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1
DE, U. S. "Climate change impact : Regional scenario." MAUSAM 52, no. 1 (December 29, 2021): 201–12. http://dx.doi.org/10.54302/mausam.v52i1.1688.
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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.
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Heinicke, Stefanie, Katja Frieler, Jonas Jägermeyr, and Matthias Mengel. "Global gridded crop models underestimate yield responses to droughts and heatwaves." Environmental Research Letters 17, no. 4 (March 18, 2022): 044026. http://dx.doi.org/10.1088/1748-9326/ac592e.
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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.
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Satyanarayana Tani and Andreas Gobiet. "Quantile mapping for improving precipitation extremes from regional climate models." Journal of Agrometeorology 21, no. 4 (November 10, 2021): 434–43. http://dx.doi.org/10.54386/jam.v21i4.278.
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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.
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SINGH, K. K., and NAVEEN KALRA. "Simulating impact of climatic variability and extreme climatic events on crop production." MAUSAM 67, no. 1 (December 8, 2021): 113–30. http://dx.doi.org/10.54302/mausam.v67i1.1153.
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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.
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Kenenbayev, S., Djura Karagic, and G. Yessenbayeva. "CLIMATE CHANGE AND PRIORITY RESEARCH AREAS IN AGRICULTURE." BULLETIN 389, no. 1 (February 10, 2021): 111–16. http://dx.doi.org/10.32014/2021.2518-1467.15.
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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.
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Sun, Qing, Yi Zhang, Xianghong Che, Sining Chen, Qing Ying, Xiaohui Zheng, and Aixia Feng. "Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields." Agriculture 12, no. 11 (October 28, 2022): 1791. http://dx.doi.org/10.3390/agriculture12111791.
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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.
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Motha, Raymond P. "Implications of climate change on long-lead forecasting and global agriculture." Australian Journal of Agricultural Research 58, no. 10 (2007): 939. http://dx.doi.org/10.1071/ar06104.
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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.
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Papadaskalopoulou, Christina, Marco Moriondo, Ioannis Lemesios, Anna Karali, Angeliki Konsta, Camilla Dibari, Lorenzo Brilli, et al. "Assessment of Total Climate Change Impacts on the Agricultural Sector of Cyprus." Atmosphere 11, no. 6 (June 9, 2020): 608. http://dx.doi.org/10.3390/atmos11060608.
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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.
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Porter, John R., and Mikhail A. Semenov. "Crop responses to climatic variation." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2021–35. http://dx.doi.org/10.1098/rstb.2005.1752.
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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.
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Chen, Yi, Zhao Zhang, and Fulu Tao. "Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C." Earth System Dynamics 9, no. 2 (May 18, 2018): 543–62. http://dx.doi.org/10.5194/esd-9-543-2018.
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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.
11
Parada-Molina, Paulo César, Víctor Luis Barradas-Miranda, Gustavo Ortiz Ceballos, Juan Cervantes-Pérez, and Carlos Roberto Cerdán Cabrera. "Climatic suitability for Coffea arabica L. front to climate events extreme: Tree cover importance." Scientia Agropecuaria 13, no. 1 (February 28, 2022): 53–62. http://dx.doi.org/10.17268/sci.agropecu.2022.005.
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Negative impacts of climate change are expected in the production of Coffea arabica L. one of the most commercialized tropical agroproducts in the world. However, most studies work with global circulation models, being of little use in making decisions on the scale of farm management. Given this, the objective of this study was to identify the suitability for the cultivation of C. arabica in the face of climate change and how tree cover mitigates the impacts of climate change in an agroforestry plot. The indices of climatic extremes were calculated (1961 to 2016 for Coatepec; 1985 to 2016 for Briones) and a trend analysis was carried out (Mann-Kendall). The temperature inside a plot, and on an open site, was monitored for two years (2017-2019). This was related to the climatic requirements of C. Arabica. Trends of increase (p < 0.05) of the minimum and minimum extreme annual temperatures were identified in the two stations near the plot (0.24 and 0.69 °C·decade-1 in Coatepec and 0.46 and 0.79 °C·decade-1 in Briones). The maximum temperature did not present significant increases, reducing the thermal amplitude. Both annual and seasonal precipitation shows trends of increase in intensity. All these conditions are still suitable for the cultivation of C. arabica. At the plot scale, the importance of tree cover is demonstrated, which in this agroforestry system allows to reduce the maximum temperature by 1.9 °C compared to an open site. Tree cover has also made it possible to mitigate extreme events.
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Galushko, Viktoriya, and Samuel Gamtessa. "Impact of Climate Change on Productivity and Technical Efficiency in Canadian Crop Production." Sustainability 14, no. 7 (April 2, 2022): 4241. http://dx.doi.org/10.3390/su14074241.
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There is a wide consensus that throughout the 20th century climate has changed globally, with many parts of the world facing increases in average temperatures as well as an increased frequency and intensity of extreme weather events. While the existing climate models can predict future changes in climate with a high degree of confidence, the potential impacts of climate change on agricultural production and food security are still not well understood. In this work, we investigate the link between climate change, output, and inefficiency in Canadian crop production using provincial data for the period of 1972–2016. This study has built a unique climate dataset from station-level weather data and uses a panel stochastic frontier model to explore the effect of climatic conditions on crop production and inefficiency. The results reveal that climatic variables are significant predictors of both the maximum potential output (frontier) and technical inefficiency. The combined effect of higher temperatures and lower precipitation, as reflected in a lower Oury index, is a downward shift of the crop production frontier. While greater variability of daily temperatures during the growing season is found to have no statistically significant effect in the frontier equation, greater variation in rainfall results in a downward frontier shift. The results also show that weather shocks measured as a deviation from historical weather normals are significant predictors of technical inefficiency.
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Todorović, Saša, Sanjin Ivanović, and Natalija Bogdanov. "The influence of extreme weather events on farm economic performance – a case study from Serbia." Italian Journal of Agrometeorology, no. 1 (August 9, 2021): 51–62. http://dx.doi.org/10.36253/ijam-1073.
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Western Balkan region, particularly Serbia, is faced with an increased frequency of extreme weather events, as a consequence of global climate change. However, there is still no enough research on how the effects of extreme weather events could be measured on the farm level. More importantly, there is no standard international methodology that is used regularly to address the issue. Therefore, the aim of this research was to evaluate the effects of extreme weather events on business performances of two the most common farm types in Serbia. To achieve this goal, the authors performed a financial loss assessment on a farm level. Panel models and R software environment were used to perform a multiple regression analysis allowing to indicate determinants of financial loss indicator depending on the farm’s production type. The results indicated that performance of both farm types is more influenced by drought than by floods. The regression analysis revealed that for both farm types financial stress is the most important independent variable.
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Iizumi, Toshichika, Mikhail A. Semenov, Motoki Nishimori, Yasushi Ishigooka, and Tsuneo Kuwagata. "ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1962 (March 13, 2012): 1121–39. http://dx.doi.org/10.1098/rsta.2011.0305.
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We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.
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Liu, S., L. Tan, X. Mo, and S. Zhang. "The need of the change of the conceptualisation of hydrologic processes under extreme conditions – taking reference evapotranspiration as an example." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 167–72. http://dx.doi.org/10.5194/piahs-371-167-2015.
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Abstract. What a hydrological model displays is the relationships between the output and input in daily, monthly, yearly and other temporal scales. In the case of climate change or other environment changes, the input of the hydrological model may show a gradual or abrupt change. There have been numerous documented studies to explore the response of output of the hydrological models to the change of the input with scenario simulation. Most of the studies assumed that the conceptualisation of hydrologic processes will remain, which may be true for the gradual change of the input. However, under extreme conditions the conceptualisation of hydrologic processes may be completely changed. Taking an example of the Allen's formula to calculate crop reference evapotranspiration (ET0) as a simple hydrological model, we analyze the alternation of the extreme in ET0 from 1955 to 2012 at the Chongling Experimental Station located in Hebei Province, China. The relationships between ET0 and the meteorological factors for the average values, minimum (maximum) values at daily, monthly and annual scales are revealed. It is found the extreme of the output can follow the extreme of the input better when their relationship is more linear. For non-liner relationship, the extreme of the input cannot at all be reflected from the extreme of the output. Relatively, extreme event at daily scale is harder to be shown than that at monthly scale. The result implicates that a routine model may not be able to catch the response to extreme events and it is even more so as we extrapolate models to higher temperature/CO2 conditions in the future. Some possible choices for the improvements are suggested for predicting hydrological extremes.
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Jeon, Hocheol. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data." Energies 12, no. 23 (November 22, 2019): 4460. http://dx.doi.org/10.3390/en12234460.
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Climate change is around us today and will affect human life in many ways. More frequent extreme weather events raise mortality and car accident rates, global warming leads to longer growing seasons for crops, which may change farmers’ crop choices, and the relationship between energy demand in residential buildings and weather is widely investigated. In this paper, we focus on the impact of weather on energy consumption, in particular, gasoline consumption through the more frequent use of both vehicles themselves and the air conditioner of the vehicle that decreases fuel economy, which has not been paid enough attention in the literature. We estimate the relationship between fuel consumption and weather using unique U.S. panel data. We find that hot days increase gasoline consumption, but in contrast to the results of residential energy consumption literature, there is no statistically significant effect on cold weather. With climate prediction data from General Circulation Models (GCMs), we simulate the impact of climate change on fuel energy consumption. The results show that the fuel consumption in the transportation sector may increase by up to 4% under the “business-as-usual” (RCP 8.5) scenario. Also, climate change has heterogeneous impacts across the continental United States.
