Bioscience Method 2026, Vol.17 http://bioscipublisher.com/index.php/bm © 2026 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
Bioscience Method 2026, Vol.17 http://bioscipublisher.com/index.php/bm © 2026 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. BioSci Publisher is an international Open Access publisher specializing in bioscience methods, including technology, lab tool, statistical software and relative fields registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. Publisher BioSci Publisher Edited by Editorial Team of Bioscience Methods Email: edit@bm.bioscipublisher.com Website: http://bioscipublisher.com/index.php/bm Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Bioscience Methods (ISSN 1925-1920) is an open access, peer reviewed journal published online by BioSci Publisher. The journal publishes all the latest and outstanding research articles, letters and reviews in all areas of bioscience, the range of topics including (but are not limited to) technology review, technique know-how, lab tool, statistical software and known technology modification. Case studies on technologies for gene discovery and function validation as well as genetic transformation. All the articles published in Bioscience Methods 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. BioSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Bioscience Methods (online), 2026, Vol.17, No.3 ISSN 1925-1920 https://bioscipublisher.com/index.php/bm © 2026 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Determination of Watering Regime for Optimal Production of Hortitom 1 and Hortitom 3 Genotypes of Solanum lycopersicum L. (Tomatoes) under Screenhouse Conditions Otitoloju Kekere, Tofunmi Hepzibah Oyetunde, Joseph Kolade Afolabi Bioscience Methods, 2026, Vol.17, No.3, 141-152 SNP-Based Heritability Is Not a Parameter but a Model-Defined Estimand: Evidence from UK Biobank Xuanjun Fang Bioscience Methods, 2026, Vol.17, No.3, 153-168 Discussion on the Operational Model of Modern Agricultural Service Centers in Socialized Rice Production Services Xinfeng Ren, Yaqin Ren Bioscience Methods, 2026, Vol.17, No.3, 169-187 Application Performance and Promotion Value of Qianjiang 661 in Rice-Rapeseed Rotation Systems Geyang Zhan Bioscience Methods, 2026, Vol.17, No.3, 188-198 Tea Seed Oil Restores Blood Pressure, Redox Balance and Lipid Homeostasis in L-NAME-Induced Hypertensive Rats L. E. Yahaya, J. F. Atanda, A. A. Oyagbemi Bioscience Methods, 2026, Vol.17, No.3, 199-207
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 141 Research Report Open Access Determination of Watering Regime for Optimal Production of Hortitom 1 and Hortitom 3 Genotypes of Solanum lycopersicum L. (Tomatoes) under Screenhouse Conditions Otitoloju Kekere , Tofunmi Hepzibah Oyetunde, Joseph Kolade Afolabi Department of Plant Science & Biotechnology, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria Corresponding author: otito.kekere@aaua.edu.ng Bioscience Methods, 2026, Vol.17, No.3 doi: 10.5376/bm.2026.17.0012 Received: 01 Apr., 2026 Accepted: 21 Apr., 2026 Published: 11 May, 2026 Copyright © 2026 Kekere 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: Kekere O., Oyetunde T.H., and Afolabi J.K., 2026, Determination of watering regime for optimal production of Hortitom 1 and Hortitom 3 genotypes of Solanum lycopersicum L. (tomatoes) under screenhouse conditions, Bioscience Methods, 17(3): 141-152 (doi: 10.5376/bm.2026.17.0012) Abstract Water availability is a major limiting factor for tomato production, particularly under changing climate conditions. This study investigated the effects of eight watering regimes twice daily (T1), once daily (T2), every 2 days (T3), every 3 days (T4), every 4 days (T5), every 5 days (T6), every 6 days (T7), and continuous waterlogging (T8) on growth, yield, and fruit nutritional quality of two Nigerian tomato genotypes (Hortitom 1 and Hortitom 3) under screenhouse conditions. The experiment was laid out in a 2 × 8 factorial arrangement in a completely randomized design with five replicates. Both genotypes exhibited 100% survival under all non-waterlogged treatments, while continuous waterlogging (T8) resulted in 100% mortality. Hortitom 1 and Hortitom 3 attained their maximum plant height at T5 (watering every 4 days), recording 58.70 cm and 62.50 cm respectively. Fruit yield (fresh weight) was highest in Hortitom 1 under T1 (5.25 g per fruit) and in Hortitom 3 under T7 (7.75 g per fruit). Nutritional composition was significantly influenced by genotype and watering regime. Crude protein content peaked at 2.06% in Hortitom 1 under T5 and 1.85% in Hortitom 3 under T4. These results demonstrate clear genotypic differences in response to water availability. Hortitom 1 performed best under moderate water stress (T5) for vegetative growth and nutritional quality, while Hortitom 3 showed superior fruit yield under more severe water restriction (T7). Both genotypes are highly susceptible to waterlogging but tolerant to drought. The findings provide genotype-specific irrigation recommendations that can enhance water-use efficiency while maintaining or improving fruit nutritional quality in tomato production under screenhouse conditions. Keywords Drought; Waterlogging; Tomato genotypes; Growth; Nutritional quality; Solanum lycopersicum 1 Introduction Tomato (Solanum lycopersicum L.) is one of the most widely cultivated and consumed vegetables worldwide. It is valued for its rich nutritional profile, including vitamins A, C, and E, as well as lycopene antioxidants that reduce risks of cardiovascular diseases and cancers (Bai and Lindhout, 2022; Natali et al., 2025). The crop also plays key economic and industrial roles. Native to western South America, particularly Peru and Ecuador, tomatoes were domesticated in Mexico. Spanish explorers introduced them to Europe in the 16th century. Initially grown as ornamentals due to their resemblance to nightshade plants, they later became a global culinary staple (Donoso et al., 2022). Tomatoes exhibit extensive morphological and genetic diversity. This diversity has produced genotypes adapted to various climates, diseases, and consumer preferences. Wild relatives contribute key traits, such as drought and salinity resistance from Solanum pimpinellifolium. Other species, including S. peruvianum, S. chilense, S. habrochaites, and S. pennellii, provide tolerances to extreme conditions, pathogens, pests, and cold (Razifard et al., 2020; Blanca et al., 2022). Successful tomato cultivation depends on optimal environmental and agronomic factors. Well-drained loamy soils with pH 5.5-6.8 and high organic matter support root health, nutrient uptake, and disease prevention (Jones, 2021). Clay soils hinder drainage, while sandy soils require irrigation and amendments. Ideal temperatures range from 20°C to 25°C for growth, flowering, and fruiting. High temperatures (>30°C) cause flower abortion, and low
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 142 temperatures (<10°C) impair pollination. Farmers mitigate these using greenhouses or shade nets (Sharma et al., 2020). Adequate light is essential for photosynthesis and fruit ripening, with supplementation needed in low-light regions. Water stress during critical growth stages reduces yields and triggers disorders like blossom-end rot. Effective countermeasures include drip irrigation (40%-60% efficiency gains), deficit irrigation, and mulching to control evaporation, regulate soil temperature, and suppress weeds (Fereres and Soriano, 2020; Makhadmeh et al., 2022; Ayana and Olika, 2024). Water is central to tomato physiology, driving cell expansion, nutrient uptake, and fruit development. Drought-induced deficits limit biomass, fruit set, and nutrient profiles, while excesses cause other issues (Bastías et al., 2020; Nguyen et al., 2021; Burato et al., 2024). Genotypic variations, such as deeper roots or osmotic adjustments, enhance tolerance (Alam et al., 2021). However, limited data exist on how irrigation regimes affect growth, yield, and fruit nutritional quality in newly developed Nigerian tomato genotypes, Hortitom 1 and Hortitom 3, under screen house conditions. In the face of water scarcity and climate variability, tomatoes require precise irrigation to sustain yields and quality (Ray and Majumder, 2024). Evaluating watering regimes for Hortitom 1 and Hortitom 3 through different irrigation methods can improve water use efficiency, root nutrient uptake, and loss reduction while maintaining nutritional content (Gheysari et al., 2021). Such insights can guide farmers toward optimal practices, enhance nutritional output for consumers, and inform breeders about genotype-environment interactions for resilient varieties (Santos et al., 2021). Therefore, this study aims to assess the impact of varying watering levels on growth, yield parameters, and fruit nutritional composition of Hortitom 1 and Hortitom 3 under screen house conditions. 2 Materials and Methods 2.1 Location of the experiment This experiment was carried out at the screen house of the Department of Plant Science and Biotechnology, Adekunle Ajasin University, Akungba-Akoko, Nigeria (latitude 7.2 0N, longitude 5.44 0E). 2.2 Sources of materials for the experiment Two tomato (Solanum lycopersicum L.) genotypes, Hortitom 1 and Hortitom 3, were obtained from the National Horticultural Research Institute (NIHORT), Ibadan, Oyo State, Nigeria. The soil was analyzed for physical and chemical properties using the standard methods of AOAC (1985). It was shade-dried and passed through a 2-mm sieve before analysis. 2.3 Soil collection and preparation Topsoil (0-15 cm depth) was collected from an arable farmland within the premises of Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria. The soil was sieved to remove debris and thoroughly mixed to obtain a homogeneous medium. Approximately 14 kg of prepared soil was filled into each perforated polythene pot. Tomato seedlings raised in the nursery for 3 weeks were transplanted into perforated polythene pots filled with 14 kg of topsoil; only pots for waterlogged conditions were not perforated. 2.4 Experimental setup The potted plants were watered regularly for two weeks after transplanting for proper seedling establishment. Thereafter, they were differentially exposed to eight watering regimes: watering twice daily (T1), once daily (T2), every 2 days (T3), every 3 days (T4), every 4 days (T5), every 5 days (T6), every 6 days (T7) and completely waterlogged (T8). Pots were laid out on the screen house floor in a completely randomized design (CRD) with each treatment replicated five times. It was a 2 x 8 factorial experiment with genotype as Factor A at 2 levels, and watering regime as Factor B at 8 levels. Except waterlogging condition that was permanently flooded, each potted plant received approximately 380 ml of water at every watering time. This was the volume required to keep the soil at field capacity based on 36% field capacity of the soil. Standard agronomic practices including weeding and pest control were carried out during the experiment.
