Much older soils data and soils
information lies idle in libraries and archives and is largely unused,
especially in developing countries like Kenya. We demonstrated the usefulness
of a stepwise approach to bring legacy soils data ‘back to life’ using the 1980
<i>Reconnaissance Soil Map of the Busia Area</i>
<i>(quarter degree sheet No. 101)</i> in
western Kenya as an example. Three studies were conducted by using agronomic
information, field observations, and laboratory data available in the published
soil survey report as inputs to several digital soil mapping techniques. In the first study, the agronomic
information in the survey report was interpreted to generate 10 land quality
maps. The maps represented the ability of the land to perform specific
agronomic functions. Nineteen crop suitability maps that were not previously
available were also generated. In the second study, a dataset of
76 profile points mined from the survey report was used as input to three
spatial prediction models for soil organic carbon (SOC) and texture. The three
predictions models were (i) ordinary kriging, (ii) stepwise multiple linear
regression, and (iii) the Soil Land Inference Model (SoLIM). Statistically, ordinary
kriging performed better than SoLIM and stepwise multiple linear regression in
predicting SOC (RMSE = 0.02), clay (RMSE = 0.32), and silt (RMSE = 0.10),
whereas stepwise multiple linear regression performed better than SoLIM and
ordinary kriging for predicting sand content (RSME = 0.11). Ordinary kriging
had the narrowest 95% confidence interval while stepwise multiple linear
regression had, the widest. From a pedological standpoint, SoLIM conformed better to the soil
forming factors model than ordinary kriging and had a narrower
confidence interval compared to stepwise multiple linear regression. In the third study, rules generated
from the map legend and map unit descriptions were used to generate a soil
class map. Information about soil distribution and parent material from the map
unit polygon descriptions were combined with six terrain attributes, to
generate a disaggregated fuzzy soil class map. The terrain attributes were
multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom
flatness (MRVBF), topographic wetness index (TWI), topographic position index
(TPI), planform curvature, and profile curvature. The final result was a soil
class map with a spatial resolution of 30 m, an overall accuracy of 58% and a
Kappa coefficient of 0.54. Motivated by the wealth of soil
agronomic information generated by this study, we successfully tested the
feasibility of delivering this information in rural western Kenya using the
cell phone-based Soil Explorer app (<a href="https://soilexplorer.net/">https://soilexplorer.net/</a>). This study
demonstrates that legacy soil data can play a critical role in providing
sustainable solutions to some of the most pressing agronomic challenges
currently facing Kenya and most African countries.<div><p></p></div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11310797 |
| Date | 06 December 2019 |
| Creators | Joshua O Minai (8071856) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Utilization_of_Legacy_Soil_Data_for_Digital_Soil_Mapping_and_Data_Delivery_for_the_Busia_Area_Kenya/11310797 |
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