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“Time to model all life on Earth” – Agriculture?

A recent Nature Comment article discusses the need for developing GCM-like General Ecosystem Models (GEM) to simulate whole ecosystems. The article also introduced few prototypes already being developed, including the Madingley Mondel that the lead author’s institute, Microsoft Research, is developing in collaboration with UNEP World Conservation Monitoring Centre (UNEP-WCMC).

The article suggests “… coupled with models from other fields, such as economics and epidemiology, they could offer a means of managing human actions and the biosphere in an integrated, consistent and evidence-based way.” This, of course, should apply to the agricultural activities and their interactions with the large ecosystems and their services. Speaking of which, ins’t it also “Time to model all agriculture on Earth” (General Agroecosystems Models – GAME)?

Microsoft Research and the UN team up to build a computational model of ecosystems across the worldMicrosoft Research and UN scientists have teamed up to build the first general-purpose computer model of whole ecosystems across the entire world. The project was detailed in a recent Nature article titled “Ecosystems: Time to model all life on Earth,” which unfortunately requires a subscription.

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GIS training on the road

IFPRI’s magazine, INSIGHTS, features a story of Emily Schmidt – who’s been working with Ethiopia’s Central Statistical Agency (CSA) to train statisticians across the country to use GIS for analyzing and visualizing the agricultural statistics data they produce. The close collaboration enabled CSA to produce their own series of atlases over the years. Now the successful training program is being expanded to Malawi and Mozambique.

On the Road | IFPRI Insights MagazineIn a vast, rapidly developing country like Ethiopia, data-about everything from literacy rates to the number of flour mills-is essential to policymaking. For the country’s Central Statistical Agency (CSA) and its regional branches, translating mountains of raw data into useful information to support policymaking in a timely manner is a major task.

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Central Statistical Agency of Ethiopia – Atlases IFPRI’s Ethiopia Strategy Support Program – Knowledge Sharing


Global forecasts of urban expansion to 2030

A new PNAS paper projects location, magnitudes, and rates of urban expansion to 2030. The supplementary information indicates the familiar Global Rural-Urban Mapping Project (GRUMP) dataset, of which IFPRI and CIAT participated the development, was used as the baseline data to create population density driver and to project for the future.

For us, working on the agricultural research, probably the rural extent is more relevant. Would the simple computation of [total pop – urban pop = rural pop] be legit, hopefully?

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon poolsAbstract Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion.

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Download full paper at PNAS (Open Access) Request to download GIS data (Esri GRID format)


Tracking farmers’ mobility silently – Holy grail?

Back in 2009, Silvia Renn (World Fish) blogged her review of a Nature news article describing the possibility of using mobile phone to monitor human behaviors and public health and noted the untapped potential of such data and technology to detect human behavioral patterns in agriculture. Following up on the story, Science recently published an article more deeply mined such mobile phone-based location data and attempted to quantify the impact of human mobility on malaria .

Quantifying the Impact of Human Mobility on MalariaHuman movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions.

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As Silvia earlier noted, I also think such mobile phone-based location data and its mining technology could start unlocking answers to so many research questions involving human, or farmers in our case, behavior. There are, of course, concerns on the subject and their privacy, but let’s put it aside for a moment.

Farmers with mobile phones that they use to look up price information about their crops

Farmers with mobile phones that they use to look up price information about their crops (IICD via Flickr)

On one possible example, not necessarily fully relying on costly surveys, we could exactly pinpoint when, how many times during the growing season, farmers visit which fields, located where, for how long (as long as they keep carrying the mobile phone, that is). We may still need some in situ surveys, but we will have much better ideas on which types of management event happens, when, where – so that we can ask a lot more informed questions and hopefully useful answers. At some point after harvest, we could also track their travel from the field to the market, the mode of transportation, travel time, etc, to understand their market accessibility and value-chain of the product. We can even guesstimate how much production they may have this season, based on their farming product transportation to the markets. We will have better ideas on the production cost side as well.

For the public health side, we’ve been hearing the location of hospital/clinic doesn’t mean much; how many doctors and nurses there are now is a lot more critical issue – but it’s very difficult to keep track. Imagine we can keep track people’s time to travel from their home/village to the health clinic, how busy the facility is, how long they stay in the facility, what’s their next destination (another clinic, bank, or going back home); good interpretation of such information could lead us to much better real-time understanding of what’s happening on their livelihood and when/whether to trigger an alarm.

Anybody wants to put more thoughts and construct a concept note together? Let me know!


Generating downscaled weather data using MarkSimGCM




A new paper describes the methodology used by CCAFS’ MarkSimGCM to downscale the outputs of a General Circulation Model and generate daily weather for the future climate . – Agricultural Systems – Generating downscaled weather data from a suite of climate models for agricultural modelling applications► Most climate model outputs need manipulation before they can be used by agricultural modellers. ► We describe a tool to generate daily data that are somewhat characteristic of future climates. ► The method uses an amalgamation of empirical downscaling, climate typing and weather generation.

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iOS 6’s new/forthcoming “Maps” uses, what else, CGIAR-CSI SRTM as elevation data

According to the San Francisco Chronicle, here is the list of data sources for the glorious (no, sorry) Apple Maps:

CoreLogic Inc.
MapData Sciences Pty LTD. Inc.
MDA Information Systems
Urban Mapping
Department of Nautral Resources Canada
CGIAR Consortium for Spatial Information
U.S. Census Bureau
U.S. Geological Survey
National Geospatial-intelligence Agency

Aboveground Live Woody Biomass from WHRC

National Dataset of Aboveground Live Woody Biomass Density by Woods Hole Research Center

Using a combination of co-located field measurements, LiDAR observations and imagery recorded from the Moderate Resolution Imaging Spectroradiometer (MODIS), WHRC researchers produced national level maps showing the amount and spatial distribution of aboveground carbon. Spatial resolution of 500m data were derived from field/LiDAR(GLAS)/MODIS.

Validating Agricultural Land Cover with Geo-Wiki

FYI, if you have experienced frustration on the quality of agricultural land use datasets out there (I know – who hasn’t), here is a place you can contribute your input:

Agriculture-Branch of the Geo-Wiki Project

The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g. to determine the potential of additional agricultural land available to grow crops in Africa). Volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved global land cover map.


(Thanks Kai for the tip!)

Agriculture gets a makeover | Geospatial World (August 2011)


From the text: “Geospatial technology, with its potential to address the complete life cycle of agriculture, is fast finding acceptance in agriculture to fulfill its responsibilities in addressing food security and as a fundamental instrument for sustainable development and poverty reduction, especially in developing nations. In the process, one of the oldest economic practices of human civilization is getting a makeover.”

Introducing UN-GGIM (Global Geospatial Information Management)

FYI, there is a new initiative by United Nations called GGIM, Global Geospatial Information Management, “… to create a formal mechanism under UN auspices where key issues and potential action can be discussed, and by involving member states as the key players.”

Website recently launched at

The UN Programme on Global Geospatial Information Management (GGIM) aims at playing a leading role in setting the agenda for the development of global geospatial information and to promote its use to address key global challenges. It provides a forum to liaise and coordinate among Member States, and between Member States and international organizations.

There will be their first UN Forum on GGIM in Seoul, South Korea, in October 2011. Concept note and agenda can be found here (you can find many familiar names in the list of speakers!).

[Jobs at IRRI] Postdoctoral Fellow – GIS and Land Use Modeller, Dhaka, Bangladesh

Postdoctoral Fellow – GIS and Land Use Modeller

* There have been 0 applications for this position

* This job opening has been viewed 48 times

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About this job

Reference number: PDF-2011-14-AN

Apply by: 22 June 2011

This three year position is for a GIS specialist with strong modeling skills to work on an IRRI-led CPWF project in the coastal zones of the Ganges in Bangladesh, namely Project G1 (Resource profiles, extrapolation domains and land-use plans). G1 is one of 5 projects in the Ganges basin related to more productive, profitable, resilient, and diversified rice-based cropping systems in the coastal zones of BGD.  The successful applicant will work closely with team members in all 5 projects to develop resource profiles at selected experimental sites and of the Southwest coastal zone; extrapolation domains to determine where new technologies are most likely to be successful land use plans under current and different hydrological scenarios in the future.

Major Accountabilities/Responsibilities:

  • Work with G1 project partners and data providers in Bangladesh to create a geodatabase of relevant information in the coastal zone of the Ganges Basin, such as soil and salinity profiles, hydrological information, land use / land cover, socioeconomic data and infrastructure at 1:50,000 scale or better.
  • Use this geodatabase to derive resource characterization profiles for the experimental sites and polders where the related Ganges projects are working.
  • Work with the Project Leader, other senior project members and the basin project partners and stakeholders to develop a robust methodology to determine ‘what work’s where’ by mapping the extrapolation domains for the technologies and policies promoted by the other basin projects.
  • Work with project partners to update or develop a current land use plan and maps of future land use change under plausible future hydrological scenarios.  This will be conducted in close collaboration with the G4 project responsible for hydrological modeling of the coastal zone of the Ganges basin.
  • Consult and work with project partners and stakeholders throughout the duration of the project on matters related to spatial analysis and map provision
  • Publish scientific outputs through national and international journals, proceedings, conferences, and workshops.
  • Based in Dhaka, with frequent field trips to the coastal zone of Bangladesh

Qualification Requirements:

  • PhD in Geography, water or land management or related fields
  • At least 5-years hands-on experience on GIS, spatial analysis, resource management and rice based cropping systems in South Asia
  • Experienced in working in multi-partner research and development programs.
  • Good experience in land use modeling, multi-criteria evaluation methods and statistical analysis tools such as R.
  • Good writing skills, and have a good publication record

Screening will start on 22 June 2011 and will continue until a suitable candidate has been found.

Please indicate the position reference number in the e-mail subject line. IRRI regrets that only shortlisted candidates will be contacted.

