PRODUCTIVE, ECONOMIC AND SOCIAL INDICATORS OF SOME CROPS IN THE SOUTH VALLEY
Bibliographic record
Abstract
After the construction of the High Dam, the High Dam Lake was formed and is one of the largest freshwater lakes in the world, extending for a length of 500 km between Egypt and Sudan, Of which 350 km are located on the Egyptian side, 150 km on the Sudanese side and the average width is about 10 km, which is the strategic water reservoir for Egypt, where the lake capacity ranges between 31, 169 billion cubic meters (Ministry of Water Resources and Irrigation, 2006). The South Valley region is one of the new industrial and agricultural development areas that attracted investors as a result of its virgin lands and an unspoiled climate. As a result of these investments, societies have emerged along Lake Nasser in the south of the valley. However, these societies have faced the problem of weak potentials of small farmers in the south of the valley, despite the fertility of the cultivated areas and their high productivity. Therefore, the research aimed to study the economic and productive indicators of some crops (melons and tomatoes) in the study area, And identify the most important obstacles affecting agricultural activity, and the reasons for the weak capabilities of small farmers in the study area. The research dealt with a descriptive and quantitative analysis method to process the data obtained by designing a questionnaire containing a number of questions aimed at identifying the average net income of a family from agricultural activity such as acre productivity and net income for watermelon and tomato crops in the south of the valley, The cultivated areas were divided into three levels according to the farm capacity (small farm capacities less than 3 acres and medium farm capacities from 3-10 acres and large farm capacities greater than 10 acres) in order to reach an estimate of the value of productive and economic indicators of agricultural activity and to identify the most important obstacles facing activity Agricultural and how to put in place the appropriate mechanism to solve these obstacles, Approximately 90% of the 120 subjects in the South Valley responded to the questionnaire during the winter season 2018. The study concluded that the statistical significance of the differences between the averages of the net acres of tomato yield estimated at 11,500 pounds in different farm capacities has increased, and this significance has returned to the increase in the net yield of the small farm capacity for medium and large capacities, as well as an increase in the average farm capacity for the largest and the significance of these differences at the level of Moral 0.001. This may be due to the interest of the study sample farmers in the different production stages, and hence the rise in the total revenue per acre, as there has been an increase in crop prices from previous years. The statistic of the average net acre yield of watermelon crop between different farm capacities, which was estimated at 14916.7 pounds. The research recommended the necessity of supporting and providing municipal, chemical fertilizers and fuel materials, and work to provide cars for the collection and transportation of agricultural products to reduce transportation and marketing costs in the study area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".