Applying PA - GRDC · histogram of this data. INTRODUCTION Understanding the causes and impact of variability FIGURE 1: An example of spatial data which includes a spatial map, data

Applying PAA reference guide for the modern practitioner

This book has been compiled by Precision Cropping Technologies Pty Ltd. Enabled by GRDC funding for the project Precision Agriculture - Building knowledge, linking agronomy, growers profiting. Practical training for practical outcomes (PCT00001).

Authors:Brooke Sauer, Precision Cropping Technologies Pty LtdTasha Wells, Site Specific Software SolutionsMichael Wells, Precision Cropping Technologies Pty LtdTim Neale, PrecisionAgriculture.com.auAndrew Smart, Precision Cropping Technologies Pty LtdWith contributions from Frank D’Emden, Precision Agronomics Australia Pty Ltd.

Cover photo courtesy John Deere Australia.

The authors acknowledge that some materials presented in this manual were sourced from:PA Training Modules for the Grains IndustryProduced by Brett Whelan and James Taylor, PA Lab (formerly Australian Centre for Precision Agriculture), University of Sydney for the Grains Research and Development Corporation 2010.

DISCLAIMERThis publication has been prepared in good faith on the basis of information available at the date of publication without any independent verification. The Grains Research and Development Corporation and Precision Cropping Technologies Pty Ltd does not guarantee or warrant the accuracy, reliability, completeness of currency of the information in this publication nor its usefulness in achieving any purpose.Readers are responsible for assessing the relevance and accuracy of the content of this publication. The Grains Research and Development Corporation or Precision Cropping Technologies Pty Ltd will not be liable for any loss, damage, cost or expense incurred or arising by reason of any person using or relying on the information in this publication.

ISBN 978-1-921779-59-6Published: November 2013

1

PA REFER

ENC

E POC

KET G

UID

E – 2013

1

PA REFER

ENC

E POC

KET G

UID

E – 2013

CONTENTSGlossary of terms 2Foreword 4

INTRODUCTIONUnderstanding the causes and impact of variability 5Data interpretation 7Data management 10Data exchange formats 11

SPATIAL AGRONOMY TOOLSPositioning 12Soil surveying • EM� 14 • Gamma�radiometrics� 16 • pH�detector� 19Crop sensing 21Yield mapping 24Elevation�surveying� 27

APPLICATIONS FOR SPATIAL AGRONOMY TOOLSSectional�control�to�improve�input�efficiencies� 30Soil compaction management 32Farm layout and design 34Soil�type�analysis�using�EM�surveys�� 35Identifying�subsoil�acidity�using�gamma�radiometric�and�EM�� 37Moisture�(capacitance)�probe�placement� 40Using�an�EM�survey�to�guide�gypsum�application�rates�� 42Using�EM�surveys�to�guide�the�placement�of�strip�trials�� 44Precision irrigation: Precision Ag for pivots 46Variable rate application of crop growth regulators using remote sensing 49Variable rate application of in-crop fertilisers using remote sensing 51Collection of biomass data using ‘on-the-go’ active sensors 53Creating a nutrient replacement map using yield data 55Surface water ponding impact analysis 57Water reservoir mapping 59

Useful resources 61

The world of PA is filled with acronyms and technical jargon. Some of the commonly used terms are compiled in the following glossary.Base station: A GPS receiver that is set up at a known location which is used collect information to

differentially correct nearby rover units. The base station calculates the error in the GPS signal and this is applied to the rover units to enhance the accuracy of their position.

Baud rate: A measure that describes how rapidly single digital elements are transmitted over a communication line.

CANBUS: An acronym for Controller Area Network. It is a method that enables communications between the electrical components within a vehicle. It contains hardware and software that is used to monitor functions of a vehicle.

CEC: Cation-exchange capacity (CEC) is the maximum quantity of total cations (of any class), that a soil is capable of holding and are freely available for exchange with the soil solution, at a given pH value.

Constellation: Refers to either the specific set of satellites used in calculating positions or all the satellites visible to a GPS receiver at one time.

Coordinate system: A reference system used to describe a unique location measured as a horizontal and vertical position.

Datum: A set of parameters and control points used to accurately define the three-dimensional shape of the earth. The datum is the basis for a planar coordinate system.

Differential correction: A network of base stations used to enhance the accuracy of GPS by removing or reducing errors associated with GPS.

EC: Electrical Conductivity is the ability of a material to conduct an electrical current. EC is often measured for soil, generating a soil EC map.

EM: Electromagnetic Induction. EM sensors are used to measure soil EC using a transmitter coil to induce a magnetic field into the soil and a receiver coil to measure the response.

ESP: The amount of sodium as a proportion of all cations in a soil is the main measure of sodicity and is termed the exchangeable sodium percentage (ESP).

Firmware: Software routines that are embedded as read-only memory (ROM) in a computer chip or hardware device to prevent modification of the routines.

Fix: A single position calculated by a GPS receiver with latitude, longitude, altitude, time and date.Geostationary satellite: A satellite which orbits at the same rate the earth rotates on its axis so

it effectively remains stationary above a point on the earth. Satellite based differential correction services utilise geostationary satellites.

GIS: Geographic Information System refers to computers, printers, software and people that are involved in collecting, storing and analysing spatially referenced data.

GNSS: A satellite navigation system with global coverage may be termed a global navigation satellite system or GNSS

GPS: Global Positioning system is a worldwide radio navigation system developed by the Department of Defence. It is comprised of a constellation of satellites and their ground stations which are used to calculate your position anywhere in the world.

Hz: Hertz: a unit of frequency, expressed in cycles per second. One megahertz (MHZ) is one million hertz. One gigahertz (GHz) is one billion hertz.

2

PA REFER

ENC

E POC

KET G

UID

E – 2013

GLOSSARY OF TERMS

Interpolation: The process of predicting unknown values between neighbouring known data values.Kinematic positioning: Kinematic positioning refers to applications in which the position of a non-

stationary object is determined.Latitude/Longitude: A polar coordinate system that describes a position on the Earth. Latitude is

the north to south position. Longitude is the east to west position. Locations are described in units of degrees, minutes and seconds.

Lightbar: Low precision GPS based guidance system. It usually consists of an LED array and a DGPS receiver and shows a vehicles location relative to the desired path, used in parallel and contour swathing.

Map projection: A systematic transformation of locations on the spherical globe to a flat plane while maintaining spatial relationships.

Multipath error: Errors caused by the interference of satellite signals. Multipath error arises when radio waves are reflected by objects such as large buildings or other elevations causing the reflected signal to take more time to reach the receiver.

NDVI: Normalised difference vegetation Index is a common index of vegetation or greenness generated using red and infrared spectral bands of an image.

PCD: Plant cell density index is the ratio of infrared to red spectral bands of an image.Pixel: Short for picture element, the smallest unit of a raster image.Raster: An image or any grid data format made up of a rectangular array of pixels which is used to

store geographic information.Remote telemetry: is the highly automated communications process by which measurements

are made and other data collected at remote or inaccessible points are transmitted to receiving equipment for monitoring.

Rover: A mobile GPS receiver which receives real time position corrections from a base station via a radio link or cell phone.

RTK: Real Time Kinematic satellite positioning is a technique where GPS signal corrections are transmitted in real time from a base station at a known location to one or more remote rover receivers. RTK GPS uses measurements of the phase of the signal’s carrier phase to produce up to centimeter level positional accuracy.

Spatial resolution: Refers to the size of the smallest object on the ground that an imaging system, such as a satellite sensor, can distinguish. Spatial resolution is measured by the pixel size.

Spatial variability: The variation found in soil and crop parameters (e.g. soil pH, crop yield) across an area at a given time.

Spectral resolution: Describes the ability of a sensor to measure fine wavelength intervals. The finer the resolution, the narrower the wavelength range for a particular band.

TWI: Topographical Wetness Index.Vector: A coordinate based data structure commonly used to represent linear geographic features.

Features are described by points, lines and polygons (e.g. field boundary).VRI: Variable Rate Irrigation is a tool which enables the application of different volumes or rates of

water to different areas of a field.VRT: Variable Rate technology refers to any equipment designed to allow the rate of farm inputs to

be precisely controlled and varied while the machine is in operation.Yield monitor: A group of sensors mounted on harvesting machinery which measures and records

crop yields at regular intervals.

3

PA REFER

ENC

E POC

KET G

UID

E – 2013

GLOSSARY OF TERMS

4

PA REFER

ENC

E POC

KET G

UID

E – 2013

Precision Agriculture is the precise application of agronomic and

management practices that impact positively in overall farm productivity and profitability.

Growers need to manage their operation to maximise whole-farm profit not just yield and the use of PA tools on-farm is the key to achieving this.

This reference guide identifies and describes a common-sense approach to utilise the available spatially based technology to develop and apply commercially appropriate, management and agronomic decisions.

Growers in the northern grains region and consultants across Australia have identified a proportion of this text content as applications of PA that they want to know more about. Alternatively other applications of spatial tools are similar to a case study.

GRDC’s investment portfolio is helping to increase the adoption of PA through training and projects developing resources that fill the knowledge gap between research and commercial application of PA on-farm.

James ClarkNorthern Panel Chair

GRDC

FOREWORD

5

PA REFER

ENC

E POC

KET G

UID

E – 2013

Precision Agriculture (PA) aims to improve a grower’s ability to manage

within field variability. PA provides practitioners with tools to quantify soil, terrain and crop variability and thereby customise agronomic practices and fine-tune resource applications to better match these variables. Variability can be both spatial and temporal.

Spatial variability: Is the variation found in soil, terrain and crop properties across an area at a given time. For example soil pH and crop yield.

Temporal variability: Is the variation found in soil and crop properties within a given area at different measurements in time. For example the difference in yield maps from one season to the next.

There are many factors that contribute to the spatial and temporal variability within a field. Some of these factors may include:

Soil attributes: Soil pH, texture, structure and depth, soil organic matter, soil water, soil chemistry and subsoil constraints.

Terrain: Soil forming processes, natural or formed elevation, depressions, aspect and slope.

Management practices: Cropping practices (e.g. control traffic farming) and management history (e.g. strategic direction of farming, crop rotations).

Environmental factors: Weather, weed, insect and disease.

The magnitude of these factors will influence the degree of variability within a field and the feasibility of managing that variability.

Wheat yield map showing the spatial variability in yield with the accompanying histogram of this data.

INTRODUCTION

Understanding the causes and impact of variability

FIGURE 1: An example of spatial data which includes a spatial map, data layer statistics and a map histogram and colour chart.

Layer name Wheat YieldField 2Season 2010Min 3.35Mean 5.2Max 6.85Std deviation 0.44CV 8.44%Total 375.5Area 72.12ha

Steps for managing variability:

• Identify and measure the variability and quantify the variability on both a spatial and temporal scale.

• Investigate the cause of the variability.

• Assess strategies to optimise the management of the variability.

Managing variability, where to begin:

• Collect, compile and utilise spatial data. Keep it simple. Begin by logging good data, organising, storing, and backing up in a systematic manner. Be meticulous about documenting events within the farm operation.

• Quantify yield variability (magnitude and spatial distribution) using yield monitors to generate accurate and good quality data.

• Large scale surface and subsoil constraints can be identified by an electromagnetic (EM) survey followed by appropriate soil sampling.

• Terrain can be assessed by collecting real time kinematic (RTK) elevation data which can be used to generate a variety of elevation derivatives such as slope, aspect and wetness index.

• Combine and compare data layers where appropriate. For example:– Assess the temporal variability of a

field by comparing the yield data over several years; and,

– Examine the spatial variability of a field by comparing the appropriate elevation derivatives with yield data to determine the impact of terrain on yield.

• Compare ground-truthed soil layers such as EM surveys with yield data to assess the impact of surface and subsoil variability. For example changes in clay content will influence water-holding capacity and yield potential.

• Utilise expert grower knowledge to help explain observations in the spatial data.

• Generally, it is more instructive to compare yield data from the same growing season (i.e. winter yield maps with other winter yield maps and summer yield maps with other summer yield maps).

• Seasonal variability can have a subsidiary effect on consecutive crops. The amount of nitrogen fixed by a legume crop may be variable creating a natural variable rate application for the next crop.

• Critically assess agronomic practices:– Can weed, disease and pest

pressures be reduced with alternate management strategies?

– Are yield and quality goals in line with current fertiliser inputs?

• Identifying the reason for observed variability will enable the appropriate management options to be considered. These options will be specific to the resources and goals of the individual and must be balanced against any environmental considerations.

6

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTRODUCTION

HISTOGRAMS AND COLOUR TABLES

The ability to create maps that display spatial variability is fundamental to precision agriculture. Maps provide a summary of the data and help to visualise the spatial variability within a field. All mapped data should be graphically summarised with a histogram, a frequency table that displays the distribution of data and shows the colours represented within the map. Histograms are a valuable tool, which highlight skews or characteristics within the data that may not be obvious when looking at the raw data or map layer. The histogram facilitates the creation of a colour chart that is meaningful for the data. Adjusting the colour chart used to display map data may illustrate a difference that was not highlighted by a previous colour chart.

There is no standardised color scheme used to display spatial data in PA. Colour schemes vary from one data provider or software manufacturer to the next. It is therefore important to look at the range of values associated with the colors in a chart, as each color will not always reflect the same value on a different map. All examples in this book are based on a red to blue color scheme where red represents the minimum data value and blue represents the maximum data value. The example below is an EM map showing the range of EM values from 110 to 265. The height of each colour bar indicates the total area (vertical axis) within the field corresponding to each EM value (horizontal axis).

It is imperative that map layers are as accurate as possible if they are to be relied upon when making management decisions. Some spatial data sets, such as yield, require processing to remove any errors and outliers from the data set. GIS packages which have customised imports for yield data, will generally offer some level of filtering to correct the data. Data filtering and correction will promote

7

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTRODUCTION

Data interpretationFIGURE 2: An example of spatial data which includes a spatial map, data layer statistics and a map histogram and colour chart.

Layer name DualEM DeepField Field 2Season 2011Min 110.93Mean 190.57Max 265.48Std deviation 34.57CV 32.52%Total 23788.62Area 72.12ha

a “cleaner” data set for map creation. However for optimum results, professional data processing is recommended. For more information on yield data processing refer to Yield Mapping on page 24.

DATA INTERPOLATIONThe interpolation of spatial data takes

the individual data points and converts them to a grid format. This regular grid output is achieved by estimating values from the surrounding values.

Common interpolation methods offered in GIS packages include Kriging, Spline, and Inverse Distance Weight. Knowledge of spatial statistics is useful when generating surfaced map layers. The interpolated output from a given data set may vary significantly depending on the model employed. Each model assumes a relationship between data points which may or may not exist.

The method of ‘smoothing’ should not be used to hide errors in a data set. Erroneous points must be removed or corrected before any data interpolation is performed. Most yield data is highly variable and an accurate map representation will show many local maxima and minima that may make the interpretation difficult. Since yield maps and other spatial data sets help to identifying trends across a field, interpolation techniques are used to mask the highly localised variation and highlight the spatial trends.

BASIC STATISTICSA GIS package should display basic

summary statistics for a data set. These include data layer information (Your Name, Farm, Field Name, Season), minimum,

maximum and mean values and the standard deviation.

Using relatively simple statistical calculations, the level of in-field variability can be quickly ascertained. Standard Deviation describes the spread or ‘dispersion’ of data around the mean or expected value. The larger the standard deviation the larger the variability in the data and the less useful the mean is as a descriptor of a typical area within the field. Another measure of data variability is coefficient of variation (CV). The CV normalises the variation in the data by expressing the standard deviation as a ratio to the mean. The higher the CV value, the greater the spatial variability. CV is also used to compare the variability between different data sets, for example yield from one crop year to the next.

DATA ANALYSISInterpretation techniques may range

from quick and simple eyeballing to a 8

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTRODUCTION

TABLE 1: Some general standards for interpreting the level of variability and the likelihood of payback for investment in VRT.

CV Value General observations

<5% Generally not enough variability to warrant any action.

>5% <10% Investigate to assess the economic benefit of managing the variability, particularly in high value crops.

>10% <15% Sufficient variability to expect to see a financial benefit in managing the variability in most crops.

>15% Highly suitable for variable rate treatment (VRT) with a high payback expected.

more rigorous statistical analysis using GIS software. Both approaches have their place in PA. Good decisions can often be made in a fraction of the time using very simple techniques. A simple approach to data interpretation may involve a visual comparison of different data layers for a field. This facilitates the identification of spatial patterns through a visual side by side comparison. Identifying patterns which are repeated across data sets and gives confidence to determine conclusions. Of equal importance to identifying reoccurring patterns, is the identification of inconsistencies between data layers. For example, several years of similar wheat yield maps and vegetative images for a field may be followed by a wheat yield map showing dissimilar patterns. Such an inconsistency will naturally raise the question ‘Why?’ and may warrant further investigation and diagnostics, providing an opportunity for the grower to learn something new about their field.

There are also occasions when a more rigorous approach to data analysis

is required. Statistical comparisons and spatial correlations between data layers can reveal relationships which may not be visually obvious. In any data interpretation human involvement is vital. A grower or consultant who is actively engaged in the analysis process is more likely to query their data, verify its integrity, and incorporate any indigenous knowledge they possess.

Human involvement can also accommodate imperfect data which is often generated in an agricultural environment. Imperfections caused by such common factors as glitches in yield monitors or clouds in aerial images can be corrected or excluded from any analysis.

Building a GIS for a farm is not an overnight process. Although there may be short term gains such as improved sampling strategies using yield maps or aerial images, the long term agronomic payback may require years of committed data gathering. Recognising stable spatial patterns and gaining an understanding of the underlying processes is a long term payoff.

9

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTRODUCTION

In agriculture, data management is a specialised skill:

• Generating large volumes of data quickly; and,

• Manage the incompatibility of data file formats when moving between different computer manufacturers.

Effective data management requires time, money, good computer skills, good organisational skills, and endless patience. Data only becomes useful once it is organised and analysed. GIS software can aid this process. For some individuals, building a good data set for their farm is a challenge they are ready to embrace. However for many growers this is a daunting and challenging task.

There are several reputable commercial software programs that can be employed to manage spatial data; SMS, Farmworks, Mapshots, and SST. Although these programs can read generic file formats and can import raw data from many hardware manufacturers, they do not allow data to be easily shared across different platforms. Additionally, the initial cost of the package, which may seem large, will actually be only a small portion of the total investment. The cost of acquiring and processing data will quickly eclipse the initial software and hardware costs.

For many growers, maintaining their own GIS may not be feasible. These growers may benefit from the specialised services of a data manager. The emergence of data analysis services

can remove the burden on individuals to manage their own data and will free up resources (such as time), that could be more effectively allocated. Specialised data managers offer a suite of services and will present the spatial data and any analysis in an accurate and usable format. Grower input will be the key to successful data interpretation. The more familiar the data manager is with the day-to-day farming operation the better.

DATA STORAGEStoring and backing up raw data is

becoming much easier with online server technology such as Drop Box, Sugar Sync and Box.net. Cloud storage is cheap, has no physical presence, has backup and recovery systems and doesn’t need replacing with time.

This form of storage provides individuals with access to their data from anywhere and sharing data between users is made simple. External hard drives are another option for data backup. Although they do not offer all the advantages of cloud storage, they are cheap, easy to use and may alleviate any data security concerns.

IN THE FUTUREThe function of Wireless Data Transfer

is becoming a new and exciting option on most multi-function tractor consoles. However, some issues remain regarding communication access, data ownership and cost. This may reduce the speed of adoption.

10

PA REFER

ENC

E POC

KET G

UID

E – 2013

Data management

INTRODUCTION

11

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTRODUCTION

In PA there are a variety of data file formats. A file ‘format’ describes the

way in which the data is stored within a computer file. Some formats produce smaller sized files (number of bytes) so that they can be easily transferred wirelessly or over the internet. Other formats may produce files of a larger size (more bytes) which are optimised for faster data access within software programs. File types with open and well known structures that can be used by many different software packages are known as generic formats.

Generic files will conform to a known standard and can be opened in commonly available software such as graphic editors, text editors or spreadsheets. These include Comma Separated Variable files (*.csv), jpg and tif files. There are

also generic spatial data file formats (vector and raster) which are used by GIS software. These include shapefiles, GeoTIFF and GeoJPG and KML files.

Another group of file formats in PA are referred to as proprietary formats. These are generally generated by a specific piece of equipment (or brand of equipment) such as a yield monitor. Proprietary files often contain large volumes of data and are stored in a compact format which can only be used with select software products. Proprietary files may be encrypted or have other security mechanisms in place to prevent tampering by unauthorised parties. It is important when working with data to be able to see the file extension in windows explorer. The optimal format for spatial data will depend on the type of data, how it is to be used and the available software tools.

Data exchange formats

TABLE 2: Generic spatial data file types used in PA.

CSV Comma-separated values stores data in a plain text format. A good format for transferring raw yield, EM or elevation data.

ESRI SHAPE A popular geospatial vector data format used by GIS software. Frequently used to transfer maps and other vector data between different hardware. A Shape file has many extensions. It should at least contain .dbf .shp and .shx.

KML/KMZ

Keyhole Markup Language is a notation for expressing geographic annotation and visualisation within Internet-based, two-dimensional maps and three-dimensional Earth browsers such as Google Earth.KML files are often distributed as KMZ files which is the zipped format with a .kmz extension.

GeoJPG, GeoTIFFAre public domain metadata standards which allow geo-referencing information to be embedded within an image file. This can include information about the map projection, coordinate systems, ellipsoids, datums, and anything else necessary to establish the exact spatial reference for the file.

ASCII Grid Formats

American Standard Code for Information Interchange is a code systems that represents text in computers. Often used as a transfer format for image (raster) files such as processed yield, EM and elevation images in either UTM or WGS 84 projections.

AutoCAD DXF Drawing Interchange Format is a data file format used mostly for drawing (lines, points and polygons) in AutoCAD programs.

The underlying technology for PA is the measurement of position on the earth’s

surface. This is made easy with the use of GPS receivers and satellite navigation systems which provide autonomous positioning and global coverage, referred to as a Global Navigational Satellite System (GNSS). There are currently 2 such systems, the US Navstar GPS and the Russian GLONASS. Each system is comprised of a satellite constellation of 20–30 orbiting satellites which transmit signals with location and time. GPS receivers passively receive these signals from multiple satellites and calculate their position in three dimensions.

The accuracy of a GPS is generally determined by the size, type and price of the receiver. Not all farming operations require the same level of positional accuracy. The level of accuracy required by an operation will determine the suitability of a GPS.

RTK is becoming increasing popular over DGPS for tractor autosteer systems. RTK uses more-expensive dual-frequency receivers; with one set up at a known

location (the Base Station) and others as mobile units, or ‘Rovers’. Rovers receive real-time position corrections from the Base station via a radio or mobile phone link. Changes in the atmosphere will affect the speed at which the radio signals are transmitted from the satellites creating a level of GPS error. Dual-frequency receivers correct this error to within a distance of 2cm.

12

PA REFER

ENC

E POC

KET G

UID

E – 2013

SPATIAL AGRONOMY TOOLS

Positioning

CHECKLIST FOR POSITIONING & AUTOSTEER SYSTEMS• How is accuracy quoted; pass to

pass or repeatability over a 24 hour period?

• Select a system that suits your budget and applications, preferably able to be upgraded.

• Is the receiver GPS or GNSS? GNSS will have access to more satellites.

• Calibration is critical for proper steering performance of an autosteer system. Terrain compensation, measured by yaw, pitch and roll of the tractor, is required for good steering accuracies.

• For an older tractor, steer assist may be a good option at about 1/2 to 1/3 of the price of full hydraulic steering.

• Hydraulic steering kits offer the most reliable performance for after-market installations.

• New tractors are becoming CANBUS steered. These systems are built in through the tractors internal management system.

Application Device Accuracy

Soil sampling

Handheld GPS & Smartphone with inbuilt GPS

+/-5 m

Spraying/spreading contractor

Differential GPS (DGPS) +/-1m

Inter-row sowing & autosteer

Real time Kinematic (RTK) GPS +/-2cm

The cost effectiveness of RTK systems have been widely documented making them an attractive option for grain growers. The use of RTK autosteer systems greatly reduce overlaps within a field with reported savings of 5-20% in input costs. RTK autosteer systems range in price from $15,000 to $40,000 depending on the machine, manufacturer, and accuracy of the system.

In addition to steering the tractor, RTK autosteer systems enable the collection of elevation data which is useful for drainage

and erosion management. Most RTK units have multi-function consoles which can record detailed field records that can assist with food and safety compliance as well as the documentation of on-farm trials.

13

PA REFER

ENC

E POC

KET G

UID

E – 2013


REAL USES AND BENEFITS OF GPS• RTK Autosteer systems can result in

significant savings (5-20%) of inputs by minimising overlap between consecutive passes.

• The ability to inter-row sow for improved plant establishment and disease management.

• Less operator fatigue, and greater focus on the task being conducted.

• RTK GPS compliments and enhances controlled traffic farming (CTF) which will result in less compaction and improved soil quality in the areas outside the wheel tracks.

• Enables VRT; precise application and placement of inputs for improved plant establishment. Boom section control for seeding spraying promotes accurate seed placement and reduces overlaps.

• Documentation and record keeping; soil sampling sites, field operations, on-farm testing.

• Ability to map, yield, elevation, EM, field boundaries and record obstacles.

• Collection of data for drainage planning and applied land levelling.

• Navigation to direct in-field sampling using a handheld GPS or mobile/tablet device.

FIGURE 1: There are many cost effective aftermarket guidance systems such as a light bar retrofitted to the steering console (a), however RTK GPS systems allow the operator to perform high accuracy field tasks such as inter-row sowing (b).

(a)

(b)

14

PA REFER

ENC

E POC

KET G

UID

E – 2013

Soil data are often key to understanding within field variability. The physical and

chemical attributes of the soil, contribute significantly to the variability within a crop. Vehicle mounted soil sensors such as EM provide a relatively quick measure of soil properties that can be used to create accurate soil maps.

Electromagnetic Induction (EM) instruments are the most commonly used soil sensors in Australia. Using a transmitting and receiving coil, the amount of electrical current flowing from the soil is measured and this is directly proportional to the degree of soil electrical conductivity. This is referred to as apparent electrical conductivity (ECa). Soil ECa is influenced by the combined relationship between:

• Clay content;

• Clay type (or depth to clay in duplex soils);

• Soil water; and,

• Soil salinity.

As each of these attributes increases in concentration in the soil, so too does ECa. EM sensors should be calibrated daily for atmospheric conditions to ensure the accuracy of the instrument. Soil and air temperature affect the relationship between ECa and soil properties preventing the derivation of a universal relationship. EM surveys are unique and relevant only to the site and time of collection. To calibrate an EM survey, it is essential to collect soil samples for laboratory analysis. The correlation of

soil ECa to other soil properties must be established for each site and this forms the foundation for the use of EM as a tool to aid management decisions.

It is essential to know the soil depth at which the EM is measured. A separate map of ECa is generated for each soil depth. Electrical conductivity is commonly measured at two soil depths. The two maps may appear to be highly correlated, however it is important to remember that the values at each depth represent a different combined relationship of chemical and physical characteristics and must be calibrated against the appropriate soil samples. Multi-depth EM surveys are useful for highlighting information about different growing regions of the soil profile. The blue areas (refer to Figure 2) in the shallow EM map highlight increasing clay content and increasing exchangeable


Soil�surveying�–�EM

CHECKLIST FOR COLLECTING EM SURVEYS• EM surveys need only be collected

once. Ensure a full soil moisture profile at the time of the survey.

• EM Data is collected at 24 to 48 metre intervals depending on variability, terrain and the requirement of elevation data (if collected simultaneously).

• Ground-truth major EM zones by soil testing for chloride, EC, CEC, clay, sand, silt, ph, boron and moisture. Samples should be collected at least for 3 depth increments: eg. 0–30 cm, 30–60 cm, 60–90 cm.

15

PA REFER

ENC

E POC

KET G

UID

E – 2013


sodium potential which is likely to impact significantly on water infiltration. Conversely the blue areas in the deep EM map are attributed to increasing clay content in addition to increasing concentrations of chloride which will reduce the plant available water capacity of the soil.

INTERPRETATION OF EM SURVEYS• The spatial variability of ECa usually

reflects changes in the PAW which is reflected in yield potential.

• The colour chart used to display an ECa map is specific to the survey of that field.

• analyse ECa for each soil depth at which it has been measured.

• Correlate ECa to soil properties to determine which properties are influencing the measurements.

• Analyse with biomass and/or yield maps to identify production limitations or potentials.

FIGURE 2: Dual EM survey (from low ECa represented by red to high ECa represented by blue) collected at a depth of (a) 0–1.2 m and (b) 0–0.5m. In the shallow EM map (b) high ECa is attributed to increasing clay content and high ESP which results in reduced water infiltration. At the lower depth (a) high ECa is attributed to high levels of clay and salt which reduces the plant available water capacity.

(a)

(b)

Gamma radiation is high frequency electromagnetic radiation which

is used as a soil sensing technique in agriculture. Gamma surveys measure the radiation emissions from the decay of naturally occurring radioisotopes in the topsoil to predict soil properties such as texture and mineralogy. In PA three channels are typically measured which relate to the decay chains of potassium, thorium and uranium. Some instruments also provide a total count reading, which is the sum of all gamma radiation, measured in counts/second.

Gamma radiometers are an effective instrument when used in conjunction with EM surveys. Combining gamma radiation with EM data has been demonstrated to improve the accuracy of predicting soil properties particularly in areas of low electrical conductivity. In Western Australia, where there is widespread sandy and gravelly duplex soils, most geophysical soil surveys use a combination of EM and gamma radiometric surveys. Similar to EM surveys, gamma radiometric data needs to be ground-truthed.

Soil cores are generally taken to a depth of 30cm as most gamma radiation detected at the soil surface is emitted from the top 30–40cms of soil. On loose, deep sands (sand depth >60cm), deeper soil cores can be of value as gamma emissions can travel from a greater soil depth.

Gamma radiometry can be used to complement EM and provide a more comprehensive definition of soil types in the following situations:

• In areas of very low soil conductivity gamma radiometrics is used to distinguish between deep sand and gravel profiles.

• In areas of high soil conductivity, gamma radiometrics can be used to distinguish between clay profiles and saline soils.

16

PA REFER

ENC

E POC

KET G

UID

E – 2013


Soil surveying – Gamma radiometrics

CHECKLIST FOR COLLECTING GAMMA RADIOMETRIC SURVEYS (OR GAMMA RADIOMETRIC DATA)• Gamma data need only be collected

once unless massive soil renovation (such as claying or spading) has been undertaken post-survey.

• Ideally soil should be dry at the time of survey.

• Ground-truthing of major gamma radiation zones should include soil laboratory testing for sand, silt and clay, gravel content, exchangeable cations, pH, EC and phosphorus sorption. Potassium and sulfur are also usually measured.

• For detailed financial analysis, gamma radiometric data should be analysed with yield data.

17

PA REFER

ENC

E POC

KET G

UID

E – 2013

• On the northern sand-plains of WA the total gamma count has helped to identify soil profiles with better water-holding capacity.

• Gamma radiometrics is effective in delineating sand profiles from decomposed granite loams.

Accurate interpretation of gamma radiation data is a specialised field which requires knowledge of landscape position and soil formation processes. For more information, refer to the CSIRO publication Guidelines for Surveying Soil and Land Resources.

Soil sensors are mounted on a vehicle with RTK GPS for simultaneous collection

of EM, gamma radiometric data and elevation simultaneously.

Shallow EM surveys frequently fail to capture the true level of field variability (as seen in Figure 3(a)). The thorium


INTERPRETATION OF GAMMA RADIOMETRICS• Ensure landscape and soil formation

processes are taken into account when interpreting gamma radiation data.

• Gamma radiometrics is a specialised soil sensing technique which is best utilised and interpreted using the services of an experienced data consulting group.

EM survey, elevation data and gamma radiometrics data collection vehicle owned by Precision Agronomics, WA. (PHOTO: Courtesy of Precision Agronomics)

radiometrics survey (b) however, highlighted significant changes in soil structure on a gravelly duplex soil in WA.

At both location one (1) and two (2) in Figure 3, the EM survey indicated a low electromagnetic currency (shown in red). The thorium channel detected contrasting values for these locations. Soil cores extracted to 60cm and subsequent

laboratory testing revealed significantly different soil profiles at each location. Site 1 was gravel dominated soil (low EM and/or high thorium), while site 2 was a deep sand (low EM and/or low thorium).

The thorium channel is recognised for its ability to help distinguish between gravel and sand soil types.

18

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 3: Shallow EM survey (a) showing minimal variability due to low electrical conductivity in 3 adjacent fields. The thorium data collected from the gamma radiometric survey (b) highlights significant field variability attributed to changes in gravel content.

(a) 1

2

(b)

Acid soils affect some of the most productive agricultural land in

Australia. The symptoms of soil acidification are not easily recognised as they are less specific than other soil constraints such as salinity and erosion. In grain crops, soil acidity impairs root growth and reduces plants ability to access water and nutrients in the soil profile. This is more significant in low rainfall regions, where the topsoil tends to dry out in the late growth stages of the crop. Soil acidification often causes a gradual decline in crop production and this decline is frequently overlooked or attributed to other causes such as seasonal affects.

The application of surface lime to ameliorate topsoil acidity is a common practice in the southern and western grain growing regions of Australia. Research shows that surface lime applications will slowly reduce acidity at lower levels in the soil profile. Although deep placement

19

PA REFER

ENC

E POC

KET G

UID

E – 2013


Soil surveying – pH�detector

CHECKLIST FOR VARIABLE RATE LIME• Use a rapid sampler on a 1ha grid or

alternatively sample zones derived from imagery or yield maps.

• Collect at least 3 calibration samples to send to the lab.

• Determine lime requirements using soil test results and convert to an application map to be loaded into the spreader controller.

FIGURE 4: A soil pH map showing variable pH levels ranging from 4.7 (red) to 7.0 (blue). This map was generated from 1ha grid samples (a) taken from the Veris Rapid pH detector mounted on an ATV (b). Less than half the field has acidity levels that impact on crop production and would benefit from liming. Field samples reveal there is no cost benefit to applying lime to the southern end of the field where the soil is more alkaline. (Photo courtesy of Andrew Whitlock, PrecisionAgriculture.com.au)

(b)

(a)

of lime is more effective, it is costly. A more viable approach is to prevent the development of subsoil acidity by the application of lime. PA technology can help to address soil acidity by enabling

rapid field tests for pH, followed by variable application of lime. In many cases, variable rate lime applications have delivered significant cost savings for the grower (25-30% is typical) over a uniform application rate.

