Demonstrating and Evaluating Multiple Soil Sensor Technology for Irrigation Scheduling in Alabama.

Yield optimization, yield quality, profitability and water conservation might be achieved if irrigation is used efficiently given that other crop management practices are carried out properly. However, there is limited up to date available information in Alabama on the irrigation timing and amount needed during the growing season to bring the soil profile to optimum soil moisture conditions to meet plant water needs. Therefore, the objectives of this study were to evaluate and demonstrate water-smart irrigation approaches including variable rate irrigation and soil sensors for irrigation scheduling to improve water use efficiency. On farm research was conducted at two locations during 2017: 1) at a 78 ac corn field at Samson, AL and 2) at a 500 ac soybean field at Greenbrier, AL. Each field was separated into management zones. Management zones were delineated based on 1) electrical conductivity data, 2) elevation, and 3) aerial imagery data. During the growing season, dynamic irrigation scheduling recommendations were developed by management zone based on soil matric potential data collected by probes that were part of a wireless, soil moisture sensing array. Each probe consisted of three Watermarks sensors installed at a depth of 6, 12 and 24 inches. The dynamic irrigation scheduling recommendations per management zone were calculated on a daily basis and were based 1) on soil matric potential data collected from the soil moisture sensing array, and 2) on soil moisture retention curves. The van Genuchten model was used to construct soil moisture retention curves and to calculate the soil volumetric water content and soil matric potential at field capacity. Additionally to Watermarks, two other commercially available soil moisture sensors (Aquaspy, and Acclima) were evaluated. Correlations between the soil moisture data of the Watermarks, Aquaspy and Acclima sensors were tested. The methodological approach, along with the calculations of the dynamic irrigation scheduling recommendations, and the results of the 2017 study will be present herein.