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2024 Volume 4
Article Contents
Abstract
Introduction
Materials and methods
Sample collection
Genomic DNA extraction, PCR amplification and SSR genotyping
Data analysis
Results
Genetic diversity, differentiation and bottleneck test of the three V. mangachapoi populations
Population genetic structure
Gene flow and effective population size
Fine-scale spatial genetic structure
Discussion
Genetic diversity, population structure and gene flow
Fine-scale spatial genetic structure
Implications for conservation
Conclusion
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

Comparative analysis of cattle production systems in Nigeria grassland agroecology

  • 1.

    Department of Animal Science, Federal University of Agriculture Zuru, Zuru 872252, Kebbi State, Nigeria

  • 2.

    Department of Animal Production, Federal University of Technology, Minna, 920101, Niger State, Nigeria

  • 3.

    Department of Animal Science, Federal University Gashua, 671105, Yobe State, Nigeria

More Information
  • This study explores herd production characteristics and phenotypic traits of indigenous dairy cattle in the grassland agroecology of Nigeria. The study highlighted the crucial role of agroecology as a modifier of cattle production operations and emphasized the need for further research to understand the genetic basis of variations in the production. Herd production data were collected through focus group meetings using FEAST software, while body measurements and phenotypic traits of lactating and breeding cattle were recorded in the agroecological zone within the grassland. The data obtained were subjected to descriptive statistics, and Moses Test of Extreme Reaction using SPSS v.20.0.0. The results indicate that the agroecology type significantly influenced various herd production characteristics (p < 0.05). Additionally, agroecology had a significant effect on body measurements and phenotypic trait expressions in the cattle, including live weight, body condition score, testis circumference, age at puberty, and age at first calving (p < 0.05). Furthermore, age differences were observed among cows based on the agroecological zones (p = 0.008), while no significant variation was found in the age of breeding bulls across both agroecology. This study concludes that within the Nigeria's grassland, agroecology plays a crucial role as a modifier of herd production characteristics and phenotypic trait expressions in smallholder dairy cattle operations. These cannot be unconnected with genetics, hence, there is a need for understanding the genetic basis of the variations.

  • Introduction

    Trees from family Dipterocarpaceae serve an important ecosystem function in the rainforest community of Asian tropical forests, where 20%−50% of the canopy layer belong to this family[1]. With high-quality timber that has a high economic value, dipterocarp forests also form a major pillar of the global tropical timber trade[2]. Due to long-term over-harvesting and land use change, tropical rainforests have become severely fragmented, and a large number of dipterocarps are today listed as endangered species and are at risk of extinction[3]. As a result, ecosystem services of Asian tropical rainforests in which dipterocarps are the dominant species have been seriously impaired[4]. Therefore, conservation genetics studies focusing on Dipterocarpaceae are urgently needed[1].

    The tropical forests in Hainan Island, China are located at the northern edge of tropical Asia and are distinct from the typical tropical rainforests of Southeast Asia in terms of species composition, community structure and appearance as a consequence of the influence of the Asian monsoon[5]. Only three species of Dipterocarpaceae, Hopea hainanensis Merrill & Chun, H. reticulata Tardieu and Vatica mangachapoi Blanco can be found on this island. Although the species diversity of Dipterocarpaceae in Hainan Island has been greatly reduced as compared to that in Southeast Asia, the three species, especially V. mangachapoi (Fig. 1), play a key role in community assembly and ecosystem functioning of the lowland rainforests of this island[5−7]. It is remarkable that V. mangachapoi has developed into a continuously distributed coastal forest growing on sand substrate with 25 kilometers-long and 400 to 500 meters-wide at Shimei Bay, Wanning City (China), which is estimated to be at least 4000 years old[8, 9]. A study showed that soil moisture and organic matters of the sand substrate are much lower than those of normal tropical soils[10]. The formation of the coastal V. mangachapoi-dominated forest on barren and harsh sandy beach is unique and rare in itself, which could serve as an example to study the underlying physiological and genetical adaptation of V. mangachapoi to arid and poor substrate. In recent years, due to such factors as coastal development and village expansion, the area of the coastal V. mangachapoi-dominated forest in Shimei Bay has been reduced, and the formerly intact population has fragmented into several isolated patches. Coupled with the presence of forest gaps and fungal disease caused by human interference, the survival of the coastal V. mangachapoi is seriously threatened[9, 11, 12]. Conservation management is thus needed to protect this unique coastal forest dominated by V. mangachapoi.

    Figure 1.  Morphology of Vatica mangachapoi. (a) An individual tree, (b) flowers, (c) fruits.
    DownLoad: Full-Size Img PowerPoint

    Habitat fragmentation can cause an intact population into small, isolated patches, with reduced gene flow between patches and increased genetic drift and inbreeding within patches[13−16]. If seed dispersal is limited and restricted within patches, genotypes are likely to be spatially clustered, producing a strong fine-scale spatial genetic structure (FSGS)[17, 18]. Vatica magachapoi has winged fruits, which may promote seed dispersal by wind. Studies indeed showed that the dispersal distance of winged fruits are generally further than non-winged fruits in dipterocarps[19, 20]. On the other hand, the strength of FSGS was also affected by pollen dispersal. Dipterocarps that are pollinated by large insects, such as bees, can achieve longer distance of pollen flow than those pollinated by small insects, such as thrips, because large pollinators can move further than small ones[21, 22]. The limited seed and pollen dispersal were confirmed as the main reason for significant FSGS of most dipterocarps in fragmented habitats[23−26]. Is there a significant FSGS in the coastal forest of V. mangachapoi? Is genetic diversity lower in the coastal V. mangachapoi populations than the undisturbed rainforest populations nearby? Does significant genetic differentiation occur between patches of the coastal V. mangachapoi forest? These questions are important for the conservation and management of the unique coastal dipterocarp forest but remain to be resolved.

    To answer the above questions, two coastal populations of V. mangachapoi (SM and RY) were sampled, and one population in the lowland rainforest near the coast (TT) were further collected for comparison. Genetic diversity, structure and population differentiation were assessed for the three V. mangachapoi populations using 12 SSR markers. Gene flow was estimated to test whether habitat fragmentation interrupted genetic exchange between them or not. Finally, FSGS were analyzed using the SM population to show whether significant spatial genetic structure has occurred within patches. Answering these questions could shed light on the conservation and continued survival of this unique coastal V. mangachapoi-dominated forest.

    Materials and methods

    Sample collection

    The study area is located in the Provincial Nature Reserve of V. mangachapoi, Wanning City, Hainan Province (China). Two V. mangachapoi populations (SM and RY) in the coastal forest separated by villages, roads and human facilities, and one population (TT) in the lowland rainforest near the coast were selected (Table 1, Fig. 2). In total, 188 V. mangachapoi trees individually spaced out more than 25 m apart and with DBH > 5 cm were sampled. Mature leaves lacking disease spots were selected, dried by silica gel and then were stored in a −20 °C refrigerator. The voucher specimens of V. mangachapoi were kept in Hainan University (Hainan, China).

