This summary analyzes the sources of uncertainty in projections of climate change impacts on agricultural crop production for 94 crop-region combinations. It finds that uncertainties related to temperature changes represent a greater contribution to uncertainty than those related to precipitation changes for most crops and regions. In particular, the sensitivity of crop yields to temperature changes is identified as a critical source of uncertainty. This is surprising given rainfall's importance to crop yields, but reflects the large magnitude of projected warming relative to historical temperature variability, as well as disagreements among climate models over regional precipitation changes. Improving understanding of crop responses to temperature and the magnitude of regional temperature projections are concluded to be two of the most important needs for reducing uncertainty in climate change impact assessments and adaptation efforts for
Climate change 2014 impacts, adaptation, and vulnerability summary for polic...Eyal Morag
הטיוטה הסופית של דו"ח שינויי האקלים של
IPCC
קבוצת העבודה 2 תקציר למקבליי ההחלטות
זהוא תקציר מודלף סביר שהיהיה דומה מאוד לניר הסופי שיפץ מחר
כמו תמיד חומר מודלף עשויי להיות שגוי
הוספתי התחלת תירגום וביחוד בתרשים או הטבלה הראשונ/ה
אני מקווה לשפר את המוצר בהמשך
Spatio-Temporal Dynamics of Climatic Parameters in TogoPremier Publishers
The detection of the variability and trends of weather variables is a necessary step in the explanation of the impacts of climate change on ecosystems and agricultural production activities. To this end, this study analyses the spatio-temporal trends of precipitation, temperature, evapotranspiration, relative humidity, wind speed (weather stations in Lomé, Atakpamé, Sokodé, Kara and Dapaong) and insolation (stations in Lomé, Atakpamé, Sokodé, Kara and Mango). The data analyzed are from the meteorological directorate-general. Trends over time were calculated over monthly, annual and seasonal intervals using analysis of variability (coefficient of variation, precipitation concentration indices) and trend analysis techniques using Mann-Kendall and Sen slope methods, allowing non-parametric statistical analysis. The results show a general upward trend in the inter-seasonally, intra and inter-yearly in precipitation, temperature (maximal and minimal) and evapotranspiration. On the other hand, a general downward trend is detected for relative humidity (maximal and minimal) and insolation. The results raise concerns for food security in Togo inasmuch as increases in rainfall, temperature and evapotranspiration coupled with decreases in relative humidity and insolation negatively affect agricultural production. It urgently needs that the public authorities take adaptation and mitigation measures.
The Dynamic Of The Main Foliar Wheat Diseases Developing At Coast Zone Of Alb...irjes
Observations were done every week starting from filleting till milk ripening in wheat production
fields. It was carried out in "Kaloshi" farm in Grabian village, Lushnja the district of Fier for the three study
years (2011, 2012, 2013). Winter wheat is one of the most important and economically beneficial crops in
Albania. Distribution of pathogens is a complex phenomenon – it is set by host distribution and susceptibility
levels, crop management and environment. Based on the data received during observations about the most
frequent foliar wheat diseases at coast zone Lushnje, for the three study years can be say that: Based on the data
obtained during surveys conducted to determine the most frequent air diseases of wheat in the low coastal area
Lushnja, for the three study years (2011, 2012, 2013) we can say that: for the three study years the first
infection of Powdery mildew (B.graminis) are seen at the first observation, march 15, with a level by 3%, while
during the mid of Aprile was 12%. During the begining of Aprile are seen infection by Septoria leaf blotch
(Septoria sp). With a value by 3% and afetr at the end of May this value was 41 %. Brown rust (P.recondita) on
the leaves is seen on mid of Aprile with a infection level by 1%, while at the end of may it was 38 %. Changes
in disease epidemics were determined and showed the differences between the analyzed diseases.
The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.
IPCC Report Climate Changes - Summary of headline statementsTheFoodChallenge
IPCC Special Report on Climate Change, Desertification, Land
Degradation, Sustainable Land Management, Food Security, and
Greenhouse gas fluxes in Terrestrial Ecosystems
Austin Journal of Irrigation is an open access, peer reviewed, and scholarly journal dedicated to publish articles in all areas of irrigation. The aim of the journal is to provide a platform for engineers, scientists, and academicians all over the world to endorse, discuss, and share various new issues and developments in diverse areas of irrigation.
Austin Journal of Irrigation accepts original research articles, review articles, case studies, commentaries, discussions, technical notes, editorials, forums, short communication, and book reviews on all the aspects of irrigation
Austin Publishing Group is a successful host of more than hundred peer reviewed, open access journals in various fields of science and medicine with intent to bridge the gap between academia and research access.
Climate change 2014 impacts, adaptation, and vulnerability summary for polic...Eyal Morag
הטיוטה הסופית של דו"ח שינויי האקלים של
IPCC
קבוצת העבודה 2 תקציר למקבליי ההחלטות
זהוא תקציר מודלף סביר שהיהיה דומה מאוד לניר הסופי שיפץ מחר
כמו תמיד חומר מודלף עשויי להיות שגוי
הוספתי התחלת תירגום וביחוד בתרשים או הטבלה הראשונ/ה
אני מקווה לשפר את המוצר בהמשך
Spatio-Temporal Dynamics of Climatic Parameters in TogoPremier Publishers
The detection of the variability and trends of weather variables is a necessary step in the explanation of the impacts of climate change on ecosystems and agricultural production activities. To this end, this study analyses the spatio-temporal trends of precipitation, temperature, evapotranspiration, relative humidity, wind speed (weather stations in Lomé, Atakpamé, Sokodé, Kara and Dapaong) and insolation (stations in Lomé, Atakpamé, Sokodé, Kara and Mango). The data analyzed are from the meteorological directorate-general. Trends over time were calculated over monthly, annual and seasonal intervals using analysis of variability (coefficient of variation, precipitation concentration indices) and trend analysis techniques using Mann-Kendall and Sen slope methods, allowing non-parametric statistical analysis. The results show a general upward trend in the inter-seasonally, intra and inter-yearly in precipitation, temperature (maximal and minimal) and evapotranspiration. On the other hand, a general downward trend is detected for relative humidity (maximal and minimal) and insolation. The results raise concerns for food security in Togo inasmuch as increases in rainfall, temperature and evapotranspiration coupled with decreases in relative humidity and insolation negatively affect agricultural production. It urgently needs that the public authorities take adaptation and mitigation measures.
The Dynamic Of The Main Foliar Wheat Diseases Developing At Coast Zone Of Alb...irjes
Observations were done every week starting from filleting till milk ripening in wheat production
fields. It was carried out in "Kaloshi" farm in Grabian village, Lushnja the district of Fier for the three study
years (2011, 2012, 2013). Winter wheat is one of the most important and economically beneficial crops in
Albania. Distribution of pathogens is a complex phenomenon – it is set by host distribution and susceptibility
levels, crop management and environment. Based on the data received during observations about the most
frequent foliar wheat diseases at coast zone Lushnje, for the three study years can be say that: Based on the data
obtained during surveys conducted to determine the most frequent air diseases of wheat in the low coastal area
Lushnja, for the three study years (2011, 2012, 2013) we can say that: for the three study years the first
infection of Powdery mildew (B.graminis) are seen at the first observation, march 15, with a level by 3%, while
during the mid of Aprile was 12%. During the begining of Aprile are seen infection by Septoria leaf blotch
(Septoria sp). With a value by 3% and afetr at the end of May this value was 41 %. Brown rust (P.recondita) on
the leaves is seen on mid of Aprile with a infection level by 1%, while at the end of may it was 38 %. Changes
in disease epidemics were determined and showed the differences between the analyzed diseases.
The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.
IPCC Report Climate Changes - Summary of headline statementsTheFoodChallenge
IPCC Special Report on Climate Change, Desertification, Land
Degradation, Sustainable Land Management, Food Security, and
Greenhouse gas fluxes in Terrestrial Ecosystems
Austin Journal of Irrigation is an open access, peer reviewed, and scholarly journal dedicated to publish articles in all areas of irrigation. The aim of the journal is to provide a platform for engineers, scientists, and academicians all over the world to endorse, discuss, and share various new issues and developments in diverse areas of irrigation.
Austin Journal of Irrigation accepts original research articles, review articles, case studies, commentaries, discussions, technical notes, editorials, forums, short communication, and book reviews on all the aspects of irrigation
Austin Publishing Group is a successful host of more than hundred peer reviewed, open access journals in various fields of science and medicine with intent to bridge the gap between academia and research access.
HOW CAN AFRICAN
AGRICULTURE ADAPT TO
CLIMATE CHANGE?
INSIGHTS FROM ETHIOPIA AND SOUTH AFRICA
Edited by Claudia Ringler, Elizabeth Bryan, Rashid M. Hassan,
Tekie Alemu, and Marya Hillesland
UNDP Support to Climate Change Adaptation Advancing Climate Resilient Livelih...ExternalEvents
The slides look at UNDPs work on resilience and climate change adaptation: training and technology, strengthening policies, institutions, capacities and knowledge and supporting the NAP process.
The presentation was made by Srilata Kammila, Regional Technical Specialist with UNDP on Day 1 of the Integrating Agriculture in National Adaptation Plans Workshop from the 5-7 April 2016, Rome, Italy
Non-Timber Forest Products: contribution to national economy and sustainable ...CIFOR-ICRAF
CIFOR scientist Robert Nasi gave this presentation on 10 October 2012 during the 11th Conference of Parties to the Convention on Biological Diversity (CBD COP11).
First appearing on the blog of Donna LaFramboise, this draft was confirmed as authentic by an IPCC spokesman, according to Justin Gills of The New York Times. Here's the blog post: http://nofrakkingconsensus.com/2013/11/01/new-ipcc-leak-working-group-2s-summary-for-policymakers/
Here's Gillis's news story, which focuses on the draft's conclusions about agriculture: Climate Change Seen Posing Risk to Food Supplies http://nyti.ms/1iBa1tR
Global Climate Change: Drought Assessment + ImpactsJenkins Macedo
This presentation outlined the purposes, methods, data analyses, results and conclusions of four selected articles in remotely sensed regional and global drought assessments and impacts for global environmental change. This presentation was developed and presented by Richard Maclean, doctoral student in Geography at Clark University and Jenkins Macedo, Master of Science candidate in Envrionmental Science and Policy at Clark University.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Drought Risk Analysis, Forecasting and Assessment.pdfssuser3f22f9
Climate change is undoubtedly one of the world’s biggest challenges in the 21st century.
Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical
but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments
are of the utmost importance for reliable drought-related decision-making. The framework of drought
risk analysis provides a unified and coherent approach to solving inference and decision-making
problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought
frequency estimation, hybrid models of forecasting and water resource management. This Special
Issue will provide researchers with a summary of the latest drought research developments in order to
identify and understand the profound impacts of climate change on drought risks and water resources.
The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring
and forecasting approaches, unique methods for drought risk estimation and creative frameworks
for environmental change assessment. These articles will serve as valuable references for future
drought-related disaster mitigations, climate change interconnections and food productivity impacts.
Presentation delivered by Dr. Graham Farquhar (The Australian National University, Australia) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
Economic perspectives on the impact of climate change on agricultureharrison manyumwa
The world's climate is changing, and the growing evidence is that the major drivers are anthropogenic, i.e. caused by humans. While humans are contributing to the changing climates the impacts of climate change on other humans range from minor to severe depending on the region one is located. As such, climate change has been viewed as a problem with a negative exernality. The diverse distributionl impacts have resulted in "winners" and "losers". But what is the way forward. I argue that "winners" should support and help the "losers" regain a normal life, by helping them to be resilient. Enjoy.
Growing Season Extension & its Impact on Terrestrial Carbon; Gardening Guidebook www.scribd.com/doc/239851313, For more information, Please see Organic Edible Schoolyards & Gardening with Children www.scribd.com/doc/239851214 - Double Food Production from your School Garden with Organic Tech www.scribd.com/doc/239851079 - Free School Gardening Art Posters www.scribd.com/doc/239851159 - Increase Food Production with Companion Planting in your School Garden www.scribd.com/doc/239851159 - Healthy Foods Dramatically Improves Student Academic Success www.scribd.com/doc/239851348 - City Chickens for your Organic School Garden www.scribd.com/doc/239850440 - Huerto Ecológico, Tecnologías Sostenibles, Agricultura Organica www.scribd.com/doc/239850233 - Simple Square Foot Gardening for Schools, Teacher Guide www.scribd.com/doc/23985111 ~
The Relationship between Surface Soil Moisture with Real Evaporation and Pote...IJEAB
The aim of this research is to determine the relationship between surface Soil Moisture (SSM) of both Real Evaporation (E) and surface Potential Evaporation (SPE) for thirty years during the period of (1985-2014) for the eight stations (Sulaymaniya, Mosul, Tikrit, Baghdad, Rutba, Kut, Nukhayib, Basrah) in Iraq, from (NOAA) and taking advantage of some statistics such as the Simple Linear Regression (SLR) and the Spearman Rho test. Calculated the monthly average for Soil Moisture, Real Evaporation and Potential Evaporation, and found to increase the values of SPE in hot months and decreased in cold months while opposite to SM There was a strong inverse relationship between them, where the correlation coefficient was in Sulaymaniya -0.91, in Mosul -0.89, in the Rutba -0.92, in Tikrit -0.89, in Baghdad -0.89, in Nukhayib -0.89, in Kut -0.87, and in Basrah -0.83, and there is a high correlation in stations (Basrah, Kut, Nukhayib, and Rutba), while there is an average correlation in the stations (Baghdad and Tikrit), and there is low correlation in the stations (Sulaymaniya, Mosul), we also note an inverse correlation between RE and PE, where there is a low correlation in Sulaymaniya and medium correlation in the Mosul and Rutba stations, and there is a high correlation in the stations (Tikrit, Baghdad, Nukhayib, Kut, and Basrah).
The presentation is qualified during his (Ganbat Bavuudorj) master thesis work in 2012. The master program was sponsored by DAAD at NUM and Heidelberg University.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
2. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
uncertainty is therefore a critical step toward reducing overall and then a multiple linear regression with production as the
uncertainty in projections [9, 11, 12]. response and T and P as the predictors was computed.
To evaluate sources of uncertainty, we begin with a simple To evaluate whether the assumption in equation (2) that
but useful approximation for the change in crop production uncertainties in T and P are independent was valid, we
( Y ) that results from a given change in growing season computed the correlation between model projections of T
average temperature ( T ) and precipitation ( P ): and P for each crop–region combination. All 94 values
were below 0.5 in absolute value, with an average squared
Y = βT T + β P P (1) correlation of R 2 = 0.06. Thus, consideration of covariance
between temperature and precipitation uncertainties would not
where βT and β P represent the sensitivity of crop production appreciably change the results presented below.
to temperature and precipitation, respectively. This equation
Another important consideration is whether equation (1)
ignores other aspects of climate change and potential nonlinear accurately describes the relationship between weather and crop
effects of temperature or precipitation, such as those that
production. One measure of this is the R 2 of the model,
arise from extreme heat waves [7] or interactions with other
which varied from a low of near zero to a high of 0.67 for
variables, but represents a useful first-order estimate for
South Asia groundnuts. An R 2 of 0.67 indicates that a linear
changes over the next few decades. In addition, results
model using growing season temperature and rainfall is able
from these simple statistical models are broadly consistent
to explain two-thirds of the variation in crop production, and
with those from studies that used process-based models, as
thus inclusion of nonlinear terms or other climatic variables
described below. If uncertainties in βT, T , β P , and P are
is not needed to predict the majority of yield variation. Note
independent, then uncertainty in Y can be expressed as [13]:
this does not preclude some role for nonlinearities, but simply
Var( Y ) = Var(βT T + β P P) = E[βT ]2 Var( T ) says that the majority of yield variation is driven by change
+ E[ T ]2 Var(βT ) + Var(βT )Var( T ) in growing season averages. In contrast, a low R 2 indicates
that these other terms may be important, that crop harvests
+ E[β P ]2 Var( P) + E[ P]2 Var(β P )
vary less according to weather than to other abiotic or biotic
+ Var(β P )Var( P) (2) stresses, and/or that reported harvests contain large amounts of
where E[ ] denotes the expected value and Var( ) denotes noise. The patterns of R 2 give some insight into the causes for
variance. low R 2 . For example, R 2 tends to be higher in some regions
(e.g., Sahel, Southern Africa, South Asia) than others (e.g.,
2. Methods Central and West Africa) and some crops (e.g. maize) than
others (e.g. cassava), indicating that R 2 may reflect differences
We evaluated each term in equation (2) for projections in the quality of data, characteristics of the climate systems,
of climate change impacts by 2030 for crops and regions or growth traits of particular crops [14]. In contrast, factors
considered in a recent analysis focused on food security [14], such as irrigation or average yields do not appear strongly
with a total of 94 crop–region combinations. Values for related to model R 2 . The implications of low model R 2 for
E[ T ], E[ P], Var( T ), and Var( P) were computed the conclusions of the current study are discussed below.
