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.

Rainfed areas in Mexico accounts for 14 million hectares where around 23 million people live and are located in places where there is a little climatic information. The severe drought that has impacted northern Mexico in the past several years as well as other parts of the country, has forced decision takers to look for improved tools and procedures to prevent and to cope with this natural hazard. For this paper, the methodology of the Food and Agricultural Organization of the United Nations (FAO) for estimating water balance variables was modified to provide crop yield estimations under rainfed agriculture in maize producer states of Mexico. The water balance accounts for the daily variation of soil water content having main input rainfall (Pp) and main output crop evapotranspiration (Eta). The algorithm computes crop yield using two distinctive approaches: 1) one based on surplus/deficit functions for each crop considered and 2) yield estimations based on soil water balance and water function productions of the crop being analyzed. For computing water balance and crop yields, a computer model is built that incorporates the FAO method for water balance (MODEL SICTOD: Computational System for Decision Taking, acronym in Spanish) which stochastically generate precipitation based on wet/dry transition probabilities using a first order Markov chain scheme. Maps of average crop yields were obtained after interpolating model outcomes for the main maize producer states of Mexico: Jalisco, Michoacan, Guerrero, Puebla Oaxaca and Chiapas. Different planting dates were analyzed, early (90 days of length period), intermediate (120 days of length period) and late (150 days of length period). Crop yield variability correlates to the transition probability on having a wet day following a dry day. Results have shown

The uncertainty of water availability is the main problem in planning for water resources in watersheds of agricultural drylands. Water availability for different uses depends on the runoff that is generated in the upper portion of the watersheds, where there are higher elevations and lower temperatures. Proximity to the ocean is a main factor that defines rainfall amounts. In this research we linked the effects of El Niño to a regional Standardized Precipitation Index (SPI) and the subsequent impact on runoff production and irrigation water allocation. Findings indicate the cascading impacts of the El Niño on the SPI, the SPI on the runoff discharge to the irrigation reservoir, and the final impact on the planted area within the irrigation district. An optimization procedure was applied to maximizing net income in agriculture under different water availability scenarios. The restrictions to the optimization model were:total available water, crop water demand, and available land. Local criteria for defining the maximum allowable planted area by crop also were taken into account. The analysis with various water availability scenarios demonstrated that with limited amounts of water for irrigation, forage area would be limited, thereby increasing the area of crops with lower water demands. In both scenarios the area of forage maize was reduced from 11 300 to 1 764 ha.Increasing irrigation water use efficiency may save water for expanding the irrigated area, or for other uses.

This study discusses how soil moisture influences the seed yield of two castor plant varieties in an arid zone in Mexico. An experiment was set up with two factors: soil moisture, with three levels (high = -0.05 MPa, medium = -0.31 MPa and low = -0.91 MPa), and castor variety (Krishna and Rincon). The combination resulted in a factorial 3 × 2 experimental design. The experiment was set up as a randomized block with four replications under a split-plot arrangement, where the whole plots were for soil moisture and the subplots were for the castor variety. The measured variables were plant height (PH), days to flowering of 50% of the plants (DF), leaf area (LA), dry weight (DW), source-sink relationship, harvest index (HI) and seed yield. Data were analyzed by ANOVA, mean tests (HSD at α = 0.05) and regression analysis. There were significant differences in PH, LA, DW, HI and yield among the treatments. The values of PH, LA and DW tended to be higher at higher soil moisture levels than at lower moisture levels. The source strength was generally lower than the sink strength in all the treatments. There were significant differences between the varieties for HI, and the interaction between soil moisture and variety was also significant. Significant differences were found in yield; the Krishna variety had a greater yield than the Rincon variety, but there was only a significant difference between the yield of the Krishna variety cultivated under low soil moisture (5200 kg ha -1 ) and that of the Rincon variety cultivated under high soil moisture (2570 kg ha -1 ). The results of this study indicate that castor plants can be cultivated in arid regions at suboptimal soil moisture levels with supplementary irrigation without compromising plant performance or yield.

La alta variabilidad en espacio y tiempo de los regímenes pluviales, hacen que la agricultura en zonas de temporal esté sujeta al riesgo climático. En esta tesitura, la mejor herramienta para sustentar la toma de decisiones lo constituye la modelación hidro-climática en donde se considera lo estocástico de los procesos hidrológicos. En el presente trabajo se hace uso de una serie de algoritmos anidados (AA) para llegar a estimar el rendimiento del cultivo maíz bajo diferentes escenarios climáticos. El algoritmo es calibrado y aplicado a una región de temporal deficiente en el norte de México (Cuencamé, Durango). Se parte de un generador climático (WXPARM) para obtener los parámetros de clima que definen a la región; posteriormente, para cuantificar el impacto del rendimiento del cultivo bajo condiciones de cambio climático, se hace uso de un modelo de reescalado para aplicar los datos de modelos climáticos globales (modelos de circulación general) a nivel parcelario (SDM) y finalmente las matrices que definen las condiciones climáticas mensuales en la región de estudio son utilizadas en un modelo para evaluar el impacto en rendimiento (EPIC) mediante la modelación del balance de humedad en el suelo. Los resultados indican que bajo escenarios de cambio climático, se esperarían incrementos en rendimiento de hasta 0.3 t ha-1 dado el cambio en los patrones climáticos esperados en los que se vislumbra un comportamiento bimodal de la lluvia. Acorde al comportamiento del clima en el futuro, sería recomendable el ajuste de fechas de siembra para que los máximos requerimientos del cultivo coincidan con la presencia de lluvias.