This annotated bibliography is a consolidation of some of the applications of Geographic Information Science and Systems in modern agriculture. The focus is the use of GIS in analyzing and monitoring an agricultural system’s soil fertility.
Note: Many of these articles were translated from other languages, making them difficult to comprehend in some cases.
Glossary of Relevant Terminology:
(Note: these definitions are directly copied from their sources and provided purely for greater understanding of soil science and not to represent my personal work)
Kringing Method: A widely used geostatistical technique for the analysis of spatial correlations and for constructing prediction maps in the field of public health
Pedometrics: the application of mathematical and statistical methods for the study of the distribution and genesis of soils
Spatial Interpolation: the process of using points with known values to estimate values at other unknown points
Anisotropism: characterizes substances that exhibit physical properties with different values
Fuzzy Mathematical Method: based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information.
Useful Acronyms:
AHP: Analytic Hierarchy Process
DSM: Digital Soil Mapping
SBNFM: Soil Basic Niche Fitness Model; refers to the closeness between actual status of soil fertility indicators of a plot and the optimum niche of soil fertility indicators
STCR: Soil Test Crop Response
DSS: Decision Support System
OM: Organic Matter (in soil), often an indicator of soil fertility
SFI: Soil Fertility Index
MCDA: Multi-Criteria Decision Analysis
OWA: Ordered Weighting Average
IDM: Inverse Distance Weighted
ICTs: Information and Communication Technologie
Annotated Bibliography
Lucà, Federica & Buttafuoco, Gabriele & Terranova, O.. (2018). GIS and Soil. 10.1016/B978-0-12-409548-9.09634-2.
https://www.researchgate.net/publication/316326588_GIS_and_Soil
This article highlights the reliance that trained soil scientists and GIS analysts have on each other when creating a GIS. It addresses the processes, such as location, information, soil harmonization, that allow legacy soil data to be used in a GIS without compatibility error. However, accuracy continues to be a challenge in soil landscape analysis, because of the extremely high cost and topographic obstacles of sampling an entire three-dimensional soil body. DSM, with the use of remote sensing, seeks to mitigate these challenges. Digital soil mapping (DSM) relies on the field and laboratory data collected and analyzed by soil scientists combined with known, and often inferred, spatial and non-spatial data. Pedometrics, the science of digitally analyzing soil within a landscape, is the discipline that combines soil science and GIS. While accurate soil mapping continues to provide unsolved challenges, this article provides a thorough overview of some of the gaps that GIS fills in soil landscape analysis.
Vishwakarma, Dinesh & Husain, Mohit & Rathore, Jagdeesh & Sharma, Anil. (2018). APPLICATIONS OF REMOTE SENSING AND GIS IN SOIL SCIENCE.
This paper addresses the three major soil body characteristics that remote sensing can provide: vegetation type and presence, degree of erosion, and slope. Based on these factors, one can even derive a soil’s taxonomy from remote sensing. Historically, obtaining this information is costly and time intensive. Soil samples are generally collected with a small tool similar to a trowel, and are assumed to be a representation of the surrounding area. In order to contextualize the sample, the collector must also observe the surrounding features through the lens of a soil scientist. Assuming homogeneity throughout the soil profile sacrifices accuracy. Remote sensing can replace or compliment this step, potentially providing more accurate information, because the traditional collector is limited to the location of their sample and their observations from where they stand. The article addresses the advantages of using remote sensing to determine a soil body’s attributes, and suggests continued potential for the use of remote sensing in other subsets of soil science, especially crop science.
Xiaolin, Liu, Yang Linnan, Peng Lin, Li Wengfeng, and Zhang Limin. “County Soil Fertility Information Management System Based on Embedded GIS.” Procedia Engineering 29.C (2012): 2388-392. Web.
There is a delay between the newest advancements of GIS and soil science technology and its practical application at the local level. This paper provides a scope into the specific challenges relating to GIS and soil science that a county may face, and the process of solving them. It addresses the need for improvement of the current soil fertility management systems used in Jianshui County in the Yunnan Province, China. The most significant challenge that the county, its policy makers, and its farmers face is the fact that most soil information systems run on a PC. This makes collection and analysis of data in the field nearly impossible.
Xiaolin, Linnan, Lin, Wengfeng, and Limin developed a system with the “class library component-based” method, using ESuperMap, SuperMap Deskpro, Visual Studio 2008, and Windows Mobile 6.5. To create the system, they collected soil spatial data for Jianshui, and created soil nutrient maps using electronic maps of the county and the Kringing spatial interpolation method on SuperMap Deskpro 6. Finally, they adapted the maps to a format recognized by mobile devices. Specifically, raster data much too large to be used on mobile devices, was saved as image data. The final product was a portable data collection and information system that provided thematic maps of over ten soil characteristics. It also provided recommendations for fertilizer application based on customizable factors, such as desired yield and time.
Guangrong Shen, Jingjing Xu, Zhenhua Qian, and Danfeng Huang. “Spatial Analysis and Assessment of Soil Fertility by Using GIS and Kriging Method.” 2010 World Automation Congress (2010): 19-23. Web.
A common challenge in soil sampling and mapping is the inaccuracy that results from sampling one point and assuming homogeneity surrounding that point. Using GIS and geostatistics can mitigate this challenge, especially in precision agriculture. This paper demonstrates how soil fertility can be an indication of the overall quality of soil, because of soil fertility’s interconnectivity with other soil characteristics, such as trace mineral presence and macronutrients. With the focal area of Chongming island, Shanghai, Shen, Xu, Qian, and Huang sought to evaluate agricultural soil fertility using GIS and known geostatistics. They used the Kriging method, the soil single variability Nemoro formula, and the ArcGIS 9.2 Geostatistal Analyst package to estimate spatial interpolation and map soil fertility values. Their findings indicated that this method is accurate enough, given the difference in cost, to warrant replacing traditional soil data collection and analysis methods.
Nie, Yan, Yu, Jing, Peng, Yating, Wu, Yunga, Yu, Lei, Jiang, Yan, and Zhou, Yong. “A Comprehensive Evaluation of Soil Fertility of Cultivated Land: A GIS-Based Soil Basic Niche-Fitness Model.” Communications in Soil Science and Plant Analysis 47.5 (2016): 670-78. Web.
With a growing population, and finite land resources, and the increase in use of chemical fertilizers, soil fertility has been degraded globally. Nie, Yu, Peng, Wu, Yu, Jiang, and Zhou used the Soil Basic Niche-Fitness model combined with GIS to evaluate agricultural soil fertility on Houhu Farm on the Jianghan Plain, Qianjiang, Hubei Province, China. To create the spatial database, they used ArcGIS 9.3 to digitize a soil map, a plot map, and a land-use map, and added the soil sample attributes. The results indicated that overall, soil fertility was favorable for agriculture. GIS, however, allowed areas where soil fertility could be improved, to be identified.
Leena, H. U, B. G Premasudha, and P. K Basavaraja. “Sensible Approach for Soil Fertility Management Using GIS Cloud.” 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016): 2776-781. Web.
This article addresses the need, in India, for improvement of national agricultural technology. Specifically, Leena, Premasudha, and Basavaraja offer a multidisciplinary approach. They suggest including public-sourced GIS cloud DSS, Decision Support System. This would give users access to information about soil fertility with geographic orientation and make suggestions regarding fertilizer application using STCR, Soil Test Crop Response equations. A common theme of modern shortcomings in the agricultural soil science sector is the transition to a multifaceted method, in which GIS plays a major role. In addition, there is a continued need for making information available to local governments, and the individual “Precision Agriculture” farmer.
Rawal, Nabin, Acharya, Keshav Kumar, Bam, Chet Raj, and Acharya, Kamal. “Soil Fertility Mapping of Different VDCs of Sunsari District, Nepal Using GIS.” International Journal of Applied Sciences and Biotechnology 6.2 (2018): 142-51. Web.
In this article, Rawal, Acharya, Bam, and Acharya seek to examine the degree of soil fertility degradation in the Sunsari district of Nepal. Sampling soils from 131 geo-referenced points, they were able to map them using a GIS. They used the Kringing method to determine interpolation. Using ArcGIS 10.1, they produced thematic maps that included attributes such as chemical composition, OM, Nitrogen, etc. The maps are now public and beneficial to individual farmers, local government officials, and students. Again, GIS is used in order to make soil fertility information more readily available and accessible.
