Soybean producers have shown interest in gaining a better understanding of how planting dates and cycle length affect soybean yield. Last season, we initiated, and we plan to persist in generating robust experimental data describing those and other interactions that are necessary to understand processes and make management decisions. This data is specific to certain places, with certain soils, and different weather patterns depending on the year. Often we attempt to interpolate between these situations or predict the direction of the yield response.
Even though we are generating good and useful data to make decisions at the field level, it is not enough to predict how the different alternatives in the management practices interact with all the universe of various soils or expected weather conditions experienced by Minnesota farmers. That is where crop simulation models come in. These tools can be useful to compare or predict yield among different alternatives, including crop management, location (soil), and weather (year). In addition, crop models also enable us to conduct a probabilistic or risk analysis for a particular location and management using available weather records (we can use past weather data).
The main problem with these models is that they need to be fine-tuned to match local conditions. In other words, we need to calibrate and validate them to make sure they can accurately predict soybean yield in the conditions we have all along the state. The Naeve Lab has been gathering a lot of information from different projects all over the state, dealing with different management, weather conditions and soil types. We now propose a project to use this information to calibrate and validate a crop simulation models specifically for Minnesota fields.
The goal of this project is to generate a tool capable of predicting soybean yield under specific management (planting date, plant population, inter-row spacing) according to the soil, the tillage, the genotype, and the weather for fields all along Minnesota.