logan and morgan counties extensionlogan.colostate.edu/agri/agri_docs/farm ranch august...

Featured Articles: MAXIMIZING THE VALUE OF LIMITED

IRRIGATION WATER: USDA RE-

SEARCHERS STUDY HOW PRODUC-

ERS ON LIMITED IRRIGATION CAN SAVE WATER AND BE PROFITABLE

SOIIL WATER CONTENT OR POTEN-

TIAL SENSORS FOR AGRICULTURE WATER MANAGEMENT

WATER IRRIGATION SCHEDULER FOR EFFICIENT APPLICATION (WISE APP)

FARMER’S DRYLAND WHEAT VARI-

ETY DECISION TREE FOR FALL 2016

WINTER TRITICALE AS A DRYLAND FORAGE FOR EASTERN COLORADO FARMS

Colorado State University Extension

Logan and Morgan Counties

508 South 10th Avenue Sterling, CO 80751

970 522-3200

www.extension.colostate.edu/

logan/

AGRICULTURE AND FARMING NEWSLETTER

Logan and Morgan Counties Extension

August 2016 Volume 1 Issue 1

Maximizing the Value of Limited Irrigation Water: USDA Researchers Study How Producers on Limited Irrigation Can Save Water and be Profitable

Dr. Louise Comas Dr. Sean Gleason Dr. Kendall DeJonge Dr. Huihui Zhang Water Management and Systems Research Unit USDA-ARS, Fort Collins CO When referencing the impact of irrigation on crop yield, the measuring stick has his-torically focused on the amount of yield gain per unit of water applied. In this era of droughts and often-inadequate irrigation water supplies, the focus has shifted to maximizing crop yield per water consumed by the crop. The key question is: what is the optimal irrigation man-agement under water-deficit conditions that will in the end save water while producing necessary yields? Based in Fort Collins, the Water Management and Systems Research Unit con-ducts deficit irrigation ex-periments about 30 miles away on the 50-acre Limited Irrigation Research Farm (LIRF) near Greeley. Lo-cated adjacent to the city’s airport, the farm is owned by Colorado State University and operated by the ARS under a long-term lease. A previous WMSRU study at the Greeley site, conducted from 2008 through 2011,

encompassed four crops grown in the High Plains: corn, wheat, sunflower and dry beans. The current pro-ject is focused on corn and sunflower, which have very different relationships be-tween crop yield and water. Corn uses slightly more water but can produce high yields, about four times the yield of sunflower, at full water availability. Sunflower’s inclusion in the research is due to its attractiveness as an alternative crop for pro-ducers with limited irrigation

capacity. We are evaluat-ing the response between y i e l d a n d E T

(evapotranspiration, the amount of water used by the crop system including both plant transpiration and evaporation) (Figure 1). If the response line is steep (as is the case with corn), you are proportionately losing a large amount in yield com-pared to what you are sav-ing water, so the benefit of saving that water is muted – unless management practices can be used to enhance yields for a given ET. With sunflower, it is a very shallow response line so there is more

‘room’ to save water and with minimal yield loss. If the response line is shallow

A corn plot with full irrigation (left) next to one with 40% of full ET during the late vegetative (right).

Northeast Colorado Extension

“REDUCING

CROP WATER

USE BY 35%

DURING THE LATE

-VEGETATIVE

STAGE, WITH

FULL OR NEARLY

FULL IRRIGATION

DURING THE REST

OF THE SEASON,

MAINTAINED

HIGH YIELDS

SIMILAR TO FULL-

IRRIGATION.”

