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Precision Agricultural Tools for Measuring Water Use Efficiency in
Sorghum
Daniel John Rigney
Supervisors: Professor David Lamb, Derek Schneider, Dr John
Stanley & Dr Mark Trotter
The Precision Agriculture Research Group The University of New England
Armidale
Submitted – November 2011
A thesis submitted in partial fulfilment of the requirements for the
Degree of Bachelor of Rural Science (Hons.) at the University of New
England, Armidale, NSW.
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Declaration
This thesis has been undertaken with the Precision Agricultural Research Group, School of
Science and Technology, University of New England, Armidale, NSW, Australia.
I certify that the substance of this report has not already been submitted for any degree or
diploma and is not currently being submitted for any other degree. I also certify that I have
duly acknowledged (to the best of my knowledge) any assistance received and all sources
used in the compilation of this thesis.
....................................................
Daniel John Rigney
October 28th 2011
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Acknowledgements
I would like to thank all members of the Precision Agricultural Research Group, for
their constant help and support throughout the last 12 months. A special thanks to my
supervisors Professor David Lamb, Derek Schneider, Dr John Stanley and Dr Mark Trotter.
Professor David Lamb, you have guided me through the project. Your time and
patience when explaining the physics behind the technologies has been much appreciated.
You developed the project concept and design which gave me a very interesting topic to
study. Thank you also for your help with the literature review and report writing process.
I simply would not have been able to complete the trial without the knowledge and
guidance of Derek Schneider. You made the field work easy and enjoyable and I am grateful
for your help. I have learnt a great deal in working with sensors and spatial data thanks to
you.
Dr John Stanley for your support during the report writing process, you spent a great
deal of time explaining results in depth and sorting through drafts. My understanding of the
topic has improved through your explanations. I appreciate your help.
Dr Mark trotter for arranging this project for me at the beginning of last year and
your continual help throughout the last 12 months is appreciated.
To all the help and support received from Jamie Barwick and Jess Roberts during
field work and while writing the report, thank you.
Finally I would also like to acknowledge the support, through project operational
funds, of the Cooperative Research Centre for Spatial Information (CRCSI), established and
supported under the Australian Governments Cooperative Research Centres Programme.
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Abstract
Water for Australian agriculture is becoming limiting. Increasing demands on
environmental flow, urban expansion and the mining sector are driving the search for ways
to increase water use efficiency (WUE). Currently farmers are relying on a few point-specific
measurements of soil water to manage vast areas, in an attempt to estimate crop WUE. But
instruments used in precision agriculture potentially provide non-invasive ways to estimate
soil water content (EM38) and plant biomass (spectral signatures) with spatial accuracy
across a paddock. Combining these two measures opens the way to estimate crop WUE on a
spatial scale. If successful this would allow farmers to make variable rate applications of
water and nutrients precisely within limiting regions. This will increase the knowledge of soil
and plant interactions as well as increase WUE.
This project reports on attempts to estimate WUE on a spatial scale by combining
surveys of biomass determined using a CropCircle (that records normalized difference
vegetation index) and surveys of soil water content via an EM38 (that records apparent
electrical-conductivity of the soil; ECa). EM38 records used in horizontal or vertical dipole
correlated reasonably well to volumetric soil water content producing R2 values of 0.58 and
0.64 respectively. NDVI over the vegetative stages of forage sorghum growth correlated well
with dry matter samples producing R2 value of 0.85. The ratio of water use (change in ECa)
and biomass (change in NDVI) showed greater than 30 fold difference in the WUE across the
site. Mapping WUE values exhibited clear regions even within a 0.44 hectare paddock.
Potential reasons for the spatial variation in WUE are discussed, demonstrating ways that
farmers might use fine-scale spatial mapping of WUE to improve management decisions.
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Table of Contents
Declaration ................................................................................................................................. 2
Acknowledgements .................................................................................................................... 3
Abstract ...................................................................................................................................... 4
List of Figures ............................................................................................................................. 7
List of Tables .............................................................................................................................. 8
Chapter 1: Introduction ............................................................................................................. 9
Chapter 2: Literature Review ................................................................................................... 11
2.1 The Importance of Soil Moisture to Plants .................................................................... 11
2.2 Sorghum (Sorghum bicolor) ........................................................................................... 12
2.3 Measuring Soil Water Content in Agriculture Fields ...................................................... 13
2.3.1 Soil Water Content and Matric Potential ................................................................ 13
2.3.2 Thermal Neutron Computed Tomography .............................................................. 13
2.3.3 Neutron Probes ....................................................................................................... 14
2.3.4 Time Domain Reflectance ........................................................................................ 16
2.3.5 Tensiometer ............................................................................................................. 17
2.3.6 Soil Electrical Conductivity....................................................................................... 18
2.3.8 Electromagnetic Induction (EM38) Sensor .............................................................. 20
2.3.9 Accuracy Issues Associated with Electro-Magnetic Induction (EMI) Measurements
.......................................................................................................................................... 22
2.3.10 The Use of EM38 to Infer Soil Moisture ................................................................ 23
2.4 Methods Used to Measure Plant Biomass production .................................................. 24
2.4.1 Remote Sensing ....................................................................................................... 24
2.4.2 Satellite Remote Sensing ......................................................................................... 27
2.4.3 Airborne Remote Sensing ........................................................................................ 28
2.4.4 Active Optical Reflectance Sensors ......................................................................... 29
2.4.5 Ground-Based Active Proximal Sensors .................................................................. 29
2.4.6 Ultra low-level Airborne (ULLA) Sensors ................................................................. 31
2.5 Importance of Differential Global Positioning Systems ................................................. 32
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2.6 Combining EMI Soil Moisture and Active Optical Canopy Sensing to Infer Water Use
Efficiency .............................................................................................................................. 33
Chapter 3: Material and Methods ........................................................................................... 34
3.1 Location and Crop .......................................................................................................... 34
3.2 Experimental Design ...................................................................................................... 34
3.3 Soil Water Content ......................................................................................................... 36
3.3.1 Electrical Conductivity (EC) ...................................................................................... 36
3.3.2 Volumetric Water Content (VMC) ........................................................................... 36
3.3.3 Electron Magnetic Induction 38 (EM38) ................................................................. 37
3.4 Sorghum Biomass ........................................................................................................... 39
3.4.1 Normalised Difference Vegetation Index (NDVI) .................................................... 39
3.4.2 Biomass Cuts ............................................................................................................ 39
3.4.3 Data Analysis: Bootstrapping technique ................................................................. 41
3.5 Estimating Water Use Efficiency .................................................................................... 41
Chapter 4: Results .................................................................................................................... 42
4.1 Introduction .................................................................................................................... 42
4.2 Biomass Calculation ....................................................................................................... 42
4.2.1 Calibration Equation ................................................................................................ 43
4.3 Soil Water Calculation .................................................................................................... 49
4.3.1 Soil Water Calibration Equation .............................................................................. 50
4.4 Water Use Efficiency ...................................................................................................... 55
Chapter 5: Discussion ............................................................................................................... 56
5.1 Calibration of Biomass to NDVI ...................................................................................... 56
5.2 Calibration of Soil Moisture to Electro-Magnetic Induction .......................................... 57
5.3 Spatial Water Use Efficiency .......................................................................................... 59
Chapter 6: Conclusion .............................................................................................................. 62
Chapter 7: Reference ............................................................................................................... 63
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List of Figures
Figure 1: Electromagnetic Induction-38, principal of operation. ............................................ 21
Figure 2: Trial design. The points show the volumetric moisture content ground truthing sites and the lines illustrate the ATV tramlines. ............................................................... 35
Figure 3: EM38, data collection procedure ............................................................................. 38
Figure 4: CropCircle, data collection procedure ...................................................................... 40
Figure 5: NDVI to biomass correlation before anthesis. ......................................................... 42
Figure 6: NDVI to biomass correlation after anthesis. ........................................................... 43
Figure 7: Bootstrapping analysis for the calibration curve between calibrated NDVI and actual biomass. The regression line indicates a linear relationship between the biomass determined via NDVI and the actual measured biomass. ................................................ 44
Figure 8: Spatial representation of biomass determined by NDVI for the 17th Feb 2011....... 46
Figure 9: Spatial representation of biomass determined by NDVI for the 28th Feb 2011. ..... 47
Figure 10: Illustrates biomass change between the two sampling date 17-25th Feb. ........... 48
Figure 11: Linear correlation between volumetric moisture content (VMC) and point specific EM38 (ECa) readings in vertical dipole mode for each sampling date. The regression line indicates a single relationship for all data. ....................................................................... 49
Figure 12: Correlation between volumetric moisture content and point specific EM38 ECa readings in horizontal dipole mode for each sampling date. Regression lines are presented for each sampling date. Circles indicate the marked difference between regressions. ....................................................................................................................... 50
Figure 13: Representation of bootstrapping analysis of the volumetric moisture content (VMC) determined via ECa (EM38) and actual VMC. The regression shows a linear relationship between the actual and predicted VMC. ..................................................... 51
Figure 14: Spatial representation of VMC variability determined by ECa on the 17th Feb 2011. ................................................................................................................................. 52
Figure 15: Spatial representation of VMC variability determined by ECa on the 28th Feb 2011. ................................................................................................................................. 53
Figure 16: Illustrates the drying out period between the 17-28th Feb 2011. .......................... 54
Figure 17: Illustrates the regions of varying WUE throughout the surveys paddock. ............ 55
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List of Tables
Table 1: NDVI and Red/NIR correlation between dry weight in corn under three different
growth stages using GreenSeeker and CropCircle sensors. ............................................. 31
Table 2: Calibration equations used to calibrate NDVI values to sorghum biomass during
different growth stages. ................................................................................................... 44
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Chapter 1: Introduction
Rapid population growth and changing climatic conditions on a world scale have
heightened the importance of understanding the limitations within agricultural systems. A
continual increase in global food production is vital to sustain the population predicted to
increase by 2.2 billion by 2050 (Buttriss, 2011). Water use efficiency has been identified as
the dominant factor limiting global crop production as water shortages occur in nearly all
continents (Hund et al., 2009). The recent decade of drought in Australia has amplified the
importance of efficient water use (Mpelasoka et al., 2008). Emerging precision agriculture
technologies such as on-the-go yield monitors in harvesters, and proximal soil moisture
surveying techniques have provided an opportunity to measure and account for soil
moisture and plant growth variability on a sub-paddock scale (Corwin and Lesch, 2005a).
The measurement of soil water content and plant biomass is currently limited to
single measures at a specific point in a field. So the management currently treats the entire
field or management unit as if it were all the same. In this thesis I explore the possibility that
two relatively commonly used, non-invasive surveys tool might spatially indicate water use
efficiency on a metre by metre scale. Access to this information might increase the
efficiency of production (biomass) by treating each area of a field with specifically required
inputs; such as variable rate water, nitrogen or seeding applications. The use of global
positioning systems (GPS) in conjunction with a Geonics EM38 provides a map of apparent
electrical conductivity (ECa). A number of researchers have reported good correlation
between soil volumetric water content and ECa in heavy clay soils Hossain (2010), but no
one appears to have used it as a direct measure. Likewise spatial measurement of biomass
using a CropCircle® sensor to determine the normalised difference vegetation index (NDVI)
can be used to map biomass.
Estimating photosynthetically active plant biomass via NDVI readings has been
extensively used for both cropping (Spackman et al., 2000) and pasture systems (Trotter et
al., 2010). Electromagnetic induction has also been shown by Hossian (2008) to correlate
well with soil volumetric moisture content. I will attempt to combine these two spatial
measures to give a map of water use efficiency to a resolution of approximately two square
metres. If the spatial measures of soil water content and plant biomass give a reasonable
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indication of the spatial variability in water use efficiency, this will enable growers to factor
these spatial differences into their management practises. The ultimate aim of the sort of
work reported in this thesis is for future farmers to be able to improve water use efficiency
by applying inputs in quantities that can be utilised efficiently on a two metre by two metre
scale.
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Chapter 2: Literature Review
2.1 The Importance of Soil Moisture to Plants
In agricultural crop production, the term ‘water use efficiency’ (WUE) can be defined
in accordance with grain yield or biomass production. In the case of biomass production it is
readily defined as the “above-ground biomass production per unit area per unit water
evapotranspired” (Kirkham, 2005). Evapotranspiration accounts for the water consumed
through the process of plant photosynthesis, the moisture that is lost through soil
evaporation, biological (eg. microbial) activity and leaching (horizontal and vertical
infiltration). In order to quantify WUE the original soil water content must be known. In-situ
measurements are possible using point probes (neutron and capacitance probes) but
expanding the spatial dimensions of any measurements, such as paddock scale has been
difficult as a result of time, cost and labour. Calculating the soil water content on a spatial
scale is now possible with advances in precision agriculture technology. Proximal sensors
such as EM38, EM31, ultrasonic displacement sensors and NIR reflectance sensors, used in
conjunction with differential global positioning systems, have enabled soil to be mapped on
an extensive scale within small time frames (Hossian, 2010). This will be discussed in more
detail in the following sections.
Photosynthetically active plant tissue is vital for the plants survival as all energy
required for plant growth is derived from this process (Reddy and Zhao, 2004). In the course
of reducing photosynthesis levels, water stress has a series of complex biochemical
implications on the plants photo-systems. Any change in activity between photo systems II
(PSII) and photo-systems I (PSI) create an imbalance in electron concentration (Reddy and
Zhao, 2004). This excess light energy originally generated from the chloroplast cells can
create active oxygen in the form of (O2, H2O2, OH) which can be hazardous to plant health.
O2 is of particular concern as it interferes with the reduction process of NADP in the electron
transport chain between PSII and PSI, which can cause a 20-30% reduction in photosynthetic
activity (Reddy and Zhao, 2004).
Another important step that occurs directly after plant stress is a reduction in the
plant’s ability to assimilate and utilise carbon (Reddy and Zhao, 2004). Reddy & Zhao (2004)
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suggests Ribulose-1, 5-bisphosphate carboxylase/oxygenase (rubisco) activity is inhibited
during water stress conditions, therefore reducing carboxylation in plants (Reddy and Zhao,
2004). Varying opinions exist in relation to the long term effects that water stress has on
rubisco and whether or not the rubisco cycles is permanently damaged after such event. If
permanent rubisco damage is a direct result of water stress this suggests that any slight
moisture limitations throughout the plants growing cycles will affect its growth and
production potential.
The overall effect water stress has on plant growth varies from species to species
although it can be assumed that any moisture stress will generally result in deleterious
effects on crop production. As such, it is important to be able to accurately quantify the
water use efficiency of a cropping plant. The ability to predict when a particular plant is
about to encounter water stress before significant biochemical growth limitations occur
would greatly improve production, especially in irrigation systems. Current advances in
precision agricultural technologies have the ability to enable producers to measure such
spatial variability.
2.2 Sorghum (Sorghum bicolor)
Sorghum (Sorghum bicolour) is a member of the Poaceae family and is used
worldwide for both grain and forage production purposes. Originally from Ethiopia the
species has spread throughout Africa, India, Southeast Asia, Australian and the United
States of America (Hannaway and Myers, 2004). Sorghum is an annual plant with C4
physiology, making it well adapted to northern regions of Australia. Most varieties require
an annual rainfall of between 400-750mm and will survive in most climates below 1000m in
altitude. Sorghum has the ability to lie dormant under adverse conditions such as drought
and resume growth once the environment becomes favourable. In order for sufficient
growth to occur sorghum requires 180 kg/ha N, 20-45 kg/ha of P and 35-80 kg/ha of K,
performing best in medium to light textured soils (Routley, 2008). In recent years exported
sorghum alone has been worth $405 million dollars to the Australian economy (Federation,
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2011). Its value, growth habits and growing location make it a desirable crop to study water
use efficiency.
2.3 Measuring Soil Water Content in Agriculture Fields
2.3.1 Soil Water Content and Matric Potential
Soil water content and matric potential are two important concepts in the
understanding of plant available moisture. Soil water content looks at the total moisture
present and does not examine the relationships between water and its surrounding
environment (Whalley et al., 2007). Matric potential is a measure of plant-related water
stress in a particular soil, and depends on the degree of saturation, soil structure, soil
texture and other external environmental effects that impact on water tension (Whalley et
al., 2007). For example, heavy clay soils can have a total soil water content of 30-40%, yet a
high matric potential may lock water up so tightly that it is not accessible for plant use.
Sandy soil will generally have a low matric potential, therefore any water in the soil profile is
accessible for plant use. Knowing both the soil water content and matric potential is
important when looking at water use efficiency in varying soil types (Whalley et al., 2007).
2.3.2 Thermal Neutron Computed Tomography
Throughout the last 18 years neutron computed tomography (NTC) has successfully
been used to examine a number of soil properties including bulk density, porosity, water
content, soil structure and soil-solute concentration (Tumlinson et al., 2008). NTC works
similarly to an X-ray in the sense that it measures variations in soil neutron concentration to
create a visual image. The level of neutron response is particularly sensitive to hydrogen
ions and therefore is very effective in demonstrating the presence of any substance
containing hydrogen, such as soil water (Tumlinson et al., 2008).
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The neutron source used is thermal and cold neutrons; the reason being is that most
materials provide a higher attenuation for low energy neutrons (Hassanein, 2006). These
neutron sources also provide higher neutron detection levels as a result of their slow
moving nature (Hassanein, 2006). The neutron radiography system consists of a number of
parallel neutron beams that are emitted towards the investigated sample, on the other side
of the sample a detector system measures remaining transmitted neutrons (Hassanein,
2006). The two main detector plates used are imaging plates and scintillator. Imaging plates
provide detail and high resolution, although scintillation screens when used with a CCD
camera, is seen as being the most effective neutron detector for tomography (Hassanein,
2006). This camera and detector system records a two-dimension image of the object in the
path of the neutron emitting collimator (Robinson et al., 2008).
A limitation to neutron tomography is the nature of scattering neutrons after they
have contacted the investigated sample. Scattered neutrons can still hit the detector after
sample refraction (Hassanein, 2006). Tomography also has trouble identifying root systems
with a small diameter, this issue can be resolved by decreasing to thickness of the soil
profile. This can create other limitations such as unnatural root growth patterns due to
confinement (Robinson et al., 2008). Soil water content can also be a problem as too much
result in a blurred image, too little and the plant becomes water stressed (Robinson et al.,
2008). Additional issues can occur in the homogeneity of the neutron beam formed in the
collimator (Robinson et al., 2008).
Whilst this technique provides an accurate image both structurally and temporally,
its applicability lies more in defining root movement than accurately estimated soil water
content. Using neutron tomography also requires growing the plant in a confined box, so
any external factors such as evapotransporation, leach and rainfall are not accounted for.
Defining soil water on a paddock scale is not possible using neutron tomography.
2.3.3 Neutron Probes
The neutron probe is one of the most effective and widely used methods to measure
soil water content. It consists of a central probe containing two main segments, the first
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being a fast neutron source and the second, a slow neutron detector (Bell, 1987). The
neutron probe also has a pulse counter (ratescaler) which is connected to the neutron
emitter and detector via a cable (Bell, 1987). The emitted neutrons are generated by a
radioactive source which fires the neutrons into the surrounding soil. As the emitted
neutrons collide with soil particles neutron scattering occurs. This scattering is
predominantly caused by the presence of hydrogen in the soil and therefore is very
responsive to soil moisture (Bell, 1987). As the collision between the rapidly moving
neutrons and the hydrogen ions occurs, the electrons reduce energy and speed. This
reduction in thermal energy results in a circular grouping of neutrons which will vary in
density depending on the neutron-hydrogen collisions (Jabro et al., 2009). The slow neutron
probe-detector then calculates the concentration of scattered neutrons, indicating the soil
moisture content for a given location. With the use of a soil moisture calibration curves the
actual moisture content can then be predicted from the ratescaler unit (Bell, 1987).
The neutron probe results when used in conjunction with gravimetric and bulk
density recordings can be used to accurately calculate the volumetric soil moisture content
(Robock et al., 2000). One of the advantages in using a neutron probes is that the results are
calculated within a spherical pattern, resulting in an average soil moisture reading within a
given zone (Robock et al., 2000). The neutron probe also gives instant results, which is
helpful in the rapid decision-making process needed in agriculture systems.
Although the neutron probe is an effective and accurate tool, there are some aspects
which limit its use, one of these issues stems from its portability, the nature of its use and
the fact that the operator (at least in Australia) requires a radiation safety licence to operate
it. The probe has to be inserted into the soil at different depths to create a soil profile
moisture reading. This process is labour intensive and does not account for spatial variation
unless a vast sampling method is employed (Robock et al., 2000). Other issues include the
high cost, handling of the radioactive content as well as the need for calibration in different
soil types and extremes in soil moisture (Robock et al., 2000).
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2.3.4 Time Domain Reflectance
Throughout the last 30 years time domain reflectance (TDR) has become a viable
instrument for measuring soil water content. Results from Weiler et al., (1998) show the
TDR sensor can be correlated to volumetric moisture content (VMC), (R²=0.92, n= 52). The
TDR device consists of two or three parallel metal rods which are inserted into the soil
surface at varying depths, depending on rod length and the moisture depth being recorded
(Weiler et al., 1998). A voltage generator attached to the rods creates sharp pulses of
electromagnetic (EM) waves. These EM wave move through the soil and send a signal back
to the TDR dependant which can be related to (VMC) (Ferre et al., 1996). The higher the
levels of moisture in the soil the greater the energy response (Ferre et al., 1996). The rods
total length along with the electromagnetic waves pulses are used to calculate the relative
dielectric permittivity, from this a square root averaging model is used to estimated
volumetric water content (Weiler et al., 1998).
The TDR probe is effective on a site-specific scale although, like the neutron probe, it
is hard to measure soil moisture variability on a spatial scale due to labour costs and time
constraints. There are some easily portable TDR probes used to rapidly measure a number
of different sites, which is effective over small areas. Although issues can arise when
physically hard soil profiles are penetrated numerous times (Long et al., 2002). An
alternative to this is to use permanently installed probes. These probes are staggered at
various depths with leads attached providing TDR signal. This method is effective in small
areas although the lead length is only efficient up to 50-80 metres due to excess signal loss
at greater distances (Long et al., 2002). This alternative is also costly and requires the leads
to be buried at large depths under cultivation farming practices.
One major source of error associated with the TDR sensor are the voids that occur
around the rods after installation. These voids can contain air or water, both of which will
impact on the predicted soil water content (Ferre et al., 1996). It is impossible to predict the
extent of the voids surrounding the probe after insertion. To account for this the inverse
dielectric averaging model is used which suggests the soil content surrounding the rods is
heterogeneous (Ferre et al., 1996). The TDR probes often contain a coating which increases
the probe’s sensitivity in dry soils, this can be of particular importance in Australian soils
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(Ferre et al., 1996). Although the TDR’s sensitivity is increased, these rods have a tendency
to underestimate the actual soil volumetric water content. Probes that penetrate through
different layers of soil with varying water contents will often show the average water
content as being the layer containing the least water. This limits the probes use (Ferre et al.,
1996).
2.3.5 Tensiometer
The Tensiometer is a simple, accurate device used to measure the amount of energy
that is required to overcome the capillary and gravimetric forces which bind moisture within
soil particles (Singh and Kuriyan, 2003). Although the Tensiometer is related to the actual
soil water content, its main purpose is to analyse the plant accessibility to the soil water.
Often dominant clay-based soils have a high volumetric water content, although have a
strong capillary force which tightly binds water molecules to soil particles. Thus it is
important, along with measuring the volumetric water content, to measure capillary tension
to enable an estimate of water availability in an agricultural system (Thalheimer, 2003).
The tensiometer calculates the soil matric potential via a ceramic cone (thimble)
containing a known concentration of water and a pressure gauge (Singh and Kuriyan, 2003).
The thimble allows water to move in or out depending on its surrounding medium. Once the
tensiometer gauge is placed in the soil, via capillary force water molecules will be pulled out
of the thimble to varying extents depending on the soil texture and water content. At the
point when the external tension and the internal thimble pressure reach equilibrium the soil
tension will be the same as the tension displayed on the pressure gauge of the tensiometer
(Bocking and Freudlund, 1979). Therefore, the more negative the water tension the higher
the energy required to extract water from the surrounding soil mass.
Although the tensiometer is a widely used instrument to measure matric potential,
errors in readings can occur as a result of various factors, the most significant being
temperature changes (Thalheimer, 2003). The length of the tensiometer tube can also lead
to inaccurate tension results, which can be accounted for by a simple equation if results are
calculated manually. However, if the water column is below the ground level or the data
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collection process is computerised, correcting these results can become difficult
(Thalheimer, 2003).
2.3.6 Soil Electrical Conductivity
The ability of a given soil volume to conduct electricity is related to its ion and water
content as well as the actual physical-chemical disposition of the ions and water in the soil
matrix. The three pathways of current flow within soil include a solid phase, solid-liquid
phase and a liquid phase. The solid phase involves movement via clay minerals. the liquid
phase, via salts in soil moisture located within soil pores and the solid phase, via particles
that are in direct and continuous contact (Hossian, 2008). The phase of interest to estimate
soil water content is the liquid phase, where the ions conduct electricity.
Calculating the apparent soil electrical conductivity (ECa) is derived from Archie’s
empirical law for saturated rocks and sand soils (Hossian, 2008).
ECa = a x σw x ϕm
a = empirical constant σw = electrical conductivity of the porous media solution (dS/m) ϕ = the porosity (m3/m3) m = the porosity exponent
It has been demonstrated by (Rhoades et al., 1989) that the movement of electrons
through a soil medium is complex and cannot simply occur via direct soil particle-to-particle
contact. Thus, the ECa reading is influenced by a number of soil characteristics including
electrical conductivity (EC) of the soil particles, the electrical conductivity of soil solution
between the small and large pores, the overall volumetric soil moisture content and the
pore filled as well as the partial based soil water content (Hossian, 2008). As the sensor is
responding to a number of different soil characteristics it is important that the actual soil
moisture content is measured for calibration purposes. This process is commonly done using
a neutron probe or soil coring to accurately estimate the soils volumetric moisture content
(Hossian, 2008).
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The electrical conduction properties may be determined either by directly measuring
the resistivity or the conductivity of the soil volume. There are two main types of
commercially available sensors that can be used to find soil electrical conductivity. One is an
electrode based sensor that requires soil contact, the other is a non-contact
electromagnetic induction sensor (Sudduth et al., 2003).
2.3.7 Vertical Electrical Sounding/Four Electrode Sensor
The electrode based-sensor, most popularly the four-electrode has been used
extensively throughout geology, soil science, surveying, archaeology, mining and
criminology fields (Tuan et al., 2006). The four electrode based sensor consists of four
probes all of which are equally spaced apart. These probes can be made of any conductive
material the most commonly used include copper and stainless steel (Tuan et al., 2006). The
two outer electrodes are charged via an electrical current source; one electrode is
negatively charged and the other positively. Within the soil, ions are attracted to the
opposing charge and thus migrate accordingly. The inner electrodes record the resistance or
speed at which the ions are travelling. This value is then used to create a soil ECa value
which is influenced by factors including water content and salinity (Tuan et al., 2006).
From an agricultural perspective soil water properties and salinity have been studied
and mapped through the use of vertical electrical sounding (VES). Studies from Tuan et al.,
(2006) illustrate that estimating the soil ECa has been achieved through the use of VES used
in conjunction with thermal conductivity probes. An alternative approach to the VES is the
use of six rolling coulters in replacement for the electrodes which calculates two individual
ECa recordings. This allows the opportunity to measure ECa without continually stopping
and inserting the probes into soil. One widely-used example of a system that utilises rolling
coulters is the Veris 3100® (Sudduth et al., 2005).
One advantage associated with the commercially available Veris 3100® sensor is
setup time. The Veris 3100® requires no prior calibration which is both time efficient and
decreases human input error (Sudduth et al., 2003). Disadvantages when using the rolling
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coulter system include its weight and its need to be pulled with a tractor. Crop damage will
often result unless controlled traffic farming practises are employed (Sudduth et al., 2003).
A modern version of the Veris 3100®, the Veris 2000XA®, has been developed with this is
mind and only has four rolling coulters reducing both its weight and width. The sensor can
now be used during the crop growing cycle if the row spacing’s exceed 76-cm in width
(Sudduth et al., 2003). This is a positive step although still limits their uses throughout
Australian cropping systems as row spacing’s are generally much narrower. Another issue
that distorts the accuracy of ECa via the probe or coulter is its contact to surrounding soil,
this is not a problem in most wet soils although error will occur in dry and stony areas
(Corwin and Lesch, 2003).
2.3.8 Electromagnetic Induction (EM38) Sensor
Electromagnetic induction (EMI) sensors are an entirely different class of sensor.
Rather than relying on direct measurement of electrical current or resistance, EMI sensors
utilise the process of electromagnetic induction. At one end of the sensor there is a
transmitter coil that emits electromagnetic circular eddy current loops that penetrate the
soil surface (Lesch et al., 2005). A small proportion of the secondary electromagnetic field is
intercepted via a receiver coil that is positioned at the opposing (Corwin and Lesch, 2005a).
Only a very small fraction of the actual secondary magnetic field is received by the receiver
coil, this is illustrated in figure 2. In order to calculate an output signal the sum of all
secondary currents at any given time are gathered then amplified (Corwin and Lesch,
2005a). The strength of the secondary field will vary depending on various soil components
such as water, clay and ion content.
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Figure 1: Electromagnetic Induction-38, principal of operation.
A very popular version of the EMI sensor is the Geonics EM38® unit (Lesch et al.,
2005).The EM38® sensor operates under two physical orientations relate to the ground
surface known as the vertical and horizontal dipoles. The vertical and horizontal dipole
orientations change the depth and width of soil being ‘examined’. The vertically positioned
EM dipole penetrates to a depth of 1.5-2 metres, while the horizontal dipole configuration
only measures the top 0.75-1 metres of the soil profile. These dipole orientations are simply
changed by standing the EM38 upright for the vertical position or laying it down for the
horizontal (Lesch et al., 2005). Measuring the soil profile with both of these dipoles can
provide information on the actual location of water within the soil. If the soil water content
is predominantly in the top 50-cm a higher reading will be observed in the horizontal
position in comparison to the vertical.
