Assessing the use of a drought-tolerant variety as adaptation strategy for maize production under climate change in the savannas of Nigeria

The Decision Support System for Agricultural Technology Transfer (DSSAT) was used to quantify the impact of climate change on maize yield and the potential benefits of the use of drought-tolerant maize variety over non-drought tolerant variety in savanna ecological zones of Nigeria. Projections of maize yields were estimated for three locations representing different agro-climatic zones and soil conditions, in the mid-century (2040–2069) and end-century (2070–2099) under representative concentration pathways scenarios (RCP 4.5 and 8.5) against the baseline period (1980–2009). Relative to the baseline period, the ensemble Global Circulation Models (GCMs) predicted significant increase in minimum and maximum temperatures and seasonal rainfall across the sites. In the mid-century, ensemble GCMs predicted temperatures increase between 1.7–2.4 °C for RCP4.5 and 2.2–2.9 °C for RCP8.5. By end-century, the temperature increases between 2.2–3.0 °C under RCP4.5 and 3.9–5.0 °C under RCP8.5. Predicted seasonal rainfall increase between 1.2–7% for RCP4.5 and 0.03–10.6% for RCP8.5 in the mid-century. By end of century, rainfall is expected to increase between 2–6.7% for RCP4.5 and 3.3–20.1% for RCP8.5. The DSSAT model predictions indicated a negative impact on maize yield in all the selected sites, but the degree of the impact varies with variety and location. In the mid-century, the results showed that the yield of the non–drought tolerant maize variety, SAMMAZ-16 will decline by 13–19% under RCP4.5 and 19–28% under RCP8.5. The projection by end-century indicates a decline in yield by 18–26% under RCP4.5 and 38–47% under RCP8.5. The yield of the drought-tolerant variety is projected to decline by 9–18% for RCP4.5 and 14–25% for RCP8.5 in the mid-century and 13–23% under RCP4.5 and 32–43% under RCP8.5 by the end-century. The higher temperatures by both emission scenarios (RCP 4.5 and 8.5) were primarily shown to cause more yield losses for non-drought-tolerant variety than that of the drought-tolerant variety. There will be 1–6% less reduction in yield when drought-tolerant variety is used. However, the higher yield reductions in the range of − 13 to − 43% predicted for the drought-tolerant variety by the end of the century across the study areas highlighted the need to modify the maize breeding scheme to combine both tolerances to drought and heat stresses in the agro-ecological zones of northern Nigeria.

www.nature.com/scientificreports/ rian Meteorological agency (NIMET) archives for the three studied sites. The protocols developed by the global Agricultural Model Intercomparison and Improvement Project (AgMIP) team 54 was used to generate the future climate scenarios (RCP4.5 and RCP8.5) for mid-century (2040-2069) and end-century (2070-2099) using the delta-based method. The future climate scenarios under RCP4.5 and RCP8.5, assume elevated CO 2 concentration of 499 and 571 ppm, respectively, compared with the current 380 ppm. The future daily rainfall, minimum and maximum temperatures were generated by perturbing the daily baseline data using delta factor method 55,56 .
For the study, we used four contrasting, bias corrected GCMs from Fifth Coupled Model Inter-comparison Project (CMIP5) including CESM1-CAM5 (National Centre for Atmospheric Research, USA), CSIRO-MK3. 6.0 (Commonwealth Scientific and Industrial Research Organization, Australia), HadGEM2-ES (Met Office Dadley Centre, UK-Exeter), and MRI-CGCM3 (Meteorological Research Institute Coupled Ocean-Atmosphere General Circulation Model 3) in our analysis. The four GCMs were selected according to their higher resolution and established ability to replicate historical rainfall and temperature over the entire country compared to the other GCMs of CMIP5 26 . Changes in rainfall and temperature in mid-and end-century relative to the baseline were estimated based on GCM outputs. The adjusted formula for modified daily precipitation and temperature were expressed in Eqs.
where P adj.fur,d is the adjusted daily rainfall for the future years, P obs,d is the observed daily rainfall for the base years, P . GCM.fur,m is the monthly mean rainfall of the GCM outputs for the future years, P . GCM.ref.m is the monthly mean rainfall of the GCM outputs for the base years, Pi is the weight of each grid cell, and k is the number of the grid cells.
