LADS research group
- Mats Söderström, SLU
- Kristin Piikki, SLU
- Sandra Wolters, SLU
- Karl Adler, SLU
- Omran Alshihabi, SLU
- Lena Engström, SLU
- Johanna Wetterlind, SLU
A programme for strengthening and further develop digital decision support systems to face new information demand for sustainable and efficient agricultural production. LADS’ research is carried out in close collaboration with authorities, industry and farmers.
Several decision support systems and geospatial datasets for precision agriculture have been and are being developed during recent years through successful collaboration between SLU and different players such as Hushållningssällskapet, DataVäxt AB, Lantmännen, Agroväst Livsmedel AB, Greppa Näringen och Jordbruksverket, Sveriges Geologiska Undersökning, Västra Götalandsregionen, Solvi AB and others.
International work is currently carried out through EU Interreg projects in northern Europe, but also in Africa, e.g. in collaboration with the International Center for Tropical Agriculture (CIAT).
Outreach and development projects, primarily for the Swedish farmers, and advisors are carried out within the framework of the collaboration network Precisionsodling Sverige (POS - Precisionsskolan; Precision Agriculture Sweden), and also with the Centre for Chemical Pesticides (CKB)
The vision of LADS is that programme outcomes shall lead to:
Read about our projects under the image slide show.
There is currently no open algorithm for optimising N rates to grain crops for general and free use. The aim of this the proposed project is to develop public algorithms between multispectral reflectance measurements of the crop and the economically optimal nitrogen rate.
We aim to facilitate optimisation of nitrogen fertilisation for most of Sweden's grain producers. To achieve this, we will develop and evaluate prediction models for economically optimal nitrogenrate (EONR) in wheat (Triticum aestivum L.) and malting barley (Hordeum vulgare L.), based on near real-time satellite reflectance data (Sentinel-2) at the time of supplemental fertilisation.
The prediction models will be designed for use in satellite-based decision support systems for precision agriculture (e.g. CropSAT), to translate satellite data into directly applicable N rate recommendation maps. Better-optimised nitrogenrates mean better profits for farmers, better scope for achieving quality goals for produce and reduced environmental risks in terms of nitrogen leaching and/or volatilisation of nitrogen compounds.
The project starts in 2019 and ends in 2021.
Stiftelsen lantbruksforskning (contract: O-18-20-162).
Well-informed decisions on land use and agricultural practices are crucial for food security and sustainable agriculture. Agronomic decisions must be based on local conditions and this call for the development of efficient soil mapping methods of East African agricultural soils.
The present project deals with pedometric considerations and outline methodologies for in situ and ex situ soil measurements by use of proximal sensor, local map adaptation of large-scale digital soil maps and inclusion of soil data in crop suitability modelling. The work focusses on soil properties that are important for crop productivity (the content of organic carbon, texture, plant-available nutrient content, and soil pH) and results are intended for use by agricultural extension officers, technicians and other scientists. An important part of the project is to disseminate the results to stakeholders and educate agricultural extension officers on how to use the digital soil map information in management decisions in smallholder farming systems in East Africa.
The project is a collaboration project between the International Centre for tropical Agriculture (CIAT) and SLU.
Various sensors (a-e) used in field work in Embu, Kenya
Kristin Piikki, SLU/ CIAT
The project started in 2014 and ends in 2019.
Formas/SIDA (contract: 2013-01975)
The long title is the name of the initial SLF-funded project that lead to the development of the web application CropSAT. CropSAT has become a widely used decision support tool primarily for precision application of nitrogen. In 2018 the system had more than 20000 users, most in Scandinavia.
Responsive fertilisation of winter wheat (Triticum aestivum L.) is often adopted, with nitrogen (N) applied two or three times between the developmental stages of tillering and booting. Satellite-based decision support systems (DSS) providing vegetation index maps calculated from satellite data are available to aid farmers in adjusting the topdressing N rate site-specifically to the current season and to variations in growth conditions within the field. In this project, the freely available CropSAT DSS was developed and evaluated. The system provides farmers with raster maps of the modified soil-adjusted vegetation index (MSAVI2) or normalized difference vegetation index (NDVI) (the latter in Denmark, the former elsewhere) calculated from data obtained from satellites Sentinel-2 (ESA,EU).
The project was a collaboration project between SLU, Hushållningssällskapet, DataVäxt AB, Agroväst Livsmedel, and Greppa Näringen (Focus-on-Nutrients). Later on SEGES in Denmark became involved in the development.
Further research and development on functionality of CropSAT and satellite-based systems is ongoing in LADS.
CropSAT – a satellite images based decision support system, free to use. The first application of its kind in Scandinavia.
Project team 2013-2014: Mats Söderström, SLU (PI); Henrik Stadig, Hushållningssällskapet; Johan Martinsson, DataVäxt AB.
The initial project started in 2013 and ended in 2014. Later external support from other donors and continued in project form until 2018, when it was acquired by DataVäxt AB. Research and development on CropSAT and satellite-based systems continuous in LADS.
Swedish Foundation for Agricultural Research (SLF) (proj. no. H1233115), and Focus-on-Nutrients (Swedish Board of Agriculture). Development have been supported by SEGES (in Denmark) and Yara (in Norway).