    Table 1.  Genetic diversity indices of the three V. mangachapoi populations based on 12 SSR markers.
    PopulationLocationNNaNeHoHeFis
    SM110.26691° E, 18.66671° N9183.6470.5470.6900.207
    RY110.17952° E, 18.59768° N3973.7040.6050.7000.142
    TT110.24941° E, 18.67744° N587.53.6940.5660.6920.167
    Average7.53.6820.5720.6940.172
     | Show Table
    DownLoad: CSV
    Figure 2.  Geographic distribution of the coastal V. mangachapoi-dominated forest and the locations of the three sampled V. mangachapoi populations. Gene flow among them was estimated, with the width of lines being proportional to the intensity of gene flow.
    DownLoad: Full-Size Img PowerPoint

    Genomic DNA extraction, PCR amplification and SSR genotyping

    A modified CTAB method[27] was used to extract the genomic DNA of V. mangachapoi. Twelve pairs of polymorphic SSR primers developed by Guo et al.[28] were used in this study. PCR amplification were performed in a total volume of 10 μL, containing 1.0 μL of genomic DNA (around 50 ng), 5.0 μL of Taq PCR Master Mix (GeneTech), 0.5 μL of forward and reverse primers, and 3.0 μL of ddH2O. Amplification was carried out as follows: pre-denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 20 s, annealing at 52−62 °C for 15 s, extension at 72 °C for 30 s, and finally extension at 72 °C for 7 min. PCR products were separated by capillary electrophoresis using ABI3730xl (Applied Biosystem) and SSR genotypes were analyzed by the GeneMarker software.

    Data analysis

    Population genetic parameters, including number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (Fis) and genetic differentiation among populations (Fst) were estimated using GenAlex 6.51[29]. Polymorphism information content (PIC) and Nei & Chessers[30] genetic distances were calculated by PowerMarker 3.25[31]. Analysis of molecular variance (AMOVA) of the V. mangachapoi population was performed using Arlequin 3.5[32]. Potential population bottleneck was examined by Wilcoxon sign-rank test and model shift using the Stepwise Mutation Model and Two-phased Mutation Model implemented in Bottleneck 1.3.2[33].

    Bayesian clustering analysis was performed using Structure 2.3.4[34]. The values of K were set from 1 to 10, and for each value of K, 10 independent replicates were run with 100,000 burn-in iterations followed by 200,000 MCMC (Markov chain Monte Carlo) iterations. The best K was determined according to the delta K of STRUCTURE Harvester[35]. The results of the 10 replicates were combined by the Greedy algorithm implemented in Clumpp1.1.2[36], and the result of individual clustering were drawn using Distruct1.1[37]. NJ trees were constructed using MEGA 11.0[38] based on Nei & Chessers[30] genetic distances. Principal co-ordinate analysis (PCoA) was performed with GenAlex 6.51.

    The effective population size (θ) of the three V. mangachapoi populations and the migration rate (M) between them were calculated using MIGRATE[39], a software based on the coalescent theory and Bayesian inference to estimate values of parameters of a user-specified population model. MIGRATE analyses were run under a Brownian motion model, and four heat chains with different temperatures of 1.0, 1.5, 3.0 and 1.0 × 105 were simulated. Gene flow was calculated according to the equation Nm = θ*M/x. For SSR markers, x was set to 4. Three independent replications were run to ensure the convergence of the Markov chain Monte Carlo methods implemented in MIGRATE.

    The fine-scale spatial genetic structure (FSGS) within-population was assessed using SPAGeDi 1.5[40]. The kinship coefficients (Fij, kinship coefficients) between any two individuals were calculated and regressed against the natural logarithm of the spatial distance to obtain the regression slope bF[41]. We divided the distance between any pair of individuals sampled from the SM population into 10 distance classes (35, 50, 75, 100, 150, 300, 500, 700, 850, 1,100 m), with at least 30 pairs of individuals per distance class[42, 43].

    The 95% confidence intervals of Fij were calculated from 9,999 permutations of spatial distance among pairs of adults for 10 distance classes. If the Fij was higher than the upper bound of the 95% confidence interval, there is significant spatial genetic structure in population and a high level of genetic similarity among individuals; if the Fij fell within the 95% confidence interval, there is no spatial genetic structure in population and individuals were considered to be spatially randomly distributed; if the Fij was less than the lower bound of the 95% confidence interval, individuals were considered to be uniformly distributed in space without spatial genetic structure. The value of the Sp statistic reflects the strength of FSGS and is defined as Sp = -bF / (1-F(1) ), where bF is the regression slope, and F(1) is the mean pairwise kinship coefficient of the first distance class.

    Results

    Genetic diversity, differentiation and bottleneck test of the three V. mangachapoi populations

    There is no significant difference in the level of genetic diversity between the coastal (SM and RY) and the rainforest (TT) populations (Table 1). The inbreeding coefficient was greater than 0, indicating inbreeding and an excess of homozygotes in the three V. mangachapoi populations. Totally 90 alleles were detected from the 12 SSR loci, and the number of alleles at a single locus ranged from 4.667 to 11.000, with an average of 7.500 alleles per locus. The observed and expected heterozygosity ranged from 0.303 to 0.769 and from 0.415 to 0.808, respectively. The primer sequences, range of allele sizes and genetic diversity indices of the 12 SSR loci are shown in Table 2.

    Table 2.  Primer sequences, allele size and genetic diversity indices of the 12 SSR markers.
    LociPrimer sequences (5’-3’)Repeat
    motif    		Allele sizeGenAlexPowerMarker
    NaNeHoHePIC
    VM1F:GAACCCTTATTGGCCTGCCTAC(AT)11166−1847.3334.2310.7400.7630.7430
    R:GGGACCAAATGACTTGAGTAATCT
    VM2F:ACCCTAACAATTCTCTTTGTTTCCT(TAA)11152−1959.6674.1200.5130.7550.7364
    R:CCCCAATCTCAGTAAGGACTCA
    VM3F:CTTGTGTCGAGCATGCATGTAT(AT)11175−1918.3334.8570.7610.7930.7659
    R:TGCTGGCCTTTTATGTTAGGGT
    VM4F:ATAGCAGGCACTTCGGAAGTAC(TA)8261−2778.6674.6130.3700.7810.7533
    R:CCTGAGAAACAAAGCAACGCAT
    VM5F:GCACTAGCACTAGCACTAGCTT(CT)11218−2264.6672.9080.6290.6510.6026
    R:GGCTTTTCCAATTTCCATGGCT
    VM6F:AGTTAAGGGACCAAATTTAGCGT(TA)7259−2695.0002.7940.5930.6360.5902
    R:GTGTTTGTCAACTGGGCTTCAA
    VM7F:CCCATGTGCTAGGCTAATGCTA(AT)6229−2395.0002.3940.3030.5820.5409
    R:AAATCAGCATGAAACTTCTCCATT
    VM8F:CACCACCACAGGCTTGAGTATA(TA)7168−1825.6671.7220.3740.4150.4044
    R:GAAGGCCAACTAATCAAGCTGC
    VM9F:TCATTTCTGTCTCACTCGACCC(TTC)10148−1685.6673.0100.6390.6660.6097
    R:TCATCGACGAATCACTGTTCGA
    VM10F:ACGGATAAGTTAACGGACTAGACA(TA)10215−2279.3334.7130.5680.7760.7997
    R:AGATTTTCCCCCAGTCATCGAC
    VM11F:GCTGGCACTTAGGATGCCTTAA(ATT)11138−15011.0003.5640.6100.7020.6657
    R:AGCAACCAATTAGCTCAAATCAA
    VM12F:GGGCAGCCTCGTAAATCAATTAC(ATT)13225−2499.6675.2530.7690.8080.7958
    R:ATTACCTGGCACAACCTTAGCC
     | Show Table
    DownLoad: CSV

    Genetic differentiation was weak among the three V. mangachapoi populations (Fst = 0.008~0.013). The result of AMOVA showed that 99% of genetic variation was partitioned within population, in line with little divergence among populations (Supplemental Table S1).

    The Wilcoxon sign rank test found that the p-values were not significant under either the S.M.M or the T.P.M model for the three V. mangachapoi populations, and their allele frequency distributions were generally L-shaped (Fig. 3), indicating that the three populations have not experienced genetic bottlenecks recently.

    Figure 3.  Allele frequency distribution of the three V. mangachapoi populations.
    DownLoad: Full-Size Img PowerPoint

    Population genetic structure

    STRUCTURE analysis found that delta K was maximized at K = 2, indicating two genetic clusters of the studied V. mangachapoi populations. The distribution of the two clusters did not differ significantly between the three populations, and this is also true for K = 3 or 5 (Fig. 4). Consistent with the results of STRUCTURE analyses, NJ tree (Supplemental Fig. S1) and PCoA analysis (Fig. 5) also suggest a homogeneous genetic structure of the three V. mangachapoi populations.

    Figure 4.  Results of STRUCTURE analysis. (a) Best K determined using the delta K method. (b) Log probabilities and delta K values for K from two to ten. (c) The results of individual assignment at K = 2, 3 and 5. Each vertical bar represents an individual, and the proportion of the colors corresponds to the posterior probability of genetic clusters assigned to each individual.
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Principal co-ordinate analysis (PCoA) based on Nei & Chessers[30] genetic distance among individual samples of V. mangachapoi.
    DownLoad: Full-Size Img PowerPoint

    Gene flow and effective population size

    The effective population sizes of the three V. mangachapoi populations estimated by MIGRATE were similar, but the intensity of gene flow varied among them (Fig. 2, Table 3). The gene flows from RY to the other two populations were less than their reverse gene flows, however, the gene flows from SM to the other two populations were greater than their reverse gene flows. These results suggested that gene flows between the V. mangachapoi populations were asymmetric.

    Table 3.  Mutation-scaled migration rate, effective population size and gene flow estimated by program MIGRATE.
    Direction of
    gene flow    		Migration
    rate (M)    		Effective population
    size (θ)    		Gene flow (Nm)
    SM→RY129.615θSM = 0.097903.172327
    SM→TT179.8394.401560
    RY→SM63.241θRY = 0.096861.531380
    RY→TT117.4902.845020
    TT→SM115.412θTT = 0.097462.812013
    TT→RY134.0623.266421
     | Show Table
    DownLoad: CSV

    Fine-scale spatial genetic structure

    No spatial genetic structure was detected at any of the 10 distance classes in the SM population (Fig. 6). The values of Fij were less than zero over multiple distance classes, indicating that individual trees of V. mangachapoi were spatially uniformly distributed. Based on the mean affinity (F(1)) for the first distance class (0.0151) and the regression slope bF (−0.004605), the strength of FSGS (Sp) was derived as 0.004675 for the V. mangachapoi population in Shimei Bay.

    Figure 6.  Fine-scale genetic structure of V. mangachapoi in Shimei Bay. The solid line represents the mean Kinship coefficient F (Loiselle et al.[41]), and the dashed lines represent the 95% confidence intervals of the mean Kinship coefficient F.
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Genetic diversity, population structure and gene flow

    Due to rapid coastal development and village expansion, the coastal population of V. mangachapoi declined and fragmented into several small and discontinuous patches[12] (Fig. 2). However, there was no significant difference in genetic diversity between the coastal (SM and RY) and the rainforest (TT) populations (Table 1), and the effective sizes of the three populations are quite close (Table 3). Besides, no recent bottleneck could be detected in these populations (Fig. 3). The results indicated that the sizes of the two coastal populations are still large to maintain a comparative level of genetic diversity relative to that of the rainforest population nearby[44]. In addition, the three V. mangachapoi populations were demonstrated to share a homogenous genetic structure and there is little differentiation between them (Figs 4 & 5). In summary, patterns of SSR variation observed in V. mangachapoi suggested either genetic connection through gene flow or not enough time to accumulate divergence after fragmentation of the coastal forest dominated by V. mangachapoi.

    Frequent gene flow can prevent rapid loss of genetic variation and differentiation between patches in a fragmented population[45, 46]. Vateriopsis seychellarum is endemic in the Seychelles, after a long period of logging, only a few hundred adult trees remained. Nevertheless, a relatively high level of genetic diversity was found in this species. Long-distance gene flow between isolated patches of Va. seychellarum was considered as the main reason to maintain genetic variation in this species[25]. If pollinators can travel across the gaps created by fragmentation, pollen-mediated gene flow could be maintained between patches, as a result, genetic drift happened within patches would be mitigated in the short term[26, 47, 48]. In this study, frequent gene flow (Nm > 1) was detected between the three V. mangachapoi populations. Besides, the time of fragmentation of the coastal V. mangachapoi forest is relatively short comparing with the generation time of this species. Differentiation between patches is probably impeded by gene flow or there is not enough time to accumulate significant divergence among them, and genetic variation could be largely maintained within the coastal V. mangachapoi forest.

    Fine-scale spatial genetic structure

    No significant FSGS was detected in the SM population of V. mangachapoi, which may result from long distance dispersal of seeds and/or pollens within population[24, 49]. Two of the five sepals of V. mangachapoi flowers keep growing and develop into functional wings of the fruits that would promote seed dispersal by wind[19, 50]. Hainan Island will experience frequent Pacific typhoon from June to October each year, and the time of fruit ripening of V. mangachapoi (mid to late July-August) coincides well with the activities of the Pacific typhoon[51]. Moreover, Wanning City is one of the locations where typhoons frequently make landfall on Hainan Island. The coastal V. mangachapoi forest at Shimei Bay would be highly likely to encounter a Pacific typhoon during its fruit ripening period. Strong convection currents can carry winged fruits of V. mangachapoi into the upper air and achieve a long-distance horizontal dispersal with hundreds of meters, which may account for the lack of significant FSGS in the SM population[52, 53].