(1) P adj,fur,d = P obs,d × www.nature.com/scientificreports/ where T adj.fur,d is the adjusted daily temperature (maximum and minimum temperatures) for the future years, T obs,d is the observed daily temperature for the base years, T GCM.fur,m is the monthly mean temperature of the GCM outputs for the future years, T . GCM,ref.m is the monthly mean temperature of the GCM outputs for the baseline, Pi is the weight of each grid cell, and k is the number of the grid cells.

Description of the crop simulation model. Decision Support Systems for Agrotechnology Transfer
(DSSAT) version 4.7 58 model was used to simulate maize grain yield using the baseline and future climate scenarios. In this study, the CERES-Maize model 39 which is embedded within the DSSAT was used to simulate the phenology and yield of maize varieties, in response to climate and management factors. CERES-Maize is a process-based, management-oriented model that utilizes water, carbon, nitrogen (N) and energy balance principles to simulate the growth and development of maize. For each cultivar, the model runs with a daily time step and simulates crop growth, development and yield based on the effects of weather, soil characteristics and crop management practices 59 . A detailed description of the original CERES-Maize model can be found in Jones and Kiniry 60 and Ritchie et al. 61 .
Model inputs data. The DSSAT model requires field level input data including daily weather (minimum and maximum temperature, rainfall, and solar radiation), soil physical and chemical characteristics, crop variety parameters, and details of the crop management 58,62 . For this study, data on maize crop management (including planting date, plant density, fertilization and crop data) were obtained from the field trials conducted for the respective sites. Soil profile data of experimental stations were obtained from field measurements, the generic horizons of the profiles and soil types were classified using the FAO guidelines 63  Model evaluation with an independent dataset. The comparison of simulated with observed yields allow the assessment of the model capacity to represent local cropping systems 41 . The independent data used for model evaluation included days to anthesis, days to physiological maturity, final grain yield and above ground biomass ha −1 . These were collected from N response trials established at Iburu in SGS and Zaria in the NGS.
Four field experiments were conducted during the rainy seasons of 2015 and 2016 each at Iburu and Zaria. Detailed climatic conditions of the two seasons were published in Tofa et al. 53 . The experiments were set up as a split plot design in randomized complete blocks with four N rates (0, 60, 120 and 180 kg ha −1 ) set as the main plot and the maize varieties were assigned to the sub-plots with three replications. The sub-plots contained four rows spaced 75 cm apart and 500 cm in length with intra-row spacing of 25 cm between stands which gave a plant density of 53,333 plants ha −1 . Phosphorus (P) and potassium (K) were applied at the rate of 60 kg ha −1 each. Triple super phosphate (TSP) and muriate of potash (MOP) were used to supply the P and K fertilizers, respectively. Urea (46% N) was used as source for the four nitrogen (N) treatments. Half of the N and full rate of P and K were properly mixed and applied 10 days after sowing (DAS). The balance N was applied 45 DAS. The detailed climate, field observations, soil, and crop management practices used for model evaluation were published previously 53 . The response of the model was evaluated using three different statistical indicators, including root mean square error (RMSE), model estimation efficiency (EF) and index of agreement (d) 5,53 .
