Adapatation of Sowing Date for Improving Sorghum Yield in Rainfed Areas in Sudan Using AQUACROP Model
Notice bibliographique
Résumé
Due to the rapid growth in world population, the pressure on water resources to feed the growing population is increasing. The Nile water share of Sudan is almost exploited; and agricultural production by rained water is threatened by the pressure of climate change. It is inevitable that the production per unit water consumed, the water productivity, must be increased to meet this challenge. This research therefore focuses on the benchmarking of physical water productivity in rain fed areas and gaining a better understanding of the temporal and spatial variations and the scope for possible improvement. A review of the available records and sources that provide measurements of crop-water productivity was consulted to assess plausible ranges of water productivity levels for rain fed Sorghum crop and to provide a first explanation for the differences that are found using AQUACROP model. As such this study may be considered as crucial step was to establish a water productivity database for the rain fed sorghum crop in the country. Sorghum (Sorghum bicolor (L.) Moench) which is the most important cereal crop in Sudan has been constrained by the detrimental effect of drought which has often caused food shortages. Almost 90% of the total sorghum cropped area is rain-fed, and 60% of that is in drought prone soil conditions. Spatial information on water use, crop production and water productivity will play a vital role for water managers to assess where scarce water resources are wasted and where in a given region the water productivity can be improved. Hence, a methodology has been developed in this study to quantify spatial variation of crop yield, evapotranspiration and water productivity using the AQUACROP model in five stations. The AQUACROP model is used to investigate optimum sowing date that result in maximization of grain yield.Benchmarking of rain fed Sorghum actual and potential grain efficiency in different agro-climate zones was made for the year 1979 to 2013. AQUACROP model was applied at five locations (Gedaref, Damazin, Dalang, El Fashir, and El Obyied) each representing an agro-climate zone. Causes of poor yield performance were investigated and consequently measures needed to improve performance were identified. The study indicates that increase in sorghum yields under historical climate conditions in the different studied stations is possible when early sowing is used and initial rain showers are utilized, yield decrease by 43% when sowing date is delayed from July 15 (the recommended date) to August 1. Stations with high rain fall (Damazin, Gadaref and Dalang) show little variations in inter-annual yields but with a tendency towards high yields, 3536, 3741, 3737 kg/fed for the above stations respectively compared to 2266 and 1086 kg/fed for El Obyied and El Fashir respectively at 15 June. The obtained WUE is lower in the driest regions (El Fashir, and El Obyied) and higher for those of high rain fall. To aid decision makers and crop growers in rain fed areas a set of recommendations for policy making and for future research were identified.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».