Knowledge Center Catalog

Field data collection methods strongly affect satellite-based crop yield estimation (Record no. 65290)

MARC details
000 -LEADER
fixed length control field 02666nab|a22003737a|4500
001 - CONTROL NUMBER
control field 65290
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230829234754.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20225s2022||||mx |||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2072-4292
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.3390/rs14091995
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Tiedeman, K.
9 (RLIN) 27487
245 10 - TITLE STATEMENT
Title Field data collection methods strongly affect satellite-based crop yield estimation
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. MDPI,
Date of publication, distribution, etc. 2022.
Place of publication, distribution, etc. Basel (Switzerland) :
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Collection
Source of heading or term AGROVOC
9 (RLIN) 9145
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Forecasting
Source of heading or term AGROVOC
9 (RLIN) 2701
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Remote sensing
Source of heading or term AGROVOC
9 (RLIN) 1986
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Maize
Source of heading or term AGROVOC
9 (RLIN) 1173
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Crop yield
Source of heading or term AGROVOC
9 (RLIN) 1066
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Chamberlin, J.
Miscellaneous information Formerly Socioeconomics Program
-- Sustainable Agrifood Systems
Field link and sequence number I1706801
9 (RLIN) 2871
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kosmowski, F.
9 (RLIN) 19573
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Hailemariam Ayalew
9 (RLIN) 9696
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Sida, T.S.
Field link and sequence number 001711262
Miscellaneous information Sustainable Intensification Program
-- Sustainable Agrifood Systems
9 (RLIN) 5724
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Hijmans, R.J.
9 (RLIN) 9465
773 0# - HOST ITEM ENTRY
Title Remote Sensing
Related parts v. 14, no. 9, art. 1995
Place, publisher, and date of publication Basel (Switzerland) : MDPI, 2022
Record control number 57403
International Standard Serial Number 2072-4292
856 ## - ELECTRONIC LOCATION AND ACCESS
Link text Open Access through DSpace
Uniform Resource Identifier https://hdl.handle.net/10883/22074
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Article
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
05/13/2022   05/13/2022 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 05/13/2022

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