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Changes Of Heat And Precipitation In Songnen Plain And Their Effects On Crop Yield

Posted on:2020-10-31Degree:MasterType:Thesis
Country:ChinaCandidate:J Y LvFull Text:PDF
GTID:2370330575486508Subject:Crop Cultivation and Farming System
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Songnen Plain is located in Heilongjiang Province and Jilin Province.It is an important commodity grain production base in China.In 2017,the grain crop planting area of the two provinces was 141.4443 million hm2 and 55.4 million hm2,and the output was 74.103 million tons and 41.51 million tons respectively.As an important factor in crop production,climatic conditions are closely related to crop growth and yield formation.Climate conditions Temperature and precipitation changes affect the growth and development of crops.In agricultural production,the physical properties of the soil,including soil temperature,moisture and microbial activity,can be changed by cropping measures to change the growth conditions of the crop.Different farming practices have different adaptability to different regional climatic conditions.Due to the large regional differences in the natural environment and human behavior,global climate change is not completely consistent with regional climate change.In the context of global climate change,there is a lack of systematic research and analysis on the characteristics of heat and precipitation in the Songnen Plain and the impact on crop yield.The adaptability of different farming methods under the climatic factors is still lack of targeted analysis.The characteristics of heat and precipitation changes in the plains and their effects on crop yields,and the choice of farming methods suitable for climate change in the Songnen Plain region are particularly important.This experiment selected 6 meteorological stations in Songnen Plain(Changchun,Harbin,Baicheng,Qiqihar,Helen and Nenjiang).Daily temperature data,monthly precipitation data,and daily evaporation data from 1951 to 2017,using linear tendency estimation method and Man-Kendall Mutation feature test was used to analyze the mutation characteristics of main climate factors in Songnen Plain from 1951 to 2017.The autoregressive moving model(ARIMA model)was used to predict the frost-free period and activity accumulation temperature in the next 10 years.For the past 5 years(2012-2016)The activity accumulated temperature of five cities(Heihe City,Suihua City,Harbin City,Siping City,Shenyang City)and the yield of soybean,corn and rice were estimated by curve regression.In 2017-2018,five tillage treatments were set up at the Xiangyang Experimental Base of Northeast Agricultural University,which were traditional tillage with straw not returning to the field(TT),no-tillage(NT)under no-tillage and no seeding Deep ridge ditch(NTS),deep ridges in the ridges and ridges+deep stalks(SCS)in the seedling stage,straw ploughing back to the field+deep mulch in the seedling stage(DS).The experiment focused on the spring drought and drought years,and explored the changes of soil temperature and water conditions under different straw tillage patterns,and the adaptability of different tillage methods under spring drought and drought conditions.The results showed that:The frost-free period in the Songnen Plain is prolonged,and the linear tendency rate is 2.3d/10a.The final frost day contributes a lot to the frost-free period.The linear tendency rate is-1.9d/10a,and the linear tendency rate after the initial frost is 0.3.d/10a.In 1990,the length of the frost-free period in the Songnen Plain began to change.The number of days of frost-free period in 2001-2017 was 10.2d higher than that in 1951-2000.The accumulated temperature of the Songnen Plain has increased significantly since 1993,and the linear tendency rate has reached 76.7 ?·d/10a.The accumulated temperature of the Songnen Plain in 2001-2017 was 310.2?·d/10a higher than that of 1951-2000.With the rising temperature in the Songnen Plain,the number of high temperature days in the Helen and Nenjiang meteorological stations in the Songnen Plain increased year by year,and increased more than other areas.The precipitation and evaporation in the Songnen Plain fluctuated greatly from year to year,and there were seasonal differences.The order of precipitation in the four seasons is summer>autumn>spring>winter,the linear tendency of annual precipitation in Songnen Plain is-4.1mm/10a;the order of evaporation is from spring to spring.>Summer>Autumn>Winter,the linear tendency of annual evaporation is-12.1 mm/10a.The annual dryness index of the Songnen Plain is 2.2,and the dryness of the four seasons is spring>winter>autumn>summer,which are 7.1,6.2,3.2,and 1.2,respectively.The dryness index of meteorological stations in Baicheng,Qiqihar,Harbin and Changchun is higher than that in Helen and Nenjiang,while the dryness index of Nenjiang meteorological stations is increasing.When the accumulated temperature of activity is ?3800?·d,the yield of soybean and rice increases with the increase of accumulated temperature,and the growth rate decreases gradually.When the accumulated temperature of activity is ?3300?·d,the yield of maize increases with the increase of accumulated temperature,and the increase gradually.Reduced.According to the ARIMA model,the activity accumulated temperature is predicted.In the next 10 years,Changchun and Baicheng are unfavorable for the increase of soybean,corn and rice production due to the decrease in activity accumulated temperature,but the impact is small.The cumulative temperature increase of Harbin,Qiqihar,Helen and Nenjiang is 96.9?.·d,72.1?·d,49.2 ?·d and 25.7 ?·d,is advantageous for the improvement of soybean,corn and rice yield.There was no significant difference in soil water content between NT and NTS treatments during spring drought,which was higher than SCS,TT and DS,respectively.When drought occurred,the soil moisture content under NT,NTS and SCS treatments was not significant,and the average was 3.0%and 4.7%higher than TT and DS.The soil accumulated temperature in DS was significantly higher than other treatments within 10 days after sowing.
Keywords/Search Tags:Songnen plain, Heat, Precipitation, Model prediction, Yield
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