The global livestock sector is rapidly transforming. Over the past few decades, many livestock systems over the world have evolved from local, small-scale mixed crop-livestock systems to global and demand-driven supply chains, in which feed and animal production stages are often disconnected. These changes, driven by economic opportunities, have altered the way livestock production impacts global nitrogen and phosphorus flows and emissions. These emissions take place in several stages of the supply chains, namely feed production, animal production and processing of animal products and threaten water, soil and air quality, but also climate, biodiversity and human health. Achieving better nutrient management is thus an important aspect of improving environmental performance in the livestock sector. Improving the efficiency of nutrient use has been identified as the main strategy to reduce environmental pressures while achieving global food security and sustainability.
To reduce nutrient losses in livestock supply chains, there is a need for methods and indicators that determine these losses or the other way around, determine the nutrient use efficiency (NUE). Most studies that evaluate NUE focus on animal, farm or regional level. For global livestock supply chains, however, that run across national and continental boundaries, such approaches over-look nutrient losses associated with off-farm activities, such as the production of feed. Some studies assess nutrient losses and NUE at a chain level, but they do not consider the entire supply chain and do not consider the effect of nutrient recycling and stock changes on NUE, or do not identify hotspots of nutrient loss along the chain that are required to support targeted nutrient improvement pathways towards sustainable nutrient use. The two objectives of this thesis, therefore, were to develop a framework of indicators to assess nutrient flows and emissions along global livestock supply chains, while identifying data, which can be improved to enhance the accuracy of the results, and to assess the impacts of the global livestock supply chains on the nitrogen flows, while exploring the improvement options.
Evaluating nutrient use and flows in livestock supply chains requires a framework and data to estimate flows, emissions and relevant indicators from each production stage. To develop such a framework, Chapter 2 first reviewed existing studies on nutrient use in the livestock sector. The review showed that four methods were used previously to analyse nutrient use in the livestock sector, namely a nutrient balance, nutrient use efficiency, material flow analysis and life cycle assessment. Among these methods, nutrient use efficiency appeared a suitable approach to benchmark nutrient management at the animal level, and to some extent at the farm level. The analysis showed that integrating the life cycle approach into NUE, therefore, could allow for the computation of supply chain level NUE, which was proposed as a valuable indicator of nutrient management sustainability.
To this end, in Chapter 3, a comprehensive framework of indicators, based on the life-cycle approach, was developed to assess the efficiency of nitrogen and phosphorus use. The framework represents nutrient flows in the typical livestock supply chain from the cradle-to-primary-processing-gate, including crop/pasture production, animal production and primary processing stage as well as the transportation of feed materials, live animals or animal products. It encompassed three indicators, including the life-cycle nutrient use efficiency (life-cycle-NUE), life-cycle net nutrient balance (life-cycle-NNB) and nutrient hotspot index (NHI). The framework was tested for a case study of mixed dairy supply chains in Europe. The proposed indicators were found to be suitable to describe different aspects of nitrogen and phosphorus dynamics and, therefore, were all needed.
This framework of indicators developed requires detailed data such as nutrient inputs into soils, herd parameters, climate, emission factors, and manure management, to estimate nutrient flows and three nutrient use indicators. These data are highly variable at the global scale, resulting in large uncertainties due to the differences in geographical representation, time boundaries, farming technology and completeness. In Chapter 4, a method was proposed to identify the important inputs parameters that contribute significantly to the variance of the results. This method, which relies on a global sensitivity analysis is tested for the cases studies of mixed cattle dairy systems in the Netherlands and Rwanda, using the Global Environmental Assessment Model (GLEAM) dataset. The results showed that uncertainties of a few important input parameters, such as manure deposited on grasslands, applied manure and synthetic fertilizer, milk production and emission factors, could explain most of the variance of N use indicators. We subsequently fixed non-important and substituted important parameters in GLEAM with new field survey data, which substantially improved the results of N use indicators. This method can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.
In Chapter 5, the framework of indicators was applied to assess N use, flows and emissions, in the global pork supply chains and to evaluate the effects of feeding swill to pigs as a strategy to integrate better livestock in a circular bio-economy. Results showed that N emissions into the environment amount to around 14.7 Tg N y-1. More than half of these emissions take place in the backyard system, although this system contributed only 27% to total pork production. Industrial systems emitted 23% of total N emissions but contributed more than half of the global pork production (56%). Intermediate systems contributed around 19% to both pork production and N emissions. We found that most of N emissions are in the form of NO3- and organic N lost to surface and groundwater, with large implications for aquatic eutrophication. Backyard and intermediate systems, with relatively high connectivity between animal and crop production were more efficient than industrial systems. These results showed that the efficiency of N use and the magnitude of N losses per unit of area depend chiefly on the region (agro-ecological and economic context), on the origin of feed, and on manure management systems. The results also showed that the substitution of swill for grains and soybeans could improve N use indicators and abate N emissions. Applied on a global scale to industrial systems, this strategy was estimated to save 31 Mt of soybeans and 20 Mt of grains on dry matter basis, equivalent to 16 M ha of land use. Implementing swill feeding, however, would require innovative policies to guide the collection, treatment, and usage of swill, and ensure safety and traceability.
In Chapter 6, the global nitrogen use and flows were evaluated for livestock supply chains using the spatially explicit Global Livestock Environmental Assessment Model and its database. The results showed that, globally, livestock supply chains are responsible for around one-third of human-induced N emissions of which 63% take place in 2 regions (i.e. South Asia and East and Southeast Asia), and 61% at the feed production stage. These emissions are in the form of NO3- (28 Tg N y-1), NH3 (26 Tg N y-1), NOx (8 Tg N y-1) and N2O (2 Tg N y-1). The magnitude and concentration of N losses imply that there is both urgent need to reduce these emissions and the opportunity to design targeted mitigation interventions. The wide range of values calculated for N use indicators further indicates that good practices are available and already implemented in parts of the value chains. Mitigation options proposed include improvement of feed fertilization, and manure management through the adoption of innovative technology and best practices. These improvement pathways can be effective because N emissions are concentrated in few regions, supply chains and steps along the chain and the wide variability of N use indicators offers opportunity to design mitigation interventions. The adoption of good practices would likely require additional investments, knowledge transfer and additional solutions to improve simultaneously the socio-economic conditions of farmers worldwide. The design and implementation of interventions should consider potential trade-offs and synergies with other sustainability dimensions, such as climate change, resource scarcity and food security.
In Chapter 7, the development of the framework of indicators, modelling challenges and data quality were discussed. The discussion revealed that the three indicators proposed in this framework: Life-cycle-NUEN, Life-cycle-NNBN and NHIN provide a comprehensive analysis of nutrient use, flows and emissions in global livestock supply chains. The discussion revealed that livestock supply chains play a role in the net transfer of soil fertility from grassland to cropland and in shifting N embodied in feed between countries, which may be lost through unregulated disposals of manure. The chapter discussed the potential improvement options but emphasised the need to consider rebound effect related to the improvement of NUE, which may result in a consumption surge. The chapter discussed nutrients challenges in connection to the overall sustainability of the livestock sector, which uses of a large number of natural resources such as land, freshwater, often with low efficiency and contributes to global human-induced greenhouse emissions. Because the livestock supply chains are embedded in the economy and culture of societies. They contribute to rural development, human diets, trade balances, risk management and other relevant development outcomes, while building resilience and adaptation to climate change. Livestock can also negatively affect these outcomes, e.g. contributing to public health issues (diets, zoonoses, Anti-microbial resistance), and offering poor conditions to livestock producers and animals themselves. Addressing N challenges will require the consideration of potential trade-offs and synergies with these wider sustainability dimensions (e.g. poverty eradication, nutrition, human health) and it will also need to be done in conjunction with other interventions that address the growth of the livestock sector. The chapter ends up by calling for a global initiative with a strong representation of livestock sector scientists and stakeholders to tackle the N pollution.