interim project report for dasnr water...

Oklahoma Water Resources Center 111 Agriculture Hall Stillwater, Oklahoma 74078 405-744-5615, FAX 405-744-5059 E-Mail: [email protected] Website: water.okstate.edu

Interim Project Report for DASNR Water Grant

Principal Investigators: Jason Warren, Scott Frazier, and Saleh Taghvaeian

Project Title: DASNR, Irrigation Management and Optimization

Descriptors: Irrigation, Corn, Cotton, Wheat, Sorghum, Energy, Salinity, Water use efficiency.

Start Date: (02/01/2015; If you received an extension, use the original project start date.)

End Date: (09/30/2016; If you received an extension, use the end date of the extension.)

Students: (Include number of students supported by the project during the project period in the table below.)

Outputs:

Warren, J., S. Taghvaeian , C. Murley, D. Sims and R. Taylor. 2016. Developing Management Strategies for Subsurface Drip Irrigation in the Oklahoma Panhandle. In T. Beedy (ed.) Oklahoma Panhandle Research and Extension Center, 2016 Research Highlights.

Presentations (Salinity Project):

1. The 36th Annual Governor's Water Conference and Research Symposium, Norman, OK, Dec 1-2, 2015.

2. Oklahoma Section Annual Meeting, OSU, Stillwater, OK, February 26, 2016 3. Student Water Conference, OSU, Stillwater, OK, March 24-25, 2016

Student Status Number Disciplines

Undergraduate 4 BAE and PaSS

M.S. 3 BAE and PaSS

Ph.D.

Post Doc

Total 7

Presentations (Subsurface Drip Project):

1. Warren, J. D. Sims, C. Murley, S. Taghvaeian, R. Kochenower and R. Taylor. 2016.

Row Placement Considerations for subsurface Drip. Presented at the Oklahoma

Irrigation Conference. Woodward, OK 7 March.

2. Warren, J., S. Taghvaeian, R. Taylor, D. Sims, and C. Murley. 2015. Alternative

Crop Row Configuration for Subsurface Drip Irrigation. Presented at the 36th Annual

Oklahoma Governor’s Water Conference and Research Symposium. Norman, OK 2

Dec.

3. Warren, J. 2015. Planting Strategies for Wheat Under SDI. Presented at the

Oklahoma Irrigation Conference. Fort Cobb, OK. 18 Aug.

4. Warren, J. 2015. Economics of Irrigated Corn vs. Grain Sorghum. Presented at the

Winter Crops Clinic. Goodwell, OK. 10 Apr.

Presentations (Sustainability Project):

1. Governors Water Conference (Norman) 2015 – Presentation and Poster 2. Water In-Service (OSU) 2015 – Presentation 3. Crop Clinic (OAES, OCES - Goodwell) 2015 – Presentation 4. Winter Crop Clinic (OSU) 2016 – Presentation 5. Irrigation Conference (OAES, OCES – Fort Cobb) 2015 – Presentation 6. Irrigation Conference (OAES, OCES – Woodward) 2016 - Presentation

Synergistic External Funding Awards:

1. Taghvaeian, S., T. Ochsner, D. Rogers, C.C. Hillyer, J.P. Bordovsky, T. Marek, J. Warren, R. Boman, R. Taylor, D. Porter, S. Frazier, and J. Aguilar. 2015. Promoting Sensor-based Technology to Improve Land and Water Resources Conservation. Submitted to NRCS-CIG for $772,029. Funded.

2. Schipanski, M., R. Waskom, P. Gowda, G. Kelly, C. Ray, M. Marsalis, K. Wagner, C. West, C. Rice, B. Guerrero, B. Auvermann, and J.G. Warren. 2016. Sustaining Agriculture through Adaptive Management Resilient to a Declining Ogallala Aquifer and Changing Climate. Submited to USDA-AFRI for $10,000,000. Funded for $9,900,000.

Publications under Development:

Two extension factsheets focusing on salinity issues in southwest Oklahoma are under preparation and will be ready for publication by December 2016.

Two peer-reviewed journal articles focused on Salinity will be completed by December 2016.

Two Extension factsheets presenting management guidance for subsurface drip irrigation and irrigated grain sorghum are currently being developed and will be completed prior to the project extension date.

An extension factsheet presenting the findings of the energy audit will also be completed prior to the project extension date.

Project Summary:

The following report contains the Interim results of three projects related to irrigation systems management in Oklahoma which were combined to facilitate the creation or an irrigation team at Oklahoma State University. First of all, the combination of these projects has resulted in the development of a strong team of faculty focused on irrigation issues which includes Dr. Frazier, Dr. Taghvaeian, and Dr. Warren as PI’s on these projects as well as Dr. Stoecker who is not include as an investigator on these projects but does collaborate to provide economic analysis. I mention the development of this team first because it has likely been the most valuable outcome of the funds provided by the DASNR water grant. As listed above this collaboration has resulted in a variety of presentations and collaborative development of publications related to irrigation management. It has also made us competitive in acquiring external funding to support our future efforts.

The assessment of salt accumulation and movement under subsurface drip irrigation in SW Oklahoma has resulted in the development of a HYDRUS-2D model used to simulate salt movement from subsurface drip irrigation. This model was then used to estimate salt movement and accumulation under a 20 year simulated weather regime for SW Oklahoma. Soil samples were collected from fields in SW Oklahoma which had been previously sampled in 2007. This field data will allow for an assessment of salt movement during this time period. The data generated from this simulation as well as data collected from soil cores is currently being analyzed and will be presented in the final report.

Evaluation or driver accuracy for planting of corn and sorghum showed that only at 50% of full irrigation will driver accuracy have a significant impact on corn yield. At this limited irrigation rate driver accuracy must be within 6 inches to ensure no negative impact on corn yield. In contrast, Grain sorghum yields were unaffected by driver accuracy regardless of irrigation rate. The stress indexes calculated using canopy temperature were sensitive to crop water stress in 2015 and show promise for use for irrigation scheduling in the Oklahoma Panhandle. Furthermore, soil moisture potential below the crop row was highly correlated to yield, suggesting that efforts to monitor soil moisture for subsurface drip irrigation management should focus on soil moisture potential directly below the crop row. Efforts to evaluate alternative row configurations for wheat found that planting solid stands of wheat optimized yield in drip irrigation and that removing rows from the dry area between the drip tape resulted in significantly reduced yields.

The sustainability analysis evaluating energy use of irrigation wells resulted in data collection from 10 wells. The analysis showed that well efficiency ranged from 10-70% of standard achievable efficiency. Improvements in the observed efficiency could reduce operating cost as much as $6,119 per pivot per year. In addition, a life cycle analysis comparing the measured efficiency of the wells and to that of standard efficiencies found that greenhouse gas emissions could be reduced by 22,913 kg CO2eq for the 10 wells evaluated. Irrigation water application uniformity was evaluated on 5 center pivots. This analysis found that application efficiency ranged from 100 to 85% of water pumped from the aquifer but that the distribution uniformity ranged from 85 to 14%.

Studying Salt Movement and Accumulation

under Subsurface Drip Irrigation Systems

Interim Research Progress Report

Saleh Taghvaeian; BAE

Introduction

Salt buildup in agricultural soils is a growing problem in Southwest Oklahoma. This is mainly due

to the fact that irrigation water quality in parts of this region low and contains salt concentrations

above the threshold level for most crops. Furthermore, many farmers in the area are seeking

strategies to apply water more efficiently. One of the common approaches is switching current

irrigation systems (furrow and sprinkler) to subsurface drip irrigation (SDI) systems. Despite all the

benefits of SDI systems such as efficient application of water and nutrients in the root zone, there is

a growing concern over the contribution of SDI systems to soil salinity buildup in the long-term.

Main Objectives

This project has two main objectives:

1) Simulation of the long-term effects of irrigation management practices on salt accumulation

in southwest Oklahoma using HYDRUS-2D

2) Evaluation of salt accumulation changes in some selected irrigated fields in the area from

2007 to 2016.

The first objective will provide information on the effect of different irrigation practices on the

pattern and accumulation of salts in the soil profile. The second objective provides an insight to the

current condition of soil salinity and change in salt accumulation over the past decade in southwest

Oklahoma.

Accomplishments

Objective 1

To achieve this objective the HYDRUS-2D model computer model has been purchased and is being

used for simulations. The graduate student working on this project has been learning and running

HYDRUS model over the past 9 months. The preliminary simulation setup was performed for one

growing season to evaluate the effects of irrigation depth and quality on soil salinity patterns under a

SDI system in the area. Therefore, the preliminary simulation setup included 6 scenarios; a

combination of 3 levels of irrigation amounts (as percentage of crop ET) and 2 levels of irrigation

water salinity (ECiw). Modeling were done based on common field and irrigation practices for cotton

in southwest Oklahoma.

Table 1. Experimental scenarios

Scenario Description Irrigation (% of ETc) ECiw (dS m-1

)

S1 Full-irrigation, non-saline water 100% 0.5

S2 Full-irrigation, highly saline water 100% 9.0

S3 Deficit-irrigation, non-saline water 80% 0.5

S4 Deficit-irrigation, highly saline water 80% 9.0

S5 Over-irrigation, non-saline water 125% 0.5

S6 Over-irrigation, highly saline water 125% 9.0

The simulation domain was assigned based on a typical SDI system in the Altus area in southwest

Oklahoma (Figure 1).

Figure 1. Physical layout and boundary conditions of the simulated domain in HYDRUS-2D

Soil salinity accumulation patterns were simulated at 60 days after planting (DAP), 120 DAP, and

160 DAP (Figure 2). In the next step, the long-term effects of irrigation management practices on

salt accumulation for the next 20 years will be simulated using HYDRUS-2D.

To simulate the future soil salinity buildup in HYDRUS-2D, future weather parameters are needed.

Future weather data projection (2016-2035) was simulated using the WeaGETS weather generator.

WeaGETS is a stochastic weather generator which can generate daily weather parameters using

daily historical precipitation, and minimum and maximum temperature. Therefore, based on the

historical weather pattern, future weather parameters (2016-2035) were simulated. In addition, the

initial soil salinity concentrations as an input for the model were determined based on soil samples

taken from 20 points in 7 fields in spring 2016 (more details in the objective 2 section).

The implemented irrigation practices, soil type, and field practices in the model were based on

common practices in the area. Irrigation management practices including different salinity levels of

irrigation water (non-saline, moderately saline, and highly saline water) and irrigation systems (SDI

and sprinkler) will be simulated in the model. The results of these simulations are still under

analyses and will be prepared as a journal manuscript by December 2016.

Figure 2. Soil salinity buildup for the 6 scenarios at 60, 120, and 160 days after planting*

*The vertical axis is soil depth (cm), the horizontal axis is distance from emitter (cm), and the color spectra is

soil salinity (dS m-1

). Objective 2

The second objective of the project has been accomplished by selecting 7 fields in southwest

Oklahoma, representing variable soil salinity conditions in the area. A number of fields were

sampled back in 2007 and soil salinity data of these samples are available. In spring 2016 we took

new soil samples from 20 selected locations, using the GPS coordinate of the same sampling

locations. A soil sampling probe was used to take soil cores (Figure 3). Each core was divided into

several sub-cores depending on the depth of the impermeable layer.

Figure 3. OSU soil sampler

Figure 4. soil sample sub-cores

Each sub-core was put in a separate bar-coded bag. Soil samples were analyzed by the OSU Soil

Laboratory, Stillwater for the 1:1 soil to water extraction test. In addition to soil salinity (EC), Na,

Ca, Mg, K, B, TSS, SAR, ESP, pH of individual samples were determined through the 1:1 soil to

water extraction test. The data are under analyses to find out the contributing factors in decreasing

or increasing salinity from 2007 to 2016.

Project Outcomes

Presentations at national and local conferences

The preliminary results of this project have been presented in the following conferences:

The 36th

Annual Governor's Water Conference and Research Symposium, Norman, OK, Dec

1-2, 2015.

Oklahoma Section Annual Meeting, OSU, Stillwater, OK, February 26, 2016

Student Water Conference, OSU, Stillwater, OK, March 24-25, 2016

The updated results will be presented in the 2016 ASABE Annual International Meeting in Orlando,

Florida on July 17-20, 2016.

Publications

Currently, two extension factsheets focusing on salinity issues in southwest Oklahoma are under

preparation and will be ready for publication by December 2016. Furthermore, the outcome of this

research project will be prepared as two peer-reviewed journal articles by December 2016.

Title: Developing Management Strategies for Subsurface Drip Irrigation in the Oklahoma Panhandle

Jason Warren, Saleh Taghvaeian , Cameron Murley, Dalton Sims and Randy Taylor; Oklahoma

State University

Summary:

Evaluation or driver accuracy for planting of corn and sorghum showed that only at 50% of full irrigation will driver accuracy have a significant impact on corn yield. At this limited irrigation rate driver accuracy must be within 6 inches to ensure no negative impact on corn yield. In contrast, Grain sorghum yields were unaffected by driver accuracy regardless of irrigation rate. The stress indexes calculated using canopy temperature were sensitive to crop water stress in 2015 and show promise for use for irrigation scheduling in the Oklahoma Panhandle. Furthermore, soil moisture potential below the crop row was highly correlated to yield, suggesting that efforts to monitor soil moisture for subsurface drip irrigation management should focus on soil moisture potential directly below the crop row.

Introduction

Various sources can be cited to demonstrate the fact that water availability in the Ogallala Aquifer is declining. For example, the USGS found that water levels had decline by as much as 100 ft under Texas County, OK between the 1940s and 1990s. The report went on to suggest that if withdrawal continued at the same rate as in 1996 that the water level would decrease by an additional 20-25 ft under Texas County, OK by 2020 (Luckey, et al. 2000). This declining water table is decreasing pumping capacity for agricultural producers in the Panhandle region. The adoption of new irrigation water management strategies are needed to improve water use efficiency in the region to offset this decline. Also, adoption of improved efficiency systems will be imperative if government restrictions on pumping are imposed in the future.

Previous research efforts in the High Plains Regions have shown that subsurface drip irrigation (SDI) provides superior water use efficiency compared to center pivot irrigation systems. In fact, Lamm and Trooien (2003) summarized 10 years of research in Kansas and concluded that irrigation water use for corn can be reduced by 35-55% using subsurface drip irrigation compared to commonly used irrigation systems in the region. Therefore research is not need to demonstrate the improved efficiency. However, research is needed to evaluate how variations in crop management will impact the performance of SDI in the region.

For the last two years we have been utilizing the subsurface drip irrigation system located at the Panhandle Research and Extension center to evaluate the yield response of corn compared to sorghum under limited irrigation water availability. This effort has provided opportunity for us to engage producers regarding the use of drip irrigation, which allows us to learn why producers are apprehensive in adopting this technology. In addition to its increased cost, producers are currently not certain that the technology will fit their production system. Specifically, information is needed on how crop row placement will impact crop performance under drip with respect to stand establishment and yield.

