INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 7, No 1, 2016

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 7, No 1, 2016 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Land suitability study for rice growing in Kisumu county Ronald Owino Abach, Moses Murimi Ngigi Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology wuodnam@gmail.com ABSTRACT Best use of available land resources for long term agricultural practice for analysis is a basic necessity to attain optimum agricultural production. In Kenya, rice being among consumed food crops but with low production rate does not meet the demands; and to alleviate this deficit there is need to identify more sites to cultivate rice crop. In locating more sites for rice farming, climate, physical and soil factors were considered. The study seeks to create a GIS model for use in locating suitable sites for rice farming. In order to achieve this goal, both remote sensing, geographical information system (GIS) tools based on multi-criteria evaluation (MCE) approach and analytical hierarchy process (AHP) were employed. Criteria variables for calculating the weights are climate, topography, soil ph, soil nutrients, soil depth, soil drainage and soil texture based on agronomist experts opinions. The analysis was carried out using application of analysis tools in the ArcGIS and obtained results indicate that 21.05% of the agricultural land is highly suited for rice farming, 41.06% is moderately suitable and 18.46% is marginally suitable. The model developed if adopted could improve the planning, locating and precisely recognizing the additional sites for rice planting. Keywords: GIS and multi-criteria evaluation, Kisumu county, land suitability analysis, rice farming. 1. Introduction Onyango, (2014) stated that rice crop has been cultivated and consumed worldwide by many people for over 10,000 years than most crops with global annual production of 500 million tons. Though rice is grown for local intake, in Kenya it has been regarded as a cash crop and this perception is rapidly changing with locals now increasingly recognizing its importance both as food crop and cash crop (Onyango, 2014). This attitude change has greatly influenced the balance between production and consumption of rice in Kenya. Kisumu County is not producing enough rice to meet her consumption demand. This is caused by the deficiency of knowledge on areas where rice crop can do well. The study, therefore, is to examine the suitability analysis of the soil to increase sites for rice cultivation. The study objective was to carryout soil suitability analysis for rice growing sites using GIS and remote sensing basing on AHP and MCE technique by identifying and gathering the necessary variables that support rice crop growing, creating the running spatial model and finding additional sites for rice farming. According to Rice Production Unit (2011/2012) report, rice growing area keeps on varying and the production also changing. Factors alluding to these variations could be lack of information on suitable sites that can sustain rice growth efficiently and effectively, and GIS technology would assist in locating the best sites for the crop to reduce haphazard choice of growing sites which are not supportive to effective rice farming. By identifying best areas for rice growth, farmers would be encouraged to increase their land sizes for the crop, hence increased production to meet existing consumption demands. Submitted on May 2016 published on August

2 Several authors have used GIS based methods to study land suitability analysis; Masoud, (2013) conducted modeling inland valley suitability for rice cultivation using computer based model in Ghana, Sailesh, (2011) also did land suitability study for rice growing in Morobe Province, Papua New Guinea using multi-criteria decision approach through GIS, and Walayat, (2012) also did research on the appropriateness of land for good yield of rice in Prachuap Khiri Khan Province, Thailand using GIS based model and realized that the appropriateness of land for the purpose of rice farming requires several factors. Though many researches have been done on soil suitability analysis all over the World and even some parts in Kenya using MCE approach, none has been done in Kisumu County. This was confirmed during face to face discussion with the expert in the area and there exists no publication so far on the same. 2. Material and methods 2.1 The study area Kisumu County (Figure 1) is the project area with its boundary follows the greater (original) Kisumu District. It has a population of 968,909 (2009 Census) and covering approximately 2086 km². It lies between South to North latitude, East to East longitude and comprises of seven sub counties namely Nyando, Nyakach, Muhoroni, Kisumu Central, Kisumu East, Kisumu West and Seme and it is warm throughout the year. The major physical features in the county are the overhanging huge granite rocks at Kisian and the legendary Kit Mikayi in Kisumu West Sub-county, the Lake Victoria, the geographically famous rice-growing Kano Plains, and lake islands (e.g. Ndere National Park which are major tourist attraction). The granite rocks are exploited (in small scale) by the local inhabitants to produce building ballast. The three major rivers draining into Lake Victoria are Nyando, Kibos and Sondu. The rivers are heavily silted, resulting in the extensive formation of lakeside swamps. The county has an annual relief rainfall that ranges between 1400mm and 2400mm which mainly fall in two seasons. The long rainy period occurs between March to May while short rains occur between October to December every calendar year and its annual mean temperature is C which is favourable for rice growth. The temperature ranges between 17 0 C and 33 0 C but seldom falls below 16 0 C. Figure 1: Study Area as an inset in the map of Kenya 34

