Webinar Series starting March 2020:
Sustainable food systems and nutrition: Food Post-Harvest Losses
There is an upcoming webinar on March 25 2020 organized by
Agreenium (l'Institut agronomique, vétérinaire et forestier de France),
UN-ESCAP (United Nations Economic and Social Commission for Asia and the Pacific), and
FAO (Food and Agriculture Organization of the United Nations),:
- How can we better measure and reduce post-harvest losses worldwide, especially in South East Asian Countries?
- Join the webinar and share your thoughts and ideas! Mar 25, 2020 02:30 PM (CET)and 9:30am US Eastern
You will find more information about the webinar here http://bit.ly/2vU0dis. The session is the first in a series.
Call for Code: Water Sustainability Datasets
Following on from the blog
Useful data sets for Call for Code we will now focus our attention on datasets and models for water sustainability and agriculture, and some basic tools to manipulate them. Agriculture is the largest consumer of water at about 70% of all withdrawals globally as
stated by the World Bank. Tio emphasize the point, an article at
Penn State entitled
How much water does it take to make a pair of jeans? says:
”
It takes around 1,800 gallons of water to grow enough cotton to produce just one pair of regular ol’ blue jeans. That’s more water than it takes to make a ton of cement"
You will find some water, agriculture, soil, and crops related datasets through the
Google Dataset Search Tool as described in the earlier
Useful data sets for Call for Code and you should pay attention to the license when you download. You will also find water related datasets in the various government websites listed in the useful datasets blog such as
https://www.data.gov/climate/water/, and in other locations such as Oak Ridge National Lab in the US
https://daac.ornl.gov/. See the Get Data section
https://daac.ornl.gov/get_data/Another great source is the
Food and Agriculture Organization (FAO) at the United Nations, whose goal is make sure that people have regular access to enough high-quality food to lead active and healthy lives, maintains. The organization compiles datasets such as
http://www.fao.org/aquastat/en/countries-and-basins/country-profiles.in collections such as http://www.fao.org/aquastat/en/ The European Data Portal
https://www.europeandataportal.eu/data/datasets has datasets in the
agriculture and other important categories. On dataworld
https://data.world/ you will find datasets about
soil. You will find further suitable datasets in many collections to help you motivate your Call for Code solution and tell the story, or to build your solution by :
- visualizing data perhaps as part of a dashboard
- using data in an application
- training or using a model to make predictions
Visualizing data The
Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the
United Nations Office for the Coordination of Humanitarian Affairs. In the exchange you will find a climate change dataset for each country, derived from world bank data. The climate change datasets typically track indicators such as arable land, land under cereal production, fertilizer consumption, etc over a number of years. The indicators will vary from country to country, but will help you tell a story around your solution. You can find and download the climate change country datasets by selecting location or by using the search option - For example:
You can also create quick graphs using the
HDX tools.
GeoJSON is sometimes used as a format for data on the HDX site for example:
https://data.humdata.org/dataset/indicator-6-3-2-proportion-of-bodies-of-water-with-good-ambient-water-quality-percent a dataset of proportion of bodies of water on earth with good ambient water quality (%) Indicator. GeoJSON is a format for encoding geographic data structures. The
Pandas Python library is an excellent tool to manipulate GeoJSON files along with
many other data formats including the popular CSV format. You can learn how to use Pandas by following this Call for Code Leaning Path
https://developer.ibm.com/technologies/analytics/tutorials/data-analysis-in-python-using-pandas.
Below is a simple visualization of the ambient quality dataset mentioned above created with
GeoPandas which is another Python library focused on Geospatial data. The top portion shows the dataset in GeoPandas with all its attributes, followed by two simple map plots illustrating the various GeoAreaNames by location.
Exploring the "proportion of bodies of water on earth with good ambient water quality (%) Indicator" dataset with GeoPandas
Another site that makes data available via GeoJSON is GreenSpin
https://www.greenspin.de/ whose stated goal is to "digitize, quantify and monitor every single agricultural field on the planet"
Using data in an applicationA simple way to access and explore any data that you have downloaded from one of the sites is to load the data in JSON format to the
Cloudant JSON database - and then access the data through
Cloudant's HTTP API. The following videos explain how to do that.
There are many easy tools to issue HTTP requests, such as
cURL, and you can find a
blog introducing the HTTP tools.
Using a model for water and agrarian solutionsAquaCrop-OS is a free, open-source version of AquaCrop, a crop water productivity model developed by the Food and Agriculture Organization of the United Nations (FAO).mentioned earlier in this blog. AquaCrop-OS simulates efficiently water-limited crop production across diverse environmental and agronomic conditions. AquaCrop-OS covers multiple crop types and environmental conditions, and is designed specifically for regions where water is a critical limiting factor in crop production. The model can be used from multiple programming languages and operating environments.
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