In order to minimize costs of SDI systems the drip tape buried at intervals such that one row of drip tape will irrigate two crop rows. Research on cotton row placement has previously been conducted in the Southern High Plains near Halfway, Texas. This research found that for cotton planted on 30 inch row spacing, yield was significantly reduced when the offset between the drip tape and crop rows was 15 inches. This yield reduction occurred at the “high” irrigation rate (approximately equal to daily evapotranspiration). At the “low” irrigation rate (approximately half of daily evapotranspiration) yield was reduced by 2% but this was not a significant reduction. The researchers evaluated yield in each row and found that at low irrigation the yield for the cotton row nearest the tape was equivalent to yields in the “high” irrigation treatments and that this compensated for the yield loss in the cotton row place 45 inches from the tape, making the average similar to the yield when rows were equidistance from the tape (each was 15 inches from the tape. In contrast, at “high” irrigation the cotton row placed 45 inches from the tape simply reduced the average of the two rows because the yield was not increased in the cotton row directly over the tape (Bordovsky et al. 2010). A similar analysis of the effect of crop row placement has not been conducted on corn or sorghum in the Southern Plains. Because the buried drip tape cannot seen from the surface, the potential for row placement error during planting is high. The use of high precision GPS systems can reduce this error, but research is needed to determine the accuracy required. Also, producers prefer to alternate row locations to improve ease of planting. This reduces the need to move root crowns out of the planting row. Producers need to know if this practice of alternating row locations from one year to the next will adversely impact crop performance. Sorghum and especially corn are more sensitive the water availability than is cotton. It is therefore expected that these crops will be more sensitive to row placement.

Objectives:

1) Evaluation of Driver Accuracy:

The objective is to evaluate how crop row placement will influence corn and grain sorghum yield response at irrigation regimes of 50, 75, and 100% of full ET replacement.

2) Row configuration for subsurface drip irrigated Wheat

The objective is to evaluate solid stands of wheat compared to planting 6 or 7 rows over the drip tape with 2 or 1 skip rows in the dry area at 50, 75 and 100% of full ET replacement

3) Canopy temperature to assess water stress in corn and sorghum

The objective was to evaluate canopy temperature measurements as a method to assess water stress in corn and sorghum. This will provide preliminary data needed to evaluate water stress thresholds and ultimately irrigation scheduling protocols.

Methodology:

05

101520

0 10 20 30 40 50 60

0 inch offset

0

5

10

15

20

0 10 20 30 40 50 60

9 inch offset

0

5

10

15

20

0 10 20 30 40 50 606 inch offset

05

101520

0 10 20 30 40 50 60

3 inch offset

0

5

10

15

20

0 10 20 30 40 50 60

15 inch offset

This research utilized the SDI system at the Oklahoma Panhandle Research and Extension Center near Goodwell, OK. This series of studies uses 9 irrigation zones, each are 630 ft long and 60 ft wide. Three zones are planted to grain sorghum, corn, and wheat and rotated annually. This was done to allow for more successful no-till management to minimize pest pressures.

Within each zone subsurface drip irrigation tape is located 12 inches below the surface and spaced 60 inches apart such that each tape will supply water to two crop rows when planted 30 inches apart. The tape contains emitters 24 inches apart along the length of the tape, designed to supply 0.18 gallons per hour at 10 psi allowing for 11 gallons per minute (GPM) being supplied to each zone. Pressure was adjusted to 13 psi at the inlet of each zone such that instantaneous flow rates of 14 GPM were achieved on each zone.

The drip tape was installed using real time kinematic global positioning (RTK GPS) Guidance. Therefore, all planting was conducting using this technology to place rows in desired locations relative to drip tape.

Objective 1) Driver Accuracy:

This objective utilized 6 zones, with 3 planted to sorghum and 3 planted to corn. Within each zone an experiment comprised of a randomized complete block design with 4 replicates and 5 treatments was imposed. The treatments, as illustrated in figure 1 consisted of crop rows being planted at 0, 3, 6, 9, and 15 inch offsets (Figure 1) from the drip tape. Each plot was 15ft (6 rows) wide and 30 ft long.

On May 5th, 2015 and April 21st, 2016 corn (hybrid Pioneer 1768AMX) was planted in 3 zones and on June 6th, 2015 and June 1st, 2016 sorghum (Hybrid Pioneer 84G62) planted in 3 zones. One zone for each crop was designated to receive irrigation at a rate equal to evapotranspiration as estimated by the Aquaplanner (www.Aquaplanner.net) irrigation scheduling program. The remaining zones were designated to receive irrigation equal to 75 and 50% of this fully irrigated rate. Efforts were made to maintain a soil profile moisture deficit of approximately 1 inch to allow for rainfall capture. At soil water deficits below this threshold, irrigation was scheduled to replace ET since the last irrigation event in the 100% replacement zones. The remaining zones received 75 and 50% of the water

Figure 1: Drip tape offset from sorghum and corn rows. (Point indicates location of drip tape at

each offset).

applied to replace ET. When rainfall was anticipated irrigation was delayed. In 2015, All Zones planted to corn received the same amount of irrigation water between planting and June 24th to ensure uniform soil profile moisture and crop emergence with irrigation initiated on May 9th, 2014. Irrigation of the sorghum was initiated on June 19th 2014. Due to sufficiency rainfall after planting in 2015 the 3 irrigation rates were imposed at the first irrigation event, which occurred on June 5th for corn and June 26th, 2015 for sorghum.

In 2014, the corn and sorghum only received 5 gal of 10-34-0 fertilizer applied in row at planting. In 2015, both crops again received this starter fertilizer in addition to in-season N fertigation. The corn received 30 lbs N acre-1 as 32-0-0 liquid fertilizer injected into the irrigation system weekly for 8 weeks starting on June 15th resulting in an application of 240 lbs N acre-1. The sorghum received 30 lbs of N acre-1 as 32-0-0 liquid fertilizer injected into the irrigation system weekly for 6 weeks starting on July 8th resulting in an application of 180 lbs N acre-1.

Corn grain yield was collected at maturity on Oct 8th, 2014 and Oct. 1st 2015. Sorghum yields were collected at maturity on Oct. 15, 2014 and Oct 14, 2015. All grain was harvested using a small plot combine to harvest the center 2 rows from each plot. In addition, the 2nd and 5th rows from each plot were harvested individually in 2014. In 2015, this protocol was modified and the center 2 rows were harvested individually.

Objective 2) Wheat Row Configurations:

The wheat was planted into the same zones with corn stubble from objective 1. Specifically, the wheat was planted with a small plot no-till drill at the opposite end of the zone where bulk corn had be planted with no offsets. Within each of the 3 wheat zone a factorial treatment structure was imposed in a randomized complete block treatment structure. The treatment factors were variety and row orientation. Billings and Iba will be used in this study. Each have good yield histories under irrigation at the Goodwell research station, however Billings does not do as well under low water availability and does not tiller as well as Iba. These varieties were planted on Oct. 22nd in 7.5 inch rows. The row configurations presented in figure 2 were created by terminating the “skip rows with Glyphosate after emergence.

Irrigation was initiated on March 10, 2015 with a final irrigation event occurring on May 4. Irrigation was terminated because sufficient rainfall occurred to maintain soil moisture according to the aquaplanner.net program as well as the mesonet irrigation scheduling tool.

Nitrogen deficiencies were observed in wheat rows between prior year’s corn crop rows. Fertigation was subsequently initiated on March 30th and applied weekly for 6 weeks at a rate of 30lbs per week to provide a total of 180 lbs N acre.

Wheat was harvested for yield at maturity on June 25, 2015. At harvest 8 rows were harvested from the middle of the solid stand treatments and the 7 and 6 rows were harvested from above the drip tape in the remaining treatments.

Objective 3) Canopy Temperature:

After emergences Apogee SI-111 Infra-Red Thermometers (IRTs) were installed and maintained one meter above the top of the canopy at a downward angle of 30° below horizon toward northeast to measure canopy temperature (Tc). Soil moisture was measured over 12-16 inch depth, using the Campbell Scientific CS-655 sensors. Data measurement and storage was performed by a CR1000 data-logger. A nearby Mesonet weather station provided required weather parameters on an hourly basis.

