Incorporating Legacy Soil pH Databases into Digital Soil Maps

Baxter, S.J.; Crawford, Deborah

doi:10.1007/978-1-4020-8592-5_27

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…viridis L.), (4) onions (Allium cepa L.), ( 5) tomatoes (Lycopersicon esculentum L.), (6) wetland and upland rice (Oryza glaberrima Steud. ), (7) citrus guava (Psidium guajava L. ), (8) cotton (Gossypium hirsutum L.), ( 9) groundnuts (Arachis hypogaea L.), (10) maize (Zea mays L.), (11) finger millet (Eleusine coracana L.), ( 12) cassava (Manihot esculenta Crantz), (13) common beans (Phaseolus vulgaris L.), ( 14) sunflower (Helianthus annuus L.), (15) Robusta coffee [Coffea canephora var. robusta (L. Linden) A. Chev)], (16) forestry, (17) fodder crops, and (18) areas suitable for grazing.…”

Section: Suitability Class Map For Rainfed Maizementioning

confidence: 99%

“…In addition, information within legacy soil data often consists of spatial distribution of soils, land quality, crop suitability, and geolocated soil profile information with their respective laboratory data, geology, and land use types. This sort of data can be analyzed and used as a primary input for digital soil mapping (DSM), especially for countries with sparse soil data infrastructures (2,(10)(11)(12)(13)(14). In a highly competitive world for resources and funding, rescuing, and utilizing available soil data while identifying gaps in soil information not provided by the legacy data can be mutually beneficial (14).…”

Section: Introductionmentioning

confidence: 99%

“…(iii) Developing the criteria that can be used to assess renewed legacy soil data (15,16). (iv) Using soil data mined from soil survey reports to either map soil properties and/or soil types using digital soil mapping techniques (6,10,13,14,(17)(18)(19)(20)(21). (v) Utilizing legacy soil data to analyze and detect spatio-temporal changes in soil properties (22)(23)(24)(25)(26)(27)(28)(29) and use of the point data to focus on resampling approaches to detect changes in soil properties (30)(31)(32).…”

Section: Introductionmentioning

confidence: 99%

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Renewal of Archival Legacy Soil Data: A Case Study of the Busia Area, Kenya

2022

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Much older soils information, collectively known as legacy soils data lies idle in libraries or in the personal collections of retired soil scientists. The probability is very high for this legacy data to be lost or destroyed. We demonstrate the stepwise process of bringing legacy soils data “back to life” using the Reconnaissance Soil Survey of the Busia Area (quarter degree sheet No. 101) in western Kenya as an example. The first step, site identification, involves meeting and deliberating with key institutions to identify a setting for the study. The second step, data archeology, involves locating and cataloging legacy soil data from key institutions, which often requires numerous site visits and the assistance of individuals familiar with the target data. The third step, data rescue, involves converting paper copies of data into a digital format by scanning the maps, narrative descriptions, and tables, and storing the information in a database. The fourth step, data renewal, consists of bringing the data to modern standards by taking advantage of technological and conceptual advances in geoinformation technology. In our example, the resulting digital (scanned) soil map of the Busia Area is a significant upgrade from the fragile paper map. The fifth step, data interpretation, entails careful interpretation of the soil information available within the legacy soil survey to provide additional agronomic information. This allowed us to produce 10 land quality maps showing the ability of the land to perform specific agronomic functions, and 18 different crop suitability maps that were not previously available. The rescued maps and their associated tabular and narrative data also provide crucial inputs for generating more detailed soil maps using digital soil mapping techniques that were unavailable when the original mapping was conducted.

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Section: Suitability Class Map For Rainfed Maizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Renewal of Archival Legacy Soil Data: A Case Study of the Busia Area, Kenya

2022

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“…Legacy surveys can provide much of this information; reducing the amount of new fieldwork required and allowing for a historical perspective. For example, Baxter and Crawford (2008) used legacy records of soil pH in a DSM exercise. Other examples are from Bui and Moran (2001) and Mayr et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Rescue and renewal of legacy soil resource inventories: A case study of the Limpopo National Park, Mozambique

Cambule

Rossiter

Stoorvogel

et al. 2015

CATENA

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“…An example is given by Baxter and Crawford (2008) who used legacy records of soil pH in a DSM exercise. The potential for legacy data re-use calls for its renewal to meet current demands.…”

Section: Introductionmentioning

confidence: 99%

Assessment of soil organic carbon stocks in the Limpopo National Park: from legacy data to digital soil mapping

Cambule¹

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In 2001, Mozambique declared an area known as "Coutada 16" (hunting zone) the Limpopo National Park (LNP), which forms part of a trans-frontier park with South Africa and Zimbabwe. The park provides ecosystem services and supports the livelihoods of thousands people living in the many communities within its boundaries, which were planned for relocation outside the park. These moves were expected to result in major land use changes, both in terms of vegetation and wildlife, affecting soil quality in and around the LNP, including in resettlement areas. Therefore, this study aimed at estimating soil organic carbon (SOC) stocks in the Limpopo National park as an indicator of livelihoods and ecosystem function. The estimation of SOC stocks was first attempted from legacy data then by both measure-andmultiply and digital soil mapping (DSM) based on a new sampling plan. Along this study issues of legacy data renewal quality, mapping data-poor and poorly-accessible areas, time-consuming and costly traditional method for SOC laboratory determination as well as uncertainty and reliability of SOC stocks estimates from different methods were also investigated. Overall this research provided (1) a guiding framework and quantitative measures for evaluation of renewed legacy survey and as such enabled an informed qualitative and quantitative SOC stocks estimation, (2) a cost-effective methodology for mapping SOC in data-poor, poorly-accessible areas following a DSM approach, (3) a rapid, cost-effect, non-destructive and pollutant-free Near-Infrared a calibration model for the determination of SOC in LNP and, (4) SOC stocks estimates, its uncertainty and reliability. Despite the high uncertainty of the estimates, which limit its use in baseline studies, achieved SOC stocks estimates are, in general, consistent with of SOC stocks estimates in the literature for similar soils in comparable environmental conditions in southern Africa.

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Incorporating Legacy Soil pH Databases into Digital Soil Maps

Cited by 4 publications

References 5 publications

Renewal of Archival Legacy Soil Data: A Case Study of the Busia Area, Kenya

Renewal of Archival Legacy Soil Data: A Case Study of the Busia Area, Kenya

Rescue and renewal of legacy soil resource inventories: A case study of the Limpopo National Park, Mozambique

Assessment of soil organic carbon stocks in the Limpopo National Park: from legacy data to digital soil mapping

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