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Halder, Subhadeep, Subodh K. Saha, Paul A. Dirmeyer, Thomas N. Chase, and Bhupendra Nath Goswami. "Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model." Hydrology and Earth System Sciences 20, no. 5 (May 10, 2016): 1765–84. http://dx.doi.org/10.5194/hess-20-1765-2016.
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Abstract. Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1–1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1–1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of LULCC over India during the monsoon season. Although, the regional climate model helps in better resolving land–atmosphere feedbacks over the Indian region, the inferences do depend on the fidelity of the model in capturing the features of Indian monsoon realistically. It is proposed that similar studies using a suite of climate models will further enrich our understanding about the role of LULCC in the Indian monsoon climate.
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Verdin, James, Chris Funk, Gabriel Senay, and Richard Choularton. "Climate science and famine early warning." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2155–68. http://dx.doi.org/10.1098/rstb.2005.1754.
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Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.
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Vannoppen, Astrid, Anne Gobin, Lola Kotova, Sara Top, Lesley De Cruz, Andris Vīksna, Svetlana Aniskevich, et al. "Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia." Remote Sensing 12, no. 14 (July 10, 2020): 2206. http://dx.doi.org/10.3390/rs12142206.
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Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.
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Pinheiro de Carvalho, Miguel Â. A., Carla Ragonezi, Maria Cristina O. Oliveira, Fábio Reis, Fabrício Lopes Macedo, José G. R. de Freitas, Humberto Nóbrega, and José Filipe T. Ganança. "Anticipating the Climate Change Impacts on Madeira’s Agriculture: The Characterization and Monitoring of a Vine Agrosystem." Agronomy 12, no. 9 (September 16, 2022): 2201. http://dx.doi.org/10.3390/agronomy12092201.
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Climate—Madeira Strategy (CMS) foresees two models to describe the climate scenarios for the Madeira region in 2050 and 2070. These scenarios anticipate an average temperature rise of 1.4 to 3.7 °C and a decrease in precipitation by 30 to 40%. Consequently, Madeira’s agriculture will suffer the impacts of climate change. To understand these impacts, a baseline of major agrosystem components needs to be established, with the ultimate goal to monitor its consequences in its functioning. CASBio project used the 1961–1991 and 2010–2020 meteorological data series to modulate climate conditions and characterize and monitor six agrosystems for 2 years. One of them was a vineyard, Quinta das Vinhas, representing a typical agrosystem in the Mediterranean climate. The annual and seasonal variation in climatic parameters, soil conditions, microbiological communities, floristic and insect diversity, and crop production was assessed, using a total of 50 parameters. The results were used to establish a baseline of the agrosystem components and their seasonal and annual variation. The major findings are: (i) winter and summer extreme events show a trend in temperature and precipitation supporting a fast change in climate; (ii) a critical imbalance between nitrogen-fixing and denitrifying bacteria was identified, especially in summer, that could be determined by the rise in temperature and drought; (iii) among floristic diversity, the therophytes and geophytes confirm to be the most suitable indicators for the rise in temperature and reduction in precipitation in the agrosystems; (iv) an imbalance in favor of C. capitata plague was observed, associated with the summer rise in temperature and decrease in precipitation; (v) despite an increase in most of the grape varieties production, the Madeiran wine local varieties were shown to be less stable in productivity under observed climate conditions. The agrosystem baseline is a starting point for long-term monitoring and allows for further quantifying the influence of climate change on agrosystem productivity, resilience, and sustainability.
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Rochette, P., G. Bélanger, Y. Castonguay, A. Bootsma, and D. Mongrain. "Climate change and winter damage to fruit trees in eastern Canada." Canadian Journal of Plant Science 84, no. 4 (October 1, 2004): 1113–25. http://dx.doi.org/10.4141/p03-177.
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Climatic conditions during the cold season represent a serious constraint to fruit production in eastern Canada. Meteorological models predict that temperatures of winter months will increase by 2 to 6°C by 2050. The possible impact of climate change on fruit trees in eastern Canada was assessed using agroclimatic indices expressing the risks associated with known causes of damage during fall, winter, and spring. Indices were calculated for 15 agricultural regions in eastern Canada for recent (1961–1990) and future periods (2010–2039 and 2040–2069) using temperature and precipitation data predicted by the Canadian Global General Circulation Model (CGCMI). Averaged across all agricultural regions, the first fall frost in 2040–2069 would be delayed by 16 d while the last spring frost (≤-2°C) would be advanced by 15 d. By 2040 to 2069, the risks of damage to fruit trees by early winter frosts in eastern Canada are likely to decrease because the shorter photoperiod at the time of the first fall frost would result in a longer hardening period. Milder winter temperatures will also reduce the cold stress as the accumulation of cold degree-days (<-15°C) would be reduced and the annual minimum temperature would be increased in all regions of eastern Canada. More frequent winter thaw events, however, would result in a loss of hardiness and in a thinner snow cover that would increase the plant vulnerability to subsequent extreme sub-freezing temperatures. The risk of damage to flower buds by a late frost would increase in southern Ontario, remain almost unchanged in the Maritimes and Ottawa Valley-southern Québec regions, and decrease in the Continental North. The projected climate change should allow for the introduction of new varieties and species where fruit trees are currently grown and for an extension further north of the commercial production in eastern Canada. Key words: Overwintering, fruit production, climatic indices, winter injury, spring frost
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Zhao, Yanxi, Dengpan Xiao, Huizi Bai, Jianzhao Tang, De Li Liu, Yongqing Qi, and Yanjun Shen. "The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms." Agriculture 13, no. 1 (December 29, 2022): 99. http://dx.doi.org/10.3390/agriculture13010099.
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The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha−1 and MAE = 498 kg ha−1). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month’s lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security.
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LALIC, B., J. EITZINGER, D. T. MIHAILOVIC, S. THALER, and M. JANCIC. "Climate change impacts on winter wheat yield change – which climatic parameters are crucial in Pannonian lowland?" Journal of Agricultural Science 151, no. 6 (August 23, 2012): 757–74. http://dx.doi.org/10.1017/s0021859612000640.
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SUMMARYOne of the main problems in estimating the effects of climate change on crops is the identification of those factors limiting crop growth in a selected environment. Previous studies have indicated that considering simple trends of either precipitation or temperature for the coming decades is insufficient for estimating the climate impact on yield in the future. One reason for this insufficiency is that changes in weather extremes or seasonal weather patterns may have marked impacts.The present study focuses on identifying agroclimatic parameters that can identify the effects of climate change and variability on winter wheat yield change in the Pannonian lowland. The impacts of soil type under past and future climates as well as the effect of different CO2 concentrations on yield formation are also considered. The Vojvodina region was chosen for this case study because it is a representative part of the Pannonian lowland.Projections of the future climate were taken from the HadCM3, ECHAM5 and NCAR-PCM climate models with the SRES-A2 scenario for greenhouse gas (GHG) emissions for the 2040 and 2080 integration periods. To calibrate and validate the Met&Roll weather generator, four-variable weather data series (for six main climatic stations in the Vojvodina region) were analysed. The grain yield of winter wheat was calculated using the SIRIUS wheat model for three different CO2 concentrations (330, 550 and 1050 ppm) dependent on the integration period. To estimate the effects of climatic parameters on crop yield, the correlation coefficient between crop yield and agroclimatic indices was calculated using the AGRICLIM software. The present study shows that for all soil types, the following indices are the most important for winter wheat yields in this region: (i) the number of days with water and temperature stress, (ii) the accumulated precipitation, (iii) the actual evapotranspiration (ETa) and (iv) the water deficit during the growing season. The high positive correlations between yield and the ETa, accumulated precipitation and the ratio between the ETa and reference evapotranspiration (ETr) for the April–June period indicate that water is and will remain a major limiting factor for growing winter wheat in this region. Indices referring to negative impact on yield are (i) the number of days with a water deficit for the April–June period and (ii) the number of days with maximum temperature above 25 °C (summer days) and the number of days with maximum temperature above 30 °C (tropical days) in May and June. These indices can be seen as indicators of extreme weather events such as drought and heat waves.
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Portalanza, Diego, Finbarr G. Horgan, Valeria Pohlmann, Santiago Vianna Cuadra, Malena Torres-Ulloa, Eduardo Alava, Simone Ferraz, and Angelica Durigon. "Potential Impact of Future Climates on Rice Production in Ecuador Determined Using Kobayashi’s ‘Very Simple Model’." Agriculture 12, no. 11 (November 1, 2022): 1828. http://dx.doi.org/10.3390/agriculture12111828.
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Rice (Oryza sativa L.) is the main staple food of more than 50% of the world’s population. However, global production may need to increase by more than 70% before 2050 to meet global food requirements despite increasing challenges due to environmental degradation, a changing climate, and extreme weather events. Rice production in Ecuador, mainly concentrated in lowland tropical plains, declined in recent years. In this paper, we aim to calibrate and validate Kobayashi’s ‘Very Simple Model’ (VSM) and, using downscaled corrected climate data, to quantify the potential impact of climate change on rice yields for Ecuador’s two main rice-growing provinces. The negative impact is expected to be highest (up to −67%; 2946 tons) under the Representative Concentration Pathway (RCP) 8.5, with a lower impact under RCP 2.6 (−36%; 1650 tons) yield reduction in the Guayas province. A positive impact on yield is predicted for Los Ríos Province (up to 9%; 161 tons) under RCP 8.5. These different impacts indicate the utility of fine-scale analyses using simple models to make predictions that are relevant to regional production scenarios. Our prediction of possible changes in rice productivity can help policymakers define a variety of requirements to meet the demands of a changing climate.
