Data
Worldwide-Crop-Production

Worldwide-Crop-Production

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context Crop production depends on the availability of arable land and is affected in particular by yields, macroeconomic uncertainty, as well as consumption patterns; it also has a great incidence on agricultural commodities' prices. The importance of crop production is related to harvested areas, returns per hectare (yields) and quantities produced. Crop yields are the harvested production per unit of harvested area for crop products. In most of the cases yield data are not recorded, but are obtained by dividing the production data by the data on area harvested. The actual yield that is captured on farm depends on several factors such as the crop's genetic potential, the amount of sunlight, water and nutrients absorbed by the crop, the presence of weeds and pests. This indicator is presented for wheat, maize, rice and soybean. Crop production is measured in tonnes per hectare, in thousand hectares and thousand tonnes. Content The csv file has 5 columns: LOCATION = the country code name SUBJECT = The type of crop(rice,soybean,etc) TIME = the year the data was recorded MEASURE = the measuring metric used VALUE = The value, according to the measuring metric specified Acknowledgements https://data.oecd.org/agroutput/crop-production.htm

5 features

LOCATIONstring48 unique values
0 missing
SUBJECTstring4 unique values
0 missing
MEASUREstring3 unique values
0 missing
TIMEnumeric37 unique values
0 missing
Valuenumeric16264 unique values
0 missing

19 properties

21165
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
40
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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