 (7.1 °C) at which the experiments were conducted were higher than the long-term average (5.7 °C). The annual total precipitation in 2021 (346.3 mm) was lower than that in 2020 (382.7 mm) and the long-term average (429.9 mm). The second year of the study was both hotter and had less precipitation as compared to the first year and the long-term averages (Figure 2). Furthermore, the years in which the planting took place were particularly favorable in terms of temperature for the cultivation of Bermudagrass plants.
A total of 102 genotypes were collected from the natural flora and two control cultivars (Coastal and Survivor) were also included in this study. The present study was carried out in three stages: plant collection, greenhouse experiments, and field experiments. The plants were collected from the natural flora, coordinates, and altitudes of collection points were recorded and soil samples were taken. Material collection was performed by removing the plants with the soil and random sampling method was used in material collection [29,30]. During the plant collection phase of this study, to represent the populations in the regions and keep the variation wide, a distance of at least 4–5 km was left between the collection points and the plants within an area of approximately 100 m2 were considered as the population. The collection process was carried out at 100 points, and 10 plants were randomly taken from each point, only by looking at the phenotype of the plants and a total of 1000 plants belonging to at least 100 populations were collected.
Plants that were removed from their original place with soil were planted in pots prepared beforehand in the greenhouses within 24 h. As such, 10 plants were taken from each population by cutting their shoots with scissors and a plant collection of 1020 pots belonging to 102 populations was created. After the collected populations were rooted in the greenhouse environment, they were transplanted into experimental fields in the form of single-row columns on 27 April 2020. Plants from each population were planted in a row at 70 cm × 70 cm spacings. Field trials were conducted in augmented experimental design. Plants were randomly distributed into six blocks and control cultivars were repeated in each block. Following the planting process, irrigations were practiced and 1 kg N per hectare was applied to the experimental plots for better establishment [31]. The first stages of synthetic variety breeding, the principles of which were determined by [32], were carried out in this study.
At the beginning of flowering, 10 plants were harvested from each population by leaving 5 cm stubble height. The fresh herbage weight per plant was determined and plants were dried at 60 °C until it reached a constant weight and the dry herbage weight per plant was determined [33]. The dried samples were ground in a feed mill for quality analysis. The nitrogen ratio of the ground samples was determined by the Kjeldahl method in accordance with the principles specified by [34]. For the crude protein (CP) ratio, the nitrogen ratio was multiplied by the coefficient of 6.25, as recommended by [35]. The neutral detergent fiber (NDF) and acid detergent fiber (ADF) ratios were determined with the use of an ANKOM fiber analyzer device in accordance with the principles specified in [36]. Relative feed values greater than 100 indicate increased feed quality and values lower than 100 indicate reduced feed quality [37,38].
Statistical analyses were carried out with the use of R statistical analysis software [39] in accordance with augmented randomized complete blocks design (AugmentedRCBD) [40,41,42]. Significant means were compared with the use of the least significant difference (LSD) multiple comparison test. Results are presented as the mean and standard deviation (SD), unless otherwise stated, and differences were considered significant for p < 0.001.
After standardizing all data, the principal component (Biplot) and hierarchical clustering analyses were performed. In the hierarchical clustering analysis, statistically appropriate distance and linkage algorithms were selected. The “cophenetic” distance and “Wald” linkage algorithms were used in this analysis. Additionally, the Jaccard index was used to determine the optimal number of clusters. All statistical analyses regarding the traits examined in the study were performed using the “cluster”, “factoextra”, “dendextend”, and “corrplot” packages in the R software [43].

3. Results and Discussion

In the combined analysis of the years, there were highly significant differences in the fresh herbage weight per plant values (Table 1). The average fresh herbage weight per plant was determined to be 37.55 g per plant, the highest value was obtained from genotype G47, and the lowest from genotype G42 (Table 1).
In the conducted study, both the test genotypes and all genotypes showed statistically significant differences at a significance level of 0.1%. These differences may have arisen due to the genetic characteristics of the utilized genotypes and the distinct abiotic conditions (as presented in Table 1) of the regions where the genotypes were collected. These factors likely played a role in the observed variations. Furthermore, the significant climatic differences between the years may have had a significant impact on the formation of this difference. Because the plants were transplanted in April, which was the first year of establishment, these may have experienced significant problems during winter emergence in the second year due to low temperatures and precipitation. Indeed, the statements made by several researchers, such as [17,44,45,46,47], that the green forage yield varies depending on the plant species and genus, plant density per unit area, utilization method, maturity period, genetic characteristics, and environmental factors support our study. The fact that the test vs. check treatment was important in this study shows that the test genotypes are significantly different from the control genotypes. As a matter of fact, although many genotypes have numerically higher green forage yield values than control genotypes, according to the results of multiple comparison tests, the G47 genotype has a higher green forage yield value than the test genotypes. A wide variation in the traits in natural populations is expected, which is a desirable outcome for breeding programs. Indeed, [15], who conducted a similar study with Medicago orbicularis L. plant, highlighted wide variation in terms of green forage yield.
