The Spatial Production Allocation Model is an effective way to map detailed patterns of crop production using much less specific input data.

A variety of information sources are used to generate plausible, disaggregated estimates of crop distribution, which are useful for understanding production and land use patterns. Identifying where trends take place is important for understanding why they take place.

Using a variety of inputs, SPAM uses a cross-entropy approach to make plausible estimates of crop distribution for 42 crops and two production systems within disaggregated units.

Moving the data from coarser units such as countries and subnational provinces, to finer units such as grid cells at 10×10 km resolution, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems.

Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

 

How Can SPAM Be Used?

Effective Mapping

By having gridded information on crop distribution and production systems it is possible to map a much finer detail than standard national or sub-national crop statistics would allow. By overlaying SPAM results with other gridded information like population density of poverty incidence, it is possible to create homogeneous units – segments – which share common characteristics and can be analyzed as a group.

Enhance Knowledge

The enhanced knowledge of agricultural production systems and associated livelihood strategies that spatially disaggregated crop data provide can form the basis of rural development strategies. Given the enormous diversity and site-specific nature of many African production systems—as well as their associated cultural, socioeconomic, and resource management issues—effective development strategies should account for such spatial patterns.

Analyze

Using GIS to analyze production and productivity patterns offers ways to understand and manipulate how geography affects agriculture through policy interventions. SPAM’s outputs are also uniform in resolution and structure, meaning they are more easily introduced and manipulated within a GIS than the irregular structures of political or administrative boundaries in which statistics are usually compiled.

Application

SPAM results have been widely used within and outside IFPRI. The model and its outputs are the key elements in the organization’s global change research, including the HarvestChoice program, climate change work, and regional research and development priority setting within IFPRI for East Africa and West. Analysts at the CGIAR centers (a global partnership that unites organizations engaged in research for a food secure future), the World Bank, the Food and Agriculture Organization of the United Nations (FAO), as well as researchers at universities and in developing country national agricultural research systems, have applied SPAM outputs in their work.

 

Who uses SPAM?

This is not an exhaustive list, please let us know if you’d like to be included.