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Tran Dai, Nghia, Hai Le Trong, Thu Doan Minh, and Ho Dinh Phi. "Impacts of Climate Shock Response Measures on Poverty Vulnerability of Farmer Households in the Mekong River Delta." Journal of Asian Business and Economic Studies 23, no. 03 (July 1, 2016): 143–60. http://dx.doi.org/10.24311/jabes/2016.23.3.03.
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The analysis of this study is based on the inherited results using the panel data of climate shocks and risks faced by farmers in 12 preventative provinces of the seven ecological regions of Vietnam as surveyed by IPSARD (2013) and the data collected from the in-depth studies with 330 farmer households sampled from six selected provinces that represent five sub-ecological areas of Mekong River Delta. The response probability models are employed to determine the impacts of weather risks on incomes of the farmer households as well as the effects of applying several climate change response measures on poverty vulnerability of the farmers. As shown by the analytical results, the poor household group is most impacted by the natural risks, which in turn also affects the level of their poverty vulnerability. To mitigate the negative impacts of extreme weather events, farmers have proactively applied different responsive measures designed to improve their resilience to climate and natural risks, such as changing crop or animal varieties, changing farming patterns, and improving production infrastructures. These measures are found to have contributed significantly and effectively in preventing productivity decline and mitigating income losses and therefore the farmers’ poverty vulnerability.
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Nyasulu, Chimango, Awa Diattara, Assitan Traore, Abdoulaye Deme, and Cheikh Ba. "Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal." Agriculture 12, no. 9 (September 15, 2022): 1473. http://dx.doi.org/10.3390/agriculture12091473.
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To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and helps farmers plan their farming activities in advance. Machine Learning is one of the technologies used in agriculture for weather forecasting and crop disease detection among others. The objective of this study is to develop Machine Learning-based models adapted to the context of daily weather forecasting for Rainfall, Relative Humidity, and Maximum and Minimum Temperature in Senegal. In this study, we made a comparison of ten Machine Learning Regressors with our Ensemble Model. These models were evaluated based on Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The results show that the Ensemble Model performs better than the ten base models. The Ensemble Model results for each parameter are as follows; Relative Humidity: Mean Absolute Error was 4.0126, Mean Squared Error was 29.9885, Root Mean Squared Error was 5.4428 and Coefficient of Determination was 0.9335. For Minimum Temperature: Mean Absolute Error was 0.7908, Mean Squared Error was 1.1329, Root Mean Squared Error was 1.0515 and Coefficient of Determination was 0.9018. For Maximum Temperature: Mean Absolute Error was 1.2515, Mean Squared Error was 2.8038, Root Mean Squared Error was 1.6591 and Coefficient of Determination was 0.8205. For Rainfall: Mean Absolute Error was 0.2142, Mean Squared Error was 0.1681, Root Mean Squared Error was 0.4100 and Coefficient of Determination was 0.7733. From the present study, it has been observed that the Ensemble Model is a feasible model to be used for Rainfall, Relative Humidity, and Maximum and Minimum Temperature forecasting.
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Frieler, K., A. Levermann, J. Elliott, J. Heinke, A. Arneth, M. F. P. Bierkens, P. Ciais, et al. "A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties." Earth System Dynamics 6, no. 2 (July 16, 2015): 447–60. http://dx.doi.org/10.5194/esd-6-447-2015.
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Abstract. Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
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Amatya, Devendra M., Thomas M. Williams, Jami E. Nettles, Richard W. Skaggs, and Carl C. Trettin. "Comparison of Hydrology of Two Atlantic Coastal Plain Forests." Transactions of the ASABE 62, no. 6 (2019): 1509–29. http://dx.doi.org/10.13031/trans.13387.