The dry herbage weights of the genotypes varied between 5.22 and 45.24 g per plant with the highest value from genotype G47, followed by genotypes G73 and G66, and the lowest value from genotype G49, followed by genotypes G42 and G93. Dry herbage weights of the Coastal and Survivor cultivars varied between 8.96 and 13.30 g per plant (Table 2). In this study, statistically significant differences were determined between the test genotypes and all genotypes, indicating that the observed differences, similarly to the green forage yield per plant, may be attributed to both genetic and ecological variations among the plants. Indeed, many researchers [48,49,50,51] stated that both ecological conditions and genetic variations among cultivars have a significant impact on the dry matter yield of plants. In addition, a test vs. check treatment was important in this study, which may have been due to the significant differences in the test genotypes in terms of dry matter yield compared to the control genotypes. Indeed, [52] conducted a similar study with Medicago sativa L. collected from natural vegetation and reported a wide variation in dry matter yield between 1143 kg ha−1 and 2183 kg ha−1. The presence of numerous genotypes surpassing the control varieties in the study is a promising result.
The average crude protein ratio, an important indicator of nutritional value and digestibility [53], was identified to be 11.32% (Table 2). As the average of years, the lowest crude protein ratio was obtained from genotype G54, followed by genotypes G61 and G27. The highest crude protein ratio was obtained from genotype G87, followed by genotypes G56 and G5. The fact that the test genotypes were different compared to the control genotypes in this study was an important factor in the importance of a test vs. check treatment. Mathematically, while 15 genotypes have a higher crude protein content compared to the control genotypes, according to the multiple comparison test, the G87 genotype has a higher crude protein content compared to the other test genotypes. The emergence of such wide variations among the collected genotypes is considered a promising development for breeders. As a matter of fact, refs. [52,54], who conducted similar studies, determined that the crude protein ratio of alfalfa plants collected from natural flora showed a wide variation between 21.7 and 24.2% and 17.4 and 22.6%, respectively.
As the average over the years, the NDF ratios of the genotypes varied between 52.14 and 67.67% with the lowest value from genotype G81 and the highest value from genotype G21 (Table 2). In this study, the test genotypes showed significant differences in terms of NDF ratio compared to the control genotypes, which led to the importance of the test vs. check treatment. These differences between the collected genotypes will provide an important opportunity for the development of new varieties to be used in breeding studies and will thus be preserved in our local varieties that are in danger of extinction. As a matter of fact, [23] and [55], who conducted similar studies with orchard grass and white sweet clover, reported significant variations in the NDF ratio.
As the average of the years, the ADF ratios of the genotypes varied between 23.09 and 33.41% with an average value of 28.97%. The lowest ADF ratio was recorded in genotype G61, followed by genotypes G64 and G22. The highest ADF ratio was seen in genotype G57, followed by genotypes G10 and G25. The ADF ratios of the Coastal and Survivor cultivars were recorded as 24.35% and 25.59%, respectively (Table 2). The ADF ratio, which consists of lignin and cellulose [56], has shown a significant difference in the test genotypes compared to the control genotypes. This difference observed between the collected genotypes and control cultivars made the test vs. control treatment important. It is expected that wild genotypes collected from natural flora exhibit significant variations in their genetic characteristics due to environmental conditions [57,58]. Indeed, many studies [55,59,60,61,62,63] have identified a wide variation in ADF content among the species collected from natural flora.
As the average over the years, the relative feed values (RFVs) of the genotypes varied between 87.18 and 123.15%, with an average value of 100.22%. The lowest RFV was obtained from genotype G44, followed by genotypes G73 and G72. The highest RFV was obtained from genotype G81, followed by genotypes G92 and G42. The relative feed value (RFV) was recorded as 117.08% for the Coastal cultivar and 115.93% for the Survivor cultivar. Only genotype G81 had a higher value than these cultivars (Table 2). The relative feed value (RFV) was used as an important feed quality indicator which was calculated by comparing the plants with alfalfa and a relative feed value of alfalfa was taken as 100 [64]. The feed quality is classified based on RFV as: <75: fifth class, 75–86: fourth class; 87–102: third class; 103–124: second class; 125–150: first class and >150: top quality [64,65,66,67]. Based on this classification of the present genotypes, 76 were in the third class, 26 in the second class and control cultivars were in the second-class quality group. It was seen that significant part of the present genotypes had a RFV of greater than the reference value of 100 (Table 2). The significant interaction between the test vs. check treatment in the conducted study indicates that there are significant differences between the test genotypes and control genotypes. Indeed, mathematically, many genotypes, except for G81, have a lower relative feed value compared to the control genotypes. However, according to the results of the multiple comparison test, the G44 genotype has a lower relative feed value compared to the test genotypes. As a result, it has been determined that there is wide variation among the collected genotypes, and this finding provides an opportunity to create valuable genetic material for further breeding studies. Indeed, many studies conducted on different plant species [68,69,70,71,72] have obtained wide variations.
Hierarchical clustering analysis was conducted with the use of the fresh herbage weight, dry herbage weight, crude protein, ADF, NDF, and RFV of 102 genotypes and control cultivars (Figure 3). The present genotypes were divided into two main clusters. The closeness and distance of genotypes from one another in terms of investigated parameters were determined and genotype G81 had the closest characteristics to the control cultivars.
