Cereal grain mineral micronutrient and soil chemistry data from GeoNutrition surveys in Ethiopia and Malawi.

The dataset comprises primary data for the concentration of 29 mineral micronutrients in cereal grains and up to 84 soil chemistry properties from GeoNutrition project surveys in Ethiopia and Malawi. The work provided insights on geospatial variation in the micronutrient concentration in staple crops, and the potential influencing soil factors. In Ethiopia, sampling was conducted in Amhara, Oromia, and Tigray regions, during the late-2017 and late-2018 harvest seasons. In Malawi, national-scale sampling was conducted during the April-June 2018 harvest season. The concentrations of micronutrients in grain were measured using inductively coupled plasma mass spectrometry (ICP-MS). Soil chemistry properties reported include soil pH; total soil nitrogen; total soil carbon (C); soil organic C; effective cation exchange capacity and exchangeable cations; a three-step sequential extraction scheme for the fractionation of sulfur and selenium; available phosphate; diethylenetriaminepentaacetic acid (DTPA)-extractable trace elements; extractable trace elements using 0.01 M Ca(NO3)2 and 0.01 M CaCl2; and isotopically exchangeable Zn. These data are reported here according to FAIR data principles to enable users to further explore agriculture-nutrition linkages.

Soil and landscape factors influence geospatial variation in maize grain zinc concentration in Malawi.

Dietary zinc (Zn) deficiency is widespread globally, and in particular among people in sub-Saharan Africa (SSA). In Malawi, dietary sources of Zn are dominated by maize and spatially dependent variation in grain Zn concentration, which will affect dietary Zn intake, has been reported at distances of up to ~ 100 km. The aim of this study was to identify potential soil properties and environmental covariates which might explain this longer-range spatial variation in maize grain Zn concentration. Data for maize grain Zn concentrations, soil properties, and environmental covariates were obtained from a spatially representative survey in Malawi (n = 1600 locations). Labile and non-labile soil Zn forms were determined using isotopic dilution methods, alongside conventional agronomic soil analyses. Soil properties and environmental covariates as potential predictors of the concentration of Zn in maize grain were tested using a priori expert rankings and false discovery rate (FDR) controls within the linear mixed model (LMM) framework that informed the original survey design. Mean and median grain Zn concentrations were 21.8 and 21.5 mg kg-1, respectively (standard deviation 4.5; range 10.0-48.1). A LMM for grain Zn concentration was constructed for which the independent variables: soil pH(water), isotopically exchangeable Zn (ZnE), and diethylenetriaminepentaacetic acid (DTPA) extractable Zn (ZnDTPA) had predictive value (p < 0.01 in all cases, with FDR controlled at < 0.05). Downscaled mean annual temperature also explained a proportion of the spatial variation in grain Zn concentration. Evidence for spatially dependent variation in maize grain Zn concentrations in Malawi is robust within the LMM framework used in this study, at distances of up to ~ 100 km. Spatial predictions from this LMM provide a basis for further investigation of variations in the contribution of staple foods to Zn nutrition, and where interventions to increase dietary Zn intake (e.g. biofortification) might be most effective. Other soil and landscape factors influencing spatially dependent variation in maize grain Zn concentration, along with factors operating over shorter distances such as choice of crop variety and agronomic practices, require further exploration beyond the scope of the design of this survey.

The nutritional quality of cereals varies geospatially in Ethiopia and Malawi.

Micronutrient deficiencies (MNDs) remain widespread among people in sub-Saharan Africa1-5, where access to sufficient food from plant and animal sources that is rich in micronutrients (vitamins and minerals) is limited due to socioeconomic and geographical reasons4-6. Here we report the micronutrient composition (calcium, iron, selenium and zinc) of staple cereal grains for most of the cereal production areas in Ethiopia and Malawi. We show that there is geospatial variation in the composition of micronutrients that is nutritionally important at subnational scales. Soil and environmental covariates of grain micronutrient concentrations included soil pH, soil organic matter, temperature, rainfall and topography, which were specific to micronutrient and crop type. For rural households consuming locally sourced food-including many smallholder farming communities-the location of residence can be the largest influencing factor in determining the dietary intake of micronutrients from cereals. Positive relationships between the concentration of selenium in grain and biomarkers of selenium dietary status occur in both countries. Surveillance of MNDs on the basis of biomarkers of status and dietary intakes from national- and regional-scale food-composition data1-7 could be improved using subnational data on the composition of grain micronutrients. Beyond dietary diversification, interventions to alleviate MNDs, such as food fortification8,9 and biofortification to increase the micronutrient concentrations in crops10,11, should account for geographical effects that can be larger in magnitude than intervention outcomes.