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Abstract. This article compares the short-term and long-term hydrology of two typical forests in the humid Atlantic Coastal Plain, including a relatively undisturbed forest with natural drainage in South Carolina (SC) and a drained pine plantation in North Carolina (NC), using monitoring and modeling approaches. Highly dynamic outflow (O) from both of these systems is driven by the water table (WT) position, as influenced by rainfall (R) and evapotranspiration (ET). The annual runoff coefficient (ROC) varied from 5% in dry years to 56% in wet years, depending on the soil water storage (SWS), with a significantly higher average value for the NC site despite its deeper WT, on average, than the SC site. Although both sites behaved similarly in extreme climate conditions, the change in SWS above the WT influenced the annual RO, ROC, and ET. The 17-year average annual ET of 1114 mm (R – O, assuming annual balanced SWS) for the SC site was significantly higher (p = 0.014) than the ET of the drained NC site (997 mm) despite the SC site’s lower mean annual R of 1370 mm, compared to 1520 mm for the NC site. This may be due to both the higher potential ET (PET) and soil water-holding capacity of the SC site. The SC site had higher frequency and duration of WT near the surface during winter, deeper summer WT, and higher correlation of annual ET to annual R (r2 = 0.90 vs. 0.15), suggesting that the SC site was often moisture-limited, particularly during the growing season. Most of the streamflow in these systems occurred during winter, with low ET demands. However, summer periods with tropical storms also resulted in large RO events, generally with higher frequency and longer durations at the drained NC site. These results are similar to an earlier short-term comparison with an unstable behavior period at the SC site after Hurricane Hugo (1989). This study highlighted (1) the differences in hydrology between coastal forests drained for silvicultural production and undrained natural forests managed only for restoration, (2) the importance of long-term monitoring and the effects of regeneration as well as vegetation management on flow regime, and (3) the application and limitations of two widely used models (MIKESHE and DRAINMOD) in describing the hydrology of these forests. Long-term studies can be a basis for testing new hypotheses on water yield, stormwater management, wetland hydrology, vegetation restoration, bioenergy production, and climate change, in addition to applications of proper models for assessing the eco-hydrologic impacts of land use and climate change on freshwater coastal forests linked with downstream riparian rivers and estuaries affected by tidal fluxes and sea level rise.HighlightsOutflow, driven by water table position on these forest systems, is highly variable, depending on its soil water storage.The hydrologic responses of both forest sites were similar during extreme climatic events or disturbances.Effect of forestry drainage on runoff was obscured by its large interannual differences.Long-term monitoring provides better insights on climate and vegetation management effects on flow regime and model validation Keywords: Drainage, Evapotranspiration, Hydrologic models, Pine forest, Poorly drained soils, Runoff coefficient, Water table.
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Cao, Wen, Chunfeng Duan, Taiming Yang, and Sheng Wang. "Higher Heat Stress Increases the Negative Impact on Rice Production in South China: A New Perspective on Agricultural Weather Index Insurance." Atmosphere 13, no. 11 (October 27, 2022): 1768. http://dx.doi.org/10.3390/atmos13111768.
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Rice is a major staple food grain for more than half of the world’s population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production.
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Deng, Zhu, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, et al. "Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions." Earth System Science Data 14, no. 4 (April 11, 2022): 1639–75. http://dx.doi.org/10.5194/essd-14-1639-2022.
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Abstract. In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).
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PIARA SINGH. "Crop models for assessing impact and adaptation options under climate change." Journal of Agrometeorology 25, no. 1 (February 17, 2023). http://dx.doi.org/10.54386/jam.v25i1.1969.
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Increased amount of green house gases (GHG) in the atmosphere will cause climate change that will adversely impact crop production especially in the arid and semi-arid regions of the developing countries. Development and implementation of field level adaptations measures to cope up with climate change are necessary to the farmers whose livelihood depends on crop-based income. Crop simulation models that incorporate soil-crop-climate processes of plant growth and that are sensitive to climate change factors can be used to quantify impact of climate change on crop production and evaluating and prioritizing adaptation measures at farm level. This paper analyses the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries. The crop models need to be linked to the improved pest, disease and weed models to analyze and predict yield losses, especially those due to climate change. The simulation models also need to incorporate the impact of extreme weather events on crop production that is projected to increase with climate change.
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Thomas, Timothy S., Richard D. Robertson, Kenneth Strzepek, and Channing Arndt. "Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change." Frontiers in Climate 4 (May 24, 2022). http://dx.doi.org/10.3389/fclim.2022.787582.
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Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks.
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Igobwa, Alvin M., Jeremy Gachanja, Betsy Muriithi, John Olukuru, Angeline Wairegi, and Isaac Rutenberg. "A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya." Climatic Change 174, no. 3-4 (October 2022). http://dx.doi.org/10.1007/s10584-022-03444-6.
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Abstract Climate models, by accurately forecasting future weather events, can be a critical tool in developing countermeasures to reduce crop loss and decrease adverse effects on animal husbandry and fishing. In this paper, we investigate the efficacy of various regional versions of the climate models, RCMs, and the commonly available weather datasets in Kenya in predicting extreme weather patterns in northern and western Kenya. We identified two models that may be used to predict flood risks and potential drought events in these regions. The combination of artificial neural networks (ANNs) and weather station data was the most effective in predicting future drought occurrences in Turkana and Wajir with accuracies ranging from 78 to 90%. In the case of flood forecasting, isolation forests models using weather station data had the best overall performance. The above models and datasets may form the basis of an early warning system for use in Kenya’s agricultural sector.
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Bai, Huizi, Dengpan Xiao, Bin Wang, De Li Liu, and Jianzhao Tang. "Simulation of Wheat Response to Future Climate Change Based on Coupled Model Inter-Comparison Project Phase 6 Multi-Model Ensemble Projections in the North China Plain." Frontiers in Plant Science 13 (February 3, 2022). http://dx.doi.org/10.3389/fpls.2022.829580.
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Global climate change results in more extreme temperature events, which poses a serious threat to wheat production in the North China Plain (NCP). Assessing the potential impact of temperature extremes on crop growth and yield is an important prerequisite for exploring crop adaptation measures to deal with changing climate. In this study, we evaluated the effects of heat and frost stress during wheat sensitive period on grain yield at four representative sites over the NCP using Agricultural Production System Simulator (APSIM)-wheat model driven by the climate projections from 20 Global Climate Models (GCMs) in the Coupled Model Inter-comparison Project phase 6 (CMIP6) during two future periods of 2031–2060 (2040S) and 2071–2100 (2080S) under societal development pathway (SSP) 245 and SSP585 scenarios. We found that extreme temperature stress had significantly negative impacts on wheat yield. However, increased rainfall and the elevated atmospheric CO2 concentration could partly compensate for the yield loss caused by extreme temperature events. Under future climate scenarios, the risk of exposure to heat stress around flowering had no great change but frost risk in spring increased slightly mainly due to warming climate accelerating wheat development and advancing the flowering time to a cooler period of growing season. Wheat yield loss caused by heat and frost stress increased by −0.6 to 4.2 and 1.9–12.8% under SSP585_2080S, respectively. We also found that late sowing and selecting cultivars with a long vegetative growth phase (VGP) could significantly compensate for the negative impact of extreme temperature on wheat yields in the south of NCP. However, selecting heat resistant cultivars in the north NCP and both heat and frost resistant cultivars in the central NCP may be a more effective way to alleviate the negative effect of extreme temperature on wheat yields. Our findings showed that not only heat risk should be concerned under climate warming, but also frost risk should not be ignored.
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Mayer, Andreas, Claudine Egger, Adeline Loyau, Christoph Plutzar, Dirk S. Schmeller, and Veronika Gaube. "Mountain pastures increase the resilience of livestock farming to extreme events in the Ariège department, France." Agronomy for Sustainable Development 42, no. 3 (June 2022). http://dx.doi.org/10.1007/s13593-022-00779-3.
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AbstractMountain pastures are embedded in highly sensitive mountain ecosystems and provide forage for livestock during summer. In years when forage in the lowlands becomes scarce due to over-grazing and land degradation, or climate-related extreme events such as droughts, increasing stocking densities or expanding grazed areas in mountain pastures provide an additional and cost-efficient forage source. Their utilization highly depends on the management decisions of farmers and practices on their own agricultural land. To predict future land use and concomitant ecological impacts, it is crucial to understand the complex interplay between the decisions of farmers as well as the socio-economic and climatic environment. To understand these interactions, we use the agent-based part of the SECLAND model to analyze the future systemic feedback between climate change, land owner’s decisions on land use, and land use change on agricultural land and mountain pastures in the department of Ariège, France. We develop three land use scenarios for a sustainability-driven, a business-as-usual, and a scenario driven by fossil-fueled economic growth. In all scenarios, 32–46% of farms cease to exist, while active farms intensify their land use. On mountain pastures, results show increasing stocking densities up to the maximum carrying capacity of 0.3 livestock units per hectare, especially under the scenario with strong climate change effects and increased extreme events. Additionally, these patterns are strongly shaped by farm succession, vegetation regrowth on unused mountain pastures, and the search for cost-efficient forage resources. Such high stocking densities on mountain pastures increase the pressure on the ecosystem through manure droppings and the introduction of alien microbes, calling for considerate management to avoid conflicting situations. Agent-based models such as that used in this study enable researchers to untangle the described complex interactions between grazing livestock, and the utilization of lowland and mountain pastures in European mountain agroecosystems.
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Lamonaca, Emilia, Amel Bouzid, Mariangela Caroprese, Maria Giovanna Ciliberti, Claudia M. d. S. Cordovil, Maria-Anastasia Karatzia, Mahmut Keskin, et al. "A framework towards resilient Mediterranean eco-solutions for small-scale farming systems." Agriculture & Food Security 11, no. 1 (January 26, 2023). http://dx.doi.org/10.1186/s40066-022-00399-w.
