Precision Agriculture
Precision agriculture is an agricultural resource management strategy that collects, processes, and evaluates data and offers insights to help farmers optimize and increase soil quality and productivity. It is used in both crop and livestock production.
Management decisions count on precision agriculture data points to improve farmland and farm produce across several key areas, including:
1. Resource use efficiency
2. Sustainability
3. Profitability
4. Productivity
5. Quality
Precision agriculture is a key component of the third wave of modern agricultural revolutions. The first agricultural revolution was the increase of mechanized agriculture. Each farmer produced enough food to feed about 26 people during this time. The 1960s prompted the Green Revolution with new methods of genetic modification, which led to each farmer feeding about 156 people. 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. With new technological advancements in the agricultural revolution of precision farming, each farmer will be able to feed 265 people on the same acreage.
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. The second wave aggregates the machine data for even more precise planting, topographical mapping, and soil data.
Prescriptive planting
Prescriptive planting is a type of farming system that delivers data-driven planting advice that can determine variable planting rates to accommodate varying conditions across a single field, in order to maximize yield. It has been described as "Big Data on the farm." Monsanto, DuPont and others are launching this technology in the US.
Principles
Precision agriculture uses many tools but here are some of the basics: tractors, combines, sprayers, planters, diggers, which are all considered auto-guidance systems. The small devices on the equipment that uses GIS (geographic information system) are what makes precision agriculture what it is. You can think of the GIS system as the “brain.” To be able to use precision agriculture the equipment needs to be wired with the right technology and data systems. More tools include Variable rate technology (VRT), Global positioning system and Geographical information system, Grid sampling, and remote sensors
★ Geolocating
Geolocating a field enables the farmer to overlay information gathered from analysis of soils and residual nitrogen, and information on previous crops and soil resistivity.
Geolocation is done in two ways
01. The field is delineated using an in-vehicle GPS receiver as the farmer drives a tractor around the field.
02. The field is delineated on a base map derived from aerial or satellite imagery. The base images must have the right level of resolution and geometric quality to ensure that geolocation is sufficiently accurate.
★ Variables
Intra and inter-field variability may result from a number of factors. These include climatic conditions (hail, drought, rain, etc.), soil (texture, depth, nitrogen levels), cropping practices (no till farming), weeds and disease. Permanent indicators—chiefly soil indicators—provide farmers with information about the main environmental constants. Point indicators allow them to track a crop's status, to see whether diseases are developing, if the crop is suffering from water stress, nitrogen stress, or lodging, whether it has been damaged by ice and so on. This information may come from weather stations and other sensors (soil electrical resistivity, detection with the naked eye, satellite imagery, etc.) soil resistivity measurements combined with soil analysis make it possible to measure moisture content. Soil resistivity is also a relatively simple and cheap measurement.
★ Strategies
NDVI image taken with small aerial system Stardust II in one flight (299 images mosaic).
Using soil maps, farmers can pursue two strategies to adjust field inputs:
1. Predictive approach: based on analysis of static indicators (soil, resistivity, field history, etc.) during the crop cycle.
2. Control approach: information from static indicators is regularly updated during the crop cycle by
✦Sampling: weighing biomass, measuring leaf chlorophyll content, weighing fruit, etc.
✦Remote sensing: measuring parameters like temperature (air/soil), humidity (air/soil/leaf), wind or stem diameter is possible thanks to Wireless Sensor Networks and Internet of things
✦Proxy-detection: in-vehicle sensors measure leaf status; this requires the farmer to drive around the entire field.
✦Aerial or satellite remote sensing: multispectral imagery is acquired and processed to derive maps of crop biophysical parameters, including indicators of disease. Airborne instruments are able to measure the amount of plant cover and to distinguish between crops and weeds.
Decisions may be based on decision-support models (crop simulation models and recommendation models) based on big data, but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the environment- a role being taken over by artificial intelligence (AI) systems based on machine learning and artificial neural networks.
It is important to realize why PA technology is or is not adopted, "for PA technology adoption to occur the farmer has to perceive the technology as useful and easy to use. It might be insufficient to have positive outside data on the economic benefits of PA technology as perceptions of farmers have to reflect these economic considerations
★ Implementing practices
New information and communication technologies make field level crop management more operational and easier to achieve for farmers. Application of crop management decisions calls for agricultural equipment that supports variable-rate technology (VRT), for example varying seed density along with variable-rate application (VRA) of nitrogen and phytosanitary products.
