The generalized second law of gravitational thermodynamics on the apparent horizon in f(R)-gravity

We investigate the generalized second law (GSL) of thermodynamics in the framework of $f(R)$-gravity. We consider a FRW universe filled only with ordinary matter enclosed by the dynamical apparent horizon with the Hawking temperature. For a viable modified gravity model as $f(R)=R-\alpha/R+\beta R^{2}$, we examine the validity of the GSL during the early inflation and late acceleration eras. Our results show that for the selected $f(R)$-gravity model minimally coupled with matter, the GSL in the early inflation epoch is satisfied only for the special range of the equation of state parameter of the matter. But in the late acceleration regime, the GSL is always respected.Comment: 10 pages, accepted by Europhys. Lett. 201

Mapping Ash CaCO3, pH, and Extractable Elements Using Principal Component Analysis

Ash cover in fire-affected areas is an important factor in the reduction of soil erosion and increased availability of soil nutrients. Thus it is important to understand the spatial distribution of ash and its capacity for soil protection and to provide nutrients to the underlying soil. In this work, we aimed to map ash CaCO3, pH, and select extractable elements using a principal component analysis (PCA). Four days after a medium to severe wildfire, we established a grid in a 9 \uc3\u9727m area on a west facing slope and took ash samples every 3m for a total of 40 sampling points. The PCA carried out retained five different factors. Factor 1 had high positive loadings for ash with electrical conductivity, calcium, and magnesium and negative with aluminum and iron. Factor 2 had high positive loadings in total phosphorous and silica and factor 3 in manganese and zinc. Factor 4 had high negative loadings in CaCO3and pH and finally, factor 5 had high positive loadings in sodium and potassium. The spatial pattern of the factors was different. The Gaussian model was the best fit for factor 1, the linear model the most accurate for factor 4, and the wave hole effect for the loadings of factors 2, 3, and 5. The map generated with the factor scores of factor 1 had a specific pattern, while the map of factor 4 scores had a low accumulation of the explained elements in one area and high in the other. The maps produced from the factor scores of factors 2, 3, and 5 showed a cycled pattern. Ordinary kriging provided the best estimate for factors 1, 2, and 4. Mapping ash in the period immediately after the fire is very important to identify the level of soil protection and the ash nutrient input in the underlying soil

Optimal irrigation scheduling, irrigation control and drip line layout to increase water productivity and profit in subsurface drip-irrigated agriculture

International audienceA new optimization framework is presented which is able to identify optimal solutions for maximum profit design of subsurface drip irrigation (SDI) systems under limited supply. To solve this complex optimization problem, decomposition is used which splits the problem into three sub-problems: (i) optimal irrigation control, which maximizes water distribution uniformity and minimizes percolation losses; (ii) optimal irrigation scheduling, which minimizes irrigation water applied in order to meet a high yield with a specified reliability; and (iii) optimal drip line layout, which includes the solutions of the other sub-problems and maximizes profitability. The multi-level optimization framework was tested in France with corn cultivated on two SDI plots with drip line spacing of 1.2m (SDI120) and 1.6m (SDI160), respectively. HYDRUS simulations estimated adequate irrigation amounts of 20-35mm per event. For optimal irrigation scheduling, initial schedules were provided at sowing and adapted weekly according to observed weather data and synthetic weather scenarios. The presented framework significantly increased profit and water productivity for deficit SDI designs. The latter was increased up to 30% (SDI120), compared to seven other irrigation experiments. The optimal SDI design was achieved by SDI160, which increased profitability by 27% compared to SDI120