from projections for 20 different general circulation models
(GCMs) that participated in the fourth assessment report of 3. Results and discussion
the Intergovernmental Panel on Climate Change, using three
emissions scenarios (SRES B1, A1b, and A2). Each model Rainfall plays a critical role in year-to-year variability of
possesses quasi-independent representations of atmosphere, production for these crops, with a change in growing season
land, and ocean dynamics, and therefore the variance of precipitation by one standard deviation associated with as
projections between models represents a common measure much as a 10% change in production (in the case of South Asia
of uncertainty related to these dynamics. Changes by 2030 millets; figure 1(a)). Temperature also plays a significant role
were computed as averages for 2020–2039 minus averages for in driving year-to-year production changes, but was slightly
1980–1999. less important than rainfall by this measure in the majority of
Values for E[βT ], E[β P ], Var(βT ), and Var(βP ) were cases. This result agrees with the intuition that rainfall is very
computed from statistical models based on historical data for important to agriculture.
crop production from the Food and Agriculture Organization In contrast, the contribution of terms in equation (2)
(FAO; http://faostat.fao.org) and temperature and rainfall from to total variance highlights a surprisingly dominant role of
the Climate Research Unit (CRU TS2.1) [15]. The details of temperature (figure 1(b)). In figure 1(b), factors related to
the regression models are provided in Lobell et al [14]. Briefly, temperature (the first three terms in equation (2)) are shown
for each crop–region combination time series of growing in shades of red while those related to precipitation are shown
season T and P for 1961–2002 were computed by averaging in blue. Only in three cases among the top 20 crop–region
monthly values in the CRU dataset for the growing season combinations (rice and millet in South Asia and wheat in West
months and for the locations where the crop is grown, based Asia) do uncertainties associated with precipitation contribute
on maps of individual crop areas [16]. The growing season T more than 25% to total variance. The single biggest source
and P averages and annual crop production from FAO were of uncertainty in most cases is the second term, relating to
transformed to first-differences to remove trend components, uncertainty in the response of crop production to temperature
2
3. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
Figure 1. (a) The predicted response of crop production (% of 1998–2002 average production) to a change in growing season average
temperature (red) or precipitation (blue) equivalent to one standard deviation of historical variability, shown for the top 20 crops most
important to global food security [14]. Absolute values of coefficients are displayed to ease comparison of magnitudes, since most temperature
coefficients are negative and most rainfall coefficients are positive. Error bars indicate ±1σ of values of βT and β P . (b) The contribution of
each term in equation (2) to total variance in climate change impact projections for 2030. All terms were normalized by the total variance,
which varies by crop, so that the sum of the six terms equals one. Dark red = E[βT ]2 Var( T ), medium red = E[ T ]2 Var(βT ), light
red = Var(βT ) Var( T ), dark blue = E[β P ]2 Var( P), medium blue = E[ P]2 Var(β P ), light blue = Var(βP ) Var( P). (c) Mean (solid)
and standard deviation (open) of projections for growing season average temperature (red) and precipitation (blue) changes by 2030, based on
output from 20 GCMs and expressed as a multiple of the standard deviation of historical temperature or precipitation.
