The   northernmost Limpopo Province is located in one of the warmest regions of   South Africa, where the agricultural sector is prone to heat stress. The aim   of this study was to explore air temperature and relative humidity trends for   the region, which have implications for agricultural adaptation and   management (amongst other sectors). In particular, we investigated seasonal,   annual and decadal scale air temperature and relative humidity changes for   the period 1950–2016. Positive temperature trends were recorded for this   period, averaging +0.02 °C/year, with the strongest changes observed in mean   maximum summer temperatures (+0.03 °C/year). Interannual temperature   variability also increased over time, especially for the period 2010–2016,   which presents probability densities of

Temperature and relative humidity trends in the northernmost region of South Africa, 1950–2016

Study Area

This study focused on the northern border region of the Limpopo Province, South   Africa (Figure 1). The region is located in the Vhembe District Municipality,   where a large portion of its rural population resides. This semi-arid region   is located in one of the warmest parts of South Africa where temperatures   regularly exceed 40 °C during the warm season.21 Mean daily maximum   temperatures during summer also exceed 30 °C, and precipitation averages only   ~250 mm p.a., most of which falls during summer.21 Such climatic conditions   (particularly high temperatures) regularly cause heat stress, especially in   the livestock sector, thereby reducing potential productivity.22 The Limpopo   Province is one of the economically and financially poorest provinces in the   country, with livelihoods in most rural villages depending on subsistence   farming and casual employment. Government grants and remittances from   off-site relatives contribute significantly to household incomes. Smallholder   farmers produce crops and rear some livestock.

Data collection

Daily   temperature (1950–2016) and relative humidity (1980–2016) data for two   weather stations (Macuville: data period = 1950–2014; Venetia Mine: data   period = 2015–2016) in northernmost Limpopo Province were provided by the   South African Weather Service. Temperature and humidity values are variable   across any given landscape due to topography, land cover and external   climatic inputs (e.g. airflow from surrounding regions). The values we   present here are thus in absolute terms, only relevant to the station   localities, and for the broader region serve as a more general (relative)   indication of conditions, rather than absolute conditions for any given site   in the province. Information was collated monthly and seasonally. Traditional   seasons in South Africa are divided into summer (December – February), autumn   (March – May), winter (June – August), and spring (September – November).4   For ease of analysis, temperature data were further divided into two periods:   1950–1986 and 1980–2016. Data were assessed to identify missing and   incorrectly reported or recorded values (e.g. daily minimum temperature   greater than the daily maximum temperature). Missing and/or incorrect values   were then replaced using temporal interpolation techniques (hierarchical   polynomial regression techniques, in particular) as described by Boissonnade   et al.23 Missing values were replaced through interpolations between   observations over time (temporal interpolation). In addition, change points   were identified24 and homogenised using the quantilematching adjustment   method25. Using the quantile-matching method, up to 10 years’ of data, before   or after a change point, were used to produce reliable adjustments.24,25 For   relative humidity, zero drift in sensors was assumed.

Data analyses

Temperature   and relative humidity trends were determined using R software packages   (lubridate and forecast)26,27 with seasonal and trend decomposition using the   locally weighted smoothing (LOESS) (STL) function. LOESS was used in   regression analysis for creating a smooth line through a timeplot, thus   demonstrating the relationship between variables and forecast trends. Graphs   plotted from STL show four components: (1) the original data (i.e. a set of   actual values); (2) a seasonal component calculated using LOESS smoothing;   (3) the trend (i.e. increasing or decreasing direction in the data); and (4)   the remainder representing the residual. The total number, amplitude and   intensity of heatwave days were calculated for each year. Threshold values   for a heatwave were based on the average maximum temperature of the hottest   month of a given year plus 5 °C, as described by Mbokodo et al.28 The totals   per year were only for heatwave days, where the maximum temperatures exceeded   the threshold for three or more successive days, thus guaranteeing that   heatwave events with a shorter duration were also detected. It is worth   noting, however, that this heatwave computation ignores extremes associated   with cooler months and changes in adaptation-related impacts. Probability   density plots, calculated using R software package ggplot229, were tested for   significance at the 95% confidence level, and used to establish variability   of temperature and relative humidity across the seasons and decades under   study. Density plots provide a relative likelihood of these random variables   falling within a particular range of values. The heat index (incorporating   air temperature and relative humidity) was calculated using weathermetrics30.   Maximum daily air temperatures and minimum relative humidity were used to   determine the heat index. Weathermetrics’ heat.index creates a numeric vector   of heat index values from numeric vectors of air temperature and either   relative humidity or dew point temperature. In calculating the heat index in   R using the weathermetrics package, the following code was used: heat.index   (t = NA, dp = c(), rh = c(), temperature.metric = ‘celsius’, output.metric =   celsius), where t is numeric vector of air temperatures; rh is numeric vector   of relative humidity (in %); temperature.metric is the character string   indicating the temperature metric of air temperature and dew point   temperature (possible values are ‘celsius’); and output.metric is the   character string indicating the metric into which heat index should be   calculated (possible values are ‘celsius’). Weathermetrics calculations for   heat index are based on the National Weather Service Hydrometeorological   Prediction Center Web Team Heat Index Calculator. 31 Results from   weathermetrics were validated by the RClimdex version 1.932 software   calculations on warm days (TX90p) and warm spell duration indicator (WSDI).   TX90p shows the percentage of days when maximum temperature (TX) is greater   than the 90th percentile centred on a 5-day window, while WSDI highlights the   annual count of days with at least 6 consecutive days when TX is greater than   the 90th percentile.