Publications

Africa

Nin-pratt, A. 2015.Agricultural Intensification in Africa. A Regional Analysis. IFPRI DISCUSSION PAPEr 01433
  • Countries: all Sub-Saharan African countries
  • Agricultural land and land suitability data
Waithaka, M., G. C. Nelson, T. S. Thomas, M. Kyotalimye. 2013. East African Agriculture and Climate Change: A Comprehensive Analysis. IFPRI Publication
  • Countries: Burundi, Democratic Republic of Congo, Eritrea, Kenya, Madagascar, Rwanda, Sudan, Tanzania, and Uganda
  • Crops: Wheat, sorghum, cassava, maize, plantains, bananas, beans, sweet potato, cotton, potatoes, millet, rice
Cervigni, R., R. Valentini, M. Santini. 2013. Toward Climate-Resilient Development in Nigeria. World Bank publication
  • Countries: Nigeria
  • Crops: Cassava, Maize, Millet, Rice, Sorghum, Yam
Benin, S., A. Nin Pratt, S. Wood, Z. Guo. 2011. Trends and Spatial Patterns in Agricultural Productivity in Africa, 1961–2010. ReSAKSS Annual Trends and Outlook Report 2011. IFPRI
  • Countries: All African countries
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potatoes, sweet potatoes, cassava, bananas and plantains, soy beans, beans, oilseeds and pulses, sugarcane, sugar beets, coffee, cotton, other fiber crops, groundnuts, and other oilseeds.
Alejandro, N., M. Johnson, E. Magalhaes, X. Diao, L. You, and J. Chamberin. 2009. Priorities for realizing the potential to increase agricultural productivity and growth in Western and Central Africa. IFPRI discussion paper 00876.
  • Countries: 21 Western and Central African countries: Benin, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Congo, Ivory Coast, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Mali, Mauritania, Niger, Nigeria, Democratic Republic of Congo, Senegal, Sierra Leone, Chad, and Togo.)
  • Crops: Maize, rice, barley, wheat, coarse grains, millet, sorghum, cassava, potato, sweet potato, beans, groundnut, soybean, banana, and cotton lint.
Dorosh, P., H. Wang, L. You, and E. Schmidt. 2009. Crop Production and Road Connectivity in Sub-Saharan Africa: A Spatial Analysis. Africa Infrastructure Country Diagnostic Working Paper 19.
  • Countries: 24 Sub-Saharan African countries: Benin, Burkina Faso,Cape Verde, Cameroon, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia,Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia.
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potato, sweet potato and yam, cassava, plantain and banana, soybean, beans, other pulses, sugarcane, sugar beets, coffee, cotton, other fibers, groundnuts, and other oil crops.
Haggblade, S., S. Longabaugh, and D. Tschirley. 2009. Spatial patterns of food staple production and marketing in South East Africa: implications for trade policy and emergency response. Food Security International Development Working Papers 100.
  • Countries: Zambia, Malawi, and Mozambique.
  • Crops: Maize and Cassava.
Lall, S.V., E. Schroeder, and E. Schmidt. 2009. Identifying Spatial Efficiency-Equity Trade Offs in Territorial Development Policies Evidence from Uganda. World Bank Policy Research Working Paper 4966.
  • Countries: Uganda.
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potatoes, sweat potatoes and yams, cassava, bananas and plantains, soybean, other pulses, sugar cane, sugar beet, coffee, cotton, other fiber crops, groundnuts, other oil crops.
Thornton, P.K., P.G. Jones, G. Alagarswamy, J. Andresen, and M. Herrero. 2009. Adapting to climate change: Agricultural system and household impacts in East Africa. Agricultural Systems In Press, Corrected Proof.
  • Countries: 5 East African countries; Burundi, Kenya, Rwanda, Tanzania, and Uganda.
  • Crops: Maize and beans.
Ulimwengu, J., J. Funes, D. Headey, and L. You. 2009. Paving the way for development: the impact of infrastructure on agricultural production and household wealth in the Democratic Republic of Congo. Presented at Agricultural and Applied Economics Association 2009 Annual Meeting. Milwaukee, Wisconsin.
  • Countries: The Democratic Republic of Congo.
  • Crops: Cassava, bean, paddy rice, plantain, sweet potato, millet, and potato.
You, L., and M. Johnson. 2008. Exploring Strategic Priorities for Regional Agricultural R&D Investments in East and Central Africa. IFPRI Discussion Paper 00776.
  • Countries: East and Central African countries: Burundi, Congo Democratic Republic, Eritrea, Ethiopia, Kenya, Madagascar, Rwanda, Somalia, Sudan, Tanzania, and Uganda.
  • Crops: Rice, maize, sorghum, cassava, plantains, potatoes, groundnuts, beans, coffee, cotton, cashew nuts, and vegetables.
You, L., S. Wood, and U. Wood-Sichra. 2009. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural Systems 99:126-140.
  • Countries: All 51 countries in Sub-Sahara Africa.
  • Crops: Barley, dry beans, cassava,cocoa, coffee, cotton, cow peas, groundnuts, maize, millet, oil palm, plantain, potato, rice, sorghum, soybeans, sugar cane, sweet potato, wheat, and yam.
You, L., C. Ringler, C.N. Gerald, U. Wood-Sichra, R. Robertson, S. Wood, Z. Guo, T. Zhu, and Y. Shn. 2009b. Torrents and Trickles:Irrigation Spending Needs in Africa. Africa Infrastructure Country Diagnostic Background Paper 9.
  • Countries: 24 Sub-Saharan African countries; Benin, Burkina Faso,Cape Verde, Cameroon, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia,Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia.
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potato, sweet potato and yam, cassava, plantain and banana, soybean, beans, other pulses, sugarcane, sugar beets, coffee, cotton, other fibers, groundnuts, and other oil crops.

Asia

Gruère, G., B.t. Antoine, and S. Mevel. 2007. Genetically modified food and international trade the case of India, Bangladesh, Indonesia, and the Philippines. IFPRI Discussion Paper 00740.
  • Countries: India, Bangladesh, Indonesia, and the Philippines.
  • Crops: Rice, wheat, maize, soybeans, and cotton.
Pender, J. 2007. Agricultural Technology Choices for Poor Farmers in Less-Favored Areas of South and East Asia. IFPRI Discussion Paper 00709.
  • Countries: South and East Asian countries: Indonesia, the Philippines, Malaysia, Thailand, Vietnam, China, Laos, Cambodia, Myanmar, Bangladesh, Sri Lanka, India, Pakistan, Mongolia, Nepal, and Bhutan.

