Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models. The predictive performance of two different statistical models (random forest and partial least square regression) was compared. Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data. In contrast, the partial least square regression model overfits the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400–700 nm). This study shows that soil organic carbon can be most accurately estimated using derivative field spectroscopy measurements and random forest regression.
Exposure to heavy metals can affect cell differentiation, neurocognitive development, and growth during early life, even in low doses. Little is known about heavy metal exposure and its relationship with nutrition outcomes in non-mining rural environments. We carried out a community-based cross-sectional study to describe the distribution of four heavy metal concentrations [arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg)] in the serum of a representative population of children aged 12 to 59 months old from the rural region of Popokabaka, Democratic Republic of Congo. The four metals were measured in 412 samples using inductively coupled plasma–mass spectrometry (ICP–MS). Limits of detection (LoD) and quantification (LoQ) were set. Percentiles were reported. Statistical and geospatial bivariate analyses were performed to identify relationships with other nutrition outcomes. Arsenic was quantified in 59.7%, while Cd, Hg, and Pb were quantified in less than 10%, all without toxicities. The arsenic level was negatively associated with the zinc level, while the Hg level was positively associated with the selenium level. This common detection of As in children of Popokabaka requires attention, and urgent drinking water exploration and intervention for the profit of the Popokabaka community should be considered.
<p> <span><span>Tropical Africa has been experiencing a long term drying trend for the last two decades. Climate change and variability has an influence on rain-fed agriculture under the Tropics. Many studies have investigated on the role of climate change and variability on crop yields, but with a limited number of predictors. We use detailed gridded crop statistics time series data to examine how recent climate inter-annual variability led to variations in maize yields. The added-value of this study is, that it integrates for the first time different sets of variables on different spatial scales, 107 in total: local, regional and global. A cross-validated model output statistics (MOS) approach is applied to choose physically motivated predictors. Both climate variables and maize yields were de-trended. The results revealed that inter-annual climate variability accounts for globally more than 35 percent of the observed maize variability in Tropical Africa. Our study uniquely illustrates spatial patterns in the relationship between climate variability and maize yield variability, highlighting where variations in different group of predictors interact and explain maize yield variability. Overall, temperature and precipitation principal component variables are preferably selected by the model. The next step of the study will consist of using the MOS equation to forecast future maize yield changes based on climate model output. The implication of the study is that, it will generate policy interventions towards buffering future crop production from climate variability.</span></span></p>
Heavy metals affect cell differentiation, neurocognitive development, and growth during early life, even in low doses. Little is known about heavy metal exposure and its relationship with nutrition outcomes in nonmining rural environments. We carried out a community-based cross-sectional study to describe the distribution of four heavy metal concentrations [arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg)] in the serum of a representative population of children aged 12 to 59 months old from the rural region of Popokabaka, Democratic Republic of Congo. The four metals were measured in 412 samples using inductively coupled plasma–mass spectrometry (ICP–MS). Limits of detection (LoD) and limits of quantification (LoQ) were set. Percentiles were reported. Statistical and geospatial bivariate analyses were performed to identify relationships with other nutrition outcomes. Arsenic was quantified in 59.7%, while Cd, Hg, and Pb were quantified in less than 10%, all without toxicities. The arsenic level was negatively associated with the zinc level, while the Hg level was positively associated with the selenium level. This common detection of As in children of Popokabaka requires attention, and urgent drinking water exploration and intervention for the profit of the Popokabaka community should be considered.
Bioclimatic variables (BCVs) are the most widely used predictors within the field of ecological niche modeling. However, recent studies indicate that BCVs alone are not sufficient to describe these limits and more (bio)climatological variables should be considered. Unfortunately, the most popular database WorldClim offers only a limited selection of predictors; thus, other gridded station-based observations or reanalysis (GSOR) datasets should be taken into account. In the present study, we investigate how well the BCVs are represented by different GSOR datasets for the extended Mediterranean area within the period 1970-2020, how deviations among the datasets differ regionally and how different calculation schemes affect the representation of BCVs. We consider different calculation schemes for quarters/months, the annual mean temperature and the maximum temperature of the warmest month and show the effects on the respective BCV. Differences resulting from different calculation schemes are presented for ERA5-Land. Selected BCVs are analyzed to show differences between WorldClim, ERA5-Land, E-OBS and CRU. Our results show that (a) deviations between the two calculation schemes for annual mean temperature (maximum temperature of the warmest month) diminish (increase) when the temporal resolution is decreased; (b) with respect to the definition of the respective month/quarter, temporal shifts can have substantially different effects on the BCVs, depending on region; (c) overall, all datasets represent the different BCVs similarly, but with partly large differences in some subregions; (d) the largest differences occur when specific month/quarters are defined by the seasonal cycle of precipitation. In summary, (a) since the definition of BCVs matches different calculation schemes, a transparent communication of the BCVs calculation schemes is inevitable; (b) the calculation, integration or elimination of BCVs has to be examined carefully for each dataset, region, period or species; (c) the GSOR-Data provide, except some subregions, a consistent representation of BCVs within the extended Mediterranean region.
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