The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
R M Patel - One of the best experts on this subject based on the ideXlab platform.
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evaluation of pedotransfer functions in predicting the soil water contents at Field Capacity and wilting point
Agricultural Water Management, 2004Co-Authors: J Givi, Shiv O Prasher, R M PatelAbstract:Abstract Thirteen pedotransfer functions (PTFs), namely Rosetta PTF, Brakensiek, Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, Mayr-Jarvis, Campbell, EPIC, Manrique, Baumer, Rawls–Brakensiek, Vereecken, and Hutson were evaluated for accuracy in predicting the soil moisture contents at Field Capacity (FC) and wilting point (WP), of fine-textured soils of the Zagros mountain region of Iran. PTFs were developed using the laboratory measurements made on soil moisture at FC and WP, particle-size distribution, bulk density, and organic matter content. PTFs were evaluated on the basis of mean-squared deviation (MSD) between the observed and predicted values. Results agreed with the concept that the PTFs developed on soils of similar properties to the ones under study generally perform better than the others. In the case of the Zagros mountain soils, the “British Soil Survey” and “Brakensiek” PTFs were found to be the best methods. Since the soils under study had a wide range of organic matter contents (0.2–5.5%), the better performance of these PTFs may also be explained by the fact that they happen to be the only ones that require organic matter content as input. Rosetta, a software package that involves an artificial neural network approach, was of intermediate value in estimating soil moistures of the soils in question. This was attributed to the fact that the texture and the bulk density of the Zagros soils were not in the range of those used to develop Rosetta.
A. Sessiz - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of wheat stubble management practices in terms of the fuel consumption and Field Capacity
Research in Agricultural Engineering, 2016Co-Authors: S. Gürsoy, B. Kolay, Ö. Avşar, A. SessizAbstract:Five wheat stubble management practices i.e. chopping the stubble by the chopper mounted on combine during harvest and transmitting the straw to trailer (SCDF), chopping the stubble by the chopper mounted on combine during harvest and spreading the straw to Field surface (SCDS), chopping the stubble on Field surface after harvest by chopper mounted on combine and transmitting the straw to trailer (SCAF), leaving the stubble on Field surface (SLS) and removing the stubble left on Field surface by baling (SSB) and the cutting height of combine header (10 and 20 cm) were evaluated in terms of fuel consumption and Field Capacity. The result of the studies showed that the cutting height of header was increased from 10 to 20 cm, the Field Capacity increased from 1.195 to 1.365 ha/h and the fuel consumption decreased from 54.472 to 38.859 l/ha. While the highest Field Capacity was determined in SLS (1.846 ha/h), SCAF and SSB treatments had the lowest Field Capacity (0.954 and 0.891 ha/h, respectively). Chopping the stubble by chopper mounted on combine and transmitting straw to trailer during harvest increased the fuel consumption of combine by 3.6 times.
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Evaluation of wheat stubble management practices in terms of the fuel consumption and Field Capacity.
Research in Agricultural Engineering, 2016Co-Authors: S. Gürsoy, B. Kolay, Ö. Avşar, A. SessizAbstract:Gursoy S., Kolay B., Avsar O., Sessiz A. (2015): Evaluation of wheat stubble management practices in terms of the fuel consumption and Field Capacity . Res. Agr. Eng., 61: 116–121.
J Givi - One of the best experts on this subject based on the ideXlab platform.
