No abstract

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.

Emergence of portable X-ray fluorescence (pXRF) systems presents new opportunities for rapid, low-cost plant analysis, both as a lab system and in situ system. Numerous studies have extolled the virtues of using pXRF for a wide range of plant applications, however, for many such applications, there is need for further assessment with regards to analytical parameters for plant analysis. While pXRF is a potential powerful research tool for elemental composition analysis, its successful use in plant analysis is made more likely by having an understanding of X-ray physics, calibration process, and ability to test a variety of homogenous and well-characterized materials for developing a matrix-specific calibration. Because potential pXRF users may often underestimate the complexity of proper analysis, this study aims at providing a technical background for plant analysis using pXRF. The focus is on elemental quantification under different analytical parameters and different methods of sample presentation: direct surface contact under vacuum, placement in a sample cup with prolene as a seal, and without the aid of a vacuum. Direct analysis on the surface of a pXRF provided highest sensitivity and accuracy (R 2 > 0.90) for light elements (Mg to P). Sulfur, K, and Ca can be reliably measured without the aid of a vacuum (R 2 > 0.99, 0.97, and 0.93 respectively), although lower detection limits may be compromised. pXRF instruments provide plant data of sufficient accuracy for many applications and will reduce the overall time and budget compared with the use of conventional techniques. Sensitivity and accuracy are dependent on the instrument's settings, make, and model. © 2015 The Authors. X-Ray Spectrometry published by John Wiley & Sons, Ltd.Additional supporting information may found in the online version of this article at publisher's web site. IntroductionKey plant nutrients, including potassium (K), phosphorus (P), and sulfur (S), play important roles in regulating processes such as photosynthesis, carbon respiration, and tissue building. Elemental analysis of plants is critical to plant studies and agriculture. However, despite the routine application of digestion-based analytical techniques in many laboratories, the slow and often hazardous sample digestion process can present a challenge in the analyses of plant nutrients, particularly where hundreds or even thousands of samples are to be analysed.[1] The conventional plant elemental analyses based on plant extracts are cumbersome and of variable accuracy and present major limitations to exploiting the full potential of plant nutrient analysis. An ideal system for analysis would include accuracy, replicability, portability, and minimal sample preparation. The technology that currently provides the best option to meet these needs is energy-dispersive X-ray fluorescence (ED-XRF), particularly in the form of a portable instrument portable X-ray fluorescence (pXRF). These instruments are commonly used for scrap metal sourcing, [2] geochemical provenience of obsidian,...

Total X-ray fluorescence spectroscopy (TXRF) determines concentrations of major and trace elements in multiple media. We developed and tested a method for the use of TXRF for direct quantification of total element concentrations in soils using an S2 PICOFOX™ spectrometer (Bruker AXS Microanalysis GmbH, Germany). We selected 15 contrasting soil samples from across sub-Saharan Africa for element analysis to calibrate the instrument against concentrations determined using the inductively coupled plasma-mass spectroscopy (ICP-MS) standard method. A consistent underestimation of element concentrations using TXRF compared to ICP-MS reference analysis occurred, indicating that spectrometer recalibration was required. Single-element recalibration improved the TXRF spectrometer's sensitivity curve. Subsequent analysis revealed that TXRF determined total element concentrations of Al, K, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, and Ga accurately (model efficacy/slope close to 1:1 line, and R(2)>0.80) over a wide range of soil samples. Other elements that could be estimated with an acceptable precision (R(2)>0.60) compared with ICP-MS although generally somewhat under- or overestimated were P, Ca, As, Rb, Sr, Y, Pr, Ta and Pb. Even after recalibration, compared to ICP-MS the TXRF spectrometer produced underestimations for elements Na, Mg, Ba, Ce, Hf, La, Nd, W and Sm and overestimations for elements Bi, Tl and Zr. We validated the degree of accuracy of the TXRF analytical method after recalibration using an independent set of 20 soil samples. We also tested the accuracy of the analysis using 2 multi-element standards as well as the method repeatability on replicate samples. The resulting total element concentration repeatability for all elements analyzed were within 10% coefficient of variability after the instrument recalibration except for Cd and Tl. Our findings demonstrate that TXRF could be used as a rapid screening tool for total element concentrations in soils assuming that sufficient calibration measures are followed.

No abstract

Abstract. Soil organic carbon (SOC) stabilization and destabilization has been studied intensively. Yet, the factors which control SOC content across scales remain unclear. Earlier studies demonstrated that soil texture and geochemistry strongly affect SOC content. However, those findings primarily rely on data from temperate regions where soil mineralogy, weathering status and climatic conditions generally differ from tropical and subtropical regions. We investigated soil properties and climate variables influencing SOC concentrations across sub-Saharan Africa. A total of 1601 samples were analyzed, collected from two depths (0–20 and 20–50 cm) from 17 countries as part of the Africa Soil Information Service project (AfSIS). The data set spans arid to humid climates and includes soils with a wide range of pH values, weathering status, soil texture, exchangeable cations, extractable metals and land cover types. The most important SOC predictors were identified by linear mixed-effects models, regression trees and random forest models. Our results indicate that geochemical properties, mainly oxalate-extractable metals (Al and Fe) and exchangeable Ca, are equally important compared to climatic variables (mean annual temperature and aridity index). Together, they explain approximately two-thirds of SOC variation across sub-Saharan Africa. Oxalate-extractable metals were most important in wet regions with acidic and highly weathered soils, whereas exchangeable Ca was more important in alkaline and less weathered soils in drier regions. In contrast, land cover and soil texture were not significant SOC predictors on this large scale. Our findings indicate that key factors controlling SOC across sub-Saharan Africa are broadly similar to those in temperate regions, despite differences in soil development history.

Diffuse reflectance Fourier transformed mid-infrared (DRIFT-MIR) spectroscopy can predict many soil properties but extractable nutrients are often predicted poorly. This paper tested the combined DRIFT-MIR and total X-ray fluorescence (TXRF) spectroscopy analysis for prediction of soil properties related to soil fertility. A total of 700 soil samples from 44 stratified randomly located 100-km 2 sentinel sites distributed across Sub-Saharan Africa (SSA) were analyzed for physicochemical composition using conventional reference methods, and compared with MIR and TXRF spectra using Random Forests (RF) regression algorithm and an internal out-of-bag (OOB) validation. Mid-infrared spectra resulted in good prediction models (R 2 > 0.80) for organic C and total N, Mehlich-3 Ca and Al, and pH. Moderately predicted (R 2 > 0.60) were extractable Mg, P sorption index, sand, silt, and clay. Models were less satisfactory (R 2 < 0.60) for Mehlich-3 extractable K, Mn, Fe, Cu, B, Zn, P, S, and Na, exchangeable acidity and electrical conductivity (Ecd). Inclusion of total element concentration data from TXRF analysis in the MIR RF models significantly reduced root mean square error of prediction by 70% for Ecd, 66% for Mehlich-3 Na, 61% for Mehlich-3 S, and 50% for Mehlich-3 B due to detection by TXRF of some saline soils that were not well predicted by MIR. Overall, both methods predicted soil properties that relate to nutrient buffering capacity, including some exchangeable bases, pH, P sorption capacity, clay content, and organic matter content, and fingerprint basic soil mineralogy. Thus, further research should test whether MIR and TXRF fingerprinting could better predict soil nutrient supply capacity, as determined by crop nutrient uptake potential, than conventional soil P, K and micronutrient tests.

No abstract