Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea.

As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.

Elaborations and Denials in Children's Responses to Yes-No Any/Some Questions in Forensic Interviews.

This study examined 379 4- to 12-year-old children's answers to any/some and other yes-no questions in forensic interviews about sexual abuse (N = 10,041). Yes-no questions that include the terms any/some (e.g., "Did he say anything?") often implicitly ask for elaboration when the answer is yes ("What did he say?"). However, children may give unelaborated responses to yes-no questions, fail to recognize implicit requests, and falsely respond "no." As predicted, children gave more wh- elaborations in response to any/some questions than other yes-no questions, but younger children elaborated less often than older children. Also as predicted, children responded "no" more often to any/some questions than to other yes-no questions, and more often to "any" than to "some" questions. "No" responses were also more common when children were asked potentially vague anything/something questions and else/other/different questions. The results highlight the potential risks of asking children any/some questions.