change. This is true even in predominantly rainfed systems, in figure 1(c)). The uncertainty surrounding temperature
such as most cases with maize, cassava, sorghum or millet. projections (σ ( T )) is also larger relative to σT than in the
Temperature also appears to dominate regardless of whether case of σ ( P) and σ P .
the crop was irrigated or the whether it was grown in high or The simple interpretation of these results is that although
low yielding environment, with a correlation of 0.03 between the sign of precipitation change is most often unknown,
average yields and the sum of temperature related factors in climate models generally agree that the magnitude of change
figure 1(b). will not be very large relative to historical year-to-year
The unexpectedly small role of precipitation can be variability, even if we consider models with the most extreme
understood by closer examination of E[ T ], E[ P], σ ( T ), precipitation projections. In contrast, even the uncertainty
and σ ( P), which are expressed for each crop in figure 1(c) surrounding temperature projections are large relative to
as a multiple of the historical standard deviation of growing historical variability, and the mean projected warming for
season temperature (σT ) or precipitation (σ P ). (We present 2030 is more than twice the historical standard deviation of
standard deviations of projections (e.g., σ ( T )) rather than temperature. These statements assume that the inter-model
variances to make all ratios unitless. Results were qualitatively standard deviation of GCM projections is a fair representation
similar when using ranges of projections rather than standard of climate uncertainty, an assumption that has been widely
deviations.) Although values of σ ( P) are typically larger challenged, particularly in the case of precipitation [17–19],
in absolute value than E[ P], which indicates that several in part on the grounds that different climate models are not
models disagree on the sign of change, both values are typically independent. Nonetheless, even if the true uncertainties in
less than 0.5 σ P . Values of E[ T ], in contrast, always exceed future precipitation changes were twice as large as those
σT , with an average change of 2.3 σT by 2030 (solid red circles estimated using inter-model differences, they would still
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4. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
Figure 3. The relationship between the R 2 of the statistical crop
model in each of 94 crop–region combinations and (a) the inferred
sensitivity of crop production to precipitation, expressed as % change
in production for each % change in precipitation, and (b) the fraction
of total variance attributed to temperature related factors in
equation (2). Models with higher R 2 tend to have greater sensitivity
to precipitation and a more important contribution from rainfall to
uncertainty in future impacts, although the role of temperature is still
significant for several models with R 2 above 0.4.
in equation (1) was replaced with monthly precipitation for
each month in the growing season and a new regression was
computed. While the model R 2 improved in nearly all cases,
the improvement in most cases was no more than expected
by chance when adding additional predictors (five additional
predictors in the case of a six month growing season), as judged
by the adjusted R 2 (figure 2).
Another possible source of error is omission of important
interactions between temperature and rainfall. To test this, a
term was added to equation (1) to represent the interaction of
precipitation and temperature [20]. Again, the R 2 increased in
some cases (particularly in the case of South Asia rapeseeds),
but overall the models were not substantially improved
(figure 2).
The results therefore appear relatively insensitive to the
Figure 2. Summary of model fits to historical crop production and
growing season climatic data for crop–region combinations, ranked
specification of monthly rainfall or interactions in the model.
by a measure of importance to global food security: model adjusted Other sources of uncertainty are more difficult to directly
R 2 for original model (solid black), model with monthly evaluate, such as those that arise from spatial aggregation over
precipitation (open black), and model with temperature–rainfall diverse climates within mountainous countries such as Kenya
interaction term (gray). Adjusted R 2 accounts for the larger number and Tanzania, and those that arise from rainfall at sub-monthly
of predictors when using monthly precipitation, which will tend to
inflate the unadjusted R 2 . (Adjusted timescales. One indication that estimates of E[β P ] may be
R 2 = 1 − (1 − R 2 )∗ (n − 1)/(n − p − 1), where n is total number of biased toward zero is that models with higher R 2 tended to
observations and p is number of predictors.) have higher values of E[β P ] (figure 3(a)). These models also
tended to have a greater contribution of precipitation to total
uncertainty, although uncertainties for many models with high
represent a fairly small component of total uncertainty (see R 2 were still dominated by temperature (figure 3(b)).
