Project Details - Full Facts for Selected Year

Parent Project: The Quest of 100 Bushel Soybean: On-Farm Approach
Checkoff Organization:Kansas Soybean Commission
Categories:Crop management systems, Environmental stress, Sustainability
Project Title (This Year):The Quest of 100 Bushel Soybean: On-Farm Approach
NCSRP, USB, QSSB Project Code:1776
Project Year:2017
Lead Principal Investigator:Ignacio Ciampitti (Kansas State University)
Co-Principal Investigators: Eric Adee (Kansas State University)
Stewart R. Duncan (Kansas State University)
Terry Griffin (Kansas State University)
Xiaomao Lin (Kansas State University)
Doug Shoup (Kansas State University)
Keywords:

Contributions

Contributing OrganizationAmount
Kansas Soybean Commission $29,972.00

Funding

Funded InstitutionAmount
Kansas State University $29,972.00

Information and Results

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Project Summary

Research information was generated in recent years relating to best management practices to increase soybean production around the USA with the USB-funded “kitchen sink” project, but only as small-scale research plots (e.g. 5-ft x 30-ft). At this point, a better characterization of our high-yielding soybean farms is urgently needed. High-yield potential can be more properly understood if it is based on farmer information (largescale plots). Weather, soil, crop, and other environmental components should be properly characterized to provide a platform and baseline of comparison across soybean fields. Plant growth rates (biomass characterization) and nutrient uptake should be characterized in large-size plots at the on-farm scale. Characterizing high-yielding soybean farmers at the on-farm scale and understanding soybean plant growth and development would encourage other farmers to question and study the factors blocking production.

Project Objectives

Identify and work closely with high-yielding soybean farmers in documenting and understanding the best management practices to increase soybean yields under varying environments (large farm areas, e.g. 50 acres) and changing climate-economic scenarios.

Project Deliverables

The project can be dissected in FIVE major components:

1) Historical component: Soybean contest winners for yield will be identified as potential collaborators (at least 4 farmers across diverse regions). Historical soybean information (last 5 yrs) on high-yielding soybean systems will be recorded from these farmers in order to better understand interaction of production practices x weather x economic scenarios from the most recent years.

2) Baseline Information: Characterize all production practices including input usage, field operation timing, and other activities implemented by the farmer in the current growing season (1st year of the project).
Example of production practices potentially to be identified in a contest winner’s farmer including input usage, field operation timing, and other activities. Specific examples may include: 1) Planting date prior to May 10; 2) Narrow row spacing (15”-7.5”); 3) Seeding rates ranged from 130 to 160 thousand per acre; 4) Seed treatment; 5) Application of foliar fungicides/ insecticides when required (based on disease infestation and insect incidence, # insects per plant); 6) Apply nutrients, P, K, S, when soil testing is low.

3) Soybean Yield Dissection: Extensively characterize soil, weather, plant growth, nutrient uptake, and main yield limiting factors during the current growing season (1st yr of the project). For this objective, proper screening of field conditions is needed. One (1) acre of the field will be jointly selected (farmer and PI-collaborators) for identifying physiological, nutrient, and all yield limiting factors. Farmers will get compensated for the use of the land at the yield obtained in the best area of their field (e.g. 75 bushels per acre x $9/bu = $675). Several 500-sq ft areas will be collected at varying growth stages.

Plant measurements to be determined:
- Leaf Area Index (LAI, derived via a LiCOR machine), light interception, and
Chlorophyll (SPAD) readings will be taken at the same moment of the biomass sampling.
- Visual disease and insect ratings from bottom, middle, and top of soybean canopy. - Grain yield components – pod number, grain number per pod, and grain weight harvested from the non-destructive areas (where “plant traits” are determined).
- Nodulation measurements at multiple growth stages (number of nodules).
- Plant biomass [e.g. Early season –V5, R3 stage-] and dry mass will be calculated and samples will be prepared for nutrient testing (complete nutrient analysis).

