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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Anim. Sci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Animal Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Anim. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2673-6225</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fanim.2026.1747486</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Effects of substituting maize with sorghum and soybean meal with canola meal on the growth performance, blood parameters, and organoleptic quality of rabbits according to sex</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Sebola</surname><given-names>N. A.</given-names></name>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1781869/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Formal analysis" vocab-term-identifier="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="methodology" vocab-term-identifier="https://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="validation" vocab-term-identifier="https://credit.niso.org/contributor-roles/validation/">Validation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Project-administration" vocab-term-identifier="https://credit.niso.org/contributor-roles/project-administration/">Project administration</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
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<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Funding acquisition" vocab-term-identifier="https://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="investigation" vocab-term-identifier="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
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</contrib-group>
<aff id="aff1"><institution>Department of Agriculture and Animal Health, College of Agriculture and Environmental Sciences, University of South Africa</institution>, <city>Johannesburg</city>,&#xa0;<country country="za">South Africa</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: N. A. Sebola, <email xlink:href="mailto:sebolan@unisa.ac.za">sebolan@unisa.ac.za</email></corresp>
<fn fn-type="other" id="fn003">
<label>&#x2020;</label>
<p>ORCID: N. A. Sebola, <uri xlink:href="https://orcid.org/0000-0002-1629-2816">orcid.org/0000-0002-1629-2816</uri></p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-16">
<day>16</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>7</volume>
<elocation-id>1747486</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>07</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Sebola.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Sebola</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-16">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<p>Increasing volatility in the cost and supply of maize and soybean meal necessitates the evaluation of locally available alternatives such as sorghum grain and canola meal, yet their combined effects on growth performance, carcass traits, serum biochemistry, and meat quality of grower rabbits, particularly in relation to sex remain inadequately documented. This study evaluated the impact of completely substituting maize with sorghum and soybean meal with canola meal on the growth performance and carcass characteristics of grower rabbits, while also examining whether sex interacts with these dietary changes to influence serum biochemical parameters and organoleptic meat quality. Forty rabbit weaners (20 bucks and 20 does; 6.13 &#xb1; 6.59 g live weight) at one month of age were randomly allocated to two dietary treatments in a two-way factorial arrangement (diet &#xd7; sex) under a completely randomized design. Each rabbit was housed individually in a 0.07 m<sup>2</sup> pen, with five replicates per treatment. Two iso-nitrogenous and iso-energetic diets were formulated: a maize&#x2013;soybean-based commercial grower diet (control) and an experimentally formulated sorghum&#x2013;canola meal (SCM) diet in which sorghum fully replaced maize and canola meal replaced soybean meal. Rabbits fed the SCM diet exhibited significantly higher (<italic>P</italic> &lt; 0.05) body weight gain, improved carcass traits, and superior meat quality compared with those on the control diet. Does gained more weight than bucks (<italic>P</italic> &lt; 0.05), regardless of diet. Rabbits on the SCM diet had higher (<italic>P</italic> &lt; 0.05) plasma sodium concentrations; however, sex had no significant effect on serum biochemical parameters or sensory meat quality, and no diet &#xd7; sex interaction was observed. These findings indicate that complete replacement of maize and soybean meal with sorghum and canola meal can enhance growth performance without adverse effects on physiological status or meat quality, irrespective of sex. Sorghum and canola meal therefore represent viable, sustainable, and cost-effective alternatives for rabbit production systems.</p>
</abstract>
<kwd-group>
<kwd>blood parameters</kwd>
<kwd>canola meal</kwd>
<kwd>grower diet</kwd>
<kwd>growth performance</kwd>
<kwd>organoleptic quality</kwd>
<kwd>rabbit</kwd>
<kwd>sex</kwd>
<kwd>sorghum</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the University of South Africa and Department of Agriculture and Animal Health.</funding-statement>
</funding-group>
<counts>
<fig-count count="0"/>
<table-count count="7"/>
<equation-count count="4"/>
<ref-count count="58"/>
<page-count count="11"/>
<word-count count="5720"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Animal Nutrition</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Rabbit production holds considerable potential to address the nutritional needs of resource-limited communities, thereby supporting sustainable food and nutrition security. In South Africa, the commercialization of rabbit farming gained momentum shortly after World War II, primarily for the production of high-quality meat (<xref ref-type="bibr" rid="B46">Steenekamp, 2023</xref>). Rabbit meat is well recognized for its superior nutritional profile, being rich in high-quality protein yet low in fat and cholesterol, making it an appealing option for health-conscious consumers (<xref ref-type="bibr" rid="B33">Mutsami and Karl, 2020</xref>).</p>
<p>Globally, intensive rabbit production has expanded rapidly in countries such as Italy, China, Korea, Spain, and Egypt, driven by the species&#x2019; favorable biological traits, including rapid growth, early maturity, high fecundity, and the capacity to thrive on grain-free diets (<xref ref-type="bibr" rid="B40">Patricci et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B48">Trocino et&#xa0;al., 2019</xref>). Despite these advantages, the intensification of rabbit production remains constrained by the high cost of feed, particularly energy- and protein-rich ingredients such as maize and soybean meal. These conventional feedstuffs are expensive and highly competitive, as they serve multiple roles in human food systems and industrial applications (<xref ref-type="bibr" rid="B47">Tenenbaum, 2008</xref>). In addition, the large-scale cultivation of maize and soybeans often involves deforestation, intensive irrigation, and heavy use of synthetic fertilizers and agrochemicals, which contribute to environmental degradation and rising production costs. In light of these challenges, it is imperative to identify and evaluate alternative, cost-effective dietary energy and protein sources that do not compete with human consumption or industrial uses. Such alternatives could play a crucial role in reducing feed costs and enhancing the sustainability and profitability of rabbit farming systems. Sorghum (<italic>Sorghum bicolor</italic> Moench L.) and canola meal have emerged as viable alternatives that can either fully or partially replace soybean meal and maize in commercial rabbit diets, respectively.</p>
<p>Sorghum is a drought-tolerant crop that thrives under harsh conditions and possesses a nutrient composition comparable to that of maize, with a slightly higher protein content (<xref ref-type="bibr" rid="B51">Walker, 1999</xref>; <xref ref-type="bibr" rid="B11">Farahat et&#xa0;al., 2020</xref>). Canola meal, a by-product of oil extraction, provides substantial protein levels (36%&#x2013;39%) and an amino acid profile similar to that of soybean meal (<xref ref-type="bibr" rid="B29">Manyeula et&#xa0;al., 2020</xref>), making it suitable for inclusion in monogastric diets. Although both ingredients contain antinutritional factors (ANFs), such as tannins, phytates, and glucosinolates (<xref ref-type="bibr" rid="B54">Wiryawan and Dingle, 1999</xref>; <xref ref-type="bibr" rid="B25">Liu et&#xa0;al., 2015</xref>), advances in plant breeding have significantly reduced their concentrations, thereby improving their feed value. Moreover, the rabbit&#x2019;s gastrointestinal tract is well adapted to high-fiber diets, enhancing its ability to utilize such ingredients efficiently (<xref ref-type="bibr" rid="B14">Gidenne, 2003</xref>).</p>
