Affiliation:
1. Universite Moulay Ismail Faculte des Sciences
2. CRRA Meknès: Centre Regional de la Recherche Agronomique de Meknes
3. Sidi Mohamed Ben Abdellah University Faculty of Science and Technology of Fez: Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes
Abstract
Abstract
The agricultural system that prevails in Morocco is typified by the triptych of cereals, cattle, and olive cultivation. The cereal sector holds significant prominence in the Moroccan agricultural industry. This phenomenon exerts a notable socio-economic influence on the country, as it constitutes a notable 10-20% of the overall Gross Domestic Product (GDP).
In the year 2017, the commonly cultivated acreage for wheat was recorded to be nearly 2500 hectares, while that for barley was approximately 2000 hectares. In relation to production, it has demonstrated substantial advancement in contrast to alternative cereals. The approximate output of Triticum aestivum, commonly known as common wheat, is 60,000 quintals.
As such, we have opted to utilize the Triticum aestivum L. cultivar of wheat, and have procured a range of biofertilizers derived from organic sources such as Shrimp waste, Potato peels, and Almond shells. These biofertilizers have been selected due to their high concentration of beneficial plant nutrients, including but not limited to nitrogen, phosphorus, and potassium.
The physico-chemical analyses were conducted at the Joint Analysis Center of the Faculty of Sciences (FSM) and the Agronomic Research and Plant Physiology Unit of the National Institute for Agronomic Research (NIAR) in Meknes. The present study placed emphasis on Fourier Transformation Infrared Spectroscopy (FTIR), X-ray diffraction (XRD), pH value, nitrogen (N) content, phosphorus (P) content, potassium (K) content, organic matter (OM) content, total organic carbon (TOC) content, and dry matter (DM).
This article explores the examination of agronomic data and soil mineralization for common wheat through the utilization of multivariate analytical techniques, including major component analysis, hierarchical classification, and self-organizing topological maps (SOM).
Publisher
Research Square Platform LLC
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