Dry spell analysis and maize yields for two semi-arid locations in East Africa

Natural Resources Management, Department of Systems Ecology, Stockholm University, 106 91 Stockholm, Sweden
Agricultural and Forest Meteorology (Impact Factor: 3.89). 06/2003; 117(1-2):23-37. DOI: 10.1016/S0168-1923(03)00037-6

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.

  • Source
    • "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). "
  • Source
    • "For seasonal precipitation, we also include a quadratic term to account for non-linear relationships, and the differential response to precipitation changes in dry vs. wet regions of the country. Given that not just the total volume of precipitation is important, but also its timing and intensity (Barron et al., 2003; Biazin et al., 2012), we define a variable with the total number of dry days from planting to harvest, as we expect long dry spells to be associated with drought-related yield declines and losses. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Nicaragua has already experienced substantial climate change, in part due to a loss of one half of its forest cover in the last half-century. In this study, we assess the extent to which historical climate trends have contributed to stagnating yields for maize (Zea mays) and bean (Phaseolus vulgaris), the two main staple crops in the country. We first analyze 40 years of historical weather data throughout Nicaragua to estimate trends, and assess the extent to which these trends correlate with spatial deforestation patterns. Then, we create a regression model linking department-level maize and bean yields with seasonal weather conditions, and use the model to estimate the impact of historical climate trends on yields. Regressions are run for yields on both harvested and sown area, with the latter accounting for the effect of complete crop losses. Results confirm strong warming trends throughout the country, with daytime temperatures in deforested areas warming at more than double the rate of global averages in the tropics. Decreases in rainfall frequency are also seen almost everywhere, along with an earlier end to the rainy season. Regression model results show, as expected, that red bean is a highly temperature-sensitive crop, and that maize is more water-limited than bean due to its longer seasonal duration. Warming temperatures and less frequent rainfall have led to drought-related losses for both crops in the main commercial production areas, while heavier rains at planting and harvest have also negatively affected yields, especially for bean. Moreover, reduced precipitation in December and January has negatively impacted production for bean in the commercially important apante, or dry season, on the humid Atlantic side of the country. In these areas, however, substantial model uncertainty remains for maize, with an alternative model formulation showing substantial benefits from drier and sunnier conditions. At an annual, national scale, beans have been more affected by climate trends since 1970 than maize, with −5% yield declines per decade on harvested area for bean and −4% for maize, and −12% and −7% yield declines respectively on sown area (with the alternative model showing gains for maize). Climate adaptation responses include government efforts to limit bean exports to control consumer prices, a switch from red to black bean for commercial sales and export, and area expansion and migration for bean in order to maintain production levels.
    Agricultural and Forest Meteorology 11/2014; 200. DOI:10.1016/j.agrformet.2014.10.002 · 3.89 Impact Factor
  • Source
    • "Finally, in the Greater Horn, a region that is highly reliant on rain-fed agriculture, dry spells within any given rainy season can be as detrimental to crops as seasonal drought (e.g., Barron et al. 2003). Here it is simply noted that on subseasonal time scales, rainfall variations associated with the Madden–Julian oscillation (MJO) have been reported for both the short and long rains (e.g., Mutai and Ward 2000; Pohl and Camberlin 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper provides a review of atmospheric circulation and sea surface temperature (SST) conditions that are associated with meteorological drought on the seasonal time scale in the Greater Horn of Africa (the region 10°S–15°N, 30°–52°E). New findings regarding a post-1998 increase in drought frequency during the March–May (MAM) “long rains” are also reported. The period 1950–2010 is emphasized, although rainfall and SST data from 1901–2010 are used to place the recent long rains decline in a multidecadal context. For the latter case, climate model simulations and isolated basin SST experiments are also utilized. Climatologically, rainfall exhibits a unimodal June–August (JJA) maximum in west-central Ethiopia with a generally bimodal [MAM and October–December (OND) maxima] distribution in locations to the south and east. Emphasis will be on these three seasons. SST anomalies in the tropical Pacific and Indian Oceans show the strongest association with drought during OND in locations having a bimodal annual cycle, with weaker associations during MAM. The influence of the El Niño–Southern Oscillation (ENSO) phenomenon critically depends on its ability to affect SSTs outside the Pacific. Salient features of the anomalous atmospheric circulation during drought events in different locations and seasons are discussed. The post-1998 decline in the long rains is found to be driven strongly (although not necessarily exclusively) by natural multidecadal variability in the tropical Pacific rather than anthropogenic climate change. This conclusion is supported by observational analyses and climate model experiments, which are presented. Bradfield Lyon, 2014: Seasonal Drought in the Greater Horn of Africa and Its Recent Increase during the March–May Long Rains. J. Climate, 27, 7953–7975. doi:
    Journal of Climate 11/2014; 27:7953-7975. DOI:10.1175/JCLI-D-13-00459.1 · 4.90 Impact Factor
Show more