An accurate irrigation scheduling methodology is necessary in crops with high water stress sensitivity and production cost. This involves the estimation of the scheduling parameters related with crop water requirements during phenological stages under different irrigation application systems. This paper presents several models to represent the parameters of irrigation scheduling based on growing degree days (GDD) such as: management allowed soil water depletion (MAD), rooting depth (R d ) and crop coefficient (K c ). The proposed models were applied accurately to schedule irrigation in two commercial fields of potato under two irrigation methods: surface and sprinkler irrigation. Results show that the model predicted irrigations in a consistent and logical manner. The proposed models are versatile, feasible and easily implemented in irrigation scheduling computer programs.

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Current climate change models predict an increased frequency and intensity of drought for much of the developing world within the next 30 years. These events will negatively affect maize yields, potentially leading to economic and social instability in many smallholder farming communities. Knowledge about the genetic resources available for traits related to drought tolerance has great importance in developing breeding program strategies. The aim of this research was to study a maize landrace introgression panel to identify chromosomal regions associated with a drought tolerance index. For that, we performed Genome-Wide Association Study (GWAS) on 1326 landrace progenies developed by the CIMMYT Genetic Resources Program, originating from 20 landraces populations collected in arid regions. Phenotypic data were obtained from early testcross trials conducted in three sites and two contrasting irrigation environments, full irrigation (well-watered) and reduced irrigation (drought). The populations were genotyped using the DArTSeq® platform, and a final set of 5,695 SNPs markers was used. The genotypic values were estimated using spatial adjustment in a two-stage analysis. First, we performed the individual analysis for each site/irrigation treatment combination. The best linear unbiased estimates (BLUEs) were used to calculate the Harmonic Mean of Relative Performance (HMRP) as a drought tolerance index for each testcross. The second stage was a joint analysis, which was performed using the HMRP to obtain the best linear unbiased predictions (BLUPs) of the index for each genotype. Then, GWAS was performed to determine the marker-index associations and the marker-Grain Yield (GY) associations for the two irrigation treatments. We detected two significant markers associated with the drought-tolerance index, four associated with GY in drought condition, and other four associated with GY in irrigated conditions each. Although each of these markers explained less than 0.1% of the phenotypic variation for the index and GY, we found two genes likely related to the plant response to drought stress. For these markers, alleles from landraces provide a slightly higher yield under drought conditions. Our results indicate that the positive diversity delivered by landraces are still present on the backcrosses and this is a potential breeding strategy for improving maize for drought tolerance and for trait introgression bringing new superior allelic diversity from landraces to breeding populations.

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

T he demand for nixtamalized products has broadened the industrialization of maize. The nejayote is a product of the alkaline cooking of grain, and unfortunately contributes to environmental deterioration after being dumped into the public sewer system. There is evidence that adequate treatment of this byproduct not only reduces pollution, but it is also a source of compounds with high added value with potential for technological applications. The objective of this review was to provide an overview of the main methodologies and technological developments which have been implemented to explore the physicochemical properties of nejayote and to assign a treatment or an application to it. With the work performed it was possible to detect that the recovery of materials with high added value (polyphenols, carbohydrates, sugars, gums and calcium components) can be used in various areas such as the food, pharmaceutical and biotechnological sectors. In addition, it was identified that the obtaining of these components can be carried out through the coupling of various bioprocesses (fermentation, filtration, centrifugation and decantation).Resumen L a demanda de productos nixtamalizados ha incrementado la industrialización del maíz. El nejayote surge de la cocción alcalina del grano, y desafortunadamente contribuye en el deterioro ambiental tras ser vertido al alcantarillado público. Existe evidencia de que el tratamiento adecuado de este subproducto no sólo disminuye la contaminación, también es fuente de compuestos de valor agregado alto con potencial para aplicaciones tecnológicas. El objetivo de esta revisión fue proveer un panorama sobre las principales metodologías y desarrollos tecnológicos que se han implementado para explorar las propiedades físico-químicas del nejayote y proveerle un tratamiento o aplicación. Con el trabajo realizado se pudo detectar que la recuperación de materiales con valor agregado alto (polifenoles, carbohidratos, azúcares, gomas y componentes de calcio) puede ser utilizada en diversas áreas como la alimenticia, farmacéutica y biotecnológica. Además, se identificó que la obtención de dichos componentes se puede llevar a cabo mediante el acoplamiento de diversos bioprocesos (fermentación, filtración, centrifugación y decantación).