The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields.

Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB.

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season

Vapor pressure deficit (VPD) can be used as an indicator to schedule greenhouse irrigation. VPD can be estimated as a function of relative humidity (RH) and temperature (T). The objective of this work was to analyze spatial variation in VPD as an indicator of water stress influenced by concentration of water vapor and intensity of near infrared (NIR). The study was carried out in an empty three-span sawtooth greenhouse with natural ventilation under the local climate in Montecillo, Mexico; these findings established a base value to analyze greenhouse field conditions prior to the influence from a crop. The experimental phase consisted of recording data (3 February 2019–24 February 2019) on temperature, humidity, solar radiation, and wind speed, which were used for developing a model in computational fluid dynamics (CFD). Then, this model was used to estimate VPD, considering changes in mass fraction of water vapor and the intensity of NIR. Scenarios with 50, 70, and 90% external RH were evaluated. It was found that without a crop, temperature was not affected by the variation in the mass fraction of water vapor and the intensity of NIR in the simulated scenarios, each of which generated a thermal gradient within the range of 4 °C. When considering the scenario of 90% external RH, we found the best VPD range along the greenhouse (2–3 kPa) that would be a favorable field condition for crops. Differences between VPD with and without a crop can be used to estimate the water quantity needs for crop growth based on the climate variables examined in this study, where higher VPD values require more water for irrigation.

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 riegoparatenerunmanejoeficientedelagua,lamayorpartede las zonas agrícolas cuentan con tecnología de riego inadecuada que genera pérdidas en la conducción y distribución del agua. El riegoporgravedadanivelparcelariotieneunabajaeficiencia 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.

Se evalúo el efecto del cambio climático sobre la evapotranspiración de referencia (ET0) en el Distrito de Desarrollo Rural Atlacomulco, la cual, es considerada una de las variables más importantes en la gestión de los recursos hídricos y la programación del riego agrícola. Los escenarios de cambio climático se proyectaron mediante el modelo de circulación general, HadGEM2-ES con dos trayectorias de concentración representativa 4.5 y 8.5, para dos horizontes de tiempo 2021-2040 y 2041-2060 mediante el software LARS-WG, posteriormente se determinó la ET0 con el programa Ref-ET, para finalmente modelar la precipitación efectiva, la evapotranspiración del cultivo y el requerimiento de riego, para el cultivo de maíz con el software de la FAO CROPWAT. Los resultados muestran que durante el periodo base 1985-2020, la ET0 oscila de 3.93 a 4.17 mm día-1, con el RCP 4.5 se proyecta un incremento de hasta 2.6 y 4.1% mientras en el RCP 8.5, sea de 4.2 y 7.4% respectivamente para ambos horizontes, este incremento se ve reflejado sobre la precipitación efectiva, evapotranspiración del cultivo y requerimiento de riego hacia los cuatro escenarios, las zonas más vulnerables son el sureste y noroeste. Mediante las variables analizadas, se pudo identificar que la precipitación efectiva no es suficiente para satisfacer las demandas de evapotranspiración del cultivo en maíz, por lo que es necesario cubrir el requerimiento de riego con algún aporte hídrico. Con los datos obtenidos se pueden generar estrategias de adaptación y mitigación para mejorar la eficiencia del uso del agua en el Distrito de Desarrollo Rural Atlacomulco y proponer alternativas de manejo agrícola.