In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole p...In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole plastic-film ridging-mulching corn seeder on double ridges was designed in this study.The key components of the sample machine was designed and its working parameters of seedbed soil covering device,crosswise-belt soil covering mechanism and profiling sowing depth adjustment device were determined.After numerical simulation on the film edge and crosswise soil covering by whole plastic-film on double ridges by discrete element method,the velocity and displacement of the oscillating plate,and the variation rule of amount of covered soil with time were explored.Field test results show that,when the advancing velocity of the machine was 0.50 m/s,the qualified rate of soil width covered on film edge of the seedbed reached 96.1%,qualified rate of crosswise soil belt width was 94.5%,qualified rate of soil thickness on seedbed was 95.3%,qualified rate of sowing depth was 89.3%,qualified rate of spacing between crosswise soil belts reached 93.6%,which all met related standards in China and satisfied design requirements,and could realize seedbed mechanization of whole plastic-film mulching on double ridges.Comparison tests on working performances of practical soil covering show a basic consistence with the seedbed soil covering simulation,and verified the effectiveness of the soil covering model built by using discrete element method.展开更多
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r...Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.7展开更多
基金The authors acknowledge that this work was financially supported by National Natural Science Foundation of China(Grant No.51775115No.51405086)China Agriculture Research System(CARS-14-1-28).
文摘In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole plastic-film ridging-mulching corn seeder on double ridges was designed in this study.The key components of the sample machine was designed and its working parameters of seedbed soil covering device,crosswise-belt soil covering mechanism and profiling sowing depth adjustment device were determined.After numerical simulation on the film edge and crosswise soil covering by whole plastic-film on double ridges by discrete element method,the velocity and displacement of the oscillating plate,and the variation rule of amount of covered soil with time were explored.Field test results show that,when the advancing velocity of the machine was 0.50 m/s,the qualified rate of soil width covered on film edge of the seedbed reached 96.1%,qualified rate of crosswise soil belt width was 94.5%,qualified rate of soil thickness on seedbed was 95.3%,qualified rate of sowing depth was 89.3%,qualified rate of spacing between crosswise soil belts reached 93.6%,which all met related standards in China and satisfied design requirements,and could realize seedbed mechanization of whole plastic-film mulching on double ridges.Comparison tests on working performances of practical soil covering show a basic consistence with the seedbed soil covering simulation,and verified the effectiveness of the soil covering model built by using discrete element method.
基金Under the auspices of National Natural Science Foundation of China(No.52079103)。
文摘Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.7