    Pollen-mediated gene flow can also influence the strength of FSGS, and restricted gene flow generally results in significant FSGS[22, 24, 54]. Kettle et al. studied the FSGS of three dipterocarp species from the tropical rainforests at Borneo[54]. No clear signal of FSGS was detected in Dipterocarpus grandiflorus, a species pollinated by large pollinators with strong mobility, which may facilitate long-distance pollen flow. On the contrary, S. xanthophylla and Parashorea tomentella had significant FSGS, probably because they were pollinated by small pollinators and consequently short distances of pollen flow. Lee et al. studied the FSGS of S. parvifolia populations in different habitats[22]. They found that populations in montane rainforests, which were mainly pollinated by large pollinators, had no FSGS, whereas populations in lowland rainforests, which were pollinated by small pollinators, had significant FSGS. Lee et al. suggested that it is the restricted pollen flow that leads to strong FSGS in the lowland dipterocarp rainforests[22]. However, as the distance of pollen-mediated gene flow of V. mangachapoi is unclear at present, the relative contribution of seed and pollen dispersal to the random distribution of genotypes in space deserve further studies.

    Implications for conservation

    In this study, we demonstrated that there was no significant difference in genetic diversity among the two coastal V. mangachapoi populations and one rainforest population near the coast. Moreover, little differentiation and frequent gene flow were detected between the three populations. Even though the coastal populations maintain a relatively high level of genetic diversity, the extremely simple community structure and poor species richness of the coastal V. mangachapoi-dominated forests indicate that this unique community is much more fragile than other dipterocarp communities[55, 56]. In addition, the coastal V. mangachapoi-dominated forest is likely to degrade into a sandy scrub community or bare sandy beach in the near future if intense anthropogenic disturbance persists. Therefore, further logging and invasion of the coastal forest must be strictly prohibited, and saplings of V. mangachapoi should be planted in forest gaps to promote the restoration of the coastal V. mangachapoi-dominated forest landscape.

    Conclusion

    The fragmented coastal V. mangachapoi-dominated forest in Shimei Bay have not yet exhibited significant genetic differentiation and diversity loss. The winged fruits of V. mangachapoi may promote seed dispersal and maintain gene flow between populations, which could mitigate genetic drift and lead to random distribution of genotypes within population. In addition, comparing with the generation time of V. mangachapoi, the time of fragmentation of the coastal V. mangachapoi forest is relatively short, so there is not enough time to accumulate differentiation for populations from the fragmented forest. Based on the above findings, we suggest to strengthen the protection of the coastal V. mangachapoi forest to prevent further deforestation. Besides, saplings of V. mangachapoi should be planted to connect isolated populations and facilitate the restoration of the unique coastal V. mangachapoi forest.

    Acknowledgments

    We thank W-Q Xiang, S-Q Zhu and Y Cai of Hainan University for their help on data analysis and collection of samples. We also thank the Provincial Nature Reserve of Vatica mangachapoi at Wanning City for their help on field research. This study was supported by the National Natural Science Foundation of China (Grant No. 32060236) and the Key R&D Projects of Hainan Province (Grant No. ZDYF2022XDNY260).

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Sikiru AB, Otu BO, Makinde OJ, Saheed S, Egena SSA, et al. 2024. Comparative analysis of cattle production systems in Nigeria grassland agroecology. Circular Agricultural Systems 4: e001 doi: 10.48130/cas-0023-0012
    Sikiru AB, Otu BO, Makinde OJ, Saheed S, Egena SSA, et al. 2024. Comparative analysis of cattle production systems in Nigeria grassland agroecology. Circular Agricultural Systems 4: e001 doi: 10.48130/cas-0023-0012
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Comparative analysis of cattle production systems in Nigeria grassland agroecology

  • 1.

    Department of Animal Science, Federal University of Agriculture Zuru, Zuru 872252, Kebbi State, Nigeria

  • 2.

    Department of Animal Production, Federal University of Technology, Minna, 920101, Niger State, Nigeria

  • 3.

    Department of Animal Science, Federal University Gashua, 671105, Yobe State, Nigeria

  • Received: 02 October 2023
  • Revised: 25 November 2023
  • Accepted: 28 November 2023
  • Published online: 09 January 2024
Circular Agricultural Systems  4,  Article number: e001  (2024)  |  Cite this article

Abstract: This study explores herd production characteristics and phenotypic traits of indigenous dairy cattle in the grassland agroecology of Nigeria. The study highlighted the crucial role of agroecology as a modifier of cattle production operations and emphasized the need for further research to understand the genetic basis of variations in the production. Herd production data were collected through focus group meetings using FEAST software, while body measurements and phenotypic traits of lactating and breeding cattle were recorded in the agroecological zone within the grassland. The data obtained were subjected to descriptive statistics, and Moses Test of Extreme Reaction using SPSS v.20.0.0. The results indicate that the agroecology type significantly influenced various herd production characteristics (p < 0.05). Additionally, agroecology had a significant effect on body measurements and phenotypic trait expressions in the cattle, including live weight, body condition score, testis circumference, age at puberty, and age at first calving (p < 0.05). Furthermore, age differences were observed among cows based on the agroecological zones (p = 0.008), while no significant variation was found in the age of breeding bulls across both agroecology. This study concludes that within the Nigeria's grassland, agroecology plays a crucial role as a modifier of herd production characteristics and phenotypic trait expressions in smallholder dairy cattle operations. These cannot be unconnected with genetics, hence, there is a need for understanding the genetic basis of the variations.

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    Introduction

    • Dairy cattle production is a crucial agricultural activity that sustains the economic and livelihood needs of approximately 150 million households worldwide[1]. In Nigeria, dairy production has proven to be beneficial for numerous households, even among those engaged in low-input operations. It has significantly contributed to their well-being, household incomes, and overall living conditions. As such, it holds substantial importance within the livestock production sector. However, one of the major factors influencing the performance of dairy cattle is the agroecological context in which they are raised. Agroecology encompasses environmental variables such as temperature, relative humidity, and heat stress, which can modulate the genetic responses of cattle, ultimately impacting their productivity[2]. The fundamental objective of sustainable dairy animal production is to ensure the animals' capacity to thrive, reproduce, and maintain productivity under a wide range of environmental conditions[3]. Consequently, understanding the expression of phenotypic traits and production characteristics within a specific agroecological context becomes imperative for optimizing productivity. This need highlights the significance of investigating these factors under smallholder operations in the forest-savanna transition agroecology of Nigeria.

