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
Hauptverfasser: Koide, Naohisa, Robertson, Andrew W., Ines, Amor V. M., Qian, Jian-Hua, DeWitt, David G., Lucero, Anthony
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container_start_page 552
container_title Journal of applied meteorology and climatology
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creator Koide, Naohisa
Robertson, Andrew W.
Ines, Amor V. M.
Qian, Jian-Hua
DeWitt, David G.
Lucero, Anthony
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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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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