Study zone

The experimental site represents the rainfed corn zone in Nayarit, Mexico. It was located near the town of San Jose de Mojarras, municipality of Santa Maria del Oro, at an altitude of 940 meters above sea level, with a strict temporary humidity regime with precipitation between June and October.

Phenological database

A database with 15 years of records was used, from the 1998-1999 to 2012-2013 cycle. This consisted of 344 records of commercial and regional varieties of corn, from which the sowing date (FS), flowering date (FF) and harvest date (FC) were obtained. To categorize the varieties according to their growth habit, the following criteria were used: early ≤ 57 days to flowering, intermediate > 57 and < 62 days to flowering and late ≥ 62 days to flowering.

Oceanic index of ‘El Niño’

ONI monthly data was obtained from the NOAA website (^{NOAA, 2017}). The values were classified into three phases: warm or ‘El Niño’ (anomaly ≥ 0.5 ºC), cold or ‘La Niña’ (anomaly ≤ -0.5 ºC) and ‘Neutral’ with anomalies between -0.5 ºC and 0.5 ºC (^{Latif and Keenlyside, 2009}).

Meteorological data

The National Meteorological Service database was available, Cerro Blanco station with code 18005 which is located at the coordinates of north latitude 21° 22’ 36” and west longitude 104° 37’ 06” at an altitude of 965 meters above sea level, the which was made up of daily records of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmed= (Tmax+Tmin)/2) and precipitation. The daily solar radiation data was obtained from the project database ‘surface meteorology and solar energy’ (^{NASA, 2017}).

Study variables

For the meteorological quantification, the period from FS to FC was considered. The variables were calculated as follows: precipitation (P)= amount of accumulated rainfall in the period. Humid days (DH)= represents the number of days with precipitation greater than 1 mm. Potential evapotranspiration (ET0 in mm day^-1)= 0.0023 x Ra x (Tmax - Tmin)^0.5 x (Tmed + 17.78), where: Ra= solar radiation. Degrees day of development (GDD)= Tmed-Tb; where: Tb= 10 °C. Yield (Ren)= Pc x Fs x Fh, where: Pc= field weight, as a function of direct grain weight (without ear weight or shelling factor) per plot as a function of the number of plants harvested with full competition. Fs (area factor)= kg plot^-1 in kg ha^-1: 10 000 m²/area occupied by harvested plants. Fh= 100-Hg (14%)/86; where: Hg= percentage of moisture from grain to harvest.

Statistical analysis

An analysis of variance was used to identify possible differences by ENSO phase. When significant differences were present, a means comparison test (Tukey with an alpha at 0.05% confidence level) was performed using the Minitab statistical program (^{Minitab, 2010}).

Influence of ENSO on the meteorological variables evaluated

The effect of the phenomenon of ‘El Niño Southern Oscillation’ on the meteorological variables evaluated in the rainfed corn-producing region of Nayarit in the 15 cycles analyzed, showed relevant differences in the different phases. ‘La Niña’ was characterized by wetter conditions (P and DH) and a lower accumulation of heat (GDD), contrary to what happens in the ‘El Niño’ phase characterized by drier conditions (less rainfall) and greater accumulation of degree days of development.

The above coincides with what was found by ^{Ruiz-Cabarcas and Pabon-Caicedo (2013}), ^{Pavia et al. (2006}) and ^{Moeletsi et al. (2011}), where the ENSO phases contrast mainly in rainfall anomalies being positive for ‘La Niña’ and negative for ‘El Niño’. In the analysis of the meteorological variables from FS to FC, distinguishing the ENSO phase, it was possible to identify how the P showed differences between phases, the ‘La Niña’ episodes being the ones with the highest accumulated quantity in the period (997 mm), which is equivalent 28.6% more than the ‘El Niño’ phase (712 mm).

Regarding DH, the years with the ‘El Niño’ phase presented the lowest accumulation of DH (50), while the years ‘Neutral’ and ‘La Niña’ accumulated 60 and 62 respectively (there being no statistical differences between these last two). The potential evapotranspiration did not show statistical differences (p> 0.05) between the three phases, with a fluctuation of only 21 mm between the extreme means. Finally, differences were identified in the cumulative GDD in ‘El Niño’ years of 6.3% greater than the ‘Neutral’ and ‘La Niña’ years, with the values fluctuating between 1 887 °C and 2015 °C. ^{Ruiz-Corral et al. (1999}), citing various authors, points out that corn thrives in multiple environmental conditions but said phenotypic plasticity is influenced by the growth habits of the crop, which is verified in the present work.

These results coincide with races of the early tropical group such as Conejo, Zapalote Chico, Raton and Nal-Tel, described by ^{Ruiz-Corral et al. (2013}). The foregoing allows us to glimpse the presence of said phenotypic plasticity in widely adapted maize, such as that existing in creole breeds, in contrast to the vulnerability of commercial maize to the specificity of its habit (improved genotypes) and the presence of an adaptation narrow both in environments where they are formed, developed and cultivated. ^{Mercer and Perales (2010}) conclude that landraces are better adapted to changing environmental conditions thanks to their greater genetic variation, which gives them the phenotypic plasticity of adapting to unfavorable environments (Table 1).

ENSO phases and yield

The corn crop established under storm conditions is exposed to meteorological variations, so the yield is highly influenced. The results of this work show that the yield was sensitive to the environmental conditions established by the three phases of ENSO, which reinforces the importance and need to know the effect of ENSO on yields, as pointed out by ^{Iizumi et al. (2014}) and ^{Zebiak et al. (2014}). The grain yields above 8 t ha^-1 were obtained by the early varieties within the ‘El Niño’ and ‘La Niña’ phases and in the intermediate varieties in the ‘El Niño’ phase. The lowest yield was obtained by the early and intermediate-cycle varieties in the ‘Neutral’ phase, while the late varieties registered the lowest yield in the ‘La Niña’ phase. Without considering the growth habit, the best yields were obtained in the ‘El Niño’ phase (< 8 t ha^-1), while the lowest yields (6.5 t ha^-1) were obtained in the ‘Neutral’ phase (Table 2).

These results coincide with other studies around the world such as in Argentina (^{De La Casa and Ovando, 2006}), in Venezuela (^{Monasterio et al., 2011}) or in China (^{Shuai et al., 2016}) where the conditions of the phase ‘El Niño’ were conducive to achieving high yields. Although ^{Mendez-Gonzalez et al. (2007}); ^{Pavia et al. (2006}); ^{Llano and Vargas (2011}) consider that the ‘El Niño’ phase is related to a deficit storm in terms of rainfall, it has been shown that for the study area the P during this phase is not a limiting factor above all for early varieties that reach their water requirements with greater efficiency in this phase (^{Álvarez-Bravo et al., 2014}).

On average the early varieties reached 78.8% of the potential yield (9.5 t ha^-1) being the highest percentage. On the contrary, the late varieties expressed 73% of the potential yield. However, the late varieties are those that present the yields with the least variation according to the ENSO phase (70.4 to 75.8%). The greatest difference in yield was presented in early habit varieties during the ‘Neutral’ phase (37.9%).

The “El Niño” phase was the one that allowed the best expression of the potential yield between vegetative cycles (84% on average). In the ‘Neutral’ phase, the greatest difference was obtained between the observed yield and the potential (lower yield) with 69% on average (Figure 1). Due to the importance that corn production implies for Mexico, results such as those of this research contribute to the construction of a system that provides food security in the face of the fluctuating influence of the environment.

Figure 1 Yield distribution by vegetative cycle and ENSO phase. 

The above reaffirms the conclusions of ^{Bojorquez-Serrano et al. (2016}) in the sense of improving the understanding of the dynamics of corn production to reduce the vulnerability of the productive sector. By classifying the yield of the different varieties according to their vegetative cycle and the effect that the different ENSO phases have on the meteorological variables, it will reduce the risk of low yields associated with climatic variability in the same way as ^{Vallejo-Nieto et al. (2011}); ^{Ponvert-Delisles et al. (2007}).