A field experiment was carried out to determine the effect of variety and plant spacing on yield and growth of groundnuts. The field experiment was laid in a 3 × 3 factorial experiment in a Randomized Complete Bl...A field experiment was carried out to determine the effect of variety and plant spacing on yield and growth of groundnuts. The field experiment was laid in a 3 × 3 factorial experiment in a Randomized Complete Block Design (RCBD) with three (3) replications. The factor A included three (3) groundnut varieties (Nkatie Sari, Sum Nutt 22 and Yenyawoso) and Factor B was the three (3) spacing of 30 cm × 15 cm, 30 cm × 30 cm and 30 cm × 40 cm. All recommended agronomic practices were followed. Data was collected from eight (8) tagged plants. Growth data were recorded on plant height, number of branches, number of leaves, and the number of flowers while yield data were collected on the number of flowers, number of pods per plant, 100 seeds weight and the pod yield (kg/ha). The plant spacing significantly influenced (P < 0.05) the growth and yield parameters. Groundnut grown at a spacing of 30 cm × 15 cm produced the maximum plant height, whereas the maximum number of leaves, number of branches and number of flowers were produced from 30 cm × 40 cm. Yenyawoso variety with a wider plant spacing performed better vegetatively among all the varieties. The Yenyawoso variety produced the highest number of pods, 100 seeds weight and pod yield as compared to the other varieties. Also, Yenyawoso at 30 cm × 40 cm spacing and Nkatie Sari at 30 cm × 15 cm spacing produced the maximum pod yield.展开更多
Effect of row space and planting density on yield of Miandan No.12 was studied. The test included four row spaces and two planting densities. Specifically, four row spaces were 50, 67, 83 and 100 cm and two planting d...Effect of row space and planting density on yield of Miandan No.12 was studied. The test included four row spaces and two planting densities. Specifically, four row spaces were 50, 67, 83 and 100 cm and two planting densities were 42 000 and 48 000 plant/hm2. The results showed that Miandan No.12 reached the highest yield when row space was 50 cm and planting density was 48 000 plantJhm2 and Miandan No.12 got the lowest yield when row space was 100 cm and planting density was 42 000 plants/hm2.展开更多
The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi-...The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.展开更多
为探索机播荠菜优质高效生产的最佳播量,更好地为荠菜种植机械化生产服务,采用双因素随机区组试验,共设置种砂(荠菜种子+黑金砂)混拌比1∶8、1∶11、1∶14和1∶17四个处理和行距14、21和28 cm 3个处理进行机器条播。结果表明,机播荠菜...为探索机播荠菜优质高效生产的最佳播量,更好地为荠菜种植机械化生产服务,采用双因素随机区组试验,共设置种砂(荠菜种子+黑金砂)混拌比1∶8、1∶11、1∶14和1∶17四个处理和行距14、21和28 cm 3个处理进行机器条播。结果表明,机播荠菜的农艺性状和产量随着种砂比的增加,表现出先增加后减少的趋势;行距与农艺性状和产量间相关性不显著。在12个种砂比和行距互作处理下,以A4B1(种砂比1∶17,行距14 cm)处理下的产量最高,每667 m^(2)产量可达2 230.84 kg。因此,推荐A4B1(种砂比1∶17,行距14 cm)为机播荠菜的最佳播量和行距。展开更多
文摘A field experiment was carried out to determine the effect of variety and plant spacing on yield and growth of groundnuts. The field experiment was laid in a 3 × 3 factorial experiment in a Randomized Complete Block Design (RCBD) with three (3) replications. The factor A included three (3) groundnut varieties (Nkatie Sari, Sum Nutt 22 and Yenyawoso) and Factor B was the three (3) spacing of 30 cm × 15 cm, 30 cm × 30 cm and 30 cm × 40 cm. All recommended agronomic practices were followed. Data was collected from eight (8) tagged plants. Growth data were recorded on plant height, number of branches, number of leaves, and the number of flowers while yield data were collected on the number of flowers, number of pods per plant, 100 seeds weight and the pod yield (kg/ha). The plant spacing significantly influenced (P < 0.05) the growth and yield parameters. Groundnut grown at a spacing of 30 cm × 15 cm produced the maximum plant height, whereas the maximum number of leaves, number of branches and number of flowers were produced from 30 cm × 40 cm. Yenyawoso variety with a wider plant spacing performed better vegetatively among all the varieties. The Yenyawoso variety produced the highest number of pods, 100 seeds weight and pod yield as compared to the other varieties. Also, Yenyawoso at 30 cm × 40 cm spacing and Nkatie Sari at 30 cm × 15 cm spacing produced the maximum pod yield.
基金Supported by National Food High-yield Science and Technology Project(2012BAD04B13-3)~~
文摘Effect of row space and planting density on yield of Miandan No.12 was studied. The test included four row spaces and two planting densities. Specifically, four row spaces were 50, 67, 83 and 100 cm and two planting densities were 42 000 and 48 000 plant/hm2. The results showed that Miandan No.12 reached the highest yield when row space was 50 cm and planting density was 48 000 plantJhm2 and Miandan No.12 got the lowest yield when row space was 100 cm and planting density was 42 000 plants/hm2.
文摘The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.