Agronomic iodine biofortification of leafy vegetables grown in Vertisols, Oxisols and Alfisols.

Iodine deficiency disorders (IDD) in sub-Saharan African countries are related to low dietary I intake and generally combatted through salt iodisation. Agronomic biofortification of food crops may be an alternative approach. This study assessed the effectiveness of I biofortification of green vegetables (Brassica napus L and Amaranthus retroflexus L.) grown in tropical soils with contrasting chemistry and fertility. Application rates of 0, 5 and 10 kg ha-1 I applied to foliage or soil were assessed. Leaves were harvested fortnightly for ~ 2 months after I application before a second crop was grown to assess the availability of residual soil I. A separate experiment was used to investigate storage of I within the plants. Iodine concentration and uptake in sequential harvests showed a sharp drop within 28 days of I application in all soil types for all I application levels and methods. This rapid decline likely reflects I fixation in the soil. Iodine biofortification increased I uptake and concentration in the vegetables to a level useful for increasing dietary I intake and could be a feasible way to reduce IDD in tropical regions. However, biofortification of green vegetables which are subject to multiple harvests requires repeated I applications.

Spatial prediction of the concentration of selenium (Se) in grain across part of Amhara Region, Ethiopia.

Grain and soil were sampled across a large part of Amhara, Ethiopia in a study motivated by prior evidence of selenium (Se) deficiency in the Region's population. The grain samples (teff, Eragrostis tef, and wheat, Triticum aestivum) were analysed for concentration of Se and the soils were analysed for various properties, including Se concentration measured in different extractants. Predictive models for concentration of Se in the respective grains were developed, and the predicted values, along with observed concentrations in the two grains were represented by a multivariate linear mixed model in which selected covariates, derived from remote sensor observations and a digital elevation model, were included as fixed effects. In all modelling steps the selection of predictors was done using false discovery rate control, to avoid over-fitting, and using an α-investment procedure to maximize the statistical power to detect significant relationships by ordering the tests in a sequence based on scientific understanding of the underlying processes likely to control Se concentration in grain. Cross-validation indicated that uncertainties in the empirical best linear unbiased predictions of the Se concentration in both grains were well-characterized by the prediction error variances obtained from the model. The predictions were displayed as maps, and their uncertainty was characterized by computing the probability that the true concentration of Se in grain would be such that a standard serving would not provide the recommended daily allowance of Se. The spatial variation of grain Se was substantial, concentrations in wheat and teff differed but showed the same broad spatial pattern. Such information could be used to target effective interventions to address Se deficiency, and the general procedure used for mapping could be applied to other micronutrients and crops in similar settings.

Selenium deficiency risks in sub-Saharan African food systems and their geospatial linkages.

Selenium (Se) is an essential element for human health. However, our knowledge of the prevalence of Se deficiency is less than for other micronutrients of public health concern such as iodine, iron and zinc, especially in sub-Saharan Africa (SSA). Studies of food systems in SSA, in particular in Malawi, have revealed that human Se deficiency risks are widespread and influenced strongly by geography. Direct evidence of Se deficiency risks includes nationally representative data of Se concentrations in blood plasma and urine as population biomarkers of Se status. Long-range geospatial variation in Se deficiency risks has been linked to soil characteristics and their effects on the Se concentration of food crops. Selenium deficiency risks are also linked to socio-economic status including access to animal source foods. This review highlights the need for geospatially-resolved data on the movement of Se and other micronutrients in food systems which span agriculture-nutrition-health disciplinary domains (defined as a GeoNutrition approach). Given that similar drivers of deficiency risks for Se, and other micronutrients, are likely to occur in other countries in SSA and elsewhere, micronutrient surveillance programmes should be designed accordingly.

Combining two national-scale datasets to map soil properties, the case of available magnesium in England and Wales.

Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant-available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross-validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties. HIGHLIGHTS: Differences in sample support mean that two datasets on a soil property cannot be combined simply.We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets.The same model allows soil properties to be mapped jointly from such data.This offers a general basis for combining soil datasets from diverse sources.

