Watch It Grow, an innovative platform for a sustainable growth of the Belgian potato production.

Belgium is the largest exporter of frozen potato products in the world. Each year, Belgian companies process over four million tons of potatoes into French fries, potato chips and other products. To ensure a sustainable growth of the potato sector, a higher potato production is needed. In this context, expansion of agricultural land is not an option.Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these technologies.The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market.In the frame of the iPot project, financed by the Belgian Science Policy Office (BELSPO), a commercial webtool called Watch iT Grow helping potato traders, the processing industry as well as farmers to monitor the potato growth has been developed.By using weather data, satellite images, aerial images (taken with drones) and data from ground measurements, users are for instance able to follow whether the crops emerge properly from the ground, how the growth is developing, whether diseases might be present or when farmers can start harvesting. The collected data are combined into crop growth models allowing the webtool to propose as well yields estimations and predictions per plot

Farming and earth observation: sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R-2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R-2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R-2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R-2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R-2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R-2 and low RMSE, with potential for precision farming management before harvest.A & nbsp;We acknowledge the support of the project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovacion, Spain. J.S. is a recipient of a FPI doctoral fellowship from the same institution (grant: PRE2020-091907) . J.L.A. acknowledges support from the Institucio Catalana de Recerca i Estudis Avancats (ICREA) , Generalitat de Catalunya, Spain) . S. C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, Spain. We acknowledge the support of Cerealto Siro Group, together with Cristina de Diego and Javier Velasco, technical staff from the company, by providing the wheat yield data. This research was also supported by the COST Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu)

Big Data in Bioeconomy

This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16

Quantifying genotypic and environmental factors affecting potato canopy growth

There is a high degree of variation in potato yield which may contribute to the current UK yield plateau. There is a strong correlation between light intercepted, total biomass produced and biomass partitioned to the tubers as yield. Variation in potato canopy growth can be analysed using non-destructive measurements and the size of the canopy determines radiation interception. Proportion of soil covered by green leaves (percentage of ground covered, GC) is a simple proxy for light intercepted. Two empirical models were compared for ability to summarize GC throughout the growing season. Both models showed a similar ability to describe GC, but differed in output and ease of interpretation, so the simpler, more descriptive model was selected for use in further canopy quantification. The effects of planting date, nitrogen rate, cultivar and stem density were examined in two multi-year experiments. Planting date had a strong effect on early growth, though subsequent growth was less sensitive to temperature. Leaf production and canopy duration varied with cultivar determinacy. Cultivar and nitrogen fertilizer rate determined the potential for branch and branch leaf production (and therefore canopy longevity), altering distribution of leaf area index (LAI) within the canopy, but duration of growth was determined by planting date, which when delayed, shortened the season. The effects of stem density were most noticeable early in the season when higher stem density resulted in faster canopy expansion and earlier canopy closure. Branch production was reduced at high stem densities, but total LAI varied little. Canopy quantification was also used to analyse historical data and across 20 cultivars, decreasing duration of early canopy expansion was the only universal response to increasing stem density. By quantifying the growth and maintenance of experimental and commercial crop canopies, causes of variation in light interception and subsequent yield can be identified. ‘Best agronomic practice’ can then be identified, enabling targeted changes to reduce the variability in yields and improve resource use efficiency. Additionally, this greater understanding of canopy development will refine existing potato yield models, enabling more specific and more accurate predictions. Future yield models should include stem density to better predict early season canopy growth and cultivar determinacy to predict interactions between canopy production and season length.This PhD was funded by the Cambridge University Potato Growers Association (CUPGRA)

Potato monitoring in Belgium with "watchITgrow"

peer reviewe