kansas field research 2015...the research program at the east central kansas experiment field is...

KansasField Research

2015

Kansas State University Agricultural Experiment Station and Cooperative Extension Service

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Contents

3 East Central Kansas Experiment Field 5 Forage Sorghum Performance Trial

7 Late-Season Nitrogen Fertilizer Application in Soybean

9 Corn Yield Response to Plant Populations

12 Kansas River Valley Experiment Field14 Effects of Seed Treatment on Sudden Death Syndrome

Symptoms and Soybean Yield

18 Effects of an Experimental Seed Treatment from DuPont on Sudden Death Syndrome Symptoms and Soybean Yield

21 Soybean Sudden Death Syndrome Influenced by Macronutrient Fertility on Irrigated Soybean in a Corn/Soybean Rotation

25 Tillage Study for Corn and Soybean: Comparing Vertical, Deep, and No-Till

28 Grain Sorghum Yield Response to Water Availability

31 Department of Agronomy31 Balanced Nutrition and Crop Production Practices for Closing

Grain Sorghum Yield Gaps

35 Corn Yield Response to Water Availability

38 Cover Crop Impacts on Soil Water Status

43 Soybean Planting Date × Maturity Group: Eastern Kansas Summary


2015

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ContributorsE.A. Adee, Assistant Professor, Kansas River Valley Experiment Field, TopekaJ.P. Broeckelman, Graduate Research Assistant, Dept. of Agronomy, Kansas State

University. ManhattanI. Campitti, Assistant Professor, Dept. of Agronomy, Kansas State, University.

ManhattanD.R. Hodges, Research Assistant, Dept. of Agronomy, Kansas State University.

ManhattanJ. Kimball, Plant Science Technician, East Central Experiment Field, OttawaG. J Kluitenberg, Professor, Dept. of Agronomy, Kansas State University. ManhattanM. Kuykenkall, Graduate Research Assistant, Dept. of Agronomy, Kansas State

University. ManhattanB. McHenry, Graduate Research Assistant, Dept. of Agronomy, Kansas State

University. ManhattanJ. L. Moyer, Professor, Southeast Agricultural Research Center, ParsonsT. Newell, Graduate Research Assistant, Dept. of Agronomy, Kansas State University.

ManhattanP.V.V. Prasad, Professor, Crop Physiology, Dept. of Agronomy, Kansas State

University. ManhattanK.L. Roozeboom, Associate Professor, Cropping Systems, Dept. of Agronomy, Kansas

State University. ManhattanD. Ruiz-Diaz, Associate Professor, Dept. of Agronomy, Kansas State University.

ManhattanG. Sassenrath, Associate Professor, Southeast Agricultural Research Center, ParsonsD. Shoup, Crops and Soils Specialist, Southeast Area Office, Chanute

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East Central Kansas Experiment Field

East Central Kansas Experiment Field Introduction The research program at the East Central Kansas Experiment Field is designed to keep area crop producers abreast of technological advances in agronomic agriculture. Specific objectives are to (1) identify top-performing varieties and hybrids of wheat, corn, soybean, and grain sorghum; (2) establish the amount of tillage and crop residue cover needed for optimum crop production; (3) evaluate weed and disease control practices using chemical, no chemical, and combination methods; and (4) test fertilizer rates, timing, and application methods for agronomic proficiency and environmental stew-ardship.

Soil Description Soils on the field’s 160 acres are Woodson. The terrain is upland and level to gently rolling. The surface soil is a dark gray-brown, somewhat poorly drained silt loam to silty clay loam over slowly permeable clay subsoil. The soil is derived from old alluvium. Water intake is slow, averaging less than 0.1 in./hour when saturated. This makes the soil susceptible to water runoff and sheet erosion.

2014 Weather Information Precipitation during 2014 totaled (27.04 in.), which was 9.7 in. below the 35-year average (Table 1). Overall, the 2014 growing season was cooler than 2013. June and October were the only months receiving above the average rainfall for each period. The summer of 2014 had 30 days exceeding 90.0ºF, and three of those days exceeded 100.0ºF. The coldest temperatures occurred in January, February and November, with 28 days with low temperatures in single digits. The last freezing temperature in the spring was April 18 (average: April 18), and the first killing frost in the fall was October 31 (average: October 21). There were 196 frost-free days, which is more than the long-term average of 185.

The early season growing conditions were very good until July after corn pollination. This is reflected in the short-season and full-season corn hybrid trials that averaged 172 and 195 bu/a, respectively. The drier July and August had a greater effect on soybean yields, with the soybean variety trial averaging 41 bu/a.

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Table 1. Precipitation at the East Central Kansas Experiment Field, Ottawa Month 2014 35-year avg. Month 2014 35-year avg.

---------------- in. ------------- ---------------- in. -------------January 0.18 1.03 July 0.85 3.37 February 0.59 1.32 August 2.88 3.59 March 0.57 2.49 September 3.39 3.83 April 3.49 3.50 October 4.35 3.43 May 1.18 5.23 November 0.38 2.32 June 7.10 5.21 December 2.08 1.45

Annual total 27.04 36.78

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Forage Sorghum Performance Trial J.L. Moyer and E.A. Adee

SummaryIn our sorghum trials, production of forage was greater (P < 0.05) for ‘FS 4’ and ‘AF 7401’ than for ‘AF 7202,’ possibly related to differences in maturity. Estimated grain production was greater for ‘AF 7401’ than for all others, except for ‘AF 7102.’

IntroductionSorghums are an efficient genus of warm-season annual grasses. They are produced largely for forage but are considered a possible dedicated energy crop. This study was established to test cultivars for their adaptation to east central Kansas and to compare their productive and agronomic potential.

ProceduresThree sorghum hybrids entered by Advanta Seeds, Inc., and two other cultivars were planted at 100,000 seeds/a in 30-in. rows on May 29, 2014, at the East Central Kansas Agronomy Experiment Field. Plots were 30 ft × 10 ft and were arranged in a random-ized, complete block with three replications. The area was fertilized preplant with 150 lb nitrogen (N)/a as urea, and sprayed preemergence on May 22 with 1.6 lb a.i./a of S-metolachlor. Plants were thinned to 35,000 plants/a on June 17.

Date of half-bloom was recorded for each plot. Measurements of height to flag leaf, number of tillers per plant, and lodging were taken at harvest, along with an estimate of relative grain production. Two rows were harvested on September 23 at 2- to 3-in. height for a length of 20 ft per plot. Subsamples were dried at 140°F for moisture content.

Results Maturity of the hybrids differed significantly (P > 0.05), in terms of both bloom date and forage dry matter content at harvest (Table 1). By both measurements, ‘AF 7202’ was earlier maturing than ‘FS 4’ and ‘AF 7401.’

Forage production was greater for ‘FS 4’ and ‘AF 7401’ than for ‘AF 7202,’ perhaps partly because of the difference in maturity (Table 1). Estimated grain production was greater for ‘AF 7401’ and ‘AF 7102’ than for the other hybrids. Plant height was greater for ‘FS4’ than the other entries, and greater for ‘Atlas’ than for the rest. Although lodg-ing differences were not significant, even at the 10% level, the greater height of ‘FS 4’ had no apparent effects on its tendency to lodge. The earlier maturity of ‘AF 7202’ may have contributed to its tendency to lodge.

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Table 1. Bloom date, dry matter (DM), yield, and other agronomic traits in 2014 for forage sorghum, Ottawa Experiment Field, Department of Agronomy

Cultivar Bloom date, Julian day1 DM, %

Yield, lb DM/a

Grain production,

0 to 102Plant

height, in. Lodging, %3 AF 7401 234 29.1 11809 8 50 3AF 7102 236 33.2 11148 7 50 4AF 7202 215 35.3 10761 5 49 16FS4 237 29.0 14129 5 102 6Atlas 224 31.4 10838 5 73 11Average 229 31.6 11737 6 64 7LSD 0.05 1 3.9 1025 2 5 NS1 Julian day 229 occurred on August 17. 2 Visually rated from 0 to 10, where 0 = no head and 10 = head fully filled with grain.3 Tillers lodged per 100 primary plants.

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Late-Season Nitrogen Fertilizer Application in SoybeanD. Hodgins, E. Adee, and I.A. Ciampitti

SummaryField experiments were conducted at the Kansas River Valley Experiment Field, located near Rossville and Topeka, KS, in the summer of 2014 to evaluate effects of late-season nitrogen (N) fertilizer application on modern soybean genotypes. A unique fertilizer N source (urea) was applied at five N rates (0, 40, 80, 120, and 160 lb N/a) to soybean at the R3 growth stage. The main objective was to determine if late-season N applica-tion has an agronomical benefit to soybean producers. Overall soybean yields ranged from 43.7 to 57.5 bu/a considering both experimental fields. At Rossville, sudden death syndrome (SDS) affected the final soybean yield potential. Application of late-season N fertilizer did not significantly increase soybean yields at the evaluated sites. Maximum soybean yields, 46 bu/a at Rossville and 57 bu/a at Topeka, were documented at the 0-N fertilizer rate.

IntroductionIncreasing soybean yields is associated with larger N demand. The ability to sustain N fixation by the rhizobia during the late season can be compromised, restricting the capability of the crop to supply all of the N required for optimum grain-filling and final grain N content. Previous studies investigating the effects of late-season N fertilizer application have shown contrasting outcomes. A common pattern is to report fertilizer N responses in sites where average soybean yields were above 50 to 60 bu/a. Therefore, the effects of extra N application late in the crop growing season might be important to consider in high-yielding soybean systems.

ProceduresThe Topeka experiment was conducted on Eudora silt loam soil. The soybean variety was Asgrow 3833, which was planted on May 21 with a Kinze split-row planter in 15-in. rows at a population 140,000 seeds/a, with no fertilizer applied before planting. Fertilizer N rates were applied at 0, 40, 80, 120, and 160 lb/a. Each fertilizer treatment was replicated four times, providing a total of 20 plots per experiment. Plot size was 20 ft (16 rows) × 30 ft. Fertilizer N was applied close to the R3 growth stage (August 18). The soybean was harvested on October 15.

The Rossville experiment was conducted on Eudora silt loam soil. Midland 3633N soybean was planted on May 14 with a Kinze split-row planter in 15-in. rows at a popu-lation 140,000 seeds/a, with no fertilizer applied before planting. Fertilizer N rates were applied at 0, 40, 80, 120, and 160 lb/a. Each fertilizer treatment was replicated four times, providing a total of 20 plots per experiment. Plot size was 10 ft (8 rows) × 20 ft. Fertilizer N was applied close to the R3 growth stage (August 18). The soybean was harvested on September 24.

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ResultsLate-season N fertilizer application did not statistically increase soybean yield in either location (Table 1). Overall yield level was 45 bu/a at Rossville and 56 bu/a at Topeka. In these environments, the application of extra N late in the season did not increase soybean yields over the no-N application check (0-N) treatment.

Table 1. Late-season nitrogen (N) application rates and yields at Rossville and Topeka, Kansas River Valley Experiment Field, 2014

N rates, lb/a Rossville Topeka---------------- Yields at 13% moisture, bu/a ----------------

0 46.1 57.540 46.1 57.080 46.0 55.6

120 45.3 55.0160 43.7 55.3

P > 0.05 NS1 NS 1 Not significant, P > 0.05.

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Corn Yield Response to Plant PopulationsD.E. Shoup, E.A. Adee, and I.A. Ciampitti

SummaryCorn hybrid development with a focus on drought tolerance has emerged in recent years, and producers have questions about their yield performance across a range of plant populations. A two-year study was conducted to determine the yield of corn hybrids across several plant populations. Corn hybrids responded differently in 2013 and 2014. In 2013, a lower yield environment occurred. The hybrid with drought toler-ance had the greatest yield of 95 bu/a at a plant population of 21,500 plants/a, whereas the non-drought-tolerant hybrid’s greatest yield was 90 bu/a at a plant population of 13,500 plants/a. In 2014, the yield environment was significantly higher. The hybrid with drought tolerance had the greatest yield of 174 bu/a at the greatest plant popula-tion of 35,500 plants/a, and the non-drought-tolerant hybrid’s greatest yield was 169 bu/a at a plant population of 29,500 plant/a.

IntroductionCorn yield can be affected by many factors in Kansas, including soil quality, fertility, crop production practices (planting date, plant population, and hybrid), and weed and pest management. The most significant factors that affect corn yield in Kansas are often related to moisture and heat stress. Several seed companies have devoted considerable resources to breeding hybrids with improved drought tolerance. Although the method of achieving drought tolerance in corn hybrids may differ among companies, the goal of improving water use efficiency can help increase yields of corn grown in water-limited environments. Producers have many questions surrounding the newer corn hybrids labeled as drought-tolerant, and data comparing yields across a range of plant popula-tions need to be evaluated. A two-year study was conducted at the East Central Experi-ment Field in Ottawa to evaluate two corn hybrids and their yield responses to various plant populations.

ProceduresThe experimental site was located on a Woodson silt loam. Plots were strip-till-fertilized into soybean stubble with a mix of 120 lb nitrogen/a, 40 lb P2O5/a, and 15 lb K2O/a. Corn was planted on 30-in. rows on April 4, 2013, with Channel hybrids 197-30 (non-DroughtGuard) and 198 (DroughtGuard) and on April 9, 2014, with Dekalb hybrids DKC50-48 (non-DroughtGuard) and DKC51-20 (DroughtGuard) (Monsanto, St. Louis, MO). The experiment was a randomized complete block design with four repli-cations in a strip-plot arrangement. Plant population was the main factor, and hybrid was the subfactor. Plots were four rows wide, 35 ft long, and planted at 36,000 seeds/a. At the V6 growth stage when the growing point was above the soil surface, plots were thinned to several plant populations. In 2013 because of low plant emergence, plots were thinned to five populations: 10,000; 13,500; 17,500; 21,500; and 27,500 plants/a. In 2014, seedling emergence was improved and plant populations were thinned to 17,500; 23,500; 29,500, and 35,500 plants/a. Plots were maintained weed-free through-out the season. Corn plots were harvested by plot combine, plot weights were deter-mined, and yields were adjusted to 13% moisture.

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Results Corn hybrids responded differently in 2013 and 2014 (Figures 1 and 2). In 2013, a lower yield environment occurred because of drier than normal weather. In 2013, only 1.37 in. of rain fell through the month of June and the first three weeks of July. The hybrid with drought tolerance had the highest yield of 95 bu/a at a plant popula-tion of 21,500 plants/a, whereas the non-drought-tolerant hybrid’s highest yield was 90 bu/a at a plant population of 13,500 plants/a. In 2014, the yield environment was considerably better because of cooler and wetter than normal conditions. The hybrid with drought tolerance had a peak yield of 174 bu/a at the highest plant population of 35,500 plants/a, and the non-drought-tolerant hybrid’s highest yield was 169 bu/a at a plant population of 29,500 plants/a. The excellent growing conditions in 2014 resulted in above-average corn yields. The highest plant population of 35,500 plants/a was likely not high enough to maximize yield with the drought-tolerant hybrid in 2014 and may have benefited from an increased seeding rate.

120

100

80

60

40

20

0

Co

rn y

ield

, bu

/a

21,50017,50013,50010,000 27,500

Hybrid 197-30

Hybrid 198DG

Corn population, plants/a

Figure 1. Corn yield response to plant populations in 2013. Corn hybrids included drought-tolerant (Channel hybrid 198; Monsanto, St. Louis, MO) and non-drought- tolerant (Channel hybrid 197-30) traits.

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29,50023,50017,500 35,500

200

180

160

140

120

100

80

60

40

20

0

Co

rn y

ield

, bu

/a

Hybrid DKC50-48

Hybrid DKC51-20

Corn population, plants/a

Figure 2. Corn yield response to plant populations in 2014. Corn hybrids included drought-tolerant (Dekalb hybrid DKC51-20; Monsanto, St. Louis, MO) and non-drought-tolerant (Dekalb hybrid DKC50-48) traits.

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Kansas River Valley Experiment Field

Kansas River Valley Experiment FieldIntroductionThe Kansas River Valley (KRV) Experiment Field was established to study management and effective use of irrigation resources for crop production in the KRV. The Paramore Unit consists of 80 acres located 3.5 miles east of Silver Lake on U.S. Highway 24, then 1 mile south of Kiro, and 1.5 miles east on 17th street. The Rossville Unit consists of 80 acres located 1 mile east of Rossville or 4 miles west of Silver Lake on U.S. Highway 24.

Soil DescriptionSoils on the two fields are predominately in the Eudora series. Small areas of soils in the Sarpy, Kimo, and Wabash series also occur. Except for small areas of Kimo and Wabash soils in low areas, the soils are well drained. Soil texture varies from silt loam to sandy loam, and the soils are subject to wind erosion. Most soils are deep, but texture and surface drainage vary widely.

2014 Weather InformationThe year was cooler and wetter than the previous year, although there were more frost-free days. The frost-free season was 194 days at the both units (average = 173 days), and 30 and 31 days in single digits at Paramore and Rossville, respectively. The last spring freeze was April 18 (average = April 21), and the first fall freeze was October 29 (aver-age = October 11). There were 30 and 31 days above 90°F at Paramore and Rossville, respectively, and 3 of those days were above 100°F at Rossville. Precipitation was below normal at both fields for the year (Table 1) but was above average for several months during the growing season. For the year, the rainfall deficit for Rossville was 3.35 in., and 8.7 in. for Paramore. The irrigated corn hybrid and soybean variety trials averaged 266 and 41 bu/a, respectively. The corn yields responded well to the cooler weather; however, the high amount of rainfall in June contributed to sudden death syndrome, a major yield-limiting factor in irrigated soybeans at KRV. Dryland corn hybrid and soybean variety trials averaged 195 and 59 bu/a, respectively, indicating a good growing season unless disease was present.

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Table 1. Precipitation at the Kansas River Valley Experiment FieldRossville Unit Paramore Unit

Month 2014 30-year avg. 2014 30-year avg.-------------------------------------- in. --------------------------------------

January 0.04 3.18 0.01 3.08February 0..59 4.88 0.65 4.45March 0.27 5.46 0.25 5.54April 3.24 3.67 2.89 3.59May 3.41 3.44 2.36 3.89June 8.26 4.64 7.05 3.81July 1.37 2.97 1.11 3.06August 4.95 1.90 3.23 1.93September 3.15 1.24 2.52 1.43October 4.37 0.95 4.00 0.95November 0.35 0.89 0.41 1.04December 2.29 2.42 0.40 2.46Total 32.29 35.64 26.53 35.23

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Effects of Seed Treatment on Sudden Death Syndrome Symptoms and Soybean YieldE.A. Adee

SummarySudden death syndrome (SDS) is a soybean disease that perennially limits yields in the Kansas River Valley. The presence of soybean cyst nematode (SCN) and saturated soils have been implicated in contributing to the severity of the disease. Selecting varieties with some degree of tolerance to SDS is the only cultural practice that can potentially reduce the severity of SDS and improve yields. Variety selection alone, however, cannot improve the production of soybeans to make them profitable. The challenge of trying to manage irrigation scheduling to avoid saturated soils further complicates efforts to increase productivity with irrigation while still avoiding SDS. A study with seed treat-ments applied to soybean was conducted at the Kansas River Valley Experiment Field in 2014, with treatments applied to a soybean variety with a high level of tolerance to SDS. The study was irrigated earlier and more often than normal to promote the disease. The most severely infested plots had more than 50% of the leaf area expressing symptoms of SDS by the R6 growth stage. Treatments with ILeVO from Bayer CropScience (Research Triangle Park, NC) reduced foliar symptoms and increased yields up to 12 bu/a, or more than 25%. These results are similar to those in a 2013 study of varieties with SDS tolerance ranging from very susceptible to more tolerant; the yield increase was up to 16 bu/a, or 40% with the ILeVO seed treatment.

IntroductionSoybean SDS is caused by the fungus Fusarium virguliforme, which infects plants through the roots, primarily before they start to flower. Foliar symptoms generally begin to show up as interveinal chlorosis and necrosis in the leaves at growth stage R3, after the seed has started to develop in the pods.

An interaction between SDS and SCN has been reported, and SCN is prevalent in the soils of the Kansas River Valley. Saturated soils also have been implicated as contribut-ing to the development of SDS. Depending on how early the symptoms begin to be visible and the symptoms’ severity, yield losses can be very significant. In severe cases, plants in which the symptoms begin early (i.e., before the seed development stage) can fail to produce any seed.

This disease has been a perennial problem in the Kansas River Valley, causing severe yield reductions to the point that soybean cannot be profitably produced in some fields. Crop rotations and tillage have had little effect on reducing the severity of the disease and reducing the subsequent yield loss. No soybean varieties are totally resistant to the fungus, but some varieties have varying degrees of tolerance that can reduce yield losses. Irrigating soybean at the wrong time also could increase the severity of SDS, further complicating production in the Kansas River Valley, where irrigation is often necessary to produce a profitable crop.

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Another method of trying to increase soybean productivity in fields with a risk of SDS is seed treatment at planting. Seed treatments could help protect the roots against initial infection by F. virguliforme.

ProceduresSoybean was planted into a field with a history of SDS at the Rossville Unit of the Kansas River Valley Experiment Field in 2014. Seed treatments were applied by Bayer CropScience (Research Triangle Park, NC) to a soybean variety with a high level of tolerance to SDS, Stine 42RE02 (Stine Seed Co., Adel, IA). The treatments included: ILeVO 0.15 mg/seed and ILeVO 0.075 mg/seed in combination with other seed treat-ment products, a check with Poncho/Votivo or Gaucho, and a test product. Soybean was planted May 6 at 140,000 seeds/a into 10- × 30-ft plots, with four replications in a randomized complete block design. The soil was Eudora silt loam, and the previous crop was soybean. Irrigation with a linear-move sprinkler irrigation system was started on June 24. Total irrigation was 7.81 in., and 21.4 in. of rain was received during the growing season. Preemergence herbicide applied at planting was Authority Maxx (FMC Corporation Agricultural Products Group, Philadelphia, PA) (5 oz) and Cinch (Syngenta Crop Protection, LLC, Greensboro, NC) (1.5 pt). Postemergence herbicide was Roundup PowerMax (Monsanto Company) (22 oz), Assure II (DuPont, Wilm-ington, DE) (12 oz), and Warrant (Monsanto Company) (1.5 qt). Foliar symptoms of SDS were rated weekly starting August 6, when the soybean crop was at the R4 (pods full length) to September 3 at the R6 (full seed) growth stages. Ratings were based on incidence and severity of the symptoms. An area under the disease progress curve (AUDPC), a unitless number describing the development of defoliation effects over time, was derived by plotting periodic measurements of disease over time and integrat-ing the area under the disease curve. A GreenSeeker meter (Trimble Navigation, Ag Division, Westminster, CO) was also used to collect normalized difference vegetation index (NDVI) readings from each plot at the R6 growth stage. The NDVI readings are higher when plants have abundant green leaves to absorb the light used in photosynthe-sis. The plots were harvested September 30.

Results The severity of the disease ratings, using both AUDPC and NDVI, correlated with yield (Figures 1 and 2). The figures also show that the more “traditional” ratings with the AUDPC and the NDVI are nearly equal in their relationship with yield. As the AUDPC increased, the yield decreased, with the AUDPC explaining more than 28% of the change in yield. The NDVI readings explained more than 16% of the change in yields, with soybean yields increasing as the NDVI increased.

The seed treatments with ILeVO increased yields from 3 to 12 bu/a, depending on the rate of the product and the additional seed treatments (Table 1). The greatest yields were with the higher rates of ILeVO.

Disease severity ratings show that the environment in which this study was conducted was very favorable for SDS, with nearly 50% of the leaves showing symptoms in the most affected plots with this variety that is highly tolerant to SDS (Table 1). To have a more than 25% yield increase owing to seed treatment with this level of severity is promising. These data are similar to a study with ILeVO conducted in 2013, indicat-

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ing that this product can consistently decrease soybean yield loss due to SDS. This seed treatment in combination with a variety with a high tolerance to SDS could make soybean more sustainable as a crop in the presence of SDS.

Table 1. Influence of seed treatment for sudden death syndrome (SDS) on yield of soybean on Stine 43RE02, Kansas River Valley Experiment Field, Rossville, 2014Seed treatments Yield SDS severity SDS severity NDVI1

bu/a % leaf area at R6 AUDPC2

Poncho/Votivo check 47.4 g3 52 a 696 ab 0.834 bcILeVO4 (0.15 mg)+ Poncho/Votivo 59.6 a 16 bc 146 c 0.846 abILeVO (0.075 mg)+ Poncho/Votivo 57.0 d 31 ab 443 bc 0.835 bcGaucho 600 check 54.0 d 25 bc 814 a 0.818 cILeVO (0.15 mg)+ Gaucho 600 57.2 c 16 bc 192 c 0.849 abILeVO (0.075 mg)+ Gaucho 600 57.1 d 7 c 153 c 0.864 aILeVO (0.15 mg)+ Gaucho 600 + Agriplier 58.3 b 13 bc 148 c 0.858 aPoncho/Votivo + test compound 50.1 f 21 bc 572 ab 0.830 bcLSD 0.05 0.06 22.9 354 0.0211 Normalized difference vegetation index.2 Area under the disease progress curve, a unitless number describing the development of defoliation effects over time.3 Values followed with different letters are significantly different at P < 0.05.4 Bayer CropScience (Research Triangle Park, NC).

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AUDPC

0 200 400 600 800 1,000 1,200

65

60

55

50

45

40

35

30

Yie

ld, b

u/a

1,400

y = -0.0057x + 57.358R² = 0.28559

Figure 1. The severity of soybean sudden death syndrome disease ratings as shown by area under the disease progress curve (AUDPC, a unitless number describing the development of defoliation effects over time) correlated with yield, Kansas River Valley Experiment Field, Rossville, 2014.

NDVI at R6

0.78 0.79 0.80 0.81 0.82 0.84 0.86

70

60

50

40

30

20

10

0

Yie

ld, b

u/a

0.88

y = 80.579x - 12.724R² = 0.16543

0.83 0.85 0.87

Figure 2. The severity of soybean sudden death syndrome disease ratings as shown by normalized difference vegetation index (NDVI) at growth stage R6 correlated with yield, Kansas River Valley Experiment Field, Rossville, 2014.

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Effects of an Experimental Seed Treatment from DuPont on Sudden Death Syndrome Symptoms and Soybean YieldE.A. Adee

SummarySudden death syndrome (SDS) is a soybean disease that perennially limits yields in the Kansas River Valley. Soybean cyst nematode (SCN) and saturated soils contribute to the severity of the disease. Selecting varieties with some degree of tolerance to SDS is the only cultural practice that can reduce the severity of SDS and improve yields. Vari-ety selection alone, however, doesn’t necessarily make soybean production profitable; an added complication is managing irrigation scheduling to avoid saturated soils. A study with seed treatments applied to soybean was conducted at the Kansas River Valley Experiment Field in 2014, with treatments applied to two soybean varieties susceptible to SDS. The study was irrigated earlier and more often than normal for soybean to promote the disease. In the most severely infested plots, more than 50% of the leaf area expressed symptoms of SDS by the R6 growth stage. Treatments with an experimental seed treatment from DuPont (Wilmington, DE) reduced the amount of foliar disease in all varieties and increased yields up to 10 bu/a, or more than 25%.

IntroductionSoybean SDS is caused by the fungus Fusarium virguliforme, which infects plants through the roots, primarily before they start to flower. Foliar symptoms generally begin to show up as interveinal chlorosis and necrosis in the leaves at growth stage R3, after the seed has started to develop in the pods.

An interaction between SDS and SCN has been reported, and SCN is prevalent in the soils of the Kansas River Valley. Saturated soils also have been implicated as contribut-ing to the development of SDS. Depending on how early the symptoms become visible and their severity, yield losses can be very significant. In severe cases, plants in which the symptoms begin early (i.e., before the seed development stage) can fail to produce any seed.

This disease has been a perennial problem in the Kansas River Valley, causing severe yield reductions in soybean to the point that the crop cannot be profitably produced in some fields. Crop rotations and tillage have had little effect on reducing the severity of the disease and reducing the subsequent yield loss. No soybean varieties are totally resis-tant to the fungus, but some varieties have varying degrees of tolerance that can reduce yield losses. Irrigating soybean at the wrong time also could increase the severity of SDS, further complicating production in the Kansas River Valley, where irrigation is often necessary to produce a profitable crop.

Another method of trying to increase soybean productivity in fields with a risk of SDS is seed treatment applied to the seeds at planting. Seed treatments could help protect the roots against initial infection by F. virguliforme.