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 143 2.5 Data collection Plant height was measured from the soil surface to the apical bud using a meter rule. Stem girth was measured at 2 cm point above the base of the plant. The number of fully expanded leaves was counted manually on each plant. The leaf area was measured using the leaf area meter (LI-COR 300 model). The number of branches produced per plant was counted manually. At harvest, plants were carefully uprooted, washed and separated into leaves, roots and stems. Root growth was determined by measuring the root length using a meter rule, and the number of roots was counted manually. Fresh weight was measured immediately after harvest, while dry weight was obtained after oven-drying at 80°C to constant weight, using Melter PC 180. Dry weight of plant parts (roots, stems, and leaves) was also measured. Yield in terms of fresh and dry mass of the fruit was also assessed using an electronic weighing balance. 2.6 Laboratory analysis of tomato fruits Dried tomato fruits were ground into fine powder for analysis. Fiber content was determined by boiling the sample in 1.25% H2SO4 and 1.25% NaOH, followed by washing and drying. Other parameters of proximate composition were analyzed using the standard methods of AOAC (1985) in which the mixture was boiled until a clear solution was obtained, and allowed to cool at room temperature. The resulting solution was quantitatively transferred into a calibrated flask and completed to 25 ml with distilled water. Moisture, crude protein, crude fat, carbohydrate and ash contents were calculated using relevant formulas. N was analyzed using the macro Kjeldahl method, while P was determined using ammonium-vanadomolybdate reagent and a calibration curve. Potassium contents were assayed through flame emission photometry. calcium contents by Ethylenediaminetetraacetic acid (EDTA) titration. 2.7 Statistical analysis All data collected were subjected to two-way Analysis of Variance (ANOVA) using SPSS (Version 27.0). Where significant differences were observed among treatment means, Tukey’s Honest Significant Difference (HSD) test was used at 95% confidence level to perform post-hoc comparisons. 3 Results 3.1 Soil used for planting The soil used for planting was a sandy soil with 5.60 pH, 6.19% clay, 4.29% silt, 89.7% sand, 2.89% C, 0.14% N, 9.02 mg/kg P, 6.24 mg/kg Ca, 1.84 mg/kg Mg, 0.34 mg/kg Na, 0.23 mg/100 K, 0.20 mg/kg H, and 8.86 mg/kg CEC. It had 1.12 mg/cm3 bulk density, 36.13% field capacity, and 19.08% permanent wilting point. 3.2 Effect of watering regime on percentage survival and growth of two genotypes of Solanum lycopersicum Table 1 below shows the effects of different watering regime on the survival of two Solanum lycopersicum genotypes. Irrigation treatments T1 to T7, applied from twice daily up to once every six days, resulted in 100% survival. In contrast, T8, which involved constant waterlogging, led to total plant death. For plant height, Hortitom1 plants measured between 41.25±3.53 cm under T7 and 58.70±6.32 cm under T5, achieving notably taller growth in T5. Hortitom 3 produced taller plants overall than Hortitom 1, with heights from 53.50±0.65 cm in T2 to 62.50±0.65 cm in T5. Stem girth in Hortitom1 varied from 2.25±0.10 cm in T6 to 2.63±0.24 cm in T1, showing no significant differences between regimes. Hortitom 3 also maintained consistent stem girth across treatments, between 2.70±0.20 cm in T5 and 2.90±0.13 cm in T2. The number of leaves in Hortitom 1 rose significantly from 11.75±0.41 leaves under T1 to 31.13±0.47 leaves under T7. Hortitom 3 displayed the opposite trend, peaking at 33.50±0.65 leaves in T1 and dropping to lower values of 19.00±0.41 in T5. Leaf area in Hortitom 1 spanned 24.15±0.31 cm²in T7 to 26.48±0.40 cm²in T1 and T3, differing significantly from other treatments. Hortitom 3 had leaf areas from 26.35±0.64 cm²in T5 to 27.31±0.08 cm²in T3. Number of roots was greater in T5 (7.50±1.04) than T7 (4.00±0.41), while root length remained similar across all treatments.
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 144 Table 1 Effect of watering regime on percentage survival and growth of two genotypes of Solanum lycopersicum Parameter Tomato genotype Watering regime T1 T2 T3 T4 T5 T6 T7 T8 Plant survival (%) H1 100.00 100.00 100.00 100.00 100.00 100.00 100.00 0.00 H3 100.00 100.00 100.00 100.00 100.00 100.00 100.00 0.00 Plant height (cm) H1 47.25±1.99a 53.13±9.44a 46.35±2.75a 55.25±1.03a 58.70±6.32a 58.07±5.94a 41.25±3.53a - H3 54.50±0.65a 53.50±0.65a 57.50±0.65b 61.50±0.65cd 62.50±0.65d 58.50±0.65b 59.50±0.65bc - Stem girth (cm) H1 2.63±0.24a 2.39±0.21a 2.35±0.22a 2.43±0.21a 2.38±0.11a 2.25±0.10a 2.33±0.12a - H3 2.73±0.13a 2.90±0.13a 2.73±0.23a 2.88±0.13a 2.70±0.20a 2.85±0.12a 2.78±0.22a - Number of leaves H1 11.75±0.41a 14.80±0.56b 18.55±0.61c 21.88±0.47d 24.88±0.47e 28.00±0.53f 31.13±0.47g - H3 33.50±0.65e 29.50±0.65d 25.50±0.65c 21.50±0.65ab 19.00±0.41a 22.50±0.65b 26.50±0.65c - Leaf area (cm2) H1 26.48±0.40c 26.10±0.38bc 26.48±0.22c 26.35±0.16c 25.97±0.31bc 24.97±0.13ab 24.15±0.31a - H3 27.00±0.26a 27.11±0.19a 27.31±0.08a 26.97±0.22a 26.35±0.64a 26.62±0.20a 26.62±0.19a - Number of roots H1 5.55±0.61ab 6.25±0.25ab 4.75±1.03ab 6.50±0.87ab 7.50±1.04b 4.75±0.75ab 4.00±0.41a - H3 7.25±0.85a 7.25±0.25a 5.25±0.25a 5.00±0.71a 5.50±0.65a 6.50±1.32a 6.75±0.48a - Root length (cm) H1 5.60±0.61a 4.58±0.64a 5.40±0.51a 5.45±0.57a 4.60±0.47a 3.58±0.41a 3.80±0.43a - H3 6.10±0.91ab 5.83±0.53ab 6.88±0.51b 5.13±0.60ab 4.20±0.63ab 3.85±0.16a 3.40±0.62a - Note: Each value is a mean ±S.E. of 5 replicates. For each value, means with the same letter(s) in superscript on the same row are not significantly different at P ≥ 0.05 (Tukey HSD test). T1: watering twice daily; T2: watering once daily; T3: watering every two days; T4: watering every three days; T5: watering every four days; T6: watering every five days; T7: watering every six days; T8: continuous waterlogging; H1: Hortitom 1 genotype; H3: Hortitom 3 genotype