      Agroecology are geographically defined areas characterized by similar climatic conditions that influence their suitability for rainfed agricultural practices. These zones are shaped by factors such as geoposition, elevation, temperature, rainfall patterns, and the distribution of rainfall during the wet season[4]. In Nigeria, there are six distinct agroecological zones, including the Mangrove Swamp, Rainforest, Derived Savanna, Guinea Savanna, Sudan Savanna, and Sahel Savanna, extending from south to north[5]. While livestock production is primarily concentrated in the northern regions of Nigeria, the Derived Savanna agroecological zone in the southern region also supports a significant population of livestock, particularly cattle. This is attributed to pastoralists migrating southward from the north in response to feed resource shortages resulting from climate impacts[6,7]. Additionally, the Southern Guinea Savanna agroecological zone, situated as the southernmost part of northern Nigeria, provides potential pasture plants and range forages for cattle consumption during the dry season when there is a scarcity of range plants in the sub-arid Sahelian regions of northern Nigeria and other West African countries[8,9].

      Consequently, the Southern Guinea and Derived Savanna agroecological zones serve as vital hubs for livestock production in Nigeria, witnessing the annual migration of animals' southwards in search of pasture. This is because this agroecology are majorly the Nigeria's grassland where extensive grazing is a common practice. While separate studies have reported smallholder livestock production in this agroecology[10,11], there is currently no comprehensive study that directly compares both areas in a single study as reported in this paper. Thus, this study aims to address this research gap by assessing livestock production characteristics and phenotypic traits of cattle under low-input production practices within the two agroecological zones. By conducting this comparative analysis, we can gain a comprehensive understanding of the unique challenges and opportunities associated with smallholder dairy cattle production in the forest-savanna transition agroecology of Nigeria.

    Materials and methods

      Study design

    • This study utilized focus group meetings with pastoralists and agropastoralists to collect data on herd production and record phenotypic traits, including body measurements, scrotal circumference, and body condition scores of indigenous dairy cattle. The focus group meetings and animal body measurements were conducted in the Southern Guinea Savanna and the Derived Savanna agroecology of Nigeria, as these regions are recognized as major hubs for cattle production in the country.

      Participants and geographic locations

    • The participants in the study were household heads who actively engaged in focus group meetings. The meetings were conducted at six different study sites, with 12 household heads participating at each location, resulting in a total of 72 household heads engaged in the focus group meetings. The geographic coordinates of each study site were determined using Google Earth (https://earth.google.com/). In the Southern Guinea Savanna, the three study sites were referred to as location A (altitude: 545 m, Longitude: 9°19'50" N, Latitude: 7°26'21" E), location B (altitude: 516 m, Longitude: 9°19'01" N, Latitude: 7°15'11" E), and location C (altitude: 730 m, Longitude: 9°18'58" N, Latitude: 7°35'24" E). In the Derived Savanna, the three study sites were referred to as location D (altitude: 343 m, Longitude: 7°59'33" N, Latitude: 3°33'35" E), location E (altitude: 375 m, Longitude: 8°39'41" N, Latitude: 3°30'36" E), and location F (altitude: 312 m, Longitude: 7°58'00" N, Latitude: 3°34'05" E). These study sites are in Niger and Oyo states of Nigeria, respectively.

      Questionnaire administration for collection of herd characteristics data

    • Structured questionnaires were administered during the focus group meetings at the selected locations. The questionnaires were designed to collect data on various aspects, including land holding capacity of the respondents, cultivation of food and fodder crops, purchased feed for livestock, animal diet and nutrition, milk and livestock prices, sources of income, and herd information such as population and categories of animals (lactating, non-lactating, heifers, male calves, female calves). The data collection followed the guidelines of focus group and individual farmers' interview procedures outlined in the Feed Assessment Tools (FEAST) software developed by the International Livestock Research Institute (ILRI).

      Animal body measurements and computation of live weights

    • A total of 40 milk-producing cows and 40 breeding bulls were selected from each agroecological zone for a comparative assessment of body measurements and phenotypic traits. The recorded body measurements included body length, measured diagonally across the body of the cattle using a flexible measuring tape, and heart girth, measured as the circumference of the girth. Scrotal circumference was measured as the maximum point of dimension around the pendulous scrotum after pushing the testes firmly into the scrotal sac[12,13]. Body condition scores were determined using attributes specific to dual-purpose cattle. Live weight was computed using a general formula for evaluating animal live weight based on the recorded body measurements[13,14].

      Statistical analysis

    • All collected data were subjected to descriptive statistical analyses. Additionally, the herd production characteristics data were subjected to Moses Test of Extreme Reaction (MTER) to determine the effect of agroecology differences on the herd production characteristics. The MTER is a statistical test that helps identify significant differences between groups. For the body measurements and phenotypic traits data, independent sample t-tests were conducted to assess the effect of agroecology and breed differences on the phenotypic traits of the cattle. The independent sample t-test is a statistical test used to compare the means of two independent groups. These statistical analyses were performed using SPSS v.20.0.0 software. The significance level for determining significant differences in means was set at p < 0.05, indicating a 5% probability threshold. The results obtained from the statistical analyses provided insights into the relationship between agroecology, herd production characteristics, and phenotypic traits of indigenous dairy cattle in the Southern Guinea Savanna and Derived Savanna agroecological zones of Nigeria.

    Results

      Effect of agroecology on herd production characteristics

    • The agroecology types also influenced herd production characteristics. Significant differences were observed in land and livestock ownership, land use for food and fodder crops production, cattle dry matter intakes by sources, metabolizable energy intake by sources, protein intake by sources, and the nutrition analysis of the cattle (Table 1).

      Table 1.  Test of significant difference (Moses Test of Extreme Reaction) for the effect of agroecology type herd production characteristics of the respondent households.

      Parametersp-values
      Land ownership0.001
      Local livestock holding0.001
      Improved livestock holding0.001
      Land use for food crops and fodder production0.001
      Dry matter intake by sources0.001
      Metabolizable energy intake by sources0.001
      Protein intake by sources0.001
      CP:ME ratio0.001
      Milk yield per ME intake0.001
      CP: crude protein; ME: metabolizable energy. The p-values represent the statistical significance levels corresponding to of each reported parameter; p = 0.001 indicates strong evidence against the null hypothesis, suggesting that there is a significant association between agroecology and the reported parameters. In other words, the p-value suggests that the observed effects are associated with agroecological factors.

      Land ownership characteristics based on agroecology

    • The land ownership categorization included small, medium, and large land ownerships. The average land size per household for large land ownership was 12.17 ± 1.82 hectares, medium land ownership was 8.05 ± 1.06 hectares, and small land ownership was 4.37 ± 0.35 hectares. The percentages of household land ownership by categories were 40.00% ± 12.40%, 18.75% ± 3.24%, and 41.25% ± 12.52% for large, medium, and small landowners, respectively. The average land size for each category of ownership (large, medium, and small) was higher for households in the derived savanna agroecology compared to the households in the southern Guinea savanna. Additionally, while most households in the southern Guinea savanna fell under the category of small landowners, most households in the derived savanna fell under the category of large landowners, indicating that households in the derived savanna agroecology have access to more land compared to households in the southern Guinea savanna (Table 2).

