通过基于物联网的滴灌和施肥优化有氧水稻的生长和产量

D. V. S. Akshay, R. M. Kumar, S. P. Sree, B. Sreedevi, M. B. B. Prasad Babu, A. S. Sakhare
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引用次数: 0

摘要

目的:通过田间试验研究不同的物联网滴灌和施肥管理方法对有氧水稻生长和产量的影响:研究地点和时间:海得拉巴印度水稻研究所(ICAR-Indian Institute of Rice Research, Hyderabad)2022-23 年和 2023-24 年的春耕和秋收季节:横向小区处理为氮管理方法(4 种),即对照(N1)、100% RDN(100% 无机)(N2)、100% RDN(50% 无机和 50% 有机)(N3)和 125% RDN(100% 无机)(N4)。垂直地块处理为灌溉管理措施(3),包括饱和(I1)、10 % DASM(I2)和 20 % DASM(I3)。有机养分通过土壤施用,无机养分通过施肥施用。基于传感器(物联网)进行了灌溉调度:125% RDN 和 100% 无机养分的氮肥施用始终能带来更高的生长参数(株高(110.4 厘米和 93.9 厘米)、每平方米分蘖数(392.2 和 291.8)和植株干重(11814.8 千克/公顷-1 和 9557.0 千克/公顷-1),其次是 100% RDN(100% 无机养分)和 100% RDN(50% 无机养分和 50% 有机养分)。与亏缺灌溉处理相比,饱和灌溉的植株更高,生长参数(株高(109.8 厘米和 91.0 厘米)、分蘖数(412.3 米-2 和 283.6 米-2)和植株干重(10916.0 千克/公顷-1 和 9463.4 千克/公顷-1)也更高。125% RDN 和 100% 无机处理的产量属性(圆锥花序数 m-2(154.3 和 141.1)、圆锥花序灌浆粒数-1(142.3 和 124.0)、圆锥花序重量(3.1 克和 3.3 克))和产量(4790.2 千克/公顷-1 和 4583.8 千克/公顷-1)较高,与 100% RDN(100% 无机)和 100% RDN(50% 无机和 50% 有机)在统计学上相似。饱和度表现出更高的产量属性(种植季和收获季的圆锥花序数 m-2(149.5 和 139.2)、圆锥花序饱满粒数-1(135.0 和 121.6)、圆锥花序重(3.0 克和 3.2 克))和产量(4554.0 千克/公顷-1 和 4425.5 千克/公顷-1),与 10% DASM 相当。交互作用不显著:结论:通过施肥施用 125% RDN 和 100% RDN 的氮肥,其生长、产量参数和产量在统计学上相似。在有氧水稻的所有生长、产量参数和产量中,基于物联网的灌溉(10% DASM)与饱和处理相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Growth and Yield in Aerobic Rice through IoT-based Drip Irrigation and Fertigation
Aim: A field experiment was conducted to study the effect of different IoT based drip irrigation and fertigation management practices on growth and yield of aerobic rice. Study Design: The experiment was laid out in a strip plot design with 4 main plots (Horizontal) and 3 sub-plots (Vertical) that are allocated randomly and replicated thrice. Place and Duration of Study: kharif and rabi seasons of 2022-23 & 2023-24 at ICAR-Indian Institute of Rice Research, Hyderabad. Methodology: The horizontal plot treatments were Nitrogen management practices (4) i.e., Control (N1), 100 % RDN (100% inorganic) (N2), 100 % RDN (50% inorganic & 50% organic) (N3) and 125 % RDN (100 % inorganic) (N4). Vertical plots treatments were Irrigation management practices (3) which include Saturation (I1), 10 % DASM (I2) and 20 % DASM (I3). Organic nutrient sources were given as soil application and the inorganic sources were applied through fertigation. Sensor based (IoT) irrigation scheduling was done. Results: Nitrogen application of 125% RDN with 100% inorganic consistently resulted in higher growth parameters (plant height (110.4 cm and 93.9 cm), number of tillers m-2 (392.2 and 291.8) and plant dry weight (11814.8 kg ha-1 and 9557.0 kg ha-1) during kharif & rabi respectively) followed by 100% RDN (100% inorganic) and 100% RDN (50% inorganic & 50% organic) in pooled data. Saturation irrigation resulted in taller plants with greater growth parameters (plant height (109.8 cm and 91.0 cm), number of tillers m-2 (412.3 and 283.6) and plant dry weight (10916.0 kg ha-1 and 9463.4 kg ha-1) during kharif & rabi respectively) compared to deficit irrigation treatments. Treatment of 125% RDN with 100% inorganic resulted in the higher yield attributes (no. of panicles m-2 (154.3 and 141.1), no. of filled grains panicle-1 (142.3 and 124.0), panicle weight (3.1 g and 3.3 g) during kharif & rabi respectively) and yield (4790.2 kg ha-1 and 4583.8 kg ha-1) which was statistically similar with 100% RDN (100% inorganic) and 100% RDN (50% inorganic & 50% organic). Saturation expressed higher yield attributes (no. of panicles m-2 (149.5 and 139.2), no. of filled grains panicle-1 (135.0 and 121.6), panicle weight (3.0 g and 3.2 g) during kharif & rabi respectively) and yield (4554.0 kg ha-1 and 4425.5 kg ha-1) which was on par with 10% DASM. The interaction was found to be non-significant. Conclusion: Nitrogen application of 125% RDN and 100% RDN through fertigation were found to have statistically similar growth, yield parameters and yield. IoT based irrigation at 10% DASM was found to be on par with saturation treatment among all the growth, yield parameters and yield of aerobic rice.
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