Data Driven Agriculture

Data Driven Agriculture
Data Driven Agriculture , Concept and Benefits

The proverbial picture is worth a thousand words; today, through sophisticated remote sensing and associated data analytics, geospatial observations can provide us with millions of data points. By integrating drone/aircraft/satellite remote sensing imagery with geographic information systems analysis, data-driven, variable rate irrigation systems, water and fertilizer applications are managed more efficiently than the status quo, with harvest productivity increased by an estimated 20% for the same fields.  Thus, Data Driven Agriculture or DDA has sometimes been called the “Fourth Revolution in Agriculture.”

DDA creates an opportunity for agriculture producers to be true stewards of valuable resources (land and labor) and the many crop inputs (irrigation, fertilizer and crop protection products) by having access to real time growth production data, and to derive real time prescriptions that can responsibly apply inputs when and where required in season. DDA offers the opportunity to produce a better quality, higher net yield crop that benefits the entire value chain, the producer, the processor, the end consumer, and ultimately, and perhaps most importantly the environmental and climactic health of the planet.

The Benefits of Data Driven Agriculture

  • Maximize crop quality and yields year-round
  • Streamline operations and improve productivity
  • Grow healthier, tastier, and more beautiful crops
  • Better allocate human and natural resources
  • Cultivate closer to the table and improve crop shelf life
  • Preserve the climate/environment and reduce waste
  • Optimize energy use and save on costs

Fertilizer Management

Maintaining an agricultural field is both a science and an art, with irrigation, fertilizer and crop protection applications being an essential piece of that process. Fertilization rates, timing, and placement, accommodating for changing weather conditions can all make the difference between having a bumper crop or a disappointing harvest. Deciding which crop inputs to use, where to apply them and how much to use depends on many factors. These include soil composition and properties, moisture content, crop health, available irrigation techniques, and forecasted weather. Artificial intelligence helps aggregate and fuse these disparate sources of information to generate actionable information for farmers to make timely decisions that both conserve resources and deploy them when needed for maximum impact.

Crop Health

Remote sensing data and information helps the farmer in two very important ways: There is, of course, the immediate, actionable content provided for day-to-day farm management. Perhaps a more important component is that archival information collection over several years can help optimize crop performance models that predict where and when problems will appear as well as generate early estimates of harvest yield. Crop Health “Early Warning” include predictions of the onset of drought, disease and pest stress and developing effective mitigation strategies to restore crop yield. Ultimately, consumer demand, effects of ever-changing markets combined with field conditions and meteorological data can help with longer term, strategic decisions such as crop selection, crop rotation, farm management during the crop cycle and ultimately timing the harvest for maximum economic benefit.

If you are considering using GIS and Remote Sensing data acquisition and analysis for crop monitoring, sustainable farm management and maximizing harvest yield, SaraniaSat CANEUS can help you with its advanced data acquisition and analysis strategies.  Please contact us at: [email protected]

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