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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title>Frontiers in Plant Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Plant Sci.</abbrev-journal-title>
<issn pub-type="epub">1664-462X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fpls.2024.1480510</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Plant Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Cultivation viability of <italic>Allium tuberosum</italic> L. in the Western Ghats: insights into crop dynamics, yield and quality</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Gavhane</surname>
<given-names>Abhishek D.</given-names>
</name>
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<sup>1</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Kale</surname>
<given-names>Rajiv B.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<name>
<surname>Khade</surname>
<given-names>Yogesh</given-names>
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<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<sup>*</sup>
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<contrib contrib-type="author">
<name>
<surname>Bhandari</surname>
<given-names>Hem Raj</given-names>
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<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<contrib contrib-type="author">
<name>
<surname>Gaikwad</surname>
<given-names>Shivam Y.</given-names>
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<sup>1</sup>
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<surname>Singh</surname>
<given-names>Sharadveer</given-names>
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<sup>1</sup>
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<surname>Shabeer T.P.</surname>
<given-names>Ahammed </given-names>
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<sup>2</sup>
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<name>
<surname>Garde</surname>
<given-names>Yogesh A.</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<surname>Khandagale</surname>
<given-names>Kiran</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<sup>*</sup>
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<name>
<surname>Mahajan</surname>
<given-names>Vijay</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<sup>*</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>ICAR-Directorate of Onion and Garlic Research</institution>, <addr-line>Pune</addr-line>,&#xa0;<country>India</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>ICAR-National Research Centre for Grapes</institution>, <addr-line>Pune</addr-line>,&#xa0;<country>India</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>N.M. College of Agriculture, Navsari Agricultural University</institution>, <addr-line>Navsari</addr-line>,&#xa0;<country>India</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Giuseppe Mannino, University of Turin, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Beppe Benedetto Consentino, University of Palermo, Italy</p>
<p>Miroslawa Chwil, University of Life Sciences of Lublin, Poland</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Rajiv B. Kale, <email xlink:href="mailto:rajiv.kale@icar.gov.in">rajiv.kale@icar.gov.in</email>; Vijay Mahajan, <email xlink:href="mailto:vijay.mahajan@icar.gov.in">vijay.mahajan@icar.gov.in</email>; Yogesh Khade, <email xlink:href="mailto:yogesh.khade@icar.gov.in">yogesh.khade@icar.gov.in</email>; Kiran Khandagale, <email xlink:href="mailto:kirankhandagale253@gmail.com">kirankhandagale253@gmail.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>18</day>
<month>11</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="ecorrected">
<day>26</day>
<month>05</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1480510</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>08</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>25</day>
<month>10</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Gavhane, Kale, Khade, Bhandari, Gaikwad, Singh, Shabeer T.P., Garde, Khandagale and Mahajan.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Gavhane, Kale, Khade, Bhandari, Gaikwad, Singh, Shabeer T.P., Garde, Khandagale and Mahajan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>
<italic>Allium tuberosum</italic>, commonly known as garlic chives, is an underutilized <italic>Allium</italic> species despite its significant culinary value for its mild garlic flavor and therapeutic potential due to the presence of sulphur-containing compounds with antimicrobial, anti-inflammatory, and antioxidant properties. This study assessed the cultivation potential of <italic>A. tuberosum</italic> in the non-traditional agro-climatic region of the Western Ghats, focusing on the effects of cultivars and seasonal variations on growth, yield, and quality in two-year field trials. Among the accessions tested, <italic>A.tuberosum</italic> All-1587 demonstrated the highest yield, producing 157.01 tons of green foliage per hectare. Bright sunshine seasons positively influenced both yield and quality, while the monsoon season induced morphological changes such as increased stem length, reduced leaf width, and decreased stem girth, traits generally considered inferior for market quality. The monsoon also led to increased waste generation, highlighting the need for careful management during this period. Nutritional analysis revealed high concentrations of potassium (5355 mg/kg), phosphorus (691 mg/kg), and sulphur (2484 mg/kg), while biochemical profiling identified bioactive compounds such as flavonoids (3.19 mg/g) and organosulfur compounds, including Allyl Methyl Thiosulfinate (269.00 mg/kg), which contribute to the plant&#x2019;s notable health benefits. These findings support the suitability of <italic>A. tuberosum</italic> for year-round cultivation in the Western Ghats and its potential for commercialization, especially in regions with similar climatic conditions.</p>
</abstract>
<kwd-group>
<kwd>underutilized <italic>Allium</italic>
</kwd>
<kwd>non-traditional cultivation</kwd>
<kwd>yield optimization</kwd>
<kwd>seasonal effects</kwd>
<kwd>Western Ghats agro-climatic region</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="4"/>
<equation-count count="2"/>
<ref-count count="84"/>
<page-count count="17"/>
<word-count count="9553"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Crop and Product Physiology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The genus <italic>Allium</italic> characterized by its distinctive scented underground bulb is one of the largest plant genera comprising nearly 1000 species with <italic>Allium cepa</italic> (onion) and <italic>Allium sativum</italic> (garlic) being commercially important crops (<xref ref-type="bibr" rid="B87">Ochar and Kim, 2023</xref>). The genus includes some underutilized and semi-domesticated species which are equally potential for commercialization. These species have significant culinary uses, primarily adding flavor and savor to food. The flavor arises from the chemical transformation of S-alk(en)yl cysteine sulfoxides into volatile compounds, including S-methyl cysteine sulfoxide, S-allyl cysteine sulfoxide, and S-trans-prop-1-enyl cysteine sulfoxide, among others (<xref ref-type="bibr" rid="B88">Jones et&#xa0;al., 2004</xref>). Despite multifaceted uses, many species within the genus remain largely underutilized, which is a missed opportunity considering their importance for food and medicinal properties as alliums are rich in secondary metabolites like organosulfur compounds, flavonoids, phenols, saponins, alkaloids, and polysaccharides. By virtue of this, they offer a range of nutritional, biological, and health benefits, such as antimicrobial, antioxidant, antitumor, immunomodulatory, antidiabetic, and anti-inflammatory properties (<xref ref-type="bibr" rid="B90">Riaz et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B91">Yan et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B89">Kim et&#xa0;al., 2023</xref>). Processed products in the form of dried bulbs, leaves, buds and flowers from wild <italic>Allium</italic> species like <italic>A. tuberosum, A. angulsorum, A. auriculatum, A. carolinianum, A. griffithianum, A. hummile, A. roylei, and A. wallichii</italic> are in demand for their culinary applications and extended shelf life (<xref ref-type="bibr" rid="B49">Negi et&#xa0;al., 2006</xref>). </p>
<p>
<italic>A.tuberosum</italic> commonly known as garlic chives, Chinese chives, or Oriental garlic, remains an underutilized species despite its substantial culinary and medicinal applications. Native to Southeast Asia and often cultivated in home gardens in northeast India (<xref ref-type="bibr" rid="B51">Pandey et&#xa0;al., 2014</xref>), this perennial species thrives in moderate climates and is particularly valued for its mild garlic flavor due to the presence of methiin, alliin other sulfur compounds (<xref ref-type="bibr" rid="B92">Xia et&#xa0;al., 2022</xref>). It is especially useful in dishes where raw garlic would be too overpowering, such as salads, egg dishes, soups, stews, stir-fries etc. The flowers are also edible and can be used as a garnish. In several countries including China, Philippines, Korea, and Thailand, garlic chives are a well-known vegetable (<xref ref-type="bibr" rid="B100">Saito, 2020</xref>; <xref ref-type="bibr" rid="B101">Alam et&#xa0;al., 2023</xref>.</p>
<p>Past research on <italic>A. tuberosum</italic> has revealed a rich profile of essential micronutrients and vitamins vital for various bodily functions, including immune response, bone health, and red blood cell formation (<xref ref-type="bibr" rid="B9">Bastaki et&#xa0;al., 2021</xref>). Medicinally, <italic>A. tuberosum</italic> seed extract is valued for its aphrodisiac properties (<xref ref-type="bibr" rid="B29">Guohua et&#xa0;al., 2009</xref>). The presence of organosulfur compounds, similar to those found in garlic, enhances its therapeutic potential by providing antimicrobial (<xref ref-type="bibr" rid="B97">Khalid et&#xa0;al., 2014</xref>), anti-inflammatory, anti-cancer (<xref ref-type="bibr" rid="B98">Park et&#xa0;al., 2013</xref>), antidiabetic benefits (<xref ref-type="bibr" rid="B74">Tang et&#xa0;al., 2017</xref>) etc.</p>
<p>Field surveys and research have highlighted the edible uses of <italic>A. tuberosum</italic> in the Himalayas, particularly in the Garhwal and Kumaon regions (<xref ref-type="bibr" rid="B39">Joseph and Pradheep, 2019</xref>). <italic>A. tuberosum</italic> consumed as raw or cooked vegetables, spices, and seasonings. Beyond these primary regions, it is gaining importance in the plains and other non-traditional areas, serving as an alternative to leaf-purpose onion cultivation, which faces challenges like susceptibility to biotic and abiotic stresses and seasonal availability cycles (<xref ref-type="bibr" rid="B93">Huang et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B5000">Khandagale et&#xa0;al., 2022</xref>). <xref ref-type="bibr" rid="B51">Pandey et&#xa0;al. (2014)</xref> stated that <italic>A. tuberosum</italic> has the potential for commercial cultivation in different onion and garlic growing part of the India. The ICAR-DOGR in Pune has been working on primary studies for <italic>A. tuberosum</italic> in this new geographical setup to cater for a year-round supply and developed a value chain model (<xref ref-type="bibr" rid="B86">Kale et&#xa0;al., 2024</xref>). Recent agronomic research has focused on improving the cultivation practices of <italic>A. tuberosum</italic> to enhance its productivity and quality. Studies have found that the use of slow-release fertilizers and mulching increased the growth, nutrient use efficiency, weed control, yield and biological value of <italic>A. tuberosum</italic> (<xref ref-type="bibr" rid="B82">Wang et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B94">Adamczewska-Sowinska and Turczuk, 2016</xref>; <xref ref-type="bibr" rid="B96">Kim, 2010</xref>).</p>
