Scielo RSS <![CDATA[Scientia Agricola]]> vol. 71 num. 6 lang. pt <![CDATA[SciELO Logo]]> <![CDATA[<b>Odor from anaerobic digestion of swine slurry</b>: <b>influence of pH, temperature and organic loading</b>]]> Farm slurry management from storage and/or treatment is the main source of odors from swine production, which are determined by factors such as operational variations (organic loading), cleaning of facilities and animal diet (pH) or environmental conditions (temperature). The aim of this study was to evaluate the influence of pH, temperature and organic loading on odor generation during anaerobic digestion of swine slurry. The methodology employed batch experimental units under controlled pH (6.0, 6.5, 7.0 and 8.0) and temperature (20, 35 and 55 °C) conditions. Additionally, an Upflow Anaerobic Sludge Blanket (UASB) system was operated under two Organic Loading Rate (OLR) conditions as Chemical Oxygen Demand (COD) (Phase I: 0.4 g L-1 d-1 of COD, Phase II: 1.1 g L-1 d-1 of COD). Odor (batch and UASB reactor) was evaluated by detection and recognition threshold as Dilution Threshold (D-T). Acidic conditions (pH 6.0) and thermophilic temperatures (55 °C) increased odors (1,358 D-T) and acidified the system (Intermediate/Total Alkalinity ratio (IT/TA): 0.85) in batch experiments. Increasing OLR on UASB reactor reduced odors from 6.3 to 9.6 D-T d-1 due to an increase in the production of biogas (0.4 to 0.6 g g-1 COD removed of biogas). <![CDATA[<b>Sward structure and livestock performance in guinea grass cv</b>: <b>Tanzania pastures managed by rotational stocking strategies</b>]]> Grazing strategy is a key element in the determination of sward structure, herbage nutritive value and animal performance. We aimed to compare the herbage characteristics and performance of livestock in pastures of Panicum maximum cv. Tanzania managed, using two rotational stocking strategies, which provided either a fixed-length rest period (FRP) of 35 days in the spring and fall and 30 days in the summer, or a variable-length rest period (VRP), determined by the time required for the canopy to achieve 70 cm in height. The pastures were evaluated in the pregrazing condition for forage mass (FM); leaf (LP), stem (SP) and dead matter (DP) percentages; and nutritive value (NV). The animals were weighed every 28 days. Pastures managed with the FRPs exhibited greater FMs, SPs and DPs and lower LPs and NVs than those managed with the VRPs. The average daily livestock weight gain was greater during the spring and summer for the VRP than for the FRP pastures, resulting in an average animal weight gain per area of 990 and 860 kg ha−¹ wet period−¹ for the pastures managed with the VRPs and FRPs, respectively. Thus, pasture rest periods that were maintained after the sward reached 70 cm in height reduced the animal performance on Tanzania guinea grass. <![CDATA[<b>Agronomic evaluation of </b><b>'Bordô'</b><b> grapevine (Ives) clones</b>]]> 'Bordô' grapevines (Vitis labrusca) have great relevance to viticulture due to the quality they can impart to wines and juices. However, this cultivar has high variation in yield, ranging from 6 to 11 t ha-1. The use of clones with superior genetic potential related to scions currently marketed may increase crop profitability and revitalize its cultivation. The aim of this study was to evaluate the agronomical responses of twelve clones of the Bordô cultivar selected over a period of 15 years according to yield and quality. The vineyard was planted in 2008. Grape plants were grafted onto '1103 Paulsen' rootstock and trained on vertical shoot positioning. The agronomical evaluations, performed in the 2011, 2012 and 2013 seasons, covered the duration of their phenological cycles, shoot growth, yield per plant, estimated total yield and physicochemical characteristics. Differences were found between clones in terms of phenology, yield components, and berry composition. Clone 6 had the lowest yield, averaging 5.0 t ha-1 whereas clone 13 was the most productive with 14.9 t ha-1. Based on the most productive vineyards in the region (10.8 t ha-1), the adoption of more productive clones can generate an increase in yield of around 38 %. <![CDATA[<b>Sequential sampling of <i>Euschistus heros</i> (Heteroptera: Pentatomidae) in soybean</b>]]> Integrated pest management programs for soybean (Glycine max (L.) Merrill) must be based on efficient sampling plans for estimating the pest population. Based on the spatial distribution of the Neotropical brown stink bug Euschistus heros (Fabricius, 1794) found on soybean, it was possible to construct a sequential sampling plan for the survey of this insect found on soybean. The experiment was carried out during two growing seasons, 2010/2011 and 2011/2012, using the transgenic soybean cultivar M 7908 RR, in plots of 10,000 m² subdivided into 100 plots of 100 m² (10 m × 10 m). Nymphs > 0.5 cm (4th and 5th instars) plus adults were counted weekly from five drop cloth technique samplings per plot. To evaluate insect dispersion in the area, the following indices were used: variance/mean ratio, Morisita's index, Green's coefficient, the k exponent of the negative binomial distribution, and estimation of the common exponent k (kc). To study probabilistic models to describe the spatial distribution of the insects, adjustments of the Poisson and negative binomial