Update:
Developing strategies that ensure that meal from U.S. soybean seeds is competitive in global markets is essential for producers. Management practices can be an inexpensive and fast-response strategy to accurately match N supply with the crop N demand. However, there is insufficient research documenting the potential of cultural practices to influence protein quantity and quality. Past regional studies and meta-analyses stressed the need of a more mechanistic understanding on the effect of cultural practices on grain protein. In this project we conducted field trials during 2019-2020 evaluating different maturity groups and cultivars across a wide range of environmental conditions (AR, KY, and MN) to: (1) quantify the potential of late season N fertilizer applications and B. japonicum inoculations to improve protein quantity and quality (amino acid and fatty acid profiles), (2) evaluate these practices in soybean grown after fallow or after a winter cereal cover crop, and (3) to quantify if aerial images can be used to detect crop N limitations and adapt inputs. Results from our study provided clear evidence that increasing late-season N availability can increase seed protein concentration, and avoid meal protein concentration below the threshold required by the food industry. Moreover, late-season N applications had a positive effect on yield that would increase net economic returns in some years and locations, making this cultural practice economical for producers. The protein quality, measured from the amino acid composition, was maintained across the different cultural practices evaluated. Spray inoculant applications of B. japonicum at R3 in previously inoculated seed and fields with soybean history did not have an effect on soybean yield or seed composition. Lastly, this project provides exciting new evidence of the potential of aerial imaging to assess in real time crop N status and the effect of cultural practices in soybean. This project generated a dataset across different environments and cultural practices that will be instrumental to improve eco-physiological and process-based soybean models for the simulation of seed protein.