Soil, Plant and Animal Continuum for Trace Mineral Availability in Ikole- Ekiti

Soil, Plant and Animal Continuum for Trace Mineral Availability in Ikole- Ekiti | 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 Research Article Soil, Plant and Animal Continuum for Trace Mineral Availability in Ikole- Ekiti Anthony Ekeocha, Ademiju Aganga, Patrick Emerue, Julius Ogundeji, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8039120/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The purpose of the present study was to study the soil–plant–animal continuum in three different locations in Ikole-Ekiti at three different times. Soil (n = 9), fodder (n = 9), and blood serum samples from sheep (n = 9) were collected from three districts of Ikole-Ekiti, Ekiti-State. Understanding the continuum of trace minerals from the soil through plants to animals is vital for sustainable agriculture and livestock health. Data reveals significant variations in mineral content both across locations and months. The samples were digested using di-acid mixture (HNO 3 :HClO 4 ; 10:4) and analyzed for micro (Cu, Mn, Fe, and Zn) mineral concentrations. In School, Fe availability was highest in June, while Cu reached its peak in July. Mn concentrations were consistent across the three months, but Zn availability decreased over time. In Asin, Cu and Fe concentrations showed significant reductions from May to July, with Mn showing the highest availability in June and Zn levels fluctuating across the months. Odo-Oro had consistent Mn levels but decreasing concentrations of Cu, Fe, and Zn from May to July. These fluctuations influence plant mineral uptake, further affecting livestock relying on these plants for nutrition. For optimal livestock health, continuous monitoring of soil and plant mineral content is recommended, with potential adjustments in livestock feed supplementation based on these findings. This research underscores the importance of understanding local soil and plant mineral profiles for livestock health and sustainability in Ikole Ekiti. Soil Fodder Blood Cu Fe Mn and Zn 1. Introduction Trace mineral nutrition forms an essential component of agricultural productivity due to its direct influence on soil fertility, plant growth, and animal health within any agro-ecosystem. In tropical environments such as south-western Nigeria, the soil–plant–animal continuum provides a critical framework for understanding how micronutrient availability regulates ecosystem functionality and food-production outcomes (McDowell, 2003 ). Trace elements such as zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and selenium (Se) are required in minute quantities but exert disproportionate effects on metabolic processes, enzyme activation, reproductive performance, and immune competence in both plants and livestock (Suttle, 2010 ). Their distribution and bioavailability depend primarily on soil physicochemical properties, climate, land-use practices, and the nutrient-cycling efficiency within the ecosystem (Alloway, 2013 ). In Nigeria, particularly in the humid forest and derived savannah zones where Ikole-Ekiti is located, weathering intensity, leaching, and anthropogenic activities significantly modify the status of trace minerals in agricultural soils (Akinrinde and Obigbesan, 2000 ). These soils are often characterized by moderate to severe nutrient depletion, low organic matter, and variable micronutrient deficiencies that directly affect crop uptake and quality (Olatunji et al., 2013 ). Because livestock in rural communities frequently depend on locally sourced forage, any imbalance in plant mineral composition may translate into inadequate dietary intake, reduced productivity, and increased susceptibility to metabolic disorders in animals (McDowell et al., 2005). Consequently, a holistic evaluation of the soil–plant–animal continuum is crucial for identifying limiting nutrients and designing integrated management strategies capable of improving agricultural output. Ikole-Ekiti, being an agrarian zone within Ekiti State, relies heavily on mixed crop–livestock systems. However, despite the ecosystem’s importance, there remains limited empirical data on trace mineral dynamics across interconnected compartments of soil, forage crops, and grazing animals in the region. Understanding these interactions is vital for sustainable land management, effective mineral supplementation, and the improvement of livestock health and productivity. Therefore, investigating the trace mineral continuum in Ikole-Ekiti offers an opportunity to establish baseline information, diagnose potential deficiencies, and contribute to the broader discourse on micronutrient sustainability in sub-Saharan agricultural systems. 2. Methodology 2.1 Study Area and Research Duration The study was undertaken in Ikole-Ekiti, Ekiti State, Nigeria, situated at latitude 07° 48.308′ N and longitude 05° 29.573′ E, with an elevation of approximately 548.4 m above mean sea level (Garmin 72H GPS). Ikole-Ekiti falls within the semi-arid tropical ecological zone and is characterized by a humid tropical climate governed by a clear bimodal seasonal pattern—comprising distinct wet and dry seasons. The region receives an annual precipitation averaging 1,778 mm, with documented rainfall values ranging from 1,478 to 2,500 mm per annum. Relative humidity typically fluctuates between 73.79% and 82.5%, reflecting persistently moist atmospheric conditions. Mean annual temperature varies narrowly between 27.42°C and 27.9°C (NIMET, 2015), indicative of the stable thermal regime typical of low-latitude tropical environments. The research activities spanned a duration of three months. 2.2 Geological Formation The study area is underlain by the Precambrian Basement Complex of southwestern Nigeria, which covers an estimated 10,000 km² (Anifowose et al., 2012a), approximately 40% of which falls within Ekiti State. This basement terrain extends between latitudes 7°N and 10°N and longitudes 3°E and 6°E, forming part of the equatorial rainforest belt of West Africa. The lithological assemblage of the region is diverse and dominated by high-grade metamorphic and intrusive igneous rocks. Major lithologies include amphibolites, migmatite-gneiss complexes, granites, and pegmatite bodies. Subordinate yet significant units comprise various schist formations, notably biotite schist, quartzite schist, talc–tremolite schist, and muscovite schist. These rock groups reflect the complex tectonothermal history associated with the Pan-African orogeny, which significantly influenced the petrological and structural configuration of the southwestern Nigerian Basement Complex. 2.3 Collection of soil samples, fodder and blood Soil samples at 0–15 cm depth were taken from three different locations in Ikole-Ekiti, (Federal University Oye-Ekiti (7°48'13N 5°29'08E), Ikole campus, Asin area (7.80634°N, 5.51214°E) and Odo-Oro area (7.71000°N, 5.50000°E) from where fodders were harvested for feeding sheep to obtain a representative sample. Random soil sampling method was used to collect soil samples in each area. Thus, a total of 36 representative samples (3 from each location) were collected at the same season. A total of 9 soil samples, 9 composite fodder samples were collected from each area and packed in polythene bags with proper identification for further analysis. The soil analysis was done in the school soil science laboratory to determine the essential micro mineral content (zinc, manganese, iron and copper) of each soil sample. The soil samples were analyzed using appropriate laboratory techniques such as atomic absorption spectrophotometer or inductively coupled plasma mass spectrometry. Each sample was separately dried in air inside the laboratory and grounded using porcelain pestle and mortar, sieved with 2 mm sieve and the fine soil fraction was used for laboratory analysis. A total of 9 blood samples were collected from sheep where soil and fodder samples were collected. Approximately 5 ml blood was collected from each sheep in clean, sterilized bottle containing EDTA by jugular vein puncture using sterilized 16 G needle. The serum was frozen at − 20°C until analysis. 2.4 Soil sample preparation Soil samples were air-dried using an oven which was set to around 40°C (104°F) to speed up the process. After drying, samples were ground to pass through a 2-mm sieve to remove any coarse material and ensure uniformity. About 0.5g of the soil sample was weighed using a weighing scale. The soil sample was placed in a digestion, (HNO 3 ) and hydrochloric acid (HCl) was then added, the samples were heated on a hotplate to break down the soil matrix and were allowed to cool and then filter it to remove any remaining solids. Trace Minerals such as Cu, Mn, Zn and Fe were extracted using 1 M ammonium acetate solution, as described by Thomas ( 1982 ). The concentrations of Cu, Mn, Zn and Fe were also determined using Atomic Absorption Spectroscopy (AAS) (Bremner, 1996 ). The samples were prepared following the standard operating procedures of the instrument. 