Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods.

While onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil.

Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes.

Traditional methods to evaluate the stability and maturity of organic wastes and composting matrices are laborious, time-consuming and generate laboratory chemical wastes. This study focused on the development of partial least square (PLS) regression models for the prediction of the stability and maturity of compost-based substrates based on Fourier transform infrared (FTIR) spectroscopy. The following parameters, selected as conventional maturity indexes, were modeled and used as dataset: dissolved organic carbon (DOC), C/N and NH4(+)/NO3(-) ratios, cation exchange capacity (CEC), degree of polymerization (DP), percentage of humic acid (PHA), humification index (HI) and humification ratio (HR).