Three stress indexes were calculated using the canopy temperature data. The time-temperature threshold (TTT) was calculated as the total time when the canopy temperature was above the biologically identified threshold. In this study a temperature of 28 °C was used. The second stress index used was the degrees above non-stressed (DANS) threshold. This was calculated as the stressed canopy temperature minus the non-stressed canopy temperature. The Third threshold calculated was the canopy temperature ration which was calculated as the non-stressed canopy temperature divided by the stressed canopy temperature. The canopy temperature for the 100% irrigation treatment was used for the non-stressed canopy temperature and the 75 and 50% irrigation treatments were used as the stressed canopy temperatures in these threshold calculations.

Results and Discussion

Irrigation Applied

Table 1 shows the in-season and pre-season irrigation applied to each zone for the corn and sorghum crops grown in 2014 and 2015 as well as the in-season rainfall totals. The irrigation reported in this table is as measured by flow meters at the inlet of each zone. The irrigation applied to corn plots in 2014 is well below the target application of 20 inches

Figure 2: Row orientation for wheat when planted as a solid stand, as 7 or 6 rows over the tape

because of overestimated flow rates early in the season. In addition, visual observations that the soil surface was consistently moist during the reproductive growth stages suggested that irrigation was sufficient to allow optimum growth. For the remaining crop years the applied irrigation reported in table 1 as measured by the flow meters is within 10% of the target irrigation rates estimated by Aquaplanner.

Table 1: Irrigation applied to the 100, 75 and 50% irrigation zones for corn and sorghum in 2014-15 and wheat harvested in 2015 as well as the pre-plant irrigation applied to all zones and the in-season rainfall.

Water Supply ------Corn----- -----Sorghum----- -----Wheat-----

2014 2015 2014 2015 2014 2015

------------------------------Inches-----------------------------

100% In-season 16.5 20 15.1 13

13.2 75% In-season 13.5 15.5 11.7 10

9.3

50% In-season 9.4 11 7.6 7

6.2

Pre-season irrigation 3 3 0 0

0

In-season Rainfall 12.7 18.5 10.6 11.7

17.5

Corn Grain Yield Table 2 shows average corn grain yields collected from the 2 center rows from each offset treatments within each irrigation regime in 2014. There were no significant yield differences within the irrigation regimes. However, when data was averaged across irrigation regimes significant differences were observed. Specifically, the yields measured in the 0 and 3 inch offset treatments were significantly higher than the yield observed in the 9 inch offset treatment. However there was no significant difference between the 6, 9 and 15 inch offset treatments. In 2015 the only significant difference among driver accuracy treatments was at the 75% irrigation regime where the 0 inch offset was significantly lower than the 3 and 9 inch offset with no difference between the offsets (Table 3). Lastly, data was pooled across years and showed that only at the 50% irrigation regime was a consistent reduction in corn yield observed (Table 4). Specifically the 2 year average yield data suggests that driver accuracy within 6 inches will be sufficient to prevent yield losses on drip irrigation regardless of irrigation rate.

Table 2: Corn grain yields in 2014 from different offsets within each irrigation regime as well as the average yield across irrigation regimes and within irrigation regimes.

Offset 50% 75% 100% Average Inches --------------------Bu acre-1--------------

0 132 178 206 172a† 3 140 177 212 177a 6 131 172 208 170ab 9 119 151 204 158b

15 120 163 206 163ab Average 129 168 207

†means followed by the same letter or no letter are not significantly different at the 0.05 probability level

Table 3: Corn grain yields in 2015 from different offsets within each irrigation regime as well as the average yield across irrigation regimes and within irrigation regimes.


0 210 222b 239 224 3 198 242a 246 229 6 197 234ab 230 220 9 182 243a 247 224

15 196 237ab 246 226 Average 197 236 241

†means followed by the same letter or no letter are not significantly different at the 0.05 probability level Table 4: Corn grain yields averaged across years from different offsets within each irrigation regime

Offset 50% 75% 100% Inches --------------------Bu acre-1--------------

0 172a 199 223

3 169a 210 229

6 164ab 204 220

9 151b 197 226

15 158ab 201 226


The yield data collected from individual rows showed that the yield of the row moved closer to the tape did not increase in any of the irrigation regime. However at less than optimum irrigation rates the yield in the row moved away from the tape decreased with increasing distance (Figure 3). This is contrary to data collected by Bordovsky et al. (2010) who found that cotton lint yields in individual rows increased as the rows were moved closer to the drip tape for deficient irrigation. Furthermore, Bordovsky et al (2010) found that this increase in yield offset the decrease in yield found in rows moved further from the drip tape.

Figure 3: Corn yield harvested from individual rows in 2015 from the 50% irrigated zone. (blue circles represent the row moved closer to tape as the offset increased and the red circles are the yield from the rows moved away from tape). Grain Sorghum Yield Table 5 shows grain sorghum yields collected in 2014 from the 2 center rows from each offset treatments within each irrigation regime. Grain sorghum yields were not significantly influenced by offset treatment. This lack of treatment affect was also observed in 2015 (Table 6) and when the data is pooled across years (Table 7).

Table 5: Average grain sorghum yields in 2014 from different row offsets within each irrigation regime as well as the average yield across irrigation regimes and within irrigation regimes.


0 120 150 152 141 3 127 164 149 147 6 128 154 152 145 9 133 146 152 144

15 126 151 154 144 Average 127 153 152

†means followed by the same letter are not significantly different at the 0.05 probability level

y = 0.2856x + 211.09 R² = 0.0549

y = -2.3778x + 235.74 R² = 0.6027

0

50

100

150

200

250

-20 -10 0 10 20 30 40

Yiel

d (

bu

/ac)

Distance From Tape (Inches)

50 % Irrigation Regime

Row Moved Closer to Tape

Row Moved away from Tape

Table 6: Average grain sorghum yields in 2015 from different row offsets within each irrigation regime as well as the average yield across irrigation regimes and within irrigation regimes.


0 164 163 164 164

3 162 164 164 163

6 158 169 168 165

9 165 165 162 164

15 163 160 166 163

Average 162 164 165 †means followed by the same letter are not significantly

different at the 0.05 probability level

Table 7: Sorghum yields averaged across years from different offsets within each irrigation regime

Offset 50% 75% 100% Inches --------------------Bu acre-1--------------

0 142 157 158

3 144 164 157

6 143 162 160

9 149 155 157

15 144 156 160


Data collected from individual rows presented if Figure 4 were similar to the observations made by Bordovsky et al. (2010). Specifically, at limited irrigation the rows moved closer to the tape showed an increase in yield while those rows moving further away producered a declining yield. However, unlike the data collected by Bordovsky et al. (2010) the sorghum yields were unaffected by driver accuracy at full irrigation.

Figure 4: Sorghum yield harvested from individual rows in 2015 from the 50% irrigated zone. (the blue circles represent the row moved closer to tape as a the offset increased and the red circles are the yield from the rows moved away from tape).

Wheat Yields: When wheat yields were pooled across variety and irrigation treatment a significant different was found between row configurations (Table 8). Specifically the wheat planted in a solid stand resulted in a significantly higher yield than the 2 skip row treatments with the treatments with 2 rows removed from the dry area between drip tapes having the lowest yield. Table 9 shows that these effects were most pronounced in the 100 and 50% irrigation treatment and that the trend was similar in the 75% irrigation treatment but not significant. This table also shows that yields were similar for the 2 varieties with no significant differences in their performance when compared within row configuration treatments.