The biplot graph showing the relationships between the genotypes and the investigated traits is presented in Figure 3. The biplot graph offers a visual assessment of the relationships between the investigated traits [73,74]. In present biplot, PC1 explains 56.90% of total variation and PC2 explains 20% of the total variation (i.e., the two principal components explain 76.90% of total variation) (Figure 4).

4. Conclusions

When the results were evaluated as a whole, the G1, G2, G3, G5, G7, G8, G10, G11, G12, G15, G17, G19, G20, G21, G22, G23, G26, G27, G28, G29, G30, G33, G34, G36, G39, G43, G44, G45, G46, G47 G48, G49. G50, G51, G52, G53, G54, G55, G56, G57, G58, G59, G62, G64, G66, G69, G70, G71, G72, G73, G74, G78, G82, G86, G87, G88, G89, G90, G91, G93, G97, G99, G100, G101, and G102 genotypes stood out as being similar or superior to the control cultivars in terms of feed quality characteristics. According to the clustering analysis, genotype G81 demonstrated characteristics similar to the control cultivars. Therefore, these genotypes, especially their distant relatives, showing superior performances, are promising for the development of Bermudagrass cultivars suitable for cold high-altitude regions. In light of the findings obtained from this study, it has been decided to continue synthetic variety development studies on the considered material, and the research is ongoing.

Author Contributions

Writing—original draft, S.S.; Writing—review & editing, M.K.G.; Project administration, M.K.G. The authors equally contributed to this work. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

This project was supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under grant number 119O287.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. De Fraiture, C.; Wichelns, D. Satisfying future water demands for agriculture. Agric. Water Manag. 2010, 97, 502–511. [Google Scholar] [CrossRef]
  2. Cetin, O.; Eylen, M.; Uzen, N. Efficient Irrigation Strategies in the GAP Region to Counter Climate Change; TMMOB Climate Change Symposium: Ankara, Turkey, 2008; pp. 264–281. [Google Scholar]
  3. Karamani, S.; Gokalp, Z. Effects of global warming and climate change on water resources. Agric. Sci. Res. J. 2010, 3, 59–66. [Google Scholar]
  4. Akalin, M. Impacts of climate change on agriculture: Adaptation and mitigation strategies to address these effects. J. Hitit Univ. Inst. Soc. Sci. 2014, 7, 351–377. [Google Scholar]
  5. Crafts-Brandner, S.J.; Salvucci, M.E. Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. J. Plant Physiol. 2002, 129, 1773–1780. [Google Scholar] [CrossRef] [PubMed]
  6. Feller, U. Drought stress and carbon assimilation in a warming climate: Reversible and irreversible impacts. J. Plant Physiol. 2016, 203, 84–94. [Google Scholar] [CrossRef] [PubMed]
  7. Ehleringer, J.R.; Cerling, T.E. C3 and C4 photosynthesis. Encycl. Glob. Environ. Chang. 2002, 2, 186–190. [Google Scholar]
  8. Harlan, J.R.; de Wet, J.M.J. Sources of variation in Cynodon dactylon (L). Pers. Crop Sci. 1969, 9, 774–778. [Google Scholar] [CrossRef]
  9. Duke, J.A. Cynodon dactylon (L.) Pers. Poaceae. Handbook of Energy Crops. Purdue University: West Lafayette, IN, USA, 1983. Unpublished. Available online: http://www.hort.purdue.edu/newcrop/duke_energy/Cynodon_dactylon.html (accessed on 25 April 2023).
  10. Kumlay, A.M.; Dursun, A. Plant genetic engineering and its applications on economically important plants. J. Atatürk Univ. Fac. Agric. 2003, 34, 34. [Google Scholar]
  11. Rivera, A.L.; Gómez-Lim, M.; Fernández, F.; Loske, A.M. Physical methods for genetic plant transformation. Phys. Life Rev. 2012, 9, 308–345. [Google Scholar] [CrossRef]
  12. Barampuram, S.; Zhang, Z.J. Recent Advances in Plant Transformation. In Plant Chromosome Engineering. Methods in Molecular Biology; Birchler, J., Ed.; Humana Press: Totowa, NJ, USA, 2011; Volume 701, pp. 1–35. [Google Scholar]
  13. Swarup, S.; Cargill, E.J.; Crosby, K.; Flagel, L.; Kniskern, J.; Glenn, K.C. Genetic diversity is indispensable for plant breeding to improve crops. Crop Sci. 2021, 61, 839–852. [Google Scholar] [CrossRef]
  14. Bouargalne, Y.; Ben Mrid, R.; Bouchmaa, N.; Zouaoui, Z.; Benmrid, B.; Kchikich, A.; Mohamed, N. Genetic diversity for agromorphological traits, phytochemical profile, and antioxidant activity in Moroccan sorghum ecotypes. Sci. Rep. 2022, 12, 5895. [Google Scholar] [CrossRef] [PubMed]
  15. Erdurmus, C. Morphological and Molecular Characterization of Button Clover (Medicago orbicularis L.) Collected from the Natural Flora of Antalya. Doctoral Thesis, Akdeniz University, Institute of Science, Antalya, Turkey, 2020. [Google Scholar]
  16. Aydin, I.; Kutbay, H.G.; Seyis, F.; Uzun, F.; Surmen, M. Collection, Characterization, and Evaluation of Annual Clovers in the Middle Black Sea Region Flora; 107O087 TUBITAK Project Final Report; TUBITAK: Samsun, Turkey, 2010. [Google Scholar]
  17. Ozyazici, M.A.; Acikbas, S. Determination of agricultural traits and yield performance of common vetch (Lathyrus sativus L.) genotypes under semi-arid climate conditions. Eur. J. Sci. Technol. 2019, 17, 1058–1068. [Google Scholar]
  18. Norton, M.R.; Malinowski, D.P.; Volaire, F. Plant drought survival under climate change and strategies to improve perennial grasses. A review. Agron. Sustain. Dev. 2016, 36, 29. [Google Scholar] [CrossRef]
  19. Koc, A.; Gokkus, A.; Bakoglu, A.; Ozaslan, A. Variation of certain chemical properties in herbage samples collected from different locations of Palandoken Mountain meadows in Erzurum during the grazing season. Int. Anim. Nutr. Cong. 2000, 44, 589–598. [Google Scholar]
  20. Polley, H.W.; Morgan, J.A.; Campbell, B.D.; Smith, M.S. Crop ecosystem responses to climatic change: Rangelands. In Climate Change and Global Crop Productivity; CABI Books: Wallingford, Oxfordshire, UK, 2000; pp. 293–314. [Google Scholar]
  21. Erdurmus, C.; Kiremitci, S.; Oten, M. Morphological traits of some vetch (Vicia sp.) species collected from natural flora. Mediterr. Agric. Sci. 2018, 31, 289–294. [Google Scholar]
  22. Mut, H.; Gulumser, E.; Dogrusoz, M.C.; Basaran, U. Determination of some quality characteristics of Bituminaria bituminosa (L.) CH stirtion genotypes collected from natural flora in Bilecik. Bilecik Şeyh Edebali Univ. J. Nat. Sci. 2020, 7, 121–128. [Google Scholar]
  23. Gulumser, E.; Mut, H.; Dogrusoz, M.C.; Basaran, U. Some quality traits of white sweet clover collected from natural flora. Turk. J. Agric.-Food Sci. Technol. 2020, 8, 324–328. [Google Scholar]
  24. Demiralay, I. Soil Physical Analysis Methods; Ataturk University, Faculty of Agriculture Publications: Erzurum, Turkey, 1993; pp. 111–120. [Google Scholar]
  25. Soil Survey Division Staff, “Soil Survey Manual ” Soil Conservation Service; Agriculture Handbook No. 18; Department of Agriculture: Washington, DC, USA, 1993.
  26. Richards, L.A. Diagnosis and Improvement of Saline and Alkali Soils; Agriculture Handbook No. 60; Department of Agriculture: Washington, DC, USA, 1954. [Google Scholar]
  27. Ulgen, N.; Yurtsever, N. Turkey Fertilizer and Fertilization Guide, 4th ed.; General Directorate of Rural Services, Ministry of Agriculture and Forestry, Soil and Fertilizer Research Institute Publications, General Publication: Ankara, Turkey, 1995; p. 230. [Google Scholar]
  28. Aydin, A.; Sezen, Y. Soil Chemistry Laboratory Manual; Ataturk University, Faculty of Agriculture Publications: Erzurum, Turkey, 1995. [Google Scholar]
  29. Sehirali, S.; Ozgen, A.M. Plant Genetic Resources; Ankara University, Faculty of Agriculture Publications: Ankara, Turkey, 2012. [Google Scholar]
  30. Tan, A.; Taskin, T. Collection of Plant Genetic Resources; Technical Brochure, No. 7; Ankara, Turkey, 2018. Available online: https://arastirma.tarimorman.gov.tr/etae/Belgeler/TeknikBrosur/7-bgk-toplama.pdf (accessed on 25 April 2023).
  31. Tan, M. Legume and Cereal Forage Crops; Ataturk University, Faculty of Agriculture Publications: Erzurum, Turkey, 2018; 356p. [Google Scholar]
  32. Demir, I. Plant Breeding; Ege University, Faculty of Agriculture Publications: Izmir, Turkey, 1990; 366p. [Google Scholar]
  33. Acikgoz, E. Forage Crops; 3. Publishing Uludag University Empowerment Foundation, Publication No: 182; VIPAS A.S. Publication: Bursa, Turkey, 2001; 584p. [Google Scholar]
  34. Kacar, B. Chemical Analysis of Plants and Soils. In Part II: Plant Analysis; Ankara University, Faculty of Agriculture Publications: Ankara, Turkey, 1972; 464p. [Google Scholar]
  35. Adesogan, A.T.; Givens, D.I.; Owen, E. Measuring Chemical Composition and Nutritive Value in Forages. In Field and Laboratory Methods for Grassland and Animal Production Research; Mannetje, L., Jones, R.M., Eds.; CABI: Cambridge, UK, 2000; pp. 263–278. [Google Scholar]
  36. Van Soest, P.J.; Robertson, J.B.; Lewis, B.A. Methods for dietery fiber, neutral detergent fiber, and non-starch polysaccharides in relation to animal nutrition. J. Dairy Sci. 1991, 74, 3583–3597. [Google Scholar] [CrossRef]
  37. Richardson, C. Relative Feeding Value (RFV), an Indicator of Hay Quality. OSO Extension Fact, F2117. 2001. Available online: http://clay.agr.okstate.edu/alfalfa/webnews/quality3 (accessed on 6 November 2012).
