No abstract

La nutrición de la papa (Solanum tuberosum) en invernadero en México se hace mediante aplicación de fórmulas sólidas de NPK, aplicaciones foliares y el uso de soluciones nutritivas hidropónicas no específicas; por lo tanto, los rendimientos son bajos, muy variados e inconsistentes. Los objetivos de esta investigación fueron: 1) determinar las concentraciones de NPK adecuadas para la producción del clon 020342.1 de papa bajo condiciones de hidroponía; y 2) evaluar los rendimientos de minitubérculos bajo condiciones de invernadero. El experimento se realizó en los invernaderos del Campo experimental Toluca en Zinacantepec, Estado de México en el año 2012. El sustrato utilizado fue perlita; se empleó el diseño San Cristóbal, con cuatro niveles de NPK respectivamente y doce tratamientos; las variables evaluadas fueron clorofila, índice de área foliar (IAF), peso fresco, número y diámetro de minitubérculos. Los tratamientos con mayor contenido de clorofila durante el ciclo del cultivo fueron: T12 y T10 con 144 y 158 μg g-1 respectivamente; la altura, peso fresco y número de tubérculos presentan diferencias significativas, donde los mejores tratamientos fueron el T10, T12, T8 y T6. Para el peso fresco de tubérculo, destacaron el T6 y T10, con 230 y 232 g respectivamente. Para el número de tubérculos totales, sobresalen el T8 con 18.6 tubérculos por planta, T12 con 18.2, T10 con 18.1 y T6 con 16.8. En conclusión, las concentraciones mayores de 150N y 300K aumentaron la tuberización y rendimiento de minitubérculo del clon 020342.1 de papa en hidroponía.

Reforestation programs have been proposed as a remedial measure to tackle deforestation and forest ecosystems degradation. Because one of the main constraints to the implementation of restoration practices is lack of funding, these programs need to be carefully planned to efficiently use the economic and human resources invested. In this study we present a geospatial decisionmaking tool to identify suitable areas for restoration. The overall approach entails (1) the use of the simple multiattribute rating technique (SMART) to identify and rank the attributes according to their importance for prioritizing areas for restoration and (2) the implementation of 0-1 integer programming to select the areas that maximize the environmental benefit. The approach is exemplified through a case study in central Mexico's mountainous state of Estado de Me ´xico, encompassing an area just above 2 million ha. Specialists in different aspects of reforestation selected the following attributes to identify priority areas for reforestation: erosion, land use/land cover, position in the watershed, soil type, terrain slope, and precipitation. In total, 644,642 ha were classified under very high priority for reforestation. Of these, 17,059 ha were selected to maximize the environmental benefit without exceeding the available budget. The selected sites were mainly in the forested zones of steeply sloped mountains. Although the multiattribute decision analysis, the optimization model, and the spatial analysis were only loosely coupled, their combination proved to be an innovative and practical approach to systematically identify priority areas for reforestation on a yearly basis.

Recurrent flooding occurs in most years along different parts of the Gulf of Mexico coastline and the central and southeastern parts of Mexico. These events cause significant economic losses in the agricultural, livestock, and infrastructure sectors, and frequently involve loss of human life. Climate change has contributed to flooding events and their more frequent occurrence, even in areas where such events were previously rare. Satellite images have become valuable information sources to identify, precisely locate, and monitor flooding events. The machine learning models use remote sensing images pixels as input feature. In this paper, we report a study involving 16 combinations of Sentinel-1 SAR images, Sentinel-2 optical images, and digital elevation model (DEM) data, which were analyzed to evaluate the performance of two widely used machine learning algorithms, gradient boosting (GB) and random forest (RF), for providing information about flooding events. With machine learning models GB and RF, the input dataset (Sentinel-1, Sentinel-2, and DEM) was used to establish rules and classify the set in the categories specified by previous tags. Monitoring of flooding was performed by tracking the evolution of water bodies during the dry season (before the event) through to the occurrence of floods during the rainy season (during the event). For detection of bodies of water in the dry season, the metrics indicate that the best algorithm is GB with combination 15 (F1m = 0.997, AUC = 0.999, K = 0.994). In the rainy season, the GB algorithm had better metrics with combination 16 (F1m = 0.995, AUC = 0.999, Kappa = 0.994), and detected an extent of flooded areas of 1113.36 ha with depths of <1 m. The high classification performance shown by machine learning algorithms, particularly the so-called assembly algorithms, means that they should be considered capable of improving satellite image classification for detection of flooding over traditional methods, in turn leading to better monitoring of flooding at local, regional, and continental scales.

The cartography of farmland classes allows generating land maps, using a methodology based on local knowledge, rapidly and at low cost, and with a greater number of cartographic units than conventional soil surveys maps. However, the results found when producing these maps with automated cartography techniques are contrasting. Precision and accuracy were evaluated in 324 computer generated farmland class (FLC) maps by applying the Inverse Distance Weighted (IDW) interpolation model. These maps were obtained by varying the sample size for the training, its spatial design, and the Power value of the interpolator. Moreover, the effort needed to obtain maps with acceptable reliability was quantified. The procedure was applied to FLC maps obtained from surveys with producers from three contrasting environmental zones in Mexico. The results show that the best sampling scheme in the three areas is the systematic sampling, and Power 8, giving the maps with the highest reliability. Through the criterion of map reliability and effort needed for sampling, the recommended sample size is 10% to 25% of the total plots.