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Wheat blast outbreaks are linked to the right climate conditions. Dec ades later it escaped from South America when it crept its way across the ocean and appeared in Bangladesh in 2016. The disease was first discovered in Brazil in 1985.
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Wheat blast disease is a major threat to smallholder farmers.
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Read More… Watch the informative video Buy the book Download brochureĬross-continental disease and crop modeling collaborations to beat back wheat blastĬross-continental collaborations facilitated by the CGIAR Platform for Big Data in Agriculture thrive to beat back the threat of wheat blast in Brazil and Bangladesh. The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data, as well as crop model inter-comparison.
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Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) linked with such models. Chapters cover topics such as integration of rotations and livestock, as well as landscape models such as agroecological zone (AEZ) models. Building on topics previously discussed in Part 1, Part 2 addresses the challenges of combining modular sub-systems into whole farm system, landscape and regional models. This collection summarises key advances in crop modelling, with a focus on developing the next generation of crop and whole-farm models to improve decision making and support for farmers.Ĭhapters in Part 1 review advances in modelling individual components of agricultural systems, such as plant responses to environmental conditions, crop growth stage prediction, nutrient and water cycling as well as pest/disease dynamics. However, the reason for under estimation of stem height for the 28 day treatment needs further investigation.Advances in crop modelling for a sustainable agriculture These results show that the APSIM NextGen lucerne phenology module was able to simulate crops grown under unconstrained growing conditions. For FD2 and FD10, two separate sets of parameters were used to improve model prediction of height to account for their contrasting seasonal C partitioning patterns. This was probably due to reduced stem extension rates, limited by low C and N reserves in perennial organs under the frequent (28 day) defoliation regime. For FD5, there was good agreement for the 84 day treatment (NSE of 0.83) and the 42 day treatment (NSE of 0.66), but it was poor for the 28 day treatment (NSE of -0.08). However, both defoliation management treatment and FD classes affected stem height. Simulation results showed good agreement for prediction of development stages (NSE of 0.77 for days to buds visible and 0.67 for days to flowering stage) and number of main stem nodes (NSE values were ranged from 0.53 to 0.84). Development stage and node appearance were shown to be independent of defoliation treatment and FD class. Development was parameterized based on thermal time targets to reach specific phenological stages and modified by photoperiod responses. These were further tested for two genotypes with contrasting FD (FD2 and FD10) under frequent (28 day: S) or long (84 day: H) defoliation regimes, all under irrigated conditions. Relationships derived from the FD5 genotype, grown under a 42 day (LL) defoliation treatment were used for model development.
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This research integrated data of lucerne phenological development into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework to develop and verify a phenology module. To date, lucerne phenological modules have not been evaluated under different defoliation regimes or with genotypes of different fall dormancy (FD) classes. A challenge for any lucerne phenology module is to capture the seasonality of development processes in response to environment, management and genotype. Prediction of lucerne phenological and morphological development is important for optimising the defoliation schedule and time of other management events.