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Universität Augsburg (2015)

Optimizing Planting Dates for Agricultural Decision-Making under Climate Change over Burkina Faso/West Africa

Waongo, Moussa

Titre : Optimizing Planting Dates for Agricultural Decision-Making under Climate Change over Burkina Faso/West Africa

Auteur : Waongo Moussa

Université de soutenance : Universität Augsburg

Grade : Doctoral Thesis 2015

Résumé partiel
Rainfed agriculture is the main source of income for population and the main driver of the economy in Africa, particularly in West Africa (WA). The agricultural system is characterized by smallholder and subsistence farming in a context of farmers’ low capacity. In water-limited regions of WA, most of the crop management decisions are made based on the perceived risk of climate and the socio-economic conditions of the farmers. Therefore, technologies and approaches in the field of agricultural water management are likely to make a difference for agricultural development and thus food security. However, only those strategies which require little resources in terms of labor and money have a chance to engage a large number of farmers. As a farming strategic decision, the planting time has the potential to sustain crop production as well as to be adopted by farmers. In the context of high rainfall variability and little irrigation options in WA, the crop planting date in WA is a crucial tactical decision for farmers and therefore their major concern. With regards to the high intra-seasonal rainfall variability in WA, early planting dates can lead to crop failure due to long dry spells which occur shortly after planting. In contrast, late planting dates have the chance to avoid crop failure but they correspond to short growing seasons which can potentially reduce crop production. In this thesis, an approach to derive an optimal planting time has been developed. Based on the crop water requirements throughout the crop growing cycle, this planting date approach uses a process-based crop model in conjunction with a fuzzy rule-based planting date definition to derive optimized planting dates (OPDs). First, by taking into account the inherent uncertainties of rainfall measurements and computations issues, three fuzzy logic memberships, which are fully determined by two fuzzy parameters each, have been developed to represent the three main criterions used to define planting date. Then, the General Large-Area Model for annual crops (GLAM) and the fuzzy rule-based planting date have been coupled with a genetic algorithm optimization technique. Finally, this has been applied to calibrate GLAM for maize cropping and subsequently to derive OPDs for maize cropping. To allow a time window for crop planting, an ensemble member principle has been applied to derive a 10-member ensemble of optimized fuzzy parameters. Burkina Faso (BF) has been selected as a case study area to derive OPDs. The performance of the OPDs approach have been evaluated by comparing maize yield derived from the OPDs method and two state-of-the-art methods which are currently in use in WA. The analysis comprises both present climate and future climate projections. Present climate data encompassed observed data and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data over BF for the period 1961-2010 and 1980-2010, respectively. Future climate encompassed eight regional climate models outputs based on the greenhouse gas emission scenarios RCP4.5 and RCP8.5 covering the period 2011-2050. Beside the climate data, soil and observed maize yield data have been involved in this study.The results show that, on average, OPDs ranged from 1 May (South-West) to 11 July (North) across the country under present climate.

Mots clés  : climate change ; crop model ; crop planting date ; genetic algorithm


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Page publiée le 29 août 2016, mise à jour le 19 septembre 2017