Browsing by Author "Mairura, F."

Now showing 1 - 5 of 5
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    Adapting African Agriculture to Climate Change
    (Springer, 2015) Kisaka, Oscar M.; Mucheru-Muna, M.; Ngetich, F.K.; Mugwe, Jayne; Mugendi, Daniel N.; Mairura, F.
    Drier parts of Embu County, Eastern Kenya, endure persistent crop failure and declining agricultural productivity which have been attributed, in part, to prolonged dry-spells and erratic rainfall. Nonetheless, understanding spatialtemporal variability of rainfall especially at seasonal level, is an imperative facet to rain-fed agricultural productivity and natural resource management (NRM). This study evaluated the extent of seasonal rainfall variability and the drought characteristics as the first step of combating declining agricultural productivity in the region. Cumulative Departure Index (CDI), Rainfall Anomaly Index (RAI) and Coefficients-of-Variance (CV) and probabilistic statistics were utilized in the analyses of rainfall variability. Analyses showed 90 % chance of below croppingthreshold rainfall (500 mm) exceeding 213.5 mm (Machanga) and 258.1 mm (Embu) during SRs for one year return-period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56 and 0.38) and in number of rainy-days (CV = 0.88 and 0.27) at Machang’a and Embu, respectively. Monthly rainfall variability was found to be equally high even during April (peak) and November (CV = 0.42 and 0.48 and 0.76 and 0.43) with high probabilities (0.40 and 0.67) of droughts exceeding 15 days in Embu and Machang’a, respectively. Dry-spell probabilities within growing months were high (81 %) and (60 %) in Machang’a and Embu respectively. To optimize yield in the area, use of soil-water conservation and supplementary irrigation, crop selection and timely accurate rainfall forecasting should be prioritized
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    Potential of deterministic and geostatistical rainfall interpolation under high rainfall variability and dry spells: case of Kenya’s Central Highlands
    (Springer, 2015-03) Kisaka, Oscar M.; Mucheru-Muna, M.; Ngetich, F.K.; Mugwe, Jayne; Mugendi, Daniel N.; Mairura, F.; Shisanya, C.A.; Makokha, G. L.
    Drier parts of Kenya’s Central Highlands endure persistent crop failure and declining agricultural productivity. These have, in part, attributed to high temperatures, prolonged dry spells and erratic rainfall. Understanding spatial-temporal variability of climatic indices such as rainfall at seasonal level is critical for optimal rain-fed agricultural productivity and natural resource management in the study area. However, the predominant setbacks in analysing hydro-meteorological events are occasioned by either lack, inadequate, or inconsistent meteorological data. Like in most other places, the sole sources of climatic data in the study region are scarce and only limited to single stations, yet with persistent missing/unrecorded data making their utilization a challenge. This study examined seasonal anomalies and variability in rainfall, drought occurrence and the efficacy of interpolation techniques in the drier regions of eastern Kenyan. Rainfall data from five stations (Machang’a, Kiritiri, Kiambere and Kindaruma and Embu) were sourced from both the Kenya Meteorology Department and on-site primary recording. Owing to some experimental work ongoing, automated recording for primary dailies in Machang’a have been ongoing since the year 2000 to date; thus, Machang’a was treated as reference (for period of record) station for selection of other stations in the region. The other stations had data sets of over 15 years with missing data of less than 10 % as required by the world meteorological organization whose quality check is subject to the Centre for Climate Systems Modeling (C2SM) through MeteoSwiss and EMPA bodies. The dailies were also subjected to homogeneity testing to evaluate whether they came from the same population. Rainfall anomaly index, coefficients of variance and probability were utilized in the analyses of rainfall variability. Spline, kriging and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model in ArcGIS environment. Validation of the selected interpolation methods were based on goodness of fit between gauged (observed) and generated rainfall derived from residual errors statistics, coefficient of determination (R 2), mean absolute errors (MAE) and root mean square error (RMSE) statistics. Analyses showed 90 % chance of below cropping-threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for 1 year return period. Rainfall variability was found to be high in seasonal amounts (e.g. coefficient of variation (CV) = 0.56, 0.47, 0.59) and in number of rainy days (e.g. CV = 0.88, 0.53) in Machang’a and Kiritiri, respectively. Monthly rainfall variability was found to be equally high during April and November (e.g. CV = 0.48, 0.49 and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a. Dry spell probabilities within growing months were high, e.g. 81 and 60 % in Machang’a and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.
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    Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya
    (Hindawi Publishing Corporation, 2015) Kisaka, Oscar M.; Mucheru-Muna, M.; Ngetich, F.K.; Mugwe, Jayne; Mugendi, Daniel N.; Mairura, F.
    This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolationmethods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1mmduring short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) inMachang’a, Kiritiri, and Kindaruma, respectively.Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.
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    Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya
    (Hindawi Publishing Corporation, 2014-08) Kisaka, Oscar M.; Mucheru-Muna, Monicah; Ngetich, F.K.; Mugwe, Jayne; Mugendi, Daniel N.; Mairura, F.
    This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.
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    Using Apsim-Model as A Decision-Support-Tool for Long-Term Integrated-Nitrogen-Management and Maize productivity under Semi-Arid Conditions in Kenya
    (Cambridge University Press, 2015-04) Kisaka, Oscar M.; Mucheru-Muna, M.; Ngetich, F.K.; Mugwe, Jayne; Mugendi, Daniel N.; Mairura, F.; Muriuki, J.
    There is continued decline in per capita agricultural productivity in the drier parts of Kenya’s central highlands. The declines have been linked to low and declining soil fertility, soil water, high atmospheric heat, prolonged dry-spells and erratic rainfall. Integrated soil fertility management (ISFM) technologies have been developed and tested in the region. Despite their significant impacts, high variability in local soils and climate contributes to large variations and inconsistency in research results among replications. Experimentation is expensive and limited to a few years, sites and scenarios. Crop-growth simulation models suitably complement experimental research, to support decision making regarding soil fertility and water management. This study evaluated the performance of the Agricultural Production Systems Simulator (APSIM) model. APSIM was parameterized and calibrated based on a rain-fed randomized complete block trial (2009–2012) at a research station in Machang’a, Embu County. The study further reported on long-term effects of integrated Nitrogen (N) management from organic residues (goat manure, Lantana camara, Tithonia diversifolia and Mucuna pruriens) and their combination with mineral fertilizers in maize production. The model adequately reproduced the observed trends of maize leaf area index (LAI) and yield response to the testNamendments. Long-termsimulations showed that application of 0, 20 and 40 Kg Nha−1 had low inter-seasonal variations (CV = 18–33%) in yields. High yield variability (CV > 56%) was observed in the application of 60 and 80 Kg N ha−1. Application of 40 Kg N ha−1 by combining mineral fertilizer and manure showed 80% chance of harvesting more than 2.5 Mg ha−1 of maize grain yield. Maize stover mulching at 5 and 6 Mg ha−1 with the same N application increased long-term guaranteed grain harvests to 3.5 Mg ha−1. This is when complemented with 90 Kg P ha−1. This integrated N and soil water management is thus recommended. For subsistence farming, low-cost recommendations are geared towards some ‘guaranteed’ yield stability each cropping season. This recommendation underpins low-cost technologies that reduce production risks among small-holder farmers who faced with intermittent financial problems, to improve food security. However, there is need to evaluate and verify that there is a positive balance of primary nutrients such as N, P and K in such a fertility and water management option. Its effects on C:N levels ought to be evaluated as well.