Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Abstract

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

“…The summer season was rainy and hot. In contrast, the winter was dry and cold [85]. On a seasonal basis, the Khanpur station during spring and the Khanpur, Multan, Okara, and Sahiwal stations during winter displayed significant (p < 0.05) trends during the period 1979-2014 (Tables 5 and 6).…”

“…The summer weather is hot and somewhat rainy, while the winter season is dry and cold. Because of the monsoons' influence on climatic events, 60-65% of precipitation is concentrated from May to September [85]. This type of behavior was due to extreme precipitation patterns and indicates that uneven atmospheric events are a reason for the climate variability over the past few years [93].…”

Drought Risk Analysis, Forecasting and Assessment under Climate Change

“…This Special Issue includes 10 peer-reviewed articles covering a wide range of research topics related to drought monitoring, drought forecasting and drought risk analysis in a changing climate. Specific issues include the development of a modified composite drought index (MCDI) and a non-stationary joint drought management index (JDMI) [36,37], climate change influences on drought patterns and crop yields [38,39], meteorological and hydrological drought risk under future climate change predictions [40,41], extreme drought assessment and its relationship with the Indian Ocean dipole (IOD) mode [42], severe drought prediction using atmospheric teleconnection patterns (ATPs) [43], drought forecasting using stochastic models [44] and hydrological drought risk estimations based on changing climate conditions and human activities [45]. These studies use statistical approaches, field measurements and mathematical methodologies.…”

Comprehensive Risk Assessment of High Temperature Disaster to Kiwifruit in Shaanxi Province, China

“…Kendall [ 52 ], and it is an effective tool recommended by the World Meteorological Organization for extracting trends in series variability. The principle of the method and the calculation steps are detailed in [ 53 ].…”