<p>The economic importance of <italic>A. tuberosum</italic> extends beyond its culinary and medicinal uses. The plant has significant market value due to its increasing popularity in gourmet cuisine and traditional medicine (<xref ref-type="bibr" rid="B103">Kalasee and Mittraparp-arthorn, 2023</xref>; <xref ref-type="bibr" rid="B101">Alam et&#xa0;al., 2023</xref>). The demand for fresh and processed products example dried leaves and flower buds, is rising globally, driven by consumer preferences for natural and health-promoting foods (<xref ref-type="bibr" rid="B104">Imahori, 2022</xref>; <xref ref-type="bibr" rid="B105">Liu et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B86">Kale et&#xa0;al., 2024b</xref>). Its unique flavor due to the presence of sulfur-containing volatiles such as Diallyl disulfides (<xref ref-type="bibr" rid="B92">Xia et&#xa0;al., 2022</xref>) and health benefits like antidiabetic, hepatoprotective, and antitumor nature (<xref ref-type="bibr" rid="B74">Tang et&#xa0;al., 2017</xref>) make it a sought-after ingredient in high-end restaurants and health food stores. Additionally, the development of value-added products including powder and feed capsules, is opening new avenues for commercial explorations (<xref ref-type="bibr" rid="B7">Annual report ICAR-DOGR, 2021</xref>). Western Ghats, characterized by its climatic variability and ecological diversity, offer a promising environment for agricultural diversification, particularly in response to the limitations faced by traditional crops such as onions and garlic, which are prone to biotic and abiotic stresses (<xref ref-type="bibr" rid="B93">Huang et&#xa0;al., 2016</xref>). This study aims to assess the adaptability of <italic>Allium tuberosum</italic> in this ecologically unique region, focusing on developing sustainable cultivation practices that can enhance agricultural diversity and resilience. Given that the Western Ghats present a warmer climate compared to the native habitat of <italic>A. tuberosum</italic>, this research seeks to explore the potential of year-round cultivation, overcoming the seasonal constraints imposed by harsh winters.</p>
<p>In light of significant agronomic variations, such as sowing methods (either from seed or bulb), planting layouts, and intercultural operations, this study will establish foundational guidelines for optimizing these practices in the Western Ghats. The primary objective is to evaluate the cultivation viability and nutritional profile of <italic>A. tuberosum</italic> in non-traditional geographical regions, with an emphasis on the Western Ghats. By analyzing its adaptability to novel environmental conditions, this research aims to provide a comprehensive framework for popularizing <italic>A. tuberosum</italic>, contributing to agricultural diversification and nutritional security. Furthermore, the study seeks to identify commercial opportunities for <italic>A. tuberosum</italic> within the expanding health food market, offering farmers and agribusinesses access to a profitable and emerging agricultural niche.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<p>The <italic>Allium</italic> germplasm in the collection was cultivated in normal conditions of western ghats to assess their production performance in the region. Three accessions viz. <italic>A.tuberosum</italic> All-1587, <italic>A. tuberosum</italic> CGN-116418 &amp; <italic>A. tuberosum</italic> CGN-16373 referred to as AT1, AT2 &amp; AT3 respectively for convenience, were used in the present study for production performance and assessment of nutraceutical properties. The nutritional and biochemical profile of the best-suited <italic>A. tuberosum</italic> line was done to assist the commercialization by advocating its health and related benefits.</p>
<sec id="s2_1">
<label>2.1</label>
<title>Location, soil and general weather conditions</title>
<p>The study was conducted at the ICAR-Directorate of Onion and Garlic Research (ICAR-DOGR), Rajgurunagar, India. The region falls under the Western Maharashtra Plain Zone agroclimatic zone as per the classification by ICAR (<xref ref-type="bibr" rid="B2">Ahmad et&#xa0;al., 2017</xref>). This is a region of the regur (black) soil. Wheat, gram, millets, cotton, pulses, groundnut, and oilseeds are the main crops in the rain-fed areas, while in the irrigated areas, sugarcane, onion and wheat, are cultivated. The experimental site is situated at 18&#xb0; 50&#x2019; 33.5112&#x2019;&#x2019; N and 73&#xb0;53&#x2019;5.7732&#x2019;&#x2019; E with an elevation of 553.8 m above MSL. The average annual temperature ranges from 5.5&#xb0;C to 42.0&#xb0;C, while the&#xa0;average annual rainfall is 669 mm. The predominant soil type at the site is black vertisol with a pH of 7.27, an electrical conductivity of 0.26 dS/m, and a soil organic carbon (SOC) content of 6.98 g/kg. The available nutrient status of the soil is favorable for crop growth, with nitrogen at 219.82 kg/ha, phosphorus at 52.82 kg/ha, potassium at 824.0 kg/ha and sulphur at 28.54 kg/ha. The general weather conditions prevailing during the experiment period are depicted in <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>General weather conditions during the experiment period.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Selection of tubers, planting and general operations</title>
<p>Three <italic>A. tuberosum</italic> accessions were evaluated employing a factorial random block design. The accessions of <italic>A. tuberosum</italic>, constituted one factor, while eight age windows each comprising a three-month unit, formed the second factor. The selected planting material i.e. rhizomes from the three-year-old mother batch were carefully extracted and separated from the clump. No treatment was applied to the rhizomes, which were then planted at 25&#xd7;25cm spacing on a raised bed of 1.2 m width and 15 cm height. Five replicates of each <italic>A. tuberosum</italic> line were planted, with each replicate consisting of 35 hills arranged in seven rows of five hills each. Two drip laterals with emitters spaced 30 cm apart were installed over the bed for irrigation. Weeding was the primary interculture operation in the research field. According to the age windows, the third, fourth, sixth, and eighth windows exhibited higher weed infestation rates, necessitating frequent weeding. Irrigation scheduling was managed based on field moisture conditions, with the second, third, fifth, and sixth age windows requiring less frequent irrigation compared to other dry and hot seasonal windows.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Observation schedule and data analyses</title>
<p>The establishment of planting was followed by observations commencing in the first month. Morphological parameters were recorded before cutting, while green foliage yield, waste generated and dry matter content were recorded after each monthly cutting. Plant height (cm), number of tillers, number of leaves, leaf length (cm), leaf width (mm), stem length (cm), stem width (mm), and crop spread (cm) were measured to determine the growth and morphology of selected <italic>A. tuberosum</italic> accessions. Crop spread was calculated as the average diameter of the clump spread over the ground. Five plants were randomly selected and tagged in each replication. The number of leaves per plant was calculated by counting the total number of leaves from each tagged plant. The data was analyzed using basic statistics and a two-factorial randomized block design analysis of variance. The quality of green foliage cuttings was further evaluated in terms of waste-to-yield ratio and stem length-to-plant height ratio.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>The analysis of the nutritional and biochemical profile of <italic>A. tuberosum</italic>
</title>
<sec id="s2_4_1">
<label>2.4.1</label>
<title>Estimation of proximate composition</title>
<p>The proximate composition of the <italic>A. tuberosum</italic> leaves was analyzed following the standard food analysis methods described in the Association of Official Analytical Chemists. The moisture was determined using a hot air oven as per AOAC 952.08, 2016 (<xref ref-type="bibr" rid="B20">Cunniff and Washington, 1997</xref>), ash content was estimated by heating plant sample in a muffle furnace for about 5-6 h at 500&#xb0;C (<xref ref-type="bibr" rid="B6000">Official Methods of Analysis of AOAC, 2016</xref>; <xref ref-type="bibr" rid="B20">Cunniff and Washington, 1997</xref>) whereas the acid insoluble ash content was determined by dissolving ash in dilute hydrochloric acid (10% w/w) (<xref ref-type="bibr" rid="B6000">Official Methods of Analysis of AOAC, 2016</xref>; (<xref ref-type="bibr" rid="B20">Cunniff and Washington, 1997</xref>), the liquid was filtered through an ashless filter paper and thoroughly washed with hot water. The filter paper was then ignited in the original dish, cooled and weighed. The crude lipid was extracted from a moisture-free sample with petroleum ether (60-80&#xb0;C) in a Soxhlet apparatus for about 6-8 h. Estimation of crude fiber content in the plant materials was carried out by treating the fat and moisture-free materials with 1.25% dilute acid and 1.25% alkali followed by washing with water and ignition of the residue. The crude protein was determined using the micro Kjeldahl method (AOAC 992.23, 2016; <xref ref-type="bibr" rid="B63">Sarkar et&#xa0;al., 2020</xref>). Total fat was determined according to the acid hydrolysis method (AOAC 948.15, 2016; <xref ref-type="bibr" rid="B20">Cunniff and Washington, 1997</xref>), using a Soxhlet extractor at 60&#xb0;C until constant weight. Dietary fiber was determined according to the enzymatic gravimetric method (AOAC 985.29, 2016; <xref ref-type="bibr" rid="B20">Cunniff and Washington, 1997</xref>), digested leaf samples with heat using alpha-amylase, protease, and amyloglucosidase at 60&#xb0;C, respectively, then added ethanol to samples to precipitate fiber (<xref ref-type="bibr" rid="B47">McCleary, 2023</xref>; <xref ref-type="bibr" rid="B57">Prosky et&#xa0;al., 1985</xref>).</p>
<p>The total carbohydrate content was estimated as described in the method of <xref ref-type="bibr" rid="B34">Hedge et&#xa0;al. (1962)</xref> by calculation having estimated all the other fractions by proximate analysis i.e. % Available carbohydrates = 100 - [% Moisture + % Ash + % Fat +% Protein + % Fiber]. The % organic matter was calculated as per <xref ref-type="bibr" rid="B38">James (2013)</xref>. The energy content was determined by multiplying the values obtained for protein, fat and available carbohydrate by 4.00, 9.00 and 4.00, respectively and adding up the values. Total sugar, reducing sugar, and non-reducing sugar were calculated as per the Lane and Eynon method (<xref ref-type="bibr" rid="B38">James, 2013</xref>) which measures sugar as reducing sugar and total sugar as invert sugar. Non-reducing sugar is determined by subtracting the total reducing sugar from the total sugar and multiplying the remainder with a 0.95 factor.</p>
</sec>
<sec id="s2_4_2">
<label>2.4.2</label>
<title>Estimation of minerals</title>
<p>The analysis of the mineral composition of <italic>A. tuberosum</italic> leaves was done using a spectrometer as per AOAC 990.08:2023 (<xref ref-type="bibr" rid="B73">Szyd&#x142;owska and Sz&#x142;yk, 2003</xref>; <xref ref-type="bibr" rid="B53">Piatkowska et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B37">Isaac and Johnson, 1985</xref>). Plant material was taken in a pre-cleaned and constantly weighed silica crucible and heated in a muffle furnace at 400&#xb0;C till there was no evolution of smoke. The crucible was cooled at room temperature in a desiccator and carbon-free ash was moistened with concentrated sulphuric acid and heated on a heating mantle till fumes of sulphuric acid ceased to evolve. The crucible with sulphated ash was then heated in a muffle furnace at 600&#xb0;C till the weight of the content was constant. One gram of sulphated ash obtained above was dissolved in 100 ml of 5% hydrochloric acid (HCl) to obtain the solution ready for determination of mineral elements through atomic absorption spectroscopy (AAS) (AA 800, Perkin-Elmer, Germany). A standard solution for each element was prepared and calibration curves were drawn for each element using AAS (<xref ref-type="bibr" rid="B36">Indrayan et&#xa0;al., 2005</xref>). Total nitrogen was estimated using the modified Kjeldahl method as per IS 7219 (<xref ref-type="bibr" rid="B16">Bremner and Mulvaney, 1982</xref>; <xref ref-type="bibr" rid="B60">S&#xe1;ez-Plaza et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B62">Sarkar et&#xa0;al., 2014</xref>). The total Sulphur content was estimated by inductively coupled plasma-optical emission spectrometry (ICP-OES) (iCAP PRO-X-Duo, Thermo-Scientific, MA, USA) after sample preparation with tetramethylammonium hydroxide (TMAH) (<xref ref-type="bibr" rid="B75">Tavares et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B33">Hansen et&#xa0;al., 2013</xref>).</p>
</sec>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Estimation of phenolic acids and flavonoids</title>
<sec id="s2_5_1">
<label>2.5.1</label>
<title>Total phenol content</title>
<p>The total phenolic content of the samples was quantitatively analyzed using the Folin-Ciocalteu reagent, following the procedure (<xref ref-type="bibr" rid="B14">Blainski et&#xa0;al., 2013</xref>). Gallic acid served as the standard for this assay. The absorbance of the reaction mixture was measured at a wavelength of 750 nm using a UV-Vis spectrophotometer (Model no. UV1800, Shimadzu Corporation, Japan). The phenolic content in the samples was determined by comparing the absorbance values with a standard curve prepared using known concentrations of gallic acid (1, 2, 5, 7, 10, 20 and 50 ppm). The results were expressed as milligrams of Gallic Acid Equivalent per gram of dry weight (mg GAE/g DW).</p>
</sec>
<sec id="s2_5_2">
<label>2.5.2</label>
<title>Total flavonoid content</title>
<p>The total flavonoid content was determined using a modified version of the aluminium chloride colorimetric method, with quercetin as the reference standard (<xref ref-type="bibr" rid="B72">Sulastri et&#xa0;al., 2018</xref>). In this assay, the sample extracts were reacted with aluminium chloride, and the resulting complex&#x2019;s absorbance was measured at 510 nm. The flavonoid content was quantified by comparing the absorbance readings against a calibration curve generated with quercetin standards by a UV-Vis spectrophotometer (Model no. UV1800, Shimadzu Corporation, Japan). The results were articulated in terms of milligrams of Quercetin Equivalents per gram of fresh weight (mg QE/g FW).</p>
</sec>
<sec id="s2_5_3">
<label>2.5.3</label>
<title>Antioxidant activity (DPPH assay)</title>
<p>The antioxidant activity of the sample extracts was evaluated using the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay (<xref ref-type="bibr" rid="B64">Scherer and Godoy, 2009</xref>). The procedure involved adding 50 &#xb5;L of the sample extract to 1.950 mL of a DPPH solution, which was prepared in 95% methanol to achieve an initial absorbance of 0.8 to 0.9 at 515 nm. The mixture was incubated at room temperature in the dark for 90 minutes to allow the reaction to occur. Post incubation, the decrease in absorbance was measured at 515 nm using a spectrophotometer (Model no. UV1800, Shimadzu Corporation, Japan). The antioxidant activity was quantified as the percentage of DPPH radical inhibition, calculated using the following formula:</p>
<disp-formula>
<mml:math display="block" id="M1">
<mml:mrow>
<mml:mtext>Percent&#xa0;inhibition</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mtext>Absorbance&#xa0;of&#xa0;control</mml:mtext>
<mml:mo>&#x2212;</mml:mo>
<mml:mtext>Absordance&#xa0;of&#xa0;sample</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mtext>Absorabnce&#xa0;of&#xa0;control&#xa0;</mml:mtext>
</mml:mrow>
</mml:mfrac>
<mml:mtext>x&#xa0;</mml:mtext>
<mml:mn>100</mml:mn>
</mml:mrow>
</mml:math>
</disp-formula>
<p>This percentage indicates the capacity of the sample to scavenge DPPH radicals, thereby reflecting its antioxidant activity</p>
</sec>
</sec>
<sec id="s2_6">
<label>2.6</label>
<title>Estimation of vitamins</title>
<p>The estimation of vitamins was outsourced to an ISO/IEC 17025:2017 accredited laboratory (Food Hygiene &amp; Health Laboratory (FHHL), Pune, India). The stock standard solutions of vitamins B1, B2, B3, B5 and B6, B7 &amp; B9 were prepared by dissolving 25 mg of each standard in 1 ml 0.1M hydrochloric acid in a 25 ml standard volumetric flask. For the preparation of standard stock solutions of vitamin B9 and B2, 25 mg of each standard were dissolved in one ml 0.1 M sodium hydroxide in a 25 ml standard volumetric flask. The standard solution was stored in amber-glass bottles in the refrigerator at 4&#xb0;C. The working standards were prepared by diluting with phosphate buffer (1 M, pH 5.5). For the preparation of the sample solution, plant materials were washed with distilled water. The washed plant materials were cut into very small pieces, frozen in liquid nitrogen and kept at 20&#xb0;C until analysis. One gram of each of the freeze-dried samples was soaked in 10 ml water and extracted with 1 ml 0.1 M NaOH and 10 ml phosphate buffer (1 M, pH 5.5) was added to it and kept in the dark for 24 hours. The solution was first filtered through a Whatman No. 1 filter paper and the resulting filtrate was taken in a 25 ml volumetric flask and the solution was topped up to the mark with HPLC grade water. The sample solution was filtered through a 0.45 &#xb5;m membrane filter before injection into the LC system. The stock solutions of the sample were kept in a refrigerator for further use. The chromatographic analysis was carried out following the method described by <xref ref-type="bibr" rid="B7000">Seal et&#xa0;al. (2017)</xref> with minor modifications on an LC-MS/MS system (API 5500-MS system connected to Exion LC, Sciex, Foster City, CA, USA). The mobile phase contains acetonitrile (Solvent A) and aqueous trifluoro acetic acid (TFA, 0.01% v/v) (Solvent B), the column was thermostatically controlled at 220&#xb0;C and the injection volume was kept at 20 ml. A gradient elution was performed by varying the proportion of solvent A to solvent B. The total analysis time per sample was 35 minutes. Estimation of vitamin B4 was done using LC-MS (API 5500-MS system connected to Exion LC, Sciex, Foster City, CA, USA) as per AOAC official method 2012.18 (<xref ref-type="bibr" rid="B45">Martin et&#xa0;al., 2013</xref>).</p>
<p>To quantify fat-soluble vitamins A, E, and K in <italic>A. tuberosum</italic> leaves the AOAC official method 2014.02 was followed (<xref ref-type="bibr" rid="B26">Gim&#xe9;nez and Martin, 2018</xref>). Freshly harvested leaves were immediately submerged in liquid nitrogen to halt enzymatic degradation and stored at -80&#xb0;C. For analysis, the frozen leaves were pulverized under liquid nitrogen and a weighed portion was extracted using a solvent system optimized for all three vitamins (methanol and buffered aqueous solution). The extract was then analyzed by UHPLC (LC 2030C-3D-Plus, Shimadzu Corporation, Japan) with a PDA detector for vitamin A and fluorescence detector for vitamins E and K. Separation was achieved using a suitable chromatographic column and optimized mobile phase conditions as outlined in the AOAC method.</p>
<p>For estimation of Vitamin C (Ascorbic acid), two grams (2 g) of the sample was dissolved in 50 ml of a 20% metaphosphoric acid-acetic acid mixture. After filtration, 10 mL of the filtrate was titrated with a solution of 2,6-dichlorophenolindophenol (DCPIP) at 0.5 g/L until a persistent pink coloration was observed. The final content of vitamin C was calculated using the following formula:</p>
<disp-formula>
<mml:math display="block" id="M2">
<mml:mrow>
<mml:mtext>Vitamin&#xa0;C&#xa0;</mml:mtext>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mtext>mg</mml:mtext>
<mml:mo stretchy="false">/</mml:mo>
<mml:mn>100</mml:mn>
<mml:mtext>g</mml:mtext>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msub>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mi>D</mml:mi>
<mml:mi>C</mml:mi>
<mml:mi>P</mml:mi>
<mml:mi>I</mml:mi>
<mml:mi>P</mml:mi>
</mml:mrow>
</mml:msub>
<mml:mo>&#xd7;</mml:mo>
<mml:mi>V</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>q</mml:mi>
<mml:mo>&#xd7;</mml:mo>
<mml:mn>5</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:mn>100</mml:mn>
</mml:mrow>
<mml:mi>w</mml:mi>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<p>Where:</p>
<p>Vitamin C: Vitamin C content (mg/100 g),</p>
<p>&#x2201;<sub>DCPIP</sub>&#x200b;: Concentration of DCPIP solution (0.5 g/L),</p>
<p>V<sub>eq</sub>&#x200b;: Volume (mL) of DCPIP used for titration,</p>
<p>W: Weight (g) of <italic>A. tuberosum</italic> sample.</p>
</sec>
<sec id="s2_7">
<label>2.7</label>
<title>Estimation of other biochemical &amp; physiochemical properties and compounds</title>
<p>The major Sulphur active compounds viz. Allicin, AMThs (Allyl methyl thiosulphinate) and ATPThs (Allyl trans-1-propenyl thiosulphinate) were estimated by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) (<xref ref-type="bibr" rid="B76">Thangasamy et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B68">Singh et&#xa0;al., 2020</xref>). commercially available certified reference standards alliin (S-alliin-L-cysteine sulphoxide) having purity &gt;90% and MS grade solvents were used. Alliin stock solution was prepared at a specific concentration and stored at -20&#xb0;C. Allicin standard solution was generated from the alliin stock using a commercially available or purified alliinase enzyme, following a conversion process with centrifugation and storage at -80&#xb0;C. Approximately 100 g of leaf sample was chopped, and crushed and a specific weight was extracted with water following a modified version of the method by <xref ref-type="bibr" rid="B40">Khar et&#xa0;al. (2011)</xref>. The final extract was filtered and injected into the LC-MS/MS system (API 5500 Q-trap LC-MS/MS, AB Sciex, Canada) for analysis with preoptimized instrument parameters (<xref ref-type="bibr" rid="B68">Singh et&#xa0;al., 2020</xref>). The analytical method was validated according to a specific guideline, assessing linearity, limit of detection, limit of quantification, and precision.</p>
<p>The total soluble solids were measured using a refractometer as per the AOAC 932.12.2023 and the results were reported in &#xb0;B. The pH was estimated using a pH meter at 10% solution as per ISO 11289:1993. Titrable acidity was measured by titration with 0.1N NaOH using phenolphthalein indicator as per Appendix A of IS 13242:2018.</p>
</sec>
<sec id="s2_8">
<label>2.8</label>
<title>Experimental design and statistical analysis</title>
<p>The study employed a factorial randomized block design to evaluate the effects of age windows (eight windows each of three months&#x2019; unit) and <italic>A. tuberosum</italic> accessions (three) on production performance. Descriptive statistics were used to summarize the collected data (e.g., mean, standard deviation). A two-factor analysis of variance (ANOVA) was then conducted to determine the statistical significance of the main effects (age windows and accessions) and their interaction effect on the measured parameters. This analysis helps identify if there are significant differences in the responses (e.g., plant height, yield) between the different age windows and accessions, and if these differences depend on each other. The nutritional and biochemical profile of the superior <italic>Allium tuberosum</italic> line, i.e. <italic>A. tuberosum</italic> CGN Kaz was represented with a simple mean value along with standard error.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<p>The performance of underutilized alliums was studied over two years, analyzing production data to assess the effects of age, season, and inherent characteristics on growth, yield, and quality.</p>
<sec id="s3_1">
<label>3.1</label>
<title>Crop growth, productivity and produce quality</title>
<sec id="s3_1_1">
<label>3.1.1</label>
<title>Morphology</title>
<p>The morphological traits of <italic>Allium tuberosum</italic> accessions exhibited significant differences across various age windows, particularly in plant height, tiller number, crop spread, leaf width, and stem length (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>). The plant height ranged from 21.53 cm to 34.61 cm, depending on the accession and age window, with AT1 showing the greatest height at 270 DAT (34.61 cm). Seasonal factors had a more profound impact on plant height than the age window alone, as indicated by higher values recorded during periods of bright sunshine hours i.e. April to June (age window 90 DAT to 180 DAT and 450 DAT to 540 DAT). The tiller counts also increased significantly with age, starting from approximately 2 tillers at 90 DAT and reaching over 43 tillers per plant at 720 DAT, with AT1 consistently producing the highest number of tillers. In terms of crop spread, AT1 exhibited the widest spread of 18.52 cm at 720 DAT, followed by AT3 and AT2.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Growth and morphology of <italic>A. tuberosum</italic> accessions at Western Ghat condition.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Age window</th>
<th valign="top" align="left">090DAT</th>
<th valign="top" align="left">180DAT</th>
<th valign="top" align="left">270DAT</th>
<th valign="top" align="left">360DAT</th>
<th valign="top" align="left">450DAT</th>
<th valign="top" align="left">540DAT</th>
<th valign="top" align="left">630DAT</th>
<th valign="top" align="left">720DAT</th>
<th valign="top" align="left">Mean</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" colspan="10" align="center">Plant height</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">22.03 (1.04)</td>
<td valign="top" align="center">30.85 (1.52)</td>
<td valign="top" align="center">34.61 (2.10)</td>
<td valign="top" align="center">33.01 (0.83)</td>
<td valign="top" align="center">27.79 (1.45)</td>
<td valign="top" align="center">30.49 (1.45)</td>
<td valign="top" align="center">32.90 (1.11)</td>
<td valign="top" align="center">22.90 (1.16)</td>
<td valign="top" align="center">29.32a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">22.24 (1.14)</td>
<td valign="top" align="center">29.99 (2.23)</td>
<td valign="top" align="center">31.95 (0.62)</td>
<td valign="top" align="center">31.74 (1.28)</td>
<td valign="top" align="center">26.83 (1.14)</td>
<td valign="top" align="center">29.90 (1.67)</td>
<td valign="top" align="center">32.01 (1.01)</td>
<td valign="top" align="center">22.85 (0.62)</td>
<td valign="top" align="center">28.44b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">21.53 (0.35)</td>
<td valign="top" align="center">30.38 (0.45)</td>
<td valign="top" align="center">32.80 (2.07)</td>
<td valign="top" align="center">32.49 (0.85)</td>
<td valign="top" align="center">25.73 (2.97)</td>
<td valign="top" align="center">27.98 (3.68)</td>
<td valign="top" align="center">31.48 (2.10)</td>
<td valign="top" align="center">24.21 (2.21)</td>
<td valign="top" align="center">28.32b</td>
</tr>
<tr>
<td valign="top" align="center">Mean</td>
<td valign="top" align="center">21.93a</td>
<td valign="top" align="center">33.12a</td>
<td valign="top" align="center">32.41a</td>
<td valign="top" align="center">32.13b</td>
<td valign="top" align="center">30.41b</td>
<td valign="top" align="center">29.45c</td>
<td valign="top" align="center">26.783d</td>
<td valign="top" align="center">23.32e</td>
<td valign="top" align="left"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety &#xd7; Age window</td>
<td valign="top" align="left" rowspan="4"/>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.2176</td>
<td valign="top" align="center">0.3553</td>
<td valign="top" colspan="6" align="center">0.6154</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.6112</td>
<td valign="top" align="center">0.9979</td>
<td valign="top" colspan="6" align="center">1.7285</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">4.7955</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">No. of tillers</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">2.622 (0.101)</td>
<td valign="top" align="center">6.17 (0.355)</td>
<td valign="top" align="center">11.76(0.455)</td>
<td valign="top" align="center">25.84(0.996)</td>
<td valign="top" align="center">27.762 (1.072)</td>
<td valign="top" align="center">32.874 (1.25)</td>
<td valign="top" align="center">36.712 (1.37)</td>
<td valign="top" align="center">43.564 (1.675)</td>
<td valign="top" align="center">23.414a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">2.366 (0.099)</td>
<td valign="top" align="center">5.944 (0.245)</td>
<td valign="top" align="center">11.27(0.452)</td>
<td valign="top" align="center">25.18 (0.955)</td>
<td valign="top" align="center">27.16 (0.838)</td>
<td valign="top" align="center">32.094 (1.221)</td>
<td valign="top" align="center">35.392 (1.359)</td>
<td valign="top" align="center">42.542 (1.635)</td>
<td valign="top" align="center">22.891b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">2.634 (0.096)</td>
<td valign="top" align="center">5.964 (0.213)</td>
<td valign="top" align="center">12.028 (0.448)</td>
<td valign="top" align="center">25.766 (0.978)</td>
<td valign="top" align="center">25.798 (0.991)</td>
<td valign="top" align="center">32.1 (1.232)</td>
<td valign="top" align="center">35.718 (1.364)</td>
<td valign="top" align="center">43.12 (1.643)</td>
<td valign="top" align="center">22.744b</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">2.541h</td>
<td valign="top" align="center">6.026g</td>
<td valign="top" align="center">11.689f</td>
<td valign="top" align="center">25.596e</td>
<td valign="top" align="center">26.907d</td>
<td valign="top" align="center">32.356c</td>
<td valign="top" align="center">35.941b</td>
<td valign="top" align="center">43.075a</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0877</td>
<td valign="top" align="center">0.1432</td>
<td valign="top" colspan="6" align="center">0.248</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.2463</td>
<td valign="top" align="center">0.4022</td>
<td valign="top" colspan="6" align="center">0.697</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">2.4089</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">Crop spread</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">3.75 (0.14)</td>
<td valign="top" align="center">5.52 (0.21)</td>
<td valign="top" align="center">8.98 (0.34)</td>
<td valign="top" align="center">12.79 (0.49)</td>
<td valign="top" align="center">15.49 (0.59)</td>
<td valign="top" align="center">16.7 (0.64)</td>
<td valign="top" align="center">17.55 (0.67)</td>
<td valign="top" align="center">18.52 (0.71)</td>
<td valign="top" align="center">12.41a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">3.14 (0.12)</td>
<td valign="top" align="center">5.48 (0.21)</td>
<td valign="top" align="center">8 (0.31)</td>
<td valign="top" align="center">12.47 (0.48)</td>
<td valign="top" align="center">15.11 (0.58)</td>
<td valign="top" align="center">15.78 (0.6)</td>
<td valign="top" align="center">17.17 (0.66)</td>
<td valign="top" align="center">17.86 (0.69)</td>
<td valign="top" align="center">12.19b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">3.63 (0.14)</td>
<td valign="top" align="center">5.92 (0.23)</td>
<td valign="top" align="center">8.53 (0.33)</td>
<td valign="top" align="center">12.55 (0.48)</td>
<td valign="top" align="center">15.31 (0.59)</td>
<td valign="top" align="center">16.35 (0.63)</td>
<td valign="top" align="center">17.29 (0.66)</td>
<td valign="top" align="center">17.97 (0.69)</td>
<td valign="top" align="center">11.88c</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">3.51h</td>
<td valign="top" align="center">5.64g</td>
<td valign="top" align="center">8.5f</td>
<td valign="top" align="center">12.6e</td>
<td valign="top" align="center">15.3d</td>
<td valign="top" align="center">16.28c</td>
<td valign="top" align="center">17.34b</td>
<td valign="top" align="center">18.12a</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0325</td>
<td valign="top" align="center">0.0531</td>
<td valign="top" colspan="6" align="center">0.092</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.0913</td>
<td valign="top" align="center">0.1491</td>
<td valign="top" colspan="6" align="center">0.258</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">1.6914</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">No of leaves</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">4.11 (0.16)</td>
<td valign="top" align="center">5.28 (0.21)</td>
<td valign="top" align="center">4.22 (0.16)</td>
<td valign="top" align="center">4.51 (0.18)</td>
<td valign="top" align="center">4.22 (0.17)</td>
<td valign="top" align="center">5.17 (0.19)</td>
<td valign="top" align="center">4.45 (0.18)</td>
<td valign="top" align="center">4.34 (0.17)</td>
<td valign="top" align="center">4.54a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">4.25 (0.16)</td>
<td valign="top" align="center">5.24 (0.2)</td>
<td valign="top" align="center">4.29 (0.15)</td>
<td valign="top" align="center">3.99 (0.17)</td>
<td valign="top" align="center">3.96 (0.15)</td>
<td valign="top" align="center">5.34 (0.34)</td>
<td valign="top" align="center">4.12 (0.16)</td>
<td valign="top" align="center">3.83 (0.14)</td>
<td valign="top" align="center">4.38ab</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">4.38 (0.19)</td>
<td valign="top" align="center">5.36 (0.21)</td>
<td valign="top" align="center">4.64 (0.19)</td>
<td valign="top" align="center">4.43 (0.18)</td>
<td valign="top" align="center">3.47 (1.28)</td>
<td valign="top" align="center">3.47 (1.28)</td>
<td valign="top" align="center">4.49 (0.28)</td>
<td valign="top" align="center">4.26 (0.16)</td>
<td valign="top" align="center">4.31b</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">4.25c</td>
<td valign="top" align="center">5.3a</td>
<td valign="top" align="center">4.38bc</td>
<td valign="top" align="center">4.31c</td>
<td valign="top" align="center">3.88d</td>
<td valign="top" align="center">4.66b</td>
<td valign="top" align="center">4.35c</td>
<td valign="top" align="center">4.14cd</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0684</td>
<td valign="top" align="center">0.1058</td>
<td valign="top" colspan="6" align="center">0.1833</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.182</td>
<td valign="top" align="center">0.2972</td>
<td valign="top" colspan="6" align="center">0.5148</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">9.2961</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">Leaf width</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">7.61 (0.31)</td>
<td valign="top" align="center">8.37 (0.34)</td>
<td valign="top" align="center">6.39 (0.26)</td>
<td valign="top" align="center">6.64 (0.27)</td>
<td valign="top" align="center">8.3 (0.33)</td>
<td valign="top" align="center">8.49 (0.35)</td>
<td valign="top" align="center">6.15 (0.24)</td>
<td valign="top" align="center">8.28 (0.31)</td>
<td valign="top" align="center">7.53a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">7.13 (0.29)</td>
<td valign="top" align="center">7.74 (0.31)</td>
<td valign="top" align="center">5.73 (0.25)</td>
<td valign="top" align="center">6.21 (0.24)</td>
<td valign="top" align="center">5.93 (0.23)</td>
<td valign="top" align="center">8.2 (0.33)</td>
<td valign="top" align="center">5.78 (0.22)</td>
<td valign="top" align="center">8.09 (0.32)</td>
<td valign="top" align="center">7.19b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">7.6 (0.3)</td>
<td valign="top" align="center">8.13 (0.32)</td>
<td valign="top" align="center">6.15 (0.24)</td>
<td valign="top" align="center">6.37 (0.23)</td>
<td valign="top" align="center">6.49 (0.25)</td>
<td valign="top" align="center">8.3 (0.32)</td>
<td valign="top" align="center">6.34 (0.32)</td>
<td valign="top" align="center">8.18 (0.31)</td>
<td valign="top" align="center">6.85c</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">7.447d</td>
<td valign="top" align="center">8.08c</td>
<td valign="top" align="center">6.091g</td>
<td valign="top" align="center">6.408f</td>
<td valign="top" align="center">6.905e</td>
<td valign="top" align="center">8.329a</td>
<td valign="top" align="center">6.091g</td>
<td valign="top" align="center">8.183b</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0157</td>
<td valign="top" align="center">0.0257</td>
<td valign="top" colspan="6" align="center">0.0445</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.0441</td>
<td valign="top" align="center">0.0722</td>
<td valign="top" colspan="6" align="center">0.125</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">1.3851</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">Stem length</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">2.3 (0.1)</td>
<td valign="top" align="center">4.39 (0.19)</td>
<td valign="top" align="center">7.33 (0.31)</td>
<td valign="top" align="center">5.58 (0.23)</td>
<td valign="top" align="center">4.67 (0.2)</td>
<td valign="top" align="center">5.31 (0.22)</td>
<td valign="top" align="center">7.81 (0.3)</td>
<td valign="top" align="center">4.74 (0.2)</td>
<td valign="top" align="center">5.27a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">3.37 (0.35)</td>
<td valign="top" align="center">4.18 (0.15)</td>
<td valign="top" align="center">7.18 (0.29)</td>
<td valign="top" align="center">4.74 (0.2)</td>
<td valign="top" align="center">4.22 (0.18)</td>
<td valign="top" align="center">4.52 (0.17)</td>
<td valign="top" align="center">7.78 (0.35)</td>
<td valign="top" align="center">4.51 (0.26)</td>
<td valign="top" align="center">5.1b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">2.9 (0.13)</td>
<td valign="top" align="center">4.14 (0.17)</td>
<td valign="top" align="center">6.43 (0.25)</td>
<td valign="top" align="center">5.4 (0.23)</td>
<td valign="top" align="center">4.34 (0.18)</td>
<td valign="top" align="center">5.13 (0.2)</td>
<td valign="top" align="center">7.9 (0.73)</td>
<td valign="top" align="center">4.56 (0.2)</td>
<td valign="top" align="center">5.06b</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">2.857h</td>
<td valign="top" align="center">4.237g</td>
<td valign="top" align="center">6.983b</td>
<td valign="top" align="center">5.241c</td>
<td valign="top" align="center">4.411f</td>
<td valign="top" align="center">4.988d</td>
<td valign="top" align="center">7.831a</td>
<td valign="top" align="center">4.601e</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0271</td>
<td valign="top" align="center">0.0443</td>
<td valign="top" colspan="6" align="center">0.0767</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.0761</td>
<td valign="top" align="center">0.1244</td>
<td valign="top" colspan="6" align="center">0.2154</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">3.3333</td>
<td valign="top" align="left" colspan="7"/>
</tr>
<tr>
<th valign="top" colspan="10" align="center">Stem girth</th>
</tr>
<tr>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">5.38 (0.19)</td>
<td valign="top" align="center">6.03 (0.32)</td>
