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This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen’s slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.

Some studies suggest that higher body mass index is associated with increased susceptibility to bacterial vaginosis (BV), but results are conflicting. Methods Female sex workers aged 16 to 45 years and participating in an open, prospective cohort study in Mombasa, Kenya between 2000 and 2014 were included in this analysis. Up to 2 years of follow-up were included per woman. Body mass index (BMI) was categorized as underweight (≤18.5), normal (18.5-24.9), overweight (25-29.9), and obese (≥30). Bacterial vaginosis was assessed using Nugent scores. Generalized estimating equations were used to estimate relative risks of the association between BMI and BV. Results At baseline, 32.1% (n = 625) of 1946 women had BV. Half of women were overweight (31.1%, n = 606) or obese (20.1%, n = 391). Participants contributed 14,319 follow-up visits. Adjusting for age, compared to women with normal BMI, overweight (adjusted relative risk, 0.91; 95% confidence interval, 0.81-1.02) and obese (adjusted relative risk, 0.82; 95% confidence interval, 0.71-0.94) women were at lower risk for BV (joint P = 0.03). Conclusions Obese women had a nearly 20% lower risk of BV compared with women with normal BMI. Potential mechanisms for this effect, including possible effects of diet, obesity-associated changes in the gut microbiome, and systemic estrogen levels, should be explored.