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 145 3.3 Effect of water stress on biomass The impact of watering regimes on biomass components shown in (Table 2) in two Solanum lycopersicum genotypes, Hortitom 1 and Hortitom 3. In Hortitom 1, fresh and dry leaf weights remained consistent across treatments at about 9.00 g and 5.51 g, respectively, while root numbers were notably higher under T5 (7.50±1.04) than T7 (4.00±0.41), though root length, fresh root weight, and dry root weight showed no differences. Stem biomass decreased steadily with water restriction, with fresh stem weight falling from 15.43±0.22 g in T1 to 10.38±0.27 g in T7, and dry stem weight from 9.13±0.43 g to 4.53±0.28 g. For Hortitom 3, fresh leaf weight increased significantly under T7 (16.75±2.10 g) compared to T1 (8.50±2.06 g), but dry leaf weight stayed similar. Root numbers did not vary, root length peaked at T3 (6.88±0.51 cm), and fresh root weight rose from 3.75±0.48 g in T2 to 7.75±0.48 g in T7, with dry root weight highest in T7 (4.92±0.42 g). Stem fresh and dry weights declined gradually from T1 (17.05±0.13 g and 10.03±0.41 g) to T7 (13.88±0.37 g and 5.70±0.20 g). 3.4 Phenological and yield parameter Table 3 outlines water regime impacts on days to first flowering and fruit yield parameters in Solanum lycopersicum genotypes Hortitom 1 and Hortitom 3. Watering regimes impacted days to first flowering in both genotypes: Hortitom 1 flowered soonest under T1 (39.75±0.32 days), with delays increasing to 65.00±2.42 days in T7. Hortitom 3 flowered later than Hortitom 1 in every case, starting at 47.50±0.65 days in T1 and extending to 71.50±0.65 days in T7. Hortitom 1 produced a steady 5.63±0.13 to 6.50±0.20 fruits per plant across treatments with no differences, alongside slightly reduced fresh fruit weights from 3.75±0.63 g to 5.25±0.32 g under drier conditions; notably, fruit length and breadth grew larger, from 16.25±0.32 cm and 18.18±0.38 cm in T1 to peaks of 19.50±0.20 cm and 21.88±0.13 cm in T6/T7. Hortitom 3 showed greater variability, with fruits numbering 3.00±0.41 to 6.00±0.41 (highest in T4), fresh weights climbing in T5-T7 to 7.75±0.48 g in T7, and dry weights maximizing at 4.92±0.42 g in T7 yet fruit length shrank from 42.35±0.65 cm in T1 to 18.35±0.65 cm in T7, while breadth fell from 75.28±0.65 cm to 51.28±0.65 cm. 3.5 Proximate and minerals composition The results in Table 4 reveal the proximate and mineral compositions of Solanum lycopersicum genotypes; Hortitom 1 and Hortitom 3 across water regimes T1-T7. Hortitom 1 generally displayed higher and more variable proximate values than Hortitom 3. Moisture content in Hortitom 1 spanned 14.28±0.05% (T3) to 19.00±0.13% (T4), exceeding Hortitom 3's narrower 15.80±0.22% (T6) to 18.71±0.04% (T3). Fat remained stable and comparable, with Hortitom 1 at 0.87±0.01% (T2) to 1.14±0.02% (T5) versus Hortitom 3 from 0.83±0.00% (T1) to 1.12±0.00% (T2). Ash was broader in Hortitom 1 (3.66±0.01% at T6 to 5.08±0.04% at T5) than Hortitom 3 (3.56±0.01% at T6 to 4.99±0.02% at T7). Crude fiber showed Hortitom 1 ranging lower to higher (5.01±0.00% at T2 to 8.31±0.03% at T6) compared to Hortitom 3 (6.03±0.01% at T1 to 8.27±0.05% at T3). Crude protein was consistently superior in Hortitom 1 (1.10±0.01% at T2 to 2.06±0.05% at T5) over Hortitom 3 (1.05±0.01% at T1 to 1.85±0.09% at T4). Carbohydrates peaked much higher in Hortitom 1 (67.37±0.23% at T7 to 74.22±0.16% at T2) than in Hortitom 3 (66.41±0.08% at T7 to 70.65±0.03% at T2). For minerals, patterns were more mixed but often favoured Hortitom 1 in range and peaks. Calcium in Hortitom 1 went from 15.85±0.05 mg/kg (T2) to 22.55±0.15 mg/kg (T6), closely matching Hortitom 3, ranging from 14.80±0.10 mg/kg (T3) to 22.75±0.25 mg/kg (T4), though the latter edged higher at its max. Potassium was notably higher in Hortitom 1 (25.60±0.20 mg/kg at T6 to 35.95±0.45 mg/kg at T1) versus Hortitom 3 (24.45±0.15 mg/kg at T4 to 32.30±0.20 mg/kg at T2). Magnesium spanned wider in Hortitom 1 (19.40±0.10 mg/kg at T6 to 26.15±0.15 mg/kg at T5) than Hortitom 3 (18.60±0.10 mg/kg at T5 to 23.37±0.04 mg/kg at T6). Iron reached a higher peak in Hortitom 1 (1.27±0.00 mg/kg at T3 to 2.01±0.00 mg/kg at T4) over Hortitom 3 (1.33±0.00 mg/kg at T4 to 1.73±0.01 mg/kg at T3). Phosphorus was similar, with Hortitom 1 at 8.92±0.19 mg/kg (T4) to 14.04±0.07 mg/kg (T2) and Hortitom 3 at 9.11±0.12 mg/kg (T6) to 14.17±0.05 mg/kg (T4). Nitrogen was marginally higher in Hortitom 1 (0.18±0.00 mg/kg at T1/T2 to 0.33±0.01 mg/kg at T5) than Hortitom 3 (0.17±0.00 mg/kg at T1 to 0.30±0.02 mg/kg at T4). These trends indicate Hortitom 1's superior nutritional profile under water stress variability.
