The agriculture sector is the largest consumer of weather and climate information worldwide. Solar radiation, precipitation and temperature are the main factors that affect crop growth and therefore productive agriculture is highly dependent on the climatic patterns of a region  . Kenya and the Great Horn of Africa (GHA) heavily rely on rain-fed agriculture and water dependent sectors for economic sustainability, and therefore, the population in this region is highly vulnerable to effects of climate variability and climate change  . Hence, understanding weather variability and its potential impact on agriculture is key to agricultural development and food security.
The Kitui County government has been promoting green gram (Vigna radiata L.) as the most suitable and profitable legume in the County and the Sahelian Solution Foundation (SASOL) and Farm Africa are prominent NGOs working to improve its farming (   ). Green gram is therefore a priority value chain in the County  .
Green gram is a short season crop adapted to the multiple cropping systems in the dry and warm conditions of lowland tropics and subtropics. It is a warm season crop that grows in a 20˚C to 40˚C temperature range with a 28˚C to 30˚C optimum    . It requires a mean minimum temperature of 20˚C - 22˚C and 350 - 1000 mm/annum of rainfall for productive growth     with 650 mm being the optimum (Mutua et al., 1990). Heavy rainfall and cool temperatures increase vegetative growth but decrease pod setting and development  .
Climatic variability over East Africa has been studied widely and rainfall has been found to exhibit high spatial and temporal variability    . Of particular interest is the observation that seasonal rainfall is decreasing   , which means that the effects of climate change are likely to negatively impact crop yields by the end of the 21st century.   studied potential variation of East African rainfall and temperature for the period 1961-2100 and found that both rainfall and temperature were projected to increase during the study period.  , on the other hand, studied 63 years of rainfall data (1950-2012) for Kenya and found the Short rains had an increasing trend and higher interannual variability than the Long rains while  used Harmonic analysis of geographical changes of rainfall seasonality over East Africa using pentad rainfall data for 36 stations for the period 1962-2006 and found that the seasonality of rainfall over the whole region does not follow the classical scheme of north-south pattern with bimodal (unimodal) dominance in areas in the vicinity (north/south) of the equator.
Understanding the spatial variation of climatic parameters and their relationship with crops can help to combat poverty and hunger by enhancing agricultural and natural resource management, making it possible to improve the socio-economic well-being of smallholder farmers  . The Analytic Hierarchy Process (AHP) is a well-established decision-making tool that uses pairwise comparison to measure a decision maker’s preference between two parameters based on their real time importance     . AHP was introduced by  and is one of the fastest growing decision-analysis techniques   that can be used to compare the relative importance of rainfall and temperature.
 reviewed land suitability studies for green gram production in Kitui County but found no study showing the best season for growing green gram. The aim of this study was to determine how rainfall and temperature varied during the two rainfall seasons in the County, the Long rains which come in March-May (MAM) and the Short rains which come in October-December (OND), and their potential for green gram production. The findings of this work would be beneficial to farmers in zoning and timing of green grams for increased productivity and the County and national governments for development of relevant policy. In addition, because green grams attract international trade, the findings would be of interest to investors who would be interested in supporting farmers or engaging in trade on the product.
2. Material and Methods
2.1. Study Area
Kitui County (Figure 1) is located in lower eastern Kenya, 150 km east of Nairobi. It has an area of 30,497 km2 of which 690 km2 is in the Tsavo East National Park  .
Figure 1. Map of Kitui County, showing Sub Counties and national park.
Being semi-arid, Kitui County is highly vulnerable to the effects of drought. The periods June to September and January to February are usually dry. The rainfall is bimodal with an annual mean of 750 mm that varies from 500 to 1050 mm with 40% reliability and annual mean temperature range between 25˚C and 30˚C  .
2.2.1. Model Input Preparation
Green gram is a rainfed crop in Kitui County and rainfall and temperature are the climatic factors with greatest impact on production in the County. Monthly rainfall and temperature data were obtained from Worldclim for the period 1950-2000. This time period was used because it has been widely studied in the past (     ). Data layers in the website were generated through interpolation techniques of mean monthly climate data from several weather stations on a 30 arc-second resolution grid  and according to  , the data from which Worldclim extracts data for Kenya are 736, 708 and 61 stations for mean, minimum and maximum temperature and rainfall.
The seasonal MAM and OND rainfall totals for each year were calculated by adding the rainfall in the corresponding three months while the MAM and OND mean temperature were determined by computing the average temperature for the three respective months. The data was then extracted, geo-referenced and reclassified into 4 suitability classes in a process summarized in Figure 2.
2.2.2. Suitability Table for Green Gram
The suitability of Kitui County for growing green gram, based on climatic factors, was summarized into four classes: highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). These classes are shown in Table 1, which is based on  guidelines for rain fed agriculture and reviewed literature and discussions with crop experts.
2.2.3. Development of Pair Wise Comparison Matrix
Rainfall and temperature were compared on a scale of 1 to 9 (Table 2) to describes
Figure 2. Flow chart for the processing of climate data.
Table 1. Suitability classes for green gram according to FAO  .
Table 2. The scale of relative importance between two factors.
(After: Saaty, 2000).
the intensity of their relative importance  . The weights express the importance of each factor relative to the other  .
2.2.4. The Consistency Ratio
AHP calculates the Consistency Ratio (CR) which measures how much variation is allowed for reasonable results and is expected to be less than 10 percent for AHP results to be accepted  . The CR is obtained from the Consistency Index (CI) and is determined as shown in Equations (1) and (2);
where is the maximum Eigen value, RI is the Random inconsistency Index and n is the number of criteria or sub-criteria being compared   . The RI is chosen depending on the number of elements being compared as shown in Table 3.
2.2.5. Green Gram Suitability Map
The rainfall and temperature maps were rated and reclassified using the AHP and assigned weights which were determined in consultation with crop experts, reflecting the influence of each factor on green gram production. The maps were subsequently overlaid to generate the final output, the green gram suitability map.
Table 3. Random Inconsistency Indices for N = 1, 2, …, 15.
Source:  .
3. Results and Discussions
3.1. Analytical Hierarchy Process Results
The results of pairwise comparison between rainfall and temperature (Table 4) showed that 67% of experts consider rainfall more important than temperature (33%), which is a reflection of the importance of water in the dry lands in limiting crop growth  and agrees with past researchers that rainfall is the most important weather parameter in East Africa as the economies of the region are dependent on rain fed agriculture   .
3.2. Rainfall Sub-Criteria
3.2.1. The MAM Rainfall
The MAM rainfall varies between 155 mm and 663 mm (Figure 3(a)) and the reclassified rainfall map (Figure 3(b)) shows that during this season, 50.4% of the county has varying degrees of suitability for growing green gram; with 4.9% having high suitability while 11.1% and 34.4% have moderate and marginal suitability, respectively (Table 5). The suitable areas in MAM are in the Central and Western regions of the County and the remaining 49.6% is unsuitable for green gram production.
3.2.2. The OND Rainfall
The OND rainfall varies between 239 mm and 729 mm (Figure 4) and the reclassified rainfall map shows that the entire County is suitable for green gram production during this season (Figure 4, Table 6), where 75.6% of the county is highly suitable while 22.5% and 1.9% have moderate and marginal suitability, respectively. The areas of marginal suitability are located in the extreme Northern and Southern part of the County. These results agree with  who observed that green gram performs best when rainfall is between 350 - 600 mm, moderately at 300 - 350 mm and 600 - 1000 mm and marginally when rainfall is between 230 - 300 mm.
3.3. The Temperature Sub-Criteria
Unclassified maps of MAM and OND show that temperature increases from west to east. Areas of high suitability in both seasons are located in the central
Figure 3. Unclassified (a) and classified (b) rainfall for Green gram production during the MAM season in Kitui County.
Figure 4. Map of Kitui County showing unclassified (mm) (a) and reclassified (b) OND Rainfall for Green gram production.
Table 4. Pairwise comparison results for rainfall and temperature.
Table 5. Green gram suitability classes in Kitui County during the MAM rainfall.
Table 6. Suitability classes for green gram production during the OND rainfall in Kitui County.
and eastern part of the County (Figure 5 and Figure 6). The suitability analysis shows that in terms of temperature Kitui County falls into two suitability classes for green gram production which are the highly and moderately suitable classes.
3.3.1. The MAM Temperature
The MAM temperature varies between 19.1˚C to 27.4˚C and the reclassified temperature map shows that during this season 78.68%, 21.29% and 0.03% of the County has high, moderate and marginal suitability for green gram production (Figure 5, Table 7).
3.3.2. The OND Temperature
The OND temperature varies between 18.6˚C to 26.9˚C (Figure 6(a), Table 8) and after reclassification (Figure 6(b)), 70.0% and 30.0% of the land is highly and moderately suitable for green gram production, respectively. The reduction in the area with potential high productivity compared with MAM could be attributed to the lower temperature during OND. Green grams perform best at a temperature of 30˚C - 24˚C and moderately at 24˚C - 20˚C    .
3.4. The Composite Green Gram Climate Suitability Map
Since the CR < 10%, the weights attained from AHP were assigned accordingly
Figure 5. Spatial variation of MAM temperature (a) and the green gram production potential after reclassification (b) for Kitui County.
Figure 6. Spatial variation of OND temperature (a) and the green gram production potential after reclassification (b) for Kitui County.
(Table 9) and the maps of rainfall and temperature were overlaid to generate composite suitability maps (Figure 7(a) & Figure 7(b)). During the MAM season, 4.9%, 33.9% and 61.3% of the land is highly, moderately and marginally suitable for green grap production, respectively. The areas of high suitability are in the west-central part of the County and the extreme south (Figure 7(a)).
Figure 7. Composite maps showing green gram production potential during. MAM (a) and OND (b) seasons.
Table 7. Suitability of green gram production in Kitui County based on temperature during the MAM season.
Table 8. Suitability of green gram production in Kitui County based on temperature during the OND season.
Table 9. Composite green gram production potential during the MAM and OND seasons.
During the OND season, 75.6% is highly suitable while 24.4% is moderately suitable for green gram production (Figure 7(b)). The higher suitability in OND over MAM is likely due to higher rainfall during this season and leads to the conclusion that it is the better season to grow green gram in Kitui County. This agrees with  conclusion that green gram yield is more dependent on adequate water supply than any other single environmental factor.
On the basis of temperature, the County is suitable for green gram production during the two seasons; being either highly (78.68%) or moderately (21.29%) suitable during MAM and 69.98% highly and 29.95% moderately suitable during OND.
The main climatic factor limiting suitability for green gram production in the County was found to be rainfall. The OND season has higher potential for green gram production compared to the MAM season, with 75.6% of the County being highly and 24.4% moderately suitable during OND while 4.9% and 33.9% of the county has high and moderate suitability, respectively, during MAM. The remaining 61.3% of the County in MAM has marginal suitability due to inadequate rainfall.
The first, second and third authors wish to thank South Eastern Kenya University for enabling them to conduct the research.
 Hossain, M.A. (2010) Global Warming Induced Sea Level Rise on Soil, Land and Crop Production Loss in Bangladesh. 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, 1-6 August 2010, 77-80. https://www.iuss.org/19th%20WCSS/Symposium/pdf/0419.pdf
 IPCC (Intergovernmental Panel on Climate Change) (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. and Miller, H.L., Eds.]. Cambridge University Press, Cambridge and New York, 996 p.
 Sahelian Solution Foundation (SASOL) (2015) Green Grams Hand Book, A Guide to Farmers within Project Enhancing Food Security with Diversified Dry Land Farming Techniques in Kitui County. Sahelian Solution Foundation, Kenya. http://www.sasolfoundation.co.ke/2013/wp-content/uploads/2015/02/Green-Grams-Manual.pdf
 Mogotsi, K.K. (2006) Vignaradiata (L.) R. Wilczek. Record from Protabase. Brink, M. and Belay, G., Eds., PROTA (Plant Resources of Tropical Africa/Ressources- végétales de l’Afriquetropicale), Wageningen. https://www.feedipedia.org/node/14363
 Indeje, M., Semazzi, F.H., Xie, L. and Ogallo, L.J. (2001) Mechanistic Model Simulations of the East African Climate Using NCAR Regional Climate Model: Influence of Large-Scale Orography on the Turkana Low-Level Jet. Journal of Climate, 14, 2710-2724. https://doi.org/10.1175/1520-0442(2001)014%3C2710:MMSOTE%3E2.0.CO;2
 Anyah, R.O. and Semazzi, F.H.M. (2006) Climate Variability over the Greater Horn of Africa Based on NCAR AGCM Ensemble. Theoretical and Applied Climatology, 86, 39-62. https://doi.org/10.1007/s00704-005-0203-7
 Ongoma, V. and Chen, H. (2016) Temporal and Spatial Variability of Temperature and Precipitation over East Africa from 1951 to 2010. Meteorology and Atmospheric Physics, 129, 131-144. https://doi.org/10.1007/s00703-016-0462-0
 Adhikari, U., Nejadhashemi, A.P. and Woznicki, S.A. (2015) Climate Change and Eastern Africa: A Review of Impact on Major Crops. Food and Energy Security, 4, 110-132. https://doi.org/10.1002/fes3.61
 Ongoma, V., Chen, H. and Gao, C. (2017) Projected Changes in Mean Rainfall and Temperature over East Africa Based on CMIP5 Models. International Journal of Climatology, 38, 1375-1392. https://doi.org/10.1002/joc.5252
 Kisaka, M.O., Mucheru-Muna, M., Ngetich, F.K., Mugwe, J.N., Mugendi, D. and Mairura, F. (2015) Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya. Advances in Meteorology, 2015, Article ID: 380404. https://doi.org/10.1155/2015/380404
 Lupia, D.F. (2014) Crop/Land Suitability Analysis by ArcGIS Tools. Technical Report. https://www.researchgate.net/profile/Flavio-Lupia/publication/268517989_CROPLAND_SUITABILITY_ANALYSIS_BY_ARCGIS_TOOLS/links/546f06830cf2b5fc1760a8d1/CROP-LAND-SUITABILITY-ANALYSIS-BY-ARCGIS-TOOLS.pdf
 Bello, D.A., Farmani, R., Javadi, A.A. and Evans, B.M. (2009) The Analytical Hierarchy Process for Contaminated Land Management. Advanced Engineering Informatics, 23, 433-441. https://doi.org/10.1016/j.aei.2009.06.006
 Jafari, S. and Zaredar, N. (2010) Land suitability analysis using multi attribute decision making approach. International Journal of Environmental Science and Development, 1, 441-445. https://doi.org/10.7763/IJESD.2010.V1.85
 Mugo, J.W., Kariuki, P.C. and Musembi, D.K. (2016) Identification of Suitable Land for Green Gram Production Using GIS Based Analytical Hierarchy Process in Kitui County, Kenya. Journal of Remote Sensing and GIS, 5, Article No. 170. https://doi.org/10.4172/2469-4134.1000170
 Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. https://doi.org/10.1002/joc.1276
 Takeshi, T. and Ruth, B. (2015, January) Using Climate Information for Supporting mung Bean Plantation over Sumbawa Region. WeADAPT. https://www.weadapt.org/knowledge-base/climate-adaptation-training/using-climate-information-for-supporting-mung-bean-plantation-over-sumbawa-region
 Muthama, N.J., Masieyi, W.B., Okoola, R.E, Opere, A.O., Mukabana, J.R., Nyakwada, W., Aura, S., Chanzu, B.A. and Manene, M.M. (2012) Survey on the Utilization of Weather Information and Products for Selected Districts in Kenya. Journal of Meteorology and Related Sciences, 6, 51-58. https://doi.org/10.20987/jmrs.08.2012.605
 Ongoma, V., Guirong, T., Ogwang, B.A. and Ngarukiyimana, J.P. (2015) Diagnosis of Seasonal Rainfall Variability over East Africa: A Case Study of 2010-2011 Drought over Kenya. Pakistan Journal of Meteorology, 11, 13-21.
 Ranawake, A.L., Dahanayaka, N., Amarasingha, U.G.S., Rodrigo, W.D.R.J. and Rodrigo, U.T.D. (2012) Effect of Water Stress on Growth and Yield of Mung Bean (Vignaradiata L). Tropical Agricultural Research and Extension, 14, No. 4. https://doi.org/10.4038/tare.v14i4.4851