Precision agriculture

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False-color images demonstrate remote sensing applications in precision farming. Daedelus comparison, remote sensing in precision farming.jpg
False-color images demonstrate remote sensing applications in precision farming.
Yara N-Sensor ALS mounted on a tractor's canopy - a system that records light reflection of crops, calculates fertilisation recommendations and then varies the amount of fertilizer spread Yara N-Sensor ALS.jpg
Yara N-Sensor ALS mounted on a tractor's canopy – a system that records light reflection of crops, calculates fertilisation recommendations and then varies the amount of fertilizer spread
Precision Agriculture NDVI 4 cm / pixel GSD DroneMapper Processed NDVI 4cm GSD.png
Precision Agriculture NDVI 4 cm / pixel GSD

Precision agriculture (PA) is a farming management strategy based on observing, measuring and responding to temporal and spatial variability to improve agricultural production sustainability. [2] It is used in both crop and livestock production. Precision agriculture often employs technologies to automate agricultural operations, improving their diagnosis, decision-making or performing. [3] [4] The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources. [5] [6]

Contents

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. [7] [8]

The practice of precision agriculture has been 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, and others). [9] Similar data is collected by sensor arrays mounted on GPS-equipped combine harvesters. These arrays consist of real-time sensors that measure everything from chlorophyll levels to plant water status, along with multispectral imagery. [10] This data is used in conjunction with satellite imagery by variable rate technology (VRT) including seeders, sprayers, etc. to optimally distribute resources. 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. [11] [12] [13]

Precision agriculture has also been enabled by unmanned aerial vehicles that are relatively inexpensive and can be operated by novice pilots. These agricultural drones 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. These multispectral images contain multiple values per pixel in addition to the traditional red, green blue values such as near infrared and red-edge spectrum values used to process and analyze vegetative indexes such as NDVI maps. [14] These drones are capable of capturing imagery and providing additional geographical references such as elevation, which allows software to perform map algebra functions to build precise topography maps. These topographic maps can be used to correlate crop health with topography, the results of which can be used to optimize crop inputs such as water, fertilizer or chemicals such as herbicides and growth regulators through variable rate applications.

History

Precision agriculture is a key component of the third wave of modern agricultural revolutions. The first agricultural revolution was the increase of mechanized agriculture, from 1900 to 1930. Each farmer produced enough food to feed about 26 people during this time. [15] The 1960s prompted the Green Revolution with new methods of genetic modification, which led to each farmer feeding about 156 people. [15] 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. [15]

Overview

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. [16] The second wave aggregates the machine data for even more precise planting, topographical mapping, and soil data. [17]

Precision agriculture aims to optimize field-level management with regard to:

Precision agriculture also provides farmers with a wealth of information to:

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. [18] [19]

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. [20]

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

Variables

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 measure moisture content. Soil resistivity is also a relatively simple and cheap measurement. [21]

Strategies

NDVI image taken with small aerial system Stardust II in one flight (299 images mosaic) SUAS StardustII Ndvi sml.jpg
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:

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 takenover 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." [25]

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. [26]

Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvesters, etc.):

Usage around the world

Pteryx UAV, a civilian UAV for aerial photography and photo mapping with roll-stabilised camera head Pteryx UAV - wiki.jpg
Pteryx UAV, a civilian UAV for aerial photography and photo mapping with roll-stabilised camera head

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. 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 [27] 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. [28]

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. [3] [4]  Research on precision agriculture for non-mechanized production is increasing and so is its adoption. [29] [30] [31] 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. [32] However, it is not clear how many agricultural producers actually use digital technologies. [32] [33]

Precision livestock farming supports farmers in real-time by continuously monitoring and controlling animal productivity, environmental impacts, and health and welfare parameters. [34]  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 (EID) that allows a milking robot to access a database of udder coordinates for specific cows. [35] Global automatic milking system sales have increased over recent years, [36] but adoption is likely mostly in Northern Europe, [37] and likely almost absent in low- and middle-income countries. [38] Automated feeding machines for both cows and poultry also exist, but data and evidence regarding their adoption trends and drivers is likewise scarce. [3] [4]