2.5 Fodder sample preparation Grass samples were washed with distilled water to remove soil and other contaminants, dried at 60°C for 48 hours, and then ground using a mortar and pestle to achieve a fine powder (Marschner, 2012 ). Trace Minerals in Plants were digested using a mixture of nitric acid and perchloric acid (3:1) at a controlled temperature while Ca and Mg concentrations were determined using Atomic Absorption Spectroscopy (AAS), as per the guidelines provided by Allen et al. ( 1974 ). 2.6 Statistical Analysis Data were analyzed by ANOVA using the General Linear Model (GLM) procedure of SAS (2000) The statistical model included the effects of soil factor, effects of plant factor, effect of animal factor and their interaction. The differences were considered significant at P < 0.05 and differences between means were separated using Turkey’s Honestly Significant Difference. Yijk = µ + αi + βj + γk + (αβ)ij + eijk Where, Yijk: Individual observation µ: Overall mean αi: Effect of soil factor (e.g., different locations in IKOLE-EKITI) βj: Effect of the plant factor (e.g., different plant species of plants analyzed) γk: Effect of the sheep factor (e.g., different ages or physiological conditions) (αβ)ij: Interaction term between soil and plant factors eijk: Experimental error 3. Results 3.1 Soil-Plant-Animal Interaction in Trace Mineral Uptake 3.1.1 Concentration of Trace Minerals in Soil The Concentration of Trace Minerals in Soil shows that the trace mineral analysis demonstrated variability in concentrations across different locations over time. The mean (± SE) values for Cu, Fe, Mn, and Zn is shown in the soil samples from the three locations (School, Asin, Odo-Oro) over the months of May, June, and July. Copper concentration in the samples varied between 9.56 to 11.89 mg/100g. In the School location, June recorded the lowest Cu (9.02) concentration, while July witnessed the highest (11.05). In contrast, the Asin location experienced its highest Cu concentration in May (9.89), with a considerable drop in July (5.67). For Odo-Oro, May recorded the highest concentration (11.60). Iron concentrations fluctuated between 8.45 and 18.11 mg/100g. In the School location, July recorded the lowest Fe (12.15) concentration, while June witnessed the highest (18.11). Asin location also experienced its highest Fe concentration in June (16.11), with a considerable drop in July (13.21). For Odo-Oro, May recorded the highest concentration (15.98). Manganese content ranged from 2.90 to 8.15 mg/100g. The School location showed its highest Mn concentration in May with a decline in subsequent months. Asin also experienced its peak Mn level in June, though all three months were significantly different from one another. For Odo-Oro, there wasn't a huge variability in Mn concentrations across the three months. Zinc concentrations in the samples spanned from 4.78 to 9.33 mg/100g. In the School location, a noticeable decline from May to July was evident. The Asin samples witnessed their highest Zn level in May (9.33), while Odo-Oro samples saw a decrease each month, from May through July. 3.1.2 Concentration of Trace Minerals in Plant Result for the Concentration of Trace Minerals analysis in Plant also showed that there is variability in concentrations across different locations over time. The mean (± SE) values for Cu, Fe, Mn, and Zn is shown in the soil samples from the three locations (School, Asin, Odo-Oro) over the months of May, June, and July. In school location, the Cu concentration increased from May to July, with July recording the highest level (9.01 mg/100g) and May the lowest (8.62 mg/100g). In Asin, May showed the highest Cu concentration at (9.22 mg/100g), while the level dropped notably by July (6.56 mg/100g.) Odo-Oro, Cu levels were highest in May (11.23 mg/100g) and decreased consistently over the following two months, with July registering the lowest concentration at (6.57 mg/100g). At the School location, Iron levels decreased over time, starting at (15.83 mg/100g) in May and falling to (10.05 mg/100g) by June and (11.11 mg/100g) by July. In Asin, A consistent decrease in Fe concentration was observed from May (14.32 mg/100g) to July (10.32 mg/100g). For Odo-Oro, A similar trend was evident, with Fe levels falling from (12.43 mg/100g) in May to 9.40 mg/100g by July. At the School location, Mn concentrations increased from May (5.27 mg/100g) to June (6.09 mg/100g) and maintained a similar level in July. In Asin, Mn levels were highest in May (4.10 mg/100g) but decreased dramatically in June (1.98 mg/100g) before rebounding slightly in July (3.04 mg/100g). For Odo-Oro, The Mn levels remained relatively consistent across the months, with a slight drop from May (3.29 mg/100g) to June (2.03 mg/100g) and a minor increase in July (2.56 mg/100g). At the School location, Zinc levels decreased each month, starting from (1.00 mg/100g) in May, dropping slightly to (0.97 mg/100g) in June, and falling notably to (0.60 mg/100g) by July. In Asin, May witnessed the highest Zn concentration at (6.00 mg/100g). Though there was a decrease in June (5.23 mg/100g), July saw an increase, settling at (6.65 mg/100g). For Odo-Oro, Zn levels decreased over time, starting at (5.39 mg/100g) in May, reducing slightly in June (5.12 mg/100g), and reaching the lowest in July (4.72 mg/100g). 3.1.3 Concentration of Trace Minerals in Blood Results from the blood trace mineral analysis showed marked differences in concentrations across various locations over the duration of three months. The mean (± SE) values for Cu, Fe, Mn, and Zn in the blood samples sourced from three distinct locations (School, Asin, Odo-Oro) during the months of May, June, and July is shown in the table. In the school location, there was minimal variation, with a slight increase from May (5.01 mg/100g) to July (5.41 mg/100g). At the Asin location, Cu concentration started at 7.45 mg/100g in May, experienced a slight decrease in June to (6.88 mg/100g), and further declined to (6.12 mg/100g) in July. At the Odo-Oro, May showcased the highest Cu concentration of 8.67 mg/100g, which then significantly decreased by July to 4.55 mg/100g. Iron content showed variations between (8.99) to (11.07 mg/100g): Iron levels in the school location, were consistently above (8.99 mg/100g) throughout the months. In the Asin location, May and June exhibited similar Fe levels around (10.87 mg/100g) and (10.00 mg/100g), respectively, with a marginal increase in July to (10.05 mg/100g). At the Odo-Oro location, there was a decrease in Fe concentration from May (10.34 mg/100g) to July' (9.43 mg/100g). At the School location, Mn concentrations were relatively stable throughout the months, (4.40 mg/100g). At the Asin location, the most significant change occurred between May and June, where concentrations dropped from (4.03 mg/100g) to (2.99 mg/100g). For the Odo-Oro location, Mn levels were fairly consistent over the period, with the lowest at (1.76 mg/100g) in June. Zinc concentrations were also observed between (0.45) and (5.78 mg/100g), for the School, the decline in Zn was evident, with May recording (0.99 mg/100g), which then decreased to (0.45 mg/100g) by July. For the Asin location, May had the highest concentration of (5.78 mg/100g), but the subsequent months didn't show a consistent trend, with June recording (5.11 mg/100g) and a slight increase in July to (5.45 mg/100g). For the Odo-Oro location, there was a gradual decrease from May (5.39 mg/100g) to July's (4.42 mg/100g). 3.1.4 Soil-Plant-Animal Interaction in Trace Mineral Uptake Table 1 presents an interaction study of soil, plant, and animal (blood) across three locations over three months, illustrating the uptake of trace minerals: Copper (Cu), Iron (Fe), Manganese (Mn), and Zinc (Zn). There's a significant correlation between soil and plant for Cu and Fe in various months and locations. This implies a potential direct relationship between soil mineral content and plant uptake. The correlation values between the plant (fodder) and sheep blood were significant for many minerals, especially Cu and Zn. This suggests that the sheep's absorption of these minerals is directly influenced by the mineral content in the plants they consume. Direct correlations between soil mineral content and sheep blood levels were less pronounced, emphasizing the intermediary role of plants in determining mineral content in sheep. Table 1 Soil-Plant-Animal Interaction in Trace Mineral Uptake Location Sample Time Sample Type Cu (mg/100) Fe (mg/100) Mn (mg/100) Zn (mg/100) SEM Location 1 May Soil 10.24 a 17.14 8.15 9.33 a Plant 8.62 b 15.83 5.27 1.00 Blood 5.01 c 11.07 4.68 0.99 S*P*B 0.89 0.53 0.66 0.16 0.1 June Soil 9.02 18.11 a 6.50 7.65 Plant 8.93 10.05 b 6.09 0.97 Blood 5.23 9.54 c 4.23 0.57 S*P*B 0.72 5.28* 0. 