SPECIAL STORY 52 FEED & ADDITIVE MAGAZINE June 2023 Bringing credible environmental footprint information to front of pack, thereby allowing consumers to make informed food choices, constitutes another opportunity for the animal protein industry and helps address the societal need for reduced emissions by generating consumer pull. It is estimated that about 10–30% of any given household’s GHG footprint is associated with food, yet there is no way a consumer can make a conscious choice of food article based on actual footprint – one kilo of beef is in effect the same as any other kilo of beef in the mind of the consumer. Based on popular belief, beef should be avoided on principle if one is environmentally conscious, irrespective of how it was produced. Many farmers and animal protein companies are already investing in sustainable solutions to significantly lower the environmental footprint of their produce, clearly differentiating their output from the industry average. This creates an opportunity to offer consumers increased choice by clearly presenting the footprint information on the packaging of milk, meat, fish and eggs. Again, however, accurate and credible measurement is a prerequisite. Transparency regarding footprint would not only identify differences within the food category; it would also, in the course of time, enable credible comparisons with meat and milk alternatives. Such transparency has the potential to influence the shift in demand for animal protein, which up to now has partly been driven by the consumer’s understandable desire to help address the environmental issues associated with animal production – although this desire has often been underpinned by high-level, average and often proxy, misleading data, rather than actual data associated with the product itself. In the course of time, it can be expected that the food industry will begin to consistently report the environmental footprints of products – this information being a prerequisite, just like the nutritional information currently provided on food packaging. IT’S ALL ABOUT DATA Undoubtedly the use of accurate farm data is critical to improve the sustainability of food systems and simultaneously report the footprint of animal protein and unlock the associated value for farmers. Accurate, data-driven decision-making is key to investing in more sustainable practices. This relies heavily on two steps. The first involves farm data collection to enter into LCA and farm footprint modelling. The second involves the practical interpretation of the foot-printing outcomes into the farm ecosystem. LCA Assessment is a method for describing the whole life cycle of a product. All phases of the product's life cycle are analyzed. In the case of animal protein, this encompasses the raw materials used in the feed – how and where they were grown and processed and the associated impact on the environment – through to details of the farming system, the energy use, the animal productivity, the volume and chemistry of manure and its subsequent use, by-products and waste streams and their processing, as well as harvesting, packaging and transport of the animal protein to the retail part of the value chain: essentially, from cradle to grave. A well run LCA qualifies, quantifies and pinpoints the environmental impacts along the chain and allows precise interventions to be made to improve the farming process, reduce its environmental footprint, and make efficiency gains that can generate savings and boost value creation. The more accurate and specific the data input and the advancement of the LCA modelling, the greater the accuracy of the LCA outcomes and the greater the likelihood of success in achieving sustainability and business goals. DSM and Blonk Consultants are long-established, leading companies in sustainable business practices. Together, we have developed an advanced foot-printing tool and service that makes it possible to improve the level of accuracy and decision-making in the direction of more sustainable animal protein production. We have worked closely together for many years and in the course of this collaboration have recognized the need for much greater LCA accuracy and transparency. We recognized that using average, high-level data and in some cases proxy data, along with varying interpretation of
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