<p>Previous research has explored the use of sorghum and canola meal in the diets of rabbits, broilers, and quails (<xref ref-type="bibr" rid="B32">Mohammed et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B8">Disetlhe et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B26">Mabelebele et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B28">Manyeula et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B30">Masenya et&#xa0;al., 2021</xref>). However, studies assessing the complete replacement of maize and soybean meal with sorghum and canola meal remain limited. Especially in both rabbit bucks and does, these animals differ in feed utilization. Therefore, testing this diet in both sexes is necessary to guarantee that the outcomes are representative of the total population. It was hypothesized that total replacement of maize and soybean meal with sorghum and canola meal, respectively, would enhance growth performance, serum metabolites, and meat quality in both male and female rabbits. Therefore, the present study aimed to evaluate the effects of substituting maize with sorghum and soybean meal with canola meal in rabbit diets, while examining the influence of sex on growth performance, serum biochemical parameters, carcass characteristics, and organoleptic quality.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Ethical statement, source of ingredients, and study location</title>
<p>This study was conducted during the summer season (25&#xb0;C&#x2013;35&#xb0;C range) at Kaffirstad Farm (Mpumalanga Province, South Africa), situated at 26.34&#xb0;S and 29.18&#xb0;E. Sorghum (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>) was acquired from the Agricultural Research Council (Potchefstroom, South Africa). SBM, corn, molasses, lime, and premix were obtained from Opti Feeds (Lichtenburg, South Africa), while canola meal was purchased from Southern Oil (Pty) Ltd., South Africa.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Ingredients and chemical composition of experimental diets (g/100 g DM).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" colspan="5" align="center">Experimental diets<sup>1</sup></th>
</tr>
<tr>
<th valign="middle" align="left"/>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">Sorghum</th>
<th valign="middle" align="left">Canola meal</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="5" align="left">Ingredients</th>
</tr>
<tr>
<td valign="middle" align="left">Corn</td>
<td valign="middle" align="center">38</td>
<td valign="middle" align="center">0</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">White sorghum</td>
<td valign="middle" align="center">0</td>
<td valign="middle" align="center">37</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Canola meal</td>
<td valign="middle" align="center">0</td>
<td valign="middle" align="center">16.5</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Hay</td>
<td valign="middle" align="center">42</td>
<td valign="middle" align="center">42</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Soybean</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">0</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Molasses</td>
<td valign="middle" align="center">1.5</td>
<td valign="middle" align="center">1.5</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Limestone</td>
<td valign="middle" align="center">1.0</td>
<td valign="middle" align="center">2.5</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Premix</td>
<td valign="middle" align="center">0.5</td>
<td valign="middle" align="center">0.5</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Total</td>
<td valign="middle" align="center">100.0</td>
<td valign="middle" align="center">100.0</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left"><italic>Chemical analysis</italic></td>
<td valign="middle" align="center">0</td>
<td valign="middle" align="center">5</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Moisture</td>
<td valign="middle" align="center">9.91</td>
<td valign="middle" align="center">10.58</td>
<td valign="middle" align="center">13.7</td>
<td valign="middle" align="center">7.0</td>
</tr>
<tr>
<td valign="middle" align="left">Crude protein</td>
<td valign="middle" align="center">13.49</td>
<td valign="middle" align="center">13.28</td>
<td valign="middle" align="center">12.4</td>
<td valign="middle" align="center">34.4</td>
</tr>
<tr>
<td valign="middle" align="left">Ether extract</td>
<td valign="middle" align="center">3.62</td>
<td valign="middle" align="center">5.19</td>
<td valign="middle" align="center">8.1</td>
<td valign="middle" align="center">23.0</td>
</tr>
<tr>
<td valign="middle" align="left">Crude fiber</td>
<td valign="middle" align="center">21.87</td>
<td valign="middle" align="center">21.23</td>
<td valign="middle" align="center">2.9</td>
<td valign="middle" align="center">2.4</td>
</tr>
<tr>
<td valign="middle" align="left">Ash</td>
<td valign="middle" align="center">5.31</td>
<td valign="middle" align="center">5.28</td>
<td valign="middle" align="center">10.4</td>
<td valign="middle" align="center">7.0</td>
</tr>
<tr>
<td valign="middle" align="left">Energy (MJ/kg)</td>
<td valign="middle" align="center">12.1</td>
<td valign="middle" align="center">11.9</td>
<td valign="middle" align="center">16.8</td>
<td valign="middle" align="center">17.6</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p><sup>1</sup>Experimental diets: CON = maize&#x2013;soybean meal diet; SCM = sorghum&#x2013;canola meal diet.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_2">
<title>Experimental diets and chemical analysis</title>
<p>Two iso-nitrogenous and iso-energetic treatment diets were formulated as follows: 1) a maize&#x2013;soybean diet = control diet; and 2) a sorghum&#x2013;canola meal (SCM) diet totally replacing maize with sorghum and soybean with canola meal in rabbits&#x2019; diets (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>), to meet the nutritional requirements recommended by the National Research Council (<xref ref-type="bibr" rid="B38">NRC, 1977</xref>). Experimental diets were milled and analyzed for dry matter, organic matter, and crude protein, as per method nos. 930.15, 924.05, and 984.13, respectively (<xref ref-type="bibr" rid="B3">AOAC, 2005</xref>), while crude fiber (ANKOM Technology, New York, USA) and minerals (calcium, phosphorus, sodium, chloride, and potassium) were analyzed in accordance with the <xref ref-type="bibr" rid="B1">AgriLasa (1998)</xref> guidelines.</p>
</sec>
<sec id="s2_3">
<title>House preparation, feeding trial, and management of rabbits</title>
<p>Thorough cleaning of the house and equipment was done with water and detergent. Thereafter, disinfection was done with F10 (Health and Hygiene (Pty) Ltd., Roodepoort, South Africa). After a 2-week downtime, the house was warmed using an infrared bulb, prior to commencing the experiment. A total of 40 New Zealand white rabbits (20 bucks and 20 does), with an average initial weight of 613 &#xb1; 6.59 g, were randomly allotted to two treatment diets in a two-way factorial arrangement in a CRD with a pen (0.07 m<sup>2</sup> per weaned rabbit) as an experimental unit, holding one rabbit, replicated five times. The house was fitted with an automatic nipple drinker, while feed was offered <italic>ad libitum</italic>, with constant lighting (24 h) for a period of 42 days.</p>
</sec>
<sec id="s2_4">
<title>Growth performance, blood collection, and analysis</title>
<p>Every morning, feed was offered, and refusals were weighed using a digital weighing scale (Explorer EX224, 0.01g, OHAUS Corp., Parsippany, NJ, USA). All the rabbits were weighed for initial weight at the start of the experiment, and thereafter on a weekly basis, to determine weekly weight gain (WWG). The feed conversion ratio (FCR) was then computed as the average feed intake (FI) over average weight gain. During slaughtering, blood was collected from three rabbits randomly selected from each treatment and sex from the neck, into a 4-mL red top anticoagulant, then stored for 45 min at room temperature to coagulate, before being refrigerated at 4&#xb0;C (<xref ref-type="bibr" rid="B52">Washington and Van Hoosier, 2012</xref>). Next, 48 h after blood collection, serum was made by centrifuging the clotted blood for 20 min at 1,500 rpm. The serum supernatant settled on top, while blood cells were deposited at the bottom. The serum was sieved into a 0.5-mL conical centrifuge tube and then stored at &#x2212;20&#xb0;C. Finally, the serum biochemical parameters (total protein, albumin, creatine, urea, sodium, potassium, calcium, magnesium, glucose, cholesterol, triglycerides, alanine transaminase, and aspartate aminotransferase) were analyzed using an automated Idexx Vex Test Chemistry Analyser (IDEXX Laboratories, Inc., Westbrook, ME, USA).</p>
