2023
Soybean Phenology Predictor Tool for MN
Category:
Sustainable Production
Keywords:
DiseaseField management Pest
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Seth Naeve, University of Minnesota
Co-Principal Investigators:
Project Code:
10-15-48-23155
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:
The ability to precisely predict the occurrence of the different soybean phenological stages could have great value in a cropping system design and could also be relevant for the effectiveness of operations such as spraying, irrigation, or harvest. Currently there are no tools to predict the occurrence of the different phenological stages for Minnesota farmers. The goal of this project is to generate a tool capable of predicting the occurrence of critical crop stages according to the location, variety maturity, and planting date. Researchers plan to integrate existing soybean phenology models and weather data to generate an app-based tool.
Key Beneficiaries:
#agronomists, #extension specialists, #farmers
Unique Keywords:
#agronomy, #cropping systems, #maturity groups, #soybean varieties, #technology
Information And Results
Project Summary

Management of grain crops such as soybeans requires acute attention to crop development and particularly to the timing and duration of reproductive stages. Successful soybean cultivar selection, for example, must consider the need to complete reproduction within the available growing season. It is also important to minimize stresses at the critical stages in which most of the yield is determined. For soybeans, this critical period, that is highly sensitive to environmental and management conditions, occurs across the R3-R6 stages. Crop management is generally oriented to avoid or reduce stresses and to optimize the crop condition during these critical stages (Monzon et al., 2021). Accordingly, decisions such as planting date and cultivar selection generally consider the available resources during these stages. Irrigation and crop pest protection strategies also may be scheduled based on these relevant crop growth stages. Lastly, crop phenology can be useful for scheduling harvest operations.

The combination of different planting dates, varieties, and in-season weather conditions creates scenarios where any specific reproductive stage may occur during a wide range of calendar dates. For example, for a specific field in MN, a 1.5 MG variety planted at the end of April will usually reach the critical phases and maturity close to two weeks earlier than a 2.2 MG variety planted by June 5th. The ability to precisely predict the occurrence of the different phenological stages could have great value in the design of cropping systems (planting date, maturity rating, etc.), and could also be relevant for the effectiveness of operations such as spraying, irrigation, or harvest. Currently there are no tools to predict the occurrence of the different phenological stages for farmers in Minnesota.

The goal of this project is to generate a tool capable of predicting the occurrence of critical crop stages according to the location, variety maturity, and planting date.

Soybean development depends on the variety (maturity rating) and is controlled by the temperature and daylength that are determined by the location and the planting date (Jones et al., 1991). The way in which these three factors interact on soybean development has been precisely modeled (Boote et al., 1998). The integration of these models with daily temperature and daylength data will allow us to predict the occurrence of specific stages for different locations across MN. Daymet (NASA; https://daymet.ornl.gov/) is a public and validated database that provides long-term, continuous, gridded estimates of daily weather variables including daily minimum, maximum temperature, and day length produced on a 1-km x 1-km gridded surface over continental North America. We propose to integrate existing soybean phenology models and Daymet weather data to generate a tool to predict the phenology of soybean across Minnesota.

Project Objectives

Generate an app-based, online tool to precisely predict the date of specific phenological stages for soybean, depending on the variety maturity rating, the planting date, and the location.

Project Deliverables

A tool to predict soybean crop phenological stages based on planting date, maturity rating, and location (lat; long). Input variables will be the coordinates, maturity ratings, and planting dates.

Progress Of Work

Update:
- We developed and calibrated a model to estimate and predict the occurrence of soybean development. It is able to estimate flowering (R1), beginning of pod set (R3), seed set (R5) and physiological maturity (R7)
- The model has been validated using data previously generated across different states and performed really well even for high latitude locations.
- Currently we are validating the model with more local data that were generated this last season across the state for contrasting maturities and planting dates.

View uploaded report PDF file

Update:
See attached

View uploaded report PDF file

Final Project Results

Benefit To Soybean Farmers

This project will provide a tool to predict the occurrence of different, relevant phenological stages. This tool will be useful to increase the operational capabilities of farmers by offering more precise and effective scheduling of critical operations: fungicide and insecticide applications; irrigation scheduling; field scouting; and harvesting. Benefits and financial returns from application of herbicides, insecticides, fertilizers, and irrigation are influenced by the stage of plant development when they are applied.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.