Soil texture and organic matter prediction using Mehlich‐3 extractable nutrients

Abstract

Abstract Soil organic matter (SOM) and texture are key properties influencing soil nutrient and water dynamics but are time‐consuming procedures for analytical laboratories. Our objective was to evaluate SOM and soil texture predictions using Mehlich‐3 nutrients and pH in Arkansas soils. Particle size was determined by the hydrometer method (2‐ and 8‐h readings) and SOM by loss on ignition. Two datasets were used to calibrate clay and sand (n = 409) and SOM (n = 1019) prediction models using simple and multiple regression. Estimated cation exchange capacity was highly correlated with clay, resulting in significant prediction models alone or combined with phosphorus (P); pH and copper (Cu); or pH, sodium (Na), and Cu (R2 = 0.84, 0.88, 0.89, and 0.90; p < 0.0001, respectively). Soil nutrients were weakly correlated with sand, resulting in a prediction model with moderate accuracy when using Mehlich‐3 P, calcium (Ca), Na, iron (Fe), and manganese (Mn) (R2 = 0.49; p < 0.0001). Clay and sand prediction models presented comparable accuracy when validated on a new dataset (n = 103). Predicted sand and clay showed good accuracy in grouping soils into medium (65%) and fine (96%) textural categories but had limited ability to define the coarse‐textural group (9%). SOM had moderate goodness‐of‐fit statistics for calibration and validation datasets using pH, P, Ca, Na, Mn, and zinc (R2 = 0.65 and 0.70, respectively; p < 0.0001). Mehlich‐3 nutrients can be used to estimate soil texture and assist with crop management decisions, but further research is needed to improve SOM prediction.