Abstract
Recent studies on yield gap analysis for rice in Southeast Asia revealed different levels of intensification across the main ‘rice bowls’ in the region. Identifying the key crop management and biophysical drivers of rice yield gaps across different ‘rice bowls’ provides opportunities for comparative analyses, which are crucial to better understand the scope to narrow yield gaps and increase resource-use efficiencies across the region. The objective of this study was to decompose rice yield gaps into their efficiency, resource, and technology components and to map the scope to sustainably increase rice production across four lowland irrigated rice areas in Southeast Asia through improved crop management. A novel framework for yield gap decomposition accounting for the main genotype, management, and environmental factors explaining crop yield in intensive rice irrigated systems was developed. A combination of crop simulation modelling at field-level and stochastic frontier analysis was applied to household survey data to identify the drivers of yield variability and to disentangle efficiency, resource, and technology yield gaps, including decomposing the latter into its sowing date and genotype components. The yield gap was greatest in Bago, Myanmar (75% of Yp), intermediate in Yogyakarta, Indonesia (57% of Yp) and in Nakhon Sawan, Thailand (47% of Yp), and lowest in Can Tho, Vietnam (44% of Yp). The yield gap in Myanmar was largely attributed to the resource yield gap, reflecting a large scope to sustainably intensify rice production through increases in fertilizer use and proper weed control (i.e., more output with more inputs). In Vietnam, the yield gap was mostly attributed to the technology yield gap and to resource and efficiency yield gaps in the dry season and wet season, respectively. Yet, sustainability aspects associated with inefficient use of fertilizer and low profitability from high input levels should also be considered alongside precision agriculture technologies for site-specific management (i.e., more output with the same or less inputs). The same is true in Thailand, where the yield gap was equally explained by the technology, resource, and efficiency yield gaps. The yield gap in Indonesia was mostly attributed to efficiency and technology yield gaps and yield response curves to N based on farmer field data in this site suggest it is possible to reduce its use while increasing rice yield (i.e., more output with less inputs). This study provides a novel approach to decomposing rice yield gaps in Southeast Asia's main rice producing areas. By breaking down the yield gap into different components, context-specific opportunities to narrow yield gaps were identified to target sustainable intensification of rice production in the region.
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Claims 12
Claim · model-written summary
Yield gap was 75% of potential in Bago.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The yield gap was greatest in Bago, Myanmar (75% of Yp)”
t0“Revisiting yield gaps and the scope for sustainable intensification for irrigated lowland rice in Southeast Asia”
Claim · model-written summary
Yield gap was 57% of potential in Yogyakarta.
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e0“intermediate in Yogyakarta, Indonesia (57% of Yp)”
Claim · model-written summary
Yield gap was 47% of potential in Nakhon Sawan.
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e0“in Nakhon Sawan, Thailand (47% of Yp)”
Claim · model-written summary
Yield gap was 44% of potential in Can Tho.
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e0“lowest in Can Tho, Vietnam (44% of Yp)”
Claim · model-written summary
Myanmar yield gap was largely a resource gap.
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e0“The yield gap in Myanmar was largely attributed to the resource yield gap”
Claim · model-written summary
Vietnam yield gap was mostly technological.
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e0“In Vietnam, the yield gap was mostly attributed to the technology yield gap”
Claim · model-written summary
Vietnam dry-season gap included a resource gap.
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e0“resource and efficiency yield gaps in the dry season and wet season, respectively”
Claim · model-written summary
Vietnam wet-season gap included an efficiency gap.
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e0“resource and efficiency yield gaps in the dry season and wet season, respectively”
Claim · model-written summary
Thailand gap was explained equally by three components.
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e0“In Thailand, where the yield gap was equally explained by the technology, resource, and efficiency yield gaps”
Claim · model-written summary
Indonesia gap was mostly efficiency and technology.
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Verbatim
e0“The yield gap in Indonesia was mostly attributed to efficiency and technology yield gaps”
Claim · model-written summary
Field response curves suggest less N use could increase rice yield in Indonesia.
- Taxon
- rice
Oryza sativae0 - Treatment
- its usee0
- Outcome
- rice yielde0
- Direction
- increasinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“possible to reduce its use while increasing rice yield”
Claim · model-written summary
The study developed a framework for decomposing yield gaps.
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- riceOryza sativae0
- Outcome
- framework for yield gap decompositione0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“A novel framework for yield gap decomposition accounting for the main genotype, management, and environmental factors explaining crop yield in intensive rice irrigated systems was developed”