Oat yield through panicle components and growth regulator

Anderson Marolli,Darlei M. Lambrecht,Ana P. Brezolin,Rubia D. Mantai,Maria E. Gzergorczick,José A. G. da Silva

doi:10.1590/1807-1929/agriambi.v21n4p261-266

Abstract

ABSTRACT The growth regulator modifies the expression of lodging and panicle components in oat plants, with reflexes in yield. The objective of this study was to define the optimal dose of growth regulator in oat for a maximum lodging of 5%. In addition, this study aimed to identify potential variables of the panicle to compose the multiple linear regression model and the simulation of grain yield in conditions of use of the regulator under low, high and very high fertilization with nitrogen. The study was conducted in 2011, 2012 and 2013 in a randomized block design with four replicates in a 4 x 3 factorial scheme, for growth regulator doses (0, 200, 400 and 600 mL ha-1) and N-fertilizer doses (30, 90 and 150 kg ha-1), respectively. The growth regulator doses of 395, 450 and 560 mL ha-1 are efficient, with maximum oat lodging of 5%, under low, high and very high nitrogen fertilization, respectively. The grain weight per panicle and panicle harvest index are potential variables to compose the multiple linear regression model. Multiple linear regression equations are efficient in the simulation of oat grain yield under the conditions of use of growth regulator, regardless of the N-fertilizer dose.

Highlights

Nitrogen (N) has strong influence on length, number and weight of grains in oat panicles, with direct reflexes on grain yield (Mantai et al, 2016)
Dalchiavon et al (2012), using multiple linear regression model, estimated rice grain yield incorporating to the model the number of panicles m-2, number of spikelets panicle-1 and the weight of one thousand grains
The relationships between oat panicle components and grain yield can favor the construction of yield simulation models in the more efficient planning of N management with the use of the regulator

Summary

Introduction

Nitrogen (N) has strong influence on length, number and weight of grains in oat panicles, with direct reflexes on grain yield (Mantai et al, 2016). Dalchiavon et al (2012), using multiple linear regression model, estimated rice grain yield incorporating to the model the number of panicles m-2, number of spikelets panicle-1 and the weight of one thousand grains. Godoy et al (2015), studying soil attributes in rice, simulated grain yield based on the use of copper, nitrogen, iron and phosphorus by multiple linear regression. It aimed to identify potential variables associated with oat panicle through the use of the regulator to compose the multiple linear regression model and the simulation of grain yield, under conditions of low, high and very high N fertilization

Objectives

Methods

Results