Table 8: Wheat yields resulting from planting

treatments presented in figure 2 when averaged

across irrigation treatments and varieties

planting treatment yield (bu/ac)

solid 66a skip 1 row 57b

Skip 2 rows 53c

y = 0.8855x + 186.79 R² = 0.518

y = -0.8549x + 163.6 R² = 0.3598

0

50

100

150

200

-20 -15 -10 -5 0 5 10 15 20 25 30 35 40

Yie

ld (

bu

/acr

e)

Distance From Tape (Inches)

50% Irrigation Regime

Row Moved Closer to Tape

Row Moved Away from Tape

Table 9: Wheat yields from goodwell, OK resulting from planting treatments presented in figure

2 for Iba and Billings

variety planting Treatment

-------Irrigaiton trt------- Average Across Irrigation

Average for each variety

100% 75% 50%

---------------------------------bu/acre----------------------------

Iba solid 72a 65 63a 67a 57

skip 1 row 60bc 51 50b 54bc

Skip 2 rows 54c 52 49b 52c Billings solid 76a 62 57ab 65a 60

skip 1 row 67ab 62 53b 60ab

Skip 2 rows 56bc 48 54b 54bc 11.3, 8.5, and 5.6 inches were applied for the 100%, 75%, and 50% irrigation treatments

Relationships between soil moisture and Yield Figure 5 shows the relationship between 60 day average volumetric soil water content measured at a depth of 12-16 inches in the south rows of corn and sorghum in treatments with offsets of 0, 6 and 15 inches (sensors and crop rows were 15, 21 or 30 inches away from drip tape). This data demonstrates that corn grain yields are more sensitive to surface soil moisture than grain sorghum. This is somewhat contradictory to the observation that sorghum yields were more sensitive to row distance from drip tape. However, the strong correlations between VWC and corn yield in figure 5 can be explained by the substantial differences in corn yield between irrigation regimes. Specifically, the average corn yields were 129, 168 and 207, for the 50, 75, and 100% irrigation regimes, respectively. In contrast, the sorghum yields were 127, 153, and 152 bushels/acre, for the 50, 75, and 100% irrigation regimes, respectively. Although single row sorghum yields within an irrigation regime were sensitive to distance to the drip tape (Figure 4), the relationship in figure 5 is stronger for corn because of greater differences in corn yield between the three irrigation regimes.

Figure 5: the relationship between yield observed in the south rows and the volumetric water content (VWC) measure within the south rows at 6 inches below the soil surface. The VWC data were averaged over a 2-month period which was Jul 9 to Sep 7 for corn and Jul 18 to Sep 15 for sorghum.

Stress indexes using Canopy Temperature Time-Temperature Threshold (TTT) Excluding the days when TTT was zero, the 2-month daily average TTT was 426, 419, and 447 minutes for 100%, 75%, and 50% irrigated corn, respectively (Figure 6). These values are similar to the 443 and 461 minutes estimates for 100% and 67% total ET replacement irrigation levels reported by Wanjura and Upchurch (2000). The maximum corn TTT was 660 minutes (11 hours), estimated on July 26th at both deficit irrigation levels. Other studies have reported smaller TTT values for similar irrigation levels of corn. But this is mainly due to the fact that these studies used a larger solar radiation threshold in filtering TTT values, thus integrating the results over a shorter period of the day (mostly afternoons). For sorghum, the average TTT was 324, 336, and 428 minutes for 100%, 75%, and 50% irrigation levels, respectively (Figure 7). The larger inter-treatment TTT difference for sorghum compared to corn could be attributed to larger water application and soil moisture difference for the sorghum. It may also indicate that this threshold is more sensitive for sorghum. The maximum TTT was 660 minutes at 50% irrigation level, measured on Sep. 3rd, 2014, when air temperature reached 37.2 °C (RH was 19%).

Figure 6: The time-temperature threshold for corn in the 100, 75, and 50% irrigation regimes.

Figure 7: The time-temperature threshold for sorghum in the 100, 75, and 50% irrigation regimes. Degrees Above Non-Stressed (DANS) The 60-day average DANS was -0.1 and 1.4 °C for 75% and 50% irrigated corn, respectively. The average values were larger at 0.2 and 2.4 °C for the same irrigation levels of sorghum. The maximum DANS was 3.9 and 4.8 °C for corn and sorghum. Figures 8 and 9 represent daily variations in DANS for the canopy temperature measurements made during 1300-1400 hr. To assist with interpreting DANS dynamics (solid lines), the relative Volumetric Water Content (VWC) is also graphed in dashed lines for each irrigation treatment. In case of corn, DANS had a rapid decline on Aug 27th, when 23 mm of rainfall was recorded by the adjacent weather station. This event was followed by another 36 mm of rain that fell during the next two days.

0

100

200

300

400

500

600

700

800

7/10 7/20 7/30 8/9 8/19 8/29 9/8

TT

T (

min

) Corn 100% 75% 50%

0

100

200

300

400

500

600

700

800

7/30 8/9 8/19 8/29 9/8 9/18 9/28

TT

T (

min

)

Sorghum 100% 75% 50%

Figure 8: Daily variations in DANS for the corn canopy temperature measurements made during 1300-1400 hr

Figure 9: Daily variations in DANS for the Sorghum canopy temperature measurements made during 1300-1400 hr Canopy Temperature ratio The canopy temperature ratio had a small range of 0.99-1.03 for 75% irrigated corn, with an average of unity. The range was larger for 50% irrigated corn at 0.90-1.00, (average 0.96). For sorghum, the range was 0.95-1.02 and 0.85-0.98 for 75% and 50% irrigations, respectively. The average Tc ratio was 0.99 and 0.92 for the same two irrigation treatments.

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C

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NS

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)

Corn 100% 75% 50%

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ela

tiv

e V

WC

DA

NS

(°C

)

Sorghum 100% 75% 50%

Figure 10: Daily variations in the temperature ratio for the corn canopy temperatures

Figure 11: Daily variations in the temperature ratio for the grain sorghum canopy temperatures

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References:

Bordovsky, J.P., and J.T. Mustian. 2010. Cotton production response to crop row offset orientation to sdi laterals in the Texas High Plains. ASABE Publication Number 711P0810cd.

Lamm, F.R., and T.P. Tooien. 2003. Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrigation Science. 22: 195-200.

Luckey, R.R., N.L.Osborn, M.F. Beker, and W. J. Andrews. 2000. Water Flow in the High Plains Aquifer in Northwestern Oklahoma. USGS Fact Sheet 081-00.

Project Title: Ogallala Aquifer Irrigation Sustainability Study

Investigator(s): Dr. Robert Scott Frazier (PI), Associate Professor, Oklahoma State University Department of Biosystems and Agricultural Engineering (BAE), 212 Ag Hall, Stillwater, OK 74078. Dr. Saleh Taghvaeian (BAE), Dr. Jason Warren (PSS), Mr. Cameron Murley (FRSU, Panhandle Research and Extension Center-Goodwell) Goal: To determine the overall efficiency and effectiveness of energy and water usage from the Ogallala underground aquifer to application to the soil for irrigation in the Panhandle and Western Oklahoma in order to minimize aquifer degradation. The project also examines important irrigation areas such as irrigation operations costs along with recommendations.

Objectives: The objective of this project is to measure the overall efficiency and effectiveness of energy and water usage from the Ogallala aquifer to application at the crop level. This study will lead to system improvements that lessen demand pressure on the aquifer. The project also will disseminate important recommendations for irrigation farmers in the area to control operation costs and use less water for the same crop production. This project is different from previous projects in that it combines the “energy efficiency” of irrigation pumping plants (engine/motor) with “water application efficiency” (pumps) of center pivot systems to provide a more comprehensive life cycle assessment (LCA) estimate of overall efficiency and sustainability of irrigation in Oklahoma Panhandle. Sometimes referred to as the “water-energy nexus”, the study will show the strong relationship and dependence between water, energy and crop production. The data resulting from this study will generate programmatic recommendations and education for Ogallala irrigation system operators and county extension personnel including improvements which will lead to less water and energy used for the same amount of crop produced. When implemented, these recommendations will lessen the pressure on the Ogallala and allow more opportunity for recharging of the aquifer. On the energy side, the life cycle assessment (LCA) of the recommended irrigation system improvements will show the lessening of environmental impacts such as greenhouse gas (GHG) and other emissions. The decrease in fuel and energy use will also show a positive economic impact for the farmers. This project therefore increases the sustainability of the aquifer and the farming communities that depend on it (environmental, economic and social aspects). Another objective of this project is disseminating project information throughout Oklahoma and to make the information readily available through use of the OSU Oklahoma Water Resources Center website which can be accessed at http://water.okstate.edu.

