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Peanut (Arachis hypogaea L.) response to low‐rate applications of selected herbicides at vegetative and reproductive growth stages

Agronomy Journal. · 1 Jan 2024 · 10.1002/agj2.21540

Abstract

Off‐target drift of herbicides can seriously reduce peanut (Arachis hypogea L.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide‐tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed‐free check. However, NDVI values could differentiate between injured and weed‐free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias.

Plant phenotyping relevance

UAV画像由来NDVIによる除草剤傷害(植物状態)の推定精度を、目視評価と比較して検証しており、植物フェノタイピング手法の技術評価が明示的な目的の一部である。

abstractThe objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury
abstractNDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating

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