基于精准农业和遥感的旱作和补灌模式棉花产量和籽油二维GIS水分根区制图

Ag. T. Filintas, Aikaterini Nteskou, Persefoni Katsoulidi, Asimina Paraskebioti, Marina Parasidou
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引用次数: 4

摘要

两次灌溉(IR1:雨灌;通过应用TDR传感器等农业新技术,研究了IR2(雨养+补充滴灌)和两种施肥(Ft1、Ft2)处理对棉花产量和籽油的影响;土壤湿度(SM);精准农业;遥感NDVI(哨兵2号卫星传感器);soil-hydraulic分析;地质统计模型;SM-rootzone,以及2D GIS制图建模。建立了土壤-水-作物-大气(SWCA)日平衡模型。双向方差分析结果显示,灌溉(IR2 =最佳)和施肥处理(Ft1 =最佳)显著影响了产量和含油量。关键生育期补灌可显著提高产量(+234.12%)和含油量(+126.44%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing
The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).
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