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Publication Year: 2022

251 to 260 of 794 Results

lc2028.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: 0cdf5182c5ff4c50d5c89651dacab504
lc2029.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: 91a63a5c8cf2c7c47ae0db1ee628f4c7
lc2029.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: ac56d03a7c93105d845ea603b5c2598a
lc2029.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: d97b4cbd60e5c7cb98d4b972d80db96a
lc2030.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: 4e0676f77e5d0afc05bce802f51896d4
lc2030.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: 68ebe57ea732f1dce7fa3c4933d667fb
lc2030.tif 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand TIFF Image - 1,7 MB - MD5: 0f9376fcdb928d15364567dcf36275df
LC_style.qml 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand Unknown - 2,2 KB - MD5: 763f4577844b1b3edb740ac8e44a28e7 QGIS style file for Land cover maps
Market.mp4 25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand MPEG-4 Video - 953,4 KB - MD5: 2c16ece2083243212762ea84e02d88b2 Video (mp4 format) to visualize land cover changes, 2016-2030, according to the "market-oriented" scenario
Instrumental textural data for boiled yam in Umudike, Nigeria 14 oct. 2022 - RTBfoods Project Chijioke, Ugo, 2022, "Instrumental textural data for boiled yam in Umudike, Nigeria", https://doi.org/10.18167/DVN1/WXBNPJ, CIRAD Dataverse, V1, UNF:6:qudjtylx6YMnt/uV5rA1RQ== [fileUNF] This dataset contains instrumental textural data of boiled yam that was collected by NRCRI team in Nigeria using a standard operating procedure.

lc2028.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2029.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2029.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2029.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2030.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2030.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

lc2030.tif

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

TIFF Image - 1,7 MB -

LC_style.qml

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

Unknown - 2,2 KB -

QGIS style file for Land cover maps

Market.mp4

25 oct. 2022 - Simulated future land cover maps according to different change scenarios, Nan Province, Thailand

MPEG-4 Video - 953,4 KB -

Video (mp4 format) to visualize land cover changes, 2016-2030, according to the "market-oriented" scenario

Instrumental textural data for boiled yam in Umudike, Nigeria

14 oct. 2022 - RTBfoods Project

Chijioke, Ugo, 2022, "Instrumental textural data for boiled yam in Umudike, Nigeria", https://doi.org/10.18167/DVN1/WXBNPJ, CIRAD Dataverse, V1, UNF:6:qudjtylx6YMnt/uV5rA1RQ== [fileUNF]

This dataset contains instrumental textural data of boiled yam that was collected by NRCRI team in Nigeria using a standard operating procedure.

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