Julien Sarron is one of the #DigitAg labelled PhDs
Spatial estimation of a perennial crop yield in West Africa : mango case study in Senegal
- Start Date: October 2017
- University: MUSE Montpellier University of Excellence / Montpellier SupAgro
- PhD School: GAIA , Montpellier
- Field(s): Agronomy
- Doctoral Thesis Advisor: :Eric Malézieux (Cirad HortSys)
- Co-supervisors : Emile Faye (Cirad HortSys)
- Funding: Cirad
- #DigitAg: Labelled PhD- Axis 6 – Challenge 1 & 8
Keywords: agroecology, landscape, geostatistics, mango orchard, yield
The current population of Africa is estimated at about 1.2 billion and should reach nearly 3.8 billion by 2100. Adding to the demographic evolution, profound modifications of the environment are superimposed. These modifications are caused by climate change that will negatively impact crop and horticultural productions in a region where rain-fed agriculture is dominant (Wheeler & von Braun 2013, Challinor et al. 2014). In this context of food insecurity, it is necessary to improve crop and horticultural production ways of management to face development issues and reduce population vulnerability.
Assessment and forecast of crop productivity are undeniably a strategic challenge for developing country. Indeed, they are key of importance for food security and autonomy but also for the economy (mastery, control and optimization of produced volume). Yield estimation is an essential and strategic information for both public (technical and research institute in agronomy, university, ministry, etc.) and private (producers, agricultural adviser, quality checker, export sector, etc.) stakeholders. However, for most crops in southern countries, tools for production forecast are still unavailable or inaccurate and they rely on weak scientific bases.
Contact: julien.sarron [AT] cirad.fr – Tél : +221 786338087
Sarron, J.; Malézieux, É.; Sané, C.A.B.; Faye, É. (2018). Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV. Remote Sensing, 10(2), 1900. https://doi.org/10.3390/rs10121900
Sarron, J., Sané, C. A. B.; Borianne, P., Malézieux, E., Nordey, T., Normand, F., Diatta, P. Is machine learning efficient for mango yield estimation when used under heterogeneous field conditions? Acta Horticulturae. (under review) – ISHS Young Minds Award : la communication présentée à HIC 2018, 30th International Horticultural Congress, 12-16 August 2018, Istanbul (Turkey) a été primée par l’International Society for Horticultural Science.