The agricultural sector in South Holland is highly productive and contributes greatly to the economy. However, current industrial agricultural practices generated several severe environmental and socio-economic challenges. Various renowned international institutions have emphasised the necessity for a radical, rather than an incremental, transformation in the agri-food system. There are growing calls to shift the paradigm from industrial agriculture to agroecology. Agroecology is a fundamental alternative vision of the agri-food system, bundling the power of sustainable farming practices that utilise ecological principles and re-establish the connection between farmers and consumers, and by extension nature. This study sets out to introduce the reader to agroecology and to discover strategies for how the province of South Holland can stimulate the transition to agroecology. To achieve this goal, the research methodology comprised a literature review and semi-structured interviews with agroecological farmers, an agroecology expert, and a provincial agricultural policymaker. The results uncovered that the underlying goal of the agri-food system should be transformed from profit and production maximisation of industrial agriculture to sustainability and social justice of agroecology. Achieving this goal shift requires fundamental changes and will be a tremendous challenge because of the complex and embedded nature of the agri-food system. Agroecology simultaneously tries to realise this transformation on three fronts: as a science, a set of agricultural practices, and a movement. Furthermore, the research identified key barriers and enablers in the transition to agroecology in South Holland from the agroecological farmers’ perspective. The province of South Holland could stimulate the transition by providing long-term affordable land, revising regulations to support forward-thinking practices, adopting an integrated approach to agriculture, bridging the gap between civil servants, farmers, and consumers, restructuring subsidy allocation, and lobbying to simplify (organic) certification processes. The research verifies the results of previous studies on several barriers and enablers but distinguishes itself by focusing specifically on the farmers’ perspective and the province of South Holland. Additional research is needed to broaden the scope beyond the viewpoint of agroecological farmers, and how to implement the policy recommendations effectively. Currently, the Dutch agri-food system is not yet ready for a paradigm shift from industrial agriculture to agroecology. However, the system is being pressured from various angles generating windows of opportunity. While being just one actor in the agri-food system, the province of South Holland could stimulate the development of agroecology by incorporating agroecology explicitly in policymaking.

The research focuses on a novel measurement-based approach to estimate the farm-specific ammonia emission potential (AEP) in the dairy sector. By measuring and evaluating the feed-manure chain, feed management strategies and manure parameters influencing AEP can be identified. Ammonia emissions from dairy farms are not only considered to be an important driver of biodiversity loss, but are also responsible for nutrient losses in the farm cycle. Currently, farm-specific ammonia emissions are calculated using the Kringloopwijzer model, which tends to over- or underestimate actual ammonia emissions. Therefore, the possibilities of a measurement-based approach are evaluated.

This study analyses the relationships within the feed-manure-AEP sequence. A comprehensive approach is used, involving 23 manure parameters and 12 feed management parameters. The most important predictors of the AEP include N, TAN, Norg, N90, and the C/N ratio, whilst urea in milk, pH, and DS showed low significance. Silage maize and VEM are identified as feed management parameters with a positive indirect relationship with the AEP, whereas other roughage and fresh grass exhibit a negative indirect relationship. The calculated TAN value plays a central role in the emission calculations of the Kringloopwijzer model. There are concerns about the accuracy of this value as well as the absence of other manure parameters in the calculation, highlighting the need for further research. Currently, it is uncertain whether the AEP measurements will be suitable for an emissions-based policy, due to the incapacity to directly represent actual ammonia emissions and the uncertainty regarding the interpretation of the results caused by the period prior to the measurements. Nonetheless, the measurements are valuable in assessing the influence of the manure composition on the AEP, and how it has been affected by feed management strategies.

The emission of reactive nitrogen species increased rapidly in the twentieth century causing a significant perturbation of the nitrogen cycle. This causes many detrimental effects for both humans and ecosystems such as eutrophication, acidification and biodiversity loss. Ammonia, which is a reactive nitrogen species is difficult to quantify because the compound is hard to observe with measurement equipment such as ground-based measurement stations and remote sensing instruments such as CrIS. Therefore, this thesis aims to improve the monitoring of surface ammonia concentrations with a machine learning technique called random forests (RF). These type of models can detect complex and non-linear relationships between variables and are frequently used in other air pollution studies. In this study, a RF model has been built to estimate the ammonia surface concentration with vertical column density (VCD) datasets, meteorological variables and land-use variables. Different combinations of VCD datasets (either modelled or CrIS VCD data) have been used to train the model and to predict the surface ammonia concentration. The result of the study is that RF models are statistically more accurate at estimating the surface ammonia concentration than the LOTOS-EUROS model when validated by ground-based measurements stations from the MAN and LML network. Especially trained RF models that have been supplied with CrIS VCD data during the prediction phase show strong performance. Moreover, when comparing the RF model that has been trained without VCD data to the ‘complete’ RF models, the complete RF models show better performance, proving the added benefit of incorporating satellite data in RF modelling. Recommendations for further research include performing similar experiments with other input variables and other machine learning algorithms, validating the performance of the RF model in different years and considering using other datasets as the ground-truth variable for RF models – such as ground-based measurement data from MAN and LML.