3 2.2 Datasets and materials Land suitability study for rice growing in Kisumu county Various datasets obtained from different sources as summarized below (Table 1). The climate data obtained from Kenya Meteorological Services for the year , the soil characteristics data got from Kenya Soil Surveys, both Landsat8 and digital elevation model (DEM) images were downloaded from United States Geological Surveys (USGS) website in May 2015 to obtain land cover and slope data respectively, and judgements comparisons questionnaire filled after having one on one discussion with experts from researchers at Kenya Agricultural and Livestock Research Organization (KALRO)-Kibos, and Ahero Irrigation Research Station to assist in the ranking of preference by different contributing factors which were later applied for weights computation. Data Soil(pH, Texture, Drainage, Depth, Nutrients) 2.3 The Study approach Table 1: Datasets sources, type and resolution Figure 2 graphically shows the steps followed in land use suitability analysis. The procedure involved factors identification required for locating best sites, data collection, data preparation, data processing, creation of AHP and integrating MCE with the GIS to generate map showing appropriate areas for rice farming. The variables identified for analysis include land cover map, the physiography, rainfall figures, temperature values, soil depth, soil texture, soil drainage, soil ph and soil nutrients of the project area for development of suitable sites. 2.4 Datasets preparation and processing Source Kenya Soil Surveys Data Type Shapefil e Resolutio n DEM USGS website Image 30m Landsat8 USGS website Image 30m Climate Administrative Boundaries Questionnaire Kenya Meteorological Services Excel Year Current 1997 For 30years Survey of Kenya Map 1:50,000 Interviewing Experts(KALRO- Kibos, NIB-Ahero) Table Current May May Variou s July 2015 The preparation and processing of data for analysis and modelling as in figure 2. The first step involved data identification and collection followed by data standardization and geodatabase creation and finally data modelling and analysis. Data harmonization or standardization involved transformation of spatial data to a uniform datum and projection while geodatabase creation entailed data vectorization, geo-referencing to same format, digitization and rasterization during reclassification of factors. The conversion of all datasets into shapefile layers was carried out and imported into the analysis file. The spatial analyst 35

4 tools in ArcGIS have capacity to perform all the spatial analysis on any spatial data to give spatial answers such as steepness within a location, rainfall and temperature interpolation. 2.5 Spatial modelling Figure 2: Processing framework The spatial modelling steps involved calculation of weights and overlays and grading the criteria basing on experts opinion and available literature where three levels; goal, main criteria and sub criteria were identified. Eight sub criteria were employed to locate suitable sites. Figure 3 indicates the three levels of relationship. AHP analysis was applied in computing criteria weights. The factor ratings were put into a matrix form to generate normalized table. The maximal Eigen values were determined and consistency ratios (CR) was calculated using consistency index (CI) and random indices (RI) table and given by CR= CI/RI. Reclassification was done by applying a common scale from 1 to 10 and values were classified to a common scale for use in a suitability analysis as per the factor scores according to the experts advice. Processed datasets were reclassified to replace values based on new information or group certain values together. 36

5 Goal Level two criteria Level three criteria Land Use Suitability Climate Topography Soil Chemical Properties Soil Physical Properties Temperature Rainfall Slope Soil ph Soil Nutrients Soil Texture Soil Drainage Soil Depth Figure 3: Criteria relationship 3. Result and conclusion 3.1 Results of variables criteria Results which were obtained from various analysis of criteria are illustrated in figures Figure 4: Land cover map The land cover comprises mainly agriculture with some spots of swampy and forest land cover. Sugarcane and rice farming are practiced in the region while major portion in the westerly area is covered by Lake Victoria. 37

