Assessing Drought, Flood, and High Temperature Disasters during Sugarcane Growth Stages in Southern China

Abstract

As a globally important sugarcane-producing region, Southern China (SC) is severely affected by various agrometeorological disasters. This study aimed to comprehensively assess multiple sugarcane agrometeorological disasters with regards to sugarcane yield in SC. The standardized precipitation evapotranspiration index and the heat degree-days were employed to characterize drought, flood, and high temperature (HT) during sugarcane growth stages in three provinces in SC in the period 1970–2020. Moreover, the relationships between sugarcane climatic yield and disaster intensities were investigated. The results indicated that the most recent decade witnessed the most intensive sugarcane agrometeorological disasters; sugarcane drought and HT intensities significantly (p < 0.05) increased in one and two provinces, respectively. Central and western SC was most drought-prone, while eastern SC was most flood-prone; sugarcane HT was concentrated in southwestern SC. The mature stage exhibited the greatest monthly intensities of drought and flood; the most HT-prone growth stage varied with provinces. The relationships between drought/flood intensity and sugarcane climatic yield were significant in seven districts; the yield-reducing effect of sugarcane flood was more obvious than that of drought. In conclusion, this study provides references for agrometeorological disaster risk reduction for sugarcane in SC.

Cotton flooding and drought analysis regarding growth stages in Hubei, China, using a daily agrometeorological index

“…Hence, it is concluded that the flowering and boll-forming stage is the period during which cotton is most vulnerable to flooding. This conclusion is consistent with the results from previous cotton field experimental studies in which various cotton growth stages were considered and compared [28,48] . The flowering and boll-forming stage is a critical reproductive period for cotton fruit development; during this stage, flooding-induced waterlogging stress not only reduces dry matter accumulation but also reduces the number of cotton bolls [27] , which directly results in a severe reduction in cotton yield.…”

Evaluating the Impacts of Waterlogging Disasters on Wheat and Maize Yields in the Middle and Lower Yangtze River Region, China, by an Agrometeorological Index

“…Moreover, since this study focused on the impacts of waterlogging, the influence of drought should be minimized when characterizing waterlogging intensity and when analyzing the impact of waterlogging on crop yield. To address this concern, this study followed previous practices [14,37] and classified the years considered in every district as 'wet conditions (the top 30%)', 'near-normal conditions (the middle 40%)', and 'dry conditions (the lowest 30%)'; this classification was performed based on annual wheat/maize AHI values. Through this filtering process, the impacts of dry years were generally excluded.…”

“…Then, by examining the correlation coefficients in each region, the impacts that waterlogging disasters have on crop yield in each region were assessed. Furthermore, when the relationships were found to be negative and significant (p < 0.05), it was considered that local waterlogging disasters had a significant negative impact on crop yield during that growth stage [14].…”

“…To date, many of the above indices have been applied to characterize agricultural drought and waterlogging disasters in various regions. For instance, in terms of studies focused on agricultural waterlogging, many scholars have employed commonly used indices such as precipitation, SPI, and SPEI to characterize waterlogging intensities during the growth stages of rice [10], maize [11,12], wheat [13], barley [1], cotton [3], and sugarcane [14], and have further examined the impacts of waterlogging on crop yield variability. However, from the perspective of agricultural waterlogging, even short-term waterlogging events can result in significant crop yield losses [15].…”

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

“…This dataset counts each month's information at a spatial resolution of 0.25 • × 0.25 • , and then forms the input data of this research model after cumulative processing. Soil data are derived from a GLDAS, which was developed jointly by GSFC and NECP in the United States [26,27]; the system has a spatial resolution of 0.25 • × 0.25 • and a temporal resolution of month by month. The selected data are processed by accumulating and averaging to generate the input for the model.…”