The DOI matches its Crossref record; 75 of the 90 references it cites were found in a registry or an index; 4 of the 48 sources its claims cite were checked for support.
A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model
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Claims the paper makes
33 Own result · 29 Own interpretation · 25 No full text of the sources · 16 No basis given · 10 Own method · 8 Sources not found · 4 Not checked yet · 3 Supported · 1 Partly supported · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Of the 14 parameters tested, Sexton and Everingham found that four had little to no influence on simulated biomass or sugar yields under well irrigated or water stressed conditions.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.2. Parameter prior distributions
What the sources say
- Supportedcites Sexton and Everingham (2014)
Section 4.1 states that the analysis tested 14 parameters. Section 5.1 reports that four—TT 2, TT 3, MSSR and TF—had percent-variance indices below 1 for all outputs, including simulated biomass and sucrose yields under irrigated and water-stressed conditions. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
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In the paper
“Particularly sucrose_delay had less influence on sucrose yields at low values (Sexton and Everingham (2014)).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
What the sources say
- Partly supportedcites Sexton and Everingham (2014)Experimental — accuracy not yet measured
Table 3 (page not available) shows that sucrose_delay (SD) had a small overall influence on simulated sucrose yields: 1.41% of variance under irrigation and 0.28% under water stress. It does not show that SD had less influence specifically at low values; the text of §5.1 likewise does not make that comparison. (LLM, conf=0.83)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Sexton and Everingham (2014) showed that sucrose_delay was weakly influential for sucrose yield under both irrigated and water stressed conditions.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
What the sources say
- Supportedcites Sexton and Everingham (2014)
Table 1 identifies SD as sucrose_delay. Table 3 (section 8; page not specified) reports that SD accounts for only 1.41% of sucrose-yield variance under irrigated conditions and 0.28% under stressed conditions, supporting the description of weak influence in both. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Both k L and sucrose_delay had total effect indices of < 5% under irrigated or stressed conditions (Sexton and Everingham, 2014).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
What the sources say
- Supportedcites Sexton and Everingham, 2014
Table 3 lists total effect indices below 5% for both KL (kL) and SD (sucrose_delay) for biomass and sucrose at harvest, under both irrigated and stressed conditions. Section 4.2 identifies these as the four simulation outputs assessed for a first ratoon crop. (LLM, conf=0.95)
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both GLUE and MCMC calibrations were able to accurately simulate green biomass and sucrose yield in both a theoretical and real world evaluation.”
- Also stated in: § 4.3. Comparisons of GLUE and MCMC calibrations · plain
“Both GLUE and MCMC calibrations were able to accurately simulate green biomass and sucrose yield in both a theoretical and real world evaluation.”
- Also stated in: § 5. Conclusions · plain
“Both GLUE and MCMC calibrations were able to accurately simulate green biomass and sucrose yield in both a theoretical and real world evaluation.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Model errors (residuals) are normally distributed.
- Also stated in: § 4.3. Comparisons of GLUE and MCMC calibrations · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Similarly leaf_size and transp_eff_cf estimates were closer when known values were low and high respectively, reflecting regions where these parameter values had a greater influence on yields.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both GLUE and MCMC were able to accurately estimate known variety parameters for two pre-defined varieties based on ARE (Table 6).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.1. Theoretical evaluation of GLUE and MCMC
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Based on ARE, the parameter sucrose_delay was particularly poorly estimated for V001 using both GLUE and MCMC.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
plainSingle case- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The distributions of rue and k L adjustment factors calculated for 89 varieties were found to be approximately normally distributed.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.2. Parameter prior distributions
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“MCMC calibration estimates of variety parameter values were physically meaningful”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Model errors (residuals) are normally distributed.
- It may state two separate claims as one. 31
- Probably not a substantial claim. 41
- The quote only partly states this claim. 2
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For both pre-defined varieties 6/10 GLUE parameter estimates and 7/10 MCMC parameter estimates were within 10% of the defined values.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.1. Theoretical evaluation of GLUE and MCMC
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While all MCMC posterior distributions captured the defined values, several parameters were located in the tail of the distributions.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although LAI was not used in the calibration process, both GLUE and MCMC calibrations produced similar NRMSE, r 2 and D statistics to the default APSIM-Sugar settings.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Based on all available samples from two validation experiments both GLUE and MCMC calibrations produced lower NRMSE and higher r 2 values for green biomass and sucrose yield model outputs compared to default APSIM values for Q117 (Table 8).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to APSIM-Sugar default values GLUE estimated a higher leaf_size and low min_sstem_sucrose while MCMC estimated a lower leaf_size and tt_emerg_to_begcane and a higher green_leaf_no.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this high leaf_size helped simulate peak values of LAI in the Grafton experiment but likely also lead to overestimating lower values of LAI”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The prior distributions for the variety parameters are as specified in Table 4.
