scholarly journals Crop insurance demand in wheat production: focusing on yield gaps and asymmetric information

2018 ◽  
Vol 15 (4) ◽  
pp. e0119
Author(s):  
Alba Castañeda-Vera ◽  
Antonio Saa-Requejo ◽  
Inés Mínguez ◽  
Alberto Garrido
Keyword(s):  
Asymmetric Information ◽  
Historical Data ◽  
Crop Insurance ◽  
Simulated Data ◽  
Crop Model ◽  
Crop Models ◽  
Actual Risk ◽  
Yield Gaps ◽  
Technical Basis ◽  
Insurance Data

Analysis of yield gaps were conducted in the context of crop insurance and used to build an indicator of asymmetric information. The possible influence of asymmetric information in the decision of Spanish wheat producers to contract insurance was additionally evaluated. The analysis includes simulated yield using a validated crop model, CERES-Wheat previously selected among others, whose suitability to estimate actual risk when no historical data are available was assessed. Results suggest that the accuracy in setting the insured yield is decisive in farmers’ willingness to contract crop insurance under the wider coverage. Historical insurance data, when available, provide a more robust technical basis to evaluate and calibrate insurance parameters than simulated data, using crop models. Nevertheless, the use of crop models might be useful in designing new insurance packages when no historical data is available or to evaluate scenarios of expected changes. In that case, it is suggested that yield gaps be estimated and considered when using simulated attainable yields.

scholarly journals Prediction of Maize Yield Response to Climate Change with Climate and Crop Model Uncertainties

2015 ◽  
Vol 54 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Yi Zhang ◽  
Yanxia Zhao ◽  
Sining Chen ◽  
Jianping Guo ◽  
Enli Wang
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Maize Yield ◽  
Crop Model ◽  
Global Climate Models ◽  
Yield Response ◽  
Yield Reduction ◽  
Climate Projections ◽  
Crop Models ◽  
Climate Change Uncertainty

AbstractProjections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021–50 period, taking 1971–2000 as the baseline period. The results indicated a yield reduction of 13% during 2021–50, with 95% probability intervals of (−41%, +12%) relative to 1971–2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021–50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.

scholarly journals A new approach to crop model calibration: phenotyping plus post-processing

2019 ◽  
Author(s):  
Pierre Casadebaig ◽  
Philippe Debaeke ◽  
Daniel Wallach
Keyword(s):  
Field Data ◽  
Mean Squared Error ◽  
Target Population ◽  
Environmental Variable ◽  
Crop Model ◽  
Crop Modeling ◽  
Initial Model ◽  
Crop Models ◽  
Empirical Correction ◽  
Or Gene

AbstractCrop models contain a number of genotype-dependent parameters, which need to be estimated for each genotype. This is a major difficulty in crop modeling. We propose a hybrid method for adapting a crop model to new genotypes. The genotype-dependent parameters of the model could be obtained by phenotyping (or gene-based modeling). Then field data for example from variety trials could be used to provide a simple empirical correction to the model, of the form a+b times an environmental variable. This approach combines the advantages of phenotyping, namely that the genotype-specific parameters have a clear meaning and are comparable between genotypes, and the advantages of fitting the model to field data, namely that the corrected model is adapted to a specific target population. It has the advantage of being very simple to apply, and furthermore gives useful information as to which environmental variables are not fully accounted for in the initial model. In this study, this empirical correction is applied to the SUNFLO crop model for sunflower, using field data from a multi-environment trial network. The empirical correction reduced mean squared error (MSE) on the average by 54% for prediction of yield and by 26% for prediction of oil content, compared to the initial model. Most of the improvement came from eliminating bias, with some further improvement from the environmental term in the regression.

scholarly journals The chaos in calibrating crop models

2020 ◽  
Author(s):  
Daniel Wallach ◽  
Taru Palosuo ◽  
Peter Thorburn ◽  
Zvi Hochman ◽  
Emmanuelle Gourdain ◽  
...  
Keyword(s):  
Experimental Data ◽  
Model Calibration ◽  
Crop Model ◽  
Process Models ◽  
Model Parameters ◽  
Model Structure ◽  
Crop Models ◽  
Estimation Of Model Parameters ◽  
Parameter Values ◽  
Made In

Calibration, that is the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.

Comprehensive global climate impact assessment for crop yields

2020 ◽  
Author(s):  
Christoph Müller ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Crop Yields ◽  
Global Climate ◽  
Model Development ◽  
Climate Impact ◽  
Crop Model ◽  
Model Intercomparison ◽  
Crop Models ◽  
Climate Impact Assessment

<p>Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.</p><p>Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (Müller et al., 2017).</p><p>However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.</p><p>Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6°C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.</p><p>We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.</p><p> </p><p>Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.</p><p>Franke (2019b)  Geoscientific Model Development, submitted</p><p>McSweeney & Jones, (2016) Climate Services, 1:24-29.</p><p>Müller (2017) Geoscientific Model Development, 10:1403-1422.</p><p> </p>

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.

scholarly journals Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield

2021 ◽  
Vol 13 (12) ◽  
pp. 2395
Author(s):  
V.S. Manivasagam ◽  
Yuval Sadeh ◽  
Gregoriy Kaplan ◽  
David J. Bonfil ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Wheat Yield ◽  
Simple Algorithm ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Yield Prediction ◽  
Area Index ◽  
Crop Models ◽  
Sentinel 2

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.

scholarly journals Double-Rice System Simulation in a Topographically Diverse Region—A Remote-Sensing-Driven Case Study in Hunan Province of China

2019 ◽  
Vol 11 (13) ◽  
pp. 1577 ◽  
Author(s):  
Jing Zhang ◽  
Zhao Zhang ◽  
Chenzhi Wang ◽  
Fulu Tao
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Hunan Province ◽  
Crop Models ◽  
Sensing Data ◽  
Average Improvement ◽  
Double Rice ◽  
Late Rice ◽  
Early Rice

Few studies have focused on the potential impacts of topography on regional crop simulation, which might constrain the development of crop models and lead to inaccurate estimations for food security. In this study, we used remote sensing data to calibrate a regional crop model (MCWLA-Rice) for yield simulation in a double-rice crop rotation system in counties of Hunan province dominated by three landforms (plain, hill, and mountain). The calibration scheme with coarse remote sensing data (Global LAnd Surface Satellite, GLASS) greatly improved model accuracy for the double-rice system and is a promising method for yield estimation in large areas. The average improvement in relative root mean square error (RRMSE) was at most 48.00% for early rice and 41.25% for late rice. The average improvement in coefficient of determination (R2) value was at most 0.54 for early rice and 0.19 for late rice. Estimation of yield in counties dominated by different landform types indicated that: (1) MCWLA-Rice tended to be unstable in areas of complex topography and resulted in unbalanced proportions of overestimations and underestimations. (2) Differences in yield simulation between early rice and late rice varied among counties; yield estimates were highest in predominantly hilly counties, followed by counties dominated by plains, and lowest in predominantly mountainous counties. The results indicated that the topography might harm the accuracy of crop model simulations. Integration of topographic factors into crop models may enable yield estimation with enhanced accuracy to promote social development.

scholarly journals Managing your risk: Weather and climate impacts on crop insurance. Fact sheet using national-scale data.

2018 ◽  
Author(s):  
Julian Reyes ◽  
Emile Elias ◽  
Andrew Eischens ◽  
Mark Shilts
Keyword(s):  
United States ◽  
Risk Management ◽  
Crop Insurance ◽  
The United States ◽  
Climate Impacts ◽  
National Scale ◽  
Fact Sheet ◽  
Weather And Climate ◽  
Insurance Data ◽  
Scale Data

A fact sheet produced by the USDA Southwest Climate Hub using publicly available crop insurance data from the USDA Risk Management Agency for the United States.

scholarly journals The GGCMI phase II emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)

2020 ◽  
Author(s):  
James Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Phase Ii ◽  
Crop Yields ◽  
Crop Model ◽  
Climate Impacts ◽  
Future Climate ◽  
Yield Response ◽  
Crop Models ◽  
Growing Seasons ◽  
Nitrogen Inputs ◽  
Temperature Water

Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

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