International Journal of Horticulture 2025, Vol.15, No.3 http://hortherbpublisher.com/index.php/ijh © 2025 HortHerb Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
International Journal of Horticulture 2025, Vol.15, No.3 http://hortherbpublisher.com/index.php/ijh © 2025 HortHerb Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Publisher HortHerb Publisher Editedby Editorial Team of International Journal of Horticulture Email: edit@ijh.hortherbpublisher.com Website: http://hortherbpublisher.com/index.php/ijh Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada International Journal of Horticulture (ISSN 1927-5803) is an open access, peer reviewed journal published online by HortHerb Publisher. The journal publishes all the latest and outstanding research articles, letters and reviews in all aspects of horticultural and its relative science, containing horticultural products, protection; agronomic, entomology, plant pathology, plant nutrition, breeding, post harvest physiology, and biotechnology, are also welcomed; as well as including the tropical fruits, vegetables, ornamentals and industrial crops grown in the open and under protection. HortHerb Publisher is an international Open Access publisher specializing in horticulture, herbal sciences, and tea-related research registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. All the articles published in International Journal of Horticulture are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. HortHerb Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
International Journal of Horticulture (online), 2025, Vol. 15, No.3 ISSN 1927-5803 http://hortherbpublisher.com/index.php/ijh © 2025 HortHerb Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Effect of Integrated Nutrient Management on Growth and Yield of Tomato Namrata Acharya, Hari Ghimire, Rejina Sapkota, Subina Acharya, Suwas Dahal, Pariwesh Gnyawali International Journal of Horticulture, 2025, Vol. 15, No. 3, 99-104 Genetic Diversity and Trait Discovery in Pineapple Germplasm: A Meta-Analysis Approach Mengting Luo, Zhonggang Li International Journal of Horticulture, 2025, Vol. 15, No. 3, 105-112 A Review of High-Yielding Potato Cultivars and Their Cultivation Techniques Wenbin Zhang International Journal of Horticulture, 2025, Vol. 15, No. 3, 113-122 Effects of Boron Foliar Sprays at Different Concentrations on Growth and Yield of Cauliflower (Brassica oleracea var. botrytis) in Marin, Sindhuli, Nepal Anish Parajuli, Dinesh Khanal, Debina Sunari, Tej Bahadur Budhathoki, Promise Shrestha, Ganesh Lamsal International Journal of Horticulture, 2025, Vol. 15, No. 3, 123-132 The Role of Canopy Management in Optimizing Grapevine Yield and Quality Hongfang Lan International Journal of Horticulture, 2025, Vol. 15, No. 3, 133-142
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 99 Research Report Open Access Effect of Integrated Nutrient Management on Growth and Yield of Tomato Namrata Acharya1 , Hari Prasad Ghimire1, Rejina Sapkota1, Subina Acharya1,2, Suwas Dahal 1, Pariwesh Gnyawali 1,3 1 Agriculture and Forestry University, Rampur, Chitwan, 44209, Nepal 2 Department of Soil Science, Agriculture and Forestry University, Rampur, Chitwan, 44209, Nepal 3 Department of Genetics and Plant Breeding, Agriculture and Forestry University, Rampur, Chitwan, 44209, Nepal Corresponding author: acharyanamrata57@gmail.com International Journal of Horticulture, 2025, Vol.15, No.3 doi: 10.5376/ijh.2025.15.0011 Received: 01 Jan., 2025 Accepted: 30 Mar., 2025 Published: 12 May, 2025 Copyright © 2025 Acharya et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Acharya N., Ghimire H.P., Sapkota R., Acharya S., Dahal S., and Gnyawali P., 2025, Effect of integrated nutrient management on growth and yield of tomato, International Journal of Horticulture, 15(3): 99-104 (doi: 10.5376/ijh.2025.15.0011) Abstract An experiment was conducted to evaluate the effect of integrated nutrient management on growth and yield of tomato. The effect of eight different types of nutrient sources were studied using randomized complete block design with three replications. Data on growth and yield parameters were taken from five randomly selected plants in each plot. The plant height was found higher in the treatment of integrated nutrient in all stages. Similarly, number of fruit clusters / plant (8.33) and number of fruits/clusters (9.66) was maximum with the treatment of integrated nutrient. Treatment of integrated nutrient has also produced the longest fruit length (4.64 cm) and highest fruit diameter (56.08 mm) at harvest. The average fruit weight and yield / plot of tomato was maximum in the treatment of integrated nutrient 62.25 g and 75.35 kg respectively at harvest. Also yield / hectare was also maximum in treatment of integrated nutrients with 186.05 mt/ha. Growth parameters, and plant height were better performing in the application of integrated nutrient. Correlation analysis showed that traits like plant height, number of branches, number of flowers, number of fruit clusters/plant, number of fruit / clusters, fruit length, fruit diameter, fruit weight of tomato was to be found positively and significantly correlated with yield of tomato. The result obtained indicated that the growth and yield of tomato is highly influenced by nutrient sources. Keywords Tomato (Solanum lycopersicumL.); Nutrients; Integrated; Management; Yield; Growth 1 Introduction The tomato is an edible berry that is widely cultivated as an annual plant all over the world. It is one of the major income generating vegetable crops of Nepal in terms of production and cultivated area (Prativa and Bhattarai, 2012). There are abundant evidences that concludes inorganic fertilizers can improve yield of crop significantly (Sharma, 2017). Although chemical fertilizers majorly contribute for sufficient crop production for rising world population, its overuse is dragging serious challenges to the present and future generations like air, water and soil pollution, land degradation, soil depletion and increased emissions of greenhouse gases (Kumar et al., 2019). Constant use of chemical fertilizer can alter the pH of soil, increase pests infestation and cause acidification, which results in decreasing organic matter load, humus load, useful organisms, stunting plant growth, and which even become responsible for emission of greenhouse gases (Pahalvi et al., 2021). Organic fertilizer used in rotational cropping systems increased crop output by at least 40%, also improved soil nutrient pools, relative fraction of soil decomposers, and stability and diversity of bacterial and fungal networks (Jiang et al., 2022). The incorporation of organic matter in soil is controlled to avoid excessive release of soluble nutrients such as nitrogen and phosphorus thereby reducing N leaching loss and P fixation; they can also supply micronutrients subsequently leading to better crop growth and production (Abbott et al., 2015). Integrated Nutrient Management (INM) is an advanced concept of modern agriculture. Application of chemical fertilizers provides a good yield but soil properties are badly affected. Keeping in mind the bad impact of chemical fertilizers uses, the concept of integrated nutrient management is taken under consideration to obtain a higher yield and good quality. INM provides organic and inorganic nutrient components to the plant for
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 100 sustainable crop production as it maintains the soil health as well as soil fertility in long term (Pandey and Chandra, 2013). INM is eco-friendly, and when used in crops, has no negative effects on the ecology and human health. Adaptation of INM practices besides increasing productivity, also improves soil health. INM has also been reported to correct micronutrient deficiency (Ramesh et al., 2023). Plant yield is a very complicated trait that depends on many different factors. As a result, understanding the degree to which yield and its characteristics are correlated is extremely useful in the field of crop improvement (Naveen et al., 2017). Assessing the interrelationships among a variety of component characters is a necessary step toward achieving the desired outcome (Sinha et al., 2020). 2 Materials and Methods 2.1 Experimental site The research was conducted in Dhulikhel municipality, Karve. This region lies in temperate mid hill of Nepal situated within 27°37’ North latitude to 85°32’ longitude with an altitude of 1,550 meter above sea level. The experimental site lies in the subtropical zone of Nepal. It is characterized by three distinct seasons: rainy season (June to October), winter season (November to Feb), and spring season (March to April). The maximum temperature during winter season rises up to 25 °C (end of February) whereas during the hottest months (May-June) it reaches up to 35 °C. The Rainy season starts from June and lasts up to October, June -July receives the highest amount of rainfall. 2.2 Experimental design The research was carried out in Randomized Complete Block Design (RCBD) with eight treatments each having three replications. The field area was divided into 24 plots. Space between plots within one replication was maintained at 0.50 m and the space between replications was maintained at 1 m. Each plot was planted with 20 tomato plants containing a total of 480 tomato plants in 24 plots. The spacing between row-to-row and plant-to-plant was maintained at 0.75 m and 0.45 m respectively. Five plants were chosen from each replication for data collection. The sample plants were chosen from among the plants that remained after the border plants were excluded. The required data were collected from the sample plants at required time intervals. 2.3 Treatment details The experiment comprised eight different nutrient management treatments designed to evaluate the effect of integrated nutrient management on the growth and yield of tomato (Table 1). These treatments included the application of various organic and inorganic fertilizers, both individually and in combination, along with a control (no nutrient input). The organic sources consisted of well-decomposed farmyard manure (FYM), vermicompost, and poultry manure, while the inorganic source was the recommended dose of chemical fertilizers (NPK at 200:180:80 kg/ha). Some treatments involved combinations of organic and inorganic fertilizers to assess their synergistic effects. Table 1 Description of nutrient management treatments applied in the experiment Treatments Description T1 Well-decomposed Farmyard Manure (FYM) at 30 t/ha T2 Recommended NPK fertilizer at 200:180:80 kg/ha T3 Vermicompost at 20 t/ha T4 Poultry manure at 10 t/ha T5 FYM at 15 t/ha + 50% of Recommended NPK T6 Vermicompost at 10 t/ha + 50% of Recommended NPK T7 FYM at 7.5 t/ha + 25% of Recommended NPK + Vermicompost at 5 t/ha + Poultry manure at 2.5 t/ha T8 Control (no organic or inorganic nutrient application)
