|
|
Hoff, H., Gerten, D., Waha, K., Warner, J., Keulertz, M., & Sojamo, S. (2013). Green and Blue Water in Africa: How Foreign Direct Investment can Support Sustainable Intensification. In T. Allan, M. Keulertz, & S. A. Sojamo (Eds.), (pp. 359–375). Handbook of Land and Water Grabs in Africa. Routledge.
|
|
|
|
Mirschel, W., Barkusky, D., Hufnagel, J., Kersebaum, K. C., Nendel, C., Laacke, L., et al. (2016). Coherent multi-variable field data set of an intensive cropping system for agro-ecosystem modelling from Müncheberg, Germany. Open Data J. Agric. Res., 2(1), 1–10.
Abstract: A six-year (1993-1998) multivariable data set for a four-plot intensive crop rotation (sugar beet – winter wheat – winter barley – winter rye – catch crop) located at Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, Müncheberg, Germany, is documented in detail. The experiment targets crop response to water supply on sandy soils (Eutric Cambisol), applying rain-fed and irrigated treatments. Weather as well as soil and crop processes were intensively monitored and management actions were consistently recorded. The data set contains coherent data for soil (water, nitrogen contents), crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content in biomass and their fractions, sugar content in beet), weather (all standard meteorological variables) and management (soil tillage, sowing, fertilisation, irrigation, harvest). In addition, observation methods are briefly described. The data set is available via the Open Research Data Portal at ZALF Müncheberg and is published under doi:10.4228/ZALF.1992.271. The data set was used for model intercomparison within the crop modelling part (CropM) of the international FACCE MACSUR project.
|
|
|
|
Ewert, F., Boote, K. J., Rötter, R. P., Thorburn, P., & Nendel, C. (Eds.). (2016). Crop modelling for agriculture and food security under global change. Abstracts. International Crop Modelling Symposium iCROPM2016, 15-17 March 2016, Berlin, Germany. Berlin.
|
|
|
|
Ewert, F., van Bussel, L. G. J., Zhao, G., Hoffmann, H., Gaiser, T., Specka, X., et al. (2015). Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments. In C. Rosenzweig, & D. Hillel (Eds.), (pp. 261–277). Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts), ICP Series on Climate Change Impacts, Adaptation, . London: Imperial College Press.
|
|
|
|
Carabano, M. J., Logar, B., Bormann, J., Minet, J., Vanrobays, M. L., Diaz, C., et al. (2016). Modeling heat stress under different environmental conditions. J. Dairy Sci., 99(5), 3798–3814.
Abstract: Renewed interest in heat stress effects on livestock productivity derives from climate change, which is expected to increase temperatures and the frequency of extreme weather events. This study aimed at evaluating the effect of temperature and humidity on milk production in highly selected dairy cattle populations across 3 European regions differing in climate and production systems to detect differences and similarities that can be used to optimize heat stress (HS) effect modeling. Milk, fat, and protein test day data from official milk recording for 1999 to 2010 in 4 Holstein populations located in the Walloon Region of Belgium (BEL), Luxembourg (LUX), Slovenia (SLO), and southern Spain (SPA) were merged with temperature and humidity data provided by the state meteorological agencies. After merging, the number of test day records/cows per trait ranged from 686,726/49,655 in SLO to 1,982,047/136,746 in BEL. Values for the daily average and maximum temperature-humidity index (THIavg and THImax) ranges for THIavg/THImax were largest in SLO (22-74/28-84) and shortest in SPA (39-76/46-83). Change point techniques were used to determine comfort thresholds, which differed across traits and climatic regions. Milk yield showed an inverted U-shaped pattern of response across the THI scale with a HS threshold around 73 THImax units. For fat and protein, thresholds were lower than for milk yield and were shifted around 6 THI units toward larger values in SPA compared with the other countries. Fat showed lower HS thresholds than protein traits in all countries. The traditional broken line model was compared with quadratic and cubic fits of the pattern of response in production to increasing heat loads. A cubic polynomial model allowing for individual variation in patterns of response and THIavg as heat load measure showed the best statistical features. Higher/lower producing animals showed less/more persistent production (quantity and quality) across the THI scale. The estimated correlations between comfort and THIavg values of 70 (which represents the upper end of the THIavg scale in BEL-LUX) were lower for BEL-LUX (0.70-0.80) than for SPA (0.83-0.85). Overall, animals producing in the more temperate climates and semi-extensive grazing systems of BEL and LUX showed HS at lower heat loads and more re-ranking across the THI scale than animals producing in the warmer climate and intensive indoor system of SPA.
|
|