This non-invasive means of measuring soil electrical characteristics has become
increasingly popular in agriculture for a number of reasons. One is that the results are
instant (Corwin and Lesch, 2003). The second is that large areas of soil can be measured in
short time frames giving an overall view or map of the soil (Corwin and Lesch, 2003). The
third is that the non-invasive sensor does not have to make direct contact with the soil and
therefore issues seen in the EM probe technique including inaccurate results in dry and
gravely soils are reduced (Corwin and Lesch, 2003).
Vast changes in ECa within and between agricultural fields are common due to
spatial variations that occur as a result of differences in soil formation, meteorological
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processes and varying management procedures (Corwin and Lesch, 2005b). Changes in
these areas will directly influence the soil structure, texture and potential water content
(Friedman, 2005). These processes can be chemical, physical and biological, all of which will
cause variations in the ECa value recorded. These factors do slowly alter over time although
the main variations within soil as a result of agriculture include cation exchange capacity,
particle size and arrangement and the soil’s ability to absorb and hold water (Friedman,
2005). These soil attributes readily change in agricultural systems due to changing
management and climatic conditions (Friedman, 2005).
2.3.9 Accuracy Issues Associated with Electro-Magnetic Induction (EMI) Measurements
The error associated with EMI measurements in precision agricultural techniques is
addressed by Corwin (2005b). Corwin suggests that there is a large amount of drift within
the EM38 sensor that can alter data accuracy. The most likely cause of this drift is external
temperature variations, due to partly overcast weather or altering shade conditions (Corwin
and Lesch, 2005b). This issue is thought to be minimised through the use of a permanent
shade source positioned over the sensor during use. Another method used to eliminate
error is to conduct regular drift runs along a given transect as the temperature changes
throughout the day. This enables the ECa to be calibrated, reducing drift effects (Corwin and
Lesch, 2005b). Another common source of error is the distance between the EM sensor and
the differential global positioning systems (DGPS) antenna (Corwin and Lesch, 2005b). This
error is often insignificant in an agricultural sense because the spatial scale of site specific
management practices are generally much larger.
Another issue that should be considered is the effectiveness of ECa reading in
relation to physical and chemical changes within the soil profile. Corwin & Lesch (2005)
suggests that ECa itself does not directly categorise spatial variation within soils, additional
information is required to analyse the exact cause of the response (Corwin and Lesch,
2005a). Instead, calculating the ECa provides an accurate and cost effective tool in locating
appropriate soil sampling locations. After the soil cores have been taken and analysed,
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correlations between the ECa and the changing chemical or physical properties can be
interpreted (Corwin and Lesch, 2005a).
2.3.10 The Use of EM38 to Infer Soil Moisture
The EM38 sensor measures the soils electrical conductivity, responding to spatial
variation in soil moisture, salinity and texture. Previous studies have indicated that soil
moisture is the most influential factor affecting soil electrical conductivity (Padhi and Misra,
2009; Hossian, 2010). Hossain et al. (2010) concluded that using the EM38 sensor in the
horizontal dipole and calibrating to a depth of at least 1.2m will provide accurate results in
soil moisture in deep vertosol soils. The root mean squared error for this experiment was
9%, which is effective for rapid spatial data collection. Padhi and Misra (2009) also analysed
soil water content in the root zone of crop fields. It must be mentioned that this trial was
also conducted on heavy clay soils which seem to show a better relationship between
volumetric water content and ECa than lighter soils. The most important message
highlighted in both papers is the need for sensor calibration. Every soil is different and the
electromagnetic current emitted into the soil will respond to a number of factors, not just
soil moisture. Calibration is commonly done using a hydraulic soil coring system to calculate
soil volumetric moisture content. These values can then be compared to the EM38 readings
over the same location.
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2.4 Methods Used to Measure Plant Biomass production
2.4.1 Remote Sensing
Remote sensing is defined as “the acquisition of data using a remotely located
sensing device, and the extraction of information from this data” (McCloy, 2006). Some of
the earliest remotely sensed data dates back to the 1850s where photographs were taken
from a hot air balloon, since then the means of data collection has advanced in a number of
directions. There are many ways in which remotely sensed data can be collected, the main
three being satellite imagery, airborne sensors and ground-based sensors.
The ability of the sensor to accurately detect variations on the earth’s surface is
influenced by its spatial, radiometric, spectral and temporal resolution (Hall et al., 2002).
Spatial resolution refers to the smallest object that is detectable on the ground which is
influenced by the image pixel size (Hall et al., 2002). Radiometric resolution refers to the
number of discrete radiometric levels that can be separately categorised from the target
(Hall et al., 2002). Temporal resolution is a measure of how often information is extracted
from a particular area. Satellites are limited with revisit-frequency as they are in orbit.
Whereas, airborne and ground-based sensors can be applied more frequently and below
cloud cover (Hall et al., 2002). Spectral resolution is the number of discrete wavebands that
can be recorded on a pixel scale. The higher the spectral resolution, the more detailed the
image (Hall et al., 2002).
Satellite, airborne and ground-based sensors collect the same information, namely
delineating variability in the spectral reflectance characteristic of the surface being
examined (Campbell et al., 2007). This difference has the ability to provide information on
what exists at a given point in time, such as roads or a wheat crop. If the pixel size is
adequate the remotely sensed data can also show variations on a small scale, for instance
spatial variability within a sorghum field. The demand for accuracy in pixel size has pushed
the trend towards ground and airborne-based sensors, as these devices are closer to their
target and increase spatial resolution.
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The measurement of reflected solar radiation (known as ‘passive’ remote sensing)
has for many years been utilised to measure the biomass in vegetation canopies. A term
used in this context, one that covers both variation within plant biomass and the
vegetation’s photosynthetic potential is photosynthetically-active biomass (PAB) (Hall et al.,
2002). Remote sensing devices can be used to calculate both of these characteristics via the
detection of reflected radiation (reflectance) in appropriate wavebands, commonly within
the RED and NIR regions of the electromagnetic spectrum (Hall et al., 2002).
Sensors with the ability to collect reflection of red, green and near-infrared radiation
from a plant surface have enabled accurate estimation of plant biomass after taking into
consideration bare soil effects (Casanova et al., 1998). The use of remote sensing techniques
such as satellite imagery, airborne and tractor based sensors to collect many spectral
wavelengths has created various options in calculating plant development, stress and yield
estimates all of which can be derived from spectral reflectance (Labus et al., 2002).
The level of plant reflectance of solar radiation varies dramatically depending on the
plant species and its health at a given time. Any light that hits the leaf surface must either
be reflected, transmitted or absorbed which will vary depending on the plants water
content, plant tissue size, pigment, physical structure, air-cell interfaces and any freezing or
thawing of the plant tissue (Woolley, 1971). The main reason why little reflectance occurs
within the visible light range is because of plant pigments that absorb visible light. Leaves
with necrosis or low pigment levels will tend to emit greater levels of both visible light and
NIR light. This is of interest as chlorophyll is a green pigment has a direct relationship to the
photosynthetic activity of a plant (Woolley, 1971). Although the chlorophyll content of a leaf
will affect visible light reflectance it has no effect on NIR light.
As NIR spectra is completely transparent to chlorophyll molecules, it is believed that
the main reflection cause is a result of water with-in the leaves (Knipling, 1970). Knipling
further states that the likely cause of reflectance of NIR light is due to the internal cellular
structure of the leaf and the outer layer of the plant. The cuticle and epidermis do cause
some scattering of light although they are mostly transparent to NIR (1970). The actual
reflection occurs in the spongy mesophyll tissue and air cavities within the leaf and is
therefore closely related to both leaf air and water content (Knipling, 1970). Only a very
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small percentage of NIR light is absorbed internally, up to 60% is scattered through the top
or bottom of the leaf surface (Campbell et al., 2007).
The reflectance of visible light is quite different to that observed in NIR as the
wavebands behave differently on leaf impact. Both red and blue wavelengths emitted from
the sun are absorbed by the leaf chlorophyll to be further used in the plants photo-systems
to excite electrons and create plant energy. While the green wavelength is predominantly
reflected from the leaf surface giving actively growing plants their green appearance
(Trotter et al., 2010). In calculating crop biomass a common method used is Normalized
Difference Vegetation Index (NDVI), which involves analysing the difference between two
spectral wavelengths (Labus et al., 2002) . These wavelength are positioned in the red (red:
630-680nm) and near-infrared (NIR: 770-1500) region of the spectrum and the formula used
to calculate NDVI = (NIR – RED/NIR + RED) (Rouse et al., 1973). NDVI and similar band ratios
have shown to be highly correlated with plant canopy stress, vigour, biomass and therefore
photosynthetic activity within a crop canopy (Labus et al., 2002).
The relationship between NDVI and photo-synthetically active biomass is well
documented, with a number of different vegetation types being accurately examined using
this technique. To date it is the most widely used indicator in estimating plant biomass
(Pettorelli et al., 2005). Calculating vegetation NDVI values on a temporal scale gives the
ability to monitor variations in both short and long term intervals. Short term includes
changes throughout the plants life cycles or seasonal patterns, while extended periods of
observation can show changes in structure, phenological and biophysical parameters of
vegetation (Tittebrand et al., 2009). Given the NDVI formula, the NDVI value is quantified by
a ratio from -1 to 1. Areas of observation with little to no vegetation reflectance will show a
low ratio value while areas of vegetation that are highly photo-synthetically active will show
a value closer to 1 (Hall et al., 2002).
Some issues that must be considered when using NDVI include non-linearity of ratio-
based indices, saturation in high photo-synthetically active vegetation and high sensitivity to
rapid changes in canopy backgrounds (Tittebrand et al., 2009). Other issues arise when
using different remote sensing techniques, from satellite sensors to active optical sensors.
These issues will be discussed further below.
