Measuring Agricultural Land–Use Intensity — A Global Analysis Using a Model–Assisted Approach

As future demand for agricultural produce rises, we must develop methods to increase production and satisfy our food needs.  The conversion of land into farms is a solution, but comes with the price of habitat destruction, altered CO2 output and input, and land erosion.  A more sensible solution would be to improve production on farmlands we currently have.  Dietrich et al. (2012) investigates and compares the agricultural land–use intensities for 10 world regions and 12 crops.  The authors use the Lund–Potsdam–Jena dynamic global vegetation model with managed Land (LPJmL) to project reference yields that are compared to current observed yields.  The results show parts of Russia, Asia, and Africa having low agricultural land–use intensities, whereas Eastern U.S., Western Europe, and parts of China have high agricultural land–use intensities.  Measuring for agricultural land–use intensity differs from the more commonly used method of calculating gap yields.  In light of the author’s results and analysis, this paper shows the value in using the t-factor for measuring land-use intensity which can be more accurate than other measuring methods.—Anthony Li
Dietrich, J. P., Schmitz, C., Mueller, C., Fader, M., Lotze-Campen, H., Popp, A., Measuring agricultural land-use intensity — A global analysis using a model-assisted approach.  Ecological Modeling May 10th, 2012

In calculating land–use intensities, the authors introduce a new measure called the t–factor.  The t–factor is the ratio between actual, observable yield to a reference, calculated yield under well–defined management and technology conditions.  The t–factor is independent of the physical environment and is proportional to the agricultural land–use intensity.  The reference yield used to calculate the t–factor can be either deduced from models or statistical analysis.  In this study, the authors use the LPJmL model to project the reference yield.  The LPJmL model simulates the growth, production, and phenology of plant and functional crop types, and reports it as maximum leaf area index, scaling factor from simulated leaf-level photosynthesis to field scale, and harvesting index.  Actual yields were compiled from the Food and Agricultural Organization of the United Nations Statistics (FAOSTAT), but were first applied to the same LPJmL model in order to account for discrepancies due to bias or systematic errors.  The authors calculated t–factors for 10 global regions and 12 different crop types for 2000, and omitted any data for crops that produced less than 0.1% of the global crop production.
The authors compared variations in t–factor, homogeneity in t–factor and the t–factor itself between the regions of the world.  Europe had the highest total t–factor, as well as the highest crop–specific t–factors for wheat, millet, field peas, and rapeseed.  The Middle East and North Africa ranked lowest in total t–factors, but was closely followed by Africa despite the fact that Africa had more crops with the lowest t–factor.  Africa and Europe had the least variation in their t–factors, which contributed to their lowest and highest t–factors, respectively.  In contrast, the Pacific Organization for Economic Cooperation and Development and the Middle East and North Africa showed the strongest variations in t–factor between crops.  In terms of homogeneity in t–factors across each country, Ireland, the United Kingdom, France, Germany, and Sweden show homogeneous values at a high t–factor while Madagascar and Mozambique had homogeneous values at a low t–factor. 
The t–factor measure used in this study differs from other measures of land–use intensity by eliminating the environmental component from observed actual yields via a reference yield.  Other typical, input–oriented measures determine land–use intensity by measuring individual drivers of land–use intensity such as fertilizer use, labor use, and machinery.  Since the t–factor is independent of natural conditions, it can be used as a measure of yield differences due to human activity.  If sample region A has twice the t–factor than sample region B, then it will have twice the yield due to human activities.  The same could be said for the reverse; assuming they have the same t–factors, if the physical conditions in region A are half as good as in region B, then the yields in both regions will be equal.  While the t–factor estimates total land–use intensity which cannot be attributed to individual factors, input–oriented measures record the relevance of certain individual inputs to the attributing factors of overall land–use intensity.  The authors make the point that low agricultural land–use intensities do not necessarily mean higher yield increases in the future.  Factors such as political instability or weak governing bodies may inhibit a nation’s opportunity to improve yields.  While these results may not accurately represent yield increases in specific countries, it is still representative of global agricultural conditions as the results are similar with FAO/OECD yield growth projections.  Without taking into account political stability or effectiveness of governing body, regions such as Africa, South and Eastern Europe, Russia, South Asia, and Latin America show long-term chances for yield increases.  This paper introduces a new method, the t–factor, for measuring agricultural land–use intensity that does not take into account environmental conditions.  Because of this, the t–factor is proportional to agriculture land–use intensity and is a good measurement if we want to calculate land–use intensity solely as a result of human activity.

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