Impact des stratégies de réduction des gaz à effet serre sur les fermes laitières
|Authors:||Velarde Guillen, Jose|
|Abstract:||Meat and dairy production are the first and second most polluting agronomic systems, respectively. In the dairy farms, the enteric fermentation and the crop production are the main sources of greenhouse gas (GHG) emissions. For this reason, the reduction of the fertilization and the dairy cows’ ration manipulation are the principal strategies used to decrease the N2O and CH4 emissions, respectively. The objective of this work was to evaluate the agro-environmental and economic impact of the strategies to decrease the GHG emissions of dairy production. For the study, the N-CyCLES model was used. It is a linear programming model in which three levels : agronomic, animal and economic are considered to find the best response (optimization) for a maximum net income or a minimum whole-farm balance of N or P; evaluating trade-offs between economic and environmental outcomes from mixed livestock-crop dairy systems. In the model, three virtual dairy farms were developed to represent the average farm of three regions of Canada: the Maritimes, Quebec/Ontario and the Prairies. For this, a data base from 166 farms of the Maritimes, from 113 farms of Quebec/Ontario and from 32 farms of the Prairies, was used. In the first study, the impact of a lower fertilization was simulated. To decrease the amount of fertilizer, the corn silage (CS) was substituted by sweet peal millet silage (SPM) in the three regions, and by sweet sorghum (SS) only in the virtual dairy farm of Quebec/Ontario. The results showed that the quantity of fertilizers declined with SPM and SS which decreased the total GHG emissions of the farm from 1 to 9 % as compared to CS. However, the N mass balance increased in the SPM scenario, but declined in the SS scenario. In addition, the farm net income (FNI) decreased from 5 to 21 % with SPM and SS in comparison with the CS scenario. In the second project, an equation to predict the enteric CH4emissions of dairy cows was developed. A database of 193 observations from 18 different treatments and 56 multiparous Holstein cows was created. In all experiments, enteric CH4 production was measured using individual respiration chambers. The dairy cows’ characteristics (dry matter intake [DMI], milk yield [MY], milk fat, milk protein and body weight [BW]) and diet characteristics (dry matter [DM], organic matter [OM], crude protein [CP], neutral detergent fiber [NDF], acid detergent fiber [ADF], gross energy [GE], ether extract [EE]and EE non-reactive in the rumen [rumen-inert fat] and starch) were used in a five-fold cross validation. The best-fit equation (r=0. 83, RMSE=40. 03) included MY, milk fat, milk protein, DMI, BW, NDF, starch and the difference between EE and Rumen-inert fat. This equation, in comparison with the IPCC Tier 2 equation allowed for a more accurate prediction of CH4 emissions from lactating dairy cows fed typical Canadian diets. Finally, in the third study, feeding strategies seeking to decrease enteric CH4 production were simulated to observe their agro-environmental and economic impact in the dairy farm. The supplementation with corn dried distillers grains with solubles (DDGS), with linseed oil (LO) or both in a CS-based diet (CDL), and the substitution of CS by brown midrib corn silage (BMR) or both supplements in a BMR-based diet (BDL) were simulated. The enteric CH4 emissions decreased in each scenario, but total GHG emissions declined only in the LO, BMR and BDL scenarios, whilst in the DDGS and CDL scenarios the emissions were higher than in the CS scenario, Economically, each scenario, except DDGS scenario, decreased FNI. The results of this study showed that the different strategies to decrease the GHG emissions of a sector of the dairy farm (cropland, cow, manure for instance) can increase the emissions in other parts of the dairy chain production. In addition, the FNI declined for most of them which can represent a problem for their adoption by the dairy farmers.|
|Document Type:||Thèse de doctorat|
|Open Access Date:||7 August 2019|
|Collection:||Thèses et mémoires|
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