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The low temperature disaster in spring is one of the main agro-meteorological disasters affecting the yield and quality of winter wheat by affecting the development process and physiological function, resulting in yield reduction. In order to clarify the quantitative relationship between spring low temperature stress and winter wheat yield and its components in Huang-Huai-Hai Region, an indicator is constructed based on yield reduction rate. Based on 1924 sets of experimental data and control data in 34 retrieved literatures, effects of low temperature stress on wheat yield and its components at green-up stage, jointing stage, booting stage and heading-flowering stage are analyzed by Meta-analysis. Using the minimum temperature and accumulated cold of the process as identification factors, the critical thresholds of 0, 10% and 30% of yield reduction rate are determined by using the Youden's Index to establish and verify the low temperature disaster grade indicator. Results show that the yield and its components of winter wheat are jointly affected by the intensity and duration of low temperature to different extent in different developmental stages. The yield and all its components decrease significantly under low temperature stress, and the sensitivity of panicle number per plant and grain number per panicle to low temperature stress is greater than that of thousand kernel weight. The low temperature disaster grade indicators are constructed according to the yield reduction rate of (0, 10%], (10%, 30%], (30%, 100%]. Taking the minimum temperature (unit: ℃) of the process as identification factor, ranges for low temperature disaster grade (Ⅰ, Ⅱ, Ⅲ) during the green-up stage are [-5.0, -2.0), [-8.5, -5.0),

Atmospheric gravity waves are one of the important dynamic processes in near space and are widely present in the atmosphere. They play a crucial role in the transfer of energy and momentum between different regions of the atmosphere. The Sun, as the ultimate source of gravity wave energy, significantly influences the intensity of gravity wave disturbances through its activity variations. This paper utilizes data from the Global Navigation Satellite System Occultation Sounder (GNOS) onboard the Fengyun-3C (FY-3C) satellite to invert global stratospheric gravity wave disturbances. It provides the global stratospheric gravity wave distribution from 2015 to 2023, nearly covering one solar activity cycle, and focuses on analyzing the response of gravity waves at different latitudes, altitudes, and wavelengths to the solar activity cycle. We found that short-wavelength gravity waves respond more noticeably to solar activity compared to long-wavelength gravity waves. Through analyzing the intensity of stratospheric gravity wave disturbances across different latitude bands, we found that in high-latitude regions, stratospheric gravity wave disturbances are most sensitive and respond most quickly to variations in solar activity. Furthermore, the Southern Hemisphere exhibits a stronger response to the current year's solar activity changes compared to the Northern Hemisphere. In the mid-latitude and equatorial regions, the response to changes in solar activity intensity is delayed. The correlation gradually strengthens with this lag, reaching a very strong level after a 2-year lag. Additionally, the correlation between the Southern Hemisphere and solar activity is generally higher than that of the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

Drought has brought great potential threat to agriculture, ecology, economy, society and available water resources of China, while accurate drought prediction can help risk management and development of early warning system, and it can reduce the destructive impact of drought. Among many prediction methods, data-driven model is a suitable tool with small data demand and fast development speed. With the development of machine learning, especially deep neural network (DNN), deep learning method shows great ability in drought prediction, and is reported to outperform traditional time series model (e.g. integrated moving average autoregressive model, ARIMA). However, its use needs to be widely estimated, further developed and adjusted for geoscience analysis. The standardized precipitation evapotranspiration index (SPEI) is reported to meet the needs of agricultural drought monitoring and early warning under the background of climate warming. SPEI at 1-, 3- and 6-month time scale are selected as the quantitative description of agricultural drought, and DNN model driven by meteorological and circulation variable is presented to explore the ability of SPEI prediction at the lead time of 1-3 months. The traditional long short-term memory neural network (TLSTM) has been used in drought prediction, which is limited by the quality of prediction factors and noise. Therefore, an improved TLSTM model (ILSTM) is proposed. With highlight of large-scale climate characteristics, a convolution neural network (CNN) module is combined with the ILSMT model. This newly-developed model (CLSTM) can extract circulation information that contributes to the regional drought change, as well as other outputs of prediction model. Evaluation of the drought prediction capabilities in different models is based on the Pearson correlation coefficient, the root mean square error, and the mean absolute error. Results indicate that overall prediction ability of DNN models outperforms the ARIMA model. And the comparative evaluation results among DNN models show that the architecture of the model has an important impact on the prediction performance. The ILSTM model can extract comprehensive information that contributes to future drought change by nonlinear coding of input variables through the full connected layer. When the correlation coefficient can be raised by 0.04-0.25, the root mean square error can be reduced by 0.07-0.32 and the mean absolute error can be reduced by 0.06-0.27 at the validation stage with different lead time comparing with the TLSTM model. Taking advantage of the circulation information as extra inputs to the ILSTM model, the CLSTM model outperform the ILSTM model, when the correlation coefficient can be raised by 0.03-0.44, the root mean square error can be reduced by 0.09-0.33 and the mean absolute error can be reduced by 0.05-0.26. Both results show that deep learning method has great ability in short-term regional climatic drought prediction.

Foxtail millet behaves strong drought resistance, but its photosynthesis process and grain yield are restricted by drought. The effects of water stress/rewatering on photosynthetic characteristics and yield of foxtail millet are studied through field water control experiment at booting and flowering stage and grain filling stage. The restraint factor on photosynthesis rate and the follow-up impact on grain yield are expounded, which may provide guidance for foxtail millet grain yield assessment and field water management under drought condition. The results are as follows: Water stress can lead to the decrease of photosynthetic rate and grain yield of characterized by larger decrease amplitude with the increase of stress intensity and duration, and increasing water use efficiency is the main survival strategy. The effect of water stress/rewatering on foxtail millet yield at grain filling stage is more obvious than that at booting and flowering stage, characterized by 22.1% production loss under 14-day mild water stress and 47.1% for FH-21 treatment group. After rewatering, photosynthesis performs compensation effect, and the photosynthetic capacity is recovered to some extent. The recovery of photosynthesis ability is lower when the the water stress intensity is stronger and the water stress duration is longer, and the recovery of photosynthetic capacity at grain filling stage is weaker than that at booting and flowering stage. Under mild and short duration water stress, the decrease of photosynthetic rate is mainly determined by stomatal factors, and the non-stomatal restriction gradually becomes the main cause for the decrease of photosynthetic rate with the increase of water stress intensity and the extension of duration. Mild negative effects of water stress on grain yield for 7 and 14 days, and severe effects for 7 days can be partially offset by the compensating effect after rewatering to some extent at booting and flowering stage, so the final effect on grain yield is not significant. In comparison, the effect of water stress on photosynthetic rate is larger, and the recovery of photosynthetic is not as good after rewatering at the grain filling stage. The formation of grain yield is more sensitive to water stress at the grain filling stage, for the closer synergistic relationship between photosynthetic rate and ultimate grain yield. The results reveal that a mild or short-term water deficit can be made up by rewatering, when the water use efficiency and photosynthesis rate will rise, and stable foxtail millet grain yield can be obtained. This can improve sustainable development by allowing deficit irrigation and water-saving agricultural practices. These critical information for optimizing water management practices is beneficial for foxtail millet sustainable development, particularly under warmer and drier climate in the future in the northern China.