- Rests on an assumption: A larger Q can help improve parameter posterior distributions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“simulated LAI using MCMC parameter estimates underestimated peak LAI in the Grafton experiment but accurately estimated lower value”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The prior distributions for the variety parameters are as specified in Table 4.
- Rests on an assumption: A larger Q can help improve parameter posterior distributions.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The sucrose_delay parameter was poorly estimated in the theoretical evaluation while the estimated value of k L in the real world evaluation could not be explained physiologically.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, for variety V001 the defined value for rue was in the lower tail of the posterior distribution and that of transp_eff_cf was in the centre of the distribution, while for variety V002 the reverse was true.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Grafton(1994-95) field experiment was characterised by cooler conditions and higher rainfall than the Ayr(1992-94) experiment (Table3).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCMC and GLUE estimates while based on only a small sample of sites, closely aligns with this recorded value and provides a measure of uncertainty not available with the original calibration.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Green biomass was most accurately simulated in all cases while simulated LAI had the highest NRMSE and lowest r 2 and D values of the three outputs analysed.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Australian sugarcane varieties have changed over the last 15 years, yet their representation in crop growth models has not.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The validation statistics used in this paper have been widely used in the assessment of crop models and other process based models such as environmental and hydrological models (Bennett et al., 2013).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > Bennet et al. (2013) highlight some of the key elements of evaluating model performance
What the sources say
- Not checked — no full text of the sourcecites Bennett et al., 2013 source
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These parameters in particular may play an important role in improving differences in response to water stress between sugarcane varieties (Inman-Bamber et al., 2012) but are difficult to measure directly.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
What the sources say
- Not checked — no full text of the sourcecites Inman-Bamber et al., 2012 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“GLUE is often referred to as a pseudo-Bayesian approach as the likelihood function L(θ i |Y) can be defined subjectively by the modeller (Beven and Binley, 1992).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.2.1 Generalized Likelihood Uncertainty Estimation
What the sources say
- Not checked — no full text of the sourcecites Beven and Binley, 1992 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Transpiration efficiency (transp_eff_cf) for C 4 crops such as sugarcane is generally accepted to be around 0.009 g kPa g -1 to 0.010 g kPa g -1 (Sinclair, 2012).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.2. Parameter prior distributions
What the sources say
- Not checked — no full text of the sourcecites Sinclair, 2012 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Of the 20 parameters calibrated in Makowski et al. (2002) using 5 data samples, 6/20 GLUE parameter estimates were within 10% of defined values while 7/20 MCMC parameter estimates were within 10% of defined values.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.1. Theoretical evaluation of GLUE and MCMC
What the sources say
- Not matchedcites Makowski et al. (2002)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainn = 5- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
In the paper
“By providing a range of performance criteria it is possible to be more confident in our results as weaknesses in one criteria may be compensated by strengths in another (Bennet et al., 2013).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > Bennet et al. (2013) highlight some of the key elements of evaluating model performance
What the sources say
- Not matchedcites Bennet et al., 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The sample yields used for calibration were considered representative of harvest yields and the dates of these samples are recorded as 'Harvest Date' in Table 2.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.1. Data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Prior distributions should be based on published data or wide enough to be relatively sure of capturing the true parameter value (Beven and Binley, 1992).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.1. Agricultural Production System sIMulator
What the sources say
- Not checked — no full text of the sourcecites Beven and Binley, 1992 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In contrast to the GLUE approach, the MCMC approach to parameter estimation requires a formal likelihood function and has a formal statistical foundation (Makowski et al., 2002).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.2.2. Markov Chain Monte Carlo
What the sources say
- Not matchedcites Makowski et al., 2002
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Transpiration efficiency in sugarcane has recently been shown to vary between varieties (Jackson et al., 2014) but the corresponding parameter in APSIM-Sugar (transp_eff_cf) remains a constant for different varieties.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.1. Agricultural Production System sIMulator
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although parameters 1-14 listed in Table 1 are classed as variety parameters in APSIM-Sugar, current definitions of varieties in APSIM-sugar differ only in (i) fully expanded area of leaves and (ii) partitioning of biomass to sucrose in the stalk.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.1. Agricultural Production System sIMulator
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Keeping APSIM-Sugar up-todate with new commercially released varieties will also allow crop modellers to investigate the impact of interactions between variety, environment and farm management practices and will help improve the model as a decision support tool.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: To date little research has considered varietal effects on such decision support research in sugarcane.
- Rests on an assumption: Some parameters representing physiological traits such as leaf area and number of green leaves are easy to measure, but accurately obtaining parameters for other traits (e.g. transpiration efficiency) can be expensive and time consuming.
- Rests on an assumption: An alternative approach is to statistically estimate parameters that represent difficult to measure traits from knowledge about traits that are routinely or more easily measured such as biomass, cane yield or sucrose yield.
In the paper
“Bennet et al. (2013) highlight some of the key elements of evaluating model performance including visual analysis to gain an overview of model performance and selection of basic performance criteria.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > Bennet et al. (2013) highlight some of the key elements of evaluating model performance
What the sources say
- Not checked — no full text of the sourcecites Bennett et al., 2013 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The remaining 10 parameters from the results of Sexton and Everingham (2014), were chosen for this calibration study as parameters that may help distinguish between varieties and have been shown to influence simulated biomass and sugar yields.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.2. Parameter prior distributions
What the sources say
- Not matchedcites Sexton and Everingham (2014)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Previous studies concluded that more formal MCMC approaches can be more efficient than the GLUE approach (Jones et al., 2011;Vrugt et al., 2009).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.2.2. Markov Chain Monte Carlo
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The use of statistical techniques for parameter estimation would provide a much needed systematic method for updating variety specific trait parameters while quantifying uncertainty about those parameter values.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 1. Introduction
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: To date little research has considered varietal effects on such decision support research in sugarcane.
- Rests on an assumption: Some parameters representing physiological traits such as leaf area and number of green leaves are easy to measure, but accurately obtaining parameters for other traits (e.g. transpiration efficiency) can be expensive and time consuming.
- Rests on an assumption: An alternative approach is to statistically estimate parameters that represent difficult to measure traits from knowledge about traits that are routinely or more easily measured such as biomass, cane yield or sucrose yield.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, Bayesian calibration of crop models generally use independent uniform prior distributions of parameter values as little information is available on parameter covariance.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.1. Agricultural Production System sIMulator
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“An increased transp_eff_cf and increased k L can improve performance in water stressed conditions (Inman-Bamber et al., 2012).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.4. Theoretical evaluation of GLUE and MCMC
What the sources say
- Not checked — no full text of the sourcecites Inman-Bamber et al., 2012 source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although crop models are highly complex, they are still simplifications of reality.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, Dumont et al. (2014) replaced the error variance with a coefficient of variation calculated as the ratio of the standard deviation and the value of the observation.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
What the sources say
- Not checked — no full text of the sourcecites Dumont et al. (2014) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“LAI was not used in the calibration process as it is not routinely measured in breeding trials or commercial production and is therefore not generally available.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unusual or unexpected parameter values from calibrations may reflect some form of model limitation and may be used to identify a source of error in the specific simulation or highlight an area in the model where further research in the underlying physiology could be explored.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Green biomass, sucrose yield and LAI measurements for the validation experiments were taken 7 times during the Grafton(1994-95) experiment and 24 times over the plant and first ratoon for the Ayr(1992-94) experiment.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Radiation use efficiency is a difficult value to measure and the rue parameter in APSIM greatly influences model outputs (Sexton and Everingham, 2014;Sexton et al., 2015).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
What the sources say
- Not matchedcites Sexton and Everingham, 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Sexton et al., 2015 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Better defining the structure of error variance within the likelihood function may have resulted in improved calibration performance in both the theoretical and real-world analysis of this study.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These two field experiments were also used in the design of APSIM-Sugar(Keating et al., 1999).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
What the sources say
- Not checked — no full text of the sourcecites Keating et al., 1999 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The authors agree with previous comparison studies such as Makowski et al. (2002) that formal techniques such as the MCMC offer advantages in having a well-documented statistical background.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
What the sources say
- Not matchedcites Makowski et al. (2002)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two additional field experiments were used as independent validations of GLUE and MCMC calibrations in the real world evaluation.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“The default value for transp_eff_cf was based on literature for other C 4 crops such as sorghum which have a value of 0.009 g kPa g -1 (Keating et al., 1999;Sinclair, 2012).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The difficulties that arose in estimating the sucrose_delay and k L parameter values highlights the need for future calibration methods to more formally consider the different sources of model error as described by Kennedy and O'Hagan (2001).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
What the sources say
- Not checked — no full text of the sourcecites Kennedy and O'Hagan (2001) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MCMC and GLUE techniques provided a reproducible and easily documented approach to model calibration.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case using a calibration technique aimed at finding the optimum solution such as MCMC may be better aligned to the crop modellers' objective.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“While Sexton and Everingham (2014) identified k L as a weakly influential parameter, later work (Sexton et al., 2015) suggested k L could be highly influential under certain growing conditions and may be influenced by soil characteristics.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
What the sources say
- Not matchedcites Sexton and Everingham (2014)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Sexton et al., 2015 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The performance of GLUE and MCMC algorithms in the real world analysis may have been improved by including data from more field trials with replicated measurements or by incorporating the available data on LAI into the calibration process.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, in practice crop models are largely used deterministically such that the calibrated parameter values are likely the only values used.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“This agrees with results from the original model validation (Keating et al., 1999).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
What the sources say
- Not checked — no full text of the sourcecites Keating et al., 1999 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For MCMC all chains converge for all parameters ( R ⌃ > 1.2) except sucrose_delay ( R ⌃ > 1.2)”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The prior distributions for the variety parameters are as specified in Table 4.