The economic and environmental benefits of precision agriculture have also been confirmed in China, but China is lagging behind countries such as Europe and the United States because the Chinese agricultural system is characterized by small-scale family-run farms, which makes the adoption rate of precision agriculture lower than other countries. Therefore, China is trying to better introduce precision agriculture technology into its own country and reduce some risks, paving the way for China's technology to develop precision agriculture in the future. [39]

In December 2014, the Russian President made an address to the Russian Parliament where he called for a National Technology Initiative (NTI). It is divided into subcomponents such as the FoodNet initiative. The FoodNet initiative contains a set of declared priorities, such as precision agriculture. This field is of special interest to Russia as an important tool in developing elements of the bioeconomy in Russia. [40] [41]

Economic and environmental impacts

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. [42] 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. [43] 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. [44]

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. [45]

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.

Precision agriculture produces large quantities of varied sensing data which creates an opportunity to adapt and reuse such data for archaeology and heritage work, enhancing understanding of archaeology in contemporary agricultural landscapes. [46]

Emerging technologies

Precision agriculture is an application of breakthrough digital farming technologies. Over $4.6 billion has been invested in agriculture tech companies—sometimes called agtech. [15]

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. [43] 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. [47] Other innovations include, partly solar powered, machines/robots that identify weeds and precisely kill them with a dose of a herbicide or lasers. [43] [48] [49]

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. [50]

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. [43]

The Internet of things

The Internet of things is the network of physical objects outfitted with electronics that enable data collection and aggregation. IoT comes into play with the development of sensors [51] and farm-management software. For example, farmers can spectroscopically measure nitrogen, phosphorus, and potassium in liquid manure, which is notoriously inconsistent. [43] 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%. [50] Moisture sensors [52] 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. [43]

Innovations are not just limited to plants—they can be used for the welfare of animals. Cattle can be outfitted with internal sensors to keep track of stomach acidity and digestive problems. External sensors track movement patterns to determine the cow's health and fitness, sense physical injuries, and identify the optimal times for breeding. [43] All this data from sensors can be aggregated and analyzed to detect trends and patterns.

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. [53]

Smartphone applications

A possible configuration of a smartphone-integrated precision agriculture system Figure 16 Components of a Precision Agriculture System (49132514563).jpg
A possible configuration of a smartphone-integrated precision agriculture system

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. [54]

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. [55] 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. [56] As more agriculture becomes ever more digital, machine learning will underpin efficient and precise farming with less manual labour.

Conferences

See also

Sources

Definition of Free Cultural Works logo notext.svg  This article incorporates text from a free content work. Licensed under CC BY-SA 3.0( license statement/permission ). Text taken from In Brief to The State of Food and Agriculture 2022 – Leveraging automation in agriculture for transforming agrifood systems​ , FAO, FAO.

Notes

  1. ↑ "Precision Farming : Image of the Day". earthobservatory.nasa.gov. 30 January 2001. Retrieved 12 October 2009.
  2. ↑ "Precision Ag Definition | International Society of Precision Agriculture". www.ispag.org. Retrieved 20 December 2021.
  3. 1 2 3 The State of Food and Agriculture 2022 − Leveraging agricultural automation for transforming agrifood systems. Rome: Food and Agriculture Organization of the United Nations (FAO). 2022. doi:10.4060/cb9479en. ISBN   978-92-5-136043-9.
  4. 1 2 3 In Brief to The State of Food and Agriculture 2022 − Leveraging automation in agriculture for transforming agrifood systems. Rome: Food and Agriculture Organization of the United Nations (FAO). 2022. doi:10.4060/cc2459en. ISBN   978-92-5-137005-6.
  5. ↑ McBratney, A., Whelan, B., Ancev, T., 2005. Future Directions of Precision Agriculture. Precision Agriculture, 6, 7-23.
  6. ↑ Whelan, B.M., McBratney, A.B., 2003. Definition and Interpretation of potential management zones in Australia, In: Proceedings of the 11th Australian Agronomy Conference, Geelong, Victoria, 2–6 Feb. 2003.
  7. ↑ Howard, J.A., Mitchell, C.W., 1985. Phytogeomorphology. Wiley.
  8. ↑ Kaspar, Thomas C.; Colvin, Thomas S.; Jaynes, Daniel B.; et al. (March 2003). "Relationship Between Six Years of Corn Yields and Terrain Attributes". Precision Agriculture. 4 (1): 87–101. doi:10.1023/A:1021867123125. ISSN   1385-2256. S2CID   40514787.
  9. ↑ McBratney, A. B.; Pringle, M. J. (September 1999). "Estimating Average and Proportional Variograms of Soil Properties and Their Potential Use in Precision Agriculture". Precision Agriculture. 1 (2): 125–152. doi:10.1023/A:1009995404447. ISSN   1385-2256. S2CID   22339888.
  10. ↑ Reyns, P., Missotten, B., Ramon, H. et al. Precision Agriculture (2002) 3: 169. https://doi.org/10.1023/A:1013823603735
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  15. 1 2 3 4 "Digital agriculture: Helping to feed a growing world". 23 February 2017. Archived from the original on 15 October 2018. Retrieved 3 April 2018.
  16. ↑ [Haneklaus, Silvia/Lilienthal, Holger/Schnug, Ewald (2016): 25 years Precision Agriculture in Germany – a retrospective. In: Proceedings of the 13th International Conference on Precision Agriculture : 31 July – 3 August 2016, St. Louis, Missouri, USA. Online unter: https://www.openagrar.de/receive/openagrar_mods_00039296]
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  26. ↑ Herring, David (29 January 2001). "Precision Farming : Feature Articles". earthobservatory.nasa.gov. Retrieved 12 October 2009.
  27. ↑ "Simon Blackmore: Farming with robots". SPIE Newsroom. 2 June 2016. Retrieved 2 June 2016.
  28. ↑ "precision agriculture with satellite imagery". Archived from the original on 7 April 2011.
  29. ↑ Nyaga, Justine M.; Onyango, Cecilia M.; Wetterlind, Johanna; Söderström, Mats (1 August 2021). "Precision agriculture research in sub-Saharan Africa countries: a systematic map". Precision Agriculture. 22 (4): 1217–1236. doi: 10.1007/s11119-020-09780-w . ISSN   1573-1618. S2CID   254944417.
  30. ↑ Onyango, Cecilia M.; Nyaga, Justine M.; Wetterlind, Johanna; Söderström, Mats; Piikki, Kristin (22 January 2021). "Precision Agriculture for Resource Use Efficiency in Smallholder Farming Systems in Sub-Saharan Africa: A Systematic Review". Sustainability. 13 (3): 1158. doi: 10.3390/su13031158 . ISSN   2071-1050.
  31. ↑ "Proceedings of 1st African Conference of Precision Agriculture – African Plant Nutrition Institute (APNI)" . Retrieved 23 December 2022.
  32. 1 2 Lowenberg-DeBoer, James; Erickson, Bruce (2019). "Setting the Record Straight on Precision Agriculture Adoption". Agronomy Journal. 111 (4): 1552–1569. Bibcode:2019AgrJ..111.1552L. doi:10.2134/agronj2018.12.0779. ISSN   0002-1962. S2CID   182858544.
  33. ↑ Van Beek, C (2020). Adoption level is the most underestimated factor in fertiliser recommendations (PDF). AgroCares. Archived from the original (PDF) on 13 December 2022. Retrieved 23 December 2022.
  34. ↑ Schillings, Juliette; Bennett, Richard; Rose, David Christian (2021). "Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare". Frontiers in Animal Science. 2. doi: 10.3389/fanim.2021.639678 . ISSN   2673-6225.
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  36. ↑ "Global milking robots market size by type, by herd size, by geographic scope and forecast". Verified Market Research. 2020. Retrieved 24 July 2022.
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  38. ↑ Lowenberg-DeBoer, J. (2022). Economics of adoption for digital automated technologies in agriculture. Background paper for The State of Food and Agriculture 2022. FAO Agricultural Development Economics Working Paper 22-10. Rome: Food and Agriculture Organization of the United Nations (FAO). doi:10.4060/cc2624en. ISBN   978-92-5-137080-3.
  39. ↑ Kendall, H.; Naughton, P.; Clark, B.; et al. (2017). "Precision Agriculture in China: Exploring Awareness, Understanding, Attitudes and Perceptions of Agricultural Experts and End-Users in China". Advances in Animal Biosciences. 8 (2): 703–707. doi:10.1017/S2040470017001066.
  40. ↑ Osmakova, Alina; Kirpichnikov, Michael; Popov, Vladimir (2018). "Recent biotechnology developments and trends in the Russian Federation". New Biotechnology. 40 (Pt A): 76–81. doi:10.1016/j.nbt.2017.06.001. PMID   28634066.
  41. ↑ "Рынки Нти".
  42. ↑ Pepitone, Julianne (3 August 2016). "Hacking the farm: How farmers use 'digital agriculture' to grow more crops". CNNMoney.
  43. 1 2 3 4 5 6 7 "The future of agriculture". The Economist. 9 June 2016.
  44. ↑ Rajvanshi, Anil K. "Is precision agriculture the solution to India's farming crisis". Archived from the original on 16 October 2013.
  45. ↑ Schieffer, J.; Dillon, C. (2015). "The economic and environmental impacts of precision agriculture and interactions with agro-environmental policy". Precision Agriculture. 16: 46–61. doi:10.1007/s11119-014-9382-5. S2CID   9071060.
  46. ↑ Opitz, Rachel (2023). "Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management - Impasses and opportunities for collaboration in agricultural areas". Internet Archaeology (62). doi: 10.11141/ia.62.10 . hdl: 1854/LU-01HM8S728JA2VW72B5MGQWYJJV .
  47. ↑ Zhang, Qin (2016). Precision Agriculture Technology for Crop Farming. Boca Raton, FL: CRC Press. p. 134. ISBN   9781482251074.
  48. ↑ Papadopoulos, Loukia (21 October 2022). "This new farming robot uses lasers to kill 200,000 weeds per hour". interestingengineering.com. Retrieved 17 November 2022.
  49. ↑ "Verdant Robotics launches multi-action agricultural robot for 'superhuman farming'". Robotics & Automation News. 23 February 2022. Retrieved 17 November 2022.
  50. 1 2 "Five technologies changing agriculture". 7 October 2016.
  51. ↑ M. Sophocleous, Thick-Film Underground Sensors. LAP LAMPERT Academic Publishing, 2016. ISBN   978-3-659-95270-8 https://www.morebooks.de/store/us/book/thick-film-underground-sensors/isbn/978-3-659-95270-8
  52. ↑ M. Sophocleous and J. K. Atkinson, “A novel thick-film electrical conductivity sensor suitable for liquid and soil conductivity measurements,” Sensors Actuators, B Chem., vol. 213, pp. 417–422, 2015. https://doi.org/10.1016/j.snb.2015.02.110
  53. ↑ "Precision beekeeping with wireless temperature monitoring". IoT ONE. Retrieved 27 April 2018.
  54. ↑ Suporn Pongnumkul, Pimwadee Chaovalit, and Navaporn Surasvadi, “Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research,” Journal of Sensors, vol. 2015.
  55. ↑ Goap, Amarendra; Sharma, Deepak; Shukla, A.K.; Rama Krishna, C. (December 2018). "An IoT based smart irrigation management system using Machine learning and open source technologies". Computers and Electronics in Agriculture. 155: 41–49. Bibcode:2018CEAgr.155...41G. doi:10.1016/j.compag.2018.09.040. S2CID   53787393.
  56. ↑ Grell, Max; Barandun, Giandrin; Asfour, Tarek; Kasimatis, Michael; Collins, Alex; Wang, Jieni; Guder, Firat (9 October 2020). "Determining and Predicting Soil Chemistry with a Point-of-Use Sensor Toolkit and Machine Learning Model". bioRxiv. doi:10.1101/2020.10.08.331371. S2CID   222348520.

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Intensive agriculture, also known as intensive farming, conventional, or industrial agriculture, is a type of agriculture, both of crop plants and of animals, with higher levels of input and output per unit of agricultural land area. It is characterized by a low fallow ratio, higher use of inputs such as capital, labour, agrochemicals and water, and higher crop yields per unit land area.

<span class="mw-page-title-main">Sustainable agriculture</span> Farming approach that balances environmental, economic and social factors in the long term

Sustainable agriculture is farming in sustainable ways meeting society's present food and textile needs, without compromising the ability for current or future generations to meet their needs. It can be based on an understanding of ecosystem services. There are many methods to increase the sustainability of agriculture. When developing agriculture within sustainable food systems, it is important to develop flexible business process and farming practices. Agriculture has an enormous environmental footprint, playing a significant role in causing climate change, water scarcity, water pollution, land degradation, deforestation and other processes; it is simultaneously causing environmental changes and being impacted by these changes. Sustainable agriculture consists of environment friendly methods of farming that allow the production of crops or livestock without damage to human or natural systems. It involves preventing adverse effects to soil, water, biodiversity, surrounding or downstream resources—as well as to those working or living on the farm or in neighboring areas. Elements of sustainable agriculture can include permaculture, agroforestry, mixed farming, multiple cropping, and crop rotation.

<span class="mw-page-title-main">Soil test</span>

Soil test may refer to one or more of a wide variety of soil analysis conducted for one of several possible reasons. Possibly the most widely conducted soil tests are those done to estimate the plant-available concentrations of plant nutrients, in order to determine fertilizer recommendations in agriculture. Other soil tests may be done for engineering (geotechnical), geochemical or ecological investigations.

<span class="mw-page-title-main">Nutrient management</span> Management of nutrients in agriculture

Nutrient management is the science and practice directed to link soil, crop, weather, and hydrologic factors with cultural, irrigation, and soil and water conservation practices to achieve optimal nutrient use efficiency, crop yields, crop quality, and economic returns, while reducing off-site transport of nutrients (fertilizer) that may impact the environment. It involves matching a specific field soil, climate, and crop management conditions to rate, source, timing, and place of nutrient application.

<span class="mw-page-title-main">Agricultural robot</span> Robot deployed for agricultural purposes

An agricultural robot is a robot deployed for agricultural purposes. The main area of application of robots in agriculture today is at the harvesting stage. Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring and soil analysis. According to Verified Market Research, the agricultural robots market is expected to reach $11.58 billion by 2025.

<span class="mw-page-title-main">Strip-till</span> Soil conservation technique

Strip-till is a conservation system that uses a minimum tillage. It combines the soil drying and warming benefits of conventional tillage with the soil-protecting advantages of no-till by disturbing only the portion of the soil that is to contain the seed row. This type of tillage is performed with special equipment and can require the farmer to make multiple trips, depending on the strip-till implement used, and field conditions. Each row that has been strip-tilled is usually about eight to ten inches wide.

Precision viticulture is precision farming applied to optimize vineyard performance, in particular maximizing grape yield and quality while minimizing environmental impacts and risk. This is accomplished by measuring local variation in factors that influence grape yield and quality and applying appropriate viticulture management practices. Precision viticulture is based on the premise that high in-field variability for factors that affect vine growth and grape ripening warrants intensive management customized according to local conditions. Precision viticulture depends on new and emerging technologies such as global positioning systems (GPS), meteorologic and other environmental sensors, satellite and airborne remote sensing, and geographic information systems (GIS) to assess and respond to variability.

The term cropping system refers to the crops, crop sequences and management techniques used on a particular agricultural field over a period of years. It includes all spatial and temporal aspects of managing an agricultural system. Historically, cropping systems have been designed to maximise yield, but modern agriculture is increasingly concerned with promoting environmental sustainability in cropping systems.

