Next week learn about the four components of water potential—osmotic potential, gravitational potential, matric potential, and pressure potential.
Take our Soil Moisture Master Class
Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together. Plus, master the basics of soil hydraulic conductivity.
Recently, we wrote about scientists who were burying their data loggers (read it here). Radu Carcoana, research specialist and Dr. Aaron Daigh, assistant professor at North Dakota State University, used paint cans to completely seal their data loggers before burying them in the fall of 2015.
Paint can setup for buried data logger.
They drilled ports for the sensor cables, sealed them up, and when they needed to collect data, they dug up the cans, retrieved the instruments, and downloaded the data in a minute or less.
Here Radu gives an update of what happened when he dug up his buried instruments in the spring.
Results of the Paint Can Experiment
In May of this year, we dug up eighteen units (one data logger and four soil moisture sensors per unit) left in the field since November 2015—over six months.
Did moisture get into the paint cans? —We found only three cans with water in them, purely due to installation techniques used for that specific unit. The other fifteen units were bone dry, although total precipitation for the month of April only amounted to 3.63 inches, plus the snow melt.
How was data recording and recovery? —For six months, every 30 minutes the soil moisture sensors took readings, the data logger recorded, and we retrieved all of the data, complete and unaltered.
Only three cans with water in them, due to installation techniques.
What about power consumption? The batteries were good —over 90% did not need replacement. The power budget provided by five AA batteries was more than enough for reading four soil moisture sensors at 30-minute intervals.
What Happens Now?
In the spring of this year, we installed 18 more units in the third farm field, right after planting soya. We now have 36 individual units (~$1,000 value each unit) buried in the ground in the middle of a field planted with corn or soybean, since the beginning of May.
On October 13-14 (after 5 months), we accessed the first twelve units (Farm A). All 30 minutes of data was read, recorded, and downloaded (since May). The batteries and the other accessories were replaced, and then we sealed and reburied the cans. Only one unit out of twelve had an issue and was replaced: the battery exploded in the can (editor’s note: battery explosion is usually caused by a manufacturing defect and the risk can be lessened by purchasing higher quality batteries, although all types are susceptible to some degree). Since battery leakage will often corrode everything the acid touches, the data logger had to be sent back for repair and there may be partial data loss. The other 24 units (Farm B and C) will be accessed next week, weather permitting.
Over 90% of batteries did not need replacement.
Is the Paint Can Method Worth it?
We will continue to monitor and retrieve the data from the buried data loggers (We don’t use data loggers suited for wireless communication, because several factors guided us not to). The paint can system works very well if the installation is done correctly, with great attention to detail, and it costs only $2.00/can. However, there are improvements that could be made in order to have this method become a standard in soil research. For instance, though we are still using paint cans and other common materials, advancements in the design of waterproof containers and sturdiness would be a huge step forward. This is just a well thought out concept – a prototype. It proves that burying electronics for a longer period of time can be done if properly executed.
Note: METER’s (formerly Decagon) official position is that you should never bury your data logger. But we couldn’t resist sharing a few stories of scientists who have figured out some innovative methods which may or may not be successful, if tried at other sites.
Alkali bee beds are maintained by farmers near Touchet, Washington to pollinate fields of alfalfa, grown there for seed. The beds are typically a few acres in size and provide a nesting place for the bees, which can increase seed production by as much as 70 percent. Alkali bees are better than honeybees for pollinating alfalfa, as they don’t mind the explosive pollen release of the alfalfa flower.
Alkali Bee
USDA-ARS entomologist, Dr. Jim Cane, is trying to understand optimal bee-soil-water relations to ensure the bees will happily reproduce next year’s pollinators. Dr. Gaylon S. Campbell recently worked with Dr. Cane to measure water relations in bee nesting beds. Here’s what they found out:
Why Water Relations Matter
Alkali bees nest underground. They prefer salty soil surfaces which retard evaporation and discourage plant growth. The soil has to be the right texture, density, and have the correct moisture levels for successful nesting. In addition, the water potential of the larval food provision mass has to be low so it does not mold. Growers apply high levels of sodium chloride to the bee bed surface, and the soil is sub-irrigated to keep the salt near the surface and the subsurface soil moist.
Bottom right: a white larvae on a gold colored provision mass inside one of the tunnels dug by the female.
