Non Technical Summary
With each degree Celsius increase in temperature, air holds roughly seven percent more water vapor. A consequence of this physical process is that as the air warms, precipitation events become fewer, but larger. This effect has been observed over the past 100 years in many parts of the world including northern Utah. Changes in precipitation intensity are particularly important in semi-arid systems where plant productivity can double in wet years. Many experiments have tested the effects of increasing or decreasing total precipitation, but relatively little is known about the effects of increasing precipitation intensity. The few studies that have examined the effects of precipitation intensity have produced variable results. In some cases, intense events can increase plant productivity by increasing water infiltration into the soil. In other cases, intense events can decrease plant productivity by increasing runoff. Experiments are needed to better parameterize models of water flow and plant productivity under various climate conditions to determine when increased precipitation intensity will increase plant productivity and when it will decrease plant productivity. Results from two current studies in northern Utah highlight this need. One experiment in a rangeland site increased soil water infiltration and shrub growth, while another experiment in a dryland agricultural site increased soil water infiltration but decreased winter wheat growth. The fact that some species in the area increased growth under increased precipitation intensity, while other species decreased growth, suggests that it may be possible to identify dryland crop varieties that respond positively rather than negatively to observed and anticipated precipitation patterns. Here we propose to test the effects of increased precipitation intensity on three varieties of winter wheat and two varieties of safflower to identify plant varieties that can best convert large precipitation events into crop production. Experimental results will be used to parameterize and test predictions of an ecohydrological model that can be used to predict how different crop varieties are likely to respond to anticipated climate conditions.
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Goals / Objectives
The overarching goal of the proposed research is to determine how increases in precipitation intensity are likely to affect dryland crop production in northern Utah. In pursuing this objective, we expect to identify crop traits that are likely to improve dryland crop production in the future. The more specific objective is to measure the response of three winter wheat and two safflower varieties to precipitation intensity levels anticipated to occur with -1, 0, 1, 2, 3, 5 and 10° C warming. Results from these experiments will be used to parameterize and test predictions of ecohydrological models of water and energy flow and plant production (i.e., Hydrus 1D; Fig. 6). Rather than attempt to replicate climate conditions that are currently predicted for the study site (e.g., wetter winters, drier summers and higher temperatures throughout the year; Gillies et al. 2012), our goal is to test a wide range of precipitation event sizes on water and energy flows and plant productivity. This data will allow us to parameterize a model that can then be used to explore the effects of different seasonal effects or temperature changes on plant growth and water cycling.Climate and plant growth data in experimental plots will be used to parameterize and test ecohydrological models that can then be used to predict the consequences of a wide range of climate conditions on plant growth. The goal of this research is not necessarily to replicate anticipated climate conditions for this area, but to parameterize and test an ecohydrological model that can be used to predict plant growth responses to a wide range of potential climate conditions. More specifically, we are interested in isolating and testing the effects of precipitation intensity. To accomplish this goal we propose to manipulate only precipitation intensity. So that model simulations can provide inference across a wide range of conditions precipitation intensity will be manipulated across a wide range of values (i.e., associated with increasing atmospheric temperatures of -1 to +10° C). By performing these experiments over several years, and across a range of treatment levels, our experiments will allow us to parameterize and test an ecohydrological model across a wide range of conditions (i.e., wet Fall, dry Fall, intense summer precipitation, less intense summer precipitation). So while constraining treatments to conditions predicted for the local site may provide better inference to our study site, this same approach is likely to limit our inference to conditions at our site. By using a wide range of treatments, applied over several years and using this data to parameterize and test an ecoydrological model, we expect to provide broad inference to the effects of a wider range of potential climate scenarios that may be applicable not only to Cache Valley, but also to ecosystems around the Intermountain West.Objectives will be addressed by answering the following questions:1) How does precipitation intensity affect water cycling and dryland forage production in Cache Valley, Utah?2) How do common dryland crop roots respond to increased precipitation intensity?3) How do common dryland crops adjust gas exchange in response to precipitation intensity?
