Applications of systems simulation for understanding and increasing yield potential of wheat and rice
Understanding and increasing yield potential of cereals is essential to meet the growing food demand in Asia. A crop growth simulation model -WTGROWS- was developed to quantify the climatically determined potential grain yields and yield gaps in wheat in tropics and sub-tropics. The model written in PCSMP simulates daily dry matter production as a function of solar radiation, maximum and minimum temperatures, and water and nitrogen stresses. Comparison of simulated and measured quantities indicated satisfactory performance of the model in reference to water and nitrogen uptake, dry matter growth and grain yield in potential as well as water- and N-limited environments. The wheat yield potential in India varied between 2.6 and 8.3 t ha -1depending upon the location. Economically optimal yields in irrigated environments were estimated for all locations based on current price ratios of N fertilizer and grain, native soil fertility, simulated crop response to N fertilizer and other costs related to transport, harvesting and market forces. Yield gaps were found to be small in irrigated regions of northwestern India but significantly large in eastern regions. Almost 35 - 50% of the gap could be ascribed to delayed sowing. Crop simulation with different amounts of nitrogen and irrigation showed significant interaction between water and N availability and climatic variability, particularly with low inputs. The effect of climate change was more pronounced in central India where yield potential is already low.The model was also used to explore the opportunities for growing wheat in irrigated and rainfed tropical southeastern Asia. The results indicated that potential yields exceed 3 t ha -1at all places and increased further with latitude and elevation. At sea level, between equator and 8°N latitude, potential grain yield was 3 t ha -1. It increased to 5 t ha -1at 21°N and 4.5 t ha -1at 10°S latitudes. Realization of the yield potential of the presently available varieties may be limited because of several agronomic constraints.A simulation framework has been developed to determine the relative importance of different plant traits in isolation or in combination for increasing yield potential. In this approach, hypothetical genotypes are 'created' by changing the specific crop parameters of a crop simulation model. The impact of simultaneous change in many traits is assessed by randomly combining different traits in the hypothetical genotype. The approach is illustrated with examples for rice in tropics. No trait individually or in combination provides more than 5% advantage in yield at the level of management typically practiced by breeders. In such environments, even though genotypes may possess traits for higher yield potential, they will not be able to express them. Another framework is presented for using crop simulation models and statistical analysis together to increase the efficiency of multi-environment genotype testing.
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| Format: | Doctoral thesis biblioteca |
| Language: | English |
| Subjects: | breeding, climatic change, crop yield, factors of production, india, quantitative analysis, simulation models, triticum, uncertainty, yields, zea mays, gewasopbrengst, klimaatverandering, kwantitatieve analyse, onzekerheid, opbrengsten, productiefactoren, simulatiemodellen, veredelen, |
| Online Access: | https://research.wur.nl/en/publications/applications-of-systems-simulation-for-understanding-and-increasi |
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| Summary: | Understanding and increasing yield potential of cereals is essential to meet the growing food demand in Asia. A crop growth simulation model -WTGROWS- was developed to quantify the climatically determined potential grain yields and yield gaps in wheat in tropics and sub-tropics. The model written in PCSMP simulates daily dry matter production as a function of solar radiation, maximum and minimum temperatures, and water and nitrogen stresses. Comparison of simulated and measured quantities indicated satisfactory performance of the model in reference to water and nitrogen uptake, dry matter growth and grain yield in potential as well as water- and N-limited environments. The wheat yield potential in India varied between 2.6 and 8.3 t ha -1depending upon the location. Economically optimal yields in irrigated environments were estimated for all locations based on current price ratios of N fertilizer and grain, native soil fertility, simulated crop response to N fertilizer and other costs related to transport, harvesting and market forces. Yield gaps were found to be small in irrigated regions of northwestern India but significantly large in eastern regions. Almost 35 - 50% of the gap could be ascribed to delayed sowing. Crop simulation with different amounts of nitrogen and irrigation showed significant interaction between water and N availability and climatic variability, particularly with low inputs. The effect of climate change was more pronounced in central India where yield potential is already low.The model was also used to explore the opportunities for growing wheat in irrigated and rainfed tropical southeastern Asia. The results indicated that potential yields exceed 3 t ha -1at all places and increased further with latitude and elevation. At sea level, between equator and 8°N latitude, potential grain yield was 3 t ha -1. It increased to 5 t ha -1at 21°N and 4.5 t ha -1at 10°S latitudes. Realization of the yield potential of the presently available varieties may be limited because of several agronomic constraints.A simulation framework has been developed to determine the relative importance of different plant traits in isolation or in combination for increasing yield potential. In this approach, hypothetical genotypes are 'created' by changing the specific crop parameters of a crop simulation model. The impact of simultaneous change in many traits is assessed by randomly combining different traits in the hypothetical genotype. The approach is illustrated with examples for rice in tropics. No trait individually or in combination provides more than 5% advantage in yield at the level of management typically practiced by breeders. In such environments, even though genotypes may possess traits for higher yield potential, they will not be able to express them. Another framework is presented for using crop simulation models and statistical analysis together to increase the efficiency of multi-environment genotype testing. |
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