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A significant increase in food production, approximately 70%, is needed to feed the estimated 9.1 billion world population by 2050. Site-specific nutrient management through adoption of Precision Agriculture (PA) technologies can potentially increase crop productivity while keeping the use of chemical fertilizers at its minimum required level. In this study, agricultural land of tehsil Chunian was delineated into management zones using georeferenced soil attributes database developed by Soil Fertility Research Institute (SFRI), Punjab. Appropriate number of zones was determined after conducting fuzzy c -means unsupervised clustering analysis by using Management Zone Analyst (MZA) software and finally Quantum Geographic Information System (QGIS) was used to differentiate study area into five zones. Analysis of variance was performed to verify whether these defined zones reflect soil attributes. We concluded that tehsil Chunian could be categorized into five statistically different management zones based on soil electrical conductivity (EC), pH, soil organic matter (SOM), available potassium and available phosphorus levels. The highest crop yield was predicted in southeastern parts of tehsil Chunian represented by management zone-2 (4.4 t ha-1 ) followed by zone-1 (4.3 t ha-1 ). Whereas, northern and northwest areas were predicted with decreasing trend of wheat yield (zone-5, 3.7 t ha-1 ; zone-4, 4.0 t ha-1 ), respectively. We found that MZA and QGIS software could be used as supportive tools for making informed-decisions and the database of soil attributes developed by SFRI would serve as primary source of information for adoption of PA technology in coming years.

Muhammad Salik Ali Khan, Shahzada Munawar Mehdi , Aftab Ahmed Sheikh, Abdul Ghaffar Khan, Muhammad Shakar, Alamgir Alvi , Hafiz Riaz Ahmad, Munir David. (2018) Delineation of nutrient management zones in Chunian using soil attributes database developed by soil fertility research institute, Punjab, Pakistan, The Journal Soil & Environment , Volume 37, Issue 2.
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