The female digs a tunnel down to a favorable depth, typically 15-20 cm or more, hollows out a spheroidal shaped cell around 1 cm diameter, and carefully coats the inside of the cell with a special secretion that appears to form a hydraulic and vapor barrier between the soil and the nest contents. She then builds a provision mass from pollen and nectar, shaped like an oblate spheroid with major axis around 6 mm and minor axis 3-4 mm. One egg is laid on the provision mass (which provides food for the larva), and the mother bee then seals up the entrance to the cell and moves on to the next one.
The female coats the inside of the cell with a special secretion that appears to form a hydraulic and vapor barrier between the soil and the nest contents.
Specialized Instruments for Each Measurement
In order to understand moisture relations between the soil, the larva, and the food provision mass, Dr. Cane carefully excavated three soil blocks from one of the bee beds, dissected them to find nests, and Dr. Campbell helped measure water potentials of the eggs, larvae, and provision masses. They also measured matric and total water potentials of bee bed soils.
A sample chamber psychrometer
A Sample Chamber Psychrometer is the only water potential device with a small enough sample chamber to be able to measure individual eggs and early-stage larvae, which it did. The provision masses were too dry to measure with the psychrometer, so several provisions were combined (to provide sufficient sample size) and measured in a Dew Point Potentiameter, along with the soil samples. Dr. Campbell measured matric potential of the highly saline soils using a tensiometer.
Water Potential Seems Important to the Bees
Dr. Campbell thinks matric potential is important in determining physical condition of the soil (how easy it is for the bees to dig and paint the inside of the nest), but probably has little to do with bee or larva water relations. The water potentials of the eggs and larvae were low (dry), but within the range one sees in living organisms. There was a consistent pattern of larva water potential decreasing with larval growth.
This alkali bee seeks shelter during the rain in a previously dug tunnel.
The exciting part of this experiment was the provision mass water potentials, which were so low that it is more convenient to talk about them in terms of water activity (another measure of the energy state of water in a system, widely used by food scientists). The intact provision masses were drier than any of the soil water potentials and not in equilibrium with the soil. Dr. Campbell says, “It’s interesting that all the provision masses were at water activities that would make them immune to degradation by almost all microbes, both bacteria and fungi.”
Another Interesting Observation
Dr. Cane found one provision mass covered with mold. Soil and plants are full of inoculum, so it is unlikely that the other provision masses lacked spores, but this one was wet enough to be compromised, and the others apparently weren’t. Dr. Campbell says, “There are two possibilities. Either it was put up too wet, or it got wet in the nest. The really interesting question is why all of them don’t get that wet. I think the hydrophobic coating of the nest eliminates all hydraulic contact from the soil to the provision mass, thus eliminating any liquid water flow, which would almost immediately wet the pollen balls. I think it also drastically reduces the vapor conductance from the soil to the ball, making water uptake through the vapor phase slow enough that the provision mass can usually be consumed before its water activity gets high enough for mold to grow.”
Tool the grower uses to punch holes in the nesting beds for the bees to tunnel into.
How Do Larvae Stay Hydrated?
The water activity of the larvae were around 0.99, much higher than either the soil or the provision mass, inspiring the scientists to wonder how they stay hydrated. Dr. Campbell speculates, “They have a water source from their metabolism, since water is a byproduct of respiration (Campbell and Norman, p. 205). It is also possible for biological systems to take up water against a potential gradient by expending energy. There are reports of a beetle which can take up water from a drop of saturated NaCl (water activity 0.75), so it is possible that the larva gets water from the environment that way. There appears to be no shortage of energy available. On the other hand, it would seem like the larval cuticle would need to be pretty impermeable to maintain water balance since the salty soil, and especially the provision mass, are so much drier than the larva.” Dr. Cane notes that, ”For a few exemplar bee species, mature larvae weigh 30-40% more than the provision they ate, with the possibility that the provision undergoes a controlled hydration by the soil atmosphere through the uncoated soil cap of the nest cell.”
In the future, Dr. Campbell is hoping to see more experiments that will answer some of the questions raised, such as measuring individual provision masses to determine why there is some variation in water potential. Dr. Cane will be undertaking experiments to measure moisture weight gain of new provisions exposed to the soil atmosphere of the Touchet nest bed soil.
Get more information on applied environmental research in our
References
Campbell, G. S. 1985. Soil Physics with BASIC: Transport Models for Soil-Plant Systems. Elsevier, New York.
Campbell, G. S. and J. M. Norman. 1998. An Introduction to Environmental Biophysics. Springer Verlag, N. Y.
Rawlins, S. L. and G. S. Campbell. 1986. Water potential: thermocouple psychrometry. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods – Agronomy Monograph 9, 2nd edition.
Soil ecologist Dr. Kathy Szlavecz and her husband, computer scientist, Dr. Alex Szalay, both at Johns Hopkins University, are testing a wireless sensor network (WSN; Mesh Sensor Network), developed by Dr. Szalay, his colleague, computer scientist Dr. Andreas Terzis, and their graduate students (read part 1). Mesh networks generate thousands of measurements monthly from wireless sensors.The husband/wife team says that WSN’s have the potential to revolutionize soil ecology by generating a previously impossible spatial resolution. This week, read about the results of their experiments.
Overall, the experiments were a scientific success, exposing variations in the soil microclimate not previously observed.
Results and Challenges:
About the performance of the network, Kathy says, “Overall, our experiments were a scientific success, exposing variations in the soil microclimate not previously observed. However, we encountered a number of challenging technical problems, such as the need for low-level programming to get the data from the sensor into a usable database, calibration across space and time, and cross-reference of measurements with external sources.
The ability of mesh networks that generate so much data also presents a data management challenge. Kathy explains, “We didn’t always have the resources or personnel who could organize the data. We needed a dedicated research assistant who could clean, handle, and organize the data. And the software wasn’t user-friendly enough. We constantly needed computer science expertise, and that’s not sustainable.”
The team also faced setbacks stemming from inconsistencies generated by new computer science students beginning work on the project as previous students graduated. This is why the team is wondering if a commercial manufacturer in the industrial sector would be a better option to help finish the development of the mesh network.
This deployment is located in the Atacama desert in Chile. Atacama is one of the highest, driest places on Earth. These sensors are co-located with the Atacama Cosmological Telescope. The goal of this deployment is to understand how the hardware survives in an extreme environment. In addition to the cold, dry climate, the desert is exposed to high UV radiation. These boxes are collecting soil temperature, soil moisture and soil CO2 data. (Image: lifeunderyourfeet.org)
What’s Next?
Kathy and Alex say that mesh sensor network design has room for improvement. Through their testing, the research team learned that, contrary to the promise of cheap sensor networks, sensor nodes are still expensive. They estimated the cost per mote including the main unit, sensor board, custom sensors, enclosure, and the time required to implement, debug and maintain the code to be around $1,000. Kathy says, “The equipment cost will eventually be reduced through economies of scale, but there is clearly a need for standardized connectors for connecting external sensors and in general, a need to minimize the amount of custom hardware work necessary to deploy a sensor network.” The team also sees a need for the development of network design and deployment tools that will instruct scientists where to place gateways and sensor relay points. These tools could replace the current labor-intensive trial and error process of manual topology adjustment that disturbs the deployment area.
This deployment is located in the fields of the farming system project at BARC. Soil temperature and moisture probes are placed at various locations of a corn-soybean-wheat rotation. The goal is to understand and explain soil heterogeneity and to provide background data for trace gas measurements. (Image: Lifeunderyourfeet.org)
Future Requirements:
According to Kathy, wireless sensor networks promise richer data through inexpensive, low-impact collection—an attractive alternative to larger, more expensive data collection systems. However, to be of scientific value, the system design should be driven by the experiment’s requirements rather than technological limitations. She adds that focusing on the needs of ecologists will be the key to developing a wireless network technology that will be truly useful. “While the computer science community has focused attention on routing algorithms, self-organization, and in-network processing, environmental monitoring applications require quite a different emphasis: reliable delivery of the majority of the data and metadata to the scientists, high-quality measurements, and reliable operation over long deployment cycles. We believe that focusing on this set of problems will lead to interesting new avenues in wireless sensor network research.” And, how to package all the data collected into a usable interface will also need to be addressed in the future.
University of Georgia researcher, Shuyang Zhen, wanted to find out if she could optimize greenhouse irrigation with reference evapotranspiration calculated from environmental factors and a crop coefficient, using NDVI measurements to adjust for canopy size (see part 1). Learn the results of the experiment and how fast growth and flowering caused problems with the NDVI measurement.
Shuyang’s experimental setup.
Fast Growth Causes Problems
Shuyang says because the plants grew so large, the canopy filled in beyond what the sensor could see. That meant there was additional leaf area that participated in vapor loss which wasn’t identified by the NDVI sensor. As the canopies approached moderate-to-high canopy densities, Shuyang observed that the NDVI readings became less responsive to increases in canopy size. To work around this problem, Shuyang tried to calculate a vegetation index called the Wide Dynamic Range Vegetation index with the spectral reflectance outputs of the two wavebands measured by the NDVI sensor. Shuyang says, “This index was supposed to improve the sensitivity at higher canopy density, so I transformed all my data and was surprised that it actually improved the sensitivity when the canopy density was lower. But at a higher canopy density it wasn’t as effective.”
The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement.
Plant flowering also caused problems with the NDVI measurement. Shuyang explains, “We had one cultivar of petunia with red flowers which formed on top of the canopy. The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement. The NDVI value gradually decreased when the plants started to flower. There was no way I could get around that issue, so in some of the replicates, I removed the flowers, and in some I kept the flowers so I could compare the different responses and characterize why it happened.”
The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.
Summary and Future Studies
During the early stages of growth, the research team saw a linear relationship between NDVI and crop coefficient. However, when the crop coefficient reached higher values, the response leveled off. Shuyang says, “The response failed to change with further increases in the crop coefficient. The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.”
Lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation,
Shuyang adds that the lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation, so the team is thinking about developing separate models for different canopy stages. She explains, “When the canopy reaches high canopy closure we may have to add an additional coefficient to compensate for that underestimation, but it’s difficult to evaluate what kind of coefficient we should use without more data. We need to do more studies to get an idea of what kind of adjustments will make the prediction more precise.”
Learn more about Shuyang’s work on the University of Georgia horticulture blog.
Greenhouse growers need irrigation strategies to maintain high plant quality, but it’s difficult to obtain quantitative information on exactly how much water will produce the highest-quality growth.
Greenhouse plant canopies are highly variable.
Estimating irrigation needs by using reference evapotranspiration calculated from environmental factors and a crop coefficient is standard for controlling field crop irrigation, but in a greenhouse this method can be challenging. Greenhouse plant canopies are highly variable, and there’s limited information on the crop coefficient values for ornamental crops.
Researchers used a sensor-controlled automated irrigation system with soil moisture sensors.
Measuring Crop Size
University of Georgia researcher, Shuyang Zhen, wanted to find out if she could solve this problem for greenhouse growers using NDVI measurements to adjust for canopy size. In a greenhouse setting, she and her team planted four types of fast growing herbaceous plants in small containers on top of greenhouse benches. They set up a small weather station to monitor environmental parameters and used that data to calculate reference evapotranspiration.
NDVI measurements are a non-destructive, continuous monitoring method to get information as to how big a crop is.
Using a sensor-controlled automated irrigation system with soil moisture sensors, the team determined the amount of water the plants used, which allowed them to calculate a crop coefficient on a daily basis. They then used NDVI measurements to monitor crop size. Shuyang says, “It’s easy to monitor environmental factors such as light, temperature, relative humidity, and wind speed, but it’s much harder to determine how big the crop is because many methods are destructive and time-consuming. We chose NDVI measurements as a non-destructive, continuous monitoring method to get information as to how big our crop was. We were specifically interested in looking at how NDVI changes with the crop coefficient and how those two parameters correlate with each other.”
Some species were more upward growing and some more sprawling.
Shuyang mounted multiple NDVI sensors on top of the benches, approximately four feet from the plants. Each sensor had a field of view of about .6 square meters and tracked the changes in plant size and NDVI values for over 8 weeks. Shuyang says, “Each species had different growth habits. Some species were more upward growing and some more sprawling. They also had different leaf chlorophyll content. Over the course of my study, three species reached reproductive stages, producing flowers. All of these factors had an effect on the NDVI measurements.”
Next week: Learn the results of the experiment and how fast growth and flowering caused problems with the measurement.
In part 2 of our PAR Measurement Series (read part 1), Dr. Gaylon S. Campbell discusses the impact of leaf arrangement, measuring light in a canopy, and why we measure PAR.
Vertical leaves absorb less radiation when the sun is at a high angle, and more radiation when the sun is at a low angle; the converse is true for horizontal leaves.
Leaf Arrangement
Leaf display (angular orientation) affects light interception. Strictly vertical or horizontally oriented leaves are extreme cases, but a large range of angles occurs. Vertical leaves absorb less radiation when the sun is at a high angle, and more radiation when the sun is at a low angle; the converse is true for horizontal leaves. The greatest photosynthetic capacity can be achieved by a change from nearly vertical to nearly horizontal leaves lower down. This arrangement leads to effective beam penetration and a more even distribution of light.
The highest LAI’s usually occur in coniferous forests.
Leaf area index (LAI), a measure of the foliage in a canopy, is the canopy property that has most effect on interception of radiation. LAI usually ranges between 1 and 12. Values of 3-4 are typical for horizontal-leafed species such as alfalfa; values of 5-10 occur in vertical leafed species such as grasses and cereals, or in plants with highly clumped leaves, such as spruce. The highest LAI’s usually occur in coniferous forests, which have overlapping generations of leaves. These forests have a photosynthetic advantage due to the longevity of individual needles.
PAR must be measured at a number of locations and then averaged.
Measuring Light in a Canopy
Variability of leaf distribution in canopies results in wide variations in light. To determine light at any height in the canopy, PAR must be measured at a number of locations and then averaged. Direct methods of measurement include using the horizontal line sensors whose output is the spatial average over the sensor length. The appropriate sensor length or number of sampling points depends on plant spacing.
Indirect methods for measuring canopy structure rely on the fact that canopy structure and solar position determine the radiation within the canopy. Because it’s hard to measure three-dimensional distribution of leaves in a canopy, models for light interception and tree growth often assume random distribution throughout the canopy; however, leaves are generally aggregated or grouped.
Models for light interception and tree growth often assume random distribution throughout the canopy; however, leaves are generally aggregated or grouped.
Why Measure Photosynthesis or PAR?
The ability to measure PAR assists with understanding the unique spatial patterns that different plants have for displaying photosynthetic surfaces. Since effective use of PAR influences plant production, knowledge of the structural diversity of canopies aids research on plant productivity. One result: researchers can use information about different plants’ abilities to intercept and use PAR to engineer canopy structure modifications that significantly improve crop yield.
The ultimate source of all energy on earth is the sun. Availability of this energy to most organisms occurs through photosynthesis, the conversion of CO2 and H2O to carbohydrates (stored energy) and O2. Photosynthesis occurs when pigments in photosynthesizers absorb the energy of photons, initiating a chain of photochemical and chemical events. Where does this energy and material exchange occur? In plant canopies. The amount of photosynthesis that occurs in canopies depends on the amount of photosynthetically active radiation (PAR) intercepted by leaves in canopies.
In canopies, leaves function collectively.
It’s More Complicated Than You Might Think
The rate at which photosynthesis occurs in one leaf might be calculated, but in canopies, leaves function collectively. Extrapolating photosynthesis from individual leaves to entire canopies is complex; the sheer numbers of leaves and their arrangement in the canopy structure can be overwhelming. Leaf area, inclination, and orientation all affect the degree to which light is captured and used in a canopy.
Average light level decreases exponentially downward through the canopy.
What Happens to Light in a Canopy?
Light varies dramatically both spatially and temporally through canopies. The average light level decreases more or less exponentially downward through the canopy, as the amount of leaf surface encountered increases. For some canopies, the greatest amount of leaf area occurs near the center. Therefore, canopy structure analysis becomes increasingly complex as one proceeds from a single plant to stands of the same plant, or to plant communities because of the variety of plants and growth forms.
Photosynthesis depends on leaf orientation.
Absorption of radiation and resulting photosynthesis depend on leaf orientation, sun elevation in the sky, spectral distribution and multiple reflections of light, and the arrangement of leaves. Patterns of light and shaded areas can be complicated and change with the sun’s position. In addition, seasonality of foliage can result in fairly small canopy interception of PAR for much of the year. PAR might also be intercepted by non-photosynthetic parts of plants (bark, flowers, etc).
In two weeks:Dr. Campbell discusses the impact of leaf arrangement, measuring light in a canopy, and why we measure PAR.
Dr. Y. Osroosh, now a researcher at Washington State University, believes that plants are the best soil moisture sensors (see part 1). He and his team have developed a new model for interpreting plant canopy signals to indirectly determine soil moisture in a Fuji apple orchard. Below are the results of their efforts and what he sees as the future of this research.
Could plants be the best indicators of soil moisture?
The Results
Osroosh says they expected to see correlations, but such strong relationships were unexpected. The team found that soil water deficit was highly correlated with thermal-based water stress indices in drip-irrigated apple orchard in the mildly-stressed range. The relationships were time-sensitive, meaning that they were valid only at a specific time of day. The measurements taken between 10:00am and 11:00am (late morning, time of maximum transpiration) were highly correlated with soil water deficit, but the “coefficient of determination” decreased quickly and significantly beyond this time window (about half in just one hour, and reached zero in the afternoon hours). Osroosh says this is a very important finding because researchers still think midday is the best time to measure canopy water stress index (CWSI). He adds, “The apple trees showed an interesting behavior which was nothing like what we are used to seeing in row crops. They regulate their stomata in a way that transpiration rate is intense late in the morning (maximum) and late in the afternoon. During the hot hours of afternoon, they close their stomata to minimize water loss.”
