Dr Ernst van den Ende and Dr Richard Finkers of Wageningen UR are on top of the recent developments in the field of agricultural science. “Growing enough food sustainably for the expanding world population presents a formidable challenge. We will need sophisticated data handling techniques to face this challenge,” says Van den Ende.
Van den Ende is the Director of the Plant Sciences Group at Wageningen University and Research and a member of the DTL Board. He explains: “In the 20th century, plant breeding techniques, artificial fertilizers, and pesticides drove green revolutions in the sense that we achieved higher yields in important crops. In recent years, many high-end technologies have been introduced in our field. For instance, omics techniques such as genomics and metabolomics are now used to characterise crop genotypes and phenotypes. And sophisticated sensors, drones, and satellites provide valuable information about, for example, climate, soil characteristics, and break-outs of infectious diseases. I think that these data will drive the next green revolution.”
Precision agriculture
The high-end technologies have resulted in the emergence of precision agriculture. This is the green counterpart of precision medicine, where a doctor chooses an optimal treatment based on a combination of data sources (e.g., a DNA test and clinical data). In precision agriculture, a farmer chooses an optimal farming strategy based on a combination of data (e.g., omics data and soil characteristics measured with sensors). Applications of precision agriculture are already available. For instance, meteorological information and data on soil conditions are linked in systems that warn farmers against break-outs of infectious diseases.
Diverse field
“In the end, precision agriculture should result in a higher yield with a reduced environmental impact and plant breeding should deliver the proper cultivars to make this possible. But we face many challenges,” says Dr Richard Finkers. He is a scientist in the Wageningen UR Plant Breeding group and a member of the DTL Scientific Team. “A major challenge is the diversity in the green sector. For example, the genomes of diverse crops show a lot of heterogeneity. The tomato genome is genetically less complex than the potato genome, even though these are related species.”
Chain optimisation
This difficulty is also encountered in another branch of agricultural science: chain optimisation. Finkers: “Chain optimisation refers to optimising the complete chain from seed to supermarket. The aim is to produce more crops using fewer resources in a sustainable way. This is very data-intensive research, including data about consumers’ behaviour in addition to omics, environmental, and farm management data.”
Data integration
Given the data-intensive nature of modern agricultural research, data integration has recently received a lot of attention. Finkers: “Wageningen researchers are contributing to this development in the context of DTL and ELIXIR-NL”. For instance, we are actively involved in the use case ‘Integrating genomic and phenotypic data for crop and forest plants’ of the ELIXIR-EXCELERATE project. We are now compiling a ‘minimal information about plant phenotype’ list. This is the minimal information that a researcher should add to his or her data in order for the data to be reusable. Among other things, it includes the geographical location where the data were collected, the researcher’s identity, and the unique identifier of the crop. Our next aim is to make data interoperable in line with the FAIR data principles, which state that research data should be Findable, Accessible, Interoperable, and Reusable. We will initially use the breeding API standard, an internationally agreed standard, but we will further FAIRify this API.
Knowledge discovery
Finkers continues: “I am also involved in the open data exchange project (ODEX4all), a project of the Netherlands e-Science Centre coordinated by Leiden University Medical Centre. Here, we combine multiple information sources, including genomic data and data from the scientific literature, to identify gene candidates for observed phenotypes, to be able to optimise breeding processes. In this approach, all available information, including information from other plant species, can be included into the reasoning process. Combined with the appropriate analytics and decision support, we expect that we will be able to breed more precisely for the novel crops needed in the (near) future. In the end, we want to use this to sustainably produce a higher yield.
Top priority
Van den Ende: “The agricultural sector faces a major challenge: to produce more crops using fewer resources while reducing any negative effects on society and the environment. Wageningen UR uses an interdisciplinary approach to find a solution. Good data exchange between the domains (plant, animal, environment, food, and social sciences) is quite a challenge; these are still silos. A Wageningen data center will be set up this year. So, data is one of our top priorities.“
Additional links
- The Farm Data Train project aims to connect agricultural data to make them more usable. It is a joint initiative of GODAN, CABI, Wageningen UR and DTL.