Shadow Analysis of Soil Surface Roughness Compared to the Chain Set Method and Direct Measurement of Micro-relief.

Erosion potential and the effects of tillage can be evaluated from quantitative descriptions of soil surface roughness. The present study therefore aimed to fill the need for a reliable, low-cost and convenient method to measure that parameter. Based on the interpretation of micro-topographic shadows, this new procedure is primarily designed for use in the field after tillage. The principle underlying shadow analysis is the direct relationship between soil surface roughness and the shadows cast by soil structures under fixed sunlight conditions. The results obtained with this method were compared to the statistical indexes used to interpret field readings recorded by a pin meter. The tests were conducted on 4-m2 sandy loam and sandy clay loam plots divided into 1-m2 subplots tilled with three different tools: chisel, tiller and roller. The highly significant correlation between the statistical indexes and shadow analysis results obtained in the laboratory as well as in the field for all the soil?tool combinations proved that both variability (CV) and dispersion (SD) are accommodated by the new method. This procedure simplifies the interpretation of soil surface roughness and shortens the time involved in field operations by a factor ranging from 12 to 20.

Influence of tillage on soil surface roughness structure andsurface porosity.

Soil erosion is a complex phenomenon involving the detachment and transport of soil particles, storage and runoff of rainwater, and infiltration. The relative magnitude and importance of these processes depends on several factors being one of them surface microtopography, usually quantified trough soil surface roughness (SSR). Surface soil porosity and SSR can be altered by tillage operation. Even though the surface porosity is an important parameter of a tilled field, however, no practical technique for rapid and non-contact measurement of surface porosity has been developed yet.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Collection of vegetation and soil surface cover in the Jena Experiment (time series since 2002)

This collection contains measurements of vegetation and soil surface cover measured on the plots of the different sub-experiments at the field site of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. The following series of datasets are contained in this collection: 1. Measurements of vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the species that have been sown into the plots to create the gradient of plant diversity.

Soil surface textures, Cumberland County, New Jersey /

"October 1986."

Soil surface textures, Cumberland County, New Jersey /

"October 1986."

Soil surface textures, Salem County, New Jersey /

"October 1986."

Soil surface textures, Salem County, New Jersey /

"October 1986."

Characterization of the soil fertility and root system of restinga forests

The Restinga vegetation consists of a mosaic of plant communities, which are defined by the characteristics of the substrates, resulting from the type and age of the depositional processes. This mosaic complex of vegetation types comprises restinga forest in advanced (high restinga) and medium regeneration stages (low restinga), each with particular differentiating vegetation characteristics. The climate along the coast is tropical (Köppen). Of all ecosystems of the Atlantic Forest, Restinga is the most fragile and susceptible to anthropic disturbances. Plants respond to soil characteristics with physiological and morphological modifications, resulting in changes in the architecture (spatial configuration) of the root system. The purpose of this study was to characterize the soil fertility of high and low restinga forests, by chemical and physical parameters, and its relation to the root system distribution in the soil profile. Four locations were studied: (1) Ilha Anchieta State Park, Ubatuba; (2) two Ecological Stations of Jureia-Itatins and of Chauás, in the municipality of Iguape; (3) Vila de Pedrinhas in the municipality of Ilha Comprida; and (4) Ilha do Cardoso State Park, Cananeia. The soil fertility (chemical and physical properties) was analyzed in the layers 0-5, 0-10, 0-20, 20-40 and 40-60 cm. In addition, the distribution of the root system in the soil profile was evaluated, using digital images and the Spring program. It was concluded that the root system of all vegetation types studied is restricted to the surface layers, 0-10 and 10-20 cm, but occupies mainly the 0-10 cm layer (70 %); that soil fertility is low in all environments studied, with base saturation values below 16 %, since most exchange sites are occupied by aluminum; and that restinga vegetation is edaphic.

Spatial and temporal variability of soil CO2 emission in a sugarcane area under green and slash-and-burn managements

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

New methods to quantify NH3 volatilization from fertilized surface soil with urea

Gaseous N losses from soil are considerable, resulting mostly from ammonia volatilization linked to agricultural activities such as pasture fertilization. The use of simple and accessible measurement methods of such losses is fundamental in the evaluation of the N cycle in agricultural systems. The purpose of this study was to evaluate quantification methods of NH3 volatilization from fertilized surface soil with urea, with minimal influence on the volatilization processes. The greenhouse experiment was arranged in a completely randomized design with 13 treatments and five replications, with the following treatments: (1) Polyurethane foam (density 20 kg m-3) with phosphoric acid solution absorber (foam absorber), installed 1, 5, 10 and 20 cm above the soil surface; (2) Paper filter with sulfuric acid solution absorber (paper absorber, 1, 5, 10 and 20 cm above the soil surface); (3) Sulfuric acid solution absorber (1, 5 and 10 cm above the soil surface); (4) Semi-open static collector; (5) 15N balance (control). The foam absorber placed 1 cm above the soil surface estimated the real daily rate of loss and accumulated loss of NH3N and proved efficient in capturing NH3 volatized from urea-treated soil. The estimates based on acid absorbers 1, 5 and 10 cm above the soil surface and paper absorbers 1 and 5 cm above the soil surface were only realistic for accumulated N-NH3 losses. Foam absorbers can be indicated to quantify accumulated and daily rates of NH3 volatilization losses similarly to an open static chamber, making calibration equations or correction factors unnecessary.

Spatial Modeling of a Soil Fertility Index using Visible-Near-Infrared Spectra and Terrain Attributes

Our objective was to develop a methodology to predict soil fertility using visible near-infrared (vis-NIR) diffuse reflectance spectra and terrain attributes derived from a digital elevation model (DEM). Specifically, our aims were to: (i) assemble a minimum data set to develop a soil fertility index for sugarcane (Sarcharum officinarum L.) (SFI-SC) for biofuel production in tropical soils; (ii) construct a model to predict the SFI-SC using soil vis-NIR spectra and terrain attributes; and (iii) produce a soil fertility map for our study area and assess it by comparing it with a green vegetation index (GVI). The study area was 185 ha located in sao Paulo State, Brazil. In total, 184 soil samples were collected and analyzed for a range of soil chemical and physical properties. Their vis-NIR spectra were collected from 400 to 2500 nm. The Shuttle Radar Topographic Mission 3-arcsec (90-m resolution) DEM of the area was used to derive 17 terrain attributes. A minimum data set of soil properties was selected to develop the SFI-SC. The SFI-SC consisted of three classes: Class 1, the highly fertile soils; Class 2, the fertile soils; and Class 3, the least fertile soils. It was derived heuristically with conditionals and using expert knowledge. The index was modeled with the spectra and terrain data using cross-validated decision trees. The cross-validation of the model correctly predicted Class 1 in 75% of cases, Class 2 in 61%, and Class 3 in 65%. A fertility map was derived for the study area and compared with a map of the GVI. Our approach offers a methodology that incorporates expert knowledge to derive the SFI-SC and uses a versatile spectro-spatial methodology that may be implemented for rapid and accurate determination of soil fertility and better exploration of areas suitable for production.