Characterizing and modeling the diversity of cropping situations under climatic constraints in West Africa

Abstract

The Sahel region is known for the high vulnerability of its agriculture to climate variability. Early warning systems that make use of agrometerological forecasts are one of the coping strategies developed by policy makers. However, the predictive quality of the tools and methods used needs improvement. In order to address some of these challenges, we conducted agronomic trials and on-farm surveys to adapt the SARRAH (Système d'Analyse Régionale des Risques Agroclimatiques, version H) crop simulation model, and also evaluated it in farmers' field conditions. The farmers' practices such as sowing dates and densities, fertilizer use and yields potentials of the millet and sorghum crops were characterized under different climatic conditions.

A Comparative Study on Satellite- and Model-Based Crop Phenology in West Africa

“…These results corroborate results from other studies [44,45]. The variability in the south, especially for the seedlings, was due to a greater variety of species and varieties; cropping calendars must be adapted, not only to plant characteristics (species and photoperiod characteristics), but also to the beginning of the season [24]. The observation that satellite-and model-derived phenometrics in Sikasso vary more from year-to-year than in Segou, especially the SOS, suggests that natural vegetation is important in a humid site, wherein the transition dates may vary due to non-homogeneous land cover.…”

“…Thus, in addition to rainfall parameters, the beginning of the crop cycle is based on previous studies [24,39] for pearl millet in Niger and sorghum in Mali [43]. For pearl millet and sorghum, the simulation starting dates were 1 March (to simulate early sowing), 1 May (to simulate intermediate sowing), and 1 July (to simulate late sowing).…”

“…The few ground phenology and/or cropping practice observations on the local scale for several years prohibits data validation from the ground and a deeper analysis of the phenology. However, a validated crop growth model for the sub-Saharan regions (SARRA-H, System for Regional Analysis of Agro-Climatic Risks) [24] provides precise agronomic information, which was well-documented using local varieties that are mainly cultivated by farmers in this area [24][25][26]. The model can reproduce the evolution of phenological stages and leaf area indices (LAI) of different tropical cereal species and varieties with mainly rainfall, temperature, global radiation, and evapotranspiration as input data.…”

Remote sensing of the land surface during the African Monsoon Multidisciplinary Analysis (AMMA)

“…Compared to passive microwave, ASAR has reduced temporal sampling, but provides higher spatial resolution, of the order of 1-10 km, depending on the ASAR mode and filtering process. LAI and SSM products were also derived at the scale of the supersite, and LC classification was developed for crop monitoring (Traore et al, 2011).…”

Untitled

“…For example, daily precipitation is required as input of rainfall-runoff models for hydrologists (Obled et al, 2009), used in climatologists' study of droughts (Usman and Reason, 2004;Gitau, 2011;Gitau et al, 2012). In agronomy, they represent the major input variable in plant growth models (Baron et al, 2005;Traore et al, 2011) and empirical statistical models (Schlenker and Lobell, 2010) whose objective is understanding and predicting changes in cereal yields for example (Ramarohetra et al, 2012;Philippon et al, 2015a). Most of these models require long series of daily rainfall.…”