scholarly journals New modelling technique for improving crop model performance - Application to the GLAM model

2019 ◽  
Vol 118 ◽  
pp. 187-200 ◽  
Author(s):  
I. Droutsas ◽  
A.J. Challinor ◽  
M. Swiderski ◽  
M.A. Semenov
Keyword(s):  
Model Performance ◽  
Crop Model ◽  
Modelling Technique

scholarly journals The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

2017 ◽  
Vol 10 (4) ◽  
pp. 1679-1701 ◽  
Author(s):  
Silvia Caldararu ◽  
Drew W. Purves ◽  
Matthew J. Smith
Keyword(s):  
Environmental Changes ◽  
Crop Yields ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Model Performance ◽  
Predictive Performance ◽  
Crop Model ◽  
Model Fit ◽  
General Process ◽  
Fitting Algorithm

Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data–space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

scholarly journals The benefits of segmentation: Evidence from a South African bank and other studies

2017 ◽  
Vol 113 (9/10) ◽  
Author(s):  
Douw G. Breed ◽  
Tanja Verster
Keyword(s):  
South African ◽  
Direct Marketing ◽  
Model Performance ◽  
Predictive Modelling ◽  
Modelling Technique ◽  
Gradient Boosting ◽  
Data Sets ◽  
Data Set ◽  
Linear Modelling ◽  
Modelling Techniques

We applied different modelling techniques to six data sets from different disciplines in the industry, on which predictive models can be developed, to demonstrate the benefit of segmentation in linear predictive modelling. We compared the model performance achieved on the data sets to the performance of popular non-linear modelling techniques, by first segmenting the data (using unsupervised, semi-supervised, as well as supervised methods) and then fitting a linear modelling technique. A total of eight modelling techniques was compared. We show that there is no one single modelling technique that always outperforms on the data sets. Specifically considering the direct marketing data set from a local South African bank, it is observed that gradient boosting performed the best. Depending on the characteristics of the data set, one technique may outperform another. We also show that segmenting the data benefits the performance of the linear modelling technique in the predictive modelling context on all data sets considered. Specifically, of the three segmentation methods considered, the semi-supervised segmentation appears the most promising.

scholarly journals Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs

2016 ◽  
Vol 55 (3) ◽  
pp. 579-594 ◽  
Author(s):  
Michael J. Glotter ◽  
Elisabeth J. Moyer ◽  
Alex C. Ruane ◽  
Joshua W. Elliott
Keyword(s):  
Arid Regions ◽  
Crop Yields ◽  
Heat Waves ◽  
Model Performance ◽  
Ground Truth ◽  
Crop Model ◽  
Systematic Evaluation ◽  
Multiple Sources ◽  
Crop Models ◽  
Maize Yields

AbstractProjections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources—reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset—and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Same soil - different climate: crop model inter-comparison with lysimeter data of translocated monoliths

2021 ◽  
Author(s):  
Jannis Groh ◽  
Horst H. Gerke ◽  
Keyword(s):  
Climate Change ◽  
Model Performance ◽  
Predictive Ability ◽  
Crop Model ◽  
Climatic Conditions ◽  
Climate Change Scenarios ◽  
Soil Ecosystem ◽  
Site Specific ◽  
Climate Conditions ◽  
Crop Models

<p>Crop model comparisons have mostly been carried out to test predictive ability under previous climate conditions and for soils of the same location. However, the ability of individual agricultural models to predict the effects of changes in climatic conditions on soil-ecosystems beyond the range of site-specific variability is unknown. The objective of this study was to test the predictive ability of agroecosystem models using weighable lysimeter data for the same soil under changing climatic conditions and to compare simulated plant growth and soil-ecosystem response to climate change between these models. To achieve this, data from the TERENO-SOILCan lysimeters-network for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters with Dedelow soil monoliths transferred to Bad Lauchstädt and Selhausen were analysed. The transfer of the soils took place to a drier and warmer location (Bad Lauchstädt) and to a warmer and wetter location (Selhausen) compared to the original location of the soils in Dedelow with the same crop rotation. After model calibration for data from the original Dedelow site, crop growth and soil water balances of transferred Dedelow soil monoliths were predicted using the site-specific boundary conditions and compared with the observations at Selhausen and Bad Lauchstädt. The overall simulation output of the models was separated into a plant-related part, ecosystem-productivity (grain yield, biomass, LAI) and an environmental part, ecosystem-fluxes (evapotranspiration, net-drainage, soil moisture). The results showed that when the soil was transferred to a drier region, the agronomic part of the crop models predicted well, and when the soil was moved to wetter regions, the environmental flow part of the models seemed to predict better. The results suggest that accounting for climate change scenarios, more consideration of soil properties and testing model performance for conditions outside the calibrated range and site-specific variability will help improve the models.</p>

scholarly journals Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

2017 ◽  
Vol 10 (4) ◽  
pp. 1403-1422 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Crop Models ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

scholarly journals On the importance of predictor choice, modelling technique, and number of pseudo‐absences for bioclimatic envelope model performance

2020 ◽  
Vol 10 (21) ◽  
pp. 12307-12317
Author(s):  
Mirza Čengić ◽  
Jasmijn Rost ◽  
Daniela Remenska ◽  
Jan H. Janse ◽  
Mark A. J. Huijbregts ◽  
...  
Keyword(s):  
Model Performance ◽  
Modelling Technique ◽  
Choice Modelling ◽  
Bioclimatic Envelope Model ◽  
Envelope Model ◽  
Bioclimatic Envelope

scholarly journals Comparison of Statistic Methods for Evaluating Crop Model Performance

2012 ◽  
Vol 14 (4) ◽  
pp. 269-276
Author(s):  
Junhwan Kim ◽  
Chung-Kuen Lee ◽  
Jiyoung Shon ◽  
Kyung-Jin Choi ◽  
Younghwan Yoon
Keyword(s):  
Model Performance ◽  
Crop Model

scholarly journals The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

2016 ◽  
Author(s):  
Silvia Caldararu ◽  
Drew W. Purves ◽  
Matthew J. Smith
Keyword(s):  
Environmental Changes ◽  
Crop Yields ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Model Performance ◽  
Predictive Performance ◽  
Crop Model ◽  
Model Fit ◽  
General Process ◽  
Fitting Algorithm

Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In the current paper we present a generic process based crop model which we parametrise using a Bayesian model fitting algorithm to three different sources of data – space based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters can largely capture the observed behaviour but the data constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improves on the prior model fit, the satellite based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge10 gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection steps for improvement in our predictions of crop yields and crop responses to environmental changes.

scholarly journals Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications

2016 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Model Evaluation ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but there so far is no general framework on how to assess model performance. We here evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that GGCMs show mixed skill in reproducing time-series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producer countries by many GGCMS and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that also other modeling groups can test their model performance against the reference data and the GGCMI benchmark.

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