Physiological and Production Profiling with TOPSIS Multi‑Criteria Ranking for Identification of Heat‑Tolerant White Fulani Cows under Tropical Farm Conditions

Physiological and Production Profiling with TOPSIS Multi‑Criteria Ranking for Identification of Heat‑Tolerant White Fulani Cows under Tropical Farm Conditions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Physiological and Production Profiling with TOPSIS Multi‑Criteria Ranking for Identification of Heat‑Tolerant White Fulani Cows under Tropical Farm Conditions Mahmood Aliyu, Akeem Babatunde Sikiru, Aliyu Haxy Dikko, Ibrahim Shuaibu Harande, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7150569/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Heat stress negatively affects dairy productivity in the humid tropics. To better understand the consequence, a longitudinally study was carried out to monitor 45 multiparous White Fulani cows for 180 days (temperature–humidity index = 81.00 ± 3.00; peaks = 88.00) with recording daily rectal temperature (RT), respiratory rate (RR), heart rate (HR) and milk yield (MY). Mixed‑effects models were used to quantify the cows’ physiological responses to heat load, while Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank individual heat stress tolerance. CRITIC‑derived weights prioritized MY (0.34) and RT (0.27). The cows in the top quartile-maintained RT 0.48°C below the herd-wide mean and produced 2.7 kg day⁻¹ more milk despite severe THI. The ranking order was evaluated for consistency using equal-weight sensitivity analysis, resulting in a strong Spearman correlation coefficient (ρ = 0.89). Findings from the study showed that the multivariate pipeline implemented provides a simple, rapid and field‑applicable tool for selecting heat‑tolerant lactating cows under smallholder conditions. The study suggests validation of the approach across additional agro‑ecological zones of Nigeria as warranted future investigations. Heat stress White Fulani cattle TOPSIS milk yield physiological responses Figures Figure 1 Figure 2 1. Introduction Heat stress has emerged as a critical obstacle for dairy production in the humid tropics, where ambient temperature and relative humidity frequently climb above the thermoneutral zone which is one of the reasons why crossbred of indigenous and exotic breeds are commonly used on most dairy farms in the humid tropic zone of Nigeria (Sikiru et al., 2023 ). The White Fulani (Bunaji) breed, widely used in West Africa is one of the most explored but remains poorly characterized for heat‑tolerance despite its robustness under pastoral conditions. There are earlier studies focused primarily on breed‑level comparisons using single traits such as rectal temperature or milk yield, yielding offering little guidance for within‑breed selection (Chawala et al., 2020 ). Meanwhile, to accelerate climate‑smart genetic improvement, there is a growing demand for multivariate field tools that integrate physiological and production indicators of thermal resilience into a single, interpretable score. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi‑criteria decision‑making algorithm fulfils these requirements but has not yet been applied to heat‑stress phenotyping of White Fulani cows under commercial farming conditions. Therefore, it was deployed as a longitudinal monitoring protocol on a Nigerian smallholder dairy farm with combined mixed‑effects modelling with CRITIC‑weighted TOPSIS ranking to achieve two objectives which are quantifying individual variation in heat‑stress responses with the cows in the same herd and identify superior cows for selective breeding. The study hypothesized that in a herd, subset of cows would maintain normothermia and higher milk yield throughout severe hot‑humid periods and such cows are potential candidates for genetic scrutiny and dissemination. Sustained selection of such adaptable animals will serve as a cornerstone of climate‑smart livestock breeding programmes in line with the Global Agenda for Sustainable Livestock (GASL). However, reliable field‑ready selection criteria remain elusive because physiological, behavioural and production indicators each capture only one dimension of the a single but highly complex heat‑stress phenotype. Multi‑criteria decision‑making (MCDM) techniques, particularly the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), offer a simple and formal way to integrate different indicators into a single biologically interpretable closeness score (Uzun et al., 2021 ). Although, there are few studies where TOPSIS have been applied but under real‑farm conditions, none has yet targeted the White Fulani breed which is a key population in Africa. By combining physiological monitoring with MCDM, this report delivers a cow‑level ranking of heat‑tolerance in White Fulani cattle and provides a practical blueprint that can be adopted for on‑farm implementation across the West African humid tropics where the White Fulani cows are popular livestock. 