MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02697nab|a22003257a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
64379 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20211015213745.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
202102s2021||||xxk|||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
2045-2322 (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.1038/s41598-021-97221-7 |
| 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 |
Ansarifar, J. |
| 9 (RLIN) |
23959 |
| 245 13 - TITLE STATEMENT |
|---|
| Title |
An interaction regression model for crop yield prediction |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
London (United Kingdom) : |
| Name of publisher, distributor, etc. |
Nature Publishing Group, |
| Date of publication, distribution, etc. |
2021. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Open Access |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Regression analysis |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
5834 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Models |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
4859 |
| 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 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11127 |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lizhi Wang |
| 9 (RLIN) |
22618 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Archontoulis, S.V. |
| 9 (RLIN) |
19736 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Related parts |
v. 11, art. 17754 |
| Place, publisher, and date of publication |
London : Nature Publishing Group, 2021. |
| International Standard Serial Number |
2045-2322 |
| Title |
Nature Scientific Reports |
| Record control number |
a58025 |
| 856 4# - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Click here to access online |
| Uniform Resource Identifier |
https://doi.org/10.1038/s41598-021-97221-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |