scholarly journals Plant density effect of organic eggplant9Solamun melongena L) on yield, biomass development and soil loss prediction

1939 ◽  
Vol 62 (4) ◽  
pp. 378-391
Author(s):  
Sixto Marquex ◽  
◽  
Keyword(s):  
Soil Loss ◽  
Plant Density ◽  
Density Effect ◽  
Loss Prediction

scholarly journals Management of erosion hazard with the agro technology in watershed aie limau kambiang on the upper watershed tarusan

2018 ◽  
Vol 229 ◽  
pp. 04016
Author(s):  
Aprisal ◽  
Bambang Istijono ◽  
Reski Permata Sari
Keyword(s):  
Soil Erosion ◽  
Random Sampling ◽  
Soil Loss ◽  
Plant Density ◽  
Surface Flow ◽  
Soil Samples ◽  
Steep Slope ◽  
Soil Infiltration ◽  
Infiltration Capacity ◽  
Soil Loss Equation

One of the hazards of the erosion is soil infiltration capacity is decreased in the place of occurrence of erosion and increasing the volume of surface flow. It will also lead to the occurrence of the superficiality of the river due to the deposition of materials of soil erosion. These hazards need alternative agrotechnology which could reduce the rate of soil erosion. This research is to know the hazard of soil erosion in the upper watershed of the Aie Limau Kambiang and find out the alternative agrotechnology for reducing the soil erosion. This research was conducted. Soil samples collected was taken in purposive random sampling based on a unit of land. The data were analyzed using the universal soil loss equation. The research results of the largest erosion threat come from the land use of traditional gardens and plant density is low. The highest erosion 151,012.00 ton/ha/year was founded on the plantation blended that have a steep slope over 35% LS value of 9.5. The better of agrotechnology with increasing plant density, that could reduce erosion to 503.40 ton/ha/year. This means that the hazard of soil erosion could be controlled with land management and selected of the better agrotechnology.

SOIL EROSION LOSSES FROM WINTER RUNOFF IN SOUTHERN ONTARIO

1981 ◽  
Vol 61 (2) ◽  
pp. 451-454 ◽  
Author(s):  
L. J. P. VAN VLIET ◽  
G. J. WALL
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Prediction Models ◽  
Universal Soil Loss Equation ◽  
Southern Ontario ◽  
Winter Runoff ◽  
Loss Prediction ◽  
Runoff And Soil Loss ◽  
Soil Loss Equation

Soil loss prediction models such as the universal soil loss equation do not usually reflect the influence of snowmelt events on annual soil loss estimates. Plot studies (2% and 6% slopes) conducted over three winters in Southern Ontario to measure runoff and soil loss from spring-plowed corn crops revealed that winter soil erosion losses represented up to 10% of annual soil loss.

Evaluating Irregular Slopes for Soil Loss Prediction

1974 ◽  
Vol 17 (2) ◽  
pp. 0305-0309 ◽  
Author(s):  
G. R. Foster ◽  
W. H. Wischmeier
Keyword(s):  
Soil Loss ◽  
Loss Prediction

Toward a framework for runoff and soil loss prediction using GUEST technology

Soil Research ◽  
1997 ◽  
Vol 35 (5) ◽  
pp. 1191 ◽  
Author(s):  
B. Yu ◽  
C. W. Rose ◽  
C. A. A. Ciesiolka ◽  
K. J. Coughlan ◽  
B. Fentie
Keyword(s):  
Soil Loss ◽  
Field Data ◽  
Rainfall Intensity ◽  
Rainfall Amount ◽  
Balance Model ◽  
Erosion Processes ◽  
Total Runoff ◽  
Physically Based ◽  
Loss Prediction ◽  
Runoff Rate

In recent years, a number of physically based models have been developed for soil loss predictions. GUEST is one such model based on fundamental physical principles and the current understanding of water erosion processes. GUEST is mainly used to determine a soil erodibility parameter. To apply the model in a predictive mode, the model is simplified in a physically meaningful manner for flow-driven erosion processes, and 2 essential hydrologic variables are identified, namely total runoff amount and an effective runoff rate. These variables are required to determine soil loss for individual runoff events. A simple water balance model was developed and used to predict runoff amount from rainfall amount. The efficiency of this runoff amount model in prediction was over 90% using field data. A 1-parameter regression model (r2 ~ 0·9) for the effective runoff rate was also established which uses peak rainfall intensity in addition to rainfall and runoff amounts. The prediction of peak rainfall intensity for a given rainfall amount and storm type was also sought. The field data were from Goomboorian, near Gympie, in south-east Queensland and these data were used to test and validate both models. Results overall are satisfactory and the approach adopted is promising. A framework for soil loss prediction is established within which individual parts can be further refined and improved.

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