distributions were tested. Two sequential sampling plans for separate fields, one for grain production and the other for seed production, were prepared. The data fitted a negative binomial distribution and a sampling plan was drawn up using the sequential likelihood ratio test (SLRT). The maximum sampling unit number expected for control-related decision making was six in grain production fields, and nine in seed production fields. <![CDATA[<b>Genetic variability among sorghum accessions for seed starch and stalk total sugar content</b>]]> Sorghum (Sorghum bicolor (L.) Moench) is a staple food grain in many semi-arid and tropical areas of the world, notably in sub-Saharan Africa because of its adaptation to harsh environments. Among important biochemical components for sorghum for processors are the levels of starch (amylose and amylopectin) and total sugar contents. The aim of this study was to determine the genetic variation for total starch in the seed, its components and total sugar in the stalks of the sorghum accessions from Ethiopia and South Africa. Samples of 22 sorghum accessions were evaluated. Significant variations were observed in total starch (31.01 to 64.88 %), amylose (14.05 to 18.91 %), the amylose/amylopectin ratio (0.31 to 0.73) and total stalk sugar content (9.36 to 16.84 %). Multivariate analysis showed a wide genetic variation within and among germplasm accessions which could be used in the selection of parental lines for the improvement of traits of interest through breeding. The variation found among the sorghum accessions shows that an improved total starch and starch components and stalk sugar contents can be achieved through crossing these selected genotypes. <![CDATA[<b>Genetic variability for carotenoid content of grains in a composite maize population</b>]]> Local maize (Zea mays L.) varieties are cultivated by small-scale farmers in western Santa Catarina (SC) State, in southern Brazil. These small areas frequently present many problems related to biotic and non-biotic stresses, which have limited the economic output and income of the farmers. Production from local varieties for human consumption would be an alternative way of improving income and stimulating on farm conservation. The genetic variability of the total carotenoid content (TCC) of kernels in a local maize population was evaluated for their economic exploitation potential as biofortified food. Two independent samples of 96 half-sib families (HSF) plus four checks were evaluated in two groups of experiments in western SC and each one was carried out in two environments. They were set out in a 10 × 10 partially balanced lattice with three replications per location; plots consisted of one row, 5.0 m long with 1.0 m between rows. TCC ranged from 11 to 23 µg g-1, averaging ≈16 µg g-1 in the pooled analysis over the two sets. The local composite population exhibited genetic variability in order to increase the TCC of grains in the second cycle of selection by the convergent-divergent scheme. <![CDATA[<b>Growth regulators and darkness increase efficiency in <i>in vitro</i> culture of immature embryos from peppers</b>]]> Common pepper (Capsicum annuum L.) is one of the most important vegetables in the world, and extensive breeding efforts are being made to develop new improved strains of this species. In this regard, in vitro culture of immature embryos may help breeders accelerate breeding cycles and overcome interspecific barriers, among other applications. In this study, we have optimized a protocol for in vitro culture of immature embryos of C. annuum. Levels of indole-3-acetic acid (IAA) and zeatin have been tested to improve the efficiency (germination rates) of this technique in C. annuum embryos at the four main immature stages (i.e. globular, heart, torpedo, and early cotyledonary) from four varietal types of this species (California Wonder, Piquillo, Guindilla, and Bola). The effect of 5-day initial incubation in the dark was also tested on the most efficient hormone formulation. On average, relatively low levels of both IAA and zeatin (0.01 mg L−¹ each) (M1) provided the highest germination rates, particularly in the advanced stages (torpedo and cotyledonary). To a lesser extent, the lack of these growth regulators (M0) or high IAA (0.2 mg L−¹)/low zeatin (0.01 mg L−¹) (M2) combination also had a positive response. On the contrary, high zeatin levels (0.2 mg L−¹) produced very low germination rates or callus development (efficiency 0-7 %). Different responses were also found between genotypes. Thus, considering the best media (M0, M1, M2), Bola embryos had the highest rates. M1 plus 5-days of initial dark incubation (M1-D) improved the efficiency rates at all embryo stages, particularly in the earliest (globular) embryos which increased from 3 % to > 20 %. <![CDATA[<b>Neural networks for predicting breeding values and genetic gains</b>]]> Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments. <![CDATA[<b>Waterlogging-induced changes in fermentative metabolism in roots and nodules of soybean genotypes</b>]]> Waterlogging blocks the oxygen supply to the root system which inhibits respiration, and greatly reduces the energy status of cells that affect important metabolic processes. This study evaluated fermentative metabolism and carbohydrate contents in the root system of two soybean (Glycine