Land-cover studies based on optical remote sensing in regions which exhibit disorderly urban growth and quick-use conversion of farmland to non-farm usage face problems due to inaccurate discrimination of cover types and hence inaccurate extent estimations. The use of data in the visible and infrared areas of the electromagnetic spectrum for classifying crop types has been extensively explored, concluding that data acquisitions must be made during critical crop development periods. This raises a concern in Central Mexico where such periods coincide with important cloud coverage and where good estimates of the extent of agricultural areas and of particular crops are keenly sought by government agencies for planning purposes. Due to the interest in accurate and updated maps for this area, repeated studies have been carried out over a number of years by the National Institute of Research for Forestry, Agriculture and Livestock for the Ministry of Agriculture of Mexico. Taking into consideration the difficulties of acquiring and analysing data derived from optical sensors, the objective of this study was to assess the advantages of combining synthetic aperture radar (SAR) and optical remote sensing in producing more accurate maps. The study area covers 15 634 ha and is located in Central Mexico in a region where agricultural plots of varied sizes and forms are interspersed with rapid urbanization spaces. We investigated alternative supervised classification schemes combining the Radarsat-1 C-band with Landsat Enhanced Thematic Mapper Plus (ETM+) bands to estimate land cover distributions and assess the quality of results with field data. Then, we set forth and evaluated a methodology which applies data fusion of selected Landsat ETM+ bands and the C-radar band. The separation and similarities for vegetated and non-vegetated cover types depends on whether the selected agricultural crops are annual or perennial, and on whether there are bare soils present. This knowledge for the particular study area influenced the selection of dates for image take and analysis. Partial fused and non-fused land-cover maps were assessed for accuracy and were combined to obtain a final map. The results demonstrate that the combined utilization of optical and radar imagery yields useful land cover information and improved classification accuracy over those obtained using either type of image on its own.

Climate change is causing detrimental changes in living organisms, including pathogens. This review aimed to determine how climate change has impacted livestock system management, and consequently, what factors influenced the gastrointestinal nematodes epidemiology in small ruminants under tropical conditions. The latter is orientated to find out the possible solutions responding to climate change adverse effects. Climate factors that affect the patterns of transmission of gastrointestinal parasites of domesticated ruminants are reviewed. Climate change has modified the behavior of several animal species, including parasites. For this reason, new control methods are required for controlling parasitic infections in livestock animals. After a pertinent literature analysis, conclusions and perspectives of control are given.

Crop yield estimation are topics of interest in Latin-American countries, for farmers and government officers responsible of managing agricultural national policies. Besides, modern remote sensing methodologies to obtain these predictions represent important steps towards attaining the goals of precision agriculture for the 21st century. Digital data from satellite images analyzed jointly with crop modelling parameters provide information that enables crop yield estimation. The objective of this study was the estimation of yield and total volume of maize production using Spot-5 satellite images and empirical models. These models expressed a) yield (Y) as a function of LAI, and b) yield as a function of NDVI. To determine the efficiency degree of the calculated predictions at the flowering stage of the crop, yield sampling was done at the physiological maturity stage in pilot plots. Regarding yield prediction in the flowering stage, the models Y = f (LAI) reported a value of 5.96 ton.ha-1 and the model Y = f (NDVI) a value of 5.04 ton.ha-1 was obtained. These data represent 114% and 97% respectively of the true yield recorded on the field. The models are specific to the maize crop and the cultivated plots location, and that the forecasts can be acceptably accurate provided the sown areas are precisely determined.

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.

In agriculture, soil and crop conditions change from day to day and throughout the growing season. Agricultural targets also vary spatially with differences observed from field to field, as well as within individual fields. The heterogeneity of corn-growing conditions in Mexico makes accurate data for crop type, crop condition and crop yield prediction difficult to obtain. Yield predictions are needed by the federal government to estimate, ahead of harvest time, the amount of corn required to be imported in order to meet the expected domestic shortfall. In this project a methodology for the estimation of corn yield ahead of harvest time is developed which uses radar and optical remote sensing and which specifically considers the corn-growing situation in Central Mexico. Radar based crop type classification requires data sets with multiple polarizations. Recent research to assess relative classification accuracies of multi-polarized combinations for target crops using airborne data has been reported. In addition to identifying crop type and variety, identifying crop growth stage is valuable. Crop condition, loosely defined as the vigor or health of a crop in a particular growth stage, is related to crop productivity and yield; however, the relationship is complex. Main crop condition indicators include biomass, height, leaf area and contents of plant water, chlorophyll and nitrogen. Crop-type and crop-condition mapping are among the applications that are expected to benefit the most from the technical enhancements embodied by RADARSAT-2. The potential of RADARSAT-1 data for these applications has been rated as "limited", whereas for RADARSAT-2 data this potential is anticipated to be "strong". The Science and Operational Applications Research for RADARSAT-2 Program (SOAR) is promoting the evaluation of Synthetic Aperture Radar (SAR) capabilities by providing images to selected research projects which include the present one.objectives of this project are: a) use RADARSAT-2 data and optical data to determine cultivated areas and monitor crop condition for obtaining better estimations of crop yield; b) obtain polarization signatures from RADARSAT-2 data for corn and relate these to Leaf Area Index and photosynthetic active radiation (PAR) crop parameters and vegetation indexes, to establish indicators of crop condition and produce estimates for crop yield; c) use field data collected for three key corn crop growth stages over 300 pilot plots during 2001-2006, and increase the number of plots to build a database to support accuracy studies using RADARSAT-2 data.The expected benefits of this project are: to obtain knowledge about crop type, crop condition and crop yield with better accuracy than with current methodologies; to support national corn farmers associations; to design agriculture related activities within State agriculture plans; to support the corn product industry and aid government decision making. Relevant results and economical impact will imply operational usage of RADARSAT- 2 data in the agricultural sector in Mexico.

Knowing the spatiotemporal variability of temperature and precipitation, as well as their future projections, is essential to assess environmental risks and plan long-term mitigation and adaptation. In this study, the General Circulation Models (GCMs) HadGEM3-GC31-LL, MRI-ESM2-0 and ACCESS-ESM1-5 of the Coupled Model Intercomparison Project (CMIP6) have been used, under two Shared Socioeconomic Pathways (SPP2-4.5 and SSP5-8.5), to project the minimum and maximum air temperature and the mean annual precipitation in Atlacomulco Rural Development District (ARDD). 30 meteorological stations were used to perform regionalized climate change projections by downscaling using LARS-WG 7. The expected changes were evaluated for three time horizons: near horizon (2021–2040), mid horizon (2041–2060) and far horizon (2061–2080); compared to the historical period (1985–2017). Based on the results, the annual minimum temperature towards the far horizon is projected to increase by 2.0 ºC with SSP2-4.5, and 3.1 ºC with SSP5-8.5; while the annual maximum temperature towards the far horizon is projected to increase between 2.5 ºC with SSP2-4.5 and 3.8 ºC using SSP5-8.5. Precipitation shows an increase for SSP2-4.5 in the near and middle horizons with 15.7 and 42.5 mm.year⁻¹, respectively, while the largest decrease is projected towards the middle horizon of SSP5-8.5 with − 26.3 mm.year⁻¹. The findings of this study provide detailed information on climate change projections in the ARDD and can serve as a useful guide for the management of different ecosystems.