✦Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvesters, etc.)
✦Positioning system (e.g. GPS receivers that use satellite signals to precisely determine a position on the globe);
✦Geographic information systems (GIS), software that makes sense of all the available data; variable-rate farming equipment (seeder, spreader).
Usage around the world
The concept of precision agriculture first emerged in the United States in the early 1980s. In 1985, researchers at the University of Minnesota varied lime inputs in crop fields. It was also at this time that the practice of grid sampling appeared (applying a fixed grid of one sample per hectare). Towards the end of the 1980s, this technique was used to derive the first input recommendation maps for fertilizers and pH corrections. The use of yield sensors developed from new technologies, combined with the advent of GPS receivers, has been gaining ground ever since. Today, such systems cover several million hectares.
Around the world, precision agriculture developed at a varying pace. Precursor nations were the United States, Canada and Australia. In Europe, the United Kingdom was the first to go down this path, followed closely by France, where it first appeared in 1997–1998. 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. Brazil established a state-owned enterprise, Embrapa, to research and develop sustainable agriculture. The development of GPS and variable-rate spreading techniques helped to anchor precision farming management practices. Today, less than 10% of France's farmers are equipped with variable-rate systems. Uptake of GPS is more widespread, but this hasn't stopped them using precision agriculture services, which supplies field-level recommendation maps.
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. Research on precision agriculture for non-mechanized production is increasing and so is its adoption. 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. However, it is not clear how many agricultural producers actually use digital technologies.
Precision livestock farming supports farmers in real-time by continuously monitoring and controlling animal productivity, environmental impacts, and health and welfare parameters. Sensors attached to animals or to barn equipment operate climate control and monitor animals’ health status, movement and needs. For example, cows can be tagged with the electronic identification that allows a milking robot to access a database of udder coordinates for specific cows. Global automatic milking system sales have increased over recent years, but adoption is likely mostly in Northern Europe, and likely almost absent in low- and middle-income countries. Automated feeding machines for both cows and poultry also exist, but data and evidence regarding their adoption trends and drivers is likewise scarce.
Economic and environmental impact
Precision agriculture, as the name implies, means application of precise and correct amount of inputs like water, fertilizer, pesticides etc. at the correct time to the crop for increasing its productivity and maximizing its yields. Precision agriculture management practices can significantly reduce the amount of nutrient and other crop inputs used while boosting yields. Farmers thus obtain a return on their investment by saving on water, pesticide, and fertilizer costs.
The second, larger-scale benefit of targeting inputs concerns environmental impacts. Applying the right amount of chemicals in the right place and at the right time benefits crops, soils and groundwater, and thus the entire crop cycle. Consequently, precision agriculture has become a cornerstone of sustainable agriculture, since it respects crops, soils and farmers. Sustainable agriculture seeks to assure a continued supply of food within the ecological, economic and social limits required to sustain production in the long term.
A 2013 article tried to show that precision agriculture can help farmers in developing countries like India and Sri Lanka.
Precision agriculture reduces the pressure of agriculture on the environment by increasing the efficiency of machinery and putting it into use. For example, the use of remote management devices such as GPS reduces fuel consumption for agriculture, while variable rate application of nutrients or pesticides can potentially reduce the use of these inputs, thereby saving costs and reducing harmful runoff into the waterways.
GPS also reduces the amount of compaction to the ground by following previously made guidance lines. This will also allow for less time in the field and reduce the environmental impact of the equipment and chemicals
Emerging Technologies
Robots
Self-steering tractors have existed for some time now, as John Deere equipment works like a plane on autopilot. The tractor does most of the work, with the farmer stepping in for emergencies. Technology is advancing towards driverless machinery programmed by GPS to spread fertilizer or plow land. Autonomy of technology is driven by the demanding need of diagnoses, often difficult to accomplish solely by hands-on farmer-operated machinery. In many instances of high rates of production, manual adjustments cannot sustain. Other innovations include, partly solar powered, machines/robots that identify weeds and precisely kill them with a dose of a herbicide or lasers.
Agricultural robots, also known as AgBots, already exist, but advanced harvesting robots are being developed to identify ripe fruits, adjust to their shape and size, and carefully pluck them from branches.
