Wheat and teff grain mineral micronutrient concentration and field data from GeoNutrition on-farm field experiments in Western Amhara region, Ethiopia

N - Datasets

Manzeke-Kangara, M.G., Amede, T., Bailey, E.H., Wilson, L., Mossa, A.W., Tirfessa, D., Kebede-Desta, M., Gashaw-Asrat, T., Agegnehu, G., Sida, T.S., Desta, G., Amare, T., Alemayehu, B., Haefele, S. M., Lark, R. M., Broadley, M. and Gameda, S. 2023. Wheat and teff grain mineral micronutrient concentration and field data from GeoNutrition on-farm field experiments in Western Amhara region, Ethiopia. Rothamsted Research. https://doi.org/10.23637/rothamsted.98y40

AuthorsManzeke-Kangara, M.G., Amede, T., Bailey, E.H., Wilson, L., Mossa, A.W., Tirfessa, D., Kebede-Desta, M., Gashaw-Asrat, T., Agegnehu, G., Sida, T.S., Desta, G., Amare, T., Alemayehu, B., Haefele, S. M., Lark, R. M., Broadley, M. and Gameda, S.

The data set comprises primary data for the concentration of over 25 mineral micronutrients including zinc (Zn) and selenium (Se) in wheat (Triticum aestivum L.) and teff (Eragrostis tef (Zucc.) Trotter) grown over two cropping seasons (2018 and 2019) in the Western Amhara region of Ethiopia. Wheat and teff were grown across a landscape gradient (hill slope, mid-slope, foot slope) and supplied with different rates of mineral nitrogen fertilizer and different Zn and Se fertilizer application methods (basal, basal + side dressing, basal + foliar fertilizer) across different farms. The data set also comprises of field data including specific study site, crop variety, fertilizer rates, and crop yields. Wheat experiments were performed in Debre Mewi and Markuma, and experiments with teff were performed in Debre Mewi and Aba Gerima. The concentrations of micronutrients in grain were measured using inductively coupled plasma mass spectrometry (ICP-MS). Laboratory standards, sample replicates and blank samples were included for quality assurance. The work provide insights on an effect of Zn, Se and N fertilizer, landscape position, and its interaction with micronutrient fertilizers on grain micronutrient concentrations. Agronomic biofortification of wheat and teff with micronutrient fertilizers was influenced by landscape position, the micronutrient fertilizer application method and N fertilizer management. The complexity of smallholder environmental settings and different farmer socio-economic opportunities calls for the optimization of nutritional agronomy landscape trials, with targeted application of micronutrient fertilizers across a landscape gradient required as an additional factor for consideration in ongoing agronomic biofortification interventions.

Year of Publication2023
PublisherRothamsted Research
Digital Object Identifier (DOI)https://doi.org/10.23637/rothamsted.98y40
Keywordsmicronutrient fertilizers
Triticum aestivum
Eragrostis tef
nitrogen fertilizers
grain crops
FunderBill and Melinda Gates Foundation
Biotechnology and Biological Sciences Research Council
Related Output
Is part ofhttps://doi.org/10.1038/s41597-022-01500-5
Funder project or codeGeoNutrition
Excellence in Agronomy (EiA) Incubation Phase
Innovation R&D on Agronomic Biofortification
S2N - Soil to Nutrition - Work package 1 (WP1) - Optimising nutrient flows and pools in the soil-plant-biota system
Growing Health [ISP]
Growing Health (WP2) - bio-inspired solutions for healthier agroecosystems: Understanding soil environments
Growing Health (WP3) - bio-inspired solutions for healthier agroecosystems: Discovery landscapes
Data files
Data type
File Access Level
Data files
Copyright license
CC BY 4.0
Data type
Additional metadata
File Access Level
Geographic location
Debre Mewi
Geographic location
Geographic location
Aba Gerima
Geographic coverageWestern Amhara, Ethiopia
Data collection method

Experimental design

The experiments were undertaken in the main cropping seasons of both 2018 and 2019. There were two sets of experiments in each season, one with wheat as the experimental crop, and the second with teff. The same treatments established on farmers’ fields, were applied to both crops. The fields were on farms in specified communities (“sites”). Wheat experiments were performed in Debre Mewi and Markuma, and experiments with teff were performed in Debre Mewi and Aba Gerima. Each farmer provided a single field for the experiment, and one replicate of each treatment was established in that field. Treatments were thus laid out in randomized complete block design, replicated across farms. Farm fields were selected at random from among those in each of three landscape positions: foot slope, mid-slope, and hillslope (Amede et al., 2022, Desta et al., 2022). There were five farms per landscape position in each of the three districts, resulting in a total of 45 farms. Each treatment plot had 5 × 5m dimensions.
In 2019, the site locations were revisited, but the treatments were established on different farms from those used in 2018 following similar patterns of landscape positions. In some cases, the same farmer was involved in both seasons, but with a different parcel of land. This was performed to avoid adding residual effects of fertilizer treatment into the factors to be considered when interpreting the results for the second season. There were 300 observations for teff (2 sites × 2 years × 3 landscapes × 5 farmers’ fields × 5 treatments) and for wheat (2 sites × 2 years × 3 landscapes × 5 farmers’ fields × 5 treatments), respectively. We also established farm history before selecting target plots and excluded farms where high fertilizer rates (>50 kg ha-1) were applied in the previous year. The replication over seasons provided information on the effect of adding fertilizer to soil which mainly contains the background concentration of those nutrients attributable to organic sources and minerals in its parent material. Similarly, a single replication of each treatment was established in each farm during the second season. The treatment was allocated to a plot independently and at random. The randomization was performed on the R platform (R Core Team, 2021), and the R code produced a plot showing the randomization which could be used in the field to record landmarks and other features to facilitate orientation on visits to the plots. The five treatments fell into three broad categories: (1) nitrogen (N) fertilizer rate; (2) micronutrient fertilizer application method; (3) sole or co-application of Zn and Se fertilizer.

