Please cite this article in press as: Quiroz, R., et al., Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy? Eur. J. Agron. (2016), http://dx.doi.org/10.1016/j.eja.2016.10.008 ARTICLE IN PRESS G Model EURAGR-25603; No. of Pages 9 Europ. J. Agronomy xxx (2016) xxx–xxx Contents lists available at ScienceDirect European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja Research Paper Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy? R. Quiroz a , H. Loayza a , C. Barreda a , C. Gavilán a,b , A. Posadas a , D.A. Ramírez a,c,∗ a International Potato Center (CIP), P.O. Box 1558, Lima 12, Peru b Present address: Soil and Water Science Department, University of Florida, P.O. Box 110290, Gainesville, FL 32611-0290, USA c Gansu Key Laboratories of Arid and Crop Science, Crop Genetic and Germplasm Enhancement, Agronomy College, Gansu Agricultural University, Lanzhou 730070, China a r t i c l e i n f o Article history: Received 2 November 2015 Received in revised form 12 September 2016 Accepted 15 October 2016 Available online xxx Keywords: Canopy photosynthesis model Chlorophyll concentration Crop growth model Leaf area index WDVI Remote sensing a b s t r a c t Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non-destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGM CC ) or the weighted difference vegetation index (CGM WDVI ). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGM WDVI improved yield prediction compared to CGM CC . Both CGM WDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incor- poration of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato. © 2016 Elsevier B.V. All rights reserved. Abbreviations: D, duration of leaf senescence; DCPM, dynamic- hourly scale- canopy photosynthesis model; DTY, dry tuber yield; LUE, light use efficiency; CGM, crop growth model; CGMCC, crop growth model parameterized using canopy cover; CGMWDVI , crop growth model parameterized using weighted difference vegetation index; Chl, total chlorophyll concentration per leaf area; Chl max , maximum total chlorophyll concentration per leaf area; f i , biomass fraction of organ i; FLINT , fraction of PAR intercepted by the foliage; G, growth respiration; G i , glucose requirement of organ i (leaves stems tubers roots); I0, PAR on a horizontal plane; I leaf , incident PAR on a leaf; k, light extinction coefficient; K i , maintenance respiration coefficient of the plant organ i; LAI, leaf area index; M, asymptotic maximum of the harvest index; m, leaf transmittance; MAE, mean absolute error; MCC, maximum canopy cover; NIR, near-infrared band; NDMA, net dry matter assimilation rate; P leaf , leaf photosynthetic rate; Pcanopy, gross canopy primary productivity; Pmax, photosynthesis at light-saturated conditions; PAR, photosynthetically active radiation; PAR0, PAR extra-terrestrial irradiance on a plane; Q10, temperature sensitivity factor; R, red band; Rm, maintenance respiration; RMSE, root mean square error; RS, remote sensing; RRMSE, relative RMSE; S df , diffuse flux of global radiation; T, daily average temperature; te , beginning of senescence; tm, time at maximum WDVI increment; Tr , reference temperature; TT, thermal time; t50, time when light interception is reduced to 50%; VI, vegetation index; WDVI, weighted difference vegetation index; W i , dry biomass of organ i; Wt , total dry biomass in the current day; Ym, maximum value of WDVI; , photosynthetic efficiency; , sharpness of the knee of the curve Pmaxvs.I leaf ; , solar zenithal angle; Z, sun angle above the horizon; Pcanopy, balance of the assimilated carbon via daily Pcanopy. ∗ Corresponding author at: International Potato Center (CIP), P.O. Box 1558, Lima 12, Peru. E-mail address: d.ramirez@cgiar.org (D.A. Ramírez). http://dx.doi.org/10.1016/j.eja.2016.10.008 1161-0301/© 2016 Elsevier B.V. All rights reserved.