In the process we develop a comprehensive and internationally comparable measure of the agro-ecological potential (AEP) based on the modeling of attainable crop yields across all agricultural areas of the globe estimated by IAASA and FAO (Tóth et al, 2012). As a proxy for urban demand we develop a measure of urban gravity (UG) that a particular location experiences with respect to all urban centers in the country with current population of over 500,000 people.<sup>2</sup> We use an estimate of the light emitted at night of each city that is derived from exiting light intensity measures across all pixels of the city<sup>3</sup> .The light emitted by each city is assumed to be highly correlated with its overall GDP. We adjust the light intensity of each city using the distance from the enumeration area (EA) in which the farmers live.

Once we show that the agro-ecological potential and UG exercise a significant influence on the observed population density, infrastructure and market access, these three variables become endogenous variables in an analysis of their impact on farming systems variables such as the crop intensity or proportion of land irrigated. That means that we cannot directly test the BR framework in a cross section framework, but we can describe how intensification differs between countries and major agro-ecological zones. We can also estimate the influence of AEZ and UG on the intensification variables via all pathways of causality, including via population density and infrastructure. Direct estimation of the impacts of population density, infrastructure and market access will have to wait for panel data to become available. A limitation of the paper is that the data sets contain no data on land investments and mechanization (other than for Nigeria).

More specifically this paper will

1. Develop internationally comparable measures of overall agro-ecological crop potential (AEP) and of Urban Gravity (UG) in the farmers’ location. 2. Describe the degree of agricultural intensification across the countries, and across the agro-ecological zones found in these countries.

3. Estimate the causal impact of agro-ecological potential and UG on population density, infrastructure and market access, and on a range of agricultural intensification variables.

**2. Agricultural intensification: Theory and findings**

The general model of the evolution of farming systems originates in the work of Ester Boserup (1965) and Hans Ruthenberg (1980) - henceforth the BR theory or framework-. In the 1980s these ideas were summarized, partially formalized, and tested for SSA in books by Prabhu Pingali, John McIntire, Yves Bigot, Daniel Bourzat and Hans Binswanger (Pingali et al 1987), Binswanger and McIntire (1987) and McIntire et al (1992). All these authors consider the evolution of farming systems, the methods of maintaining soil fertility, the technology in use and the labor input per hectare as endogenous, being influenced by the both agro-ecological and the socioeconomic characteristics of the environments with which the farmers are confronted. The main driving forces of the evolution of the farming systems towards higher intensification and croplivestock interaction are population pressures (often measured as population density) and market access, both of which define the opportunities of households in the areas. Additional factors

> 2 We leave out the smaller cities, as their income as measured via light emissions could be affected by the agro-ecological potential of the zone in which they sit, making them endogenous to the system analyzed. 3 As a proxy of light intensity we used sum of nighttime lights in 2009.  Input values ranging from 0-63, indicate average intensity of light observations, regardless of frequency of observation. Ephemeral events such as lightning strikes and fires have been discarded.  The satellite source is DMSP F16, inter-calibrated for comparison between years. The range of 0-63 refers to the pixel-level value (the source data are gridded at 30 arc seconds).  The variable we are using is aggregated at the 5 arc minute block level (resolution of SPAM, GAEZ and other harvest choice variables), which would include many pixels from the lights data.

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