caa a22 4500 445864370 CHVBK 20180317145453.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s11119-011-9233-6 doi (NATIONALLICENCE)springer-10.1007/s11119-011-9233-6 Site-specific early season potato yield forecast by neural network in Eastern Canada [Elektronische Daten] [Jérôme Fortin, François Anctil, Léon-Étienne Parent, Martin Bolinder] Deterministic potato (Solanum tuberosum L.) growth models hardly rely on driving seasonal field variables that directly characterize spatial variation of plant growth. For example, the SUBSTOR model computes the leaf area index (LAI) as an auxiliary variable from meteorological conditions and soil properties. Empirical models may account for seasonal LAI functions and accurately predict potato yield. The objective was to evaluate multiple linear regression (MLR) and neural networks (NN) as predictive models of potato yield. Using data from several replicated on-farm experiments conducted over 3years, model performance was evaluated for their capacity to forecast tuber yields 9, 10 and 11weeks before harvest compared to SUBSTOR. A 3-input NN using LAI functions and cumulative rainfall yielded the most accurate estimations and forecasts of tuber yields. This NN showed that tuber yield of contrasting zones was mostly a function of meteorological conditions prevailing during the first 5-8weeks after planting. Subsequent development of tubers was essentially controlled by biomass allocation to tubers. The NN models were more coherent than MLR and SUBSTOR for two reasons: (1) the use of seasonal LAI directly as input rather than computed as an auxiliary variable and (2) the non-linearity of the modeling process resulting in more accurate estimation of the temporal discontinuities of potato tuber growth. This model showed potential for application in precision agriculture by accounting for temporal and spatial real-time climatic and crop data. Springer Science+Business Media, LLC, 2011 Crop growth model nationallicence Neural network nationallicence SUBSTOR nationallicence Genetic algorithm nationallicence Leaf area index nationallicence Fortin Jérôme Department of Civil and Water Engineering, Université Laval, Québec, Canada aut Anctil François Department of Civil and Water Engineering, Université Laval, Québec, Canada aut Parent Léon-Étienne Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada aut Bolinder Martin Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada aut Precision Agriculture Springer US; http://www.springer-ny.com 12/6(2011-12-01), 905-923 1385-2256 12:6<905 2011 12 11119 https://doi.org/10.1007/s11119-011-9233-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11119-011-9233-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fortin Jérôme Department of Civil and Water Engineering, Université Laval, Québec, Canada aut NATIONALLICENCE 700 1- Anctil François Department of Civil and Water Engineering, Université Laval, Québec, Canada aut NATIONALLICENCE 700 1- Parent Léon-Étienne Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada aut NATIONALLICENCE 700 1- Bolinder Martin Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada aut NATIONALLICENCE 773 0- Precision Agriculture Springer US; http://www.springer-ny.com 12/6(2011-12-01), 905-923 1385-2256 12:6<905 2011 12 11119 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer