Major crop yields are currently not increasing fast enough to meet demand on existing farmland. Ensuring food security while protecting rainforests, wetlands, and grasslands depends on achieving the highest possible yields with limited land, if we hope to feed a population of more than 9 billion people by 2050.
Crop productivity varies across the globe, depending on environment, inputs, and practices (Sadras et al., 2015). Calculating an area’s yield gap––the difference between irrigated or rainfed crops and actual yields––will allow us to estimate future yield increase and productivity gaps of crops and cropping systems.
The Global Yield Gap Atlas (GYGA) seeks to provide the best available estimates of yield gaps globally using current average farm yields and yield potential (Yp) for irrigated environments, or water-limited yield potential (Yw) for rainfed environments (Van Ittersum et al., 2013). GYGA has calculated yield gaps for major food crops in participating countries across agroecological zones.
Yield gaps have been calculated with wide variety of crop growth models that are not beyond discussion (Affholder et al., 2012), based on weather data that are incomplete and not always accessible and quality soil data that are still in the process of being developed at scale by, for instance, the African Soil Information System (AFSIS).
According to the Food and Agricultural Organization’s (FAO) recent publication “Yield gap analysis of field crops – Methods and case studies,” realistic solutions are required to close yield gaps in both small- and large-scale cropping systems worldwide (Sadras et al., 2015). One of the solutions that GYGA addresses is creating definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short, long term). But this alone is not enough to provide solutions to close the yield gap.
Identifying the causes of gaps between yield levels, creating management options to reduce the gaps where feasible and implementing policies that favor adoption of gap-closing technologies are also necessary to close the yield gap. Research organizations such as CIMMYT can help work towards this end by specifying socioeconomic contexts and genotype × environment × management (GxExM) components across different environments (e.g., Keil, D’souza, & McDonald, 2015; Pannell, Llewellyn, & Corbeels, 2014). These contexts must be analyzed through close collaboration between socioeconomic and biophysical scientists.
With a more bottom-up approach based on the growing body of knowledge from socioeconomic studies, farming systems analysis and field trials, yield gap analysis can go to the next level, where it can aid in targeted and ex-ante impact assessment of both improved varieties and associated technologies.
Affholder, F., Tittonell, P., Corbeels, M., Roux, S., Motisi, N., Tixier, P., & Wery, J. (2012). Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years? Agronomy Journal, 104(3), 735–748.
Keil, A., D’souza, A., & McDonald, A. (2015). Zero-tillage as a pathway for sustainable wheat intensification in the Eastern Indo-Gangetic Plains: does it work in farmers’ fields? Food Security.
Pannell, D. J., Llewellyn, R. S., & Corbeels, M. (2014). The farm-level economics of conservation agriculture for resource-poor farmers. Agriculture, Ecosystems & Environment, 187, 52–64.
Sadras, V. O., Cassman, K. G. G., Grassini, P., Hall, A. J., Bastiaanssen, W. G. M., Laborte, A. G., … Steduto, P. (2015). Yield gap analysis of field crops – Methods and case studie. Rome: FAO and DWFI.
Van Ittersum, M., Cassman, K., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance – A Review. Field Crops Research, 143, 4–17.