The rapid soil pH meter is simple to use. With an easy push mechanism, the pH probe is inserted into the soil for a few seconds. An integrated GPS enabled data logger references the sample site. The pH electrode is automatically cleaned after each sample.

Many lime spreaders are capable of variable rate applications or can be upgraded to do so. A controller changes the belt speed on the spreader or controls the height of the trapdoor, thereby changing the application rate according to a prescription map.

20

PA REFER

ENC

E POC

KET G

UID

E – 2013


INTERPRETATION OF pH MAPS• pH is the measure of acidity. The

Veris and other pH detectors measures pH in a water solution.

• The pH data points need to be interpolated to produce a soil pH map from which a zonal application map is derived.

• The black points on the map indicate the location of sample sites

• Significant changes in soil pH often indicate significant changes in soil type and yield potential.

In agriculture, optical sensing is commonly used to measure variability

in soil and vegetation. Optical imaging utilises the visible, near-infrared (NIR) and thermal portions of the electromagnetic spectrum.

Variations in the surface of the earth causes sunlight to be reflected absorbed or transmitted. Vegetation and soil exhibit all 3 energy exchanges and these interactions vary across the EM spectrum. Knowledge of which wavelengths are absorbed by different land features and the intensity of the reflectance can help one to understand the state of an object.

Optical sensors will generally use at

least two different bands of light, most commonly the red and NIR. Using the distinct spectral properties of plants with low reflectance in the visible and very

21

PA REFER

ENC

E POC

KET G

UID

E – 2013


Crop sensing

CHECKLIST FOR COLLECTING REMOTE SENSING IMAGERY• Satellite imagery is collected during

set orbit times irrespective of cloud cover.

• The pricing of satellite imagery is set using specific scene sizes, which are often significantly larger than the area of interest.

• Aerial imagery offers more flexible acquisition of data and is often more cost effective. When selecting an image source consider the spatial and spectral resolution needed for the application.

• For small areas, biomass data can be acquired using active optical sensors (see PA Applications: Collection of biomass data using ‘on-the-go’ active sensors on page 53).

FIGURE 5: Shows the affect of increasing spatial resolution of a PCD image: (a) 2 m resolution; (b) 5 m resolution; (c) 10 m resolution; and, (d) 25 m resolution. (Image: Courtesy of PCT)

(a)

(b)

(c)

(d)

high reflectance in the NIR region of the solar spectrum, the spectral contrast can be used for identifying the presence of green vegetation and evaluating some characteristics (e.g. cover and biomass) through various vegetation indices.

Plant cell density (Figure 6), the ratio of infrared to red reflectance, provides a measure of crop vigor. The PCD values cannot be used to indicate specific levels of biomass. This indicates a level of biomass variability within the field. A high level of variability is indicated by the PCD image in Figure 6 (CV: 32.5%). This can provide a basis for differential management of inputs such as fertiliser, water and growth regulators.

22

PA REFER

ENC

E POC

KET G

UID

E – 2013


UTILISING REMOTE SENSED IMAGERY• Enhancement tools help make

imagery more interpretable for specific applications. Enhancement and classification tools are often used to highlight features.

• Classified images and vegetation indices (e.g. NDVI & PCD) are frequently used as a substitute for biomass. The analysis of imagery in conjunction with other ancillary data helps enhance ones understanding of within field variability and its causes.

• Imagery is only a surrogate for physical plant characteristics at a specific time. Field validation is essential to measure the attribute of interest.

• Relationships between reflectance and plant characteristics such as biomass, leaf area index and yield, are inconsistent, except in environments with a very reliable growing season.

FIGURE 6: High resolution (2m) PCD imagery showing a large variation in early season cotton biomass. Red and blue areas correspond to areas in the field with lowest and highest PCD respectively.

Layer name PCD Field 27Season 2010Min 2Mean 130.29Max 255Std deviation 42.36CV 32.51%Total 13703.66Area 105.18ha

Ratios between other spectral bands provide information about other physical information such as plant water content and chlorophyll concentration or absorption. Ratios of narrow spectral bands will generally provide more specific information than those created from sensors with very broad spectral bands.

The spatial resolution determines the level of detail which can be distinguished in an image. It is determined by the size of the pixels within an image. As spatial resolution increases, each pixel represents a smaller area on the ground which increases the level of detail contained within an image.

IN THE FUTUREUnmanned Aerial Vehicles (UAV) are

generating interest as an alternative to imagery collected by satellite and aerial platforms. While they offer an attractive alternative to real time data acquisition, there are some issues to consider, i.e. Civil Aviation Safety Authority (CASA) compliance requirements, coverage area per flight, data processing and camera quality.

23

PA REFER

ENC

E POC

KET G

UID

E – 2013


Monitoring yield is a simple and economical method used to measure

the impact of environmental, agronomic and management factors on yield. It is often considered a logical starting point for developing information about inherent field variability. Most new harvesting machines come equipped with a yield monitor. Older machines can be retrofitted with a system.

Using yield maps to quantify spatial and temporal variability may have an immediate impact on management decisions or the usefullness of this information may increase over time as it is interpreted with other spatial data.

Yield data can be used for:

• Estimating nutrient removal from a field.

• Generating variable rate application maps for subsequent crops.

• Analysis with soil data layers such as EM to determine changes in production potential within a field.

• Developing accurate gross margin information.

• Post-harvest analysis or insurance claims.

• Multi-season analysis and the generation of permanent management zones.

• Analysis of on-farm trials.

• Analysis with terrain data layers for economic assessment of land forming.

24

PA REFER

ENC

E POC

KET G

UID

E – 2013

Yield mapping

ERRORS IN YIELD DATA WHICH CAN BE RECTIFIED POST HARVEST ARE:• Inaccurate yield totals or data

spikes.• Depending on the amount and

location of missing yield data, interpolation techniques may be used to overcome the loss of data.

• Time delays (e.g. mass flow), GPS and positioning offsets in yield data collection.

• Overlaps in data or gaps due to incorrect or differing cutting widths.

• Correcting and eliminating overlaps.

• Incorrect field labelling on the yield console or multiple files for a single field.

• Compatibility and accuracy issues when merging data from multiple and/or different branded machines.


Accurate yield data is essential if it is to be used as the basis for making decisions. Most people recognise that correct installation and calibration of a yield monitor is required, but it is also necessary to clean and process the data generated by a yield monitor. The data taken directly from a harvester is often highly variable and will contain errors.

It is paramount that yield data is ‘cleaned’ using the appropriate filters. Erroneous data points must be removed or corrected before the data is interpolated. Most commercial mapping programs try to smooth out errors in yield data but accurate removal is considerably better. Growers may choose to do this themselves or may consult a professional data service who can offer experience and expertise, filtering the data to produce an accurate data set suitable for mapping and further analysis. See page 7, Spatial Data Interpretation for specific information about interpreting yield data

25

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 7: Common yield data errors that can be rectified post harvest: Position off-sets (a); incorrect cutting width (b); varying output from multiple harvesters (c); and, GPS feed issues (d).

(a)

(b)

(c)

(d)

26

PA REFER

ENC

E POC

KET G

UID

E – 2013

FIGURE 8: Basic yield processing involves the removal of overlaps, filtering for maximum and minimum values and cleaning data at the end of rows (a). This improves the quality of the data (b) which can be interpolated to produce a map that can be used for spatial analysis (c).

(a)

(b)

(c)


CHECKLIST FOR COLLECTING ACCURATE YIELD DATA• Install the latest firmware on the

yield monitor and update any PC software used for data processing.

• If the data is professionally cleaned and processed, calibration of harvesters does not need to be accurate. It is simpler to rectify yield totals post-harvest.

• Ensure 50% (or more) of the harvesters are monitoring yield, stagger yield monitors with non-monitoring harvesters instead of monitoring a continuous block.

• Make sure the monitor is set-up correctly; refer to the user manual or dealer for queries.

• Verify data is being recorded to the storage device soon after harvest begins, it is too late when harvest is completed.

• Where possible, harvest with a full comb width.

• Consider providing contractors with a memory card and/or USB stick.

• Record the actual tonnage from each field if calibration is to be performed post-harvest. Calibrate the data and ensure that errors are removed before the data is interpolated.

• Make a copy and a backup of raw yield data on an external hard drive or cloud server for safe keeping.

• Record and collect yield data every season even during poor seasons.

The use of elevation data in the agricultural sector has expanded to

include a wide range of applications. High accuracy RTK GPS provides producers with a new and affordable opportunity to collect elevation data which can be used for land forming, assessing the direction of farming, control traffic design and erosion control.

With an increasing number of agricultural vehicles equipped with auto guidance systems, the measuring of elevation data during farming operations has become more practical.

Topography is an important soil forming factor which will affect soil variability and crop growth. The topography of a field

will determine the rate of precipitation run-off and the rate of erosion and soil formation at the soil surface. Steeper slopes will produce higher rates of run-off and erosion of topsoil, while depressions will accumulate water and minerals. Water and nutrient movement and changes in soil type (and/or attributes) can often be better understood with a digital elevation model (DEM). A DEM, which provides a digital model of a terrain surface can also be used to calculate topographical derivatives that provide specific information that can aid agronomic and management decisions.

The depression maps in Figure 9 highlight the low lying areas (green) through

27

PA REFER

ENC

E POC

KET G

UID

E – 2013


Elevation�surveying

FIGURE 9: Elevation data collected using RTK GPS over three adjacent fields highlights the natural slope and water flow of the land from east to west (a). The depression depth is calculated for the existing north/south direction of farming (b). To assess the impact of field layout on elevation (water flow?), a depression map is created based on an east/west row configuration (c).

(a) (b) (c)

to the areas of higher elevation (red). Depression maps can be used to quantify the depth of stagnant water in a field. Approximately 215ha (83%) of this field is low lying. This impacts the natural flow of water from east to west. This field is susceptible to stagnant water pooling which can have a significant impact on final yield.

28

PA REFER

ENC

E POC

KET G

UID

E – 2013

INTERPRETATION OF ELEVATION DATA• The interpretation of elevation data

is relatively simple as the values are absolute.

• Elevation derivatives can be helpful when accounting for variability in yield within a field.

• Selecting an appropriate derivative will depend on the individual field, its topography and the purpose of the investigation. For example, in irrigated fields, aspect maps will have limited value, while an error from planr (EFP) map can be used to assess the impact of natural or management induced field depressions on yield.

CHECKLIST FOR COLLECTING ELEVATION DATA• Data can be collected during any

farming operation which uses an RTK GPS.

• Avoid using elevation data collected during operations where weight is continuously changing or where compaction varies.

• Data should be logged at regular intervals (1 second) over the field.

• As a standalone operation, collect data on a 24m swath.

TABLE 1: A list of commonly used elevation derivatives.

Derivative Description Units Applications

Slope

A multi-directional calculation of the fall of land. Steeper slopes increase water speed and reduces infiltration (depending on soil type). Flatter slopes reduce speed and increase infiltration, but often create more water-logging events. Slope can also be calculated along the pass or row where wheel tracks or beds/hills encourage water movement in limited directions.

Degrees Percentage (%)

• Locate moisture probes in neutral slopes.

• Can be used to fine-tune soil based management zones, i.e.. will water move to or away from a zone?

• Use to calculate yield losses and cost benefit in landforming.

• Use for farm layout and contour design.

Aspect Describes the direction a piece of land is facing.

Degrees (0-360°)

• Aspect can be used to identify areas of a field that receive less or more sunshine which can impact on crop yield.