<td valign="top" align="center">4.51 (0.1)</td>
<td valign="top" align="center">5.64 (0.15)</td>
<td valign="top" align="center">4.28 (0.25)</td>
<td valign="top" align="center">4.68 (0.15)</td>
<td valign="top" align="center">4.35 (0.19)</td>
<td valign="top" align="center">4.56 (0.17)</td>
<td valign="top" align="center">4.93a</td>
</tr>
<tr>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">5.13 (0.2)</td>
<td valign="top" align="center">6.11 (0.23)</td>
<td valign="top" align="center">4.1 (0.17)</td>
<td valign="top" align="center">5.59 (0.21)</td>
<td valign="top" align="center">4.09 (0.17)</td>
<td valign="top" align="center">4.75 (0.25)</td>
<td valign="top" align="center">4.25 (0.17)</td>
<td valign="top" align="center">4.46 (0.17)</td>
<td valign="top" align="center">4.81b</td>
</tr>
<tr>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">5.15 (0.22)</td>
<td valign="top" align="center">5.72 (0.28)</td>
<td valign="top" align="center">4.24 (0.17)</td>
<td valign="top" align="center">5.25 (0.21)</td>
<td valign="top" align="center">4.13 (0.17)</td>
<td valign="top" align="center">4.63 (0.17)</td>
<td valign="top" align="center">4.2 (0.17)</td>
<td valign="top" align="center">4.18 (0.19)</td>
<td valign="top" align="center">4.69c</td>
</tr>
<tr>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">5.22c</td>
<td valign="top" align="center">5.95a</td>
<td valign="top" align="center">4.28f</td>
<td valign="top" align="center">5.49b</td>
<td valign="top" align="center">4.17g</td>
<td valign="top" align="center">4.68d</td>
<td valign="top" align="center">4.27f</td>
<td valign="top" align="center">4.4e</td>
<td valign="top" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">Variety</td>
<td valign="top" align="center">Age window</td>
<td valign="top" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="top" align="center">SEm</td>
<td valign="top" align="center">0.0117</td>
<td valign="top" align="center">0.0191</td>
<td valign="top" colspan="6" align="center">0.033</td>
</tr>
<tr>
<td valign="top" align="center">LSD</td>
<td valign="top" align="center">0.0329</td>
<td valign="top" align="center">0.0536</td>
<td valign="top" colspan="6" align="center">0.0927</td>
</tr>
<tr>
<td valign="top" align="center">CV</td>
<td valign="top" align="center">1.5364</td>
<td valign="top" align="left" colspan="7"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Figures in the parenthesis show the standard deviation. The LSD represents least square values at 5%. The mean values indicated with different letters show significant difference. AT1: <italic>A.tuberosum</italic> All-1587, AT2: <italic>A. tuberosum</italic> CGN-116418 and AT3: <italic>A. tuberosum</italic> CGN-16373.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The variations in leaf width and stem length were similarly pronounced across different age windows. AT1 had the widest leaves at 720 DAT (8.28 cm), while the longest stem length was also observed at 720 DAT, with AT1 and AT3 recording lengths of 7.81 cm and 7.9 cm, respectively. The leaf count showed moderate differences across accessions, with AT1 consistently producing a higher number of leaves, peaking at 5.17 leaves per plant at 540 DAT. These results suggest that both genetic makeup and the age window strongly influenced the morphology of the accessions, with seasonal effects playing an equally important role in shaping plant structure, especially during windows of bright sunshine.</p>
<p>
<xref ref-type="table" rid="T2">
<bold>Tables&#xa0;2</bold>
</xref>, <xref ref-type="table" rid="T3">
<bold>3</bold>
</xref> show that statistical significance was evident for most morphological traits. The least-square difference (LSD) values from the tables confirmed that the differences in plant height, tiller count, crop spread, and other morphological parameters were indeed significant across accessions and age windows, further reinforcing the impact of age and genetic variation on plant structure. This significance emphasizes the necessity of considering both genetic factors and environmental conditions when evaluating the morphology of <italic>A. tuberosum</italic>.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Green foliage yield and related parameters of <italic>A. tuberosum</italic> accessions at Western Ghat condition.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Age window</th>
<th valign="middle" align="left">090DAT</th>
<th valign="middle" align="left">180DAT</th>
<th valign="middle" align="left">270DAT</th>
<th valign="middle" align="left">360DAT</th>
<th valign="middle" align="left">450DAT</th>
<th valign="middle" align="left">540DAT</th>
<th valign="middle" align="left">630DAT</th>
<th valign="middle" align="left">720DAT</th>
<th valign="middle" align="left">mean</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="10" align="center">Green foliage yield</th>
</tr>
<tr>
<td valign="middle" align="center">AT1</td>
<td valign="middle" align="center">4.56 (0.18)</td>
<td valign="middle" align="center">8.98 (0.34)</td>
<td valign="middle" align="center">14.23 (0.55)</td>
<td valign="middle" align="center">18.95 (0.73)</td>
<td valign="middle" align="center">24.17 (0.93)</td>
<td valign="middle" align="center">26.69 (1.02)</td>
<td valign="middle" align="center">28.68 (1.1)</td>
<td valign="middle" align="center">30.75 (1.18)</td>
<td valign="middle" align="center">19.63a</td>
</tr>
<tr>
<td valign="middle" align="center">AT2</td>
<td valign="middle" align="center">3.79 (0.14)</td>
<td valign="middle" align="center">8.53 (0.33)</td>
<td valign="middle" align="center">13.44 (0.52)</td>
<td valign="middle" align="center">18.31 (0.7)</td>
<td valign="middle" align="center">21.89 (0.84)</td>
<td valign="middle" align="center">26.07 (1)</td>
<td valign="middle" align="center">28.13 (1.08)</td>
<td valign="middle" align="center">29.67 (1.14)</td>
<td valign="middle" align="center">18.73b</td>
</tr>
<tr>
<td valign="middle" align="center">AT3</td>
<td valign="middle" align="center">4.13 (0.16)</td>
<td valign="middle" align="center">8.32 (0.32)</td>
<td valign="middle" align="center">14.05 (0.54)</td>
<td valign="middle" align="center">18.17 (0.7)</td>
<td valign="middle" align="center">22.8 (0.88)</td>
<td valign="middle" align="center">25.08 (0.96)</td>
<td valign="middle" align="center">28.02 (1.08)</td>
<td valign="middle" align="center">29.21 (1.12)</td>
<td valign="middle" align="center">18.72b</td>
</tr>
<tr>
<td valign="middle" align="center">Mean</td>
<td valign="middle" align="center">4.16h</td>
<td valign="middle" align="center">8.61g</td>
<td valign="middle" align="center">13.9f</td>
<td valign="middle" align="center">18.48e</td>
<td valign="middle" align="center">22.95d</td>
<td valign="middle" align="center">25.95c</td>
<td valign="middle" align="center">28.28b</td>
<td valign="middle" align="center">29.88a</td>
<td valign="middle" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="center">Variety</td>
<td valign="middle" align="center">Age window</td>
<td valign="middle" colspan="6" align="center">Variety &#xd7;Age window</td>
</tr>
<tr>
<td valign="middle" align="center">SEm</td>
<td valign="middle" align="center">0.0547</td>
<td valign="middle" align="center">0.0893</td>
<td valign="middle" colspan="6" align="center">0.1546</td>
</tr>
<tr>
<td valign="middle" align="center">LSD</td>
<td valign="middle" align="center">0.1536</td>
<td valign="middle" align="center">0.2508</td>
<td valign="middle" colspan="6" align="center">0.4342</td>
</tr>
<tr>
<td valign="middle" align="center">CV</td>
<td valign="middle" align="center">1.8169</td>
<td valign="bottom" align="left"/>
</tr>
<tr>
<th valign="middle" colspan="10" align="center">Waste</th>
</tr>
<tr>
<td valign="middle" align="center">AT1</td>
<td valign="middle" align="center">0.5 (0.02)</td>
<td valign="middle" align="center">1.08 (0.04)</td>
<td valign="middle" align="center">2.71 (0.11)</td>
<td valign="middle" align="center">2.65 (0.1)</td>
<td valign="middle" align="center">2.9 (0.11)</td>
<td valign="middle" align="center">2.67 (0.1)</td>
<td valign="middle" align="center">6.38 (0.25)</td>
<td valign="middle" align="center">5.23 (0.2)</td>
<td valign="middle" align="center">3.01a</td>
</tr>
<tr>
<td valign="middle" align="center">AT2</td>
<td valign="middle" align="center">0.3 (0.01)</td>
<td valign="middle" align="center">1.02 (0.04)</td>
<td valign="middle" align="center">2.55 (0.1)</td>
<td valign="middle" align="center">2.56 (0.1)</td>
<td valign="middle" align="center">2.63 (0.1)</td>
<td valign="middle" align="center">2.61 (0.1)</td>
<td valign="middle" align="center">6.19 (0.23)</td>
<td valign="middle" align="center">5.04 (0.2)</td>
<td valign="middle" align="center">2.88b</td>
</tr>
<tr>
<td valign="middle" align="center">AT3</td>
<td valign="middle" align="center">0.43 (0.02)</td>
<td valign="middle" align="center">1(0.04)</td>
<td valign="middle" align="center">2.67 (0.1)</td>
<td valign="middle" align="center">2.54 (0.1)</td>
<td valign="middle" align="center">2.74 (0.11)</td>
<td valign="middle" align="center">2.51 (0.1)</td>
<td valign="middle" align="center">6.16 (0.24)</td>
<td valign="middle" align="center">4.96 (0.19)</td>
<td valign="middle" align="center">2.86b</td>
</tr>
<tr>
<td valign="middle" align="center">mean</td>
<td valign="middle" align="center">0.41g</td>
<td valign="middle" align="center">1.03f</td>
<td valign="middle" align="center">2.64d</td>
<td valign="middle" align="center">2.59e</td>
<td valign="middle" align="center">2.75c</td>
<td valign="middle" align="center">2.6de</td>
<td valign="middle" align="center">6.25a</td>
<td valign="middle" align="center">5.08b</td>
<td valign="middle" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="center">Variety</td>
<td valign="middle" align="center">Age window</td>
<td valign="middle" colspan="6" align="center">Variety &#xd7; Age window</td>
</tr>
<tr>
<td valign="middle" align="center">SEm</td>
<td valign="middle" align="center">0.0112</td>
<td valign="middle" align="center">0.0182</td>
<td valign="middle" colspan="6" align="center">0.0316</td>
</tr>
<tr>
<td valign="middle" align="center">LSD</td>
<td valign="middle" align="center">0.0315</td>
<td valign="middle" align="center">0.0511</td>
<td valign="middle" colspan="6" align="center">0.0888</td>
</tr>
<tr>
<td valign="middle" align="center">CV (%)</td>
<td valign="middle" align="center">2.42</td>
<td valign="bottom" align="left"/>
</tr>
<tr>
<th valign="middle" colspan="10" align="center">Dry matter</th>
</tr>
<tr>
<td valign="middle" align="center">AT1</td>
<td valign="middle" align="center">11.42 (0.51)</td>
<td valign="middle" align="center">13.01 (0.38)</td>
<td valign="middle" align="center">9.51 (0.28)</td>
<td valign="middle" align="center">10.61 (0.3)</td>
<td valign="middle" align="center">10.76 (0.65)</td>
<td valign="middle" align="center">13.3 (0.85)</td>
<td valign="middle" align="center">10.36 (1.03)</td>
<td valign="middle" align="center">10.44 (0.27)</td>
<td valign="middle" align="center">11.18a</td>
</tr>
<tr>
<td valign="middle" align="center">AT2</td>
<td valign="middle" align="center">10.59 (0.47)</td>
<td valign="middle" align="center">12.06 (0.35)</td>
<td valign="middle" align="center">8.82 (0.25)</td>
<td valign="middle" align="center">9.83 (0.28)</td>
<td valign="middle" align="center">9.98 (0.6)</td>
<td valign="middle" align="center">12.33 (0.79)</td>
<td valign="middle" align="center">9.61 (0.96)</td>
<td valign="middle" align="center">9.68 (0.25)</td>
<td valign="middle" align="center">10.47b</td>
</tr>
<tr>
<td valign="middle" align="center">AT3</td>
<td valign="middle" align="center">10.69 (0.48)</td>
<td valign="middle" align="center">12.17 (0.35)</td>
<td valign="middle" align="center">8.9 (0.26)</td>
<td valign="middle" align="center">9.93 (0.28)</td>
<td valign="middle" align="center">10.07 (0.61)</td>
<td valign="middle" align="center">12.45 (0.79)</td>
<td valign="middle" align="center">9.7 (0.96)</td>
<td valign="middle" align="center">9.88 (0.26)</td>
<td valign="middle" align="center">10.36b</td>
</tr>
<tr>
<td valign="middle" align="center">mean</td>
<td valign="middle" align="center">10.9b</td>
<td valign="middle" align="center">12.41a</td>
<td valign="middle" align="center">9.08d</td>
<td valign="middle" align="center">10.12c</td>
<td valign="middle" align="center">10.27c</td>
<td valign="middle" align="center">12.7a</td>
<td valign="middle" align="center">9.89c</td>
<td valign="middle" align="center">10c</td>
<td valign="middle" align="left" rowspan="5"/>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="center">Variety</td>
<td valign="middle" align="center">Age window</td>
<td valign="middle" colspan="6" align="center">Variety x Age window</td>
</tr>
<tr>
<td valign="middle" align="center">SEm</td>
<td valign="middle" align="center">0.0833</td>
