Identification of Suitable Land for Green Gram Production Using GIS Based Analytical Hierarchy Process in Kitui County, Kenya

Mugo, Jane Wangui; Kariuki, Patrick Chege; Musembi, David K.

doi:10.4172/2469-4134.1000170

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…For evaluating the land suitability of pulse crop (green gram), eleven criteria (Table 1) are considered which belong mostly to topography (slope and elevation), climate (rainfall, land surface temperature), land use land cover (LULC), and soil characteristics (topsoil texture, soil drainage, soil salinity, soil pH, soil depth, and inundation land type) based on a relevant literature review [42][43][44][45][46] and the opinions of experts like agriculturists, agronomists, and government personnel from the agriculture ministry.…”

Section: Multicriteria Evaluation Of the Land Suitability Analysismentioning

confidence: 99%

Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh

2021

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The agricultural potential of Bangladesh’s coastal region has been threatened by the impact of climate change. Pulse crops with high nutritional value and low production costs such as green gram constitute an important component of a healthy and accessible diet for the country. In order to optimize the production of this important staple, this research aims to promote climate-smart agriculture by optimizing the identification of the appropriate land. The objective of this research is to investigate, estimate, and identify the suitable land areas for green gram production based on the topography, climate, and soil characteristics in the coastal region of Bangladesh. The methodology of the study included a Geographic Information System (GIS) and the Multicriteria Decision-Making approach: the Analytical Hierarchy Process (AHP). Datasets were collected and prepared using Landsat 8 imagery, the Center for Hydrometeorology and Remote Sensing (CHRS) data portal and the Bangladesh Agricultural Research Council. All the datasets were processed into raster images and then reclassified into four classes: Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable. Then, the AHP results were applied to produce a final green gram suitability map with four classes of suitability. The results of the study found that 12% of the coastal area (344,619.5 ha) is highly suitable for green gram production, while the majority of the land area (82.3% of the area) shows moderately suitable (S2) land. The sensitivity analysis results show that 3.3%, 63.4%, 28.0%, and 1.2% of the study area are S1, S2, S3, and NS, respectively. It is also found that the highly suitable land area belongs mostly to the southeastern part of the country. The result of this study can be utilized by policymakers to adopt a proper green gram production strategy, providing special agricultural incentive policies in the highly suitable area as a provision for the increased food production of the country.

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Section: Multicriteria Evaluation Of the Land Suitability Analysismentioning

confidence: 99%

Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh

2021

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“…Agriculture is likely to be the most vulnerable sector to the changing climate with stress on subsistence farming in the tropical region, as smallholder farmers lack sufficient resources to acclimatize to climate change (Eriksen et al, 2005;Adhikari et al, 2015). Understanding the spatial variability of climate parameters can help fight hunger and poverty and improve the wellbeing of smallholder farmers by improving their management of natural resources for agricultural production (Mugo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rainfall Variability under Present and Future Climate Scenarios Using the Rossby Center Bias-Corrected Regional Climate Model

Mugo¹,

Opijah²,

Ngaina³

et al. 2020

AJCC

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This study sought to determine the spatial and temporal variability of rainfall under past and future climate scenarios. The data used comprised stationbased monthly gridded rainfall data sourced from the Climate Research Unit (CRU) and monthly model outputs from the Fourth Edition of the Rossby Centre (RCA4) Regional Climate Model (RCM), which has scaled-down nine GCMs for Africa. Although the 9 Global Climate Models (GCMs) downscaled by the RCA4 model was not very good at simulating rainfall in Kenya, the ensemble of the 9 models performed better and could be used for further studies. The ensemble of the models was thus bias-corrected using the scaling method to reduce the error; lower values of bias and Normalized Root Mean Square Error (NRMSE) were recorded when compared to the uncorrected models. The bias-corrected ensemble was used to study the spatial and temporal behaviour of rainfall under baseline (1971 to 2000) and future RCP 4.5 and 8.5 scenarios (2021 to 2050). An insignificant trend was noted under the baseline condition during the March-May (MAM) and October-December (OND) rainfall seasons. A positive significant trend at 5% level was noted under RCP 4.5 and 8.5 scenarios in some stations during both MAM and OND seasons. The increase in rainfall was attributed to global warming due to increased anthropogenic emissions of greenhouse gases. Results on the spatial variability of rainfall indicate the spatial extent of rainfall will increase under both RCP 4.5 and RCP 8.5 scenario when compared to the baseline; the increase is higher under the RCP 8.5 scenario. Overall rainfall was found to be highly variable in space and time, there is a need to invest in the early dissemination of weather forecasts to help farmers adequately prepare in case of unfavorable weather. Concerning the expected increase in rainfall in the fu-

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“…Mugo et al [14] found some areas in Kitui County, an ASAL region in Kenya, suitable for production in terms of climate. There is, however, a lack of knowledge on whether other ASAL areas in Kenya are currently suitable for green gram production, and how climate change will affect their current suitability.…”

Section: Introductionmentioning

confidence: 99%

Suitability of Green Gram Production in Kenya under Present and Future Climate Scenarios Using Bias-Corrected Cordex RCA4 Models

Mugo¹,

Opijah²,

Ngaina³

et al. 2020

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Green gram is considered as one of the legumes suitable for cultivation in the Arid and Semi-Arid Lands (ASALs) of Kenya. However, climate change may alter the areas suitable for green gram production. This study sought to model green gram suitability in Kenya under present and future conditions using bias-corrected RCA4 models data. The datasets used were: maps of soil parameters extracted from Kenya Soil Survey map; present and future rainfall and temperature data from an ensemble of nine models from the Fourth Edition of the Rossby Centre (RCA4) Regional Climate Model (RCM); and altitude from the Digital elevation model (DEM) of the USGS. The maps were first reclassified into four classes of suitability as Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable (N). The classes represent the different levels of influence of a factor on the growth and yield of green grams. The reclassified maps were then assigned a weight generated using the Analytical Hierarchy Process (AHP). A weighted overlay of climate characteristics (past and future rainfall and temperature), soil properties (depth, pH, texture, CEC, and drainage) and altitude found most of Kenya as moderately suitable for green gram production during the March to May (MAM) and October to December (OND) seasons under the baseline, RCP 4.5 and RCP 8.5 scenarios with highly suitable areas being found in Counties like Kitui, Makueni, and West Pokot among others. During the MAM season, the area currently highly suitable for green gram production (67,842.62 km 2) will increase slightly to 68,600.4 km 2 (1.1%) during the RCP 4.5 and reduce to 61,307.8 km 2 (−9.6%) under the RCP 8.5 scenario. During the OND season, the area currently highly suitable (49,633.4 km 2) will increase under both RCP 4.5 (22.2%) and RCP 8.5 (58.

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Identification of Suitable Land for Green Gram Production Using GIS Based Analytical Hierarchy Process in Kitui County, Kenya

Cited by 17 publications

References 9 publications

Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh

Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh

Rainfall Variability under Present and Future Climate Scenarios Using the Rossby Center Bias-Corrected Regional Climate Model

Suitability of Green Gram Production in Kenya under Present and Future Climate Scenarios Using Bias-Corrected Cordex RCA4 Models

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