29

PA REFER

ENC

E POC

KET G

UID

E – 2013

TABLE 1: A list of commonly used elevation derivatives.

Derivative Description Units Applications

Depression

Indicates areas where water will be stagnant. Can typically cause significant yield losses due to water-logging. Wheel tracks and beds may create additional depressions, where water cannot move in a natural direction.

Depth (cm)

• Remedial action cost benefit analysis when overlaid with yield data.

• Use in landforming software to guide surface drain building.

Landscape Change

Indicates the relative change up or down-slope, above or below the derived natural surface plane.

Negative or Positive (cm)

• Used as an analytical layer for yield variability due to water shedding and accumulation.

• Often used in zoning along with soil information.

• In some landscapes can be a surrogate for soil type.

• Often describe low areas susceptible to frost.

Cut & Fill

Cut and fill data is a by-product of fitting a plane of best fit (to the natural surface). The ‘cut and fill’ values estimate the amount of soil to be ‘cut’ or filled above or below the plane of best fit. With the emergence of GPS landforming multi-directional and multiple slope cut and fill is a more cost effective solution, especially in dryland farming.

Negative (Cut) or Positive (fill) (cm)

• Used mostly for land forming operations.

• Cut and fill layers can be used as variable rate maps post landforming for applications of variable rate gypsum or nutrients.

Topographical Wetness Index (TWI)

Calculated derivative of local up-slope and slope values to identify and quantify the spatial distribution of soil moisture and surface saturation.

Index (0-15)

• Can be similar to landscape change in use.

Contour Map

A vector map, showing elevation lines: areas of the same height. Any point on a line will be the same elevation.

(m)

• Useful in overlay analysis which can be used to understand the direction of water flow.

• Aids contour design and implementation.

Flow Map

Also a vector map generally show the direction water will move and where it will link with water from other areas of the field.

(m)

• Used for visual analysis of water movement and accumulation.

• Used for design of surface drains.


(continued)

30

PA REFER

ENC

E POC

KET G

UID

E – 2013

APPLICATIONS FOR SPATIAL AGRONOMY

SECTION BOOM CONTROLSection boom control used in

conjunction with precision GPS/GNSS guidance can significantly reduce overlap during the application of agricultural chemicals. Reductions in input application overlap will result in savings in chemical, fuel and time required to perform the operation.

GPS-controlled boom sections will automatically turn off sections of the spray boom to avoid applying products to previously treated areas. The higher the accuracy of the GPS system, the better the result. The section controller

connects to the GPS and the electronic section switches. As the field is sprayed, the coverage map is recorded; application equipment is automatically turned OFF in previously treated areas or ON and OFF at terraces, waterways and headland rows. There are several aftermarket boom control options which can be retrofitted to an existing spray rig with prices starting from $3000. In most cases, these devices are generic and can control the electronic switching system of many different manufacturers.

Savings on spraying costs due to precision boom control have been well

Section control to improve input efficiencies

Example of a sprayer equipped with GPS-based section control when approaching a non-cropped inclusion such as a waterway or terrace. The relevant boom section or individual nozzles are turned OFF over the non-cropped areas while the remaining boom sections continuetotreatthecroppedareaofthefield. (Image courtesy of Teejet.com)

31

PA REFER

ENC

E POC

KET G

UID

E – 2013


documented and will be greater for hilly, irregular shaped fields which contain obstructions such as drainage ditches. On flat or regular shaped fields, estimates of savings due to reduced overlap are generally less.

The economic benefit of precision boom control will be incremental with savings increasing with each application and increasing at a higher rate as more expensive inputs are applied.

PRECISION ROW CONTROLAnother control system which is

becoming increasingly popular is individual planter and/or seeder row control. This is typically a system that pneumatically or electronically controls individual row shutoff for a planter through a GPS signal or manual controller, and can be directed from the cab of the tractor. The technology works by turning planter sections or rows OFF in areas that have been previously planted or ON and OFF at headland turns and waterways. Most manufacturers provide this technology as an option on new planters and some older planters can be retrofitted with this hardware. Savings on seed costs due to seeder row control will vary with the size and shape of the field. Savings are generally greater in small irregular shaped fields which contain obstructions (e.g. waterways and terraces).

BENEFITS AND CONSIDERATIONS OF SECTION CONTROL• Up to 20% reduction in the overlap

of inputs using RTK GPS technology.• Up to 25% savings on spraying

costs due to the reduction of spray overlap.

• Payback on investment is generally short term depending on the scale of the farm.

• Improved overall application accuracy.

• Increased operator efficiency.• Reduced operator fatigue by not

having to manually control the boom sections and/or planter.

• Reduced environmental impact due to precise placement of inputs.

Research and on-farm experience suggest that continuous cropping

systems perform extremely well under a zero till, controlled traffic farming (CTF) system. CTF divides fields into two sections:

• A healthy well-structured soil for promoting crop growth; and,

• A track for supporting vehicles and machinery.

In a continuous cropping system up to 85% of the field becomes compacted from machinery and will result in a loss of yield. The aim of CTF is to have all vehicle wheels using the same tracks when performing field operations. By doing so, soil compaction is limited to specific areas in the field that are (normally) taken out of production. A GPS guided system used in conjunction with CTF will produce minimal soil compaction, improved driver

32

PA REFER

ENC

E POC

KET G

UID

E – 2013


Soil compaction management

Permanent, hard wheel tracks in a CTF system have been shown to reduce diesel use by up to 50%. (Source: Andrew Whitlock, PrecisionAgriculture.com.au)

performance and reduced input overlap. With good design, compaction can be limited to as little as 10% of a field using CTF.

In grain farming, CTF is based on the track width of the harvester which is limited to a minimum of approximately 3 metres (120 inches). All other machinery should then be configured to this spacing, ensuring that the opportunities are maximised for the system.

There are some engineering risks associated with modifying tractors and machinery to wider wheel spacings. Damage often occurs to bearings and axles and many of the aftermarket modifications are not warranted by the manufacturer. Many companies now offer warranted 3m front/rear axles to specifically accommodate CTF.

Operating widths in Australian grain production have focused on 9m (30ft), 10.5m (35ft), and 12m (40ft) spacings.

33

PA REFER

ENC

E POC

KET G

UID

E – 2013


BENEFITS OF CTF• Reduced compaction due to fewer

passes in a field – 1 tractor pass using CTF produces 90% less compaction than up to 5 passes using conventional farming.

• Reduced input costs due to reduced area of overlap and increased operational efficiency.

• Reduced diesel use by up to 50% due to harder, permanent tracks.

• Greater accuracy for operations such as placing seed and inter-row sowing as the same tracks are consistently used.

• Reduced operator fatigue when combined with RTK GPS.

• Improved water infiltration and storage due to reduced soil compaction.

• Improved drainage and reduced water-logging.

• Improved soil erosion control.• Improved timeliness of operations as

permanent tracks enable operations to resume faster after a rainfall event.

• Improved soil erosion control.• Complements and integrates

with precision farming tools and management practices.

Overland water flow does not stop at boundaries or fence lines. When

designing or changing a farm layout, particularly the direction of farming within a field, consideration must be given to the natural water flow of the area.

If adopting CTF, optimal placement and orientation of permanent tramlines will enable water to flow away from areas subject to water-logging. In regions which experience high intensity rainfall events, farm layout is an important factor in the elimination or reduction of erosion due to concentrated water run-off.

Important considerations when designing a farm layout include:

• Topographical mapping; this provides information on the surface hydrology of a field and will play a key role in the design of an efficient farm layout (use elevation data collected with an RTK GPS).

• A whole farm approach; what happens beyond man made boundaries?

• Assess drainage; each row will carry its own water.

• Optimise operational efficiency for the farm (e.g. length of run, field shape).

• Access to fields and removal of grain; place access tracks on ridgelines as these naturally shed water faster and often provide the driest area of the field to place field bins.

• Cross contour banks at right angles to minimise the impact on the tractor and implement existing infrastructure.

• Plan access points and farming direction to compliment existing infrastructure.

34

PA REFER

ENC

E POC

KET G

UID

E – 2013


Farm layout and designBENEFITS OF A GOOD FARM LAYOUT• Reduced water-logging which can

significantly improve yield.• Ability to get onto the land sooner

after rainfall events.• Reduced incidence of significant

erosion by preventing water movement from intensifying and becoming concentrated in a field.

CTF farming up and down the natural slope and over contour banks to complement thenaturalflowofwater.Thislayoutwillreduce the concentration of water in the fieldtherebyminimisingpotentialsoilerosion. (Source: Rob McCreath)

Geospatial EM surveys are an efficient and effective method of characterising

soil properties, especially on vertosol and duplex soils in Eastern Australia. Apparent Electrical Conductivity (ECa) is measured by an electromagnetic induction (EM) instrument and is well correlated with three agronomically important soil properties – clay, water and salt content.

EM measurements should be taken when the soil moisture profile is full. Soil moisture is required for conduction between soil layers, hence measurements taken in dry soil will be underestimated. Higher soil moisture levels will result in more salts in solution and a higher measure of EM. As clay content increases,

35

PA REFER

ENC

E POC

KET G

UID

E – 2013


Soil�type�analysis�using�EM�surveys

BENEFITS OF SOIL TYPE ANALYSIS• Quantification of changing soil

properties within the field. This provides a base layer for subsequent analysis with other spatial data.

• Aids the selection of suitable crop types.

• Identification and quantification of areas with subsoil constraints such as increasing sodicity or salt loads.

• Forms the basis for zonal application maps.

TABLE 2: Correlation matrix showing a strong correlation (expressed as r2) between Dual EM (0–125cm) value and CEC, clay and silt.

Name CEC Chloride Clay EC dS/M ESP Moisture Sand Silt Dual EM

0–125cmCEC 0.69 0.46 0.62 0.71 0.21 0.01 0.71 0.83Chloride 0.69 0.77 0.96 0.94 0.43 0.41 0.64 0.73Clay 0.46 0.77 0.79 0.46 0.49 0.37 0.81 0.79EC dS/M 0.62 0.96 0.79 0.98 0.36 0.45 0.46 0.75ESP 0.71 0.94 0.46 0.98 0.37 0.35 0.47 0.31Moisture 0.21 0.43 0.49 0.36 0.37 0.45 0.05 0.25Sand 0.01 0.41 0.37 0.45 0.35 0.20 0.49 0.07Silt 0.71 0.64 0.81 0.46 0.47 0.05 0.49 0.90

Dual EM 0–125cm 0.83 0.73 0.79 0.75 0.31 0.25 0.07 0.90

FIGURE 1: EM Zone map showing six soil classes. The numbers displayed on the map (1-11) represent the location of soil samples collected to ground-truth the EM survey.

the soil’s ability to store moisture and nutrients increases producing a higher EM reading than a sandier textured soil at the same moisture level. In the absence of salt, a high ECa reading is attributed to high clay content and a higher yield potential.

Since the interpretation of an EM survey can be confusing, it must be ground-truthed. Soil samples taken at the time of the survey must be used to calibrate and interpret the EM data. The spatial variability of ECa across a field often reflects changes in yield potential. In a non-saline environment, clay content is often the primary factor affecting yield potential. This is due to its affect on water-holding capacity and the movement of

nutrient through the soil. The analysis of ECa can help to identify the underlying soil processes affecting crop yield, therefore aiding management decisions aimed at maximising yield and profitability.

EM maps can be used in a variety of ways; one such example is the calculation of effective rooting depth (Figure 2). This map is derived from an EM survey and the accompanying soil water laboratory samples, and is specific to the current crop of barley. The data reveals areas within the field which contain significant levels of chloride at various soil depths (25cm-130cm). This reduces the plant’s ability to access available water, and this effects the crop productivity.

