World Hunger Map
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?
Maps serve the purpose of raising awareness about global hunger and places that are in need like the FAO Hunger Map and World Food Programme Food Security Analysis.
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?