</sec>
<sec id="s2_5">
<title>Slaughter protocol</title>
<p>All rabbits were starved for 13 h at the end of the feeding trial, then weighed (slaughter weight) using a digital weighing scale, and transported to a local poultry abattoir for slaughtering. The rabbits were electrically stunned and hung upside down on a movable metal rack. The jugular vein was cut with a sharp knife for bleeding, and they were then skinned.</p>
</sec>
<sec id="s2_6">
<title>Meat color and pH measurement</title>
<p>Muscle color was determined 24 h after slaughter. The fillet and leg color, including lightness (L*), redness (a*), and yellowness (b*), were determined by a spectrophotometer (CM 2500c model, Konica Minolta, Japan), replicated three times to obtain the average value of the color. A pH meter (CRISON pH25, CRISON Instruments SA, Spain) was used to determine the pH of each rabbit at 45 min (pH<sub>i</sub>) and 24 h (pH<sub>u</sub>) post-mortem, as described by <xref ref-type="bibr" rid="B29">Manyeula et&#xa0;al. (2020)</xref>.</p>
</sec>
<sec id="s2_7">
<title>Drip loss</title>
<p>From the 24 sampled rabbits (12 males and 12 females), approximately 30 g of muscle strips were cut from the fillet muscle, parallel to the fiber direction, then weighed (W<sub>i</sub>) on a weighing scale (Explorer EX224, OHAUS Corp.) of 0.01 g sensitivity. The muscle strips were suspended in a cold room (4&#xb0;C) for 72 h and reweighed (W<sub>F</sub>) to determine drip loss, following the method proposed by <xref ref-type="bibr" rid="B18">Honikel and Hamm (1994)</xref>, as indicated in <xref ref-type="disp-formula" rid="eq1">Equation 1</xref>:</p>
<disp-formula id="eq1"><label>(1)</label>
<mml:math display="block" id="M1"><mml:mrow><mml:mi>D</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>p</mml:mi><mml:mo>&#xa0;</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mo>=</mml:mo><mml:mo>&#xa0;</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>F</mml:mi></mml:msub><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#xa0;</mml:mo><mml:mo>&#xd7;</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math>
</disp-formula>
</sec>
<sec id="s2_8">
<title>Cooking loss</title>
<p>Raw muscle cubes from 24 sampled rabbits were cut from the fillet muscles, initially weighed (W<sub>i</sub>), and oven-heated at 75&#xb0;C for 45 min. Thereafter, the oven-heated muscle was cooled down at room temperature for 15 min and dried with soft tissue and reweighed (W<sub>F</sub>) (<xref ref-type="bibr" rid="B44">Sanka and Mbanga, 2014</xref>). Cooking loss was calculated using <xref ref-type="disp-formula" rid="eq2">Equation 2</xref>:</p>
<disp-formula id="eq2"><label>(2)</label>
<mml:math display="block" id="M2"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>o</mml:mi><mml:mi>k</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mo>&#xa0;</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mo>=</mml:mo><mml:mo>&#xa0;</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>F</mml:mi><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>I</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>F</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#xd7;</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math>
</disp-formula>
</sec>
<sec id="s2_9">
<title>Sensory evaluation</title>
<p>A total of 50 untrained consumers were recruited from among the participants in a 1-day conference held at the North-West University (Mafikeng, North-West Province, South Africa). Information regarding sensory evaluation was collected via a questionnaire before the sensory assessment of the samples, which was conducted in the class, before lunch at noon. Each individual panelist was issued with an eight-point hedonic scale to evaluate each meat sample from the two treatments and deionized water to cleanse the mouth after each sample was offered. Verbal instructions on the test were highlighted prior to commencing. Approximately 5-cm portions of meat were cut and baked for 30 min in an oven (105&#xb0;C). Immediately after cooking, the meat was cut into 1 cm<sup>3</sup>, randomly placed (one/treatment) on a plastic form pack, and divided into two equally sized wedges. Each panelist assessed two samples, one from each of the dietary treatments, and numbers were used to identify the samples. The panelists were asked to score each sample using an eight-hedonic scale, as follows: extremely bland = 1; very bland = 2; fairly bland = 3; slightly bland = 4; slightly intense = 5; fairly intense = 6; very intense = 7; and extremely intense = 8, following the procedure proposed by <xref ref-type="bibr" rid="B53">William et&#xa0;al. (2023)</xref>.</p>
</sec>
<sec id="s2_10">
<title>Statistical analysis</title>
<p>The data on average weekly weight gain were analyzed using repeated measures analysis (<xref ref-type="bibr" rid="B45">SAS, 2010</xref>), employing the following statistical model:</p>
<disp-formula>
<mml:math display="block" id="M3"><mml:mrow><mml:msub><mml:mi>&#x3b3;</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub><mml:mo>&#xa0;</mml:mo><mml:mo>=</mml:mo><mml:mo>&#xa0;</mml:mo><mml:mi>&#x3bc;</mml:mi><mml:mo>+</mml:mo><mml:mo>&#xa0;</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mo>&#xa0;</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mtext>&#xa0;</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>D</mml:mi><mml:mo>*</mml:mo><mml:mi>S</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#xa0;</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>D</mml:mi><mml:mo>*</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mo>&#xa0;</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>
</disp-formula>
<p>Where, <inline-formula>
<mml:math display="inline" id="im1"><mml:mrow><mml:msub><mml:mi>Y</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = respond variables, <inline-formula>
<mml:math display="inline" id="im2"><mml:mi>&#x3bc;</mml:mi></mml:math></inline-formula> = population mean, <inline-formula>
<mml:math display="inline" id="im3"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> = effects of diets, <inline-formula>
<mml:math display="inline" id="im4"><mml:mrow><mml:msub><mml:mtext>S</mml:mtext><mml:mtext>j</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mtext>effects&#xa0;of&#xa0;sex</mml:mtext><mml:mo>,</mml:mo><mml:mo>&#xa0;</mml:mo></mml:mrow></mml:math></inline-formula><inline-formula>
<mml:math display="inline" id="im5"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>= time effect in week, <inline-formula>
<mml:math display="inline" id="im6"><mml:mrow><mml:mo>&#xa0;</mml:mo><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>D</mml:mi><mml:mo>*</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = interaction effect (diet and sex), <inline-formula>
<mml:math display="inline" id="im7"><mml:mrow><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>D</mml:mi><mml:mo>*</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = diets and weekly interaction effect, and <inline-formula>
<mml:math display="inline" id="im8"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = random error.</p>
<p>Overall growth performance, serum parameters, and meat quality data were analyzed using the general linear model (GLM) procedure of the Statistical Analysis System (<xref ref-type="bibr" rid="B45">SAS, 2010</xref>), with the dietary treatment and sex as the factors, using the model below:</p>
<disp-formula>
<mml:math display="block" id="M4"><mml:mrow><mml:msub><mml:mi>&#x3b3;</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>&#x3bc;</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>D</mml:mi><mml:mo>*</mml:mo><mml:mi>S</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#xa0;</mml:mo><mml:mo>+</mml:mo><mml:mo>&#xa0;</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>
</disp-formula>
<p>Where, <inline-formula>
<mml:math display="inline" id="im9"><mml:mrow><mml:msub><mml:mi>&#x3b3;</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = dependent variable, <inline-formula>
