Climate change, climate variability and adaptation options in smallholder cropping systems of the Sudano - Sahel region in West Africa
Notice bibliographique
Résumé
In the Sudano-Sahelian zone of West Africa (SSWA) agricultural production remains the main source of livelihood for rural communities, providing employment to more than 60 percent of the population and contributing to about 30% of gross domestic product. Smallholder agricultural production is dominated by rain-fed production of millet, sorghum and maize for food consumption and of cotton for the market. Farmers experience low and variable yields resulting in increasing uncertainty about the ability to produce the food needed for their families. Major factors contributing to such uncertainty and low productivity are climate variability, climate change and poor agricultural management. The objective of this thesis was to evaluate through experimentation, modelling and participatory approaches the real and perceived characteristics of climate variability and change and their effects on crop production in order to identify opportunities for enhancing the adaptive capacity of farmers in the Sudano - Sahelian zone. The general approach was based on, first, understanding the past trend of climate and its effect on the yield of main crops cultivated in southern Mali; second, evaluating together with farmers different adaptation options in the field; third, evaluating climate adaptation options through experimentation on station; and fourth, evaluating the consequences of different adaptation options under different long term scenarios of climate change. Minimum daily air temperature increased on average by 0.05oC per year during the period from 1965 to 2005 while maximum daily air temperature remained constant. Seasonal rainfall showed large inter-annual variability with no significant change over the 1965 - 2005 period. However, the total number of dry days within the growing season increased significantly indicating a change in rainfall distribution. There was a negative effect of maximum temperature, number of dry days and total seasonal rainfall on cotton yield. Farmers perceived an increase in annual rainfall variability, an increase in the occurrence of dry spells during the rainy season, and an increase in temperature. Drought tolerant, short maturing crop varieties and appropriate planting dates were the commonly preferred adaptation strategies to deal with climate variability. Use of chemical fertilizer enhances the yield and profitability of maize while the cost of fertilizer prohibits making profit with fertilizer use on millet. Training of farmers on important aspects of weather and its variability, and especially on the onset of the rains, is critical to enhancing adaptive capacity to climate change. A field experiment (from 2009 to 2011) indicated that for fertilized cereal crops, maize out yielded millet and sorghum by respectively 57% and 45% across the three seasons. Analysis of 40 years of weather data indicated that this finding holds for longer time periods than the length of this trial. Late planting resulted in significant yield decreases for maize, sorghum and cotton, but not for millet. However, a short duration variety of millet was better adapted for late planting. When the rainy season starts late, sorghum planting can be delayed from the beginning of June to early July without substantial reductions in grain yield. Cotton yield at early planting was 28% larger than yield at medium planting and late planting gave the lowest yield with all three varieties. For all four crops the largest stover yields were obtained with early planting and the longer planting was delayed, the less stover was produced. Analysis of predicted future climate change on cereal production indicated that the temperature will increase over time. Generally stronger increases occur in the rcp8.5 scenario compared to the rcp4.5 scenario. The total annual rainfall is unlikely to change. By midcentury predicted maize grain yield losses were 45% and 47% with farmer's practice in the rcp4.5 and rcp8.5 scenarios respectively. The recommended fe
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».