Full textAbstract:
Abstract Background The impacts of climate change on crop and livestock sectors are well-documented. Climate change and its related events (e.g., high temperatures, extreme events, disease outbreaks) affect livestock production in various ways (e.g., nutrition, housing, health, welfare), and tend to compromise the physical productivity and the economic performances. Understanding animal responses to climate change may help planning strategies to cope with the adverse climatic conditions and also to reduce polluting emissions. Through an interdisciplinary approach, we develop a conceptual framework to assess and develop new organisational models for Mediterranean small-scale farming systems so as to mitigate the impacts of climate change, to improve farm management and farming technologies, and to achieve an effective adaptation to the climate changes. The conceptual framework consists of four phases: (i) community engagement, (ii) strategies development, (iii) data collection and analysis, (iv) business model generation and sustainability assessment. We assess strengths, weaknesses, opportunities, and threats of the eco-solutions by mean of a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis technique. Results The proposed eco-solutions are expected to increase the sustainability of agriculture and food production systems by introducing new and efficient uses of natural resources. The proposed models are expected to have an impact not only on the environment (in terms of mitigation), but also on the economic and social performances, as they are expected to foster the responses of small-scale farms to the increasingly frequent effects of climate change (adaptation solutions). Among the positive impacts, we emphasise the importance of more stable revenues, a tendency that would help farmers to raise their revenues. Last but not least, we found that the proposed models are likely to increase the social resilience of the farming systems to the challenges imposed by the climate change. Conclusions The eco-solutions can support stakeholders involved in Mediterranean small-scale farming systems by suggesting novel land, crop, and livestock management approaches to optimise revenue flows, business models and climate change mitigation strategies thanks to the adoption of a systemic approach, that is not only focused on specific components of the system but instead based on the linkages between environmental, social, and economic aspects.
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Yu, Jina, David Hennessy, Jesse Tack, and Felicia Wu. "Climate change will increase aflatoxin risk in US corn." Environmental Research Letters, April 5, 2022. http://dx.doi.org/10.1088/1748-9326/ac6435.
Full textAbstract:
Abstract The impacts of climate change on agricultural production are a global concern and have already begun to occur (1, 2), with major drivers including warmer temperatures and the occurrence of extreme weather events (3-8). An important dimension of the climate change-crop yield relationship that has often been overlooked in the empirical literature is the influence that warming temperatures can have on plant damage arriving through biotic channels, such as pest infestation or fungal infection (5). Aflatoxins are carcinogenic chemicals produced by the fungi Aspergillus flavus and A. parasiticus, which commonly infect food crops. Currently, in the United States, aflatoxin is a perennial contaminant in corn grown in the South, but rare in the Corn Belt and northern states. Climate change may expand aflatoxin’s geographical prevalence, however; because hot, dry summers promote aflatoxin accumulation. Here we model aflatoxin risk as a function of corn plant growth stages and weather to predict US regions with high aflatoxin risk in 2031-2040, based on sixteen climate change models. Our results suggest that over 89.5% of corn-growing counties in fifteen states, including the Corn Belt, will experience increased aflatoxin contamination in 2031-2040 compared to 2011-2020. Interestingly, the results are spatially heterogeneous and include several Southern counties expected to have lower aflatoxin risk, because the causative fungi become inactivated at very high temperatures.
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Villalobos-Soublett, Emilio, Nicolás Verdugo-Vásquez, Irina Díaz, and Andrés Zurita-Silva. "Adapting Grapevine Productivity and Fitness to Water Deficit by Means of Naturalized Rootstocks." Frontiers in Plant Science 13 (May 24, 2022). http://dx.doi.org/10.3389/fpls.2022.870438.
Full textAbstract:
Climate change effects are unbalanced in all regions and cultivars linked to the wine industry. However, the impact of extreme weather events, such as drought and rising global temperatures, highlight the potential vulnerability in plant productivity, phenology, and crop water requirements that affect quality and harvests. Among adaptative measures for grapevine cultivars in existing or new winegrowing areas, the use of tolerant rootstocks to abiotic stress has been regarded as a mid-term strategy to face emerging constrains. The aim of this study was to compare naturalized or autochthonous rootstocks influence over grapevine cultivar performance and to characterize their response to deficit irrigation conditions. Data was collected from Cabernet Sauvignon and Syrah grafted plants for over 3 growing seasons (2018–2021) from a hyper-arid experimental field in Vicuña, Chile. Morpho-physiological parameters were determined throughout seasons and combinations where significant effects from rootstocks, irrigation treatment, and cultivar were observed over An and gs, thus modifying CO2 assimilation and intrinsic Water Use Efficiency (WUEi). Primary productivity and yield were also modified by rootstock depending upon cultivar hydric behavior. Interestingly, cluster and berry traits were unaffected despite how water productivity and integral water stress were modulated by rootstock. In both cultivars, it was observed that trait responses varied according to the irrigation conditions, rootstocks, and their respective interactions, thus highlighting a relative influence of the rootstocks in the processes of adaptation to the water deficit. Moreover, harvest date and acidity were modified by deficit irrigation treatment, and rootstocks did not modify phenological stages. Adaptation of grapevines to expected lower water availability might be improved by using suitable tolerant rootstocks, and maturity index can be modified through irrigation management.