<mml:math display="inline" id="im10"><mml:mrow><mml:mo>&#xa0;</mml:mo><mml:mi>&#x3bc;</mml:mi></mml:mrow></mml:math></inline-formula> = population mean, <inline-formula>
<mml:math display="inline" id="im11"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>= diets effect, <inline-formula>
<mml:math display="inline" id="im12"><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mtext>Sex&#xa0;effect</mml:mtext><mml:mo>,</mml:mo><mml:mtext>&#xa0;</mml:mtext></mml:mrow></mml:math></inline-formula><inline-formula>
<mml:math display="inline" id="im13"><mml:mrow><mml:mi>D</mml:mi><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> = interaction of diet and sex, and <inline-formula>
<mml:math display="inline" id="im14"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>= random error associated with observation, <inline-formula>
<mml:math display="inline" id="im15"><mml:mrow><mml:mo>&#xa0;</mml:mo><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:math></inline-formula> = assumed to be normally and independently distributed. Significance was declared at <italic>P</italic> &#x2264; 0.05, and the means were separated using the probability of difference option in the LSMEANS statement of SAS.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Growth performance</title>
<p>The main rabbits&#x2019; performance parameters, with values of FI, FCR, and AWG, are summarized in <xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>. They showed that there was a diet and sex (male or female) interaction (<italic>P</italic> &lt; 0.05) effect on AWG (<italic>P</italic> = 0.01) but not on FI <italic>(P</italic> = 0.96) and FCR (<italic>P</italic> = 0.89). Likewise, significant diet was also noted on AWG (<italic>P</italic> = 0.03), but not on FI <italic>(P</italic> = 0.98) and FCR (<italic>P</italic> = 0.36). Diet and sex did not affect overall FI and FCR but influenced overall weight gain.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Overall feed intake and feed conversion ratio of rabbits, as affected by the main effects (diet and sex) and their interaction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Parameters</th>
<th valign="middle" colspan="2" align="left">Diet<sup>1</sup></th>
<th valign="middle" colspan="3" align="left">Sex<sup>2</sup></th>
<th valign="middle" colspan="3" align="left">Main effects</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">&#x2642;&#x2642;</th>
<th valign="middle" align="left">&#x2640;&#x2640;</th>
<th valign="middle" align="left">SEM</th>
<th valign="middle" align="left">Diet</th>
<th valign="middle" align="left">Sex</th>
<th valign="middle" align="left">Interaction</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Feed intake (g/rabbit)</td>
<td valign="middle" align="left">94.6</td>
<td valign="middle" align="left">92.9</td>
<td valign="middle" align="left">91.9</td>
<td valign="middle" align="left">95.5</td>
<td valign="middle" align="left">1.09</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Average weight gain</td>
<td valign="middle" align="left">23.0<sup>b</sup></td>
<td valign="middle" align="left">34.4<sup>a</sup></td>
<td valign="middle" align="left">29.3</td>
<td valign="middle" align="left">32.5</td>
<td valign="middle" align="left">4.2</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Feed conversion ratio</td>
<td valign="middle" align="left">3.9</td>
<td valign="middle" align="left">3.5</td>
<td valign="middle" align="left">4.0</td>
<td valign="middle" align="left">3.36</td>
<td valign="middle" align="left">0.31</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NS, not significant; SEM, standard error of the mean.</p></fn>
<fn>
<p><sup>1</sup>Diets: CON = commercial rabbit grower diets; SCM = total replacement of maize with sorghum and soybean with canola meal in rabbits&#x2019; diets.</p></fn>
<fn>
<p><sup>2</sup>Sex: &#x2642;&#x2642; = males; &#x2640;&#x2640; = females.</p></fn>
<fn>
<p>*<italic>P</italic> &lt; 0.05.</p></fn>
<fn>
<p>a, b: Mean on the same row with different superscripts differ significantly (<italic>p</italic> &lt; 0.05).</p></fn>
</table-wrap-foot>
</table-wrap>
<p>In weeks 3 and 6, only the diet had significant effects (<xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>). However, in weeks 4 and 5, the effects (<italic>P</italic> &lt; 0.05) were on diet and sex only, whereas in weeks 7 and 8, only sex had a significant effect. Rabbits fed SCM were the heaviest in weeks 1, 3, 4, 5, and 6. In week 1, the bucks were heavier (<italic>P</italic> &lt; 0.05) than the does, whereas in weeks 4, 5, 7, and 8, the opposite was true. No significant effects, based on sex, were detected in weeks 2 and 6.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Means of body weight gain (g/rabbit) in subsequent weeks of rabbit growth in grams, as affected by the main effects (diet and sex) and their interaction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Age</th>
<th valign="middle" colspan="2" align="left">Diet<sup>1</sup></th>
<th valign="middle" colspan="3" align="left">Sex<sup>2</sup></th>
<th valign="middle" colspan="3" align="left">Main effects</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">&#x2642;&#x2642;</th>
<th valign="middle" align="left">&#x2640;&#x2640;</th>
<th valign="middle" align="left">SEM</th>
<th valign="middle" align="left">Diet</th>
<th valign="middle" align="left">Sex</th>
<th valign="middle" align="left">Interaction</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Week 1</td>
<td valign="middle" align="left">19.0<sup>b</sup></td>
<td valign="middle" align="left">49.3<sup>a</sup></td>
<td valign="middle" align="left">37.5<sup>a</sup></td>
<td valign="middle" align="left">30.1<sup>b</sup></td>
<td valign="middle" align="left">2.09</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Week 2</td>
<td valign="middle" align="left">24.9</td>
<td valign="middle" align="left">45.3</td>
<td valign="middle" align="left">28.9</td>
<td valign="middle" align="left">41.4</td>
<td valign="middle" align="left">7.14</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 3</td>
<td valign="middle" align="left">15.7<sup>b</sup></td>
<td valign="middle" align="left">20.9<sup>a</sup></td>
<td valign="middle" align="left">18.3</td>
<td valign="middle" align="left">18.3</td>
<td valign="middle" align="left">1.35</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 4</td>
<td valign="middle" align="left">24.6<sup>b</sup></td>
<td valign="middle" align="left">39.2<sup>a</sup></td>
<td valign="middle" align="left">24.5<sup>b</sup></td>
<td valign="middle" align="left">39.3<sup>a</sup></td>
<td valign="middle" align="left">1.68</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 5</td>
<td valign="middle" align="left">20.4<sup>b</sup></td>
<td valign="middle" align="left">36.1<sup>a</sup></td>
<td valign="middle" align="left">22.5<sup>b</sup></td>
<td valign="middle" align="left">34.1<sup>a</sup></td>
<td valign="middle" align="left">3.26</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 6</td>
<td valign="middle" align="left">25.4<sup>b</sup></td>
<td valign="middle" align="left">31.1<sup>a</sup></td>
<td valign="middle" align="left">29.2</td>
<td valign="middle" align="left">27.3</td>
<td valign="middle" align="left">0.64</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 7</td>
<td valign="middle" align="left">27.7</td>
<td valign="middle" align="left">31.1</td>
<td valign="middle" align="left">23.6<sup>b</sup></td>
<td valign="middle" align="left">35.3<sup>a</sup></td>
<td valign="middle" align="left">1.39</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Week 8</td>
<td valign="middle" align="left">26.5</td>
<td valign="middle" align="left">26.3</td>
<td valign="middle" align="left">24.2<sup>b</sup></td>
<td valign="middle" align="left">28.6<sup>a</sup></td>
<td valign="middle" align="left">0.84</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NS, not significant; SEM, standard error of the mean.</p></fn>
<fn>
<p><sup>1</sup>Experimental diets: CON = commercial rabbit diet; SCM = total replacement of maize with sorghum and soybean with canola meal in rabbit diets.</p></fn>
<fn>
<p><sup>2</sup>Sex: &#x2642;&#x2642; = males; &#x2640;&#x2640; = females.</p></fn>
<fn>
<p>*<italic>P</italic> &lt; 0.05. a, b: Mean on the same row with different superscripts differ significantly (p &lt; 0.05).</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Blood biochemical parameters</title>