2. Materials and methods Forty-five multiparous White Fulani cows (parity 2–4) were observed for 180 days (Oct 2024 – Mar 2025) on a smallholder farm (7°59′ N, 3°33′ E; Köppen Aw). The ambient THI was computed using daily observation of temperature and relative humidity with the equation validated for Nigerian climates (Sikiru et al. 2018 ). The rectal temperature (RT), respiratory rate (RR), heart rate (HR) and milk yield (MY) were logged twice daily; ambient temperature and relative humidity were captured at 15-min intervals (HOBO ® loggers). The White Fulani cows (parity 2–4; 414 ± 28.00 kg body mass) were housed under open‑sided sheds and milked once daily at 06:00 h, received a total mixed ration formulated to supply 2088.50Kcal kg⁻¹ DM, and 15.50% crude protein (Table 1 ) and had ad libitum access to borehole water. Table 1 Ingredients composition and proximate composition (% on DM basis) of the on-farm total mixed ration (TMR) used by smallholder dairy farmers Ingredient Quantity (%) Crude Protein Neutral Detergent Fibre Acid Detergent Fibre Ether Extract Elephant grass 40.00 8.00 65.00 40.00 2.00 Palm Kernel Cake 25.00 16.00 60.00 30.00 8.00 Maize bran 20.00 10.00 30.00 12.00 4.00 Soybean meal 13.00 44.00 13.00 7.00 1.50 Mineral mix 1.00 0.00 0.00 0.00 0.00 Molasses 0.50 4.50 0.00 0.00 0.00 Salt 0.30 0.00 0.00 0.00 0.00 Urea 0.20 281.00 0.00 0.00 0.00 Nutrients composition Crude Protein (%) 15.50 Neutral Detergent Fibre (%) 48.69 Acid Detergent Fibre (%) 26.81 Ether Extract (%) 3.80 Ash (%) 8.17 Nitrogen Free Extract (%) 46.08 Metabolizable Energy (kcal/kg) 2088.50 The values represent proximate compositions of feed ingredients as determined in the TMR obtained from the farm for laboratory analyses. Crude Protein (CP), Ether Extract (EE), Crude Fibre (CF ≈ ADF), Ash, and Nitrogen-Free Extract (NFE) are expressed on a dry matter basis (% DM) and Metabolizable Energy (ME) expressed as kcal/kg DM. Urea included in the ration as a non-protein nitrogen source, its CP value is theoretical (281% on DM basis). The mineral mix and salt are considered inert for CP, EE, fibre, and energy but contribute significantly to total ash content. The data were checked for readings > 3.5 SD from each cow’s mean then flagged and this led to 0.9% records discarded as measurement artefacts. The non-normal variables were subjected to Box-Cox transformation while the statistical modelling Linear mixed-effects models (cow random intercept; AR(1) residuals) were used to quantified linear + quadratic THI effects on RT, RR and HR. The multivariate ranking at Cow-level least-square means for RT, RR, HR, MY and cumulative heat-load units were entered into TOPSIS. The criterion weights were derived by the CRITIC objective-weighting algorithm and contrasted with an equal-weights scenario for robustness. The Principal-component analysis (PCA) and Ward hierarchical clustering were used to visualise multivariate patterns, mirroring the phenotyping strategy. All analyses were executed in R 4.4.0 with lme4, lmerTest, FactoMineR and MCDA. All the procedures conformed to the guidelines of the Department of Animal Production, Federal University of Technology Minna, approved by the Institutional Animal Care and Use Committee of the University (Approval code: 000093). 3. Results The mean THI over the 180‑day observation period was 81.00 ± 3.00, with mid-day peaks reaching 88.00, while the pre‑dawn THI seldom dropped below 74, confirming chronic thermal load (Fig. 1 A). The RT rose by 0.11°C per unit THI (p < 0.001); RR and HR increased by 3.4 breaths min⁻¹ and 1.9 beats min⁻¹ per unit THI, respectively (Fig. 1 B). Nevertheless, considerable inter‑cow variability was evident (random‑effects SD = 0.36°C for RT). The aggregate MY averaged 7.90 ± 1.40 kg d⁻¹ but declined by 0.26 kg d⁻¹ per 10‑unit THI rise (p = 0.032). The cows in the most tolerant quartile produced 2.7 kg d⁻¹ more milk than the least tolerant group despite similar feed intake (Fig. 1 C). Based on inter‑criterion contrast and correlation structure, MY received the greatest weight (0.34) followed by RT (0.27), RR (0.22) and HR (0.17). The closeness coefficients ranged from 0.19 to 0.84, and five cows (IDs GDF‑03, ‑11, ‑19, ‑24, ‑41) consistently topped the ranking across CRITIC‑weighted and equal‑weight scenarios (Spearman ρ = 0.89, p < 0.001) as presented in Fig. 1 D. The PCA of the physiological–production matrix explained 68% of variance on the first two axes and clearly separated high‑tolerance cows along PC1 loadings dominated by lower RT and higher MY (Fig. 2 ). The combined heat‑tolerance index derived from the mixed‑model residuals and TOPSIS ranks classified 22% of cows as highly tolerant, 47% as intermediate and 31% as susceptible. The sensitivity and robustness optimization focusing on leave‑one‑trait‑out analysis showed that removing RR or HR altered no cow’s quartile assignment, whereas excluding MY demoted three cows from high heat tolerant to intermediate heat tolerant. The ranking therefore hinges chiefly on production resilience and core body temperature regulation. The observations showed that the RT ranged 38.40–41.50°C (mean 39.90°C), RR 26–108 breaths min⁻¹ (mean 62.00), HR 52.00–108.00 beats min⁻¹ (mean 78.00). The coefficient of variation for MY (18%) was higher than that of RT (3%), this showed that milk yield is the most discriminating trait that can be used to distinguish the animals under heat stress condition. Further, Ward’s hierarchical clustering based on scaled trait means split the herd into three phenotypic clusters closely matching the highly tolerant/intermediate/susceptible categories (Adjusted Rand Index = 0.82). The observation also showed that for breeding programmes, animals GDF‑03 and GDF‑11 combined low RT (–0.48°C relative to herd mean) with the highest MY (+ 3.1 kg d⁻¹) and stable HR, making these animals as prime candidate for genomic exploration