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 101 3 Results and Analysis 3.1 Growth parameter At 60 days after transplanting, plant height was measured and found significantly higher in T7 (7.5 t/ha of well-decomposed FYM + 25% of recommended NPK +5 t/ha of vermicompost + 2.5 t/ha poultry manure) with 116.46 cm, which is statistically higher than other treatments (Table 2). While the minimum height was recorded with control (101.66 cm) which is due to the application of major and minor nutrients, through different organic manure and bio fertilizers, which increased the photosynthetic activity, chlorophyll formation, nitrogen metabolism, and auxin contents in the plants which ultimately improved the plant height. Table 2 Mean performance of yield attributing traits of tomato under different treatments Treatment PH NF FC/P F/C FL FD FW Y/P Y/H T1 105.60c 50.00bc 6.33b 6.66b 3.88c 52.11d 51.83bc 45.60d 112.59d T2 108.87d 53.33bc 6.66b 6.66b 4.30bc 55.00b 53.88bc 50.57cd 124.00cd T3 106.70e 48.33bc 6.33b 6.66b 4.25bc 53.83c 55.21b 48.91cd 120.77cd T4 105.70e 47.66bc 6.33b 6.33b 4.07cd 50.43c 49.56c 45.89d 113.30d T5 110.75c 58.33b 7.00ab 7.30b 4.45ab 55.56ab 53.61bc 60.04b 148.25b T6 114.22b 54.33bc 7.33ab 7.00b 4.44ab 56.02a 54.68a 53.76c 132.79c T7 116.46a 84.33a 8.33a 9.66a 4.64a 56.08a 62.25d 75.35a 186.05a T8 101.66c 43.00c 3.00c 4.00c 3.21d 45.22f 37.83** 28.38e 70.07e LSD 1.38*** 10.99** 1.27* 1.64* 0.21** 0.81** 6.76 6.01** 14.83** SEm(±) 0.16 1.28 0.14 0.19 0.02 0.07 0.78 0.70 1.72 CV(%) 0.71 11.50 10.71 13.25 2.33 0.87 7.42 6.72 6.72 Grand mean 108.86 54.91 6.41 6.79 4.13 50.03 52.23 51.06 126.09 Note: LSD= Least Significant Difference, SEM=Standard Error of Mean, CV= Coefficient of Variation, GM= Grand Mean, PH= Plant height, NF= Number of flowers, FC/P= Number of fruit cluster per plants, F/C= Fruits per cluster, FL= Fruit length, FD= Fruit diameter, FW= Fruit weight, Y/P= Yield per plot, Y/H= Yield per harvest 3.2 Flower, fruit characteristics and yield Number of flowers were significantly influenced by different types of nutrients (Table 3). The average number of flowers were found to be 54.91. The highest number of flowers (84.33) were observed in T7 followed by T5 (58.33). The lowest number of flowers were recorded in control (43.00). The average number of fruit clusters/plants was 6.41. The highest number of fruit clusters/plant was recorded in T7 (8.33) followed by T6 (7.33) and the minimum number of fruit clusters/plant was found in control (3.00). The mean number of fruits/clusters was 6.79 and the highest was observed with T7 (9.66) and the lowest was found in control (4.00). The average fruit length was found to be 4.13 cm with highest in T7 (4.64 cm). The highest fruit diameter was obtained from T7 with 56.08 mm, which is significantly at par with T6. The lowest diameter was obtained from the control with 45.22 mm. The average fruit diameter was 50.03 mm. The average fruit weight was calculated to be 52.23 g. The highest fruit weight was obtained from T7 with 62.25 g followed by T3 and the lowest was found in the control. And the highest yield/hectare was obtained from T7 (186.05 mt/ha) which is followed by T5 (148.25 mt/ha) and T6 (132.79 mt/ha) and the lowest was found in control with 70.07 mt/ha. 3.3 Correlation between different plant parameters The study showed that the traits plant height and yield were positively and significantly correlated (r=0.77) which means an increase in plant height increase the yield of tomato significantly (Table 4). Number of branches and yield were found significantly and positively correlated. Similar findings were reported for number of branches and plant height by Naveen et al. (2017). Similarly, number of flowers, number of fruits cluster per plant, number of fruits per cluster, and yield were found positively and significantly correlated with correlation coefficient r=0.82, r=0.65 and r=0.70, respectively, which means increase in number of flowers, number of fruits cluster per plant, and number of fruits per cluster
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 102 significantly increase the yield of tomato. Sinha et al. (2020) has also reported that fruit per cluster, fruit cluster per plant and plant height significantly influence the fruit yield in tomato. Furthermore, fruit length (r=0.79), fruit diameter (r=0.77) and fruit weight (r=0.81) were also found positively significant with the yield indicating their contribution to the yield. Table 3 Mean performance of yield attributing traits of tomato under different treatments Treatment PH NF FC/P F/C FL FD FW Y/P Y/H T1 105.60c 50.00bc 6.33b 6.66b 3.88c 52.11d 51.83bc 45.60d 112.59d T2 108.87d 53.33bc 6.66b 6.66b 4.30bc 55.00b 53.88bc 50.57cd 124.00cd T3 106.70e 48.33bc 6.33b 6.66b 4.25bc 53.83c 55.21b 48.91cd 120.77cd T4 105.70e 47.66bc 6.33b 6.33b 4.07cd 50.43c 49.56c 45.89d 113.30d T5 110.75c 58.33b 7.00ab 7.30b 4.45ab 55.56ab 53.61bc 60.04b 148.25b T6 114.22b 54.33bc 7.33ab 7.00b 4.44ab 56.02a 54.68a 53.76c 132.79c T7 116.46a 84.33a 8.33a 9.66a 4.64a 56.08a 62.25d 75.35a 186.05a T8 101.66c 43.00c 3.00c 4.00c 3.21d 45.22f 37.83** 28.38e 70.07e LSD 1.38*** 10.99** 1.27* 1.64* 0.21** 0.81** 6.76 6.01** 14.83** SEm(±) 0.16 1.28 0.14 0.19 0.02 0.07 0.78 0.70 1.72 CV(%) 0.71 11.50 10.71 13.25 2.33 0.87 7.42 6.72 6.72 Grand mean 108.86 54.91 6.41 6.79 4.13 50.03 52.23 51.06 126.09 Note: LSD= Least Significant Difference, SEM=Standard Error of Mean, CV= Coefficient of Variation, GM= Grand Mean, PH= Plant height, NF= Number of flowers, FC/P= Number of fruit cluster per plants, F/C= Fruits per cluster, FL= Fruit length, FD= Fruit diameter, FW= Fruit weight, Y/P= Yield per plot, Y/H= Yield per harvest Table 4 Correlation