The important contribution of rice to global food security requires an understanding of yield gaps in rice-based farming systems. However, estimates of yield gaps are often compromised by a failure to recognize the components that determine them at a local scale. It is essential to define yield gaps by the biological limitations of the genotype and the environment. There exist a number of methods for estimating rice yield gaps, including the use of crop growth simulation models, field experiments and farmer yields. We reviewed the existing literature to (i) assess the methods used to estimate rice yield gaps at a local scale and to summarize the yield gaps estimated in those studies, (ii) identify practical methods of analysis that provides realistic estimates of exploitable rice yield gaps, and (iii) provide recommendations for future studies on rice yield gaps that will allow accurate interpretation of available data at a local level. Rice yield gap analysis can be simplified without sacrificing precision and context specificity. This review identifies the comparison of the attainable farm yield (the mean of the top decile) with the population mean, as a practical and robust approach to estimate an exploitable yield gap that is highly relevant at the local level, taking into account what is achievable given the local socioeconomic conditions. With this method we identified exploitable yield gaps ranging from 23-42% for one particular season in four different rice growing areas in Southeast Asia. To enable accurate estimation and interpretation of yield gaps in rice production systems, we propose a minimum dataset needed for rice yield gap assessment. Future studies on rice yield gaps should consider the region, season and crop ecosystem (e.g. upland rainfed, lowland irrigated) as a minimum to facilitate decisions at a local level. In addition, we recommend taking into account the cultivar, soil type, planting date, crop establishment method and N application rates, as well as field topography and topographic sequence for rainfed systems. A good understanding of rice yield gaps and the factors leading to yield gaps will allow better targeting of agricultural research and development priorities for livelihood improvement and sustainable rice production.
Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps.
Ludwigia is an important broadleaf weed of direct-seeded rice in Asia. Crop interference that relies on shading may have potential as a component of integrated weed management strategies but it requires understanding the extent to which rice can interfere with weed growth and how these weeds may respond. The growth of ludwigia was studied when grown alone and in competition with 4 and 12 rice (cv. IR72) plants. Rice interference reduced ludwigia height, number of branches, and shoot and root biomass. However, ludwigia showed the ability to reduce the effects of rice interference by increasing leaf weight ratio, increasing stem and leaf biomass in the upper half of the plant, and increasing specific stem length. At 11 wk after seeding, for example, ludwigia grown with 12 rice plants had 38% greater leaf weight ratio compared to plants grown alone. When grown with 12 rice plants, the weed had 82% of its leaf biomass in upper half of the plant compared to only 25% in weeds grown alone. The results showed that ludwigia responded to rice interference with a combination of adaptations typical of many weed species. Despite such plasticity, the control of ludwigia may be achieved by dense rice stands and increasing interference.
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