below). A more thorough sensitivity analysis of equation (2)
In addition to errors in estimates of climate uncertainties, was conducted to evaluate how far off the estimates of each
our estimates of crop responses and their uncertainties are variable in equation (2) could be before the results qualitatively
prone to errors that could potentially bias the results. First, changed. The empirical distributions of each variable across all
one could argue that the importance of rainfall to crops (β P ) 94 crop–region combinations are illustrated in figure 4, with
relative to temperature is underestimated by using region and the median value indicated by the thin vertical line. A two-
growing season averages. For example, it is possible that at-a-time sensitivity analysis was conducted based on these
rainfall during critical stages of crop growth is both more distributions, with each term in equation (2) computed as
important than, and poorly correlated with, average growing two variables were systematically varied across the range of
season rainfall. To test the importance of the intra-seasonal observed values, holding all other variables at their median
temporal distribution of rainfall, the average precipitation term value.
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5. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
Figure 4. Histogram of the estimates for each variable in equation (2) from 94 crop–region combinations. Units are % per ◦ C for βT , % per %
for β P , ◦ C for T , and % for P . Vertical solid line shows median value.
Figure 5. Sensitivity analysis of the fraction of total variance in equation (2) contributed by the first three terms relating to temperature. Each
panel displays this fraction as β P and one other variable are varied over the range of observed values, with all other variables fixed at their
median values. The x -axis shows variation in β P and the panel label indicates the variable on the y -axis. For most combinations, temperature
terms contribute most of the total uncertainty.
Results indicated that the relative importance of terms of climate change rest largely on impacts of rainfall changes.
involving temperature was most sensitive to the value of E[β P ] However, these situations are the exception rather than the rule,
(figure 5). Precipitation uncertainties became most important as indicated by the relatively small amount of the parameter
for high values of E[β P ] coupled with low values of E[βT ] space with more than half of total variance contributed by
and Var(βT ) and high values of Var( P ). This result makes precipitation terms.
intuitive sense: in cases where crops are relatively sensitive to As another measure of robustness that considers all
rainfall and future rainfall is very uncertain, then future impacts possible interactions not captured by two-at-a-time tests, we
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6. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
Figure 6. Sensitivity of results in figure 1(b) to an increase in the response of crops to rainfall (β P ) by two or five times. This is intended to
show the potential effects of under-estimating the sensitivity of crop production to rainfall.
took 10 000 random combinations of the eight variables Prospects for reducing uncertainty for βT are less
in equation (2), with the distributions defined in figure 3, clear. Two general approaches exist for estimating crop
and computed the fraction of total variance arising from temperature sensitivity: detailed studies of processes through
temperature. In fully 92% of cases, temperature terms field and laboratory experiments, or statistical analyses of
contributed more than half of total variance, and in 81% of past temperature and crop production variations. The former
cases more than three-fourths. Thus, the qualitative result require models to extrapolate results to broad scales relevant
that temperature related uncertainties drive most of overall to impact assessments, although the temperature responses
impact uncertainty appears robust. As a final test, the results of these models are often poorly constrained by experiments
in figure 1(b) were re-generated after inflating the estimates and not well understood [22]. The latter approach, which
of E[β P ] in each crop–region combination by a factor of two underlies the models used in this study, is often hampered by
and five (figure 6). Only if the importance of rainfall to crops variations in other yield controlling factors, the quality and
(β P ) is roughly five times as large relative to βT as we estimate spatial scale of available data, and by relatively small number
here would terms related to rainfall contribute more than half of observations (∼40 years). Thus, some level of uncertainty
of total uncertainties for most crops. is inevitable.