19


ProceduresSoybean was planted into a field with a history of SDS at the Rossville Unit of the Kansas River Valley Experiment Field in 2014. Seed treatments were applied by DuPont (Wilmington, DE) to two soybean varieties susceptible to SDS, Sloan and Pioneer 93Y91 (Pioneer Hi-Bred, Johnston IA). The treatments included: the DuPont experimental seed treatment at 0.65X, 1.0X, 2.0X, 3.0X; a competitor’s seed treat-ment; and an untreated check. Soybean was planted May 6 at 140,000 seeds/a into 10-ft × 30-ft plots, with four replications in a randomized complete block design. The soil was Eudora silt loam, and the previous crop was soybean. Irrigation with a linear-move sprinkler irrigation system was started on June 24. Total irrigation was 7.81 in., and 21.4 in. of rain was received during the growing season. Preemergence herbicide applied at planting was Authority Maxx (FMC Corporation Agricultural Products Group, Philadelphia, PA) (5 oz) and Cinch (Syngenta Crop Protection, LLC, Greens-boro, NC) (1.5 pt). Postemergence herbicide was Roundup PowerMax (Monsanto Company, St. Louis, MO) (22 oz), Assure II (DuPont, Wilmington, DE) (12 oz), and Warrant (Monsanto Company) (1.5 qt). Foliar symptoms of SDS were rated weekly starting July 21, when the soybean crop was at the R4 (pods full length) stage, through August 18, when plants were at the R6 (full seed) growth stage. Ratings were based on incidence and severity of the symptoms. An area under the disease progress curve (AUDPC), a unitless number describing the development of defoliation effects over time, was derived by plotting periodic measurements of disease over time and integrat-ing the area under the disease curve. A GreenSeeker meter (Trimble Navigation, Ag Division, Westminster, CO) was also used to collect normalized difference vegetation index (NDVI) readings from each plot at the R6 growth stage; NDVI readings are higher when there are abundant green leaves to absorb the light used in photosynthesis. The plots were harvested September 22.

Results The experimental seed treatment from DuPont reduced the severity of foliar symp-toms of SDS (Table 1). The single rating at R6 on August 18 and the AUDPC, which measured disease severity throughout the season, both showed a reduction in SDS severity. The NDVI rating taken at R6 also showed higher ratings for the treatments with the experimental product from DuPont, especially at higher application rates. Yields were higher with the two higher rates of the experimental product (Table 1), which agrees with the higher NDVI ratings and the lower severity ratings for SDS. There was no interaction between variety and seed treatment (data not shown) because the product performed similarly with both varieties.

These data suggest the experimental product from DuPont has the potential to increase soybean yield in the presence of SDS. The environment was very favorable for SDS, and both varieties in the trial were highly susceptible to SDS, showing that this product can reduce yield loss even when the pressure from SDS is severe. Caution should be used in drawing strong conclusions because these data are from only one site, but the results are promising.

20


Table 1. Influence of an experimental seed treatment for sudden death syndrome (SDS) on soybean yield, Kansas River Valley Experiment Field, Rossville, 2014

Seed treatments YieldSDS foliar

at R6 SDS severity NDVI1

bu/a % AUDPC2

DuPont3 experimental treatment, 0.65X 29.7 c4 39.1 b 410 b 0.705 bcDuPont experimental treatment, 1.0X 31.9 bc 45.3 b 377 b 0.739 abDuPont experimental treatment, 2.0X 35.3 ab 41.0 b 326 b 0.750 aDuPont experimental treatment, 3.0X 40.0 a 26.6 b 232 b 0.767 aCompetitor’s product 28.4 c 68.0 a 806 a 0.669 cUntreated check 29.6 c 71.1 a 777 a 0.688 cLSD 0.05 4.8 15.7 183 0.036 1 Normalized difference vegetation index determined by a GreenSeeker meter (Trimble Navigation, Ag Division, Westminster, CO).2 Area under the disease progress curve, a unitless number describing the development of defoliation effects over time. 3 DuPont (Wilmington, DE).4 Values with the same letter are not statistically different at P < 0.05.

21


Soybean Sudden Death Syndrome Influenced by Macronutrient Fertility on Irrigated Soybean in a Corn/Soybean RotationE.A. Adee and D. Ruiz Diaz

SummaryThe effects of nitrogen (N), phosphorus (P), and potassium (K) fertilization on a corn/soybean cropping sequence were evaluated from 1983 to 2014, with corn planted in odd years. We observed a relationship between the P rate applied during the corn years and the severity of sudden death syndrome (SDS) in 2014 soybean.

IntroductionA study was initiated in 1972 at the Topeka Unit of the Kansas River Valley Experi-ment Field to evaluate the effects of N, P, and K on furrow-irrigated soybean. In 1983, the study was changed to a corn/soybean rotation with corn planted and fertilizer treat-ments applied in odd years. Study objectives were to evaluate the effects of N, P, and K applications on a corn crop on grain yield of corn, yield of the following soybean crop, and soil test values.

ProceduresThe initial soil test in March 1972 on this silt loam soil was 47 lb/a available P and 312 lb/a exchangeable K in the top 6 in. of the soil profile. Rates of P were 50 and 100 lb/a P2O5 (1972–1975) and 30 and 60 lb/a P2O5 (1976–2011), except in 1997 and 1998, when a starter of 120 lb/a of 10-34-0 (12 lb/a N + 41 lb/a P2O5) was applied to all plots of corn and soybean. Rates of K were 100 lb/a K2O (1972–1975), 60 lb/a K2O (1976–1995), and 150 lb/a K2O (1997–2011). Nitrogen rates included a factorial arrangement of 0, 40, and 160 lb/a of preplant N (with single treatments of 80 and 240 lb/a N). The 40 lb/a N rate was changed to 120 lb/a N in 1997. Treatments of N, P, and K were applied every year to continuous soybean (1972–1982) and every other year (odd years) to corn (1983–1995, 1999–2013). Soil cores were pulled from each plot in the spring of 2014, prior to planting. Analyses for macronutrients were performed from soil for each 1-ft increment to a depth of 4 ft.

Soybean varieties planted in even years were: Douglas (1984), Sherman (1986, 1988, 1990, 1992, 1996, 1998), Edison (1994), IA 3010 (2000), Garst 399RR (2002), Stine 3982-4 (2004), Stine 4302-4 (2006), Midland 9A385 (2008), Asgrow 4005 (2010), Asgrow 3832 (2012), and Asgrow 3833 (2014). Soybean was planted in early to mid-May. Herbicides were applied preplant each year, and postemergent herbicides were applied as needed. Plots were cultivated, furrowed, and furrow-irrigated through 2001 and sprinkler-irrigated with a linear-move irrigation system from 2002 to 2014. Percentage of leaf area infested by SDS was rated visually, and normalized difference vegetation index (NDVI) ratings were measured with a GreenSeeker meter (Trimble

22


Navigation, Ag Division, Westminster, CO) on August 28 at growth stage R6. A plot combine was used to harvest grain.

ResultsThe severity of foliar SDS symptoms in soybean was related to the rate of P applied to the corn in the corn/soybean rotation for the previous years (Table 1). The SDS was most severe, and the NDVI (measure of greenness), heights, and yields decreased as the rate of P decreased. The level of P in the soil was different at the different rates in a soil sample taken in the spring of 2014 (Table 2). The largest difference between P rates was in samples collected from the top foot of soil. There was no effect of N, K, nor any inter-actions of the three macronutrients with these four measurements (data not shown).

SDS had not been observed to this degree in these plots in previous years. In addition, the effect of P on yield has not been this great, with average yield response for 1984 to 2012 from the check to the 60 lb rate less than 6 bu/a. The development of SDS was probably related to the above-average rainfall in June of 8.26 in., which is 3.62 in. more than the 30-year average.

The negative correlation between foliar symptoms of SDS and NDVI was very strong (-0.82, <0.0001; Figure 1). The NDVI measurements are an objective measurement based on near-infared light reflectance off the crop canopy, which can be affected by the greenness of leaves and density of the canopy, both of which can be influenced by multiple factors. Height of plants, development of branches, number and size of leaves, and amount of chlorophyll in leaves are some of the factors that can affect NDVI read-ings. The visual ratings of foliar symptoms tend to be more subjective but can focus on a single aspect of crop health, in this case foliar symptoms of SDS. The strength of this correlation indicates that SDS was a primary factor affecting the health of this crop, even though height differences were related to P rates.

Yield of soybean correlated well with both the visual rating for SDS (-0.74, <0.0001) and NDVI (0.83, <0.0001) (Figures 2 and 3). This result suggests that SDS was a major factor affecting yield of soybean in this study. Combined with the strong relationship between the rate of P applied during the corn year of the rotation with yield and NDVI, the negative relationship with foliar symptoms of SDS indicates that P had a significant role in the severity of SDS and subsequent yield loss. To our knowledge, this relation-ship between P applied as a fertilizer and SDS has not been previously reported.

23


Table 1. Effects of phosphorus (P) applied to corn on sudden death syndrome (SDS) and yield of soybean, Kansas River Valley Experiment Field, 2014

P rate on corn SDS severity NDVI1 Height Yieldlb/a % foliage affected in. bu/a

0 58 0.758 29.8 34.030 43 0.777 36.0 44.860 23 0.799 37.0 52.9

LSD (0.05) 16 0.018 2.2 4.3 1 Normalized difference vegetation index.

Table 2. Soil test values for phosphorus (P) in macro-fertility study, Kansas River Valley Experiment Field, 2014

P rate 1st ft 2nd ft 3rd ft 4th ftlb/a

0 13 15 22 16.630 30 17.4 24.2 17.260 92 27.2 30.6 18.4

LSD (0.05) 8.8 1.9 2.7 NS1 1 Not significant.

