The environmental impact of dairy production: 1944 compared with 2007
A common perception is that pasture-based, low-input dairy systems characteristic of the 1940s were more conducive to environmental stewardship than modern milk production systems. The objective of this study was to compare the environmental impact of modern (2007) US dairy production with historical production practices as exemplified by the US dairy system in 1944. A deterministic model based on the metabolism and nutrient requirements of the dairy herd was used to estimate resource inputs and waste outputs per billion kg of milk. Both the modern and historical production systems were modeled using characteristic management practices, herd population dynamics, and production data from US dairy farms. Modern dairy practices require considerably fewer resources than dairying in 1944 with 21% of animals, 23% of feedstuffs, 35% of the water, and only 10% of the land required to produce the same 1 billion kg of milk. Waste outputs were similarly reduced, with modern dairy systems producing 24% of the manure, 43% of CH4, and 56% of N2O per billion kg of milk compared with equivalent milk from historical dairying. The carbon footprint per billion kilograms of milk produced in 2007 was 37% of equivalent milk production in 1944. To fulfill the increasing requirements of the US population for dairy products, it is essential to adopt management practices and technologies that improve productive efficiency, allowing milk production to be increased while reducing resource use and mitigating environmental impact.[1]
The carbon footprint of dairy production systems through partial life cycle assessment
Greenhouse gas (GHG) emissions and their potential effect on the environment has become an important national and international issue. Dairy production, along with all other types of animal agriculture, is a recognized source of GHG emissions, but little information exists on the net emissions from dairy farms. Component models for predicting all important sources and sinks of CH4, N2O, and CO2 from primary and secondary sources in dairy production were integrated in a software tool called the Dairy Greenhouse Gas model, or DairyGHG. This tool calculates the carbon footprint of a dairy production system as the net exchange of all GHG in CO2 equivalent units per unit of energy-corrected milk produced. Primary emission sources include enteric fermentation, manure, cropland used in feed production, and the combustion of fuel in machinery used to produce feed and handle manure. Secondary emissions are those occurring during the production of resources used on the farm, which can include fuel, electricity, machinery, fertilizer, pesticides, plastic, and purchased replacement animals. A long-term C balance is assumed for the production system, which does not account for potential depletion or sequestration of soil carbon. An evaluation of dairy farms of various sizes and production strategies gave carbon footprints of 0.37 to 0.69 kg of CO2 equivalent units/kg of energy-corrected milk, depending upon milk production level and the feeding and manure handling strategies used. In a comparison with previous studies, DairyGHG predicted C footprints similar to those reported when similar assumptions were made for feeding strategy, milk production, allocation method between milk and animal coproducts, and sources of CO2 and secondary emissions. DairyGHG provides a relatively simple tool for evaluating management effects on net GHG emissions and the overall carbon footprint of dairy production systems.[2]
Evaluation of indicators to assess the environmental impact of dairy production systems
Current awareness of environmental pollution of animal production in Western Europe has triggered research on development of environmental indicators at farm level. Only when the environmental impact of commercial farms can be quantified effectively, important differences in impact can be demonstrated among contrasting systems, which subsequently can contribute to reducing the environmental impact from animal production. Therefore, the aim of this study was to evaluate the effectiveness of environmental indicators derived from three methods used widely in animal production, i.e., input–output accounting, ecological footprint analysis and life cycle assessment (LCA). Evaluation of the effectiveness of indicators was based on an assessment of their relevance, quality and availability of data. Such a systematic evaluation of these environmental indicators has never been performed yet. To evaluate the effectiveness of the 13 environmental indicators, data from eight organic, commercial dairy farms in the Netherlands were used. Results show that indicators derived from input–output accounting are effective, because of their high relevance, good quality and easy availability of data. These indicators, however, do not include all environmental impact categories (e.g., land use, energy use, global warming potential), and focus on on-farm emission. The environmental indicator derived from ecological footprint analysis is not effective for land and fossil energy use, because of its limited relevance and low quality, whereas LCA resource-based indicators are effective because of their high relevance, good quality and availability of data. LCA indicators for global warming, acidification and eutrophication potential are effective also, because of their good relevance and good quality. Data of these LCA indicators are difficult to collect. To give a good insight into the environmental impact of a dairy production system, besides input–output accounting indicators, LCA indicators are required. [3]
Descriptive Analysis of the Effectiveness of Livestock Extension Services Delivery among Dairy Farmers in District Peshawar
The instant study analyzes farmer’s perception about the livestock extension services in district Peshawar. Multi-stage sampling technique was used to select three villages and proportional allocation method was utilized to select 80 dairy farmers from the three selected villages. Primary data was collected through interview schedules from the selected respondents. Field data showed that 36% of the respondents contacted livestock and 67% of the respondents responded that livestock officer paid visit to them. Majority of the respondents (69%) were not satisfied from the services of livestock and dairy development department. The study concludes that livestock and dairy development department did not provide satisfactory facilities nor training regarding improved dairy technologies was provided. Livestock extension officers were also not found fulfilling their jobs accordingly by visiting the farmers on regular basis which compels the dairy farmers to consult private clinics at high cost. It is suggested therefore that independent monitoring unit should be established to ensure livestock officer to pay regular and frequent visits to the farmers to educate the dairy farmers which will build and restore the trust and confidence of the dairy farmers on livestock extension department alongside improving their dairy production.[4]
Prevalence of Endometritis and Its Associated Risk Factors in Dairy Cattle of Central Uganda
Aims: Endometritis is a major postpartum disease that affects dairy cattle productivity which is accompanied by heavy economic losses to the farmer. The status of Clinical endometritis (CE) and Sub-Clinical Endometritis (SCE) in sub-Saharan Africa is poorly understood, thus the study was carried out to provide information on the prevalence and associated risk factors that are responsible for the occurrence of SCE and CE in dairy cattle of Central Uganda.
Study Design: This was a prospective study involving 140 dairy cattle within 60 days postpartum from 35 commercial dairy farms in Central Uganda. The dairy herds were visited in both the dry (n=90) and wet season (n=50) and subsequent visits were conducted after 3 months and 5 months to collect data for reproductive performance
Methodology: A metricheck® device was used to collect the cervico-vaginal discharge which was examined for color, odor, texture, and measurement of its pH during the postpartum period for diagnosis of endometritis. Further examination of the reproductive tract was carried out using a vaginal speculum and subsequently rectal palpations were performed. The objectives of the study were to determine the prevalence of CE and SCE in the Central Uganda and assess the risk factors involved.
Results: In this study, the prevalence of CE and SCE was established at 3.6% and 18.6% respectively, this burden was slightly higher in the wet than in the dry season but with no statistical significance P=0.126. Dairy cattle that had calved more than three times were shown to be associated with a higher body appearance (BCS > 3) than those that had calved down fewer times. Dystocia, Retained After Birth and Abortion were identified as associated risk factors (P = 0.00) to SCE and CE whereas infertility and Prolonged days calving to conception (>90 d) were postpartum implications (P=0.00) associated with SCE and CE in this study. This would be attributed to the poor management of postpartum dairy cattle in the farms visited as no farm was found with a maternity pen. SCE caused infertility in 65.5% of the dairy cattle whereas the CE is a major influencing factor to long calving to first AI and calving to conception interval (306d±90.6), On basis of reproduction, there was no major difference towards use of AI or Natural service.
Conclusion: Management of endometritis in the region should address pre-partum and postpartum dairy herd management through improved extension service delivery and technical farm support to construct maternity pens, Endometritis is a multifactorial disease that requires a multidisciplinary approach to boost nutrition and health thus reducing incidence of risk factors such as dystocia and Retained fetal birth) .subsequent studies should be carried out to explore the national burden of SCE and ascertain the cause of the abortion.[5]
Reference
[1] Capper, J.L., Cady, R.A. and Bauman, D.E., 2009. The environmental impact of dairy production: 1944 compared with 2007. Journal of animal science, 87(6), pp.2160-2167.
[2] Rotz, C.A., Montes, F. and Chianese, D.S., 2010. The carbon footprint of dairy production systems through partial life cycle assessment. Journal of dairy science, 93(3), pp.1266-1282.
[3] Thomassen, M.A. and de Boer, I.J., 2005. Evaluation of indicators to assess the environmental impact of dairy production systems. Agriculture, ecosystems & environment, 111(1-4), pp.185-199.
[4] Nawaz, A., Khan, M.Z., Rehman, A. and Ullah, R., 2016. Descriptive Analysis of the Effectiveness of Livestock Extension Services Delivery among Dairy Farmers in District Peshawar. Asian Journal of Agricultural Extension, Economics & Sociology, pp.1-6.
[5] Tayebwa, D.S., Bigirwa, G., Byaruhanga, J. and Kasozi, K.I., 2015. Prevalence of endometritis and its associated risk factors in dairy cattle of Central Uganda. Journal of Experimental Agriculture International, pp.155-162.
