Response of fragrant pear quality and water productivity to lateral depth and irrigation amount

灌溉 含水量 环境科学 农学 多年生植物 用水效率 地面灌溉 滴灌 土壤水分 农业工程 土壤科学 园艺 生物 地质学 工程类 岩土工程
作者
Jiaxin Wang,Xinlin He,Ping Gong,Tong Heng,Danqi Zhao,Chunxia Wang,Quan Chen,Jie Wei,Ping Lin,Guang Yang
出处
期刊:Agricultural Water Management [Elsevier BV]
卷期号:292: 108652-108652 被引量:4
标识
DOI:10.1016/j.agwat.2023.108652
摘要

This study addresses the low water productivity (WP) of traditional flood irrigation (FI) and the mismatch between the wet zone of surface drip irrigation (SDI) and the root activity layer of fragrant pear. A fixed field experiment was conducted in Xinjiang, northwest China in 2021 − 2023. The experiment was designed as a two-factor, completely randomized trial with three lateral depths of Subsurface drip irrigation (SSDI) gradients set at 20 cm (D1), 30 cm (D2) and 40 cm (D3) and three irrigation gradients: W1 (3750 m³ ha−1); W2 (5250 m³ ha−1); and W3 (6750 m³ ha−1), using SDI (6750 m³ ha−1) and FI (9750 m³ ha−1) as the controls. The effects of these factors on soil moisture, crop water consumption (ETc), fruit quality, yield, WP and net profit were analyzed. The results revealed that the average soil moisture content increased with the lateral depth and irrigation amount. Principal component analysis determined D2W3 to be the treatments with the best overall quality in 2021–2023. On average, compared with SDI and FI, SSDI increased yield by 13.14% and 17.03%, and WP by 44.65% and 137.23%, respectively. A comprehensive Moisture-Yield-Quality-Efficiency evaluation model was established using the Projection Pursuit Classification model based on the Real Coded Accelerating Genetic Algorithm (RAGA-PPC). The comprehensive evaluation was optimized at the lateral depth of 30 cm and an irrigation amount of 6750 m³ ha−1. This study is a crucial reference for further research on perennial fruit trees to improve fruit quality and WP.
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