The influence of water uptake on matric head variability in a drip-irrigated root zone

Abstract Irrigation is a service provided to agricultural crops in order to improve their yields. This is also a rare situation where the location of the costumers (roots) is not known and the heterogeneity in the water delivery system (soil) effects the water distribution. This non-uniformity of soil-plant conditions in the agricultural field complicates decision-making parameters regarding irrigation management (where to place sensors, how many sensors are needed, when and how much to irrigate). Many aspects of this non-uniformity have been studied in the past decades, including soil hydraulic properties, tillage history, irrigation method, sensor location, measured volume, and more. One aspect that has been relatively neglected is how plant roots affect variability. In this work, we focus on the effect that growing roots have on the variability of tensiometer measurements. Corn seeds were sown in a pre-designed experimental plot that consisted of three very distinct soils (sand, sandy loam, clay) to create high variation in investigated conditions. Subsurface drip irrigation was installed in the plot, and the drippers of the center line were wrapped with geotextile to create a geotextile-drip interface (GDI), i.e., a volume which has preferred conditions for root growth. Tensiometers were installed in the GDI (GDI treatment) and 10 cm away from the drip lines (S10 treatment); the matric head was monitored throughout the season. The weekly averaged coefficient of variation (CV) of the GDI treatment decreased as the corn plants grew, for each soil type and the entire plot. As time passed and the corn plants grew, CV peaks, due to irrigation, and the duration of high CV decreased because of the increasing influence of root water uptake on fluxes in the measured volume. After harvest, the CV returned to initial values. S10 tensiometers showed higher CV values than the GDI tensiometers during the last week of growth. After harvest, the CV of the S10 tensiometer measurements decreased, contrary to the trend in the GDI tensiometers. The results point to the major influence of roots on matric head variability, first, by decreasing variability with time and second, through the effect of tensiometer location since the GDI tensiometers were located in volumes with high root density. This influence has major consequences regarding the methodology of tensiometer measurements to represent an agricultural field, and might provide means for global guidelines for tensiometer placement and irrigation management.

[1]  Jochen Hemming,et al.  Root Zone Sensors for Irrigation Management in Intensive Agriculture , 2009, Sensors.

[2]  Jacob Bear,et al.  Physical principles of water percolation and seepage , 1968 .

[3]  Steven R. Evett,et al.  SOIL WATER SENSORS FOR IRRIGATION SCHEDULING: CAN THEY DELIVER A MANAGEMENT ALLOWED DEPLETION? , 2011 .

[4]  Soil Bulk Density and Penetration Resistance under Different Tillage and Crop Management Systems and Their Relationship with Barley Root Growth , 2003 .

[5]  Dani Or,et al.  Flow and uptake patterns affecting soil water sensor placement for drip irrigation management , 1996 .

[6]  W. L. Bland,et al.  SOIL TEMPERATURE AND ROOT GROWTH , 1992 .

[7]  B. Kay,et al.  Optimum versus non-limiting water contents for root growth, biomass accumulation, gas exchange and the rate of development of maize (Zea mays L.) , 2006 .

[8]  P. Wierenga,et al.  SPATIAL VARIABILITY OF SOIL WATER TENSION IN AN IRRIGATED SOIL , 1985 .

[9]  P. Wierenga,et al.  Variability of soil water tension in a trickle irrigated Chile pepper field , 2004, Irrigation Science.

[10]  Christos Goumopoulos,et al.  Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support , 2014 .

[11]  A. Gutjahr,et al.  Stochastic Analysis of Unsaturated Flow in Heterogeneous Soils: 2. Statistically Anisotropic Media With Variable α , 1985 .

[12]  E. Segal,et al.  Water Uptake and Hydraulics of the Root Hair Rhizosphere , 2008 .

[13]  Steve H. L. Liang,et al.  Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support , 2014 .

[14]  U. Schmidhalter,et al.  Evaluation of Very High Soil-Water Tension Threshold Values in Sensor-Based Deficit Irrigation Systems , 2014 .

