Distinct spatial dependency of carbon distribution between soil pools in grassland SOIL

Grassland soils play a key role in climate change and food security, and carbon (C) and nitrogen (N) mineralization is central to this. Although there are a number of mathematical models available to estimate C and N mineralization, they do not encompass the variability of the process and there is uncertainty in their predictions. The input parameters of the SOMA model (Soil Organic Matter “A”) have been conceptualized and validated to predict mineralization in arable soils. The objective of this research was to measure the spatial dependence of the input parameters in order to further ob - tain spatial predictions of mineralisation in a grassland system. A nested design was applied using sampling intervals of 30 m, 10 m, 1 m, and 0.12 m as sources of variation. From each sampling point a soil sample was taken (0-23 cm) and physical sequential fractionation was applied to obtain the free light fraction (FLF) and intra-aggregate light fraction (IALF). The C and N contents in the fractions were measured by mass spectrometry, and the results analysed by residual maximum likelihood (REML) to obtain components of variance at each stage, and then accumulated to plot the approach to a variogram. Both fractions showed spatial dependence at the finest scales measured, and the general pattern was different from that in an arable site. The recommended soil sampling interval where C and N mineralization predictions would be spatially distributed according to the correlation of input light fractions parameters of SOMA is 0.5m

Uncertainty in geological interpretations : Effectiveness of expert elicitations

We would like to thank all those who took part in our elicitations, as well as all those who helped in their facilitation. This work was undertaken while C.H. Randle held a joint University of Aberdeen, College of Physical Science Ph.D. Award and British Geological Survey University Funding Initiative (BUFI) Ph.D. Studentship at Aberdeen University, through Natural Environment Research Council (NERC). The contributions by C.H. Randle, R.M. Lark, and A.A. Monaghan are published with the permission of the Executive Director of BGS (NERC). The authors would like to thank Hazel Gibson and an anonymous reviewer for their comments on the manuscript and confirm that all views expressed are the opinions of the authors.Peer reviewedPublisher PD

Can uncertainty in geological cross-section interpretations be quantified and predicted?

This work was undertaken while C.H. Randle held a joint British Geological Survey University Funding Initiative (BUFI) and University of Aberdeen, College of Physical Sciences Ph.D. Studentship at Aberdeen University. The contributions by C.H. Randle, R.M. Lark, and A.A. Monaghan are published with the permission of the Executive Director of the British Geological Survey Natural Environment Research Council. We would also like to thank all those who took part in both experiments as well as the many people who have given input on our results.Peer reviewedPublisher PD

Communicating the uncertainty in estimated greenhouse gas emissions from agriculture

In an effort to mitigate anthropogenic effects on the global climate system, industrialised countries are required to quantify and report, for various economic sectors, the annual emissions of greenhouse gases from their several sources and the absorption of the same in different sinks. These estimates are uncertain, and this uncertainty must be communicated effectively, if government bodies, research scientists or members of the public are to draw sound conclusions. Our interest is in communicating the uncertainty in estimates of greenhouse gas emissions from agriculture to those who might directly use the results from the inventory. We tested six methods of communication. These were: a verbal scale using the IPCC calibrated phrases such as ‘likely’ and ‘very unlikely’; probabilities that emissions are within a defined range of values; confidence intervals for the expected value; histograms; box plots; and shaded arrays that depict the probability density of the uncertain quantity. In a formal trial we used these methods to communicate uncertainty about four specific inferences about greenhouse gas emissions in the UK. Sixty four individuals who use results from the greenhouse gas inventory professionally participated in the trial, and we tested how effectively the uncertainty about these inferences was communicated by means of a questionnaire. Our results showed differences in the efficacy of the methods of communication, and interactions with the nature of the target audience. We found that, although the verbal scale was thought to be a good method of communication it did not convey enough information and was open to misinterpretation. Shaded arrays were similarly criticised for being open to misinterpretation, but proved to give the best impression of uncertainty when participants were asked to interpret results from the greenhouse gas inventory. Box plots were most favoured by our participants largely because they were particularly favoured by those who worked in research or had a stronger mathematical background. We propose a combination of methods should be used to convey uncertainty in emissions and that this combination should be tailored to the professional grou

Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography

The spatial distribution and accessibility of organic matter (OM) to soil microbes in aggregates – determined by the fine-scale, 3-D distribution of OM, pores and mineral phases – may be an important control on the magnitude of soil heterotrophic respiration (SHR). Attempts to model SHR on fine scales requires data on the transition probabilities between adjacent pore space and soil OM, a measure of microbial accessibility to the latter. We used a combination of osmium staining and synchrotron X-ray computed tomography (CT) to determine the 3-D (voxel) distribution of these three phases (scale 6.6 μm) throughout nine aggregates taken from a single soil core (range of organic carbon (OC) concentrations: 4.2–7.7 %). Prior to the synchrotron analyses we had measured the magnitude of SHR for each aggregate over 24 h under controlled conditions (moisture content and temperature). We test the hypothesis that larger magnitudes of SHR will be observed in aggregates with (i) shorter length scales of OM variation (more aerobic microsites) and (ii) larger transition probabilities between OM and pore voxels. After scaling to their OC concentrations, there was a 6-fold variation in the magnitude of SHR for the nine aggregates. The distribution of pore diameters and tortuosity index values for pore branches was similar for each of the nine aggregates. The Pearson correlation between aggregate surface area (normalized by aggregate volume) and normalized headspace C gas concentration was both positive and reasonably large (r D0.44), suggesting that the former may be a factor that influences SHR. The overall transition probabilities between OM and pore voxels were between 0.07 and 0.17, smaller than those used in previous simulation studies. We computed the length scales over which OM, pore and mineral phases vary within each aggregate using 3-D indicator variograms. The median range of models fitted to variograms of OM varied between 38 and 175 μm and was generally larger than the other two phases within each aggregate, but in general variogram models had ranges <250 μm. There was no evidence to support the hypotheses concerning scales of variation in OM and magnitude of SHR; the linear correlation was 0.01. There was weak evidence to suggest a statistical relationship between voxel-based OM–pore transition probabilities and the magnitudes of aggregate SHR (r D0.12).We discuss how our analyses could be extended and suggest improvements to the approach we used

Assessing urinary flow rate, creatinine, osmolality and other hydration adjustment methods for urinary biomonitoring using NHANES arsenic, iodine, lead and cadmium data

Background There are numerous methods for adjusting measured concentrations of urinary biomarkers for hydration variation. Few studies use objective criteria to quantify the relative performance of these methods. Our aim was to compare the performance of existing methods for adjusting urinary biomarkers for hydration variation. Methods Creatinine, osmolality, excretion rate (ER), bodyweight adjusted ER (ERBW) and empirical analyte-specific urinary flow rate (UFR) adjustment methods on spot urinary concentrations of lead (Pb), cadmium (Cd), non-arsenobetaine arsenic (AsIMM) and iodine (I) from the US National Health and Nutrition Examination Survey (NHANES) (2009–2010 and 2011–2012) were evaluated. The data were divided into a training dataset (n = 1,723) from which empirical adjustment coefficients were derived and a testing dataset (n = 428) on which quantification of the performance of the adjustment methods was done by calculating, primarily, the correlation of the adjusted parameter with UFR, with lower correlations indicating better performance and, secondarily, the correlation of the adjusted parameters with blood analyte concentrations (Pb and Cd), with higher correlations indicating better performance. Results Overall performance across analytes was better for Osmolality and UFR based methods. Excretion rate and ERBW consistently performed worse, often no better than unadjusted concentrations. Conclusions Osmolality adjustment of urinary biomonitoring data provides for more robust adjustment than either creatinine based or ER or ERBW methods, the latter two of which tend to overcompensate for UFR. Modified UFR methods perform significantly better than all but osmolality in removing hydration variation, but depend on the accuracy of UFR calculations. Hydration adjustment performance is analyte-specific and further research is needed to establish a robust and consistent framework

