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Remote Sens. 2016, 8(3), 170; doi:10.3390/rs8030170

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

1
Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, B-2400 Mol, Belgium
2
Département Sciences et Gestion de l’Environnement, Université de Liège, Avenue de Longwy 185, 6700 Arlon, Belgium
3
Climate Risk Management Unit, Institute for Environment and Sustainability (IES), European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Yoshio Inoue and Prasad S. Thenkabail
Received: 20 October 2015 / Revised: 28 January 2016 / Accepted: 1 February 2016 / Published: 25 February 2016
View Full-Text   |   Download PDF [4525 KB, uploaded 25 February 2016]   |  

Abstract

According to Monteith’s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO2 fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith’s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999–2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R2 values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84–0.61, RRMSE (%) reduced from 13.1–8.9, MBE (t/ha) decreased from 0.05–0.03 and the index of model performance (E1) increased from 0.08–0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world. View Full-Text
Keywords: dry matter productivity; yield estimate; SPOT VEGETATION; water stress; maize; common wheat dry matter productivity; yield estimate; SPOT VEGETATION; water stress; maize; common wheat
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Durgun, Y.Ö.; Gobin, A.; Gilliams, S.; Duveiller, G.; Tychon, B. Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity. Remote Sens. 2016, 8, 170.

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