enough, however, a producer may be better off growing that crop under dryland – or nearly dryland – conditions, and either using the irrigation water of that crop elsewhere on the farm, or leasing it to users who will pay more for it than the profit from increased crop yields. Divided in half between corn and sunflower, the main LIRF experiment contains a total of 96 plots, each composed of 12 rows about 145 feet long and planted in 30-inch row spacing. The 12 treatments of strategic seasonal deploy-ment are replicated four times. Crops in the plots are grown under surface drip irrigation in order to have as “tight” control of the water applications as possible and to maximize efficient water

application (minimize evapo-ration). With drip irrigation, once the crop reaches full canopy cover, evaporation losses are negligible. Meas-urements are taken from the center four rows of each plot to avoid “edge effects” of neighboring plots. A research priority at LIRF is exploring options to minimize crop yield losses through strategic deployment of deficit irrigation. Treat-ments in this experiment were established with the understanding that yield will be reduced if the crop ex-periences water shortages during certain growth peri-ods, such as flowering and seed set. But, there are other times when water shortages do not necessarily

impact final yield to a sig-nificant degree. Thus, the treatments explore how crops respond under specific seasonally-varied deficit irrigation regimens (avoiding deficit during flowering and seed set, and applying defi-cits during the late vegeta-tive and maturation periods) in order to see which distri-bution and amount of deficit irrigation best saves water while maintaining yields. The 12 different water treat-ments — all applied through the farm’s drip irrigation system — range from 100/100 all the way down to 40/40. The first number equates to the percent of maximum ET applied during late vegetative growth stage, with the second num-

ET, mm

0 200 400 600 800 1000

Yie

ld,

Kg

ha-1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Z. maysH. annuus

2012

2012

2013

2010

Figure 1. Water production functions showing grain yield per annual ET used in maize (Zea mays) and sunflower (Helianthus annuus) cropping systems under five levels of irrigation (five treatments in each year). 2010 and 2013 were typical years. Data is from USDA-ARS WMSRU experiments conducted in Greeley, CO. 2012 was an extremely hot and dry year, which led to both maize and sunflower using substantially more water. Relationships between yield and ET were linear in all years for both crops.

August 2016 Volume 1

“WATER

DEFICITS

DURING SEED

FILL RESULTED

IN

CONSIDERABLE

YIELD LOSSES.”

ber being percent of maxi-mum ET applied during the crop maturation stage. All treatments receive 100 per-cent of maximum ET during flowering and seed set. The “100/100” is considered full irrigation, while the “40/40” is similar to dryland condi-tions with supplemental irri-gation during flowering and seed set. The plots are irri-gated every four to five days. Should the Greeley research site receive rainfall during the experiment, subsequent irrigations are adjusted to compensate. Before and after irrigation and precipi-tation events, soil moisture readings are recorded to determine how much water is in the soil profile. From that information, water balance is used to determine how much ET the plants have actually extracted from the soil and used. Data is collected on several fronts. We measure volu-metric soil water content by neutron probe, ET by water balance, crop canopy tem-perature, vegetative canopy cover, and growth stage/biomass. We also collect and evaluate multispectral and thermal imagery, root phenology and density, sap flow (movement of water through the plant), gas ex-change, stomatal conduc-tance — and, of course, crop yield. An on-site CoAgMet (Colorado Agricultural Mete-orological Network) weather station provides the baseline reference ET data used in calculating and interpreting plot measurements. The current LIRF experiment has one season remaining,

but some findings are taking shape. Early results show that reducing crop water use by 35% during the late-vegetative stage, with full or nearly full irrigation during the rest of the season, main-tained high yields similar to full-irrigation treatments. This seasonally applied irri-gation deficit saved ap-proximately 17% of crop water used over the total season. Water shortfalls during the late-vegetative stage pro-duced short plants with slightly reduced leaf area (Picture). Plants under this treatment in both corn and sunflower appear able to achieve nearly maximum seed yield if water is ap-plied during the reproductive

and seed fill stages (Figure 3; 65/80 treatment). In con-trast, though, a treatment with similar annual ET but water deficits during seed fill resulted in considerable yield losses (Figure 3; 100/50 treatment). Thus, seasonally varied deficit irrigation, with deficits in appropriate times of the season, shows promise for increasing crop water pro-ductivity (yield per amount of water used by the crop) when water is limited. Work in 2016 will include repeating the experiment to test the reliability of the re-sponses found thus far. We also will evaluate more ex-treme treatments to push the boundaries on how much water can be saved during

Figure 3. Water production function (WPF) showing the rela-tionship between grain yield per annual ET used by maize in 2013. Treatments are designated by the amount of deficit irri-gation targeted in the late vegetative stage (number to the left of the ‘/’) and the amount targeted in the maturation or grain filling stage (number to the right). The WPF (the line) is fit through the five treatments with equal targeted deficit during both stress periods (black symbols). White, yellow and green symbols show the result of different targeted deficit during the two stress periods. “A” represents a hypothetical treatment that could push the boundary of ET savings while maintaining similar yields of fully irrigated treatments. “B” represents hypothetical increases in yield from improve hybrids that could increase pro-ductivity under limited water supply.


“MANY

METHODS OF

DETERMINING

SOIL MOISTURE

HAVE BEEN

DEVELOPED,

FROM SIMPLE

MANUAL

GRAVIMETRIC

SAMPLING TO

MORE

SOPHISTICATED

REMOTE

SENSING.”

the late-vegetative stage with minimal impacts on yields (i.e. point A in Figure 3). In adjacent fields, we are exploring a broad range of genetic lines of corn to identify viable plant mechanism for increasing crop yields per water con-sumed, which can be used by breeders and geneticist to develop more efficient germ-plasm for limited water sys-

tems (i.e. point B in Figure 3). Additionally, we are working on remote sensing techniques to provide farmers with spa-tially variable water stress information that can be use-ful in making good irrigation management decisions. The research goal of the unit is to develop better answers about the relationship be-tween deficit irrigation, wa-

ter stress, and final crop yield, and ultimately in-crease crop production per water consumed by the crop. Given the increasing demand for water — by urban enti-ties as well as agricultural — and the full likelihood of future drought events, it is more important than ever to use available water supplies as effectively as possible.

Soil Water Content or Potential Sensors for Agriculture Water Management

The general categories or types in which most sensors fall are: a) tensiometers, b) electrical resistance based (soil water tension or poten-tial), c) capacitance based (frequency oscillation), d) time domain reflectometry (soil dielectric permittivity based sensors), and e) nu-clear (neutron probe/gauge). Hignett and Evett (2008) indicated the following: “in general, a manufacturer’s calibration is commonly per-formed in a temperature con-trolled room, with distilled water and in easy to manage homogeneous soil materials (loams or sands) which are uniformly packed around the sensor. This calibration proce-dure produces a very precise and accurate calibration for the conditions tested. How-ever, in field conditions varia-tions in clay content, tempera-ture, and salinity may affect the manufacturer’s calibra-tion.” If an in-situ calibration of a given sensor is not available, then the soil water content or potential sensor accuracy needs to be assessed in or-der to better manage water

and to realize the reliability

of the sensor. In addition, appropriate sensor calibra-tion curves can be devel-oped during the sensor evaluation process. In 2011, the performance of a Digitized Time Domain Transmissometry (TDT) soil water content sensor, a type of TDR sensor (Acclima, Inc. (Meridian, ID), and a resis-tance-based soil water po-tential sensor (Watermark 200, Irrometer Company,

Soil gravimetric sampling per-formed by Jordan Varble, a CSU graduate student.

Dr. José L. Chavez Colorado State University Civil and Environmental Engi-neering Department Soil moisture is an important factor used in irrigated agri-culture to make decisions regarding irrigation schedul-ing and for land managers making decisions concerning livestock grazing patterns, crop planting, and soil stabil-ity for agricultural machinery operations. Many methods of determin-ing soil moisture have been developed, from simple man-ual gravimetric sampling to more sophisticated remote sensing and Time Domain Reflectometery (TDR) meas-urements. One common tech-nique is to measure dielectric constant, that is, the capaci-tive and conductive parts of a soil’s electrical response. Through the use of appropri-ate calibration curves, the dielectric constant measure-ment can be directly related to soil moisture (Topp et al. 1980). However, there are several different types of sensors commercially avail-able which present different levels of soil water content or potential readings’ accuracy.

sistent in measuring soil mois-ture. In the case of the wa-termark sensor the accuracy was less than expected. However, more field data still are needed to further conclude on the accuracy and reliability of the water-mark sensor. Furthermore, it is imperative to have a good soil-sensor probe contact for a good reading. Otherwise, the dielectric property of the air would be measured which is considerable different than that for water. Thus, good installation of sensors is criti-cal. Similarly, soil salts con-centration may affect the sensor soil water content readings and care should be exercised to account for salts through a proper calibration of the sensor.

soil volumetric water content. In the case of the Water-mark sensor, the factory-recommended equation, evaluated with measured soil water content from a corn irrigated field, in average overestimated soil water content by 11.2±12.6% of soil volumetric water content. Finally, field-derived cali-bration equations developed for both sensors resulted in higher accuracy than the factory- or laboratory-derived equations. The re-sulting mean bias error (MBE) and root mean square error (RMSE) for the TDT sensor was 1.8±2.6% and for the Watermark sen so r -4.3±5.0%, respectively. These results indicate that the TDT soil water content sensor was more accurate and con-

Inc., Riverside, CA) were evaluated on a sandy clay loam soil from an agricultural field near Greeley, CO. Sensor measured soil water content values were com-pared with corresponding values derived from gravim-etric samples. Soil potential (tension) values from the wa-termark were converted to volumetric soil water content for the evaluation. Linear calibration equations were developed for the TDT sen-sor while a logarithmic cali-bration equation was devel-oped for the Watermark sensor. According to laboratory tests, the TDT’s factory-recommended calibration performed very well with errors less than 1.2±3.9% of


“GOOD

INSTALLATION

OF SENSORS IS

CRITICAL.”

Three sensors install at 4-5 inches of depth: a) Decagon 5TE sensor (left), b) Acclima TDF sensor (middle), and c) CS616 Campbell Scientific sensor (right).


“IRRIGATION

WATER

MANAGEMENT

PLAYS A KEY

ROLE IN WATER

CONSERVATION,

PREVENTION OF

WATER

POLLUTION,

AND ENHANCED

CROP

PRODUCTIVITY.”

Water Irrigation Scheduler for Efficient Application: (WISE App)

Dr. Allan A. Andales Colorado State University Soil and Crop Sciences De-partment Improved irrigation water management (IWM) in ap-proximately 2.3 million acres of irrigated farm land in Colorado can play a key role in water conservation, prevention of water pollu-tion, and enhanced crop pro-ductivity. There is a need for a widely accessible decision tool that will increase the capacity of producers and water managers to deter-mine real-time irrigation wa-ter demand for a field or region of interest. An online IWM system named Water Irrigation Scheduler for Effi-cient Application (WISE; http://wise.colostate.edu/) has been developed and pilot-tested in Colorado. WISE is accessible via a web browser, with soil profile water status information also accessible via mobile apps. Early in its development, a stakeholder committee (10 individuals) was formed rep-resenting progressive crop producers and advisers, re-searchers, conservation agency personnel, farm man-agers, and crop commodity group representatives to test and provide suggestions for improving the tool. In addi-tion, WISE has been demon-strated at more than 15 pro-ducer- or conservation agency-conferences and workshops. WISE is a cloud-based tool that has been developed and deployed on the Envi-ronmental Risk Assessment

and Management Systems Platform (eRAMS.com). The web browser interface is GIS-enabled with friendly graphical user interfaces. After the user draws the boundaries of an irrigated field, the tool automatically collects local soils and daily weather data from publicly available data sources such as SSURGO soils database from USDA-Natural Re-sources Conservation Service (NRCS) and the Colorado Agricultural Meteorological Network (CoAgMet.com). To completely set up a field for irrigation scheduling, the user also has to input the follow-ing information: (a) crop in-formation: type, emergence or green-up date, managed root depth; (b) irrigation system information: type and application efficiency; and (c) soil information: initial soil moisture content at emer-gence or green-up. Once a crop type is selected, default values of crop coefficients (used to estimate crop water use from weather data) are provided. The crop coeffi-cients incorporate the effects of crop development on wa-ter use. Advanced users can modify the default values to better represent their crop variety. The tool will then estimate the daily soil water deficit (net irrigation require-ment) of the root zone using local weather data and user-inputted values of actual applied irrigation (inches of water entered into the pivot control panel, for example). An iPhone® or Android® app on a smartphone can synchronize with the cloud server to display soil water

status information for each individual field. The figure below shows the WISE iPhone® app with a “water bucket” representa-tion of soil water status of a field. Field capacity (FC) and wilting point (WP) show

the upper and lower limits of plant available water (inches of water) in the root zone, respectively. The red bar shows the estimated amount of deficit or depletion (irrigation needed) relative to management allowed depletion (MAD). To learn how to set up and use the tool , visit http://wise.colostate.edu/. Acknowledgements The development and testing of WISE was funded by the

http://wise.colostate.edu/�






“PREMIUM

PROGRAM CAN

PAY AN EXTRA

$.030 TO $0.90

A BUSHEL.”

USDA–National Institute of Food and Agriculture (NIFA), the Colorado Water Conser-vation Board, and the Colo-

rado Agricultural Experiment Station. Funding was pro-vided by Western Sugar Cooperative to test WISE on

sugar beet fields. The Coca-Cola Company provided support for promoting and improving WISE in Colorado.

Dr. Jerry Johnson Sally Sauer Colorado State University Soil and Crop Sciences De-partment This tree can be used to se-lect a few varieties for

planting. Decisions need to be made by you to end up with the best possible variety that meets your needs. In the beginning you must decide whether you are going to plant hard red or hard white winter wheat. It is normally an easy decision and proba-bly decided well before anybody would look at this decision tree. For those farmers choosing to

grow hard white wheat you can decide whether you want to get into a premium program (CWRF Ardent Mills Ultragrain® Premium Pro-gram) that can pay an extra $.30 to $.90 a bushel. Or, you can forego the premium

program and plant Antero –a high yielding wheat adapted to the Great Plains. Making the decision about which hard red winter wheat you are going to plant is a little more complicated but still follows all of the rules of a decision tree. The first de-cision to be made is whether you are going to plant a Clearfield variety or not. Again, this may be a deci-

sion that you have already made in advance. One of the Clearfield varieties, Brawl CL Plus, is a double gene Clearfield variety meaning that the Clearfield herbicide Beyond can be mixed with methylated seed

oil to make it more potent on some of the more intractable winter annual grasses, and especially volunteer rye. Brawl CL Plus, a release from Colorado State University (like most of the varieties we will describe talk about to-day), is marketed by Plains-Gold and has excellent test weight, straw strength, and milling and baking quality. It is early maturity and has an intermediate reaction to both

Farmer’s Dryland Wheat Variety Decision Tree for fall 2016


“TRITICALE IS A

HYBRID CROSS

BETWEEN RYE

AND DURUM

WHEAT.”

stripe rust and leaf rust. There are several high-yielding single-gene Clear-field wheat varieties. Oak-ley CL was the second high-est yielding variety on a three-year average in our trials. It is from Kansas State University-Hays and has good test weight, good stripe rust resistance, and good wheat streak mosaic virus resistance. Among the high-yielding non-Clearfield varieties, WB Grainfield is the only early maturing variety. It is a re-lease from Westbred (a pri-vate seed company now owned by Monsanto). It has

good leaf and stripe rust resistance and has a lower test weight. For the high yielding medium maturing varieties, Byrd, LCS Mint, and Winterhawk are equal yielding and the new variety Avery was high-yielding in the two-year av-erage yield summary. This should be a category of pri-mary importance for selec-tion of a variety as the va-rieties are high-yielding and stable. The last group of high-yielding non-Clearfield me-dium-to-late maturity varie-ties includes Denali and SY

Wolf. Denali has been near the top of every variety trial since its release in 2011. It was third from the top for yield in the 2015 three-year summary. SY Wolf from Agripro/Syngenta has per-formed moderately well in dryland variety trials since its release in 2011. It seems even better adapted to irri-gated conditions. It is resis-tant to leaf rust and moder-ately resistant to known races of stripe rust. (Note: A separate decision tree is required for irrigated conditions)