<p>The findings in <xref ref-type="table" rid="T4"><bold>Table&#xa0;4</bold></xref> showed that the diet significantly affected sodium and potassium, while magnesium was affected by both diet and interaction. Protein, creatinine, and cholesterol were significantly affected (<italic>P</italic> &gt; 0.05) by interaction (diet * sex). With regard to diet effects, potassium and magnesium were higher in rabbits fed the CON diet, compared to those on the SCM diet, whereas sodium was higher in the rabbits reared on the SCM diet than those on the CON diet. Sex did not have any significant effect (<italic>P</italic> &gt; 0.05) on the plasma protein, minerals, energy, and enzyme profiles of the rabbits.</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Blood biochemical parameters (mmol/L, unless otherwise stated) in rabbits as affected by the main effects (diet and sex) and their interaction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Parameters</th>
<th valign="middle" colspan="2" align="left">Diet<sup>1</sup></th>
<th valign="middle" colspan="3" align="left">Sex<sup>2</sup></th>
<th valign="middle" colspan="3" align="left">Main effects</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">&#x2642;&#x2642;</th>
<th valign="middle" align="left">&#x2640;&#x2640;</th>
<th valign="middle" align="left">SEM</th>
<th valign="middle" align="left">Diet</th>
<th valign="middle" align="left">Sex</th>
<th valign="middle" align="left">Interaction</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="9" align="left">Plasma protein profile (&#xb5;mol/L)</th>
</tr>
<tr>
<td valign="middle" align="left">Total protein</td>
<td valign="middle" align="left">54.9</td>
<td valign="middle" align="left">60.8</td>
<td valign="middle" align="left">60.6</td>
<td valign="middle" align="left">55.1</td>
<td valign="middle" align="left">2.24</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Albumin</td>
<td valign="middle" align="left">46.2</td>
<td valign="middle" align="left">44.2</td>
<td valign="middle" align="left">50</td>
<td valign="middle" align="left">40.4</td>
<td valign="middle" align="left">4.62</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Creatinine</td>
<td valign="middle" align="left">62.2</td>
<td valign="middle" align="left">57</td>
<td valign="middle" align="left">57.9</td>
<td valign="middle" align="left">61.3</td>
<td valign="middle" align="left">2.6</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Urea</td>
<td valign="middle" align="left">0.3</td>
<td valign="middle" align="left">0.06</td>
<td valign="middle" align="left">0.1</td>
<td valign="middle" align="left">0.2</td>
<td valign="middle" align="left">0.22</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<th valign="middle" colspan="9" align="left">Plasma mineral profile</th>
</tr>
<tr>
<td valign="middle" align="left">Sodium</td>
<td valign="middle" align="left">136.8<sup>b</sup></td>
<td valign="middle" align="left">141.0<sup>a</sup></td>
<td valign="middle" align="left">139.5</td>
<td valign="middle" align="left">138.3</td>
<td valign="middle" align="left">1.22</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Potassium</td>
<td valign="middle" align="left">6.0<sup>a</sup></td>
<td valign="middle" align="left">4.6<sup>b</sup></td>
<td valign="middle" align="left">5.4</td>
<td valign="middle" align="left">5.3</td>
<td valign="middle" align="left">0.38</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Calcium</td>
<td valign="middle" align="left">3.2</td>
<td valign="middle" align="left">3.3</td>
<td valign="middle" align="left">3.3</td>
<td valign="middle" align="left">3.2</td>
<td valign="middle" align="left">0.06</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Magnesium</td>
<td valign="middle" align="left">1.9<sup>a</sup></td>
<td valign="middle" align="left">1.6<sup>b</sup></td>
<td valign="middle" align="left">1.8</td>
<td valign="middle" align="left">1.6</td>
<td valign="middle" align="left">0.07</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<th valign="middle" colspan="9" align="left">Plasma energy profile</th>
</tr>
<tr>
<td valign="middle" align="left">Glucose</td>
<td valign="middle" align="left">5.6</td>
<td valign="middle" align="left">5.4</td>
<td valign="middle" align="left">5.3</td>
<td valign="middle" align="left">5.6</td>
<td valign="middle" align="left">0.32</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Cholesterol</td>
<td valign="middle" align="left">1.8</td>
<td valign="middle" align="left">1.6</td>
<td valign="middle" align="left">1.7</td>
<td valign="middle" align="left">1.7</td>
<td valign="middle" align="left">0.12</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Triglycerides</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">0.9</td>
<td valign="middle" align="left">1.1</td>
<td valign="middle" align="left">0.01</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<th valign="middle" colspan="9" align="left">Plasma enzyme profile (IU/L)</th>
</tr>
<tr>
<td valign="middle" align="left">Alanine transaminase</td>
<td valign="middle" align="left">39.7</td>
<td valign="middle" align="left">41.7</td>
<td valign="middle" align="left">42.1</td>
<td valign="middle" align="left">39.2</td>
<td valign="middle" align="left">2.93</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Aspartate aminotransferase</td>
<td valign="middle" align="left">61.6</td>
<td valign="middle" align="left">61.3</td>
<td valign="middle" align="left">65.3</td>
<td valign="middle" align="left">57.7</td>
<td valign="middle" align="left">8.61</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NS, not significant; SEM, standard error of the mean.</p></fn>
<fn>
<p><sup>1</sup>Diets: CON = commercial rabbit grower diets; SCM = totally replacing maize with sorghum and soybean with canola meal in rabbit grower diets.</p></fn>
<fn>
<p><sup>2</sup>Sex: &#x2642;&#x2642; = males; &#x2640;&#x2640; = females.</p></fn>
<fn>
<p>*<italic>P</italic> &lt; 0.05. a, b: Mean on the same row with different superscripts differ significantly (p &lt; 0.05).</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<title>Physicochemical characterization</title>
<p>The interaction (diet * sex) was significant on leg b*, pH<sub>u</sub>, and drip loss compared with the other experimental groups (<xref ref-type="table" rid="T5"><bold>Table&#xa0;5</bold></xref>). Fillet L* and b*, pH<sub>u</sub>, cooking, and drip loss were affected by the diet, whereas sex affected (<italic>P</italic> &lt; 0.05) pH<sub>u</sub> and drip loss only in terms of meat quality. Rabbits fed the SCM diet had the highest fillet L*, b*, and pH<sub>u</sub>, while those fed the control diet had the highest values on cooking and drip loss. Nonetheless, sex significantly affected pH<sub>u</sub> and drip loss only (<xref ref-type="table" rid="T5"><bold>Table&#xa0;5</bold></xref>). Other meat quality parameters were not significantly (<italic>P</italic> &gt; 0.05) affected by the main effects. Does recorded the highest value of pH<sub>u</sub> but had the lowest value of drip loss.</p>
<table-wrap id="T5" position="float">
<label>Table&#xa0;5</label>
<caption>
<p>Meat quality parameters in rabbit meat as affected by diet, sex, and their interaction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Parameters</th>
<th valign="middle" colspan="2" align="left">Diet<sup>1</sup></th>
<th valign="middle" colspan="3" align="left">Sex<sup>2</sup></th>
<th valign="middle" colspan="3" align="left">Main effects</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">&#x2642;&#x2642;</th>
<th valign="middle" align="left">&#x2640;&#x2640;</th>
<th valign="middle" align="left">SEM</th>
<th valign="middle" align="left">Diet</th>
<th valign="middle" align="left">Sex</th>
<th valign="middle" align="left">Interaction</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="9" align="left">Fillet color</th>
</tr>
<tr>
<td valign="middle" align="left">L* (lightness)</td>
<td valign="middle" align="left">47.6<sup>b</sup></td>
<td valign="middle" align="left">48.5<sup>a</sup></td>
<td valign="middle" align="left">45</td>
<td valign="middle" align="left">47.3</td>
<td valign="middle" align="left">1.16</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">a* (redness)</td>
<td valign="middle" align="left">10.7</td>
<td valign="middle" align="left">11.4</td>
<td valign="middle" align="left">11.1</td>
<td valign="middle" align="left">11.6</td>
<td valign="middle" align="left">0.26</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">b* (yellowness)</td>
<td valign="middle" align="left">10.1<sup>b</sup></td>
<td valign="middle" align="left">12.7<sup>a</sup></td>
<td valign="middle" align="left">11</td>
<td valign="middle" align="left">11.7</td>
