Land Use Management: Improving Crop Yield Land Use Management: Improving Crop Yield Land Use Management: Improving Crop Yield

Dr Samuel Almond

MSc, PhD

samuel-almond

Biography

Samuel is the Earth Observation and Land Specialist accessing satellite data to provide answers for land based queries including the effects of climate change.

Dr Samuel Almond’s paper on Land Use Management: Improving Crop Yield.

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Land Use Management: Improving Crop Yield.

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Land Use Management: Improving Crop Yield

The Problem

G-STEP is working with agronomists Field Technique and their consortium partners to develop and apply Earth Observation solutions that improve the information available to those involved in the production, processing and marketing of arable crops. In current practices of precision agriculture, management activities are guided by changes in crop and soil conditions over a large area but not within individual fields. This study has shown the potential of remote sensing to identify small scale changes within single fields opening up micro scale management for small producers.

The Solution

This project is utilising satellite remote sensor reflectance data measured in the visible and near infrared region of the electromagnetic spectrum. Reflectance in these wavebands is linked with vegetation variables such as Leaf Area Index (LAI), which characterises canopy development and chlorophyll content - both indicators of crop health. The reflectance data is used to work out the Normalised Difference Vegetation Index (NDVI).This is a measure by which thicker healthier crops can be distinguished from thinner canopies and bare soil. NDVI data is processed to produce digital maps of crop health and canopy development at key phases during the crop growth cycle. These can be used either to monitor crop development throughout a single growing season or growth can be compared with previous years using archived geo-referenced satellite data. This allows for precise, targeted management solutions – such as the application of nutrients - and over time historical data sets will aid in future crop management planning. This new approach to precision agriculture is more sustainable, saves time and increases crop yield – increasing profits.