projects in the herd. Conversely, GDF‑29, GDF‑30 and GDF‑32 displayed persistently elevated RT (> 40.5°C) and rapid MY decline and as such they are candidates for culling or management interventions and removal from breeding programmes. The expected gain analyses showed that selection of the top 15% most tolerant cows would raise average MY by 0.9 kg d⁻¹ and reduce mean RT by 0.22°C in the next generation, assuming moderate heritability (h 2 = 0.25) for the herd. 4. Discussion This study demonstrates that an integrated MCDM protocol can be effectively used to distinguishes heat‑tolerant White Fulani cows under challenging tropical farm conditions leveraging routinely measurable physiological traits and milk yield. The mixed‑effects models quantified the physiological burden of chronic THI, similar to patterns reported in Thai Holstein–Zebu crosses (Tao et al 2020 ) and Colombian Romosinuano cows (Wanjala et al 2023 ). By coupling these outputs to CRITIC‑weighted TOPSIS, the report captured both the magnitude and stability of individual responses and thus avoided over‑reliance on any single indicator. One of the salient findings of this study is the dominant influence of MY in the objective weights of the cows. Although MY often deteriorates first under heat load, its inclusion alongside thermal and cardiorespiratory metrics provides a direct link to farmer revenue. The strong correlation between CRITIC‑weighted and equal‑weight rankings also indicates that the pipeline is robust to moderate uncertainty in weight specification, an advantage over subjective scoring systems. The identification of five highly tolerant cows in the herd presents immediate oppourtunities for within‑breed herd-wide selection for genetic improvement. Genomic analyses targeting heat‑shock proteins and vasodilatory pathways are suggested from these animals. Further, the clustering outcomes could guide pragmatic management, enabling resource management of the farm by allowing the farmer to allocate scarce shade or fan installations preferentially to susceptible cows. This is a report of single‑farm design which could limit extrapolation across ecological zones, also the absence of milk‑composition data may understate the full economic impact of heat stress. These are suggested as future studies to validate the pipeline across peri‑urban dairies in other agroecological zones of the country including the Guinea savannah and Sudan savannah belts while incorporate infrared thermography to reduce handling‑induced artefacts. Conclusion This study concluded that under a sustained THI, approximately one‑fifth of the White Fulani cows investigated maintained normothermia and superior milk yield which showed that the CRITIC‑TOPSIS pipeline can serve as a low cost, rapid, objective and field‑ready tool for ranking individual heat‑tolerance of a herd. The tool is recommended for adoption as a means to accelerate genetic gain towards climate‑resilient dairy production in Nigeria and other sub‑Saharan Africa countries. These findings align with emerging consensus that heat‑tolerance is a multifactorial trait influenced by evaporative capacity, metabolic efficiency and behavioural adaptability. The top‑ranked cows in this study exhibited lower afternoon RT yet did not elevate RR excessively, suggesting superior peripheral vasodilation and sweat gland function rather than a simple shift to panting. This phenotype mirrors sweat‑efficient normotherms category observed in Gir cattle which are also heat tolerant cows like White Fulani which deserves physiological scrutiny. Declarations Data Availability Statement Data associated with this study are publicly available in the Open Science Framework (OSF) repository under the project link https://osf.io/sz2dc/ Declaration of Competing Interests The authors declare no known competing financial interests or personal interests. Funding Sources This research did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics declarations The protocol implemented in this study was approved according to Institutional Animal Care and Use Committee, of the Federal University of Technology Minna, Nigeria (000093). Competing interests The authors declare no competing interests. Clinical trial number Not applicable Consent to Publish declaration Not applicable Consent to Participate declaration Not applicable References Abdulsalam W, Egena SSA, Otu BO, et al. Location and Season Interaction on Reproductive Phenotypic Traits and Blood Biochemical Profile. of White Fulani Breeding Bulls under Smallholder Production System; 2023. De Campos JS, Onasanya GO, Ubong A, et al. Potentials of single nucleotide polymorphisms and genetic diversity studies at HSP90AB1 gene in Nigerian White Fulani, Muturu, and N’Dama cattle breeds. Trop Anim Health Prod. 2024;56:58. Egger-Danner C, Cole JB, Pryce JE, et al. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal. 2015;9:191–207. https://doi.org/10.1017/S1751731114002614 . Habimana V, Nguluma AS, Nziku ZC, et al. Heat stress effects on milk yield traits and metabolites and mitigation strategies for dairy cattle breeds reared in tropical and sub- tropical countries. Front Vet Sci. 2023;10:1121499. Mazurov V, Sanova Z. Milk yield and its relationship in highly productive cows during robotic milking. Agrarian Bull the. 2023;230:43–54. https://doi.org/10.32417/1997-4868-2023-230-01-43-54 . Rashamol VP, Sejian V, Bagath M, et al. Physiological adaptability of livestock to heat stress: an updated review. J Anim Behav Biometeorol. 