Today’s farmers use sophisticated agriculture technology because they can save time and money.
Crops grow in specific locations. This makes Geographic Information Systems (GIS) an EXTREMELY relevant tool for farmers.
Precision farming uses GPS on the field. While satellites and drones collect vegetation and weather information from the sky.
Where does this data all go? That’s why agriculture maps serve it to the people. Let’s look at some agriculture technology examples.
Data From the Machine – Precision Farming
Farmers use precision agriculture because they can reduce the amount of fertilizer applied on the field.
Not only do farmers save money on fertilizer, but they are saving the environment from over-application. This is because of lot of the excess fertilizer tend to end up in streams and rivers by run-off.
Precision farming applies fertilizer only where it’s needed. It’s site-specific.
Sensors on a machine gather information about the crops. In addition, the GPS gives you the exact position on the field. Precision farming then applies a variable rate of fertilizer to nutrient-deficient sites.
The farmer who uses precision farming can save anywhere in the ballpark of 2-15$ per acre. Over time, this makes an incredible investment.
Data From the Sky – Satellites and Drones
What do crops need to grow?
Other than sunlight and nutrients, plants need the right amount of water. Too much (flooding) or little (drought) water impacts crop growth.
Satellite technology like Soil Moisture Ocean Salinity (SMOS) collect real-time (passive) microwave energy from the Earth’s surface. This can better forecast crop production and monitor drought and flooding.
Landsat satellites analyze the greenness of vegetation using indices like Normalized Difference Vegetation Index (NDVI). We have a local and global estimate of crop productivity for the entire planet from a long list of satellites orbiting the Earth.
What can drones collect on a field?
- Plant height, count and biomass estimates
- Presence of disease and weeds
- Plant health and field nutrients
- 3D elevation and volumetric data
Instead of workers scouting fields, agriculture technology like drones can cover more ground.
Drones can inspect crop health from the sky and where plant stress is occurring. Farmers can make actionable decisions on applying nitrogen and monitoring yields. Farmers can use precision watering sensors because they know where it’s needed most. They can combat the spread of pests by identifying critical intervention areas.
One small drone can help make some VERY powerful decisions for farmers.
Data online – Real-time mapping
The United States Department of Agriculture (USDA) National Agricultural Statistics Services (NASS) developed the mapping application CropScape where farmers can obtain acreage estimates of crop types.
Simply, farmers can see what crops are growing where and how much. In addition, government have also used CropScape on issues like food security, land-cover change and pesticide control.
At a global scale, the Food and Agriculture Organization of the United Nations AgroMap breaks down primary food crops with its global spatial database of agricultural land-use statistics.
For large-scale planning, online tools like plant hardiness defines suitable climatic growing areas for different types of crops. Likewise, the Canadian Land Inventory was the first GIS data set produced. It’s purpose was to classify the varying potential for agricultural production in Canada.
Modelling – Mashing data sets
Researchers are mashing together various inputs to better model and understand crop production.
The Crop Assimilation Model (CAM) and Soil-Water-Air-Plant (SWAP) in GRASS GIS serve as crop productivity monitoring tools by simulating soil, water and crop processes.
The Length of Growing Period (LGP) is when crops meet the full evapotranspiration demands of precipitation and soil moisture holding capacity. Each crop type has specific moisture requirements making LGP difficult to calculate.
The Erosion-Productivity Impact Calculator (EPIC) models crop yields and irrigation requirements to climate change. While the Agricultural Non-Point Source (AGNPS) Model predicts the effects of agriculture on water quality.
…And the Versatile Soil Moisture Budget (VSMB) simulates soil moisture conditions of cropland areas taking into account evapotranspiration, rainfall, runoff and other factors.
Geographic Information Systems assists decision-support. IDRISI’s Reducing Emissions from Deforestation and forest Degradation (REDD) determines the opportunity cost of potential agricultural revenue versus deforestation.
Aspect and microclimate can locate potential areas that can be harvested like the south-facing slopes of the Swiss Alps. The south-facing slopes shelter from cold and dry winds which is critical to successful crop growth.
Meeting Future Food Demand
How can we fulfill the needs of a growing and increasingly affluent population?
Agriculture technology is helping diagnose food security like in this Feeding the World Story Map.
Can we learn from historical data? Landsat satellite data provides a unique view of historical agricultural land. When you plot historical trends of land use over time, is there enough arable land to serve a growing population?
Satellite, mobile-collected and GIS data storage are safeguarding food-insecure populations by establishing underlying causes
Conclusion – Agriculture Technology
Precision farming, satellites, drones, webmaps and sophisticated models – this list represents some of the agriculture technology being used on farmland today.
The modern-day farmer needs to understand a lot more than just what to seed – soils, weeds, nutrients, weather, insects, disease, machinery and climate .
These emerging trends provide the location intelligence farmers need to get the job done faster and with more knowledge.
What are some of the other growing trends for agriculture technology?