Quantitative soil - landscape models for the Haldon and Hurunui soil sets, New Zealand

Soil Research ◽  
2005 ◽  
Vol 43 (2) ◽  
pp. 127 ◽  
Author(s):  
Jochen Schmidt ◽  
Phil Tonkin ◽  
Allan Hewitt
Keyword(s):  
New Zealand ◽  
Soil Properties ◽  
Soil Survey ◽  
Fuzzy Classification ◽  
Aerial Photographs ◽  
Soil Profiles ◽  
Land Resource ◽  
Landscape Models ◽  
Soil Landscape ◽  
Landscape Units

Limited resources and large areas of steeplands with limited field access forced soil and land resource surveyors in New Zealand often to develop generalised models of soil–landscape relationships and to use these to produce soil maps by manual interpretation of aerial photographs and field survey. This method is subjective and non-reproducible. Recent studies showed the utility of digital information and analysis to complement manual soil survey. The study presents quantitative soil–landscape models for the Hurunui and Haldon soil sets (New Zealand), developed from conceptual soil–landscape models. Spatial modelling techniques, including terrain analysis and fuzzy classification, are applied to compute membership maps of landform components for the study areas. The membership maps can be used to derive a ‘hard’ classification of land components and uncertainty maps. A soil taxonomic model is developed based on field data (soil profiles), which attaches dominant soil profiles and soil properties, including their uncertainties, to the defined land components. The method presented in this study is proposed as a potential technique for modelling land components of steepland areas in New Zealand, in which the spatial soil variation is dominantly controlled by landform properties. A soil map was developed that includes the uncertainty in the fundamental definitions of landscape units and the variability of soil properties within landscape units.

scholarly journals An expert system for allocating soils to landforms through the application of soil survey tacit knowledge

2005 ◽  
Vol 85 (1) ◽  
pp. 103-112 ◽  
Author(s):  
R. A. MacMillan ◽  
W. W. Pettapiece ◽  
J. A. Brierley
Keyword(s):  
Tacit Knowledge ◽  
Original Data ◽  
Soil Survey ◽  
Process Models ◽  
Rule Base ◽  
Landscape Models ◽  
Soil Landscape ◽  
The Individual ◽  
Relationship Of ◽  
The Relationship

Soil survey is a paradigm-based science that relies heavily on the application of conceptual soil-landscape models, which in turn are based upon tacit pedological knowledge. This tacit knowledge is generally acquired by systematic field observation and recording the relationships between the occurrence of soils and associated landform positions. Soil survey databases identify the types of soils within a delineated area but they do not generally describe the relationship of specific soils with specific landscape positions. A case in point is the recently completed 1:100 000 scale soil landscape database prepared for the agricultural region of Alberta, Canada. In order to utilize this database with various interpretative algorithms a procedure for allocating soils to specific landform positions needed to be developed. The development of this procedure initially involved capturing the local tacit pedological knowledge in a series of tables and programs. The procedure was then applied to the Alberta soil survey database to automatically assign soils to landform positions and then to assign specific slope characteristics to the individual soils. The resulting soil-landform product was more useable than the original data for input to land based process models. Key words: Soil survey, tacit knowledge, soil-landscape modeling, heuristic rule base, predictive mapping

Is pedology dead and buried?

Soil Research ◽  
1997 ◽  
Vol 35 (5) ◽  
pp. 979 ◽  
Author(s):  
L. R. Basher
Keyword(s):  
Soil Properties ◽  
Land Management ◽  
Land Development ◽  
Developed Countries ◽  
General Purpose ◽  
Fund Raising ◽  
Soil Survey ◽  
Sustainable Land Management ◽  
Landscape Models ◽  
Soil Information

Pedology, the field study of soils as natural landscape bodies, has suffered serious cutbacks in stang and funding in many developed countries. Soil survey, a strong focus for pedology, has been most affected by this recession. The cutbacks to pedology reflect the reduction in funding for general purpose soil resource inventories and a decline in central government planning and land development, as well as changing needs for soil information and perceived failure of soil survey to respond by delivering relevant, timely information at affordable cost. A refocusing of research effort in pedology is required to contribute to research into environmental issues of sustainable land management, and global change processes and impacts. The adoption of modern, ecient approaches to collecting, analysing, interpreting and presenting field soil data will improve the fund-raising capability of pedology and enhance its institutional stature. The general purpose paper soil map and soil survey report has largely been superseded as a medium for presenting soil information. Increasingly, it will be replaced by computer-generated, special purpose, interpretive soil maps that are based on soil–landscape models and include more objective, statistically estimated information on soil variability. There is a continuing role for pedology to define the extent, distribution, properties, suitability, and vulnerability of soils as a basis for sustainable land management. There is a need for increasing focus on temporal changes in soil properties, greater attention to soil properties that determine soil functioning and influence soil use, and interpretation of the environmental record contained in soils and regolith.

