Peanut (Arachis species) plants originated in South America where they have existed for thousands of years. Successively, peanut culture has been introduced in many African countries and was incorporated into local traditional food cultures. Numerous studies showed peanut nutritive importance and capacity to prevent several human diseases. The target of the present survey aimed to create a germplasm benchmark of peanut varieties in the north region of Côte d'Ivoire (West Africa country) since this plant is weakly studied in this geographic area. For this purpose, six peanut varieties were processed and pre and/or post-harvest measurements have been brought on seedlings. In addition, biochemical composition of peanut seed for each considered varieties were measured. Statistical analysis based on several R software functions showed a good quality of collected peanut data and proposed post-harvest parameters as an adequate factor clustering the present analyzed peanut varieties. Then, statistical analysis performed in this study, allowed to cluster analyzed peanut varieties in two different groups. Moreover, the same survey evidenced a strong agreement between both postharvest and biochemistry parameters assessing the difference between the two detected peanut variety groups (p-value < 0.05). Finally, the findings exhibited protein, glucose as well as ash biochemistry parameters as decent indicators selecting and clustering the present managed peanut varieties (p-value <0.05). In conclusion, this study proved a methodology demarche suggesting the possibility to hypothesize peanut germplasm benchmark in the savanna region of Côte d'Ivoire.

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Usually, quantitative data standardization and/or normalization procedures requested in biological and as well in biomedical data analysis with the purpose to infer about linear regression relationship between processed variables and/or conditions. Here, we embarked to understand performance of quantitative data transformation systems in terms of reducing data variability as well as assessing data distribution normality by a computational statistic approach. For this purpose, we performed several multivariate descriptive and analytical statistical tests. Even if results shown drastic reduction of data variability by applying presently data transformation procedures, it is noteworthy to underline the relative opposite attitude of Exponential (Expo) data standardization system in that sense. In addition although, results revealed variance homogeneity for data processed by both Maximum and Logarithm data transformation methods, it is noteworthy to underline a relative variance homogeneity with regard data submitted to Box-Cox, Z-score, Minimum-Maximum and Square Root data transformation methods. Further, findings exhibited high aptitude of Square Root, Box-Cox and Logarithm quantitative data standardization methods, in stabilizing processed data variability. Interestingly, results shown high performances of Logarithm and Box-Cox data standardization systems in term of adjusting data normal distribution. In addition, multiple comparison of mean by Turkey contrast test suggested the high performance in term of data normality with regard Box-Cox standardization method. In conclusion, even if our results revealed heterogenic performances of presently processed quantitative data transformation methods, it is noteworthy to underline the high performances of both Box-Cox and Logarithm methods

Fertile soil pressure represents a crucial concern vis-à-vis of agricultural crop yield improvement in several southern countries. Environmental concerns and soil low fertility as well as a rapid demographic development and as well intensive industrial exploitation with regard ground resource, drastically contribute in reducing agricultural land availability. We believe that multiple culture and/or intercropping experimentation designs, integration in southern countries agricultural practices, could partially overcame fertile soil pressure issues. The main types of intercropping include mixed intercropping, row intercropping and strip intercropping. Here we assessed maize/cowpea intercropping patterns as well as maize monoculture systems. For this purpose, experimental dispositive is as following; 5 parcels including sole maize plants, 5 parcels with alternation of maize and cowpea plots on the same row (strip intercropping) and 5 parcels including maize and cowpea alternation rows (row intercropping), by a computational statistical approach with the purpose to promote mixed crops practices in contrasting fertile soil availability concerns, optimizing maize plants growth process. Growth data (plant height and plant leave number) apropos 52 maize plants for each above described experimental sites were collected during 9 weeks and processed by own R script, including descriptive and analytical statistical surveys. Findings clearly shown a positive impact of intercropping practices in accelerating maize early growth process. Maize and cowpea rows intercropping exhibited a good performance in term of accelerating maize growth process as opposite to maize monoculture (p<0.03) and maize/cowpea strip intercropping parcels (p<0.001). This study highlighted the usefulness mixed cultures intercropping system based on maize/cowpea rows alternation planting pattern in improving maize early development and as well promoted experimental design as a valuable solution in agronomical research for contrasting agricultural concerns vis-àvis of limited productive and as well low fertile ground resources.