Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data.
Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0306266 |
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| author | Miranda Y Mortlock David Carey Hamish Murray Peter J Baker Paul G Corry |
| author_facet | Miranda Y Mortlock David Carey Hamish Murray Peter J Baker Paul G Corry |
| author_sort | Miranda Y Mortlock |
| collection | DOAJ |
| description | Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government's SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected. |
| format | Article |
| id | doaj-art-59aadc59900a4e52952c770173767136 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-59aadc59900a4e52952c7701737671362025-08-20T03:08:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e030626610.1371/journal.pone.0306266Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data.Miranda Y MortlockDavid CareyHamish MurrayPeter J BakerPaul G CorryClimate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government's SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected.https://doi.org/10.1371/journal.pone.0306266 |
| spellingShingle | Miranda Y Mortlock David Carey Hamish Murray Peter J Baker Paul G Corry Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. PLoS ONE |
| title | Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. |
| title_full | Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. |
| title_fullStr | Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. |
| title_full_unstemmed | Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. |
| title_short | Enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis, crop records and weather data. |
| title_sort | enhancing green bean crop maturity and yield prediction by harnessing the power of statistical analysis crop records and weather data |
| url | https://doi.org/10.1371/journal.pone.0306266 |
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