The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared (MIR) reflection from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable

A soil sampling program for the Netherlands

Soil data users in The Netherlands were inventoried for current and future data needs. Prioritized data needs were used to design the Netherlands Soil Sampling Program (NSSP) as a framework containing 3 groups of related projects: map upgrading, map updating and upgrading of pedotransfer functions. In each one group, the sampling design, performance criteria and optimal sample size were defined. This paper focuses on the upgrading of the existing soil map of The Netherlands at scale 1:50,000, and extensively treats the user inventory and the sampling strategy. The sampling design, performance criteria of the sampling and associated optimal sample size were obtained by statistical analysis of soil data available before the sampling. The Phosphate Sorption Capacity (PSC) was chosen as target variable to optimize sampling, because it dominated total cost per sample. A prior analysis of a performance criterion related to the sampling error of PSC resulted in a cost saving of 13% relative to total cost determined earlier by expert judgment. A posterior analysis showed that the set quality criterion was reached or better in 6 out of 7 cases. The NSSP resulted in a data base with soil data from 2524 sample points selected by stratified random sampling, and a collection of 5764 aliquots taken at these points. The NSSP has been showing its usage potential for various kinds of environmental studies and could be a sound future basis for a national scale monitoring program

Spatial and temporal variability of plant-available soil water in Congo Basin and its relationship with tree species distributions

Regional-scale patterns of tropical rainforest tree composition can be due to climate (rainfall, dry season length), geology and/or soil properties (chemical fertility, available water). In Amazonia, soil fertility and dry season length appears to be the main factor to explain this pattern. However, in the Congo Basin, geology has been proposed to explain the pattern of some commercial timber species. Since the geological substrates of this area have similar chemical properties, we hypothesized that this pattern could be explained by the plant-available soil water (PAW). We used a soil water balance model similar to RisQue in the Congo Basin over the period from 2000 to 2010, with a decade time step, and with a spatial resolution of 8 km. The input parameters of this model were the maximum plant-available soil water (PAWmax), rainfall and evapotranspiration. The output parameter was the maximum number of successive decades when PAW was null, named extreme drought index (EDI). Finally we carried out a map of EDI at Congo Basin scale that we compared with maps of the spatial pattern of 31 commercial species. We showed that Arenosols, as expected, but also other soils like Ferralsols, have the lowest PAWmax of the Congo Basin. We evidenced no or low correlations between the map of EDI and maps of the spatial pattern of each of the 31 commercial species. Other factors, not taken into account in this study, might explain this result like the water table level and variable forest rooting depth in function of soil type. (Résumé d'auteur

Misc. Pub. 85-4

Paper copies in Archives, Acc #:2013-0059Soils have been surveyed in various parts of Alaska to meet resource -development needs since territorial days. These surveys have been conducted and published by the National Cooperative Soil Survey since 1952 and are a joint effort of the United States Department of Agriculture Soil Conservation Service and the Alaska Agricultural Experiment Station. Initially, government agencies were the major users of such soil surveys because land ownership was controlled almost entirely by government agencies. However, the demand for soils and geographic information increased substantially as population increased and urban areas grew following the discovery of oil on the Kenai Peninsula during the 1950s and on the North Slope in the late 1960s. Interest also heightened when the state gained titles to a large portion of land following statehood in 1959. The National Cooperative Soil Survey (NCSS) published many soil surveys for areas of intensive land use or potential land development. These soil surveys often are underutilized or misused. This publication, "Soil Survey and Its Use in Alaska," was developed over three years based on my field reviews of NCSS activities in Alaska as well as on my discussions with users of soil surveys regarding questions and problems arising from using the reports. In this publication, soil surveys and their use in Alaska are reviewed and discussed.Preface -- Introduction: What is Soil? Early Works, Current Status, Table 1: Status of National Cooperative Soil Survey (NCSS) in Alaska (Dec. 1984), National Cooperative Soil Survey -- How Soil Surveys are Made -- How Soils are Classified and Named: Soil Classification, Map Units -- The Use of Soil Survey: Soil: A Valuable Resources, General Resource Planning, Regional Land-Use or Watershed Planning, Community Planning, Agricultural Development, Engineering Interpretation, Environmental Protection, Recreation and Wildlife Management, Other Potential Uses in Alaska -- Problems and Questions About Soil Surveys: Map Scale and Order of Survey, Map Unit Inclusion, Table 2: General guidelines for identifying intensity of soil surveys, Land Capability Classification, Misuse of Soil Surveys, Over-Interpretation of Soil Surveys, Automated Data-Processing in Soil Survey, Taxonomic Unit vx. Map Unit, Soil Survey Report Format, Soil Mapping on the Arctic Slope -- Future Challenges of NCSS in Alaska -- Conclusions -- Reference

