| Maize (Zea mays L.) is the second-largest food crop in China after rice (China Statistics Yearbook2012), and it plays an important role in agricultural production and in the national economy in the country. However, this production may be affected by climate changes in regional areas. The future prediction indicates that the nationwide annual mean air temperature would increase by1.3-2.1℃in2020and2.3-3.3℃in2050as compared with that in2000. This change may dramatically affect maize production in this part of the world and thus will decrease the world maize production, which will have huge damage on world’s food supply. This thesis discussed the effect of climate change on maize growth and the impact on the predictive production in Hebei Province of China using the Providing Regional Climates for Impacts Studies (PRECIS) climate prediction under SRES A2and B2scenarios, and then proposed some appropriate adaptations and strategies. About9sites were selected and data from the meteorological station in Hebei province were analyzed in this study, baseline1980to2010. The Maize Production Emulation System (MPES) simulation model was used to simulate the maize growth under extreme climate scenarios, which is the propriety of the Agriculture Intelligent Technology Laboratory of the Agricultural Information Institute of Chinese Academy of Agricultural Science. The applicability of MPES model was evaluated in China, and its sensitivity analysis to the weather was checked. The weather forecast data used for this research were derived by PRECIS simulation baseline (1961-1990). The results of the simulation growth using MPES model is a combination of both connectivity verification techniques and simulation analysis of each site in the SRES A2, B2scenarios under2020s,2050s and2070s future weather. Hence, the adjustment type of sowing and cultivation, the introduction of measures to adapt to the new breed to predict better yield were considered. The simulation data was collected from different meteorological stations across Hebei Province area and some from the Nation Statistics Bureau of China. These data include historical observed field information, daily meteorological data and soil parameters data. The data obtained was used to build a database about growth period and simulated yield. The settings and calibration of MPES model were based on further validation and analysis of adaptation and weather sensitivity were established by using the combination of temperature settings, rain and crops parameters. The main results were as follows:1. The sensibility to weather of MPES was obtained by changing the initial weather inputs. Its capacity of climate adaptation was also evaluated by setting up a basis as a model for evaluation of future climate conditions.2. The maize yield has excellent simulation with the Normalized Root Mean Square Error (NRMSE) of9.81%, for the simulation to the growing period of summer maize in most of the sites, apart from a few years’ simulation of growth period day’s deviation. The trends of the simulated and observed values were quite close. The measured data in9locations compared with the simulated gave a correlation coefficient R2of0.70with a confidence level σ=0.01. Simulation performance results were also significant in all sites. Simulation for different years showed wide variation, very high or low depending on the sites performance, but with a small error in the set. The model has shown its effectiveness in most of the study areas.3. The MPES model can reproduce the effect of variations in seed and simulate the best period of growth for better performance. However, some results showed some errors with reasonable limits. The performance of the model expressed by a better applicability of the simulation compared to the observed maize yield can be maximized.4. The MPES model is sensitive to temperature and precipitation. It can provide better approach for future climate changes impacts on maize production. The Sensitivity analyses of genetic parameters have demonstrated that the model is more sensitive to maize growth parameters, which provide a theoretical basis for determining the future direction of maize breeding.5. Observed weather data in Qingxian, Quzhou and7other sites were analyzed and revised to simulate the growth and yield of maize in each site when compared with the measured values. The results showed that the climate model SRES were compatible with the parameters of the model MPES and capacity can be adapted to assess the impact of future climate change.6. Prediction in Luanxian, Shenzhou, Ningji and other6sites under A2and B2scenarios in2070s of different changes in climatic factors were evaluated. In addition, the A2scenario, which provides a higher temperature increase in scenario B2, predicted an increase in precipitation, except for Dahe and Huailai under scenario A2which showed a slight downward trend sites. Overall, most sites showed a tendency to increased precipitation.7. Climate change will lead to a shorter period of maize growth. Maize growth period under A2scenario is longer than that of B2scenario. The predictions in yield provided a tendency to an average of20%decrease in A2scenario in2070s, as mean reduction of B2scenario is11%.8. To adjust appropriately seeding and cultivation, the introduction of new varieties can effectively improve the yield of maize. The management system of agriculture must be improved and the efficiency of fertilizers is necessary to be promoted for future agricultural sustainability. |
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