Abstract: The determination of the impact of climate change on crop yield at a regional scale requires the development of new modelling methodologies able to generate accurate yield estimates with reduced available data. In this study, different simulation approaches for assessing yield have been evaluated. In addition to two well-known models (AquaCrop and Stewart function), a methodological proposal considering a simplified approach using an empirical model (SOM) has been included in the analysis. This empirical model was calibrated using rainfed sunflower experimental field data from three sites located in Andalusia, southern Spain, and validated using two additional locations, providing very satisfactory results compared with the other models with higher data requirements. Thus, only requiring weather data (accumulated rainfall from the beginning of the season fixed on September 1st, and maximum temperature during flowering) the approach accurately described the temporal and spatial yield variability observed (RMSE = 391 kg ha(-1)). The satisfactory results for assessing yield of sunflower under semi-arid conditions obtained in this study demonstrate the utility of empirical approaches with few data requirements, providing an excellent decision tool for climate change impact analyses at a regional scale, where available data is very limited.

Abstract: The study was based on data collected during 5 yr (2003-2007) and was aimed at assessing the effects of the month, slaughter house of destination (differing for stocking density, openings, brightness, and cooling device types), length of the journey, and temperature-humidity index (THI) on mortality of heavy slaughter pigs (approximately 160 kg live weight) during transport and lairage. Data were obtained from 24,098 journeys and 3,676,153 pigs transported from 1,618 farms to 3 slaughter houses. Individual shipments were the unit of observation. The terms dead on arrival (DOA) and dead in pen (DIP) refer to pigs that died during transport and in lairage at the abattoir before slaughtering, respectively. These 2 variables were assessed as the dependent counts in separate univariate Poisson regressions. The independent variables assessed univariately in each set of regressions were month of shipment, slaughter house of destination, time traveled, and each combination of the month with the time traveled. Two separate piecewise regressions were done. One used DOA counts within THI levels over pigs transported as a dependent ratio and the second used DIP counts within THI levels over pigs from a transport kept in lairage as a dependent ratio. The THI was the sole independent variable in each case. The month with the greatest frequency of deaths was July with a risk ratio of 1.22 (confidence interval: 1.06-1.36; P < 0.05) and 1.27 (confidence interval: 1.06-1.51; P < 0.05) for DOA and DIP, respectively. The lower mortality risk ratios for DOA and DIP were recorded for January and March (P < 0.05). The aggregated data of the summer (June, July, and August) versus non-summer (January, March, September, and November) months showed a greater risk of pigs dying during the hot season when considering both transport and lairage (P < 0.05). The mortality risk ratio of DIP was lower at the slaughter house with the lowest stocking density (0.64 m(2)/100 kg live weight), large open windows on the roof and sidewalls, low brightness (40 lx) lights, and high-pressure sprinklers as cooling devices. The mortality risk ratio of DOA increased significantly for journeys longer than 2 h, whereas no relationship was found between length of transport and DIP. The piecewise analysis pointed out that 78.5 and 73.6 THI were the thresholds above which the mortality rate increased significantly for DOA and DIP, respectively. These results may help the pig industry to improve the welfare of heavy slaughter pigs during transport and lairage.

Abstract: Irrigated agriculture constitutes the largest consumer of freshwater in the Mediterranean region and provides a major source of income and employment for rural livelihoods. However, increasing droughts and water scarcity have highlighted concerns regarding the environmental sustainability of agriculture in the region. An integrated assessment combining a gridded water balance model with a geodatabase and GIS has been developed and used to assess the water demand and energy footprint of irrigated production in the region. Modelled outputs were linked with crop yield and water resources data to estimate water (m(3) kg(-1)) and energy (CO2 kg(-1)) productivity and identify vulnerable areas or `hotspots’. For a selected key crops in the region, irrigation accounts for 61 km(3) yr(-1) of water abstraction and 1.78 Gt CO2 emissions yr-1, with most emissions from sunflower (73 kg CO2/t) and cotton (60 kg CO2/t) production. Wheat is a major strategic crop in the region and was estimated to have a water productivity of 1000 tMm(-3) and emissions of 31 kg CO2/t. Irrigation modernization would save around 8 km(3) of water but would correspondingly increase CO2 emissions by around +135\%. Shifting from rain-fed to irrigated production would increase irrigation demand to 166 km(3) yr(-1) (+137\%) whilst CO2 emissions would rise by +270\%. The study has major policy implications for understanding the water-energy-food nexus in the region and the trade-offs between strategies to save water, reduce CO2 emissions and/or intensify food production.