A note on the Hybrid Soil Moisture Deficit Model v2.0

peer-reviewedThe Hybrid Soil Moisture Deficit (HSMD) model has been used for a wide range of applications, including modelling of grassland productivity and utilisation, assessment of agricultural management opportunities such as slurry spreading, predicting nutrient emissions to the environment and risks of pathogen transfer to water. In the decade since its publication, various ad hoc modifications have been developed and the recent publication of the Irish Soil Information System has facilitated improved assessment of the spatial soil moisture dynamics. In this short note, we formally present a new version of the model (HSMD2.0), which includes two new soil drainage classes, as well as an optional module to account for the topographic wetness index at any location. In addition, we present a new Indicative Soil Drainage Map for Ireland, based on the Irish Soil Classification system, developed as part of the Irish Soil Information System

The arable farmer as the assessor of within-field soil variation

Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems

Quantification of soil mapping by digital analysis of LANDSAT data

Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class

Comparing soil boundaries delineated by digital analysis of multispectral scanner data from high and low spatial resolution systems

The author has identified the following significant results. Computer-aided analysis techniques used with aircraft MSS data showed that the spatial resolution was sufficient to recognize each soil mapping unit of the test site. Some difficulties occurred where different soil series were intricately mixed, and this mixture showed as a separate spectral mapping unit, or where the difference between two soils depended on the depth of silty surface material. Analysis of LANDSAT data with computer-aided techniques showed that it was not possible to find spectrally homogeneous soil features of the seven soil series on the 40 ha test site on the digital display or on a picture print map. Cluster techniques could be used on an extended test area to group spectrally similar data points into cluster classes

ERTS-1 MSS imagery: Its use in delineating soil associations and as a base map for publishing soils information

ERTS 1 imagery is a useful tool in the identification and refinement of soil association areas and an excellent base map upon which soil association information can be published. Prints of bands 5 and 7 were found to be most useful to help delineate major soil and vegetation areas. After delineating major soil areas, over 4800 land sale prices covering a period of 1967-72 were located in the soil areas and averaged. The soil association then were described as soil association value areas and published on a 1:1,000,000 scale ERTS mosaic of South Dakota constructed using negative prints of band 7. The map is intended for use by state and county revenue officers, by individual buyers and sellers of land and lending institutions, and as a reference map by those planning road routes and cable lines and pipelines

Mapping cacao fertiliser requirements in Côte d'Ivoire

In Côte d'Ivoire, soils in cacao plantations are depleted due to the absence or underuse of fertilisation. A digital map of 130 landunits was created from soil and climatic parameters. A soil diagnosis software was combined with GIS (geographical information system) to convert the current unique fertiliser “Engrais cacao” into a greater number of recommendations more adapted to local conditions, thus more actual. Cacao fertiliser requirements were calculated from soil samples taken in mature cacao plantations in each landunit. The relationships between nutrient requirements and soil chemical parameters enabled building a map of the actual cacao fertiliser recommendations. Soils with identical characteristics were compared regarding their cacao nutritional needs. Highly significant correlations between soil nutrients were found; particularly, Ca and Mg were highly correlated with K, making it possible to calculate the Ca and Mg amounts in fertiliser formulae as function of K (i.e. Ca = 8.5×K and Mg = 3×K). The final map contains 23 N-P-K-Ca-Mg fertiliser formulae. Among them, the currently recommended blanket fertiliser represents 16.5% of the cacao areas. The comparison of our results with a previous study, done 40 years ago, evidenced that the soil nutrients under cacao have significantly decreased over the period, reinforcing the need for fertilizers. (Résumé d'auteur