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 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 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.
“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.
In the spatial domain, soil moisture variability arises from differences in soil texture.
Other than the fact that most situations call for more than a single sensor if you are working in the field, it turns out that there are few hard and fast rules that can be applied universally. In fact, one study that attempted to answer this question found that the optimum number of samples ranged from four to 250 (Loescher et al., 2014). Obviously, study objectives, accuracy requirements, scale, and site-specific characteristics must be taken into account on a case-by-case basis. Although no single answer can capture all scenarios, there are some generalities that you can rely on for guidance.
Keep in mind that soil moisture is dynamic in both temporal and spatial domains. Having an understanding of the driving forces of variability in both of these domains provides insight into how to go about sampling.
In the spatial domain, soil moisture variability arises from differences in soil texture (Baroni et al., 2013; Vereecken et al. 2014), amount and type of vegetation cover (Baroni et al., 2013; Loescher et al., 2014; Tueling & Troch, 2005), topography (Brocca et al., 2010; Jacobs et al., 2004; Tueling & Troch, 2005), precipitation and other meteorological factors (Vereecken et al., 2014), management practices (Bogena et al., 2010; Korres et al., 2015; Vereecken et al., 2014), and soil hydraulic properties (García et al., 2014). As you plan your study, consider the variability in these landscape features to get a sense of how many sample locations you will need to capture the heterogeneity in soil moisture across your study domain.
Soil moisture changes in predictable patterns associated with seasonal weather and vegetation dynamics.
Soil water content can be highly variable in the temporal domain as well. This is no big surprise since we expect soil moisture to change with precipitation, drought, irrigation, and evapotranspiration, and in predictable patterns associated with seasonal weather and vegetation dynamics (Wilson et al., 2004). While this is an easy concept to grasp for any given location, it becomes more complex when we consider the variability that arises from the interaction between temporal and spatial dynamics.
Although studies have found conflicting results (primarily due to differences in spatial and temporal sampling scales), there is growing consensus that spatiotemporal variability in soil moisture content behaves in the following predictable manners. The standard deviation of soil moisture is lowest under extreme wet and dry conditions and highest under intermediate soil moisture conditions (Famiglietti et al., 2008). At the same time, the coefficient of variation (CV) is negatively related to soil moisture (Bogena et al., 2010; Brocca et al., 2007; Famiglietti et al., 2008; Korres et al., 2015). In other words, soil moisture CV is highest under dry conditions and lowest under wet conditions. Finally, the probability distribution of soil moisture content values is negatively skewed under wet conditions and positively skewed under dry conditions (Bogena et al., 2010; Famiglietti et al., 2008). All of the above characteristics appear to be scale-independent (see Fig. 10 in Famiglietti et al., 2008).
The standard deviation of soil moisture is lowest under extreme wet and dry conditions.
The following examples use simulated data to help illustrate the effects of spatial and temporal heterogeneity on soil moisture content. In the first example, we simulated soil moisture content for the same study site under wet and dry conditions and calculated the probability density functions (PDF). Under wet conditions (blue line in Fig. 1) the standard deviation was low and the PDF was negatively skewed. In contrast, dry conditions resulted in a larger standard deviation and a positively skewed PDF. This example demonstrates that the parameters describing the soil moisture PDFs are not static, but instead change through time depending on soil moisture conditions.
Figure 1. Probability density function (PDF) of soil moisture content from the same field under dry (red) and wet (blue) conditions.
In the second example, we simulated soil water content for a single point in time when conditions were neither wet or dry. The resulting PDF is bimodal, indicating that there is more than one “population” of soil moisture content within the study site (Fig. 2). There are several reasons that soil moisture content can exhibit this type of multimodal distribution. It may be that there are areas with different soil textures (e.g., drier sandy and wetter silt loam areas), that the study area includes low-lying topography and adjacent hillslopes, or that the study area has heterogeneous vegetation cover.
Figure 2. PDF for a snapshot in time at a location that has a heterogenous landscape.
The two simple examples above demonstrate the complex nature of soil moisture across time and space. Both examples suggest that parametric statistics and an assumption of normality may not always be valid when working with soil water content in field conditions (Brocca et al., 2007; Vereecken et al., 2014).
How Many Soil Moisture Sensors?