Defining the habitat niche of Alopecurus myosuroides at the field scale.

The distribution of Alopecurus myosuroides (black-grass) in fields is patchy. The locations of these patches can be influenced by the environment. This presents an opportunity for precision management through patch spraying. We surveyed five fields on various types of soil using a nested sampling design and recorded both A. myosuroides seedlings in autumn and seed heads in summer. We also measured soil properties at those sampling locations. We found that the patches of seed heads within a field were smaller than the seedling patches, suggesting that techniques for patch spraying based on maps of heads in the previous season could be inherently risky. We also found that the location of A. myosuroides patches within fields can be predicted through their relationship with environmental properties and that these relations are consistent across fields on different soil types. This improved understanding of the relations between soil properties and A. myosuroides seedlings could allow farmers to use pre-existing or suitably supplemented soil maps already in use for the precision application of fertilisers as a starting point in the creation of herbicide application maps.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales.

Weeds tend to aggregate in patches within fields, and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at various scales, the strength of the relations between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We developed a general method that uses novel within-field nested sampling and residual maximum-likelihood (reml) estimation to explore scale-dependent relations between weeds and soil properties. We validated the method using a case study of Alopecurus myosuroides in winter wheat. Using reml, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales, we optimised the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Wavelet analysis of the variability of nitrous oxide emissions from soil at decameter to kilometer scales.

We analyzed data on nitrous oxide emissions and on soil properties that were collected on a 7.5-km transect across an agricultural landscape in eastern England using the discrete wavelet packet transform. We identified a wavelet packet "best basis" for the emission data. Wavelet packet basis functions are used to decompose the data into a set of coefficients that represent the variation in the data at different spatial frequencies and locations. The "best basis" for a set of data is adapted to the variability in the data by ensuring that the spatial resolution of local features is good at those spatial frequencies where variation is particularly intermittent. The best basis was shown to be adapted to represent such intermittent variation, most markedly at wavelengths of 100 m or less. Variation at these wavelengths was shown to be correlated particularly with chemical properties of the soil, such as nitrate content. Variation at larger wavelengths showed less evidence of intermittency and was found to be correlated with soil chemical and physical constraints on emission rates. In addition to frequency-dependent intermittent variation, it was found that the variance of emission rates at some wavelengths changed at particular locations along the transect. One factor causing this appeared to be contrasts in parent material. The complex variation in emission rates identified by these analyses has implications for how emission rates are estimated.

Mapping aerial metal deposition in metropolitan areas from tree bark: a case study in Sheffield, England.

We investigated the use of metals accumulated on tree bark for mapping their deposition across metropolitan Sheffield by sampling 642 trees of three common species. Mean concentrations of metals were generally an order of magnitude greater than in samples from a remote uncontaminated site. We found trivially small differences among tree species with respect to metal concentrations on bark, and in subsequent statistical analyses did not discriminate between them. We mapped the concentrations of As, Cd and Ni by lognormal universal kriging using parameters estimated by residual maximum likelihood (REML). The concentrations of Ni and Cd were greatest close to a large steel works, their probable source, and declined markedly within 500 m of it and from there more gradually over several kilometres. Arsenic was much more evenly distributed, probably as a result of locally mined coal burned in domestic fires for many years. Tree bark seems to integrate airborne pollution over time, and our findings show that sampling and analysing it are cost-effective means of mapping and identifying sources.

The use of soil survey data to determine the magnitude and extent of historic metal deposition related to atmospheric smelter emissions across Humberside, UK.

When a smelter has ceased operation, and in the absence of historical emission data, high-resolution geochemical surveys of the soil can reveal historical loads to the surrounding land. We use measurements of lead and tin in the soil at two depths to estimate the total quantities of these metals deposited on 286 km(2) of land around the former Capper Pass smelter (north-east England). We subtracted median background concentrations for three parent material types outside the region of deposition from the data within it. We then constructed a statistical model of metal deposition based on the adjusted data. The data were from irregularly spaced sites and were strongly skewed with a spatial trend. We mapped the concentrations of the metals by lognormal universal kriging with the parameters for the trend and residuals modelled simultaneously by residual maximum likelihood (REML). The maps suggest that metal was deposited up to 24 km to the north-east of the smelter by the prevailing wind. We estimated total excess metal in the soil over the area of deposition to be 2500 t of lead and 830 t of tin.