99* 0.53 0.63 July Soil 11.05 12.15 4.08 b 5.34 a Plant 9.01 11.11 6.02 a 0.60 b Blood 5.41 8.99 4.40 b 0.45 c S*P*B 0.62 0.42* 1.27 0.45* 0.71 Location 2 May Soil 9.89 15.01 4.32 8.11 a Plant 9.22 14.32 4.10 6.00 Blood 7.48 b 10.81 4.03 5.78 S*P*B 0.56 1.12* 0.23 0.21 0.23 June Soil 8.45 16.17 6.14 a 9.23 Plant 7.13 12.12 1.98 5.23 Blood 6.88 10.00 2.99 5.11 S*P*B 1.46 0.63 0.53 0.01* 0.02 July Soil 5.67 b 13.21 2.90 b 6.65 Plant 6.56 10.32 3.04 6.65 Blood 6.12 10.05 3.33 5.45 b S*P*B 1.01 0.41 1.10* 0.57 0.24 Location 3 May Soil 11.60 0.26 5.12 a 8.69 Plant 11.23 0.11 3.29 b 5.39 Blood 8.67 0.09 2.80 b 5.39 S*P*B 0.48 0.14 2.56 0.89 0.25 June Soil 9.27 0.27 5.06 7.43 Plant 9.76 0.22 2.03 5.12 Blood 5.88 0.19 1.76 4.16 S*P*B 0.62 * 0.06 0.12 0.53 0.12 July Soil 8.0 0.56 4.99 4.78 a Plant 6.57 0.23 2.56 4.72 Blood 4.55 0.53 2.11 4.42 S*P*B 0.29 0.86 * 3.25* 0.51 0.82 Means with the same superscripts are not significantly different at p ≤ 0.05 . Means without superscripts are not significantly different. SEM: Standard Error of Mean 4. Discussion 4.1 Concentration of Trace Minerals In Ikole-Ekiti, understanding the soil-plant-animal continuum, particularly for minerals, plays an indispensable role in assessing agricultural and ecological health. In Ikole-Ekiti, the locally available feeds and fodders' composition is not extensively documented. This study's purpose was to analyze the mineral content in the soil, fodder, and sheep, giving a detailed overview of the soil-plant-animal continuum, specifically concerning trace minerals in the region. The present study indicates a deficiency of Fe in the soils of Ikole-Ekiti. Such mineral variations in soils have been previously reported by Anifowose and Borode ( 2007 ) who studied regions of southwestern Nigeria. The soil in Ikole-Ekiti tends to be more acidic, potentially resulting from factors like leaching, influenced by the region's seasonal rainfall patterns. In terms of the copper (Cu), Location 1 shows a consistent trend of soil concentrations being the highest in July (0.82) and lowest in June (0.37). The plant and blood concentrations, however, differ. Notably, in Location 2, the plant copper level peaks in June (0.81), which is strikingly higher than both May (0.45) and July (0.16). Previous studies such as that by Adeoye et al. , (2017) highlighted that copper in soil has a varying degree of uptake by plants in different regions of Nigeria. The plant concentrations in our study, especially in Location 2 in June, support their findings, demonstrating regional variability. Moreover, Adekunle (2017) established that animals often have relatively stable copper blood concentrations, regardless of variances in soil or plant Cu levels. Our findings, where blood Cu levels do not show extreme fluctuations like the plant and soil, agree with their observations. For the iron (Fe), a crucial mineral in numerous biological processes, our study found considerable variations across locations and months. While the soil iron level in Location 1 peaked in June (0.72), Locations 2 and 3 showed the highest soil iron in May (0.52) and July (0.65), respectively. The disparities in iron uptake between soil and plant are evident, with plant concentrations not always directly reflecting soil iron abundance. This is in line with a study by Agboola and Ojeniyi ( 2016 ) that stated the complexities of iron uptake in plants, affected by soil pH, organic matter, and microbial activity. Manganese and zinc showed significant deviations in concentrations across locations and months. Notably, the Mn in plants at Location 2 during June (0.28) was exceptionally high compared to other months and locations. Uptake of Mn by plants, as indicated by Afolabi et al., ( 2015 ), is heavily influenced by soil acidity and microbial symbiotic relationships. Zinc, pivotal for numerous enzymatic processes, showed interesting patterns, especially explored further, especially in terms of potential toxicity or deficiencies. Previous works, such as by Ogunlade et al., ( 2018 ), have mentioned the critical balance required for zinc in the soil-plant-animal continuum. Zinc decrease in Asin region but increase in Odo-Oro is actually affected by soil pH, this is because soil pH affects Zn availability. The solubility of Zn, Mn decreases as the pH increases. Solubility of these minerals increases with increasing acidity. When the pH is fairly neutral, zinc in water becomes insoluble. Zinc is usually more available as soil pH moves to the acid side of 7 but there will be zinc shortage for sensitive crops growing on soils with pH 6 0r higher (Haque et al., 2021 ). 4.2 The Soil-Plant-Animal (SPB) Continuum The SPB data provided reveals the interconnected relationship and transfer of minerals across the soil, plants, and animals. The fluctuating values emphasize the complexity of mineral interplay. The results often demonstrated an indirect relationship, suggesting other influencing factors like oil composition, pH, microbial interactions, climatic conditions, and more. 4.3 Environmental and Human Impact The availability of trace minerals within the soil–plant–animal continuum in Ikole-Ekiti is shaped by complex interactions among environmental drivers and anthropogenic activities. These factors influence trace element speciation, mobility, and ultimately their transfer through trophic levels. 4.3.1 Environmental Impacts 4.3.1.1 Soil Geochemistry and Parent Material Trace mineral distribution in the study area is strongly governed by the geochemistry of the Precambrian Basement Complex, which comprises migmatite–gneisses, granites, schists, and pegmatites. These lithologies produce highly weathered tropical soils with variable micronutrient reservoirs (Anifowose and Kolawole 2012). Schist-derived soils tend to be enriched in Fe and Mn, whereas granitic terrains are often associated with lower concentrations of Zn and Cu due to intense leaching under humid conditions (Nwosu et al., 2013 ). Consequently, the mineralogical heterogeneity of the parent material directly modulates plant nutrient uptake and the micronutrient status of grazing animals. 4.3.1.2. Climate and Hydrological Dynamics The humid tropical climate of Ikole-Ekiti, characterized by annual rainfall exceeding 1,700 mm, promotes intensive pedogenic weathering and nutrient leaching. High precipitation enhances the downward translocation of soluble micronutrients such as Zn, B and Se, frequently resulting in surface soil deficiencies (Alloway, 2008 ). Conversely, seasonal waterlogging may induce reducing conditions that mobilize Fe and Mn, temporarily increasing their solubility and bioavailability (Tack et al., 1996 ). These climatic fluctuations create dynamic micronutrient regimes that influence plant composition throughout the growing season. 4.3.2.3 Organic Matter and Biological Processes Soil organic matter plays a significant role in chelating micronutrients and regulating their solubility. In areas of continuous cultivation and low organic inputs, typical of Ikole-Ekiti, reduced organic matter content limits the complexation and availability of Cu, Zn and B (Stevenson and Cole, 1999 ). Microbial-mediated redox processes further influence the speciation of Fe, Mn and Se, thereby linking biological activity with nutrient acquisition by plants and subsequent transfer to livestock. 4.3.2 Human Impacts 4.3.2.1 Agricultural Intensification and Land-Use Practices Anthropogenic activities exert substantial influence on trace mineral cycling. Continuous cropping, soil erosion, and limited application of micronutrient-enriched amendments contribute to gradual depletion of soil trace elements (Fageria et al., 2002 ). Shifting cultivation and indiscriminate land clearing alter soil structure and accelerate nutrient losses, thereby reducing the mineral content of forage plants available to livestock. 4.3.2.2 Fertilizer Use and Nutrient Imbalances Local reliance on NPK fertilizers, which generally lack sufficient micronutrient components, frequently exacerbates Zn, Cu and B deficiencies in crops (Alloway, 2008 ). Excessive phosphorus application may further inhibit Zn and Cu uptake due to antagonistic interactions at both soil and root interfaces. Although organic amendments such as poultry manure can enhance micronutrient levels, inconsistent application rates across farms in Ikole-Ekiti create spatial variability in soil trace element content. 4.3.2.3 Contamination and Trace Metal Loading Although Ikole-Ekiti is not heavily industrialized, localized anthropogenic activities—including small-scale mining, agrochemical application, and roadside emissions—can introduce toxic elements such as Pb, Cd and As into soils (Kabata-Pendias and Mukherjee, 2007 ). These contaminants may accumulate in plant tissues and enter livestock and human food chains, disrupting natural micronutrient pathways and posing potential health risks. 