If your objective is to determine the “true” mean soil water content for your study area, then your sampling scheme will need to account for the sources of variability described above. If your study area has substantial topographical relief, heterogeneous canopy cover, and strong seasonality in precipitation, then you are likely going to need sensors located in areas that represent the major sources of heterogeneity. If instead, your study site is fairly homogenous or you are simply interested in the temporal pattern of soil water content (e.g., for irrigation scheduling), then you can likely get away with fewer soil moisture sensors due to temporal autocorrelation in the data (Brocca et al. 2010; Loescher et al., 2014).
It is labor intensive and difficult to capture all soil moisture dynamics using spot sampling.
It is clear that soil water content is highly dynamic in time and space. It is labor intensive and difficult to capture all of these dynamics using spot sampling, although some people do choose to go this route. Like so many other areas of environmental science, some of the deepest insights into soil moisture behavior are emerging from studies using networks of in-situ sensors (Bogena et al., 2010; Brocca et al., 2010). We believe that for most applications, the use of in-situ, continuous measurements will provide you with a superior understanding of soil water content.
For a more in-depth treatment of this topic, read the articles listed below. We recommend the review by Vereecken et al. (2014) as a good place to start.
Baroni G, Ortuani B, Facchi A, Gandolfi C. (2013) The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field. Journal of Hydrology, 489:148-159.
Brocca L, Melone F, Moramarco T, Morbidelli R. (2010) Spatial‐temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46, doi:10.1029/2009WR008016.
Brocca L, Morbidelli R, Melone F, Maramarco T. (2007) Soil moisture spatial variability in experimental areas of central Italy. Journal of Hydrology, 333:356-373.
Bogena HR, Herbst M, Huisman JA, Rosenbaum U, Weuthen A, Vereecken H. (2010) Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone Journal, 9:1002-1013.
Famiglietti JS, Dongryeol R, Berg AA, Rodell M, Jackson TJ. (2008) Field observations of soil moisture variability across scales. Water Resources Research, 44, doi:10.1029/2006WR005804.
García GM, Pachepsky YA, Vereecken H. (2014) Effect of soil hydraulic properties on the relationship between the spatial mean and variability of soil moisture. Journal of Hydrology, 516:154-160.
Korres W, Reichenau TG, Fiener P, Koyama CN, Bogena HR, Cornelissen T, Baatz R, Herbst M, Diekkrüger B, Vereecken H, Schneider K. (2015) Spatio-temporal soil moisture patterns – A meta-analysis using plot to catchment scale data. Journal of Hydrology 520:326-341.
Loescher H, Ayres E, Duffy P, Luo H, Brunke M. (2014) Spatial variation in soil properties among North American Ecosystems and Guidelines for Sampling Designs. PLoS ONE 9, doi:10.1371/journal.pone.0083216
Tueling AJ, Troch PA. (2005) Improved understanding of soil moisture variability dynamics. Geophysical Research Letters, 32, doi:10.1029/2004GL021935
Vereecken H, Huisman JA, Pachepsky Y, Montzka C, van der Kruk J, Bogena H, Weihermüller L, Herbst M, Martinez G, Vanderborght J. (2014) On the spatio-temporal dynamics of soil moisture at the field scale. Journal of Hydrology, 516:76-96.
Wilson DJ, Western AW, Grayson RB. (2004) Identifying and quantifying sources of variability in temporal and spatial soil moisture observations. Water Resources Research, 40, doi:10.1029/2003WR002306.
Dr. Gaylon S. Campbell discusses how to measure light and photosynthesis (PAR) in canopies and why it’s helpful to researchers.
The source of all energy on earth is the sun.
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 talks about the impact of leaf arrangement, measuring light in a canopy, and why we measure PAR.
Get more information on applied environmental research in our
Dr. Yasin 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?
Yasin 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). Yasin 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.
Yasin 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.”
Yassin 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 Yasin 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.”
Get more information on applied environmental research in our
As a young university student, Dr. Yasin 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?
Yasin 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, Yasin 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 Yasin 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 Yasin performed his research.
What Data Was Used?
Yasin 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.
Yasin 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 Yasin’s experiments, the future of this research, and about other researchers who are trying to achieve similar goals.
Get more information on applied environmental research in our
Rachel Rubin, PhD candidate at Northern Arizona University and her team at Northern Arizona University are investigating the role soil microbes play in plant response to heat waves, including associated impacts to microbial-available and plant-available water (see part 1). Because heat waves threaten plant productivity, they present a growing challenge for agriculture, rangeland management, and restoration. Below are the results of Rachel’s experiments, some of the challenges the team faced, and the future of this research.