Simulation protocol and analysis. The calibrated CERES-Maize model was used to access the impact of climate change on grain the yield of the two different maize varieties in the three agro-ecological zones of Nigeria. The effects of climate change on yield of each variety was simulated for 30-year baseline (1980-2009) weather data with the default atmospheric CO 2 concentration of 380 ppm, while the future (2040-2069 and 2070-2099) climate scenarios were both simulated at 499 ppm CO 2 (RCP4.5) and 571 ppm CO 2 (RCP8.5). Other variables such as soil, cultivar and crop management practices are held constant. The seasonal analysis program in DSSAT was used to generate 30-year simulations and examine the variation in maize yield relative to the baseline. Two maize varieties, SAMMAZ-26 and SAMMAZ-16, were used which represents the drought-tolerant and nondrought-tolerant varieties, respectively. The long-term simulations were done on an Alfisol soil from Kano representing the SS, a Typic Haplustalf from Zaria representing the NGS and a Ferric Luvisols from Abuja to represent the SGS. Nitrogen, soil water content, and organic matter content was allowed to be carried over between seasons, thereby not necessitating the need for re-initialization. The planting date was set at June 20 in the SS and June 30 for both NGS and SGS as recommended by Tofa et al. 53 . Plant population was set according to the national recommendation of 5.3 plants m -2 . A constant inter row spacing of 75 cm and planting depth of 5 cm were maintained. For all simulated scenarios, the model was set up to supply the recommended rate of 120 kg N ha −1 using split application method. Half of N was set to apply at 10 days after sowing, and the other half at 45 days after sowing. Phosphorus and potassium were assumed to be non-limiting, so P and K sub-models were switched-off. A simple mathematical averaging was performed using excel to access the climate model ensemble mean. The impact of climate scenarios on maize yield was compiled and relative yield deviation from the baseline was computed according Faye et al. 65  www.nature.com/scientificreports/ where ΔYield is the yield change due to climate change, Yield Scenario and Yield baseline are yields obtained under scenario and baseline weather conditions respectively. Comparison was made for future water and N stress relative to baseline at each growing stage including emergence, end of juvenile, beginning of flowering, 75% silking, beginning of grain feeling, end of grain feeling and maturity. Values of the ensemble four GCMs were compared to the present for both varieties within each location to determine any difference in the actual water and N stresses in the model during the mid-and endof-century using DSSAT output. The stress was measured on a scale of between 0 (minimum) and 1 (maximum) in the model each for water and N.

Results and discussions
Model calibration. Table 1 shows the values for genetic coefficients used in this study. The generated cultivar coefficients are within the range of DSSATv4.7 cultivar database. There was variation between the varieties in all the coefficients generated using Generalized Likelihood Uncertainty Estimation (GLUE). SAMMAZ-26 had higher potential kernel number (780 plant -1 ) than that of SAMMAZ-16 (743 plant -1 ). SAMMAZ-26 also recorded high thermal time from emergence to the end of juvenile phase (302 °C days) and from silking to physiological maturity (806 °C days) and high optimum kernel filling rate (6.5 mg day -1 ) and thermal time between successive leaf tip appearances (40 °C days). However, the delay in development for each hour that day-length is above 12.5 h was slightly higher (0.424 days) for SAMMAZ-16. The parameterized CERES-Maize model was used to assess the accuracy of cultivar coefficients by simulating days to anthesis, days to physiological maturity, grain yield kg ha -1 , and above ground biomass kg ha -1 for the two maize varieties (Table 2). There was high prediction accuracy for all the tested parameters as indicated by low RMSE and high d-index values for the two varieties. Excellent statistics were achieved for calibration of the phenological parameters (anthesis and maturity) with RMSE below 3 days and d-index above 0.92 for of both varieties. For SAMMAZ-16, the RMSE between the observed and simulated values of grain yield and above ground biomass, were 245 kg ha -1 and 1152 kg ha -1 , respectively, with corresponding d-index of 0.91 and 0.80. For variety SAMMAZ-26, the RMSE for grain yield was 157.7 kg ha -1 with d-index 0.61 while for above ground biomass the RMSE was 784 kg ha -1 with d-index 0.88. This result suggests accurate predictions of all parameters for model application.
Model evaluation. The model evaluation results using an independent dataset showed that the model accurately predicted days to anthesis and maturity with low RMSE values of < 2 days for both parameters with  (Table 3). We found the model's performance satisfactory based on the closeness of fit between observed and simulated parameters evaluated, thereby suggesting that the model is robust and near accurate to make wider assessments across different environments under study. These results are in agreement with previous findings from the same agro-ecologies of Nigeria 5,53,66,67 .
Projected trends in minimum and maximum temperatures. The thirty-year temperature projection under the medium (RCP 4.5) and a high (RCP 8.5) emission scenarios for the future climates (mid-and end-century) in the three locations indicated significant increases in temperatures, compared to baseline (Figs. 2, Table 3. Comparisons of simulated and observed mean values on days to anthesis, days to maturity, grain yield, and above ground biomass for model evaluation with their respective statistical indices for the calibrated maize varieties.  www.nature.com/scientificreports/    www.nature.com/scientificreports/ In Abuja in the SGS, the minimum temperature is projected to increase by 1.8 °C in the mid-century and 2.3 °C by the end-century under RCP4.5, meanwhile under RCP8.5 minimum temperature will rise by 2.5 °C in the mid-century and 4.5 °C by the end-century compared to baseline. The maximum temperature was projected to increase by 1.7 and 2.23 °C in the mid-and end-century, respectively, compared to the baseline period under the RCP4.5 scenario. Under the RCP8.5 scenario, the maximum temperature was expected to increase by 2.2 and 3.9 °C in the mid-and end-century periods, respectively. In Zaria in the NGS, the minimum and maximum temperatures will increase by 2.4 and 2.2 °C in the midcentury, and 3.0 and 2.8 °C at the end-century under RCP4.5 compared to baseline period. Under high emission scenario (RCP8.5), the minimum and maximum temperatures were projected to increase by 3.2 and 2.8 °C in the mid-century, and increase by 5.0 and 4.5 °C by the end of century compared to baseline period.
In Kano in the SS, the projections under RCP 4.5 indicates minimum and maximum temperatures will increase by 2.1 and 2.0 °C in the mid-century and by 2.9 and 2.6 °C by end-century, respectively. Under RCP 8.5, the minimum and maximum temperatures will increase by 2.9 and 2.7 °C, respectively in the mid-century while projected increase by end-century will be 4.8 and 4.4 °C for minimum and maximum temperatures, respectively. These results agree with previous reports that indicated future warming of the air in the different parts of Nigeria 20,21,25,31 . The temperature increases in our study fall within the ranges reported by Niang et al. 69 , who projected temperature increase to exceed 2 °C by 2050 across much of Africa and to reach between 3 and 6 °C by the end of the century. Our results showed that the temperature changes vary spatially and increase from the SGS to the SS. This temperature distribution is consistent with those predicted for Nigeria by Abiodun et al. 70 . They reported increase in temperature in all the ecological zones, with the higher value between 1 and 4 °C in the SS.
In all the study areas, the minimum temperature increases faster than that of the maximum temperature under both RCPs. Our findings agreed with Dike et al. 71 who found that the magnitude of the increasing trend of minimum temperature is higher when compared with the increasing trend in maximum temperature over Nigeria. They stated that, minimum temperature increased at a rate of 0.51 °C per decade during 1971-2013, which by far exceeds the 0.17 °C per decade increase in maximum temperature. Analysis by Meehl et al. 72 revealed that daily minimum temperatures will increase more rapidly than daily maximum temperatures leading to the increase in the daily mean temperatures and a greater likelihood of extreme events and these changes could have detrimental effects on grain yield. High night temperature (minimum temperature in the range of 22-25 °C) could contribute to the lower yield of maize crops grown in the humid tropics because of higher rate of respiration 73 . Nightly weight losses by whole plant averaged 40% of daily weight gain 73 . According to Hatfield 74 , maize grain yields decreased from 84 to 100% because of exposure to high night time temperatures and disruption of the pollination process as evidenced by the large reduction in kernels per ear. During the grain-filling period, however, exposure to higher night temperatures shortened the grain-filling period by increasing the rate of senescence 74 . Projected changes in seasonal rainfall. Figure 6 presents the seasonal changes in rainfall computed from baseline to future climates for each GCM under both RCP4.5 and 8.5 scenarios. Based on the ensemble GCMs (Table 4), the seasonal rainfall is expected to increase in all study areas, for both RCPs, with the higher increases in the SS. The change in rainfall, increases in time from mid-to end-of-century in all the locations under RCP8.5. However, under the RCP4.5 scenario, the rainfall increase is limited with the slight decrease (0.3%) for SS from mid-century to the end of century. With the ensemble GCM, the largest projected mean rainfall increases of 10.6% in the mid-century and 20.1% in the end of century were expected in the SS zone under RCP8.5. Under both RCP4.5 and RCP8.5 an increase in rainfall is consistently projected to be lower for SGS during both the mid-and end-century periods. In this agro-ecology, the increase in rainfall ranged between 0.03% in the mid-century and 3.28% in the end-century, both under RCP8.5. The study by Shiru et al. 75 also reported a significant increase in seasonal rainfall in the range of 0-20% in most parts of the northern Nigeria. Adhikari et al. 76 observed that large uncertainty exists in projecting precipitation, and changes would range from − 15 to + 27% by 2060s in Africa. This projected increase in rainfall in the Guinea and Sudan savanna may pose a risk for annual flooding which may affect crop performance. Tables 5 and 6 shows the simulated maize yield with the baseline (1980-2009) and relative yield change in the mid-century (2040-2069) and end-century (2070-2099) under RCP4.5 and RCP8.5 scenarios. Irrespective of variety, the simulated ensemble of four GCMs showed a consistent decline in maize yield for both future climates relative to the baseline period in all locations. The two maize varieties performed differently