Broadly, this research will use experimental roofs to collect rain and snow and re-deposit collected precipitation as relatively large, intense events. Treatments collect and redeposit rain at a rate of roughly 1 cm in 10 minutes. Precipitation data collected every 15 minutes on the USU campus over the past 45 years suggests that this rate occurred in 14 of 7375 observed time periods with precipitation (i.e., treatments create 'intense' precipitation events). It is likely that natural events may be more intense over very short time periods than our drip irrigation lines. These very short, intense events are more likely to disturb the soil surface, but are unlikely to produce different infiltration rates except on very steep slopes. In short, treatments redistribute ambient precipitation as fewer, larger events that are very intense relative to historical precipitation recorded in 15-minute intervals.The experimental design will generally follow that of Kulmatiski and Beard (2013) with several important improvements. Control shelters will be used that immediately redeposit collected precipitation. This will allow a test of shelter artifacts. Second, both rain and snow events will be manipulated. Third, rather than comparing plant and soil water responses in one set of treated plots and one set of control plots, a combined ANOVA / regression experimental design will be used. This combined approach will provide inference to a broader range of precipitation intensity conditions. This approach has been recommended because it produces data that is needed by large-scale biosphere-atmosphere models (Smith et al. 2014). Finally, 100% roofing will be used allowing better control of precipitation patterns.Study Site: Our dryland agriculture site is at the Emily Godfrey Fonnesbeck Research Farm in Clarkston, UT (41° 53' 44" N; 112° 2' 39" W; elevation 1485 m). The area has mean annual precipitation of 44 cm (including 45 cm of snow) and mean monthly temperatures from -3.7 °C in January to 23.6 °C in July. The plots and surrounding area are in a crop rotation consisting of alternating years of winter wheat and fallow. The area was historically a shrub steppe ecosystem.Experimental Design: Precipitation manipulations will be achieved using 11, 2.1 m x 2.5 m x 2 m (w x l x h) shelters and three shelter-free control plots. Treatments began spring 2016. Rainwater from roofs is collected in holding tanks adjacent to the plots. For the three sheltered-control plots, water from the tanks is routed onto the plot passively through gravity-fed drip irrigation lines immediately as it is collected. For treatments associated with roughly -1, 1, 2, 3, 5 and 10 °C increases in atmospheric temperature, water is held in the tanks until there was enough water to create 2, 3, 5, 9, and 21 mm rain events at which point floating outlets or 'flouts' in the tanks sink and allow the water to run onto the plots via drip irrigation lines. There are three replicates of the 0 and 3 °C treatments. Remaining treatments have one replicate each. Treatment levels represent a 7% increase in average precipitation event sizes for each 1 °C of warming. For example, if average precipitation event sizes were 10 mm a 1°C treatment would receive precipitation events with an average size of 10.7mm. The -1°C treatments are achieved by routing water from the tanks back onto the plot between rain events, thereby creating additional small (~1 mm) "rain events." To be clear, all plots receive the same total amount of precipitation. As with the rainfall manipulations, snowfall manipulations are used to create fewer, larger precipitation events. Snow treatments are applied by shoveling collected snow 9, 8, 7, 6, 5, 4, and 2 times during the growing season. These snow addition frequencies are roughly based on historical data (1928-2014) of snow events >4 cm. When historical snow event sizes are increased by 7% for each 1 °C of anticipated warming (removing the smallest events so that the yearly totals remains unchanged) then there is a median of 13, 11, 10, 8, 7 and 4 events per year for increases of 0 °C (unchanged), 1 °C, 2 °C, 3 °C, 5 °C and 10 °C, respectively. We used fewer snow additions than this because snow treatments were not applied for the entire winter. As with rain the -1 °C treatment increased the frequency of snow events.Crop plantings will follow the schedule designed by the farm manager. Plots will be seeded at a rate of roughly 125 kg/ha with a row spacing of 15 cm, using a plot seed planting drill. Wheat will be harvested late July each growing season. Herbicide (Roundup PowerMax) will be used once each spring for weed suppression. Safflower will be planted in the Fall of 2019 and 2021.Soil moisture will be measured roughly biweekly during the growing season at six depths in each plot (time domain reflectrometry; Sentek Sensors, Stepney Australia). Hourly measurements of soil water potential (Campbell Scientific 229 heat dissipation sensors; Logan, UT, USA) will be taken at one sheltered-control plot and one 3 °C treatment plot.At the end of each growing season, the target crop will be harvested from a 1 m x 1 m subplot at plot. Wet biomass will be recorded and seed will be collected using a stationary thresher, and wet and dried seed mass measured (oven dried to constant weight at 60 °C). Total crop mass from the remaining plot will be harvested and weighed wet and dry. Wet and dry weed biomass (primarily Lactuca serriola) will also be measured for each plot. Mean plant height will also be recorded prior to harvest. Additional within-season measures of growth will include leaf area index (LAI; AccuPAR LP-80, Meter Group, Inc. Pullman, WA), normalized difference vegetation index measurements (NDVI; Spectral reflectance sensor for NDVI, Meter Group, Inc. Pullman, WA), photochemical reflectance index (PRI; spectral reflectance sensor for PRI, Meter Group, Inc. Pullman, WA), and canopy temperature (infrared radiometer SI-421, Apogee Instruments, Logan, UT).Root growth will be measured using one root observation chamber in each plot (Bartz Technology Co, Carpenteria, CA) and two soil cores (0-15cm) will be taken from each plot during peak growing season.Regressions of above and belowground and seed biomass as a function of treatment (i.e., Fig. 4) will be conducted. The repeated measures data (LAI, NDVI etc.) will be analyzed use a mixed effects model with fixed effects of treatment and time and random effect of plot ("lmer" function in the lme4 package; Bates et al. 2015). Due to the hybrid experimental design, regression analyses, as described above, will be conducted using all treatment levels and mixed models will be run using just the treatments with replicated plots (shelter-less control, sheltered control, and 3°C treatment). Statistical analysis will be done using R (R Core Team 2017).Additional analyses will be performed using the Hydrus 1D model (Šim?nek et al. 1999). This model describes water movement through the soil profile and through plant tissues. Environmental parameters needed by this model (i.e. temperature, relative humidity, precipitation, wind speed and light intensity) will be obtained from a nearby meteorological station and we have soil texture data. Root profiles will be determined from rhizotron samples. LAI will be measured directly as described above. Soil moisture data collected during the experiment will be used to validate Hydrus predictions of soil moisture content (and thus ecohydrological consequences of fewer, larger precipitation events).