Bernard Tychon

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Abstract Crowdsourcing, understood as outsourcing tasks or data collection by a large group of non-professionals, is increasingly used in scientific research and operational applications. In this paper, we reviewed crowdsourcing initiatives in agricultural science and farming activities and further discussed the particular characteristics of this approach in the field of agriculture. On-going crowdsourcing initiatives in agriculture were analysed and categorised according to their crowdsourcing component. We identified eight types of agricultural data and information that can be generated from crowdsourcing initiatives. Subsequently we described existing methods of quality control of the crowdsourced data. We analysed the profiles of potential contributors in crowdsourcing initiatives in agriculture, suggested ways for increasing farmers’ participation, and discussed the on-going initiatives in the light of their target beneficiaries. While crowdsourcing is reported to be an efficient way of collecting observations relevant to environmental monitoring and contributing to science in general, we pointed out that crowdsourcing applications in agriculture may be hampered by privacy issues and other barriers to participation. Close connections with the farming sector, including extension services and farm advisory companies, could leverage the potential of crowdsourcing for both agricultural research and farming applications. This paper coins the term of farmsourcing asxa0a professional crowdsourcing strategy in farming activities and provides a source of recommendations and inspirations for future collaborative actions in agricultural crowdsourcing.

On the Use of Decision-Support Tools for Improved Irrigation Management: AquaCrop-Based Applications

Feeding more people with less water is putting efficient irrigation practices worldwide high on the agendas. As a reaction, over the last decades, numerous irrigation decisionsupport tools have been developed. For several reasons, the gap between farmer and modeler remained in most cases too large. The Food and Agriculture Organization of the United Nations (FAO) contributes to alleviate the encountered adoption limitations with AquaCrop and its stand-alone AquaCrop plug-in. This simple and robust fieldcrop-water balance has been successfully tested for a wide range of crops and regions, and its database is still expanding through worldwide contributions. The present chapter describes how AquaCrop can help irrigation advisory services draft efficient irrigation calendars that are easily applicable and adoptable: either by the elaboration of site-specific irrigation schedule calendars in chart format when the user has no access to the needed data or by the integration of its plug-in in a server/client ICT application offering centralized data management. As for the irrigation charts, studies prove 10-30% water savings, while maintaining yield and requiring minimum data. The server/client application offers an all-in advice tool, including real-time irrigation advice and yield forecasts. No adoption assessments have yet been carried out, but several ongoing pilot studies are promising.

Phytomass estimation using eMODIS NDVI and ground data in arid rangelands of Morocco

The assessment of rangeland productivity in semi-extensively grazed arid rangelands is a prerequisite for livestock management in relation to sustainable use of pastoral resources. The objective of this study was to assess rangeland productivity based on normalised difference vegetation index (NDVI) images. Data on phytomass were measured on 61 field samples in arid rangelands of Morocco, covering various rangeland categories during autumn (November) and spring (April), i.e. when phytomass is at low and high levels, respectively, for two consecutive years (2008 and 2009). Dekadal EROS Moderate Resolution Imaging Spectroradiometer (eMODIS) NDVI data were linearly regressed to field measurements for these four periods. Results show that phytomass values were correlated with NDVI during spring, with R2 and RMSE values of 0.82 and 0.3 t ha−1, respectively. This study indicates there is a high potential for operational use of remotely sensed data to estimate rangeland phytomass of semi-extensively grazed rangelands.

Mapping of zones at risk (ZAR) in West Africaby using NGI, VCI and SNDVI from the e-station.

Issa Garba 1 , Illa Salifou 4 , Abdoul Hamid Sallah 2 , Abdallah Samba 1 , Ibra Toure 3 , Yapi Yapo 1 , Alio Agoumo 1 , Salamatou Soumana 1 , Amina Oumarou 1 , Bernard Tychon 2 And Bakary Djaby 2 . 1. AGRHYMET Regional Centre, CILSS. 2. University of Liège (ULg). 3. Center for Agricultural Research for Development (CIRAD). 4. University of Niamey (UAM). ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History

Potato monitoring in Belgium with “WatchITGrow”

WatchITGrow is a web-based application developed for potato monitoring in Belgium. The different components encompass a back-end with biophysical parameters derived from high resolution satellite imagery, agrometeorological algorithms, phenological development and crop models; and a front-end with dashboards to visualize spatio-temporal information and insert potato field information.

Assessment of AquaCrop for winter wheat using satellite derived fCover data

The integration of crop growth models with remote sensing has presented great potential in (regional) crop yield forecasting; although so far few field-level applications exist. Based on crowd/farm-sourced observations (phenological stages and yield measurements) and a basic assimilation procedure using satellite (DMC) and digital hemispherical pictures (DHP) derived green fractional cover data (fCover), the AquaCrop plug-in model was assessed for winter wheat fields in Belgium. A semi-automated R-environment was developed to simultaneously run, assess and evaluate the ensemble of field-level simulations. The root mean square error (RMSE) was 0.8 ton/ha. It was concluded that the presented approach might be promising for large scale field-level yield forecasting.