between different plant parameters YPP PH NOB NOF NOFCP NOFC FD FL FW YPP 1 - - - - - - - - PH 0.77** 1 - - - - - - - NOB 0.56** 0.52** 1 - - - - - - NOF 0.82** 0.72** 0.44* 1 - - - - - NOFCP 0.65** 0.66** 0.44* 0.70** 1 - - - - NOFC 0.70* 0.61** 0.34 0.90** 0.36 1 - - - FD 0.77** 0.72** 0.33 0.75** 0.60** 0.67** 1 - - FL 0.79** 0.78** 0.51** 0.51** 0.53** 0.38 0.74** 1 - FW 0.81** 0.68** 0.45* 0.57** 0.63** 0.39 0.64** 0.82** 1 Note: ** Correlation is significant at the 0.01 level; *Correlation is significant at the 0.05 level; YPP= Yield / plot, PH= Plant height, NOB= No. of branches, NOF= No. of flowers, NOFCP=No. of fruit clusters/plant, NOFC=No. of fruit / clusters, FL= Fruit Length, FD= Fruit Diameter, FW= Fruit Weight 4 Discussion The experimental findings showed that among the eight treatments used in the experiment, the integrated nutrient combination (7.5 t/ha of well-decomposed FYM + 25% of recommended NPK + 5 t/ha of vermicompost + 2.5 t/ha of poultry manure) was found to be superior in terms of plant growth and performance including fruit yield. The enhanced growth can be attributed to the synergistic effects of organic and inorganic nutrient sources, which improved soil structure, microbial activity, and nutrient availability, thereby promoting better physiological responses in tomato plants. Correlation study revealed that yield per plot of tomato was positively correlated and highly significant with number of fruits followed by fruit weight, fruit length, fruit diameter, plant height, number of fruits per cluster and number of fruit cluster per plant. Thus, yield can be further improved by improving these traits.
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 103 A study conducted in Lalitpur also revealed that the integration of organic manures with inorganic fertilizers significantly improved overall plant growth, yield, and soil macronutrient status compared to the sole application of either nutrient (Prativa and Bhattarai, 2012). Another study conducted at Rajasthan, India, also suggested that the combination of biofertilizer, farm yard manure, vermicompost and poultry manure is the best nutrient combination for enhancing crop yield (Sharma et al., 2023). The findings suggest that use of integrated nutrient management should be adopted for tomato cultivation in the Kavrepalanchok district. Furthermore, the use of organic amendments like vermicompost and poultry manure contributes to long-term soil fertility and reduces dependency on synthetic fertilizers. While the study presents promising results, some limitations must be acknowledged. The experiment was conducted in a specific agro-climatic region, and findings may not be applicable to all locations. Thus, trials in different climatic zones and soil conditions can help validate the broader applicability of the results. Moreover, future research should explore long-term soil health impacts and economic feasibility of this nutrient combination. Authors’ contributions NA develop the concept of the experiment, design the experiment and involved in data interpretation and report draft review. RS involved in data collection, analysis and content writing. HPG also engaged in data collection, analysis and content writing. Likewise, SD, SA and PG all involved in data collection, analysis and content writing. All authors read and approved the final manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abbott L.K., and Manning D.A., 2015, Soil health and related ecosystem services in organic agriculture, Sustainable Agriculture Research, 4(3). https://doi.org/10.22004/AG.ECON.230386 Hasnain M., Chen J., Ahmed N., Memon S., Wang L., Wang Y., and Wang P., 2020, The effects of fertilizer type and application time on soil properties, plant traits, yield and quality of tomato, Sustainability, 12(21): 9065. https://doi.org/10.3390/su12219065 Jiang Y., Zhang J., Manuel D.-B., Op De Beeck M., Shahbaz M., Chen Y., Deng X., Xu Z., Li J., and Liu Z., 2022, Rotation cropping and organic fertilizer jointly promote soil health and crop production, Journal of Environmental Management, 315: 115190. https://doi.org/10.1016/j.jenvman.2022.115190 Kumar R., Kumar C., Kumar R., and Prakash O., 2019, The impact of chemical fertilizers on our environment and ecosystem, pp.69-86. Lin Y., Ye G., Kuzyakov Y., Liu D., Fan J., and Ding W., 2019, Long-term manure application increases soil organic matter and aggregation, and alters microbial community structure and keystone taxa, Soil Biology and Biochemistry, 134: 187-196. https://doi.org/10.1016/j.soilbio.2019.03.030 Naveen B.L., Saidaiah P., Ravinderreddy K., and Geetha A., 2017, Correlation and path coefficient analysis of yield and yield attributes in tomato (Solanum lycopersicumL.), Journal of Pharmacognosy and Phytochemistry, 6(6): 665-669. Pahalvi H.N., Rafiya L., Rashid S., Nisar B., and Kamili A.N., 2021, Chemical fertilizers and their impact on soil health, in Microbiota and Biofertilizers, Vol 2: Ecofriendly Tools for Reclamation of Degraded Soil Environs, pp.1-20. https://doi.org/10.1007/978-3-030-61010-4_1 Pandey S.K., and Chandra K.K., 2013, Impact of integrated nutrient management on tomato yield under farmers field conditions, Journal of Environmental Biology, 34(6): 1047-1051. Prativa K.C., and Bhattarai B.P., 2011, Effect of integrated nutrient management on the growth, yield and soil nutrient status in tomato, Nepal Journal of Science and Technology, 12: 23-28. https://doi.org/10.3126/njst.v12i0.6474 Rajawat K.S., Ameta K.D., Kaushik R.A., Dubey R.B., Jain H.K., Jain D., and Kaushik M.K., 2019, Effect of integrated nutrient management on growth attributes and soil nutrient status of tomato under naturally ventilated polyhouse, International Journal of Current Microbiology and Applied Sciences, 8(10): 512-517. https://doi.org/10.20546/ijcmas.2019.810.056 Ramesh E., Sikder S., and Vandana K.S., 2023, Effect of integrated nutrient management for growth, yield and post-harvest quality of tomato, International Journal of Environment and Climate Change, 13(5): 1-10. https://doi.org/10.9734/ijecc/2023/v13i51736
International Journal of Horticulture, 2025, Vol.15, No.3, 99-104 http://hortherbpublisher.com/index.php/ijh 104 Sharma A., and Chetani R., 2017, A review on the effect of organic and chemical fertilizers on plants, International Journal of Research in Applied Science and Engineering Technology, 5(2): 677-680. https://doi.org/10.22214/ijraset.2017.2103 Sharma H.L., Tailor S.P., Rajawat K.S., and Kurmi K.P., 2023, Effect of integrated nutrient management on the growth, yield parameters and economics in tomato (Lycopersicon esculentumL.) under Southern Rajasthan conditions, Pharma Innovation Journal, 12(3): 321-326. https://doi.org/10.22271/tpi.2023.v12.i3c.18948 Sinha A., Singh P., Bhardwaj A., and Kumar R., 2020, Genetic variability and character association analysis for yield and attributing traits in tomato (Solanum lycopersicumL.) genotypes for protected cultivation, Journal of Pharmacognosy and Phytochemistry, 9(1): 2078-2082. https://doi.org/10.22271/phyto.2020.v9.i1ai.10772
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 105 Meta Analysis Open Access Genetic Diversity and Trait Discovery in Pineapple Germplasm: A Meta-Analysis Approach Mengting Luo 1,2 , Zhonggang Li 1 1 Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China 2 Hainan Institute of Tropical Agricultural Resources, Sanya, 572000, Hainan, China Corresponding author: mengting.luo@jicat.org International Journal of Horticulture, 2025, Vol.15, No.3 doi: 10.5376/ijh.2025.15.0012 Received: 20 Mar., 2025 Accepted: 22 Apr., 2025 Published: 25 May, 2025 Copyright © 2025 Luo and Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Luo M.T., and Li Z.G., 2025, Genetic diversity and trait discovery in pineapple germplasm: a meta-analysis approach, International Journal of Horticulture, 15(3): 105-112 (doi: 10.5376/ijh.2025.15.0012) Abstract In this study, we used a meta-analysis to describe the genetic diversity of pineapple germplasm resources and the results of excellent traits mining, revealed the population structure differences among different germplasm types and geographical sources, analyzed the integration rule of QTL related to high Brix traits through a case study, and proposed a breeding strategy based on molecular markers and genome selection. The results show that genetic variation exists between different regions and varieties of pineapple, and molecular markers are very effective in assessing germplasm diversity and trait associations. This study is expected to provide scientific basis for the targeted breeding of high-quality pineapple varieties and efficient utilization of genetic resources, and promote the development of pineapple industry. Keywords Pineapple germplasm; Genetic diversity; Trait discovery; Meta-analysis; Molecular markers 1 Introduction As one of the major tropical fruits, pineapple (Ananas comosus) has attracted much attention due to its economic value and rich genetic composition, which play a key role in variety improvement and adaptation (Hossain, 2016; Cheng et al., 2018; Chaudhary et al., 2019; Ali et al., 2020). Genetic diversity in pineapple germplasm resources is important for breeding programs to improve yield, fruit quality and stress resistance. Genetic diversity is mainly attributed to the heterozygous characteristics of pineapple and the presence of several varieties such as Perola, Queen, and Abacaxi, each of which exhibits its own unique and potential traits (Duval et al., 2001; Zhao and Qin, 2018). Studies by Paz et al. (2012), Makaranga et al. (2018) and Zhao and Qin (2018) show that the application of molecular markers such as SSR, SNP and AFLP is conducive to the assessment and understanding of genetic diversity. It provides important information for genetic structure analysis and character improvement. The utilization of pineapple germplasm is still facing challenges. The low genetic diversity of pineapples observed by researchers in areas such as Tanzania and Cuba limits the improvement and breeding of local varieties (Paz et al., 2005; Paz et al., 2012; Makaranga et al., 2018). The complex reproductive biology of pineapple, such as open pollination uncertainty and somatic cell variation, increases the complexity of the breeding process and hinders the development of new varieties (Reinhardt et al., 2018; Wang and Paull, 2018; Chen et al., 2019; Nureszuan et al., 2021; Jia et al., 2024). Studies by Makaranga et al. (2018) and Jia et al. (2024) suggest that more advanced breeding strategies must be introduced to effectively exploit and utilize the genetic potential of pineapple germplasm. This study will evaluate the genetic diversity and character discovery of pineapple germplasm through meta-analysis, and propose strategies to overcome the bottleneck of germplasm utilization by integrating data from multiple studies. This study hopes to provide theoretical support for cultivating more resistant and higher quality pineapple varieties, and then promote the development and productivity of pineapple cultivation.
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 106 2 Genetic Diversity Studies in Pineapple: Literature Landscape 2.1 Common molecular techniques Various molecular techniques have been widely used in the study of pineapple genetic diversity to assess and characterize genetic variation between and within different genotypes. Commonly used molecular markers include RAPD, RFLP, AFLP, SSR, and SNP (Duval et al., 2001; Kato et al., 2005; Paz et al., 2012; Zhou et al., 2015; Zhao and Qin, 2018). SSR markers are widely used in genetic diversity assessment due to their high polymorphism and good repeatability (Wang et al., 2017; Ismail et al., 2020; Nashima et al., 2020). AFLP markers have been used to reveal genetic relationships and diversity in specific germplasm resources (Carlier et al., 2010; Paz et al., 2012; Sheeja et al., 2021). 2.2 Traits commonly studied In pineapple genetic diversity studies, researchers often focus on traits related to breeding potential, such as yield, fruit size, fruit quality, and productivity (Zhao and Qin, 2018; Adje et al., 2019; Junior et al., 2021; Chen et al., 2024a; Chen et al., 2024b). Researchers also focused on specific traits, such as leaf margin phenotype and flesh color, and mined their contributing genes and QTLS by genome sequencing (Figure 1) (Nashima et al., 2022). The study of Sinaga and Marpaung (2024) showed that the study of stress resistance traits is also the focus, and the study of stress resistance traits is conducive to cultivating disease-resistant and stress-resistant pineapple varieties. Figure 1 Phenotype of leaf margin and flesh color in pineapples (Adopted from Nashima et al., 2022) Image caption: (a) Pipe-type leaf margin phenotype. (b) Spiny-type leaf margin phenotype. (c) White flesh color phenotype. (d) Yellow flesh color phenotype (Adopted from Nashima et al., 2022) 2.3 Need for meta-analytic consolidation It is necessary to conduct a meta-analysis in the field of pineapple genetic diversity. Meta-analyses integrate different studies to provide an understanding of genetic diversity patterns and trait associations for different germplasm resources. Junior et al. ’s study in 2021 demonstrated that the integration of meta-analyses helps to identify consistent genetic markers and traits that can be used in breeding programs to improve pineapple varieties. Meta-analysis is great for standardizing methods and results, which makes it easier to compare results across studies and regions.
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 107 3 Meta-Analytic Insights on Genetic Diversity 3.1 Global diversity patterns Pineapple germplasm showed significant genetic diversity among different regions and varieties, and SSR, AFLP and ISSR molecular markers were used to reveal rich genetic variation. According to the study of Ismail et al. (2020), the SSR study in Malaysia showed moderate polymorphism, with an average of 3.9 alleles detected at each locus and an average PIC value of 0.433, indicating that its genetic diversity was at a medium level. Paz et al. (2012) showed that the genetic diversity of Cuban germplasm was low through AFLP analysis, and most of the materials were clustered at genetic distance less than 0.20, indicating a limited range of variation. Hayati and Kasiamdari (2024) showed that Indonesian varieties showed high genetic diversity with 89.38% polymorphisms detected by ISSR markers. 3.2 Population structure across studies Studies in different regions revealed the differences in population structure of pineapple germplasm resources. Ismail et al. 's study in 2020 showed that the population structure analysis in Malaysia used the delta K method to identify two major genetic clusters, and the findings were supported by UPGMA systematic cluster maps. Rattanathawornkiti et al. ’s study in 2016 showed that AFLP studies in Thailand identified 9 independent genetic populations in 37 materials, which were closely related to morphological characteristics of breeds (such as Cayenne and Queen taxa). The population structure of pineapple is complex, which can be influenced by both genetic and environmental factors. 3.3 Variation among germplasm types Variability between different types of pineapple germplasm has been demonstrated in multiple studies. According to Zhao and Qin (2018), the genetic diversity of pineapple is driven by cross-pollination and somatic variation, resulting in wide differences in plant morphology and fruit traits among different varieties. Zhou et al. (2015) reported that the application of SNP markers revealed high genetic redundancy in the germbank, and somatic mutations were considered to be the main source of intraspecific variation. Continuous intraspecific variability in wild species such as Ananas ananassoides and Ananas parguazensis is an important contributor to overall genetic diversity (Duval et al., 2001). 4 Trait Discovery through Meta-Analysis 4.1 Fruit quality traits The fruit quality traits of pineapple are critical for fresh food and processing purposes. Several studies have highlighted that genetic diversity in germplasm resources is beneficial for improving fruit quality traits (such as size, sweetness, and flesh color). Ismail et al. (2020) used SSR markers to study Malaysian pineapple materials and found that they had moderate polymorphism, which could be used to improve fruit sweetness and taste traits. Genes associated with flesh color, such as carotenoid cleaved dioxygenase 4 (AcCCD4), identified in Nashima et al. (2022) provide the genetic basis for the development of high-quality fruit color varieties through marker-assisted selection (Figure 2). Zhou et al. 's study in 2015 showed that the application of SNP markers revealed significant variability within varieties, providing rich selection resources for the improvement of fruit quality traits. 4.2 Stress tolerance and resistance Stress and pest resistance traits help ensure sustainable cultivation of pineapples in a varied environment. Studies on genetic diversity of AFLP and ISSR markers suggest that genetic variation in pineapple germplasm can be used to breed cultivars with high resistance (Paz et al., 2012; Wang et al., 2017). Studies of pineapple germplasm from Cuba and Indonesia have revealed material resources with great potential to resist biological and abiotic stresses (Paz et al., 2012; Hayati and Kasiamdari, 2024). The identification of genetic populations with specific stress resistance traits provides a scientific basis for parental selection (Rattanathawornkiti et al., 2016).
