Academic literature on the topic 'Gwydir River System (N S W )'

The drivers that determine the hydrological connectivity (HC) are complex and interrelated, and disentangling this complexity will improve the administration of the river–lake interconnection system. Dongting Lake, as a typical river–lake interconnected system, is freely connected with the Yangtze River and their HC plays a major role in keeping the system healthy. Climate, hydrology, and anthropogenic activities are associated with the HC. In this study, hydrological drivers were divided into the total flow of three inlets (T-flow) and the total flow of four tributaries (F-flow). To elucidate the HC of the Dongting Lake, HC was calculated by geostatistical methods in association with Sentinel-2 remote sensing images. Then, the structural equation model (SEM) was used to quantify the impacts of hydrology (F-flow, and T-flow) and meteorology (precipitation, evaporation, and temperature) on HC. The geostatistical analysis results demonstrated that the HC showed apparent seasonal change. For East and West Dongting Lake, the dominant element was north–south hydrological connectivity (N–S HC), and the restricted was west–east hydrological connectivity (W-E HC), but the dominant element was E–W HC and the restricted was N–S HC in South Dongting Lake. The results of SEM showed that N–S HC was mainly explained by T-flow (r = 0.49, p < 0.001) and F-flow (r = 0.28, p < 0.05). T-flow, temperature (r = 0.33, p < 0.05), and F-flow explained E–W HC. The finding of this work supports the management of both the Dongting Lake floodplain and other similar river–lake floodplain systems.

Two crossed fault systems with NW-SE and E-W directions affect on the central and southern part of the Almyros basin. The uplift movement in the western part of the basin, with importance vertical displacement (up to 200m) of the lignite layers and the formation river terraces are related with the activity of the first fault NWSE direction. The second fault with E-W direction, located along Xerias river, affect on drainage system with hydrographie network from the south to the north development. In the southern part of the basin and on the Orthrys mountain a fault system with E-W trending affects on alpine basement and neogene deposits. This fault system forms the southern boundary of the Almyros basin. The recent brittle tectonic during Neogene-Quaternary is connected with the evolution and the configuration of the Almyros basin as well as volcanic activity of the area. The morphological differentiations of Almyros basin, the drainage system and the recent landforms with morphogenic activity are controlled by the recent brittle tectonics. The normal fault systems in the studied area caused by the extensional stress field (σ3), trending N-S to NNW-SSE, which controls the geodynamic regime since Lower Pleistocene. This geodynamic regime has defined the recent morphological and morphotectonic evolution of the studied area.

Many water sources around us which have kinetic energy to run waterwheels are not optimally utilized. This energy can be converted into an energy source that can produce electricity. Therefore this study produced a design of a waterwheel that could be used in low-flow rivers to produce electricity by adding generators. Waterwheel modeling using Ansys is calculated based on flow assumptions. Modeling using this system provides advantages in the form of computational power efficiency, the stability of numerical calculations and the accuracy of the resulting solutions. Numerical analysis of the waterwheel is assumed that the waterwheel is half floating on the surface of the water. As stated in the limitation of the problem that the incoming water flowing at a speed of 5 m/s from the flow moves the wheel. The flow rate of water that hit the blade on the waterwheel causes the waterwheel to rotate which is pressured by the flow of water with a number of 12 blades. With a relatively simple design, the waterwheel produces a wheel rotation I of 91 Rpm and II of 78 Rpm, with a torque of 39.2 N by using some analysis of this design can be applied to river flow with low flow velocity. The relatively simple design makes it easy to be produced and maintenance. River flow used is in the Malang District with a flow velocity of 1 m/s gets a power of 1128 W on waterwheel I while on waterwheel II gets a power of 967 W.