<td valign="middle" align="center">0.136</td>
<td valign="middle" colspan="6" align="center">0.2356</td>
</tr>
<tr>
<td valign="middle" align="center">LSD</td>
<td valign="middle" align="center">0.234</td>
<td valign="middle" align="center">0.382</td>
<td valign="middle" colspan="6" align="center">0.6617</td>
</tr>
<tr>
<td valign="middle" align="center">CV (%)</td>
<td valign="middle" align="center">4.93</td>
<td valign="bottom" align="left"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Figures in the parenthesis show the standard deviation. The LSD represents least square values at 5%. The mean values indicated with different letters show significant difference. AT1: <italic>A.tuberosum</italic> All-1587, AT2: <italic>A. tuberosum</italic> CGN-116418 and AT3: <italic>A. tuberosum</italic> CGN-16373.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Eigenvectors.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Variables</th>
<th valign="middle" align="center">PC1</th>
<th valign="middle" align="center">PC2</th>
<th valign="middle" align="center">PC3</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Plant height</td>
<td valign="middle" align="center">0.122</td>
<td valign="middle" align="center">0.345</td>
<td valign="middle" align="center">0.503</td>
</tr>
<tr>
<td valign="middle" align="center">No of tillers</td>
<td valign="middle" align="center">0.319</td>
<td valign="middle" align="center">-0.371</td>
<td valign="middle" align="center">0.062</td>
</tr>
<tr>
<td valign="middle" align="center">No of leaves</td>
<td valign="middle" align="center">-0.127</td>
<td valign="middle" align="center">0.055</td>
<td valign="middle" align="center">0.599</td>
</tr>
<tr>
<td valign="middle" align="center">Leaf width</td>
<td valign="middle" align="center">-0.167</td>
<td valign="middle" align="center">-0.480</td>
<td valign="middle" align="center">0.212</td>
</tr>
<tr>
<td valign="middle" align="center">Stem length</td>
<td valign="middle" align="center">0.322</td>
<td valign="middle" align="center">0.286</td>
<td valign="middle" align="center">0.260</td>
</tr>
<tr>
<td valign="middle" align="center">Stem girth</td>
<td valign="middle" align="center">-0.292</td>
<td valign="middle" align="center">0.051</td>
<td valign="middle" align="center">0.302</td>
</tr>
<tr>
<td valign="middle" align="center">Crop spread</td>
<td valign="middle" align="center">0.328</td>
<td valign="middle" align="center">-0.347</td>
<td valign="middle" align="center">0.078</td>
</tr>
<tr>
<td valign="middle" align="center">Green foliage</td>
<td valign="middle" align="center">0.336</td>
<td valign="middle" align="center">-0.338</td>
<td valign="middle" align="center">0.104</td>
</tr>
<tr>
<td valign="middle" align="center">Waste</td>
<td valign="middle" align="center">0.382</td>
<td valign="middle" align="center">-0.108</td>
<td valign="middle" align="center">0.074</td>
</tr>
<tr>
<td valign="middle" align="center">Stem to leaf ratio</td>
<td valign="middle" align="center">0.360</td>
<td valign="middle" align="center">0.110</td>
<td valign="middle" align="center">0.038</td>
</tr>
<tr>
<td valign="middle" align="center">Waste to yield ratio</td>
<td valign="middle" align="center">0.327</td>
<td valign="middle" align="center">0.272</td>
<td valign="middle" align="center">0.054</td>
</tr>
<tr>
<td valign="middle" align="center">Dry matter %</td>
<td valign="middle" align="center">-0.218</td>
<td valign="middle" align="center">-0.305</td>
<td valign="middle" align="center">0.392</td>
</tr>
<tr>
<th valign="middle" align="center">Statistics</th>
<th valign="middle" colspan="3" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">Standard deviation</td>
<td valign="middle" align="center">2.471</td>
<td valign="middle" align="center">1.540</td>
<td valign="middle" align="center">1.301</td>
</tr>
<tr>
<td valign="middle" align="center">Proportion of Variance</td>
<td valign="middle" align="center">0.509</td>
<td valign="middle" align="center">0.198</td>
<td valign="middle" align="center">0.141</td>
</tr>
<tr>
<td valign="middle" align="center">Cumulative Proportion</td>
<td valign="middle" align="center">0.509</td>
<td valign="middle" align="center">0.707</td>
<td valign="middle" align="center">0.848</td>
</tr>
<tr>
<td valign="middle" align="center">Eigen Values</td>
<td valign="middle" align="center">6.108</td>
<td valign="middle" align="center">2.371</td>
<td valign="middle" align="center">1.694</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_1_2">
<label>3.1.2</label>
<title>Crop growth</title>
<p>The crop growth patterns over the study period, specifically regarding tillering, crop spread, and dry matter content, showed steady increases with age across all accessions. Tillering, which began with an average of around 2.5 tillers at 90 DAT, grew progressively to over 43 tillers by 720 DAT. AT1 consistently led in tiller production across all age windows, although the other accessions (AT2 and AT3) followed closely behind. Similarly, crop spread increased with age, starting at 3.75 cm at 90 DAT and reaching a maximum of 18.52 cm at 720 DAT for AT1, while AT3 and AT2 showed slightly smaller, yet significant, values. Dry matter content was influenced by both crop age and seasonal variations. The highest dry matter yields were recorded during the 90&#x2013;180 DAT and 450&#x2013;540 DAT windows, coinciding with periods of bright sunshine and elevated temperatures. AT1 maintained the highest average dry matter content (11.18%), while AT2 and AT3 trailed behind slightly. This seasonal trend demonstrates the strong interplay between environmental conditions and crop physiology, with the brightest sunshine hours contributing to higher dry matter accumulation.</p>
<p>The significant differences between accessions, as shown by the LSD values, underscore the importance of considering both genetic traits and environmental conditions when evaluating the crop growth of <italic>A. tuberosum</italic>. The growth patterns observed suggest that tillering and dry matter content are not only dependent on age but also heavily influenced by external factors such as sunshine hours and temperature, further validating the role of environmental variables in crop development.</p>
</sec>
<sec id="s3_1_3">
<label>3.1.3</label>
<title>Yield and quality of harvest</title>
<p>The quality of the harvest was assessed through a range of parameters, including green foliage yield, waste generated, the waste-to-yield ratio, and the stem-to-leaf ratio. Fresh foliage yield increased significantly with crop age across all accessions, with AT1 producing the highest overall yield. In the 630-720 DAT window, AT1 yielded 30.75 tons/ha, whereas AT2 and AT3 produced slightly lower yields of 29.67 tons/ha and 29.21 tons/ha, respectively. Over the two years, AT1 accumulated a total yield of 157.01 tons/ha, significantly higher than the other accessions, illustrating its superior productivity. However, the higher productivity also led to a proportional increase in waste generation, with AT1 producing 6.38 tons/ha of waste in the 540-630 DAT window, while AT2 and AT3 followed closely behind. The waste-to-yield ratio was highest during the 180-270 and 540-630 DAT windows, coinciding with the monsoon season, which negatively impacted foliage quality.</p>
<p>The stem-to-leaf ratio, which served as a critical measure of harvest quality, indicated that a higher ratio corresponded to poorer quality, as the crop is primarily cultivated for its leaves. Moreover, the results suggest that seasonal variations, particularly during monsoon windows, had a profound effect on yield quality. The stem-to-leaf and waste-to-yield ratios were notably higher during these periods, confirming that environmental conditions played a critical role in determining harvest quality. These findings, combined with the significant LSD values, further validate the hypothesis that age, genetics, and seasonality collectively influence both yield and its quality (<xref ref-type="fig" rid="f2"><bold>Figures 2</bold></xref>, <xref ref-type="fig" rid="f3"><bold>3</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Stem length to plant height ratio across different age windows.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g002.tif"/>
</fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Waste generated to green foliage yield ratio across different age windows.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g003.tif"/>
</fig>
</sec>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>The association of different growth and yield parameters</title>
<p>The correlations between different morphological and yield parameters of <italic>A. tuberosum</italic> as given in <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref> reveal intriguing results. The green foliage yield was found positively correlated with the tillering. As these tillers are added with the age of the plant, it widens the crop spread which is supported by a highly positive correlation between crop spread with yield (r = 0.89) and the tillers (r = 0.98). However, this crop spread increment was also associated with inferior quality traits i.e. the waste (r = 0.89) and stem-to-leaf ratio (r = 0.58). This hints at a possible trade-off i.e. increased vegetative growth might come at the cost of generating more stem and inferior quality harvest. This connection is further strengthened by the strong positive correlation between waste and waste-to-yield ratio (waste-waste to yield ratio: r = 0.813, green foliage yield-waste to yield ratio: r = 0.836). Interestingly, the table reveals some counterpoints. A negative correlation of both leaf width and stem girth with the number of tillers (leaf width-tillers: -0.237, stem girth-tillers: r = -0.523) was observed.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Correlations between different yield and morphological parameters of <italic>A. tuberosum</italic>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g004.tif"/>
</fig>
<p>In addition to the aforementioned correlations, other interesting patterns emerged from the correlogram. The leaf length showed a positive yet moderate correlation with green foliage yield (r = 0.55), implying that longer leaves contribute somewhat to the overall yield, though not as significantly as tillering. On the contrary, the stem-to-leaf ratio was negatively correlated with yield quality parameters like leaf width (r = -0.42), indicating that a higher stem-to-leaf ratio might reduce leaf quality, potentially due to a thicker, less desirable stem structure. Moreover, the waste-to-yield ratio was inversely related to both leaf length (r = -0.31) and leaf width (r = -0.36), reinforcing that plants with thinner or shorter leaves might generate more waste relative to the total yield, pointing to an indirect relationship between morphological attributes and overall yield efficiency. These additional correlations highlight the complexity of the trade-offs between vegetative growth and yield quality in <italic>Allium tuberosum</italic>.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>The principal component analysis</title>
<p>
<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref> shows that the flat portion of the curve begins at component 4, indicating we should use the first three principal components. The first three principal components have eigenvalues greater than 1 (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). The eigen analysis also indicated that PC1, PC2 and PC3 accounted for 50.9%, 19.8% and 14.1% of the variance, respectively. These principal components accounted for 84.8% of the total variability.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Scree plot component vs Eigenvalues.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g005.tif"/>
</fig>
<p>The coefficient of the principal component (Eigenvectors) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>) revealed that the first principal component had large positive associations with waste, stem-to-leaf ratio, green foliage yield, crop spread, waste-to-yield ratio, stem length and the number of tillers, so this component primarily measures continuing stability at 630DAT. The second component had large negative associations with dry matter %, green foliage, crop spread, number of tillers and leaf width, so this component primarily measures yield at 720DAT. The third component had positive associations with the number of leaves, stem girth, plant height, and dry matter %, so this component mostly measures steady variability at 90DAT and 180DAT. The principal component analysis - biplot (PC1 vs PC2) visually shown in <xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Principal component analysis- Biplot PC1 vs PC2 based on the 12 characters studied.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-15-1480510-g006.tif"/>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>The Nutritional and biochemical profile of <italic>A.tuberosum</italic> All-1587</title>
