8 resultados para Pantano de Sau

em Universidad Politécnica de Madrid


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El río Cacín, es uno de los afluentes más importantes del río Genil, que fue declarado principal en la cuenca del río - Guadalquivir por el R.D. Ley de 20 de Septiembre de 1.927(creación de la Confederación Sindical Hidrográfica del Guadalquivir), en el que se dice: "Articulo 1 º - De conformidad con lo dispuesto por el artículo 1º del Real Decreto de 5 de Marzo de 1.926, se formara la Confederación Sindical Hidrográfica del Guadalquivir, quedando declarados principales a los efectos del artículo 4º , los - ríos Guadalquivir, Guadalimar, Guadiana menor, Jándula, Guadalmellato, Guadiato, Rembezar, Genil y Viar . . .”

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De acuerdo con lo propuesto por el Ilmo. Sr. Director de la Escuela Especial de Ingenieros de Montes, esta Direccion General acuerda designar a Vd. la redaccion del siguiente proyecto: "Anteproyecto de restauracion del pantano de Yasa (margen izquierdo del rio Aragon) croquizando en el plano las zonas de mas urgente necesidad de correccion y con una relacion detallada a modo de catalogo de las fincas forestales y muy especialmente de las que tengan mas de 250 has: tanto publicas como particulares, con indicacion de si podrian adquirirse por comvenio, expropiacion o consorcio".

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Estudiemos la población y su riqueza agrícola, forestal, ganadera e industrial, es decir sus medios de vida y veamos como se pueden o deben modificar para elevar el nivel medio o que este no decaiga y fijémonos en el valor de la desviación del punto de equilibrio qu la modificación que uno de ellos puede producir en los otros, con un desplazamiento, en consecuencia, de la resultante general, cuyo sentido y valor debemos intentar calcular.

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Models may be useful tools to design efficient crop management practices provided they are able to accurately simulate the effect of weather variables on crop performance. The objective of this work was to accurately simulate the effects of temperature and day length on the rate of vegetative node expression, time to flowering, time to first pod, and time to physiological maturity of faba bean (Vicia faba L.) using the CROPGRO-Fababean model. Field experiments with multiple sowing dates were conducted in northwest Spain during 3 yr (17 sowing dates: 12 used for calibration and five for validation). Observed daily minimum and maximum air temperatures were within the range of ?9.0 and 39.2°C and observed photoperiods within 10.1 to 16.6 h. Optimization of thermal models to predict leaf appearance raised the base temperature (Tb) from the commonly used value of 0.0 to 3.9°C. In addition, photothermal models detected a small accelerating effect of day length on the rate of leaf appearance. Accurate prediction of the flowering date required incorporating day length, but the solved Tb approached negative values, close to ?4°C. All the reproductive phases after flowering were affected only by temperature, but postanthesis Tb was also mayor que0°C and approached values close to 8°C for time to first pod set and 5.5°C for time from first pod to physiological maturity. Our data indicated that cardinal base temperatures are not the same across all phenological phases.

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Está situada al S.O. del rio Alcaide y Rambla Mayor y forma con ellas la cabecera del Rio Guadalentin: la parte mas alta de Rambla Seca pertenece al termino municipal de Velez-Blanco, del partido judicial de Velez-Rubio, provincia do Almeria; su parte baja esta enclavada en el termino municipal de Murcia. Limita por su mareen izquierda, con la Serrata de Guadalupe, Cerro Calderón, Cerro Colorado y la sierra de las Almohallas y por la derecha con piedra de Casanova, Collado de los Carrascales, Morra de los troncos, Morro del Tabaco, Cerro de la Cruz y sierra de la culebrina. El origen de ella está en la vertiente de piedra Casanova y la Hoya del Carrascal y su desembocadura en el pantano de Valdeinfierno.

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Este artículo evalúa los impactos de la Reforma de la PAC de 2003 y la implementación del Pago Único (PU) sobre superficies, rendimientos de secano y diversidad de cultivo en España. Empleando datos de superficie, se hace un análisis estadístico para aislar el efecto del PU con respecto a la tendencia del periodo 1995-2009. Adicionalmente, se analiza el impacto sobre los rendimientos de las principales producciones de secano. Finalmente, se analiza la diversidad productiva. Desde la introducción del PU en secano se ha producido una mayor respuesta al mercado, caracterizada por una concentración e intensificación de la producción. También produjo un efecto marcado de reducción en la SAU total, y las SAUs de secano y regadío.

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Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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All crop models, whether site-specific or global-gridded and regardless of crop, simulate daily crop transpiration and soil evaporation during the crop life cycle, resulting in seasonal crop water use. Modelers use several methods for predicting daily potential evapotranspiration (ET), including FAO-56, Penman-Monteith, Priestley-Taylor, Hargreaves, full energy balance, and transpiration water efficiency. They use extinction equations to partition energy to soil evaporation or transpiration, depending on leaf area index. Most models simulate soil water balance and soil-root water supply for transpiration, and limit transpiration if water uptake is insufficient, and thereafter reduce dry matter production. Comparisons among multiple crop and global gridded models in the Agricultural Model Intercomparison and Improvement Project (AgMIP) show surprisingly large differences in simulated ET and crop water use for the same climatic conditions. Model intercomparisons alone are not enough to know which approaches are correct. There is an urgent need to test these models against field-observed data on ET and crop water use. It is important to test various ET modules/equations in a model platform where other aspects such as soil water balance and rooting are held constant, to avoid compensation caused by other parts of models. The CSM-CROPGRO model in DSSAT already has ET equations for Priestley-Taylor, Penman-FAO-24, Penman-Monteith-FAO-56, and an hourly energy balance approach. In this work, we added transpiration-efficiency modules to DSSAT and AgMaize models and tested the various ET equations against available data on ET, soil water balance, and season-long crop water use of soybean, fababean, maize, and other crops where runoff and deep percolation were known or zero. The different ET modules created considerable differences in predicted ET, growth, and yield.