Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset

Jones, Matthew R.; Singels, A.; Chinorumba, S.; Patton, A.; Poser, Christophe; Singh, Maninder P.; Martiné, Jean-François; Christina, Mathias; Shine, James M.; Annandale, J. G.; Hammer, Graeme

doi:10.1016/j.fcr.2019.107622

Cited by 15 publications

(19 citation statements)

References 28 publications

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“…Our development of a sugarcane growth-stage-specific yield model can be integrated into other models that are more process based and not growth stage specific. These processbased models include DSSAT-Canegro [41,42] and APSIM-Sugar [43,44], specifically for the APSIM-Sugar model's specifications for sugarcane plant transpiration efficiency and supply of water to plant roots [45]. Our results were consistent with other studies that have used these models to estimate sugarcane yields based off of historical yield data in the major production region for sugarcane in southeastern Brazil.…”

Section: Application To Previous Sugarcane Modelingsupporting

confidence: 84%

Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

et al. 2023

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Brazil is the largest producer of sugarcane (Saccharum spp.) in the world, and this crop’s response to climate and soil water storage is essential for optimal management and genetic/yield improvements. The objective of our study was to build a multivariate model to estimate sugarcane yield in the subtropical conditions of the northwestern Paraná region using climatic and soil water storage variables. Observed yield data was used from experiments conducted at the Experimental Station of the Sugarcane Genetic Improvement Program of the Universidade Federal do Paraná. The sugarcane varieties RB72454, RB867515, RB966928, and RB036066 were analyzed in the 1998–2006, 2008, 2018 and 2019 harvest years. Stepwise multiple linear regression analysis with repeated cross-validation was developed to estimate sugarcane yield given climate and soil water storage variables for crop growth phases. The accumulated degree days in Phases I and II and soil water storage in Phase II of development significantly impacted sugarcane yield. The multiple linear regression model, with accumulated degree days and soil water storage in Phases I and II of development, successfully predicted sugarcane yield for analyzed varieties. Sugarcane production models like the one we developed can improve crop management for greater sustainability and climate change adaption in Brazil and other areas.

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Section: Application To Previous Sugarcane Modelingsupporting

confidence: 84%

Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

et al. 2023

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“…A CGM can predict crop yield by integrating mathematical descriptions of plant physiological processes in response to changes in their environment and in management practices throughout the cropping cycle. In general modeling studies, cultivar characteristics are expressed by the cultivar-specific parameters used in the mathematical equations that describe a plant’s physiological processes, and the parameters are used for genomic analysis of plant yield 19 , 42 , 43 , leaf expansion 44 , and flowering time 45 , 46 . Our method eliminates the need to parameterize the many process models in a CGM by combining easily available climate data with parameterization of two cultivar-specific variables, α and β, that determine the overall ability of yield formation by means of linear regression in response to Y p calculated with fixed default parameters in the CGM for the attainable yield to represent the effects of the growing environment.…”

Section: Discussionmentioning

confidence: 99%

“…The regression provides a simple expression of a cultivar’s yield characteristics (α and β). This analysis differs from previous modeling studies that required the measurement and parameterization of many physiological processes for each cultivar, including leaf expansion, photosynthesis, biomass production, and carbon allocation to harvestable organs 19 , 20 . Our method can be applied in a ‘big data’ context using the accumulated yield data from many previous studies, including studies that recorded only yield without measuring the wide range of physiological processes required to parameterize a CGM.…”

Section: Introductionmentioning

confidence: 89%

Upcycling rice yield trial data using a weather-driven crop growth model

et al. 2023

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Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate.

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“…Our method allowed the use of a large volume of yield data since only yield and phenology measurements, two parameters that are commonly recorded in yield trials, were needed. This approach has strong advantages over previous modeling studies that required the measurement and parameterization of many physiological processes for each cultivar (Yoshida et al, 2009;Gu et al, 2014;Akinseye et al, 2019;Jones et al, 2019;Kadam et al, 2019). Another advantage was using the observed phenology as input data for calculating Yp.…”

Section: Discussionmentioning

confidence: 99%

“…This analysis differs from previous modeling studies that required the measurement and parameterization of many physiological processes for each cultivar, including leaf expansion, photosynthesis, biomass production, and carbon allocation to harvestable organs (Yoshida et al, 2009;Gu et al, 2014;Akinseye et al, 2019;Jones et al, 2019;Kadam et al, 2019). Our method can be applied in a "big data" context using the accumulated yield data from many previous studies, including studies that recorded only yield without measuring a wide range of physiological processes.…”

Section: Introductionmentioning

confidence: 99%

Upcycling yield trial data using a weather-driven crop growth model

Shimono

Abe

Kim

et al. 2022

Preprint

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Numerous breeding efforts have improved crop yields by screening new high-yield cultivars through yield trials in multiple environments. However, the accumulated data from these trails has not been effectively upcycled to guide future breeding programs because of the strength of the genotype by environment (G × E) interaction. Here, we propose a new method that accounts for these interactions by using a weather-driven crop growth model; the recorded yields of each cultivar were expressed using a unique linear regression in response to the potential yield (Yp) calculated by a weather-driven crop growth model. We call this approach the YpCGM method. We applied this method to 72 510 independent datasets from yield trials of paddy rice that used 237 core cultivars (n = 20 to 6342 field trials) measured at 110 locations in Japan during the 38 years from 1980 to 2017. The 237 core cultivars were selected from a pedigree matrix of 14 032 Japanese cultivars by using the k-medoids method (k = 200 clusters), and the genomic information for 91 800 single-nucleotide polymorphisms was obtained. The genotypic coefficients of yield-ability and yield-plasticity differed among the 237 cultivars, with values ranging from 2.5 to 7.3 t/ha and from –0.23 to +0.95, respectively. Genomic prediction validated the values of these two parameters by leave-one-out cross-validation based on the pedigree and genome information. Our study represents a novel “big data” method for managing and generalizing G × E interactions using a crop growth model supported by large amounts of field data.

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Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset

Cited by 15 publications

References 28 publications

Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

Upcycling rice yield trial data using a weather-driven crop growth model

Upcycling yield trial data using a weather-driven crop growth model

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