Egypt faces wheat insecurity due to the limited cropped area of agricultural lands and the limited horizontal expansion disproportionate to the population increase. The issue of food security, crop consumption rates, and self-sufficiency is considered one of the most important problems facing countries that seek to improve sustainable agriculture and economic development to eliminate poverty or hunger. This research aims to use data mining classification techniques and decision tree algorithms to predict the food security status of strategic agricultural crops (e.g., wheat) as an Agro intelligence technique. Also, the outputs and extracted information from the prediction process will help decision-makers to take an appropriate decision to improve the self-sufficiency rate of wheat, especially in epidemic crises and hard times such as COVID-19, political, and economic disturbances. On the other hand, the research investigates the patterns of wheat production and consumption for the Egyptian population from 2005 to 2020. This research presents a methodology to predict the food security status of strategic agricultural crops through the case study of wheat in Egypt. The proposed model predicts the food security status of wheat with an accuracy of 92.3% to determine the self-sufficiency ratio of wheat in Egypt during the years from 2015 to 2020. Also, it identifies the factors affecting the food security status of wheat in Egypt, their impact on determining and improving the food security state and its rate of self-sufficiency.

With climate change in the world, changes in temperature and deficiency of irrigation water is considered as a serious threat affecting growth and crop production. Therefore, developing genotypes with existence stable high-yielding and high water use efficiency is a necessary. The purpose of this study was to detect the most yield adaptability/stability of eight bread wheat across 24 environments (combination of three irrigation regimes × four nitrogen levels × two seasons) based on AMMI and GGE methods. A randomized complete block designs with three replicates for each environment was used. AMMI analysis for, wheat grain yield exhibited the highly significant difference of genotypes, environment, GEI and first two interaction principal components (IPCA's). Based on AMMI stability value (ASV) and total rank (ASV and yield) discriminated genotypes Line 3 (G3), Line 1 (G1), Line 4 (G4) and Line 2 (G2) as the most stable and suited for water stress. Using GGE biplot facilitate comparison revealing Line 3 (G3) was the best and ideal genotype with high grain yield potential and stability. Generally, AMMI analysis and GGE biplot methods showed that genotypes Line 3 (G3) was detected to be the most adapted/stable under water deficiency. Thus they should be recommended for release with wider environmental adaptability in Egypt. Moreover, treatment 75 kg nitrogen with medium irrigation regime may be considered as alternative treatment for recommended high irrigation requirements and should be recommended for release with wider environmental adaptability in water deficiency of irrigation requirements

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• The 54th Annual Conference of Statistics, Computer Science and Operations Research, Agricultural Economics Research Institute (AERI), Giza, Egypt, 11-9 December 2012.
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Economics for Vegetable Oil Industry and its Effects on Food Security in Egypt
It was found that soybean and sunflower yields are stable as their farmers do not get high revenues such as other crops competing for the area such as maize, whether white or yellow, and vegetable crops such as tomatoes, peppers, zucchini, etc., and this is probably why some farmers are reluctant to grow it besides poor productivity Their yields are about 1.4 and 1.04, respectively, for soybean and abed el-Shams crops, as they did not achieve high profits compared to other crops competing for the unit area. Some indicators of the productive and economic efficiency of oil producing plants were estimated. Egypt, due to the lack of sufficient data and difficulty in obtaining it, has been estimated the rate of return on cost and net worth of revenues, where the results showed that the average number of oil-producing enterprises about 27 units of oil production, and the average number of workers by about 139943 workers, while the ratio of return to costs about 114.5% means that each pound will invest in this area to achieve a return of about 114.5 pounds, the net present value is estimated at 404974.7 pounds for the average period 2010-2017.
It was found that the average available production capacity is estimated at 2600358.67 million pounds, and the actual production is estimated at 1938334 million pounds, representing about 80.5 of the available capacity, where the idle capacity reached about 662024.7 million pounds, representing about 26.03% of the available energy for the average period 2015-2017. There are some factories are still operating at half of their production capacity and did not achieve the actual production required of them, where some companies resorted to esters of crude oil, manufacturing and refining because of the cheap price in addition to providing labor and saving their salaries.
The results of the estimated model now predict that the expected values of the variables of the model are (quantity produced oils, average per capita oil consumption per capita ?2, volume of imports ?3, during the period (2020-2025), as it is expected that the volume of total production Of oil from about 2755.4 thousand tons in 2020 to about 2908.0 thousand tons in 2025 with a growth rate of about 15.3%, while the average per capita consumption of oil is expected to increase from about 14.7 kg In 2020, the national consumption increased from 1458.5 million tons in 2020 to about 1665.2 million tons in 2025.
Recommendations
1 - appropriate pricing policies should be developed to contribute to increases in oil production rates.
4. Providing support from the government to producers of oil crops to encourage them to grow these crops of economic importance.
5 - Development of new varieties with high oil recovery rates.
6- Encouraging investors to establish production units for oil extraction.
8. Develop a policy and legislation to impose contract farming on farmers and motivate them to cultivate these crops, and put the mechanism for implementation and circulation to all regions of the Republic.

