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.

Kinetic parameters related to nitrogen uptake in 'Okinawa' peach rootstocks are altered by 'Chimarrita' scion Nitrogen uptake in 'Okinawa' peach rootstock.

Background: The kinetic parameters of peach rootstock are a major factor contributing to its nitrogen (N) uptake efficiency, which directly affects the yield and quality parameters. However, the impact of grafting on N kinetic parameters is not sufficiently known as rootstocks are grafted with other scion cultivars before being transplanted to the field. The objective of this study was therefore to evaluate whether the grafting of 'Okinawa' rootstock with the scion cultivar 'Chimarrita' can alter the kinetic parameters related to N uptake.

Nutrient Diagnosis of at the Factor-Specific Level Using Machine Learning and Compositional Methods.

Brazil is home to 30% of the world's trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact.