Traditional methods for studying tree roots are destructive and labor intensive, but available nondestructive techniques are applicable only to small scale studies or are strongly limited by soil conditions and root size. Soil electrical resistivity measured by geoelectrical methods has the potential to detect belowground plant structures, but quantitative relationships of these measurements with root traits have not been assessed. We tested the ability of two-dimensional (2-D) DC resistivity tomography to detect the spatial variability of roots and to quantify their biomass in a tree stand. A high-resolution resistivity tomogram was generated along a 11.75 m transect under an Alnus glutinosa (L.) Gaertn. stand based on an alpha-Wenner configuration with 48 electrodes spaced 0.25 m apart. Data were processed by a 2-D finite-element inversion algorithm, and corrected for soil temperature. Data acquisition, inversion and imaging were completed in the field within 60 min. Root dry mass per unit soil volume (root mass density, RMD) was measured destructively on soil samples collected to a depth of 1.05 m. Soil sand, silt, clay and organic matter contents, electrical conductivity, water content and pH were measured on a subset of samples. The spatial pattern of soil resistivity closely matched the spatial distribution of RMD. Multiple linear regression showed that only RMD and soil water content were related to soil resistivity along the transect. Regression analysis of RMD against soil resistivity revealed a highly significant logistic relationship (n = 97), which was confirmed on a separate dataset (n = 67), showing that soil resistivity was quantitatively related to belowground tree root biomass. This relationship provides a basis for developing quick nondestructive methods for detecting root distribution and quantifying root biomass, as well as for optimizing sampling strategies for studying root-driven phenomena.

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The findings revealed that mean burden level was 31.59 (SD 19.51). A difference between experimental and CGs was found for sexual and affective marital satisfaction (p<0.05). The same variables showed a rather negative correlation with total burden levels (r=-0.374, p<0.001; r=-0.448, p<0.001).

Monitoring soil water content at high spatio-temporal resolution and coupled to other sensor data is crucial for applications oriented towards water sustainability in agriculture, such as precision irrigation or phenotyping root traits for drought tolerance. The cost of instrumentation, however, limits measurement frequency and number of sensors. The objective of this work was to design a low cost “open hardware” platform for multi-sensor measurements including water content at different depths, air and soil temperatures. The system is based on an open-source ARDUINO microcontroller-board, programmed in a simple integrated development environment (IDE). Low cost high-frequency dielectric probes were used in the platform and lab tested on three non-saline soils (ECe1: 2.5 < 0.1 mS/cm). Empirical calibration curves were subjected to cross-validation (leave-one-out method), and normalized root mean square error (NRMSE) were respectively 0.09 for the overall model, 0.09 for the sandy soil, 0.07 for the clay loam and 0.08 for the sandy loam. The overall model (pooled soil data) fitted the data very well (R2 = 0.89) showing a high stability, being able to generate very similar RMSEs during training and validation (RMSEtraining = 2.63; RMSEvalidation = 2.61). Data recorded on the card were automatically sent to a remote server allowing repeated field-data quality checks. This work provides a framework for the replication and upgrading of a customized low cost platform, consistent with the open source approach whereby sharing information on equipment design and software facilitates the adoption and continuous improvement of existing technologies.

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Multi-electrode soil electrical resistivity (ρ) tomography was used for the non-invasive study of tree roots in situ and their spatial distribution in an agricultural soil. The quantitative relations of ρ and root biometry and the contribution of different root size classes were investigated with two-and three-dimensional 48-electrode tomograms in an orchard in southern Italy on a Typic haploxeralf fine, mixed termic soil. Root biomass density (RD) and root length density (RLD) were measured destructively on coarse (>2 mm diameter) and fine roots, and soil paste electrical conductivity, water content, stone content, texture, organic matter and pH were measured on soil samples taken up to 0.48-m deep. Areas of large ρ values (up to 460 ohm m) were found close to tree trunks and variability in ρ was related to RD (0-0.137 Mg m −3 ) only; the resistive response was from coarse roots. The effect of other soil variables on ρ was overshadowed by the presence of roots and therefore no significant multivariate relationship was found. A highly significant ρ-RD gamma GLM model used to fit positively skewed data provides a useful framework for regression analysis when ρ is dominated by roots. Soil electrical resistivity is promising as a proxy for RD in orchards, but not for RLD, and the effect of tree roots on ρ needs to be taken into account in electrical surveys of soils.

The demand for sources of nutraceuticals has led to the rediscovery and diffusion of traditional crops such as chia (Salvia hispanica L.), whose leaves and fruits are rich in W3 fatty acids and anti-oxidants. Chia originates in Central America but it is rapidly expanding to new areas. A field experiment conducted at Atella in Basilicata (Southern Italy) was set up to test the response of chia to N top-dress fertilisation (0 and 20 kg ha -1 ) and to sowing density (D1=125, D2=25, D3=8 and D4=4 plants m -2 ) in a split-plot design with three replications. First results show maximum leaf area index values up to 7.1 and fresh vegetative biomass production at early flowering ranging between 50.87 (D4) and 59.71 (D1) t ha -1 . Yield increased with plant density: a significantly (P<0.01) higher production (398 kg ha -1 ) was reached in D1. N top-dressing had a detrimental effect on yield and corresponded to higher lodging and lower maturation percentage of seeds, though non-significant. Based on our first results it seems worthwhile to continue agronomical trials for chia in herbaceous systems of southern Italy for leaf production based on traditional genotypes, while fruit production might be pursued by adopting high sowing density and the search for longer-day genotypes. IntroductionSalvia hispanica L, known as chia, is a multifunctional plant whose food use may be traced back as far as 2500 B.C. Domesticated in Mesoamerica around 2600 B.C. it has been a staple food in Mexico between 1500 and 900 B.C. (Pozo Pozo, 2010). It is an annual herbaceous species belonging to the Labiatae family, and its fruits were one of the four main food source traditionally used in pre-Colombian times. It has recently been re-discovered (Ayerza and Coates, 2009) due to its high content of nutraceuticals and therefore the potential for a functional food and a feed capable of increasing the nutritional value of milk and meat products (Ayerza and Coates, 2006;Peiretti and Meineri, 2008).

Wheat yield and protein content are spatially variable because of inherent spatial variability of factors affecting the yield at field scale. In Mediterranean environments, yield variability is often caused by the irregular weather pattern, particularly rainfall and by position on the landscape. The objective of this study was to determine the effects of landscape position and rainfall on spatial variability of wheat yield and protein in a rolling terrain field of Southern Italy, and to propose stable management areas through simulation modelling and georesistivity imaging in rolling landscape. The study was carried out in Southern Italy, during 2 years of wheat monoculture; extensive soil properties and in‐season plant measurements were measured. This study showed that soil water content was the main factor affecting spatial variation of yield for both years. The interactions between rainfall, topography and soil attributes increase the chances to observe yield variability among years. The principal component analysis demonstrated that for both years, soil water content explained most of the variability. The crop simulation model provided excellent results when compared with measured data with root mean square error of 0.2 t ha−1. The simulated cumulative probability function showed that the model was able to confirm the yield temporal stability of three different zones.