Forecasting of Banana Crop Productivity Using Geospatial Approach: A Case Study of Anand District

Abstract

The banana is one of the main fruit crops in the world as it has gained importance in the global market for many industries due to its high source of nutrients and its fibre content. Owing to climate change and irregular precipitation, the yield of banana crops is becoming very unpredictable and, thus, there is a need to understand the impact of climatic parameters on the yield. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. This study employs the geospatial approach to establish a relationship between climatic variables and the banana crop productivity of the Anand district of Gujarat, India. Sentinel data was utilized to derive various indices like Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Enhanced Vegetation Index (EVI), and Normalised Difference Water Index (NDWI). Land Surface Temperature (LST) was also derived using a Landsat dataset. Evapotranspiration (ET) data was also considered while understanding the impact of these parameters on yield. Values were extracted based on the ground control points (GCP) of different agricultural fields of the study area. Derived data was analysed using different statistical tools to understand the relationship between different indices and the productivity of the banana crop. Results indicated that the banana yield is highly dependent on water availability and the ET of the study area, proving that these parameters can be utilized for generating predicting models of banana yield.

Keywords

• banana
• sentinel data
• Landsat
• EVI
• NDWI
• evapotranspiration