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Lessire, F., Hornick, J. L., Minet, J., & Dufrasne, I. (2015). Rumination time, milk yield, milking frequency of grazing dairy cows milked by a mobile automatic system during mild heat stress. Advances in Animal Biosciences, 6(01), 12–14.
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Montesino-San Martín, M., Olesen, J. E., & Porter, J. R. (2015). Can crop-climate models be accurate and precise? A case study for wheat production in Denmark. Agricultural and Forest Meteorology, 202, 51–60.
Abstract: Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical and mechanistic wheat models to assess how differences in the extent of process understanding in models affects uncertainties in projected impact. Predictive power of the models was tested via both accuracy (bias) and precision (or tightness of grouping) of yield projections for extrapolated weather conditions. Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher accuracy comes at the cost of precision of the mechanistic model to embrace all observations within given boundaries. The approaches showed complementarity in sensitivity to weather variables and in accuracy for different extrapolation domains. Their differences in model precision and accuracy make them suitable for generic model ensembles for near-term agricultural impact assessments of climate change.
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Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., et al. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Env. Model. Softw., 72, 402–417.
Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved.
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Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., et al. (2015). The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev., 8(2), 261–277.
Abstract: We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12-15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.
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De Pascale, S., Maggio, A., Orsini, F., Stanghellini, C., & Heuvelink, E. (2015). Growth response and radiation use efficiency in tomato exposed to short-term and long-term salinized soils. Scientia Horticulturae, 189, 139–149.
Abstract: Farmlands are increasingly exposed to degradation phenomena associated to climate change and agricultural practices, including irrigation. It is estimated that about 20% of the world’s irrigated land is salt affected. In this paper we aimed at evaluating the effect of seasonal and multiannual soil satinization on growth, yield, and radiation use efficiency of tomato in open field. Two field experiments were carried out at the Experimental Station of the University of Naples Federico II (latitude 40 degrees 31’N longitude 14 degrees 58’E) (Italy) on tomato during 2004 and 2005 to study the effect of five levels of water salinity: NSC (EC = 0.5 dS m(-1)), SW1 (EC= 2.3 dS m(-1)), SW2 (EC= 4.4 dS m(-1)), SW3 (EC= 8.5 dS m(-1)) and SW4 (EC= 15.7 dS m(-1)) in a soil exposed to one-season salinization (ST = short-term) and an adjacent soil exposed to >20 years salinization (LT = long-term). Plant growth, yield and fruit quality (pH, EC, total soluble solids and the concentration of reducing sugars and of titratable acids), and plant water relations were measured and radiation use efficiency (RUE) was calculated. Increasing water salinity negatively affected the leaf area index (LAI), radiation use efficiency (RUE) and above-ground dry weight (DW) accumulation resulting in lower total and marketable yield. Maximum total and marketable yield obtained with the NSC treatment were respectively 117.9 and 111.0 Mg ha(-1) in 2004 and 113.1 and 107.9 Mg ha(-1) in 2005. Although the smaller leaf area of salinized plants was largely responsible for reduced RUE, we found approximately 50% of this reduction to be accounted for by processes other than changed crop architecture. These may include an increased stomatal resistance, increased mesophyll resistance and other impaired metabolic functions that may occur at high salinity. Remarkably, we found that LT salinized plants had a slightly better efficiency of use of intercepted radiation (RUEIR) at a given EC of soil extract than ST salinized plants indicating that LT salinization, and consequent permanent modifications of the soil physical properties, may trigger additional physiological mechanisms of adaptation compared to ST salinized plants. These differences are relevant in light of the evolution of salinized areas, also in response to climate change.
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