Precision Agriculture: A Revolution in Farming Skip to main content

Precision Agriculture: A Revolution in Farming

Precision agriculture (PA) is a farming management strategy based on observing, measuring and responding to temporal and spatial variability to improve agricultural production sustainability². It is used in both crop and livestock production. PA often employs technologies to automate agricultural operations, improving their diagnosis, decision-making or performing³⁴.

What are the benefits of PA?

PA can offer many benefits to farmers, consumers and the environment, such as:

- Increasing crop yield and quality by applying the right amount of inputs (such as fertilizers, pesticides, water, seeds) at the right time and place².
- Reducing costs and waste by optimizing the use of resources and minimizing losses due to pests, diseases, weeds or environmental factors².
- Enhancing environmental stewardship by reducing greenhouse gas emissions, soil erosion, water pollution and biodiversity loss².
- Improving farm management and profitability by providing timely and accurate information for decision making and planning².

How does PA work?

PA relies on various sources of data to monitor and assess the variability of crop and soil conditions within a field or a farm. Some of the data sources include:

Global Positioning System (GPS) and Global Navigation Satellite System (GNSS): These systems enable the farmer's and/or researcher's ability to locate their precise position in a field and create maps of the spatial variability of different variables (such as crop yield, terrain features, soil properties, etc.).
Remote sensing: This involves using sensors mounted on satellites, airplanes or drones to capture images of the field at different wavelengths (such as visible, near infrared, thermal, etc.). These images can be processed and analyzed to derive various indicators of crop health, growth and stress (such as vegetation indices, biomass, chlorophyll content, water status, etc.) .
Proximal sensing: This involves using sensors mounted on tractors, combines or other machinery to measure various parameters of the crop or soil in real time (such as plant height, leaf area index, moisture content, nutrient levels, etc.) . Some sensors can also be placed directly in the soil to wirelessly transmit data without human presence .
Soil sampling: This involves collecting soil samples from different locations within a field and analyzing them in a laboratory for various physical, chemical and biological properties (such as texture, pH, organic matter content, microbial activity, etc.).

The data collected from these sources can be integrated and processed using various software tools and algorithms to generate maps, models and recommendations for farm management. Some of the tools and techniques include:

Geographic Information System (GIS): This is a system that stores, manages and analyzes spatial data using layers of information that can be overlaid and manipulated.

Variable Rate Technology (VRT): This is a technology that allows for the application of different rates of inputs (such as seeds, fertilizers, pesticides, water) to different parts of a field based on the variability of crop or soil conditions.

Decision Support System (DSS): This is a system that provides guidance and advice for farm management based on data analysis and optimization models.

Artificial Intelligence (AI): This is a branch of computer science that aims to create machines or systems that can perform tasks that normally require human intelligence (such as learning, reasoning, problem-solving, etc.). AI can be used to enhance PA by developing machine learning algorithms that can learn from data and make predictions or recommendations for farm management.

What are some examples of PA applications?

PA has been applied to various crops and livestock systems around the world. Some examples are:
Corn: PA can help optimize nitrogen fertilizer application by using remote sensing or proximal sensing to estimate crop nitrogen status and VRT to apply variable rates of nitrogen based on crop needs.

Wheat: PA can help improve wheat quality by using remote sensing or proximal sensing to monitor grain protein content and VRT to apply variable rates of nitrogen or fungicides based on grain quality targets.

Cotton: PA can help reduce pesticide use by using remote sensing or proximal sensing to detect pest infestations and VRT to apply variable rates of insecticides based on pest thresholds or economic injury levels.

(1) Precision agriculture - Wikipedia.
(2) Field Notes - Precision Agriculture - CropLife Canada.
(3) What is precision agriculture/precision farming?.
(4) Precision Agriculture | Home - Springer.


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