[15]  Erik Lichtenberg,et al.  Advancing Wireless Sensor Networks for Irrigation Management of Ornamental Crops: An Overview , 2013 .

[16]  A. Warrick.  Soil Water Dynamics , 2003 .

[17]  Michael J. Delwiche,et al.  Wireless sensor network with irrigation valve control , 2013 .

[18]  Nicholas Dercas,et al.  Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems , 2015 .

[19]  U. Shani,et al.  Soil-limiting flow from subsurface emitters. II: Effect on uniformity , 1996 .

[20]  A. G. Bengough,et al.  Penetrometer resistance, root penetration resistance and root elongation rate in two sandy loam soils , 1991, Plant and Soil.

[21]  R. Webster.  THE MEASUREMENT OF SOIL WATER TENSION IN THE FIELD , 1966 .

[22]  William Armstrong,et al.  Root Growth and Metabolism Under Oxygen Deficiency , 2002 .

[23]  Michael D. Dukes,et al.  Evaluating irrigation applied and nitrogen leached using different smart irrigation technologies on bahiagrass (Paspalumnotatum) , 2013, Irrigation Science.

[24]  J. Meynard,et al.  Non-uniformity of environmental conditions in greenhouse lettuce production increases the risk of N pollution and lower product quality , 2001 .

[25]  John D. Lea-Cox,et al.  Sensors for Improved Efficiency of Irrigation in Greenhouse and Nursery Production , 2013 .

[26]  N Michelakis,et al.  Water use, wetted soil volume, root distribution and yield of avocado under drip irrigation , 1993 .

[27]  D. J. Greenwood,et al.  Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology , 2009, The Journal of Agricultural Science.

[28]  George M. Hornberger,et al.  Estimating spatial variability in soil moisture with a simplified dynamic model , 1983 .

[29]  A. Ben‐Gal,et al.  An index for plant water deficit based on root-weighted soil water content , 2015 .

[30]  W. R. Gardner,et al.  Water Uptake by Plants: I. Divided Root Experiments , 1977 .

[31]  Jan M. H. Hendrickx,et al.  Variability of soil water tension and soil water content , 1990 .

[32]  F. Capraro,et al.  Intelligent Irrigation in Grapevines: A Way to Obtain Different Wine Characteristics , 2008 .

[33]  A. W. Warrick,et al.  13 – Spatial Variability of Soil Physical Properties in the Field , 1980 .

[34]  Naftali Lazarovitch,et al.  Optimal tensiometer placement for high-frequency subsurface drip irrigation management in heterogeneous soils , 2015 .

[35]  D. Russo.  A Stochastic Approach to the Crop Yield-Irrigation Relationships in Heterogeneous Soils: I. Analysis of the Field Spatial Variability1 , 1986 .

[36]  Sterling A. Taylor,et al.  Managing Irrigation Water on the Farm , 1965 .

[37]  A. G. Bengough,et al.  Mechanical impedance to root growth: a review of experimental techniques and root growth responses , 1990 .

[38]  R. Geneve,et al.  Comparing substrate moisture-based daily water use and on-demand irrigation regimes for oakleaf hydrangea grown in two container sizes , 2014 .

[39]  R. G. Evans,et al.  Methods and technologies to improve efficiency of water use , 2008 .

[40]  J. Bear.  Dynamics of Fluids in Porous Media , 1975 .

[41]  Calvin D. Perry,et al.  A real-time wireless smart sensor array for scheduling irrigation , 2008 .

[42]  G. Katul,et al.  Soil water depletion by oak trees and the influence of root water uptake on the moisture content spatial statistics , 1997 .

[43]  D. Tessier,et al.  Effect of Compaction on Soil Physical and Hydraulic Properties: Experimental Results and Modeling , 1997 .

[44]  Effi Tripler,et al.  Plant response to the soil environment: An analytical model integrating yield, water, soil type, and salinity , 2007 .