Results 141 to 150 of about 12,029,699 (234)

IGBP-DIS soil data set for pedotransfer function development

, 1996
At the request of the Global Soil Data Task (GSDT) of the Data and Information System of the International Geosphere Biosphere Programme (IGBP-DIS), ISRIC prepared a uniform soil data set for the development of pedotransfer functions.
van Engelen, V.W.P., Engelen, V.W.P., van, Batjes, N.H., Tempel, P.   +3 more
core  

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. [PDF]

PLoS One
Moosavi AA, Nematollahi MA, Omidifard M.
europepmc   +1 more source

Modeling Calcite's Sensitivity to Biogenic CO₂ Production: A Pathway to Soil CO₂ Efflux Partitioning. [PDF]

Environ Sci Technol
Tetteh K, Guggenberger G, Sauheitl L, Zamanian K.   +3 more
europepmc   +1 more source

Comparison of Three Regression Models for Determining Water Retention Curves

Journal of Hydrology and Hydromechanics, 2011
Skalová Jana, Čistý Milan, Bezák Juraj   +2 more
doaj   +1 more source

Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications. [PDF]

Sci Data, 2019
Abbaspour KC, Vaghefi SA, Yang H, Srinivasan R.   +3 more
europepmc   +1 more source

Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils. [PDF]

PLoS One
Mozaffari H, Moosavi AA, Nematollahi MA.
europepmc   +1 more source

Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method

, 2014
Amir Haghverdi, H. Öztürk, W. Cornelis
semanticscholar   +1 more source

Comparison of geostatistical and response surface methodology for estimating soil saturated hydraulic conductivity. [PDF]

Sci Rep
Cheng L.
europepmc   +1 more source

Datasets on bulk density and coarse fragment content from the French soil quality monitoring network. [PDF]

Data Brief
Munera-Echeverri JL, Boulonne L, Saby NPA, Arrouays D, Bertouy B, Lacarce E, Serré F, Toutain B, Millet F, Loiseau T, Martin M.   +10 more
europepmc   +1 more source

A machine learning-coupled APSIM model pipeline for projected oil palm yield in Surat Thani, Thailand. [PDF]

PLoS One
Jantaraprasit N, Promchote P, Wang SS, Daengnui S, Motlagh SK, de Lima Moraes AG, França L, Yoon JH, Phumichai C, Kittipadakul P.   +9 more
europepmc   +1 more source