Re-sequencing of vrs1 and int-c loci shows that labile barleys (Hordeum vulgare convar. labile) have a six-rowed genetic background

Labile-barleys (Hordeum vulgare convar. labile (Schiem.) Mansf.) are found in the highlands of Ethiopia, Eretria and North India-Pakistan districts. They represent a distinct spike form showing row-type alterations even within individual spikes of the same genotypes. Variation at the six-rowed spike 1 (vrs1) locus is sufficient to control barley lateral spikelet fertility, which is also modified by alleles at the intermedium-c (int-c) locus. This study aimed at re-sequencing these two loci to investigate whether labile-barleys have a two-rowed genetic background, resulting in increased lateral spikelet fertility, or show reduced lateral spikelet fertility if they possess a six-rowed genetic background. The Vrs1 re-sequencing results of 221 supposedly labile-barley accessions from Ethiopia revealed 13 accessions with two novel vrs1.a1 haplotypes. Following the current nomenclature of vrs1 haplotypes, the new haplotypes were named as haplotypes 66 and 67. Re-sequencing at the int-c locus showed that 118 of the labile-barleys possessed the previously described Int-c.a allele but only one accession was found having a novel Int-c.a haplotype in the homozygous state (termed Int-c.a haplotype1; Hap_1). Interestingly, 101 labile-barleys carried the Int-c.a allele and Int-c.a haplotype1 simultaneously, suggesting maintained heterozygosity or recent gene duplication at this locus. Only one accession had a two-rowed haplotype (Vrs1.b3, int-c.b1) and one accession possessed the Vrs1.t (deficiens) and Int-c.a alleles (six-rowed). These two accessions were considered as misclassified labile genotypes and not included in further analysis. Thus, these results confirmed that all of the 219 labile accessions studied in this work showed six-rowed alleles at vrs1 but reduced lateral spikelet fertility. This reduction is most likely caused by the recessive labile (lab) locus which we are in the process to characterize further.     

Determination of surface area of soil by adsorption methods

How significant are estimates of surface areas of soils obtained by adsorption methods? To answer this we tested the applicability of an equation that relates monolayer coverage (N) of an adsorbent with the area (A) of adsorbate, NA^z = k, for six soil samples using six adsorbates of different sizes. The data obtained confirm the validity of the equation with the z parameter ranging between 1.25 and 1.41. Hence each soil sample showed preferences towards small‐sized adsorbates for which the product NA (calculated surface area) is inversely related to the size of adsorbate. We also demonstrated that the equation has roots in the Langmuir adsorption equation.     

Sorption of 2,2′-bipyridine on clays and oxides

The sorption of 2,2′-bipyridine onto some clays and oxides was studied as a function of concentration, pH, time and the exchangeable cation of the clays. The reaction was found to include both physical, cation exchange, and chemisorption, was irreversible and multilayered at high concentrations. The sorption was also pH dependent attaining a maximum in the pH range 4.5 to 5.5 depending on the sorbent; this dependence was attributed to chemisorption. The maximum in sorption was observed to take place above the pH value where the molecular form is the only bipyridine species present in solution thus indicating its preference by the sorbents over the positive species. Chemisorption, ion exchange against metalic cations and molecular aggregation on the surface are the factors thought to be responsible for the desorption of bipyridine at pH values beyond that at the maximum sorption.     

The electric potential midway between overlapping clay flat double layers: A compilation of theoretical treatments

A compilation of some useful approaches used for the obtention of the minimum potential between interacting flat clay double layers is given. Values of the minimum potential obtained from published nomograms, tables and equations are compared. The effect of dissimilar double layers on the potential is also illustrated.     

Will the Conventional Soil-Plant Analysis Pass into Oblivion? Rapid and Low-Cost Determination Using Spectroscopy

Is it time for replacing the traditional soil-plant analysis methods by spectroscopy? Traditional analytical methods are subject to significant sources of errors that commonly result in compromise of soil characteristics and gross underestimation of chemical concentrations in soil-plant for a wide range of analyses. Given the magnitude of the decisions that are made based on these data, the technical and economic impacts of using traditional methods can be significant. Therefore, it is now time for soil-plant spectroscopy to enter an operational phase. Spectroscopy has opened a new era in which traditional analyses are being left behind. Spectroscopy can be used to accurately predict certain soil and plant properties, making it a valuable tool in precision farming. Soil spectra contain much information relevant to soil-plant properties. Multivariate regressions of spectra can accurately predict several soil properties. Spectroscopy requires only a few seconds to analyze a soil sample, but the relevant information needs to be mathematically extracted from the spectra so that it can be correlated with soil properties. Therefore, the successful application of spectroscopy to quantify and evaluate the relationships between soil reflectance and soil properties depends largely on the development of accurate and robust calibration models. These procedures appear to be sufficiently accurate and precise to enable their use in soil and plant analysis. There are many advantages to using this technique.     

Early sensing of peanut leaf spot using spectroscopy and thermal imaging

Early and late leaf spot are the most devastating, important fungal foliar diseases affecting peanut. The economic loss caused by peanut diseases justifies the necessity to develop more reliable detection methods. Point and image spectroscopy and thermal imaging were used in this study for the early detection of peanut leaf spot. The spectral reflectance factors differ significantly according to the health condition. The leaves of the healthy peanut showed a decreasing reflection in 1015 nm, whereas the heavily diseased leaves showed an increasing reflection. At the thermal infrared range, affected plants show a higher temperature than healthy ones. A presymptomatic decrease in leaf temperature using thermal imagery was found about 1.3°C lower than the healthy leaves. However, the diseased plant’s temperature was 2.2°C higher than that of the healthy one. The temperature difference allowed the discrimination between the infected and healthy leaves before the appearance of visible necrosis on leaves. Two simple indices, early leaf spot index (ELSI) and late leaf spot index (LLSI), were developed to allow early prediction of the peanut disease severity. The disease severity estimation using ELSI and LLSI has an overall accuracy of 78% and 89%, respectively. This demonstrates the efficiency of the proposed indices to estimate the peanut disease.