scholarly journals Climate change and U.S. agriculture: Accounting for multidimensional slope heterogeneity in panel data

2020 ◽  
Vol 11 (4) ◽  
pp. 1391-1429 ◽  
Author(s):  
Michael Keane ◽  
Timothy Neal
Keyword(s):  
Climate Change ◽  
Panel Data ◽  
Climate Models ◽  
Crop Yields ◽  
Greenhouse Gas Emission ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Spatial And Temporal Variation ◽  
Local Climate

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

scholarly journals Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

2021 ◽  
Vol 2069 (1) ◽  
pp. 012070
Author(s):  
C N Nielsen ◽  
J Kolarik
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Weather Data ◽  
Building Performance ◽  
Comprehensive Overview ◽  
Local Climate ◽  
The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

scholarly journals Impact Assessment of Climate Change on Paddy Yield: A Case Study of Nepal Agriculture Research Council (NARC), Tarahara, Nepal

1970 ◽  
Vol 8 (3) ◽  
pp. 147-167 ◽  
Author(s):  
Yam K Rai ◽  
Bhakta B Ale ◽  
Jawed Alam
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
Research Council ◽  
Crop Yields ◽  
Rice Yield ◽  
Weather Data ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
The Impact

Climate change and global warming are burning issues, which significantly threat agriculture and global food security. Change in solar radiation, temperature and precipitation will influence the change in crop yields and hence economy of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Rice model of DSSAT v4.0 was used to simulate the rice yield of the region under climate change scenarios using the historical weather data at Nepal Agriculture Research Council (NARC) Tarahara (1989-2008). The Crop Model was calibrated using the experimental crop data, climate data and soil data for two years (2000-2001) and was validated by using the data of the year 2002 at NARC Tarahara. In this study various scenarios were undertaken to analyze the rice yield. The change in values of weather parameters due to climate change and its effects on the rice yield were studied. It was observed that increase in maximum temperature up to 2°C and 1°C in minimum temperature have positive impact on rice yield but beyond that temperature it was observed negative impact in both cases of paddy production in ambient temperature. Similarly, it was observed that increased in mean temperature, have negative impacts on rice yield. The impact of solar radiation in rice yield was observed positive during the time of study period. Adjustments were made in the fertilizer rate, plant density per square meter, planting date and application of water rate to investigate suitable agronomic options for adaptation under the future climate change scenarios. Highest yield was obtained when the water application was increased up to 3 mm depth and nitrogen application rate was 140 kg/ha respectively. DOI: http://dx.doi.org/10.3126/jie.v8i3.5941 JIE 2011; 8(3): 147-167

scholarly journals The impact of climate change in wheat and barley yields in the Iberian Peninsula

2021 ◽  
Author(s):  
Virgílio A. Bento ◽  
Andreia F.S. Ribeiro ◽  
Ana Russo ◽  
Célia M. Gouveia ◽  
Rita M. Cardoso ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Crop Yields ◽  
Greenhouse Gas Emission ◽  
Hot Spot ◽  
Historical Period ◽  
Temperature And Precipitation ◽  
The Impact

<p>World food and drink production largely depends on wheat and barley crops, which are the basis of nutrition for both humans and animals. The Iberian Peninsula (IP), and particularly Spain, is responsible for a large percentage of farming areas dedicated to these two crops. Furthermore, the IP is known as a prominent climate change hot spot, with expected rising temperatures and a decrease in mean precipitation (with more extreme events). Thus, it is vital to understand the effects of climate change in wheat and barley yields in the IP.</p><p>Multiple linear regression (MLR) models were developed based on the relation between temperature and precipitation and both crop yields, with the aim of projecting these into the future. Three main objectives were pursued: (1) to establish the existence of a relationship between wheat and barley yields and temperature and precipitation, taking advantage of data from the EURO-CORDEX regional climate models (RCMs) forced with ERA-Interim; (2) to calibrate and validate MLR models using a selection of predictors from the same EURO-CORDEX RCMs; and (3) to apply these MLR models to EURO-CORDEX RCMs forced with global climate models (GCMs) for an historical period (1971-2000) and two future periods (2041-2070 and 2071-2100) according to two greenhouse gas emission scenarios (RCP4.5 and RCP8.5). Results show a dichotomic behaviour of wheat and barley future yields depending on the crop’s production region. Projections for the southern cluster of the IP show severe yield losses for both cereals, which may be a consequence of the increase in maximum temperatures in spring, particularly for RCP8.5 at the end of the 21st century. Conversely, projections for the northern cluster of the IP show an increase in yield output, which may be a result of the projected warming taking place within the early winter months.</p><p>This study reinforces the worth to implementing changes in the society to mitigate losses and to assess production gains/losses due to climate change. These may be implemented locally (different cultivar species), countrywide (implementing sustainable policies), or even globally (alleviate greenhouse gas emissions). This work was supported by project IMPECAF (PTDC/CTA-CLI/28902/2017), LEADING (PTDC/CTA-MET/28914/2017) and by IDL (UIDB/50019/2020).</p>

scholarly journals Potential rice yields in future weather conditions in different parts of Asia.

