Texture specific regression models for predicting soil ECe values from EC1: 2.5 for effective soil salinity assessment in Tanzania

Simple item page
dc.contributor.authorIsdory, D. P.
dc.contributor.authorMassawe, B. H.
dc.date.accessioned2025-04-29T14:05:15Z
dc.date.available2025-04-29T14:05:15Z
dc.date.issued2023
dc.descriptionJournal article pg. 54-61
dc.description.abstractElectrical conductivity of saturated soil paste extract (ECe) is a standard laboratory soil salinity measurement. However, due to difficulty of ECe measurement, electrical conductivity of soil to water suspensions (ECsoil:water) such as EC1:2.5 are used and its values converted to ECe for salinity interpretation in crop production. This study was conducted to develop texture specific regression models for predicting ECe values from EC1:2.5 for Tanzanian soils. A total of 198 composite soil samples at 0 – 30 cm depth were collected from Kiwere, Dakawa, Sakalilo and Mwamapuli irrigation schemes in Iringa, Morogoro, Rukwa and Katavi Regions respectively and analyzed for soil texture, EC1:2.5 and ECe using standard laboratory methods. The dominant soil textural classes were clay, sandy clay loam, sandy clay, and clay loam. There were significant differences (P<0.05) between mean values of EC1:2.5 and ECe (dS m-1) in all textural classes. The regression models indicated significantly strong linear relationships between values of EC1:2.5 and ECe for all textural classes with R2>0.90 and P<0.001 for both regression models with and without intercept. The regression models without intercept performed better in predicting soil ECe from EC1:2.5 than regression models with intercept by having higher P-values, slope value closer to 1.0 and lower RMSE values between measured and predicted ECe. The study recommends regression models expressed as ECe = 2.0963 EC1:2.5 for clay; ECe = 2.7714 EC1:2.5 for sandy clay loam; ECe = 2.3519 EC1:2.5 for sandy clay and ECe = 2.0811 EC1:2.5 for clay loam soils for predicting soil ECe from EC1:2.5 in Tanzania.
dc.identifier.citationhttps://www.ajol.info/index.php/tjags/article/view/263059
dc.identifier.urihttps://www.suaire.sua.ac.tz/handle/123456789/6689
dc.language.isoen
dc.publisherTanzania Journal of Agricultural Sciences
dc.subjectSoil salinity
dc.subjectregression models
dc.subjectEC1:2.5
dc.subjectECe
dc.subjectTanzania
dc.titleTexture specific regression models for predicting soil ECe values from EC1: 2.5 for effective soil salinity assessment in Tanzania
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
ajol-file-journals_510_articles_263059_65ae361f00501.pdf
Size:
943.02 KB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Articles, Conference and Workshop Papers Collection