- Rests on an assumption: A larger Q can help improve parameter posterior distributions.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Convergence of the three chains to a single posterior distribution was monitored”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“While maximum leaf area has been measured as high as 70000 mm 2 under high input glasshouse studies (Inman-Bamber, 2013), under field conditions in Australia values of 38000 mm 2 were more common (Robertson et al., 1998).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The main limitation of this research was the use of simplistic GLUE and MCMC algorithms.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This kind of analysis has not been performed before for a sugarcane model and strongly supports the use of statistical calibration techniques in calibrating APSIM-Sugar for different varieties.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This matches earlier studies which have found that GLUE and MCMC can perform similarly under theoretical conditions (Makowski et al., 2002;Vrugt et al., 2009).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
What the sources say
- Not matchedcites Makowski et al., 2002
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Vrugt et al., 2009 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Both field experiments used for validation were well irrigated and fertilized to remove water and nutrient stresses.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This would seem a more realistic calibrated value based on field measures of yield.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Previous GLUE calibration studies such as Marin et al. (2011) and Jones et al. (2011) have used Q values of as low as 6000 as a trade-off between statistical power and time efficiency.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This allowed for the inclusion of observed data throughout the growing season.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.4. Limitations
What the sources say
- Not checked — no full text of the sourcecites Dumont et al. (2014) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Makowski et al. (2002) concluded that by discretising the parameter space the GLUE process may inaccurately represent the posterior parameter distribution.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
What the sources say
- Not matchedcites Makowski et al. (2002)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The ranges of uniform prior distributions for parameters cane_fraction, sucrose_fraction_stalk, sucrose_delay, min_sstem_sucrose and tt_emerg_to_begcane, were based on varieties defined in APSIM-Sugar documentation”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The ability to implement these statistical techniques in freely available programs such as R means that calibration toolboxes could readily be developed for other crop models.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With appropriate variety definitions, APSIM-Sugar could be used for early risk assessment of adopting new varieties in different growing environments.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the simplifications made it seems likely that there should be parameter sets throughout the parameter space capable of performing equally well (equifinality).”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.3. Comparisons of GLUE and MCMC calibrations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is possible that a weak influence on yields complicated the estimation techniques.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
Model errors (residuals) are normally distributed.
“where y o is the o th observation and y ⌃ o (θ) is the o th simulated value using parameter set θ, assuming that model errors (residuals) are normally distributed (Dumont et al., 2014;He et al., 2010;Iizumi et al., 2009;Jones et al., 2011;Makowski et al., 2006;Makowski et al., 2002;Marin et al., 2011;Pathak et al., 2012).”
The paper relies on this without support.
To date little research has considered varietal effects on such decision support research in sugarcane.
“To date little research has considered varietal effects on such decision support research in sugarcane.”
The paper relies on this without support.
Some parameters representing physiological traits such as leaf area and number of green leaves are easy to measure, but accurately obtaining parameters for other traits (e.g. transpiration efficiency) can be expensive and time consuming.
“Some parameters representing physiological traits such as leaf area and number of green leaves are easy to measure.However, accurately obtaining parameters for other traits (e.g.transpiration efficiency) can be expensive and time consuming.”
The paper relies on this without support.
An alternative approach is to statistically estimate parameters that represent difficult to measure traits from knowledge about traits that are routinely or more easily measured such as biomass, cane yield or sucrose yield.
“An alternative approach is to statistically estimate parameters that represent difficult to measure traits from knowledge about traits that are routinely or more easily measured such as biomass, cane yield or sucrose yield.”
The authors state this as an assumption.
Model errors are normally distributed.
“Model errors were assumed normally distributed and a normal likelihood function based on (Eq.( 2)) was used.”
The authors state this as an assumption.
Error variances for harvest green biomass and sucrose yield are known with no uncertainty.
“These variances were assumed known with no uncertainty.”
The authors state this as an assumption.
Parameters are independent such that the covariance matrix is diagonal with variances derived from the prior distributions.
“Parameters were assumed independent such that the covariance matrix (Ʃ) was diagonal with variances derived from the prior distributions.”
The authors state this as an assumption.
Prior distributions of parameters are independent.
“In this study, the prior distributions of parameters were assumed to be independent as no data on the relationship between parameters were available.”
The authors state this as an assumption.
For MCMC all chains converged for all parameters.
“For MCMC all chains were assumed to converge for all parameters as R ⌃ was less than 1.2 for all parameters”
The authors state this as an assumption.
The prior distributions for the variety parameters are as specified in Table 4.
“Prior distributions assumed for statistical calibration of variety parameters.”
The paper relies on this without support.
A larger Q can help improve parameter posterior distributions.
“However, a larger Q can help improve parameter posterior distributions”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 3.3.2. Implementing MCMC
“exp((ln[P(θ * )] + ln[L(θ * |Y)]) -(ln[P(θ t-1 )] -ln[L(θ t-1 |Y)]))”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.2.2. Markov Chain Monte Carlo
“r = P(θ * |Y) P(θ t-1 |Y) = P(θ * )P(Y| θ * ) P(θ t-1 )P(Y| θ t-1 ) (6)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 4.2. Real world evaluation of GLUE and MCMC for variety Q117
“Based on NRMSE and D index, green biomass, sucrose yield and LAI simulations were slightly better using the MCMC calibration compared to the GLUE calibration.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
“| | All Data | 31 14.84 0.78 0.93 14.92 0.85 0.93 14.97 0.78 0.94 | |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
“The defined values for 4 parameter of V001 (sucrose_delay, min_sstem_sucrose, green_leaf_no and rue) were not within a 95% credible interval of their GLUE empirical posterior distribution.”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > O
“min_sstem_sucrose 550 661.98(100.54)7 3 20.4”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 5. Conclusions
“For MCMC all chains converge for all parameters ( R ⌃ > 1.2) except sucrose_delay ( R ⌃ > 1.2)”
§ A Theoretical and Real World Evaluation of Two Bayesian Techniques for the Calibration of Variety Parameters in a Sugarcane Crop Model > 2.2.2. Markov Chain Monte Carlo
“(1997) recommend R ⌃ values of less than 1.2 are acceptable for most applications.”
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BackgroundTraits for canopy development and light interception by twenty-seven Brazilian sugarcane varieties
Dias, H. B., Inman‐Bamber, N. G., Everingham, Y. L., Sentelhas, P. C., Bermejo, R., & Christodoulou, D. (2020) · Field Crops Research · doi:10.1016/j.fcr.2020.107716 · Cited by 25
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Bayesian multi-level calibration of a process-based maize phenology model
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BackgroundDifferences in parameter estimates derived from various methods for the ORYZA (v3) Model
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MethodApplication of Parameter Optimization Methods Based on Kalman Formula to the Soil—Crop System Model
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“GLUE and Markov Chain Monte Carlo (MCMC) were compared for the parameter identification of the APSIM-Sugar model [28,29].”
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BackgroundViswanathan, M. (2022) · doi:10.5194/bg-2021-238-ac2
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A Bayesian sequential updating approach to predict phenology of silage maizePreprint
Viswanathan, M., Weber, T. K. D., Gayler, S., et al. (2021) · doi:10.5194/bg-2021-238
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Background
Metadata
- Authors
- Sexton, J., Everingham, Y., & Inman-Bamber, G.
- Affiliations
- James Cook University (AU)
- Corresponding author
- Sexton, J.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 83
- Pages
- 126-142
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2406337265
- Semantic Scholar
- cab01478d3f3834dfb8e4a2813d6c9618c8eae55
- Cited by
- 61
- References
- 90
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 8 · 2023: 7 · 2022: 11 · 2021: 10 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 1
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- Locations
- Environmental Modelling & Software (publishedVersion)
- ResearchOnline at James Cook University (James Cook University) (submittedVersion)
- Funders
- Sugar Research Australia
- James Cook University (STU076)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agriculture, Soil, Plant Science
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Sugarcane Cultivation and Processing
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- sugarcane, crop model calibration, Bayesian parameter estimation, generalized likelihood uncertainty estimation, Markov chain Monte Carlo, sucrose yield, green biomass
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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