Winter Triticale as a Dryland Forage for Eastern Colorado Farms Dr. Merle F. Vigil David J. Poss USDA - ARS, Akron Colorado Dr. Wilma Trujillo Colorado State University Extension Because we are researchers stationed at the USDA-ARS Central Great Plains Re-search Station, farmers often ask about annual forage options for dryland. Forage millet, sorghum-sudan grass mixes and hay-grazer are often discussed and then we get to ”what about grazing wheat”? When the wheat question comes we tell them “have you ever considered winter triticale”? Sometimes they look at us funny and ask “triti-what?.” We then share that triticale is a hybrid cross between rye and durum wheat. When they hear that, they often get a worried look and ask about triticale becoming a weed like rye. Especially if they are a wheat farmer, we let them

know that we have not had that problem because the triticale gets harvested for hay way before any grain forms. Then we relate that triticale, being a hybrid be-tween wheat and rye, is a competitive crop and ex-presses true hybrid vigor (heterosis is the scientific term). Heterosis basically means that the genes have

combined to make the hybrid plant tougher and more com-petitive resulting in rapid growth and development. Because triticale is a hybrid, it puts on more biomass and leafy forage growth than any rye or wheat cultivar on the market. Triticale also fits in our systems since it has a different growing season than millet and other annual


“THE WINTER

TRITICALE VA-

RIETIES OUT-

PERFORM THE

SPRING VA-

RIETIES.”

forages often grown in East-ern Colorado. This helps reduce the risk due to weather. If there would be an untimely hail storm or dry spell not all of the forage acres would be severely affected if a producer has planted both a warm season forage, such as millet, and a cool season forage, such as triticale. How are we growing it? We plant winter triticale in the fall, mid-September to early October (same as win-ter wheat). We have been planting 50-60 lbs of seed per acre. But probably we could get by with less than that. Perhaps 45 lbs of seed per acre would be enough to get decent stands. Because this is research, we did not want stand problems to be an issue; hence the 50-60 lb seeding rate. We planted the cultivar NE422T in our

studies but there are several other cultivars that have been competitive. In repli-cated field trials the follow-ing yields were realized (Table 1). Presto, Boreal, Windrift, NE422T and Pika were in the top half of the varieties tested. The cultivar that had the highest two-year average yield was NE422T followed by Presto. Both winter and spring varie-ties are available, however our experience is that the winter varieties outperform the spring varieties. For ex-ample, in 2009 we had both a winter variety trial and a spring variety trial. The best winter variety yield was 2.8 ton/acre compared to the best spring variety yield of 1.7 ton/acre. Likewise, the average yield across all varieties was 2.5 ton/acre and 1.5 ton/acre for the

winter and spring varieties, respectively. In our field trials, winter triti-cale was direct seeded (no-till) into millet or wheat stub-ble with a no-till disc drill about 2 inches deep. The triticale was planted in rows spaced 7.5 inches apart. We harvested the crop just as the heads start to emerge from the boot, when the awns are still green and tender. Actual harvest dates most years have been in late May to early June. One year, har-vest was as late as June 30th. What about N and other plant nutrients? Much of our triticale research has focused on developing nitrogen (N) response func-tions for the crop under dry-land conditions. Those efforts conducted over several years have produced N re-sponse equations that are

2007 2006 Mean of 2 years Triticale Cultivar Forage Yield Forage Yield Forage Yield

Lbs/acre Lbs/acre Lbs/acre

Presto 5883 A 5350 5617

Boreal 5879 A 4938 5409

Wind Drift 5597 A 5467 5532

NE422T 5549 A 5708 5629

Pika 5487 A 5348 5418

Grazing 5005 AB

NE42GT 4380 B

Bobcat 4361 B

NT04151 4291 B

LSD (0.10) 910

P >F 0.015**

Table 1. Dryland winter triticale forage yields in a two year replicated trial (4, replications each year). Data in bold are the greatest and the second greatest yields measured each year and/or those that averaged at the top of the test in two years.


