Crop Area and Production by State
Data as of 2022Production and planted area of crops by state from 2017 to 2022, broken down by crop type.
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How is this data produced?
Data on crops in Malaysia is collected via establishment-level surveys and censuses conducted by the Department of Statistics Malaysia (DOSM) in collaboration with the Department of Agriculture (DOA). This data is then used both for the production of agricultural statistics, as well as in the compilation of Malaysia's Gross Domestic Product (GDP) data.
For this dataset specifically, the following classifications are helpful to understand (note: paddy, coconut, and flowers are considered self-explanatory):
- Cash crops are crops that can provide quick return (within 1 year) to farmers. They include sweet corn, groundnuts, cassava, yam, sweet potato and sugarcane (juice).
- Industrial crops are crops grown to manufacture raw industrial materials. They are divided into several groups such as fats (coconut), and groups that produce fresheners (coffee and tea). Industrial crops are grown in large quantities with a relatively long period of planting, ranging from one year to many years.
- Herbs refer to shrubs that usually have soft stems and contain plenty of water. This generally means all plants which have value to medicine, food (health and supplements), perfumes, fragrance and cosmetics.
- Spices are aromatic crops used as coloring and flavoring for culinary purposes such as nutmeg and ginger.
- Fruits and Vegetables refer specifically to the tropical species grown in Malaysia, which has an equatorial climate. Examples of tropical fruits are banana, mango, papaya, pineapple, durian, rambutan and jackfruit, while examples of tropical vegetables are spinach, okra, eggplant, water spinach, long beans, loofah and bitter gourd.
What caveats I should bear in mind when using this data?
Values for breakdowns may be slightly different to totals when summed, due to rounding.
Publication(s) using this data
This data was also published as part of the Ministry of Agriculture and Food Security's annual Agrofood Statistics report.
Metadata
Dataset description
Production and planted area of crops by state from 2017 to 2022, broken down by crop type.
Variable definitions
- Date
- State
- Crop Type
- Planted Area
- Production
Name in Dataset | Variable | Definition |
|---|---|---|
date (Date) | Date | The date in YYYY-MM-DD format, with MM-DD set to 01-01 as the data is at annual frequency |
state (Categorical) | State | One of 16 states, or Malaysia. Note that there is no data for W.P. Putrajaya, due to the lack of commercial agriculture in the state. |
crop_type (Categorical) | Crop Type | The type of crop in snake case, covering cash crops, industrial crops, coconut, paddy, flowers, herbs, spices, fruits and vegetables |
planted_area (Float) | Planted Area | The total planted area of the crop in hectares |
production (Float) | Production | The total production of the crop in metric tonnes |
Last updated:
31 Dec 2023, 12:00
Next update:
31 Dec 2024, 12:00
Data source(s)
- Ministry of Agriculture and Food Security
- Department of Agriculture
- Department of Statistics Malaysia
URLs to dataset
License
This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A copy of the license is available Here.
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Data
Full Dataset (CSV)
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Full Dataset (Parquet)
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Code
Connect directly to the data with Python.
# If not already installed, do: pip install pandas fastparquet
import pandas as pd
URL_DATA = 'https://storage.data.gov.my/agriculture/crops_state.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)Sample OpenAPI query
The following code is an example of how to make an API query to retrieve the data catalogue mentioned above. You can use different programming languages by switching the code accordingly. For a complete guide on possible query parameters and syntax, please refer to the official Open API Documentation.
import requests
import pprint
url = "https://api.data.gov.my/data-catalogue?id=crops_state&limit=3"
response_json = requests.get(url=url).json()
pprint.pprint(response_json)
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