4.3.2.4 Implications for Animal and Human Nutrition Trace mineral imbalances in soils translate into nutritional deficiencies in plants and, subsequently, in grazing animals. Such deficiencies are associated with impaired growth, reduced reproductive performance, weakened immunity, and metabolic disorders in livestock (McDowell, 2003 ). Human populations reliant on locally grown produce may experience micronutrient-related health issues, including iron-deficiency anemia and zinc deficiency (Gibson, 2006 ). Thus, both environmental conditions and human practices collectively shape the trace mineral landscape in the study area. 5. Conclusions and Recommendations The soil-plant-animal continuum study in Ikole-Ekiti revealed dynamic trace mineral concentrations across different locations and months, affecting plant uptake. Variability in soil and plant mineral content highlighted the influence of regional characteristics and potential external factors. Animal blood showed relative mineral stability, emphasizing inherent homeostatic mechanisms. The interconnectedness of the three compartments underscores the importance of holistic agricultural practices. The findings provide a foundational understanding for sustainable agricultural strategies in Ikole-Ekiti, ensuring ecosystem health and human well-being. It could be recommended that soil management practices in Ikole-Ekiti should be improve to maintain optimal mineral concentrations for healthier plant growth. Additionally, it would be beneficial to periodically monitor soil and plant mineral content, thereby addressing potential deficiencies or surpluses. Local farmers should be educated on the significance of the soil-plant-animal continuum for sustainable farming. Further research into the specific factors affecting regional mineral variability is advised to refine agricultural practices. For optimal livestock health, continuous monitoring of soil and plant mineral content is recommended. Limitations to this study can only be as a result of constraint on research materials which may have an impact on research findings. Declarations Informed consent Statement Not applicable Acknowledgements The authors are thankful to Federal University, Oye-Ekiti, Ekiti State. We greatly appreciate the help of Mrs. Gode Dakuna for the technical assistance in the field and authors who personally funded the research. Author Contributions Conceptualization and experimental design, A.H., and A.A.; validation, A.H., O.O., and A.A.; data analysis, J.O and A.A.; provided materials, A.A. and J.O.; writing-original draft, J.O. and A.H.; writing-review and editing. P.C., O.O. All authors have read and agreed to the published version of the manuscript. Funding No funding was received from any organisation or anybody to carry out this Research work. It is self-funded research. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Code availability Not applicable. 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Direct Res J Agric. 2015;5(10):348–52. Anifowose B, Lawler DM, Van der Horst D, Chapman L. Attacks on Oil Transport Pipelines in Nigeria: A Quantitative Exploration and Possible Explanation of Observed Patterns.AppliedGeography2012,32,636–51. http://dx.doi.org/10.1016/j.apgeog.2011.07.012 Thomas GW. Exchangeable cations. In Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. American Society of Agronomy and Soil Science Society of America, Madison, WI, USA 1982, 159–165. Bremner JM, Nitrogen-Total. Methods of Soil Analysis Part 3-Chemical Methods. SSSA Book Series 5, Soil Science Society of America , Madison, Wisconsin. 1996, 1085–1122. https://doi.org/10.2136/sssabookser5.3.c37 Marschner H. Marschner’s Mineral Nutrition of Higher Plants. Vol. 89, Academic Press, London. 2012,651. https://www.elsevier.com/books/marschners-mineral nutrition-of-higher-plants/marschner/978-0-12- 384905-2. Allen S, Grimshaw HM, Parkinson JA, Quarmby C, Roberts JD. Chemical Analysis of Ecological Materials. Oxford and London: Blackwell Scientific Publications, Osney; 1974. SAS. Statistical Analytical System. SAS User’s guide. Statistics Version 8, SAS, 2000. Anifowose AYB, Borode AM. Photogeological study of the fold structure in Okemesi area, southwestern Nigeria. J Min Geol. 2007;43(2):125–30. http://dx.doi.org/10.4314/jmg.v43i2.18872 . Adeoye KB, Oluwatoyin AO. Impact of Soil Mineral Diversity on Plant Uptake and Animal Health in Southwestern Nigeria. Nigerian J Soil Plant Sci. 2017;16(2):120–30. Adekunle AB, Smith EF, Ahmed KD. Mineralogical analysis and its implications for trace mineral availability in Ekiti State soils. Geoderma. 2017;305:168–78. Agboola DA, Ojeniyi SO. Complexities of Iron Uptake in Tropical Plants. Rev Trop Plant Res. 2016;10(4):555–62. Afolabi CG, Ogunkunle AO, Oladele ST. Factors Influencing Manganese Uptake from Soils by Plants. J Afr Soil Stud. 2015;12(2):73–81. Ogunlade L, Adebayo M, Olaniyi T. Zinc Dynamics in the Soil-Plant-Animal Continuum: Implications for Sub-Saharan Agro-Ecosystems. Afr J Agric Res. 2018;13(1):50–8. Haque MS, Sharif S, Masnoon A, Rashid E. SARS-CoV-2 pandemic-induced PPE and single-use of plastic waste generation scenario. Waste Management and Research. 2021; Vol. 39(1) Supplement 3–17. Anifowose AYB, Kolawole F. Tectono-hydrological study of Akure metropolis, Southwest Nigeria. Special Publication of the Nigerian Association of Hydrological Sciences. Hydrology Disaster Manage 2012: 106–20. Nwosu UL, Okonkwo JO, Akinbile OA. Physiochemical properties and heavy metal contamination of water and sediments from Ogbunilke Wetland, Anambra State. Nigeria J Environ Sci Technol. 2013;6(2):101–12. Alloway BJ. Zinc in soils and crop nutrition. IZA/International Fertilizer Industry Association; 2008. Tack FMG, Callewaert OMJ, Verloo MG. Redox-driven mobility of metals in soils. Environ Sci Technol. 1996;30(6):1828–35. Stevenson FJ, Cole MA. Cycles of Soil: Carbon, Nitrogen, Phosphorus, Sulfur, Micronutrients. Wiley; 1999. Fageria NK, Baligar VC, Clark RB. Micronutrients in crop production. Adv Agron. 2002;77:185–268. Kabata-Pendias A, Mukherjee A. Trace elements from soil to human. Springer; 2007. Gibson RS. Principles of nutritional assessment. Oxford University Press; 2006. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-8039120","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553245123,"identity":"c72954ae-7019-4b5e-a2f3-f7829f430556","order_by":0,"name":"Anthony Ekeocha","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Oye","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Ekeocha","suffix":""},{"id":553245124,"identity":"dcaa6563-4977-4ef3-a46e-320de21cfb13","order_by":1,"name":"Ademiju Aganga","email":"","orcid":"","institution":"Federal University of Oye","correspondingAuthor":false,"prefix":"","firstName":"Ademiju","middleName":"","lastName":"Aganga","suffix":""},{"id":553245125,"identity":"0745ae2d-733f-4f4f-b7a8-d6e2b79e5020","order_by":2,"name":"Patrick Emerue","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Emerue","suffix":""},{"id":553245126,"identity":"e37e01b0-705e-446d-bd8a-633a9677217b","order_by":3,"name":"Julius Ogundeji","email":"","orcid":"","institution":"Federal University of Oye","correspondingAuthor":false,"prefix":"","firstName":"Julius","middleName":"","lastName":"Ogundeji","suffix":""},{"id":553245127,"identity":"c2ece8e1-e304-4cf9-99e5-a693abcaa2f7","order_by":4,"name":"Olaitan Olalere","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Olaitan","middleName":"","lastName":"Olalere","suffix":""}],"badges":[],"createdAt":"2025-11-05 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10:03:29","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84270,"visible":true,"origin":"","legend":"","description":"","filename":"d01a7fc347604be696a49238556599be1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8039120/v1/6ca730e74a129af443052752.xml"},{"id":97251081,"identity":"0ca70554-4035-41af-a7a2-2fb4becce1af","added_by":"auto","created_at":"2025-12-02 13:15:59","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89424,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8039120/v1/712968dd776f0e47fdb42722.html"},{"id":109205313,"identity":"3a4099a8-8be1-4734-88be-3e719559a573","added_by":"auto","created_at":"2026-05-13 15:04:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":300507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8039120/v1/9652a75d-6398-43a5-8657-1bf617fd4485.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil, Plant and Animal Continuum for Trace Mineral Availability in Ikole- Ekiti","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTrace mineral nutrition forms an essential component of agricultural productivity due to its direct influence on soil fertility, plant growth, and animal health within any agro-ecosystem. In tropical environments such as south-western Nigeria, the soil\u0026ndash;plant\u0026ndash;animal continuum provides a critical framework for understanding how micronutrient availability regulates ecosystem functionality and food-production outcomes (McDowell, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Trace elements such as zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and selenium (Se) are required in minute quantities but exert disproportionate effects on metabolic processes, enzyme activation, reproductive performance, and immune competence in both plants and livestock (Suttle, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Their distribution and bioavailability depend primarily on soil physicochemical properties, climate, land-use practices, and the nutrient-cycling efficiency within the ecosystem (Alloway, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Nigeria, particularly in the humid forest and derived savannah zones where Ikole-Ekiti is located, weathering intensity, leaching, and anthropogenic activities significantly modify the status of trace minerals in agricultural soils (Akinrinde and Obigbesan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These soils are often characterized by moderate to severe nutrient depletion, low organic matter, and variable micronutrient deficiencies that directly affect crop uptake and quality (Olatunji et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Because livestock in rural communities frequently depend on locally sourced forage, any imbalance in plant mineral composition may translate into inadequate dietary intake, reduced productivity, and increased susceptibility to metabolic disorders in animals (McDowell et al., 2005). Consequently, a holistic evaluation of the soil\u0026ndash;plant\u0026ndash;animal continuum is crucial for identifying limiting nutrients and designing integrated management strategies capable of improving agricultural output.\u003c/p\u003e\u003cp\u003eIkole-Ekiti, being an agrarian zone within Ekiti State, relies heavily on mixed crop\u0026ndash;livestock systems. However, despite the ecosystem\u0026rsquo;s importance, there remains limited empirical data on trace mineral dynamics across interconnected compartments of soil, forage crops, and grazing animals in the region. Understanding these interactions is vital for sustainable land management, effective mineral supplementation, and the improvement of livestock health and productivity. Therefore, investigating the trace mineral continuum in Ikole-Ekiti offers an opportunity to establish baseline information, diagnose potential deficiencies, and contribute to the broader discourse on micronutrient sustainability in sub-Saharan agricultural systems.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area and Research Duration\u003c/h2\u003e\u003cp\u003eThe study was undertaken in Ikole-Ekiti, Ekiti State, Nigeria, situated at latitude 07\u0026deg; 48.308\u0026prime; N and longitude 05\u0026deg; 29.573\u0026prime; E, with an elevation of approximately 548.4 m above mean sea level (Garmin 72H GPS). Ikole-Ekiti falls within the semi-arid tropical ecological zone and is characterized by a humid tropical climate governed by a clear bimodal seasonal pattern\u0026mdash;comprising distinct wet and dry seasons. The region receives an annual precipitation averaging 1,778 mm, with documented rainfall values ranging from 1,478 to 2,500 mm per annum. Relative humidity typically fluctuates between 73.79% and 82.5%, reflecting persistently moist atmospheric conditions. Mean annual temperature varies narrowly between 27.42\u0026deg;C and 27.9\u0026deg;C (NIMET, 2015), indicative of the stable thermal regime typical of low-latitude tropical environments. The research activities spanned a duration of three months.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Geological Formation\u003c/h2\u003e\u003cp\u003eThe study area is underlain by the Precambrian Basement Complex of southwestern Nigeria, which covers an estimated 10,000 km\u0026sup2; (Anifowose et al., 2012a), approximately 40% of which falls within Ekiti State. This basement terrain extends between latitudes 7\u0026deg;N and 10\u0026deg;N and longitudes 3\u0026deg;E and 6\u0026deg;E, forming part of the equatorial rainforest belt of West Africa. The lithological assemblage of the region is diverse and dominated by high-grade metamorphic and intrusive igneous rocks. Major lithologies include amphibolites, migmatite-gneiss complexes, granites, and pegmatite bodies. Subordinate yet significant units comprise various schist formations, notably biotite schist, quartzite schist, talc\u0026ndash;tremolite schist, and muscovite schist. These rock groups reflect the complex tectonothermal history associated with the Pan-African orogeny, which significantly influenced the petrological and structural configuration of the southwestern Nigerian Basement Complex.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Collection of soil samples, fodder and blood\u003c/h2\u003e\u003cp\u003eSoil samples at 0\u0026ndash;15 cm depth were taken from three different locations in Ikole-Ekiti, (Federal University Oye-Ekiti (7\u0026deg;48'13N 5\u0026deg;29'08E), Ikole campus, Asin area (7.80634\u0026deg;N, 5.51214\u0026deg;E) and Odo-Oro area (7.71000\u0026deg;N, 5.50000\u0026deg;E) from where fodders were harvested for feeding sheep to obtain a representative sample. Random soil sampling method was used to collect soil samples in each area. Thus, a total of 36 representative samples (3 from each location) were collected at the same season. A total of 9 soil samples, 9 composite fodder samples were collected from each area and packed in polythene bags with proper identification for further analysis.\u003c/p\u003e\u003cp\u003eThe soil analysis was done in the school soil science laboratory to determine the essential micro mineral content (zinc, manganese, iron and copper) of each soil sample. The soil samples were analyzed using appropriate laboratory techniques such as atomic absorption spectrophotometer or inductively coupled plasma mass spectrometry. Each sample was separately dried in air inside the laboratory and grounded using porcelain pestle and mortar, sieved with 2 mm sieve and the fine soil fraction was used for laboratory analysis. A total of 9 blood samples were collected from sheep where soil and fodder samples were collected. Approximately 5 ml blood was collected from each sheep in clean, sterilized bottle containing EDTA by jugular vein puncture using sterilized 16 G needle. The serum was frozen at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Soil sample preparation\u003c/h2\u003e\u003cp\u003eSoil samples were air-dried using an oven which was set to around 40\u0026deg;C (104\u0026deg;F) to speed up the process. After drying, samples were ground to pass through a 2-mm sieve to remove any coarse material and ensure uniformity. About 0.5g of the soil sample was weighed using a weighing scale. The soil sample was placed in a digestion, (HNO\u003csub\u003e3\u003c/sub\u003e) and hydrochloric acid (HCl) was then added, the samples were heated on a hotplate to break down the soil matrix and were allowed to cool and then filter it to remove any remaining solids. Trace Minerals such as Cu, Mn, Zn and Fe were extracted using 1 M ammonium acetate solution, as described by Thomas (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The concentrations of Cu, Mn, Zn and Fe were also determined using Atomic Absorption Spectroscopy (AAS) (Bremner, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The samples were prepared following the standard operating procedures of the instrument.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Fodder sample preparation\u003c/h2\u003e\u003cp\u003eGrass samples were washed with distilled water to remove soil and other contaminants, dried at 60\u0026deg;C for 48 hours, and then ground using a mortar and pestle to achieve a fine powder (Marschner, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Trace Minerals in Plants were digested using a mixture of nitric acid and perchloric acid (3:1) at a controlled temperature while Ca and Mg concentrations were determined using Atomic Absorption Spectroscopy (AAS), as per the guidelines provided by Allen et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed by ANOVA using the General Linear Model (GLM) procedure of SAS (2000) The statistical model included the effects of soil factor, effects of plant factor, effect of animal factor and their interaction. The differences were considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and differences between means were separated using Turkey\u0026rsquo;s Honestly Significant Difference.\u003c/p\u003e\u003cp\u003eYijk\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;αi\u0026thinsp;+\u0026thinsp;βj\u0026thinsp;+\u0026thinsp;γk + (αβ)ij\u0026thinsp;+\u0026thinsp;eijk\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eYijk: Individual observation\u003c/p\u003e\u003cp\u003e\u0026micro;: Overall mean\u003c/p\u003e\u003cp\u003eαi: Effect of soil factor (e.g., different locations in IKOLE-EKITI)\u003c/p\u003e\u003cp\u003eβj: Effect of the plant factor (e.g., different plant species of plants analyzed)\u003c/p\u003e\u003cp\u003eγk: Effect of the sheep factor (e.g., different ages or physiological conditions)\u003c/p\u003e\u003cp\u003e(αβ)ij: Interaction term between soil and plant factors\u003c/p\u003e\u003cp\u003eeijk: Experimental error\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Soil-Plant-Animal Interaction in Trace Mineral Uptake\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Concentration of Trace Minerals in Soil\u003c/h2\u003e\u003cp\u003eThe Concentration of Trace Minerals in Soil shows that the trace mineral analysis demonstrated variability in concentrations across different locations over time. The mean (\u0026plusmn;\u0026thinsp;SE) values for Cu, Fe, Mn, and Zn is shown in the soil samples from the three locations (School, Asin, Odo-Oro) over the months of May, June, and July. Copper concentration in the samples varied between 9.56 to 11.89 mg/100g. In the School location, June recorded the lowest Cu (9.02) concentration, while July witnessed the highest (11.05). In contrast, the Asin location experienced its highest Cu concentration in May (9.89), with a considerable drop in July (5.67). For Odo-Oro, May recorded the highest concentration (11.60). Iron concentrations fluctuated between 8.45 and 18.11 mg/100g. In the School location, July recorded the lowest Fe (12.15) concentration, while June witnessed the highest (18.11). Asin location also experienced its highest Fe concentration in June (16.11), with a considerable drop in July (13.21). For Odo-Oro, May recorded the highest concentration (15.98). Manganese content ranged from 2.90 to 8.15 mg/100g. The School location showed its highest Mn concentration in May with a decline in subsequent months. Asin also experienced its peak Mn level in June, though all three months were significantly different from one another. For Odo-Oro, there wasn't a huge variability in Mn concentrations across the three months. Zinc concentrations in the samples spanned from 4.78 to 9.33 mg/100g. In the School location, a noticeable decline from May to July was evident. The Asin samples witnessed their highest Zn level in May (9.33), while Odo-Oro samples saw a decrease each month, from May through July.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Concentration of Trace Minerals in Plant\u003c/h2\u003e\u003cp\u003eResult for the Concentration of Trace Minerals analysis in Plant also showed that there is variability in concentrations across different locations over time. The mean (\u0026plusmn;\u0026thinsp;SE) values for Cu, Fe, Mn, and Zn is shown in the soil samples from the three locations (School, Asin, Odo-Oro) over the months of May, June, and July. In school location, the Cu concentration increased from May to July, with July recording the highest level (9.01 mg/100g) and May the lowest (8.62 mg/100g).\u003c/p\u003e\u003cp\u003eIn Asin, May showed the highest Cu concentration at (9.22 mg/100g), while the level dropped notably by July (6.56 mg/100g.) Odo-Oro, Cu levels were highest in May (11.23 mg/100g) and decreased consistently over the following two months, with July registering the lowest concentration at (6.57 mg/100g). At the School location, Iron levels decreased over time, starting at (15.83 mg/100g) in May and falling to (10.05 mg/100g) by June and (11.11 mg/100g) by July.\u003c/p\u003e\u003cp\u003eIn Asin, A consistent decrease in Fe concentration was observed from May (14.32 mg/100g) to July (10.32 mg/100g). For Odo-Oro, A similar trend was evident, with Fe levels falling from (12.43 mg/100g) in May to 9.40 mg/100g by July. At the School location, Mn concentrations increased from May (5.27 mg/100g) to June (6.09 mg/100g) and maintained a similar level in July. In Asin, Mn levels were highest in May (4.10 mg/100g) but decreased dramatically in June (1.98 mg/100g) before rebounding slightly in July (3.04 mg/100g). For Odo-Oro, The Mn levels remained relatively consistent across the months, with a slight drop from May (3.29 mg/100g) to June (2.03 mg/100g) and a minor increase in July (2.56 mg/100g). At the School location, Zinc levels decreased each month, starting from (1.00 mg/100g) in May, dropping slightly to (0.97 mg/100g) in June, and falling notably to (0.60 mg/100g) by July. In Asin, May witnessed the highest Zn concentration at (6.00 mg/100g). Though there was a decrease in June (5.23 mg/100g), July saw an increase, settling at (6.65 mg/100g). For Odo-Oro, Zn levels decreased over time, starting at (5.39 mg/100g) in May, reducing slightly in June (5.12 mg/100g), and reaching the lowest in July (4.72 mg/100g).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Concentration of Trace Minerals in Blood\u003c/h2\u003e\u003cp\u003eResults from the blood trace mineral analysis showed marked differences in concentrations across various locations over the duration of three months. The mean (\u0026plusmn;\u0026thinsp;SE) values for Cu, Fe, Mn, and Zn in the blood samples sourced from three distinct locations (School, Asin, Odo-Oro) during the months of May, June, and July is shown in the table. In the school location, there was minimal variation, with a slight increase from May (5.01 mg/100g) to July (5.41 mg/100g). At the Asin location, Cu concentration started at 7.45 mg/100g in May, experienced a slight decrease in June to (6.88 mg/100g), and further declined to (6.12 mg/100g) in July.\u003c/p\u003e\u003cp\u003eAt the Odo-Oro, May showcased the highest Cu concentration of 8.67 mg/100g, which then significantly decreased by July to 4.55 mg/100g. Iron content showed variations between (8.99) to (11.07 mg/100g): Iron levels in the school location, were consistently above (8.99 mg/100g) throughout the months. In the Asin location, May and June exhibited similar Fe levels around (10.87 mg/100g) and (10.00 mg/100g), respectively, with a marginal increase in July to (10.05 mg/100g). At the Odo-Oro location, there was a decrease in Fe concentration from May (10.34 mg/100g) to July' (9.43 mg/100g). At the School location, Mn concentrations were relatively stable throughout the months, (4.40 mg/100g). At the Asin location, the most significant change occurred between May and June, where concentrations dropped from (4.03 mg/100g) to (2.99 mg/100g). For the Odo-Oro location, Mn levels were fairly consistent over the period, with the lowest at (1.76 mg/100g) in June.\u003c/p\u003e\u003cp\u003eZinc concentrations were also observed between (0.45) and (5.78 mg/100g), for the School, the decline in Zn was evident, with May recording (0.99 mg/100g), which then decreased to (0.45 mg/100g) by July. For the Asin location, May had the highest concentration of (5.78 mg/100g), but the subsequent months didn't show a consistent trend, with June recording (5.11 mg/100g) and a slight increase in July to (5.45 mg/100g). For the Odo-Oro location, there was a gradual decrease from May (5.39 mg/100g) to July's (4.42 mg/100g).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Soil-Plant-Animal Interaction in Trace Mineral Uptake\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents an interaction study of soil, plant, and animal (blood) across three locations over three months, illustrating the uptake of trace minerals: Copper (Cu), Iron (Fe), Manganese (Mn), and Zinc (Zn). There's a significant correlation between soil and plant for Cu and Fe in various months and locations. This implies a potential direct relationship between soil mineral content and plant uptake. The correlation values between the plant (fodder) and sheep blood were significant for many minerals, especially Cu and Zn. This suggests that the sheep's absorption of these minerals is directly influenced by the mineral content in the plants they consume. Direct correlations between soil mineral content and sheep blood levels were less pronounced, emphasizing the intermediary role of plants in determining mineral content in sheep.\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\u003eSoil-Plant-Animal Interaction in Trace Mineral Uptake\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCu (mg/100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFe (mg/100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMn (mg/100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZn (mg/100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.33 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.62 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.01 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.11 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.05 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.54 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.28*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 99*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.08 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.34 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.02 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.40 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.11 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.67 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.12 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.78 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS*P*B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.25*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMeans with the same superscripts are not significantly different\u003c/em\u003e \u003cb\u003eat p\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/b\u003e.