essicolo / site-specific-multilevel-modeling-of-potato-response-to-nitrogen-fertilization

🥔 Supplementary material for Parent, S.-É., Leblanc, M., Parent, A.-C., Coulibali, Z. and Parent, L.E. (2017). Site-specific multilevel modeling of potato response to nitrogen fertilization. DOI: 10.3389/fenvs.2017.00081

Site-Specific Multilevel Modeling of Potato Response to Nitrogen Fertilization

Serge-Étienne Parent1*, Michael Leblanc1, Annie-Claude Parent2, Zonlehoua Coulibali1, Léon Etienne Parent1

1. Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada G1V0A6
2. Department of Civil and Water Engineering, Université Laval, Québec, Canada G1V0A6

• Corresponding author: Serge-Étienne Parent, email: [email protected]  

ABSTRACT

Technologies of precision agriculture, digital soil maps and meteorological stations provide a minimum data set to guide precision farming operations. However, determining optimal nutrient requirements for potato (Solanum tuberosum L.) crops at subfield scale remains a challenge given specific climatic, edaphic and managerial conditions. Multilevel modeling can generalize yield response to fertilizer additions using data easily accessible to growers. Our objective was to elaborate a multilevel N fertilizer response model for potato crops using the Mitscherlich equation and a core data set of 93 N fertilizer trials conducted in Quebec, Canada. Daily climatic data were collected at 10 km 𝗑 10 km resolution. Soils were characterized by organic matter content, pH and texture in the arable layer, and by texture and tools of pedometrics across a gleization-podzolization continuum in subsoil layers. There were five categories of preceding crops and five cultivar maturity orders. The three Mitscherlich parameters (Asymptote, Rate and Environment) were most often site-specific. Sensitivity analysis showed that optimum N dosage increased with non-leguminous high-residue preceding crops, coarser soils, podzolization, drier climatic condition and late cultivar maturity. The inferential model could guide site-specific N fertilization using an accessible minimum data set to support fertilization decisions. As decision-support system, the model could also provide a range of optimum N doses across a large spectrum of site-specific conditions including climate change.

DOI: 10.3389/fenvs.2017.00081