Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts
Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using...
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Veröffentlicht in: | Journal of applied meteorology and climatology 2013-03, Vol.52 (3), p.552-569 |
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description | Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the “dry” January–June and “rainy” July–December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October–December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June–September and in October–December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season. |
doi_str_mv | 10.1175/jamc-d-11-0254.1 |
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M. ; Qian, Jian-Hua ; DeWitt, David G. ; Lucero, Anthony</creator><creatorcontrib>Koide, Naohisa ; Robertson, Andrew W. ; Ines, Amor V. M. ; Qian, Jian-Hua ; DeWitt, David G. ; Lucero, Anthony</creatorcontrib><description>Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the “dry” January–June and “rainy” July–December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October–December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June–September and in October–December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season.</description><identifier>ISSN: 1558-8424</identifier><identifier>EISSN: 1558-8432</identifier><identifier>DOI: 10.1175/jamc-d-11-0254.1</identifier><identifier>CODEN: JOAMEZ</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage ; Agricultural and forest meteorology ; Agricultural production ; Agronomy. Soil science and plant productions ; Biological and medical sciences ; Cereal crops ; Climatic models of plant production ; Climatology. Bioclimatology. Climate change ; Crop production ; Cyclones ; Dry season ; Dry seasons ; Earth, ocean, space ; El Nino ; Exact sciences and technology ; External geophysics ; Forecasting models ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Generalities. Techniques. Climatology. Meteorology. 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M.</creatorcontrib><creatorcontrib>Qian, Jian-Hua</creatorcontrib><creatorcontrib>DeWitt, David G.</creatorcontrib><creatorcontrib>Lucero, Anthony</creatorcontrib><title>Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts</title><title>Journal of applied meteorology and climatology</title><description>Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the “dry” January–June and “rainy” July–December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October–December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June–September and in October–December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season.</description><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage</subject><subject>Agricultural and forest meteorology</subject><subject>Agricultural production</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Cereal crops</subject><subject>Climatic models of plant production</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Crop production</subject><subject>Cyclones</subject><subject>Dry season</subject><subject>Dry seasons</subject><subject>Earth, ocean, space</subject><subject>El Nino</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Forecasting models</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Generalities. Techniques. Climatology. Meteorology. 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Climate change</topic><topic>Crop production</topic><topic>Cyclones</topic><topic>Dry season</topic><topic>Dry seasons</topic><topic>Earth, ocean, space</topic><topic>El Nino</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Forecasting models</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Generalities. Techniques. Climatology. Meteorology. 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M.</au><au>Qian, Jian-Hua</au><au>DeWitt, David G.</au><au>Lucero, Anthony</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts</atitle><jtitle>Journal of applied meteorology and climatology</jtitle><date>2013-03-01</date><risdate>2013</risdate><volume>52</volume><issue>3</issue><spage>552</spage><epage>569</epage><pages>552-569</pages><issn>1558-8424</issn><eissn>1558-8432</eissn><coden>JOAMEZ</coden><abstract>Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the “dry” January–June and “rainy” July–December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October–December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June–September and in October–December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/jamc-d-11-0254.1</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural and forest climatology and meteorology. Irrigation. Drainage Agricultural and forest meteorology Agricultural production Agronomy. Soil science and plant productions Biological and medical sciences Cereal crops Climatic models of plant production Climatology. Bioclimatology. Climate change Crop production Cyclones Dry season Dry seasons Earth, ocean, space El Nino Exact sciences and technology External geophysics Forecasting models Fundamental and applied biological sciences. Psychology General agronomy. Plant production Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production Harvesting Irrigation systems Marine Meteorology Rain Rainfall Rainy season Rainy seasons Rice Sea surface temperature Seasons Tropical cyclones Weather forecasting Wind |
title | Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts |
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