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Author |
Vitali, A.; Lana, E.; Amadori, M.; Bernabucci, U.; Nardone, A.; Lacetera, N. |
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Title |
Analysis of factors associated with mortality of heavy slaughter pigs during transport and lairage |
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Journal Article |
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Year  |
2014 |
Publication |
Journal of Animal Science |
Abbreviated Journal |
J. Anim. Sci. |
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Volume |
92 |
Issue |
11 |
Pages |
5134-5141 |
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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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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. |
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1525-3163 (Electronic) 0021-8812 (Linking) |
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LiveM, ft_macsur |
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MA @ admin @ |
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4641 |
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Author |
Perego, A.; Sanna, M.; Giussani, A.; Chiodini, M.E.; Fumagalli, M.; Pilu, S.R.; Bindi, M.; Moriondo, M.; Acutis, M. |
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Title |
Designing a high-yielding maize ideotype for a changing climate in Lombardy plain northern Italy |
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Journal Article |
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Year |
2014 |
Publication |
Science of the Total Environment |
Abbreviated Journal |
Science of the Total Environment |
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Volume |
499 |
Issue |
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Pages |
497-509 |
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Agriculture/*methods/standards; *Climate Change; Droughts; Italy; Nitrogen/analysis; Soil; Water Supply/statistics & numerical data; Zea mays/*growth & development/standards; Climate change; Crop model; Maize; Water use adaptation |
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• ARMOSA model simulated a maize ideotype with drought adaptation under climate change. • The ideotype needs less water for higher yield compared to current hybrids. • Higher production involves more crop residues that enhance soil C sequestration. • Soil organic C may generally decrease and N leaching will increase in sandy soil. The expected climate change will affect the maize yields in view of air temperature increase and scarce water availability. The application of biophysical models offers the chance to design a drought-resistant ideotype and to assist plant breeders and agronomists in the assessment of its suitability in future scenarios. The aim of the present work was to perform a model-based estimation of the yields of two hybrids, current vs ideotype, under future climate scenarios (2030–2060 and 2070–2100) in Lombardy (northern Italy), testing two options of irrigation (small amount at fixed dates vs optimal water supply), nitrogen (N) fertilization (300 vs 400 kg N ha− 1), and crop cycle durations (current vs extended). For the designing of the ideotype we set several parameters of the ARMOSA process-based crop model: the root elongation rate and maximum depth, stomatal resistance, four stage-specific crop coefficients for the actual transpiration estimation, and drought tolerance factor. The work findings indicated that the current hybrid ensures good production only with high irrigation amount (245–565 mm y− 1). With respect to the current hybrid, the ideotype will require less irrigation water (− 13%, p < 0.01) and it resulted in significantly higher yield under water stress condition (+ 15%, p < 0.01) and optimal water supply (+ 2%, p < 0.05). The elongated cycle has a positive effect on yield under any combination of options. Moreover, higher yields projected for the ideotype implicate more crop residues to be incorporated into the soil, which are positively correlated with the SOC sequestration and negatively with N leaching. The crop N uptake is expected to be adequate in view of higher rate of soil mineralization; the N fertilization rate of 400 kg N ha− 1 will involve significant increasing of grain yield, and it is expected to involve a higher rate of SOC sequestration. |
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0048-9697 |
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CropM |
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MA @ admin @ |
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4633 |
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Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
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Journal Article |
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Year |
2014 |
Publication |
Precision Agriculture |
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Precision Agric. |
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15 |
Issue |
3 |
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255-272 |
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stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
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The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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1385-2256 1573-1618 |
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CropM |
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MA @ admin @ |
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4621 |
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Nelson, G.C.; Valin, H.; Sands, R.D.; Havlík, P.; Ahammad, H.; Deryng, D.; Elliott, J.; Fujimori, S.; Hasegawa, T.; Heyhoe, E.; Kyle, P.; Von Lampe, M.; Lotze-Campen, H.; Mason d’Croz, D.; van Meijl, H.; van der Mensbrugghe, D.; Müller, C.; Popp, A.; Robertson, R.; Robinson, S.; Schmid, E.; Schmitz, C.; Tabeau, A.; Willenbockel, D. |
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Title |
Climate change effects on agriculture: economic responses to biophysical shocks |
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Journal Article |
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Year |
2014 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
Proc. Natl. Acad. Sci. U. S. A. |
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Volume |
111 |
Issue |
9 |
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3274-3279 |
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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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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. |
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0027-8424 1091-6490 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4535 |
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Piontek, F.; Müller, C.; Pugh, T.A.; Clark, D.B.; Deryng, D.; Elliott, J.; Colón González, F.J.; Flörke, M.; Folberth, C.; Franssen, W.; Frieler, K.; Friend, A.D.; Gosling, S.N.; Hemming, D.; Khabarov, N.; Kim, H.; Lomas, M.R.; Masaki, Y.; Mengel, M.; Morse, A.; Neumann, K.; Nishina, K.; Ostberg, S.; Pavlick, R.; Ruane, A.C.; Schewe, J.; Schmid, E.; Stacke, T.; Tang, Q.; Tessler, Z.D.; Tompkins, A.M.; Warszawski, L.; Wisser, D.; Schellnhuber, H.J. |
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Multisectoral climate impact hotspots in a warming world |
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Journal Article |
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Year |
2014 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
Proc. Natl. Acad. Sci. U. S. A. |
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Volume |
111 |
Issue |
9 |
Pages |
3233-3238 |
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Keywords |
Agriculture/statistics & numerical data; Computer Simulation; Conservation of Natural Resources/*methods; Ecosystem; *Environment; Geography; Global Warming/economics/*statistics & numerical data; Humans; Malaria/epidemiology; *Models, Theoretical; *Public Policy; Temperature; Water Supply/statistics & numerical data; Isi-mip; coinciding pressures; differential climate impacts |
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The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty. |
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0027-8424 |
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MA @ admin @ |
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4538 |
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