Radiation use efficiency and its use for a better characterization of ecosystem change syndromes in grasslands

Photo by http://www.elefete.com/

The Aerial Net Primary Productivity (ANPP) is an integrating variable of the ecosystem functioning that determines the ecosystem services provision. In livestock systems, the ANPP is the main determinant of energy available for the cattle, similarly to natural systems determines the energy available to wild herbivores. Therefore, estimate the ANPP and understand how it varies in space and over time, is an aspect of special interest. Estimates from harvests biomass are very time- and effort-demanding. ANPP can also be estimated from spectral data provided by remote manual sensors or on-board satellites. These estimates are derived from Monteith’s radiative model, which establishes that ANPP results from the product between incident Photosynthetically Active Radiation (PAR), the fraction of this that is absorbed by the vegetation (fPAR) and the Radiation Use Efficiency (RUE). PAR can be estimated using weather stations or models based on remote sensors. fPAR can be derived from spectral indices such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI) and represents the key link between remote sensing and ANPP estimation. Finally, the RUE can be estimated from the quotient between independent estimates of ANPP and absorbed radiation (the product between PAR and fPAR), or models based on climate information. Recently, it was shown that the RUE can be estimated from spectral signals such as fluorescence and Photochemical Reflectance Index (PRI). The RUE is the most elusive variable in the model and about which there is much uncertainty. Regarding the ANPP variations, its main determinant is the precipitation, with different patterns depending on the variation in space and over time. Recently, a typology of ecosystem change syndromes was defined which represents the set of response patterns of ANPP to precipitation. This typology is based on the Precipitation Use Efficiency (PUE) and the Marginal Response to Precipitation (MRP). In this project we propose, on the one hand, to improve knowledge of the RUE variations and the possibility to estimate RUE by remote sensing. On the other hand, we propose to characterize syndromes of ecosystem change in temperate grasslands. While these two goals are independent, improving the knowledge of the RUE will imply a better estimate of ANPP, which will result in a more precise characterization of the syndromes of ecosystem change and its most probable biophysical and anthropic controls.


Regional Analysis and Remote Sensing Lab, University of Buenos Aires, Argentina ( Dr Texeira & Dr Oesterheld)

Funded by

Argentinian Agency for the Promotion of Research, Technological Development and Innovation PICT-2019-I-D

Grant amount


Jonathan Ojeda (Jony)
Crop Ecophysiologist - Cropping Systems Modeller - Data Scientist

I use crop models to understand GxExM interactions and quantify sources of uncertainties in agricultural predictions.