Effect of Organic, Chemical and Combined Fertilizers on Some Quantitative and Qualitative Characteristics of Three Sunflower (Heliantus annuus L.) Cultivars

Document Type : Research Paper

Authors

1 Department of Agronomy- Faculty of Agriculture- University of Zabol, Zabol, Iran

2 Department of Agronomy,Faculty of Agriculture,University of Zabol,Zabol,Iran

3 Department of Agronomy- Faculty of Agriculture- University of Zabol

4 Seed and Plant Improvement Institute , Karaj

Abstract

Background & Objective: This study was conducted to evaluate the effect of chemical, organic and integrated fertilizers on some characteristics of sunflower cultivars.
Materials and Methods: The experiment was carried out in the form of split plots in the form of a randomized complete block design in three replications at the Zahak Agricultural Research Station in Sistan and Baluchistan province. The main factor including control (no fertilizer application), compost (40 tons per hectare), humic acid (5 liters per hectare), chemical fertilizer (150 kg urea, 100 kg phosphorus and 100 kg potash) , compost and humic acid (20 tons per hectare of compost + 2.5 liters per hectare) and humic acid + chemical fertilizer (2.5 liters per hectare + 75 kg of urea, 50 kg of phosphorus and 50 kg of potash). The sub-factor was three sunflower cultivars Shams, Qasem and Haysan 25 .
Results: The highest head diameter from humic acid + compost and Shams cultivar , weight 1000 seeds from humic acid + chemical fertilizer and Shams, number of seeds per head from humic acid + compost and Shams, and biological yield were obtained from humic acid + chemical fertilizer and Shams cultivar. Biological yield of Shams, Ghasem and Haysan 25 cultivars in humic acid + chemical fertilizer compared to the control increased by 39.2%, 31.5% and 34.5%, respectively.
Conclusion: Use of organic fertilizers and integrated nutrition management are among the effective methods to improve the quantitative and qualitative characteristics of sunflower, which reduces the use of chemical fertilizers.

Keywords

Main Subjects

References

Abdollahi Hesar A, Sofalian O, Alizadeh B, Asghari A and Zali H. 2020. Evaluation of some autumn canola genotypes based on agronomy traits and SIIG index. Journal of Crop Breeding, 12(34): 93-104. (In Persian)..
Barati A, Zali H, Marzoqian A, Koohkan Sh and Gholipour A. 2021. Selection of barley pure lines with high yield and desirable agronomic characteristics in warm areas of Iran. Journal of Crop Production, 14 (1): 199-218. (In Persian)..
Benakanahalli NK, Sridhara S, Ramesh N, Olivoto T, Sreekantappa G, Tamam N, Abdelbacki AMM, Elansary HO and Abdelmohsen SAM. 2021. A framework for identification of stable genotypes based on MTSI and MGDII Indexes: an example in guar (Cymopsis tetragonoloba L.). Agronomy, 11: 1221.
Bhandari H, Bhanu A, Srivastava K, Singh M and Shreya HA. 2017. Assessment of genetic diversity in crop plants-an overview. Advances in Plants & Agriculture Research, 7(3):279-286.
Bizari EH, Pedroso Val BH, Pereira EM, Di Mauro AO and Uneda-Trevisoli S. 2017. Selection indices for agronomic traits in segregating populations of soybean. Revista Ciencia Agronomica, 48:110–117
Emami S, Asghari A, Mohammaddoust Chamanabad H, Rasoulzadeh A and Ramzi E. 2019. Evaluation of osmotic stress tolerance in durum wheat (Triticum durum L.) advanced lines. Environmental Stresses in Crop Sciences, 12 (3): 697-707. (In Persian).
FAO. 2019. Statistical data. www. Fao. Org/faostat.
Gholizadeh A, Ghaffari M and Shariati F. 2021. Use of selection index of ideal genotype (SIIG) in order to select new high yielding sunflower hybrids with desirable agronomic characteristics. Journal of Crop Breeding, 13 (38): 116-123. (In Persian)..
Holland JB. 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46: 642-654.
Hussain T, Akram Z, Shabbir G, Manaf A and Ahmed M. 2021. Identification of drought tolerant chickpea genotypes through multi trait stability index. Saudi Journal of Biological Sciences, doi:10.1016/j.sjbs.2021.07.056
Haghighatnia H and Alhani F. 2020.  Evaluation of irrigation water salinity tolerance indices in new cultivars and lines of safflower. Iranian Journal of Soil and Water Research, 51(7): 1181-1821. (In Persian)..
Jahufer MZZ and Casler MD. 2015. Application of the Smith-Hazel selection index for improving biomass yield and quality of switch grass. Crop Science, 55:1212–1222.
Meier C, Marchioro VS, Daniela Meira D, Olivoto T, Klein LA. 2021. Genetic parameters and multiple-trait selection in wheat genotypes. Pesquisa Agropecuária Tropical, 51: e67996.
Mirzaei MR and Hemayati, SS. 2021. The effect of environment and maternal plant on germination traits of sugar beet seeds and an approach to select the superior genotype. Agricultural Research, https://doi.org/10.1007/s40003-021-00607-2
Najafi Mirak T, Dastfal M, Andarzian B, Farzadi H, Bahari M and Zali H. 2018. Stability analysis of grain yield of durum wheat promising lines in warm and dry areas using parametric and non-parametric methods. Journal of Crop Production and Processing, 8 (2): 79-96. (In Persian).
Oliveira ICM, Marcal TDS, Bernardino KDC, Ribeiro PCDO, Parrella RADC, Carneiro PCS, Schaffert RE and Carneiro JEDS. 2019. Combining ability of biomass sorghum lines for agroindustrial characters and multitrait selection of photosensitive hybrids for energy cogeneration. Crop Science, 59(1): 1554-1566.
Olivoto T, Licio ADC, da Silva JAG, Sari BG and Diel MI. 2019a. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111(6): 2961-2969.
Olivoto T, Licio ADC, da Silva JAG, Marchioro VS, de Souza VQ and Jost E. 2019b. Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6): 2949-2960.
Olivoto T and Lucio AD. 2020. Metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11: 783-789
Olivoto T and Nardino M. 2020. MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics, 1–22.
Olivoto T and Nardino M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37: 1383–1389.

Pour-Aboughadareh A. and Poczaib P. 2021. Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data in Brief, 36: 107596.

Pour-Aboughadareh A. and Poczaib P. 2021. A dataset on multi-trait selection approaches for screening desirable wild relatives of wheat. Data in Brief, 39: 107541.
Pour-Aboughadareh A, Sanjani S, Nikkhah-Chamanabad H, Mehrvar MR, Asadi A, Amini A. 2021. Identification of salt-tolerant barley genotypes using multi-traits index and yield performance at the early growth and maturity stage. Bulletin of the National Research Centre, 45:1-16.
JOURNAL OF AGRICULTURAL SCIENCE AND SUSTAINABLE PRODUCTION
Volume 33, Issue 3
October 2023
Pages 25-37

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