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evaluation of pedotransfer functions in predicting the soil water contents at Field Capacity and wilting point
Agricultural Water Management, 2004Co-Authors: J Givi, Shiv O Prasher, R M PatelAbstract:Abstract Thirteen pedotransfer functions (PTFs), namely Rosetta PTF, Brakensiek, Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, Mayr-Jarvis, Campbell, EPIC, Manrique, Baumer, Rawls–Brakensiek, Vereecken, and Hutson were evaluated for accuracy in predicting the soil moisture contents at Field Capacity (FC) and wilting point (WP), of fine-textured soils of the Zagros mountain region of Iran. PTFs were developed using the laboratory measurements made on soil moisture at FC and WP, particle-size distribution, bulk density, and organic matter content. PTFs were evaluated on the basis of mean-squared deviation (MSD) between the observed and predicted values. Results agreed with the concept that the PTFs developed on soils of similar properties to the ones under study generally perform better than the others. In the case of the Zagros mountain soils, the “British Soil Survey” and “Brakensiek” PTFs were found to be the best methods. Since the soils under study had a wide range of organic matter contents (0.2–5.5%), the better performance of these PTFs may also be explained by the fact that they happen to be the only ones that require organic matter content as input. Rosetta, a software package that involves an artificial neural network approach, was of intermediate value in estimating soil moistures of the soils in question. This was attributed to the fact that the texture and the bulk density of the Zagros soils were not in the range of those used to develop Rosetta.
Anna Förster - One of the best experts on this subject based on the ideXlab platform.
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PEMWN - Artificial Neural Network based Soil VWC and Field Capacity Estimation Using Low Cost Sensors
2018 IFIP IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), 2018Co-Authors: Idrees Zaman, Nandit Jain, Anna FörsterAbstract:Volumetric Water Content (VWC) is used for determining the Field Capacity of any soil. Sensor nodes equipped with VWC and temperature sensor are deployed underground to understand the properties of soil for agricultural activities. The cost of VWC sensors becomes the bottleneck in the deployment of these sensors over a large Field. In this paper, we analyze the use of low-cost moisture and temperature sensors to estimate the VWC values and Field Capacity of any Field using machine learning techniques i.e. Artificial Neural Network and Random Forests.
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Artificial Neural Network based Soil VWC and Field Capacity Estimation Using Low Cost Sensors
2018 IFIP IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), 2018Co-Authors: Idrees Zaman, Nandit Jain, Anna FörsterAbstract:Volumetric Water Content (VWC) is used for determining the Field Capacity of any soil. Sensor nodes equipped with VWC and temperature sensor are deployed underground to understand the properties of soil for agricultural activities. The cost of VWC sensors becomes the bottleneck in the deployment of these sensors over a large Field. In this paper, we analyze the use of low-cost moisture and temperature sensors to estimate the VWC values and Field Capacity of any Field using machine learning techniques i.e. Artificial Neural Network and Random Forests.
Shiv O Prasher - One of the best experts on this subject based on the ideXlab platform.
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evaluation of pedotransfer functions in predicting the soil water contents at Field Capacity and wilting point
Agricultural Water Management, 2004Co-Authors: J Givi, Shiv O Prasher, R M PatelAbstract:Abstract Thirteen pedotransfer functions (PTFs), namely Rosetta PTF, Brakensiek, Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, Mayr-Jarvis, Campbell, EPIC, Manrique, Baumer, Rawls–Brakensiek, Vereecken, and Hutson were evaluated for accuracy in predicting the soil moisture contents at Field Capacity (FC) and wilting point (WP), of fine-textured soils of the Zagros mountain region of Iran. PTFs were developed using the laboratory measurements made on soil moisture at FC and WP, particle-size distribution, bulk density, and organic matter content. PTFs were evaluated on the basis of mean-squared deviation (MSD) between the observed and predicted values. Results agreed with the concept that the PTFs developed on soils of similar properties to the ones under study generally perform better than the others. In the case of the Zagros mountain soils, the “British Soil Survey” and “Brakensiek” PTFs were found to be the best methods. Since the soils under study had a wide range of organic matter contents (0.2–5.5%), the better performance of these PTFs may also be explained by the fact that they happen to be the only ones that require organic matter content as input. Rosetta, a software package that involves an artificial neural network approach, was of intermediate value in estimating soil moistures of the soils in question. This was attributed to the fact that the texture and the bulk density of the Zagros soils were not in the range of those used to develop Rosetta.