/AnMtgsAbsts2009.52841 Active Organic Matter as a Simple Measure of Field Soil Quality.

Monday, November 2, 2009: 3:30 PM
Convention Center, Room 333, Third Floor
Irfan Aziz, Dept. of Agronomy, Univ. of Arid Agriculture, Rawalpindi, Pakistan, T. Mahmood, Dept. of Environmental Sciences, Univ. of Arid Agriculture, Rawalpindi, Pakistan, Y. Raut, Ohio State Univ. South Centers, Piketon, OH, Wayne Lewis, Ohio State Univ., South Centers, Piketon, OH, Rafiq Islam, Soil and Water, Ohio State Univ., OARDC, Piketon, OH and Ray R. Weil, Environmental Science and Technology, Univ. of Maryland, College Park, MD
Soil is a vital resource that directly and/or indirectly supports all natural systems on earth. Maintaining a healthy and productive soil is the foundation of sustainable agriculture. Understanding the importance of sustainable agriculture and the role of management practices to influence soil quality are critical to define economically viable, environmentally sound, socially participatory, and morally responsible future agriculture. There is a strong interest among farmers, Ag-consultants, Extension educators, and state and federal agency personnel for simple evaluation of soil quality in the field. Most important to evaluate soil’s capability to perform is the soil labile (active) organic matter fraction. We report on a highly simplified, non-toxic, sensitive, reliable, and quick test of active organic matter as a measure of soil quality which based on color from 2 minutes shaking of air-dried soil with dilute solution of potassium permanganate reagent. Soil quality is evaluated by changes in the deep purple color to different shades of pink or colorless in poor, fair, good and excellent ratings by using a color chart. The lighter the color of the solution after reacting with soil is the better the quality of the soil. In addition, the active C determined by this procedure is significiantly related to microbially active nitrogen, total biological activity, total C and N content, aggregate stability of soil, and closely predicted crop yields.