      Table 2.  Land ownership and access characteristics of the respondents in the Southern Guinea savanna and derived savanna agroecology by land ownership size and ownership categories.

      Land ownership parametersSouthern Guinea savannahDerived savannahMean
      Large land ownership (ha)9.29 ± 0.4315.00 ± 2.8512.17 ± 1.82
      Medium land ownership (ha)6.10 ± 0.2710.00 ± 1.488.05 ± 1.06
      Small land ownership (ha)3.75 ± 0.385.00 ± 0.004.37 ± 0.35
      Large landowners (%)15.00 ± 8.6665 ± 8.6540.00 ± 12.40
      Medium landowners (%)20.00 ± 5.7017.50 ± 4.3318.75 ± 3.24
      Small landowners (%)65.00 ± 14.4617.50 ± 4.3341.25 ± 12.52
      ha: hectare.

      Tropical Livestock Units (TLUs) of local and improved livestock

    • The Tropical Livestock Units (TLUs) of the local dairy cattle per household were higher in the southern Guinea savanna compared to the derived savanna. Conversely, the TLUs of improved dairy cattle per household were higher in the derived savanna compared to the southern Guinea savanna agroecological zone. Moreover, the TLUs of non-milk-producing animals, including dry cows, heifers, and young male or female calves, were lower for the derived savanna agroecology compared to the southern Guinea savanna agroecology (Table 3 & 4).

      Table 3.  Per household Tropical Livestock Units (TLUs) of local livestock ownership of the respondents in the Southern and derived savanna agroecology.

      ParametersSouthern Guinea savannaDerived savannaMean
      Local dairy cattle (TLUs/HH)53.68 ± 2.8551.82 ± 17.8852.75 ± 8.07
      Fattening and draught cattle (TLUs/ HH)1.80 ± 0.5512.30 ± 7.147.05 ± 3.92
      Local dairy cows - lactating (TLUs/HH)27.24 ± 1.5526.97 ± 0.4727.10 ± 0.79
      Local dairy cows - non lactating (TLUs/HH)11.00 ± 0.5713.23 ± 0.3012.11 ± 0.56
      Local dairy heifers (6 months old - 1st calving) (TLUs/HH)5.72 ± 0.0927.51 ± 6.9616.61 ± 5.71
      Local dairy female calves (less than 6 months old) (TLUs/HH)2.12 ± 0.2611.62 ± 2.976.87 ± 2.50
      Local dairy calf's male (less than 6 months old) (TLUs/HH)0.33 ± 0.060.79 ± 0.130.56 ± 0.18
      Local bulls less than 2 years (TLUs/HH)1.75 ± 0.416.50 ± 3.784.12 ± 1.97
      Local bulls older than 2 years (TLUs/HH)0.61 ± 0.1015.20 ± 8.727.90 ± 4.41
      TLUs: tropical livestock units; HH: household.

      Table 4.  Per household Tropical Livestock Units (TLUs) of improved livestock ownership of the respondents in the southern and derived savanna agroecology.

      ParametersSouthern Guinea savannahDerived savannahMean
      Improved dairy cattle (TLUs/HH)0.00 ± 0.0022.73 ± 8.7422.73 ± 8.74
      Improved dairy cows - lactating (TLUs/HH)0.00 ± 0.0015.90 ± 9.1715.90 ± 9.17
      Improved dairy cows - non-lactating (TLUs/HH)0.00 ± 0.003.08 ± 1.743.08 ± 1.74
      Improved dairy heifers (6 months old - 1st calving) (TLUs/HH)0.00 ± 0.003.60 ± 2.063.60 ± 2.06
      Improved dairy female calves (less than 6 months old) (TLUs/HH)0.00 ± 0.001.20 ± 0.691.20 ± 0.69
      Improved dairy calf's male (less than 6 months old) (TLUs/HH)0.00 ± 0.001.00 ± 0.551.00 ± 0.55
      Improved bulls older than 2 years (TLUs/HH)0.00 ± 0.003.00 ± 1.753.00 ± 1.75
      TLUs: tropical livestock units; HH: household.

      Land use for fodder crops production

    • The land use for fodder crop production varied between the two agroecological zones. In the southern Guinea savanna, the mean total crop area per household was 4.93 ± 0.78 hectares, while in the derived savanna, it was slightly higher at 5.78 ± 1.02 hectares. Similarly, the total forage area per household was higher in the derived savanna (1.49 ± 0.75 hectares) compared to the southern Guinea savanna (1.02 ± 0.55 hectares). When considering specific yield measurements, it was found that the crop residue yield (kg DM/ha) in the derived savanna was higher (1,886.76 ± 415.63) than in the southern Guinea savanna (1,425.50 ± 317.43). Additionally, the forage yield (kg DM/ha) was significantly higher in the derived savanna (23,650.37 ± 9,516.69) compared to the southern Guinea savanna (16,874.43 ± 7,303.12). This indicates that the derived savanna agroecology has a higher capacity for fodder production. Moreover, the forage crop area as a percentage of the cropped area was higher in the derived savanna (21.20% ± 8.87%) compared to the southern Guinea savanna (17.29% ± 7.22%). This suggests that farmers in the derived savanna allocate a larger proportion of their cropped area specifically for fodder crop cultivation, highlighting the importance of fodder production in agroecology (Table 5).

      Table 5.  Per household total crop area, crop residue yield and percentage of fodder crop area in the southern and derived savanna agroecology.

      ParametersSouthern Guinea savannaDerived savannaMean
      Total crop area per household (ha/HH)4.04 ± 0.025.83 ± 1.484.93 ± 0.78
      Total forage area per household (ha/HH)0.50 ± 0.142.47 ± 1.491.49 ± 0.75
      Crop residue yield (kg DM/ha)986.89 ± 5.632,786.62 ± 232.631,886.76 ± 415.63
      Forage yield (kg DM/ha)40,960.00 ± 11,824.136,340.74 ± 3,660.8323,650.37 ± 9,516.69
      Forage crop area as percentage of cropped area (%)12.41 ± 3.6330.00 ± 17.3121.20 ± 8.87
      ha: hectare; HH: household; Kg: kilogramme; DM: dry matter.

      Feeds, feeding management, and animal nutrition efficiency

    • Grazing was found to be the major source of dry matter intake in both agroecological zones, contributing to an average of 62.00% ± 13.90% of the total feed supply. However, there were notable differences between the two zones. In the southern Guinea savanna, grazing contributed a higher percentage (75.00% ± 0.00%) to the total feed supply compared to the derived savanna (49.00% ± 28.26%). This could be attributed to the availability of more grazing land and favorable climatic conditions for natural pasture growth in the southern Guinea savanna. Regarding metabolizable energy intake, grazing remained the primary source of feed in both zones. However, there was a significant difference in the contribution of cultivated fodder. In the derived savanna, the percentage supply of metabolizable energy from cultivated fodder was higher (31.00% ± 28.26%) compared to the southern Guinea savanna (15.00% ± 0.00%), indicating a greater emphasis on cultivated fodder production in the derived savanna agroecology (Table 6).

