Water has a dielectric of approximately 80, so if we assume that a dry soil has a dielectric of 5 (VWC = 0.00 m3/m3), then changes to the bulk dielectric read by the sensor will be attributable to changes in water content. If you read a METER sensor in air, which has a dielectric constant of 1, you will quite naturally get a negative number.
Improving accuracy of dielectric soil moisture sensors
There are two common causes for negative readings on a METER soil moisture sensor:
1) Poor contact with the soil resulting from improper installation or disturbance
Air gaps next to a sensor will contribute the lower dielectric of air to the measurement resulting in an underestimation of VWC. Air gaps can arise if enough care is not taken to pack soil around the sensor body to approximate native bulk density. Sensors that have been disturbed, such as having a cable tripped over, can also develop air gaps that can result in negative results in dry soils. (To reduce the possibility of air gaps when installing METER sensors, use the new TEROS borehole installation tool)
2) A calibration that is inappropriate for the soil in which the sensor is installed
If the standard mineral calibration is used, an error of ~ 3-4% can be expected in METER sensor readings. Negative numbers can be observed in oven-dry soils (by definition a VWC of 0.0 m3/m3) down to ~ – 0.02 m3/m3 with no malfunction of the sensor. The dielectric constant of the soil is assumed to be 5 and this is a valid assumption in the majority of soils of primarily mineral composition. If your soil has a different dielectric constant, such as can occur in soils with high organic matter content, then the uncertainty in your measurements will increase. This is not a large problem because METER sensors can be calibrated to match a given soil with very little investment in resources.
Want more details?
Watch our webinar titled Why Does My Sensor Read Negative below. This webinar is designed for those who use electromagnetic sensors (capacitance/TDR/FDR) to measure soil water content. Learn about the theory behind the measurements. Dr. Doug Cobos discusses:
What is volumetric water content?
Dielectric measurement theory basics
Dielectric mixing models
Why might a sensor read a negative VWC?
Can a sensor really have 2% VWC accuracy for all soils?
Sources of error in dielectric measurement methods
Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.
Before selecting a site, scientists should clearly define their goals for gathering data.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
Sensor installation: Sensors are not installed correctly, causing inaccurate readings
Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.
The HYPROP and WP4C provide the ability to make fast, accurate soil moisture release curves (soil water characteristic curves-SWCCs), but lab measurements have some limitations: sample throughput limits the number of curves that can be produced, and curves generated in a laboratory do not represent their in situ behavior. Lab-produced soil water retention curves can be paired with information from in situ moisture release curves for deeper insight into real-world variability.
Soil water characteristic curves help determine soil type, soil hydraulic properties, and mechanical performance and stability
Moisture release curves in the field? Yes, it’s possible.
Colocating matric potential sensors and water content sensors in situ add many more moisture release curves to a researcher’s knowledge base. And, since it is primarily the in-place performance of unsaturated soils that is the chief concern to geotechnical engineers and irrigation scientists, adding in situ measurements to lab-produced curves would be ideal.
In this brief 20-minute webinar, Dr. Colin Campbell, METER research scientist, summarizes a recent paper given at the Pan American Conference of Unsaturated Soils. The paper, “Comparing in situ soil water characteristic curves to those generated in the lab” by Campbell et al. (2018), illustrates how well in situ generated SWCCs using the TEROS 21 calibrated matric potential sensor and METER’s GS3 water content sensor compare to those created in the lab.
Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, continues (see part 1) to discuss the strengths and limitations of IoT technologies for irrigation water management.
Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost.
LoRaWAN (a vendor-managed solution see part 1) is ideal for monitoring applications where sensors need to send data only a couple of times per day with very high battery life at very low cost. Cellular IoT, on the other hand, works best for agricultural applications where sensors are required to send data more frequently and irrigation valves need to be turned on/off. Low-Power Wide-Area Networking (LPWAN) technologies need gateways or base stations for functioning. The gateway uploads data to a cloud server through traditional cellular networks like 4G. Symphony Link has an architecture very similar to LoRaWAN with higher degree of reliability appropriate for industrial applications. The power budget of LTE Cat-M1 9 (a network operator LPWAN) is 30% higher per bit than technologies like SigFox or LoRaWAN, which means more expensive batteries are required. Some IoT technologies like LoRa and SigFox only support uplink suited for monitoring while cellular IoT allows for both monitoring and control. LTE-M is a better option for agricultural sensor applications where more data usage is expected.