<td valign="middle" align="left">0.51</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<th valign="middle" colspan="9" align="left">Leg color</th>
</tr>
<tr>
<td valign="middle" align="left">L* (lightness)</td>
<td valign="middle" align="left">47</td>
<td valign="middle" align="left">48.8</td>
<td valign="middle" align="left">47.7</td>
<td valign="middle" align="left">48.1</td>
<td valign="middle" align="left">1.21</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">a* (redness)</td>
<td valign="middle" align="left">11.1</td>
<td valign="middle" align="left">11.8</td>
<td valign="middle" align="left">11.6</td>
<td valign="middle" align="left">11.3</td>
<td valign="middle" align="left">0.34</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">b* (yellowness)</td>
<td valign="middle" align="left">12.8</td>
<td valign="middle" align="left">11.3</td>
<td valign="middle" align="left">12.2</td>
<td valign="middle" align="left">11.9</td>
<td valign="middle" align="left">0.67</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">pH<sub>i</sub></td>
<td valign="middle" align="left">7.2</td>
<td valign="middle" align="left">7.3</td>
<td valign="middle" align="left">7.1</td>
<td valign="middle" align="left">7.4</td>
<td valign="middle" align="left">0.14</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">pH<sub>u</sub></td>
<td valign="middle" align="left">6.9<sup>b</sup></td>
<td valign="middle" align="left">7.1<sup>a</sup></td>
<td valign="middle" align="left">6.4<sup>b</sup></td>
<td valign="middle" align="left">7.1<sup>a</sup></td>
<td valign="middle" align="left">0.03</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Peak force</td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">0.6</td>
<td valign="middle" align="left">0.4</td>
<td valign="middle" align="left">0.05</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Cooking loss</td>
<td valign="middle" align="left">39.9<sup>a</sup></td>
<td valign="middle" align="left">26.3<sup>b</sup></td>
<td valign="middle" align="left">34.8</td>
<td valign="middle" align="left">31.3</td>
<td valign="middle" align="left">2.67</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Drip loss</td>
<td valign="middle" align="left">59.0<sup>a</sup></td>
<td valign="middle" align="left">53.1<sup>b</sup></td>
<td valign="middle" align="left">60.9<sup>a</sup></td>
<td valign="middle" align="left">51.2<sup>b</sup></td>
<td valign="middle" align="left">1.42</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NS, not significant; SEM, standard error of the mean.</p></fn>
<fn>
<p><sup>1</sup>Diets: CON = commercial rabbit diet; SCM = total replacement of maize with sorghum and soybean with canola meal in rabbit diets.</p></fn>
<fn>
<p><sup>2</sup>Sex: &#x2642;&#x2642; = males; &#x2640;&#x2640; = females.</p></fn>
<fn>
<p>*<italic>P</italic> &lt; 0.05. a, b: Mean on the same row with different superscripts differ significantly (p &lt; 0.05).</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<title>Carcass traits and internal organs</title>
<p>There was no diet and sex (male or female) interaction in the heart, liver, kidneys, lungs, and small intestine (<xref ref-type="table" rid="T6"><bold>Table&#xa0;6</bold></xref>). Likewise, no significant dietary effects were observed on the fillet, heart, kidney, lungs, and small intestines. Notably, sex significantly affected the hot carcass weight (HCW), cold carcass weight (CCW), hind legs, heart, liver, and large intestine (<xref ref-type="table" rid="T6"><bold>Table&#xa0;6</bold></xref>). Rabbits fed the SCM diet had the highest values (<italic>P</italic> &lt; 0.05) of HCW, CCW, hind leg, heart, liver, and large intestine weights. Does had significantly higher HCW, CCW, and hind leg weight. On the other side, the liver and large intestine weights were higher (<italic>P</italic> &lt; 0.05) in bucks, while other parameters were not affected.</p>
<table-wrap id="T6" position="float">
<label>Table&#xa0;6</label>
<caption>
<p>Carcass characteristics and internal organs (% of HCW, unless otherwise stated) of rabbits, as affected by diet, sex, and their interaction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Parameters</th>
<th valign="middle" colspan="2" align="left">Diet<sup>1</sup></th>
<th valign="middle" colspan="3" align="left">Sex<sup>2</sup></th>
<th valign="middle" colspan="3" align="left">Main effects</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">&#x2642;&#x2642;</th>
<th valign="middle" align="left">&#x2640;&#x2640;</th>
<th valign="middle" align="left">SEM</th>
<th valign="middle" align="left">Diet</th>
<th valign="middle" align="left">Sex</th>
<th valign="middle" align="left">Interaction</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">HCW</td>
<td valign="middle" align="left">933.0<sup>b</sup></td>
<td valign="middle" align="left">1,037.7<sup>a</sup></td>
<td valign="middle" align="left">946.0<sup>b</sup></td>
<td valign="middle" align="left">1,074.7<sup>a</sup></td>
<td valign="middle" align="left">19.52</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">CCW</td>
<td valign="middle" align="left">881.7<sup>b</sup></td>
<td valign="middle" align="left">1,033.3<sup>a</sup></td>
<td valign="middle" align="left">894.0<sup>b</sup></td>
<td valign="middle" align="left">1,025.0<sup>a</sup></td>
<td valign="middle" align="left">19.48</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Fillet legs</td>
<td valign="middle" align="left">7.1</td>
<td valign="middle" align="left">6.9</td>
<td valign="middle" align="left">6.5<sup>b</sup></td>
<td valign="middle" align="left">7.5<sup>a</sup></td>
<td valign="middle" align="left">0.11</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Hind legs</td>
<td valign="middle" align="left">0.1<sup>b</sup></td>
<td valign="middle" align="left">0.1<sup>a</sup></td>
<td valign="middle" align="left">0.1<sup>b</sup></td>
<td valign="middle" align="left">0.1<sup>a</sup></td>
<td valign="middle" align="left">0.002</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Fillet</td>
<td valign="middle" align="left">5.3</td>
<td valign="middle" align="left">5.3</td>
<td valign="middle" align="left">4.9</td>
<td valign="middle" align="left">5.6</td>
<td valign="middle" align="left">0.23</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
</tr>
<tr>
<td valign="middle" align="left">Heart</td>
<td valign="middle" align="left">0.4<sup>b</sup></td>
<td valign="middle" align="left">0.5<sup>a</sup></td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">0.4</td>
<td valign="middle" align="left">0.03</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Liver</td>
<td valign="middle" align="left">4.2<sup>b</sup></td>
<td valign="middle" align="left">5.6<sup>a</sup></td>
<td valign="middle" align="left">5.3<sup>a</sup></td>
<td valign="middle" align="left">4.5<sup>b</sup></td>
<td valign="middle" align="left">0.23</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Kidneys</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.04</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Lungs</td>
<td valign="middle" align="left">1.4</td>
<td valign="middle" align="left">1.4</td>
<td valign="middle" align="left">1.6</td>
<td valign="middle" align="left">1.3</td>
<td valign="middle" align="left">0.13</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">SI (mm)</td>
<td valign="middle" align="left">3,425.00</td>
<td valign="middle" align="left">3,000.00</td>
<td valign="middle" align="left">3,176.10</td>
<td valign="middle" align="left">3,248.30</td>
<td valign="middle" align="left">153.1</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">LI (mm)</td>
<td valign="middle" align="left">504.2<sup>b</sup></td>
<td valign="middle" align="left">576.2<sup>a</sup></td>
<td valign="middle" align="left">585.0<sup>a</sup></td>
<td valign="middle" align="left">443.0<sup>b</sup></td>
<td valign="middle" align="left">21.91</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
<td valign="middle" align="left">*</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p><sup>1</sup>Diets: CON = commercial rabbit diet; SCM = total replacement of maize with sorghum and soybean with canola meal in rabbit diets.</p></fn>
<fn>
<p><sup>2</sup>Sex: &#x2642;&#x2642; = males; &#x2640;&#x2640; = females.</p></fn>
<fn>
<p>HCW, hot carcass weight; CCW, cold carcass weight; SI, small intestine; LI, large intestine; SEM, standard error of the mean; NS, not significant.</p></fn>
<fn>
<p>*<italic>P</italic> &lt; 0.05. a, b: Mean on the same row with different superscripts differ significantly (p &lt; 0.05).</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Sensory attributes</title>
