Agriculture: Difference between revisions
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[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]] |
[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]] |
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Agriculture is responsible for about 14% of GHG emissions [26]. This might come as a surprise, since plants take up CO<sub>2</sub> from the air. However, modern industrial agriculture involves more than just growing plants. First, the land is stripped of trees, releasing carbon sequestered there. Second, the process of tilling exposes topsoil to the air, thereby releasing carbon that had been bound in soil aggregates and disrupting organisms in the soil that contribute to sequestration. Finally, because such farming practices strip soil of nutrients, nitrogen-based fertilizers must be added back to the system. Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption [386]. Moreover, while some of this nitrogen is absorbed by plants or retained in the soil, some is converted to nitrous oxide, a greenhouse gas that is about 300 times more potent than CO<sub>2</sub>. |
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Plants, microbes, and other organisms have been drawing CO2 from the atmosphere for millions of years. Most of this carbon is continually broken down and recirculated through the carbon cycle, and some is stored deep underground as coal and oil, but a large amount of carbon is sequestered in the biomass of trees, peat bogs, and soil. Our current economy encourages practices that are freeing much of this sequestered carbon through deforestation and unsustainable agriculture. On top of these effects, cattle and rice farming generate methane, a greenhouse gas far more potent than CO2 itself. Overall, land use by humans is estimated to be responsible for about a quarter of global GHG emissions<ref>{{Cite book|title=Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth |
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Assessment Report of the Intergovernmental Panel on Climate Change|url=https://www.ipcc.ch/report/ar5/wg3/|date=2014|coeditors=O. Edenhofer, R. Pichs-Madruga, Y. |
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Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. |
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Savolainen, S. Schlomer, C. von Stechow, T. Zwickel, J.C. Minx}}</ref>(and this may be an underestimate<ref>{{Cite journal|title=Are the impacts of land use on warming underestimated in climate policy?|url=https://iopscience.iop.org/article/10.1088/1748-9326/aa836d|coauthors=Natalie M Mahowald, Daniel S Ward, Scott C Doney, Peter G Hess, and James T Randerson|date=2017}}</ref>). In addition to this direct release of carbon through human actions, the permafrost is now melting, peat bogs are drying, and forest fires are becoming more frequent as a consequence of climate change itself – all of which release yet more carbon<ref>{{Cite journal|title=The study of Earth as an integrated system|url=https://climate.nasa.gov/nasa_science/science/#:~:text=The%20Study%20of%20Earth%20as%20an%20Integrated%20System&text=Earth%20system%20science%20is%20the,whole%2C%20including%20its%20changing%20climate.}}</ref>. |
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Such industrial agriculture approaches are ultimately based on making farmland more uniform and predictable. This allows it to be managed at scale using basic automation tools like tractors, but can be both more destructive and less productive than approaches that work with the natural heterogeneity of land and crops. Increasingly, there is demand for sophisticated tools which would allow farmers to work at scale, but adapt to what the land needs. This approach is often known as “precision agriculture.” |
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The large scale of this problem allows for a similar scale of positive impact. According to one estimate<ref>{{Cite book|title=Drawdown: The most comprehensive plan ever proposed to reverse global warming|author=Paul Hawken|date=2015}}</ref>, about a third of GHG emissions reductions could come from better land management and agriculture. ML can play an important role in some of these areas. Precision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation. |
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Satellite imagery are often useful for monitoring land use. Some widely accessed resources include, |
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== Problem Areas == |
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* [https://developers.google.com/earth-engine/datasets/catalog/landsat/ Landsat] |
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* [https://developers.google.com/earth-engine/datasets/catalog/sentinel/ Sentinel] |
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* [https://developers.google.com/earth-engine/datasets/catalog/ Earth Engine Data Catalog] |
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* [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane?tab=overview Methane detection from satellite] |
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=== Precision Agriculture === |
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* Weed and pest detection |
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* Soil sensing |
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*[https://www.agr.gc.ca/eng/scientific-collaboration-and-research-in-agriculture/agricultural-research-results/holos-software-program/?id=1349181297838 Holos]: A crop simulator for Canadian farms. |
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* Farming assistant |
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=== Robotic Agriculture === |
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Development of autonomous robots for performing specific tasks on crops. |
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=== Monitoring Agriculture === |
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* Estimating carbon in soil and soil health |
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* Estimating emissions of methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O) |
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* Segmentation and classification of crops |
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* Quantifying cattles and other farm animals |
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== Background Readings == |
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*[https://www.mdpi.com/2072-4292/11/6/676 Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review] |
*[https://www.mdpi.com/2072-4292/11/6/676 Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review] |
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== Online Courses and Course Materials == |
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<br /> |
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== Community == |
== Community == |
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=== Journals |
=== Journals === |
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*[https://www.journals.elsevier.com/computers-and-electronics-in-agriculture Computers and Electronics in Agriculture] |
*[https://www.journals.elsevier.com/computers-and-electronics-in-agriculture Computers and Electronics in Agriculture] |
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*[https://www.springer.com/journal/11119 Precision Agriculture] |
*[https://www.springer.com/journal/11119 Precision Agriculture] |
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== Library and Tools == |
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=== Past and upcoming events === |
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=== Remote Sensing === |
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[[Remote Sensing Datasets]] offers great opportunity to monitor agriculture and can be georeferenced to match ground measurements. In particular, [https://earthengine.google.com/ Google earth engine] offers a convenient interface over freely available satellite imagery such as [https://developers.google.com/earth-engine/datasets/catalog/landsat/ Landsat] and [https://developers.google.com/earth-engine/datasets/catalog/sentinel/ Sentinel]. |
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=== Soil Measurements === |
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== References == |
== References == |
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Revision as of 20:08, 28 August 2020
Agriculture is responsible for about 14% of GHG emissions [26]. This might come as a surprise, since plants take up CO2 from the air. However, modern industrial agriculture involves more than just growing plants. First, the land is stripped of trees, releasing carbon sequestered there. Second, the process of tilling exposes topsoil to the air, thereby releasing carbon that had been bound in soil aggregates and disrupting organisms in the soil that contribute to sequestration. Finally, because such farming practices strip soil of nutrients, nitrogen-based fertilizers must be added back to the system. Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption [386]. Moreover, while some of this nitrogen is absorbed by plants or retained in the soil, some is converted to nitrous oxide, a greenhouse gas that is about 300 times more potent than CO2.
Such industrial agriculture approaches are ultimately based on making farmland more uniform and predictable. This allows it to be managed at scale using basic automation tools like tractors, but can be both more destructive and less productive than approaches that work with the natural heterogeneity of land and crops. Increasingly, there is demand for sophisticated tools which would allow farmers to work at scale, but adapt to what the land needs. This approach is often known as “precision agriculture.”
Problem Areas
Precision Agriculture
- Weed and pest detection
- Soil sensing
- Farming assistant
Robotic Agriculture
Development of autonomous robots for performing specific tasks on crops.
Monitoring Agriculture
- Estimating carbon in soil and soil health
- Estimating emissions of methane (CH4) and nitrous oxide (N2O)
- Segmentation and classification of crops
- Quantifying cattles and other farm animals
Background Readings
- Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices
- Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review
Online Courses and Course Materials
Community
Journals
Library and Tools
Some packages for working with remote sensing data are,
- eo-learn: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing
Data
Remote Sensing
Remote Sensing Datasets offers great opportunity to monitor agriculture and can be georeferenced to match ground measurements. In particular, Google earth engine offers a convenient interface over freely available satellite imagery such as Landsat and Sentinel.