2020;6:62–71. Sakowski T, Kuczyńska B, Puppel K, et al. Relationships between physiological indicators in blood, and their yield, as well as chemical composition of milk obtained from organic dairy cows. J Sci Food Agric. 2012;92:2905–12. https://doi.org/10.1002/jsfa.5900 . Sikiru AB, Egena SSA, Alemede IC, Makinde OJ. Environmental source of stress in livestock productivity - a study of Minna climate data. Biotechnol Anim Husb. 2018;34:159–70. Sikiru AB, Mullakkalparambil VS, Nair R, et al. Sustaining livestock production under the changing climate: Africa scenario for Nigeria resilience and adaptation actions. Climate Change Impacts on Nigeria. Environment and Sustainable Development). Springer; 2023. Spiers DE, Spain JN, Sampson JD, Rhoads RP. Use of physiological parameters to predict milk yield and feed intake in heat-stressed dairy cows. J Therm Biol. 2004;29:759–64. https://doi.org/10.1016/j.jtherbio.2004.08.051 . Tao S, Rivas RMO, Marins TN, et al. Impact of heat stress on lactational performance of dairy cows. Theriogenology. 2020;150:437–44. Uzun B, Taiwo M, Syidanova A, Uzun Ozsahin D. (2021) The Technique For Order of Preference by Similarity to Ideal Solution (TOPSIS). pp 25–30. Wanjala G, Kusuma Astuti P, Bagi Z, et al. A review on the potential effects of environmental and economic factors on sheep genetic diversity: Consequences of climate change. Saudi J Biol Sci. 2023;30:103505. https://doi.org/10.1016/j.sjbs.2022.103505 . Chawala AR, Mwai AO, Peters A, Banos G, Chagunda MG. (2020). Towards a better understanding of breeding objectives and production performance of dairy cattle in sub- Saharan Africa: a systematic review and meta-analysis. CABI Reviews, (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor invited by journal 22 Jul, 2025 Editor assigned by journal 22 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 17 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7150569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":490007115,"identity":"fb911c13-8b43-4d0e-bf39-83a2c1a58bc1","order_by":0,"name":"Mahmood Aliyu","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Mahmood","middleName":"","lastName":"Aliyu","suffix":""},{"id":490007116,"identity":"5ef8b5a9-e7e7-40d3-8706-bd7f72534fc7","order_by":1,"name":"Akeem Babatunde Sikiru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACHiBmbGBIYGBmbHwA4vMRr4W9udkAxGcjXgvP8TYJkABBLfw9hw8+urnDLo9/RmJb5dccOxk2BuaHj27g0SJxti3ZOPdMcrHEjcS227LbkoEOYzM2zsFnzXkeM+ncNubEBpAWyW3MQC08bNL4tMif5//+O7etPnE+UEux5LZ6wloMzvawMee2HU7ccOZgG+PHbYcJazE8c8xYOvfM8WLD443N0ozbjvOwMRPwi9yZ5Iefc3dU58kdZn/48ee2ant+9uaHj/F6Hxkw84BJYpWDAOMPUlSPglEwCkbBiAEA+yJM557H1T0AAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Agriculture Zuru","correspondingAuthor":true,"prefix":"","firstName":"Akeem","middleName":"Babatunde","lastName":"Sikiru","suffix":""},{"id":490007117,"identity":"36b2ca25-cdf5-4111-aee9-3f9dc5fd2972","order_by":2,"name":"Aliyu Haxy Dikko","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Aliyu","middleName":"Haxy","lastName":"Dikko","suffix":""},{"id":490007118,"identity":"94cfd8f5-687f-4028-a67d-058f20e21d85","order_by":3,"name":"Ibrahim Shuaibu Harande","email":"","orcid":"","institution":"Federal University of Agriculture Zuru","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"Shuaibu","lastName":"Harande","suffix":""},{"id":490007119,"identity":"db91b24b-f00b-47bf-bfe6-91c83b6b7635","order_by":4,"name":"Stephen Sunday Acheneje Egena","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"Sunday Acheneje","lastName":"Egena","suffix":""},{"id":490007120,"identity":"8a5b5d4f-ed68-40d9-8940-8c2d091a111b","order_by":5,"name":"Nurulfiza Bint Mat Isa","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nurulfiza","middleName":"Bint Mat","lastName":"Isa","suffix":""},{"id":490007121,"identity":"0fca8287-7cee-488c-be11-394a6edc1845","order_by":6,"name":"Kasim Sakran Abass","email":"","orcid":"","institution":"University of Kirkuk","correspondingAuthor":false,"prefix":"","firstName":"Kasim","middleName":"Sakran","lastName":"Abass","suffix":""},{"id":490007124,"identity":"8b09b6f3-47a9-457a-8b2a-ccd1700bfbe2","order_by":7,"name":"Veerasamy Sejian","email":"","orcid":"","institution":"ICAR-National Institute of Animal Nutrition and Physiology","correspondingAuthor":false,"prefix":"","firstName":"Veerasamy","middleName":"","lastName":"Sejian","suffix":""}],"badges":[],"createdAt":"2025-07-17 15:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7150569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7150569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87580759,"identity":"efcc12a2-737c-4886-b0d0-2cbc9c4c9a2d","added_by":"auto","created_at":"2025-07-25 12:48:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1480509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComposite visualizations of physiological and milk yield indicators in livestock.\u003c/strong\u003e \u003cstrong\u003ea)\u003c/strong\u003eDistribution histograms show the density of observations for each physiological indicator, highlighting variability across samples. \u003cstrong\u003eb)\u003c/strong\u003e The PCA biplot is color-coded by Ward clusters, representing three distinct groups of observations based on principal component values. \u003cstrong\u003ec)\u003c/strong\u003e The heatmap illustrates the Z-score scaled values of physiological traits, providing a visual comparison of standard deviations across all observations for each trait. \u003cstrong\u003ed)\u003c/strong\u003e The TOPSIS ranking chart lists the top 20 observations based on their closeness coefficient, with colour intensity representing the CRITIC-weighted performance. Higher closeness values indicate better alignment with the ideal solution in the TOPSIS analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7150569/v1/35bba71f8e4b0b18fc7ab7b2.png"},{"id":87580758,"identity":"506b1a35-7a9f-4a45-98c8-34104e63464b","added_by":"auto","created_at":"2025-07-25 12:48:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":485466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar Chart of TOPSIS Scores Representing Individual Heat Stress Tolerance Ranking. \u003c/strong\u003eThe bar chart displays the TOPSIS scores of individual cows, ranked from most to least heat stress tolerant. Higher scores (left side) indicate animals with superior thermophysiological performance and greater overall resilience to heat stress, while lower scores (right side) reflect increased vulnerability. The colour gradient provides a visual scale of the tolerance spectrum, supporting data-driven selection for thermal robustness.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7150569/v1/0863d8e5338f7a4b0d3d89f4.jpeg"},{"id":87581552,"identity":"5c9b87c7-74e4-4e36-9ef0-07760f09e6af","added_by":"auto","created_at":"2025-07-25 12:56:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7150569/v1/5538f0fe-5592-4a17-8d4e-c2754ac8ea73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physiological and Production Profiling with TOPSIS Multi‑Criteria Ranking for Identification of Heat‑Tolerant White Fulani Cows under Tropical Farm Conditions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeat stress has emerged as a critical obstacle for dairy production in the humid tropics, where ambient temperature and relative humidity frequently climb above the thermoneutral zone which is one of the reasons why crossbred of indigenous and exotic breeds are commonly used on most dairy farms in the humid tropic zone of Nigeria (Sikiru et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The White Fulani (Bunaji) breed, widely used in West Africa is one of the most explored but remains poorly characterized for heat‑tolerance despite its robustness under pastoral conditions. There are earlier studies focused primarily on breed‑level comparisons using single traits such as rectal temperature or milk yield, yielding offering little guidance for within‑breed selection (Chawala et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, to accelerate climate‑smart genetic improvement, there is a growing demand for multivariate field tools that integrate physiological and production indicators of thermal resilience into a single, interpretable score. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi‑criteria decision‑making algorithm fulfils these requirements but has not yet been applied to heat‑stress phenotyping of White Fulani cows under commercial farming conditions.\u003c/p\u003e\u003cp\u003eTherefore, it was deployed as a longitudinal monitoring protocol on a Nigerian smallholder dairy farm with combined mixed‑effects modelling with CRITIC‑weighted TOPSIS ranking to achieve two objectives which are quantifying individual variation in heat‑stress responses with the cows in the same herd and identify superior cows for selective breeding. The study hypothesized that in a herd, subset of cows would maintain normothermia and higher milk yield throughout severe hot‑humid periods and such cows are potential candidates for genetic scrutiny and dissemination. Sustained selection of such adaptable animals will serve as a cornerstone of climate‑smart livestock breeding programmes in line with the Global Agenda for Sustainable Livestock (GASL). However, reliable field‑ready selection criteria remain elusive because physiological, behavioural and production indicators each capture only one dimension of the a single but highly complex heat‑stress phenotype.\u003c/p\u003e\u003cp\u003eMulti‑criteria decision‑making (MCDM) techniques, particularly the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), offer a simple and formal way to integrate different indicators into a single biologically interpretable closeness score (Uzun et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although, there are few studies where TOPSIS have been applied but under real‑farm conditions, none has yet targeted the White Fulani breed which is a key population in Africa. By combining physiological monitoring with MCDM, this report delivers a cow‑level ranking of heat‑tolerance in White Fulani cattle and provides a practical blueprint that can be adopted for on‑farm implementation across the West African humid tropics where the White Fulani cows are popular livestock.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eForty-five multiparous White Fulani cows (parity 2\u0026ndash;4) were observed for 180 days (Oct 2024 \u0026ndash; Mar 2025) on a smallholder farm (7\u0026deg;59\u0026prime; N, 3\u0026deg;33\u0026prime; E; K\u0026ouml;ppen Aw). The ambient THI was computed using daily observation of temperature and relative humidity with the equation validated for Nigerian climates (Sikiru et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The rectal temperature (RT), respiratory rate (RR), heart rate (HR) and milk yield (MY) were logged twice daily; ambient temperature and relative humidity were captured at 15-min intervals (HOBO \u0026reg; loggers). The White Fulani cows (parity 2\u0026ndash;4; 414\u0026thinsp;\u0026plusmn;\u0026thinsp;28.00 kg body mass) were housed under open‑sided sheds and milked once daily at 06:00 h, received a total mixed ration formulated to supply 2088.50Kcal kg⁻\u0026sup1; DM, and 15.50% crude protein (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and had \u003cem\u003ead libitum\u003c/em\u003e access to borehole water.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIngredients composition and proximate composition (% on DM basis) of the on-farm total mixed ration (TMR) used by smallholder dairy farmers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIngredient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantity\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrude\u003c/p\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003cp\u003eDetergent Fibre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcid\u003c/p\u003e\u003cp\u003eDetergent Fibre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEther Extract\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElephant grass\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePalm Kernel Cake\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaize bran\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoybean meal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMineral mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMolasses\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSalt\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNutrients composition\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCrude Protein (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutral Detergent Fibre (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcid Detergent Fibre (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEther Extract (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAsh (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNitrogen Free Extract (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolizable Energy (kcal/kg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2088.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe values represent proximate compositions of feed ingredients as determined in the TMR obtained from the farm for laboratory analyses. Crude Protein (CP), Ether Extract (EE), Crude Fibre (CF\u0026thinsp;\u0026asymp;\u0026thinsp;ADF), Ash, and Nitrogen-Free Extract (NFE) are expressed on a dry matter basis (% DM) and Metabolizable Energy (ME) expressed as kcal/kg DM. Urea included in the ration as a non-protein nitrogen source, its CP value is theoretical (281% on DM basis). The mineral mix and salt are considered inert for CP, EE, fibre, and energy but contribute significantly to total ash content.\u003c/p\u003e\u003cp\u003eThe data were checked for readings\u0026thinsp;\u0026gt;\u0026thinsp;3.5 SD from each cow\u0026rsquo;s mean then flagged and this led to 0.9% records discarded as measurement artefacts. The non-normal variables were subjected to Box-Cox transformation while the statistical modelling Linear mixed-effects models (cow random intercept; AR(1) residuals) were used to quantified linear\u0026thinsp;+\u0026thinsp;quadratic THI effects on RT, RR and HR. The multivariate ranking at Cow-level least-square means for RT, RR, HR, MY and cumulative heat-load units were entered into TOPSIS. The criterion weights were derived by the CRITIC objective-weighting algorithm and contrasted with an equal-weights scenario for robustness. The Principal-component analysis (PCA) and Ward hierarchical clustering were used to visualise multivariate patterns, mirroring the phenotyping strategy. All analyses were executed in R 4.4.0 with lme4, lmerTest, FactoMineR and MCDA. All the procedures conformed to the guidelines of the Department of Animal Production, Federal University of Technology Minna, approved by the Institutional Animal Care and Use Committee of the University (Approval code: 000093).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe mean THI over the 180‑day observation period was 81.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00, with mid-day peaks reaching 88.00, while the pre‑dawn THI seldom dropped below 74, confirming chronic thermal load (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The RT rose by 0.11\u0026deg;C per unit THI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); RR and HR increased by 3.4 breaths min⁻\u0026sup1; and 1.9 beats min⁻\u0026sup1; per unit THI, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Nevertheless, considerable inter‑cow variability was evident (random‑effects SD\u0026thinsp;=\u0026thinsp;0.36\u0026deg;C for RT). The aggregate MY averaged 7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 kg d⁻\u0026sup1; but declined by 0.26 kg d⁻\u0026sup1; per 10‑unit THI rise (p\u0026thinsp;=\u0026thinsp;0.032). The cows in the most tolerant quartile produced 2.7 kg d⁻\u0026sup1; more milk than the least tolerant group despite similar feed intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on inter‑criterion contrast and correlation structure, MY received the greatest weight (0.34) followed by RT (0.27), RR (0.22) and HR (0.17). The closeness coefficients ranged from 0.19 to 0.84, and five cows (IDs GDF‑03, ‑11, ‑19, ‑24, ‑41) consistently topped