Targeting management practices for rice yield gains in stress-prone environments of Myanmar

Article


Radanielson, A. M., Kato, Yoichiro, Palao, Leo Kris, Feyisa, Gudina, Malabayabas, Arlene Julia, Aunario, Jorrel K., Garcia, Cornelia, Balanza, Jane G., Win, Khin Thawda, Singh, Rakesh K., Zamora, Chenie, Myint, Daw Tin Tin and Johnson, David E.. 2019. "Targeting management practices for rice yield gains in stress-prone environments of Myanmar." Field Crops Research. 244, pp. 1-12. https://doi.org/10.1016/j.fcr.2019.107631
Article Title

Targeting management practices for rice yield gains in stress-prone environments of Myanmar

ERA Journal ID5309
Article CategoryArticle
AuthorsRadanielson, A. M. (Author), Kato, Yoichiro (Author), Palao, Leo Kris (Author), Feyisa, Gudina (Author), Malabayabas, Arlene Julia (Author), Aunario, Jorrel K. (Author), Garcia, Cornelia (Author), Balanza, Jane G. (Author), Win, Khin Thawda (Author), Singh, Rakesh K. (Author), Zamora, Chenie (Author), Myint, Daw Tin Tin (Author) and Johnson, David E. (Author)
Journal TitleField Crops Research
Journal Citation244, pp. 1-12
Article Number107631
Number of Pages12
Year2019
PublisherElsevier
Place of PublicationNetherlands
ISSN0378-4290
1872-6852
Digital Object Identifier (DOI)https://doi.org/10.1016/j.fcr.2019.107631
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0378429018316903
Abstract

ice in Myanmar is grown in diverse environments, including inland dry zone and salt-affected coastal deltas. This study evaluated management options that could improve productivity and reduce risks of rice crop in stress-prone areas of the country. We selected four sites from two regions in the central dry zone (Wundwin) and the Ayeyarwady delta (Labutta, Bogale and Mawlamyinegyun). We used experimental and survey datasets on farmers’ practices and rice yields from 2012 to 2014 to run the ORYZA model to simulate the climatic yield potential (YP; yield without stress) and the attainable yield under rainfed conditions (YW; yield limited by water), saline conditions (YS; yield limited by salinity), and under conditions of current farmers’ practices (YF; yield in farmers’ practices). Simulated yield responses to different management practices showed spatial variability within and among the selected sites. YP ranged from 5.4 to 11.1 t ha−1, YW ranged from 0.5 to 7.5 t ha−1, and YF ranged from 2.2 to 4.2 t ha−1. In salt-affected areas, average YS ranged from less than 0.1 t ha-1 to 5.6 t ha−1. Yield gains with the choice of an improved variety and adjusted sowing date were estimated at up to 53% above YF. Changing the time of sowing and using improved rice varieties provided the greatest yield gains in salt-affected and drought-prone areas where YF was the least. In areas where YF was greater, the improvement of nitrogen management provided larger benefits than in areas with lower YF. We conclude that an integrated approach using remote-sensing technologies, crop modeling, and a geographic information system is valuable for targeting the best management options to close the yield gap in unfavorable rice environments in Asia.

KeywordsClimate; Cropping systems; Modeling; Productivity; Salinity; Soil
ANZSRC Field of Research 2020300206. Agricultural spatial analysis and modelling
300205. Agricultural production systems simulation
300207. Agricultural systems analysis and modelling
Byline AffiliationsCentre for Sustainable Agricultural Systems
International Rice Research Institute, Philippines
International Centre for Tropical Agriculture, Philippines
Addis Ababa University, Ethiopia
University of Tokyo, Japan
Department of Agricultural Research, Myanmar
Institution of OriginUniversity of Southern Queensland
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