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Conventional tillage (CT) and cover crops (CC) trigger different runoff (Q) and runon (Qin) magnitudes and patterns in woody crops. The spatial and temporal stability of these patterns is not well known yet. In this study, we run the uncalibrated DR2-2013© SAGA v1.1 model (0.5 × 0.5 m of cell size) to simulate time to ponding (Tp), runoff duration (TQ), initial runoff per raster cell (q0), Qsim and Qin in six olive plots (480 m2 per plot) during two years (108 rainfall events and 648 simulations). Two plots were managed with a mixture of plant species (CC-I), two with one single plant species (CC-II) and two with CT. Runoff yield from each plot was collected (Qobs) in gauging-stations during 27 time-integrated samples and used for modelling validation (162 control points). On average, Qobs was 9% higher under CT than under CC-I, and 8% higher than under CC-II. Topsoil saturation was simulated for the entire plots during 29 events (test-period), and Qsim appeared in another 51 and 52 events in the plots with CC and CT. Tp with CT was 2.3 times higher (59 s) than the average duration with CC and the topsoil became saturated 3.3 times faster in the inter-rows than below the trees. Values of q0 with CC were 2.3% lower than with CT and total Qsim with CC was 2% higher than with CT. However, the differences of Qsim between the different treatments were not statistically significant. The mean observed and simulated runoff coefficients were of 11 and 14%, with median values of 7 and 10%. Qsim correlated well with Qobs (Pearson ca. 0.861), and Qsim was overestimated ca. 10%. The model performed better when rainfall depth and intensity were high, and the range of variability of both Qsim and Qobs was similar. The average, best and worst Nash–Sutcliffe coefficients were 0.665, 0.791 (P6) and 0.512 (P3) and thus model simulations were satisfactory. The four plots with CC presented on average a worse performance (Kling–Gupta coefficient = 0.607) than the two plots with CT (KGE = 0.769). The lowest spatial variability of q0, Qobs, Qsim and actual available water (Waa, the sum of Qin and stored water in the soil surface) were found in the plots with CC. CT triggered higher spatial variability of runoff and higher temporal variability of runon than CC. ; The postdoctoral contract of Dr. López-Vicente and part of this research was funded by the project "Soil and organic carbon loss and redistribution in olive groves of Jaen, Granada and Seville: numerical simulation and assessment of the particle size effect" of the University of Jaen (group RNM296) (Spain), the Regional Government of Andalusia (Spain) and the European Union (FEDER funds). The experimental results from the runoff plots has been possible since 2009 through projects Biosuelo, P08-AGR-03643 (Andalusian Government), RESEL (Spanish Ministry for Environment and Rural and Marine Affairs) and FEDER funds. ; Peer reviewed

This open access book provides a panoramic view of the evolution of Spanish agriculture from 1900 to the present, offering a more diverse picture to the complex and multidimensional reality of agrarian production. With a clear transdisciplinary ambition, the book applies an original and innovative theoretical and methodological tool, termed Agrarian Social Metabolism, combining Social Metabolism with an agroecological perspective. This integrative analysis is especially interesting for environmental scientists and policy makers being the best way to design sustainable agroecosystems and public policies capable of moving us towards a more sustainable food system. Spanish agricultural production has experienced impressive growth during the 20th century which has allowed it to ensure the supply of food to the population and even to transform some crops into important chapters in foreign trade. However, this growth has had its negative side since it was based on the injection of large amounts of external energy, on the destruction of employment and the loss of profitability of agricultural activity. But perhaps the most serious part is the strong impact of the current industrialised agriculture model on Spanish agroecosystems, exposed to the overexploitation of hydric resources, pollution of the water by nitrates and pesticides, high erosion rates and an alarming loss of biodiversity; damage which in the immediate future will end up reducing production capacity.