Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The influence of global climate change on agricultural productivity is an essential issue of ongoing concern. The growth and development of wheat, maize, and rice are influenced by elevated atmospheric CO concentrations, increased temperatures, and seasonal rainfall patterns. However, due to differences in research methodologies (e.g., crop models, climate models, and climate scenarios), there is uncertainty in the existing studies regarding the magnitude and direction of future climate change impacts on crop yields. In order to completely assess the possible consequences of climate change and adaptation measures on crop production and to analyze the associated uncertainties, a database of future crop yield changes was developed using 68 published studies (including 1842 samples). A local polynomial approach was used with the full dataset to investigate the response of crop yield changes to variations in maximum and minimum temperatures, mean temperature, precipitation, and CO concentrations. Then, a linear mixed-effects regression model was utilized with the limited dataset to explore the quantitative relationships between them. It was found that maximum temperature, precipitation, adaptation measure, study area, and climate model had significant effects on changes in crop yield. Crop yield will decline by 4.21% for each 1 °C rise in maximum temperature and increase by 0.43% for each 1% rise in precipitation. While higher CO concentrations and suitable management strategies could mitigate the negative effects of warming temperatures, crop yield with adaptation measures increased by 64.09% compared to crop yield without adaptation measures. Moreover, the uncertainty of simulations can be decreased by using numerous climate models. The results may be utilized to guide policy regarding the influence of climate change and to promote the creation of adaptation plans that will increase crop systems' resilience in the future.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385928 | PMC |
http://dx.doi.org/10.3390/plants12142709 | DOI Listing |
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