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K-State Agronomy eUpdates

Department of Agronomy

Kansas State University

1712 Claflin Rd.

2004 Throckmorton PSC

Manhatan, KS 66506

785-532-6101

agronomy@ksu.edu

Extension Agronomy

2016 Forecasted corn yield potential and attainable yields

At this point in the season, a slightly higher-than-average percentage of Kansas corn has already passed the flowering (pollination) stage and is entering into the grain filling period. The most recent Kansas Agricultural Statistics Service crop progress report (July 17) projected 63% of the Kansas’ corn crop is at the silking stage, near last year’s number and a bit higher than average (59%). Overall, close to 70% of the corn crop in Kansas was classified by the USDA as in good or better condition. Pollination conditions around the state were OK (for Central and Eastern regions of the state; the West region is at pollination). However, high temperatures and episodic drought stress could present a challenge for the coming weeks, and could potentially affect the final effective grain number and kernel size per ear. From now until harvest, weather will be the main primary factor driving changes and affecting maximum corn yield.

Potential corn yield estimation

Estimating potential corn yields can help us understand the maximum yield attainable if management is optimal and in absence of unmanageable adversities, such as hail or flooding. A research team based at the University of Nebraska is continuing a project (see  the full article) for forecasting corn yield using historical and current weather and management information in collaboration with faculty and extension educators from 10 universities across the U.S. Corn Belt (http://cropwatch.unl.edu/2016/2016-corn-yield-forecasts-july-13).

The corn simulation model -- Hybrid-Maize Model (http://hybridmaize.unl.edu) -- was developed by researchers in the Agronomy and Horticulture Department at UNL and takes into consideration several factors such as weather, plant population, hybrid relative maturity, planting date, and soil type, among other factors. The model assumes optimal management, with no limitation imposed by nutrients or biotic factors (weeds, insect pests, pathogens) and no adversities such as flooding, hail or abiotic factors (heat, drought). Thus, the model provides maximum yield is conditions are optimal.

A yield gap, difference between final attainable yield and maximum yield predicted, will develop if management is sub-optimal or there are other adverse factors not accounted by the model that may reduce corn yield. Simulations can be performed to forecast current-season corn yields. Factors such as site-specific weather conditions from planting until the simulation date and historical weather information to simulate the rest of the 2016 growing season are used for the simulation. Myriad yield scenarios could be produced depending on the growing conditions from the simulation date until harvesting time, but forecasts are more accurate and reliable as the simulation time approaches corn maturity.

Simulation results for Kansas

A total of 41 sites were simulated for corn yields across the U.S. Corn Belt, including 5 sites for Kansas – rainfed, irrigated, or both water scenarios -- and 1 site in Missouri (Fig. 1) that is relevant for the northeast Kansas area. Sites include Garden City, Hutchinson, Silver Lake, Manhattan, Scandia, and St. Joseph, Mo. A separate yield forecast was performed for irrigated and dryland corn for Scandia and Silver Lake, while only irrigated corn was simulated at Garden City. The dryland scenarios for corn yield forecast were Manhattan, Hutchinson, and St. Joseph, Mo.

Daily weather data used for simulating these locations were retrieved from the High Plains Regional Climate Center (HPRCC http://www.hprcc.unl.edu/). For Kansas, local agronomists provided information about soil properties and crop management (hybrid maturity, plant populations, and historical and 2016 planting dates) required for the simulations (Table 1). The following agronomists should be properly acknowledged for investing their time and providing their expertise: Eric Adee, Agronomist-in-Charge, Kansas River Valley Experiment Field, Topeka; Gary Cramer, Agronomist-in-Charge, South Central Kansas Experimental Field, Hutchinson; and John Holman, Southwest Research-Extension Center Cropping Systems Agronomist, Garden City.

The current locations represent just a sample of the corn area in the state, but more sites could be added in the coming years to increase the site-specificity of the corn yield forecast analysis.

Figure 1. Locations utilized for simulation purposes for Kansas.

 

 

Table 1. Management and soil data used for forecasts in Kansas.

Location

Water regime

Density (plant per acre)

Hybrid RM (days)

2016 planting date1

Average yield (bu/acre) 2

Manhattan

Dryland

25,000

110

April 17

109

Scandia

Irrigated

34,000

116

April 27

173

 

Dryland

24,000

107

May 2

100

Silver Lake

Irrigated

34,000

117

April 14

171

 

Dryland

24,000

109

April 18

99

Hutchinson

Dryland

20,000

105

April 28

74

Garden City

Irrigated

26,000

113

May 8

191

Data were retrieved by state collaborators and DuPont Pioneer agronomists. 1 Approximate date at which 50% of final corn area was planted in 2016 at each location. Soil water balance was initialized around prior crop harvest in the previous year (2015), assuming 50% available soil water. 2 Average (2005-2014) actual yield reported by USDA-NASS for the counties located near the simulated location (source: Global Yield Gap Atlas www.yieldgap.org)

 

Forecasted corn yield potential was compared to the long-term average yield from 2005-2014. The model then calculated 2016 forecasted yield potential, utilizing current-season weather. The 2016 in-season yield potential forecasts for Kansas is presented in Table 2.

At almost all sites simulated in Kansas, there is close to 50% probability of achieving near average yields for the current season as relative to the long-term yield potential.

Under irrigated conditions (Scandia, Silver Lake, and Garden City), there is a 30% greater probability of having above-average yields (relative to the long-term yield potential) for Scandia and Silver Lake, and a 20% chance of having above-average yields for Garden City. Under rainfed conditions, there is a higher probability of being above average for 2016 corn yields in the northeast corner of the state (if the corn was planted before first week of May; Table 1), but still a fair probability (>=30%) for the rest of the dryland sites (except for Manhattan; 29% - Table 2). It should be emphasized that forecasted yield for corn is showing adequate probabilities for average yield expectation across all locations evaluated in this analysis.

 

 

Table 2. 2016 In-season Yield Potential Forecasts for KS (July 13).

 

 

 

Range of 2016 forecasted yields as of July 13 (bu/acre)

Probability (%) of 2016 yield to be (relative to the long-term average yield):

Simulated current crop stage

Location

Water regime

Long-term (2005-14) avg. yield (bu/a)

25th

75th

Below

Near

Above

 

Garden City

Irrigated

191

184

207

10%

70%

20%

R1, Silking

Hutchinson

Dryland

74

71

86

12%

54%

35%

R3, Milk

Manhattan

Dryland

109

104

121

10%

61%

29%

R3, Milk

Scandia

Dryland

100

104

118

0%

57%

43%

R2, Blister

 

Irrigated

173

178

203

0%

70%

30%

R2, Blister

Silver Lake

Dryland

99

94

113

17%

50%

33%

R3, Milk

 

Irrigated

171

172

204

20%

50%

30%

R3, Milk

 

Summary

Stress conditions during this week and expected in the coming week, primarily related to heat and episodic drought stress, could have a critical impact on corn yields across the state via an impact on kernel number (abortion process) and also on the kernel weight (grain filling occurring in southeast Kansas).

Further details related to current and expected weather conditions, see our previous Agronomy eUpdate article:
https://webapp.agron.ksu.edu/agr_social/eu_article.throck?article_id=1021

As clarified in the UNL article (see below link), these predictions do not consider current or past production problems (e.g. saturated soils, replanting, hail/flooding, nitrate leaching and nutrient deficiencies), or the influence of biotic (e.g. disease, insects) or abiotic (e.g. heat, drought) stress factors.

You can read the full paper related to forecasted yields in 41 locations around the Corn Belt at: http://cropwatch.unl.edu/2016/2016-corn-yield-forecasts-july-13

 

Ignacio Ciampitti, Cropping Systems and Crop Production Specialist
ciampitti@ksu.edu