NB-IoT is more popular in EU and China and LTE Cat-M1 in the U.S. and Japan. T-Mobile is planning to deploy NB-IoT network in the U.S. by mid-2018 following a pilot project in Las Vegas. Verizon and AT&T launched LTE Cat-M1 networks last year and their IoT-specific data plans are available for purchase. Verizon and AT&T IoT networks cover a much greater area than LoRa or Sigfox. An IoT device can be connected to AT&T’s network for close to $1.00 per month, and to Verizon’s for as low as $2 per month for 1MB of data. A typical sensor message generally falls into 10-200 bytes range. With the overhead associated with protocols to send the data to the cloud, this may reach to 1KB. This can be used as a general guide to determine how much data to buy from a network operator.
Studies show there is a potential for over 50% water savings using sensor-based irrigation scheduling methods.
What the future holds
Many startup companies are currently focused on the software aspect of IoT, and their products lack the sensor technology. The main problem they have is that developing good sensors is hard. Most of these companies will fail before batteries of their sensors die. Few will survive or prevail in the very competitive IoT market. Larger companies who own sensor technologies are more concerned with the compatibility and interoperability of these IoT technologies and will be hesitant to adopt them until they have a clear picture. It is going to take time to see both IoT and accurate soil/plant sensors in one package in the market.
With the rapid growth of IoT in other areas, there will be an opportunity to evaluate different IoT technologies before adopting them in agriculture. As a company, you may be forced to choose specific IoT technology. Growers and consultants should not worry about what solution is employed to transfer data from their field to the cloud and to their computer or smart phones, as long as quality data is collected and costs and services are reasonable. Currently, some companies are using traditional cellular networks. It is highly likely that they will finally switch to cellular IoT like LTE Cat-M1. This, however, may potentially increase the costs in some designs due to the higher cost of cellular IoT data plans.
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Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, discusses where and why IoT fits into irrigation water management. In addition, he explores possible price, range, power, and infrastructure road blocks.
Wireless sensor networks collect detailed data on plants in areas of the field that behave differently.
Studies show there is a potential for water savings of over 50% with sensor-based irrigation scheduling methods. Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost. Wireless sensor networks can collect data on plants in a lot of detail in areas of the field that behave differently. The need for wireless sensors and actuators has led to the development of IoT (Internet of Things) solutions referred to as Low-Power Wide-Area Networking or LPWAN. IoT simply means wireless communication and connecting to some data management system for further analysis. LPWAN technologies are intended to connect low-cost, low-power sensors to cloud-based services. Today, there are a wide range of wireless and IoT connectivity solutions available raising the question of which LPWAN technology best suits the application?
IoT Irrigation Management Scenarios
The following are scenarios for implementing IoT:
buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
relying on the infrastructure from a network operator LPWAN (e.g., LTE Cat-M1, NB-IOT, Sigfox, Ingenu, LoRWAN).
This is how cellular network operators or cellular IoT works. LPWAN technology fits well into agricultural settings where sensors need to send small data over a wide area while relying on batteries for many years. This distinguishes LPWAN from Bluetooth, ZigBee, or traditional cellular networks with limited range and higher power requirements. However, like any emerging technology, certain limitations still exist with LPWAN.
Individual sensor subscription fees in cellular IoT may add up and make it very expensive where many sensors are needed.
IoT Strengths and Limitations
The average data rate in cellular IoT can be 20 times faster than LoRa or Symphony Link, making it ideal for applications that require higher data rates. LTE Cat-M1 (aka LTE-M), for example, is like a Ferrari in terms of speed compared to other IoT technologies. At the same time, sensor data usage is the most important driver of the cost in using cellular IoT. Individual sensor subscription fee in cellular IoT may add up and make it very expensive where many sensors are needed. This means using existing wireless technologies like traditional cellular or ZigBee to complement LPWAN. One-to-many architecture is a common approach with respect to wireless communication and can help save the most money. Existing wireless technologies like Bluetooth LE, WiFi or ZigBee can be exploited to collect in-field data. In this case, data could be transmitted in-and-out of the field through existing communication infrastructure like a traditional cellular network (e.g., 3G, 4G) or LAN. Alternatively, private or public LPWAN solutions such as LoRaWAN gateways or cellular IoT can be used to push data to the cloud. Combination of Bluetooth, radio or WiFi with cellular IoT means you will have fewer bills to pay. It is anticipated that, with more integrations, the IoT market will mature, and costs will drop further.
Many of LPWAN technologies currently have a very limited network coverage in the U.S. LTE Cat-M1 by far has the largest coverage. Ingenu, which is a legacy technology, Sigfox and NB-IOT have very limited U.S. coverage. Some private companies are currently using subscription-free, crowd-funded LoRaWAN networks to provide service to U.S. growers: however, with a very limited network footprint. Currently, cellular IoT does not perform well in rural areas without strong cellular data coverage.
In two weeks: Dr. Osroosh continues to discuss IoT strengths and limitations in part 2.
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Whether researchers measure soil hydraulic properties in the lab or in the field, they’re only getting part of the picture. Laboratory systems are highly accurate due to controlled conditions, but lab measurements don’t take into account site variability such as roots, cracks, or wormholes that might affect soil hydrology. In addition, when researchers take a sample from the field to the lab, they often compress soil macropores during the sampling process, altering the hydraulic properties of the soil.
Roots, cracks, and wormholes all affect soil hydrology
Field experiments help researchers understand variability and real-time conditions, but they have the opposite set of problems. The field is an uncontrolled system. Water moves through the soil profile by evaporation, plant uptake, capillary rise, or deep drainage, requiring many measurements at different depths and locations. Field researchers also have to deal with the unpredictability of the weather. Precipitation may cause a field drydown experiment to take an entire summer, whereas in the lab it takes only a week.
The big picture—supersized
Researchers who use both lab and field techniques while understanding each method’s strengths and limitations can exponentially increase their understanding of what’s happening in the soil profile. For example, in the laboratory, a researcher might use the PARIO soil texture analyzer to obtain accurate soil texture data, including a complete particle size distribution. They could then combine those data with a HYPROP-generated soil moisture release curve to understand the hydraulic properties of that soil type. If that researcher then adds high-quality field data in order to understand real-world field conditions, then suddenly they’re seeing the larger picture.
Table 1. Lab and field instrument strengths and limitations
Below is an exploration of lab versus field instrumentation and how researchers can combine these instruments for an increased understanding of their soil profile. Click the links for more in-depth information about each topic.
Particle size distribution and why it matters
Soil type and particle size analysis are the first window into the soil and its unique characteristics. Every researcher should identify the type of soil that they’re working with in order to benchmark their data.
Particle size analysis defines the percentage of coarse to fine material that makes up a soil
If researchers don’t understand their soil type, they can’t make assumptions about the state of soil water based on water content (i.e., if they work with plants, they won’t be able to predict whether there will be plant available water). In addition, differing soil types in the soil’s horizons may influence a researcher’s measurement selection, sensor choice, and sensor placement.
Different readings in soil moisture sensors are caused by spatial variation in water content (see part 1). These readings provide researchers valuable information about soil texture, watering patterns, and water use. This week, learn two more strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site.
In some crop studies, it may be important to account for horizontal variation.
Strategy #2: Crop Studies—Representing Variation in a Homogeneous Environment
In some research projects, it will be important to account for horizontal variation. How variable is the water content across a field? We did an experiment in which we set out a transect across a field of bare, tilled soil. Using a METER EC-5 soil moisture probe connected to a Procheck meter, we sampled water content at one-meter intervals over a 58-meter distance. The individual readings are shown in Figure 1.
Figure 1. You can determine how many samples are necessary to characterize a homogeneous area in about an hour using and EC-5 soil moisture sensor and a ProCheck.
In this data set the samples are not spatially correlated. The variation is apparent. The mean water content of the data set is 0.198 m3m-3. The standard deviation is 0.023 m3m-3. The coefficient of variation is 12%. Using some simple geostatistics, we determined that three carefully placed sites would adequately represent the variation present in this very homogeneous environment. Of course, in some environments, samples will not be independent. If a semivariogram indicates that some underlying spatial factor influences soil moisture variability, you will have to consider that in your experimental design.
By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw conclusions from your data.
On a forested hillside, horizontal variation in soil moisture will obviously be significant. Determining how many sensors to use and where to place them is not at all trivial. Stratified sampling—systematically sampling from more uniform subgroups of a heterogeneous population—may be a better way to deal with this kind of variety. The researcher classifies the site into strata (eg., forested canopy, brush, hillside, valley) and evaluates the number of samples needed to statistically represent the variation present within each stratum.
Many people allow for the variation in soil moisture values that come from slope, orientation, vegetation, and canopy cover. Some fail to consider the important soil-level variations that come from soil type and density. By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw reasonable conclusions from your data. Choose too few locations, and you run the risk of missing the patterns that will lead to higher level understanding. Choose too many, and not only will you be unable to afford your experiment, you may miss the patterns altogether as your experiment overflows with random abundance.
Sometimes researchers want to compare dissimilar sites.
Comparing Data from Different Sites or Strata
Comparing absolute water content numbers can give confusing results. Both measurements are volumetric water content, but 35% here vs. 15% there actually tells us very little. Was the site in sand or clay, or something in between? If conditions at the two sites are virtually identical, the comparison may make some sense. But often, researchers want to compare dissimilar sites.
Figure 2. Changes in VWC with depth (convention: negative values indicate depths below soil surface) for the same time period at Site 1.
Water potential measurements determined by converting absolute volumetric water content to soil water potential using a moisture characteristic curve specific to each soil type can be used to compare results across sites. Comparing relative values—quantities of water used in centimeters for example—can also be both useful and valid.
Figure 3 below illustrates an experiment we performed in a dryland field where water content measurements were made over a growing season at 30, 60, 90, 120, and 150 cm below a wheat crop. The graph of soil moisture data shows how water is taken up from successively deeper layers. By subtracting one profile from another and summing over the layers where change occurs (for instance, in Figure 2 above, subtract the far left line from the far right line to see how much water was used from May 10th to August 21st), you can determine the amount of water used by the plants over a particular period. If similar data were taken at different sites or in different strata, these relative values, in terms of quantified water use, could form the basis of solid comparison studies.
Figure 3. Soil water content in winter wheat measured at 30 cm increments
Read more about accurate soil moisture: Can you sample the profile without a profile probe? Find out.
How Do you Know You’re Getting Accurate Soil Moisture?
Researchers and irrigators may wonder if their soil moisture sensors are accurate because probes at different locations in the same field have different water content readings. Different readings in soil moisture sensors are caused by spatial variation in water content. These readings provide researchers valuable information about soil texture, watering patterns, and water use. Here are some ideas and strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site. Click the links for more in-depth information about accurate soil moisture.
One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard.
Horizontal vs. Vertical Variation
It’s helpful to distinguish variation in the vertical from variation in the horizontal. Most people expect strong vertical variation due to wetting and drying patterns, soil horizonation, and compaction. Water content can vary drastically over distances of only a few centimeters, especially near the soil surface. Horizontal variation is typically less pronounced in a bare or uniformly planted field, and at a given depth, it might be quite small. But surprisingly large variations can exist, indicating isolated patches of sand or clay or differences in topography. One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard. Knowing that sand has a low field capacity water content, he surmised (correctly) that he had found the sandy areas in the vineyard.
Soil moisture sensors sometimes measure unexpected things.
Because properly installed dielectric soil moisture sensors lie in undisturbed (and therefore unanalyzed) soil, they sometimes measure unexpected things. One researcher buried a probe in what appeared to be a very dry location and was startled to measure 25 to 30% volumetric water content. Those readings made the soil appear saturated, but obviously it wasn’t. She dug down to the sensor and found a pocket of clay. As she discovered, it is impossible to get much information from an absolute water content measurement without knowing what type of soil the sensor is in.
Since we expect variation, how do we account for it? How many probes are needed to adequately characterize the water content in an application or experiment? There is no simple answer to this question. The answer will be affected by your site, your goals, and how you plan to analyze your data. Here are some things you might consider as you plan.
If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot.
Strategy #1: Irrigation—Use Soil Moisture as an Indicator
What information do you have when you know a field’s volumetric water content? That number independently tells an irrigator very little. Soil moisture can be used like a gauge to show when a field is full and when it needs to be refilled, but the “full” and “empty” are only meaningful in context.
The goals of irrigation are to keep root zone water within prescribed limits and to minimize deep drainage. Understanding and monitoring the vertical variation lets you correlate a real-time graph of water use data with above-ground field conditions and plant water needs. It makes sense to place probes both within and below the root zone.
By contrast, measuring horizontal variation—placing sensors at different spots in the field—is not very helpful. If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot. Because there’s no way to adjust water application in specific spots, there’s no benefit to quantifying spatial variation in the horizontal. Like a float in a gas tank, a set of soil moisture sensors in the right spot will adequately represent the changing soil moisture condition of the whole field.
We recommend a single probe location in each irrigation zone with a minimum of one probe in the root zone and one probe below it. Additional probes at that site, within and below the root zone, will increase the reliability of the information for the irrigation manager, at minimal additional cost.
In two weeks: Learn two more techniques researchers use in crop studies and ecology studies to account for variability in order to obtain an accurate soil moisture picture.
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This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.
Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.
Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.
New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.
Current textbook knowledge of plant dehydration
The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.
Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.
How transpiration varies with water potential
There is a strong correlation between transpiration and plant water potential: as plant water potential becomes more negative, transpiration decreases. Some species are sensitive and show a rapid decrease in transpiration; other species exhibit a slower decrease.
Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which transpiration is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between transpiration and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).
Figure 1. The relationship between transpiration (stomatal conductance) and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.
Taking advantage of P50
The strong, and universal, relationship between transpiration and water potential is vital information for growers. A transpiration versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that transpiration is 80% of its maximum.
Irrigation Curves – a rapid assessment of plant hydration
Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between transpiration and hydration that indicates an optimal hydration point for a specific species or variety.
Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.
P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.
Example irrigation curves
Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Transpiration was measured daily, between 11am and 12pm, with an SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 (now called TEROS 21) Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When transpiration was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.
Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).
Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.
Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).
From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.
Further research is required
Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.
For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: email@example.com
Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289
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