To better understand the range of adaptation of maize (Zea mays L.) landraces, climatic adaptation intervals of 42 Mexican maize races were determined. A database of 4161 maize accessions was used to characterize altitudinal and climatic conditions where the 42 maize races grow, yielding ecological descriptors for each race. Using the geographical coordinates of the collection sites of each accession, their climatic conditions were characterized using the geographic information system IDRISI and a national environmental information system. Analyses of variance and cluster analyses of the racial ecological descriptors were performed to determine possible environmental groupings of the races. We found a very high level of variation among and within Mexican maize races for climate adaptation and ecological descriptors. The general overall climatic ranges for maize were 0 to 2900 m of altitude, 11.3 to 26.6°C annual mean temperature, 12.0 to 29.1°C growing season mean temperature, 426 to 4245 mm annual rainfall, 400 to 3555 mm growing season rainfall, and 12.46 to 12.98 h mean growing season daylength. These climatic ranges of maize surpass those from its closest relative, teosinte (Z. mays ssp. parviglumis Iltis and Doebley), indicating that maize has evolved adaptability beyond the environmental range in which ancestral maize was first domesticated.

Adaptation of crops to climate change has motivated an increasing interest in the potential value of novel traits from wild species; maize wild relatives, the teosintes, harbor traits that may be useful to maize breeding. To study the ecogeographic distribution of teosinte we constructed a robust database of 2363 teosinte occurrences from published sources for the period 1842–2016. A geographical information system integrating 216 environmental variables was created for Mexico and Central America and was used to characterize the environment of each teosinte occurrence site. The natural geographic distribution of teosinte extends from the Western Sierra Madre of the State of Chihuahua, Mexico to the Pacific coast of Nicaragua and Costa Rica, including practically the entire western part of Mesoamerica. The Mexican annuals Zea mays ssp. parviglumis and Zea mays ssp. mexicana show a wide distribution in Mexico, while Zea diploperennis, Zea luxurians, Zea perennis, Zea mays ssp. huehuetenangensis, Zea vespertilio and Zea nicaraguensis had more restricted and distinct ranges, representing less than 20% of the total occurrences. Only 11.2% of teosinte populations are found in Protected Natural Areas in Mexico and Central America. Ecogeographical analysis showed that teosinte can cope with extreme levels of precipitation and temperatures during growing season. Modelling teosinte geographic distribution demonstrated congruence between actual and potential distributions; however, some areas with no occurrences appear to be within the range of adaptation of teosintes. Field surveys should be prioritized to such regions to accelerate the discovery of unknown populations. Potential areas for teosintes Zea mays ssp. mexicana races Chalco, Nobogame, and Durango, Zea mays ssp. huehuetenangensis, Zea luxurians, Zea diploperennis and Zea nicaraguensis are geographically separated; however, partial overlapping occurs between Zea mays ssp. parviglumis and Zea perennis, between Zea mays ssp. parviglumis and Zea diploperennis, and between Zea mays ssp. mexicana race Chalco and Zea mays ssp. mexicana race Central Plateau. Assessing priority of collecting for conservation showed that permanent monitoring programs and in-situ conservation projects with participation of local farmer communities are critically needed; Zea mays ssp. mexicana (races Durango and Nobogame), Zea luxurians, Zea diploperennis, Zea perennis and Zea vespertilio should be considered as the highest priority taxa.

The structural pattern of rainfall data exhibits random fluctuations over time and space. Utilizing concepts of fractal theory, it has been possible to identify characteristics of rainfall data beyond simple statistical indicators of their randomness. The objective of this research was to identify the spatial variation of the Hurst exponent, extracted through standard wavelet techniques from time series of daily rainfall data in the state of Zacatecas, Mexico. The Hurst exponent was extracted for 26 locations using the reference techniques for auto-affine traces-in particular, the wavelets method. Results have shown that the Hurst exponents of rainfall time series are negatively influenced by altitude; thus, stations located at higher altitudes were characterized by Hurst exponents indicating more nonpersistent behavior. The trends among geographical variables (west longitude and latitude) and climatic parameters (annual rainfall and number of rainy days) and their relationship with the Hurst exponent were also analyzed.

La degradación continua de los pastizales debido a una deficiente planeación en la producción ganadera de tipo extensivo, hace necesario el monitoreo rutinario del estado de salud de los agostaderos. Por ello, se requiere establecer un protocolo de seguimiento por medio de metodologías confiables, rápidas y económicas. Una metodología que se ha utilizado es por medio del registro de 17 indicadores del suelo, agua y vegetación. La metodología se aplicó en 34 sitios del pastizal mediano abierto del estado de Zacatecas con cuatro repeticiones por sitio. Con los valores estimados por componentes principales se calculó un semivariograma y por interpolación con Kriging, se generaron tres mapas y se compararon con mapas de referencia generados por estudios de tipo cuantitativo para valorar su coincidencia. El primer mapa se asoció con estadios de salud del pastizal utilizando un mapa de referencia con valores del “índice de vegetación de diferencia normalizada” (NDVI); el segundo mapa identifica las zonas más afectadas por el escurrimiento, y el tercero identifica las zonas afectadas por la erosión hídrica. Los mapas se generaron con modelos matemáticos. La coincidencia entre mapas generados por componentes principales y mapas de referencia fue de 59.2, 31.2 y 17.2 %, los cuales fueron cercanos a los valores de varianza estimados por los componentes principales, así como la igualdad (P>0.05) entre los valores categóricos, permitieron validar la correspondencia entre ambos métodos.