<sec id="s3_4_1">
<label>3.4.1</label>
<title>The proximate analysis &amp; mineral composition</title>
<p>The proximate analysis of <italic>A.tuberosum</italic> All-1587 (AT1) revealed a high moisture content of 88.94% in the fresh leaf cuttings (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). This translates to a low caloric density of 36.13 Kcal per 100g. Interestingly, this moisture content is slightly higher than reported by <xref ref-type="bibr" rid="B55">Pooja (2021)</xref>, suggesting latent variations based on cultivation practices. Rest, the ash content of 1.47 g per 100g, with a minimal acid-insoluble fraction (0.04 g), indicates the presence of dietary minerals. The low-fat content (0.85 g per 100g) and the lower cholesterol (&lt;0.50 g per 100g) of AT1 are promising, especially considering the commendable ratio of unsaturated to saturated fats. The presence of a notable amount of dietary fiber (2.56 g per 100g) and protein (3.10 g per 100g) in AT1 is important. Both, the protein and dietary fiber content are quite higher than the A. cepa bulbs (1.1 g and 1.7g per 100g respectively) (<xref ref-type="bibr" rid="B12">Bhattacharjee et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B43">Mahmood et&#xa0;al., 2021</xref>).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Nutritional and biochemical profile of <italic>A. tuberosum</italic> Kazakhsthan CGN-1587.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Serial No.</th>
<th valign="middle" align="left">Particular</th>
<th valign="middle" align="left">Estimation</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="3" align="center">Proximate analysis</th>
</tr>
<tr>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">Moisture</td>
<td valign="middle" align="center">88.94 &#xb1; 7.55 Kcal/100g</td>
</tr>
<tr>
<td valign="middle" align="center">2</td>
<td valign="middle" align="center">Ash Content</td>
<td valign="middle" align="center">1.47 &#xb1; 0.12 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">3</td>
<td valign="middle" align="center">Acid insoluble ash</td>
<td valign="middle" align="center">0.04 &#xb1; 0.0028 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">4</td>
<td valign="middle" align="center">% Organic Matter</td>
<td valign="middle" align="center">9.59 &#xb1; 0.81 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">5</td>
<td valign="middle" align="center">Total Fat</td>
<td valign="middle" align="center">0.85 &#xb1; 0.07 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">6</td>
<td valign="middle" align="center">Saturated Fat</td>
<td valign="middle" align="center">0.14 &#xb1; 0.01 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">7</td>
<td valign="middle" align="center">Unsaturated Fat</td>
<td valign="middle" align="center">0.71 &#xb1; 0.06 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">8</td>
<td valign="middle" align="center">Cholesterol</td>
<td valign="middle" align="center">&lt;0.5 &#xb1; g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">9</td>
<td valign="middle" align="center">Protein</td>
<td valign="middle" align="center">3.1 &#xb1; 0.26 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">10</td>
<td valign="middle" align="center">Total Carbohydrates</td>
<td valign="middle" align="center">5.3 &#xb1; 0.45 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">Dietary Fiber</td>
<td valign="middle" align="center">2.56 &#xb1; 0.22 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">12</td>
<td valign="middle" align="center">Total sugar</td>
<td valign="middle" align="center">2.15 &#xb1; 0.18 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">13</td>
<td valign="middle" align="center">Energy</td>
<td valign="middle" align="center">36.13 &#xb1; 3.07 Kcal/100g</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="center">Mineral composition</th>
</tr>
<tr>
<td valign="middle" align="center">14</td>
<td valign="middle" align="center">N</td>
<td valign="middle" align="center">5608.32 &#xb1; 475.88 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">P</td>
<td valign="middle" align="center">691.68 &#xb1; 58.69 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">K</td>
<td valign="middle" align="center">5355.3 &#xb1; 454.41 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">S</td>
<td valign="middle" align="center">2484.97 &#xb1; mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">Mg</td>
<td valign="middle" align="center">569.39 &#xb1; 48.31 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">Mn</td>
<td valign="middle" align="center">5.62 &#xb1; 0.48 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">Si</td>
<td valign="middle" align="center">19.98 &#xb1; 1.7 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">Se</td>
<td valign="middle" align="center">42.17 &#xb1; 5.18 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">Zn</td>
<td valign="middle" align="center">53.42 &#xb1; 4.53 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">Cu</td>
<td valign="middle" align="center">16.2 &#xb1; 1.37 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">Fe</td>
<td valign="middle" align="center">397.46 &#xb1; 33.73 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">Ca</td>
<td valign="middle" align="center">1126.99 &#xb1; 95.63 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">26</td>
<td valign="middle" align="center">Na</td>
<td valign="middle" align="center">14.72 &#xb1; 1.25 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">B</td>
<td valign="middle" align="center">1.93 &#xb1; 0.16 mg/kg</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="center">Vitamin profile</th>
</tr>
<tr>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">A (Retinol)</td>
<td valign="middle" align="center">3.15 &#xb1; 0.27 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">29</td>
<td valign="middle" align="center">B1 (Thiamine)</td>
<td valign="middle" align="center">0.309 &#xb1; 0.03 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">B2 (Riboflavin)</td>
<td valign="middle" align="center">5.27 &#xb1; 0.45 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">31</td>
<td valign="middle" align="center">B3 (Niacin)</td>
<td valign="middle" align="center">4.87 &#xb1; 0.41 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">32</td>
<td valign="middle" align="center">B4 (Adenine)</td>
<td valign="middle" align="center">264.45 &#xb1; 22.44 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">33</td>
<td valign="middle" align="center">B5 (Pantothenic Acid)</td>
<td valign="middle" align="center">0.91 &#xb1; 0.08 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">34</td>
<td valign="middle" align="center">B6 (Pyridoxine)</td>
<td valign="middle" align="center">0.053 &#xb1; 0.0045 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">35</td>
<td valign="middle" align="center">B7 (Biotin)</td>
<td valign="middle" align="center">&lt;0.2 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">36</td>
<td valign="middle" align="center">B9 (Folates)</td>
<td valign="middle" align="center">&lt;0.2 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">37</td>
<td valign="middle" align="center">C (Ascorbic acid)</td>
<td valign="middle" align="center">18.12 &#xb1; 3.35 mg/100g</td>
</tr>
<tr>
<td valign="middle" align="center">38</td>
<td valign="middle" align="center">E (Tocopherol)</td>
<td valign="middle" align="center">5.38 &#xb1; 0.46 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">39</td>
<td valign="middle" align="center">K (Phytonadione)</td>
<td valign="middle" align="center">85 &#xb1; 7.21 mcg/kg</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="center">Biochemical properties</th>
</tr>
<tr>
<td valign="middle" align="center">40</td>
<td valign="middle" align="center">pH (10% solution)</td>
<td valign="middle" align="center">6.7 &#xb1; 0.57</td>
</tr>
<tr>
<td valign="middle" align="center">41</td>
<td valign="middle" align="center">Titrable acidity</td>
<td valign="middle" align="center">0.36 &#xb1; 0.03 g/100g</td>
</tr>
<tr>
<td valign="middle" align="center">42</td>
<td valign="middle" align="center">TSS</td>
<td valign="middle" align="center">7.93 &#xb1; 0.67&#xb0;Brix</td>
</tr>
<tr>
<td valign="middle" align="center">43</td>
<td valign="middle" align="center">Total phenolic compounds</td>
<td valign="middle" align="center">1.187 &#xb1; 0.1 mg GAE/g</td>
</tr>
<tr>
<td valign="middle" align="center">44</td>
<td valign="middle" align="center">Reducing Sugar</td>
<td valign="middle" align="center">1.63 &#xb1; 0.14 mg/100g</td>
</tr>
<tr>
<td valign="middle" align="center">45</td>
<td valign="middle" align="center">Non-reducing Sugar</td>
<td valign="middle" align="center">0.49 &#xb1; 0.04 mg/100g</td>
</tr>
<tr>
<td valign="middle" align="center">46</td>
<td valign="middle" align="center">Antioxidant Activity</td>
<td valign="middle" align="center">64.45 &#xb1; 5.47% RSA DPPH</td>
</tr>
<tr>
<td valign="middle" align="center">47</td>
<td valign="middle" align="center">Allicin</td>
<td valign="middle" align="center">18.16 &#xb1; 1.58 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">48</td>
<td valign="middle" align="center">AMThs (Allyl methyl thiosulphinate)</td>
<td valign="middle" align="center">301.55 &#xb1; 25.59 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">49</td>
<td valign="middle" align="center">ATPThs (Allyl trans-1-propenyl thiosulphinate)</td>
<td valign="middle" align="center">4.41 &#xb1; 0.37 mg/kg</td>
</tr>
<tr>
<td valign="middle" align="center">50</td>
<td valign="middle" align="center">Flavonoid (mg/g)</td>
<td valign="middle" align="center">3.19 &#xb1; 0.27 mg/g</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>*Values are given as &#xb1; standard error from the mean.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The analysis of AT1 reveals a rich mineral profile dominated by essential macro-elements. Potassium (5355.30 mg/kg), followed closely by phosphorous (691.68 mg/kg), calcium (1126.99 mg/kg) and magnesium (569.39 mg/kg). In the macro-elements, a noteworthy aspect is the substantial amount of Sulphur (2484.97 &#xb1; 104 mg/kg) present in AT1. This aligns with the characteristic pungent aroma of Alliums, attributed to various volatile Sulphur compounds (<xref ref-type="bibr" rid="B1">Abe et&#xa0;al., 2020</xref>). Similarly, as the assimilation of selenium is carried out through the metabolic absorption route of sulfur (<xref ref-type="bibr" rid="B8">Barak and Goldman, 1997</xref>) the concentration of Se was found 42.17 mg/kg which is in line with the previous findings (<xref ref-type="bibr" rid="B54">Pilon-Smits et&#xa0;al., 2009</xref>). The presence of essential micronutrients like manganese (5.62 mg/kg), zinc (5.34 mg/kg), copper (1.62 mg/kg), and iron (39.74 mg/kg) further strengthen the nutritional value of AT. Additionally, the low sodium content (14.72 mg/kg) was also reported. The B, Cu and Zn content was comparatively lower in concentration than the <italic>A. cepa</italic> leaves as reported by <xref ref-type="bibr" rid="B25">Ferreira et&#xa0;al. (2022)</xref>.</p>
</sec>
<sec id="s3_4_2">
<label>3.4.2</label>
<title>The vitamin profile</title>
<p>The vitamin profile of <italic>A tuberosum</italic> (AT1) reveals a captivating interplay of water-soluble and fat-soluble vitamins, impacting human health (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). AT stands out for its particularly high content of vitamin B4, also known as choline (264.45 mg/kg) which is approximately four times more than in <italic>A. cepa</italic> bulb as reported by <xref ref-type="bibr" rid="B52">Pareek et&#xa0;al. (2017)</xref>. While the content of these B vitamins is not exceptionally high, their combined presence contributes to the overall nutritional value of AT1. Interestingly, pyridoxine (vitamin B6) is present in very low quantities (0.053 &#xb1; 0.0045 mg/kg), suggesting that AT may not be a significant dietary source for this particular vitamin. The presence of vitamin C (18.12 mg/100g) highlights AT1&#x2019;s contribution to immune function and antioxidant activity (<xref ref-type="bibr" rid="B21">Carr and Maggini, 2017</xref>). This vitamin C quantity is quite less than previous reports where <xref ref-type="bibr" rid="B81">&#x17b;urawik and Jadczak (2008)</xref> reported 43.0 - 56.1 mg/100g of L-ascorbic acid in the leaves <italic>A. tuberosum</italic>. The presence of vitamin E (5.38 mg/100g) further strengthens the antioxidant properties of AT1 (<xref ref-type="bibr" rid="B78">Traber and Atkinson, 2007</xref>). Vitamin E helps to protect cells from free radical damage, which has been linked to various chronic diseases (<xref ref-type="bibr" rid="B78">Traber and Atkinson, 2007</xref>). While vitamin K content is relatively low (85.00 mcg/100g), it can contribute modestly to blood clotting and bone health (<xref ref-type="bibr" rid="B66">Shearer and Newman, 2008</xref>). The limited detection of folates (vitamin B9) and biotin (vitamin B7) suggests that AT1 may not be a primary source of these essential nutrients.</p>
