**5. Limitations**

It was difficult to harmonize our results based on the various AMRS approaches used in studies available for assessment. Moreover, in many of the studies, the Kirby-Baeur method was used, but adequate reference to standards from either Clinical & Laboratory Standards Institute (CLSI) or the European Committee on Antimicrobial Susceptibility Testing (EUCAST) was not provided.

Studies on antibiotics residues were scarce in Nigeria during the years under consideration, with limited studies available for analysis. The methods in most of the studies considered commercial kits, with a dearth of in-depth information on the procedures, which should have given ample opportunity to compares the biases in the methods used in the various residue studies. Relevant equipment that should support such studies on residue testing was wanting at the time of these studies in Nigeria.

### **6. Conclusions**

Multidrug resistance has shown a heightened rise in Nigeria based on this study. The need to use international standards to evaluate most studies on AMR nationwide, in view of the variance of these standards, is necessary. Most of the antimicrobials observed in this study are on the WHO 2017 list of essential antimicrobials and are also listed in OIE 2017 Terrestrial animal health code has, thus, necessitated the evaluation of the situation of AMR in humans [76,80].

It is necessary to design a carefully planned, multi-sectoral, surveillance plan, which can be adopted for research and diagnostic purposes in various aspect of AMR. The need for standardization in all studies in the future and, possibly, the development of guidelines that should harmonize studies across platforms using the "One Health Approach" is imperative. This should target the promotion of good practices and antimicrobial stewardship, which should be enforced by the government, with the cooperation of all stakeholders

The relevant ministries and government departments should enforce: Registration and monitoring of animal production premises, especially, food producing animals; improvement of biosecurity compliance of food animal environments; prohibition of the use of antibiotics for growth promotion and prophylactic treatment; and putting in place a system to implement drug withdrawal periods in food animals.

More detailed descriptions of the results (figures) are available in the Supplementary materials, which are available online.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-4601/15/6/1284/ s1, Figure S1: Flow chart of the methodological strategy (PRISMA 2009 Flow Diagram), Figure S2: Nigeria geopolitical zonal spread of the AMRS reports, Figure S3: Geopolitical zonal spread of the Antimicrobial Residue reports, Figure S4a: Level of resistance within generation of antimicrobials tested, Figure S4b: Proportional (%) pattern of resistance levels within generation of antimicrobials tested, Figure S5a: Frequency of Antimicrobial Resistance levels of classes of antibiotics, Figure S5b: Antimicrobial resistance patterns within classes along generation of antibiotics, Figure S6: Antimicrobial resistance patterns of β-lactam derivatives antibiotics, Figure S7: Antimicrobial resistance patterns of Quinolones, Figure S8: Antimicrobial resistance patterns of Aminoglycosides, Figure S9: Antimicrobial resistance patterns of Macrolide, Phenicol, and Tetracycline, Figure S10: Antimicrobial resistance patterns of Sulfonamides derivatives, Figure S11: Frequency of antimicrobial resistance levels of other classes of antibiotics, Figure S12: Antimicrobial resistance patterns of other classes of antibiotics, Figure S13: Pattern of antimicrobial resistance of *Escherichia coli,* Figure S14: Pattern of antimicrobial resistance of *Salmonella,* Figure S15: Pattern of antimicrobial resistance of *Staphylococcus,* Figure S16: Pattern of antimicrobial resistance of *Pseudomonas,* Figure S17: Pattern of antimicrobial resistance of *Klebsiella,* Figure S18: Pattern of antimicrobial resistance of other bacteria, Table excel S1: Raw data AMRS, S2: Comprehensive AMRS data, S3: Categorized AMRS data analytical.

**Author Contributions:** The study was conceived by all the authors; designed and supervised by S.F. and F.O.F.; search and review were done by M.G., S.O.O., E.J.A., M.K.A., H.A., and N.O.O.; analysis of the data and writing of the manuscript was done by I.A.O. and N.O.O. F.O.F. provided the overall supervision from design, data gathering, analysis, manuscript preparation, review, journal selection and proof reading of the final manuscript.

**Funding:** The corresponding author acknowledged the support of University of Pretoria for the Doctoral Research Support Scholarship for 2016 and 2017 funding years for the partial funding.

**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. The funding was part of regular funding block by the University of Pretoria to support research works of their PhD students.
