Precision Agriculture or Precision Farming or satellite farming or site specific crop management (SSCM)
The “precision farming” or Global Positioning System (GPS)” offers the promise of increasing productivity, while decreasing production costs and minimizing the environmental impact of farming.
OR
The electronics revolution of the last several decades has spawned two technologies that will impact agriculture in the next decade. These technologies are Geographic Information Systems (GIS) and Global Positioning System (GPS). Along with GIS and GPS there have appeared a wide range of sensors, monitors and controllers for agricultural equipment such as shaft monitors, pressure transducers and servo motors. Together they will enable farmers to use electronic guidance aids to direct equipment movements more accurately, provide precise positioning for all equipment actions and chemical applications and, analyze all of that data in association with other sources of data (agronomic, climatic, etc). This will add up to a new and powerful toolbox of management tools for the progressive farm manager.
Precision farming should not be thought of as only yield mapping and variable rate fertilizer application and evaluated on only one or the other. Precision farming technologies will affect the entire production function (and by extension, the management function) of the farm. A brief overview of the components in precision farming is presented in Figure 1 and listed below.
Yield Monitoring:
Instantaneous yield monitors are currently available from several manufacturers for all recent models of combines. They provide a crop yield by time or distance (e.g. every second or every few metres). They also track other data such as distance and bushels per load, number of loads and fields.
Yield mapping:
GPS receivers coupled with yield monitors provide spatial coordinates for the yield monitor data. This can be made into yield maps of each field.
Variable rate fertilizer:
Variable rate controllers are available for granular, liquid and gaseous fertilizer materials. Variable rates can either be manually controlled by the driver or automatically controlled by an on board computer with an electronic prescription map.
Weed mapping:
A farmer can map weeds while combining, seeding, spraying or field scouting by using a keypad or buttons hooked up to a GPS receiver and data logger. These occurrences can then be mapped out on a computer and compared to yield maps, fertilizer maps and spray maps.
Variable spraying:
By knowing weed locations from weed mapping spot control can be implemented. Controllers are available to electronically turn booms on and off, and alter the amount (and blend) of herbicide applied.
Topography and boundaries:
Using high precision DGPS a very accurate topographic map can be made of any field. This is useful when interpreting yield maps and weed maps as well as planning for grassed waterways and field divisions. Field boundaries, roads, yards, tree stands and wetlands can all be accurately mapped to aid in farm planning.
Salinity mapping:
GPS can be coupled to a salinity meter sled which is towed behind an ATV (or pickup) across fields affected by salinity. Salinity mapping is valuable in interpreting yield maps and weed maps as well as tracking the change in salinity over time.
Guidance systems:
Several manufacturers are currently producing guidance systems using high precision DGPS that can accurately position a moving vehicle within a foot or less. These guidance systems may replace conventional equipment markers for spraying or seeding and may be a valuable field scouting tool.
Records and analyses:
Precision farming may produce an explosion in the amount of records available for farm management. Electronic sensors can collect a lot of data in a short period of time. Lots of disk space is needed to store all the data as well as the map graphics resulting from the data. Electronic controllers can also be designed to provide signals that are recorded electronically. It may be useful to record the fertilizer rates actually put down by the application equipment, not just what should have been put down according to a prescription map. A lot of new data is generated every year (yields, weeds, etc). Farmers will want to keep track of the yearly data to study trends in fertility, yields, salinity and numerous other parameters. This means a large database is needed with the capability to archive, and retrieve, data for future analyses.
Farming management concept based on observing, measuring and responding to inter and intra-field variability in crops. Crop variability typically has both a spatial and temporal component which makes statistical/computational treatments quite involved. The holy grail of precision agriculture research will be the ability to define a Decision Support System (DSS) for whole farm management with the goal of optimizing returns on inputs while preserving resources. The reality today is that seemingly simple concepts such the ability to define management zones, areas where different management practices will apply, for a single crop type on a single field over time are difficult to define (see, for example, McCartney et al. (2005).
Whelan et al. (2003) Whelan and McCartney (2003) articulate a number of approaches that are currently being used to define management zones (mostly by the research community), these include hand drawn polygons on yield maps, supervised and unsupervised classification procedures on satellite or aerial images, identification of yield stability patterns across seasons, etc. Among these many approaches is a phytogeomorphological approach which ties multi-year crop growth stability/characteristics to topological terrain attributes. The interest in the phytogeomorphological approach stems from the fact that the geomorphology component typically dictates the hydrology of the farm field. Multi-year datasets are now becoming available that show this stability and these effects (Kaspar et al., (2003, however, there is a lot of work remaining to create an actual DSS system that could universally help farmers.
It can be said that the practice of precision agriculture was enabled by the advent of GPS and GNSS. The farmer's and/or researcher's ability to locate their precise position in a field allows for the creation of maps of the spatial variability of as many variables as can be measured (e.g. crop yield, terrain features/topography, organic matter content, moisture levels, nitrogen levels, pH, EC, Mg, K, etc.). Further, these maps can be interpolated onto a common grid for comparison (see Whelan et al. (2003) and the reference to the VESPER kriging system). Spatial and temporal variability of crop variables are at the heart of PA, while the spatial and temporal behaviours of that variability are key to defining amendment strategies, or 'recipe maps'. Recipe maps would be the output of any generalized decision support system that could be defined for farm use. Precision agriculture has also been enabled by technologies like crop yield monitors mounted on GPS equipped combines, the development of variable rate technology (VRT) like seeders, sprayers, etc., the development of an array of real-time vehicle mountable sensors that measure everything from chlorophyll levels to plant water status, multi- and hyper-spectral aerial and satellite imagery, from which products like NDVI maps can be made, although the costs of these are high, information technology, and geospatial tools.
Precision agriculture also provides farmers with a wealth of information to:
build up a record of their farm
improve decision-making
foster greater traceability
enhance marketing of farm products
improve lease arrangements and relationship with landlords
enhance the inherent quality of farm products (e.g. protein level in bread-flour wheat)
Stages and Tools:
Precision agriculture is a four-stage process using techniques to observe spatial variability:
Geolocation of Data:
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:
The field is delineated using an in-vehicle GPS receiver as the farmer drives a tractor around the field.
The field is delineated on a basemap 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.
Characterizing Variability:
Intra and inter-field variability may result from a number of factors. These include climatic conditions (hail, drought, rain, etc. ), soils (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, i.e., 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 precisely map agro-pedological conditions.
Using soil maps, farmers can pursue two strategies to adjust field inputs:
Predictive approach: based on analysis of static indicators (soil, resistivity, field history, etc.) during the crop cycle.
Control approach: information from static indicators is regularly updated during the crop cycle by:
o sampling: weighing biomass, measuring leaf chlorophyll content, weighing fruit, etc.
o remote sensing: measuring parameters like temperature (air/soil), humidity (air/soil/leaf), wind or stem diameter is possible thanks to Wireless Sensor Networks[6]
o proxy-detection: in-vehicle sensors measure leaf status; this requires the farmer to drive around the entire field.
o aerial or satellite remote sensing: multispectral imagery is acquired and processed to derive maps of crop biophysical parameters.
Decisions may be based on decision-support models (crop simulation models and recommendation models), but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the environment.
Implementing practices to address variability
New information and communication technologies (NICT) 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.[7]
Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvestors, etc.):
positioning system (e.g. GPS receivers that use satellite signals to precisely determine a position on the globe);
geographic information systems (GIS), i.e., software that makes sense of all the available data;
variable-rate farming equipment (seeder, spreader).
History (First Started):
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. 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. Fertilizer that would have been spread in areas that don't need it can be placed in areas that do, thereby optimizing its use. 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. The actual scenario of agriculture in Brazil walks towards efficient production with environment protection therefore Embrapa established the Brazilian Precision Agriculture Research Network, with the objective of knowledge generation, tools and technologies development on precision agriculture to soybean, maize, wheat, rice, cotton, pasture, eucalyptus, pines, grapes, peach, orange and sugar cane crops. 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.
Economic and environmental impacts:
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 phytosanitary and fertilizer costs. The second, larger-scale benefit of targeting inputs—in spatial, temporal and quantitative terms—concerns environmental impacts. Applying the right amount of inputs 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. Precision agriculture therefore seeks to use high-tech systems in pursuit of this goal.
A recent article has tried to show that precision agriculture can help farmers in developing country like India.
Where to start?
Precision farming does not "happen" as soon as one purchases a GPS unit or yield monitor. It occurs over time as a farmer adopts a new level of management intensity on the farm. Implicit in this is an increased level of knowledge of the precision farming technologies such as GPS. What is perhaps more important for the success of precision farming, at least initially, is the increased knowledge that a farmer needs of his natural resources in the field. This includes a better understanding of soil types, hydrology, microclimates and aerial photography. A farmer should identify the variance of factors within the fields that effect crop yield before a yield map is acquired. A yield map should serve as verification data to quantify the consequences of the variation that exists in a field. Management strategies and prescription map development will likely rely on sources other than yield maps. The one important key source of data a farmer should not start precision farming without is an aerial photograph.
By Tom Goddard, Conservation and Development Branch, Alberta Agriculture, Food and Rural Development
(Proceedings: Precision Farming Conference, January 20 - 21, 1997, Taber, Alberta, Canada)
References:
• Hurn, J. 1993. GPS A Guide to the Next Utility. Trimble Navigation. Sunnyvale, Calif., http://www.trimble.com/gps/index.html
• Stombaugh, T.S. and Clement, B.R. 1999. Unraveling the GPS Mystery, AEX-560-99, Ohio State University Fact Sheet, Food, Agricultural and Biological Engineering, 590 Woody Hayes Dr., Columbus, OH 43210-1058, http://ohioline.osu.edu/aex-fact/0560.html