Global warming is expected to affect yield-determining factors of soybean (Glycine max (L.) Merr.), including the number of flowers and pods. However, little is known about the effects of high temperature on the temporal patterns of flowering and pod set. Experiments in the temperature-controlled greenhouses were conducted to examine the temporal pattern of flowering in determinate soybean cultivar “Sinpaldalkong” and to assess the effects of high temperature on the flower number, pod-set ratio, and pod number of the early- and late-opened-flowers and their contributions to overall pod number. The experiment comprised five sowing dates in 2013–2015 and four temperature treatments, namely ambient temperature (AT), AT + 1.5 °C, AT + 3.0 °C, and AT + 5.0 °C. Flowering duration (i.e., days between the first flowering and the last flowering) was extended by higher temperature and earlier sowing. The temporal distribution of flowering showed a bimodal distribution except for the experiment with the shortest flowering duration, i.e., second sowing in 2014. More flowers were produced in the late flowering period at high temperatures; however, most of these late-opened-flowers failed to reproduce, regardless of temperature conditions, resulting in a negligible contribution to the overall pod number. For the early-opened-flowers, the number of flowers was not significantly affected by temperature, while the pod-set ratio and pod number decreased with high temperatures resulting in a decrease in the overall pod number at temperatures above 29.4 °C.

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

We propose an approach to develop a solar radiation model with spatial portability based on deep neural networks (DNNs). Weather station networks in South Korea between 33.5–37.9° N latitude were used to collect data for development and internal testing of the DNNs, respectively. Multiple sets of weather station data were selected for cross-validation of the DNNs by standard distance deviation (SDD) among training sites. The DNNs tended to have greater spatial portability when a threshold of spatial dispersion among training sites, e.g. 190 km of SDD, was met. The final formulation of the deep solar radiation (DSR) model was obtained from training sites associated with the threshold of SDD. The DSR model had RMSE values <4 MJ m⁻² d⁻¹ at external test sites in Japan that were within ±6° of the latitude boundary of the training sites. The relative difference between the outputs of crop yield simulations using observed versus estimated solar radiation inputs from the DSR model was about 4% at the test sites within the given boundary. These results indicate that the identification of the spatial dispersion threshold among training sites would aid the development of DNN models with reasonable spatial portability for estimation of solar radiation.