Skip to main content
ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #394666
Research Project: Contributions of Climate, Soils, Species Diversity, and Management to Sustainable Crop, Grassland, and Livestock Production Systems

Location: Grassland Soil and Water Research Laboratory

Title: Mapping soil health and grain quality variations across a corn field in Texas

Author
item Adhikari, Kabindra
item Smith, Douglas
item Hajda, Chad

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/5/2022
Publication Date: 8/1/2022
Citation: Adhikari, K., Smith, D.R., Hajda, C.B. 2022. Mapping soil health and grain quality variations across a corn field in Texas. In: Proceedings of the 15th International Conference on Precision Agriculture, June 26-29, 2022, Minneapolis, Minnesota. p. 1-5.

Interpretive Summary: Soil health and grain quality maps can help farmers to fine-tune precision agriculture applications. We mapped soil health and grain protein and oil content across a corn field using apparent electrical conductivity (ECa) and terrain attributes as their predictors. We found that ECa and field topography were related to soil health and grain protein and oil content. Soil types also influenced soil health and grain protein and oil content across the field.

Technical Abstract: Soil health status is related to grain yield and quality. Within-field mapping of soil health index and grain quality can help farmers and managers to adjust site-specific farm management decisions for economic benefits. A study was conducted to map within-field soil health and grain protein and oil content variations using apparent electrical conductivity (ECa) and terrain attributes as their predictors. Two hundred and two topsoil samples were analyzed to determine soil health index based on the Haney Soil Health Tool. Grain protein and oil content were measured using CropScan monitor and ECa with DualEM sensor. Soil health index, protein and oil content were predicted using ECa and 14 terrain attributes derived from the digital elevation model. We found ECa a good predictor of soil health index and protein content, terrain attributes such as wetness index and elevation were also important. We found the field had a good soil health status and, areas with higher soil health index had higher protein content. Soil types also influenced soil health index and grain protein and oil content across the field.