A simplified Excel® algorithm for estimating the least limiting water range of soils
Gorde:
| Egile nagusia: | |
|---|---|
| Argitaratze data: | 2004 |
| Beste egile batzuk: | |
| Formatua: | Article |
| Hizkuntza: | eng |
| Baliabidea: | Repositório Institucional da UnB |
| Download full: | http://repositorio.unb.br/handle/10482/7989 https://dx.doi.org/10.1590/S0103-90162004000600013 |
Gaia: | ABSTRACT: The least limiting water range (LLWR) of soils has been employed as a methodological approach for evaluation of soil physical quality in different agricultural systems, including forestry, grasslands and major crops. However, the absence of a simplified methodology for the quantification of LLWR has hampered the popularization of its use among researchers and soil managers. Taking this into account this work has the objective of proposing and describing a simplified algorithm developed in Excel® software for quantification of the LLWR, including the calculation of the critical bulk density, at which the LLWR becomes zero. Despite the simplicity of the procedures and numerical techniques of optimization used, the nonlinear regression produced reliable results when compared to those found in the literature. __________________________________________________________________________________________ RESUMO |
| Gaia: | ABSTRACT: The least limiting water range (LLWR) of soils has been employed as a methodological approach for evaluation of soil physical quality in different agricultural systems, including forestry, grasslands and major crops. However, the absence of a simplified methodology for the quantification of LLWR has hampered the popularization of its use among researchers and soil managers. Taking this into account this work has the objective of proposing and describing a simplified algorithm developed in Excel® software for quantification of the LLWR, including the calculation of the critical bulk density, at which the LLWR becomes zero. Despite the simplicity of the procedures and numerical techniques of optimization used, the nonlinear regression produced reliable results when compared to those found in the literature. __________________________________________________________________________________________ RESUMO |
|---|