Sample characteristics

Type of sample: Grain
Sample collection: Grain samples were collected from representative heads of each treatment at physiological maturity, and a composite sample was considered for nutrient analysis. Grain samples were oven dried, cleaned and milled. Sample milling was done in a domestic stainless-steel coffee grinder, which was wiped clean before use and after each sample with a non-abrasive cloth. All preparation was done away from sources of contamination by soil or by dust.

Analytical methods

Analytical method name: Microwave digestion
Analytical method description: Approximately 0.2 g (dry weight, dw) of each finely ground plant sample was weighed and microwave digested in 6 mL trace analysis grade HNO3 in perfluoroalkoxy (PFA) vessels (Multiwave; Anton Paar GmbH, St. Albans, UK). The digested samples were diluted 1-in-10 with Milli-Q water immediately prior to multi-element analysis by inductively coupled plasma mass spectrometry (ICP-MS). Each digestion batch included a minimum of 6 blanks and a certified wheat flour standard (NIST 1567a) for QA purposes; recoveries were 94% for Zn and 95% for Se. Zinc and Se elemental analysis of diluted solutions was undertaken by ICP-MS (Thermo-Fisher Scientific iCAP-; Thermo Fisher Scientific, Bremen, Germany). Samples were introduced (flow rate 1.2 mL min−1) from an autosampler (Cetac ASX-520 Teledyne CETAC Technologies, Omaha, NE, USA) incorporating an ASXpress™ rapid uptake module through a perfluoroalkoxy (PFA) Microflow PFA-ST nebulizer (Thermo Fisher Scientific, Bremen, Germany). Sample processing was undertaken using Qtegra™ software (Thermo-Fisher Scientific) utilizing external cross-calibration between pulse-counting and analogue detector modes when required. The iCAP-Q employed in-sample switching between two modes using a collision cell (i) charged with He gas with kinetic energy discrimination (KED) to remove polyatomic interferences and (ii) using H2 gas as the cell gas. The latter was used only for Se determination. Internal standards Sc, Ge, Rh, and Ir, to correct for instrumental drift, were introduced to the sample stream on a separate line. Calibration standards included a multi-element solution including Zn and Se, in the range 0–100 µg L−1 (Claritas-PPT grade CLMS-2 from SPEX Certiprep Inc., Metuchen, NJ, USA), a bespoke external multi-element calibration solution (PlasmaCAL, SCP Sci-ence , Courtaboeuf, France) with Ca, Mg, Na and K in the range 0–30 mg L−1 and, a mixed phosphorus, boron and sulfur standard made in-house from salt solutions (KH2PO4, K2SO4 and H3BO3). The matrices used for internal standards, calibration standards and sample diluents were 2% Primar grade HNO3 (Fisher Scientific, Lough-borough, UK) with 4% methanol (to enhance ionization of Se).

[Datasets have been safeguarded because they contain some sensitive information]

Data preparation and processing activities

Data are divided into four spreadsheets according to type of crop and year.
- tef_w_amhara_2018
- wheat_w_amhara_2018
- tef_w_amhara_2019
- wheat_w_amhara_2019
They correspond to CSV files.

The data dictionary is also provided:
- Data_dictionary.csv

Retention review action:
The datasets presented in this study are currently available on request from the corresponding author. Meta-data associated with this data is available at https://doi.org/10.23637/rothamsted.98y40. Open access to the datasets will be available at https://harvestirr.rothamsted.ac.uk/ once the data is published.

[Datasets have been safeguarded because they contain some sensitive information]

Analytical method references:
- Amede, T.; Gashaw, T.; Legesse, G.; Tamene, L.; Mekonen, K.; Thorne, P.; Schultz, S. Landscape positions dictating crop fertilizer responses in wheat-based farming systems of East African Highlands. Renew. Agric. Food Syst. 2022, 37, 1–13. https://doi.org/10.1017/S1742170519000504.
- Desta, G.; Amede, T.; Gashaw, T.; Legesse, G.; Agegnehu, G.; Mekonnen, K.; Whitbread, A. Sorghum yield response to NPKS and NPZn nutrients along sorghum-growing landscapes. Exp. Agric. 2022, 58, E10. https://doi.org/10.1017/S0014479722000072.
-R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. Available online: https://www.R-project.org/ (accessed on 15 December 2022).

Author ORCIDs:

- Kangara, G. https://orcid.org/0000-0002-3784-4915
- Bailey, E.H. https://orcid.org/0000-0002-5266-3792
- Mossa, A.W. https://orcid.org/0000-0002-9540-2023
- Tirfessa, D. https://orcid.org/0000-0002-3360-624X
- Asrat, T.G. https://orcid.org/0000-0001-7939-023X
- Haefele, S. https://orcid.org/0000-0003-0389-8373
- Lark, R.M. https://orcid.org/0000-0003-2571-8521
- Broadley, M. https://orcid.org/0000-0003-3964-7226
- Gameda, S. https://orcid.org/0000-0003-1761-6373

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