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fsm python tute 6 Texture Processor
In this tutorial, we will make texture zones using the textureProcessor.
This processor uses sand and clay attributes to create texture zones based on the Australian texture triangle.
If your paddocks have been persisted, it's easy to make a texture map. Just be sure to use the same names for sand and clay.
from fsm.processors.textureProcessor import textureProcessor
from fsm.models.gradients import farmSoilMappingGradients
from fsm.graphicsRenderer import GraphicsRenderer
from fsm.configs import GraphicsConfig, TextureZonerConfig
txz = TextureZonerConfig(SAND, CLAY, depth_range=(0, 20))
pbrs = textureProcessor(paddocks, txz)
grad =farmSoilMappingGradients.TEXTURE_ZONES
graphics_config = GraphicsConfig(
attribute="AUS_Textures",
gradient=grad,
output_dir=output_dir,
fit_to_data=True)
gr = GraphicsRenderer(pbrs)
gr.build(graphics_config)Paper: A conditioned Latin hypercube method for sampling in the presence of ancillary information
Minasny, Budiman, and Alex B. McBratney. 2006. “A Conditioned Latin Hypercube Method for Sampling in the Presence of Ancillary Information.” Computers & Geosciences 32 (9): 1378–88.
Paper: Predicting and Mapping the Soil Available Water Capacity of Australian Wheatbelt.
Padarian, J., B. Minasny, A. B. McBratney, and N. Dalgliesh. 2014. “Predicting and Mapping the Soil Available Water Capacity of Australian Wheatbelt.” Geoderma Regional 2-3 (November): 110–18.
Paper (non-journaled thesis): Provision of soil water retention information for biophysical modelling: an example for Australia
Padarian, J. 2014. "Provision of soil water retention information for biophysical modelling: an example for Australia". Provision of soil information for biophysical modelling: 19-50. Faculty of Agriculture and Environment. The University of Sydney
Paper: Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm
Florin, M. J., A. B. McBratney, B. M. Whelan, and B. Minasny. 2011. “Inverse Meta-Modelling to Estimate Soil Available Water Capacity at High Spatial Resolution across a Farm.” Precision Agriculture 12 (3): 421–38.
Paper: Modelling soil attribute depth functions with equal-area quadratic smoothing splines
Bishop, T. F. A., A. B. McBratney, and G. M. Laslett. 1999. “Modelling Soil Attribute Depth Functions with Equal-Area Quadratic Smoothing Splines.” Geoderma 91 (1): 27–45.
Paper: Validation of digital soil maps at different spatial supports
Bishop, T. F. A., A. Horta, and S. B. Karunaratne. 2015. “Validation of Digital Soil Maps at Different Spatial Supports.” Geoderma 241-242 (March): 238–49.
Paper: Boundaryline analysis of fieldscale yield response to soil properties
Shatar, T. M., and A. B. McBratney. 2004. “Boundary-Line Analysis of Field-Scale Yield Response to Soil Properties.” The Journal of Agricultural Science 142: 553.
Paper: A segmentation algorithm for the delineation of agricultural management zones
Pedroso, Moacir, James Taylor, Bruno Tisseyre, Brigitte Charnomordic, and Serge Guillaume. 2010. “A Segmentation Algorithm for the Delineation of Agricultural Management Zones.” Computers and Electronics in Agriculture 70 (1): 199–208.
Paper: Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data
Plain, M. B., B. Minasny, A. B. McBratney, and R. W. Vervoort. 2008. “Spatially Explicit Seasonal Forecasting Using Fuzzy Spatiotemporal Clustering of Long-Term Daily Rainfall and Temperature Data.” https://hal.archives-ouvertes.fr/hal-00298949/.
Paper: Spatiotemporal monthly rainfall forecasts for south-eastern and eastern Australia using climatic indices
Montazerolghaem, Maryam, Willem Vervoort, Budiman Minasny, and Alex McBratney. 2016. “Spatiotemporal Monthly Rainfall Forecasts for South-Eastern and Eastern Australia Using Climatic Indices.” Theoretical and Applied Climatology 124 (3): 1045–63.