Dry spell analysis and maize yields for two semi-arid locations in East Africa
ABSTRACT High variability in rainfall occurrence and amounts together with high evaporative demand create severe constraints for crop growth and yields in dry sub-humid and semi-arid farming areas in east Africa. Meteorological analyses on rainfall distribution are common, but generally focus on assessing drought occurrence on annual and seasonal basis. This paper presents two types of seasonal dry spell analysis, using easy accessible data on daily rainfall and evapotranspiration for two semi-arid locations in east Africa for 20–23 years. The meteorological dry spell analysis was obtained by Markov chain process, and the agricultural dry spell analysis used rainfall data in a simple water balance model also describing impact on maize (Zea mays L.) growth due to water availability on clay or sandy soil. The meteorological dry spell analysis showed a minimum probability of 20% of dry spells exceeding 10 days at both sites, increasing to 70% or more depending on onset of season, during approximate flowering and early grain filling stage. The agricultural dry spell analysis showed that maize was exposed to at least one dry spell of 10 days or longer in 74–80% of seasons at both sites. Maize on sandy soil experienced dry spells exceeding 10 days, three–four times more often than maize on clay soil during flowering and grain filling stages. In addition, the water balance analysis indicated substantial water losses by surface runoff and deep percolation as the crop utilised only 36–64% on average of seasonal rainfall. Such large proportion of non-productive water flow in the field water balance may provide scope for dry spell mitigation through improved water management strategies.
- SourceAvailable from: N. Philippon
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- "Besides inter-annual variability, intra-seasonal variability also has a potential effect on crop yields (Cooper et al., 2008). Barron et al. (2003) noted high dry spells probabilities during maize flowering and grain filling at semi-arid sites in Kenya and Tanzania, resulting in substantial losses of final grain yields, especially on sandy soils. Araya and Stroosnijder (2011) demonstrated that in northern Ethiopia a short growing period or a total lack of rain were the main factors of crop failure, before dry spells occurrence and false starts of the rainy season. "
ABSTRACT: Mount Kenya is an equatorial mountain whose climatic setting is fairly simple (two rainy seasons in March – May, the Long Rains, and October – December, the Short Rains) though concealing significant spatial variations related to elevation and aspect (part I, Camberlin et al., 2014). This part II is dedicated to the sensitivity of sorghum yields to climate variability in space and time, with a focus on the intra-seasonal characteristics of the rainy seasons. To that aim we use the crop model SARRA-H calibrated for the region and fed with rainfall, temperature, wind speed, humidity and solar radiation data over the period 1973 – 2001 at three stations located on the eastern slopes of Mount Kenya. The crop model is run independently for the two rainy seasons. Four groups of simulations are conducted by varying the initialization date of the simulation, the sowing dates and the type of soil, in order to test sorghum sensitivity to water availability. Evidence is found that potential sorghum yields are dominantly controlled by variations in seasonal rainfall amounts: mean yields are higher at higher and wetter locations, and are higher during the wettest rainy season and years. However, beyond this apparent simplicity, more complex aspects emerge of the crop – climate relationships. First, the yield – elevation relationship is altered at high elevation due to lower temperature. Second, despite a strong link with the seasonal rainfall amounts, we evidence an underlying role of some intra-seasonal rainfall characteristics such as the number of rainy days (itself mainly determined by the rainy season duration) or the occurrence of long dry spells. Third, unseasonal rains occurring after the end of the rainy season, especially after the Short Rains, play a role in final crop yield. Fourth, variations of climate variables such as solar radiation by modulating the potential evapotranspiration concur to yield variations at the wettest locations.International Journal of Climatology 06/2015; DOI:10.1002/joc.4394 · 3.16 Impact Factor
- "The use of seasonal amounts can be detrimental when the largest rainfall amounts near the seasonal peak are not spatially coherent and may hide potentially predictable signals, notably at the beginning and end of the rainy seasons, that can be important for crops (Sivakumar 1993; Barron et al. 2003; Sultan et al. 2005; Marteau et al. 2011). Our results demonstrate that the interannual variability of the long (FMAMJ) rains in Kenya and north Tanzania is consistent with such behavior, with the most spatially coherent signals confined to the early stage or at least the first half of the rainy season (Figs. 2, 6). "
Dataset: moron&al jcl2013
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- "Studies by Sivakumar (1991), Seleshi and Zanke (2004) and Tilahun (2006) noted high variations in annual and seasonal rainfall totals and rainy days in Ethiopia and Sudano- Sahelian regions. Studies on rainfall patterns in the region have been based principally on annual averages, thus missing on within-season rainfall characteristics (Barron et al. 2003). "
ABSTRACT: 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.Theoretical and Applied Climatology 03/2015; DOI:10.1007/s00704-015-1413-2 · 2.02 Impact Factor