\u003cem\u003eMeans without superscripts are not significantly different. SEM: Standard Error of Mean\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Concentration of Trace Minerals\u003c/h2\u003e\u003cp\u003eIn Ikole-Ekiti, understanding the soil-plant-animal continuum, particularly for minerals, plays an indispensable role in assessing agricultural and ecological health. In Ikole-Ekiti, the locally available feeds and fodders' composition is not extensively documented. This study's purpose was to analyze the mineral content in the soil, fodder, and sheep, giving a detailed overview of the soil-plant-animal continuum, specifically concerning trace minerals in the region. The present study indicates a deficiency of Fe in the soils of Ikole-Ekiti. Such mineral variations in soils have been previously reported by Anifowose and Borode (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) who studied regions of southwestern Nigeria. The soil in Ikole-Ekiti tends to be more acidic, potentially resulting from factors like leaching, influenced by the region's seasonal rainfall patterns.\u003c/p\u003e\u003cp\u003eIn terms of the copper (Cu), Location 1 shows a consistent trend of soil concentrations being the highest in July (0.82) and lowest in June (0.37). The plant and blood concentrations, however, differ. Notably, in Location 2, the plant copper level peaks in June (0.81), which is strikingly higher than both May (0.45) and July (0.16). Previous studies such as that by Adeoye \u003cem\u003eet al.\u003c/em\u003e, (2017) highlighted that copper in soil has a varying degree of uptake by plants in different regions of Nigeria. The plant concentrations in our study, especially in Location 2 in June, support their findings, demonstrating regional variability. Moreover, Adekunle (2017) established that animals often have relatively stable copper blood concentrations, regardless of variances in soil or plant Cu levels. Our findings, where blood Cu levels do not show extreme fluctuations like the plant and soil, agree with their observations. For the iron (Fe), a crucial mineral in numerous biological processes, our study found considerable variations across locations and months. While the soil iron level in Location 1 peaked in June (0.72), Locations 2 and 3 showed the highest soil iron in May (0.52) and July (0.65), respectively. The disparities in iron uptake between soil and plant are evident, with plant concentrations not always directly reflecting soil iron abundance. This is in line with a study by Agboola and Ojeniyi (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) that stated the complexities of iron uptake in plants, affected by soil pH, organic matter, and microbial activity.\u003c/p\u003e\u003cp\u003eManganese and zinc showed significant deviations in concentrations across locations and months. Notably, the Mn in plants at Location 2 during June (0.28) was exceptionally high compared to other months and locations. Uptake of Mn by plants, as indicated by Afolabi et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), is heavily influenced by soil acidity and microbial symbiotic relationships. Zinc, pivotal for numerous enzymatic processes, showed interesting patterns, especially explored further, especially in terms of potential toxicity or deficiencies. Previous works, such as by Ogunlade et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), have mentioned the critical balance required for zinc in the soil-plant-animal continuum.\u003c/p\u003e\u003cp\u003eZinc decrease in Asin region but increase in Odo-Oro is actually affected by soil pH, this is because soil pH affects Zn availability. The solubility of Zn, Mn decreases as the pH increases. Solubility of these minerals increases with increasing acidity. When the pH is fairly neutral, zinc in water becomes insoluble. Zinc is usually more available as soil pH moves to the acid side of 7 but there will be zinc shortage for sensitive crops growing on soils with pH 6 0r higher (Haque et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 The Soil-Plant-Animal (SPB) Continuum\u003c/h2\u003e\u003cp\u003eThe SPB data provided reveals the interconnected relationship and transfer of minerals across the soil, plants, and animals. The fluctuating values emphasize the complexity of mineral interplay. The results often demonstrated an indirect relationship, suggesting other influencing factors like oil composition, pH, microbial interactions, climatic conditions, and more.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Environmental and Human Impact\u003c/h2\u003e\u003cp\u003eThe availability of trace minerals within the soil\u0026ndash;plant\u0026ndash;animal continuum in Ikole-Ekiti is shaped by complex interactions among environmental drivers and anthropogenic activities. These factors influence trace element speciation, mobility, and ultimately their transfer through trophic levels.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Environmental Impacts\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section4\"\u003e\u003ch2\u003e4.3.1.1 Soil Geochemistry and Parent Material\u003c/h2\u003e\u003cp\u003eTrace mineral distribution in the study area is strongly governed by the geochemistry of the Precambrian Basement Complex, which comprises migmatite\u0026ndash;gneisses, granites, schists, and pegmatites. These lithologies produce highly weathered tropical soils with variable micronutrient reservoirs (Anifowose and Kolawole 2012). Schist-derived soils tend to be enriched in Fe and Mn, whereas granitic terrains are often associated with lower concentrations of Zn and Cu due to intense leaching under humid conditions (Nwosu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consequently, the mineralogical heterogeneity of the parent material directly modulates plant nutrient uptake and the micronutrient status of grazing animals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section4\"\u003e\u003ch2\u003e4.3.1.2. Climate and Hydrological Dynamics\u003c/h2\u003e\u003cp\u003eThe humid tropical climate of Ikole-Ekiti, characterized by annual rainfall exceeding 1,700 mm, promotes intensive pedogenic weathering and nutrient leaching. High precipitation enhances the downward translocation of soluble micronutrients such as Zn, B and Se, frequently resulting in surface soil deficiencies (Alloway, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Conversely, seasonal waterlogging may induce reducing conditions that mobilize Fe and Mn, temporarily increasing their solubility and bioavailability (Tack et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). These climatic fluctuations create dynamic micronutrient regimes that influence plant composition throughout the growing season.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section4\"\u003e\u003ch2\u003e4.3.2.3 Organic Matter and Biological Processes\u003c/h2\u003e\u003cp\u003eSoil organic matter plays a significant role in chelating micronutrients and regulating their solubility. In areas of continuous cultivation and low organic inputs, typical of Ikole-Ekiti, reduced organic matter content limits the complexation and availability of Cu, Zn and B (Stevenson and Cole, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Microbial-mediated redox processes further influence the speciation of Fe, Mn and Se, thereby linking biological activity with nutrient acquisition by plants and subsequent transfer to livestock.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Human Impacts\u003c/h2\u003e\u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\u003ch2\u003e4.3.2.1 Agricultural Intensification and Land-Use Practices\u003c/h2\u003e\u003cp\u003eAnthropogenic activities exert substantial influence on trace mineral cycling. Continuous cropping, soil erosion, and limited application of micronutrient-enriched amendments contribute to gradual depletion of soil trace elements (Fageria et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Shifting cultivation and indiscriminate land clearing alter soil structure and accelerate nutrient losses, thereby reducing the mineral content of forage plants available to livestock.