  38. Redfearn, D.; Zhang, H.; Caddel, J. Forage Quality Interpretations; Oklahoma Cooperative Extension Service, Oklahoma State University Division of Agricultural Sciences and Natural Resources: Stillwater, OK, USA, 2004. [Google Scholar]
  39. Aravind, J.; Mukesh Sankar, S.; Wankhede, D.P.; Kaur, V. AugmentedRCBD: Analysis of Augmented Randomized Complete Block Designs. R Package Version 0.1.5.9000. 2022. Available online: https://aravind-j.github.io/augmentedRCBD (accessed on 25 April 2023).
  40. Federer, W.T. Augmented (or Hoonuiaku) Designs. Cornell University Mimeo; Biometrics: New York, NY, USA, 1956. [Google Scholar]
  41. Federer, W.T. Augmented designs with one-way elimination of heterogeneity. Biometrics 1961, 17, 447–473. [Google Scholar] [CrossRef]
  42. Searle, S.R. Computing Formulae for Analyzing Augmented Randomized Complete Block Designs; Technical Report; Cornell University: New York, NY, USA, 1965. [Google Scholar]
  43. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 25 April 2023).
  44. Sengul, S. Investigation of Some Morphological and Cytological Characteristics in Clover (Medicago sativa L.) Ecotypes Cultivated in the Van Region. Doctoral Thesis, Ataturk University, Institute of Science, Department of Field Crops, Erzurum, Turkey, 1995. [Google Scholar]
  45. Kara, S.M.; Deveci, M.; Dede, O.; Sekeroglu, N. Effects of Different Crop Rotations and Nitrogen Levels on Green Forage Yield and Some Characteristics in Silage Maize. In Proceedings of the Turkey 3rd Field Crops Congress, Adana, Turkey, 15–18 November 1999; pp. 172–177. [Google Scholar]
  46. Yilmaz, S.; Gozubenli, H.; Can, E.; Atis, I.; Avci, S. The Effect of Sowing Time on Yield Performance of Some Main Crop Maize (Zea mays L.) Varieties. In Proceedings of the Turkey 7th Field Crops Congress, Erzurum, Turkey, 25–27 June 2007; pp. 293–296. [Google Scholar]
  47. Tenekecier, H.S.; Orak, A.; Gurbuz, M.A.; Cubuk, M.G. Determining the Performance of Chickling Vetch (Lathyrus sativus L.) Varieties under Thrace Region Conditions. KSU J. Nat. Sci. 2017, 20, 102–108. [Google Scholar]
  48. Frate, C.A.; Davis, R.M. Alfalfa Diseases and Management. Irrigated Alfalfa Management for Mediterranean and Desert Zones; Summers, C.G., Putnam, D.H., Eds.; University of California, Division of Agriculture and Natural Resources: Oakland, CA, USA, 2008; p. 8296. [Google Scholar]
  49. Djaman, K.; Smeal, D.; Koudahe, K.; Allen, S. Hay yield and water use efficiency of alfalfa under different irrigation and fungicide regimes in a semiarid climate. Water 2020, 12, 1721. [Google Scholar] [CrossRef]
  50. Donmez, H.B.; Hatipoglu, R. Adaptation of Some Annual Ryegrass (Lolium multiflorum Lam.) Varieties under Mediterranean Climate Conditions. Cukurova J. Agric. Food Sci. 2021, 36, 369–380. [Google Scholar]
  51. Temel, S.; Keskin, B.; Tosun, R.; Cakmakci, S. Determination of Forage Yield and Quality Performance in Summer-Sown Field Pea Varieties. Turk. J. Agric. Nat. Sci. 2021, 8, 411–419. [Google Scholar]
  52. Acikbas, S.; Albayrak, S.; Turk, M. Determination of Forage Yield and Quality of Some Clover (Medicago sativa L.) Genotypes Collected from Natural Vegetation. Turk. J. Agric. Res. 2017, 4, 155–162. [Google Scholar]
  53. Lithourgidis, A.S.; Vasilakoglou, I.B.; Dhima, K.V.; Dordas, C.A.; Yiakoulaki, M.D. Forage yield and quality of common vetch mixtures with oat and triticale in two seeding ratios. Field Crops Res. 2006, 99, 106–113. [Google Scholar] [CrossRef]
  54. Cacan, E.; Kokten, K.; Seydosoglu, S. Determining the performance of alfalfa population collected from a narrow agroeceological zone of Turkey. Ciênc. Rural. 2020, 50, e20190721. [Google Scholar] [CrossRef]
  55. Can, M.; Ayan, I. Variation of Yield and Some Characteristics in Cock’s-foot (Dactylis glomerata L.) Populations Based on Growth Stages. KSU J. Nat. Sci. 2017, 20, 160–166. [Google Scholar]
  56. Balthrop, J.; Brand, B.; Cowie, R.A.; Danier, J.; de Boever, J.L.; de Jonge, L.H.; Piotrowski, C. Quality assurance for animal feed analysis laboratories. In Animal Production and Health Manual; Food and Agriculture Organization (FAO): Rome, Italy, 2014; ISSN 1810-1119. [Google Scholar]
  57. Geber, M.A.; Dawson, T.E. Genetic variation in stomatal and biochemical limitations to photosynthesis in the annual plant, Polygonum arenastrum. Oecologia 1997, 109, 535–546. [Google Scholar] [CrossRef]
  58. Maherali, H.; Sherrard, M.E.; Clifford, M.H.; Latta, R.G. Leaf hydraulic conductivity and photosynthesis are genetically correlated in an annual grass. New Phytol. 2008, 180, 240–247. [Google Scholar] [CrossRef]
  59. George, M.; Sands, P.; Wilson, C.; Ingram, R.; Connor, J. Irrigated warm-and cool-season grasses compared in Northern California pastures. Calif. Agric. 1992, 46, 21–25. [Google Scholar] [CrossRef]
  60. Anderson, W.F. Digestibility and fiber of a forage Bermuda grass core collection. In Proceedings of the ASA-CSSA-SSSA International Meetings, Indianapolis, IN, USA, 15 November 2006; Volume 1216. [Google Scholar]
  61. Zhao, D.; Starks, P.J.; Brown, M.A.; Phillips, W.A.; Coleman, S.W. Assessment of forage biomass and quality parameters of Bermudagrass using proximal sensing of pasture canopy reflectance. Grassl. Sci. 2007, 53, 39–49. [Google Scholar] [CrossRef]
  62. Basbag, M.; Cacan, E.; Sayar, M.S.; Firat, M. Determination of Some Quality Characteristics of Horned Sainfoin (Astragalus hamosus L.) Herbage Collected from Different Locations in Southeastern Anatolia Region. Int. J. Agric. Wildl. Sci. 2019, 5, 346–354. [Google Scholar]
  63. Teka, H.; Madakadze, I.C.; Angassa, A.; Hassen, A. Effect of seasonal variation on the nutritional quality of key herbaceous species in semi-arid areas of Borana, Ethiopia. Indian J. Anim. Nutr. 2012, 29, 324–332. [Google Scholar]
  64. Kaya, S. Relative Feed Value and Relative Forage Quality Index in the Evaluation of Roughage Feeds. Turk. J. Sci. Rev. 2008, 1, 59–64. [Google Scholar]
  65. Rohweder, D.A.; Barnes, R.; Jorgensen, N. Proposed hay grading standards based on laboratory analyses for evaluating quality. J. Anim. Sci. 1978, 47, 747–759. [Google Scholar] [CrossRef]
  66. Ball, D.M.; Hovelend, C.S.; Lacefield, G.D. Forage Quality in Southern Forages; Potash & Phosphate Institute: Norcross, GA, USA, 1996; pp. 124–132. [Google Scholar]
  67. Geren, H.; Kir, B.; Kavut, Y.T. Effect of Different Harvesting Times on Yield and Some Forage Quality Parameters of Sweet Sorghum (Sorghum bicolor var. saccharatum) Varieties. Ege Univ. J. Agric. Fac. 2019, 56, 249–255. [Google Scholar]
  68. Lema, M.; Kebe, S.; Opio, R. Growth rate, carcass trait and blood chemistry of cross-bred meat goats grazing Puna chicory, Rackmaster refuge mix and Sahara Bermudagrass. J. Appl. Anim. Res. 2008, 33, 1–6. [Google Scholar] [CrossRef]
  69. Cinar, S. Determination of Suitable Mixtures of Some Perennial Warm-Season Cereal Forage Crops with Alfalfa (Medicago sativa L.) under Cukurova Plain Condition. Doctoral Thesis, Cukurova University, Institute of Science, Department of Field Crops, Adana, Turkey, 2012. [Google Scholar]
  70. Joines, D.K.; Savoy, H.J. Evaluation of sulfur fertilization effects on yield, quality, and nutrient content of Bermudagrass hay. Commun. Soil Sci. Plant Anal. 2014, 45, 934–943. [Google Scholar] [CrossRef]
  71. Yilmaz, S.; Hur, N.; ve Ertekin, I. Determination of Forage Yield and Quality in Selected Lines of Bermudagrass [Cynodon dactylon (L.) Pers. var. dactylon]. J. Mustafa Kemal Univ. Fac. Agric. 2018, 23, 232–241. [Google Scholar]
  72. Pang, K.; Van Sambeek, J.W.; Navarrete-Tindall, N.E.; Lin, C.H.; Jose, S.; Garrett, H.E. Responses of legumes and grasses to non-moderate, and dense shade in Missouri, USA. II. Forage quality and its species-level plasticity. Agrofor. Syst. 2019, 93, 25–38. [Google Scholar] [CrossRef]
  73. Akcura, M.; Topal, A. Comparison of Grain Yield and Quality Characteristics of Selected Pure Lines from Local Bread Wheat Populations in Central Anatolia Region with Some Registered Varieties; National Grain Symposium, Selcuk University, Faculty of Agriculture: Konya, Turkey, 2008. [Google Scholar]
  74. Demirel, F.; Gurcan, K.; Akar, T. Clustering analysis of morphological and phenological data in einkorn and emmer wheats collected from Kastamonu region. Int. J. Sci. Technol. Res. 2019, 5, 25–36. [Google Scholar]
Agronomy 13 01471 g001 550
Figure 1. The location of the provinces where the Bermudagrass was collected in relation to the map of Turkey.
