From Climate to Agriculture Limited Predictand Relevance

The scale mismatch issue becomes more challenging indeed when agricultural applications are at stake. In Sudano-Sahelian West Africa, proper understanding of intra-seasonal variability patterns is of critical importance because of the highly unstable onset of the rainy season and the frequence of dry spells (Ati et al. 2002; Dodd and Jolliffe 2001; Omotosho et al. 2000; Ward et al. 1999). The length of the growing period (LGP) is mainly a function of the date of the first rains (Sivakumar 1988), which is delayed with latitude and varies widely from year to year (Fig. 19.1a). This important relationship basically results from the independence between the onset and end dates of the rainy season (Fig. 19.1bc). The ability to predict seasonal rainfall is then relatively less important, with the exception of the northernmost desert margins, where LGP is 'invariably' very short and water availability - as opposed to water distribution - becomes the central issue (Ingram et al. 2002). In that marginal agricultural environment running from southern Mauritania to northern Burkina Faso, southern Niger and central Chad, there might be scope for the application of selected seasonal forecasts (e.g. JAS rainfall), for which reasonable skill is observed with short lead times (Neil Ward, IRI, New York, personal communication). However southwards across Sahelian, Sudanian and northern Guinean agro-ecologies, the relationship between

Fig. 19.1. a Duration of rainy season as a function of onset date in julian days for Sikasso (northern Guinea zone, 11°21' N) and Mopti (Sahelian zone, 14°31' N); normal period: 1971-2000; b relationship between onset and end dates for Sikasso; average end date is highlighted by continuous line with ±2 standard deviations (dashed lines); average end: 6 October, standard deviation: 12.0 days; average onset: 23 May, standard deviation: 12.9 days; c relationship between onset and end dates for Mopti; average end date is highlighted by continuous line with ±2 standard deviations (dashed lines); average end: 10 September, standard deviation: 12.2 days; average onset: 15 July, standard deviation: 11.9 days

Fig. 19.1. a Duration of rainy season as a function of onset date in julian days for Sikasso (northern Guinea zone, 11°21' N) and Mopti (Sahelian zone, 14°31' N); normal period: 1971-2000; b relationship between onset and end dates for Sikasso; average end date is highlighted by continuous line with ±2 standard deviations (dashed lines); average end: 6 October, standard deviation: 12.0 days; average onset: 23 May, standard deviation: 12.9 days; c relationship between onset and end dates for Mopti; average end date is highlighted by continuous line with ±2 standard deviations (dashed lines); average end: 10 September, standard deviation: 12.2 days; average onset: 15 July, standard deviation: 11.9 days

3-monthly rainfall, soil water regimes and plant growth patterns is less clear cut: the relevance of seasonal products for agricultural applications therefore decreases.

Prospects for Temporal Downscaling: Disciplinary Divergences

Important ongoing work in the framework of the Multidisciplinary Analysis of the African Monsoon project (AMMA 2005) indicates that any potential application of downscaled seasonal forecasts will need to overcome a persistent dichotomy between climatologists and agriculturalists when it comes to farm decision-making advisories. The former advocate later sowing dates synchronous with an abrupt northward shift of the ICTZ, which they connect to monsoon onset, as opposed to the pre-onset (Sul tan and Janicot 2003). The latter insist that the risk linked to delayed sowing (N leaching, lower radiation and temperatures, rainfall aggressivity on younger shoots, waterlogging, pest pressure and mostly competition from weeds) is considerably larger than the risk, associated with farmer earlier planting strategies, of losing 2-3 kg ha-1 of seeds (Vaksmann et al. 2005). Many of the biotic and abiotic stresses that impact final yield are not taken into account by water balance models (Sultan et al. 2005) and could explain these divergent views of what could be a safe 'agronomic' start to the cropping season.

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