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Author |
Schauberger, B.; Rolinski, S.; Müller, C. |
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Title |
A network-based approach for semi-quantitative knowledge mining and its application to yield variability |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Volume |
11 |
Issue |
12 |
Pages |
123001 |
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Keywords  |
yield variability; crop models; interaction network; plant process; wheat; maize; rice; Global Food Security; Climate-Change; Crop Production; Stress Tolerance; Wheat Yields; Heat-Stress; Temperature Variability; Environmental-Factors; United-States; Elevated CO2 |
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Abstract |
Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields. |
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2017-04-07 |
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1748-9326 |
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Review |
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CropM, ft_macsur |
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MA @ admin @ |
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4942 |
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Author |
Ferrise, R.; Toscano, P.; Pasqui, M.; Moriondo, M.; Primicerio, J.; Semenov, M.A.; Bindi, M. |
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Title |
Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin |
Type |
Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
7-21 |
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Keywords |
yield predictions; seasonal forecasts; analogue forecasts; stochastic weather generator; empirical forecasting models; durum wheat; crop modelling; mediterranean basin; general-circulation model; scale climate indexes; crop yield; grain-yield; forecasts; simulation; region; precipitation; australia; europe |
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Abstract |
Uncertainty in weather conditions for the forthcoming growing season influences farmers’ decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of <20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4696 |
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Author |
Sharif, B.; Makowski, D.; Plauborg, F.; Olesen, J.E. |
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Title |
Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark |
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Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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82 |
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Pages |
11-20 |
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Keywords |
Winter oilseed rape; Statistical models; Yield; Climate; Regression |
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Abstract |
Highlights • Regularization techniques for regression outperformed the classical regression techniques in predicting crop yields. • Different regression techniques with similar prediction accuracy showed different responses of major climatic variables to crop yield. • The regression models showed some responses of crop yield to climatic conditions that is mostly absent in process based crop models. Abstract Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input variables. Several statistical regression methods were fitted to a dataset including 689 observations of winter oilseed rape yield from replicated field experiments conducted in 239 sites in Denmark, covering nearly all regions of the country from 1992 to 2013. Regression methods were compared by cross-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C temperature increase during flowering was estimated to range between 0 and +6 %, depending on choice of regression method. Precipitation was found to have an adverse effect on yield during autumn and winter. It was estimated that an increase in precipitation of +1 mm/day would result in a relative yield change ranging from 0 to −4 %. Soil type was also important for crop yields with lower yields on sandy soils compared to loamy soils. Later sowing was found to result in increased crop yield. The estimated effect of climate on yield was highly sensitive to the chosen regression method. Regression models showing similar performance led in some cases to different conclusions with respect to effect of temperature and precipitation. Hence, it is recommended to apply an ensemble of regression models, in order to account for the sensitivity of the data driven models for projecting crop yield under climate change. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4966 |
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Author |
Tao, F.; Roetter, R.P.; Palosuo, T.; Diaz-Ambrona, C.G.H.; Ines Minguez, M.; Semenov, M.A.; Kersebaum, K.C.; Nendel, C.; Cammarano, D.; Hoffmann, H.; Ewert, F.; Dambreville, A.; Martre, P.; Rodriguez, L.; Ruiz-Ramos, M.; Gaiser, T.; Hohn, J.G.; Salo, T.; Ferrise, R.; Bindi, M.; Schulman, A.H. |
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Title |
Designing future barley ideotypes using a crop model ensemble |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
82 |
Issue |
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Pages |
144-162 |
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Keywords |
Water-Use Efficiency; Climate-Change; Nitrogen Dynamics; Systems; Simulation; Wheat Cultivars; Grain Weight; Yield; Growth; Fertilization; Adaptation; Adaptation; Breeding; Climate change; Crop simulation models; Impact; Genotype; Genetic traits |
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Abstract |
Climate change and its associated higher frequency and severity of adverse weather events require genotypic adaptation. Process-based ecophysiological modelling offers a powerful means to better target and accelerate development of new crop cultivars. Barley (Hordeum vulgare L) is an important crop throughout the world, and a good model for study of the genetics of stress adaptation because many quantitative trait loci and candidate genes for biotic and abiotic stress tolerance have been identified in it. Here, we developed a new approach to design future crop ideotypes using an ensemble of eight barley simulation models (i.e. APSIM, CropSyst, HERMES, MCWLA, MONICA, SIMPLACE, Sirius Quality, and WOFOST), and applied it to design climate-resilient barley ideotypes for Boreal and Mediterranean climatic zones in Europe. The results showed that specific barley genotypes, represented by sets of cultivar parameters in the crop models, could be promising under future climate change conditions, resulting in increased yields and low inter-annual yield variability. In contrast, other genotypes could result in substantial yield declines. The most favorable climate-zone-specific barley ideotypes were further proposed, having combinations of several key genetic traits in terms of phenology, leaf growth, photosynthesis, drought tolerance, and grain formation. For both Boreal and Mediterranean climatic zones, barley ideotypes under future climatic conditions should have a longer reproductive growing period, lower leaf senescence rate, larger radiation use efficiency or maximum assimilation rate, and higher drought tolerance. Such characteristics can produce substantial positive impacts on yields under contrasting conditions. Moreover, barley ideotypes should have a low photoperiod and high vernalization sensitivity for the Boreal climatic zone; for the Mediterranean, in contrast, it should have a low photoperiod and low vernalization sensitivity. The drought-tolerance trait is more beneficial for the Mediterranean than for the Boreal climatic zone. Our study demonstrates a sound approach to design future barley ideotypes based on an ensemble of well-tested, diverse crop models and on integration of knowledge from multiple disciplines. The robustness of model-aided ideotypes design can be further enhanced by continuously improving crop models and enhancing information exchange between modellers, agro-meteorologists, geneticists, physiologists, and plant breeders. (C) 2016 Elsevier B.V. All rights reserved. |
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2017-01-20 |
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1161-0301 |
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CropM, ft_MACSUR |
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MA @ admin @ |
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4935 |
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Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.C.; Olesen, J.E.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.A.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Gaiser, T.; Palosuo, T.; Tao, F.; Nendel, C.; Roggero, P.P.; Bartošová, L.; Asseng, S. |
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Title |
Crop modelling for integrated assessment of risk to food production from climate change |
Type |
Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
72 |
Issue |
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Pages |
287-303 |
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Keywords |
uncertainty; scaling; integrated assessment; risk assessment; adaptation; crop models; agricultural land-use; change adaptation strategies; farming systems simulation; agri-environmental systems; enrichment face experiment; high-temperature stress; change impacts; nitrogen dynamics; atmospheric co2; spring wheat |
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The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4521 |
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