IRRI provides a multicultural work environment that reflects the values of gender equality, teamwork, and respect for diversity, with a competitive compensation and benefit package. Women are encouraged to apply.

Join us to deliver rice science for a better world

Interested candidates  should apply online (click apply now). Should you encounter problems applying online please email at


Shared by Andy Nelson at IRRI – Please help Andy spread the word!

Meet CSI’s New Logo!


Finally! After the heated contest with 49 entries from all around the world and votes amongst our reps, we finally have a new logo here. The colors in the globe represents various research themes we’re working on, and the globe shows our specialty – spatial analysis (We also had pretty interesting designs with very explicit icons of crop, animal, fish, human, and maps – but most votes favored this simple/abstract design).

Congrats to our winning designer, hendra264 (in Indonesia), and thanks all for participating in the process!

I’ve uploaded some various sizes/formats of the logo to for your own use. Additionally, Kai suggested to design a more compacted/stacked version of the logo to put in size-challenged layout of maps, etc – which is a great idea! I asked Hendra to try out few designs, so stay tuned for the update.

“New logo wanted for CGIAR-CSI” – Contest launched through

Hi all,

It’s not a secret that we’ve been wanting to have a new/cool/21st-century-ish logo for CSI for a while. It’s not that the old one is wrong, we just felt that it’s time to update our look. Our desire has been especially more escalated recently while planning for upcoming events and e-Atlas and so on.

So, *surprise* – we just launched a 7-day logo designing contest through a crowdsourcing company called 99designs (

·         Contest: New logo wanted for CGIAR CSI

·         Link:

·         Prize: $295 (minus some fee)

With the level of prize, we’re targeting young creative graphics designers out there. However, that doesn’t stop you or one of your colleagues to become a winner! In fact, given the complexity of design concept (I just hope someone can creatively/cleverly figure how to combine images of agriculture – crop, forest, livestock, fish – and spatial aspects), our colleagues may have comparative advantages. Who knows. Also, if you happened to know a talented logo designer in your ring, feel free to forward this message and encourage them.

After the 7-day period, we’ll ask each center’s representative to be a judge and vote for the best one. Sounds exciting? Wish us a good luck finding an exciting logo soon!



Presentations at the Global Land and Poverty Summit 2010


In September, Esri organized the Global Land and Poverty Summit 2010 in Washington, DC, focusing on “…how to use geographic technology to help solve diverse problems faced by the poor and to create low-cost, practical solutions for healthy, sustainable societies.”

As announced at the earlier AAGW 2010, CGIAR-CSI led an agriculture-themed session called “Dirt Poor: Seeking Solutions to Poverty from the Ground Up“, together with USDA-ERS. We collectively made three presentations that largely encompassed recent research highlights:

  • Prioritizing, Targeting and Monitoring Science, Technology and Policy Options For Poor Smallholders Examples from the CGIAR – Chris Legg (GLCI), Lieven Claessens (CIP), Mario Herrero (ILRI), Philip Thornton (ILRI), and Stanley Wood* (IFPRI)
  • Payments for Wildlife Conservation (PWC) and Poverty in East African Rangelands – Philip Osano* (ILRI), Jan de Leeuw (ILRI), Mohammed Said (ILRI), Shem Kifugo (ILRI), Dickson Kaelo (Basecamp Foundation), Norbert Henniger (World Resource Institute), Katherine Homewood (University College London)
  • Seeing is Believing: Very High Resolution for Smallholders in West Africa – Pierre Sibiry Traore* (ICRISAT)

Overall the session was very well received, and we impressively (as usual) showcased our colleagues’ hard work on this area. You can take a look at (or download) the presentations at

Sincere thanks (and big cheers) to everyone for attending/contributing/supporting the event!

[Wanted] Global raster layer of land surface area..??


Source: Map Analysis by Joseph K. Berry (

Does anybody have/know a publicly-available global raster dataset of land surface area with terrain considered (see the attached illustration describing the surface area)?

All I want is, very simply (?), for each grid cell (at 5’ or higher resolution) what’s the land surface area (in ha, for example). Can’t believe nobody has done/published it yet (hey, we’re in the age of 30 m DEM..), but I couldn’t find one – other than some theoretical backgrounds here and there, and the TIN stuff in ArcGIS (Thanks, Kai!).

For mapping crop land globally, we conveniently ignored the existence of slope/terrain (duh..) and simply calculated the grid cell area = f(latitude) – waterbody (or sometimes even included waterbody), assuming most crops are grown in flat surface,, in the grand scheme of things. Besides, I’ve been hearing that we don’t gain much by including terrain in the function (“Don’t sweat the small stuff!”). However, even if that’s true – as we’re getting more serious about socioeconomic variables beyond crop production, I think we really need to be more accurate calculating the grid cell area (and this potentially propagates through all sorts of indicators we’re estimating cell by cell). And,

So,, anyone?

If I don’t hear from anyone within next couple of weeks, I might just go ahead and scratch my own itch again – but thought worth asking.  : )