Prospect of Developing Soil Health Index in Bangladesh

Volume 6 - Issue 2

Sadiq MS¹*, Singh IP², Ahmad MM³, Lawal M⁴ and Kabeer YM¹

Received: January 23, 2019;   Published:February 04, 2019

DOI: 10.32474/CIACR.2019.06.000234

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Abstract

National economy of Bangladesh yet highly depends on agricultural development. The country has to produce foods for its everincreasing population from limited cultivable areas even from unfavorable ecosystems. Food demand has compelled the farmers to grow two to four crops in a year with and without balanced fertilizers and thus soil health has deteriorated. Soils of many parts of the country are deficient in essential macro- or micro-nutrients viz. potassium, zinc, boron, sulphur, and molybdenum. Different national research institutes have analyzed most representative soils of the country but no soil quality index (SQI) is available. The SQI needs to be developed on the basis of primary soil physical, chemical and biological attributes. These indices will help to classify soil capability to plan for sustainable agri-management strategies in terms of water and fertilizers management, adoption of appropriate tillage practices, residue and solid waste management. The development of pedo-transfer functions for characterizing soil moisture and soil nutrients characteristics are important for its subsequent use. There is also need to develop soil managers for handling problem soils in terms of crop choices and land resources management. Existing literature in Bangladesh is enough to develop SQI for subsequently relating with the resources and inputs management, crop simulation models for land use planning, inputs and resource management, yield estimates, climate change and environmental impact assessment. In the present article we have tried to link SQI for operational advisory service to the farmers for maximizing profits and also to ensure the environmental safety and sustainable soil health.

Keywords: Pedo-Transfer Functions; Decision Support System; Water and Nutrients Availability; Soil Managers; Cloud Computing; Machine Learning; Soil Information System

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