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 finetune 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.
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 waterholding 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.
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 these tutorials 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 left is an EM map showing the range of EM values from 2 to 25. The height of each colour bar indicates the total area within the field corresponding to each EM value. 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.
Raw yield on the left and cleaned yield data on the right ready for surfacing.
However, for optimum results, professional data processing is recommended. For more information on yield data processing refer to Yield Mapping on page 24.
The 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.
A 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.
Interpretation techniques may range from quick and simple eyeballing to a 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 yield maps and vegetative images for a field may be followed by a 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 the following example from PCT Agcloud colour line help visualise a statistical comparison.
- Red lines are r squared <0.3 and generally indicating a poor correlation.
- Orange lines are r squared >0.3 and <0.6 and should be considered as ‘ok but with caution.
- Green lines are r squared greater than 0.6 and should consider highly correlated.
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 implementation stratergy 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.
Some general standards for interpreting the level of variability and the likelihood of payback for investment in VRT.
CV Value and 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. >- Generally not enough variability to warrant any action.
5-10% <15% Sufficient variability to expect to see a financial benefit in managing the variability in most crops. >- 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.
Soil Surveying – DualEM, EM38, Veris and TSM
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.
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. Older EM sensors should be calibrated daily for atmospheric conditions to ensure the accuracy of the instrument. Newer sensors such as those from DualEM are factory calibrated and should be more stable over time. However be sure to keep checking by rerunning a previous days pass and check the results.
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.
Below is a visual representation of the soil site and in this case Soil Chloride at different depths over the DualEM 100cm
Electrical conductivity is commonly measured at two or 4 soil depths. The maps from different depths 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.
Below is using PCT Agcloud Analytics to observe corrleations of soil coring data at different depth with soil survey layers – DualEM and Gamma Radiometrics K
Multi-depth EM surveys are useful for highlighting information about different growing regions of the soil profile.
The blue areas in one field for an EM map may highlight increasing clay content and improved PAW. Conversely in another field the blue areas in the deep EM map maybe attributed attributed to increasing clay content in addition to increasing concentrations of chloride which will reduce the plant available water capacity of the soil.
They may both be blue in a colour table but have very different ECa readings and soil sampling will help distinguish between them.
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.
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.
• 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 absorption. Potassium and sulfur are also usually measured.
• For detailed financial analysis, gamma radiometric data should be analysed with yield data.
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 30 cm as most gamma radiation detected at the soil surface is emitted from the top 30–40 cm of soil. On loose, deep sands (sand depth >60 cm), 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.
• 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.
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 shown here.
The thorium 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 60 cm 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.
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.
Soil Surveying – On the go pH
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
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 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.
Below shows the process of collection bottom left, interpolated layer top left and prescription map on the right.
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.
• Significant changes in soil pH often indicate significant changes in soil type and yield potential.
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
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 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.
One example is plant cell density, is the ratio of infrared to red reflectance, provides a measure of crop vigour. The PCD values cannot be used to indicate specific levels of biomass. This indicates a level of biomass variability within the field.
When a high level of variability is indicated by the PCD image this can provide a basis for differential management of inputs such as fertiliser, water and growth regulators.
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.
Below shows the effect 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.
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.
This is a quick summary of the vegetation indices available in PCT Agcloud.
NDVI, PCD & SVI
These indices produce similar results with a few caveats and have been used for such purposes as:
- Photosynthetic capacity of plant canopies
- General ‘condition’ or ‘health’ of vegetation
- Other correlations/estimations made from NDVI include:
- Leaf area index
- Chlorophyll concentration of leaves
Note that all these correlations cannot hold at once as they are not all completely related to each other.
Normalized Difference Vegetation Index (NDVI)
The granddaddy of vegetation Indices, NDVI conceived around 1970, normalizes the different between red and near infrared reflectance.
- Operates in functional range 0 to 1
- Tends to saturate easily on higher biomass crops
- A good starting point
The NDVI Wikipedia page is very good at detailing performance and limitations: https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index
Plant Cell Density (PCD)
A non-normalized vegetation index which is simply NIR / Red.
- Operations in functional range 0 to infinity. Often values 0 to 20.