max L. Merril) genotypes under hypoxic and post-hypoxic conditions. Nodulated plants (genotypes Fundacep 53 RR and BRS Macota) were grown in vermiculite and transferred to a hydroponic system at the reproductive stage. The root system was submitted to hypoxia by flowing N2 (nitrogen) gas in a solution for 24 and 72 h. For recovery, plants returned to normoxia condition by transfer to vermiculite for 24 and 72 h. Fermentative enzyme activity, levels of anaerobic metabolites and carbohydrate content were all quantified in roots and nodules. The activity of alcohol dehydrogenase, pyruvate decarboxylase and lactate dehydrogenase enzymes, as well as the content of ethanol and lactate, increased with hypoxia in roots and nodules, and subsequently returned to pre-hypoxic levels in the recovery phase in both genotypes. Pyruvate content increased in nodules and decreased in roots. Sugar and sucrose levels increased in roots and decreased in nodules under hypoxia in both genotypes. Fundacep RR 53 was more responsive to the metabolic effects caused by hypoxia and post-hypoxia than BRS Macota, and it is likely that these characteristics contribute positively to improving adaptation to oxygen deficiency. <![CDATA[<b>Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification</b>]]> The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations. <![CDATA[<b>Treatment of cattle-slaughterhouse wastewater and the reuse of sludge for biodiesel production by microalgal heterotrophic bioreactors</b>]]> Microalgal heterotrophic bioreactors are a potential technological development that can convert organic matter, nitrogen and phosphorus of wastewaters into a biomass suitable for energy production. The aim of this work was to evaluate the performance of microalgal heterotrophic bioreactors in the secondary treatment of cattle-slaughterhouse wastewater and the reuse of microalgal sludge for biodiesel production. The experiments were performed in a bubble column bioreactor using the microalgae Phormidium sp. Heterotrophic microalgal bioreactors removed 90 % of the chemical oxygen demand, 57 % of total nitrogen and 52 % of total phosphorus. Substantial microalgal sludge is produced in the process (substrate yield coefficient of 0.43 mg sludge mg chemical oxygen demand−¹), resulting in a biomass with high potential for producing biodiesel (ester content of more than 99 %, cetane number of 55, iodine value of 73.5 g iodine 100 g−¹, unsaturation degree of ~75 % and a cold filter plugging point of 5 ºC). <![CDATA[<b>Sequential path analysis</b>: <b>what does "sequential" mean?</b>]]> Studying relationships among plant and crop traits is crucial for crop scientists to understand complex biological systems that occur in plants and the field. Such knowledge constitutes the basis for more practical information on how to manage breeding and production to provide better or more suitable cultivars, higher yields, lower yield gaps, and resistance to pests etc. To acquire such knowledge, however, representative models of associations between plant and crop traits must be constructed. In path analysis - one of the major methods for analyzing multivariate relationships between quantitative traits - it is important to decide on an appropriate model for these associations, a model that is representative of the corresponding biological phenomena that are of interest to crop researchers. Adopting this "point of view", we asked various questions relating to such model building: (i) how should sequentiality in sequential path analysis be understood? (ii) how should it be interpreted? (iii) how should such sequential models be formulated? We discussed these issues in the context of crop science. Differences in simple and complex (sequential) models of path analysis are presented. Based on crop science examples, we show how important it is to correctly represent the biological relationships for a path analysis model. <![CDATA[<b>Exploring interactions of plant microbiomes</b>]]> A plethora of microbial cells is present in every gram of soil, and microbes are found extensively in plant and animal tissues. The mechanisms governed by microorganisms in the regulation of physiological processes of their hosts have been extensively studied in the light of recent findings on microbiomes. In plants, the components of these microbiomes may form distinct communities, such as those inhabiting the plant rhizosphere, the endosphere and the phyllosphere. In each of these niches, the "microbial tissue" is established by, and responds to, specific selective pressures. Although there is no clear picture of the overall role of the plant microbiome, there is substantial evidence that these communities are involved in disease control, enhance nutrient acquisition, and affect stress tolerance. In this review, we first summarize features of microbial communities that compose the plant microbiome and further present a series of studies describing the underpinning factors that shape the phylogenetic and functional plant-associated communities. We advocate the idea that understanding the mechanisms by which plants select and interact with their microbiomes may have a direct effect on plant development and health, and further lead to the establishment of novel microbiome-driven strategies, that can cope with the development of a more sustainable agriculture.