“ACTUAL

HARVEST

DATES MOST

YEARS HAVE

BEEN IN LATE

MAY TO

EARLY JUNE. .”

based on both residual inor-ganic N found in the soil (top 2 feet of soil profile only) plus an imposed N regime composed of several N rates. Because that work has been through scientific peer review and published we are confident in developing the following tables (Tables 2 and 3) to help guide produc-ers in making nitrogen (N) fertilizer recommendations for this crop. We have not done any phosphorous (P) or other nutrient work for this crop. However, because triti-cale is developed from wheat we have been using a s t a r t e r o f 1 0 - 3 4 - 0 (ammonium polyphosphate) in all of our trials. No P defi-ciency has been observed in our studies. We have fertil-ized our triticale with a P rate of 15-20 lbs of P as

reasonable N rate. Because N currently costs only about $0.50 per lb. of actual N and if a neighbor has agreed to buy the hay at $80/ton, Table 2 indicates the farmer will need about 70 lbs. of N (actually 60 could be applied, subtract-ing off the 10 found as re-sidual N in the soil). An analysis of the data in the two tables indicate that as N costs go down, and hay prices and expected hay yields go up, a farmer can justify putting on more N fertilizer and still make money from that investment in fertilizer. If hay prices drop, and N cost increases less N is recommended. Also, with a low yield potential, as in dry years, optimal N rates tend to decrease.

P2O5 placed with the seed at planting time. In wet years, dryland forage yields have been as high as 8 dry tons per acre. Most years yields have averaged around 3 - 4 tons per acre. To use the Tables 2 and 3, we assume that a farmer has pulled a soil sample and has the soil analyzed for lbs. of nitrate-N available in the top 2 feet of the soil profile. Hopefully the soil sample is pulled in the fall just before planting the crop. If in that analysis the farmer finds 10 lbs of available N in the top two feet of his soil profile and is confident (based on soil profile moisture) that he/she will grow a triticale for-age crop of 1 ton per acre, the farmer can then use these tables to help decide on a

Table 2. The economic optimum N rates (EONR) for fertilizing dryland winter triticale hay when N cost 0.50 per lb of actual N. The table takes into account the residual N in the top 2 feet of the soil profile.

$60/ton $80/ton $100/ton $120/ton $140/ton

0.5 0 0 30 50 70

0.75 10 50 70 90 100

1 50 70 90 100 110

1.5 80 100 110 120 130

2 100 120 120 130 130

2.5 110 120 130 140 140

3 120 130 140 140 140

3.5 130 130 140 140 150

4 130 140 140 140 150

5 140 140 150 150 150

6 140 140 150 150 150

Dry years

Ave years

Wet years

EONR (Lbs N/acre)

ConditionsTriticale Hay

Yield (Ton/acre) Hay Price

August Volume 1

“AS N COSTS

GO DOWN,

AND HAY

PRICES AND

EXPECTED HAY

YIELDS GO UP,

A FARMER

CAN JUSTIFY

PUTTING ON

MORE N FER-

TILIZER.”

Table 3. The economic optimum N rates (EONR) for fertilizing dryland winter triticale hay when N cost 0.75 per lb of actual N. The table takes into account the residual N in the top 2 feet of the soil profile.

$60/ton $80/ton $100/ton $120/ton $140/ton

0.5 0 0 0 20 40

0.75 0 0 30 50 70

1 0 40 60 80 90

1.5 40 70 90 100 110

2 70 90 110 120 120

2.5 90 110 120 120 130

3 100 120 130 130 140

3.5 110 130 130 130 140

4 120 130 130 140 140

5 120 130 140 140 140

6 130 140 140 140 150

Wet years

ConditionsTriticale Hay

Yield (Ton/acre)

EONR (Lbs N/acre)

Hay Price

Dry years

Ave years

Logan County Extension 508 South 10th Avenue Sterling, CO 80751 Tel: 970 522-3200

Logan and Morgan Counties CSU Extension

Logan County Extension 914 East Railroad Avenue Fort Morgan, CO 80701

PRESORTED STANDARD

US POSTAGE PAID

FORT MORGAN, CO

PERMIT NO. 50

Return Service Requested

Morgan County Extension 914 East Railroad Avenue Fort Morgan, CO 80701 Tel: 970 542-3540

Or find us on the web at: http://www.extension.colostate.edu/logan/ http://www.coopext.colostate.edu/morgan/ CSU Extension offers up-to-date, unbiased, research-based information to families across Colorado.

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