Precision agriculture
Precision agriculture (PA) is a management strategy that gathers, processes and analyzes temporal, spatial and individual plant and animal data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.”[2] It is used in both crop and livestock production.
The interest in the phytogeomorphological approach stems from the fact that the geomorphology component typically dictates the hydrology of the farm field.
These arrays consist of real-time sensors that measure everything from chlorophyll levels to plant water status, along with multispectral imagery.
However, recent technological advances have enabled the use of real-time sensors directly in soil, which can wirelessly transmit data without the need of human presence.
[12][13][14] Precision agriculture can benefit from unmanned aerial vehicles, that are relatively inexpensive and can be operated by novice pilots.
These agricultural drones[15] can be equipped with multispectral or RGB cameras to capture many images of a field that can be stitched together using photogrammetric methods to create orthophotos.
In classrooms, conferences, workshops, and field days, educators of precision agriculture have struggled to keep up with the number of questions being asked.
Training people to use precision agriculture technologies has proven difficult, in contrast to teaching the fundamental ideas and concepts, which have been intuitive and rather straightforward.
[18] It is expected that by 2050, the global population will reach about 9.6 billion, and food production must effectively double from current levels in order to feed every mouth.
[18] The first wave of the precision agricultural revolution came in the forms of satellite and aerial imagery, weather prediction, variable rate fertilizer application, and crop health indicators.
[21][22] Precision agriculture uses many tools, but some of the basics include tractors, combines, sprayers, planters, and diggers, which are all considered auto-guidance systems.
This information may come from weather stations and other sensors (soil electrical resistivity, detection with the naked eye, satellite imagery, etc.).
In the American Midwest (US), it is associated not with sustainable agriculture but with mainstream farmers who are trying to maximize profits by spending money only in areas that require fertilizer.
This practice allows the farmer to vary the rate of fertilizer across the field according to the need identified by GPS guided Grid or Zone Sampling.
In Latin America the leading country is Argentina, where it was introduced in the middle 1990s with the support of the National Agricultural Technology Institute.
Uptake of GPS is more widespread, but this hasn't stopped them using precision agriculture services, which supplies field-level recommendation maps.
[31] While digital technologies can transform the landscape of agricultural machinery, making mechanization both more precise and more accessible, non-mechanized production is still dominant in many low- and middle-income countries, especially in sub-Saharan Africa.
[32][33][34] Examples include the AgroCares hand-held soil scanner, uncrewed aerial vehicle (UAV) services (also known as drones), and GNSS to map field boundaries and establish land tenure.
[35][36] Precision livestock farming supports farmers in real-time by continuously monitoring and controlling animal productivity, environmental impacts, and health and welfare parameters.
[38] Global automatic milking system sales have increased over recent years,[39] but adoption is likely mostly in Northern Europe,[40] and likely almost absent in low- and middle-income countries.
[41] Automated feeding machines for both cows and poultry also exist, but data and evidence regarding their adoption trends and drivers is likewise scarce.
Precision agriculture management practices can significantly reduce the amount of nutrient and other crop inputs used while boosting yields.
[50] Other innovations include, partly solar powered, machines/robots that identify weeds and precisely kill them with a dose of a herbicide or lasers.
Aerial photography from light aircraft can be combined with data from satellite records to predict future yields based on the current level of field biomass.
External sensors track movement patterns to determine the cow's health and fitness, sense physical injuries, and identify the optimal times for breeding.
Monitoring of a honeybee colony's health via wireless temperature, humidity, and CO2 sensors helps to improve the productivity of bees, and to read early warnings in the data that might threaten the very survival of an entire hive.
[58] Machine learning may also provide predictions to farmers at the point of need, such as the contents of plant-available nitrogen in soil, to guide fertilization planning.