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The relationship between a single leaf reflectance and an entire crop canopy
reflectance in quite different due to variations in leaf position and overlay. Other issues that
contribute to the change in reflectance include shadowing and variations in exposed ground
cover (Campbell et al., 2007). The major advantage when using NIR light for biomass
calculation is that it is readily transmitted and reflected from the leaf surface. Campbell et
al., (2007) suggests that as much as 50-60% of emitted NIR light is re- transmitted
throughout the crop canopy enabling further reflection from leaves within the lower
sections (Campbell et al., 2007). The end result is a brighter NIR reflectance that is receiving
data from both the canopy surface as well as vegetation within the canopy. One issue that
must be considered in agricultural remote sensing is the NIR reflectance that occurs from
soil. To account for this unwanted reflectance mathematical models have been developed
and are a vital tool in ensuring accurate results when measuring canopy biomass (Campbell
et al., 2007).
2.4.2 Satellite Remote Sensing
The use of satellites is a conventional and feasible method to observe and assess
changing global conditions, including both the earth’s surface and atmospheric conditions
(Trishchenko et al., 2002). Sensors onboard a satellites used to capture these changes
include photographic equipment, television cameras, scanning radiometers and image
radars, which all gather information on reflected and emitted electromagnetic radiation
(Trishchenko et al., 2002). The most commonly used sensor for agricultural research
purposes in relation to biomass calculation is the Advanced Very High Resolution
Radiometer (AVHRR). This technology has allowed images to be captured using a wide range
of wavelengths allowing the collection of NDVI on a broad scale (Trishchenko et al., 2002).
One of the major advantages in using satellite sensor imagery to calculate NDVI is that the
satellite is constantly orbiting the earth’s surface, which allows for holistic biomass
estimation updates with high revisit frequency. This data then creates the potential to
evaluate how a particular crop is changing throughout its growth cycle, without physically
collecting the information.
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Although temporally convenient, with satellite imagery comes some limitations, one
of which is spatial resolution. The technology is rapidly improving although currently the
pixel size is limited to 20-30 metres squared for commercial purposes (Ozesmi and Bauer,
2002). This is adequate for large scale cropping systems although it limits data information
quality and accuracy within smaller paddocks. Additionally there can be issues with satellite
data caused by changing ground and atmospheric conditions such as cloud cover, deviation
from the satellites original orbital path, sensor calibration issues as well as digital
quantization errors (Pettorelli et al., 2005). Studies undertaken by Huete et al., (2002)
analysed the performance of the MODIS-NDVI satellite sensor, highlighting atmospheric
water vapour content lowered the NIR response accuracy. This study further concluded
that the majority of noise associated with satellite imagery is caused by the sensor view
angle effects and to a lesser extent from aerosol contamination (Huete et al., 2002).
2.4.3 Airborne Remote Sensing
Similar to the satellite sensors, the airborne based sensors detect reflected light
from the earth’s surface. The resolution required to detect spatial variation will depend on
the nature of the change, whether it is a discrete patterns or continuous gradual variation.
The continuous change in vegetation will require significantly higher spatial resolution to
account for smaller units of deviation throughout a paddock (Lamb, 2000). This is where the
airborne sensors close proximity allows for increased resolution and a more accurate
examination of sub-paddock scale variation. Another advantage of the airborne sensor is
that the aircraft can be flown any time excluding harsh weather conditions, unlike satellites
which are completely controlled by satellite orbit. Aeroplanes also have the ability to fly
under cloud where satellite vision would be obscured.
A case study by Spackman et al. (2002) showed the performance of airborne high
resolution images in relation to NDVI calculation (Spackman et al., 2000). Images were taken
over a rice field at 1400m in altitude at four different stages during the plant’s life cycles
which gave a 1m image resolution. The NDVI results calculated from the spatial images were
then calibrated with field data to estimate the following R2 values for each. The mid-tillering
stage R2=0.73 which was the highest, panicle initiation R2=0.50, flowering showed the
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lowest with a R2=0.19 and pre-harvest R2=0.45 (Spackman et al., 2000). From the study it
was concluded that metre-resolution airborne multispectral images could be effectively
used to estimate crop biomass in rice during early stages of growth and late maturity. These
estimates were shown to be less accurate during flowering (Spackman et al., 2000).
2.4.4 Active Optical Reflectance Sensors
Continual advances in active optical sensors and light-emitting diodes (LEDs) have
led to the development of compact sensing devices which can be used to estimate NDVI.
Active sensors have their own light source which is used to illuminate the target. The scatter
and reflectance of this emitted light is collected via onboard sensors (Trotter et al., 2010).
Limitations of the sensors light emitting intensity have only enabled use close to the target
surface. Most sensors currently used are limited to hand held and boom mounted devices
that can be moved throughout a paddock via a vehicle (Trotter et al., 2010). More recent
studies (Lamb et al., 2009) have shown that these sensors can be mounted on a low-level
aircraft and record PAB accurately at distances ranging from metres (Lamb et al., 2009) to
tens of metres (Lamb et al., 2011) above the targeted canopy. One benefit of the LED based
sensor is its low production costs. The current trend in both laser for visual displays and
optical fibre for communication has seen an increase in technological advances within this
field, with future increased accessibility and reduced sensor cost (Trotter et al., 2010).
2.4.5 Ground-Based Active Proximal Sensors
Two of the most commonly used, active, proximal plant canopy sensors include
Greenseeker® (NTech Industries, Ukiah, CA) and CropCircle® (Holland Scientific, Lincoln, NE),
both of which contain their own light source in Red (650 nm) and NIR (770 nm) bands. These
are then used to accurately estimate NDVI (Solari et al., 2008). All of the methods
mentioned to estimate plant biomass, are based around detecting VIS and NIR wave bands
from their target to estimate NDVI.
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CropCircle® creates a NVDI value by collecting plant light reflectance in two
wavebands, one in the visible spectrum of 590±5.5 nm and the other within the NIR 880±10
nm (Hong et al., 2007). The visible light band can be selected 530-600 nm as the chlorophyll
reflectance value remains reasonably constant throughout this range (Hong et al., 2007).
One issue that may prevent using visible reflectance values from the green bands is the
possibility of saturation at high biomass levels. In this case red or yellow would provide
better results. The sensor uses a bank of polychromatic diodes to produce its own active
light source in the form of modulated light beams. These diodes are projected with a field of
view of 320 by 60 (Hong et al., 2007). Detecting the reflected light is possible via two banks of
silicon photodiodes positioned on the senor head. These sensors accept a spectral range of
light from 320 to 1100 nm, the unwanted light is filtered out leaving only the two desired
wavebands (Hong et al., 2007).
There are a number of advantages when using active proximal sensors in conjunction
with DGPS technology to map biomass variation. The sensors are relatively inexpensive,
although additional costs will occur when purchasing GPS technology for spatial accuracy
(Lamb et al., 2011). Another advantage in comparison to satellite derived data is that ratio-
based spectral indices values do not change at different altitudes from the crop canopy,
which means the data received will contain absolute values and immediate data use is
possible (Lamb et al., 2011). Finally, the active sensor enables data collection during adverse
weather conditions, such as heavy cloud cover, variations in light concentration and at night
(Lamb et al., 2011).
A study by Hong (2007) analysed NDVI and RED/NIR over several vegetation indices
and correlated their relationship with dry weight of corn leaves, using both the
GreenSeeker® and CropCircle® sensors (Hong et al., 2007). Results indicated that both
CropCircle® and GreenSeeker® NDVI values were highly correlated with dry weight samples
at the V6-7 growth stage. The GreenSeeker® was used alone in the V8-9 stage and during
flowering, showing R2 values of -0.63 and 0.81 respectively when comparing with NDVI
values (Hong et al., 2007).
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Table 1: NDVI and Red/NIR correlation between dry weight in corn under three different growth stages using GreenSeeker and CropCircle sensors.
Measurements Index V6-7 stage V8-9 stage Flowering
GreenSeeker NDVI 0.79 0.63 0.81
GreenSeeker Red/NIR -0.76 -0.64 -0.79
CropCircle NDVI 0.78 CropCircle Red/NIR -0.78 CropCircle GNDVI 0.82
Significant at the 0.01 level. (Hong et al., 2007)
Issues associated with ground-based active systems in comparison to airborne
sensors include the requirement of the sensor to be moved throughout the paddock via a
vehicle. This procedure becomes a problem when the crop exceeds the height of the vehicle
being used or if control traffic farming systems are not employed (Lamb et al., 2009).
2.4.6 Ultra low-level Airborne (ULLA) Sensors
Active spatial sensors have the ability to gather the same information at high speeds
and over much larger areas when attached to low flying aircraft. Due to the power of the
LED light source used, the aircraft has to be flown at 3-5m above the canopy in order for the
receiver to pick up the transmitted light (Lamb et al., 2009). Although the low altitude
precludes operation over heavily undulating country, it is highly effective in flat cropping
areas when, for example used in conjunction with existing dusting applications. Resent
advances in the strength of the LEDs has shown positive results in obtaining data at higher
altitudes with very similar accuracy (Lamb et al., 2011). The altitudes flown ranged from 15-
45m from the canopy surface with height-related sensor deviations so small (in comparison
to the ground based sensors), they had no significant impact on the NDVI biomass
estimation (Lamb et al., 2009).
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2.5 Importance of Differential Global Positioning Systems
Global positioning systems (GPS) have enabled very precise navigation technologies
to be incorporated into agricultural practices, increasing both efficiency and production. The
GPS provides three dimensional data which incorporates time, latitude, longitude and
elevation (Proffit, 2006). This is possible via satellites in space that are constantly orbiting
the earth. Each satellite repeatedly broadcasts information on the satellites orbit using an
atomic (caesium vapour) clock which has a universal time standard (Cox, 2002). With this
information the satellites position can be calculated by the GPS receiver. The information
when triangulated with a number of orbiting satellites provides data on the receiver’s
position on earth. The major agricultural limitation of non-differential GPS is their accuracy.
In Australia they are only accurate to ± 6 metres, 90% of the time (Proffit, 2006).
