Plants of Agave spp. perform Crassulacean acid metabolism (CAM) and are highly drought-tolerant, but little is known concerning seed germination under low water availability. The aim of this study was to assess the effect of substrate water potential (ΨW) on seed germination and contrast hydrotime parameters of seven valuable and commercially-important Agave species from different geographical distributions and climatic regions of Mexico. Our hypothesis was that seed germination of Agave species is not affected by low water availability independently of seed biomass and the climate of their distribution area. Seed germination (at 25°C and in the dark) between 85 and 100% for all species occurred within 80–180 h at -0.03 MPa and 250–430 h at -1.0 MPa. Seed germination at -1.5 MPa declined to less than 50% (p < 0.05) for A. asperrima and A. cupreata but did not change significantly for A. americana var. marginata, A. lechuguilla and A. striata, although they showed the lowest mean base water potential (-2.01 to -2.64 MPa). Seed germination of 40% Agave species, from arid and semi-arid climates in this study, was not affected by the lower ΨW. Germination of seeds of Agave species is moderately affected by low water availability, is partially dependent of their ecological distribution, and is independent of seed mass.

Zea mays and Sorghum bicolor are important crops for animal and human nutrition worldwide. In the Central Highland Valley of Mexico, both crops are extremely important, and research is aimed toward increasing yield, disease resistance, and crop adaptation from 1,900- to 2,700-m elevation. In a 3-year field breeding experiment (2004 to 2006), leaf blight and vascular wilt symptoms were frequently observed in contiguous plots of maize and sorghum crops in Montecillo, Mexico and maize plots in Tecamac, Mexico. To identify and characterize the causal agent of these symptoms, isolations were conducted on leaves from areas where healthy and diseased tissues converged. Leaf sections of 1 cm2 from both crops were disinfested, placed on casamino acid-peptone-glucose (CPG) medium, and incubated at 28°C. After 48 h, only yellow colonies were observed and 12 isolates were selected for further characterization. Physiological and biochemical tests indicated that the isolates were nonfluorescent on King's B medium, and API 50 CHE (bioMérieux, Marcy l'Etoile, France) revealed that they were negative for gelatin hydrolysis, indole production, acid production from raffinose and positive for utilization of glycerol, D-glucose, mannitol, arbutine, esculine, salicine, cellobiose, maltose, melibiose, D-fucose, and D-arabitol; all characteristics of Pantoea agglomerans. Further identification of these isolates was accomplished by DNA analysis. For DNA analysis, 1.4-kbp fragments of the 16S rRNA gene were amplified with primer set 8F/1492R (3) and sequenced with U514F/800R universal primers (2). Five sequences were obtained and deposited in GenBank (Accession Nos. EF050806 to EF050810). A phylogenetic tree was constructed using the UPGMA method (mega version 3.1). Results of the phylogenetic analysis grouped the species P. ananatis, P. stewartti, and P. agglomerans into three clusters. The five unknown sequences were grouped into the P. agglomerans cluster. There was a 98 to 99% similarity of the five 16S rRNA gene sequences with P. agglomerans strain type ATCC 27155. Pathogenicity of the 12 isolates was confirmed by injecting 10⁸ CFU mL^–1 of inoculum into stems of 3-week-old maize cv. Triunfo and sorghum cold tolerant hybrid (A1×B5)×R1 seedlings in the greenhouse at 28°C and 80% relative humidity. Also, seedlings were inoculated with water, nonpathogenic isolates of P. agglomerans from maize (GM13, and HLA1), and not inoculated as negative controls. Three replications were included for each isolate and control. All test strains developed water-soaked lesions on juvenile leaves at 8 days postinoculation and were followed by chlorotic to straw-colored leaf streaks and then leaf blight symptoms at 3 weeks postinoculation. All negative control seedlings did not develop symptoms. In addition, the 12 isolates were infiltrated at 10⁷ CFU mL^–1 into tobacco leaves that displayed a hypersensitive response at 4 days, indicating the presence of the type III secretion system (1). Isolates were reisolated, and the 16S rRNA gene fragments were 100% similar to their original isolate sequences. P. agglomerans has been reported to affect other crops, including chinese taro in Brazil (2007), onion in the United States (2006) and South Africa (1981), and pearl millet in Zimbabwe (1997); however, to our knowledge, this is the first report of P. agglomerans associated with leaf blight and vascular wilt symptoms in maize and sorghum in the Central Highland Valley of Mexico. References: (1) J. Alfano and A. Collmer. Annu. Rev. Phytopathol 42:385, 2004. (2) Y. Anzai et al. Int. J. Syst. Evol. Microbiol. 50:1563, 2000. (3) M. Sasoh et al. Appl. Environ. Microbiol. 72:1825, 2006.

