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Nitrogen (N) is an essential nutrient for plant growth and development and is especially important in the production of high quality leafy green vegetables. In this experiment, leaf N concentration, chlorophyll concentration (Chl) and weight above fresh matter (AFM) of romaine lettuce (Lactuca sativa L. var. longifolia) were estimated by correlations between in situ SPAD and atLEAF readings. Lettuce was grown in high tunnels during 42 days and was irrigated at five nitrogen levels: 0, 4, 8, 12 and 16 mEq·L-1 of NO3-, based on the Steiner nutrient solution. The N concentration, Chl concentration and AFM were determined in the laboratory, while SPAD and atLEAF readings were measured in situ weekly. SPAD readings had high, positive and significant linear correlations with N (R2 = 0.90), Chl (R2 = 0.97) and AFM (R2 = 0.98); atLEAF readings had a similar linear correlation with N (R2 = 0.91), Chl (R2 = 0.92) and AFM (R2 = 0.97). Besides, SPAD and atLEAF readings had high, positive, and significant linear correlation (R2 = 0.96). Thus, SPAD and atLEAF meters can be used to non-destructively and accurately estimate the N status of lettuce, in a reliable and quick manner during the crop production cycle. In addition, atLEAF is currently more affordable than SPAD.   ********* In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 3, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue. *********

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Precipitation and its distribution greatly influence the evolution of ecosystems and the development of society. The objective was to analyse trends in extreme precipitation indices at 25 weather stations of Aguascalientes State. Eleven extreme precipitation indices were obtained. The time series of these indices were analysed with the non‐parametric Mann–Kendall test. The number of days above 50 mm, consecutive wet days, and extremely wet days did not have significant trends at any of the weather stations. Each of the indices maximum 1 day precipitation amount, maximum 5 day precipitation amount and number of very heavy precipitation days showed a significant positive trend at 12% of the weather stations; both the number of heavy precipitation days and the number of very wet days had significant positive trends at 8% of the weather stations; the simple daily intensity index, consecutive dry days and annual total wet‐day precipitation showed significant positive trends at 20%, 36% and 4% of weather stations, respectively. The intensity index was the only one that showed a significant negative trend, which happened at 4% of the weather stations. In a small part of Aguascalientes, the precipitation intensity, the number of rainy days and the accumulated total increased in short periods. Also, in a reduced region, daily precipitation intensity decreased; and in another very small area the number of dry days increased. For mitigating the effects of these phenomena, it is suggested that water be used more efficiently for sustained agricultural production systems and ecosystem management. The results of the present study will be of great importance in future economic planning in the Aguascalientes State.

<strong>Introduction:</strong> Evapotranspiration is key in the management of arid agricultural areas. In Chihuahua, the volume of irrigation water is based on reference evapotranspiration (ET_o) calculated with empirical methods and extrapolated to the cropped area, which is inaccurate. The alternative is to calculate ET_o variation by spatial interpolation.</br> <strong>Objective:</strong> To analyze the spatio-temporal variation of ET_o using empirical methods and spatial interpolation in Chihuahua, Mexico.</br> <strong>Methodology:</strong> Records from 33 meteorological stations from 1960-2013 and seven ET_o estimation methods were used. The results were compared with the Penman-Monteith method, modified by FAO (PMMF), ANOVA analysis (P ≤ 0.05), and homogeneous ET_o surfaces built from the point values by spatial interpolation.</br> <strong>Results:</strong> The Hargreaves method (R² = 0.91, RMSE = 1.16 and ME = -0.69 mm-day^-1) had a smaller bias with respect to PMMF. ET_o values ranged from 2.5 to 7.1 mm-day^-1 in a west-east direction, with maximum values at low elevations and minimum values at high elevations, which showed the influence of the Sierra Madre Occidental on ET_o. This characteristic was most noticeable in the warm months (June to September).</br> <strong>Limitations of the study:</strong> The use of estimated data needs field validation.</br> <strong>Originality:</strong> The ET_o estimation with seven empirical methods and one spatial interpolation method to extrapolate values to areas with scarce meteorological data.</br> <strong>Conclusions:</strong> The Hargreaves method allows estimating the spatio-temporal variation of ET_o in large extensions and areas with limited meteorological information.</br>

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (fv). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and fv variables. The spectral Kcfv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-cGDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kcfv:VI model showed a strong linear correlation with Kc-cGDD (R2 > 0.80). The model precision increases with plant densities, and the Kcfv:NDVI with 80,000 plants/ha had the best fitting performance (R2 = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and fv crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas.