Journal of Electronics and Informatics
IRO Journals
Volume 3 — Issue 1 — March 2021

Analysis of Soil Nutrients based on Potential Productivity Tests with Balanced Minerals for Maize-Chickpea Crop

https://doi.org/10.36548/jei.2021.1.003
H. James Deva Koresh
Research Scholar, Karunya University, Coimbatore, India
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Koresh, H. James Deva. 2021. “Analysis of Soil Nutrients Based on Potential Productivity Tests With Balanced Minerals for Maize-Chickpea Crop”. Journal of Electronics and Informatics 3 (1): 23-35. https://doi.org/10.36548/jei.2021.1.003.
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Keywords

— Soil nutrition
— Crop productivity
Published: 22-03-2021

Abstract

Generally, a soil nutrients test has been performed for determining the productivity measures of any plant. It includes many challenges of environmental impacts and climate adaptation. To maintain the crop nutrients quality without affecting previous performance from the soil, it is required to minimize the challenges in the soil health sector can be increased economic returns from crop productivity. This article represents the review on improving productivity for soil nutrition. Soil nutrition was tested and assessed using the existing method, and deficiencies in the soil were identified that could be improved using some standardized methods. This productivity function of soil supply is measured by a various spatial scale which is a part of this research. The objective aims to achieve high productivity in the context of soil and also to realize environmental impact for soil functionality, productivity function, and resources information. The classification of soils corresponding multitude of approaches developed globally for potential soil productivity. The main focus is to determine strategies for the effects of a balanced nutrition system of maize-chickpea. The treatment and control can be developed and tested every year on crop yield. Besides, this research presents a future enhancement of improved productivity tests for a balanced soil nutrition system for better crop yield. The soil classification will be categorized with a knowledge base algorithm for further accuracy for the system.

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