Agricultural Yields

Agricultural productivity is modeled explicitly in FeliX in order to quantify the likely effect of several important factors on crop yields. These factors are:

Each factor is modeled on a global scale, a level of aggregation which obscures the disparate and often divergent local manifestations of each of these factors. Because it is not possible to derive rigorous analytical solutions with global applicability to these parameters, the model assigns each a net positive or negative effect of conservative magnitude. Follow links in the list above for more details on individual factors.

The table below lists the six independent, dimensionless productivity factors which parametrize agricultural yields in the model. INT, land management, and carbon fertilization are determined to boost productivities, while water availability (or lack thereof), pollution, and climate change effects threaten to reduce yields. The right-most column lists the product of all six factors, by which baseline productivity (calibrated to 1.2E6 kCal/ha/year) is scaled annually.

Agricultural yields are calculated as the product of six independently-derived scaling factors in the FeliX model. These include input-neutral technologies (INT), land management systems, water availability, ozone and black carbon pollution, carbon fertilization effects (C Fert), and climate change. The first seven columns show the temporal evolution of individual factors, while the final column calculates the final product, by which baseline productivity is scaled.

Agricultural yields are calculated as the product of six independently-derived scaling factors in the FeliX model. These include input-neutral technologies (INT), land management systems, water availability, ozone and black carbon pollution, carbon fertilization effects (C Fert), and climate change. The first seven columns show the temporal evolution of individual factors, while the final column calculates the final product, by which baseline productivity is scaled.

The figure below translates productivity factors into cropland yields, which represent an aggregate over all global regions as well as crop types. The columns at bottom depict the baseline population projection, while the grey bars display present and future cropland yield projections. Based on historical data, these projections from an independent analysis parametrize crop yields as a function of GPD [1]. They represent the range of likely productivity levels due to the spread of existing INTs and the development of additional yield-enhancing technologies.

The green curve represents global, aggregate cropland yields through 2100 in the FeliX model. The shaded region propagates the effects of high and low population estimates, which affect yields indirectly through water availability. The gray bars note econometric predictions of yield growth due to INT and land management (fertilizer use). At bottom, median population projections are also shown. 

The green curve represents global, aggregate cropland yields through 2100 in the FeliX model. The shaded region propagates the effects of high and low population estimates, which affect yields indirectly through water availability. The gray bars note econometric predictions of yield growth due to INT and land management (fertilizer use). At bottom, median population projections are also shown. 

1. Herrero, M., Havlik, P., McIntire, J., Palazzo, A. and Valin, H. 2014. African Livestock Futures: Realizing the Potential of Livestock for Food Security, Poverty Reduction and the Environment in Sub-Saharan Africa. Office of the Special Representative of the UN Secretary General for Food Security and Nutrition and the United Nations System Influenza Coordination (UNSIC), Geneva, Switzerland, 118 p.