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section4\"\u003e\u003ch2\u003e4.3.2.2 Fertilizer Use and Nutrient Imbalances\u003c/h2\u003e\u003cp\u003eLocal reliance on NPK fertilizers, which generally lack sufficient micronutrient components, frequently exacerbates Zn, Cu and B deficiencies in crops (Alloway, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Excessive phosphorus application may further inhibit Zn and Cu uptake due to antagonistic interactions at both soil and root interfaces. Although organic amendments such as poultry manure can enhance micronutrient levels, inconsistent application rates across farms in Ikole-Ekiti create spatial variability in soil trace element content.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section4\"\u003e\u003ch2\u003e4.3.2.3 Contamination and Trace Metal Loading\u003c/h2\u003e\u003cp\u003eAlthough Ikole-Ekiti is not heavily industrialized, localized anthropogenic activities\u0026mdash;including small-scale mining, agrochemical application, and roadside emissions\u0026mdash;can introduce toxic elements such as Pb, Cd and As into soils (Kabata-Pendias and Mukherjee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These contaminants may accumulate in plant tissues and enter livestock and human food chains, disrupting natural micronutrient pathways and posing potential health risks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section4\"\u003e\u003ch2\u003e4.3.2.4 Implications for Animal and Human Nutrition\u003c/h2\u003e\u003cp\u003eTrace mineral imbalances in soils translate into nutritional deficiencies in plants and, subsequently, in grazing animals. Such deficiencies are associated with impaired growth, reduced reproductive performance, weakened immunity, and metabolic disorders in livestock (McDowell, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Human populations reliant on locally grown produce may experience micronutrient-related health issues, including iron-deficiency anemia and zinc deficiency (Gibson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Thus, both environmental conditions and human practices collectively shape the trace mineral landscape in the study area.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusions and Recommendations","content":"\u003cp\u003eThe soil-plant-animal continuum study in Ikole-Ekiti revealed dynamic trace mineral concentrations across different locations and months, affecting plant uptake. Variability in soil and plant mineral content highlighted the influence of regional characteristics and potential external factors. Animal blood showed relative mineral stability, emphasizing inherent homeostatic mechanisms. The interconnectedness of the three compartments underscores the importance of holistic agricultural practices. The findings provide a foundational understanding for sustainable agricultural strategies in Ikole-Ekiti, ensuring ecosystem health and human well-being.\u003c/p\u003e\u003cp\u003eIt could be recommended that soil management practices in Ikole-Ekiti should be improve to maintain optimal mineral concentrations for healthier plant growth. Additionally, it would be beneficial to periodically monitor soil and plant mineral content, thereby addressing potential deficiencies or surpluses. Local farmers should be educated on the significance of the soil-plant-animal continuum for sustainable farming. Further research into the specific factors affecting regional mineral variability is advised to refine agricultural practices. For optimal livestock health, continuous monitoring of soil and plant mineral content is recommended.\u003c/p\u003e\u003cp\u003eLimitations to this study can only be as a result of constraint on research materials which may have an impact on research findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInformed consent Statement\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors are thankful to Federal University, Oye-Ekiti, Ekiti State. We greatly appreciate the help of Mrs. Gode Dakuna for the technical assistance in the field and authors who personally funded the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization and experimental design, A.H., and A.A.; validation, A.H., O.O., and A.A.; data analysis, J.O and A.A.; provided materials, A.A. and J.O.; writing-original draft, J.O. and A.H.; writing-review and editing. P.C., O.O. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e No funding was received from any organisation or anybody to carry out this Research work. It is self-funded research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: Not applicable\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable\u003c/p\u003e\n\u003cp\u003eEthics declaration: not applicable\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcDowell LR. Minerals in Animal and Human Nutrition. 2nd ed. Elsevier; 2003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuttle NF. The Mineral Nutrition of Livestock. 4th ed. CABI; 2010.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlloway BJ. Micronutrients in Agriculture. 4th ed. Wiley; 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkinrinde EA, Obigbesan GO. Evaluation of the fertility status of selected soils for crop production in five ecological zones of Nigeria. \u003cem\u003eProceedings of the 26th Annual Conference of the Soil Science Society of Nigeria\u003c/em\u003e 2000, 279\u0026ndash;288.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlatunji O, Ayeni LS, Adeleye EO. Soil micronutrient dynamics under continuous cropping systems in south-western Nigeria. Int J Agricultural Res. 2013;8(4):123\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDowell LR, Valle G. Major minerals in forage: Implications for animal nutrition. J Anim Sci. 2005;83(2):26\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNIMET. Nigeria Metrological Agency. Direct Res J Agric. 2015;5(10):348\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnifowose B, Lawler DM, Van der Horst D, Chapman L. Attacks on Oil Transport Pipelines in Nigeria: A Quantitative Exploration and Possible Explanation of Observed Patterns.AppliedGeography2012,32,636\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.apgeog.2011.07.012\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2011.07.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas GW. Exchangeable cations. In Methods of Soil Analysis. Part 2. 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Oxford University Press; 2006.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil, Fodder, Blood, Cu, Fe, Mn, and Zn","lastPublishedDoi":"10.21203/rs.3.rs-8039120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8039120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe purpose of the present study was to study the soil\u0026ndash;plant\u0026ndash;animal continuum in three different locations in Ikole-Ekiti at three different times. Soil (n\u0026thinsp;=\u0026thinsp;9), fodder (n\u0026thinsp;=\u0026thinsp;9), and blood serum samples from sheep (n\u0026thinsp;=\u0026thinsp;9) were collected from three districts of Ikole-Ekiti, Ekiti-State. Understanding the continuum of trace minerals from the soil through plants to animals is vital for sustainable agriculture and livestock health. Data reveals significant variations in mineral content both across locations and months. The samples were digested using di-acid mixture (HNO\u003csub\u003e3\u003c/sub\u003e:HClO\u003csub\u003e4\u003c/sub\u003e; 10:4) and analyzed for micro (Cu, Mn, Fe, and Zn) mineral concentrations. In School, Fe availability was highest in June, while Cu reached its peak in July. Mn concentrations were consistent across the three months, but Zn availability decreased over time. In Asin, Cu and Fe concentrations showed significant reductions from May to July, with Mn showing the highest availability in June and Zn levels fluctuating across the months. Odo-Oro had consistent Mn levels but decreasing concentrations of Cu, Fe, and Zn from May to July. These fluctuations influence plant mineral uptake, further affecting livestock relying on these plants for nutrition. For optimal livestock health, continuous monitoring of soil and plant mineral content is recommended, with potential adjustments in livestock feed supplementation based on these findings. This research underscores the importance of understanding local soil and plant mineral profiles for livestock health and sustainability in Ikole Ekiti.\u003c/p\u003e","manuscriptTitle":"Soil, Plant and Animal Continuum for Trace Mineral Availability in Ikole- Ekiti","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 10:03:24","doi":"10.21203/rs.3.rs-8039120/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c7e26a6-f2bb-45ec-9726-4fbf9d12dfac","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-13T05:23:44+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T05:42:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 10:03:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8039120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8039120","identity":"rs-8039120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}