Drones and satellite imagery
Drone and satellite technology are used in precision farming. This often occurs when drones take high quality images while satellites capture the bigger picture. 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. Aggregated images can create contour maps to track where water flows, determine variable-rate seeding, and create yield maps of areas that were more or less productive. Super resolution enhancement methods are seeing increased use in crop disease surveillance from low flying aircraft. Klapp et al. 2021 demonstrates a significantly improved infrared super-resolution method using a convolutional neural network.
The Internet of things
The Internet of things is the network of physical objects outfitted with electronics that enable data collection and aggregation. To come into play with the development of sensors and farm-management software. For example, farmers can spectroscopically measure nitrogen, phosphorus, and potassium in liquid manure, which is notoriously inconsistent. They can then scan the ground to see where cows have already urinated and apply fertilizer to only the spots that need it. This cuts fertilizer use by up to 30%. Moisture sensors in the soil determine the best times to remotely water plants. The irrigation systems can be programmed to switch which side of tree trunk they water based on the plant's need and rainfall.
As another example, monitoring technology can be used to make beekeeping more efficient. Honeybees are of significant economic value and provide a vital service to agriculture by pollinating a variety of crops. 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.
Smartphone applications
Smartphone and tablet applications are becoming increasingly popular in precision agriculture. Smartphones come with many useful applications already installed, including the camera, microphone, GPS, and accelerometer. There are also applications made dedicated to various agriculture applications such as field mapping, tracking animals, obtaining weather and crop information, and more. They are easily portable, affordable, and have high computing power.
Machine learning
Machine learning is commonly used in conjunction with drones, robots, and internet of things devices. It allows for the input of data from each of these sources. The computer then processes this information and sends the appropriate actions back to these devices. This allows for robots to deliver the perfect amount of fertilizer or for IoT devices to provide the perfect quantity of water directly to the soil. 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. As more agriculture becomes ever more digital, machine learning will underpin efficient and precise farming with less manual labour.
Benefits of Precision Agriculture
1. Reduced costs
Being able to accurately decrease fertilizer, herbicide or seed rates in areas where it will not be economical to utilize is one of the key benefits of precision agriculture.
2. Increased Profitability
Increasing yields because of applying agronomic principles at a high resolution, while reducing costs increases overall profitability. Farmers Edge offers one of the lowest-priced, high-value packages in the industry through our unique application of technology.
3. Enhanced Sustainability
Ensuring that crop input products applied actually get into the plant and not elsewhere affecting the environment delivers not only a superior bottom line but also supports a safer environment, and in the future, can even give you access to new markets for your crops. Using our precision services, we have been able to quantify that, on average, a Farmers Edge Variable Rate (VR) customer reduced their carbon footprint by over 10% while increasing output!
4. Better Harvestability
One of the most significant benefits of precision agriculture is the ability to understand the farm nutrient levels and soil types across the farm. We know that fields and geographies are not created equal, and this can impact the amount of nitrogen mineralizations, water holding capacity, and much more. When we understand these variances, we can ensure we do not over apply nitrogen, which can lead to lodging, or we can increase nutrients like potassium that help with standability in areas where it is low. To top it off, we can do VR desiccation, meaning we can have a lower amount of desiccant on hilltops or sandy areas and higher rates in low spots to ensure crop can be harvested with ease.
5. Increased Land Values
We know precision is an excellent practice from an agronomic perspective, but it can also drive the value of land prices, as discussed here.
“The evidence that precision agriculture makes land more productive and profitable clearly translates into higher rent value and market value for farmland to aggressive users of precision ag.” – AgDaily
6. Higher Resolutions Understanding of Your Farm
Farmers know their land better than anyone. Precision agriculture gives the ability to understand why certain areas of farm under produce, or are producing better, giving the foundation to make decisions that continually improve the farm.
7. Better In season Yield Understanding
Using precision imagery, or precision weather services can help to not only gain an understanding of areas of your farm that are seeing challenges or need more attention but through this combination of information regarding the farms and fields, we can provide accurate yield prediction in-season, empowering better decisions agronomically as well as for marketing or asset purchasing purposes.
These are all interconnected, yet are just the start of the value delivered from precision agriculture. As technology improves, usage of the technology improves, and we continue to improve the product, experience and outcomes for farmers
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