36

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 2: Effective rooting depth map developed using information from the grower, soil water testing and an EM survey. The effective rooting depth highlights changes in plant available water as a result of changing levels of chloride in the soil profile.

Subsoil acidity is a major constraint to agricultural production across the

Western Australian wheat belt. This acidity is attributed to a range of factors which include:

• The prevalence of soils with naturally low pH buffering capacity; and,

• Historical use of nitrogenous fertilisers and leguminous pasture rotations.

At low pH (<4.8 in CaCl2), aluminium present in the soil becomes soluble and is toxic to plant roots. Low pH also

hinders the uptake of key nutrients such as phosphorus and can have a dramatic effect on crop yield. Lime is the most efficient way of ameliorating soils with low pH, however it is a costly input (between $50 and $150ha), with a long payback period (up to five years in soils with severe subsoil acidity). The ability to rapidly and efficiently identify soils with low pH for targeted lime applications is critical for long-term sustainability within a large part of the WA wheat belt.

Substantial variations in subsoil pH have been observed across the WA wheat belt and these variations have been strongly correlated to soil type. Electromagnetic and gamma radiometric surveys are used to identify low pH soils and this data is used for variable rate lime applications.

Figure 3 highlights the variation in soil profile conditions in WA. Soil variability was mapped using EM and gamma radiometric sensors. This data provides a template to guide targeted soil sampling which is sent for laboratory testing. Sample sites were selected across the range of EM values and the thorium and potassium channels of the gamma survey. Both topsoil (0–10cm) and mid-soil (10–30cm) were analysed for a range of attributes including pH, exchangeable cations and texture. Sites EM1 and Th2 both had low EM readings (~2mS/m ECa @ 0–75cm), with a sandy textured topsoil. Site Th2 however, had a highly elevated

37

PA REFER

ENC

E POC

KET G

UID

E – 2013


Identifying subsoil acidity using gamma�radiometrics�and�EM

BENEFITS AND CONSIDERATIONS• Combining EM and Gamma

Radiometrics provides a more comprehensive definition of soil variability in certain landscapes.

• Soil Survey maps provide a guide for targeting soil tests to ground-truth survey data.

• Collect soil samples at depths of 0–10cm and 10–30cm across the range of gamma values.

• Liming rates can be targeted to address actual pH levels which reduces production risks and improves return on investment Savings in lime costs using VRT can be achieved over uniform lime management.

• Soil sensors identify soil types predisposed to developing acidity under high production.

thorium reading, approximately 5 times higher than Site EM1. This was associated with a layer of very coarse ironstone gravel present between 10cm and 40cm. The soil core photos illustrate the vast differences between the subsoils of the two sites. The use of gamma radiometrics enhanced the EM survey to better quantify and understand soil variability.

There is a very strong correlation between EM and midsoil (10-30cm) pH.

The gravelly soils (as represented by Site Th2) are outliers due to their low water-holding capacity and low productivity. The sand dominant soils (i.e. those with low EM and low Th) have a higher water-holding capacity and historically are more productive. With a lower pH buffering capacity, the pH of these sand dominated soils has fallen over time, resulting in a decline in production potential.

Variable rate lime zones were

38

PA REFER

ENC

E POC

KET G

UID

E – 2013

FIGURE 3: Shallow EM Survey (a) showing low electrical conductivity (red areas) of the soil and subsequently minimal field variability. However the gamma radiometrics (Thorium) survey (b) indicates significant soil variability due to changes in actual soil type despite the low electrical conductivity.

(a)

(Th1)(Th1)

(Th2)(Th2)

(b)


developed for this field to target optimum lime rates to the pH of each zone. This resulted in a saving of $18/ha for lime inputs (compared to a uniform rate applied to the entire field). With repeat lime applications every four to five years (depending on production), surveying costs (approximately $20/ha for areas over 1000ha) would be accounted for in the medium term. It is also anticipated that improvements in productivity due

to targeted amelioration will provide additional future benefits.

39

PA REFER

ENC

E POC

KET G

UID

E – 2013

FIGURE 4: A strong positive relationship between pH and ECa was used to generate a VR Zone Map with 3 rates of lime (0.5, 1 and 1.5t/ha). Savings in lime due to the VRT averaged at $18/ha.


Zone (ha)

VR lime (total t)

Blanket (total t)

Saving ($50/t)

1 (11) 0.5t (5.5t) 1.5t (16.5t) $550

2 (18) 1.0t (18t) 1.5t (27t) $450

3 (27) 1.5t (40t) 1.5t (40t) $0

56ha 1.03t (48t) 1.5t (84t) $1000

($18/t)

The use of soil moisture monitoring tools has increased to achieve on-farm

water use efficiencies. Moisture probes are primarily used to calculate soil water deficits for irrigation scheduling. However their use in dryland farming systems to determine available soil water and crop water use, prior to in crop nutrient applications, is increasing.

Accurate irrigation scheduling will ensure that crop performance is not constrained by too much or too little water, which can negatively impact on yield. Optimising the timing and duration of irrigations will improve the overall efficiency of the crop and contribute significantly to the productivity and profitability.

C-probes monitor soil moisture continuously, highlighting diurnal plant water use. The probes can measure moisture at multiple depths in the root zone, effectively mapping plant water use on a daily basis. Using remote telemetry, this information can be accessed by the grower in real time.

Fertilisers and systemic chemicals applied during the cropping season will have their maximum uptake when the

40

PA REFER

ENC

E POC

KET G

UID

E – 2013


Moisture�(capacitance)�probe�placement

BENEFITS AND CONSIDERATIONS OF SOIL MOISTURE PROBES• Improve water efficiency by

monitoring crop water; a useful tool to calculate crop water deficit.

• Ensures optimal timing of in-crop fertiliser.

• Promotes a more responsive approach to crop management.

• EM surveys provide information about the majority soil type and can be used as a guide to site a soil moisture probe. Analysis of elevation data will ensure the probe is located in an area where normal water movement occurs.

John Deere Field connect moisture probe powered with a solar panel and remote telemetry antennas are used for real-time online observations.

crop is photosynthetically active. Using measurements of daily water use, the application of foliar fertiliser can be timed to coincide with periods of strong plant growth. This is a valuable tool in both irrigated and dryland systems. The ability to access real-time crop growth information enables a more responsive management approach. Growers can make decisions based on changing crop conditions which reduces the production risk and optimises yield.

Siting the probe in a suitable position in the field is crucial for ensuring good management decisions. Probes should be located in the predominant soil type to ensure that the majority of the crop will receive optimum watering. The probe should not be located in areas that are either higher or lower than a plane of best fit. In these areas, surface water will pool

or run more quickly than the other areas of the field. EM Surveys overlaid with elevation data will assist with optimal sensor placement in the field (see Figure 5).

41

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 5: EM survey (a) used to identify the majority soil type (highlighted in black) for optimal placement of a C-probe. Elevation data (b) can be used to further refine moisture probe placement by eliminating areas that pool or shed water quickly. The black areas (c) indicate the most suitable areas for moisture probe placement.

(a) (b) (c)

Sodicity is a term given to the amount of sodium held loosely on clay particles

within the soil. When exchangeable sodium percentage (ESP) reaches 5% (as a proportion of all cations) the soil is considered to be sodic. This is often indicated by structural problems.

The sodium weakens the bonds between soil particles when wetted resulting in excessive swelling of clay particles which often become detached and dispersed. Dispersion of clay particles can result in clogging of soil

pores. Swelling and dispersion reduces water infiltration and drainage resulting in water-logging and increased run-off. The weakening of soil structure makes sodic soils susceptible to soil degradation including erosion.

Sodicity can occur at any depth in the soil and is a common soil constraint across Australia. If the sodicity is predominately on the soil surface it can be effectively treated with the application of gypsum. Gypsum improves the soil structure and productivity of plants.

42

PA REFER

ENC

E POC

KET G

UID

E – 2013


Using�an�EM�survey�to�guide�gypsum application rates

FIGURE 6: Bar graph showing the wheat yield for each shallow EM soil zone. The blue line indicates the number of hectares in each EM zone. Soil zone 4 (green) is the majority soil type. Wheat yield is impacted most significantly by sodicity in zones 4, 5 and 6.

Sodicity levels may vary across a field and can be managed by applying gypsum at a variable rate. Shallow EM surveys are useful to determine the extent of surface sodicity within a field. Areas with high sodium levels will have a higher ECa. A simple correlation between yield data and shallow EM zone map will indicate areas where the sodicity level is impacting on yield. Several gypsum rates should be

trialled across the affected areas of the field or in a single soil zone. Strip trials are an effective method for determining gypsum response rates. The EM survey can provide a guide for the placement of a strip trial within a field.

43

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 7: Gypsum application map showing 3 major soil types. A four (4) treatment (40m width) strip trial is conducted to assess varying rates of gypsum (0t/ha – orange, 2.5t/ha – yellow, 5t/ha – red and 10t/ha – green). Wheat yield is impacted by increasing sodicity (red areas on yield map correspond with blue areas on soil class map).

Strip trials can be used to compare the effectiveness of different

agronomic practices such as crop type, variety, seed treatments and in-crop spraying applications on varying nutrient applications. Yield data collected at the end of the season is used to analyse field trials. With VRT, growers are no longer dependent on general agronomic recommendations but can generate their own local data to determine optimal input rates and management practices specific to their operation.

Soil and yield mapping tools have shown that soil characteristics and yield potential can change dramatically within

a field. This variability can be transferred to a strip trial and can compound any subsequent trial analysis. It is important to have an understanding of the underlying variability in a field before conducting any trials. A well collected and ground-truthed EM survey provides a synopsis of soil type variability and is a useful base layer when designing, siting and analysing a strip trial.

The placement of a field trial will depend in part on the variables to be tested. For example a trial assessing seeding rates or fertiliser rates, should be laid out to cover the range of major soil types. However, a trial assessing varieties should be placed within a single soil

44

PA REFER

ENC

E POC

KET G

UID

E – 2013


Using�EM�surveys�to�guide�the�placement of strip trials

FIGURE 8: A variable rate fertiliser response trial overlaid on EM soil zones. The trial tested 3 rates of nitrogen fertiliser, applied as a starter and later as an in-crop foliar application. The trial revealed the application of lower rates of DAP (on all soil types) at sowing, reduced early establishment of barley. Low rates of in-crop nitrogen on areas with a high salt load (and subsequently reduced plant available water shown in blue), further reduced the canopy of the crop and the use of soil water. Although this translated to a lower yield in these areas, the quality of the barley was improved with lower protein content and a reduction in the quantity of screenings.

Zone Area Colour Rate

1 22.3 50

2 81.5 50

3 62.4 50

4 13.3 100

5 13.3 50

6 13.3 10

7 27.8 50

zone which offers relative uniformity and minimises the influence of soil variables.

When trials are placed over a range of soil types, treatments including a control (or ‘do nothing different’ treatment) may be analysed and compared using EM zones. Comparing treatments by soil type will prevent the analysis becoming confounded by variable soil properties. Often the effect of the treatment is not visually obvious, particularly in raw yield data. Analysing the averaging yield and standard deviation by zone is more likely to highlight a treatment effect.

45

PA REFER

ENC

E POC

KET G

UID

E – 2013


BENEFITS AND CONSIDERATIONS• Use an EM survey to determine if the

trial should run across all soil types or within a representative soil type.

• Consider the number and rate of treatments:– Usetreatmentsthatarelikelyto

make a difference; and,– Keepinputratetrialssimple.

• If you use a yield monitor to assess treatments, ensure it is properly calibrated.

• If the yield map doesn’t reveal visual differences between treatments, statistical analysis of treatments by EM zone will be more likely to show treatment differences.