An important remaining question is whether the prospects Of the 94 crop–region combinations considered in our
for reducing uncertainty in T or βT are particularly study [14], the lowest uncertainty for βT was found for South
good or bad. Roe and Baker [21] recently argued that Asia wheat, with σ (βT ) = 0.7% per ◦ C. To represent an
uncertainty in global climate sensitivity to CO2 is unlikely to optimistic scenario of relatively low uncertainty, we applied
be reduced substantially in the near term, because it results this value to all other crops, resulting in a reduction of
from climate system feedbacks that are hard to constrain total variance of impact projections by over half in most
with observations. Model projections of regional temperature cases (figure 7). Constraining temperature sensitivities of all
change are determined largely (but not exclusively) by the crops to the level of precision in South Asia wheat would
model’s global climate sensitivities [3], so that uncertainties thus substantially improve our ability to predict agricultural
in T may be expected to persist for some time. responses to climate change.
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7. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
impact of temperature uncertainties, and in particular the
uncertainties in crop response to temperature, should receive
increased attention. The common approach of representing
uncertainty only by examining output from different climate
models risks a gross over-estimation of our current ability
to predict agricultural responses to climate change. This
conclusion is also supported by the few studies that have
examined yield impacts with two separate process-based crop
models. These studies have reported discrepancies between
crop models as big or greater than those that result from
different climate models, and attributed these discrepancies
largely to the temperature coefficients in the models [10, 23].
Thus, the results presented here are unlikely to result from the
exact specification of yield responses in equation (1), or to the
fact that we relied on time series models rather than process-
based models or cross-sectional data.
We considered here only uncertainties that relate
to growing season average temperature and precipitation.
Impacts of extreme events, pests and diseases, changes in solar
radiation, and many other factors also add uncertainties to
projections. In our opinion, all of these effects are likely to
be captured by or secondary to those of average temperature
change, but further work is needed to test this point. For
example, the distribution of rainfall within growing seasons
may change, with heavier but less frequent rainfall events
in many regions [3], which could substantially change the
relationship between growing season average precipitation and
crop production. We also do not consider here adaptive
management changes, which represent an additional but poorly
known source of uncertainty in future impacts, even for the
relatively short-term year of 2030 discussed here.
Despite these other uncertainties, the uncertain nature of
crop responses to mean temperature change will remain an
important factor for any risk assessment of climate change
impacts that relies on accurate quantification of uncertainty.
Crop model inter-comparison projects, similar to those used
Figure 7. Fraction of total variance in climate change impact to assess climate model uncertainty [3], may be useful to
projections for 2030 remaining if the estimated value of σ (βT ) is this end in the short-run. Unlike climate sensitivity, however,
replaced by the lowest observed value across all crops (0.7% per ◦ C), the sensitivity of crops to warming can be experimentally
which represents an optimistic scenario of improved knowledge of
crop temperature responses. tested. Warming trials for major crops across the range of
environmental and management conditions in which they are
most commonly grown may therefore be a particularly useful
4. Conclusions means of further prioritizing and focusing adaptation efforts.
We find that, in general, uncertainties in average growing
season temperature changes and the crop responses to these Acknowledgments
changes represent a greater source of uncertainty for future
impacts than do associated changes in precipitation. This We thank D Battisti, W Falcon, C Field, R Naylor, C Tebaldi
finding stems from the fact that future temperature changes for helpful discussions and two anonymous reviewers for
will be far greater relative to year-to-year variability than comments on the manuscript. We acknowledge the modeling
changes in precipitation, even when considering the most groups, the PCMDI and the WCRP’s Working Group on
extreme precipitation scenarios. These results do not imply Coupled Modeling (WGCM) for their roles in making available
that reduced uncertainties in rainfall projections would be the WCRP CMIP3 multi-model dataset. Support of this dataset
useless, as projections for several critically important crops is provided by the Office of Science, US Department of Energy.
still derive much of their uncertainty from rainfall projections, This work was supported by the Rockefeller Foundation and
such as rice in South Asia and wheat in West Asia. Moreover, by National Aeronautics and Space Administration Grant
the spatial scale of the datasets used here likely mute the No. NNX08AV25G to DL issued through the New Investigator
importance of rainfall. Rather, it is our belief that the Program in Earth Science.
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8. Environ. Res. Lett. 3 (2008) 034007 D B Lobell and M B Burke
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