<span class="mw-page-title-main">Agricultural machinery</span> Machinery used in farming or other agriculture

Agricultural machinery relates to the mechanical structures and devices used in farming or other agriculture. There are many types of such equipment, from hand tools and power tools to tractors and the countless kinds of farm implements that they tow or operate. Diverse arrays of equipment are used in both organic and nonorganic farming. Especially since the advent of mechanised agriculture, agricultural machinery is an indispensable part of how the world is fed. Agricultural machinery can be regarded as part of wider agricultural automation technologies, which includes the more advanced digital equipment and robotics. While agricultural robots have the potential to automate the three key steps involved in any agricultural operation, conventional motorized machinery is used principally to automate only the performing step where diagnosis and decision-making are conducted by humans based on observations and experience.

<span class="mw-page-title-main">Driverless tractor</span> Autonomous farm vehicle

A driverless tractor is an autonomous farm vehicle that delivers a high tractive effort at slow speeds for the purposes of tillage and other agricultural tasks. It is considered driverless because it operates without the presence of a human inside the tractor itself. Like other unmanned ground vehicles, they are programmed to independently observe their position, decide speed, and avoid obstacles such as people, animals, or objects in the field while performing their task. The various driverless tractors are split into full autonomous technology and supervised autonomy. The idea of the driverless tractor appears as early as 1940, but the concept has significantly evolved in the last few years. The tractors use GPS and other wireless technologies to farm land without requiring a driver. They operate simply with the aid of a supervisor monitoring the progress at a control station or with a manned tractor in lead.

The combine grain yield monitor is a device coupled with other sensors to calculate and record the crop yield or grain yield as a modern-day combine harvester operates. Yield monitors are a part of the precision agriculture products available to producers today that provide producers with the tools to reduce costs, increase yields, and increase efficiency. The present day grain yield monitor is designed to measure the harvested grain mass flow, moisture content, and speed to determine total grain harvested. In most cases today this is coupled with global positioning system to record yield and other spatially variable information across a field. This allows for the creation of a grain yield map which provides information on spatial variability and supports management decisions for producers.

Phytogeomorphology is the study of how terrain features affect plant growth. It was the subject of a treatise by Howard and Mitchell in 1985, who were considering the growth and varietal temporal and spatial variability found in forests, but recognized that their work also had application to farming, and the relatively new science of precision agriculture. The premise of Howard and Mitchell is that landforms, or features of the land's 3D topography significantly affect how and where plants grow. Since that time, the ability to map and classify landform shapes and features has increased greatly. The advent of GPS has made it possible to map almost any variable one might wish to measure. Thus, a very increased awareness of the spatial variability of the environment that plants grow in has arisen. The development of technology like airborne LiDAR has enabled the detailed measurement of landform features to better than sub-meter, and when combined with RTK-GPS enables the creation of very accurate maps of where these features are. Comparison of these landform maps with mapping of variables related to crop or plant growth show a strong correlation.

<span class="mw-page-title-main">Variable rate application</span> Precise use of a material in agriculture

In precision agriculture, variable rate application (VRA) refers to the application of a material, such that the rate of application is based on the precise location, or qualities of the area that the material is being applied to. This is different from uniform application, and can be used to save money, and lessen the environmental impact.

<span class="mw-page-title-main">Agricultural technology</span> Use of technology in agriculture

Agricultural technology or agrotechnology is the use of technology in agriculture, horticulture, and aquaculture with the aim of improving yield, efficiency, and profitability. Agricultural technology can be products, services or applications derived from agriculture that improve various input/output processes.

An agricultural drone is an unmanned aerial vehicle used in agriculture operations, mostly in yield optimization and in monitoring crop growth and crop production. Agricultural drones provide information on crop growth stages, crop health, and soil variations. Multispectral sensors are used on agricultural drones to image electromagnetic radiation beyond the visible spectrum, including near-infrared and short-wave infrared.

Digital agriculture, sometimes known as smart farming or e-agriculture, is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution. Other definitions, such as those from the United Nations Project Breakthrough, Cornell University, and Purdue University, also emphasize the role of digital technology in the optimization of food systems.

Guy Lafond was a research scientist for over 30 years with Agriculture and Agri-Food Canada at the Indian Head Research Farm in Saskatchewan. He was instrumental in establishing the Indian Head Agricultural Research Foundation (IHARF) in the early 1990s and had a major impact on cropping practices and soil conservation.

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