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.
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
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.