America

Elliott, J., M. Glotter, N. Best, K. Boote, J. Jones, J. Hatfield, C. Rosenzweig, L. A. Smith, I. Foster. 2013. Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling. RDCEP Working Paper No. 13-01.
  • Countries: United States.
  • Crops: Maize.
Nelson, G.C., and R.D. Robertson. 2008. Green gold or green wash: environmental consequences of biofuels in the developing world. Review of Agricultural Economics 30:517-529.
  • Countries: Brazil.
  • Crops: Maize.
Wood, S., L. You, and X. Zhang. 2004. Spatial patterns of crop yields in Latin America and the Caribbean. EPTD discussion paper No.124.
  • Countries: Latin America and the Caribbean countries.
  • Crops: Rice, maize, and soybean.
You, L., and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems 90:329-347.
  • Countries: Brazil.
  • Crops: Wheat, rice, maize, sorghum, potato, cassava, bean, and soybean.
Zapata-Caldas, E., G. Hyman, H. Pachón, F.A. Monserrate, and L.V. Varela. 2009. Identifying candidate sites for crop biofortification in Latin America: case studies in Colombia, Nicaragua and Bolivia. International Journal of Health Geographics 8.
  • Countries: Colombia, Nicaragua and Bolivia.
  • Crops: Bean, rice, maize, sweet potato, and cassava.

Cross regions

Gruère, G., S. Mevel, and A. Bouët. 2009. Balancing productivity and trade objectives in a competing environment: should India commercialize GM rice with or without China? Agricultural Economics 40:459-475.
  • Countries: Global
  • Crops: Rice.
Kostandini, G., B.F. Mills, S.W. Omamo, and S. Wood. 2009. Ex ante analysis of the benefits of transgenic drought tolerance research on cereal crops in low-income countries. Agricultural Economics 40:477-492.
  • Countries: India, Indonesia, Bangladesh, the Philippines, Kenya, Ethiopia, Nigeria, and South Africa.
  • Crops: Maize, rice, wheat.
Skalsky, R., Z. Tarasovicov , J. Balkovic, E. Schmid, M. Fuchs, E. Moltchanova, G. Kindermann, and P. Scholtz. 2009. GEO-BENE global database for bio-physical modeling v. 1.0. Geobene Public Research Documents.
  • Countries: Global.
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potatoes, sweat potatoes and yams, cassava, bananas and plantains, soybean, other pulses, sugar cane, sugar beet, coffee, cotton, other fiber crops, groundnuts, other oil crops.
You, L., S. Wood, and U. Wood-Sichra. 2006. Generating global crop distribution maps: from census to grid. Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, California, July 23-27, 2006.
  • Countries: Global.
  • Crops: wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soybean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops.
You, L., S. Wood, and K. Sebastian. 2008. Comparing and synthesizing different global agricultural land datasets for crop allocation modeling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37:1433-1440.
  • Countries: Global.
  • Crops: Wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soybean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops.
You, L. 2009. A tale of two countries: spatial and temporal patterns of rice productivity in China and Brazil. IFPRI Discussion Paper 00758.
  • Countries: Brazil and China.
  • Crops: Rice.

Blogs

Projects

Models

Maps

ESRI Image Services:

The Team

A large team is responsible for all the work and resources included in MapSPAM.

  • Liangzhi You

    Senior Research Fellow, HarvestChoice/IFPRI

  • Ulrike Wood-Sichra

    Research Analyst, HarvestChoice/IFPRI

  • Steffen Fritz

    Research Scholar, IIASA

  • Zhe Guo

    GIS Coordinator, HarvestChoice/IFPRI

  • Linda See

    Research Scholar, IIASA

  • International Food Policy Research Institute

    Jawoo Koo

    Research Fellow, HarvestChoice/IFPRI

Special Thanks

Many people and institutes provided data or comments/feedback to SPAM.

In particular we’d like to thank the following people: Ayumi Arai, Carlo Azzarri, Melanie Bacou, Surajit Baruah, Jordan Chamberlin, Maria Comanescu, Cindy Cox, Günther Fischer,  Yue Gong, Beliyou Haile, Daniel Hawes, Mario Herrero, Robert Hijmans, Dave Hodson, Glenn Hyman, Shahnila Islam, Andy Jarvis, Nanga Kaye, Amy Kapp, Erwin  Kalvelagen, Zahia Khan, Ho Young Kwon, Chris Legg, Gucheng Li, Daniel Mason-D’Croz, Gerald Nelson, An Notenbaet, Navin Ramankutty, Ingrid Rhinehart, Cleo Roberts, Ricky Robertson, Ivy Romero, Cynthia Rossi, Kate Sebastian, Dongsheng Sun, Philip Thornton, H.T. van Velthuizen, Ivan Vidangos, Ojo Williams, Stanley Wood, Wenbin Wu, Hua Xie, Han Xu, Biwen Zhou.

Please forgive us if we haven’t mentioned you.

Also thank you to  HarvestChoice for your support and Hormel Foods for the inspiration of the name.