SDS severityAugust 28, % leaf area infested

10 20 30 40 60 90

0.84

0.82

0.80

0.78

0.76

0.74

0.72

0.70

0.68

ND

VI

100

R² = 0.67726

50 70 800

Figure 1. Relationship between visual ratings for severity of foliar symptoms of sudden death syndrome (SDS) and normalized difference vegetation index (NDVI) measurements with a GreenSeeker meter (Trimble Navigation, Ag Division, Westminster, CO) in a long-term macronutrient fertility study at the Kansas River Valley Experiment Field, 2014.

24


SDS severity August 28 at R6, % leaf area infested

10 20 30 40 60 90

70

60

50

40

30

20

10

0

Yie

ld, b

u/a

100

y = -0.2557x + 54.789R² = 0.53331

50 70 800

Figure 2. Relationship between foliar symptoms of sudden death syndrome (SDS) and yield of soybean at the Kansas River Valley Experiment Field, 2014.

NDVI readingAugust 28

0.70 0.72 0.74 0.76 0.80

70

60

50

40

30

20

10

0

Yie

ld, b

u/a

0.84

y = 259.1x - 157.51R² = 0.67168

0.78 0.820.68

Figure 3. Relationship between normalized difference vegetation index (NDVI) and yield of soybean at the Kansas River Valley Experiment Field, 2014.

25


Tillage Study for Corn and Soybean: Comparing Vertical, Deep, and No-TillE.A. Adee

IntroductionThe need for tillage in corn and soybean production in the Kansas River Valley contin-ues to be debated. The soils of the Kansas River Valley are highly variable, with much of the soil sandy to silty loam in texture. These soils tend to be relatively low in organic matter (<2%) and susceptible to wind erosion. Although typically well drained, these soils can develop compaction layers under certain conditions. A tillage study was initi-ated in the fall of 2011 at the Kansas River Valley Experiment Field near Topeka to compare deep vs. shallow vs. no-till vs. deep tillage in alternate years. Corn and soybean crops are rotated annually. This is intended to be a long-term study to determine if soil characteristics and yields change in response to a history of each tillage system.

ProceduresA tillage study was laid out in the fall of 2011 in a field that had been planted with soybean. The tillage treatments were (1) no-till, (2) deep tillage in the fall and shal-low tillage in the spring every year, (3) shallow tillage in the fall following both crops, and (4) deep tillage followed by a shallow tillage in the spring only after soybean, and shallow tilled in the fall after corn. The fall of 2010, prior to the soybean crop, the entire field was subsoiled with a John Deere (John Deere, Moline, IL) V-ripper. After soybean harvest, 30-ft × 100-ft individual plots were tilled with a Great Plains (Great Plains Mfg., Salina, KS) TurboMax vertical tillage tool at 3 in. deep or a John Deere V-ripper at 14 in. deep. Spring tillage was with a field cultivator. In the fall of 2012, the treatments were with the TurboMax or a Great Plains Sub-Soiler Inline Ripper SS0300. Spring tillage in 2013 and 2014 was with the TurboMax on the required treat-ments. Each tillage treatment had four replications. Dry fertilizer (11-50-0 and 0-0-60 nitrogen-phosphorus-potassium, or NPK) was applied at 200 lb/a for each product to the entire field prior to fall tillage. Nitrogen (150 lb in 2012 and 2013, 185 lb in 2014) was applied in March prior to corn planting. Corn hybrid Pioneer 1395 was planted at 30,600 seeds/a on April 12, 2012; P1498HR on April 30, 2013; and P1105 at 32,000 seeds/a on April 21, 2014. Soybean variety Pioneer 93Y92 was planted at 155,000 seeds/a on May 14, 2012; P94Y01 on May 15, 2013; and Asgrow 3833 at 140,000 on May 21, 2014. Soybean was planted after soybean in the setup year. Irrigation to meet evapotranspiration (ET) rates began May 26 and concluded August 1 for corn, and began July 5 and concluded August 23 for soybean in 2012. Irrigation for corn started June 24, 2013, and concluded August 1. Irrigation for soybean in 2013 started June 30 and concluded September 8. Irrigation in 2014 started July 1 and ended Aug 16 for corn, and started July 22 and ended August 22 for soybeans. Two yields were taken from each plot from the middle 2 rows of planter passes. Corn was harvested on August 31, 2012; September 25, 2013; and September 11, 2014. Soybean was harvested on October 5, 2012; October 10, 2013; and October 9, 2014.

26


ResultsYields of corn or soybean did not differ due to tillage in the setup year of the study (Table 1). The yields were respectable considering the extreme heat and drought experi-enced this growing season. Growing conditions were better in 2013, resulting in higher yields in both corn and soybean, but no significant differences were detected among till-age treatments (Table 2). In 2014, corn yields were very good and soybean yields were lowered by sudden death syndrome (SDS), but no differences were detected among tillage treatments (Table 3). Combining data from 2013 and 2014 for analysis did not result in any differences among tillage treatments (Table 3). We anticipate that it will take several years for any characteristics of a given tillage system to build up to the point of influencing yields.

Table 1. Effects of tillage treatments on corn and soybean yields, Kansas River Valley Experiment Field, 2012Tillage treatment Corn yield Soybean yield

------------------ bu/a ------------------No-till 196 57.2Fall subsoil/spring field cultivation 202 58.1Fall vertical till 198 58.1LSD 0.05 NS1 NS1 Not significant.

Table 2. Effects of tillage treatments on corn yields, Kansas River Valley Experiment Field, 2013 and 2014

Corn yieldTillage treatment 2013 2014 Average

--------------------- bu/a ---------------------No-till 221 243 232Fall subsoil/spring field cultivation 217 259 238Fall vertical till 196 259 228Fall subsoil after soybean/vertical till after corn 219 256 238LSD 0.05 NS1 NS NS 1 Not significant.

27


Table 3. Effects of tillage treatments on soybean yields, Kansas River Valley Experiment Field, 2013 and 2014

Soybean yieldTillage treatment 2013 2014 Average

--------------------- bu/a ---------------------No-till 62.4 52.8 57.6Fall subsoil/spring field cultivation 64.3 54.6 59.4Fall vertical till 64.4 55.5 60.0Fall subsoil after soybean/vertical till after corn 66.3 53.4 59.8LSD 0.05 NS1 NS NS 1 Not significant.

28


Grain Sorghum Yield Response to Water AvailabilityJ. Broeckelman, E. Adee, K. Roozeboom, G. Kluitenberg, and I.A. Ciampitti

SummaryYield effects of irrigation on sorghum and corn were compared, but this report is merely focused on the sorghum phase of the crop rotation. Mean yield based on 12.5% grain moisture for irrigated sorghum was 168 bu/a, whereas dryland yield was 145 bu/a. The latter represents a yield improvement of 23 bu/a, an increase of approximately 2 bu/a per unit (in.) of water applied (considering a total of 11 in. of water applied in the irri-gation block).

The irrigated sorghum used a mean of 7.8 in. more water than the dryland, which suggests that the dryland sorghum consumed 3.4 in. more water from the soil profile than the irrigated sorghum (this value assumes no water losses due to runoff or deep percolation and is calculated from total precipitation and irrigation as well as changes in profile water status). Water use efficiency, or WUE, was calculated as the ratio of yield to water use. A trend for superior WUE of 6.5 bu/in. was documented under dryland conditions, compared with 5.6 bu/in. for irrigated sorghum.

IntroductionDecreases in available irrigation water and increased water restrictions necessitate exploration of more economical ways to use available irrigation water. Under low- yielding environments (<80 bu/a grain sorghum), sorghum has a yield advantage over corn because of its lower input costs and superior WUE and heat tolerance. Sorghum’s yield potential is not as high as corn’s, however, so the goal of this study is to deter-mine at what point in available water, both under dryland and irrigation management scenarios, it is better to plant sorghum rather than corn.

ProceduresIn a randomized complete block design, grain sorghum was planted in dryland and fully irrigated blocks at the Topeka Unit of the Kansas River Valley Experiment Field. Within each block, three treatments of different grain sorghum hybrids were planted (Pioneer 84G62, Pioneer 85Y40, and DKS 53-67) with four replications. The plot size was 10 ft × 30 ft, and sorghum was planted in 30-in. rows (four rows per plot). The center two rows were harvested to determine final grain yield and its components.

Plant populations and fertility were based on yield goals of 170 bu/a for the fully irri-gated and 130 bu/a for the dryland. Grain sorghum was planted on May 21 with seed-ing rates based on a goal of 90,000 plants/a in the irrigated block and of 60,000 plants/a in the dryland block. Fertilizer was applied based on recommendations for corn because sorghum fertilizer recommendations for the target yield were lower, and we wanted to eliminate variables that would cause different yields for corn vs. sorghum. Nitrogen (N) was applied preplant at 142 lb/a on both the dryland and the irrigated treatments and

29


was supplemented with 80 lb/a N on the irrigated block (applied June 6). Phosphorus (P) and potassium (K) were applied preplant along with the N at 52 lb/a and 60 lb/a, respectively, for both water scenarios.

Because the study is considering crop production under limited irrigation, water usage was measured at diverse growth stages. After emergence, 6-ft aluminum tubes were installed in the center of each plot halfway between the center two rows. These tubes were used to take water content measurements throughout the growing season using a neutron probe at depths of 0.5, 1.5, 2.5, 3.5, and 4.5 ft. The moisture readings were taken at emergence, mid-vegetation, flowering, mid-reproductive phase, harvest, and 40 days after harvest.

ResultsBoth dryland and irrigated sorghum yielded very well because of high early-season rain-fall amounts. Dryland sorghum yields were highly variable because of the non-uniform soils of the river bottom, which was also seen in the variability in neutron probe mois-ture readings.

The Pioneer 85Y40 hybrid yielded best of the three genotypes on both irrigated and dryland blocks, with yields of 178 bu/a and 150 bu/a, respectively (Tables 1 and 2). The second-best-yielding hybrid was Pioneer 84G62, with an irrigated yield of 165 bu/a and a dryland yield of 143 bu/a. DKS 53-67 yielded 162 bu/a on irrigated and 142 bu/a on dryland. Overall, irrigated sorghum yielded 168 bu/a (Table 1), whereas dryland sorghum yielded 145 bu/a. The irrigated averaged approximately 24 bu/a higher yield than the dryland block (Table 2).

Throughout the growing season, 11 in. of water was applied through irrigation. This can be calculated as an approximately 2-bu increase for every inch of water applied.

Although there were no significant differences between hybrids in terms of water use, differences in WUE for irrigated sorghum were significant (Table 1). The Pioneer 85Y40 hybrid had a WUE of 5.9 bu/in., whereas 84G62 and DKS 53-67 had similar values of 5.5 and 5.4 bu/in., respectively. For the dryland environment, no significant differences in yield, water use, or WUE were documented among the evaluated hybrids (Table 2).

30


Table 1. Sorghum water use, yield (12.5% grain moisture) and water use efficiency (WUE) parameters under the irrigated environment

Irrigated MeansHybrid Water use, in. Yield, bu/a WUE, bu/in.85Y40 30.1 A1 177.6 A 5.9 A84G62 30.2 A 166.3 B 5.5 B

DKS 53-67 29.7 A 160.8 B 5.4 BP-value 0.4861 0.0334 0.0265

1 Values with the same letters are not significantly different (P > 0.05).

Table 2. Sorghum water use, yield (12.5% grain moisture) and water use efficiency (WUE) parameters under the dryland environment

Dryland MeansHybrid Water use, in. Yield, bu/a WUE, bu/in.85Y40 22.8 A1 149.7 A 6.6 A84G62 22.0 A 142.6 A 6.5 A

DKS 53-67 21.7 A 141.6 A 6.5 AP-value 0.1515 0.8087 0.9902

1 Values with the same letters are not significantly different (P > 0.05).

31

Department of Agronomy

Balanced Nutrition and Crop Production Practices for Closing Grain Sorghum Yield GapsB. McHenry, E. Adee, V. Prasad, and I.A. Ciampitti

SummaryA field experiment was conducted at the East Central Kansas Experiment Field near Ottawa, KS, and at the Kansas River Valley Experiment Field near Rossville, KS, in the summer of 2014 to evaluate diverse cropping systems approaches on closing sorghum yield gaps. Yield gaps can be understood as the difference between maximum yield and attainable on-farm yields. The factors that were tested include narrow row spacing; plant population; balanced nutrition practices, including various timings of nitrogen, phosphorus, and potassium (NPK) and micronutrient applications; crop protection with fungicide and insecticide applications; plant growth regulator effects; and the use of precision ag technology for maximizing yields, including a GreenSeeker meter (Trimble Navigation, Westminster, CO) for more precisely determining fertilizer nitrogen needs for sorghum. In addition, this project seeks to quantify the comparison between corn and grain sorghum grown side by side at two production input levels (low vs. high). Only sorghum grain yields are presented in this report. Grain sorghum yields were 115 to 135 bu/a in Rossville (under irrigation) and 60 to 80 bu/a in Ottawa (dryland). Rainfall was limited in Ottawa during the flowering and reproductive stages of growth, which drastically limited yield potential.

IntroductionKansas sorghum producers face the problem of low attainable yield. Grain sorghum is one of the major crops grown in the state of Kansas, and addressing this problem will improve short-term yield and crop productivity. Using better genotypes and best management practices are essential to closing grain sorghum yield gaps. This project is unique in that it takes into account the multitude of factors that influence farmers’ decisions in an effort to quantify the diverse interactions that can maximize yields.

ProceduresAt the two locations, Ottawa, KS (dryland), and Rossville, KS (irrigated), the plots were set up with 5 replications with 11 treatments in each replication for the sorghum phase (Table 1). A randomized complete block design was used for the grain sorghum treat-ments, and side-by-side corn comparison plots were grown on each side of the sorghum replications (2 extra treatments, low vs. high production input for corn). The plots were 10 ft × 50 ft, or 0.01 acres. The hybrids used were Sorghum Partners NK7633 for sorghum and Pioneer 1151 for corn. Measurements for plant characterization were taken at the V5 growth stage, flowering, mid-reproductive stage, and at harvest. The measurements taken included: plant population stand counts, leaf area index (LAI) at V5 and flowering, chlorophyll (SPAD) readings at V5 and flowering, canopy tempera-ture at flowering, aboveground biomass and nutrient concentrations at diverse growth stages, and grain yield and its components (grain number/head and seed weight).

32


Results Grain sorghum yields were 60 to 80 bu/a in Ottawa (dryland), with lower yield poten-tial related to the limited precipitation experienced during the reproductive period (Figure 1). At Rossville, grain sorghum yields were 115 to 135 bu/a, with higher yield potential related to the irrigation scheduling system (irrigated site) (Figure 2). At Ottawa, the cropping system approach did not influence sorghum grain yields, which may be related to the low yield potential explored in this location (reproductive-stage drought stress). At Rossville, the maximum yield gap documented between the highest-yielding treatment (“kitchen sink,” or all inputs are applied but without chloride, treat-ment 9) and the lowest-yielding scenario (check, treatment 10) was close to 20 bu/a (135 vs. 114 bu/a, respectively). The diverse systems evaluated did not differ in sorghum grain yield, with a statistically significant yield difference from all treatments versus the check, a common-practice approach (treatment 10).

33


Tab

le 1

. Des

crip

tion

of so

rghu

m tr

eatm

ents

impl

emen

ted

in th

is st

udy

Tre

atm

ents

12

34

56

78

910

11Se

edin

g rat

eO

ptim

umN

orm

alO

ptim

umO

ptim

umO

ptim

umO

ptim

umO

ptim

umO

ptim

umO

ptim

umN

orm

alO

ptim

umR

ow sp

acin

g15

in.

15 in

.30

in.

15 in

.15

in.

15 in

.15

in.

15 in

.15

in.

30 in

.15

in.

N p

rogr

amG

SG

SG

SSt

anda

rdG

SG

SG

SG

SG

SSt

anda

rdG

SFu

ngic

ide/

inse

ctic

ide

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

No

Yes

Mic

ronu

trie

nts

Fe, Z

nFe

, Zn

Fe, Z

nFe

, Zn

Fe, Z

nN

one

Fe, Z

nFe

, Zn

Fe, Z

nN

one

Fe, Z

nPG

RYe

sYe

sYe

sYe

sYe

sYe

sN

oYe

sYe

sN

oYe

sSt

arte

r fer

tiliz

erN

PKSZ

nN

PKSZ

nN

PKSZ

nN

PKSZ

nN

PKSZ

nN

PKSZ

nN

PKSZ

nN

PN

PKSZ

nN

PN

PKSZ

nC

hlor

ide

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Gre

enSe

eker

+ N

No

No

No

No

No

No

No

No

No

No

Yes

Opt

imum

seed

ing r

ate =

80,

000

plan

ts/a

; Nor

mal

= 5

0,00

0 pl

ants

/a; 1

5 in

. = n

arro

w ro

w sp

acin

g; 3

0 in

. = w

ide r

ow sp

acin

g; G

S =

Gre

enSe

eker

met

er (T

rimbl

e Nav

igat

ion,

Wes

tmin

ster

, CO

);

Stan

dard

= co

nven

tiona

l N ap

plic

atio

n (w

ithou

t pre

cisio

n ag

tech

nolo

gy);

Fe =

Iron

; Zn

= Zi

nc; P

GR

= p

lant

grow

th re

gula

tor;

N =

nitr

ogen

; P =

pho

spho

rus;

K =

pot

assiu

m; S

= su

lfur.

34


80

75

70

65

60

55

50

45

40

Yie

ld, b

u/a

8641 10

Treatments

2 3 5 7 9 11

Figure 1. Sorghum grain yield under diverse cropping systems approaches at the Ottawa Unit of the East Central Kansas Experiment Field. See Table 1 for treatment details.

145

135

125

115

105

95

85

75

Yie

ld, b

u/a

8641 10

Treatments

2 3 5 7 9 11

Figure 2. Sorghum grain yield under diverse cropping systems approaches at the Rossville Unit of the Kansas River Valley Experiment Field. See Table 1 for treatment details.

35


Corn Yield Response to Water AvailabilityT. Newell, K. Roozeboom, G. Kluitenberg, and I. Ciampitti

SummaryDrought-tolerant technologies have become popular in hybrids for low-yielding corn environments across central and western Kansas and are marketed for their ability to produce higher grain yields with less water. The objective of this study was to compare water use, yield, and water use efficiency (WUE) of two types of drought-tolerant (DT) corn hybrids and a high-yielding non-DT hybrid. Water use and yield of two DT and one non-DT, high-yielding hybrid were compared in both dryland and irrigated situ-ations. The average yield for the irrigated corn was 217 bu/a, and the average was 127 bu/a in dryland, representing a yield increase of 90 bu/a. The irrigated corn received a total of 10 in. more water than the dryland corn over the course of the growing season, resulting in 9 bu for each additional inch of water use averaged across the three hybrids. The irrigated corn used a mean of 20.85 in. of water, and the dryland corn used a mean of 11.66 in. of water. The WUE was 10.71 bu/in. and 10.43 bu/in. for dryland and irrigated corn, respectively. Although hybrid yields differed in the irrigated environ-ment, water use and WUE were similar for all hybrids in both dryland and irrigated environments. One DT hybrid exhibited more stable yields across dryland and irrigated environments compared with the other DT hybrid and the non-DT hybrid.

