Natalia Uribe Rivera earns PhD for research on a framework and method to help farmers protect watersheds

My PhD research helps farmers protect watersheds. By choosing the right practices, farmers can reduce the release of contaminants into the water while improving or maintaining crop productivity and minimizing costs.

But how do you know what the right practices, known as Agricultural Best Management Practices (Ag-BMPs) are? This is the question my research answers. I developed a framework and methodology and a model that shows how to optimize farming practices (for example; fertilizer management, conservation tillage, crop rotations, living fences) in terms of space and time – that is, spatio-temporal optimization.

In our case study, we worked with tree tomato, potato and grass crops used by milk farmers in the Andes mountains in the Riogrande basin in Antioquia, Colombia. We used the modelling framework and methodology to suggest Ag-BMPs that could be used by the farmers.

We made the model, the framework and the results available to the farmers so that they can use these resources to determine which practices are optimal. The model and the framework could be adapted to other crops and used worldwide. We expect them to be accessible and affordable also to those in poorer regions of the world. The methodology framework can be a complementary policy instrument to control non-point source water pollutants and encourage sustainable agro-industrial activities.

A memorable moment of my PhD was when I travelled with my professor and mentor to the basin of our case study in Colombia. We talked with farmers and learned about their needs and daily life. This allowed us to collect data in the field, and more importantly, it helped us gain a clear perception of the reality of the peasants. This was very different from working only from a computer.

The most challenging part of my PhD study was working individually for so many years. I am passionate about teamwork, so this was a challenge for me. Also, being a mom in the last year of my doctorate was very challenging, mainly because it was in the middle of the Covid pandemic. Being isolated for so long with a new-born baby made it very hard to focus on finishing writing.

I hope to be able to help people who need to make day-to-day decisions in the management of their crops. I have been working on the NASA-USAID programme SERVIR-Amazonia for a year now. This program develops geospatial tools that help in decision-making to solve environmental problems in the Amazon. I have work with many people from the Amazon region and countries in training programs.

This year, I want to develop an application to enable farmers to easily apply the results of the doctoral research.

I have started conversations with the Colombian government about the results of my doctoral research. In this way, we can look at options to apply the framework/methodology in other regions of the country and for other crops. It would be great if it could become one of the country's tools that allows decision-makers to create policies that benefit of farmers and the national economy while also reducing water pollution.

My PhD path was like a roller coaster of emotions that allowed me to grow academically and personally. The only advice that I would give myself when I started would be: approach the doctorate as a job. In this way, the difficulties will not take away my motivation to finish and affect me so much emotionally.

Spatio-Temporal Multi-Objective Optimization of Agricultural Best Management Practices

In an effort to meet the global growing food demand, nutrient pollutants in runoff have also increased due to intensified agricultural practices. For this reason, stakeholders have tried to shift from conventional agricultural practices to best management practices (BMPs). However, the selection and allocation of agricultural BMPs (Ag-BMPs) at a watershed scale, in practice, is very complex. Optimization approaches for selecting and allocating Ag-BMPs have been used, with limitations on the inclusion of temporal and dynamic spatial aspects. To address this issue, the dissertation’s main objective is to build a spatio-temporal multi-objective optimization modelling framework that provides new insights to improve the selection and allocation of Ag-BMPs in a watershed. To achieve the objective, the optimization framework has been developed, and it allows for incorporation of a greater number of crops and Ag-BMPs scenarios in the optimization model, as well as contemplating the space and time variations to allocate Ag-BMPs. In this framework, the SWAT hydrological model is coupled with the multi-objective optimization algorithm (NSGA-II). Minimization of nitrate (NO3-N) losses and maximization of crop yields at field level were the objective functions. The developed framework and experience with it on the considered case study in Latin America is seen as a useful hydroinformatics tool for supporting management decisions in agriculture.