• Design each treatment width to fit within harvesting widths.

• Sow and harvest the trial in the same direction.

• Ensure the trial is harvested so the harvester’s comb does not enter the neighbouring treatment and keep the comb full through the centre of the treatment.

• Treatment effects can vary seasonally with weather and other external growing conditions.

• Treatments can interact and have a ‘follow on’ effect between seasons, particularly with input based treatments.

Variable-rate irrigation (VRI) is a tool that delivers irrigation water at rates that

match the needs of individual areas within fields.

Most agricultural fields have variable topographic and soil conditions which result in variable water-holding capacities, drainage rates and infiltration rates. Centre pivots are relatively efficient in their application of water, however their uniform application does not produce optimal irrigation efficiencies. The solution to this problem is offered by a VRI which can automatically adjust water application rates to match the variable needs of the field.

The first step in assessing the need for VRI is to determine the degree of field variability. The greater the non-uniformity of a field, the greater the potential benefit from variable rate inputs. In most cropping systems, water is the primary input driving crop production. Irrigation is required in many instances due to inadequate rainfall. Variable application of water improves yield production by ensuring the crop receives optimal rates of water. This can also have beneficial effects on the efficiency of fertiliser inputs. VRI can also offer substantial water savings over time where non-cropped areas such as roads or drainage lines exist under the pivot.

Field variability can be measured by mapping soil properties and topography using EM and RTK GPS respectively.

Analysing each variable’s influence on yield production will help identify how best to vary the irrigation application depth.

In its simplest form, VRI can be inexpensively implemented by dividing a pivot into pie sections with different water needs and adjusting the pivot travel speed every few degrees as it rotates through the sections. This can be done manually or through the pivots control panel. Pressure and flow rate remain constant while application depth increases or decreases based on a pre-assigned prescription (Rx) in the form of pie slice-shaped sectors.

Using this system, the irrigation application rates are targeted to meet the average water requirement for each pie-shaped area. For example, if the average EM value within a given sector indicates a lighter soil with a lower water-holding capacity, the pivot speed may be decreased to apply more water. Conversely, if the average EM value within a given sector indicates a heavier soil with a higher water-holding capacity, the pivot speed can be increased to apply less water. Landscape changes will identify areas where speeds can be increased for depressions and decreased over ridges. This level of irrigation application management is commonly referred to as VRI Speed Control. VRI Speed Control is the low-cost way to match irrigation application depth to soil type and can be easily applied to any centre pivot

46

PA REFER

ENC

E POC

KET G

UID

E – 2013


Precision irrigation: Precision Ag for pivots

equipped with position-based speed control.

A more advanced VRI system is VRI Zone Control which provides true variable rate irrigation with a much higher level of irrigation application management. Individual sprinklers or banks of sprinklers along the pivot span can be controlled independently of one another using a series of pulsing valves. The number of zones this system can accommodate is virtually unlimited enabling water management on a fine scale. By creating smaller management zones, the opportunity to minimise the yield limiting effects of both under and over-irrigation increases substantially. By meeting soil and crop needs with exact water amounts spatially, growers have the opportunity to save both water and increase yields. An optimal VRI design must be assessed on

47

PA REFER

ENC

E POC

KET G

UID

E – 2013


FIGURE 9: Demonstrates the use of VRI speed control to adjust the rate of water applied to pie shaped regions of a field. Soil variability is characterised using EM where low ECa (red) correlates to the sandier areas and higher ECa (blue) represents heavier soils (a). The optimal irrigation design (b) is generated for the field where higher rates of water are represented by red (slower pivot speeds) and lower water rates are represented by blue (higher pivot speeds). The optimal design is modified to produce a VRI Speed control map (c) which applies variable rates of water in pie-shaped regions.

BENEFITS AND CONSIDERATIONS OF USING PRECISION IRRIGATION• The aim of VRI is to increase water

use efficiency, improve yields and maximise profitability over the entire field.

• Variable application of water improves yield production through the delivery of optimal irrigation rates and will increase the efficiency of other inputs such as fertiliser or seeding rate.

• VRI Speed Control is the low-cost way to match irrigation application depth to soil type and can be easily applied to any centre pivot equipped with position-based speed control.

a field by field basis and will be dependent on the degree of field variability weighed against the economic and environmental benefit of increased irrigation control.

48

PA REFER

ENC

E POC

KET G

UID

E – 2013

FIGURE 10: The VRI Zone control map applies water on a finer scale and will be depend on the configuration of the sprinklers. The rate of water application is determined by the frequency with which the sprinklers cycle off. When the sprinklers cycle off infrequently (shown in red) this results in a higher rate of water application. Conversely when the sprinklers cycle off at a higher frequency (shown in blue) this will produce a lower rate of water application.


Some crops, such as cotton, have a very structured yet indeterminate

growth pattern. They are susceptible and highly responsive to management and environmental factors. Upon cessation of flowering, competition for water and

nutrients between the vegetative and reproductive growth phases begins within the plant. It is important to keep these growth phases balanced to maximise yield.

Cotton growers use chemical plant

49

PA REFER

ENC

E POC

KET G

UID

E – 2013


Variable rate application of crop growth regulators using remote sensing

FIGURE 11: PCD imagery (a) showing variability in plant biomass (blue = high biomass; red = low biomass),(b) a PIX® application map generated from the PCD image; higher rates of Pix® are applied to areas with higher biomass.

(b)(a)

Zone Area Colour Rate1 1.3 0

2 9.6 0

3 49.3 250

4 42.7 500

5 11.4 1000

growth regulators such as Mepiquat chloride (Pix®) to regulate cotton growth to their advantage. Applying plant growth regulators to modify early and mid-season growth offers the potential to influence yield. However the benefits to growth

regulators are limited by a grower’s ability to understand and apply them properly.

The key to modifying plant growth is understanding plant requirements during the various stages of development and how these requirements influence yield and quality. As the cotton plant responds rapidly to its environmental conditions, growth is often variable within a field. High resolution multi-spectral imagery, strategically acquired during the growing season can be used to identify variability in crop growth. Plant Cell Density (PCD) imagery, captures the reflectance of a crop in the near-infrared and red light spectrum providing valuable information about crop biomass and crop vigour. Quantifying variability in crop growth forms the basis for delineating zones for variable rate application of Pix® where rates will be set according to the level of vegetative growth in each zone.

50

PA REFER

ENC

E POC

KET G

UID

E – 2013

BENEFITS AND CONSIDERATIONS OF IMAGERY• Collect remotely sensed imagery

with a set purpose in mind, this will determine the timing of the acquisition.

• Ground-truth the vegetation index through directed sampling to determine appropriate Pix® rates.

• Imagery can be useful for future seasonal management.

• Variable biomass at flowering may be attributed to variable levels of excess nutrients; this will have an impact on future crops.

• Imagery is not a reliable method to predict final yield.


Yield variability, on a field scale is very common and in some circumstances

can be directly attributed to changes in available nutrients. Nutrient availability is related to:

• The chemical structure of the soil and its ability to make nutrients available to the plant; and,

• The variable uptake of nutrients by the previous crop.

In fields where significant inherent variability exists, it is financially viable to treat

the field differentially using VRT. Uniform fertiliser applications will in all likelihood result in over-fertilisation in some areas and under-fertilisation in other areas of the field. As a result yield potential will not be met.

Split applications of fertiliser minimises the risk of applying all the fertiliser at planting giving growers greater flexibility in their nutrient programs. VRT within season can target the changing demands of the crop ensuring yield potential is met in a cost effective and environmentally neutral manner.

51

PA REFER

ENC

E POC

KET G

UID

E – 2013


Variable rate application of in-crop fertilisers using remote sensing

FIGURE 12: Early season PCD image of cotton crop showing a range of PCD values: low biomass (red) to high PCD (dark blue) (a) with the accompanying histogram of the PCD image (b). The VRA map for in-crop fertiliser (c) with application rates (d) is generated from the PCD image. The biomass level within the crop is highly variable early in the season. To address this variability a VR nitrogen map is generated to apply additional N to low biomass areas.

(c)

(d)

(a)(b)

Zone Area Colour Rate1 13.2 0

2 20.5 0

3 41.1 250

4 7.0 500

Remotely sensed imagery can be used to provide information about crop growth and performance at mid-season. A vegetation index generated

from an image, gives a measure of crop conditions. The lower the index, the lower the biomass and vigour of the crop. The higher the index the greater the plant biomass. A vegetation index map can be used as a guide for optimising yield by determining areas which would benefit from additional fertiliser.

It is important to understand why biomass is variable within a field as it may not always be attributed to nutrient availability. Additional data layers such as EM, elevation (in irrigated and high rainfall scenarios) and yield are important in understanding the underlying cause of crop variability. Interpreting imagery in conjunction with EM layers is particularly useful in ensuring that within season fertiliser applications match plant available water-holding capacity and do not jeopardise yield or product quality.

52

PA REFER

ENC

E POC

KET G

UID

E – 2013

BENEFITS AND CONSIDERATIONS OF IMAGERY• Collect remotely sensed imagery

prior to applying top-up fertiliser.• By applying fertiliser to suit variable

production potential and seasonal conditions, initial input costs can be reduced. This minimises the risk of applying all the fertiliser at planting.

• Split fertiliser applications give growers greater flexibility with their fertiliser program.

• Ground-truth the vegetation index map by directed sampling to determine appropriate fertiliser application rates.

• Variable biomass in one season can indicate variable uptake of available nutrients during the season. This could be used to create a CR (crop response) map for subsequent crops.


Commercially available optical sensors or more commonly known as ‘on-the-

go’ sensors are designed to collect reflectance of the crop or soil while traversing a field.

The reflectance values and calculated biomass index can be stored and extracted from the device to create a biomass map. Vehicle based optical sensors use the same principles as airborne and satellite systems. However,

the use of these traditional remote sensing platforms is limited by cost, cloud and timing.

‘On-the-go’ sensors are typically mounted on the boom of a spray rig or on top of the tractor cab depending on the sensor height requirements. A continuous flow of information is sent to a virtual terminal (monitor). Data may be extracted from the terminal as raw reflectance values or a biomass index using a USB

53

PA REFER

ENC

E POC

KET G

UID

E – 2013


Collection of biomass data using ‘on-the-go’ active sensors

(PHOTO: Courtesy of Yara Australia)

54

PA REFER

ENC

E POC

KET G

UID

E – 2013

or wireless connection). Data can be processed and interpreted with other spatial information using a GIS program.

The biomass map can be used in a variety of ways:

• To create a variable rate nitrogen map which can be loaded into a VRT controller. Data should be ground-truthed with biomass samples and the appropriate laboratory testing.

• Taken late season, can highlight the impact of anomalies (e.g. spray-drift or hail damage) on a crop.

• Can compliment other spatial data and enhance a growers understanding of within field variability.

‘On-the-go’ sensors can be connected to a VRA system to sense and treat in real time. Sensor based VRA systems measure crop and/or soil properties as they travel across a field. An input is delivered by a controller at the location at which the sensor measurements are taken.


FIGURE 13: NDVI data collected using a pair of tractor cab mounted Cropspec® NDVI cameras and interpolated to create a mid-season (July) biomass map. The map was used with other data layers including an EM survey to create an in-crop VR foliar nitrogen map.

Layer name NDVIField 1Season 2010Min 1.49Mean 25.02Max 46.36Std deviation 6.03CV 24.09%Total 16675.14Area 666.52ha

Since all crops are variable in their growth, level of biomass and final

grain yield, there will be a variable uptake of nutrients from the soil. A VR nutrient replacement map is calculated using the mean yield value of each yield zone to generate a series of nutrient replacement

values. Using standard nutrient removal values; which describe the amount of nutrient carried by each tonne of grain, and fertiliser recommendations for the

55

PA REFER

ENC

E POC

KET G

UID

E – 2013


Creating a nutrient replacement map using yield data

BENEFITS AND CONSIDERATIONS OF USING YIELD DATA FOR GENERATING A NUTRIENT REPLACEMENT MAP• Zone based nutrient replacement

maps take variable yield into consideration and ensure that appropriate fertiliser rates are applied to the field.

