Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

peer-reviewedAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises

Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

peer-reviewedThe global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practice

Effect of introducing weather parameters on the accuracy of milk production forecast models

peer-reviewedThe objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy

Dairy Sector: Opportunities and Sustainability Challenges

Achieving success along the entire production and supply chain of a dairy sector depends explicitly on adopting a sustainable 'state of the art' approach. In this regard, understanding key sustainability indicators and challenges with a holistic approach is vital. Appropriate design, application of novel technologies, implementation of life cycle analysis, upgradation and optimization of the entire production line are some of the key factors to be measured. In addition, it is vital that due consideration is given to demands of the producers, consumers, and dependent industries. Nevertheless, concern for the environment, social security and economy of the region should not be ignored. Precise planning ('on-farm' and 'off-farm') assumes importance especially when circular economy strategies needs to be considered. With these as background, this book is focused towards identifying present opportunities and overcoming future sustainability challenges in the global dairy sector

Marginal Abatement Cost Curves for Latin American Dairy Production: A Costa Rica case study

This study utilises data collected from Costa Rican dairy farmers to conduct a cradle to farm gate Life Cycle Assessment and the first Marginal Abatement Cost Curve (MACC) for dairy production in Latin America. Ninety dairy farms across five farm typologies were assessed, reflecting Costa Rica's diverse agroclimatic zones and varying degrees of dairy/beef specialisation. The efficacy and cost-effectiveness of specific mitigation measures depend on farm typology, but several promising technologies are identified that increase efficiency whilst substantially reducing emissions across most farms – in particular, measures that improve animal health and increase pasture quality. Pasture measures are synergistic with silvopastoral practises and are highly effective at emission mitigation, although relatively expensive. The replacement of lower quality by-product feeds with high quality concentrate feed is a cost-effective mitigation measure at farm level, but emission reductions could be negated by indirect land use change outside the scope of the MACC analyses. Achieving carbon neutrality at farm level is not likely to be possible for most farms, with the exception of extensive farm typologies. Not all measures are suitable in every context, and additional policy support will be needed to offset financial and technical challenges related to adoption. Results of this first tropical dairy MACC study are constrained by lack of high-resolution data, but they highlight the need for farm-typology-specific mitigation recommendations. Overall, there is a high potential for pasture improvement and silvopastoral measures to mitigate the globally significant contribution of Latin American livestock production to climate change.</p

Strategies for sustainable livestock production in Brazil and the European Union

Livestock provide as much as one-third of all protein consumed by humans, but have a disproportionate and growing environmental impact. Livestock production occupies 50-75% of agricultural land, contributes 15% of anthropogenic greenhouse gas emissions, and drives agricultural expansion in the tropics through the global trade in animal feed. This thesis therefore evaluates two strategies for shrinking the environmental impact of the livestock sector. First, I evaluate the potential for food losses (i.e. foods which were intended for human consumption, but which ultimately are not directly eaten by people) to replace grain- and soybean-based pig feeds in Europe. While food losses have been included in animal feed for millennia, the practice is all but banned in the European Union, because of disease control concerns. Several East Asian states have in the last 20 years, however, introduced regulated systems for safely recycling food losses into animal feed. I combine data from multiple sources (including government reports, the animal science literature, and factory-floor data from South Korean swill-feed factories), and find that the introduction of East Asian practices for recycling food losses as animal feed could reduce the land use of EU pork (20% of world production) by one fifth, potentially saving 1.8 million hectares of agricultural land. This would also reduce 12/14 other assessed environmental impacts and deliver economic savings for pig farmers, as swill (cooked food losses) costs 40-60% less than conventional grain-based feeds. In a survey of pig farmers (n=82) and other agricultural stakeholders (n=81) at a UK agricultural trade fair, we found high support (>75%) for the relegalisation of swill. Support for swill feeding arose in part because respondents thought that swill would lower costs, increase profitability, and be better for the environment. Our results also confirmed the critical importance of disease control and consumer communication when considering relegalisation, as respondents who thought that swill would increase disease risks and be unpalatable to consumers were less supportive of relegalisation. Any new system for the use of swill will require careful design of regulation to ensure that heat-treatment is sufficient, and to reduce to a negligible level the risk of uncooked animal by-products entering feed. Our results suggest, however, that if such a system can be established, there would be multiple benefits and widespread support for its relegalisation. Second, I evaluated the potential to increase the productivity of cattle ranching in the Brazilian Amazon. While high hopes have been placed on the potential for intensification of low-productivity cattle ranching to spare land for other agricultural uses, cattle productivity in the Amazon biome (29% of the Brazilian cattle herd) remains stubbornly low, and it is not clear how to realize theoretical productivity gains in practice. I therefore (a) surveyed six on-the-ground initiatives which have been working with local farmers to improve cattle ranching, quantifying their farm practices, animal performance, and economic results; and (b) analysed the progress that has already been made in reconciling agriculture and forest conservation, by evaluating the impact of the flagship anti-deforestation policy, the priority list (Municípios Prioritários). The survey showed that cattle intensification initiatives operating in four states have used a wide range of technologies to improve productivity by 30-490%, while supporting compliance with the Brazilian Forest Code. Using two complementary difference-in-difference estimators, I then found no evidence for trade-offs between agriculture and forest conservation under the priority list; instead, reductions in deforestation in priority list municipalities were paired with increases in cattle production and productivity (cattle/hectare). The policy had no effect on dairy or crop production. Together, these results provide real-world evidence that increases in cattle production in Brazil do not need to come at the expense of the country’s remaining native vegetation.This research was funded by BBSRC DTP grant BB/J014540/1 and the Tim Whitmore Trust Fund

Multi-level processes of integration and disintegration. Proceedings of the Third Green Week Scientific Conference

CONTENTS: ACKNOWLEDGEMENTS ... I; ABOUT THE MACE PROJECT... III; PLENARY PRESENTATION ... 1; Landscape agroecology: Managing interactions between agriculture, nature and socio-economy... 3, Tommy Dalgaard; DEVELOPMENT CHALLENGES IN RURAL AREAS ... 13; Patterns of rural development in mountainous areas of the Mediterranean: Between innovation and tradition ... 15, Angela Guarino; Agro ecology: Hypothesis for a sustainable local development?... 22, Silvia Doneddu; The farmers' early retirement scheme as an instrument of structural changes in the rural areas after Poland's accession to the EU ... 29, Michal Dudek; FOOD MARKETS AND AGRICULTURAL MARKETING... 37; G/Local brand challenges in the Austrian agricultural food market ... 39, Bernadette Frech, Ana Azevedo, Hildegard Liebl; Willingness of food industry companies to co-finance collective agricultural marketing actions... 48, Anikó Tóth, Csaba Forgács; MULTIFUNCTIONAL AGRICULTURE ... 57; The role of multifunctional agriculture for rural development in Bulgaria... 59, Violeta Dirimanova; A methodological review of multifunctional agriculture ... 66, Concettina Guarino, Francesco Di Iacovo; A spatially explicit decision-making support tool for integral rural development ... 75, Catherine Pfeifer, Jetse Stoorvogel; AGRICULTURAL EXTENSION AND NETWORKS IN RURAL AREAS... 89; Feasibility and implementation strategies of dairy extension in Ulaanbaatar/Mongolia... 91, Baast Erdenebolor, Volker Hoffmann; The relevance of social networks for the implementation of the LEADER programme in Romania ... 99, Doris Marquardt, Gertrud Buchenrieder, Judith Möllers; Quality assessment problems of agricultural advisory centres' services... 113, Gunta Grinberga; INTEGRATION PROCESSES INTO INTERNATIONAL MARKETS... 125; Competition or market power in the Ukrainian meat supply chain? ... 127, Andriy Matyukha, Oleksandr Perekhozhuk; Integration of the Hungarian cereal market into EU 15 markets ... 138, Attila Jambor; Regional specialisation of agriculture and competitive advantages of East-European countries... 146, Oleksandr Zhemoyda, Stephan J. Goetz; GOVERNANCE AND USE OF NATURAL RESOURCES ... 155; An analysis of biodiversity governance in the Kiskunság National Park according to the GoverNat Framework... 157, Cordula Mertens, Eszter Kelemen, György Pataki; Hierarchical network modelling and multicriteria analysis for agri-environmental measures in Poland ... 168, Jadwiga Ziolkowska; Assessing rural livelihood development strategies combining socioeconomic and spatial methodologies ... 179, K.C. Krishna Bahadur; SUSTAINABLE AGRICULTURAL LAND USE... 189; Linking economic and energy modelling with environmental assessment when modelling the on-farm implementation of Anaerobic Digestion ... 191, Andreas Muskolus, Andrew M. Salter, Philip J. Jones; Phytoremediation of a heavy metal-contaminated agricultural area combined with energy production. Multifunctional use of energy maize, rapeseed and short rotation crops in the Campine (BE)... 200, Nele Witters, Stijn Van Slycken, Erik Meers, Kristin Adriaensen, Linda Meiresonne, Filip Tack, Theo Thewys, Jaco Vangronsveld --

Reducing Uncertainty in Life Cycle Assessment of Livestock Production Systems

Life cycle assessment (LCA) has been increasingly applied to livestock production systems to estimate their environmental footprints, but the degree of uncertainties associated with these values is known to be generally high. This thesis explores novel methods of LCA modelling to reduce uncertainty associated with environmental footprints of meat production systems, with the view to contribute to objective and transparent debates about the role of livestock in global food security. Three innovative approaches are proposed in this thesis. First, as information on individual animals is often unavailable, livestock data are often aggregated at the time of inventory analysis. To investigate the level of bias caused by this aggregation, Chapter 3 uses primary data collected at the North Wyke Farm Platform in Southwest England and calculates emission intensities for individual animals and their intra-farm distributions, providing a step towards deriving optimal animal selection strategies based on livestock LCA. Second, the severity of greenhouse gas emissions from agricultural production is known to vary spatially and temporally, yet available LCA frameworks often fail to sufficiently consider these differences due to data constraints. To evaluate the degree of avoidable uncertainties attributable to this practice, Chapter 4 conducts an original field experiment to derive site-specific nitrous oxide emission factors, which are subsequently used in Chapter 5 to compare LCA results derived under these localised values and generic alternatives intended for the widest possible users. Finally, while LCA results are typically communicated in the form of environmental burdens per output of mass, it is gradually becoming recognised that product quality also needs to be accounted for to truly understand the value of each farming system to society. Using data from seven livestock production systems encompassing cattle, sheep, pigs, and poultry, Chapter 6 develops new methods to incorporate nutritional values of meat products into livestock LCA

Reducing uncertainty in life cycle assessment of livestock production systems

Life cycle assessment (LCA) has been increasingly applied to livestock production systems to estimate their environmental footprints, but the degree of uncertainties associated with these values is known to be generally high. This thesis explores novel methods of LCA modelling to reduce uncertainty associated with environmental footprints of meat production systems, with the view to contribute to objective and transparent debates about the role of livestock in global food security. Three innovative approaches are proposed in this thesis. First, as information on individual animals is often unavailable, livestock data are often aggregated at the time of inventory analysis. To investigate the level of bias caused by this aggregation, Chapter 3 uses primary data collected at the North Wyke Farm Platform in Southwest England and calculates emission intensities for individual animals and their intra-farm distributions, providing a step towards deriving optimal animal selection strategies based on livestock LCA. Second, the severity of greenhouse gas emissions from agricultural production is known to vary spatially and temporally, yet available LCA frameworks often fail to sufficiently consider these differences due to data constraints. To evaluate the degree of avoidable uncertainties attributable to this practice, Chapter 4 conducts an original field experiment to derive site-specific nitrous oxide emission factors, which are subsequently used in Chapter 5 to compare LCA results derived under these localised values and generic alternatives intended for the widest possible users. Finally, while LCA results are typically communicated in the form of environmental burdens per output of mass, it is gradually becoming recognised that product quality also needs to be accounted for to truly understand the value of each farming system to society. Using data from seven livestock production systems encompassing cattle, sheep, pigs, and poultry, Chapter 6 develops new methods to incorporate nutritional values of meat products into livestock LCA