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The objective of this study was to explore the heterosis of partial interspecific hybrids between African rice (Oryza glaberrima Steud.) and Asian rice (Oryza sativa L.) and its correlation with genetic distance. Sixty‐nine rice accessions including 57 introgression lines (ILs) were tested for population structure and genetic distance. Forty‐nine crosses between ILs and a test variety, Shengtai1 (ST1), were evaluated for midparent heterosis of nine agronomic traits. Population structure, genetic distance, and the proportion of O. glaberrima genome (PGG) of ILs and heterosis of the test crosses were estimated and analyzed. The PGG of the ILs ranged from 1.22 to 49.71%, with an average of 15.29%. The genetic distance between parents of the tested crosses was positively correlated to the PGG of ILs (r = .95, P < .01). Positive heterosis was scored on plant height (12.23%), panicles per plant (19.97%), panicle length (10.37%), spikelets per panicle (24.06%), thousand‐grain weight (14.00%), length/width ratio of grain (0.03%), and grain yield per plant (60.77%) of the partial interspecific hybrids. Although the introgression of more African genomic genes could effectively increase the genetic distance between ILs and the test variety that lead to positive heterosis for most agronomic traits, it could also increase the possibility of the occurrence of interspecific sterility that might decrease the seed setting rate and result in negative heterosis (−2.57%). These data indicated that the introgression of African rice genes into the genome of Asian rice could effectively broaden the genetic diversity, and the deployment of ILs as parents could be a potential way for exploiting interspecific heterosis in rice.

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.