Within-Field Variability in Granular Matrix Sensor Data and its Implications for Irrigation Scheduling

Abstract

Highlights Within-field variability was larger for individual depths than for the profile average across multiple depths. Distributions of the profile average were approximately normal, with increasing variances as the soil was drying. Probability theory was applied to quantify the effect of sensor set number on irrigation scheduling. The benefit of additional sensors sets may decrease for longer irrigation cycles and for more heterogeneous fields. Abstract. Even when located within the same field, multiple units of the same soil moisture sensor rarely report identical values. Such within-field variability in soil moisture sensor data is caused by natural and manmade spatial heterogeneity and by inconsistencies in sensor construction and installation. To better describe this variability, daily soil water tension values from 14 to 23 sets of granular matrix sensors during the middle part of four soybean site-years in the Mississippi Delta were analyzed. The soil water tension data were found to follow approximately normal distributions, to exhibit moderately high temporal rank stability, and to show strong positive correlation between mean and variance. Based on these observations and the existing literature, a probabilistic conceptual framework was proposed for interpreting within-field variability in granular matrix sensor data. This framework was then applied to investigate the impact of sensor set number (i.e., number of replicates) and irrigation triggering threshold on the scheduling of single-day and multi-day irrigation cycles. If a producer’s primary goal of irrigation scheduling is to keep soil water adequate in a particular fraction of land on average, the potential benefit from increasing sensor set number may be smaller than traditionally expected. Improvement, expansion, and validation of this probabilistic framework are welcomed for developing a practical and robust approach to selecting the sensor set number and the irrigation triggering threshold for diverse soil moisture sensor types in diverse contexts. Keywords: Irrigation scheduling, Probability, Sensors, Soil moisture, Soil water tension, Variability, Watermark.

Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors

“…Owing to phenomena such as hardware inconsistencies (Kelleners et al, 2005), environmental differences (Wilson et al, 2004), installation disturbances (Rothe et al, 1997), and microscale heterogeneities (Evett et al, 2012), replicates are not guaranteed to report similar values despite being co-located in a seemingly homogeneous area. What one replicate reports can be described as a random observation of a probability distribution whose spread increases with the magnitude of interreplicate variability (Lo et al, 2020b). In other words, the replicates of a sensor with high interreplicate variability are likely to report values deviating substantially from the true mean and to reach the irrigation trigger at vastly different times.…”

Beyond 2020, Vision of the Future: Selected Papers from the Sixth Decennial National Irrigation Symposium

“…The use of soil water sensors for scheduling agricultural irrigation is growing in the U.S. The within-field variability in granular matrix soil water sensor data was examined in a field study in Mississippi (Lo et al, 2020). The authors found that variability was greater for individual depths than for the complete soil profile, and that variability increased as the soil water decreased.…”

“…Overall, eight journal articles for this ASABE Special Collection were related to agricultural irrigation in the humid region, with three articles specifically discussed in this section (Leib et al, 2020;Lena et al, 2020;Reinhart et al, 2020). The other five articles were discussed in the Technology and Innovation section (Chiu and Reba, 2020;Henry, et al, 2020;Lo et al, 2020), the Irrigation Systems section (Reba and Massey, 2020), and the Irrigation Management section (Zamora-Re et al, 2020).…”