Selenium deficiency is widespread and spatially dependent in Ethiopia

Selenium (Se) is an essential element for human health and livestock productivity. Globally, human Se status is highly variable, mainly due to the influence of soil types on the Se content of crops, suggesting the need to identify areas of deficiency to design targeted interventions. In sub-Saharan Africa, including Ethiopia, data on population Se status are largely unavailable, although previous studies indicated the potential for widespread Se deficiency. Serum Se concentration of a nationally representative sample of the Ethiopian population was determined, and these observed values were combined with a spatial statistical model to predict and map the Se status of populations across the country. The study used archived serum samples (n = 3269) from the 2015 Ethiopian National Micronutrient Survey (ENMS). The ENMS was a cross-sectional survey of young and school-age children, women and men. Serum Se concentration was measured using inductively coupled plasma mass spectrometry (ICPMS). The national median (Q1, Q3) serum Se concentration was 87.7 (56.7, 123.0) μg L−1. Serum Se concentration differed between regions, ranging from a median (Q1, Q3) of 54.6 (43.1, 66.3) µg L−1 in the Benishangul-Gumuz Region to 122.0 (105, 141) µg L−1 in the Southern Nations, Nationalities, and Peoples’ Region and the Afar Region. Overall, 35.5% of the population were Se deficient, defined as serum Se < 70 µg L−1. A geostatistical analysis showed that there was marked spatial dependence in Se status, with serum concentrations greatest among those living in North-East and Eastern Ethiopia and along the Rift Valley, while serum Se concentrations were lower among those living in North-West and Western Ethiopia. Selenium deficiency in Ethiopia is widespread, but the risk of Se deficiency is highly spatially dependent. Policies to enhance Se nutrition should target populations in North-West and Western Ethiopi

The potential of spot urine as a biomarker for zinc assessment in Malawian children and adults

Population-level assessment of zinc deficiency remains a challenge due to the lack of suitable biomarkers. Spot urinary zinc concentration (UZC) has the potential to provide information on population zinc status in large-scale surveys, but there is no established cut-off point indicating deficiency. A strong correlation between this biomarker and an established biomarker such as serum zinc concentration (SZC) in paired samples (i.e., from the same individual), could identify the thresholds indicating zinc deficiency. This study, therefore, aimed to regress spot UZC from school-aged children and women from the Malawi micronutrient survey with paired SZC data using a linear mixed-effects model. The nested variance components indicated no linear relationship between the UZC and SZC data, irrespective of adjustments for inflammation and hydration. Thresholds of urinary zinc excretion that have been suggested by expert panels were applied to the spot UZC data, as a post-hoc analysis. The zinc deficiency prevalence estimates derived from these suggested thresholds were not similar to the estimates from the SZC data, and further research is required to understand whether spot UZC can still provide useful information in population zinc assessment

Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

© 2021, The Author(s). A study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe

Can nitrogen fertilizer management improve grain iron concentration of agro-biofortified crops in Zimbabwe?

Improving iron (Fe) concentration in staple grain crops could help reduce Fe-deficiency anaemia in communities dependent on plant-based diets. Co-application of nitrogen (N) and zinc (Zn) fertilizers has been reported to improve both yield and grain Zn concentration of crops in smallholder farming systems. This study was conducted to determine if similar effects are observed for grain Fe concentration. Field experiments were conducted in two years, in two contrasting agro-ecologies in Zimbabwe, on maize (Zea mays L.), cowpea (Vigna unguiculata [L.] Walp) and two finger millet (Eleusine coracana (L.) Gaertn.) “seed pools”. The two finger millet “seed pools” were collected during previous farmer surveys to represent “high” and “low” Fe concentrations. All plots received foliar Fe-ethylene diamine tetra-acetic acid (EDTA) fertilizer and one of seven N treatments, representing mineral or organic N sources, and combinations thereof. Higher grain yields were observed in larger N treatments. Grain Fe concentration increased according to species: maize < finger millet < cowpea but varied widely according to treatment. Significant effects of N-form on grain Fe concentration were observed in the low finger millet “seed pool”, for which mineral N fertilizer application increased grain Fe concentration to a greater extent than other N forms, but not for the other species. Whilst good soil fertility management is essential for yield and grain quality, effects on grain Fe concentration are less consistent than reported previously for Zn