Agriculture production has been found to be the most sensitive sector to climate change. Northeast China (NEC) is one of the world’s major regions for spring maize production and it has been affected by climate change due to increases in temperature and decreases in sunshine hours and precipitation levels over the past few decades. In this study, the CERES-Maize model-v4.7 was adopted to assess the impact of future climatic change on the yield of spring maize in NEC and the effect of adaptation measures in two future periods, the 2030s (2021 to 2040) and the 2050s (2041 to 2060) relative to the baseline (1986 to 2005) under RCP4.5 and RCP8.5 scenarios. The results showed that increased temperatures and the decreases in both the precipitation level and sunshine hours in the NEC at six representative sites in the 2030s and 2050s periods based on RCP4.5 and RCP8.5 climate scenarios would shorten the maize growth durations by (1–38 days) and this would result in a reduction in maize yield by (2.5–26.4%). Adaptation measures, including altered planting date, supplemental irrigation and use of cultivars with longer growth periods could offset some negative impacts of yield decrease in maize. For high-temperature-sensitive cultivars, the adoption of early planting, cultivar change and adding irrigation practices could lead to an increase in maize yield by 23.7–43.6% and these measures were shown to be effective adaptation options towards reducing yield loss from climate change. The simulation results exhibited the effective contribution of appropriate adaptation measures in eliminating the negative impact of future climate change on maize yield.

This study analyzed the climate variability trends in Bahati Sub-County, Kenya for the period 1985 to 2015.The climate parameters used included rainfall onset, rainfall cessations, seasonal rain, annual rainfall, annual mean temperature, maximum and minimum temperature. Data on rainfall and temperature was collected from Nakuru Meteorological Station. The strength and direction of the relationship between rainfall and temperature was determined using SPSS software version 23 (correlation analysis) and Microsoft Office Excel 2010 to generate frequency tables, pie charts, graphs and moving averages. The findings revealed that there is a significant, negative and strong relationship between annual rainfall trend and maximum temperature (r= -0.716, p = 0.000). However, the study findings also revealed that the relationship between annual rainfall and minimum temperature had an insignificant, weak and negative relationship (r= 0.151, p= 0.418).