• In the northern cropping region it is difficult to predict the levels of nitrogen fixed by legume crops, especially in areas where variable levels of mineral N are found.

• The amount of residual stubble can be estimated using grain yield and harvest index (HI). HI is the ratio of grain yield to the above ground biomass (0.2-0.5 for most winter cereals).

• Using yield data to calculate nutrient removal assumes that yield potential was attained across the field and that soil nutrient levels (for the nutrient under investigation) were not limiting for the growth of the previous crop.

• Amount of Stubble (t/ha) = grain yield (t/ha X (1-HI)/HI).

FIGURE 14: A variable rate nutrient replacement map can be calculated using the yield map from the previous crop and a formula which describes the amount of nutrient associated with each tonne of grain.

Zone Area Colour N in Soil VR N to apply

1 86.2 45 20

2 208.6 30 50

3 217.8 10 90

4 72.7 0 120

following crop (see Table 3), an application rate can be calculated for each zone. This zone approach will account for the variability within the field. A nutrient replacement strategy has the potential to offer environmental benefits over a conventional application without sacrificing profitability.

In the case of a legume crop, which may be used as part of the cropping rotation, nitrogen contribution to the soil due to nitrogen fixation must be

calculated. Unlike grain crops, where regions of better growth will translate to increased nutrient removal, regions of better growth in a legume crop will result in greater nitrogen fixation which is then available for a future crop. The variable fixing of nitrogen by a legume will have the effect of a natural variable rate fertiliser.

56

PA REFER

ENC

E POC

KET G

UID

E – 2013


TABLE 3: Nutrient removal estimations in grain and stubble for wheat, barley, canola and seed cotton (6 bales/ha and 10 bales/ha). Nitrogen contributions by chickpeas and faba beans to soil N are also listed.

Nutrient N Removal (per t/grain)

Removal (per t/Stubble)

CanolaSource: Canola best management practice guide for south eastern Australia page 31.

Nitrogen 40kg/ha 10kg/haPhosphorus 7kg/ha 2kg/haSulfur 10kg/ha 3.2kg/haPotassium 9kg/ha 26kg/ha

WheatSource: Canola best management practice guide for south eastern Australia page 31.

Nitrogen 21kg/ha 8kg/haPhosphorus 3kg/ha 0.7kg/haSulfur 1.5kg/ha 1.5kg/haPotassium 5kg/ha 7kg/ha

Barley Source: Canola best management practice guide for south eastern Australia page 31.

Nitrogen 20kg/ha 21kg/haPhosphorus 2.5kg/ha 0.7kg/haSulfur 1.5kg/ha 1.5kg/haPotassium 4.5kg/ha 18kg/ha

Cotton Source: NUTRIpak page 1.

Nutrients removed in seed & lint

6 bales/ha (Lint Yield)

10bales/ha (Lint Yield)

Nitrogen 43kg/ha 116kg/haPhosphorus 15kg/ha 26kg/haSulfur 4kg/ha 10kg/haPotassium 25kg/ha 47kg/ha

N contributions by Legumes: Source GRDC: : www.grdc.com.au/GRDC-UpdatePaper-Herridge2009-NitrogenContribution

Crop Crop N fixedChickpeas 107kg/haFaba Beans 122kg/ha

The object of any surface water management is to maximise crop

yield, reduce the area subject to ponding and minimise the cost of drainage work. Agricultural fields which have variable topography, or are very flat, will often experience ponding in depressions. These poorly drained areas are subject to water-logging after excessive rain. In the absence of artificial drainage, the soil in these areas will become saturated, affecting crop root development and

causing stress to the crop. Water-logging slows crop growth, depresses tillering of cereals and in some situations may even kill the crop. There is also a high risk of de-nitrification caused by anaerobic (lack of air) conditions.

Some common drainage techniques include:

• Adding a ‘spoon’ drain or ditch;

• Laser or GPS controlled levelling;

• Changing the direction of farming in very flat areas; and,

• Adding raised beds.

These operations allow water to leave

57

PA REFER

ENC

E POC

KET G

UID

E – 2013


Surface water ponding impact analysis

BENEFITS AND CONSIDERATIONS OF ASSESSING SURFACE WATER IMPACT• Examining surface water flow

when re-designing farm-layout can significantly improve yield potential.

• RTK GPS is required to collect elevation data that can be used to develop topographic layers which describe surface water movement.

• Contour maps can be used to understand the direction of water flow in a field.

• Analysing depression maps with high resolution imagery and yield data, quantifies the impact of surface water movement on yield potential.

• GPS controlled levelling is suited to both dryland and irrigated farming where advances in software enabling measureable reductions in earthworks.

FIGURE 15: Image shows the yield data overlayed on a mid-season satellite image. The standing water can be clearly seen (grey) in the satellite image, and is reflected by low yielding areas in the yield map. Yield losses due to water-logging were calculated at $1,200 per hectare.

a field and reduce water ponding. A drainage design will be field specific and will depend on the topography, soil type and cropping system. Surface drainage will improve field conditions following a rainfall event, allowing more timely planting and harvesting.

High accuracy elevation data collected with an RTK GPS can be used to create a variety of topographical maps that can show the natural flow of water or identify areas of the field that are high or low lying. Overlaying this data with crop information (imagery or yield data) can help to explain crop growth anomalies that can be attributed to elevation changes. Elevation data is useful for determining the

economic benefit of implementing surface drainage including how and where any work should be undertaken. An estimation of soil erosion and deposition from a flood or intense rainfall event can be calculated using pre-event and post-event elevation data. Using this information, an estimate of the cost of remedial action can be weighed against the projected improvement in productivity.

Elevation data can be collected using an RTK GPS steering system during a grower’s normal field operations. In the absence of an RTK system, a grower may employ the services of a contractor.

58

PA REFER

ENC

E POC

KET G

UID

E – 2013

FIGURE 16: A high resolution NDVI satellite image (orange is poor growth, blue is good growth), overlayed with high accuracy contour lines. The image clearly shows that poor surface water drainage is having a significant impact on productivity with more than 50 percent of the field severely affected by water-logging. (Source Tim Neale)


Increasing pressure on the availability of water resources and anticipated future

accountability to natural resource groups has lead to water becoming a highly valuable and scarce agricultural commodity.

Consequently, irrigators are concerned about their ability to harvest rainwater and store it efficiently on-farm. Seepage and evaporation are the greatest contributors to water loss from storage reservoirs. EM technology and elevation mapping offer an opportunity to reduce water loss from storage dams and improve water efficiencies and profitability.

Elevation mapping provides an accurate measure of water reservoir

capacity, enabling growers to assess their on-farm water capacity. Data collected with an RTK GPS can be used to accurately (+/-2cm) map the depth of a reservoir. This data can then be used to calculate the volume of the reservoir. An accurate measure of water storage capacity will assist growers in their assessment and budgeting of water through the cropping season. EM surveys are regularly used to map bulk electrical conductivity of soils, identifying areas of changing clay content and salinity. As clay content and soil mineralogy influence deep drainage, EM surveys offer a basic tool that can identify the location of potential leaks within the reservoir.

59

PA REFER

ENC

E POC

KET G

UID

E – 2013


Water reservoir mapping

FIGURE 17: A 3-dimensional view of a water storage showing changes in elevation (a) and ECa (b). Apparent electrical conductivity is highly correlated to clay content (blue = high clay content; red = lower clay content).

(a) (b)

The Irrimate™ Storage Meter is a simple yet accurate pressure sensor that continuously measures and records storage depth, volume and water surface area. It is a particularly useful tool for calculating water balances as it shows a continuous record of inflows and outflows of water allowing the calculation of seepage and loss through evaporation.

Mapping soils using EM provides an opportunity to evaluate suitable reservoir sites and guide any excavation to avoid exposing soils that are vulnerable to deep drainage.

60

PA REFER

ENC

E POC

KET G

UID

E – 2013BENEFITS AND CONSIDERATIONS OF MAPPING WATER RESERVOIRS• EM surveys can increase on-farm

efficiencies by helping to optimise the layout of irrigated fields, supply channels and water reservoirs.

• EM can identify potential deep drainage issues with existing storages.

• Elevation mapping gives an accurate measure of water storage capacity.


61

PA REFER

ENC

E POC

KET G

UID

E – 2013

USEFUL RESOURCESGRDC Precision Agriculture Manual

Part 1: Precision Agriculture Manual – http://www.grdc.com.au/Resources/Publications/2006/08/PA-Manual-2006

Advanced Field-Scale Experimentation for Grain Growers – A guide to using precision agriculture to improve trial results: www.grdc.com.au/AdvancedFieldScaleExperimentation

Proximal Crop Reflectance Sensors – A guide to their capabilities and uses www.grdc.com.au/ProximalCropReflectanceSensors

PA in Practice – Grain growers’ experience of using variable rate and other PA technologies www.grdc.com.au//uploads/documents/PA-in-practice1.pdf www.grdc.com.au/PAinPractice2

Where does PA Pay? – WA Crop Updates 2011 www.grdc.com.au/GRDC-UpdatePaper-Lawes2011-PAPays

Barriers to PA Adoption – www.grdc.com.au/GRDC-UpdatePaper-Mandel2011-PAobstacles

When does VRT Fertiliser Pay – www.grdc.com.au/GRDC-UpdatePaper-RobertsonEtAl2009-FertiliseSoilType

Using PA to Identify Nutrient Hotspots – www.grdc.com.au/GRDC-UpdatePaper-Lawes-NHotspots

Designing Farm Trials with PA Equipment – www.grdc.com.au/GRDC-UpdatePaper-Lawes-PATrialDesign

GRDC publications Ground Cover Issue 88 – Precision Agriculture Supplement: www.grdc.com.au/GCS88 • Adoption need not be complicated • The dollar return from variable rate • Next steps in PA • Trials not tribulations • Soil scientists tap into the radio waves • Variable rates for constrained subsoils • Site-specific management of weeds • A third eye for canopy management • Precision placement for disease control • Selective grain harvesting • PA beyond guidance

Agronomy jigsaw – Finding the pieces that maximise water use efficiency: www.grdc.com.au/DAW00193

PA Educational Resources – sydney.edu.au/agriculture/pal/publications_references/educational_resources.shtml

N Contributions by legumes – www.grdc.com.au/GRDC-UpdatePaper-Herridge2009-NitrogenContribution

62

PA REFER

ENC

E POC

KET G

UID

E – 2013

Precision Cropping Technologies Pty Ltd – www.pct-ag.com

Precision Ag Help Desk – www.pahelpdesk.com

GRDC PA Links – http://www.grdc.com.au/PALinks

PA Lab (University of Sydney) – http://sydney.edu.au/agriculture/pal/

Precision Ag Research Group (UNE) – www.une.edu.au/parg/

PA Research and Application in Australasia – http://sydney.edu.au/agriculture/pal/research_symposia/archive_of_proceedings.shtml

The PA Help Desk – www.pahelpdesk.com is all things PA from machinery to services. There is a comprehensive PA Library to help you find information to answer your questions, resources, and training materials or you can post a technical question in the help desk.

USEFUL RESOURCES

www.grdc.com.au

Documents

Applying PA - GRDC · histogram of this data. INTRODUCTION Understanding the causes and impact of variability FIGURE 1: An example of spatial data which includes a spatial map, data