Problem: Soybean lodging (plants that fall over) results in a 9–34% yield loss. In Japan, there is high demand for indigenous cultivars, and it is often difficult to switch to lodging-resistant cultivars. As a countermeasure against lodging, "pinching", which is pruning the upper part of the trunk when overgrowth is expected, is attracting attention. However, pinching reduces the yield when the risk of lodging is low. Therefore, it is important to determine the risk of lodging before pinching is implemented. Although previous studies have shown that lodging is caused by the effects of main stem length and wind speed, there are still some questions that require clarification, e.g., the growth stages that have a high influence on lodging. Objective: The objective of this study was to analyze the effects of main stem elongation and wind speed on lodging for the establishment of a future method to predict lodging. Methods: We used data obtained from experimental plots with different cultivation management in the years 2018, 2020, and 2021 (n = 32). The cultivar tested was “Miyagishirome”, which is a major cultivar in Miyagi Prefecture and has characteristics that make it easy for lodging. The lodging angles were studied at R3 and R8, and classified the R3 lodging as the “Early lodging” and the R8 lodging as the “Late lodging”. Results: In the multiple regression analysis of “Early lodging”, the main stem length was not significant, only wind speed was significant at the 0.1% level. In the “Late lodging”, the effect of main stem elongation from V6 to R1 was high, and the result of a single regression analysis was R2 = 0.70 (p <0.001). Multiple regression analysis showed that the R2 (R2 = 0.81) was highest in the model with R6 main stem length added as an explanatory variable, and wind speed was not significant. Conclusion: The results indicated that “Early lodging” was strongly affected by wind speed, and “Late lodging” was strongly affected by main stem elongation (especially the main stem elongation from V6 to R1), resulting in more severe lodging. Implication: Prediction of main stem elongation after V6 should be focused for judging the application of pinching as a countermeasure of late lodging.

In soybean, lodging is sometimes caused by strong winds and rains, resulting in a decrease in yield and quality. Technical measures against lodging include “pinching”, in which the main stem is pruned when excessive growth is expected. However, there can be a decrease in yield when pinching is undertaken when the risk of lodging is relatively low. Therefore, it is important that pinching is performed after the future risk of lodging has been determined. The lodging angle at the full maturity stage (R8) can be explained using a multiple regression model with main stem elongation from the sixth leaf stage (V6) to the blooming stage (R1) and main stem length at the full seed stage (R6) as the explanatory variables. The objective of this study was to develop an areal lodging prediction method by combining a main stem elongation model with areal main stem length estimation using UAV remote sensing. The main stem elongation model from emergence to R1 was a logistic regression formula with the temperature and daylight hours functions f (Ti, Di) as the explanatory variables. The main stem elongation model from R1 to the peak main stem length was a linear regression formula with the main stem length of R1 as the explanatory variable. The model that synthesized these two regression formulas were used as the main stem elongation model from emergence to R8. The accuracy of the main stem elongation model was tested on the test data, and the average RMSE was 5.3. For the areal main stem length estimation by UAV remote sensing, we proposed a soil-adjusted vegetation index (SAVIvc) that takes vegetation cover into account. SAVIvc was more accurate in estimating the main stem length than the previously reported vegetation index (R2 = 0.78, p < 0.001). The main stem length estimated by the main stem elongation model combined with SAVIvc was substituted into a multiple regression model of lodging angle to test the accuracy of the areal lodging prediction method. The method was able to predict lodging angles with an accuracy of RMSE = 8.8. These results suggest that the risk of lodging can be estimated in an areal manner prior to pinching, even though the actual occurrence is affected by wind.