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 108 Figure 2 Carotenoid accumulation and AcCCD4 expression during fruit ripening in ‘Yugafu’, (Yu) and ‘Yonekura’ (Yo) (Adopted from Nashima et al., 2022) Image caption: (a) Flesh appearance. (b) Carotenoid content. (c) AcCCD4 relative gene expression. Three biological replicates for each sample were examined to determine carotenoid quantities and conduct gene expression analysis. Error bars indicate SE. VIO, violaxanthin; cis-VIO, 9-cis-violaxanthin; LUT, lutein; ZEA, zeaxanthin; BCR, β-cryptoxanthin; ACA, α-carotene; BCA, β-carotene (Adopted from Nashima et al., 2022) 4.3 Flowering and growth traits Flowering and growth characteristics are the key points to optimize the production cycle and increase the yield of pineapple. Some studies have found that there are genetic differences in flowering and growth traits among different materials. The genetic analysis of half-sib lines by Junior et al. (2021) showed that traits such as fruit quality and soluble solid content provided scientific basis for breeding superior parents. The use of molecular markers such as RFLP and SNP can help to identify key genetic variants that affect flowering time and plant structure, and promote growth traits (Duval et al., 2001; Zhou et al., 2015). The study of Wang et al. (2017) showed that genetic cluster analysis of materials will be conducive to analyzing the genetic basis of flowering and growth traits and formulating targeted breeding strategies. 5 Case Study: Meta-Analysis of Brix-Related QTLs 5.1 Data aggregation process The meta-analysis summarized QTL data related to Brix (soluble solid content) by integrating multiple studies using different molecular markers and genetic analysis methods. Studies included in the analysis included studies using SSR, AFLP, and SNP markers (Paz et al., 2012; Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018; Ismail et al., 2020). The subjects of the study were germplasm resources from Malaysia, Cuba, Indonesia and other regions, which ensured the diversity and representativeness of the data (Paz et al., 2012; Ismail et al., 2020; Hayati and Kasiamdari, 2024).
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 109 5.2 Findings and statistical interpretation The results of the meta-analysis revealed genetic differences in Brix-related traits in different materials. SSR and SNP markers performed well in identifying polymorphic sites related to Brix content (Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018). Multiple QTLS were consistently associated with high Brix levels and could be considered as breeding target sites. Statistical analysis showed that the genetic variation of Brix traits was influenced by both intraspecific and interspecific diversity, and the increase of Brix level in some materials was closely related to the existence of specific alleles (Duval et al., 2001; Junior et al., 2021; Nashima et al., 2022). Rattanathawornkiti et al. (2016) and Wang et al. (2017) successfully identified several genetic populations associated with superior Brix traits through principal component analysis (PCA) and cluster analysis. 5.3 Implications for breeding programs The results of the meta-analysis provided scientific basis for pineapple breeding. The identification of specific QTLS associated with high Brix values can provide theoretical basis for marker-assisted selection and help breeders develop new varieties with sweeter and better quality (Zhou et al., 2015; Junior et al., 2021; Nashima et al., 2022). The genetic diversity revealed in the study indicates that researchers can use heterosis to breed new varieties with excellent Brix characteristics (Wang et al., 2017; Ismail et al., 2020; Hayati and Kasiamdari, 2024). The incorporation of molecular markers into the breeding process will significantly improve the efficiency of parental screening and accelerate the breeding of high-quality and high-yield pineapple varieties (Duval et al., 2001; Rattanathawornkiti et al., 2016). 6 Applications in Breeding and Genomic Innovation 6.1 Marker-assisted and genomic selection Marker-assisted selection and genomic selection play a key role in modern pineapple breeding programs. The use of SSR, AFLP, SNP and other molecular markers is very helpful for assessing genetic diversity and identifying desirable traits in germplasm. Studies have shown that SSR markers are more efficient than ISSR markers in assessing genetic diversity, which is conducive to researchers' selection of parents with excellent traits (Wang et al., 2017; Ismail et al., 2020). SNP markers can provide stable and accurate DNA fingerprints, which are helpful for genotype identification and germplasm resource management, and can accelerate the screening and breeding of good genotypes (Zhou et al., 2015). 6.2 Genomic prediction models informed by meta-data The wealth of genetic information is used by genomic prediction models to predict the performance of undetermined genotypes. By integrating metadata from different studies into predictive models, researchers can significantly improve their accuracy. Nashima et al. ’s study in 2022, showed that haplotype-based genome sequencing has enabled the localization of genes associated with important traits such as leaf margin morphology and pulp color. Studies by Zhou et al. (2015) and Nashima et al. (2022) demonstrate that combining genomic information with phenotypic data can help build models that predict the performance of new hybrid combinations, speeding up the breeding cycle and improving selection efficiency. 6.3 Bridging research and practical use Translating genetic research results into practical breeding strategies will help promote the development of the pineapple industry. A number of studies have revealed that genetic identification of pineapple germplasm materials using AFLP and ISSR markers will help reveal the genetic relationship and population structure, and provide scientific basis for hybrid selection and breeding program design (Paz et al., 2012; Rattanathawornkiti et al., 2016; Wang et al., 2017). The application of genomic tools in breeding programs can help select parents with high heterosis potential and accelerate the development of quality varieties (Junior et al., 2021). 7 Limitations and Future Directions 7.1 Methodological constraints SSR and ISSR markers show moderate to high effectiveness in polymorphism detection, but the efficiency of SSR and ISSR markers is different, and some studies have shown that SSR markers are more advantageous in genetic
International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 110 diversity assessment (Wang et al., 2017; Ismail et al., 2020; Hayati and Kasiamdari, 2024). AFLP markers show low diversity in some germplasm populations (Gerber et al., 2000; Paz et al., 2012). Existing studies mainly focus on some specific geographical regions or germplasm banks, which cannot fully represent the global genetic diversity of pineapple (Paz et al., 2012; Ismail et al., 2020), it is difficult for researchers to fully reveal the genetic relationships and diversity patterns among germplasm (Wang et al., 2017). 7.2 Uncovered traits and geographic gaps Scientists have now identified genes associated with leaf margin morphology and pulp color (Nashima et al., 2022), but further research is needed on other traits such as yield, fruit quality and productivity (Zhao and Qin, 2018; Junior et al., 2021). In terms of geographical representation, current genetic diversity studies mainly focus on Malaysia, Cuba, Indonesia and Thailand (Paz et al., 2012; Rattanathawornkiti et al., 2016; Ismail et al., 2020; Hayati and Kasiamdari, 2024), while other important pineapple production areas have not been adequately sampled, geographic gaps may cause breeders to bias their perceptions of global genetic diversity, limiting the effectiveness of breeding programs. 7.3 Multi-omics integration in future meta-analyses Future meta-analysis studies should focus more on the integration of multi-omics data to analyze the germplasm resources of pineapple more comprehensively. Integrating genomic, transcriptome, proteome, and metabolome data is helpful for identifying key genes and their pathways that regulate important traits (Zhao and Qin, 2018; Nashima et al., 2022). The use of SNP markers can provide robust and comparable DNA fingerprints for the identification and management of global germplasm resources (Zhou et al., 2015). Through the multi-omics integration method, researchers can break through the limitation of single marker study, fully grasp the genetic diversity and character framework of pineapple, and promote the management of germplasm resources, the formulation of breeding strategies and the protection of varieties (Zhou et al., 2015; Wang et al., 2017). Acknowledgments The authors appreciate the comments from Mr. Rudi Mai and Mr. Qixue Liang on the manuscript of this study. Funding This study was supported by the Research and Training Fund of the Hainan Institute of Tropical Agricultural Resources (Project No. H2025-02). Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adje C.A.O., Achigan-Dako E.G., d'Eeckenbrugge G.C., Yedomonhan H., and Agbangla C., 2019, Morphological characterization of pineapple (Ananas comosus) genetic resources from Benin, Fruits, 74(4): 167-179. https://doi.org/10.17660/th2019/74.4.2 Ali M., Hashim N., Aziz A., and Lasekan O., 2020, Pineapple (Ananas comosus): A comprehensive review of nutritional values, volatile compounds, health benefits, and potential food products, Food Research International, 137: 109675. https://doi.org/10.1016/J.FOODRES.2020.109675 Carlier J., Sousa N., Santo T., d'Eeckenbrugge G., and Leitão J., 2010, A genetic map of pineapple (Ananas comosus (L.) Merr.) including SCAR, CAPS, SSR and EST-SSR markers, Molecular Breeding, 29: 245-260. https://doi.org/10.1007/s11032-010-9543-9 Chaudhary V., Kumar V., Singh K., Kumar R., Kumar V., and Chaudhary C.V., 2019, Pineapple (Ananas comosus) product processing: a review, Journal of Pharmacognosy and Phytochemistry, 8: 4642-4652. Chen B., Hou J.F., Cai Y.F., Wang G.Y., Cai R.X., and Zhao F.C., 2024a, Utilizing genetic diversity for maize improvement: strategies and success stories, Maize Genomics and Genetics, 15(3): 136-146. https://doi.org/10.5376/mgg.2024.15.0014 Chen B., Hou J.F., Cai Y.F., Wang G.Y., Cai R.X., and Zhao F.C., 2024b, Genetic diversity in the genus zea: insights from chloroplast genome variability, Maize Genomics and Genetics, 15(5): 228-238.
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