IntroductionBecause irrigation water in central and western Kansas has decreased and water restric-tions have increased, producers are looking for a more economical way to use available irrigation water and maximize dryland corn yields. Drought-tolerant hybrids such as Monsanto’s DroughtGard (Monsanto, St. Louis, MO) and Pioneer’s AQUAmax (Pioneer Hi-Bred, Johnston, IA) have been marketed as providing hybrids with supe-rior drought and heat tolerance that result in the ability to produce in low-yielding environments. The objective of this study is to compare water use, yield, and water use efficiency of two types of DT corn hybrids and a high-yielding non-DT hybrid.

ProceduresThree corn hybrids were planted into both dryland and fully irrigated blocks in a randomized complete block design with four replications. Each block contained four replications of three hybrids: Pioneer 1151 AQUAmax (native traits, water-optimized hybrid), Croplan 6000 DroughtGard (WinField Solutions, Shoreview, MN; native traits plus transgenic trait, water-optimized hybrid), and Croplan 6274 (high yield potential in well-watered conditions). The plot size was 10 ft × 45 ft, and corn was planted into 30-in. rows (four rows per plot). The center two rows were hand-harvested (20 ft) to determine the final yield and yield components.

Soil water content was measured at various growth stages using a neutron moisture meter (NMM). After emergence, 6-ft aluminum tubes were installed in the row between corn plants in one of the two center rows of each plot. These tubes were used to take NMM water readings at depths of 6, 18, 30, 42, and 54 in. Soil moisture read-

36


ings were taken at emergence; at mid-vegetative, flowering, and mid-reproductive stages; at harvest; and 30 days postharvest.

Seeding rates and fertilizer applications were based on yield goals of 110 bu/a dryland and 190 bu/a irrigated. The corn was planted May 2 with a seeding rate based on a goal of 28,000 plants/a dryland and 34,000 plants/a irrigated. Nitrogen (N) was applied preplant at 100 lb/a on both dryland and irrigated and was supplemented (at V4) with 130 lb/a N and 35 lb/a P2O5 in the irrigated block and only 30 lb/a P2O5 in the dryland block. Means were calculated, and mean separations were conducted using SAS 9.3 PROC GLIMMIX (α = 0.10).

ResultsTreatment differences were observed for grain moisture, test weight, and irrigated grain yield (Table 1). In both dryland and irrigated environments, the non-DT hybrid had the greatest grain moisture. This could be because many of the current hybrids adapted for irrigated conditions have an extended grain-fill period and a longer stay-green period. Croplan 6000DG had the driest grain at harvest in both environments, but it did not differ from Pioneer 1151AM in the dryland environment. The only difference in test weight was that Pioneer 1151AM was greater than the other two hybrids in the dryland environment. In the irrigated environment, Pioneer 1151AM had the greatest yield, Croplan 6000DG the least, and Croplan 6274 was intermediate (Table 1).

Figure 1 illustrates the yield response to estimated water use for each plot. The nearly parallel lines for Croplan 6274 and Pioneer 1151AM imply that these two hybrids responded similarly for yield as water input increased, which represents the capacity of both hybrids to increase grain production efficiently as more water is introduced to the growing environment. Although Croplan 6000DG did not have the top yield in well-watered conditions, the smaller slope of the yield-water use curve (Figure 1) indicates that yield of this hybrid may be more stable in environments with less available water.

37


Table 1. Means per hybrid/environment of water use, yield, yield components, and water use efficiency (WUE)

Means

Water use, in.

Grain moisture, %

Test weight, lb/bu

Yield, bu/a

WUE, bu/in.

DrylandPioneer 1151AM 11.84 a1 14.60 b 59.18 a 129.38 a 10.93 aCroplan 6000DG 11.75 a 14.30 b 56.80 b 133.80 a 11.40 aCroplan 6274 12.00 a 19.50 a 57.08 b 118.04 a 9.80 a

IrrigatedPioneer 1151AM 21.05 a 14.35 b 62.75 a 228.81 a 10.87 aCroplan 6000DG 20.66 a 13.95 c 62.08 a 205.28 b 9.93 aCroplan 6274 20.84 a 14.90 a 62.43 a 218.10 ab 10.50 a

1 Values within column and a water environment followed by the same letter are not different, α = 0.10.

Water use, in.

8 10 12 14 16 20

275

250

225

200

175

150

125

100

75

50

25

0

Yie

ld, b

u/a

24

y = 11.06x - 13.59R² = 0.86

18 22

y = 7.98x + 40.27R² = 0.87

y = 10.73x + 2.69R² = 0.89

Croplan 6274

Pioneer 1151AM

Croplan 6000DG

Figure 1. Corn grain yield response to estimated water use at Scandia in 2014.

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Cover Crop Impacts on Soil Water StatusM. Kuykendall, K. Roozeboom, G. Kluitenberg, P.V.V. Prasad

SummaryWater is a primary concern for producers in the Great Plains; as such, research is warranted to quantify how much cover crops affect the amount of soil water available to subsequent cash crops. Cover crop mixes have been marketed as a means to conserve water in no-till cropping systems following winter wheat (Triticum aestivum L.) harvest. The objectives of this study are to quantify changes in soil profile water content in the presence of different cover crops and mixtures of increasing species complexity, to quantify their biomass productivity and quality, and to quantify the impact of cover crops on subsequent corn (Zea mays L.) yields. We hypothesized the change in soil water brought on by the cover crop treatments would be correlated to the quantity of biomass produced and the species composition, rather than mixture complexity. Soil moisture was measured using a neutron probe to a depth of 9 ft. Results from 2013–14 showed no difference in water use between cover crop mixtures and single species. Cover crops depleted the soil profile by a maximum of 3.5 in. during growth, but fallow was able to gain 0.75 in. of water during the same period. At the time of corn planting, soil moisture under all cover crops had replenished to levels at cover crop emergence, except for the brassicas, which had extracted water from deeper in the profile. Corn yields were reduced following the grass cover crops and the six-species mix. Corn yields were more closely related to the carbon:nitrogen (C:N) ratio of the cover crop residue than to profile soil moisture at corn emergence. The fact that yields were similar for corn after fallow and for corn after brassica cover crops implied that water was not the cause of yield reductions after the other cover crops.

IntroductionCover crops have become increasingly popular in no-till systems in recent years as a tool to increase cropping system intensity and diversity. One of the main concerns of Kansas producers is the possibility that cover crops may reduce the amount of soil water stored in the profile for the next grain crop, potentially reducing yields. Some have suggested that complex cover crop mixtures may extract water differently than individual species. To quantify changes in soil profile water content under different cover crops and mixtures, 11 treatments were imposed during the fallow period between winter wheat harvest and corn planting at Manhattan and Belleville, KS. Treatments include both single species and mixtures of increasing complexity as well as a chemical fallow control (Table 1). Results are presented as the average for each type of cover crop as indicated in Table 1 and include results from Manhattan, the only location to have a full rotation to the next corn crop.

ProceduresCover crops were drilled immediately following wheat harvest in 2013 using a Great Plains no-till drill and were terminated with herbicide in late September at flowering of most species. Seeding rates and ratios were based on recommendations from prominent cover crop seed marketers and publications. Biomass was hand-harvested shortly after termination. Soil water below each treatment was measured using a neutron probe at

39


1-ft increments to a depth of 9 ft. Neutron probe readings were collected at intervals beginning at cover crop emergence until just before corn planting the following spring. Readings were taken approximately weekly during cover crop growth and monthly up to and throughout corn growth. Readings taken after corn emergence were taken only in the chemical fallow and the brassica species that had extracted the most water during growth.

ResultsFigure 1 shows that all cover crop types extracted a similar amount of water to a depth of 3.5 ft by October 16, 2013. The soil profile under the fallow treatment contained more water than all cover crop treatments to a depth of 3.5 ft on that date. The soil contained less water below the brassicas at depths of 5.5 to 7.5 ft compared with the other treatments. By the next spring (Figure 1), differences in soil water at depths of 1.5 ft and less had mostly disappeared, but soil water was greatest in the chemical fallow plots at depths of 2.5 to 4.5 ft. At depths greater than 4.5 ft, the brassica plots still contained the least soil water.

At termination of the cover crops on September 22, the soil profiles held roughly 3 to 3.5 in. less water than at cover crop emergence to a depth of 9 ft (Table 2). The chemi-cal fallow plots held approximately 0.75 in. more water to the same depth, exhibiting a 40% precipitation storage efficiency based on 1.9 in. of precipitation during this period. Complexity of the cover crop mixture did not affect how much water was extracted from the soil profile. Soil water was monitored throughout the winter until just before corn planting in the spring of 2014. By April 15, the plots in fallow contained roughly 1.5 in. more stored soil water to the 9-ft depth than they had the previous August, stor-ing only 23% of the 6.6 in. of precipitation that fell since the previous August (Table 2). Most of the plots with cover crop treatments regained much of the soil profile water lost the previous summer so they contained nearly the same amount of water in the soil profile as they had at cover crop emergence. The exceptions were the plots with brassica cover crops and those with the mix of nine species, both with less water than when the cover crops emerged the previous August (Table 2). This resulted in 2.9 in. less stored soil water in the plots previously in brassicas and 2.1 in. less water stored in plots previ-ously planted with the nine-species mix compared with fallow prior to corn planting.

Corn was planted in the spring of 2014 to assess the influence of the previous cover crop’s soil water depletion on corn growth and yield. Neutron probe access tubes were installed in the two corn treatments that had the least (fallow) and most (tillage radish) water extraction the previous season. Soil water was tracked for those treatments from soon after corn emergence until physiological maturity. After corn had emerged in early May, soil water content was essentially equal down to 2.5 ft for the chemical fallow plots and plots that had been planted to tillage radish (Figure 2). At depths from 3 to 9 ft, the chemical fallow plots contained 1.8 in. more soil water. A difference in soil water between the two treatments continued throughout the corn growing season but was reduced to 1.3 in. by mid-August when corn reached maturity (Figure 2). The fact that soil water content increased at depths greater than 4 ft and decreased at shallower depths implies that corn rooting did not extend beyond 4 ft in this environment.