Methods: This project will be conducted on a sample of ten different center pivot locations reflecting the diversity of existing systems. In order to measure overall efficiency, the irrigation system is divided into three subsystems: the engine/motor, the pumping station (transferring water from the aquifer to the center pivot) and the center pivot (transferring water from the pivot point to crop root zone). Each subsystem will be tested separately and together. The efficiency of the system engine/motor will be directly measured via electrical or fuel

http://thinkwater.okstate.edu/

inputs via wattmeter and fuel flow loggers. Simultaneously the output shaft power will be monitored with a load cell dynamometer and recorded (Eaton Mod 7530 and transducer). The energy input versus the shaft power output will give the efficiency of the driver (motor/engine).The pump efficiency will be measured via flow, head and power input measurements. Finally the water delivery subsystem (pivot) will be tested and observed. Water losses from the pivot point (pump outlet) to the soil at plant level will be measured under variable climatic conditions (wind speed, relative humidity, etc.). The head measurement will also give the aquifer water table level at the time. These studies will give the overall energy and water delivery efficiency of the entire center pivot pump system. In addition, the life cycle assessment of the system will report GHG and other emissions, water usage, local water supply and ecosystem impacts and other life cycle impacts will be measured and reported. Areas for suggested improvement will be identified and quantified. Much of the equipment for the engine efficiency measurements (Eaton Mod 7530 and transducer load cell) is already on loan from the Cimarron County NRCS office. Some water flow measurement equipment has been provided by other faculty members in BAE. Fuel flow and water flow measuring equipment will still be needed. Findings and Significance: Tests were conducted on the ten selected center pivot sites (Table 1). Table 1. Selected Ogallala Irrigation Center Pivot Test Sites

Site No.

Site

Power

County

Depth to water

1

NG 1

Natural gas

Texas

222.4 ft

2

NG 2

Natural gas

Texas

278.0 ft

3

ELECT 1

Electricity

Beaver

15.4 ft

4

ELECT 2

Electricity

Beaver

10.8 ft

5

ELECT 3

Electricity

Beaver

10.4 ft

6

ELECT 4

Electricity

Beaver

27.4 ft

7

ELECT 5

Electricity

Beaver

17.5 ft

8

ELECT 6

Electricity

Beaver

20.2 ft

9

ELECT 7

Electricity

Beaver

10.4 ft

10

ELECT 8

Electricity

Beaver

27.4 ft

The same principle of input fuel energy applies to evaluating electric motors, although nameplate efficiencies generally are used in determining the output power. This is sometimes difficult to measure output power of an electric motor during on-site testing and because electric motors generally retain efficiency until failure. The most reliable method for testing electrical consumption is to measure voltage and current of the incoming power using a True-RMS clamp meter. The input power measured can then be multiplied by the nameplate motor efficiency to provide the power produced by the motor. The output horsepower from the engine or motor also serves as the input horsepower to the pump and is used to evaluate the combined pump and well efficiency. The required horsepower for a pump during any given pumping condition is calculated using the water horsepower equation based on water flow, pumping depth, water pressure and well column friction. The overall efficiency of a pumping plant is the ratio of the water horsepower to the potential energy. This can be calculated by multiplying the engine efficiency and the pump/well efficiency or it can be figured directly. The overall efficiency typically is calculated in cases where a torque cell is not available or not able to be installed. Irrigation Uniformity and Water Application Efficiency

This irrigation efficiency study is unique in that is measures both pumping-plant energy efficiency and water application efficiency. The study measured both the amount of pumped water delivered via the spray nozzles and the relative uniformity of this application. Irrigation uniformity was evaluated based on the readings from catch cans placed under the irrigation boom/nozzles. Two commonly-used indicators are Christiansen's Uniformity (UC) and Distribution Uniformity (DU)

Catch cans were placed across the irrigated area on 10 or 20 ft spacing to collect applied water and estimate distribution uniformity.

Irrigation uniformity indicators and irrigation application efficiency are demonstrated below for a system with poor uniformity (Figure 1) and a better uniformity system (Figure 2). Note that the water delivery efficiency is not that bad in the former but the uniformity is bad.

Site: NG 2; Pivot radius: 1260 ft UC: 31% DU: 14% Efficiency: 83%

Figure 1: Example of poor uniformity in tested Ogallala irrigation system

Site: ELECT 5;Pivot radius: 640 ft UC: 76% DU: 69% Efficiency: 93%

Figure 2: Example of acceptable uniformity in tested Ogallala irrigation system

PER

CEN

TAG

E

Data Analysis Ten wells were tested including two natural gas and eight electric systems. The overall efficiency of the natural gas systems was much lower than the electric systems. Nearly all of this inefficiency was due to the aging engines since the pump efficiencies for all systems were (surprisingly) relatively similar. Using the Nebraska Pumping Plant Performance Criteria (NPPPC) [UGA, 2016] we can compare the energy efficiency to an accepted standard (See Table 2). The standard efficiency assumes a well-designed and maintained pumping plant. The “potential reduction” in Figure 6 is the amount of energy savings possible if the inefficient system was operating at standard efficiency.

The following charts (Figs 3-6) demonstrate the calculated pump efficiency (%), the overall plant efficiency (%), the percent overall efficiency compared to NPPPC, and the potential reduction (%) for all tested systems.

Calculated Pump Efficiency

90

80

70

60

50

40

30

20

10

0

NG ONE NG TWO ELECT

ONE ELECT TWO

ELECT THREE

ELECT FOUR

ELECT FIVE

ELECT SIX ELECT

SEVEN ELECT EIGHT

Figure 3. Efficiency of pump unit – not power plant

Figure 4. Percent of standard efficiency of only the pump unit

Figure 5. Overall efficiency of the plant and pump compared to NPPPC acceptable efficiency (black line)

Figure 6. Percentage reduction in energy and cost from bringing systems to NCCCP efficiency recommendations

Table 2: Fuel and Electrical Pumping Performance Per Nebraska Pumping Plant Performance Criteria (NPPPC) Ref (UGA)

$/Hour 5.91 12,167 2.98 6,129 2.93 6,039 $/Acre-In 5.15 - 2.86 - 2.29 -

$/Hour 6.68 13,758 3.71 7,639 2.97 6,119 $/Acre-In 2.07 - 1.54 - 0.53 -

$/Hour 2.72 2,724 2.03 2,029 0.69 695 $/Acre-In 1.95 - 1.84 - 0.11 -

$/Hour 2.75 2,747 2.59 2,593 0.15 154 $/Acre-In 3.41 - 2.63 - 0.78 -

$/Hour 3.53 3,535 2.73 2,728 0.81 807

$/Acre-In

2.26 -

2.51 -

- 0.25 -

$/Hour

3.89

3,886

4.32

4,320

- 0.43 (434)

$/Acre-In 1.54 - 1.29 - 0.25 -

$/Hour 1.03 1,027 0.86 860 0.17 166 $/Acre-In 1.74 - 1.42 - 0.32 -

$/Hour 2.25 2,253 1.84 1,835 0.42 418 $/Acre-In 3.54 - 3.49 - 0.05 -

$/Hour 6.43 6,430 6.34 6,340 0.09 90 $/Acre-In

2.96 -

3.46 -

- 0.51 -

Economics of Improving Irrigation Efficiency

By improving the efficiency of the energy use (NG and Electricity) to the NPPPC standards, the cost to irrigate is lowered. Table 3 below shows the projected savings in dollars per acre-in per year for the ten systems examined. Further cost reduction is possible by improving the water delivery efficiency.