A soil-landscape model for close-jointed mudstone, Gisborne-East Cape, North-Island, New Zealand

Soil Research ◽  
1995 ◽  
Vol 33 (3) ◽  
pp. 381 ◽  
Author(s):  
M Mcleod ◽  
WC Rijkse ◽  
JR Dymond
Keyword(s):  
New Zealand ◽  
Digital Elevation Model ◽  
Aerial Photographs ◽  
Computer Algorithms ◽  
Landscape Model ◽  
Soil Class ◽  
Soil Group ◽  
Digital Elevation ◽  
Soil Landscape ◽  
Elevation Model

A soil-landscape model, comprising 12 land components at a scale of 1 : 5000, has been developed in Neogene close-jointed mudstone in the Gisborne-East Cape region of the North Island, New Zealand. In a validation, soil order was predicted correctly in 81% of observations, soil group in 80%, soil subgroup in 63% and soilform in 60% of observations. A simplified model based on 11 land components for use at a scale of 1 : 50 000 has also been validated. Here soil order was predicted correctly in 71% of observations, soil group in 73% and soil subgroup in 49% of observations. For application with a digital elevation model (1 : 50 000), the number of land components was amalgamated to five. Here the soil order and soil group were predicted correctly in 63% of observations and soil subgroup in 40% of observations during validation. In all trials, the percentage of correct observations increased if a second choice or subdominant soil class was allowed. It took 2 person-weeks to produce a soil map from the 1 :50 000 form of the model over 400 km2 of steep and hilly country by photo interpretation of stereo aerial photographs, compared with 1 day of applying computer algorithms on the digital elevation model (DEM). The soil-landscape model succinctly relates soil class to land component and it enables improved targeting of farm and planning inputs by empowering existing research into soil fertilizer requirements and soil physical properties.

An expert system to predict intricate saline - sodic subsoil patterns in upland South Australia

Soil Research ◽  
2009 ◽  
Vol 47 (6) ◽  
pp. 602 ◽  
Author(s):  
M. Thomas ◽  
R. W. Fitzpatrick ◽  
G. S. Heinson
Keyword(s):  
Expert System ◽  
Computer Model ◽  
A Priori ◽  
South Australia ◽  
Soil Survey ◽  
A Priori Knowledge ◽  
Environmental Covariates ◽  
Landscape Models ◽  
Soil Landscape ◽  
Priori Knowledge

Digital soil mapping (DSM) offers apparent benefits over more labour-intensive and costly traditional soil survey. Large cartographic scale (e.g. 1 : 10 000 scale) soil maps are rare in Australia, especially in agricultural areas where they are needed to support detailed land evaluation and targeted land management decisions. We describe a DSM expert system using environmental correlation that applies a priori knowledge from a key area (128 ha) soil–landscape with a regionally repeating toposequence to predict the distribution of saline–sodic subsoil patterns in the surrounding upland farming region (2275 ha) in South Australia. Our predictive framework comprises interrelated and iterative steps, including: (i) consolidating a priori knowledge of the key area soil–landscape; (ii) refining existing mentally held and graphic soil–landscape models; (iii) selecting suitable environmental covariates compatible with geographic information systems (GIS) by interrogation via 3D visualisation using a GIS; (iv) transforming the existing soil–landscape models to a computer model; (v) applying the computer model to the environmental variables using the expert system; (vi) performing the predictive mapping; and (vii) validation. The environmental covariates selected include: digital terrain attributes of slope gradient, topographic wetness index and plan curvature, and airborne gamma-radiometric K%. We apply selected soil profile physiochemical data from a prior soil survey to validate mapping. Results showed that we correctly predicted the saline–sodic subsoils in 10 of 11 reference profiles in the region.

Creating and testing a geometric soil-landscape model in dry steeplands using a very low sampling density

Soil Research ◽  
2000 ◽  
Vol 38 (1) ◽  
pp. 101 ◽  
Author(s):  
P. D. McIntosh ◽  
I. H. Lynn ◽  
P. D. Johnstone
Keyword(s):  
New Zealand ◽  
Soil Properties ◽  
Slope Position ◽  
Landscape Model ◽  
Sampling System ◽  
Sampling Density ◽  
Seasonally Dry ◽  
Phosphate Retention ◽  
Soil Landscape ◽  
Nutrient Amounts

