Littérature scientifique sur le sujet « Climate change, crop models, extreme events, grapevine »

Climate change and global warming are going to be the major issues for the 21st century. Their impacts on agriculture, water availability and other natural resources are of serious concern. The paper briefly summarizes the existing information on global warming, past climatic anomalies and occurrence of extreme events vis-a-vis their impact on south Asia in general and Indian in particular. Use of GCM models in conjunction with crop simulation models for impact assessment in agriculture are briefly touched upon. The impact on hydrosphere in terms of water availability and on the forests in India are also discussed. A major shift in our policy makers paradigm is needed to make development sustainable in the face of climate change, global warming and sea level rise.

Abstract Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models’ capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models’ capacity to adequately respond to extreme weather events.

The potential of quantile mapping (QM) as a tool for bias correction of precipitation extremes simulated by regional climate models (RCMs) is investigated in this study. We developed an extended version of QM to improve the quality of bias-corrected extreme precipitation events. The extended version aims to exploit the advantages of both non-parametric methods and extreme value theory. We evaluated QM by applying it to a small ensemble of hindcast simulations, performed with RCMs at six different locations in Europe. We examined the quality of both raw and bias-corrected simulations of precipitation extremes using the split sample and cross-validation approaches. The split-sample approach mimics the application to future climate scenarios, while the cross-validation framework is designed to analyse “new extremes”, that is, events beyond the range of calibration of QM. We demonstrate that QM generally improves the simulation of precipitation extremes, compared to raw RCM results, but still tends to present unstable behaviour at higher quantiles. This instability can be avoided by carefully imposing constraints on the estimation of the distribution of extremes. The extended version of the bias-correction method greatly improves the simulation of precipitation extremes in all cases evaluated here. In particular, extremes in the classical sense and new extremes are both improved. The proposed approach is shown to provide a better representation of the climate change signal and can thus be expected to improve extreme event response for cases such as floods in bias-corrected simulations, a development of importance in various climate change impact assessments. Our results are encouraging for the use of QM for RCM precipitation post-processing in impact studies where extremes are of relevance.

Wide range of inter-annual climatic variability and frequent occurrence of extreme climatic events in Indian context is a great concern. There is a need to assess the impact of these events on agriculture production as well suggest the agri-management options for sustenance. The appropriate region specific agro-advisory needs to be established for the farmers and other stake holders. Crop simulation models are effective tools for assessing the crops’ response to these climate related events and for suggesting suitable adaptation procedures for ensuring higher agricultural production. Remote sensing and GIS are effective tools in this regard to prepare the regional based agro-advisories, by linking with the crop simulation models and relational database layers of bio-physical and socio-economic aspects. For effective agro-advisory services, there is a need to link the other biotic and abiotic stresses for accurate estimates and generating window of suitable agri-management options. Crop simulation models can effectively integrate these stresses for crop and soil processes understanding and ultimate yield formation. In this review article, we have discussed about the inter-annual/ seasonal climatic variability and occurrence of extreme climatic events in India and demonstrated the potential of crop models viz., INFOCROP, WTGROWS, DSSAT to assess the impact of these events (also including climate change) on growth and yield of crops and cropping systems and thereby suggesting appropriate adaptation strategies for sustenance. The potential of remote sensing for crop condition assessment and regional/national yield forecast has been demonstrated. Crop simulation tools coupled with remote sensing inputs through GIS can play an important role in evolving this unique operational platform of designing weather based agro-advisory services for India.

The effect of global climate warming on agriculture, as the most threatened sector of the economy, in the form of reduced crop yields and more frequent manifestations of extreme weather events, is one of the urgent problems that need to be paid close attention. Adaptation of agriculture to climate change is becoming one of the key priorities that need to be developed through the creation of new models of farming systems that would combine the effectiveness of traditional and alternative farming systems while being environmentally friendly and cost-effective. This article considers research issues in agriculture, including the creation of stress-resistant varieties, soil and water and resource-saving technologies, adapted to climate changes, adaptive-landscape, accurate and biological farming systems.

Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change.

Variations in crop yields and agricultural productivity are strongly influenced by fluctuations in seasonal weather conditions during the growing season. The El Niño/Southern Oscillation (ENSO) cycle, and other similar ocean/atmosphere teleconnections in the North Pacific and North Atlantic, contribute to extreme weather events and climatic variability. As seasonal forecasting skills improve with greater knowledge of these teleconnections and improved Global Circulation Models (GCMs), farmers and agricultural planners will be able to make better use of long-lead forecasts for strategic decisions in agriculture. Issues related to climate variability and climate change pose significant risks to agriculture as the frequency of natural disasters tends to increase worldwide.

In this paper, the results of a climate change impact and vulnerability assessment conducted for the agricultural sector of Cyprus are presented. The assessment is based on the outputs of specialized climatic and crop models, while it incorporates quantified socio-economic vulnerability indicators of the Cypriot agriculture. The results are aggregated at municipal level in order to support regional and local adaptation planning. The assessment was performed for two representative concentration pathways (RCP4.5, RCP8.5), as well as for extreme climatic scenarios. Following, an economic assessment was made on the expected change in revenues of the agricultural sector. The results of climatic simulations indicated that future increases in temperature will be characterized by a strong seasonal trend, with the highest increases occurring in summer. Precipitation is expected to decrease throughout the island, where the highest decreases (50%) are expected during summer (RCP8.5). This trend will affect mainly tomato, grapevine, and olive tree, whose growing cycle takes place during summer. By contrast, crops covering autumn-winter season, such as potato, barley, and wheat, are expected to partially avoid harsh summer conditions. The results of the economic assessment show that the changes in total revenues are insignificant, because, under all scenarios, a loss in one crop is compensated by a gain in another crop. However, the farmers as well as the government should take action to increase the resilience of the agricultural sector, with a special focus on those crops and areas that are expected to be adversely affected by climate change impacts.