Along with climate warming, extreme heat events have become more frequent, severe, and seriously threaten rice production. Precisely evaluating rice heat levels based on heat duration and a cumulative intensity index dominated by temperature and humidity is of great merit to effectively assess regional heat risk and minimize the deleterious impact of rice heat along the middle and lower reaches of the Yangtze River (MLRYR). This study quantified the response mechanism of daytime heat accumulation, night-time temperature, and relative humidity to disaster-causing intensity in three categories of single-season rice heat (dry, medium, and wet conditions) using Fisher discriminant analysis to obtain the Heat Comprehensive Intensity Index daily (HCIId). It is indicated that relative humidity exhibited a negative contribution under dry heat, i.e., heat disaster-causing intensity increased with decreasing relative humidity, with the opposite being true for medium and wet heat. The Kappa coefficient, combined with heat duration and cumulative HCIId, was implemented to determine classification thresholds for different disaster levels (mild, moderate, and severe) to construct heat evaluation levels. Afterwards, spatiotemporal changes in heat risk for single-season rice through the periods of 1986–2005, 2046–2065 and 2080–2099 under SSP2-4.5 and SSP5-8.5 were evaluated using climate scenario datasets and heat evaluation levels carefully constructed. Regional risk projection explicitly revealed that future risk would reach its maximum at booting and flowering, followed by the tillering stage, and its minimum at filling. The future heat risk for single-season rice significantly increased under SSP5-8.5 than SSP2-4.5 in MLRYR. The higher risk would be highlighted in eastern Hubei, eastern Hunan, most of Jiangxi, and northern Anhui. As time goes on, the heat risk for single-season rice in eastern Jiangsu and southern Zhejiang will progressively shift from low to mid-high by the end of the twenty-first century. Understanding the potential risk of heat exposure at different growth stages can help decision-makers guide the implementation of targeted measures to address climate change. The proposed methodology also provides the possibility of assessing other crops exposure to heat stress or other extreme events. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of low-temperature (LT) stress during the jointing stage on three most representative wheat varieties in the Huang-Huai-Hai region: "Shannong 38" (a robust winter wheat variety), "Jimai 22" (a semi-winter wheat variety), and "Zhenmai 12" (a weak winter wheat variety). The objective was to explain the sensitivity and change thresholds of various physiological and yield indicators of three winter wheat varieties to low temperatures during the jointing stage and to construct an index for the grading of LT disasters during the jointing stage using principal component analysis. Controlled environment experiments were conducted using an artificial climate chamber during the jointing stage of winter wheat. Five daily minimum temperature treatments were applied, namely (T1, −6 °C); (T2, −3 °C); (T3, 0 °C); (T4, 3 °C); (T5, 6 °C); and control (CK, 8 °C). The duration of treatments was divided into three levels: (D1: 2 days), (D2: 4 days), and (D3: 6 days). It was found that the photosynthetic parameters and chlorophyll content showed a decreasing trend with the increase in the degree of LT stress. The activities of protective enzymes and endogenous hormones increased during the early stages of LT stress or at relatively high temperatures. However, they decreased significantly with an increase in LT stress. Among the varieties, "Zhenmai 12" exhibited a yield reduction rate exceeding 10% under 3 °C LT stress for more than 4 days and a yield reduction rate exceeding 20% under 0 °C LT stress for more than 6 days. "Jimai 22" showed a yield reduction rate exceeding 10% under 3 °C LT stress lasting more than 2 days, and a yield reduction rate exceeding 20% under −3 °C LT stress lasting more than 4 days. "Shannong 38" experienced a yield reduction rate exceeding 10% under 0 °C LT stress lasting more than 4 days and a yield reduction rate exceeding 20% under −6 °C LT stress lasting more than 6 days. Principal component analysis (PCA) conducted on all trait indicators of the three winter wheat varieties revealed that "Zhenmai 12" experienced mild LT stress at 6 °C for 2 days, moderate LT stress at 0 °C for 6 days, and severe LT stress at −3 °C for 6 days. "Jimai 22" experienced mild LT stress under 6 °C for 6 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 2 days. "Shannong 38" experienced mild LT stress under 3 °C for 4 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 6 days. [ABSTRACT FROM AUTHOR]

As global warming continues, heat stress events are expected to increase and negatively affect rice production. Spatiotemporal changes in single-season rice exposure to heat stress were explored along the middle and lower reaches of the Yangtze River (MLRYR) in China during 1971–2020 based on created heat thresholds in different phenological stages, derived from comparison of historical heat records for single-season rice and agro-meteorological data. The feature importance (IF) provided by the Random Forest model was used to modulate the relationship between threshold accumulated temperature and yield reduction rate caused by heat stress. In addition, critical temperature thresholds at different phenological stages were determined by combining Overall Accuracy and the Receiver Operating Characteristic (ROC) curve. According to historical disaster records, the heat stress occurred before the reproductive phase (i.e., the tillering–jointing stage) and ended in the filling stage. Critical temperature thresholds of Tmax at tillering–jointing, booting, flowering and filling were quantified as 36, 35, 35 and 38 °C, with higher IF values of 13.14, 10.93, 17.15 and 13.15, respectively. The respective values of Overall Accuracy and the areas under the ROC curve were greater than 0.85 and 0.930, implying that each threshold performed excellently in identifying heat occurrence. Based on the determined critical thresholds, accumulated harmful temperature (Tcum), number of heat days (HD), first heat date (FHD) and last heat date (LHD) were presented to characterize heat exposure. It was clear that Tcum and HD exhibited a north-to-south increasing trend from 1971 to 2020, with the obvious increasing occurrence in most parts of the study region through the period of 2010 to 2020. FHD occurred earlier in most stations except the northeast parts, while LHD ended later in southern MLRYR. Exploring heat critical thresholds at different phenological stages highlighted in this study can help decision-makers monitor and evaluate heat exposure to single-season rice in MLRYR and further develop mitigation strategies to ensure rice production security. [ABSTRACT FROM AUTHOR]