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Ecocrop

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585: 204:, and other soil characteristics. The crop database is particularly useful if there is no alternative but to use environmental ranges. Once these inputs are determined, the system produces a suitability index as a percentage. The suitability index score is generated from 0 to 1 with the former indicating totally unsuitable while the latter indicates optimal or excellent suitability. The output also include separated suitability values for temperature and precipitation. 169: 146:
Ecocrop first emerged in 1991 after planning and initial expert consultancies were completed concerning the development of a database. This system was developed by the Land and Water Development Division of FAO (AGLL) and was launched in 1992. The goal was to create a tool that can identify plant
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As a prediction model, the Ecocrop algorithm yields data that are more generic than those produced by other models such as DOMAIN and BIOCLIM. The information is generic with respect to the nature of the requirements and is attributed to the lack of information concerning specific crops. Another
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concept. In 1994, the Ecocrop database already permitted the identification of more than 1,700 crops and 12-20 environment requirements covering all of the agro-ecological settings of the world. Succeeding iterations of the database from 1998 to 1999 mainly involved improvements to the
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Ecocrop evaluates whether climatic conditions are adequate within a growing season for temperature and precipitation every month. It involves the calculation of climatic suitability based on
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The Ecocrop model determines a crop's suitability to a location by evaluating different variables. Specifically, the plant descriptors include category, life form, growth habit, and
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Aside from serving as a plant identifier, Ecocrop is also used for other purposes. For instance, it can assess the influence of future
134:(FAO) it provided information predicting crop viability in different locations and climatic conditions. It also served as a catalog of 232: 620: 208:
limitation is that the results depend solely on bioclimatic factors and discounts other variables such as soil requirements,
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The Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future
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Yadav, Shyam Singh; Redden, Robert J.; Hatfield, Jerry L.; Lotze-Campen, Hermann; Hall, Anthony J. W. (2011).
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Assessing Carbon Stocks and Modelling Win-win Scenarios of Carbon Sequestration Through Land-use Changes
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Robyn, Johnston; Hoanh, Chu Tai; Lacombe, Guillaume; Lefroy, Rod; Pavelic, Paul; Fry, Carolyn (2012).
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Improving water use in rainfed agriculture in the Greater Mekong Subregion.: Summary report.
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Crop viability database developed by the UN Food and Agriculture Organization
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Ponce-Hernandez, Raul; Koohafkan, Parviz; Antoine, Jacques (2004).
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Monitoring and Predicting Agricultural Drought: A Global Study
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The Economic, Social and Political Elements of Climate Change
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Rosenstock, Todd S.; Nowak, Andreea; Girvetz, Evan (2018).
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Climate Change and Food Security: Africa and the Caribbean
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Millington, James D. A.; Wainwright, John (2018-09-27).
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Food and Agriculture Organization of the United Nations
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Index

Food and Agriculture Organization
Open access
Plant taxonomy
gaez.fao.org/pages/ecocrop-find-plant
database
crop
Food and Agriculture Organization of the United Nations
plants
information system
Land Use Planning
user interface

life span
temperature
precipitation
Köppen climate classification
photoperiodism
latitude
altitude
pests
diseases
rainfall
climate change on crop suitability
"FAO Ecocrop"


ISBN
9789290907480
Monitoring and Predicting Agricultural Drought: A Global Study
287

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