Table 3: Economics of Current Operations and Cost Reduction Possibilities from Improvements

Seasonal Runtime (hours)

Current Costs Potential Costs Cost Reduction Unit Seasonal Unit Seasonal Unit Seasonal

NG ONE 2060 $/Acre-In 5.62 - 2.83 - 2.79 -

NG TWO

1000

ELECT 1 1000

ELECT 2 1000

ELECT 3 1000

ELECT 4 1000

ELECT 5 1000

ELECT 6 1000

ELECT 7 1000

ELECT 8 1000

$/Hour 5.28 5,279 6.18 6,180 -0.9 (901)

Water Distribution Efficiency and Effectiveness The ‘water application at distances from the pivot’ figures graphically show how well, or uniform, the water is being delivered to the ground (Figures 7, 8, 9, 10, 11). The statistical uniformity is shown by using the accepted Distribution and Christiansen Uniformity coefficients (DU, CU). In general, the DU (and CU to a lesser extent) shows us how uniform the center pivot applies water to ground level as the boom passes along the ground with the nozzles spraying (see Tables 4, 6, 8, 10, 12). These are several points of interest in the results of these water distribution studies: Is the average amount supplied to the ground of the correct quantity? And, how uniform is this water application? Examination of Figure 9 and Tables 8 & 9 shows a widely varying application of water from one end of the spray boom to the other. In some cases there is no water reaching the ground (clogged nozzles) and on other sections an excess of water. This leads to poor consistency in crop production. This may also cause producers to increase the flow and pressure to make up for the variability in the application. This, of course, is the worst thing to do from an aquifer conservation and energy minimization point of view – however, we see some evidence of this in the higher than needed pressures and flow rates employed by some producers. The water application efficiency (Tables 5, 7, 9, 11, 13) shows the calculated amount of water (total) being delivered to the ground. A value of 100% indicates that all of the water leaving the pump is being delivered to the ground (plant base). Values less than 100% are typically due to leaks in the distribution piping. An example of the combined energy/water study is examining the possible inter- dependence between energy and water use. In the case of Natural Gas site #2, the energy efficiency of the plant is a rather low at about 45% of nominal efficiency (old detuned engine). Yet, the water delivery efficiency is very good at 100% and the DU is acceptable at 85%. In this case the power plant energy use and water application use efficiency is somewhat decoupled. However, for the water applied – the energy use is excessive. Therefore, water and energy in irrigation are never completely independent. Improvements on this site should probably first focus on the power-plant (NG engine).

able 4 : Uniformity Coefficients

Sum of lowest quarter weighted catches 90

Sum of lowest quarter used can numbers 362

Lowest quarter average catch (in) 0.25

Distribution Uniformity - DUlq 73%

Sum of absolute value of diff from avg catch

115.00

Christiansen Uniformity - CU 84%

Figure 7: Water application amount at distances from pivot

T

Table 5

Pivot Settings and Water Efficiency

Pivot Wetted Radius (ft) 1290

Inflow Rate (gpm) 817

% Setting During Eval. 75

Fraction of Full Circle % 100

Application Efficiency 99.7%

Figure 8: Water application amount at distances from pivot Table 6: Uniformity Coefficients





Sum of absolute value of diff from avg catch 25.19


Table 7:














Table 9:














Table 11: Pivot Settings and Water Efficiency






Figure 11: Water application amount at distances from pivot

Table 12: Uniformity Coefficients






Table 13:







Life Cycle Assessment (LCA) of Efficiency Improvements The LCA analysis for this study examines the environmental impacts that could be avoided by improving the ten systems (power plants only for this section) to NPPPC recommended efficiency levels. Further improvements would be possible by optimizing the water operations management but are not examined in this study. The LCA study uses the GREET v1.3.0.12704 (Argonne National Lab) software ([email protected]) and examines the electrical (kWh) and fuel (Natural Gas) that could be avoided by getting the irrigation system power plants to NPPPC standards. The natural gas analysis is from a file “NG from Shale and Regular Recovery”. The LCA of utility electricity is from the GREET file “Distributed – U.S. Central and Southern Plains”. The LCA analysis is a “well to product or wheel” type study where the impacts from extraction (coal/electricity, petroleum), through refining, generation, transmission and end use (transportation) are included. Therefore, some of the impacts may not be in the immediate vicinity of the irrigation site but certainly are in the overall ecosystem. While the GREET model is a transportation-centric model, but can still be used to simulate stationary engines/motors. In the case of natural gas, we include the end-use, which for GREET, is a natural gas powered vehicle. This is not too far of a stretch as the irrigation engines are essentially automotive based to begin with. We let the efficiency calculations from the center pivot test affect the fuel usage – and consequently the LCA outputs. A word of explanation on some of the varied LCA outputs. One may wonder why there is nuclear energy in the natural gas production outputs. Utility electric is used in various stages of the NG production. The grid, on average, has some percentage of nuclear- produced power that varies according to geographic region. The electricity used in this LCA was from the Southwest Power Pool. What follows are the avoided environmental (LCA) “impacts” projected by improving tested center pivots to NPPPC standards (natural gas and electric):

Table 14: Avoided IC natural gas fuel production emissions

Well to Product - Emissions Units Saved by NPPPC

VOC g 1,874.5

CO g 3,041.7

NOx g 3,981.7

PM10 mg 164,401.5

PM2.5 mg 136,276.3

SOx g 3,664.3

CH4 g 31,180.0

N2O mg 10,194.9

CO2 kg 1,557.7

PM10_TBW g 0.0

PM2.5_TBW g 0.0

VOC_evap g 0.0

CO2C g 0.0

CO2 Biogenic g -1,411.1

CO2 Land Use Change g 0.0

CO2 Fertilizer g 0.0

Black carbon mg 30,962.0

Primary organic carbon mg 26,412.3

Groups 0.0

Greenhouse Gas kg 2,531.3

Flow properties 0.0

Biogenic carbon mass ratio 0.0

Table 15: Avoided natural gas fuel production resources consumed Well to Product - Resources Saved by NPPPC

Resources mmbtu 327.5

Natural Gas mmbtu 325.0

Crude Oil btu 1,114,213.1

Coal Average btu 987,824.5

Nuclear Energy btu 160,807.6

Bitumen btu 110,101.8

Hydroelectric Power

btu

67,604.0

Wind Power btu 24,575.5

Forest Residue btu 10,867.6

Renewable (Solar, btu 4,103.6

GeoThermal Power btu 3,952.7

Pet Coke btu 1,870.6

Solar btu 31.1

Water gal 1,712.6

Uranium Ore mg 1,607.1

Groups 0.0

Fossil Fuel mmbtu 327.2

Natural Gas Fuel mmbtu 325.0

Petroleum Fuel btu 1,226,185.5

Coal Fuel btu 987,824.5

Non Fossil Fuel btu 271,942.7

Nuclear btu 160,807.6

Renewable btu 111,134.8

Biomass btu 10,867.6

Table 16: Avoided IC natural gas engine end-use emissions (Stationary Engine at Irrigation Pump)

Vehicle Name: Dedicated CNGV Car or Stationary

Fuel Blend: Compressed Natural Gas (100%)

Vehicle

Operation Total Total Saved by NPPPC

Total Energy 1000 kJ/MJ 1168.132 kJ/MJ 376,255.3 KJ

Fossil Fuel _ _ 1159.242 kJ/MJ 373,391.8 KJ

Coal Fuel _ _ 32.3 kJ/MJ 10,403.8 KJ

Natural Gas Fuel _ _ 1121.878 kJ/MJ 361,356.9 KJ

Petroleum Fuel _ _ 5.064 kJ/MJ 1,631.1 KJ

Water _ _ 0.108 L/MJ 34.8 L

Emissions

Engine Operation Only

VOC 20.62 6641.702 31.518 mg/MJ 10,151.9 mg

CO 703.312 226536.8 728.649 mg/MJ 234,697.8 mg

NOx 26.48 8529.208 66.683 mg/MJ 21,478.6 mg

PM10 1.521 489.9141 3.203 mg/MJ 1,031.7 mg

PM2.5 1.409 453.8389 2.54 mg/MJ 818.1 mg

SOx 254.551 81990.88 21.558 mg/MJ 6,943.8 mg

CH4 27.419 8831.66 296.869 mg/MJ 95,621.5 mg

CO2 55.051 17731.93 65.438 g/MJ 21,077.6 g

N2O 2.254 726.0134 2.337 mg/MJ 752.7 mg

Black carbon 133.489 ug/MJ 42,996.8 ug

PM10_TBW 3.85 1240.085 3.85 mg/MJ 1,240.1 mg

PM2.5_TBW 1.371 441.5991 1.371 mg/MJ 441.6 mg

VOC_evap 5.446 1754.157 5.446 mg/MJ 1,754.2 mg

Primary organic carbon 152.361 ug/MJ 49,075.5 ug

CO2Biogenic 0 -43.72 mg/MJ -14,082.2 mg

Greenhouse Gas 57.657 18571.32 76.289 g/MJ 24,572.7 g

Table 17: Avoided utility electrical production emissions (Stationary Motor at Irrigation Pump) – local LCA emissions negligible for electric motor

Well to Product - Emissions

Saved by NPPPC

Imperial #'s

VOC 22.372 g 2,000.1 g 4.4 Lbs

CO 61.187 g 5,470.1 g 12.0 Lbs

NOx 249.801 g 22,332.2 g 49.1 Lbs

PM10 62.685 g 5,604.0 g 12.3 Lbs

PM2.5 36.912 g 3,299.9 g 7.3 Lbs

SOx 531.461 g 47,512.6 g 104.5 Lbs

CH4 405.459 g 36,248.0 g 79.7 Lbs

N2O 2.699 g 241.3 g 0.5 Lbs

CO2 214.979 kg 19,219.1 kg 42,282.1 Lbs

PM10_TBW 0 g 0.0 g 0.0 Lbs

PM2.5_TBW 0 g 0.0 g 0.0 Lbs

VOC_evap 0 g 0.0 g 0.0 Lbs

CO2C 0 g 0.0 g 0.0 Lbs

CO2Biogenic -1.408

kg

-125.9 kg

-276.9

Lbs

CO2LandUseChange 0 g 0.0 g 0.0 Lbs

CO2Fertilizer 0 g 0.0 g 0.0 Lbs

Black carbon 1.801 g 161.0 g 0.4 Lbs

Primary organic carbon 3.56 g 318.3 g 0.7 Lbs

Groups

Greenhouse Gas 227.991 kg 20,382.4 kg 44,841.3 Lbs

Flow properties 0.0

Biogenic carbon mass ratio

0.165

%

14.8

%

0.0

Saved by NPPPC**

238.4

mmbtu

140.1

mmbtu

81,669,620.3 btu

9,735,357.1 btu

2,332,711.3 btu

2,332,095.7 btu

969,680.8 btu

389,165.2 btu

374,857.7 btu

315,152.4 btu

230,507.8 btu

3,916.3 btu

2,956.9 btu

36,029.2 gal

97.3 g

224.3 mmbtu

140.1 mmbtu

81,669,620.3 btu

14,119,265.8 btu

9,735,357.1 btu

4,383,908.7 btu

2,567,135.5 btu

969,680.8 btu

Table 18: Avoided utility electrical production resource usage (Stationary Motor at Irrigation Pump) – local LCA resource usage negligible for electric motor

Well to Product - Resources

** Includes all efficiencies

Resources

2.667 mmbtu

Coal Average

1.567 mmbtu

Natural Gas 913530.4 btu

Nuclear Energy 108896.6 btu

Crude Oil 26092.97 btu

Wind Power 26086.08 btu

Forest Residue 10846.54 btu

Renewable (Solar 4353.078 btu

GeoThermal Power 4193.039 btu

Hydroelectric Power 3525.195 btu

Bitumen 2578.387 btu

Pet Coke 43.807 btu

Solar 33.075 btu

Water 403.011 gal

Uranium Ore 1.088 g

Groups

Fossil Fuel 2.509 mmbtu

Coal Fuel 1.567 mmbtu

Natural Gas Fuel 913530.4 btu

Non Fossil Fuel 157933.6 btu

Nuclear 108896.6 btu

Renewable 49037.01 btu

Petroleum Fuel 28715.16 btu

Biomass 10846.54 btu

Combined avoided LCA outputs for All Tested Irrigation Systems: Total GHG Saved (NPPPC Stds) = 22,913 Kg (50,409 Lbs) Total Energy- Process Water Saved = 37,751 Gallons (Not Irrigation Water)

Ultrasonic Flow Meter Testing Early in the project the team discovered that the NRCS (loaned) GE PT-878 portable flow meter was giving very erratic and odd results for water flow in metal pipes. The PT-878 was taken to the Payne County ARS Hydraulics lab where calibrated water flow in 12” metal pipes could be observed and tested. Again, the PT-878 delivered unusual readings (sometimes negative). This led the OSU team to purchase a Fuji Portaflow C ultrasonic flow meter for the main irrigation efficiency project. The Fuji has proven to be accurate and reliable under a wide variety of conditions. The inconsistent PT-878 readings caused the NRCS offices to conduct a mass test of their PT-878 meters at the ARS lab. OSU grant personnel assisted with tests and observed the outcomes. Essentially, the tests revealed that almost all the 15 meters were in need of rework and calibration. A fact-sheet regarding the tests and operational details surrounding the portable meters is under construction.

Conclusions Work in progress for final report

Recommendations for Improving Systems:

Work in progress for final report

Future Work: Work in progress for final report

References

USGS (2008) Groundwater Depletion in the United States (1900-2008). Scientific

Investigations Report #2013-5079, 23 pp.

MSSOY.org (2014). SOYBEAN IRRIGATION GUIDE FOR THE MIDSOUTHERN US,

Mississippi Soybean Production Board Fact Sheet, March 2014.

USDA-NASS (2008) Farm and Ranch Irrigation Survey. 268 pp.

Burt, C. (2011) Characteristics of Irrigation Pump Performance in Major Irrigated Areas of

California. ITRC Report No. R 11-004.

Fipps, G., and Neal, B. (1995) Texas Irrigation Pumping Plant Efficiency Testing Program.

Final Report Submitted to the State Energy Conservation Office, April 7, 1995. 122 pp.

Keller, J., and Bliesner, R. (1990) Sprinkler and Trickle Irrigation. The Blackburn Press,

Caldwell, NJ, USA. ISBN-10: 1-930665-19-9

Kenny, N. (2013) Basics of On-site Pumping Plant Evaluations. LSU AgCenter Pub. 3241-

F.

UGA: Nebraska Pumping Plant Performance Criteria, Accessed 2016,

http://extension.uga.edu/publications/detail.cfm?number=C965

Lyle, W.M., and Bordovsky, J.P. (1981) New irrigation system design for improving

irrigation efficiencies and enhancing energy conservation. Final Report to the Depart. of



Energy, Doe/g/015123, 77 pp.

http://extension.uga.edu/publications/detail.cfm?number=B837


EVALUATING ENERGY USE FOR PUMPING IRRIGATION WATER, 2011, Derrel L.

Martin, Tom W. Dorn, Steve R. Melvin, Alan J. Corr, William L. Kranz, Proceedings of the 23rd

Annual Central Plains Irrigation Conference, Burlington, CO., February 22-23, 2011

http://extension.uga.edu/publications/detail.cfm?number=B837


interim project report for dasnr water...

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