Improvements in the accuracy of GPS have led to the introduction of differential
global positioning systems (DGPS). The major difference is that DGPS needs a receiving
station that is situated at a known location (Cox, 2002). The stationary receiver corrects a
certain proportion of the satellite error or noise and this information is then sent to the GPS
being used in the field. The closer in proximity the two receivers are, the more accurate the
correction is as both receivers are collecting very similar satellite transmissions. Three
dimensions real-time kinematic differential global positioning systems (RTK DGPS) works in
a similar fashion the only difference being that the base station must be in close proximity
(kilometres) providing accuracy down to 2.5cm (Proffit, 2006).
There are two GPS systems that are predominantly used in conjunction with the
mobile EM units, these include self-contained systems and stand-alone GPS receivers
(Corwin and Lesch, 2005b). The difference between the two is that stand-alone systems
have to be connected to an external processor in order to store data logger points, whereas
the self contained systems have inbuilt data loggers as well as the ability to process and edit
the data (Corwin and Lesch, 2005b). The two most commonly-used, commercially-available
GPS systems include the Trimble Pathfinder and Ag132 (Corwin and Lesch, 2005b).
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2.6 Combining EMI Soil Moisture and Active Optical Canopy Sensing to Infer
Water Use Efficiency
As mentioned previously the aim of this project is to estimate water use efficiency
through the use of precision agricultural tools discussed throughout this brief review. The
most effective method of measuring soil water content on both a spatial and temporal scale
is the use of non-invasive sensors that measure electrical conductivity (EM-38). This sensor
is the only viable instrument that maybe used to estimates the soil water content without
physically penetrating the soil. The other methods mentioned such as the neutron probe,
time domain reflectance and the tensiometer all require contact with the soil which
dramatically slows the sampling process down and limits the ability to sample numerous
points throughout a paddock over a small timeframe. The performance characteristics of
the EM-38 whereby ECa information is recorded within the root zone of sorghum, which
typically grows to a depth of 98cm (Blum and Ritchie, 1984), is another key reason behind its
selection for this project. Using the EM-38 device in conjunction with DGPS technology will
hopefully provide an effective means to estimate soil water content fluctuations as a result
of rainfall, leaching and evapotranspiration.
To be able to analysis how well the sorghum plant is utilising the available soil
water, biomass estimations are also necessary. It is likely that the best spatial method of
determining plant biomass changes would be through calculating NDVI indices. There are a
number of means in which this information can be gathered, such as satellite imagery,
airborne sensors and active optical sensors. After taking into account cost, time and
paddock area it was decided the most viable method was to mount the active optical sensor
(CropCircle®) to an ATV and run tramlines through the sorghum paddock at different time
intervals to estimate biomass content.
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Chapter 3: Material and Methods
3.1 Location and Crop
Forage sorghum (Sorghum bicolor x S. Sudanense var. sweet jumbo) was sown at
1.5kg per ha over a 30m x 150m site at the University of New England (UNE), Armidale on
their northern property ‘Clarks Farm’, (latitude -30.48 S, longitude 151.65 E). The soil type is
a black, self-mulching vertosol varying in clay content down the slope, descending to the
south by 1-2m. Sorghum was selected to remove water rapidly from the soil and produce
large amounts of biomass. This location, with reasonable variations in elevation and soil
type, was selected to give a range of biomass production and soil water use that would in-
turn be expected to generate a range of water use efficiencies for the survey.
3.2 Experimental Design
Figure 1 displays the site set out with 4m tramlines for surveys from an ATV carrying
EM38 and NDVI sensors. The sorghum rows were spaced at 25cm, north to south. The ATV
passed every 4m making six complete passes running in a north-south direction on each
survey date.
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Figure 2: Trial design. The points show the volumetric moisture content ground truthing sites and the lines illustrate the ATV tramlines.
Twelve points were selected throughout the block to collect volumetric water
content for calibrating the sensor to actual soil water content. The ECa values from an initial
EM38 site survey were segregated in 12 zones (highest to lowest interval) using the Vesper®
(Minasny et al., 2002) kriging program and ARCmap (ESRI GIS, version 9.3.1). This made sure
that the full range of ECa zones were sampled. One point was selected at random from each
zone for volumetric water content and ECa (EM38) and direct electrical conductivity
measures (EC). These 12 points are marked as blue circles in Figure 2.
The data points collected by the EM38 sensor during the ATV survey were uploaded
onto a computer in the form of dbf files. These files were then converted to shape-files for
data cleaning, where extraneous points are removed that simply represent areas where the
vehicle was stationary or reduced speed. The cleaned shape-files were used as input for the
Vesper® kriging program which created raster files. Once kriged, the raster files were
manipulated with ARCmap (again if not done before) software to generate 12 different ECa
zones. These 12 points were marked throughout the plot to locate sampling sites, to be
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subsequently located in the plot by GPS (Trimble® ProXRS Receiver coupled to a TSCe data
logger, California USA).
3.3 Soil Water Content
3.3.1 Electrical Conductivity (EC)
On each sampling date soil cores were taken from a depth of 5-85cm at each of the
12 calibration sites. The cores were divided into 10cm segments, after discarding the top
5cm. Segments 15-25cm and 35-45cm were used to determine soil EC. Once placed in an
aluminium drying container (45mm dia. X 120mm length) the soil samples were dried at
40°C for 10 days. The dry cores were then broken up using a soil grinder into fragments
smaller then 5mm in diameter. From every sample 10g of soil and 50g of distilled water was
added into an 80ml beaker (1:5 soil to water ratio). This solution was then mixed using a
stainless steel spatula until homogenous and allowed to settle for 60 seconds, before
immersing the EC probe (Beta-81 Conductivity Meter, CHK Engineering).
3.3.2 Volumetric Water Content (VMC)
The same cores but different segments were used for the EC readings were used to
estimate VMC. VMC was determined for 10cm segments from depths of 5-15, 25-35, 45-55,
55-65, 65-75 and 75-85cm. Each segment was sealed in an aluminium drying container
(45mm dia. X 120mm). Wet weight was recorded before removing the lids and placing in an
oven for drying at 110°C for 7 days. The difference between the wet and dry weight was
determined to be the water content of each sample. The soil core volume and dry weight
was also used to calculate soil bulk density.
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3.3.3 Electron Magnetic Induction 38 (EM38)
An (Geonics Limited., Ontario EM38) sensor was used to estimate VMC on a spatial
scale over the plot. The data collection procedure is illustrated in figure 3.The sensor was
towed by a four wheel ATV through the plot along the tramlines shown in Figure 2. Before
data collection the EM38 was allowed to adjust to its surrounding temperature and was
then calibrated, as directed by the manufactures operation manual. Further information on
the calibration procedure can be shown by (McNeill, 1986). The EM38 data points were
collected every second and referenced to a location point determined by a Trimble DGPS
carried by the ATV at the same time. The ATV moved throughout the plot at 10km/h at a
constant speed to provide a stream of records with approximately the same distance
between each data point. One survey was carried out with the EM38 on its side,
immediately after that the EM38 was placed in the vertical dipole position and the survey
transects repeated. Logged survey data was then uploaded onto a computer in a dbf file.
After data cleaning, Vesper® was used to krig (interpolate) data for the areas between the
data collection points to create raster cells. The ECa and VMC records from the single site
measures were then used to generate a calibration curve that was used to interpret the ECa
survey readings into a map of soil moisture.
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EM-38
12v Power Supply
GPS Screen GPS Computer
GPS Satellite Receiver Computer Analysis
Figure 3: EM38, data collection procedure
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3.4 Sorghum Biomass
3.4.1 Normalised Difference Vegetation Index (NDVI)
A ‘Red head’ CropCircle® ACS-210 sensor (Holland Scientific Inc., Lincoln, NE, USA)
was used to estimate crop biomass on a spatial scale. This active sensor produced a signal in
the 650-880nm wavebands. The methods as described in the EM38 data collection process
(section 2.2.3) were used for surveying from the ATV however the CropCircle® was mounted
on an arm that held the sensor directly over the sorghum row. Data was collected with the
GeoScout® GLS-400 (Holland Scientific Inc., Lincoln, NE, USA) data logger. The data was
processed via Vesper® Krig software to produce the geospatial maps of NDVI. The data
collection procedure is shown in figure 4.
3.4.2 Biomass Cuts
Plant biomass cuts were collected from 10 points throughout the plot. To ensure the
full range of biomass production was represented, the sampling sites were again randomly
selected but from zones already determined by that days survey. Once the CropCircle® data
was kriged the results were divided into 10 segments, varying in biomass concentration.
Points within each of the 10 zones were selected randomly on ArcMap®. These points were
then uploaded to a differential global positioning system (DGPS) for location within the plot.
At each of the 10 sites, sorghum plants were cut at ground level form a quadrant (40cm X
40cm) and NDVI reading were recorded for the same area. The samples were placed in large
paper bags (50cm X 75cm) for oven drying at 40°C for 5 days. A polynomial calibration
equation was then used to relate biomass to NDVI.
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CropCircle® Sensor
GPS Screen
GPS Receiver
Computer Analysis
GeoSCOUT® Data Logger
12v Power Supply (GPS)
GPS Computer
12v Power Supply (GeoSCOUT®)
Figure 4: CropCircle, data collection procedure
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3.4.3 Data Analysis: Bootstrapping technique
To assess the accuracy and repeatability of the surrogate results obtained (ECa and
NDVI), the bootstrapping statistical analysis method was used. This method involved
randomly dividing the surrogate measure sample points (ECa or NDVI) into two equal
groups. One half of the group was used to create a calibration equation and the other half
was the validation data set. The calibration curve created from the first half of data points
was applied to the second half (validation) of data points. The root mean squared error
prediction (RMSEP) for each of the validation data points was then calculated. An average
error value of all RMSEP results was obtained which provided an indication of surrogate
accuracy and repeatability.
3.5 Estimating Water Use Efficiency
The calibration curves, determined from the point measures of VMC to ECa and
biomass cuts to NDVI were then applied to the spatial survey data. This converted the ECa
(EM38) survey data into a spatial measure of VMC and the NDVI (CropCircle®) survey data
into a spatial measure of crop biomass. These surrogates were then combined: water use
over biomass production, that is ECa (calibrated to VMC)/NDVI (calibrated to biomass cuts)
to produce the ratio of water use efficiency (WUE) on a spatial scale. Note that only one
period of WUE could be calculated as rainfall obscured crop water use during the remaining
sampling intervals.