Researchers have found good relationships between CWSI and soil water content in the root zone near the end of the season at high soil water deficits in row crops.
Other Research
Osroosh points to other efforts which have tried to correlate remotely-sensed satellite-based thermal or NIR measurements to soil water content. He says, “The closest studies to ours have been able to find good relationships between CWSI and soil water content in the root zone near the end of the season at high soil water deficits in row crops. Paul Colaizzi, a research agricultural engineer did his PhD research in part on the relationship between canopy temperature, CWSI, and soil water status in Maricopa, Arizona; also motivated by Jackson et al. (1981). Steve Evett and his team at Bushland, Texas are continuing that research as they try to develop a relationship between CWSI and soil water status that will hold up. They are using a CWSI that is integrated over the daylight hours and have found good relationships between CWSI and soil water content in the root zone near the end of the season when plots irrigated at deficits begin to develop big deficits.”
Osroosh wants to study other apple cultivars, tree species, and perhaps even row crops, under other irrigation systems and climates.
What’s The Future?
In the future, Osroosh hopes to study the limitations of this approach and to find a better way to monitor a large volume of soil in the root zone in real-time (as reference). He says, “We would like to see how universal these equations can be. Right now, I suspect they are crop and soil-specific, but by how much we don’t know. We want to study other apple cultivars, tree species, and perhaps even row crops, under other irrigation systems and climates. We need to monitor crops for health, as well, to make sure what we are measuring is purely a water stress signal. One of our major goals is to develop a sensor-based setup which, after calibration, can be used for “precise non-contact sensing of soil water content” and “stem water potential” in real-time by measuring canopy temperature and micrometeorological parameters.”
As a young university student, Dr. Y. Osroosh, now a researcher at Washington State University, wanted to design the most accurate soil moisture sensor. Over the years, however, he began to realize the complexity and difficulty of the task. Inspired by the work of Jackson et al. (1981) and researchers in Bushland, TX, he now believes that plants are the best soil moisture sensors. He and his team developed a new model for interpreting plant canopy signals to indirectly determine soil moisture.
The team measured microclimatic data in an apple orchard.
How Can Plants Indicate Water in Soil?
Osroosh and his team wanted to use plant stress instead of soil sensors to make irrigation decisions in a drip-irrigated Fuji apple tree orchard. But, the current practice of using the crop water stress index (CWSI) for detecting water stress presented some problems, Osroosh comments, “Currently, scientists use either an empirical CWSI or a theoretical one developed using equations from FAO-56, but the basis for FAO-56 equations is alfalfa or grass, which isn’t similar to apple trees.” One of the main differences between grass and apple trees is that apple tree leaves are highly linked to atmospheric conditions. They control their stomata to avoid water loss.
There is high degree of coupling between apple leaves and the humidity of the surrounding air.
So Osroosh borrowed a leaf porometer to measure the stomatal conductance of apple trees, and he developed his own crop water stress index, based on what he found. He explains, “We developed a new theoretical crop water stress index specifically for apple trees. It accounts for stomatal regulations in apple trees using a canopy conductance sub-model. It also estimates average actual and potential transpiration rates for the canopy area which is viewed by a thermal infrared sensor (IRT).”
Fuji apple orchard (Roza Farm, Prosser, WA) where Osroosh performed his research.
What Data Was Used?
Osroosh says they established their new “Apple Tree” CWSI based on the energy budget of a single apple leaf, so “soil heat flux” was not a component in their modeling. He and his team measured soil water deficit using a neutron probe in the top 60 cm of the profile, and they collected canopy surface temperature data using thermal infrared sensors. The team also measured microclimatic data in the orchard.
Neutron probes were problematic, as they did not allow collection of data in real time.
Osroosh comments, “The accuracy of this approach greatly depends on the accuracy of reference soil moisture measurement methods. To establish a relationship between CWSI and soil water, we needed to measure soil water content in the root zone precisely. We used a neutron probe, which provides enough precision and volume of influence to meet our requirements. However, it was a labor and time intensive method which did not allow for real-time measurements, posing a serious limitation.”
Next week:Learn the results of Dr. Osroosh’s experiments, the future of this research, and about other researchers who are trying to achieve similar goals.