<p><xref ref-type="table" rid="T7"><bold>Table&#xa0;7</bold></xref> shows that diet did not affect (<italic>P</italic> &gt; 0.05) meat quality. The consumers classified the meat from rabbit-fed treatment diets as slightly intense in all attributes.</p>
<table-wrap id="T7" position="float">
<label>Table&#xa0;7</label>
<caption>
<p>Descriptive statistics for organoleptic quality of meat from rabbits fed diets containing sorghum as maize substitute and canola meal as soybean substitute in rabbits&#x2019; diets.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left" rowspan="2">Attributes</th>
<th valign="middle" colspan="3" align="left">Diets<sup>1</sup></th>
<th valign="middle" rowspan="2" align="left">Significance</th>
</tr>
<tr>
<th valign="middle" align="left">CON</th>
<th valign="middle" align="left">SCM</th>
<th valign="middle" align="left">SEM</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Aroma</td>
<td valign="middle" align="left">5.4</td>
<td valign="middle" align="left">5.5</td>
<td valign="middle" align="left">0.29</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Juiciness</td>
<td valign="middle" align="left">4.8</td>
<td valign="middle" align="left">5.3</td>
<td valign="middle" align="left">0.31</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">First bite</td>
<td valign="middle" align="left">5.5</td>
<td valign="middle" align="left">5.6</td>
<td valign="middle" align="left">0.32</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Sustained juiciness</td>
<td valign="middle" align="left">5.2</td>
<td valign="middle" align="left">5.5</td>
<td valign="middle" align="left">0.27</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Overall tenderness</td>
<td valign="middle" align="left">5.4</td>
<td valign="middle" align="left">5.5</td>
<td valign="middle" align="left">0.27</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Connective tissue</td>
<td valign="middle" align="left">4.5</td>
<td valign="middle" align="left">5.0</td>
<td valign="middle" align="left">0.29</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Flavor overall</td>
<td valign="middle" align="left">5.2</td>
<td valign="middle" align="left">5.2</td>
<td valign="middle" align="left">0.27</td>
<td valign="middle" align="left">NS</td>
</tr>
<tr>
<td valign="middle" align="left">Typical flavor</td>
<td valign="middle" align="left">4.8</td>
<td valign="middle" align="left">5.4</td>
<td valign="middle" align="left">0.30</td>
<td valign="middle" align="left">NS</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SEM, standard error of the mean; NS, not significant.</p></fn>
<fn>
<p><sup>1</sup>Diets: CON = commercial rabbit diet; SCM = total replacement of maize with sorghum and soybean with canola meal in rabbit grower diets.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<sec id="s4_1">
<title>Growth performance</title>
<p>Sorghum offers a nutritive value comparable to that of maize and other conventional feed grains, confirming its suitability as a feed ingredient (<xref ref-type="bibr" rid="B58">Zarei et&#xa0;al., 2022</xref>). Similarly, canola meal ranks second only to soybean meal as a high-quality protein source in animal diets and is rich in essential minerals, vitamins, and beneficial phenolic compounds (<xref ref-type="bibr" rid="B34">Newkirk, 2009</xref>). In this study, previous results showed that the interaction (diet * sex) affected weight gain, suggesting that the weight gain of rabbits is affected by age and sex. A lack of interaction (diet * sex), observed in <xref ref-type="table" rid="T2"><bold>Tables&#xa0;2</bold></xref>-<xref ref-type="table" rid="T6"><bold>6</bold></xref>, indicates that diet and sex acted independently on those parameters. However, significant interaction in the tables revealed that those parameters were acted on jointly by diet and sex. A lack of interaction effects (diet * sex) on FI concurs with the findings of <xref ref-type="bibr" rid="B19">Hussein and Abdelfattah (2020)</xref>, who reported no interaction (diet * sex) in FI in growing rabbits fed a concentrate meal. The interaction (diet * sex) on FCR contradicts the findings of the study of <xref ref-type="bibr" rid="B19">Hussein and Abdelfattah (2020)</xref>: diet and sex did not have effects on FI and FCR, implying that the feed was consumed and converted to muscle in the same way as in the control diet. This aligns with what was reported by <xref ref-type="bibr" rid="B15">Gugo&#x142;ek et&#xa0;al. (2018)</xref>, who found similar FI and FCR in rabbits fed rapeseed meal, white lupine, and pea seeds as SBM substitutes. Also, <xref ref-type="bibr" rid="B19">Hussein and Abdelfattah (2020)</xref> reported that sex had no significant effect on the FI of California rabbits fed a commercial diet. The lack of significant diet and sex effects on FI suggests that substituting maize with sorghum and SBM with canola meal as energy and protein ingredients in rabbit diets does not change the palatability of the diets, irrespective of the sex of the animal. The non-significant interaction (diet * sex) on weight gain aligns with the findings of a study by <xref ref-type="bibr" rid="B36">North et&#xa0;al. (2018)</xref>. Rabbits have been reported to possess well-developed GITs when it comes to utilizing fibrous diets and tolerating antinutritional compounds such as tannin (<xref ref-type="bibr" rid="B56">Wolf and Cappai, 2020</xref>). This may explain why rabbits fed SCM diets had similar (weeks 2, 7, and 8) or higher in other weeks in BWG, compared to those reared on the control diet. These observations match those of <xref ref-type="bibr" rid="B35">Njoku et&#xa0;al. (2013)</xref>, who reported an increased average daily weight gain with increasing sorghum milling dust in the diets of growing rabbits. Indeed, it was noted by <xref ref-type="bibr" rid="B16">Hamlin (2012)</xref> that rabbits are anatomically and physiologically developed for a high-fibrous (20%&#x2013;25%) diet. The faster weight gain of does (more than bucks) in week 1 was surprising, and there is no evidence as yet to show that this was reported in previous rabbit- or livestock-related research. The current results in weeks 4, 5, 7, and 8 match what is reported in the literature (<xref ref-type="bibr" rid="B39">Ologbose et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B24">Lamptey et&#xa0;al., 2022</xref>), where bucks were reportedly slightly heavier than does at all ages, possibly due to better feed utilization. Contrary to these results, <xref ref-type="bibr" rid="B4">Birolo et&#xa0;al. (2020)</xref> reported similar weights in male and female rabbits, in line with the results from weeks 2, 3, and 6, suggesting that in those weeks rabbits utilized feeds to the same degree of efficiency, regardless of their sex. This could be because, 6 weeks after weaning, bucks and does frequently use diets similarly because they have not reached sexual maturity, when physiological and metabolic disparities depending on sex become more noticeable.</p>
</sec>
<sec id="s4_2">
<title>Blood biochemical parameters</title>
<p>Interaction (diet * sex) was observed on protein, creatinine, magnesium, and cholesterol, suggesting that these serum metabolite parameters are controlled jointly by diet and sex. The plasma proteins and energy were not affected by diets and sex, suggesting that the intake of the sorghum&#x2013;canola-based diet had no negative post-ingestive feedback, irrespective of sex. The lack of post-ingestive feedback was further explained by similar (<italic>P</italic> &lt; 0.05) plasma enzymes (ALT and AST) profiling, which are indicators of hepatocellular damage (<xref ref-type="bibr" rid="B2">Ambrosy et&#xa0;al., 2015</xref>), and suggests that the combination of sorghum and canola has the potential to be used in the diets of rabbits, as indicated by values falling within the normal ranges for healthy rabbits (<xref ref-type="bibr" rid="B20">Jenkins, 2008</xref>). The higher plasma sodium in rabbits reared on an SCM diet may be a reflection of the high concentration of this mineral reported in sorghum (9.04 mg/100 g), compared to maize (3.74 mg/100 g) (<xref ref-type="bibr" rid="B21">Jocelyne et&#xa0;al., 2020</xref>). Rabbits are more tolerant of high sodium, but a plasma sodium concentration above 185 mmol/L can induce hypernatremia (<xref ref-type="bibr" rid="B41">Patson and Sladky, 2020</xref>), which is why rabbits fed SCM did not show any sign of hypernatremia at plasma concentrations of 141.0 mmol/L in this study. Sorghum contains antinutritional factors such as tannins, kafirin, ferulic acid, endosperm, and phytate that hinder its utilization by monogastric animals and result in a lowering of the digestibility of carbohydrates, proteins, and minerals (<xref ref-type="bibr" rid="B30">Masenya et&#xa0;al., 2021</xref>), which could explain why rabbits fed a sorghum&#x2013;canola-based diet had lower plasma potassium and magnesium levels. Moreover, canola is known to contain low levels of potassium and magnesium (<xref ref-type="bibr" rid="B27">Mahan et&#xa0;al., 2005</xref>).</p>