Jiang, Qiuxiang. “Comprehensive Evaluation of Soil Fertility Based on GIS and Attribute Recognition Model.” Transactions of the Chinese Society of Agricultural Engineering 25.6 (2009): 76-80. Web.
Jiang Qiuxiang reported on the use of GIS and an attribute recognition model that identified soil entropy variance on the Sanjiang Plain in China. 100 soil entropy samples were taken and assigned several attributes that were added to a GIS to produce a soil fertility map. The modeling allowed Qiuxiang to identify several areas of low phosphorous and potassium. The article highlights that using GIS allows for a nearly immediate representation to be utilized for improvements in agricultural regions of China, such as the Sanjiang Plain.
Akponikpè, P.B.I, J. Minet, B. Gérard, P. Defourny, and C.L Bielders. “Spatial Fields’ Dispersion as a Farmer Strategy to Reduce Agro-climatic Risk at the Household Level in Pearl Millet-based Systems in the Sahel: A Modeling Perspective.” Agricultural and Forest Meteorology 151.2 (2011): 215-27. Web.
This paper highlights a study of millet yield variance with various climatic factors and growing techniques in the Fakara region of Southwest Niger. While the greatest factors affecting yield are soil fertility and annual rainfall, these are already well documented and accounted for. This study challenges a locally adopted hypothesis that asserts that distribution of a single household’s various fields around the village ensures the greatest millet yield. This is likely because this practice ensures that every household has at least some of their fields in high-yield areas, regardless of the year’s climatic factors.
To test this hypothesis, they combined the use of crop simulation models, land tenure data, satellite-obtained precipitation data, the CERES-Millet model, and historical precipitation data into a GIS. Millet fields were cropped by each of the 107 households studied, and this vector data was converted to raster and various climatic attributes were added. With the use of GIS, a model was created, and the previously un-tested “local knowledge” hypothesis was testd and confirmed factual. Once again, a multidimensional approach is the most time and cost effective.
Arif Özyazici, M., Orhan Dengiz, Mustafa Sağlam, Aylin Erkoçak, and Ferhat Türkmen. “Mapping and Assessment-based Modeling of Soil Fertility Differences in the Central and Eastern Parts of the Black Sea Region Using GIS and Geostatistical Approaches.” Arabian Journal of Geosciences 10.2 (2017): 1-9. Web.
Arif, Dengiz, Sağlam, Erkoçak, and Türkmen conducted a soil fertility analysis of the eastern and central Black Sea region. The goal of the study was to create a SFI model specific to the region, using methods typical of larger-scale soil fertility studies. From 3400 soil samples, they were able to input the geospecific points, along with various fertility-indicating attributes, such as OM, chemical composition, etc. Using the geostatistical method, they were able to create a regional SFI map on a much more specific scale than had be created before for the eastern, central Black Sea regions. This is yet another example of using global satellite-retrieved data to study soil fertility at a local level and make that information available to the farmers, policy makers, and other individuals that will utilize it.
Mokarram, Marzieh, and Majid Hojati. “Using Ordered Weight Averaging (OWA) Aggregation for Multi-criteria Soil Fertility Evaluation by GIS (case Study: Southeast Iran).” Computers and Electronics in Agriculture 132.C (2017): 1-13. Web.
A study near the agricultural center of Shiraz, Fars Province, Iran sought to analyze soil fertility and make geospatial soil data more available to regional wheat farmers. With 45 sample points, using MCDA combined with GIS, surveryors were able to classify soil polygons based on observed attributes related to fertility. First, they used the IDW interpolation method to assign values areas in between data points. In order to assess several criteria per polygon, they then used the weighted linear combination and Boolean overlay methods, drawing from the previously-obtained OWA data to provide a framework. The results were several soil fertility maps highlighting different attributes that may inform the decision making of local farmers.
Mokarram, Marzieh, and Majid Hojati. “Using Ordered Weight Averaging (OWA) Aggregation for Multi-criteria Soil Fertility Evaluation by GIS (case Study: Southeast Iran).” Computers and Electronics in Agriculture 132.C (2017): 1-13. Web.
A study near the agricultural center of Shiraz, Fars Province, Iran sought to analyze soil fertility and make geospatial soil data more available to regional wheat farmers. With 45 sample points, using MCDA combined with GIS, surveryors were able to classify soil polygons based on observed attributes related to fertility. First, they used the IDW interpolation method to assign values areas in between data points. In order to assess several criteria per polygon, they then used the weighted linear combination and Boolean overlay methods, drawing from the previously-obtained OWA data to provide a framework. The results were several soil fertility maps highlighting different attributes that may inform the decision making of local farmers.
Alexis, Stervins, Luis García-Montero, G. Hernández, Ana García-Abril, and J. Pastor. “Soil Fertility and GIS Raster Models for Tropical Agroforestry Planning in Economically Depressed and Contaminated Caribbean Areas (coffee and Kidney Bean Plantations).” Agroforestry Systems 79.3 (2010): 381-91. Web.
Alexis, García-Montero, Hernández, García-Abril, and Pastor reported on the design process of a GIS depicting soil fertility of the jaragua-bahoruco-enriquillo biosphere reserve for future land-use decisions. The site is situated on the border of the Dominican Republic and Haiti. Using the Kringing method to fill in the gaps of the data from eighty soil samples, they were able to link multiple soil attributes to sample locations. They created a raster combination and reclassification tool within ArcGIS 9 that helped to analyze soil water pH, OM, and chemical composition. From their models, they calculated that land previously allocated for bean and coffee production, may be more efficiently used for forestry.
Chitdeshwari, Santhi, Radhika, Sivagnanam, Hemalatha, Dey, and Subba Rao. “GPS and GIS BASED Soil Fertility Mapping for Cuddalore District of Tamil Nadu.” Madras Agricultural Journal 104.7-9 (2017): 251. Web.
Seven researchers with the Department of Soil Science &Agricultural Chemistry, Tamil Nadu Agricultural University in Coimbatore, and the Indian Institute of Soil Science (ICAR) in Bhopal sought to analyze the soil distribution of the Cuddalore district of Tamil Nadu. They based their studies on 474 soil samples from 79 villages. Their main attributes of focus were macronutrient content, OM, salinity, and pH. Using ArcGIS 9.3, they created thematic maps the distribution of soil fertility, based on these factors, assigning categories of “high,” “medium,” and “low” fertility. The intention of these maps was to make soil fertility information available to the department of agriculture, fertilizer companies, researchers, government officials, land-use planners, and local farmers.
Miller B, Burras L, Semalulu O, Tenywa M. “Strengthening an indigenous soil classification system using GIS-based mapping of the Buganda catena, Uganda.” Geophysical Research Abstracts. 2018;20:@Abstract EGU2018-3748. Web.
In this study, researchers Miller, Burras, Semalulu, and Yenywa sought to expand upon indigenous soil knowledge of the Buganda catena, Uganda with the use of GIS. Honoring the traditional ecological knowledge of an area, while balancing agribusiness pressure of technological innovation, can present challenges. This study aimed to serve the farmers of the studied area first by giving them greater access to and understanding of their soil quality. The researchers used soil maps drawn by local farmers combined with spatial analysis, in order to map soil fertility, slope, and elevation. The results, however, indicated that more information, specifically regarding stony soil distribution, could be obtained using further spatial analysis in the future.
Munyua, Hilda, Edith Adera, and Mike Jensen. “Emerging Icts And Their Potential In Revitalizing Small-Scale Agriculture In Africa.” Citeseerx.ist.psu.edu. N.p., 2009. Web. 2 Dec. 2019.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.855.2532&rep=rep1&type=pdf
Munyua, Adera, Jensen reported on a study initiated by the International Developmental Research Center on the potential of ICTs in small-scale agriculture in Africa. The technologic advancements in the agricultural sector are funded and accessible by large-scale agriculture and fertilizer companies. This is in part because large-scale food production operations are often more cost-effective than localized small-scale agriculture, excluding global environmental degradation. The goal of this study was to identify the shortcomings of small-scale agriculture in Africa and how ICTs may be implemented. GIS is a major part of these developments, especially as many small-scale agricultural operations transition to precision agriculture methods. In addition, as farmers are able to gain access to new technologies, they may also contribute geospatial data, using a mobile device to take coordinates that may later be utilized in a GIS. The major challenge of implementing universal technologies is providing consistent information and resources to rural areas.