New species of Hydraenidae are described in the genera Prosthetops Waterhouse (1), Pterosthetops Perkins (1), Parasthetops Perkins & Balfour-Browne (13), and Mesoceration Janssens (24). New collecting locality data are given for the following species described by Perkins & Balfour-Browne (1994): Parasthetops aeneus, P. nigritus, P. spinipes, P. curidius, Mesoceration distinctum, M. rivulare, M. jucundum, M. splendorum, M. rubidum, M. fusciceps, M. languidum, M. dissonum, M. rufescens, and M. brevigranum. High resolution digital images of the holotypes of new species are presented (online version in color), and male genitalia are illustrated. Distribution maps are provided for all prosthetopine species in the genera Prosthetops, Pterosthetops, Parasthetops, and Mesoceration. The following 39 new species are described (type locality in South Africa unless otherwise given): Prosthetops gladiator (Eastern Cape Province, summit of Prentjiesberg); Pterosthetops hawequas (Western Cape Province, Hawaquas radio tower); Parasthetops benefossus(Western Cape Province, Wiedouw farm), P. buunicornus (Lesotho: Drakensberg, Sani Pass Valley), P. confluentus (Eastern Cape Province, Little Karroo, Baviaanskloof N valley), P. lemniscus (Lesotho: Drakensberg, Sani Pass Valley), P. namibiensis (Namibia: Windhoek, Eros Mt.), P. pampinus (Western Cape Province, Dorps River into Prins Albert, Swartbergpas), P. parallelus (Northern Cape Province, Richtersveld, Oemsberg), P. propitius (Lesotho: Drakensberg, Sani Pass Valley), P. retinaculus (Eastern Cape Province, Sundays River system, Letskraal), P. sebastiani (Lesotho: Drakensberg, Sani Pass Valley), P. semiplanus (Eastern Cape Province, Sundays River system, Letskraal), P. striatus (Northern Cape Province, Namaqualand, Kamieskroon), P. unicornus (Eastern Cape Province, Naudes Nek, 12 miles ENE Rhodes); Mesoceration barriotum (Western Cape Province, Cape-Swartberg, Seweweekspoort Kloof), M. bicurvum (Eastern Cape Province, Wildebees River), M. bispinum (KwaZulu-Natal Province, Weza, Impetyene Forest), M. compressum (Eastern Cape Province, S. coast, Dwesa forest reserve), M. concavum (Mpumalanga Province, Blyderiver Canyon), M. curvosum (KwaZulu-Natal Province, Umtamvuna River), M. disjunctum (Eastern Cape Province, Nature's Valley Reserve), M. drakensbergensis (Lesotho, Drakensberg, Sani Pass Valley), M. durabilis (Western Cape Province, 2 miles SW of Citrusdal), M. granulovestum (Western Cape Province, Cederberg, Eikenboom), M. incarinum (Lesotho, Drakensberg, Sani Pass Valley), M. integer (KwaZulu-Natal Province, Busheladi Stream on Lundy's Hill near Deepdale), M. littlekarroo (Western Cape Province, Little Karroo, Rus-en-vredewaterf), M. longipennis (Western Cape Province, W. Wiedouw farm), M. maluti (Lesotho, Drakensberg, Sani Pass Valley), M. natalensis (KwaZulu-Natal Province, Umkomaas River, where crossed by Himeville to Impendhle road), M. periscopum (Western Cape Province, Cederberg, Eikenboom), M. piceum (Western Cape Province, Cederberg, Eikenboom), M. rapidensis (Western Cape Province, S. W. Cape Mts., Hawequas SE), M. repandum (Western Cape Province, Cederberg, Eikenboom), M. reticulatum (Western Cape Province, Nuweberg Forest Station), M. semicarinulum (Western Cape Province, Groot Toren farm), M. tabulare (Western Cape Province, Platteklip Gorge, north face of Table Mountain), M. umbrosum (Western Cape Province, Wiedouw farm).

Minimum tillage reportedly reduce erosion, avoid soil degradation and improve crop productivity. This study aimed to determine how tillage operations may affect either nutrient accumulation or nutrient losses by erosion. The study was, carried out from December, 2000 to March, 2001, in the watershed of the Caetés River, in Rio de Janeiro State, Brazil (22º25'43"S, 43º25'07"W). The experiment was set up in sandy clay Kandiudult soil, 60% slope, under cucumber (Cucumis sativus L.) crop. Soil samples were collected before planting and after harvest, on 22.0 X 4.0 m Greeoff plots. After each rainfall, fine sediments carried by runoff were deposited into two collecting tanks in a row, installed at the end of each plot, and were later dried, weighed and stored for analyses. Treatments (n = 4) were characterized by different tillage systems: (i) downhill plowing followed by the burning