Fluoride uptake and translocation in food crops grown in fluoride-rich soils.

BACKGROUND: The East African Rift Valley (EARV) area is characterized by an intense volcanic activity, which largely influences the nature of soils, ground and surface waters causing a transfer of fluoride from volcanic emissions to the environment. Field experiments were conducted in fluorine-contaminated areas of Ngarenanyuki (Arumeru district) in north Tanzania. In order to evaluate the potential fluoride exposure from diet and the related health risk for the local population, the content of fluoride in soil and plant tissues was assessed, focusing on the edible portions (leaves, fruits or seeds) of the main cultivated and consumed food crops in the area. RESULTS: Average fluoride contents of 8.0, 11.4, 11.3 and 14.2 mg kg-1 of dry matter were observed respectively for maize (Zea mays L.), tomato (Lycopersicon esculentum Mill.), bean (Phaseolus vulgaris L.) and kale (Brassica sp. pl.) edible parts. The cumulative estimated average daily dose (EADD) ranged from 0.026 to 0.165 mg F d-1 kg-1 among different rural population groups and considering two different hypotheses of absorption fraction (75% or 100%), i.e. the amount of fluoride that is absorbed during the digestion process. The associated hazard index (HI) values varied from 0.43 to 2.75. CONCLUSIONS: Considering the dietary habits of the local population, the outcomes of the present study suggest that the investigated crops can substantially contribute to fluoride related diseases, especially in earlier ages. © 2020 Society of Chemical Industry.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.