The goal of this study was to generate a method to examine seasonal variability by climatic classification and Pacific seasonal factors to identify extreme wet and dry events in northern Mexico for the period 1952–2013. Using the standardized precipitation and evapotranspiration index (SPEI) on scales of three months (SPEI-3) and 24 months (SPEI-24), the variability of extreme wet and dry events were measured. The SPEI-3 and SPEI-24 anomalies were divided by the standard deviation (standardized Z anomalies). A Pearson correlation for SPEI-3, SPEI-24, Pacific decadal oscillation (PDO) and the oceanic El Niño index (ONI) was applied. Wet extreme events were recorded in 1954, 1968, 1976–1977, 1981, 1984, 1986 and 2003, of which the greatest magnitude was recorded in 1984 for the Sinaloa-very dry region. Extreme dry events were recorded in 1952–1953, 1990, 1997, 2003 and 2011–2013. The Z anomalies of the wet extreme events observed coincide with +PDO phase anomalies. In this study, the El Niño southern oscillation (ENSO) has little influence on wet and dry extreme events in northern Mexico. The negative phase anomalies of sea surface temperature (−SST) in the equatorial and eastern Pacific are indicators of extreme wet events. This study shows for the first time the influence of the PDO and the ONI on seasonal variability of extreme wet and dry events by climatic classification through the SPEI index in northern Mexico.

In the state of Sinaloa, rainfall presents considerable irregularities, and the climate is mainly semiarid, which highlights the importance of studying the sensitivity of various indices of meteorological drought. The goal is to evaluate the sensitivity of four indices of meteorological drought from five weather stations in Sinaloa for the prediction of rainfed maize yield. Using DrinC software and data from the period 1982–2013, the following were calculated: the standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI). The observed rainfed maize yield (RMY_ob) was obtained online, through free access from the database of the Agrifood and Fisheries Information Service of the government of Mexico. Sensitivities between the drought indices and RMY_ob were estimated using Pearson and Spearman correlations. Predictive models of rainfed maize yield (RMY_pr) were calculated using multiple linear and nonlinear regressions. In the models, aSPI and eRDI with reference periods and time steps of one month (January), two months (December–January) and three months (November–January), were the most sensitive. The correlation coefficients between RMY_ob and RMY_pr ranged from 0.423 to 0.706, all being significantly different from zero. This study provides new models for the early calculation of RMY_pr. Through appropriate planning of the planting–harvesting cycle of dryland maize, substantial socioeconomic damage can be avoided in one of the most important agricultural regions of Mexico.

ObjectiveTo evaluate five indigenous Mexican plants [Hippocratea excelsa, Hippocratea celastroides, Argemone mexicana (A. mexicana), Tagetes lucida, and Pseudosmodingium perniciosum (P. perniciosum)] toxicity against the fourth instar larvae of the dengue primary vector, Aedes aegypti (A. aegypti).MethodsEach plant part was treated successively with hexane, ethyl acetate, acetone, and methanol to extract potential active components of the plants against the dengue vector.ResultsThere was a range of toxicity at 24 or 48 h post-exposure for the different plant parts and organic solvent used (LC50 values ranged between 20 and 890 μg/mL). Extracts from seeds of A. mexicana (hexane washing with methanol and acetone) and stem-bark of P. perniciosum (hexane) showed highest toxicity to Ae. aegypti larvae at 48 h post-exposure (LC50 values were 80, 50, and 20 μg/mL, respectively), thus making them potential candidates as biolarvicides. Efforts are on-going to characterize the bioactive components of the extracts, through chromatography, for their use as biological tools for the control of the primary dengue vector.ConclusionsA. mexicana and P. perniciosum are good candidates to combat the dengue vector, Ae. aegypti, as they were highly toxic to the larvae

This study is motivated by the problem of erosivity (R), exacervated in semiarid zones by intense seasonal storms. The purpose was to estimate the spatial variation of R in a coastal area covering 37500 km2 which is one of the most important agricultural areas in northwestern Mexico. Four methods were used. Rainfall data from 11 SMNCONAGUA weather stations (from 1966 to 2013) were used to calculate R. The annual average R1 was 1181.08, and R2 was 1084.51 MJ mm ha–1 h–1 with ranges of 2.35–5220.55 and 2.93–4711.38 MJ mm ha–1 h–1. Statistical tests showed that a transformation of the data of the form y = log (x), was appropriate for an ANOVA analysis of the data. The value of the test statistic was F = 1.77 with p = 0.149, showing interdependence between the indicators P (α = 0.05). The values of the correlation coefficients for the data were P vs. R1 = 0.96, P vs. R2 = 0.99, P vs. AIm = 0.98, P vs. MFI = 0.99. The classification of risk in this region showed that 2017.5 km2 of the study area was at a very high risk of rain erosion, 2407.5 km2 under high risk, 5662.5 km2 under medium to high risk, and 14250 km2 under low risk. The results are shown on 1:10,000 maps. Results are a set of useful information for soil management programs and for cultivation planning that takes the seasonal variation of R into account in this region where large volumes of extensive crops are grown.