The yield and quality of food crops is central to the well being of humans and is directly affected by climate and weather. Initial studies of climate change on crops focussed on effects of increased carbon dioxide (CO 2 ) level and/or global mean temperature and/or rainfall and nutrition on crop production. However, crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and yield. Thus, climate variability and changes in the frequency of extreme events are important for yield, its stability and quality. In this context, threshold temperatures for crop processes are found not to differ greatly for different crops and are important to define for the major food crops, to assist climate modellers predict the occurrence of crop critical temperatures and their temporal resolution. This paper demonstrates the impacts of climate variability for crop production in a number of crops. Increasing temperature and precipitation variability increases the risks to yield, as shown via computer simulation and experimental studies. The issue of food quality has not been given sufficient importance when assessing the impact of climate change for food and this is addressed. Using simulation models of wheat, the concentration of grain protein is shown to respond to changes in the mean and variability of temperature and precipitation events. The paper concludes with discussion of adaptation possibilities for crops in response to drought and argues that characters that enable better exploration of the soil and slower leaf canopy expansion could lead to crop higher transpiration efficiency.

Abstract. A new temperature goal of “holding the increase in global average temperature well below 2 ∘C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘C above pre-industrial levels” has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 ∘C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0 ∘C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (−1.7 %), respectively, in a warming scenario of 1.5 ∘C (2.0 ∘C). In general, the warming scenarios would bring more opportunities than risks for crop development and food security in China. Moreover, although the variability of crop yield would increase from 1.5 ∘C warming to 2.0 ∘C warming, the probability of a crop yield decrease would decrease. Our findings highlight that the 2.0 ∘C warming scenario would be more suitable for crop production in China, but more attention should be paid to the expected increase in extreme event impacts.

Grapevine (Vitis vinifera L.) is a valuable fruit crop characterized by a worldwide importance. In particular, Italy, France and Spain are the biggest wine-producing countries and they play a relevant role in the world wine economy. However, the specific climate conditions of the narrow geographical areas in which grapevine is currently cultivated expose the viticulture suitability to the great risk of the climate change and extreme events. In this context, the grapevine simulation models represented useful tools for investigating the main physiological plant processes under different pedo-climatic conditions. Accordingly, the objective of this thesis is to provide a new software component for estimating grapevine growth, yield and quality in different environments. The new software component UNIFI.GrapeML is presented in Chapter 2 as an extendible model library in which the fine-granularity of the model structure allows an easier discretization and implementation of the code. In Chapter 2, UNIFI.GrapeML was tested in a specific case of study in Northeastern of Spain on a Chardonnay vineyard. A sensitivity analysis using Latin Hypercube and Sobol method was performed for evaluating the sensitivity of the model parameters on the final fruit biomass considering the environmental conditions of the study area. The results evidenced the strong impact of leaf area expansion and crop partitioning parameters on final output. The model was then calibrated on soil water content, phenology and fruit biomass data showing satisfactory results. Afterwards, UNIFI.GrapeML was implemented with a quality approach for assessing sugar concentration during ripening period according to the berry water content. This approach was evaluated in a specific case of study in Montalcino wineproducing region (Italy) for Sangiovese variety over the period 1998-2015. In this case, the phenological phases (budbreak, flowering, veraison and maturity) of Sangiovese variety were calibrated using data from Susegana and Montalcino for determining the length of grapevine cycle. Then, the model calibration and validation on observed sugar content data was performed for Montalcino site. The results showed high correlations in both calibration and validation procedures considering the yearto- year variability of the dataset. Finally, the impact of climate change and extreme events was evaluated on the phenological cycle of very early, early, middle and late varieties at European scale (Chapter 4). The effect of the mean climate change on phenology was assessed using a chilling-forcing model while the effect of extreme events was accounted through the frost events at budbreak and the effect of suboptimal temperatures at flowering stages (fruit-set impact implemented in UNIFI.GrapeML). The results highlighted an overall advance of budbreak and flowering phases of all varieties across a latitudinal and IV longitudinal geographical gradient, especially in central/eastern Europe. In particular, the climate change showed a high impact on budbreak of late compared to very early and early varieties in western European region. Moreover, a higher decrease of frost events was evidenced in western regions compared to central/eastern Europe and a more relevant effect of these events was found on very early and early compared to late varieties. On the other hand, the estimation of the temperature stress at flowering stage evidenced a lower variability between varieties and scenarios while relevant differences were showed between European regions.

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

The increase in extreme climate events due to climate change has resulted in crop losses, quality losses, environmental and social impacts in agricultural areas. Insurance against extreme events is a vital tool adaptation to deal with the impacts of those hazards. However, few works consider the optimization of different dimensions related to this tool. This work proposes a framework to use multi-objective optimization models to better design and evaluate crop insurance premiums and conducts a case study for sugarcane premiums at São Paulo state in 2010. The framework can be adopted for different crops, objectives, and models. The case study showed that around 20% of the policies evaluated were efficient solutions from the farmer's point of view.