1990 ◽  
Vol 38 (4) ◽  
pp. 661-680 ◽  
Author(s):  
D.M. Jansen
Keyword(s):  
Climate Change ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Air Humidity ◽  
Physiological Processes ◽  
Rice Yields ◽  
Use Efficiency ◽  
Co2 Level ◽  
Crop Growth Simulation

The effects of future climate change on potential yields of rice cv. IR36 grown at present and in 2020 and 2100 in China, India, Indonesia, Thailand and South Korea were estimated using the crop growth simulation model MACROS which combines the effects of temp., radiation, wind speed, air humidity and crop status on physiological processes. Historic weather data of these sites were adapted to possible changes in temp. and CO2 level, to mimic climate change. Simulated yields rose in low and middle temp. change scenarios, but decreased in the high temp. scenario. Water use efficiency decreased in the high temp. scenario irrespective of CO2 scenario, and increased otherwise. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Predicted Future Benefits for an Endemic Rodent in the Irano-Turanian Region

Climate ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Suzanna Meeussen ◽  
Anouschka Hof
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Species Distribution Modelling ◽  
Geographic Range ◽  
Future Climate ◽  
Future Climate Change ◽  
Distribution Ranges ◽  
Bioclimatic Variables ◽  
Suitable Area ◽  
The Impact

Climate change is expected to have an impact on the geographical distribution ranges of species. Endemic species and those with a restricted geographic range may be especially vulnerable. The Persian jird (Meriones persicus) is an endemic rodent inhabiting the mountainous areas of the Irano-Turanian region, where future desertification may form a threat to the species. In this study, the species distribution modelling algorithm MaxEnt was used to assess the impact of future climate change on the geographic distribution range of the Persian jird. Predictions were made under two Representative Concentration Pathways and five different climate models for the years 2050 and 2070. It was found that both bioclimatic variables and land use variables were important in determining potential suitability of the region for the species to occur. In most cases, the future predictions showed an expansion of the geographic range of the Persian jird which indicates that the species is not under immediate threat. There are however uncertainties with regards to its current range. Predictions may therefore be an over or underestimation of the total suitable area. Further research is thus needed to confirm the current geographic range of the Persian jird to be able to improve assessments of the impact of future climate change.

scholarly journals Climate Change Trends and Impacts on California Agriculture: A Detailed Review

Agronomy ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 25 ◽  
Author(s):  
Tapan Pathak ◽  
Mahesh Maskey ◽  
Jeffery Dahlberg ◽  
Faith Kearns ◽  
Khaled Bali ◽  
...  
Keyword(s):  
Climate Change ◽  
Agricultural Sector ◽  
Climate Adaptation ◽  
Crop Yields ◽  
Heat Waves ◽  
Sufficient Evidence ◽  
Future Climate Change ◽  
Future Research ◽  
Detailed Review ◽  
California Agriculture

California is a global leader in the agricultural sector and produces more than 400 types of commodities. The state produces over a third of the country’s vegetables and two-thirds of its fruits and nuts. Despite being highly productive, current and future climate change poses many challenges to the agricultural sector. This paper provides a summary of the current state of knowledge on historical and future trends in climate and their impacts on California agriculture. We present a synthesis of climate change impacts on California agriculture in the context of: (1) historic trends and projected changes in temperature, precipitation, snowpack, heat waves, drought, and flood events; and (2) consequent impacts on crop yields, chill hours, pests and diseases, and agricultural vulnerability to climate risks. Finally, we highlight important findings and directions for future research and implementation. The detailed review presented in this paper provides sufficient evidence that the climate in California has changed significantly and is expected to continue changing in the future, and justifies the urgency and importance of enhancing the adaptive capacity of agriculture and reducing vulnerability to climate change. Since agriculture in California is very diverse and each crop responds to climate differently, climate adaptation research should be locally focused along with effective stakeholder engagement and systematic outreach efforts for effective adoption and implementation. The expected readership of this paper includes local stakeholders, researchers, state and national agencies, and international communities interested in learning about climate change and California’s agriculture.

Assess 21st century Flash Drought in the United States using high resolution regional climate models

2021 ◽  
Author(s):  
Brandi Gamelin ◽  
Jiali Wang ◽  
V. Rao Kotamarthi
Keyword(s):  
Climate Change ◽  
United States ◽  
Soil Moisture ◽  
Climate Models ◽  
Wrf Model ◽  
The United States ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Regional Variability ◽  
Groundwater Levels

<p>Flash droughts are the rapid intensification of drought conditions generally associated with increased temperatures and decreased precipitation on short time scales.  Consequently, flash droughts are responsible for reduced soil moisture which contributes to diminished agricultural yields and lower groundwater levels. Drought management, especially flash drought in the United States is vital to address the human and economic impact of crop loss, diminished water resources and increased wildfire risk. In previous research, climate change scenarios show increased growing season (i.e. frost-free days) and drying in soil moisture over most of the United States by 2100. Understanding projected flash drought is important to assess regional variability, frequency and intensity of flash droughts under future climate change scenarios. Data for this work was produced with the Weather Research and Forecasting (WRF) model. Initial and boundary conditions for the model were supplied by CCSM4, GFDL-ESM2G, and HadGEM2-ES and based on the 8.5 Representative Concentration Pathway (RCP8.5). The WRF model was downscaled to a 12 km spatial resolution for three climate time frames: 1995-2004 (Historical), 2045-2054 (Mid), and 2085-2094 (Late).  A key characteristic of flash drought is the rapid onset and intensification of dry conditions. For this, we identify onset with vapor pressure deficit during each time frame. Known flash drought cases during the Historical run are identified and compared to flash droughts in the Mid and Late 21<sup>st</sup> century.</p>

scholarly journals An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

2017 ◽  
Vol 30 (17) ◽  
pp. 6701-6722 ◽  
Author(s):  
Daniel Bannister ◽  
Michael Herzog ◽  
Hans-F. Graf ◽  
J. Scott Hosking ◽  
C. Alan Short
Keyword(s):  
Climate Change ◽  
Sichuan Basin ◽  
Climate Models ◽  
Distribution Functions ◽  
Future Climate ◽  
First Century ◽  
Future Climate Change ◽  
Change Scenario ◽  
The Sichuan Basin ◽  
Twenty First Century

The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade−1), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.

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