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 146 Table 2 Effect of watering regime on the vegetative biomass of two genotypes of Solanum lycopersicum Biomass parameter (g) Tomato genotype Watering regime T1 T2 T3 T4 T5 T6 T7 T8 Leaf fresh weight H1 9.00±1.47a 9.00±1.78a 8.25±1.38a 5.00±1.22a 8.00±0.71a 7.00±0.91a 5.00±0.71a - H3 8.50±2.06a 9.75±1.49ab 13.75±1.93ab 12.50±0.96ab 12.50±1.32ab 13.50±0.65ab 16.75±2.10b - Stem fresh weight H1 15.43±0.22c 15.28±0.62c 13.85±0.67bc 13.03±0.91bc 12.23±0.34ab 11.63±0.36ab 10.38±0.27a - H3 17.05±0.13d 17.43±0.38d 16.95±0.13d 16.35±0.21cd 15.75±0.17bc 14.78±0.11ab 13.88±0.37a - Root fresh weight H1 5.00±0.41a 4.75±0.48a 3.75±0.48a 3.75±0.48a 4.25±0.63a 4.00±0.58a 4.00±0.41a - H3 4.75±0.48ab 3.75±0.48a 5.50±0.65abc 4.75±0.75ab 7.25±0.48bc 6.67±0.88bc 7.75±0.48c - Leaf dry weight H1 5.51±1.74a 5.55±1.42a 3.75±1.85a 2.16±0.85a 6.60±2.92a 2.04±0.79a 1.14±0.29a - H3 5.86±1.69a 5.98±2.23a 7.29±2.05a 8.34±0.60a 8.43±0.78a 9.29±0.96a 10.48±1.09a - Stem dry weight H1 9.13±0.43b 8.88±0.34b 7.45±0.59b 7.45±0.31b 5.58±0.21a 5.03±0.43a 4.53±0.28a - H3 10.03±0.41d 9.15±0.46cd 8.25±0.37bc 7.20±0.35ab 7.03±0.41ab 6.48±0.41a 5.70±0.20a - Root dry weight H1 0.81±0.19a 1.75±0.41a 0.97±0.29a 0.73±0.28a 1.05±0.06a 0.83±0.27a 0.74±0.17a - H3 2.64±0.27ab 1.18±0.39a 2.80±0.77ab 2.56±0.60a 1.82±0.38a 3.10±0.36ab 4.92±0.42b - Total biomass H1 15.24±2.21ab 18.58±2.0b 12.80±1.52a 13.63±1.16a 13.71±1.69a 12.90±0.71a 13.03±0.91a - H3 13.43±0.72a 11.92±0.77a 17.65±0.88ab 15.57±0.97ab 18.38±0.93b 15.71±0.47a 16.35±0.21ab - Note: Each value is a mean ±S.E. of 5 replicates. For each value, means with the same letter(s) in superscript on the same row are not significantly different at P ≥ 0.05 (Tukey HSD test). T1: watering twice daily; T2: watering once daily; T3: watering every two days; T4: watering every three days; T5: watering every four days; T6: watering every five days; T7: watering every six days; T8: continuous waterlogging; H1: Hortitom 1 genotype; H3: Hortitom 3 genotype
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 147 Table 3 Effect of watering regime on the phenological and yield parameters of two genotypes of Solanum lycopersicum Growth parameters Tomato genotype Watering regime T1 T2 T3 T4 T5 T6 T7 T8 Number of days to first flowering H1 39.75±0.32a 40.00±0.20a 43.75±1.80ab 45.75±1.65ab 52.00±2.35bc 60.00±2.80cd 65.00±2.42d - H3 47.50±0.65a 51.50±0.65b 55.50±0.65c 59.50±0.65d 63.50±0.65e 67.50±0.65f 71.50±0.65g - Number of fruit H1 6.50±0.20a 6.50±0.20a 5.75±0.14a 6.00±0.20a 6.00±0.20a 5.63±0.13a 5.63±0.24a - H3 5.50±0.65ab 4.50±0.65ab 3.50±0.65ab 6.00±0.41b 3.00±0.41a 5.50±0.65ab 4.50±0.65ab - Fruit fresh weight (g) H1 5.25±0.32a 5.00±0.20a 4.00±0.46a 4.00±0.41a 4.25±0.63a 3.75±0.63a 4.00±0.41a - H3 4.75±0.48ab 3.75±0.48a 5.50±0.65abc 4.75±0.75ab 7.25±0.48bc 6.50±0.65bc 7.75±0.48c - Fruit dry weight (g) H1 0.81±0.19a 1.75±0.41a 0.97±0.29a 0.66±0.30a 1.05±0.06a 0.85±0.19a 0.74±0.17a - H3 2.64±0.28ab 1.68±0.65a 2.80±0.77ab 2.55±0.60a 1.81±0.39a 2.93±0.28ab 4.92±0.42b - Fruit length (cm) H1 16.25±0.32a 18.00±0.20bc 17.50±0.20b 18.50±0.20cd 19.25±0.14de 19.50±0.20e 19.25±0.14de - H3 42.35±0.65g 38.35±0.65f 34.35±0.65e 30.35±0.65d 26.35±0.65c 22.35±0.65b 18.35±0.65a - Fruit breadth (cm) H1 18.18±0.38a 20.00±0.20b 19.50±0.20b 20.50±0.20b 21.38±0.24bc 21.50±0.20cd 21.88±0.13d - H3 75.28±0.65g 71.28±0.65f 67.28±0.65e 63.28±0.65d 59.28±0.65c 55.28±0.65b 51.28±0.65a - Note: Each value is a mean ±S.E. of 5 replicates. For each value, means with the same letter(s) in superscript on the same row are not significantly different at P ≥ 0.05 (Tukey HSD test). T1: watering twice daily; T2: watering once daily; T3: watering every two days; T4: watering every three days; T5: watering every four days; T6: watering every five days; T7: watering every six days; T8: continuous waterlogging; H1: Hortitom 1 genotype; H3: Hortitom 3 genotype
Bioscience Methods 2026, Vol.17, No.3, 141-152 http://bioscipublisher.com/index.php/bm 148 Table 4 Effect of watering regime on the fruit proximate and mineral compositions of two genotypes of Solanum lycopersicum Proximate/Mineral composition Tomato genotype Watering regime T1 T2 T3 T4 T5 T6 T7 T8 Moisture (%) H1 17.61±0.06d 15.13±0.13b 14.28±0.05a 19.00±0.13e 15.00±0.02b 16.41±0.08c 17.44±0.06d - H3 18.65±0.00cd 15.86±0.05a 18.71±0.04d 16.80±0.22b 18.01±0.01c 15.80±0.22a 18.21±0.03cd - Fat (%) H1 1.06±0.06b 0.87±0.01a 1.12±0.00b 1.02±0.03a 1.14±0.02b 1.13±0.02b 1.07±0.06b - H3 0.83±0.00a 1.12±0.00c 0.99±0.02abc 0.93±0.08ab 0.97±0.01abc 0.84±0.01ab 1.01±0.01bc - Ash (%) H1 5.02±0.00d 3.68±0.01a 4.19±0.06b 3.70±0.01a 5.08±0.04d 3.66±0.01a 4.50±0.01c - H3 4.05±0.00b 3.89±0.03b 3.60±0.10a 4.64±0.00c 4.94±0.02d 3.56±0.01a 4.99±0.02d - Crude fibre (%) H1 5.39±0.02a 5.01±0.00a 7.47±0.12c 5.67±0.12b 7.93±0.08cd 8.31±0.03d 8.01±0.21cd - H3 6.03±0.01a 7.24±0.00c 8.27±0.05e 6.59±0.01b 7.94±0.08d 7.90±0.07d 7.95±0.02d - Crude protein (%) H1 1.12±0.01a 1.10±0.01a 1.68±0.01c 1.99±0.02d 2.06±0.05e 1.34±0.02b 1.74±0.01c - H3 1.05±0.01a 1.26±0.00b 1.83±0.00d 1.85±0.09d 1.54±0.01c 1.53±0.01c 1.45±0.02bc - Carbohydrate (%) H1 