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha⁻¹ and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Linear and non‐linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic‐enabled prediction models have been developed for predicting complex traits in genomic‐assisted animal and plant breeding. These models include linear, non‐linear and non‐parametric models, mostly for continuous responses and less frequently for categorical responses. Several linear and non‐linear models are special cases of a more general family of statistical models known as artificial neural networks, which provide better prediction ability than other models. In this paper, we propose a Bayesian Regularized Neural Network (BRNNO) for modelling ordinal data. The proposed model was fitted using a Bayesian framework; we used the data augmentation algorithm to facilitate computations. The proposed model was fitted using the Gibbs Maximum a Posteriori and Generalized EM algorithm implemented by combining code written in C and R programming languages. The new model was tested with two real maize datasets evaluated for Septoria and GLS diseases and was compared with the Bayesian Ordered Probit Model (BOPM). Results indicated that the BRNNO model performed better in terms of genomic‐based prediction than the BOPM model.

The objective of the present study was to determine the nutritive value, the presence of antioxidant compounds, and the antioxidant capacity of coffee pulp ensiled or non-ensiled. Dry matter (DM), crude protein (CP), ash, acid detergent fiber (ADF), neutral detergent fiber (NDF), and lignin, as well as the antioxidant compounds present in coffee pulp and their antioxidant capacity, were determined. A completely randomized design was used. Data were analyzed by analysis of variance. Ensiling of coffee pulp increased the CP content from 98.6 to 111.6 g kg⁻¹ DM, NDF from 414.6 to 519.5 g kg⁻¹ DM, ADF from 383.9 to 439.3 g kg⁻¹ DM, and lignin from 122.9 to 133.6 g kg⁻¹ DM. Caffeine decreased from 5.72 to 5.02 mg g⁻¹ DM. Three antioxidant compounds were detected. Caffeic acid decreased due to ensiling (16.49 vs 14.69 mg g⁻¹ DM). Gallic acid (2.88 vs 2.58 mg g⁻¹ DM) and chlorogenic acid (62.12 vs 56.00 mg g⁻¹ DM) did not differ, and there was similar antioxidant capacity of non-ensiled (215.66 µmol trolox g⁻¹ DM) and ensiled coffee pulp (206.59 µmol trolox g⁻¹ DM). Despite the decrease in the caffeic acid content due to the ensiling process, it is possible to use either ensiled or non-ensiled coffee pulp for animal feeding because of its high antioxidant capacity.

A goodness-of-fit test for the Gumbel distribution is proposed. This test is based on the Kullback–Leibler discrimination information methodology proposed by Song ( 2002 Song , K. S. ( 2002 ). Goodness-of-fit tests based on Kullback–Leibler discrimination information . IEEE Trans. Inform. Theor. 48 : 1103 – 1117 . [Crossref], [Web of Science ®] [Google Scholar] ). The critical values of the test were obtained by using Monte Carlo simulation for small sample sizes and different levels of significance. The proposed test is compared with the tests developed by Stephens ( 1977 Stephens , M. ( 1977 ). Goodness-of-fit tests for the extreme value distribution . Biometrika 65 : 730 – 737 . [Google Scholar] ), Chandra et al. ( 1981 Chandra , M. , Singpurwalla , N. D. , Stephens , M. A. ( 1981 ). Kolmogorov statistics for tests of fit for the extreme value and Weibull distributions . J. Amer. Statist. Assoc. 74 : 729 – 735 . [Google Scholar] ), and the test given by Kinnison ( 1989 Kinnison , R. (1989). Correlation coefficient goodness of fit test for the extreme value distribution. Amer. Statistician 43:98–100. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ) in terms of their power by considering various alternative distributions. Simulation results show that the Kullback–Leibler information test has higher power than some of the studied tests.