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Chapter 4: Results
4.1 Introduction
This chapter is broken up into three major sections, the first being the biomass
calculation using a CropCircle® sensor. The second section focuses on using apparent
electrical conductivity (ECa) to estimate soil water content and the final section combines
these two surrogate measures to estimate water use efficiency (WUE) on a spatial scale.
4.2 Biomass Calculation
This section presents the correlation between NDVI and plant biomass. The
relationship differs before and after anthesis so the data has been placed on separate
graphs to illustrate this change (Figure 5 and 6). Figure 5 shows a good correlation between
NDVI and biomass accross the first three sampling dates (10th to 28th Feb) before anthesis.
The NDVI ratio accurately predicts plant biomass with a polynomial trend line, the R2 = 0.85
and the RMSEP = 7.47g/m2.
Figure 5: NDVI to biomass correlation before anthesis.
R² = 0.85
0.22
0.32
0.42
0.52
0.62
0.72
0.82
0.92
0 10 20 30 40 50 60 70 80
ND
VI (
Cro
pC
ircl
e)
Plant Biomass (g/0.4m2)
10/02/2011 17/02/2011 28/02/2011
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After anthesis the trend line changed particularly during the final sampling date
(figure 6). While still maintaining a strong correlation (R2 = 0.88), the polynomial curve for
the 30th Feb, shows that NDVI begins to decrease when plant biomass exceeds 350g/0.4m2.
The final sampling shows a poor correlation with NDVI (R2 = 0.33). Biomass increased but
the NDVI reduced considerably even for biomass levels lower than on previous sampling
dates.
Figure 6: NDVI to biomass correlation after anthesis.
4.2.1 Calibration Equation
The following calibration equations were extracted from the trend lines in figures 5
and 6. The only equation used to generate a water use efficiency map was the first one
from the 10-28th February because rainfall occurred to obscure water use on the other
sampling dates.
R² = 0.876
R² = 0.3266
0.5
0.55
0.6
0.65
0.7
0.75
0 200 400 600 800
ND
VI(
Cro
pC
ircl
e)
Plant Biomass (g/0.4m2)
30/03/2011 12/04/2011
44 | P a g e
Table 2: Calibration equations used to calibrate NDVI values to sorghum biomass during different growth stages.
Sampling Dates Calibration Equation
1 (10-2-11, 18-2-11 and 28-2-11) Growth Stage 3-5 Biomass = 0.3756*EXP (6.6518(NDVI))
2 (30-3-11) Growth Stage 5 Biomass = 0.1692*EXP (10.262(NDVI))
3 (12-4-11) Growth Stage 6 Biomass = 9.6111*EXP (5.7511(NDVI))
The experiment repeatability of the first three sampling dates was analysed using
bootstrapping statistical analysis, Figure 7. The points lie within close proximity to the line
(R2 = 0.88 and RMSEP of 7.47g/0.4m2).
Figure 7: Bootstrapping analysis for the calibration curve between calibrated NDVI and actual biomass. The regression line indicates a linear relationship between the biomass determined via NDVI and the actual measured biomass.
This calibration equation using all data points was then applied to the spatial NDVI
values to obtain estimates of biomass for all points on the CropCircle® survey. Two maps of
plant biomass were produced, the first on the 17th Feb (Figure 8) and the second on the 28th
Feb (Figure 9). Spatial variation in biomass is evident in both maps with high biomass
illustrated by dark blue areas and the low yellow. Figure 8 shows poor biomass production
R² = 0.88 0
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80
Cal
ibra
ted
ND
VI
Val
ue
Actual Biomass
45 | P a g e
on the top left hand side of the map (to the north-west), and vigorous growth occurring
throughout the bottom lower half. The greatest biomass occurred in a circular pattern
towards the top of the map.
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Figure 8: Spatial representation of biomass determined by NDVI for the 17th Feb 2011.
Both figures 8 and 9 show similar patterns with poor yields at the northern and
southern most ends of the plot. Limited biomass content can also be seen in regions on the
eastern side.
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Figure 9: Spatial representation of biomass determined by NDVI for the 28th Feb 2011.
The bulk of the biomass increase occurred throughout the western side of the site.
Limitations to growth over this period can be seen at the northern and southern most ends
of the site as well as the central regions of the eastern side. Biomass production overall
(Figure 8 or 9) appears to match the change in biomass (Figure 10).
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Figure 10: Illustrates biomass change between the two sampling date 17-25th Feb.
The western side shows a marked increase in plant growth while the northern and southern
ends highlight growth limitations.
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4.3 Soil Water Calculation
Correlations between soil volumetric water content (VMC) and soil electrical
conductivity (ECa) using an EM38 sensor in both the vertical and horizontal dipole where
correlations were analysed. ECa responds in a linear fashion to (VMC) in the vertical dipole
with an R2 =0.64 (Figure 11).
Figure 11: Linear correlation between volumetric moisture content (VMC) and point specific EM38 (ECa) readings in vertical dipole mode for each sampling date. The regression line indicates a single relationship for all data.
The correlation between ECa and VMC in the horizontal dipole showed two different
response trends (Figure 12). These individual trends have been illustrated by the blue and
red rings. The reason behind this variation in ECa response could not be easily accounted for
so the data from the horizontal dipole was not used in the final water use efficiency
calculation.
R² = 0.64
60
80
100
120
140
160
180
200
0.46 0.48 0.5 0.52 0.54 0.56
EM3
8 (
ECa)
VMC
3/02/2011
10/02/2011
18/02/2011
28/02/2011
30/03/2011
12/04/2011
50 | P a g e
Figure 12: Correlation between volumetric moisture content and point specific EM38 ECa readings in horizontal dipole mode for each sampling date. Regression lines are presented for each sampling date. Circles indicate the marked difference between regressions.
4.3.1 Soil Water Calibration Equation
The calibration equation VMC = (ECa + 379.99)/974.28, from the trend line in Figure
11 was applied to the surveyed ECa spatial data. This produced the VMC maps shown in
Figures 14 and 15. To determine the repeatability of ECa to predict soil moisture
bootstrapping statistical analysis was used (Figure 13). The RMSEP was 0.01m3/m3 VMC.
This is equivalent to a 1% error in soil moisture estimation between the ECa prediction and
the VMC.
60
80
100
120
140
160
180
0.452 0.472 0.492 0.512 0.532 0.552 0.572
EM-3
8 E
c
VMC
3/02/2011
10/02/2011
18/02/2011
28/02/2011
30/03/2011
12/04/2011
51 | P a g e
Figure 13: Representation of bootstrapping analysis of the volumetric moisture content (VMC) determined via ECa (EM38) and actual VMC. The regression shows a linear relationship between the actual and predicted VMC.
R² = 0.70
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.45 0.47 0.49 0.51 0.53 0.55 0.57 0.59
EM3
8 P
red
icte
d V
MC
Actual VMC
52 | P a g e
Figure 14: Spatial representation of VMC variability determined by ECa on the 17th Feb 2011.
Figure 14 shows low VMC throughout the northern regions of the paddock (47% VMC). The
central and southern regions illustrate an increase in VMC, the highest point is 55% soil
moisture at the southern most end.
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Figure 15: Spatial representation of VMC variability determined by ECa on the 28th Feb 2011.
Figure 15 shows a very similar distribution of soil moisture to figure 14. As there was
no rainfall between these sampling dates it is assumed that the major difference in soil
water content has been lost via the crop. The highest VMC values have dropped by 4% in
comparison to values in figure 14, suggesting a drying out period.
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Figure 16: Illustrates the drying out period between the 17-28th Feb 2011.
The dark red regions show the most soil water loss between sampling dates. Overall
the loss is reasonably constant only varying from 2.5-5% VMC. The most apparent and
extreme VMC change has occurred throughout the top third of the paddock.
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4.4 Water Use Efficiency
A water use efficiency (WUE) map (Figure 17) was created using change in biomass
and soil VMC for each of the pixels surveyed across the sites between the 17-28th Feb. The
map illustrates areas of high WUE with blue regions, yellow and orange for intermediate
and red for lower performing areas. There are distinct regions of differing WUE. The
northern and southern points of the paddock show poor WUE values, 81. The western edge
has the highest biomass increase. The greatest WUE determined in this region was 2296.
The water use efficiency map (Figure 17) resembles the biomass production maps (Figs. 9
and 10) more clearly than the change in soil water content (Figure 16).
Figure 17: Illustrates the regions of varying WUE throughout the surveyed paddock.
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Chapter 5: Discussion
5.1 Calibration of Biomass to NDVI
The first process in measuring water use efficiency on a spatial scale was proving
that Normalised Difference Vegetation Index (NDVI) is a good predictor of plant biomass.
Previous research has shown calibration curves can be created for crops and pastures
(Solari, 2006; Trotter et al., 2010). The first section of the experiment reproduces this by
correlating CropCircle® sensors NDVI values with sorghum biomass levels at ‘Clarks Farm’.
Combing the first three sampling dates produced a polynomial calibration that related NDVI
to biomass with an R2 = 0.85. Despite this strong relationship further analysis suggested that
the trend deteriorated as the sorghum neared maturity. Although the relationship was very
good before anthesis two separate calibration equations were necessary from the 30th
March (R2= 0.88). The final date did not produce a useful calibration (R2 = 0.33). The
relationship between NDVI and sorghum biomass becomes disjointed after the onset of
senescing leaf materials. As the NDVI registers photosynthetically active chlorophyll content
any change in leaf senescence will alter the red light reflectance. This will influence the NDVI
ratio, thus changing the value. This has been previously observed by Mutanga & Skidmore
(2004) in dense vegetation. The first three sampling date’s calibration is used to estimate
sorghum water use efficiency in section three.