40


Although several measures of corn performance were influenced by previous cover crop treatment, corn plant density was unaffected. Plant density was relatively high for this environment to maximize water use and increase the likelihood of detecting cover crop treatment differences associated with soil water status. Corn planted in plots following fallow had only three fired leaves in mid-July, and corn planted after the legume and mixture cover crops had four (Table 2). Corn planted after the grass and brassica cover crops showed nearly five fired leaves. Yields after grass cover crops and the mixture of six lagged by 12 to 15 bu/a compared with the highest-yielding plots (Table 2).

The fact that corn yields were similar for both the fallow and the brassica plots implies that soil water was not the primary driver of the yield response to the previous cover crop, because those treatments had the greatest difference in soil water at corn planting. Instead, reductions in corn yield appear to be more closely related to the presence of a grass cover crop, either alone or in mixtures, and the greater C:N ratio of those cover crop residues (Table 2).

Table 1. Cover crop treatments and groupings by cover crop typeTreatment Cover crop type1 Chemical fallow Fallow2 3

Sorghum-sudan grass (SS) Pearl millet (PM) Grasses

4 5

Tillage radish (TR) Winfred rape (WR) Brassicas

6 7

Medium red clover (RC) Sunn hemp (SH) Legumes

8 9

Mix of SS/TR/RC Mix of PM/WR/SH Mix

10 Mix of SS/PM/TR/WR/RC/SH Mix

11 Mix of SS/TR/PM/WR/German millet/cowpeas/ hairy vetch/Ethiopian cabbage/Hunter brassica Mix

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Table 2. Change in soil water, corn yield, and number of fired leaves, and carbon:nitrogen (C:N) ratio of the aboveground residue from cover crop treatments

Cover crop type

Change in soil water content, 0- to 9-ft. depth Corn after cover crops C:N ratio

of cover crop residue

August 17 to Sept. 221

August 17 to April 153 Fired leaves Yield

--------------- in. --------------- number bu/a X:1Fallow 0.76 a2 1.54 a 3.0 d 83.6 ab -----Grasses -3.18 b -0.25 b 4.9 a 72.6 c 55.5 aBrassicas -3.52 b -1.38 c 4.6 ab 83.0 ab 27.6 cdLegumes -2.97 b -0.28 b 4.1 bc 84.6 a 25.7 dMixes of 3 -3.14 b -0.05 b 4.2 abc 75.7 ab 36.9 bcMix of 6 -2.92 b 0.10 b 4.0 bc 69.7 c 44.4 abMix of 9 -3.19 b -0.58 bc 4.2 abc 75.4 ab 42.3 b1 Period of cover crop growth.2 Values within a column followed by different letters are different at α = 0.05. 3 From cover crop emergence to corn planting.

FallowGrassesLegumesBrassicas

Mixes of 3Mix of 6Mix of 9

Soil volumetric water content, in./in.

0.25 0.3 0.35 0.4

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

Dep

th, f

t

0.2

October 6, 2013


0.25 0.3 0.35 0.4

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

Dep

th, f

t

0.2

April 15, 2014

Figure 1. Soil volumetric water content beneath the cover crop treatments and fallow near cover crop termination (left) and before corn planting (right).

42


Tillage radishFallow


0.25 0.3 0.35

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

Dep

th, f

t

0.2

May 3, 2014


0.25 0.3 0.35

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

Dep

th, f

t

0.2

August 11, 2014

Figure 2. Soil profile volumetric water content under plots previously in chemical fallow or tillage radish the previous year at corn emergence (left) and physiological maturity (right).

43


Soybean Planting Date × Maturity Group: Eastern Kansas SummaryI. Ciampitti, D. Shoup, G. Sassenrath, J. Kimball, and E. Adee

SummaryOptimum planting time for soybean depends on the interaction between genotype and environment (G × E). Four field studies were conducted during the 2014 growing season across eastern Kansas (Manhattan, Topeka, Ottawa, and Parsons). This study explores the impact of planting date (early, mid, and late planting times) on yield for modern soybean cultivars from a range of maturity groups (early, medium, and late groups).

IntroductionSoybean yield is largely defined by the interaction between the genotype, maturity group, and environment. In the last 20 years, Kansas producers have shifted soybean planting dates earlier at a rate of about 0.5 day/year. Although the environment is the primary determinant of soybean yields, producer decisions are the one controllable factor that drives Kansas soybean production. Within those decisions, agronomic practices such as planting dates and maturity groups are important components that producers can manage.

New genetic interactions with environment and management practices require an updated version of optimum planting dates × maturity groups. The optimal planting times for these cultivars under the varying soils and environmental conditions across the state of Kansas are not well known. This project involves a coordinated regional effort to perform research trials. The outcomes of this project will provide excellent information for equipping key stakeholders in the decision-making process.

ProceduresSoybean cultivars from early, medium, and late maturity groups were planted at three times during the 2014 growing season. Maturity groups at Topeka and Manhattan loca-tions were 2.0, 3.8, and 4.8, all planted at different times (April 22, May 15, and June 3 at Manhattan, and May 2, May 20, and June 18 at Topeka). Maturity groups at Ottawa were 3.7, 4.2, and 4.8 and were planted May 5, May 28, and June 26; at Parsons, groups 3.9, 4.8, and 5.6 were planted May 2, June 3, and June 26. At all field locations, total yield from each cultivar and planting date was determined by harvesting the center rows of each plot at maturity using a plot combine.

Results and DiscussionUnder the dryland scenario at Manhattan, the mid-maturity group (3.8) was the high-est-yielding group for the early or late planting date, with the latest maturity group (4.8) outyielding the other groups evaluated for the mid planting date (May 15) (Figure 1).Under full irrigation at Topeka, medium- and late-maturity groups (3.8 and 4.8) maxi-mized soybean yields for the earliest planting time (May 2), with yields above 70 bu/a (Figure 1). Lower yields were documented for the mid-May planting time compared

44


with the early planting date, with the exception of the late-maturing group (4.8). For the late planting time (June 18), the maturity group 3.8 (medium group) (yields >60 bu/a) significantly outyielded the early (2.0) and late (4.8) soybean groups (yields below 45 bu/a).

Under the dryland scenario at Ottawa, soybean yields were similar across all maturity groups for the early (May 5) and mid planting dates (May 28), but yields were generally greater at the mid planting date (around 35 bu/a) (Figure 2). For the late planting time (June 26), soybean yield increased with the maturity group evaluated, and overall yields were the greatest among planting dates, with averages near 40 bu/a.

Under the dryland scenario at Parsons, the trend in soybean yield indicated that the earlier maturity group (3.9) yielded highest at the earliest planting date (May 3). Yield declined for this maturity group at later planting. Conversely, the longer-maturing cultivars yielded better at the later planting dates (Figure 2). Although a trend in the data supported timing of planting to capture fall rains to enhance yield, the results were not statistically significant between the later maturity groups.

E M LE M LE M L E M L E M L E M L

Early = MG 2.0

Medium = MG 3.8

Late = MG 4.8

100

90

80

70

60

50

40

30

20

10

0

Soyb

ean

yie

ld, b

u/a

June 3May 15April 22

June 18

May 20May 2

Soybean maturity group (MG)

DrylandManhattan

Average Manhattan = 29 bu/a

IrrigatedTopeka

Average Topeka = 58 bu/a

Figure 1. Soybean yields under varying planting dates (early, mid, and late) and maturity groups (E = early, M = medium, L = late maturing groups) for Manhattan and Topeka.

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E M LE M LE M L E M L E M L E M L

Early = MG 3.7–3.9

Medium = MG 4.2–4.8

Late = MG 4.8–5.6

100

90

80

70

60

50

40

30

20

10

0

Soyb

ean

yie

ld, b

u/a

June 26May 28

April 5

June 26June 3

May 2

Soybean maturity group (MG)

DrylandOttawa

Average Ottawa = 37 bu/a

DrylandParsons

Average Parsons = 43 bu/a

Figure 2. Soybean yields under varying planting dates (early, mid, and late planting times) and maturity groups (E = early, M = medium, L = late maturing groups) for Ottawa and Parsons.

Kansas State University Agricultural Experiment Station and Cooperative Extension Service

August 2015

K-State Research and Extension is an equal opportunity provider and employer.

Copyright 2015 Kansas State University Agricultural Experiment Station and Cooperative Extension Service. Contents of this publication may be freely reproduced for educational purposes. All other rights reserved. In each case, give credit to the author(s), Kansas Field Research 2015, Kansas State University, August 2015. Contribution no. 15-393-S from the Kansas Agricultural Experiment Station.

Chemical DisclaimerBrand names appearing in this publication are for product identification purposes only. No endorsement is intended, nor is criticism implied of similar products not mentioned. Experiments with pesticides on nonlabeled crops or target species do not imply endorsement or recommendation of nonlabeled use of pesticides by Kansas State University. All pesticides must be used consistent with current label directions. Current information on weed control in Kansas is available in 2015 Chemical Weed Control for Field Crops, Pastures, Rangeland, and Noncropland, Report of Progress 1117, available from the KSRE Bookstore, 24 Umberger Hall, Kansas State University, or at: www.bookstore.ksre.ksu.edu/ (type Chemical Weed Control in search box).

These and other articles are available at the Kanasas Agricultural Experiment Station Research Reports site at: http://newprairiepress.org/kaesrr

Publications from Kansas State University are available at: www.ksre.ksu.edu


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