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Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., et al. (2014). Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl. Acad. Sci. U. S. A., 111(9), 3274–3279.
Abstract: Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.
Keywords: Agriculture/*economics; Carbon Dioxide/analysis; *Climate Change; Commerce/statistics & numerical data; Computer Simulation; Crops, Agricultural/*growth & development; Forecasting; Humans; *Models, Economic; agricultural productivity; climate change adaptation; integrated assessment; model intercomparison
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Vitali, A., Lana, E., Amadori, M., Bernabucci, U., Nardone, A., & Lacetera, N. (2014). Analysis of factors associated with mortality of heavy slaughter pigs during transport and lairage. J. Anim. Sci., 92(11), 5134–5141.
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.
Keywords: Abattoirs/*statistics & numerical data; Animals; *Data Interpretation, Statistical; Humidity/adverse effects; Light/adverse effects; *Mortality; Retrospective Studies; Seasons; Swine/*physiology; Temperature; Time Factors; Transportation/*statistics & numerical data; lairage; mortality; pigs; temperature-humidity index; transport
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Sandor, R., Ehrhardt, F., Grace, P., Recous, S., Smith, P., Snow, V., et al. (2020). Ensemble modelling of carbon fluxes in grasslands and croplands. Field Crops Research, 252, 107791.
Abstract: Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).
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Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J., et al. (2016). Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS One, 11(4), e0151782.
Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
Keywords: systems simulation; nitrogen dynamics; winter-wheat; crop models; data resolution; scale; water; variability; calibration; weather
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