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Super high-yielding rice (SHYR) (>15 t ha−1) plays a crucial role in global food production and security. We hypothesized that the external environment of different ecological regions could improve biomass accumulation in different periods and thus increase the rice yield. Two SHYR varieties, i.e., Xiangliangyou900 (XLY900) and Yliangyou900 (YLY900), were cultivated in the YONGSHENG and LONGHUI ecoregions, China. The results indicated that the average yield of the two SHYRs in the LONGHUI ecological region was 15.27–15.45 t ha−1 and 18.81–20.10 t ha−1 in YONGSHENG. The high grain yield in the YONGSHENG ecoregion was mainly due to the increased number of spikelets per panicle, crop growth rate, and total biomass during the transplanting–heading stage (TP-HS) and heading–maturity stage (HS-MS), and harvest index. The yield of SHYR was significantly correlated with external environment conditions, i.e., average minimum temperature, average daytime, and night-time temperature, and average daily temperature at the TP-HS, HS-MS, and transplanting–maturity (TP-MS) stages. The rice yield was significantly and positively correlated with the cumulative daily radiation. Therefore, it can be concluded that the final yield of super high-yield rice is closely related to the utilization of temperature and radiation resources during the growth process in the ecological environment. [ABSTRACT FROM AUTHOR]

Abstract: China is one of the most important rice production countries in the world, and maintaining high rice productivity in China is very important for world food security. While previous studies showed that rice production in China has been and will be negatively impacted by global warming, the confounding effects of climatic change, variety improvement and agronomic managements have not been separately investigated. In this paper we combine an analysis of climate and rice growth data with crop modeling to investigate the impact of changes in climate, rice varieties, and agronomic management on rice productivity at four sites (Wuchang, Xinyang, Zhenjiang and Hanyuan) in China. The results showed a significant increase in minimum temperature during all rice growth stages at Wuchang and Zhenjiang, and from heading to maturity at Xinyang, but little change at Hanyuan. Global warming would have led to a reduction in the length of rice growing period and a reduction in grain yield at all study sites, if no varietal changes had occurred. However, the adoption of new rice varieties stabilized growing duration, increased harvest index and grain yield at three of the four sites. In the face of future warming, a planned breeding effort may be needed to offset the negative impact of future climate change. [Copyright &y& Elsevier]

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease. [ABSTRACT FROM AUTHOR]