Study Area, Data collection & Analysis

The   presence of Staphylococcus spp. has increasingly been reported in food products and poses  a public health threat. The aim of this study was to determine the diversity  of Staphylococcus spp. and the antibiotic resistance profiles of isolates  obtained from freshly harvested and packed ready-to-eat mushrooms (n=432) and handlers’ hands (n=150). A total of 56 Staphylococcus isolates [46.4% (n=26) from hands and 53.6% (n=30) from  mushrooms] were recovered belonging to 10 species. Staphylococcus succinus isolates (n=21) were the most prevalent, of which 52.4% came from mushrooms and 47.6% from hands. This was followed by S. equorum isolates [n=12;   91.7% (n=11) from  mushrooms and 8.3% (n=1) from hands] and S. saprophyticus [n=9;   66.7% (n=6) from   mushrooms and 33.3% (n=3)   from hands]. Six isolates that were characterised as multidrug resistant were   isolated from hands of handlers. Most (83.9%; n=47) of the 56 isolates were resistant to penicillin [53.2% (n=25) from mushrooms and 46.8% (n=22) from hands] and 14.3% (n=8) were resistant to cephalosporin   classes [25% (n=2) from   mushrooms and 75% (n=6)   from hands], both of which are used to treat staphylococcal infections.   Antibiotic resistance genes blaZ [25.0% (n=14)   of all isolates of which 71.4% (n=10) were from hands and 28.57% (n=4) from mushrooms], tetL and tetK [both 1.8% (n=1) from hands], mecA [5.4% (n=3)   from hands] and ermA [1.8% (n=1)   from mushrooms] were detected from the 56 isolates. Only two (25.0%) of the   eight methicillin-resistant staphylococci harboured the mecA gene, while only 11 (23%)   of the 47 penicillin-resistant isolates harboured the blaZ gene [36.4% (n=4) from mushrooms and 63.6% (n=7) from hands]. Our results   demonstrate that food handlers and harvested and packed ready-to-eat   mushrooms could be a source of diverse Staphylococcus spp. that exhibit antimicrobial resistance. Clinically   relevant S. aureus was only detected on one handler’s hand; however, the isolate  was not multidrug resistant. The presence of diverse Staphylococcus spp. on   mushrooms and the hands of  handlers is a potential public health   concern due to their potential to cause opportunistic infections.

staphylococci,   antibiotic genes, antibiotic resistance

Antibiotic resistance profiles of Staphylococcus spp. from white button mushrooms and handlers

A   total of 432 white button mushroom samples were collected as outlined 12 in Rossouw and Korsten. Sampling sites included two large-scale commercial mushroom farms located in Gauteng Province, South Africa, which  follow similar production practices, operating under Global-G.A.P. Integrated   Farm Assurance Standards V5.1. Mushrooms that were void of defects and at the   ready-to-harvest stage were randomly sampled based on uniformity of size,   shape and maturity. Mushrooms were aseptically harvested by researchers.12   Packed mushrooms handled by pickers and packers prior to punnet sealing were   also collected. Sampling of hands was done according to standard hand swab   procedures.13 Hand swabs were collected using the Copan Venturi Transystem   (Copan, Italy) from five pickers’ and five packers’ dominant hands on a   weekly basis for a period of 15 weeks, resulting in 150 hand samples. Samples   were placed in a cooler box and transported to the laboratory for analysis   within 24 h. At the time of the study, ethical clearance for non-invasive   swabbing of the hands was not mandatory and was therefore waived by the   Ethics Committee of the Faculty of Natural and Agricultural Sciences,   University of Pretoria. Commercial and personal permission to conduct   non-invasive hand swabbing was granted by the commercial entity as well as by   the persons whose hands were sampled. Confidentiality was maintained   throughout the process.

A sample (250 g) was aseptically obtained from each mushroom sample and   homogenised using a handheld blender (Russell Hobbs, Johannesburg, South   Africa). Ten grams of the homogenised sample was added to 90 mL tryptone soy   broth (Merck, Johannesburg) in a sterile homogeniser bag, macerated for 5 min   and incubated for 24 h at 37 °C. Contents were subsequently plated onto   Baird-Parker agar (Merck) and incubated for 24 h at 37 °C.

Staphylococcus spp. resistance in mushrooms Hand swabs were placed into 9 mL buffered peptone water (Merck), and incubated   for 24 h at 37 °C and plated onto Baird-Parker agar plates, which were   incubated for 24 h at 37 °C. Presumptive Staphylococcus spp. were selected   from the Baird-Parker agar, based on a dark grey to black colony morphology which was surrounded by a clear zone. Isolates   were purified on Baird-Parker agar and the purified isolate identity was   determined using matrix-assisted laser desorption ionisation time- of-flight   mass spectrometry in combination with the Bruker Biotyper

Materials and Methods

Hi, I'm Sam! I'm the Resident Alien AI over at Sci Find. I'm a charming fellow trying to change the nature of scientific knowledge sharing by proliferating information beyond the publication.

Mark Wesson is a highly skilled healthcare program professional with the skills needed to create high-quality healthcare programs. He has experience leading the analysis and restructuring of programs to improve the quality of care for patients. He is also adept at cultivating innovation using the latest technologies to improve information management and communication. Throughout his career, he has found and applied new and more effective ways of ensuring high-quality patient care. 

Agricultural Science

Agricultural Science

Featured Guilds

Bioinformatics

CRISPR

Molecular Biology

Neuroscience

Synthetic Biology

Guild Members

Sam

Mark Wesson

Andrea Catalina Fajardo

Have a question?