</sec>
<sec id="s3_4_3">
<label>3.4.3</label>
<title>The biochemical properties</title>
<p>The reported biochemical properties of AT1 shed light on some interesting aspects that can influence its sensory characteristics, preservation, and culinary applications (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). A slightly acidic pH of 6.7 in a 10% AT solution indicated its weakly acidic nature (<xref ref-type="bibr" rid="B61">S&#xe1;nchez-Rodr&#xed;guez et&#xa0;al., 2019</xref>). Further investigation revealed the presence of organic acids (0.36 g/100g) within AT1, evident from the titrable acidity value. The total soluble solids (TSS) measured at 7.93&#xb0;Brix signifies the concentration of soluble solids present in AT1 which is lower than some previous reports i.e. 8.6&#xb0;Brix as reported by <xref ref-type="bibr" rid="B55">Pooja (2021)</xref>. This TSS content is at par with that of Welsh onion leaves (7.51 &#xb1; 0.31&#xb0;Brix) and lower than A. cepa leaves (5.87 &#xb1; 0.18) as reported by Masahiro <xref ref-type="bibr" rid="B80">Yuasa et&#xa0;al. (2022)</xref>.</p>
<p>The biochemical profile of <italic>A. tuberosum</italic>, revealed a promising composition with various health-promoting compounds (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). The presence of a significant amount of total phenolics (1.187 mg GAE/g fresh weight basis) suggests strong antioxidant property (<xref ref-type="bibr" rid="B13">Bindon et&#xa0;al., 2010</xref>). This is further supported by the high free radical scavenging activity (64.45% RSA DPPH). These findings align with observations in other <italic>Allium</italic> species for their antioxidant properties (<xref ref-type="bibr" rid="B58">Rahman, 2007</xref>). Interestingly, <italic>A. tuberosum</italic> displayed a unique profile of organosulfur compounds. While allicin, the characteristic pungent compound of garlic (<italic>A. sativum</italic>), was present at relatively low levels (16.60 mg kg<sup>-1</sup>), the dominant organosulfur compound was allyl methyl thiosulfinate (AMThs) at a considerably higher concentration (269.00 mg kg<sup>-1</sup>). This suggests <italic>A. tuberosum</italic> might possess a distinct flavor and potentially different biological activities compared to garlic (<xref ref-type="bibr" rid="B42">Lanzotti, 2006</xref>). The presence of flavonoids (3.19 mg/g) and ascorbic acid (18.12 mg/g) adds further value to the nutritional profile of AT1.</p>
</sec>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>The two-year production study highlight the suitability of <italic>A. tuberosum</italic> cultivation at Western Ghats as the yield scenarios of the green leaf cuttings were comparable with the traditional growing belt. The <italic>A. tuberosum</italic> yielded 157.01 tons/ha in two years by monthly cuttings, which was three times higher than the reported by <xref ref-type="bibr" rid="B82">Wang et&#xa0;al. (2020)</xref>. Where the <italic>A. tuberosum</italic> chives production in main growing regions (i.e China, Uzbekisthan, Kazakisthan, Eastern Europe and Korea) is limited by the winter low temperatures, the Western ghat conditions provide a year-round growth season with monthly cutting intervals. More importantly, the leaf purpose market of <italic>Allium</italic> species is majorly dominated by <italic>A. cepa</italic> cultivars. It faces a seasonal slag due to monsoon and high summer heat months. Comparably the yield variations in case of <italic>A. tuberosum</italic> are less and can be projected as alternative to later as also supported by consumer studies by <xref ref-type="bibr" rid="B86">Kale et&#xa0;al. (2024b)</xref>. The yield related studies carried out for <italic>A. tuberosum</italic> (<xref ref-type="bibr" rid="B59">Rathore et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B55">Pooja 2021</xref>) were mostly short durational, thus this study is the first insight at Indian planes for the comparable multi-year production.</p>
<p>The temporal and varietal variations in <italic>A. tuberosum</italic> growth dynamics present compelling insights into optimizing cultivation strategies, especially when considering the intricate interaction between environmental factors and plant morphology. Seasonal fluctuations, particularly the heightened sensitivity of AT1 to photoperiod and temperature, reaffirm the species&#x2019; dependence on these external variables for growth regulation (<xref ref-type="bibr" rid="B59">Rathore et&#xa0;al., 2020</xref>). Notably, the monsoon season&#x2019;s influence, leading to an exaggerated stem elongation, supports the hypothesis of light-seeking behavior in low-sunlight conditions (<xref ref-type="bibr" rid="B27">Golan et&#xa0;al., 2024</xref>). However, this adaptive elongation, while advantageous for light capture, results in a proportional reduction in stem girth and leaf width. These findings invite deeper exploration into the physiological trade-offs plants make to cope with fluctuating environmental constraints. The distinctive patterns observed in foliage yield, especially the progressive increase in tillering, reveal a double-edged sword. Although an increase in tillers is correlated with higher yields, it simultaneously escalates waste production, a phenomenon corroborated by <xref ref-type="bibr" rid="B81">&#x17b;urawik and Jadczak (2008)</xref> and <xref ref-type="bibr" rid="B79">Uddin et&#xa0;al. (2019)</xref>. This apparent yield-waste paradox challenges conventional notions of productivity, suggesting that beyond a certain threshold, the drive for higher foliage production may compromise overall harvest quality. Producers must thus navigate this trade-off, optimizing tillering while controlling waste accumulation to balance economic returns with quality.</p>
<p>A critical reflection on the crop&#x2019;s morphological adjustments under different seasonal windows reveals fascinating behavioral responses. For instance, the reduction in leaf width during the monsoon, despite lower light availability, contradicts the general expectation of increased leaf area in low-light environments. This anomaly suggests a possible compensatory mechanism where the plant may prioritize structural integrity over expansive leaf growth to maintain balance, a notion in line with <xref ref-type="bibr" rid="B15">Brand (1997)</xref> and <xref ref-type="bibr" rid="B69">Sladek et&#xa0;al. (2009)</xref> but contrast with the findings of <xref ref-type="bibr" rid="B44">Marenco and Reis (1998)</xref> and <xref ref-type="bibr" rid="B69">Sladek et&#xa0;al. (2009)</xref>. This observation invites further investigation into the physiological underpinnings of such adaptive strategies, particularly in light-limited environments.</p>
<p>The results of this study establish A. tuberosum (AT1) as a nutritionally rich crop, exhibiting significant concentrations of potassium, phosphorus, and sulphur, which are vital for cardiovascular health and metabolic regulation (<xref ref-type="bibr" rid="B65">Schwalfenberg and Genuis, 2017</xref>; <xref ref-type="bibr" rid="B35">Humphries and Dart, 2015</xref>). The high sulfur content enhances its culinary appeal and suggests therapeutic applications (<xref ref-type="bibr" rid="B74">Tang et&#xa0;al., 2017</xref>). Furthermore, the presence of organosulfur compounds, particularly allyl propyl disulfide, comparable with <xref ref-type="bibr" rid="B68">Singh et&#xa0;al. (2020)</xref> other <italic>Allium</italic> species as reported by <xref ref-type="bibr" rid="B59">Rathore et&#xa0;al. (2020)</xref>. positions AT1 as a possible mitigator of oxidative stress associated with chronic diseases (<xref ref-type="bibr" rid="B58">Rahman, 2007</xref>). The favorable fat profile and low caloric content of AT1 further support its role in dietary management for individuals with metabolic disorders and low calory diets (<xref ref-type="bibr" rid="B18">Campos, 2018</xref>; <xref ref-type="bibr" rid="B77">Thompkinsons et&#xa0;al., 2014</xref>).</p>
<p>Notably, the plant&#x2019;s micronutrient profile includes selenium, zinc, copper, and iron, which are essential for metabolic and immune functions (<xref ref-type="bibr" rid="B85">Elmadfa and Meyer, 2019</xref>). The Selenium&#x2019;s countable presence justifies their common assimilation through the metabolic absorption route of sulphur as reported by <xref ref-type="bibr" rid="B28">Gonz&#xe1;lez-Morales et&#xa0;al. (2017)</xref> and <xref ref-type="bibr" rid="B83">Jiang et&#xa0;al. (2021)</xref>. The vitamins present, particularly B vitamins, vitamin C, and vitamin E bolster AT1&#x2019;s antioxidant and immune-supportive properties (<xref ref-type="bibr" rid="B48">Mehedint and Zeisel, 2013</xref>; <xref ref-type="bibr" rid="B24">Dosed&#x11b;l et&#xa0;al., 2021</xref>). Overall, AT1 emerges as a significant player in dietary strategies aimed at combatting chronic diseases. To optimize the benefits of AT1, further research is necessary to validate them which is crucial for translating its nutritional advantages into effective dietary interventions.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion and implication</title>
<p>This study offers a comprehensive analysis of the agronomic performance, yield potential, quality traits, and nutritional profile of <italic>A. tuberosum</italic> under the specific environmental conditions of the Western Ghats. The <italic>A.tuberosum</italic> All-1587 line demonstrated the highest foliage yield, making it ideal for large-scale cultivation. The outcomes of the study give a deeper understanding of the complex interactions between agronomic traits and overall crop performance. The key implications of the study are as follows:</p>
<list list-type="bullet">
<list-item>
<p>The study guides farmers in tailoring harvesting schedules and spatial arrangements to specific seasonal conditions for a stable and reliable yields.</p>
</list-item>
<list-item>
<p>The <italic>A.tuberosum</italic> All-1587 line&#x2019;s superior yield and quality traits suggest significant potential for vegetable supply chains and the production of value-added products.</p>
</list-item>
<list-item>
<p>High bioactive compound content stages <italic>A.tuberosum</italic> All-1587 as an attractive ingredient for health and wellness products, contributing to nutritional security in regions with prevalent deficiencies.</p>
</list-item>
<list-item>
<p>Resilience to seasonal variability, with minimal yield fluctuations, highlight <italic>A. tuberosum</italic>&#x2019;s high adaptability to diverse environmental conditions in the region.</p>
</list-item>
</list>
<p>Developing guidelines and best practices based on these insights will help farmers adopt optimal cultivation techniques. These findings promotes sustainable and economically viable farming practices, by expanding the cultivation in unconventional zones. The findings provide valuable insights for optimizing and commercializing <italic>A. tuberosum</italic>, benefiting farmers, entrepreneurs, processing industries, and supply chain developers.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>AG: Data curation, Formal analysis, Investigation, Methodology, Software, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. RK: Conceptualization, Project administration, Resources, Supervision, Writing &#x2013; review &amp; editing. YK: Writing &#x2013; review &amp; editing, Conceptualization, Resources. HB: Writing &#x2013; review &amp; editing. SG: Formal analysis, Investigation, Visualization, Writing &#x2013; original draft. SS: Investigation, Writing &#x2013; original draft. AS: Formal analysis, Investigation, Resources, Writing &#x2013; original draft. YG: Visualization, Writing &#x2013; original draft, Formal analysis. KK: Writing &#x2013; review &amp; editing, Visualization, Writing &#x2013; original draft. VM: Funding acquisition, Project administration, Resources, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research and/or publication of this article. This work was financially supported by the ICAR-Directorate of Onion and Garlic Research, Pune, India.</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10">
<title>Correction note</title>
<p>A correction has been made to this article. Details can be found at: <ext-link xlink:href="https://doi.org/10.3389/fpls.2026.1881329" ext-link-type="uri">10.3389/fpls.2026.1881329</ext-link>.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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