in each agro-ecological zone. In SGS, relative to the baseline, the grain yield of SAMMAZ-26 is expected to decrease by 14 and 16% under RCP4.5 and RCP8.5, respectively, in the mid-century. The declines are 17% (RCP4.5) and 37% (RCP8.5) by the end of century. The grain yield of SAMMAZ-16 will decrease by 15% under RCP4.5 and 19% under RCP8.5 in the mid-century. Yield decrease of 21% (RCP4.5) and 42% (RCP8.5) are projected for the end of century. Future maize grain yields simulated for the NGS also shows consistent differences between the two varieties. Grain yield of SAMMAZ-26 will decrease by 9% under RCP4.5 and 14% under RCP8.5 during the mid-century. The decrease will be 13% under RCP4.5 and 32% under RCP8.5 by the end of the century period. For variety SAMMAZ-16, the yields are expected to decrease by 13 and 19%, under RCP4.5 and RCP8.5, respectively, in the mid-century. By the end of century yield will decrease by 18% under RCP4.5 and 38% under RCP8.5. In SS, the results shows that future yields of maize also differ strongly between the two varieties. Relative to the baseline climate, grain yield of SAMMAZ-26 will reduce by 18% under RCP4.5 and 25% under RCP8.5 in the mid-century. For end-of-century yield will reduce by 23% and 43% under RCP4.5 and RCP8.5, respectively. For SAMMAZ16, the yield is expected to decrease by 19% for RCP4.5 and 28% for RCP8.5 in mid-century. The estimated yield reduction of SAMMAZ-16 is 26% under RCP4.5 and more than 46% for RCP8.5 by the end of century.   Table 6. Baseline and simulated grain yield (kg ha -1 ) of different maize varieties and the relative yield change (%) at end-century in three agro-ecologies of Nigeria. www.nature.com/scientificreports/ Higher percentage reduction in maize yield was predicted by the end of century compared to the middle future climate. Ensemble grain yield decline was between 9 and 19% under RCP4.5 and between 14 and 28% under RCP8.5 in the mid-century. In the end of century, yield reductions were between 13 and 26% under RCP4.5 and 32-47% under RCP8.5. Thus, ensemble grain yield change of between − 9 and − 47% in our study falls within the wide ranges (− 98 to + 16%) of climate change impact reported for SSA 77 . Roudier et al. 78 found that the response of crop yield to climate change in West Africa can vary from − 50 to + 90% in a selection of 16 publications. This range is even wider in the review made by Müller et al. 79 which showed that projected impacts relative to current African production levels range from − 100 to + 168%.

Projected impact of climate change on maize yield.
Based on the comparative study of water and N stress under current and future climate change scenarios (Figs. 7, 8), the effects of future water and N stress on the crop were determined to be less throughout the growing season. This could be due to the increase in rainfall predicted in this study (Tables 5,6) which is anticipated to significantly reduce water stress and enhance N uptake on maize production in both future climates. The larger reduction in grain yield in the late future production system could be from the intensified heat stress due to increases in maximum temperature (ranged 3.85-4.48 °C) and minimum temperature (ranged 4.48-4.95 °C) across the locations. This agrees with previous reports that indicated the greatest reduction in maize yield is due to increase in temperature than a decrease in rainfall 33,40,68,[80][81][82] . A study by Tesfaye et al. 33 under hotter and drier climate change scenarios in South Africa showed significant yield reductions of local maize varieties by 21, 33 and 50% when temperature increased by 1, 2 and 4 °C, respectively. Similarly, Lobell and Burke 80 showed that an increase in temperature by 2 °C would result in a greater reduction in maize yields than a decrease in precipitation by 20%. Traore et al. 40 also estimated maize grain yield reductions of between 51 and 57% under current farmers' practices for southern Mali. In Nigeria, Abiodun et al. 70 , stated that the warming increases with latitudes, with the lowest warming over the southern region and the highest at the northern region. The southern regions receive lower warming than the interior because the cooling effect from the Atlantic Ocean reduces the warming near the coast. Hence, the northern stations are expected to be warmer than southern stations, therefore, more yield reduction in the Sudan savanna.
