Jul 17, 2014 by Ulrike Wood-Sichra

Characterizing global production systems just got easier. IFPRI/HarvestChoice has updated SPAM, from circa 2000 to centering on the year 2005. That’s right; SPAM 2005 is out for release and freely available to the public. That means more accurate and up-to-date spatially disaggregated crop production statistics and more of it.

Aug 15, 2013 by Ulrike Wood-Sichra

It may get stuck in your junk folder, confused with a tin of mysterious food, or buried in a Google search (try adding a few more key terms), but for now the name sticks (and sticks in your memory); SPAM (Spatial Production Allocation Model) is not getting a new name anytime soon. That’s just one of the items key players discussed at a recent SPAM workshop.

Jul 16, 2012 by Susana Crespo

Picture it. You're working at HarvestChoice processing incoming crop statistics and you come across some figures for harvested area of dolichos in Kenya.

Feb 21, 2012 by Jeff Horwich

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) and HarvestChoice are now working together to apply innovative, “bio-economic” approaches to improve the food security of poor people in sub-Saharan Africa and South Asia. Together, our organizations will merge biological, environmental and economic tools to track global food pests and better target strategic investments to improve global food security. 

CSIRO and HarvestChoice

Feb 11, 2012 by Ulrike Wood-Sichra

SPAM2000 (any version) is based on a large set of data which centers as much as possible on the year 2000: land cover / land use (Boston University’s MODIS-derived land cover 2000 and JRC’s GLC2000), crop suitability (Global AEZ Zones 2000 by FAO and IIASA), irrigated areas (FAO and CESR of University of Kassel), population density (CIESIN), and, most importantly, national and sub-national crops statistics for 2000.

Feb 4, 2011 by Jawoo Koo

Crop systems models can help researchers estimate the future of food security under climate scenarios. Many crop models are known to exist around the world - for different crops with varying complexities, yet it is not easy to find the right model for the right problem. To better understand the global extent of crop model development and to identify gaps in capabilities, HarvestChoice participated in an initiative to conduct a rapid meta-analysis of crop models using on-line survey to the crop modeling community in the world. Here are the key findings.

Word cloud of crops that respondents modeled

Sep 17, 2010 by Jawoo Koo

In August, we posted a new collection of more than 3,400 soil profiles that are converted/formatted for crop model applications, based on the WISE 1.1 Soil Profile Database. Utilizing this new soil profiles, as described in the post, we anticipate crop modeling studies to expand their coverage areas even to the locations where no soil measurement data was previously available. For the questions of exactly how, here is a quick example application that can help you find the one(s).

Which soil profiles?

Sep 17, 2010 by Jawoo Koo

Many of HarvestChoice spatial datasets are organized and released on 10-km grids. To make spatial analyses easier for researchers (even without having access to GIS platform), we put data layers from multiple themes together in one denormalized big table. This post describes the methodology and presents a prototype.

Layered information on a grid cell

Aug 15, 2010 by Jawoo Koo

Conceptually, in its simplest form, one may liken the cultivation of a crop in the field to a mathematical function.

YIELD RESPONSES TO NITROGEN

Aug 12, 2010 by Jawoo Koo

As a quick demonstration to estimate crop yield levels at regional-scale with various management assumptions, this post describes how crop systems models can be used to assess yield gap of rainfed maize due to the limited supply of soil nitrogen. This methodology can help researchers to find what is the most critical factor that limits crop yield productivity in a given environment condition and how to address the constraint.