In agronomy, there has been a rise in field experiments measuring the effects of management practices on indicators related to crop yield, soil quality and environmental impacts, as well as meta-analysis studies on these reported impacts, to enhance sustainable agriculture. At the same time, there is the need for more holistic agronomic recommendations across varying site properties (e.g., soil, climate, and local conditions) while optimising various objectives of maintaining crop production, soil health and environmental quality. A major restraint in building decision support tools for this purpose is a lack of empirical data, which we aim to improve by incorporating meta-analytical data. In our study, we build upon an agronomic dataset synthesized from published meta-analyses on long-term field experiments and design a decision support framework to evaluate the performance of best management practices under different site properties.
We first provide an overview of our open-source dataset which quantifies the overall impacts of many recommended agronomic measures on various indicators, essentially providing the observed mean of an effect over many fields in different climate zones, soil types, and crop types. We explore the nutrient management strategies in this dataset with several main objectives:
- Assess how the effect of a measure varies under local conditions
- Evaluate the performance of measures at meeting multiple agronomic objectives (e.g., increasing crop yield, increasing soil carbon, and decreasing N losses to the environment)
- Integrate these findings into a decision support model framework
- Map expected impacts of measures in various European regions.
To our knowledge, this is the first study that, (1) synthesizes existing field data to this extent on agronomic measures under varying local conditions, and (2) incorporates these results in a multi-objective analysis evaluating measures based on sustainability targets. This analysis focuses on nutrient management strategies as well as the feasibility of applying those measures, including mineral and organic fertilisation, right application of fertilisers (e.g., 4R strategies), crop residue management, and use of soil amendments. We focus on several indicators, including crop yield, soil organic carbon (SOC), and nitrogen losses to air and water. The impacts of measures are evaluated by comparing expected changes in those indicators to their current levels and distance to existing target levels (crop yield and SOC) and critical levels (nitrogen losses). Finally, we use a spatially explicit upscaling technique for Europe to map outcomes in the EU-27 region.
A main goal of our study is to highlight the trade-offs and synergies that can arise in these expected impacts under contrasting local conditions and multiple objectives. For example, certain practices may benefit crop yield and nutrition within a season but may indirectly negatively impact longer-term production through soil health impacts (e.g., declining organic matter and fertility levels) or ecosystem decline (e.g., excessive nitrogen loss to waterways). Based on outcomes of our model we explore examples of field types under different conditions (e.g., temperate versus Mediterranean climate, clay versus sandy soil, different crop rotations) to illustrate our findings on the performance of measures. Our study contributes to the empirical evidence base quantifying the impacts of management as well as the optimisation of both agronomic and environmental goals together. We also give an overview of the ongoing development of an empirically driven decision support tool which, based on location and user-defined objectives, aims to summarise the results of this modelling framework in a user-oriented interface.
Co-authors: Gerard H. Ros, Wim de Vries