The aim of this study was to determine whether a predictive geometric soil-landscape model, potentially applicable to 400 000 ha of seasonally dry greywacke steeplands in New Zealand, could be created for 29 soil properties, using a very low soil sampling density. We postulated that in these deeply dissected steeplands which have relatively uniform geology and slope form, landscape geometry (through its effects on microclimate), rather than vegetation, geology, or slope form will control the soil pattern. To create and test the soil-landscape model we sampled the 26 000 ha Benmore Range, South Canterbury, New Zealand, in a formally stratified way so that trends of soil carbon, soil nutrients, and profile characteristics could be established for predominant slopes, at various altitudes and aspects. We used a factorial sampling system (3 land systems × 3 altitudes × 4 aspects × 2 slope positions), giving 72 sampling sites in total, and a sampling density of one site per 360 ha. Altitude and aspect had significant (P < 0.05) effects on many topsoil characteristics, particularly those likely to be related to soil moisture status, leaching, and weathering (e.g. topsoil pH, carbon, nitrogen, and phosphate retention). For most soil properties the effect of slope position was not significant (P > 0.05). The soil-landscape model was tested by comparing predicted and actual soil properties at a further 22 sites. Soil properties that were laboratory-determined were generally satisfactorily predicted by the model, but properties based on several measurements (e.g. nutrient amounts in units of kg/ha) were less satisfactorily predicted, presumably because they incorporate more measurement error. Trends of soil properties that showed strong altitude and aspect relationships were effectively illustrated using 360° ‘radar diagrams’. We conclude that for dry steeplands of uniform geology, with simple and repeated landforms at the output scale being used, a geometric soil-landscape model based on a very low sampling density successfully predicts soil properties on dominant landscape units. The methodology has application to national resource inventories.

Linking Land Use Capability classes and APSIM to estimate pasture growth for regional land use planning

Soil Research ◽  
2016 ◽  
Vol 54 (1) ◽  
pp. 94 ◽  
Author(s):  
Iris Vogeler ◽  
Rogerio Cichota ◽  
Josef Beautrais
Keyword(s):  
Land Use ◽  
New Zealand ◽  
Land Use Planning ◽  
Management Practices ◽  
Growth Patterns ◽  
Land Resource ◽  
Pastoral Systems ◽  
System Models ◽  
Farm System ◽  
The Impact

Investigation of land-use and management changes at regional scales require the linkage of farm-system models with land-resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (NZLRI) and associated Land Use Capability (LUC) database include estimates of the potential stock-carrying capacity across the country, which can be used to derive estimates of average annual pasture yields. Farm system models and decision support tools, however, require information on the seasonal patterns of pasture growth. To generate such pasture growth curves (PGCs), the Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate PGCs for three regions of New Zealand. Simulated annual pasture yields were similar to the estimates of annual potential pasture yield in the NZLRI spatial database, and they provided information on inter-annual variability. Simulated PGCs generally agreed well with measured long-term patterns of seasonal pasture growth. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand for use in farm system models and for assessing the impact of management practices and climate change on the regional sustainability.

Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe

2017 ◽  
Vol 60 (3) ◽  
pp. 683-692 ◽  
Author(s):  
Yongjin Cho ◽  
Kenneth A. Sudduth ◽  
Scott T. Drummond
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Property ◽  
Soil Strength ◽  
Reflectance Spectra ◽  
Land Resource ◽  
Strength Data ◽  
Soil Sensors ◽  
Combining Data

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

scholarly journals Subsoil microbial community responses to air exposure and legume growth depend on soil properties across different depths

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hongmei Yan ◽  
Fan Yang ◽  
Jiamin Gao ◽  
Ziheng Peng ◽  
Weimin Chen
Keyword(s):  
Microbial Community ◽  
Soil Properties ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Soil Profiles ◽  
Air Exposure ◽  
Structure Variation ◽  
Α Diversity ◽  
Different Depths ◽  
Legume Growth

AbstractAnthropogenic disturbance, such as agricultural and architectural activities, can greatly influence belowground soil microbes, and thus soil formation and nutrient cycling. The objective of this study was to investigate microbial community variation in deep soils affected by strong disturbances. In present study, twelve soil samples were collected from different depths (0–300 cm) and placed onto the surface. We investigated the structure variation of the microbial community down through the soil profiles in response to disturbance originated by legume plants (robinia and clover) cultivation vs. plant-free controls. The high-throughput sequencing of 16S rRNA genes showed that microbial α-diversity decreased with depth, and that growing both plants significantly impacted the diversity in the topsoil. The soil profile was clustered into three layers: I (0–40 cm), II (40–120 cm), and III (120–300 cm); with significantly different taxa found among them. Soil properties explained a large amount of the variation (23.5%) in the microbial community, and distinct factors affected microbial assembly in the different layers, e.g., available potassium in layer I, pH and total nitrogen in layer II, pH and organic matter in layer III. The prediction of metabolic functions and oxygen requirements indicated that the number of aerobic bacteria increased with more air exposure, which may further accelerate the transformation of nitrogen, sulfur, carbon, and pesticides in the soil. The diversity of soil microorganisms followed a depth-decay pattern, but became higher following legume growth and air exposure, with notable abundance variation of several important bacterial species, mainly belonging to Nitrospira, Verrucomicrobia, and Planctomycetes, and soil properties occurring across the soil profiles.

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

Effects of irrigation, dairy effluent dispersal and stocking on soil properties of the Waimate District, New Zealand

2016 ◽  
Vol 7 (1) ◽  
pp. 59-66 ◽  
Author(s):  
B.O. Manono ◽  
H. Moller ◽  
R. Morgan
Keyword(s):  
New Zealand ◽  
Soil Properties ◽  
Dairy Effluent
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