Rainfed rice production needs to contribute more to the current and future world food security due to the increasing competition for limited water supplies including irrigation water. However, it is vulnerable to climate variabilities and extremes hence the utilization of climate predictions is crucial. In this study, the predictive accuracy and applicability of a seasonal climate predictions (SINTEX-F) were evaluated for rainfed rice areas where climate uncertainties are main constraints for a stable and high production. Outputs from SINTEX-F such as daily rainfall, maximum and minimum air temperatures, and wind speed were tested for Indonesia and Lao PDR through the cumulative distribution function-based downscaling method (CDFDM), which is a simple, flexible and inexpensive bias reduction method through removing bias from the empirical cumulative distribution functions of the GCM outputs. The CDFDM outputs were compared with historical weather data. Obtained results showed that discrepancies between SINTEX-F and the historical weather data were significantly reduced through CDFDM for both sites. ORYZA, an ecophysiological rice growth model that simulate agroecological rice growth processes, was used to evaluate the applicability of the SINTEX-F for grain yield predictions. Obtained results from on-farm field validation showed that the predicted grain yield was close to the actual grain yield that was obtained through optimum sowing timing given by the predictions. A normalized root mean square error between predicted and actual grain yield showed satisfactory model fit in predictions. This implies that SINTEX-F was applicable for improving rainfed rice production through CDFDM. However, CDFDM has a limitation in orographic precipitation, the high-resolution daily weather data or a sophisticated special interpolation method should be considered in order to improve the representation of the geographical pattern for the parameters derived from CDFDM.
Agricultural Systems – Elsevier
Published: May 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera