On Farm Soil Moisture Monitoring Summary

Kevin Heaton, USU Extension Agent

Background:

            Monitoring soil moisture is an effective way to manage irrigation scheduling.  During the spring of 2006, twenty Farmers volunteered to have soil moisture sensor installed in their fields.  A team including: Wally Dodds, Chrystal Reeve, Kevin Heaton, Kristi Hatch and participating farmers, installed approximately 120 Watermark® soil moisture sensors.  Most of the sensors were left in the field throughout the winter of 2006-07.  Some of the sensors were removed and reinstalled in different fields to allow more farmers to participate in the program.  Watermark® sensors work on the principle of electrical resistance between two electrodes.  The sensors collect and absorb moisture as the soil wets and dries.  The higher the soil moisture content the lower the electrical resistance.  Because there is a known and consistent calibration between electrical resistance and soil moisture, Watermark® sensors perform well at estimating soil moisture.  Watermark® meters and data loggers connect to sensors by wires and convert electrical resistance to centibars (cb).  An important point to remember, is that the lower the centibar reading, the higher the soil moisture.  In fact, a “0 cb” means soil saturated with water, usually shortly after an irrigation event.  Most soil moisture data is reported between 0-200 cb.  For most soils and forage crops, the point at which to apply irrigation is between 70-90 cb.  However, very sandy soils may need irrigation applied at 40-50 cb.  The point at which to apply irrigation for heavy soils is between 90-120 cb.  At each field location sensors were installed at 3 depths, 1 ft, 2 ft and 4 ft.  Installation at the three depths offers allows producers to monitor depth of water infiltration shortly after the irrigation event and they are able to monitor the depletion of available water in the root zone.  Twice a week, the technician read the sensors with a portable meter.  Participating farmers were given the opportunity to borrow a meter so that they could read the sensor themselves.  Five Watermark® data loggers were installed in 5 field in 2006.  In 2007, 10 more data loggers were installed in fields throughout Kane and Garfield Counties.  The data loggers took readings 3 times per day.  All data was input into an Excel spreadsheet and then graphed.  Each graph was given a numerical number based on the following scale: 1 = extreme over watering, 2 = slight over watering, 3 = good scheduling, 4 = slight under watering, 5 = extreme under watering or 10 = undetermined due to sensor malfunction/data integrity.

Results and Discussion:

            The 14 fields with data loggers installed in them provided the highest quality and most reliable data.  Eight of the fields were over watered a significant portion of the season while only 5 were under watered a significant portion of the season.  A good example of extreme under watering the first half of the season and extreme over watering the second half of the season can be identified in graph #271, page 16.  An example of over watering can be seen in graphs #272, page 17, and #273, page 18.  It is important to note that in very sandy soils the sensors may not react quickly enough to accurately reflect the decreasing soil moisture conditions, thus data could reflect wetter conditions than actually exist.  Graph #274, page 19, and #275, page 20, are examples of extreme under watering.  Graph #281, page 281, indicates good water scheduling and soil moisture management. 

            Of the 26 fields with sensors installed, 14 graphs had inconclusive results.  The sensors in these fields were either lost in the field, inaccessible, installed incorrectly, inconsistently read, or needed to be reinstalled. 

            Of the 12 sensor locations that provided management quality data, approximately 45 % indicated under irrigated conditions, 10 % indicated well scheduled irrigation management and 45 % indicated over irrigation.  An example of well managed soil moisture is graph 938662-B.   Examples of over irrigation and under irrigation are graphs 938668-A and 938667-A, respectively.  It is highly possible that the sensors could have been placed in an unrepresentative part of the field such as a sub-irrigated corner or a dry part of the field.  In either case, the data would not provide adequate information to manage irrigation over the whole part of the field. 

Conclusions:

            Soil moisture monitoring is an effective way to improve irrigation scheduling if data is collected consistently, sensors are installed correctly and equipment is functioning properly.  Over irrigation is apparent.  However, the degree to which over irrigation is a problem is not yet understood.  Before applying water conservation measures, individual farmers should take into account the economic and environmental factors associated with future management changes.