Gypsum supplies calcium to Ultisol soil and its effect on <i>Phytophthora nicotianae</i>, pineapple (<i>Ananas comosus</i>) growth, yield and fruit quality in lower single row bed under climate change issue

Supriyono Loekito; Afandi; Auliana Afandi; Naomasa Nishimura; Hiroyuki Koyama; Masateru Senge; Supriyono Loekito; Afandi; Auliana Afandi; Naomasa Nishimura; Hiroyuki Koyama; Masateru Senge

doi:10.3934/agrfood.2022044

AIMS Agriculture and Food

2022, Volume 7, Issue 3: 721-736. doi: 10.3934/agrfood.2022044

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Research article

Gypsum supplies calcium to Ultisol soil and its effect on Phytophthora nicotianae, pineapple (Ananas comosus) growth, yield and fruit quality in lower single row bed under climate change issue

1.
Department of Research and Development, PT. Great Giant Pineapple, Jl. Raya Terbanggi Besar Km 77, Central Lampung, Lampung 34163, Indonesia
2.
Department of Soil Science, Faculty of Agriculture, Lampung University, Jl. Sumantri Brojonegoro 1, Bandar Lampung, Lampung 35145, Indonesia
3.
Research Center for Horticultural and Estate Crops, National Research and Innovation Agency, Cibinong Science Center Jl. Raya Bogor Km. 46, Cibinong, Bogor 16911, Indonesia
4.
Faculty of Applied Biological Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
5.
Gifu University Laboratory, Ltd. Union, 1-1 Yanagido, Gifu 502-1193, Japan

Received: 22 March 2022 Revised: 06 July 2022 Accepted: 15 July 2022 Published: 06 September 2022

Lower bed single-row pineapple cultivation could protect pineapple from soil erosion during the rainy season and drought period; however, disease problems could arise as a result of water logging. Two experiments were conducted in Ultisol soil using a lower bed single row to investigate the ability of gypsum to provide soil calcium (Ca) to the pineapple plant, plant resistance to heart rot disease, and provide a better effect on crop growth and fruit quality. In the first trial, four levels of gypsum (0, 1.0, 1.5 and 2.0 Mg·ha⁻¹) and dolomite (2 Mg·ha⁻¹) were spread and incorporated into soil that had been saturated with Phytophthora nicotianae inoculums. In the second trial, gypsum treatments (0, 1.0, 1.5, 2.0 and 2.5 Mg·ha⁻¹) were used as a basic fertiliser in the row between the single beds. P. nicotianae attacked all treatments at 6 weeks after planting (WAP), and at 10 WAP, the mortality of the dolomite treatment reached 63.8%, significantly higher than that of the gypsum treatments (3.3%–14.3%). In the second experiment, gypsum significantly increased plant weight from 3 to 9 months after planting (MAP), especially when applied at 1.5–2.5 Mg·ha⁻¹. Fruit texture, total soluble solids (TSS), and titratable acidity (TA) were not significantly different between treatments, but they all met the standards for canned pineapple grades. The results showed that soil-applied gypsum before planting provides soil calcium, meets the plant Ca requirement during an early and fast growth stage, and is free of heart rot disease.

Keywords:

Citation: Supriyono Loekito, Afandi, Auliana Afandi, Naomasa Nishimura, Hiroyuki Koyama, Masateru Senge. Gypsum supplies calcium to Ultisol soil and its effect on Phytophthora nicotianae, pineapple (Ananas comosus) growth, yield and fruit quality in lower single row bed under climate change issue[J]. AIMS Agriculture and Food, 2022, 7(3): 721-736. doi: 10.3934/agrfood.2022044

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Abstract

References

[1]	Von Uexkull HR, Bossart RP (1989) Management of acid upland soils in Asia, In: Craswell ET, Pushparajah E (Eds.), Management of acid soils in the humid tropics of Asia, NSW: RodenPrint, 2–19.
[2]	Prasetyo BH, Suriadikarta DA (2006) Karakteristik, potensi, dan teknologi pengelolaan tanah ultisol untuk pengembangan pertanian lahan kering di Indonesia. J Litbang Pertanian 25: 39–46.
[3]	Malezieux E, Bartholomew DP (2003) Plant nutrition, In: Bartholomew DP, Paul RE, Rohrbach KG (Eds.), The pineapple: Botany, production and uses, Wallingford: CABI Publishing, 143–165. https://doi.org/10.1079/9780851995038.0143.
[4]	Uchida R, Hue NV (2000) Soil acidity and liming, In: Silva JA, Uchida R (Eds.), Plant nutrient management in Hawaii's soils, approaches for tropical and subtropical agriculture, Hawaii: University of Hawaii, 101–106.
[5]	Taiz L, Zeiger E, Moller IM, et al. (2015) Plant physiology and development, 6 Eds., Sunderland, Massachusetts: Sinaur Associates, 16–23.
[6]	Tailep WMAK, El-Saady AM, El-Dahshouri MF, et al. (2019) Influence of foliar spray of different calcium sources on nutritional status, seed yield and quality for some peanut genotypes. Biosci Res 16: 309–319.
[7]	Olle M, Bender I (2009) Causes and control of calcium deficiency disorders in vegetables: A review. J Hortic Sci Bitech 84: 577–584. https://doi.org/10.1080/14620316.2009.11512568 doi: 10.1080/14620316.2009.11512568
[8]	Von Uexkull HR (1986) Efficient fertilizer use in acid upland soils of the humid tropics, Rome: FAO, 1–59.
[9]	Rohrbach KG, Johnson MW (2003) Pests, diseases and weeds, In: Bartholomew DP, Paul RE, Rohrbach KG (Eds.), The pineapple: Botany, production and uses, Wallingford: CABI Publishing, 203–252.
[10]	Pegg KG (1993) Diseases, In: Broadly RH, Wassman RC, Sinclair E (Eds.), Pineapple pest and disorder, Queensland: Department of Primary Industries, 11–32.
[11]	Mite F, Espinosa J, Medina L (2010) Liming effect on pineapple yield and soil properties in volcanic soils. Better Crops 94: 7–9.
[12]	Vasquez-Jimenez J, Bartholomew DP (2018) Plant nutrition, In: Sanewski GM, Bartholomew DP, Paull RE (Eds.), The pineapple: Botany, production and uses, 2 Eds., Wallingford: CABI Publishing, 175–202.
[13]	Silva JA, Hamasaki R, Ogoshi R, et al. (2006) Lime, gypsum, and basaltic dust effects on the calcium nutrition and fruit quality of pineapple. Acta Hortic 702: 123–131. https://doi.org/10.17660/ActaHortic.2006.702.15 doi: 10.17660/ActaHortic.2006.702.15
[14]	Pegg K, Giblin F (2008) Principle of Phytophthora root rot management in establish orchards Queensland. Talking Avocados 19: 36–38.
[15]	Messenger BJ, Menge JA, Pond E (2000) Effect of gypsum on zoospores and sporangia of Phytophthora cinnamomi in field soil. Plant Dis 84: 617–621. https://doi.org/10.1094/PDIS.2000.84.6.617 doi: 10.1094/PDIS.2000.84.6.617
[16]	Correia AF, Neves LG, Serafim ME, et al. (2017) Chemical attributes of soil with use of a cover crop and agricultural gypsum in pineapple. Rev Cienc Agrar 60: 25–32.
[17]	Afandi A, Subandiyah S, Wibowo A, et al. (2021) Population genetic analysis of Phytophthora nicotianae associated with heart rot in pineapple revealed gen flow between population. Biodiversitas 22: 3342–3348. https://doi.org/10.13057/biodiv/d220830 doi: 10.13057/biodiv/d220830
[18]	Loekito S, Afandi, Afandi A, et al. (2022) Study on soil properties and species conformity of Phytophthora species in a pineapple field. Int J Agric Biol 27: 361–370. https://doi.org/10.17957/IJAB/15.1936 doi: 10.17957/IJAB/15.1936
[19]	Tsao PH, Draft GC, Sztejnberg A, et al., Potting Mixes for Control Phytophthora Root Rot, 1986. Available from: https://slosson.ucdavis.edu/newsletters/Tsao_198629144.pdf.
[20]	Zook MN, Rush JS, Kuc JC (1987) A role for Ca²⁺ in the elicitation of rishitin and lubimin accumulation in potato tuber tissue. Plant Physiol 84: 520–525. https://doi.org/10.1104/pp.84.2.520 doi: 10.1104/pp.84.2.520
[21]	Liming C, Warren AD, Gypsum as an Agricultural Amendment, General Use Guidelines, 2011. Available from: https://www.academia.edu/35029694/Bulletin_945.
[22]	Kelly DS (1993) Nutritional disorder, In: Roger HB, Rudolf CW, Eric S (Eds.), Pineapple pest and disorders, Queensland: Department of Primary Industries, 33–42.
[23]	Nome C, Magalhaes PC, Oliveira E, et al. (2009) Differences in intracellular localization of corn stunt spiroplasmas in magnesium treated maize. Biocell 33: 133–136. https://doi.org/10.32604/biocell.2009.33.133 doi: 10.32604/biocell.2009.33.133
[24]	Huber DM, Jones J (2013) The role of magnesium in plant disease. Plant Soil 368: 73–85. https://doi.org/10.1007/s11104-012-1476-0 doi: 10.1007/s11104-012-1476-0
[25]	Taiz L, Zeiger E (2002) Plant physiology, 3 Eds. Sunderland, Massachusetts: Sinaur Associates.
[26]	Souza LFS, Reinhardt DH (2007) Pineapple, In: Johnson AE (Ed.), Tropical fruits of Brazil, Horgen, Switzerland: International Potash Institute, 179–201.
[27]	Marschner H (2012) Mineral nutrition and yield response, In: Marschner's mineral nutrition of higher plants, 2 Eds, London, San Diego: Academic Press, 184–200. https://doi.org/10.1016/B978-0-08-057187-4.50012-6
[28]	Favaretto N, Norton LD, Joern BC, et al. (2006) Gypsum amendment and exchangeable calcium and magnesium affecting phosphorus and nitrogen in runoff. Soil Sci Soc Am J 70: 1788–1796. https://doi.org/10.2136/sssaj2005.0228 doi: 10.2136/sssaj2005.0228
[29]	Cano-Reinoso DM, Soesanto L, Kharisun, et al. (2021) Effect of pre-harvest fruit covers and calcium fertilization on pineapple thermotolerance and flesh translucency. Emir J Food Agr 33: 834–845. https://doi.org/10.9755/ejfa.2021.v33.i10.2766 doi: 10.9755/ejfa.2021.v33.i10.2766
[30]	JB Jones Jr (2012) Soil fertility and principals, In: Plant nutrition and soil fertility manual, 2 Eds., Boca Raton: CRC press, 5–11. https://doi.org/10.1201/b11577-5
[31]	De Freitas ST, Mitcham EJ (2012) Factors involved in fruit calcium deficiency disorders. Hortic Rev 40: 107–146.
[32]	Saure M (2014) Why calcium deficiency is not the cause of blossom-end rot in tomato and pepper fruit—a reappraisal. Sci Hortic 174: 151–154. https://doi.org/10.1016/j.scienta.2014.05.020 doi: 10.1016/j.scienta.2014.05.020
[33]	Gerendás J, Führs H (2013) The significance of magnesium for crop quality. Plant Soil 368: 101–128. https://doi.org/10.1007/s11104-012-1555-2 doi: 10.1007/s11104-012-1555-2
[34]	Villanueva MJ, Tenorio MD, Esteban MA, et al. (2004) Compositional changes during ripening of two cultivars of maskmelon fruits. Food Chem 87: 179–183. https:/doi.org/10.1016/j.foodchem.2003.11.009 doi: 10.1016/j.foodchem.2003.11.009
[35]	Lobo MG, Yahia E (2017) Biology and postharvest physiology of pineapple, In: Lobo MG, Paull RE (Eds.), Handbook of pineapple technology: Production, postharvest science, processing and nutrition. Chichester, Hoboken: John Wiley & Sons, 39–61. https://doi.org/10.1002/9781118967355.ch3
[36]	United States Department of Agriculture, United States Standards for Grades of Canned Pineapple, 1990. Available from: https://ams.usda.gov/sites/default/files/media/canned_pineapple_standard.pdf.

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