69.81±0.10c 74.22±0.16e 71.27±0.02d 68.65±0.28b 68.80±0.11b 69.16±0.12c 67.37±0.23a - H3 69.43±0.01b 70.65±0.03c 66.63±0.20a 69.21±0.19b 66.61±0.05a 70.39±0.13c 66.41±0.08a - Calcium (mg/kg) H1 20.40±0.10d 15.85±0.05a 17.40±0.00b 21.80±0.30e 18.50±0.10c 22.55±0.15e 20.40±0.30d - H3 18.50±0.10c 17.05±0.15b 14.80±0.10a 22.75±0.25d 17.80±0.30bc 16.65±0.15b 17.50±0.30bc - Potassium (mg/kg) H1 35.95±0.45e 30.65±0.05c 27.45±0.35b 27.70±0.20b 31.05±0.35c 25.60±0.20a 32.80±0.30d - H3 28.60±0.10bc 32.30±0.20e 29.30±0.20c 24.45±0.15a 27.65±0.15b 30.60±0.10d 28.35±0.25bc - Magnesium (mg/kg) H1 25.60±0.20c 23.65±0.15b 19.85±0.25a 19.80±0.30a 26.15±0.15c 19.40±0.10a 20.50±0.30a - H3 20.55±0.15b 23.15±0.05d 21.75±0.05c 20.40±0.10b 18.60±0.10a 23.37±0.04d 21.70±0.20c - Iron (mg/kg) H1 1.85±0.01f 1.44±0.00b 1.27±0.00a 2.01±0.00g 1.81±0.00e 1.50±0.00c 1.65±0.00d - H3 1.65±0.00e 1.37±0.00b 1.73±0.01f 1.33±0.00a 1.48±0.00c 1.72±0.00f 1.62±0.00d - Phosphorus (mg/kg) H1 12.20±0.10d 14.04±0.07e 9.57±0.01b 8.92±0.19a 10.67±0.02c 12.18±0.03d 10.94±0.01c - H3 10.67±0.01c 13.16±0.01e 12.30±0.17d 14.17±0.05f 13.13±0.01e 9.11±0.12a 9.58±0.01b - Nitrogen (mg/kg) H1 0.18±0.00a 0.18±0.00a 0.27±0.00c 0.32±0.01d 0.33±0.01d 0.22±0.01b 0.28±0.00c - H3 0.17±0.00a 0.20±0.00ab 0.29±0.00d 0.30±0.02d 0.25±0.00c 0.25±0.01c 0.23±0.00bc - Note: Each value is a mean ±S.E. of 5 replicates. For each value, means with the same letter(s) in superscript on the same row are not significantly different at P ≥ 0.05 (Tukey HSD test). T1: watering twice daily; T2: watering once daily; T3: watering every two days; T4: watering every three days; T5: watering every four days; T6: watering every five days; T7: watering every six days; T8: continuous waterlogging; H1: Hortitom 1 genotype; H3: Hortitom 3 genotype
Bioscience Methods 2026, Vol.17, No.3, 141- http://bioscipublisher.com/index.php/bm 149 3.6 Leaf total chlorophyll content Table 5 illustrates the impact of water regimes on leaf chlorophyll content (µm) in Solanum lycopersicum genotypes Hortitom 1 and Hortitom 3. Hortitom 1 exhibited total chlorophyll ranging from 36.46 µm under T4 (watering every three days) to a peak of 66.92 µm under T6 (every five days), with high values also at T5 (64.53 µm), indicating optimal retention under moderate water stress. In contrast, Hortitom 3 displayed substantially higher total chlorophyll peaks at 82.00 µm (T3, every two days) and 84.61 µm (T4, every three days), far exceeding other treatments, driven by elevated chlorophyll a in these regimes while chlorophyll b remained lower but slightly increased. Overall, Hortitom 3 outperformed Hortitom 1 in maximum chlorophyll accumulation, particularly under frequent watering, suggesting better photosynthetic adaptation to specific regimes. Table 5 Effect of watering regime on the leaf chlorophyll content (mg/g fresh weight) of two genotypes of Solanum lycopersicum Tomato genotype Chlorophyll content (mg/g fresh weight) Watering regime T1 T2 T3 T4 T5 T6 T7 T8 Hortitom 1 a 22.83 28.10 19.16 16.79 29.27 29.84 23.36 ̶ b 23.98 27.36 20.07 19.67 35.26 37.08 23.04 ̶ Total 46.82 55.46 39.23 36.46 64.53 66.92 46.39 ̶ Hortitom 3 a 22.15 22.00 68.21 67.86 21.19 28.33 26.31 ̶ b 22.76 21.80 13.79 16.75 22.32 29.82 25.15 ̶ Total 44.91 43.81 82.00 84.61 43.51 58.15 51.45 ̶ Note: Each value is a mean of 5 replicates. T1: watering twice daily; T2: watering once daily; T3: watering every two days; T4: watering every three days; T5: watering every four days; T6: watering every five days; T7: watering every six days; T8: continuous waterlogging; H1: Hortitom 1 genotype; H3: Hortitom 3 genotype 4 Discussion Tomato genotypes Hortitom 1 (H1) and Hortitom 3 (H3) demonstrated distinct physiological adaptations to water stress regimes, from optimal twice daily watering (T1) to severe restriction every six days (T7), with complete mortality under waterlogging (T8) due to root hypoxia (Sharma and Pathak, 2020). 4.1 Effect on growth parameters Water stress regimes showed genotypic differences in vegetative growth. In Hortitom 1, plant height peaked under moderate stress (T5), reflecting adaptive enhancements typical of mild drought responses that optimize resource allocation (Alomari-Mheidat et al., 2024; Mustapha et al., 2025). Conversely, Sillo (2022) noted generally reduced height and stem diameter under deficits, underscoring genotype dependency, while Hortitom 3 consistently displayed taller plants, indicative of superior water use efficiency (Tüzel et al., 2025). Leaf number progressed upward in Hortitom 1 from T1 to T7, enabling sustained production amid restriction, aligning with leaf area adjustments for stress acclimation (Koch et al., 2019). Hortitom 3, however, exhibited declines at intermediate intervals, potentially signaling adaptive senescence to conserve water (Petrović et al., 2021). Leaf area fluctuated markedly in Hortitom 1 but remained stable in Hortitom 3, suggesting the latter's conservative strategy for optimized transpiration (Razouk et al., 2022; Chiofalo et al., 2025). Stem girth showed remarkable stability across treatments in both, a trait likely genetically governed to preserve vascular function under fluctuating moisture (Rodriguez et al., 2021; Amankwaa-Yeboah et al., 2023). Reproductive timing was also disturbed, with both genotypes experiencing progressively delayed first flowering under rarer watering, attributable to curtailed carbon fixation and hormonal shifts (Fernández-García et al., 2021; Sillo et al., 2022). 