      Table 6.  Per household sources of dry matter intake of cattle from different sources of the feed supply in the Southern Guinea savanna and derived savanna agroecology.

      ParametersSouthern Guinea savannaDerived savannaMean
      Purchase feed (%)5.33 ± 0.8216.50 ± 9.5810.97 ± 4.91
      Grazing (%)75.00 ± 0.0049.00 ± 28.2662.00 ± 13.90
      Collected fodder (%)2.66 ± 0.670.00 ± 0.002.66 ± 0.67
      Crop residue (%)7.66 ± 0.676.00 ± 2.306.83 ± 1.14
      Cultivated fodder (%)9.00 ± 0.0028.50 ± 16.4818.75 ± 8.51

      A similar pattern was observed for crude protein supply percentage. Grazing contributed the largest proportion of crude protein in both zones, but the percentage supply from cultivated fodder was higher in the derived savanna (26.00% ± 15.81%) compared to the southern Guinea savanna (9.00% ± 0.00%). This indicates that farmers in the derived savanna agroecology focus more on providing protein-rich fodder to their cattle. Furthermore, the metabolizable energy quantity supply in millijoules (MJ) per household was higher in the derived savanna agroecology compared to the southern Guinea savanna, reflecting the overall higher availability and quality of feed resources in the derived savanna. These findings suggest that while grazing remains a significant source of feed in both agroecological zones, the derived savanna shows a greater emphasis on cultivated fodder production, leading to higher nutritional efficiency and potential for improved animal productivity presented in Tables 7, 8 & 9.

      Table 7.  Per household sources of metabolizable energy intake of cattle from different sources of the feed supply in the Southern Guinea savanna and derived savanna agroecology.

      ParametersSouthern Guinea savannaDerived savannaMean
      Purchase feed (%)8.00 ± 1.1011.50 ± 6.089.75 ± 2.85
      Grazing (%)74.33 ± 0.6748.50 ± 28.0961.47 ± 13.77
      Collected fodder (%)2.00 ± 0.000.00 ± 0.002.00 ± 0.00
      Crop residue (%)9.00 ± 1.004.00 ± 1.106.50 ± 1.32
      Cultivated fodder (%)6.67 ± 2.0936.00 ± 20.7121.33 ± 11.47

      Table 8.  Per household sources of crude protein intake of cattle from different sources of the feed supply in the Southern Guinea savanna and derived savanna agroecology.

      ParametersSouthern Guinea savannaDerived savannaMean
      Purchase feed (%)9.67 ± 1.4712.50 ± 6.6311.03 ± 3.18
      Grazing (%)73.33 ± 1.6748.50 ± 28.1960.97 ± 13.75
      Collected fodder (%)2.00 ± 0.000.00 ± 0.002.00 ± 0.00
      Crop residue (%)7.33 ± 1.335.50 ± 2.036.47 ± 1.10
      Cultivated fodder (%)7.66 ± 2.3333.50 ± 19.3320.53 ± 10.40

      Table 9.  Analysis of nutrients intakes of the cattle kept by each household with respect to milk production per cattle in each household.

      ParametersSouthern Guinea savannaDerived savannaMean
      Dry matter quantity (kg/HH)64,511.25 ± 5,664.02504,597.04 ± 227,307.56284,554.15 ± 141,505.96
      ME quantity (MJ/HH)583,470.06 ± 51,226.914,836,953.98 ± 1,882,599.732,710,212.02 ± 1,270,420.18
      CP quantity (kg/HH)5,191.85 ± 455.8440,726.10 ± 17,989.4422,958.98 ± 11,309.27
      CP:ME ratio (g CP/MJ)8.9 ± 0.007.79 ± 0.878.34 ± 0.48
      Milk yield per ME (Ltr/MJ)0.01 ± 0.000.03 ± 0.000.017 ± 0.07
      Kg: kilogramme; HH: household; ME: metabolizable energy; CP: crude protein; MJ: milojoules; Ltr: Litre.

      Animal body measurements and expression of phenotypic traits

    • The agroecology types had a significant effect on the animals' measurements and phenotypic traits of the cattle. Significant differences were observed in body condition scores, testis circumference, live weight, age at puberty, and age at first calving between the two agroecological zones. There was a significant difference in the age of the cows under production in the agroecological zones (p = 0.008), but no significant difference in the age of the bulls (Table 10).

      Table 10.  The descriptive statistics of the animal body measurements and expression of the phenotypic traits of the cows and bulls under low external input operations in the selected agroecology.

      Southern Guinea savannaDerived savannaMeanp-values
      Live weight of the bulls (kg)205.24 ± 16.30309.62 ± 27.88257.43 ± 22.090.010
      BCS of the bulls5.79 ± 0.906.90 ± 0.596.35 ± 0.750.010
      Testis circumference of the bulls28.42 ± 0.5835.20 ± 2.1731.81 ± 1.380.040
      Age of the bulls (years)3.00 ± 0.252.70 ± 0.242.87 ± 0.250.430
      Live weight of the cows (kg)157.33 ± 6.15313.84 ± 22.73235.58 ± 14.440.006
      BCS of the cows3.50 ± 0.226.71 ± 0.335.10 ± 0.280.001
      Age of the cows (years)3.90 ± 0.376.14 ± 0.385.02 ± 0.370.008
      Age of the cows at puberty (years)2.00 ± 0.002.40 ± 0.152.20 ± 0.050.018
      Age of the cows at first calving (years)2.75 ± 0.003.15 ± 0.152.95 ± 0.080.016
      BCS: body condition scores; kg: kilogram.
    Discussion

    • This study aimed to assess the impact of agroecology differences on herd production characteristics and phenotypic traits of indigenous and improved cattle used for dairy production under low external input operations in Nigeria. The chosen agroecology, the Southern Guinea savanna and derived savanna, are significant regions for livestock production in the country. The results of this study indicate that the derived savanna agroecology is more favorable for dairy production in Nigeria compared to the southern Guinea savanna agroecology.

      The southern Guinea savanna, like other savanna regions in Africa, is characterized as a rainfed grassland with a relatively shorter and less intense dry season compared to the Sahelian agroecology. This makes it a relatively suitable agroecology for livestock production in the northern parts of Nigeria compared to other areas in the region[15,16]. On the other hand, the derived savanna agroecology is more suitable for dairy cattle production due to its abundance of feed resources and less dense forest cover compared to the tropical rainforest agroecology.

      The derived savanna is a transitional zone between the lowland rainforest and Guinea savanna agroecological zones, resulting from human-induced forest degradation and subsequent regrowth into savanna-type grasses[17]. This conversion of forests into savanna landscapes is often associated with deforestation, which is a growing concern due to the loss of biodiversity[18]. In Nigeria, forest conversion in the derived savanna agroecology has primarily been driven by crop food production and urban development, but more recently, it has also been attributed to cattle ranching, particularly in the southwestern parts of the country where laws have been enacted to restrict extensive cattle production on natural grasslands. The derived savanna agroecology is characterized by a forest-savanna transition terrain, making it suitable for both crop and livestock production[19]. As a result, it has become a major hub for dairy cattle production, attracting pastoralists from the northern regions in search of greener pastures as well as investment-driven smallholder dairy farms[20,21].

      In line with previous studies conducted in the derived savanna agroecology, which indicated a higher number of cows than bulls in the herd composition, suggesting a prevalent dairy farming practice among smallholders in the area, this study observed an improvement in the herd composition[22]. While the indigenous Bunaji breed still represents a significant portion of the herd (80%), there is now a substantial number of crossbred cattle used for milk production. This shift can be attributed to the growth of the dairy industry development being promoted by both the government and the private sector, as recommended in a previous study conducted in the area[23].

      Furthermore, the husbandry practices in the derived savanna agroecology appear to be more advanced compared to those in the southern Guinea savanna. For instance, feeding and animal nutrition management in the derived savanna heavily rely on cultivated fodders, industrial compounded feeds, and crop residues. This improved feeding strategy is reflected in the significantly higher nutrient intake per cattle kept for milk production in the derived savanna compared to the southern Guinea savanna, indicating better efficiency in nutrient utilization among cattle in the derived savanna agroecology. Overall, the findings of this study highlight the importance of considering agroecology differences in dairy cattle production. The derived savanna agroecology in Nigeria offers better conditions for dairy farming due to its favorable climate, availability of feed resources, and improved husbandry practices[24]. Therefore, an understanding of these agroecological variations can contribute to the development of targeted interventions and policies aimed at promoting sustainable and efficient dairy production in specific regions.

      The results of this study shed light on the significant impact of agroecology as a modifier of cattle herd production characteristics and phenotypic traits in the context of dairy production under low external input operations. The investigation focused on two key agroecology in Nigeria, namely the southern Guinea savanna and derived savanna, which are crucial regions for livestock production in the country. By examining the differences between these agroecology, the study aimed to address a notable knowledge gap in understanding the specific factors contributing to variations in cattle performance. The background of the study emphasized the importance of considering agroecology differences in dairy cattle production. While the southern Guinea savanna agroecology is known for its suitability for livestock production in the northern parts of Nigeria, the derived Savanna agroecology offers more favorable conditions for dairy farming due to its feed resources and less dense forest cover. This distinction is particularly significant considering the ongoing climate change and its potential impact on agroecological zones, including the transition from rainforests to savanna landscapes. Therefore, exploring the relationships between agroecology, climate change, and cattle performance becomes crucial for sustainable and efficient dairy production.

      This study addressed this knowledge gap by comparing herd production characteristics and phenotypic traits between the two agroecology. The results revealed substantial differences, highlighting the superior performance of cattle in the derived savanna agroecology compared to the southern Guinea savanna. Key findings included higher tropical livestock units (TLUs) of crossbred cattle used for milk production in the derived savanna, indicating the growth and adoption of improved dairy breeds. Moreover, the husbandry practices in the derived savanna, such as the reliance on cultivated fodders, compounded feeds, and crop residues, contributed to higher nutrient intake and efficiency compared to the southern Guinea savanna. These findings underscore the influence of agroecology on cattle performance and emphasize the need for tailored interventions and policies to support specific regions. Further research is warranted to delve into the specific factors driving the observed differences between the agroecology investigated. Exploring the relationships between climate change, agroecology, and cattle performance would provide valuable insights into the adaptability and resilience of dairy systems in the face of changing environmental conditions.

      Climate change poses challenges to agricultural systems worldwide and understanding its implications for livestock production is crucial. Investigating how climate change influences agroecological zones and subsequently affects cattle performance can inform strategies to mitigate its adverse effects[25]. Factors such as temperature, precipitation patterns, and forage availability are likely to play significant roles in shaping the productivity and resilience of dairy cattle in different agroecological contexts[26]. By considering the interactions between climate change and agroecology, future research can contribute to the development of climate-smart practices and adaptation strategies in dairy production.

    Conclusions

    • This study provides valuable insights into the role of agroecology as a modifier of cattle herd production characteristics and phenotypic traits in dairy systems. The results highlight the superiority of the derived savanna agroecology in Nigeria for dairy production, attributed to its favorable climate, abundant feed resources, and improved husbandry practices. The study bridges an important knowledge gap and emphasizes the need for further research to unravel the specific factors underlying the observed differences between agroecology. Understanding the complex relationships between climate change, agroecology, and cattle performance will contribute to the development of sustainable and resilient dairy systems in the face of evolving environmental conditions.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Sikiru AB, Egena SSA; data collection, analysis and interpretation of results: Sikiru AB, Saheed S, Otu BO, Makinde OJ; draft manuscript preparation: Sikiru AB. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • All data generated or analyzed during this study are included in the manuscript.

    Acknowledgments

    • The authors are thankful to the Fulani Cattle Breeders in Tafa Local Government Area and Bosso Local Government Areas of Niger State, Jere in Kagarko Local Government Area of Kaduna State, Iseyin Local Government Area, and Shaki West Local Government Area, of Oyo State, Nigeria for their cooperation for execution of the study. Special thanks to management and staff of Genius Farms, Iseyin, Oyo State, Nigeria. Our profound gratitude to Mr. Yusuf Adeyemo and Mr. Abdulrazak for providing us with free accommodation while carrying out the fieldwork.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Cite this article
    Sikiru AB, Otu BO, Makinde OJ, Saheed S, Egena SSA, et al. 2024. Comparative analysis of cattle production systems in Nigeria grassland agroecology. Circular Agricultural Systems 4: e001 doi: 10.48130/cas-0023-0012
    Sikiru AB, Otu BO, Makinde OJ, Saheed S, Egena SSA, et al. 2024. Comparative analysis of cattle production systems in Nigeria grassland agroecology. Circular Agricultural Systems 4: e001 doi: 10.48130/cas-0023-0012
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  • Introduction
  • Materials and methods

  • Study design

  • Participants and geographic locations

  • Questionnaire administration for collection of herd characteristics data

  • Animal body measurements and computation of live weights

  • Statistical analysis

  • Results

  • Effect of agroecology on herd production characteristics

  • Land ownership characteristics based on agroecology

  • Tropical Livestock Units (TLUs) of local and improved livestock

  • Land use for fodder crops production

  • Feeds, feeding management, and animal nutrition efficiency

  • Animal body measurements and expression of phenotypic traits

  • Discussion
  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
  • About this article
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