In the current study, four methods were used to compare the efficiency of different half-diallel analyses in determining the gene action mode in the half-diallel in bread wheat crosses, excluding reciprocals. The experiment was grown at Sakha Agricultural Research Station, using the randomized complete block design with three replications under natural infection of rusts. Regardless of the analyses restrictions, comparison of the tested methods indicated that Griffing model has features of formulas definition for estimating the effects and their variances. Morley-Jones model partitioned sum of square into additive and non-additive items. Hayman’s method was characterized by graphical analyses and genetic parameters. Meanwhile, Gardner and Eberhart method has more features than the others giving a clear-cut idea about the genetic aspects of heterosis by partitioning it into different components (average, varieties and specific heterosis). All methods agreed unanimously on, significant variation among genotypes and their components that were detected for all studied traits, mainly due to genetic diversity. The parents Gemmeiza11 and Giza168 were reported as the best combiners for grain yield and rust reaction. The cross Misr2 * Giza168 and Gemmeiza11* Giza168 were found to be superior and exhibited the highest specific combining ability effects and heterosis for grain yield. There were significant differences for both additive and non-additive types of gene action for all traits except for 100-kernel weight, confirming that both were almost equally important for the inheritance, as well as implying the presence of directional dominance and imbalance of gene distribution for resistance to wheat rusts. All traits revealed that the inheritance was controlled by over- and partial-dominance with additive gene action. The genetic information obtained would be utilized in wheat breeding program for improving grain yield per plant and resistance to wheat rusts.

The present study was carried out at Sakha Agricultural Research Station, ARC during 2013/14 to 2016/17 growing seasons. The aim was to investigate the gene mode of action and inheritance pattern of yield traits for use as indicators of high grain yield in four bread wheat crosses, namely Giza 171 x Vorobey, Gemmeiza11x Vorobey, Sids12 x Vorobey and Line 1 x Vorobey. Analysis of variance showed significant differences among the generation means for all studied traits. Scaling test showed that most studied characteristics were significant, indicating the presence of non-allelic interactions. Dominance gene effects were generally higher in magnitude than those of additive ones in the four crosses, indicating that dominant genes play an important role in the inheritance of such traits beside the additive one. Significant positive heterotic and inbreeding depression values were detected for grain weight and number of spikes per plant with the highest heritability and genetic advance, especially in cross3 (Sids12 x Vorobey). Partial-dominance was detected for days to maturity and 100-kernel weight and days to heading traits, however over-dominance towards the higher parent (Vorobey) was detected for plant height, number of spikes per plant, number of kernels per spike and grain weight per plant. All crosses gave high negative effect of the additive x additive to most of the traits, indicating that the materials used in this study had a decreasing allele expression, making the improvement of these traits in early generations ineffective. Based on these results, the studied crosses would be of interest in a breeding program for genetic improvement of wheat yield with delaying the selection to later segregating generations.

Development of high yield genotypes is one of the most important aims of sesame breeding programs. This study was carried out during 2016 and 2017 growing seasons in order to determine the genetic behavior of sesame set of 28 diallel F1 crosses and their parents. Genotypes were grown in Toshka Research Station Farm, South Valley, Egypt. Results elucidated a highly significant variation among genotypes, indicating a wide genetic variability for the studied traits and the possibility of genetic improvement using such genetic pools of sesame. The (GCA/SCA) ratio indicated great additive gene action for all studied traits. Parents: Introduced 589, Sohag 1, Shandweel 3 and Line119 were identified as good general combiner, whereas crosses (Shandweel 3 x Local 535), (Line11 x Line119), (Line119 x Local 535), (Sohag 1 x Shandweel 3) and (Introduced 142 x Introduced 589) were the best specific combiner for seed yield and its component traits. Additive (D) and dominance (H1) component of genetic variability were highly significant for most traits; however H1 estimates were greater than D, resulting in the average degrees of dominance (H1/ D)0.5 were more than unity, indicating, the role of over-dominance gene effects in the inheritance of the studied traits. Value of H1 was greater than H2, resulting in H2/4H1 that was below (0.25), indicating that positive and negative alleles at loci for these traits were not in equal proportional for parents. The covariance of additive and dominance effect (F) coupled with ratio of dominant/recessive alleles in the parents KD/KR was greater than one for most traits, indicating that the recessive genes were more in the parents than dominant ones. Narrow sense heritability recorded moderated estimates ranged from 0.29 to 0.68, meaning probability improvement some yield-related traits in sesame through selection.

Available data increase quickly every moment, this eventually drags to big data flooding. Hence there is an emergent need for exploiting big data in order to extract valuable knowledge from it. Adoption of distributed architecture and data intensive algorithms facilitates handling and processing big data. This paper introduces a distributed single pass clustering algorithm based on MapReduce in order to reduce running time of processing big data. Also, it introduces median based single pass clustering in order to mitigate the order of the input data problem that is associated with single pass clustering. Furthermore, it introduces a new hybrid approach which integrates median based single pass clustering and k-means algorithm. The proposed integration improves the median based clustering to work well with sparse data such as text.