Figure 1. The location of the provinces where the Bermudagrass was collected in relation to the map of Turkey.
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Figure 2. Climate data of Erzurum province for the years 2020 and 2021 and the long-term averages *.
Figure 2. Climate data of Erzurum province for the years 2020 and 2021 and the long-term averages *.
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Figure 3. Hierarchical clustering analysis.
Figure 3. Hierarchical clustering analysis.
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Figure 4. Biplot graph for the investigated parameters of the genotypes.
Figure 4. Biplot graph for the investigated parameters of the genotypes.
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Table
Table 1. Variance analysis for the FHW, DHW, CP, NDF, ADF, and RFV of the genotypes 1.
Table 1. Variance analysis for the FHW, DHW, CP, NDF, ADF, and RFV of the genotypes 1.
Source of VariationDFFHWDHWCPNDFADFRFV
Block (eliminating treatments)50.00 ns0.06 ns0.00 ns0.00 ns0.00 ns0.00 ns
Treatment: check10.06 ns2.29 ns0.08 ns0.02 ns0.94 ns0.07 ns
Treatment: test1013.64 ***2.64 ***0.14 ns0.67 ns0.75 ns0.69 ns
Treatment (ignoring blocks)1033.91 ***2.90 ***0.15 ns1.03 ns1.11 ns1.15 ns
Treatment: test vs. check135.03 ***29.73 ***1.14 ns38.88 ***36.76 ***49.08 ***
1 DF: Degree of freedom, FHW: fresh herbage weight, DHW: dry herbage weight, CP: crude protein, NDF: neutral detergent fiber, ADF: acid detergent fiber, RFV: relative feed value, ns: non-significant, ***: p ≤ 0.001.
Table
Table 2. Coordinate, altitude, FHW, DHW, CP, NDF, ADF, and RFV values of the genotypes 1.
Table 2. Coordinate, altitude, FHW, DHW, CP, NDF, ADF, and RFV values of the genotypes 1.
GenotypeAltitude
(m)		FHW
(g per plant)		DHW
(g per plant)		CP
(%)		NDF
(%)		ADF
(%)		RFV
G1176845.2622.1011.8161.1528.85101.96
G2167131.7914.1311.4661.3429.31100.85
G3141037.5117.4610.5362.6429.9497.52
G4142418.5011.8711.8663.3928.1498.30
G5154436.0019.7713.0261.2528.48101.17
G683623.0211.5810.9263.1931.2295.32
G7152226.5617.0712.4463.4631.3794.48
G8166344.2123.6711.6263.4330.3295.94
G9126517.0111.1411.4859.9427.86104.17
G1081753.7529.129.9863.1033.3993.34
G11180884.8035.9210.0766.6329.3692.41
G1297151.8224.8511.3762.8326.41101.60
G13156414.249.5111.3961.5130.5098.38
G14156413.6510.2811.1659.2230.83101.77
G1581785.4736.5011.4564.4030.1494.92
G16144423.9814.039.7855.3727.69112.95
G17138636.5122.7411.2161.5326.66102.92
G18135426.3118.2111.5367.5629.6190.67
G19184559.6234.7110.4164.9230.1793.59
G20112146.6422.3310.2061.7929.21101.30
G21101341.0120.7410.3767.6729.7691.57
G22166217.5913.2012.5258.0325.75110.75
G23184639.3920.0112.4159.6525.84107.12
G24141113.109.1910.9858.7727.94107.19
G25135528.9520.8911.4561.9532.7195.17
G26177559.8025.2910.8263.9929.4296.11
G27162561.7732.759.7061.4228.00102.84
G28158524.3916.5712.4358.6428.82105.58
G29114533.1721.9611.3357.9327.25108.77
G30115877.6433.6211.9164.0929.1296.10
G31177928.4217.0210.7564.3431.2093.35
G32135515.1512.5512.2163.8229.9195.57
G33177544.1218.4011.4558.9228.57105.26
G34167344.5723.9312.4863.2926.43100.26
G35170546.5019.609.9965.2030.1894.59
G36171227.2713.5211.7962.3730.5497.90
G37179517.297.5110.1266.3230.6791.29
G38140023.8115.2611.4865.0330.8792.93
G39197041.7523.1810.5060.9129.00101.74
G40126220.2714.0911.0364.0429.1296.25
G41129612.716.8511.7159.4828.95103.83
G4214639.206.5310.6755.5227.42114.31
G4382446.7119.3211.0565.6831.2291.54
G44190740.4817.9310.2163.3930.3387.18
G45122830.6113.1810.4563.3632.0094.96
G46118747.1719.6312.1262.3029.0299.20
G47140895.3745.2412.0261.2628.28102.28