The prediction power of the calibration curves statistically analysed by determining
the root mean squared error prediction (RMSEP). The RMSEP value was 7.47g/0.4m2, before
senescence which is equivalent to an accuracy of 168kg/ha or a 9% average error. This
average error prediction is impressive when compared to other biomass estimation
techniques, suggesting NDVI in an accurate measurement tool prior to anthesis. The final
sampling date (after anthesis) gave a much poorer calibration, (RMSEP = 165.18g/0.4m2)
equivalent to 4127.5kg/ha or a 20% error. This poor correlation after senescence has been
seen before in rice (Spackman et al., 2000).
The poor correlation beyond growth stage 6 could be explained by the CropCircles®
inability to penetrate the leaf canopy a high densities. The sensor does not emit a light
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beam strong enough once the canopy is denser than 5-6 layers of leaves (Solari, 2006). This
could explain the poor correlation in the last sampling date 14th April. Dry biomass beyond
500g/40cm2 can result in reduced near infra-red canopy penetration.
The lower correlation between NDVI and dry weight biomass during the plants
reproductive phase could be attributed to RED light saturation. As senescence begins the
levels of RED light absorbed by photo-synthetically active plant biomass (PAB) decreases
which increases RED reflectance. This increased reflectance will change the NDVI ration
(NDVI = NIR+RED/NIR-RED) lowering the values. The NDVI will drop while the sorghum
biomass is unchanged. Although not tried in this project CropCircle® manufacture a sensor
that targets the yellow spectrum in replacement of the red, this might provide a better
correlation after anthesis.
Trotter & Frazier (2008) demonstrated that R2 ~ 0.94 is achievable in sorghum
cropping systems. They emphasised the importance of matching the plant samples to the
actual optical measurement location, which improves calibration reliability (Trotter &
Frasier, 2008). In my project the CropCircle® calibration sites were measured using a hand
held sensor in a stationary position directly over sorghum plants within a row. This
minimises the possibility of soil reflectance and absorbance effecting NDVI. Once the
CropCircle® is mounted on the ATV to provide spatial readings along transects it proves
difficult to keep the sensor positioned directly over rows at all times. Thus the hand held
sensor is more likely to be estimating biomass more efficiently than the mobile unit which is
also reflecting off bare soil. Irrespective of these potential problems the correlations
between NDVI values and physical biomass samples suggest that the CropCircle® sensor
might well be a useful surrogate for biomass in a water use efficiency calculation.
5.2 Calibration of Soil Moisture to Electro-Magnetic Induction
The use of Electro-Magnetic Induction (EM38) to infer soil moisture content is not as
well established as NDVI to PAB (Corwin & Lesch, 2005). Literature shows that apparent
conductivity (ECa, measured by an EM38) can correlate to soil water content in heavy clay
soils (Hossian, 2010). Although, no literature was found attempting to use ECa as a temporal
58 | P a g e
measurement of soil water change. The results in this project demonstrate that ECa
readings can correlate well with soil volumetric water content in a linear fashion. EM38
readings were taken in both the vertical and horizontal dipole with the best correlation
produced in the vertical, as a result this is the spatial means used to estimate soil volumetric
water content throughout the discussion.
The RMSEP value obtained (0.01) indicates that the calibration curve fits the data
well. The variation from the actual VMC was on average only 1%. So the results suggest the
EM38 readings are a good predictor of VMC. It must be mentioned that these RMSEP values
were calculated post kriging, which is likely to improve RMSEP.
In an attempt to further understand the content of the soil that may have been
altering EM38 readings, information on soil electrical conductivity (EC) was gathered.
Experimental results show that there was small fluctuation within EC reading between both
dates and locations within each date. Statistical analysis indicated that the between location
variation was not significant (0.34) and the between sampling dates was (0.047). Although
significant it was decided that adjusting EM38 results accordingly would not improve the
original correlation.
Sampling error is important to consider when correlating ECa to volumetric water
content VMC readings. During the selected time interval (no rainfall occurred) the lowest
and highest VMC recorded were 0.49 and 0.57 respectively. This indicates that the largest
fluctuation in VMC throughout the plot was only 8%. To accurately record such small
changes in soil water it is vital that sampling error is minimised. The soil cores used to
calibrate the ECa readings were cut into 10cm segments using a knife and a basic measuring
scale. Assuming the core volume is accurate (42mm dia. and 10cm lengths) it would be
relatively easy to create error in readings if the cut was not done accurately. Future research
in this area could explore a method of using a soil cutting device that increases accuracy and
reduces human error.
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5.3 Spatial Water Use Efficiency
The previous two sections have demonstrated that soil water content and plant
biomass can be estimated rapidly on a spatial scale with non-invasive sensors. By combining
soil water loss and plant biomass growth over a specified period, change in plant WUE
throughout a paddock can be illustrated. During this experiment only one time window
without rainfall disturbance could be used to create a WUE map. This window enabled
spatial interpretation of WUE, exhibiting clear difference in WUE throughout the paddock.
The accuracy in which NDVI to biomass and ECa related to soil water gave confidence to the
reliability of the WUE map. The following discussion will attempt to explain the spatial zone
differences in WUE.
In an attempt to estimate if WUE changed spatially across the paddock the change in
plant biomass was divided by the change in soil moisture and a map was created accordingly
(Figure 17). Results clearly show that some regions within the paddock utilised the soil
moisture efficiently, others areas show large losses in soil moisture with small amounts of
plant growth. This implies that WUE will change not just from one season to the next but
spatially across a field within seasons. The change in some locations is greater than 30 times
more plant growth to the same amount of soil water utilised.
Poor WUE values obtained at the northern most end of the plot might be attributed
to poor soil texture and depth. This zone of the field was hard to penetrate during soil
coring below 50cm due to a rocky B horizon. Poor WUE values can be a factor of low soil
moisture availability, low biomass production or a combination of the two. The original
water content values at the northern end show a low water content, perhaps a poor
availability of soil nutrients which reduced water uptake. This horizon had a higher sand
content in comparison to the rest of the block, reducing water holding capacity and thus
water and nutrient retention.
The poor WUE values at the southern end of the plot are most likely a result of water
logging. The plot drains to this point which limited plant growth throughout the season.
Reduced WUE in this area maybe due to high evaporation with poor sorghum growth,
indicating water is being wasted in this region. The determining factor of WUE in this case is
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the rate of plant growth, the change in water content is unlikely to be efficient if the plant is
not productive.
There is no direct measure of WUE, so comparison to analyse the accuracy of my
spatial data is not possible. Exact biomass changes between sampling dates is not
achievable as the plant material is removed, so further explanation on small changes in WUE
become more speculative. The road that was once present on the eastern fence line may
have caused compaction, resulting in WUE variation throughout central regions of the plot,
Figure 17. If compacted, water infiltration, plant water availability and thus plant vigour
would be reduced, lowering WUE. Based on Figure 14 & 15 is can also be stated that water
maybe shedding off the compacted zone into the centre and western side of the plot,
increasing water accessibility and plant growth, subsequently increasing WUE. To validate
the interpretations future experiments should look at totally excluding water infiltration in
areas. Also limiting data collection in close proximity to paddock boundaries will reduce the
boarder effect as the outer few rows of the crop have access to additional water.
One factor that have a negatively influence the accuracy of the WUE map produced
is the interpolations process. The Vesper® Kriging program was used to interpolate both
plant biomass and soil water content between the spatial points gathered throughout the
plot. The actual values at each of these points is not known, they are simply the best
estimate from neighbouring points. The quality of the WUE map is directly related to the
accuracy of the calibration equations, the correlations used for calibration can result in
averaging particularly if only a small number of ground truthing samples are gathered. This
error will be minimised if a greater number of spatial data points are gathered within the
plot so the raster grid size can be minimised. Creating WUE maps on a larger scale with less
spatial variation will lower the averaging error, improving spatial representation of crop
growth and soil moisture. More realistic WUE results in larger paddocks using these
technologies have been shown by (Schneider, 2010).
Ground truthing links the actual measures to surrogate measures of the sensor that
estimate biomass and soil water. The estimates of WUE are only as accurate as the
calibration procedure used. The more truthing points the more accurate the surrogate
estimate will become. Only ten biomass cuts were used to calibrate the CropCircle®
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readings. This number could be increase to improve the calibration after anthesis, it must be
noted that ten was a sufficient number before anthesis and this was the period were WUE
was calculated. As the WUE ratio is reported from the same area of soil (raster), any error in
prediction of biomass or soil water will introduce error in that raster cell. The EM38 sensor
was towed inter-row while the NDVI values were measured directly over the row. The inter-
row moisture is a result of water removal from rows on either side. Poor growth habits on
the measured side maybe a result of plant vigour in the opposing row which will lower WUE.
This highlights the impact data interpolation can have, particularly throughout a small plot
where a few incorrect data points could greatly influence the maps appearance.
This project has demonstrated that WUE maps can be calculated using spatial non-
invasive sensors that measure biomass and soil water content. This provides farmers with
information on the cropping potential of their paddocks, illustrating underperforming areas
they may be managed differently as a result. Agronomist and farmers often have a broad
understanding of their current systems, additional information such as WUE on yield and
elevation maps provides quantitative evidence to further justify changes in management
practises.
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Chapter 6: Conclusion
This project was designed to determine if water use efficiency could be estimated on
a spatial scale by combining surveys of biomass determined using a CropCircle® (NDVI) and
surveys of soil water content via an EM38 (VMC). Water use efficiency is becoming
increasingly important, particularly as the demand from other sectors such as
environmental conservation and mining companies grow. Improving technologies are
providing farmers with the opportunity to manage zones throughout paddocks separately,
increasing water, nutrient and seeding application efficiencies. This experiment adds
another dimension in the understanding of paddock variation, proving that a water use
efficiency map can be calculated with spatial accuracy.
The CropCircle® sensor was shown to correlate well with sorghum biomass R2 = 0.85
before anthesis. In addition to providing data for a water use efficiency map this is a positive
result for a number of in-season management practises such as nitrogen application and
disease management. The EM38 sensor was also proven to be a good surrogate measure of
soil volumetric moisture content R2 = 0.64. This supports existing evidence that non-invasive
electro-magnetic induction devices can be used to infer soil moisture in heavy vertisol soils.
Combining the two procedures to estimate water use efficiency have created a rapid
spatially accurate tool that could markedly improve current management practises.
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