Solvi.nu is web application for management of drone images. One special feature in the system is management of images acquired over field crop trials. The aim of this tool was to facilitate the use drones in crop trials, which is of world-wide interest.
Much of what we know about how new crop varieties react under field conditions, as well as how to optimize fertilisation and other management actions, emanates from field trials. A field trial may consist of hundreds of small plots in which crop response to management can be assessed in a statistically rigorous manner. During later years the use of crop sensors have become more common in this work, and drones (unmanned aerial vehicles, UAV) as a platform for measurement is regarded as very promising.
In order to automate the management of drone images from field trials, we have developed a new cloud-based tool that requires a very limited input from the user. The tool automatically detects boundaries of each plot in a mosaic image through a neural network model, and calculates summary statistics for individual bands and selected vegetation indices. Plot-wise data can then be exported as text files or in geographical formats for further analysis in geographical information systems.
The Zonal statistics and Trial plot extraction tool was implemented in Solvi (www.solvi.nu) - a web application for drone imagery analysis. Further development is ongoing.
Through the Zonal statistics and Trial plot extraction tool in Solvi.nu it becomes much easier to use drones in field trials.
Mats Söderström, SLU; Sofia Delin, SLU; Igor Tihonov, Solvi.
The project started in 2018, and a functioning version is currently online. Further development is ongoing.
Vinnova (dnr: 2016–04248) within the Testbed UAV project, coordinated by RISE Research Institute of Sweden AB.
DSMS is an open geodatabase with information on the topsoil of the arable land in Sweden. The aim is that the data shall be accurate and detailed enough to be useful for precision agriculture, i.e. for management decision within fields.
The spatial resolution of DSMS map layers is 50 m × 50 m. Currently, it covers > 90% of the arable land of Sweden (~2.5 million ha). Non-agriculture land and areas with organic soil are not covered. Access to a number of national datasets made it possible to build the DSMS. Results from two soil sampling campaigns (~15 000 samples) were combined with remote sensing data (gamma radiation data from airborne radiometric scanning and a digital elevation model derived from airborne laser scanning) plus a Quaternary soil deposit map. So far, multivariate adaptive regression splines models were parameterized for content of clay, sand and organic matter, as well as buffering capacity and target-pH. DSMS data is provided for free by the Geological Survey of Sweden (SGU). Examples of applications developed based on the DSMS are web applications used by farmers to generate prescription files for variable-rate seeding and variable-rate liming – e.g. Markdata.se.
Later research has focused on developing interactive functionality between digital soil mapping databases and the user’s local data. This means that a user can combine local data with e.g. DSMS maps, and automatically downscale the database to generate improved local maps.
Currently, research is ongoing for developing new map layers of DSMS.
DSMS is free to use and can be used for the development of web applications suitable for precision agriculture. The image shows a map for variable-rate liming developed in Markdata.se, an application based on DSMS data.
DSMS development project team: Mats Söderström and Kristin Piikki, SLU; Gustav Sohlenius and Lars Rodhe, SGU. Development of Markdata.se was carried out by Kristin Piikki and Mats Söderström, SLU; Henrik Stadig, Hushållningssällskapet; Johan Martinsson, DataVäxt AB. Current research is carried out by PhD student Karl Adler, SLU.
A pre-study was carried out by SLU in collaboration with Hushållningssällskapet in 2010-2011 (Swedish National Space Board, proj no. 199/10). The initial development of DSMS started in 2013 was also financed by the Swedish National Space Board (proj no. 214/13) and SGU. Further developments have been funded by the Swedish Foundation for Agricultural Research (SLF; O-15-20-566). Current projects for the development of DSMS are funded by Västra Götalands Regionen and SLU (VGR/SLU project RUN 2018-00141).
DSMS could be created thanks to availability of data från Lantmäteriet, SGU, the Swedish Board of Agriculture (Jordbruksverket) and Swedish Environmental Protection Agency (Naturvårdsverket).
Only the best batches of cereal grains are used to produce porridges and cereal drinks for toddlers. There are strict quality regulations on heavy metals and mycotoxins levels in wheat and oats for baby food. In addition, any negative environmental impact from the crop growing needs to be minimized, in our striving towards sustainability goals. To meet these challenges, the entire chain must work - from the field where grain is grown to the producer of baby food – it is essential to keep track of and handle selected lots such that quality and sustainability can be guaranteed.
The Baby Grain Passport (BGP) concept will lead to traceability and sustainability in the production of high-quality baby food cereals. The project is organized in four work packages (WPs), closing the loop between research and business. Three WPs are dealing with grain production issues using artificial intelligence and data mining techniques to transfer big data into useful information. The fourth WP will develop the BGP concept and a prototype information system for strategic planning and traceability along the baby food value chain.
The project starts in 2019 and ends in 2021.
Formas – a Swedish Research Council for Sustainable Development (contract: 2019-02280)
LADS was initiated within the VGR/SLU project RUN 2018-00141, including financial support from Dataväxt AB, Sweden. Project specific donors are acknowledged under the project descriptions.