- Can be useful to use as extremes are not normalized out as they are for NDVI so it can appear ‘less noisy’ than that SVI and ‘less saturated’ than NDVI
- Can be hard to work with as the range is never really known without examining the data whereas normalized indices will have functional range 0 to 1.
Satamap Vegetation Index (SVI) / Modified Triangular Vegetation Index 2 (MTVI2)
Historically Satamap took on MTVI2 and applied a fixed, unique colour scale and bundled up named it Satamap Vegetation Index (SVI). MTVI2 can still be used as a vegetation index with the rainbow color scale to compare alongside the other indices.
- MTVI2 / SVI is a solid choice in high biomass situations (resistant to saturation https://www.tandfonline.com/doi/abs/10.5589/m08-071) where you want the range to remain between 0 and 1
- Alongside red and NIR it exploits the green band. This attempts to account for soil color.
- MTVI2 is commonly used in narrow band and hyperspectral applications but we find it performs excellent with Sentinel 2 imagery, especially high biomass situations.
- Identical results to MCARI2
Figure 1: Common vegetation indices compared for drought stressed (low biomass) barley
Figure 2: Common vegetation indices compared for high biomass crop. Chemical damage bottom left, and fertiliser trial top right.
LAI & CCC estimations using neural networks
For LAI & CCC we use an on-the-fly implementation of the biophysical variables in the Scientific Toolbox Exploitation Platform (STEP) developed by European Space Agency (ESA). STEP take a neural network approach to calculating these parameters. To train this model the process uses simulated data to form a ‘generic’ algorithm. What this means is it should have reasonable performance in most geographic locations over several vegetation types but to use with caution. ESA have used this method extensively and successfully in the past for many other satellite missions.
Leaf Area Index (LAI)
“LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. It determines the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere.” – Sentinel2 ToolBox Level2 Products, 2016
Canopy Chlorophyll Content (CCC)
The chlorophyll content is a very good indicator of stresses including nitrogen deficiencies. It is strongly related to leaf nitrogen content (Houlès et al. 2001). – Sentinel2 ToolBox Level2 Products, 2016
Normalized Difference Red Edge (NDRE)
NDRE follows the same concept as NDVI, except in place of the Near infrared band, uses what is called ‘Red Edge’.
A healthy leaf will generally absorb red light and be highly reflective of NIR, by moving to a point somewhere between red and near infrared in place of infrared there is lower chance of saturation in high biomass crops as top of canopy reflectance will be less intense. In addition, there is some evidence to suggest NDRE can correlate with leaf nitrogen content (reference).
We generally don’t use NDRE much as we can mitigate saturation with PCD and SVI and experiment with leaf nitrogen content with CCC. Also, the red edge band is lower resolution (20m as opposed to 10m).
Moisture Stress Index (MSI)
MSI is an estimation of leaf water content. NIR is dived by shortwave infrared (SWIR). SWIR will reflect more as leaf water content decreases. NIR reflectance is not directly impacted by water content and is therefore used as a reference.
Similar to PCD, MSI is no normalised so we cannot know exactly what the range will be, but generally we see values from 0.4 to 2.
It is important to note that the values are inverted to a normal vegetation index. A high value indicates low water content/high plant stress.
Below is a chart that compares the values of MSI (blue), SVI (red), NDVI (green) & NDRE (yellow) throughout the growing season. You can quickly see the inverse nature of MSI and the non-saturating characteristic of SVI.
There are several different vegetation indices available and this document just scratches the surface. Each one has its own specific strengths and weaknesses. Having a brief practical understanding of how these can be applied will help in both interpreting the data and figuring out what to use in specific situations.
Sentinel2 ToolBox Level2 Products, 2016, https://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf
Houlès, V., Mary, B., Machet, J.M., Guérif, M., & Moulin, S. (2001). Do crop characteristics available from remote sensing allow to determine crop nitrogen status? In G. Grenier, & S. Blackmore (Eds.), 3rd European Conference on Precision Agriculture (pp. 917-922). Montpellier: Agro Montpellier
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.
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 usefulness 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.
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.
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.
Below is a list of cleaning/editing and erroneous data removal tools from PCT Agcloud. For a more detailed description of each please consult PCT.
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.
After passing through the filtering and editing phase of data import an automated processed yield map is the result.
The raw v clean data is shown here.