6 Figure 5: Interpolated rainfall map The interpolated rainfall map indicate the variances in values. The rainfall values range from to ml. Figure 6: Interpolated temperatue map The interpolated mean temperature map indicates the variances in values. The temperature values range from to degree Celsius. The slope indicates the degree variance of slope. The slope degree ranges from 0 degree to 9.27 degree being the highest elevation. It is important to note that most regions in Kisumu County are relatively flat. The flat areas ranges from degrees while relatively high areas ranges from degrees. 38

7 Figure 7: Slope map Figure 8: Soil depth map An area where soil depth data is inadequate is marked as no data and is shown with grey colour in the map. Figure 9: Soil drainage map 39

8 The drainage pattern of the area is classified as well-drained, moderately-well drained, poorly drained and imperfectly drained. Figure 10: Soil texture map Distribution of soil texture in the project area 4. Criteria weighting Results of criteria weighting was achieved after calculating the factors weights from the pairwise comparison matrix (Table 2). The consistency ratio (CR) of 0.05 for the matrix was acceptable level. Table 2: Pairwise comparison matrix of criteria. Climate Topography Soil Physical Properties Soil Physical Properties Climate 1 5 1/3 1/3 Topography 1/5 1 1/7 1/7 Soil Physical Properties Soil Physical Properties 3 7 1/2 1 Nth root of values product Weights Ranking ʎmax=4.14 CI = 0.05 CR = 0.05 The obtained suitability map (Figure 11) for rice crop is shown below. The suitability map depicts pixels with varying degrees from 4 to 6. The suitability classes based on FAO structure (FAO, 1976). 6-9 values represent highly suitable, 5-6 represent moderately suitable, 4-5 indicate marginally suitable, and 2-4 represent not suitable. 40

9 Figure 11: Rice suitability map Table 3 is a summary of each class extent. Sites represented by 21.05% of the land are highly suitable. Highly suitable areas are mainly flat with high temperatures and rainfall, poor soil drainage with a lot of nutrients and very fine soil texture (Clayey). Moderate suitable sites are 41.06% of agricultural land. 5. Conclusion Table 3: Rice Crop Suitability Classes in Kisumu County Suitability Class Area (Ha) Area (%) S S S N The study objective was to create a spatial model for use in the establishment of suitable sites for rice growing using GIS and remote sensing basing on MCE technique. From the results obtained, it was observed that Kisumu County has many attributes and potentials that can be explored and utilized for successful rice farming. Therefore, the results achieved show that the integration of remote sensing, GIS and application of MCE using AHP could provide a superior database for decision makers to plan for a sustainable and cheaper agricultural activity and this method is recommended for mapping land suitability of other crops in the county and across the country. This study has positive implication in the sense that rice farming is expected to multiply as a result of identification of more suitable sites. 6. References 1. Food and Agriculture Organization (FAO) (1976), A Framework for Land Evaluation. FAO Soils bulletin 32, FAO, Rome, pp Masoud. (2013), Modeling Inland Valley Suitability for Rice Cultivation, ARPN Journal of Engineering and Applied Sciences, 8(1), pp Onyango, A. O. (2014), Exploring Options for Improving Rice Production to Reduce Hunger and Poverty in Kenya. World Environment, 4(4), pp

10 4. Sailesh, S. (2011), Land Suitability Analysis for Rice Cultivation Based on Multi- Criteria Decision Approach through GIS. International Journal of Science Emerging Technology, 2(1), pp Rice Production Unit. (2011/2012), National Rice Development Strategy. Nairobi: Ministry of Agriculture. 6. Walayat, H. (2012), Geographical Information System Based Model of Land Suitability for Good Yield of Rice in Prachuap Khiri Khan Province, Thailand. Science, Technology and Development, 31(1), pp