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Climate-Smart Holistic Management System Criteria’s Effectiveness on Milk Production in Lithuania

Vilma Naujokienė, Rolandas Bleizgys, Kęstutis Venslauskas and Simona Paulikienė
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Vilma Naujokienė: Department of Mechanics, Energy and Biotechnology Engineering, Vytautas Magnus University, Studentų Str. 11, Kaunas District, LT-52261 Akademija, Lithuania
Rolandas Bleizgys: Department of Mechanics, Energy and Biotechnology Engineering, Vytautas Magnus University, Studentų Str. 11, Kaunas District, LT-52261 Akademija, Lithuania
Kęstutis Venslauskas: Department of Mechanics, Energy and Biotechnology Engineering, Vytautas Magnus University, Studentų Str. 11, Kaunas District, LT-52261 Akademija, Lithuania
Simona Paulikienė: Department of Mechanics, Energy and Biotechnology Engineering, Vytautas Magnus University, Studentų Str. 11, Kaunas District, LT-52261 Akademija, Lithuania

Agriculture, 2022, vol. 12, issue 6, 1-18

Abstract: One of the problematic sectors according to GHG (greenhouse gas) and ammonia (NH 3 ) emission quantities is agriculture. Without endangering food production (and intensifying), GHG emissions come from all sources in animal husbandry. The aim of this study was to comprehensively reduce GHG emissions by applying a holistic process management model to one of the most popular cowsheds in Lithuania (260-seat boxing cowshed, cows are milked on site, computerized management of technological processes, productivity of 8600 kg of milk, barn system, and liquid manure). Considering the cow keeping technology applied on the farm, the equipment used, and the feed production and ration system, a model for the management of technological parameters of production processes was prepared for the farm. This model balanced trade-offs among animal welfare, cow productivity, production costs, and GHG and NH 3 emissions. The aim of the research was the adaptation of the integrated model to fully control, manage, and optimize milk production processes through bio- and engineering innovations to implement climate-friendly feed production and feeding and feed rationing systems, to improve animal housing and working conditions, and to reduce GHG and NH 3 emissions without increasing production costs. The environmental impact assessment was performed with SimaPro 9.1 process modeling software. Data from milk production, biomass cultivation, and feed preparation, transportation, and equipment were used from the Ecoinvent v3 database. Based on the LML-I calculation methodology, the effect of processes was determined. To quantify the potential emissions in the dairy farm, the emission factors were estimated using a life cycle assessment method per functional unit—1000 kg—of standardized milk. Grass silage, maize silage, and feed concentrate were found to account for the largest share of gas emissions—26.09% (107.39 kg CO 2 eq. FU −1 ), 22.70% (93.44 kg CO 2 eq. FU −1 ), and 21.85% (89.92 kg CO 2 eq. FU −1 ) of the total CO 2 emissions during the process, respectively. Considering the critical points of the classic SC scenario, the cultivation technology was adjusted, where 50% of N fertilizers were replaced by bioproducts (biological preparations). Both scenarios—classic SC (control variant) and Bio SC (variants using bioproducts)—were evaluated for comparison. The use of biopreparations in the categories reduced the environmental impact from 0.1% to 45.7% in dairy production technology grass silage, barley grain, hay production, and corn silage stocks. The carbon footprint of the sustainable bio-based milk production (0.393 kg CO 2 eq. kg −1 FPCM (fat- and protein-adjusted milk)) was lower by 4.6% compared to the average Lithuanian classic dairy farm (0.412 kg CO 2 eq. kg −1 FPCM). Based on this methodology, it is possible to assess many dairy farms and address critical points in an integrated way, which can help to improve the quality of dairy production and the environment.

Keywords: global warming potential; greenhouse gas; holistic management; GHG reduction; milk production; fat- and protein-adjusted milk (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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