Crop Yield Modeling and Estimation of Paddy: A Case Study of land Pooling Area Ward No.1 and 3 of Bandipur Rural Municipality of Tanahun, Nepal


Authors : Milan KC; Dr. Krishna Prasad Bhandari; Bikash Sherchan

Volume/Issue : Volume 7 - 2022, Issue 9 - September

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3S3kCoX

DOI : https://doi.org/10.5281/zenodo.7212619

Rice is largely grown type of crop in Nepal and has an important role in food security and nutrition. Therefore proper and timely monitoring, forecasting, and prediction are necessary for planning and management purposes for governmental and nongovernmental bodies that have as been working for food security. So the forecasting and estimation of crops and monitoring of different phases of the crop are necessary for food and crop management. For proper monitoring, remote sensing imagery and NDVI series are capable of the identification of crop growth and health as well as crop yield prediction model development. This research reflects a model for paddy monitoring using Sentinel 2 imagery of every 5 days interval by extracting NDVI series. The land pooling area of seratar, and Nahala, Bandipur Rural Municipality of Nepal has been selected as a study area for understanding and analyzing the NDVI value of different phonological stages of paddy with different land management factors like water level, level of damages, soil type, fertilizers used, date of transplantation, amount of seed, sowing date, etc. Google Earth Engine (GEE) is an ideal platform that is used to study and extract the NDVI of multiple sentinel images of study areas. Importantly, GEE reduces time and space for data processing and analysis. The hectic process of getting satellite imagery from a different sources, the downloading process of large images, their correction and classification collectively known as remote sensing processes can be resolved with this open source platform. By using the time series NDVI value, we can assess the correlation between the NDVI values and land management factors. Along with the paddy monitoring, the rice yield prediction and estimation model is developed using the regression model. The model classifies only those parameters which are highly correlated to the NDVI values. And from that, we are, able to develop the crop yield prediction model. Besides that, we have prepared the rice distribution map, LULC map, and agriculture map from the data that we collected from the field survey. And there is also a socioeconomic analysis of crop yield model in the scenario of world and Nepal. It reflects its importance in planning, policy-making maintaining the demand-supply chain in distribution of agriculture production etc.

Keywords : Estimation, LULC, time series, Regression, NDVI, GEE.

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