Wheat production area in Mexico occupies about 700,000 hectares, a figure that has been stable since mid-1990s, where 3.8 million t of grains are produced annually. In the post-green revolution period, wheat yields in Mexico fluctuated around 4 t/ha for much of the 1980s before rising and stabilizing around 5.2 t/ha by mid-2000s. Even with this yield gain, net imports of wheat more than tripled from about 0.8 Mt in 1980 to 3.5 Mt in recent years (FAOSTATS, 2019).

About 88% of total wheat area in Mexico is under irrigation, where average yield is 5.6 t/ha at national level ( The most important area for irrigated wheat is located in the Northwest region (Baja California, Sonora, and Sinaloa states), while other relevant areas for this crop can be found in the Northeast (Nuevo Leon state) and in El Bajio region in the central states of Mexico (Guanajuato, Queretaro, Michoacán, and Jalisco). Wheat crop systems in Mexico include one crop cycle per year, in the northwestern part of the country, which usually goes from November-December to March-April, but also as a rotation with sorghum or maize in El Bajio region. In Baja California and Sonora states, durum wheat is the most typical type, whereas the rest of the country mostly cultivate soft wheat varieties.


Data sources and assumptions (following GYGA protocols)

Weather data and reference weather stations

CLICOM meteorological stations network (Climate Computing project; was used to retrieve daily weather data for 15+ years. Based on crop harvested area distribution (MAPSPAM, 2005) and the agro-climatic zones defined for Mexico (Van Wart et al., 2013), a total of 11 reference weather stations (RWS) were selected. RWS buffer zones accounted for 70% of total harvested area of irrigated wheat, while the agro-climatic zones where these locations were located accounted for 81% of national area for this crop. NASA-POWER ( was used as source of incident solar radiation data at all RWS because daily measured solar radiation was lacking. Quality control and filling/correction of the weather data was performed based on correlations between the target RWS and two adjacent weather stations following van Wart et al. (2013). Number of corrections/filled data was always lower than 3%. No rainfall data was used for simulations, as we assumed no water limitations for crop growth. Hence, complete weather records (daily solar radiation, Tmax, Tmin,) were available for the 1998-2012 interval for which yield potential (Yp) was simulated. The first simulated crop season was 1998-1999 (harvested year 1999) and the last one 2011-2012 (harvested year 2012), hence, a total of 14 cropping seasons were simulated.

Harvest area and actual yields.

District-level data on crop harvested area and average yields for each crop were retrieved from official national statistics (available at Weighted average actual yields were calculated for each buffer-year based on their harvested area during the period 2010-2015. Year 2013 was discarded from the analysis due to extremely low yields in extended areas.

Crop system and management information for crop simulations

Management practices for each RWS buffer zone were retrieved from local agronomists. Requested information included average and optimal planting dates, dominant cultivar name and crop cycle length, and actual and optimal plant population density. The provided data were subsequently corroborated by other local and national experts. Simulations of widespread varieties for each region were performed using CERES-wheat model embedded in DSSAT v (Jones et al., 2003). Genetic coefficients were derived and validated for this analysis based on data from 32 well-managed irrigated experiments located in Ciudad Obregon (Sonora), Mexicali (Baja California), General Teran (Nueva Leon), La Barca (Jalisco) and Los Mochis (Sinaloa). Crop phenology was calibrated for three different cultivars types: CIRNO C 2008 (durum wheat), an intermediate cycle soft-wheat, and a short cycle soft-wheat (RMSE emergence-anthesis and emergence-maturity= 4-7 days). Many of these experiments may have experienced nutrient and water deficiencies, incidence of biotic adversities, and other yield-reducing factors. Therefore, genetic coefficients were adjusted to address highest yields obtained at each experimental site. A durum wheat variety (CIRNO C 2008) was simulated for buffers located in Sonora and Baja California states. An intermediate soft wheat variety was simulated in Sinaloa and Nuevo Leon states. Finally, the average of short and intermediate wheat varieties was used in the remaining central states of Mexico.



Hoogenboom, G., J.W. Jones, P.W. Wilkens, C.H. Porter, K.J. Boote, L.A. Hunt, U. Singh, J.I. Lizaso, J.W. White, O. Uryasev, R. Ogoshi, J. Koo, V. Shelia, and G.Y. Tsuji. 2015. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 ( DSSAT Foundation, Prosser, Washington.

Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. DSSAT Cropping System Model. European Journal of Agronomy 18:235‐265.

Van Wart, J., Van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Mueller, N.D., Claessens, L., Cassman, K.G., Van Ittersum, M.K., 2013a. Reviewing the use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143, 44-55.


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Ivan Ortiz-Monasterio


Eduardo Villasenor