4.2 Effect on biomass accumulation Hortitom 1 preserved stable fresh and dry leaf weights across regimes, minimizing photosynthetic losses, while root numbers surged under moderate drought to exploit deeper soil water, though stem biomass decline progressively, showing resource shifts from structure to acquisition (Arif et al., 2022; Kou et al., 2022). In Hortitom 3, fresh leaf weights increased with decreasing irrigation frequency, implying leaf carbon gain (Flexas et al., 2020); root fresh and dry weights similarly amplified. Stem biomass declined steadily in both genotypes, a conserved response to curtail non essential growth (Tüzel et al., 2025).
Bioscience Methods 2026, Vol.17, No.3, 141- http://bioscipublisher.com/index.php/bm 150 4.3 Effect on yield components Yield responses revealed adaptive strategies under water stress. Hortitom 1 kept fruit numbers fairly steady, with slight drops in fresh weight but clear gains in fruit length and width during water shortages, directing more resources to individual fruits for bigger sizes (Poomkokrak et al., 2024; Zahedifar et al., 2025). In comparison, Hortitom 3 had greater changes in fruit count and size, boosting fresh and dry weights with less frequent watering while fruit length and width decreased, matching patterns of stress-induced fruit drop and limited growth (Medyouni et al., 2021; Zhang et al., 2025). 4.4 Effect on fruit nutritional and proximate compositions Irrigation frequency greatly affects the quality of tomato fruits. In Hortitom 1, watering every 2-5 days raised moisture, crude fiber, and protein levels by concentrating these nutrients with less water dilution (Hasanuzzaman et al., 2021; Wadood et al., 2024). Hortitom 3 showed similar changes, with varying levels of moisture, fat, ash, fiber, and protein that improved under the same moderate stress. Minerals and heavy metals also shifted: Hortitom 1 built up more calcium during moderate stress to help it adapt, while potassium was highest with frequent watering to support water balance and leaf pore control (White and Broadley, 2020). Factors like genotype root links and soil microbe effects further shaped these trends (Ojewumi et al., 2025; Tripodi et al., 2025). 4.5 Effect on total chlorophyll content Chlorophyll levels, which show how well plants photosynthesize, improved best under mild water limits. Hortitom 1 built up higher total chlorophyll, chlorophyll a, and chlorophyll b with watering every few days, helping it capture light more effectively (Flexas et al., 2021; Akhlaq et al., 2025; Atanassova et al., 2025). Hortitom 3 reached even higher peaks under certain moderate watering schedules, pointing to strong photosystem activity and built-in toughness for its type, even though drought often slows photosynthesis overall (Argentel-Martínez et al., 2024; Karami et al., 2025). 5 Conclusion and Recommendations In conclusion, the two tomato genotypes exhibited distinct responses to different watering regimes, highlighting the importance of genotype-specific irrigation management. Hortitom 1 performed optimally under moderate water stress (watering every 4 days, T5), where it achieved maximum plant height and the highest crude protein content. Hortitom 3, on the other hand, showed superior fruit yield under more severe water restriction (watering every 6 days, T7). Both genotypes attained their highest plant height at T5, demonstrating good tolerance to moderate drought conditions. The study further revealed that continuous waterlogging (T8) caused complete mortality in both genotypes, indicating high susceptibility to excess water. However, both Hortitom 1 and Hortitom 3 displayed strong drought tolerance, maintaining 100% survival even under watering intervals of up to six days. These findings suggest that adopting genotype-specific watering regimes can significantly improve water productivity and fruit quality in resource-limited environments. For optimal performance, Hortitom 1 should be irrigated every 4 days, while Hortitom 3 performs better with irrigation every 6 days under screenhouse conditions. Both varieties should be grown only on well-drained soils to avoid waterlogging. Future studies should validate these results under field conditions across different seasons and soil types to enhance the applicability of the recommendations for smallholder farmers. Author’s contribution Otitoloju Kekere designed and supervised the research, and prepared draft of the manuscript. Hepzibah Tofunmi Oyetunde set up the experiment and collected data. Hepzibah Tofunmi Oyetunde and Joseph Kolade Afolabi co-designed and monitored the experimental process. Joseph Kolade Afolabi performed statistical analyses of the data. All authors read and approved the final manuscript. Funding This research received no external funding.
