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Record W2905491862

Assessment of the Suitability of Raised Bed – Furrow Irrigation Technique in Saving Water for Wheat Production in New Halfa Area, Sudan

2018· dissertation· en· W2905491862 on OpenAlexaboutno aff
Mohamed Yousif Daffalla Ebdelrahman

Bibliographic record

VenueMELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas)) · 2018
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationMathematicsEnvironmental scienceAgronomyBiology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Agriculture in Sudan is the principal source of income and livelihood for 60 to 80% of the population (Elgali,2010). Agriculture is divided into two main sectors; irrigated and rainfed. The irrigated sector covers about 1.8 million ha including the Gezira, Rahad, New Halfa, Elsuki,White Nile and Blue Nile schemes. Gezira, Rahad and New Halfa are considered the most important, and they produce cotton, groundnut, wheat, sorghum and vegetables (Mahir and Abdelaziz, 2010). Sudan has the largest irrigated area in sub - Saharan Africa and ranked second only to Egypt on the continent in terms of irrigated agriculture. Commercial agricultural activities are mostly concentrated in a belt across the center of the country, known as the central clay plain, which extends approximately 1100 km from south to north between latitudes 10º and 14º North, in the arid, semi - arid dry Savannah zone ( UNEP, 2007). Agriculture in Sudan accounts for 97% of the country's water use ( Sullivan, 2010, Barton and Writer, 2012).The diversion of water to mechanized farms and intensive cultivation by rural farmers is contributing to the spread of arid conditions across Sudan (Barton and Writer,2012). Water is in high demand to meet the needs of the rapid population growth and food production and plans to expand agriculture through irrigation further raises the demand for water (Taha, 2010). Water requirement will become severe if the environmental factors are considered, such as increased desertification and degradation, which have intensified Sudan's water problems (Ashok, 2008). 2 Mahgoub (2014) stated that Sudan would face water deficit if it implemented its policy to extensively increase the area of irrigated land. Wheat is a cereal grain grown all over the world. It is the third most produced cereal after maize and rice and the staple food of millions of people. The world map (2017) shows a list of top ten wheat producers in the world. The European Union, which is an amalgamation of several European nations, tops the list. However, among other nations, China is the world’s second largest wheat producing nation, followed by Russia, USA and Canada. The total world wheat production between 1996-2011 is listed in Table 1.1. Sudan total cereals production is usually sufficient to meet domestic needs, especially in terms of sorghum and millet, but is a net importer of wheat (Ahmed, 2010). Wheat imports started to increase since 1990s up to 2006 which reflected the change in the population feeding patterns (Abbadi and Ahmed, 2006). Bread consumption has been widespread in both rural areas as a consequence of changing tastes, convenience and consumer subsidies. Table 1.2 shows the wheat production in Sudan from 1999 to 2004 and from 2010 to 2014. Sudan imports of wheat during 2009- 2014 shown in Table 1.3. Moreover, according to Sudan tribune (2014), Sudan imports about 2millions metric tons yearly which costs about 1.5 milliard Dollars. They added that Sudan wheat production covers about 35% of its demand in the best situations. Wheat is a strategic crop in Sudan produced under irrigation during the dry and comparatively cool and short growing season ( November - March).The main production areas of wheat in Sudan are the Northern State and in the central clay plains in the schemes of Gezira ( 168,000 ha) New Halfa (25,200 ha) and Rahad (18,900 ha) in the semi arid climate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.353
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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