Ulrike Wood-Sichra

Mapping global cropland and field size

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

Generating Plausible Crop Distribution Maps for Sub-Saharan Africa Using a Spatial Allocation Model

Agricultural production statistics are fundamental parameters for agriculture policy research. Information on acreage and yields of important crops is critical for understanding trends within what is the most important economic sector of many developing countries. Sub-national data — i.e. data organized by administrative units such as regions or districts — enable the analysis of patterns within countries that may highlight important policy issues, such as the need to allocate resources to underproductive areas. However, collecting sub-national data is difficult for developing countries with limited resources. Even with great effort, and often only on broad regional scales, enormous data gaps exist and are unlikely to be filled. As a result, information is often only available at national or very broad sub-national levels (such as provinces). Such geographically coarse data are unable to reflect important variations within countries and are insufficient for the spatial analysis of production patterns and trends. To fill these spatial data gaps we developed a model to disaggregate production data from coarser to finer spatial units. Using a cross-entropy approach, our spatial allocation model attempts to make plausible allocations of crop production from large reporting units such as a country or state, into smaller spatial units organized as cells of a regularly-spaced grid. In addition to more detailed information, the organization of production information in geographic grids allows for greater analytical possibilities through geographic information systems. The allocation model works on the basis of available evidence of mapped indicators of agricultural production, which include farming systems, land cover, crop biophysical suitability surfaces, commodity prices and local market access. This article describes the generation of crop distribution maps for Sub-Saharan Africa for the year 2000 using the spatial allocation model and discusses the importance of such maps for development analysis and planning.

A Comparative Analysis of Global Cropping Systems Models and Maps

This study aims to explore and quantify systematic similarities and differences between four major global cropping systems products: the dataset of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000), the spatial production allocation model (SPAM), the global agroecological zone (GAEZ) dataset, and the M3 dataset developed by Monfreda, Ramankutty, and Foley. The analysis explores not only the final cropping systems maps but also the interdependencies of each product, methodological differences, and modeling assumptions, which will provide users with information vital for discerning between datasets in selecting a product appropriate for each intended application.

Agriculture production and transport infrastructure in east Africa : an application of spatial autoregression

Africa is estimated to have great potential for agricultural production, but there are a number of constraints inhibiting the development of that potential. Spatial data are increasingly important in the realization of potential as well as the associated constraints. With crop production data generated at 5-minute spatial resolution, the paper applies the spatial tobit regression model to estimate the possible impacts of improvements in transport accessibility in East Africa. It is found that rural accessibility and access to markets are important to increase agricultural production. In particular for export crops, such as coffee, tea, tobacco, and cotton, access to ports is crucial. The elasticities are estimated at 0.3–4.6. In addition, the estimation results show that spatial autocorrelation matters to the estimation results. While a random shock in a particular locality would likely affect its neighboring places, the spatial autoregressive term can be positive or negative, depending on how fragmented the current production areas are.

Crop production, transport infrastructure, and agrobusiness nexus : evidence from Madagascar

The literature suggests a wide range of impacts of improved transport connectivity on agricultural growth. Still, the infrastructure-growth nexus remains somewhat mysterious, particularly in the African context, because many rural farmers do not have their own transport means. Using data from Madagascar, the paper reexamines the important roles of agrobusinesses. By applying the spatial autoregressive model, it is shown that proximity to input-oriented agrobusinesses, such as input dealers and equipment suppliers, is particularly important to increase rice production. Fertilizer and irrigation use is also found important, indicating the needs for intensification in rice production. Market accessibility is always found as a significant determinant: transport infrastructure connecting farmers and markets, especially the capital city, Antananarivo, is therefore important to develop and maintain.

Spatial autocorrelation panel regression : agricultural production and transport connectivity

Spatial analysis in economics is becoming increasingly important as more spatial data and innovative data mining technologies are developed. Even in Africa, where data often crucially lack quality analysis, a variety of spatial data have recently been developed, such as highly disaggregated crop production maps. Taking advantage of the historical event that rail operations were ceased in Ethiopia, this paper examines the relationship between agricultural production and transport connectivity, especially port accessibility, which is mainly characterized by rail transport. To deal with endogeneity of infrastructure placement and autocorrelation in spatial data, the spatial autocorrelation panel regression model is applied. It is found that agricultural production decreases with transport costs to the port: the elasticity is estimated at -0.094 to -0.143, depending on model specification. The estimated autocorrelation parameters also support the finding that although farmers in close locations share a certain common production pattern, external shocks, such as drought and flood, have spillover effects over neighboring areas.

CELL5M: A geospatial database of agricultural indicators for Africa South of the Sahara.

Recent progress in large-scale georeferenced data collection is widening opportunities for combining multi-disciplinary datasets from biophysical to socioeconomic domains, advancing our analytical and modeling capacity. Granular spatial datasets provide critical information necessary for decision makers to identify target areas, assess baseline conditions, prioritize investment options, set goals and targets and monitor impacts. However, key challenges in reconciling data across themes, scales and borders restrict our capacity to produce global and regional maps and time series. This paper provides overview, structure and coverage of CELL5M—an open-access database of geospatial indicators at 5 arc-minute grid resolution—and introduces a range of analytical applications and case-uses. CELL5M covers a wide set of agriculture-relevant domains for all countries in Africa South of the Sahara and supports our understanding of multi-dimensional spatial variability inherent in farming landscapes throughout the region.