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Bioscience Methods 2026, Vol.17, No.3, 153-168 http://bioscipublisher.com/index.php/bm 153 Research Article Open Access SNP-Based Heritability Is Not a Parameter but a Model-Defined Estimand: Evidence from UK Biobank Xuanjun Fang Hainan Provincial Key Laboratory of Crop Molecular Breeding, Hainan Institute of Tropical Agricultural Resources (HITAR), Sanya, 572025 Corresponding author: xuanjunfang@hitar.org Bioscience Methods, 2026, Vol.17, No.3 doi: 10.5376/bm.2026.17.0013 Received: 06 Apr., 2026 Accepted: 07 May, 2026 Published: 18 May, 2026 Copyright © 2026 Fang, 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: Fang X.J., 2026, SNP-based heritability is not a parameter but a model-defined estimand: evidence from UK biobank, Bioscience Methods, 17(3): 153-168 (doi: 10.5376/bm.2026.17.0013) Abstract SNP-based heritability is widely interpreted as a fundamental property of complex traits, yet estimates vary substantially across methods. Here we show that this variation arises because different approaches do not estimate the same quantity: SNP-based heritability is a model-defined estimand rather than a single biological parameter. Using UK Biobank height data as a representative case, we systematically compare estimates from individual-level methods (GCTA-GREML and related estimators) and summary-statistics-based approaches (LD Score Regression and SumHer). We find that GREML-based methods consistently yield higher estimates (~0.60-0.69), LDSC produces systematically lower values (~0.56), and SumHer yields intermediate or higher estimates (~0.63). These differences persist under matched samples and SNP sets, indicating that they cannot be attributed to sampling variation alone. We demonstrate that the discrepancies arise from differences in data representation, model assumptions, and the treatment of linkage disequilibrium (LD) and allele frequency. Accordingly, each method targets a distinct estimand: GREML captures variance explained through genomic relationships, LDSC estimates LD-weighted marginal effects, and SumHer models MAF- and LD-dependent architectures. This framework resolves apparent inconsistencies in SNP heritability estimates and clarifies that cross-method comparisons are generally not statistically valid without alignment of underlying assumptions. More broadly, our results redefine SNP-based heritability as a model-dependent functional determined by SNP coverage, LD structure, and estimation framework. These findings provide a principled basis for interpreting heritability estimates and have implications for genetic studies ranging from biobank-scale analyses to genomic prediction. Keywords SNP heritability; Estimand; Estimand mismatch; GCTA-GREML; LD Score Regression (LDSC); UK Biobank; Linkage disequilibrium; Genetic architecture 1 Introduction Heritability is a central parameter in quantitative genetics, used to quantify the contribution of genetic factors to phenotypic variation. In classical frameworks, heritability is typically estimated using pedigree or twin-based designs, where genetic variance is inferred from known relatedness structures. However, with the advent of genome-wide association studies (GWAS) and high-throughput genotyping technologies, the paradigm of heritability estimation has undergone a fundamental shift-from pedigree-based inference to SNP-based heritability derived from molecular markers (SNP-based heritability, hSNP 2 ) (Yang et al., 2010). The evolution of statistical genetic methods-from linkage analysis and candidate gene approaches to GWAS-has fundamentally reshaped how genetic variation is quantified and interpreted (Fang and Wu, 2026). Within this paradigm shift, SNP-based heritability estimation, particularly under the GCTA-GREML framework, represents a transition from pedigree-based inference to genotype-driven variance decomposition (Fang, 2026). SNP-based heritability is typically defined as the proportion of phenotypic variance explained by observed or imputed SNP markers across the genome. Its estimation is commonly based on linear mixed models (LMMs) or their extensions. Among these, the GCTA-GREML framework estimates genetic variance components using individual-level genotype data by constructing a genomic relationship matrix (GRM), and is widely regarded as approximately unbiased and statistically efficient under appropriate model assumptions (Yang et al., 2016). In contrast, LD Score Regression (LDSC) and its extensions (e.g., S-LDSC) estimate heritability using GWAS summary statistics, enabling large-scale analyses when individual-level data are unavailable (Bulik-Sullivan et al.,
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