On-farm field characterization of all four-soybean environments (Chris Bodenhausen, Muscotah; Andy Winsor, Perry; Justin Knopf, Salina; and Ron Ohlde, Morganville).

4) Outreach:
A multifaceted extension and outreach program will include participation from faculty in cooperation with grower organizations, and producers. Information from this study will be presented at extension activities and the topics tailored to each specific audience. Presenting in field days, production schools, summer tours, and grower-oriented meetings will be key-component of this proposal. The PI will collaborate with Area Agronomists, Kansas Soybean, and agriculture and natural resources extension agents to identify farmers for this project and the needs of local clientele. All the information produced from this study will be available via the utilization of diverse communication venues
(websites, social media, extension programming, radio, television interviews, and press).

Progress of Work

Update:
Summary:
Nowadays good agronomical practices demand the adoption of new technologies that deliver better resource efficiency. The objective of this study was to identify and work closely with high-yielding soybean farmers in order to implement Ag precision tools, in this case: satellite imagery. Fields were selected for the 2017 growing season. The study is based on working with the field variation and the selection of three productivity zones outlined according to normalized difference vegetation index (NDVI) values.

Introduction
Vast information about crop health and development can be obtained via characterization of the temporal and spatial variability in the field, for example with the utilization of satellite imagery. Satellite imagery may provide crucial information that could potentially influence the decision-making process related to all farming inputs such as fertilizer, seeding rate, genotype selection, and pesticide application, among others.
The main objectives of this study are to: 1) explore the potential use of satellite imagery to identify productivity zones and evaluate soybean development across the growing season at the on-farm scale, and 2) explore relationships between satellite imagery data and ground-truth based plant traits such as plant growth and final yield.

Procedure
Sites Description
Field sites were established for 2017. Agronomical practices were those suitable per site.
Determination of Productivity Zones
A map defining productivity zones will be elaborated with previous year data for NDVI obtained from satellite imagery. See, Example of previous season productivity map.

View uploaded report

Update:
Summary:
Nowadays good agronomical practices demand the adoption of new technologies that deliver better resource efficiency. The objective of this study was to identify and work closely with high-yielding soybean farmers in order to implement precision Ag tools, in this case: satellite imagery. Fields were selected for the 2017 growing season. The study is based on working with the field variation and the selection of three productivity zones outlined according to normalized difference vegetation index (NDVI) values.

Introduction
Vast information about crop health and development can be obtained via characterization of the temporal and spatial variability in the field, for example with the utilization of satellite imagery. Satellite imagery may provide crucial information that could potentially influence the decision-making process related to all farming inputs such as fertilizer, seeding rate, genotype selection, and pesticide application, among others.
The main objectives of this study are to: 1) explore the potential use of satellite imagery to identify productivity zones and evaluate soybean development across the growing season at the on-farm scale, and 2) explore relationships between satellite imagery data and ground-truth based plant traits such as plant growth and final yield.

Progress
Sites Description
Field sites were established for 2017. Agronomical practices were those suitable per site. Productivity zone maps for each location were established. This current growing season we have partnered with Justin Knopf in Gypsum, Andy Winsor in Perry, Matt Everhart in Salina, and Chris Bodenhausen in Muscotah,
In-season measurements: Plant phenology, soil moisture and satellite information were gathered to delineate zone management zones prior to soybean harvesting time.

Impact
Information related to this project was presented in a field tour coordinated by our group in collaboration with Tom Maxwell, county Ag Extension Agent (K-State Research and Extension, Central KS district). The below information was presented at this event.

View uploaded report

Update:
Summary:
Nowadays good agronomical practices demand the adoption of new technologies that deliver better resource efficiency. The objective of this study was to identify and work closely with high-yielding soybean farmers in order to implement precision Ag tools, in this case: satellite imagery. The study is based on working with the field variation and the selection of three productivity zones outlined according to normalized difference vegetation index (NDVI) values.

Introduction
Vast information about crop health and development can be obtained via characterization of the temporal and spatial variability in the field. Satellite imagery may provide crucial information that could potentially influence the decision-making process related to all farming inputs such as fertilizer, seeding rate, genotype selection, and pesticide application, among others.
The main objectives of this study are to: 1) explore the potential use of satellite imagery to identify productivity zones and evaluate soybean development across the growing season at the on-farm scale, and 2) explore relationships between satellite imagery data and ground-truth based plant traits such as plant growth and final yield.

Progress
Field data collection was completed for the 2017 growing season. Productivity zone maps for each location were established. This current growing season we have partnered with Justin Knopf in Gypsum, Andy Winsor in Perry, Matt Everhart in Salina, and Chris Bodenhausen in Muscotah, and Ray Flickner in McPherson. At this point, we did provide to all farmers/cooperators a report from the current growing season with in-season satellite data per field. We are only waiting to receive yield monitor data so we can work during the winter on this to finalize the process for this year project.

Impact
Data related to this project was presented in the KS Soybean tour stop at Muscotah.

View uploaded report

Updated April 13, 2018:
Summary:
Nowadays good agronomical practices demand the adoption of new technologies that deliver better resource efficiency. The objective of this study was to identify and work closely with high-yielding soybean farmers in order to implement Ag precision tools, in this case: satellite imagery. Fields were selected for the 2017 growing season. The study is based on working with the field variation and the selection of three productivity zones outlined according to normalized difference vegetation index (NDVI) values.

Introduction
Vast information about crop health and development can be obtained via characterization of the temporal and spatial variability in the field, for example with the utilization of satellite imagery. Satellite imagery may provide crucial information that could potentially influence the decision-making process related to all farming inputs such as fertilizer, seeding rate, genotype selection, and pesticide application, among others.
The main objectives of this study are to: 1) explore the potential use of satellite imagery to identify productivity zones and evaluate soybean development across the growing season at the on-farm scale, and 2) explore relationships between satellite imagery data and ground-truth based plant traits such as plant growth and final yield.

Procedure
Sites Description
Field sites were established for 2017. Agronomical practices were those suitable per site.
Determination of Productivity Zones
A map defining productivity zones will be elaborated with previous year data for NDVI obtained from satellite imagery.

Outcomes:
Reports were prepared and sent to farmers.

Attached is the final report for this growing season for your field and also a complementary report to show how we use the information and what kind of data we process, in this example, we didn’t find significant differences that mean we don’t have differences between the different row spacing treatments.

About your field:
The present report includes:
- Characterization of the soil type within your field (data gathered from SSURGO)
- Map of the field altitude with LIDAR images obtained with radars
- Maps of the changes in greenness of the crop along the growing season characterized by normalized difference vegetation index (NDVI*), utilizing satellite imagery data with different spatial resolution (Landsat 8-L8- with 30 m x30 m; Sentinel 2-S2- with 10 m x 10 m) throughout the cropping season.

View uploaded report

View uploaded report 2

View uploaded report 3

View uploaded report 4

Final Project Results

This project help identifying on-farm production practices that are blocking yield potential. This information is currently helping other farmers in the region to FINE-TUNE their management practices for closing yield gaps. All project outcomes will be disseminated in diverse research and extension communication outlets to better educate Kansas soybean producers and agri-business professionals in the use of best management practices for maximizing financial returns and preserve the land and water resources under their control. The main outcomes were already presented in the Kansas Soybean Schools for the winter of 2018.

Benefit to Soybean Farmers

Farmers are benefited by working together with Extension professionals to understand the main factors limiting soybean yields at the field-scale.

Performance Metrics

Farmers are better educated in the use of new technologies and the project is helping farmers to fine-tune management practices and improve profits by optimizing the use of inputs based on establishing field management zones.

Project Years

YearProject Title (each year)
2017The Quest of 100 Bushel Soybean: On-Farm Approach