Heat waves present a growing challenge for agriculture, rangeland management, and restoration.
Rachel says the experiment was not without its difficulties. After devoting weeks towards custom wiring the electrical array, the team had to splice heat resistant romex wire leading from the lamps to the dimmer switches, because the wires inside the lamp fixtures kept melting. Also, automation was not possible with this system. She explains, “We were out there multiple times a day, checking the treatment, making sure the lamps were still on, and repairing lamps with our multi-tools. We used an infrared camera and an infrared thermometer in the field, so we could constantly see how the heating footprint was being applied to keep it consistent across all the plots.”
Rachel says her biggest finding was that all of the C4 grasses survived the field heat wave, whereas only a third of the Arizona Fescue plants survived. She adds that the initially strong inoculum effects in the greenhouse diminished after outplanting, with no differences between intact, heat-primed inoculum or sterilized inoculum for either plant species in the field. “It may be related to inoculum fatigue,” she explains, “the microbes in the intact treatment may have become exhausted by the time the plants were placed in the field, or maybe they became replaced, consumed, or outcompeted by other microbes within the field site”. Rachel emphasizes that it’s important to conduct more field experiments on plant-microbe interactions. She says, “Field experiments can be more difficult than greenhouse studies, because less is under our control, but we need to embrace this complexity. In practice, inoculants will have to contend with whatever is already present in the field. It’s an exciting time to be in microbial ecology because we are just starting to address how microbes influence each other in real soil communities.”
Diminished effects may be related to inoculum fatigue.
What’s In Store?
Now that the team has collected data from the greenhouse and from the heat wave itself, they have started looking at mycorrhizal colonization of plant roots, as well as sequencing of bacterial and archaeal communities from the greenhouse study. Rachel says, “It’s quite an endeavor to link ‘ruler science’ plant restoration to bacterial communities at the cellular level. I’m curious to see if heat waves simply reduce all taxa equally or if there is a re-sorting of the community, favoring genera or species that are really good at handling harsh conditions.”
Get more information on applied environmental research in our
Rachel Rubin, PhD candidate at Northern Arizona University, is interested in the intersection of extreme climate events and disturbance, which together have a much greater impact on plant communities. She and her team at Northern Arizona University are investigating the role soil microbes play in plant response to heat waves, including associated impacts to microbial-available and plant-available water.
Plants have a tight co-evolutionary history with soil microbes. It has been said that there is no microbe-free plant on earth.
Because heat waves threaten plant productivity, they present a growing challenge for agriculture, rangeland management, and restoration.
Can Soil Microbes Increase Heat Resistance?
Many plants maintain mutualistic associations with a diverse microbiome found within the rhizosphere, the region of soil that directly surrounds plant roots. These “plant growth-promoting rhizobacteria” and arbuscular mycorrhizal fungi provision limiting resources including water, phosphorus and nitrogen in exchange for photosynthetically derived sugars. However, we understand very little about whether extreme events can disrupt these interactions.
Fig. 1 Fine roots exploring the inoculum that was added as a band between layers of potting mixture.
Rachel and her team exposed rhizosphere communities to heat stress and evaluated the performance of native grasses both in the greenhouse, and transplanted under an artificial heat wave. They hypothesized that locally-sourced inoculum (a sample of local soil containing the right microbes) or even heat-primed inoculum would help alleviate water stress and improve survival of native grasses.
Rubin started in the greenhouse by planting Blue Grama (Bouteloua gracilis, C4 grass) and Arizona Fescue (Festuca arizonica, C3 grass) and assessed their responsiveness to locally collected soil inoculum that had either been left intact, pre-heated or sterilized (Fig. 1). Rubin says, “We expected that our plants would benefit the most from having intact soil microbe communities. But, we were surprised to find very large differences between plant species. Blue Grama performed the best with intact inoculum, whereas Arizona Fescue performed better with pre-heated or sterilized soil”. This could mean that Blue Grama is more dependent on its microbiome, whereas Arizona Fescue engineers a rhizosphere that contains more parasitic microbes rather than mutualistic microbes. Rachel says that understanding this relationship is important for tailoring plant restoration projects to local conditions. Plants that exhibit high levels of mutualisms with their rhizosphere might require an extra inoculum “boost” in order to successfully establish in highly degraded soil, whereas we should not bother to inoculate plants that tend to harbor parasites within their rhizosphere.
