Open AccessWithin-Field Variability in Granular Matrix Sensor Data and its Implications for Irrigation Scheduling
Author(s) -
Tsz Him Lo,
H. C. Pringle,
Daran R. Rudnick,
Geng Bai,
L. Jason Krutz,
Drew M. Gholson,
Xin Qiao
Publication year - 2020
Publication title -
applied engineering in agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.276
H-Index - 54
eISSN - 1943-7838
pISSN - 0883-8542
DOI - 10.13031/aea.13918
Subject(s) - irrigation , irrigation scheduling , environmental science , soil science , water content , soil water , hydrology (agriculture) , statistics , mathematics , remote sensing , geotechnical engineering , engineering , geography , agronomy , biology
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.