Alerce (Fitzroya cupressoides (Mol.) Johnst.) and Guaitecas cypress (Pilgerodendron uviferum (Don) Florin) are two of the three closely-related species of conifers in the Cupressaceae that are endemic to southern Chile and Argentina. Both are listed in Appendix I of the Convention on International Trade in Endangered Species of Fauna and Flora (CITES). The presence or absence of nodular (conspicuously pitted) end walls in the parenchyma cells provide good diagnostic characters to separate the two species wood anatomically, but the latter is sometimes difficult to distinguish. Therefore, a collaborative project was designed to study the chemical-molecular expression of these species by analyzing the heartwood using DART TOFMS (Direct Analysis in Real-Time (DART) Time-of-Flight Mass Spectrometry (TOFMS). This study compares the anatomical features of heartwood for both species and demonstrates that anatomy in conjunction with chemistry can separate them. DART TOFMS analysis combined with PCA was able to unequivocally determine taxonomic source with a statistical certainty of 99%. The mass spectra results obtained from heartwood demonstrated that identification is feasible after a few seconds, using a very small sample. DART TOFMS is a robust tool for reliable species identification and is useful to identify the taxonomic source of finished products or timber that are suspected of being illegally harvested.
Identifying wood species using wood anatomy is an important tool for various purposes. The traditionally used method is based on the macroscopic description of the physical and anatomical characteristics of the wood. This requires that the identifier has thorough technical knowledge about wood anatomy. A possible alternative to this task is to use intelligent systems capable of identifying species through an analysis of digital images. In this work, 21 species were used to generate a set of 2000 macroscopic images. These were produced with a smartphone under field conditions, from samples manually polished with knives. Texture characteristics obtained through a gray level co-occurrence matrix were used in developing classifiers based on support vector machines. The best model achieved a 97.7% accuracy. Our study concluded that the automated identification of species can be performed in the field in a practical, simple and precise way.
Low Zn in staple food grains like rice is closely related to large scale Zn malnutrition in many countries of the World. Zinc biofortification of rice grains by some cost effective agronomic method is important for low income farmers. To explore the possibility of enhancing the bioavailability of Zn in rice grains besides higher yields of two cultivars, the combinations of varying Zn fertilizer doses with or without inoculation of rhizobacteria consortium under split plot design set up were evaluated in two years field trials. Microbial inoculation + 5 kg Zn ha-1 to I year rice crop resulted in the highest number of effective tillers, grain yields, Zn concentration and uptake in grains and straw and total Zn uptake in both years. Grain yield of rice during two years increased by 19.7-27.9 and 17.1-20.4 percent over control under treatments receiving microbial inoculation + 5 kg Zn ha-1 to I year rice and 5 kg Zn ha-1 alone to I year rice crop, respectively. The highest concentration of Zn (10.9-19.1 mg kg-1) and the lowest concentration of phytic acid (18.5-25.3 g kg-1) in dehulled rice grains were recorded with soil application of 5 kg Zn ha-1; however, the values were at par with those observed under microbial inoculation + 5 kg Zn ha-1 (12.0-17.0 mg Zn kg-1 and 19.2-26.9 g phytic acid kg-1). The percent utilization of soil applied Zn increased with microbial inoculation in both the years and it was relatively higher in NDR 359 as compared to PD 16.