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Aim of study: Mexico's large irrigation areas demand non-structural actions to improve the irrigation service, such as monitoring crop phenology; however, its application has been limited by the large volumes of field information generated, diversity of crop management and climatic variability. The objective of this study was to generate and validate a methodology to monitor corn (Zea mays L.) phenology from the historical relationship of the vegetation indexes (VIs), EVI and NDVI, with the phenological development (PD) of corn grown in large irrigation zones.Area of study: Irrigation District (ID) 075 “Valle del Fuerte”, northern Sinaloa, Mexico.Material and methods: We used a database of 20 years of climate, field crop growth and crop phenology data, and Landsat satellite images. A methodology was proposed on a large scale supported with GIS and remote sensing data series.Main results: The methodology was validated in 19 plots with an acceptable correlation between observed PD and estimated PD for the two VIs, with slightly better values for EVI than for NDVI. NDVI and EVI models agreed with experimental PD observations in 92.1% of the farms used to validate the methodology, in 2.5% only the NDVI model coincided with the real, in 3.1% only the EVI model coincided, and in 2.3% both models disagreed with observation, generated a stage out of phase with respect to the real phenological stage.Research highlights: is possible to generalize the methodology applied to large irrigation zones with remote sensing data and GIS.

In arid and semi-arid zones, evapotranspiration represents a great loss of water into the atmosphere, which is why it is necessary to understand and quantify this process. The work was carried out in the Valle del Fuerte Experimental Field during the autumn-winter 2012-13 agricultural cycle. A meteorological station was used to measure the climatic variables and the atmometer to measure ETo. The mean square of the error (MSE), mean square root of the error (CTMR) and the linear regression were obtained to statistically evaluate the performance of the atmometer. The estimate of ETo were 1495 and 1455 mm for the atmometer and the weather station. The linear regression coefficient was R2 = 87%, the MSE and CTMR were 0.71 and 0.84 mm. The yield was 2.66 and 2.64 t ha-1 for the atmometer and IrriModel, with water productivity of 0.71 and 0.68 kg m-3. The linear regression analysis of the atmometer presents a high level of confidence to estimate ETo. The atmometer with proper management and maintenance is an alternative to estimate the ETo for the scheduling of irrigation in crops., RESUMEN: En zonas áridas y semiáridas la evapotranspiración representa gran pérdida de agua hacia la atmósfera. Por eso es necesario entender y cuantificar este proceso. El trabajo se realizó en el Campo Experimental Valle del Fuerte durante el ciclo agrícola otoño-invierno 2012-13. Se utilizó una estación meteorológica para medir las variables climáticas y el atmómetro para calcular la ETo. Se obtuvo el cuadrado medio del error (MSE), raíz del cuadrado medio del error (REMC) y la regresión lineal para evaluar estadísticamente el desempeño del atmómetro. La estimación de ETo fue de 1495 y 1455 mm para el atmómetro y la estación meteorológica, res pectivamente. El coeficiente de regresión lineal fue de R2 = 87% y el MSE y REMC fueron de 0,71 y 0,84 mm. El rendimiento fue de 2,66 y 2,64 t ha-1 para el atmómetro e IrriModel, con productividad del agua de 0,71y 0,68 kg m-3. El análisis de regresión lineal del atmómetro presenta alto nivel de confianza para estimar la ETo. Con manejo y mantenimiento adecuado el atmómetro es una alternativa para calcular la ETo con fines de calendarización del riego en cultivos.

Computational fluid dynamics has been successfully used in protected agriculture to simulate greenhouse weather as physical processes. The variables involved are velocity, wind direction related to either absolute or relative humidity, temperature as well as deficit vapor pressure, and carbon dioxide, among others. The research evolution is changing from the traditional validation of new designs and management to testing efficient production with less environmental pollution. This work points out this kind of assessment based on the physical principles of conservation of mass, momentum, and energy. Constitutive relationships like Darcy-Forchheimer porosity model in the momentum equation as well as the geometry and physical properties of the materials involved are needed to fulfill the particular solutions of temperature, wind, and humidity. This chapter is enhanced by the effect of solar radiation in more processes like crop transpiration with dynamical meshes and condensation.

Maize is one of the main crops grown in the State of Sinaloa; however, in this region the application of irrigation is performed without considering the physical characteristics of the soil increasing losses of water and fertilizer. It is important to develop technologies to optimize the use of inputs (water, fertilizers, pesticides) increasing the productive potential of crops and reducing production costs, for this reason a series of experiments were conducted during the 2006-2007 autumn-winter cycles and 2011-2012 in the INIFAP-CEVAF located in the north of Sinaloa, Mexico, in order to know the effect of gravity irrigation by different techniques in use efficiency of nitrogen (N) in the maize crop. These experiments consisted of a randomized complete block to the implementation of two low pressure irrigation systems (multi-gates), one of PVC pipe and the other of Lay flat hose, in the first cycle and three surface irrigation techniques (alternate rows, beds and reducing spending) in the second cycle. It was found that using the pipe multi-gates irrigation system and the implemented irrigation techniques in this study was possible to increase irrigation efficiency on averaged 80% with a gradual increase in the efficiency of nitrogen fertilizer reducing losses and saving water volumes that can be used in scenarios of low water availability and establishment of second crops. Keywords: low pressure irrigation systems, nitrogen use efficiency, surface irrigation techniques, El maíz es uno de los principales cultivos sembrados en el estado de Sinaloa; sin embargo, en esta región la aplicación de riegos se realiza sin considerar las características físicas del suelo incrementando las pérdidas de agua y fertilizantes. Es importante desarrollar tecnologías que permitan optimizar el uso de insumos (agua, fertilizantes, pesticidas) incrementando el potencial productivo de los cultivos y reduciendo los costos de producción, por tal motivo una serie de experimentos se condujeron durante los ciclos otoño-invierno 2006-2007 y 2011-2012 en el INIFAP-CEVAF ubicado en el norte de Sinaloa, México, con el propósito de conocer el efecto del riego por gravedad por diferentes técnicas en la eficiencia de uso del nitrógeno (N) en el cultivo de maíz. Dichos experimentos consistieron en bloques completos al azar con la aplicación de dos sistemas de riego de baja presión (multicompuertas), uno de tubería PVC y el otro de manguera Lay flat en el primer ciclo y tres técnicas de riego por superficie (surcos alternos, camas y reducción de gasto) en el segundo ciclo. Se comprobó que usando el sistema de riego de tuberías multicompuertas y las técnicas de riego implementadas en este estudio fue posible incrementar la eficiencia del riego en promedio 80% con un incremento gradual en la eficiencia del nitrógeno reduciendo las pérdidas de fertilizante y ahorrando volúmenes de agua que puedan utilizarse en escenarios de baja disponibilidad de agua y/o establecimiento de segundos cultivos. Palabras-clave: eficiencia de uso de nitrógeno; sistemas de riego de baja presión; técnicas de riego por superficie

An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML) is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS), root relative square error (RRSE), normalized mean absolute error (MAE), and correlation factor (R). Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5- Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91), the lowest RRSE errors (79.46% and 79.78%), the lowest average MAE errors (18.12% and 19.42%), and the highest average correlation factors (0.41 and 0.42). Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning

In Mexico, the availability of water in arid and semi-arid areas is highly critical. The agricultural sector requires large volumes of water to meet the water requirements of crops. Despite the acquisition of irrigation systems to have an efficient management of water, most of the agricultural areas have inadequate irrigation technology that generates conveyance losses and water distribution. Gravity irrigation at plantation level has a low efficiency in implementation and there is no measurement and irrigation scheduling to meet the demands of the crop in terms of timeliness and efficiency as well. This research was conducted in northern Sinaloa, Mexico during the autumn-winter 2006-2007 season, sowing maize (Zea mays L.) in the Fort Valley Experimental Field (INIFAP) and with a cooperating producer. The water conservation techniques in surface irrigation evaluated were: irrigation in beds, irrigation expenditure reduction, alternate furrow irrigation and conventional irrigation. Alternate furrow technique was the best alternative in terms of irrigation efficiency and performance, getting in the Experimental 13.8 t ha-1 on average, 83.65% efficiency ofapplication and 88.19% uniformity, and producer 11.86 t ha -1 with 79% efficiency of application and 83.42% uniformity., En México, la disponibilidad de agua en zonas áridas y semiáridas es altamente critica. El sector agrícola requiere de grandes volúmenes de agua para satisfacer los requerimientos hídricos de los cultivos. Apesar de la adquisición de sistemas de riego para tener un manejo eficiente del agua, la mayor parte de las zonas agrícolas cuentan con tecnología de riego inadecuada que genera pérdidas en la conducción y distribución del agua. El riego por gravedad a nivel parcelario tiene una baja eficiencia en su aplicación y además, no existe una programación y medición del riego para satisfacer las demandas de los cultivos en términos de oportunidad y eficiencia. La presente investigación se realizó en el norte de Sinaloa, México durante el ciclo agrícola otoño-invierno 2006-2007, se sembró maíz (Zea mays L.) en el Campo Experimental Valle del Fuerte (INIFAP) y con un productor cooperante. Las técnicas de conservación del agua en riego por gravedad evaluadas fueron las siguientes: riego en camas, riego por reducción de gasto, riego por surcos alternos y riego convencional. La técnica de surcos alternos fue la mejor alternativa en términos de eficiencia de riego y rendimiento; obteniendo en el Campo Experimental 13.8 t ha-1 en promedio, 83.65% de eficiencia de aplicación y 88.19% de uniformidad, y con el productor 11.86 t ha-1 con 79% de eficiencia de aplicación y 83.42% de uniformidad.