the ranking across CRITIC‑weighted and equal‑weight scenarios (Spearman ρ\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe PCA of the physiological\u0026ndash;production matrix explained 68% of variance on the first two axes and clearly separated high‑tolerance cows along PC1 loadings dominated by lower RT and higher MY (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The combined heat‑tolerance index derived from the mixed‑model residuals and TOPSIS ranks classified 22% of cows as highly tolerant, 47% as intermediate and 31% as susceptible. The sensitivity and robustness optimization focusing on leave‑one‑trait‑out analysis showed that removing RR or HR altered no cow\u0026rsquo;s quartile assignment, whereas excluding MY demoted three cows from high heat tolerant to intermediate heat tolerant. The ranking therefore hinges chiefly on production resilience and core body temperature regulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe observations showed that the RT ranged 38.40\u0026ndash;41.50\u0026deg;C (mean 39.90\u0026deg;C), RR 26\u0026ndash;108 breaths min⁻\u0026sup1; (mean 62.00), HR 52.00\u0026ndash;108.00 beats min⁻\u0026sup1; (mean 78.00). The coefficient of variation for MY (18%) was higher than that of RT (3%), this showed that milk yield is the most discriminating trait that can be used to distinguish the animals under heat stress condition. Further, Ward\u0026rsquo;s hierarchical clustering based on scaled trait means split the herd into three phenotypic clusters closely matching the highly tolerant/intermediate/susceptible categories (Adjusted Rand Index\u0026thinsp;=\u0026thinsp;0.82). The observation also showed that for breeding programmes, animals GDF‑03 and GDF‑11 combined low RT (\u0026ndash;0.48\u0026deg;C relative to herd mean) with the highest MY (+\u0026thinsp;3.1 kg d⁻\u0026sup1;) and stable HR, making these animals as prime candidate for genomic exploration projects in the herd. Conversely, GDF‑29, GDF‑30 and GDF‑32 displayed persistently elevated RT (\u0026gt;\u0026thinsp;40.5\u0026deg;C) and rapid MY decline and as such they are candidates for culling or management interventions and removal from breeding programmes. The expected gain analyses showed that selection of the top 15% most tolerant cows would raise average MY by 0.9 kg d⁻\u0026sup1; and reduce mean RT by 0.22\u0026deg;C in the next generation, assuming moderate heritability (h\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25) for the herd.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrates that an integrated MCDM protocol can be effectively used to distinguishes heat‑tolerant White Fulani cows under challenging tropical farm conditions leveraging routinely measurable physiological traits and milk yield. The mixed‑effects models quantified the physiological burden of chronic THI, similar to patterns reported in Thai Holstein\u0026ndash;Zebu crosses (Tao et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Colombian Romosinuano cows (Wanjala et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By coupling these outputs to CRITIC‑weighted TOPSIS, the report captured both the magnitude and stability of individual responses and thus avoided over‑reliance on any single indicator. One of the salient findings of this study is the dominant influence of MY in the objective weights of the cows. Although MY often deteriorates first under heat load, its inclusion alongside thermal and cardiorespiratory metrics provides a direct link to farmer revenue.\u003c/p\u003e\u003cp\u003eThe strong correlation between CRITIC‑weighted and equal‑weight rankings also indicates that the pipeline is robust to moderate uncertainty in weight specification, an advantage over subjective scoring systems. The identification of five highly tolerant cows in the herd presents immediate oppourtunities for within‑breed herd-wide selection for genetic improvement. Genomic analyses targeting heat‑shock proteins and vasodilatory pathways are suggested from these animals. Further, the clustering outcomes could guide pragmatic management, enabling resource management of the farm by allowing the farmer to allocate scarce shade or fan installations preferentially to susceptible cows.\u003c/p\u003e\u003cp\u003eThis is a report of single‑farm design which could limit extrapolation across ecological zones, also the absence of milk‑composition data may understate the full economic impact of heat stress. These are suggested as future studies to validate the pipeline across peri‑urban dairies in other agroecological zones of the country including the Guinea savannah and Sudan savannah belts while incorporate infrared thermography to reduce handling‑induced artefacts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study concluded that under a sustained THI, approximately one‑fifth of the White Fulani cows investigated maintained normothermia and superior milk yield which showed that the CRITIC‑TOPSIS pipeline can serve as a low cost, rapid, objective and field‑ready tool for ranking individual heat‑tolerance of a herd. The tool is recommended for adoption as a means to accelerate genetic gain towards climate‑resilient dairy production in Nigeria and other sub‑Saharan Africa countries. These findings align with emerging consensus that heat‑tolerance is a multifactorial trait influenced by evaporative capacity, metabolic efficiency and behavioural adaptability. The top‑ranked cows in this study exhibited lower afternoon RT yet did not elevate RR excessively, suggesting superior peripheral vasodilation and sweat gland function rather than a simple shift to panting. This phenotype mirrors sweat‑efficient normotherms category observed in Gir cattle which are also heat tolerant cows like White Fulani which deserves physiological scrutiny.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData associated with this study are publicly available in the Open Science Framework (OSF) repository under the project link https://osf.io/sz2dc/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no known competing financial interests or personal interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol implemented in this study was approved according to Institutional Animal Care and Use Committee, of the Federal University of Technology Minna, Nigeria (000093).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulsalam W, Egena SSA, Otu BO, et al. 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A review on the potential effects of environmental and economic factors on sheep genetic diversity: Consequences of climate change. Saudi J Biol Sci. 2023;30:103505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sjbs.2022.103505\u003c/span\u003e\u003cspan address=\"10.1016/j.sjbs.2022.103505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChawala AR, Mwai AO, Peters A, Banos G, Chagunda MG. (2020). Towards a better understanding of breeding objectives and production performance of dairy cattle in sub- Saharan Africa: a systematic review and meta-analysis. CABI Reviews, (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-animals","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Animals](https://link.springer.com/journal/44338)","snPcode":"44338","submissionUrl":"https://submission.springernature.com/new-submission/44338/3","title":"Discover Animals","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heat stress, White Fulani cattle, TOPSIS, milk yield, physiological responses","lastPublishedDoi":"10.21203/rs.3.rs-7150569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7150569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeat stress negatively affects dairy productivity in the humid tropics. To better understand the consequence, a longitudinally study was carried out to monitor 45 multiparous White Fulani cows for 180 days (temperature\u0026ndash;humidity index\u0026thinsp;=\u0026thinsp;81.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00; peaks\u0026thinsp;=\u0026thinsp;88.00) with recording daily rectal temperature (RT), respiratory rate (RR), heart rate (HR) and milk yield (MY). Mixed‑effects models were used to quantify the cows\u0026rsquo; physiological responses to heat load, while Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank individual heat stress tolerance. CRITIC‑derived weights prioritized MY (0.34) and RT (0.27). The cows in the top quartile-maintained RT 0.48\u0026deg;C below the herd-wide mean and produced 2.7 kg day⁻\u0026sup1; more milk despite severe THI. The ranking order was evaluated for consistency using equal-weight sensitivity analysis, resulting in a strong Spearman correlation coefficient (ρ\u0026thinsp;=\u0026thinsp;0.89). Findings from the study showed that the multivariate pipeline implemented provides a simple, rapid and field‑applicable tool for selecting heat‑tolerant lactating cows under smallholder conditions. The study suggests validation of the approach across additional agro‑ecological zones of Nigeria as warranted future investigations.\u003c/p\u003e","manuscriptTitle":"Physiological and Production Profiling with TOPSIS Multi‑Criteria Ranking for Identification of Heat‑Tolerant White Fulani Cows under Tropical Farm Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 12:48:33","doi":"10.21203/rs.3.rs-7150569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-05T10:31:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T12:12:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T14:53:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86929443034559105429108255377804534935","date":"2025-08-12T20:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86400322059142046463021137696934600331","date":"2025-08-05T10:29:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T05:59:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100062972745817353587015014559363720769","date":"2025-07-24T00:44:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T10:21:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T12:03:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T06:45:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T06:45:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Animals","date":"2025-07-17T15:20:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-animals","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Animals](https://link.springer.com/journal/44338)","snPcode":"44338","submissionUrl":"https://submission.springernature.com/new-submission/44338/3","title":"Discover Animals","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"489fead6-1adb-4224-bab9-c835b726ee08","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T13:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 12:48:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7150569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7150569","identity":"rs-7150569","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}