Fig. 2 A heated plot in the foreground connected to infrared lamps, water content and matric potential sensors, and EM50 data loggers.
After the team studied these responses, they planted the grasses into a degraded section of a grassland and installed an array of 1000-Watt ceramic infrared lamps mounted on steel frames (Fig. 2) to address whether inoculation influenced plant performance and survival. With help from a savvy undergraduate electrical engineering major (Rebecca Valencia), Rubin simulated a two-week heat wave while monitoring soil temperature and moisture using water content and water potential sensors. She also measured plant performance (height, leaf number and chlorophyll content) before, during, and after the event. Control plots had aluminum “dummy lamps” to account for shading.
An infrared photo, which is how Rachel determined that the heating footprint was evenly distributed on all the plants. The scale bar on the right is in degrees C.
Data obtained from soil sensors helped Rachel to measure heating treatment effects as well as rule out a potential cause for plant mortality: soil moisture. “Soil temperature was on average 10 degrees hotter in heated plots than control plots, but matric potential and soil water content were completely unaffected by heating. This tells us that the grasses died from reasons other than water stress– perhaps a top-kill effect.” Although growing concern over heat waves in agriculture is centered around accompanying droughts, this experiment demonstrates that heating can produce negative effects on some species even when water is in plentiful supply.
When faced with a reluctant spring thaw, dryland wheat growers know they have only two weeks to remove the snow from their fields, or large sections of their crop will die off from snow mold. Melting the snow artificially can be an expensive process, but one southern Idaho wheat grower has found a unique solution that could save both money and the environment (see part 1). He covers the snow with displaced topsoil, which speeds up the warming process and is less expensive than using traditional fly ash. This week, find out which techniques Campbell uses to save time and money while redistributing his soil.
Campbell says the number of passes he has to make with his tractor makes a difference.
Campbell says the number of passes he has to make with his tractor makes a difference, so he spreads the soil in patches, rather than evenly, in order to save time and money. “What we’ve found is, if you can get the soil deposited every 40 feet (9 m), you start changing the temperature in the whole area, and not just where the soil was dropped. The breeze will spread the warmth, and the snow in between the patches of soil will melt a lot faster. We do adjust our spacing depending on the amount of snow. If there’s more snow, that means we’ve got less time, and it’s got to melt faster, so we’ll place the patches of soil closer together for more even coverage.
Using a tractor to spread the soil in patches, rather than evenly, saves time and money.
Campbell adds that he is selective on which field he spreads the soil. If the weather conditions are not looking good, and they have deep snow, then they may skip a field they think they can’t save. Or if the fields further south have less snow, and the weather looks warm, they may also skip that field because the snow may melt on it’s own. There are challenges in trying to make those predictions, however. He says, “It’s hard to make those guesses based on the weather forecast because the predictions just aren’t very accurate a couple of weeks out. One year the forecast was sunny, so we applied fly ash to the fields. But the next day it snowed a foot, and it was two weeks before that snow melted so the sun could get to the ash. We lost quite a bit of grain that year.”
Campbell is selective on which field he spreads the soil.
Next year Campbell will try mixing the soil with some fly ash, to take advantage of the ash’s darker color. He says, “We’re going to keep putting the soil back on the field. It’s less expensive, and we don’t have to haul it. Next year we’ll try mixing the soil with about ⅓ ash so we can get it a little blacker. It will be a nice middle ground where the dark color will melt the snow, yet it won’t cost too much. And we can redistribute the eroded, nutrient-rich topsoil.”
Get more information on applied environmental research in our
Each year in early spring, dryland wheat farmers battle for their crop’s survival. As temperatures climb to 0 degrees C, the dark, wet microclimate underneath the snow begins to propagate snow mold.
Wheat spikes in a wheat field.
Soil scientist, Dr. Colin Campbell says, “Soil under a blanket of snow can warm as spring temperatures rise, despite their icy covering. Temperatures above freezing and the water from snowmelt are a perfect environment for mold to grow.“
When faced with these weather conditions, wheat growers know they have only a couple of weeks to remove the snow, or large sections of their crop will die off. Melting the snow artificially can be an expensive process, but one southern Idaho wheat grower has found a unique solution that could save both money and the environment.
Temperatures above freezing and the water from snowmelt are a perfect environment for mold to grow.
The Old Method
Traditionally, wheat growers have spread fly ash (ash from coal) on the spring snow to try and speed the melting process. The black fly ash creates a warmer microclimate by absorbing more solar radiation rather than reflecting it. To demonstrate its effectiveness, the USDA performed studies using fly ash to speed snow melt, with positive results. However, growers say the challenge is using the method in a way that is economical on dryland wheat where the profit margin is narrow. Bryce Campbell, a dryland wheat farmer near Burley, ID, says, “Some people use fly ash to get in the field faster or to get the water flowing into the soil, butour primary goal is to prevent snow mold from killing the winter wheat. If that happens, we have to replant the crop to a spring crop which yields a lot less. Our goal is to try and keep our crop alive.”
During heavy rain events, topsoil washed down to the edges of the field, collecting in dikes Campbell constructed.
An Inexpensive New Method
Campbell has used fly ash in the past, but last year, he had a better idea. He noticed that during heavy rain events, some of his topsoil washed down to the edges of the field, collecting in dikes he constructed and eventually becoming dried and powdery. He wondered if he could use that soil as an economical replacement for fly ash. In the fall, he collected some in a truck and left it to dry completely in the back of his shed; then this season, he spread it over the spring snow. Seeing the results, he decided it was worth the effort, both economically and environmentally. Campbell estimated the fly ash melt to be approximately 30% faster than the powdered soil because of its darker color, but the soil was free, which made a difference in his bottom line.
If snow mold kills the wheat crop, growers have to replant the crop to a spring crop which yields less.
He adds, “Some of the wheat farmers down the road are using a finely ground coal dust product to melt their snow. It’s a great product, and it works really well for melting snow, but their cost is about $20/acre. When you spread that on a thousand acres, that’s $20,000. I can put my soil on a thousand acres, and my only cost is two hours of gathering up the soil plus a day and a half in the tractor for application.” Next week: Find out which techniques Campbell uses to save time and money redistributing his displaced soil.
Get more information on applied environmental research in our
Scientists often evaluate Low Impact Development (LID) design by quantifying how much stormwater rain garden systems (cells) can divert from the sewer system. But Dr. Amanda Cording and her research team want to understand what’s happening inside the cell in order to improve the effectiveness of rain garden design (see part 1). Below are the results of their research.
Deep rooted systems were found to have a much better ability to hold the soil in place and remove nutrients.
Cording says that some of her key findings were that the soil media and vegetation selection is absolutely crucial to the performance of these systems. Cording’s team looked at the root layering perspective in three dimensions and found that deep rooted systems were found to have a much better ability to hold the soil in place and remove nutrients throughout the life cycle of the cell. The more surface area the roots covered, the more pollutants the cell would remove. She adds, “Cells with deep-rooted plants were found to be resilient during increased precipitation due to climate change, did well at retaining peak flow rates, and performed well at removing total suspended solids and nutrients predominantly associated with particulates.” Labile nutrients, Cording says, were a completely different story. She says the bioretention systems have to be specifically designed to remove those nutrients through sorption (P) and denitrification (N).
Compost was found to have a negative effect on water quality.
Compost, which is often used as an organic amendment in the soil media to help remove heavy metals and provide nutrients for the plants, was found to have a negative effect on water quality overall, due to the high pre-existing labile N and P content. She says, “It’s intuitive, but at the same time, a lot of these systems are designed based on bloom time and color, and not necessarily on the physical and chemical pollutant removal mechanisms at work.”
Green algal bloom in a small freshwater lake in New Zealand. (Image: Massey University)
What Lies Ahead?
Cording also tested a proprietary bioengineered media in two of her cells which was designed to remove the phosphorous that causes algal blooms in the rivers and streams. She says, “It did a phenomenal job. There was very little phosphorous coming out compared to the traditionally-designed retention cells. Cording, who is now based in Honolulu and works for an ecological engineering company called EcoSolutions, is looking at how to use natural, highly-leached iron rich soils, to get a similar amount of phosphorous removal, and how bioretention can be designed with anoxic storage zones to remove nitrate via denitrification. She says, “These nutrients can be easily removed from stormwater with a little conscious design effort and a splash of chemistry.”
Get more information on applied environmental research in our