</sec>
<sec id="s4_3">
<title>Physicochemical characterization</title>
<p>Interaction (diet * sex) had significant effects on meat yellowness, pH<sub>u</sub>, and drip loss, implying that these traits are controlled jointly by diet and sex, which contradicts the findings of the study of <xref ref-type="bibr" rid="B7">Daszkiewicz and Gugo&#x142;ek (2020)</xref>. The decline in the pH value hinders the growth of undesired bacteria, rates of conversion of color, and the formation of undesired flavors in the meat, which was observed in the meat from rabbits reared on the CON diet in this study. It may be deduced that combining sorghum and canola in the diet of rabbits reduced the conversion rate of glycogen levels to lactic acid after slaughter, hence improving meat quality. This finding concurs with the literature reporting the effects of sorghum- (<xref ref-type="bibr" rid="B17">Hern&#xe1;ndez-Mart&#xed;nez et&#xa0;al., 2018</xref>) and canola-based diets (<xref ref-type="bibr" rid="B9">El-Medany and El-Reffaei, 2015</xref>) on the femoris and biceps muscles of rabbits. Investigators (<xref ref-type="bibr" rid="B57">Yal&#xe7;&#x131;n et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B36">North et&#xa0;al., 2018</xref>) reported higher muscle ultimate pH in bucks, whereas other studies found no sex-related effects on meat pH (<xref ref-type="bibr" rid="B6">Dalle Zotte et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B13">Frunza et&#xa0;al., 2019</xref>). Cooking and drip loss were higher in rabbits fed the control diet, which corroborates the findings reported by <xref ref-type="bibr" rid="B9">El-Medany and El-Reffaei (2015)</xref>. Those authors reported significantly lower cooking loss in rabbits fed canola than in those fed a control diet. During meat processing (cooking and drying), juices are released together with the nutrients, due to protein denaturation and muscle atrophy (<xref ref-type="bibr" rid="B43">Purslow et&#xa0;al., 2016</xref>), resulting in poor-quality products. In this study, low cooking and drip loss observed in rabbits fed the SCM diet imply that utilizing sorghum and canola (SCM) in the diet improved the meat quality in terms of tenderness, juiciness, and flavor by promoting muscle structure to reduce the loss of juices (nutrients). Furthermore, drip loss can also be influenced by secondary metabolites (methylglyoxal), changing the properties of muscle proteins (<xref ref-type="bibr" rid="B42">Przybylski et&#xa0;al., 2022</xref>). Loss of water and the application of heat to the meat led to the formation of pores and shrinkage, which can affect the texture and taste, leading to poor meat quality (<xref ref-type="bibr" rid="B22">Koc et&#xa0;al., 2008</xref>). The significantly higher drip loss on the meat from does contradicted the findings of <xref ref-type="bibr" rid="B37">North et&#xa0;al. (2019)</xref>, who reported similar drip loss values from the meat of males and females. The current results suggest that bucks possess better meat quality than does, as drip loss determines the appearance, texture, nutritional value, and attractiveness of the meat (<xref ref-type="bibr" rid="B12">Filho et&#xa0;al., 2016</xref>).</p>
</sec>
<sec id="s4_4">
<title>Carcass traits and internal organs</title>
<p>The heavier HCW, CCW, heart, hind leg, liver, and large intestine in rabbits reared on SCM could be associated with final weight. Indeed, <xref ref-type="bibr" rid="B31">Michalik et&#xa0;al. (2009)</xref> reported a strong positive relationship between rabbit body and carcass weight, which is indicative of an adequate diet and nutrition (<xref ref-type="bibr" rid="B5">Cardoso et&#xa0;al., 2011</xref>). However, the presence of toxins in the diet is reflected by hypertrophy or hypotrophy of the liver (<xref ref-type="bibr" rid="B10">Ewuola et&#xa0;al., 2003</xref>), which could be another reason why rabbits fed the SCM diet had heavier livers, as mentioned in the study of <xref ref-type="bibr" rid="B30">Masenya et&#xa0;al. (2021)</xref>. The kidneys, heart, and large intestine were not affected by diet, suggesting that replacing maize with sorghum and canola with soybean in rabbit diets did not show any anatomical adaptation responses in the rabbits. The present results align closely with similar findings reported by many researchers (<xref ref-type="bibr" rid="B17">Hern&#xe1;ndez-Mart&#xed;nez et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B50">Wafar et&#xa0;al., 2020</xref>). The authors reported similar carcass traits and internal organs when weaner rabbits were fed a malted sorghum cultivar. In this study, it was clear that the sorghum&#x2013;canola-based diet (SCM), when compared to the control diet, can improve the profitability of sustainable rabbit farming, as it is associated with heavier internal and external organs that determine market prices in the commercial milieu. The higher (<italic>P</italic> &lt; 0.05) HCW, CCW, fillet, and hind leg weights of does contradict the findings of <xref ref-type="bibr" rid="B37">North et&#xa0;al. (2019)</xref>, who found similar HCW, CCW, and hind leg weights in male and female rabbits. Bucks had heavier livers and hearts and longer large intestines than does, which correlate with the findings of <xref ref-type="bibr" rid="B19">Hussein and Abdelfattah (2020)</xref>, who reported significantly heavier internal organs in bucks than in does, which could explain why males grew faster than females (<xref ref-type="bibr" rid="B19">Hussein and Abdelfattah, 2020</xref>). The results reported by <xref ref-type="bibr" rid="B49">T&#x16f;mov&#xe1; et&#xa0;al. (2022)</xref> match the findings of the current study, i.e., the internal organ weights of rabbits were not affected by their sex.</p>
</sec>
<sec id="s4_5">
<title>Sensory attributes</title>
<p>The consumers classified the meat from rabbits fed with all treatment diets as slightly intense (5) in all attributes, implying that rabbit meat from an SCM diet would equally be patronized as the meat from rabbits fed the control diet. This indicates that the combination of sorghum (maize replacer) and canola (soybean replacer) in the rabbits&#x2019; diet did not have an adverse effect on meat quality. However, meat juiciness is an important parameter for consumer choice, as it is associated with fat content (<xref ref-type="bibr" rid="B55">Wognin et&#xa0;al., 2018</xref>), and this led the researchers to deduce that the lack of a significant effect in this study could be due to similar lipid meat contents. Similar to these current results, <xref ref-type="bibr" rid="B23">Kpehe et&#xa0;al. (2020)</xref> reported a lack of difference (<italic>P</italic> &lt; 0.05) in the organoleptic quality of the meat of rabbits fed diets containing graded levels of rice offal.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusion</title>
<p>The total substitution of maize with sorghum, and soybean meal with canola meal, did not have any adverse effect on the growth performance, serum biochemical indices, and sensory evaluation of rabbits. A sorghum&#x2013;canola-based diet has been found to promote weight gain. It can be concluded that sorghum&#x2013;canola can be employed to substitute maize&#x2013;soybean in rabbit diets.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The animal study was approved by the University of Mpumalanga Research and Ethics Committee. The study was conducted in accordance with the local legislation and institutional requirements.</p></sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>NS: Formal analysis, Methodology, Validation, Data curation, Project administration, Conceptualization, Software, Writing &#x2013; review &amp; editing, Funding acquisition, Investigation, Writing &#x2013; original draft, Resources.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>Our thanks go to Opti Feeds (South Africa).</p>
</ack>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work 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="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s12" 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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