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Kabindra Adhikari on Muck Rack

Kabindra Adhikari

As seen in: Wiley Online Library, MDPI
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Articles

Machine Learning and Geospatial Modelling of Soil Zinc: Implications for Crop Nutrition in the Brazilian Cerrado

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By Kabindra Adhikari, Andrew J. Margenot, Raul Roberto Poppiel
| Wiley Online Library Verified
1 Introduction The Cerrado biome covers 200 million hectares (22% of Brazil) and produces 60% of the Brazilian soybean, corn, cotton, and sugarcane (IBGE 2017). As a neotropical savanna on ancient weathered surfaces under a warm and humid climate, it is characterized by the predominance of acidic soils with low micronutrient availability, particularly Zn, which has reduced crop growth and yields by up to 40% in cereals, often without visible symptoms (Lopes 1983; Cakmak et al. 1999; Noulas et al.
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?

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By Efrain Noa-Yarasca, Chad B. Hajda, Kabindra Adhikari, Douglas Smith
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US

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By Sayantan Sarkar, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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