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.