Characterization of the dry bean polygalacturonase-inhibiting protein (PGIP) gene family during Sclerotinia sclerotiorum (Sclerotiniaceae) infection.

Polygalacturonase-inhibiting proteins are leucine-rich repeat proteins that inhibit fungal endopolygalacturonases. The interaction of polygalacturonase-inhibiting protein with endopolygalacturonases limits the destructive potential of endopolygalacturonases and may trigger plant defense responses induced by oligogalacturonides. We examined the expression of fungal pg and plant Pvpgip genes in bean (Phaseolus vulgaris) stems infected with Sclerotinia sclerotiorum to determine whether any of them are associated with the infection process. Transcriptional analysis was carried out by means of semi-quantitative reverse transcription PCR or real-time PCR. The sspg1 gene was highly expressed during infection; sspg3 was regulated during the later phases of infection; sspg5 was more uniformly expressed during infection, whereas sspg6 was only weakly expressed. During the course of infection, Pvpgip1 transcripts were not detected at early stages, but they appeared 72 h post-inoculation. High levels of Pvpgip2 expression were observed during the initial phase of infection; the transcript peaked by 48 h post-inoculation and declined by 72 h post-inoculation. Pvpgip3 expression increased strongly at 96 h post-inoculation. Pvpgip4 was constantly present from 24 h post-inoculation until the end of the experiment. However, we detected higher levels of the Pvpgip4 transcript in the necrotic lesion area than in plants that had been mechanically wounded. Remarkably, only Pvpgip4 appeared to be moderately induced by mechanical wounding. These results provide evidence that endopolygalacturonases contribute to the infection process during host colonization by promoting the release of plant cell oligogalacturonides, which are powerful signaling molecules and may also activate plant defenses, such as polygalacturonase-inhibiting proteins

Mineração em metadados aplicados ao processo de desenvolvimento de coleções

The increasing of the new scientific areas, along with the multitude of information items, it's pushing the librarians to find creatively novel ways to gather information for the development of the collection under their responsability. This must definitely change the usual practice of these professionals towards mastering moderns IT tools in order to better analyse a huge volume of data in a shorter amount of time. Hence, this paper discusses a proposal of building metadata of the syllabus plans, within a university to easy the minering some important information out of them as part of a master collection development policy

Avaliação de sistema de produção alternativo para feijão com uso de fungicidas.

bitstream/item/56911/1/PA-15610001.pd

Combining unsupervised and supervised neural networks in cluster analysis of gamma-ray burst

The paper proposes the use of Kohonen's Self Organizing Map (SOM), and supervised neural networks to find clusters in samples of gammaray burst (GRB) using the measurements given in BATSE GRB. The extent of separation between clusters obtained by SOM was examined by cross validation procedure using supervised neural networks for classification. A method is proposed for variable selection to reduce the "curse of dimensionality". Six variables were chosen for cluster analysis. Additionally, principal components were computed using all the original variables and 6 components which accounted for a high percentage of variance was chosen for SOM analysis. All these methods indicate 4 or 5 clusters. Further analysis based on the average profiles of the GRB indicated a possible reduction in the number of clusters

Características físico-químicas de sucos de uvas Isabel Precoce e BRS Violeta elaborados no Nordeste do Brasil.

A avaliação da adaptação de variedades de uva destinadas à elaboração de sucos a determinadas condições de clima e solo requer estudos específicos, contando com uma avaliação analítica do produto elaborado. O objetivo do presente trabalho foi comparar os sucos obtidos a partir de duas variedades de uvas labruscas produzidas no Vale do São Francisco. Os sucos foram elaborados em triplicata pelo método artesanal, a partir de uvas das variedades BRS Violeta e Isabel Precoce. As variáveis analisadas foram pH, sólidos solúveis totais, acidez total e volátil, dióxido de enxofre livre e total, teor alcoólico, índice de cor, antocianinas totais e índice de polifenóis totais (I-280). As médias foram comparadas pelo teste de Tukey a 5% de probabilidade. Apenas para o pH não houve diferença significativa, sendo as demais variáveis significativamente diferentes entre as variedades, nos sucos analisados. A variedade BRS Violeta apresentou excelente potencial para a elaboração comercial de sucos na região

Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil

Revista oficial de la Asociación Española de Teledetección[EN] Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas.[ES] En las últimas décadas se han perdido grandes áreas de pequeñas marismas interiores (SIM) a través de la conversión a tierras agrícolas, urbanas e industriales. Las marismas restantes enfrentan varias amenazas, como el drenaje para la agricultura, la construcción de carreteras e instalaciones portuarias, la eliminación de residuos, entre otras. Este estudio integra 17 índices espectrales de teledetección y un método basado en árboles de decisión (DT) para cartografiar áreas de pequeñas marismas interiores utilizando imágenes del satélite Sentinel 2A de verano e invierno. Los resultados muestran que los índices de teledetección, aunque no han sido desarrollados específicamente para la delimitación de marismas, presentan resultados satisfactorios para clasificar estos ecosistemas. Los índices que demostraron ser más útiles para la clasificación de marismas mediante técnicas de DT en el área de estudio fueron el NDTI, BI, NDPI y BI_2, con 25.9%, 17.7%, 11.1% y 0.8%, respectivamente. En general, la proporción correcta encontrada fue de 95.9% y 77.9% para las imágenes de verano e invierno, respectivamente. Nuestra hipótesis es que esta variación significativa de la proporción correcta está relacionada con el período de siembra del arroz en verano y/o con el período de oscilación del nivel del agua en invierno. Para futuras investigaciones, recomendamos el uso de sensores remotos activos (por ejemplo, radar) y mapas de suelo además de los índices espectrales de teledetección para obtener mejores resultados en la delimitación de pequeñas áreas de marismas interiores.João Paulo Delapasse Simioni thanks the CAPES agency for providing a doctoral fellowship. The au-thors acknowledge the Center for Remote Sensing and Meteorology (CEPSRM) at the Federal University of Rio Grande do Sul (UFRGS) for the support provided for this research.Simioni, JPD.; Guasselli, LA.; Ruiz, LFC.; Nascimento, VF.; De Oliveira, G. (2018). Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil. Revista de Teledetección. (52):55-66. doi:10.4995/raet.2018.10366SWORD556652Artigas, F. J., Yang, J. 2006. Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. Wetlands, 26(1), 271. https:// doi.org/10.1672/0277-5212(2006)26[271:sdomvt]2. 0.co;2Belloli, T. F. 2016. Environmental Impacts Due to Rice, Large Banhado Environmental Protection Area - RS. Federal University of Rio Grande do Sul. Retrieved from https://www.lume.ufrgs.br/bitstream/ handle/10183/158968/001023034.pdf?sequence=1Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., Marani, M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1), 54-67. https://doi.org/10.1016/j.rse.2006.06.006Canadian Wetland Inventory Technical Group. 2016. Canada Wetland Inventory (Data Model). Stonewall. Retrieved from http://www.ducks.ca/assets/2017/01/ CWIDMv7_01_E.pdfClevers, J. G. P. W., Leeuwen, H. J. C. Van, Sensing, R., Verhoef, W. 1989. Estimanting apar by means of vegetation indeces: a sensitivity analysis. XXIX ISPRS Congress Technical Commission VII: Interpretation of Photographic and Remote Sensing Data, 691-698.Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. https:// doi.org/10.1016/0034-4257(91)90048-BDeering, D. W. 1975. Measuring forage production of grazing units from Landsat MSS data. Proceedings of 10th International Symposium on Remote Sensing of Environment, 1975, 1169-1178.Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063Di Vittorio, C. A., Georgakakos, A. P. 2018. Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1-17. https://doi.org/10.1016/j.rse.2017.11.001Dong, Z., Wang, Z., Liu, D., Song, K., Li, L., Jia, M., Ding, Z. 2014. Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China. Journal of the Indian Society of Remote Sensing, 42(3), 569-576. https://doi.org/10.1007/s12524-013-0357-1Dvorett, D., Davis, C., Papeş, M. 2016. Mapping and Hydrologic Attribution of Temporary Wetlands Using Recurrent Landsat Imagery. Wetlands, 36(3), 431- 443. https://doi.org/10.1007/s13157-016-0752-9Environmental Protection Agency. 2001. Functions and Values of Wetlands. Watershed Academy Web. Washington. Retrieved from https://www.epa.gov/wetlandsfunctionsvaluesEscadafal, R. 1989. Remote sensing of arid soil surface color with Landsat thematic mapper. Advances in Space Research, 9(1), 159-163. https://doi.org/10.1016/0273-1177(89)90481-XEtchelar, C. B. 2017. Erosive Processes in Wetlands. Rio Grande do Sul Federal University. Retrieved from https://www.lume.ufrgs.br/bitstream/ handle/10183/171041/001054625.pdf?sequence=1Fariña, J. M., He, Q., Silliman, B. R., Bertness, M. D. 2017. Biogeography of salt marsh plant zonation on the Pacific coast of South America. Journal of Biogeography, 12, 238-247. https://doi.org/10.1111/ jbi.13109Fluet-Chouinard, E., Lehner, B., Rebelo, L. M., Papa, F., Hamilton, S. K. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158, 348-361. https://doi.org/10.1016/j.rse.2014.10.015Friedl, M.A. M. A., Brodley, C. E. C. E. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399- 409. https://doi.org/10.1016/S0034-4257(97)00049-7Gao, B. C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257- 266. https://doi.org/10.1016/S0034-4257(96)00067-3Gedan, K. B., Crain, C. M., Bertness, M. D. 2009. Smallmammal herbivore control of secondary succession in New-England tidal marshes. Ecology, 90(2), 430- 440. https://doi.org/10.1890/08-0417.1Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-XJensen, J. R. 2007. Remote sensing of the environment : an earth resource perspective. Pearson Prentice Hall.Judd, C., Steinberg, S., Shaughnessy, F., Crawford, G. 2007. Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California. Wetlands, 27(4), 1144-1152. https://doi.org/10.1672/0277- 5212(2007)27[1144:msmvua]2.0.co;2Junk. 2013. Definição e Classificação das Áreas Úmidas (AUs) Brasileiras : Base Científica para uma Nova Política de Proteção e Manejo Sustentável Prefácio : Lista dos autores e suas instituições : Centro de Pesquisa Do Pantanal, BrazilJunk, W. J., Bayley, P. B., Sparks, R. E. 1989. The Flood Pulse Concept in River-Floodplain Systems. International Large River Symposium.Junk, W. J., Piedade, M. F. 2015. Áreas Úmidas (AUs) Brasileiras: Avanços e Conquistas Recentes. Boletim Ablimno, 41(2), 20-24.Junk, W. J., Piedade, M. T. F., Lourival, R., Wittmann, F., Kandus, P., Lacerda, L. D., Agostinho, A. A. 2014. Brazilian wetlands: Their definition, delineation, and classification for research, sustainable management, and protection. Aquatic Conservation: Marine and Freshwater Ecosystems, 24(1), 5-22. https://doi. org/10.1002/aqc.2386Kandus, P., Minotti, P., Malvárez, A. I. 2008. Distribution of wetlands in Argentina estimated from soil charts. Acta Scientiarum - Biological Sciences, 30(4), 403-409. https://doi.org/10.4025/actascibiolsci.v30i4.5870Kaplan, G., Avdan, U. 2017. Mapping and Monitoring Wetlands Using SENTINEL 2 Satellite Imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV, 271-277. https:// doi.org/10.5194/isprs-annals-IV-4-W4-271-2017Kaplan, G., Avdan, U. 2017. Wetland Mapping Using Sentinel 1 SAR Data. In Suha Ozden, R. Cengiz Akbulak, Cuneyt Erenoglu, Oznur Karaca, Faize Saris, & Mustafa Avcioglu (Eds.), International Symposium on GIS Applications in Geography & Geosciences.Kaufman, Y., Tanre, D. 1992. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2). https://doi.org/10.1109/36.134076Kulawardhana, R. W., Thenkabail, P. S., Vithanage, J., Biradar, C., Islam, M. A. a, Gunasinghe, S., Alankara, R. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 7(2), 62-96. https://doi. org/10.1017/CBO9780511806049Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., Lafaye, M. 2007. Classification of ponds from highspatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74. https://doi.org/10.1016/j. rse.2006.07.012Leite, M. G., Guasselli, L. A. 2013. Spatio-temporal dynamics of aquatic macrophytes in Banhado Grande, Gravataí River basin,. Para Onde!?, 7(1), 17-24.Liu, L., Liu, Y. H., Liu, C. X., Wang, Z., Dong, J., Zhu, G. F., Huang, X. 2013. Potential effect and accumulation of veterinary antibiotics in Phragmites australis under hydroponic conditions. Ecological Engineering, 53, 138-143. https://doi.org/10.1016/j. ecoleng.2012.12.033Mahdavi, S., Salehi, B., Amani, M., Granger, J. E., Brisco, B., Huang, W., Hanson, A. 2017. ObjectBased Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data. Canadian Journal of Remote Sensing, 43(5), 432-450. https://doi.org/10.1080/07038992.2017.1342206Maltchik, L., Rolon, A. S., Guadagnin, D. L., Stenert, C. 2004. Wetlands of Rio Grande do Sul, Brazil: a classification with emphasis on plant communities. Acta Limnol. Bras, 16(2), 137-151.Mao, R., Ye, S.-Y., Zhang, X.-H. 2018. SoilAggregate-Associated Organic Carbon Along Vegetation Zones in Tidal Salt Marshes in the Liaohe Delta. CLEAN - Soil, Air, Water, 1-7. https://doi.org/10.1002/clen.201800049McFeeters, S. K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714Mcowen, C. J., Weatherdon, L. V, Bochove, J.-W. Van, Sullivan, E., Blyth, S., Zockler, C., Fletcher, S. 2017. A global map of saltmarshes. Biodiversity Data Journal, 5(5), e11764. https://doi.org/10.3897/BDJ.5.e11764Miranda, C. de S., Paranho Filho, A. C., Pott, A. 2018. Changes in vegetation cover of the Pantanal wetland detected by vegetation index: a strategy for conservation. Biota Neotropica, 18(1), 1-6. https://doi.org/10.1590/1676-0611-bn-2016-0297Mondal, I., Bandyopadhyay, J. 2014. Coastal Wetland Modeling Using Geoinformatics Technology of Namkhana Island, South 24 Parganas, WB, India. Open Access Library Journal, 975, 1-17. https://doi.org/10.4236/oalib.1100975Nielsen, S. 1994. Geomorfologia da bacia do rio GravataíRS. In Bacia do rio Gravataí-RS: informações básicas para a gestão territorial (pp. 1-18). Porto Alegre: Proteger.Nunes da Cunha, C., Piedade, M. T. F., Junk, W. J. 2015. Classificação e Delineamento das Áreas Úmidas Brasileiras e de seus Macrohabitats. EdUFMT (Vol. 1). Cuiaba. https://doi.org/10.1017/CBO9781107415324.004Pearson, R. L., Miller, L. D. 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie. Remote Sensing of Environment, 8, 1355-1365.Pontius, R. G., Millones, M. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. https://doi.org/10.1080/01431161.2011.552923Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. https://doi.org/10.1016/0034-4257(94)90134-1Ramos, R. A., Pasqualetto, A. I., Balbueno, R. A., Quadros, E. L. L. de, Neves, D. D. das. 2014. Mapeamento e diagnóstico de áreas úmidas no Rio Grande do Sul, com o uso de ferramentas de geoprocessamento. In Anais do Simposio de Áreas Protegidas (pp. 17-21). Viçosa.Ramsar. 2002. A Framework for Wetland Inventory. 8th Meeting of the Conference of the Contracting Parties to the Convention on Wetlands. Valencia. Retrieved from http://archive.ramsar.org/pdf/inventoryframework-2002.pdfRichardson, A. J., Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.Rossato, M. S. 2011. Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia. Universidade Federal do Rio Grande do Sul.Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309-317. https://doi.org/citeulike-article-id:12009708Ruiz, L. F. C., Caten, A. ten, Dalmolin, R. S. D. 2014. Árvore de decisão e a densidade mínima de amostras no mapeamento da cobertura da terra. Ciência Rural, 44(6), 1001-1007. https://doi.org/10.1590/S0103-84782014000600008Sakané, N., Alvarez, M., Becker, M., Böhme, B., Handa, C., Kamiri, H. W., Langensiepen, M., Menz, G., Misana, S., Mogha, N. G., Möseler, B. M., Mwita, E. J., Oyieke, H. A., Van Wijk, M. T. 2011. Classification, characterisation, and use of small wetlands in East Africa. Wetlands, 31, 1103. https://doi.org/10.1007/s13157-011-0221-4Sharma, A., Panigrahy, S., Singh, T. S., Patel, J. G., Tanwar, H. 2014. Wetland Information System Using Remote Sensing and GIS in Himachal Pradesh , India. Asian Journal of Geoinformatics, 14(4), 13-22.Sharpe, P. J., Kneipp, G., Forget, A. 2016. Comparison of Alternative Approaches for Wetlands Mapping: A Case Study from three U.S. National Parks. Wetlands, 36(3), 547-556. https://doi.org/10.1007/s13157-016-0764-5Silva, R. C. da. 2016. Estudo da dinâmica da fragilidade ambiental na Bacia Hidrográfica do Rio Gravataí, RS. Universidade Federal da Bahia.Simioni, J. P. D., Guasselli, L. A., Etchelar, C. B. 2017. Connectivity among Wetlands of EPA of Banhado Grande, RS Conetividade entre Áreas Úmidas, APA do Banhado Grande, RS. Brazilian Journal of Water Resources, 22(15). https://doi.org/10.1590/2318-0331.011716096Stefano, L. de. 2003. WWF ' s Water and Wetland Index Summary of Water Framework Directive results. WWF European Living Waters Programme c/o. San Francisco.Subramaniam, S., Saxena, M. 2011. Automated algorithm for extraction of wetlands from IRS resourcesat LISS III data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 193-198). Bhopal.Teixeira, S. G. 2011. Radar de abertura sintética aplicado ao mapeamento e reconhecimento de zonas úmidas costeiras. Universidade Federal do Pará.Visser, J. M., Sasser, C. E. 1999. Marsh Vegetation of the Mississippi River Deltaic Plain. Estuaries, 21(4B), 818-828.Walsh, N., Bhattasali, N., Chay, F. 2014. Mapping Tidal Salt Marshes.White, D. C., Lewis, M. M., Green, G., Gotch, T. B. 2016. A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin. Ecological Indicators, 60, 1309-1320. https://doi.org/10.1016/j.ecolind.2015.01.032Xu, H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. https://doi.org/10.1080/01431160600589179Yan, D., Wünnemann, B., Hu, Y., Frenzel, P., Zhang, Y., Chen, K. 2017. Wetland evolution in the Qinghai Lake area, China, in response to hydrodynamic and eolian processes during the past 1100 years. Quaternary Science Reviews, 162, 42-59.Zhou, Q., Jing, Z., Jiang, S. 2003. Remote sensing image fusion for different spectral and spatial resolutions with bilinear resampling wavelet transform. In Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems 2, 1206-1213. Shanghai: IEEE. https://doi.org/10.1109/ITSC.2003.125267

Variação fenotípica em populações segregantes de mamoeiro.

O mamoeiro (Carica papaya L.) é uma cultura de expressiva importância para o Brasil, um dos principais produtores da fruta, com produção de 1,9 milhão de toneladas em 2008, participando com 20,8% do mercado mundial. Atualmente, há uma tendência de crescimento das exportações brasileiras, iniciada pela abertura do mercado-americano, o que deverá assegurar a estabilidade e maior rentabilidade da cultura (BRAPEX, 2006; ANUÁRIO..., 2010). A ocupação da maior parte dos plantios comerciais por apenas três cultivares tem restringido a variabilidade genética e aumentado a vulnerabilidade da cultura do mamoeiro, ao ataque de pragas e doenças, limitando o seu desenvolvimento. Para dar suporte a essa produção, são desenvolvidos programas de melhoramento visando o desenvolvimento de linhagens, variedades ou híbridos com alta produtividade, boa precocidade de frutificação, porte baixo, resistência às doenças, ocorrência mínima de flores hermafroditas carpelóides, pentândricas e estéreis, elevado teor de sólidos solúveis (> 14o Brix) e padrões de peso cor e firmeza, de acordo com as exigências do mercado. A obtenção de linhagens e híbridos de mamoeiro é favorecida pela possibilidade de o mamoeiro poder ser autopolinizado sem expressiva perda de vigor (DANTAS & LIMA, 2001).pdf 2744 CHAGAS NETO, F., i. e, FRANCISCO DAS CHAGAS VIDAL NET