Food Industry 1

LOCATION: 2301
08:30 Andrew HendersonKarin VeltmanCarolyn MattickYing Wang and Olivier Jolliet

Understanding Ranges of Nutrient Losses in Agriculture, Focusing on Dairy Farms

ABSTRACT. Nutrient management represents both a challenge and opportunity to agriculture, as lost nutrients may impact water and air quality. Such losses may also have direct economic implications for farms, via possible phosphorus supply shortages [1] or future nutrient regulation (e.g., for eutrophication or greenhouse gases).

Recent LCA efforts to assess farm nutrient losses have either relied on default emission factors or aggregated results from process models (e.g., [2], [3]). Though spatial and process variability complicates this task, there is still a need in LCA to improve the understanding of farm-level nutrient inventory. Using a combination of literature meta-analysis and case study modeling, this research outlines and estimates the range of nutrient losses on dairy farms, as well as the potential scale of improved nutrient cycling on these farms. Dairy farms were chosen in order to capture crop and livestock losses.

For the meta-analysis, we compiled ~300 research articles for nutrient flows on dairy farms, normalizing by cow and by milk production. We cataloged the nitrogen and phosphorus flows crossing the farm boundary (e.g., purchased feed) and internal to the farm (e.g., manure application to crops). Some flows had large coefficients of variation, such as total excretion of nitrogen (c.o.v. = 2.3, n = 43) and total intake of nitrogen (c.o.v. = 2.7, n = 94).

We compared these results to modeled nutrient flows on a commercial dairy farm, using the process-based models IFSM [4] and Manure-DNDC [5]. Field N2O emissions differed between the models (3.8-11.3 tonnes N2O/ yr), but other emissions, such as P and N losses to (ground)water through leaching, run-off and erosion are comparable across models. Whole-farm ammonia emissions are also similar in models (87.6 – 122.1 tonnes NH3/yr).

The variation among farms in the literature and models will be used to bound nutrient losses for LCA studies: for those flows with large reported and modeled variation, future work will identify controlling factors that can be captured in an LCA framework. For those flows with minimal variation, average values would be appropriate for LCA studies.

References:

[1] D. P. Van Vuuren, A. F. Bouwman, and A. H. W. Beusen, “Phosphorus demand for the 1970–2100 period: A scenario analysis of resource depletion,” Glob. Environ. Change, vol. 20, no. 3, pp. 428–439, Aug. 2010.

[2] M. Guerci, L. Bava, M. Zucali, A. Sandrucci, C. Penati, and A. Tamburini, “Effect of farming strategies on environmental impact of intensive dairy farms in Italy,” J. Dairy Res., vol. 80, no. 03, pp. 300–308, 2013.

[3] A. D. Henderson, A. C. Asselin-Balençon, M. C. Heller, and Jolliet, Olivier, “Spatial LCA of resource use in agricultural productions: A U.S. case study,” presented at the SETAC North America 34th Annual Meeting, Nashville, TN, USA, Nov-2013.

[4] C. A. Rotz, M. S. Corson, D. S. Chianese, F. Montes, S. Hafner, H. F. Bonifacio, and C. U. Coiner, “The Integrated Farm System Model (IFSM): Reference Manual Version 4.1,” Pasture Systems and Watershed Management Research Unit – Agricultural Research Service – United States Department of Agriculture, Washington, DC, USA, 2014.

[5] C. Li, W. Salas, R. Zhang, C. Krauter, A. Rotz, and F. Mitloehner, “Manure-DNDC: a biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems,” Nutr. Cycl. Agroecosystems, vol. 96, pp. 163–200, May 2012.

08:45 Shannan LittleKaren BeaucheminChaouki BenchaarLilong ChaiHenry JanzenRoland KröbelEmma McGeough and Andrew Vanderzaag

Demonstrating the effect of diet on the carbon footprint of a Canadian dairy scenario using whole-systems analysis and the Holos model: corn silage vs. alfalfa silage

ABSTRACT. The effect of diet on enteric methane production in dairy cattle diets is well studied.1,2 While we know the largest contributor to the on-farm carbon footprint of milk is enteric methane, the impacts of diet on the overall, on-farm carbon foot print of milk production are less widely known.3 Before recommending a feeding strategy for greenhouse gas (GHG) mitigation, it is important to conduct a holistic assessment of all related emissions, ranging from those arising from feed production, digestion of these feeds, managing the resulting manure, and other on-farm production processes and inputs. The impact of diet on milk yield and composition must also be assessed.

Using a whole-systems approach, the Holos model,4 and experimentally measured data, this study compares the effects of corn silage vs. alfalfa silage based diets on GHG estimates in a simulated Canadian dairy production system, based in the province of Quebec. A previous study demonstrates that feeding a corn silage based diet reduces enteric methane by 10% based on a percentage of gross energy intake as compared to an alfalfa silage based diet.4 Accounting for enteric and manure methane, cropping/ soil and manure nitrous oxide (direct and indirect), and energy and lime application carbon dioxide, preliminary model results demonstrate that overall farm GHG emissions are reduced by over 10% with the corn silage based diet. Total farm area required to grow the required feed is also reduced.

However, the whole-system results will demonstrate the impact of diet choice on overall net farm GHG emissions as the effects of diet choice on soil carbon change must also be assessed. The comparison of ammonia emissions associated with the diets is also important. Results are expressed with the functional units of kg of fat and protein corrected milk, kg of live-weight of meat, kg of protein, and MJ of energy. The system boundary is at the farm-gate. The choice of allocation between milk and meat functional units is also assessed.

This study serves to reinforce the essential need to utilize the whole-systems or life cycle approach instead of focussing on single elements of a farm system without investigating interrelated effects of management choices. Reported GHG reduction factors cannot be simply combined as effects of farm management transfer through the entire system, sometimes with counter-intuitive effects. It is necessary to apply this whole-systems approach before implementing changes in management intended to reduce greenhouse gas emissions and improve sustainability.

Keywords: dairy farm, greenhouse gas (GHG) emissions, life cycle assessment, carbon footprint, mitigation

1 Boadi, D., C. Benchaar, J. Chiquette and D. Massé. 2004. Mitigation strategies to reduce enteric methane emissions from dairy cows: Update review. Canadian Journal of Animal Science 84: 319–335.

2 Beauchemin, K.A., M. Kreuzer, F. O’Mara, and T.A. McAllister. 2008. Nutritional management for enteric methane abatement: A review. Australian Journal of Experimental Agriculture 48: 21-27.

3 Mc Geough, M.J., S.M. Little, H.H. Janzen, T.A. McAllister, S.M. McGinn and K.A. Beauchemin. 2012. Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: A case study. Journal of Dairy Science 95: 5164-5175.

4 Little, S., K. Beauchemin, H. Janzen, R. Kroebel and K. Maclean. 2013. Holos – A tool to estimate and reduce greenhouse gases from farms. Methodology and Algorithms for Version 2.0. Agriculture and Agri-Food Canada, 104 pgs.

5 Hassanat, F., R. Gervais, C. Julien, D.I. Massé, A. Lettat, P.Y. Chouinard, H.V. Petit, and C. Benchaar. 2013. Replacing alfalfa silage with corn silage in dairy cow diets: Effects on enteric methane production, ruminal fermentation, digestion, N balance, and milk production. Journal of Dairy Science 96: 4553-4567.

09:00 Kiyotada HayashiTakanori KitagawaYoshitaka MikageSatoko Takasaki and Takaomi Yasuhara

A cost-effectiveness approach to comparative life cycle assessment of agricultural production systems: The case of rice cultivation using a fertilizer derived from brewing by-products

ABSTRACT. Many functional materials have been developed using food by-products and applied as agricultural inputs such as fertilizers and pesticides. Since the purpose of the application is to establish sustainable agricultural production systems, life cycle assessment (LCA) will play an important role in judging whether the application contributes to sustainability. Actually, environmental impacts of organic fertilizers made from animal manure, for example, have been analyzed using the framework of comparative LCA of agricultural production systems [1].

However, there is room to take advantage of the problem characteristics: application of the material affects crop yields and environmental impacts, which can be depicted as a simple influence diagram. Therefore, we integrated cost-effectiveness measures with comparative LCA in order to clearly support the decision whether to use the material. A rice production system using a fertilizer made from yeast cell wall extract, a by-product of beer brewing, was compared with a conventional rice production system.

The framework can be illustrated using land-oriented expression [2], in which performance of production systems is depicted in a coordinate with yields per area (horizontal axis) and environmental impacts per area (vertical axis). In contrast to conventional comparative LCA, in which degrees between two lines (horizontal lines and lines connecting the origin and each point for production systems) are compared, we pay attention to incremental production costs (an input) and incremental yields and environmental impacts (outputs to measure effectiveness). The word “incremental” is based on the usage in economic evaluation [3] and is different from “marginal” in LCA [4]. On the basis of the framework, we will discuss cost-effectiveness (∆yield/∆cost and ∆environmental impact/∆cost) and trade-offs between productivity and environmental impacts (∆environmental impact/∆yield).

The results can be summarized as follows: ∆GHG/∆cost (∆GHG=incremental increase of greenhouse gas emissions) increased slightly, although the result was dependent on inventory modelling (system expansion or allocation); ∆yield/∆cost increased drastically; and these results can be connected to the small trade-off rate between the two outputs (∆GHG/∆yield), which implies the performance measured on GHG emissions per product unit was improved. The framework can be extended to include working hours (an input) and revenue (an output).

Citations:

[1] Martinez-Blanco, J., Munoz, P., Anton, A. and Rieradevall, J. (2011). Assessment of tomato Mediterranean production in open-field and standard multi-tunnel greenhouse, with compost or mineral fertilizers, from an agricultural and environmental standpoint. Journal of Cleaner Production, 19, 985-997. [2] Hayashi, K. (2013). Practical recommendations for supporting agricultural decisions through life cycle assessment based on two alternative views of crop production: the example of organic conversion. The International Journal of Life Cycle Assessment, 18, 331-339. [3] Drummond, M.F., Sculpher, M.J., Torrance, G.W., O’Brien, B.J. and Stoddart, G.L. (2005). Methods for the Economic Evaluation of Health Care Programmes, Third Edition. Oxford University Press, New York. [4] Weidema, B.P, Frees, N. and Nielsen, A.M. (1999). Marginal production technologies for life cycle inventories. The International Journal of Life Cycle Assessment, 4, 48-56.

09:15 Markus Frank

AgBalance Farm – from socio-economic LCA to farm management

ABSTRACT. Life Cycle Assessment has proved capable to reveal the key drivers of sustainable agriculture and thus to serve as a guardrail for improvement strategies (Frank et al. 2012). However, the translation of the results of LCA studies into on-farm decision support has mostly failed. Here, we present the concept AgBalance Farm that uses key learnings of socio-economic LCA studies for the development web-based crop management support applications for farmers. Soybean production in India was selected as a test case. India is the fifth largest producer of soybean in the world but soybean yields currently reach only half the global average of 2.4mt/ha. Lack of knowledge about good farming practices comprises the key reasons for the low productivity. Through the training program ‘Samruddhi’ (Sanskrit for ‘prosperity’), farmers are educated not only on the timely usage of crop protection in-puts, but also about correct fertilization, seed rate and spacing to enable higher yields (GIZ 2013). While the contribution of Samruddhi to the profitability of the Indian soybean farmers had been shown (PWC 2013), its contribution to the sustainability of the production was largely unknown (Voeste 2012). Against this background, a holistic socio-economic life cycle assessment using AgBalanceTM methodology was conducted, comparing soybean production under ‘Samruddhi’ and ‘non-Samruddhi’ in the state of Madhya Pradesh. The AgBalanceTM revealed that the ‘Samruddhi’ production practice outperfomed ‘non-Samruddhi’ in all three dimensions of sustainability. Based upon this AgBalanceTM study, 12 sustainability indicators with a substantial impact on the study result were selected. Through regression analysis of data sets of approx. 100 individual farmers, mathematical functions describing the interdependencies between the respective indicators were derived, e.g. between yield and nutrient management. A web-based application was generated in order to conduct scenario analysis interactively, which can be used by farmers or technical advisors to help soybean farmers optimizing their production protocol towards higher yield, profitability and sustainability. With this “AgBalance Farm” strategy, we aim to effectively use the potential of socio-economic LCA to support crop management decisions of individual smallholder farmers.