| Records |
| Author |
Bernabucci, U.; Biffani, S.; Buggiotti, L.; Vitali, A.; Lacetera, N.; Nardone, A. |
| Title |
The effects of heat stress in Italian Holstein dairy cattle |
Type |
Journal Article |
| Year |
2014 |
Publication |
Journal of Dairy Science |
Abbreviated Journal |
J. Dairy Sci. |
| Volume |
97 |
Issue |
1 |
Pages |
471-486 |
| Keywords |
Animals; Breeding; Cattle; Dietary Fats/analysis; Dietary Proteins/analysis; Female; Genetic Variation; Heat Stress Disorders/*veterinary; *Hot Temperature; Humans; Humidity; *Lactation; Linear Models; Milk/chemistry; Parity; Phenotype; Weather; dairy cow; heritability; production trait; temperature-humidity index breaking point |
| Abstract |
The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives. |
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English |
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1525-3198 (Electronic) 0022-0302 (Linking) |
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LiveM, ft_macsur |
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MA @ admin @ |
Serial |
4617 |
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| Author |
Özkan, Ş.; Hill, J.; Cullen, B. |
| Title |
Effect of climate variability on pasture-based dairy feeding systems in south-east Australia |
Type |
Journal Article |
| Year |
2014 |
Publication |
Animal Production Science |
Abbreviated Journal |
Animal Production Science |
| Volume |
55 |
Issue |
9 |
Pages |
1106-1116 |
| Keywords |
carry-forward surplus; conserved-hay; probability; winter deficit; grown forage consumption; new-zealand; nutritive characteristics; interannual variation; botanical composition; herbage accumulation; crop; systems; cows; management; profit |
| Abstract |
The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in climate affects plant growth, leading to uncertainty in dryland pasture supply. This paper models the impact of climate variability on pasture production and examines the potential of two pasture-based dairy feeding systems: (1) to experience winter deficits; (2) to carry forward the conserved pasture surpluses as silage for future use; and (3) to conserve pasture surpluses as hay. The two dairy feeding systems examined were a traditional perennial ryegrass-based feeding system (ryegrass max. – RM) and a system that incorporated double cropping into the perennial ryegrass pasture base (complementary forage – CF). The conditional probability of the RM and CF systems to generate pasture deficits in winter were 94% and 96%, respectively. Both systems could carry forward the surplus silage into the following lactation almost once in every 4-5 years with the RM system performing slightly better than the CF system. The proportions of the grain-based concentrates fed in the two systems were 25% and 27% for the RM and CF systems, respectively. This study suggests that double-cropping systems have the potential to provide high-quality feed to support the feed gaps when pasture is not available due to increased variability in climatic conditions. |
| Address |
2015-09-23 |
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1836-5787 |
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LiveM |
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no |
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MA @ admin @ |
Serial |
4689 |
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Biewald, A.; Rolinski, S.; Lotze-Campen, H.; Schmitz, C.; Dietrich, J.P. |
| Title |
Valuing the impact of trade on local blue water |
Type |
Journal Article |
| Year |
2014 |
Publication |
Ecological Economics |
Abbreviated Journal |
Ecol. Econ. |
| Volume |
101 |
Issue |
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Pages |
43-53 |
| Keywords |
virtual water; blue and green water; water scarcity; agricultural trade; global vegetation model; virtual water; crop trade; resources; scarcity; food; footprints; products; flows; green |
| Abstract |
International trade of agricultural goods impacts local water scarcity. By quantifying the effect of trade on crop production on grid-cell level and combining it with cell- and crop-specific virtual water contents, we are able to determine green and blue water consumption and savings. Connecting the information on trade-related blue water usage to water shadow prices gives us the possibility to value the impact of international food crop trade on local blue water resources. To determine the trade-related value of the blue water usage, we employ two models: first, an economic land- and water-use model, simulating agricultural trade, production and water-shadow prices and second, a global vegetation and agricultural model, modeling the blue and green virtual water content of the traded crops. Our study found that globally, the international trade of food crops saves blue water worth 2.4 billion US$. This net saving occurs despite the fact that Europe exports virtual blue water in food crops worth 3.1 billion US$. Countries in the Middle East and South Asia profit from trade by importing water intensive crops, countries in Southern Europe on the other hand export water intensive agricultural goods from water scarce sites, deteriorating local water scarcity. (C) 2014 Elsevier B.V. All rights reserved. |
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0921-8009 |
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CropM, TradeM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4512 |
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| Author |
Nelson, G.C.; van der Mensbrugghe, D.; Ahammad, H.; Blanc, E.; Calvin, K.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; von Lampe, M.; Mason, d’C., Daniel; van Meijl, H.; Müller, C.; Reilly, J.; Robertson, R.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Takahashi, K.; Valin, H.; Willenbockel, D. |
| Title |
Agriculture and climate change in global scenarios: why don’t the models agree |
Type |
Journal Article |
| Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
| Volume |
45 |
Issue |
1 |
Pages |
85-85 |
| Keywords |
climate change impacts; economic models of agriculture; scenarios; system model; demand; cmip5 |
| Abstract |
Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. |
| Address |
2016-10-31 |
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English |
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0169-5150 |
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Article |
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| Notes |
CropM, TradeM, ft_macsur |
Approved |
no |
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MA @ admin @ |
Serial |
4796 |
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Robinson, S.; van Meijl, H.; Willenbockel, D.; Valin, H.; Fujimori, S.; Masui, T.; Sands, R.; Wise, M.; Calvin, K.; Havlik, P.; Mason d’Croz, D.; Tabeau, A.; Kavallari, A.; Schmitz, C.; Dietrich, J.P.; von Lampe, M. |
| Title |
Comparing supply-side specifications in models of global agriculture and the food system |
Type |
Journal Article |
| Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
| Volume |
45 |
Issue |
1 |
Pages |
21-35 |
| Keywords |
global agricultural models; global food system scenario analysis; general equilibrium; partial equilibrium; growth; trade |
| Abstract |
This article compares the theoretical and functional specification of production in partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two model families differ in their scopepartial versus economy-wideand in how they represent technology and the behavior of supply and demand in markets. The CGE models are deep structural models in that they explicitly solve the maximization problem of consumers and producers, assuming utility maximization and profit maximization with production/cost functions that include all factor inputs. The PE models divide into two groups on the supply side: (1) shallow structural models, which essentially specify area/yield supply functions with no explicit maximization behavior, and (2) deep structural models that provide a detailed activity-analysis specification of technology and explicit optimizing behavior by producers. While the models vary in their specifications of technology, both within and between the PE and CGE families, we consider two stylized theoretical models to compare the behavior of crop yields and supply functions in CGE models with their behavior in shallow structural PE models. We find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of implicit supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In practice, however, the applied models are more complex and differ in their empirical sensitivity to variations in specificationcomparability of results given parameter choices is an empirical question. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models. |
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English |
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0169-5150 |
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| Notes |
CropM, TradeM, ft_macsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
4735 |
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