of crop residues (DPB); (ii) downhill plowing with no burning of the crop residues (DPNB); (iii) animal traction contour plowing, with strips of guinea grass planted at a spacing of 7.0 m (AT); and (iv) minimum tillage (MT). Samples of the soil-plowed layer were collected before planting and after harvest, between the rows and from the plants. Total concentration of Ca, Mg, K and P were determined after extraction with nitric perchloride digestion. Labile P and exchangeable K were extracted with the Mehlich 1 extractant solution. The MT system reduced losses of both exchangeable bases (15%) and P (8%), and affected the distribution of labile and organic P. Crop residues left on soil surface in the MT system, resulted in increased organic matter content. Downhill plowing, the most used tillage operation in the region, resulted in the greatest losses of Ca, Mg, K, and P.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. 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Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. 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Can Gio mangrove forest (CGM) is located downstream of Ho Chi Minh City (HCMC), situated between an estuarine system of Dong Nai - Sai Gon river and a part of Vam Co river. The CGM is the largest restored mangrove forest in Vietnam and the UNESCO’s Mangrove Biosphere Reserve. The CGM has been gradually facing to numeric challenges of global climate change, environmental degradation and socio-economic development for the last decades. To evaluate sediment quality in the CGM, we collected 13 cores to analyze for sediment grain size, organic matter content, and trace element concentration of Cd, Cr, Cu, Ni, Pb, Zn. Results showed that trace element concentrations ranged from uncontaminated (Cd, Cu, and Zn) to very minor contaminated (Cr, Ni, and Pb). The concentrations were gradually influenced by suspended particle size and the mangrove plants.ReferencesAnh M.T., Chi D.H., Vinh N.N., Loan T.T., Triet L.M., Slootenb K.B.-V., Tarradellas J., 2003. 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The alkali volcanics and intrusive rocks, dated at around 35-33Ma, are cut by mineralised northeast and east trending faults showing predominant evidence for strike-slip. Mineralisation includes haematite-Au-Cu and is accompanied by iron-rich alteration of the volcanic rocks. Detailed assessment of the geometry of the fault system at Pu Sam Cap suggests that the faults formed as a Riedel shear system during left-lateral slip within the Song Hong-Song Chay shear zone and the numerous contemporaneous northwest trending faults to the south; the northeast trending faults are interpreted as dextral “book-end’’ faults between major northwest trending faults enclosing the Pu Sam Cap massif. As mineralisation is hosted within these faults and is also associated with lamprohyric dykes it confirms a thermal event younger than the alkaline volcanics and syenitic intrusives at Pu Sam Cap, suggesting a hidden, young porphyry system. The age of faulting, and thus the maximum age for this young intrusive event, is attributed to the 23-21Ma period of late-stage left-lateral strike-slip motion across northwest Vietnam.ReferencesAnczkiewicz R., Viola G., Muntener O., Thrirlwall M., Quong N.Q., 2007. Structure and shearing conditions in the Day Nui Con Voi massif: implications for the evolution of the Red River Fault. Tectonics 26: TC2002.Cao Shunyun, Liu Junlai, Leis B., Zhao Chunquiang 2010. New zircon U/Pb geochronology of the post-kinematic granitic plutons in Diancang Shan Massif along the Ailao-Shan-Red River Shear Zone and its geological implications. Acta Geologica Sinica (English Edition), 84, 1474-1487.Chung S.-L., Lee T., Lo C., et al., 1997. Intraplate extension prior to continental extrusion along the Ailao Shan-Red River shear zone.Geology, 25, 311-314.Cloos H., 1928. Experimentezurinnern Tektonik. Zentralblatt fur Mineralogie und Palaeontologie, 1928, 609-621.Findlay R.H., Phan Trong Trinh 1997. 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PRODUÇÃO E RESPOSTAS FISIOLÓGICAS DA MANGUEIRA CV. KEITT SOB DIFERENTES SISTEMAS DE IRRIGAÇÃO NO SUBMÉDIO DO SÃO FRANCISCO WELSON LIMA SIMÕES1; PEDRO PAULO BEZERRA FERREIRA2; MARIA APARECIDA DO CARMO MOUCO3; MARIA AUXILIADORA COELHO DE LIMA4; MIGUEL JULIO MACHADO GUIMARÃES5 E JOSÉ ALIÇANDRO BEZERRA DA SILVA6 1 Embrapa Semiárido, rodovia BR-428, Km 152, s/n, Zona Rural, EMBRAPA, Petrolina, PE, CEP 56302-970. E-mail: welson.simoes@embrapa.br2 Universidade Federal do Vale do São Francisco, UNIVASF, Juazeiro, BA, CEP 48.902-300. E-mail: pedro_k77@hotmail.com3 Embrapa Semiárido, rodovia BR-428, Km 152, s/n, Zona Rural, EMBRAPA, Petrolina, PE, CEP 56302-970. E-mail: maria.mouco@embrapa.br4 Embrapa Semiárido, rodovia BR-428, Km 152, s/n, Zona Rural, EMBRAPA, Petrolina, PE, CEP 56302-970. E-mail: auxiliadora.lima@embrapa.br5 Engenharia Agrícola - Universidade Federal Rural de Pernambuco, UFRPE, Recife, PE, CEP 52171-900. E-mail: mjmguimaraes@hotmail.com6 Departamento de fisiologia, Universidade Federal do Vale do São Francisco, UNIVASF, Juazeiro, BA, CEP 48.902-300. E-mail: jose.alicandro@univasf.edu.br 1 RESUMO O objetivo deste trabalho foi avaliar a influência de quatro arranjos de sistemas de irrigação sobre a fisiologia, a produtividade e a qualidade pós-colheita dos frutos da mangueira (Mangifera indica L.) cv. Keitt, no Submédio do Vale São Francisco. O experimento foi conduzido no delineamento experimental em blocos casualizados, com 04 tratamentos e 05 repetições, durante dois ciclos de cultivo. Os tratamentos foram: T1 – Um microaspersor sob copa; T2 – Um microaspersor entre plantas; T3 – Duas linhas laterais de gotejadores por fileira de planta; e T4 – Uma faixa de gotejo em formato de anel ou espiral (rabo de porco) ao redor da planta. Foram avaliadas: as respostas fisiológicas das plantas (fotossíntese líquida, condutância estomática, transpiração e temperatura foliar); o peso médio dos frutos; a produtividade; a quantidade de frutos por planta; e a qualidade dos frutos: volume, densidade, firmeza da polpa, teor de sólidos solúveis e acidez titulável. O sistema de irrigação por gotejamento é o mais indicado para o cultivo da mangueira cv. Keitt no Submédio do Vale do São Francisco, por interferir positivamente na fisiologia e na produtividade da planta e no número e firmeza dos frutos. Palavras-chave: fotossíntese, produtividade, qualidade de fruto SIMÕES, W. L.; FERREIRA, P. P. B.; MOUCO, M. A. do C.; LIMA, M. A. C.; GUIMARÃES, M. J. M.; SILVA, J. A. B. da.PRODUCTION AND PHYSIOLOGICAL RESPONSES OF MANGO CV. KEITT UNDER DIFFERENT IRRIGATION SYSTEMS IN SÃO FRANCISCO RIVER’S LOWER MIDDLE 2 ABSTRACT In order to assess the effect of four irrigation systems on post-harvest physiology, productivity and quality of mango (Mangifera indica L.) cv. Keitt fruits, in São Francisco river’s lower middle, an experiment was conducted in a randomized block design with 04 treatments and 05 repetitions for two crop cycles. The treatments were: T1 - One micro-sprinkler under plant; T2 – One micro-sprinkler between plants; T3 - Two lines of drippers per plant; and T4 - One line of drippers around the plant. The physiological characteristics of plants (net photosynthesis, stomatal conductance, transpiration and leaf temperature); the average weigh of fruits; productivity; amounts of fruits per plant; and fruit quality (volume, density, pulp firmness, content of soluble solids, and titratable acidity) were assessed. It was found that the drip irrigation system is best suited for the cultivation of mango cv. Keitt in São Francisco river lower middle, for positively affecting the plant physiology and productivity and the number firmness of fruits. Keywords: photosynthesis, productivity, fruit quality

The results of long-term studies of the vegetation of chalk outcrops of the Orenburg region (Russian Federation) and North-West Kazakhstan on Sub-Ural plateau and adjacent territories are presented. Chalk outcrops are unique botanical-geographical sites located in steppe and desert zones of Eurasia. Specific communities of calcephyte plant species have spread in these areas, in places of outcrops or close occurrence from the surface of upper-Cretaceous carbonate rocks. The flora of chalk outcrops is characterized by a great amount of rare species, mainly ende­mic, associated with peculiar substrates, the locality of habitats, and the historical past of the area of outcrops location (Matyshenko, 1985) The history of the study of flora and vegetation of chalk outcrops is given. Synthaxonomic studies of chalk vegetation as part of the ecological-floristic approach cover only territories west of the Volga river (Poluyanov, 2009; Averinova, 2011, 2016; Demina, 2014; Demina et al., 2016; Didukh et al., 2018). Chalk highlands of the North-West Kazakhstan and adjacent regions of the Russian Federation occupy quite large areas. However, up to date, there is no data on the vegetation diversity of these territories based on complete geobotanical relevés, that is why their synthaxonomy remains undeveloped. The study area with 15 massifs of chalk outcrops (Fig. 1) includes the Orenburg region (Novosergievsky, Perevolotsky, Sol-Iletskiy, Akbulak and Gaysky districts), and Aktobe (Hobdinsky, Uilsky and Bayganinsky district) and Atyrau (Zhylyoysky district) regions of the Republic of Kazakhstan. The largest massifs in the Orenburg region of the Russian Federation are: Starobelogorskie (Fig. 2), Chesnokov­skie (Fig. 3), Verkhnechibendinskie (Fig. 6), Troits­kie (Fig. 7), Pokrovskie Chalk Mountains (Fig. 4) and Durtel mountain (Fig. 5). Chalk massif Akshatau (Fig. 8) and the range Aktolagai (Fig. 9) are the largest within Aktobe region. The investigated sites are mostly located on the Sub-Ural Plateau, which extended from the southern regions of the Orenburg region to the Emba River in the territory of Aktobe region. They are less common within the Obschiy Syrt and sporadic in the Guberlinskie mountains. The study area covers a wide range of zonal vegetation from dry steppes in the northern part of the gradient to northern deserts in the southern one. The dataset includes 270 relevés of chalk outcrops communities performed by the authors in 2014–2019. The primary classification was carried out using TWINSPAN algorithm. As a result three groups of communities are established. The first group is communities of the Emben Plateau, the most southern area; second is communities on relatively developed soils in the slopes bases, depressions between chalk ridges and on their flat tops; third is widespread communities on most of the Podural Plateau and Obschy Syrt, excluding the Emben Plateau. Comparison with associations of calcephyte, semidesert and steppe vegetation (Golub, 1994; Kolomiychuk, Vynokurov, 2016; Lysenko, Yamalov, 2017; Didukh et al., 2018; Korolyuk, 2017) was made to determine the position of studied communities in the system of ecological-floristic classification of the herbasceous vegetation of Eurasia. Cluster analysis results (Fig. 10) revealed the significant specificity the chalk outcrops of the Sub-Ural Plateau in comparison with calciphytic communities of Eastern Europe, as well as with deserts and steppes zonal vegetation. That was the reason to describe a new class for vegetation of the studied chalk outcrops. The class Anabasietea cretaceae Golovanov class nov. hoc loco. Diagnostic species: Anabasis cretacea, Anthemis trotzkiana, Artemisia salsoloides, Atraphaxis decipiens,Crambe aspera, Echinops meyeri, Jurinea kirghisorum, Hedysarum tscherkassovae, Lepidium meyeri, Limonium cretaceum, Linaria cretacea, Matthiola fragrans, Nanophyton erinaceum, Seseli glabratum, Zygophyllum pinnatum;holotypus is order Anabasie­talia cretaceae ord. nov. hoc loco. Class combines calciphytic, mainly semi-shrub communities on the outcrops of chalk and marl rocks of the south of the Orenburg region and North-West Kazakhstan within the steppe (subzones of the true and desert steppes) and desert zone. The central order, Anabasietalia cretaceae Golovanov ord. nov. hoc loco, is described;holotypus is alliance Anthemido trotzkianae–Artemision salsoloidis all. nov. hoc loco. Three alliances identified within the order reflect both community distribution along the latitudinal gradient and succession stages. The alliance Sileno fruticulosae–Nanophytonion erinacei Lebedeva all. nov hoc loco is poor-species communities, located mainly on the chalk massifs in the southern part of the Sub-Ural Plateau (Emben Plateau) and adjacent territories. Holotypus of the alliance is ass. Onosmo staminei–Anabasietum cretaceae ass. nov. hoc loco with highly constant desert plant species (Anabasis salsa, Artemisia terrae-albae, Atriplex cana, Limonium suffruticosum, Rhammatophyllum pachyrhizum, etc.). It includes the ass. Onos­mo staminei–Anabasietum cretaceae ass. nov. hoc loco (Table 3, syntaxa 1–3; Tables 4–6). Holotypus hoc loco: Table 4, rel. no. 9 (YS19-034): Republic of Kazakhstan, Atyrau region, Zhylyojskij district, 10 km W Aktologay ridge, 47.48514° N, 54.97647° E, 19.05.2019, collector Yamalov S. M.) The alliance Anabasio cretaceae–Agropyrion desertorum Korolyuk all. nov hoc loco.Holotypus is ass. Agropyro desertorum–Artemisietum lessingianae ass. nov. hoc loco. Alliance includes communities in flat habitats with well-developed soils at the foot of the chalk hills in the central and northern parts of the Sub-Ural Plateau, on the chalk rock outflows, as well on their tops. Active are species of deserts and galophytic communities of the classes Artemisietea lerchianae and Festuco-Puccinellietea, as well as these of dry and desert steppes of the order Tanaceto achilleifolii–Stipetalia lessingianae. There are 2 associations: Agropyro desertorum–Artemisietum lessingianae ass. nov. hoc loco (Table 3, syntaxon 4; Table 7; fig. 23; holotypus hoc loco: Table 7, rel. no 8 (YS15-019)), Russian Federation, Orenburg region, Sol-Ilets­kiy district, Troitsk Chalk Mountains, 10 km SW vil. Troitsk, 50.65317° N, 54.542° W, 06.06.2015, collector Yamalov S. M.) and Psephello marschallianae–Artemisietum lerchianae ass. nov. hoc loco ((Table. 3, syntaxon 5; Table 8; fig. 24); holotypus hoc loco: Table 8, rel. no 15 (YS19-050), Republic of Kazakhstan, Aktyubinsk region, Hobdinsky district, chalk mountains 16 km NE vil. Zhantalap, 50.39986° N, 56.05054° N, 21.05.2019, collector Yamalov S. M.). The alliance Anthemido trotzkianae–Artemision salsoloidis Yamalov all. nov hoc loco.Holotypus is ass. Anthemido trotzkianae–Artemisietum salsoloi­dis ass. nov. Alliance includes the cenoses of the chalk highlands of the Sub-Ural Plateau (except for its extremely southern part) and the Obschiy Syrt. These are both communities of the initial and more advanced succession stages. The high constancy of Anthemis trotzkiana and Artemisia salsoloides, as well as the presence of petrophytic species widely distributed in the rocky steppes of the Southern Ural (Alyssum tortuosum, Centaurea marchalliana, Euphorbia seguieriana, Galium octonarium) are character for the alliance cenophlora. There are three associations— Nanophytono erinacei–Jurinetum kirghisori ass. nov. hoc loco (Table 3, syntaxon 6; Table 9; Fig. 25; holotypus hoc loco: Table 9, rel. no 7 (GY18-070)), Russian Federation, Orenburg region, Sol-Iletskiy district, Verhnechibendinskie Chalk Mountains, 10 km W vil. Troitsk, 50.6562° N, 54.44272° W, 07.06.2016, collector Golovanov Ya. M.); Anthemido trotzkianae–Artemisietum salsoloidis ass. nov. hoc loco (Table 3, syntaxa 7, 8; Tables 10, 11; Fig. 26; holotypus hoc loco: Table 10, rel. no 20 (GY15-047)), Russian Federation, Orenburg region, Sol-Iletskiy district, Troitsk Chalk Mountains, 10 km NW vil. Troitsk, 50.65267° N, 54.54217° E, 06.06.2015, collector Golovanov Ya. M.); Onosmo simplicissimae–Anthemietum trotzkianae ass. nov. hoc loco (Table 3, syntaxon 9; tab. 12; Fig. 27); holotypus hoc loco: Table 12, rel. no 1 (GY19-011)), Republic of Kazakhstan, Aktyubinsk region, Uilskii district, Terektytau, 10 km NE vil. Akshatau, 49.43507° N, 54.60127° E, 15.05.2019, collector — Golovanov Ya. M.). There are 2 associations in the class Festuco-Brometea. Within the dry steppe order Tanaceto achilleifolii–Stipetalia lessingianae this is Bassio prostratae–Agropyretum desertorum ass. nov. hoc loco (Table 3, syntaxa 10, 11; Table 13), holotypus hoc loco: Table 13, rel. no 8 (GY19-004)), Republic of Kazakhstan, Aktyubinsk region, Uilskii district, Terektytau, 10 km NE vil. Akshatau, 49.42942° N, 54.60047° E, 15.05.2019, collector Golovanov Ya. M.); within the true steppe order Helictotricho-Stipetalia this isass. Anthemido trotzkianae–Thymetum guberlinensis ass. nov. hoc loco (Table 3, syntaxon 12; Table 14); holotypus hoc loco: Table 14, rel. no 8 (GY14-012)), Russian Federation, Orenburg region, Gayskii district, chalk mountain Dyurtel, 4 km NE vil. Starohalilovo, 51.504° N, 58.157° E, 27.06.2014, collector Golovanov Ya. M.). The result of the research of chalk outcrops ve­getation of Sub-Ural plateau and adjacent territories is new class Anabasietea cretaceae which includes 1 order, 3 alliances, 6 associations, 3 subassociations, 2 variants and 9 facies.