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Data · dataset · 2026

Soil nutrient status at three depths across 57 grid points, Kushtia, Bangladesh (2024)

Listed in Teesside University Research Data Repository

This dataset contains the physicochemical analysis of 171 soil samples collected from Poradaha Union, Mirpur Upazila, Kushtia District, Bangladesh, an intensively cropped tract of the Ganges floodplain under rice, wheat, jute, sugarcane and pulse rotations.

Description

Fifty-seven sampling locations were generated as a systematic grid at 890 m spacing using the fishnet tool in ArcGIS 10.7.1 and located in the field with a handheld GPS during March 2024.

At each location, samples were collected by manual auger from three depth intervals (0–20, 20–40 and 40–60 cm), giving a fully paired design in which depth is nested within location. Field pH and volumetric moisture were measured on site with a Takemura No. 10051 tester. Samples were air-dried, sieved to 0.15 mm and analysed at the Soil Resource Development Institute (SRDI) Regional Laboratory, Kushtia, Ministry of Agriculture, Government of Bangladesh, under laboratory numbers 13013–13183.

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Determinations were: organic matter by Walkley–Black wet oxidation; total nitrogen reported under the Kjeldahl method; available phosphorus by the modified Olsen procedure for neutral and calcareous soils; available sulfur turbidimetrically; available zinc by DTPA extraction; available boron by hot calcium chloride extraction; and exchangeable potassium by 1 N ammonium acetate extraction. Each observation carries WGS 84 coordinates, land use, land type, soil texture class, soil series, crop, and the SRDI (2020) fertility class for each property.

Data are supplied in tidy long format (171 rows) and in the original wide format (57 rows), with a data dictionary and the signed SRDI laboratory report. Users should note that total nitrogen in this dataset is an almost exact linear function of organic matter (TN = 0.0492 × OM − 0.0065; r = 0.978), indicating it was estimated from organic matter by a conversion factor rather than determined independently for every sample.

Any organic matter–nitrogen correlation computed from these data is therefore arithmetic rather than empirical. The dataset supports analyses of vertical nutrient distribution, soil fertility assessment against national classes, and geostatistical work on the spatial structure of soil properties in floodplain agriculture.

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Catalogue records · 1

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Inferred from text
Genome sequencing 65% · Soil biology 76%
Provenance · 1 source records, 13 field assertions
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