Aggregation Index in Representative Soils of the Paraiba State

The stability of soil aggregates is an important physical parameter as it mainly influences the flow of water and air in the soil. In this sence, the research aimed to determine the stability aggregates index in six representative soil classes in the Paraíba State-Brazil at different stages of pedological development. The soil classes analyzed is localized on the Microregions of the Curimataú Occidental (Ferrasol, Planosol, Leptosol, Arenosol) and Brejo Paraibano (Acrisol, Lixisol), on the Paraíba state, Brazil. At depths of 0-5 cm, 5-10 cm and 10-20 cm was collected three undeformed soil samples were collected per mini trench (one per depth), making a total of 15 samples per soil class and 90 total samples. In each soil class was analysed: chemical parameters, granulometrics parameters, stability of agregates, Weighted average diameter via wet sieving (DMPAu) and Weighted average diameter via dry sieving (DMPAs); agregate stability index (IEA). For the conditions that research was developed can conclude that: The IEA values tended to increase with clay contents in all soil classes; Among the less developed soils, the IEA values were better in the Planosol soil, denoting that there is a probable relationship with the low contents of K, Na and Al; In the more developed soils, the high values of Na and Al may have contributed to the lower IEA in the PVd, while the lower values may have contributed to the higher IEA in Ferrasol and Lixisol soils.

Conflito de uso do solo em Áreas de Preservação Permanente da Bacia Hidrográfica do rio Marapanim, nordeste do Pará

Permanent preservation areas contribute significantly to maintaining the quality of water resources and protecting against erosive events. In this sense, this work mains of is to analyze the land use conflict in the permanent preservation areas of the Marapanim River basin, located in the northeast of the State of Pará. To this end, it data were processed using images Landsat of the years of 1988, 1998, 2008 and 2017 to discuss changes in land use for each year. Afterwards, land use was overlapped with the Permanent Preservation Areas of the basin (PPA), to identify the types of use that are interfering in the limits that should be maintained as PPA. The results showed that in three of the four years of analysis, the exposed soil class was present in more than 20% of the inadequate land use in PPA, followed by the Farming class, which increased almost 300% the occupation in PPA in the first to last year of study. Thus, this study can support public policies in order to monitor the sources and margins of water courses for a better management of water resources in this region.

Correspondence Between Vegetation Patterns and Soils in Wet and Wet-mesic Grasslands of Hanság and Tóköz (Hungary)

Our research focused on the causes responsible for the fine mosaic pattern of plant associations on wet and wet-mesic meadows. The study area is located in the Little Hungarian Plain, including the former swamp basins of Hanság and Tóköz in Hungary. The vegetation survey data were evaluated by statistical methods (TWINSPAN method), and vegetation maps of the areas were prepared. Topsoil samples near the relevés were gathered for further laboratory tests. Soil profiles were opened by a Pürckhauer soil sampler for on-site description of the soil horizons and classification. Surface models provided a base for the preparation of contour maps that could be compared with the vegetation pattern. We found that of the two dominant vegetation types, mesotrophic wet meadows were associated with Mollic Gleysols, while non-tussock sedge beds were associated with Histic Gleysols. At the transitions of the two soil classes, the subgroup of non-tussock sedge beds is the dominant type. The soil class only determined the plant association on a habitat level, but it could not reason the fine pattern of the plant communities on the same soil class. Canonical correspondence analysis (CCA) was performed to investigate the relationship between the distribution of dominant species and soil parameters. Several soil parameters combined have a significant effect on the distribution of dominant species. In conclusion, we found that the formation of association types strongly depends on the soil characteristics of the area, and that it is closely related to it. However, in the formation of the fine mosaic pattern, the driving ecological factors are the microrelief and the length of the saturated or flooded soil conditions.

Abacus to Predict Groundwater Recharge at Non-Instrumented Hydrographic Basins

One of the first steps to implement a policy for groundwater resources management is knowing the groundwater recharge. However, the unavailability of data and resources to execute field studies increase the uncertainty associated with the estimation of groundwater recharge. To fill this gap, the present work aimed to propose a method to predict groundwater recharge at non-instrumented hydrographic basins. The approach proposed is based on using an abacus to execute the transposition and/or regionalization of results generated in an experimental basin. The methodology comprised the estimation and mapping of recharge rates in the experimental basin using three distinct approaches—numerical modelling of the saturated zone, distributed hydrological modelling of the vadose zone, and the method of fluctuation of the water table elevation—and the following generation of the abacus, with average recharge values for combinations of soil class, land use/cover and slope using geographic information systems. The results indicate that the abacus is consistent for some Ferrasol areas, that the reliability of average regionalized values depends on the complexity of the physical environment—soil class, land use/cover, and slope—and that new studies, focusing on the hydro-physical characterization of soils, might produce more reliable estimations.

Agroclimatic zoning of off-season (safrinha) corn for the first ten days of February in Goiás, Brazil

Through maps, agroclimatic zoning can help us understand the guidelines for the most suitable locations for sowing, minimizing risks of produce loss due to climatic adversities. The aim of the present study is to develop an agroclimatic zoning for the cultivation of a non-irrigated off-season (safrinha) corn in the state of Goiás, sown in the first ten days of February. The methodological steps were the delimitation of the representative bands of the climatic requirements for the crop; the spatialization of the climatic elements in the region; the spatial reclassification of climatic elements based on the climatic requirements for the crop; elaboration of the agroclimatic zoning map. Average, minimum, and maximum air temperature, accumulated precipitation, actual crop evapotranspiration and relative humidity of the air registered between February and July for the years 2013 to 2017 were used. The most suitable areas for the cultivation of the non-irrigated off-season corn sown in the first ten days of February in Goiás established by the agroclimatic zoning are concentrated in the South-Southwest and Northwest regions. The expressive presence of Latosol soil class and the proximity of the values of the climatic elements accumulated precipitation, temperature and altitude, which are proper for the development of the off-season corn, favour suitability of the areas.

Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.