G48158838.6718.9411.5361.7329.18100.89
G49132013.195.2212.8659.3326.19107.50
G50153644.4617.7112.1159.5229.56103.32
G51153671.8828.7311.9361.8728.79100.78
G52175947.5124.0310.7265.7931.7391.19
G53168059.0228.0610.6265.0831.1293.35
G54168656.2624.209.0761.8729.40100.60
G55180538.6322.5510.8065.3031.6691.91
G56147628.6718.6813.2561.5429.14101.39
G57100846.0818.7710.4664.8033.4190.86
G58159232.7221.4311.2058.9829.86103.66
G59116335.7018.3011.0959.7025.80107.61
G60141326.4017.2611.3460.9129.94100.33
G61115819.708.119.4458.3423.09113.29
G6285461.8926.1212.0064.1429.7095.95
G63141025.7718.2511.0561.6029.16101.76
G64121856.5024.5112.2960.4024.91107.39
G65149625.6911.7411.1363.5127.7398.76
G66179867.1738.8111.7567.2630.6490.27
G67125820.3211.4712.2161.4329.7099.76
G68133920.9713.1411.2764.8727.7196.67
G69154550.1424.1510.3864.5530.3895.91
G70116950.8424.6510.4861.6626.67103.70
G71171336.5723.3311.0962.6629.2198.36
G72186690.3942.0011.9367.2731.0889.73
G7381759.7824.0510.5567.0132.1989.66
G74133920.0715.7412.3764.7330.3494.04
G75150713.896.5911.5361.7326.78103.24
G76116721.3510.239.9162.1128.01100.63
G77135072.0131.0310.2763.4430.7995.65
G78165547.0627.3211.7365.6531.6591.16
G79133117.0211.3211.3557.6127.16111.43
G80128622.9214.0310.6261.5729.01100.49
G81133520.6712.4711.1052.1426.07123.15
G8283964.1428.1012.5762.7930.2297.42
G83138632.1218.0710.8763.9327.0098.97
G84186024.1916.1011.7663.0227.9099.83
G85183826.5115.1111.0162.6931.5995.75
G86135528.0318.1512.0862.1826.78101.94
G87154931.5419.5113.3864.7731.0093.50
G88103548.6623.3710.9862.5827.34100.58
G89115829.1018.6012.2962.5726.00102.33
G90116339.3621.0411.6860.3326.90106.38
G91173437.7621.8112.4563.3729.0997.90
G92166125.6113.4610.0655.8626.21115.53
G93143410.566.5812.6560.2429.98101.32
G94117615.908.7510.3657.5226.47111.07
G95113022.608.8510.7756.4427.91112.33
G96130225.7612.8311.7463.8528.6497.82
G97165429.0312.1412.7859.5928.69104.30
G98162425.7711.039.9665.4730.9893.63
G99174370.7031.0010.3865.3829.4094.11
G10081852.0525.8410.9662.6830.5497.18
G101192235.7518.9712.1759.5227.72105.15
G102171062.1329.8412.6861.3428.28101.56
K1 19.048.9611.8955.6024.35117.08
K2 20.4413.3012.2855.3125.59115.93
Mean37.5519.2411.3262.0528.97100.22
CV (%)39.5037.9429.778.1410.9010.80
SD19.148.130.903.011.986.79
LSD (control treatments)16.258.043.875.723.5712.55
LSD (test treatments in different blocks)48.7424.1211.6217.1510.7037.65
LSD (test treatments in the same block)39.7919.699.4714.008.7430.74
LSD (test treatment and control treatment)37.2218.428.8713.108.1728.76
1 FHW: fresh herbage weight, DHW: dry herbage weight, CP: crude protein, NDF: neutral detergent fiber, ADF: acid detergent fiber, RFV: relative feed value.
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Severoglu, S.; Gullap, M.K. Determination of Feed Yield and Quality Parameters of Bermudagrass (Cynodon dactylon L. (Pers.)) Populations Collected from Natural Flora. Agronomy 2023, 13, 1471. https://doi.org/10.3390/agronomy13061471

AMA Style

Severoglu S, Gullap MK. Determination of Feed Yield and Quality Parameters of Bermudagrass (Cynodon dactylon L. (Pers.)) Populations Collected from Natural Flora. Agronomy. 2023; 13(6):1471. https://doi.org/10.3390/agronomy13061471

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Severoglu, Sedat, and M. Kerim Gullap. 2023. "Determination of Feed Yield and Quality Parameters of Bermudagrass (Cynodon dactylon L. (Pers.)) Populations Collected from Natural Flora" Agronomy 13, no. 6: 1471. https://doi.org/10.3390/agronomy13061471

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