Grain yield reduction due to climate change vary spatially over the three study areas. Under both RCPs, the effect was more pronounced in the SS in both mid-and end-centuries, while the least decline in yield was observed in the NGS where the predicted increase in temperature was higher under both RCPs in two centuries. This is probably due to the better soil condition in the NGS that could reduce the adverse effect of the higher temperatures. Similarly, Sowunmi and Akintola 83 , concluded that Guinea savannah zone is suitable for maize production regarding good soil and temperature. In contrary, another study in Nigeria indicated that yield reduction increases from the southern Guinea savanna to the Sudan savanna. They projected that an increase in temperature by 4 °C reduced maize yields by 11.7%, 19.7% and 21.6% in the SGS, NGS and Semi-Arid, respectively, at 330 ppm CO 2 concentration 2 .
Under both RCPs, the results shows that, the predicted temperature increase is likely to have the biggest adverse effect on yields of both varieties at the end of the century in all the three study sites. However, the simulated reduction in grain yield would be lower using SAMMAZ-26 in both climate change scenarios compared to the SAMMAZ-16. This suggests that the drought-tolerant variety would perform better at both future climate periods compared to non-drought-tolerant variety. Our results are consistent with findings of Cairns et al. 36 who reported that drought tolerant varieties are more resilient to the effects of high temperature due to climate change in SSA.
The high yield reductions in the range of − 13 to − 23% under RCP4.5 and − 32 to − 42% under RCP8.5 observed for the drought-tolerant variety by the end of the century across the study areas highlighted the need to breed maize varieties and hybrids that combine tolerance to drought and heat stress. This is because drought and heat stress occur simultaneously in farmers' fields. The breeding strategies in maize that had been adopted so far have focused on improvement of each stress separately 28 . Cairns et al. 36 suggested that the genetic control of drought, heat, and combined drought and heat tolerance are largely independent of each other. Preliminary results of maize evaluated under the Drought Tolerant Maize for Africa (DTMA) project revealed that tolerance to drought stress does not necessarily confer tolerance to heat stress or combine drought and heat stress, which have implications to breed for adaptation to climate change in maize production systems in SSA 36 . This is because higher temperatures are often associated with increases in evapotranspiration and a reduction in soil moisture levels which hasten the onset and severity of drought stress, especially in rainfed drylands 33,84 . This study suggests that breeding maize for combine drought and heat stress could be an effective adaptation strategy in dealing with the adverse effects of high temperature in the future climate change.

Conclusion
This study evaluated the impacts of climate change on maize production in northern Nigeria in two future time periods (mid-and end-centuries) compared to a baseline period , under RCPs 4.5 and 8.5 climate change emission scenarios. The climate projections for major maize growing zones in Nigeria showed rise in temperatures. Our climate modelling results suggest that with ensemble GCM, both the minimum and maximum temperatures will increase from the mid-century period to the end of the century in all locations. The annual mean temperature under RCP8.5 scenario was the highest compared with RCP4.5 within each century period for all locations. Rainfall is expected to increase at all the study areas, for both RCPs, with the higher increases of 10.6% in the mid-century and 20.1% in the end of century in the SS. As projected by the ensemble GCMs in combination with 2 RCPs and DSSAT model, climate change will result in reduction of yield of between 9 and 28% for the mid-century (2040-2069) and between 13 and 47% for end of the century (2070-2099), depending on the environment and variety, with farmers in the Sudan savanna environment having more negative impact. Results shows that the use of improved and drought-tolerant variety as adaptation strategy can reduce the www.nature.com/scientificreports/ negative impact of climate change. On average, the yield difference between SAMMAZ-26 and SAMMAZ-16 is 2% under RCP4.5 and up to 4% under RCP8.5 during the mid-century, while in the end of century, the average yield difference between the two varieties is 4% and 5% under RCP4.5 and RCP8.5, respectively, across the three study areas. Though the yield reduction due to climate change was less for the drought-tolerant variety compared to the non-drought tolerant variety, the high yield reduction of the drought-tolerant variety suggests that breeders should develop varieties that combine tolerance to drought with that of heat to withstand the impacts of increased temperatures in the agro-ecological zones of northern Nigeria.  www.nature.com/scientificreports/

Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. www.nature.com/scientificreports/