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Optimization of Planting Date and Density of Cotton Through Crop Mechanistic Model and Field Experimentation in Semi-Arid Conditions

Pakistan Journal of Botany(2024)SCI 4区

Univ Agr Faisalabad

Cited 0|Views16
Abstract
Climate variability and trend affect crop growth, development, and ultimately seed yield. Selection of appropriate planting date and density is essential for improving crop performance under changing climate. A field experiment was conducted under semi-arid climatic conditions to evaluate the performance of cotton crop under different planting dates viz. 22(nd) April, 7(th) May, 22(nd) May and 6(th) June and densities viz 88890, 59260 and 44445 plants/ha. Treatments were arranged by using randomized complete block design with split plot arrangement. The phenological parameters i.e., square initiation, flower initiation, boll formation and boll opening and yield- and yield- components i.e., number of bolls per plant, monopodial branches, sympodial branches, seed cotton yield and seed index were significantly affected by planting dates and densities. Results showed that maximum seed cotton yield (3464 kg ha(-1)) was recorded when cotton was sown on 22(nd) April. However, plant population also affected cotton crop significantly. Maximum seed cotton yield (2751 kg ha(-1)) was recorded for 22.5 cm planting density followed by 15 cm and 30 cm. Furthermore, OZCOT-DSSAT cotton model showed that the simulated phenological parameters with the average error of 9%, 3% and 4% in days to flowering, day to maturity and seed cotton yield, respectively. In sum, simulated data and observed data showed cotton could be planted on 22(nd) April with 59260 plants/ha to achieve maximum productivity.
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Key words
Cotton,Phenology,Planting date,Planting density,DSSAT
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要点】:本文通过田间实验和作物机理模型,研究了半干旱条件下棉花的最佳种植日期和密度,以提高作物产量。

方法】:研究采用随机完全区组设计,以分裂小区的方式安排处理,对比分析了不同种植日期和密度对棉花生长和产量的影响。

实验】:实验在半干旱气候条件下进行,种植日期分别为4月22日、5月7日、5月22日和6月6日,密度分别为88890、59260和44445株/公顷,使用的数据集为田间实验观测数据。结果显示,4月22日播种且密度为59260株/公顷时,籽棉产量最高(3464千克/公顷)。同时,OZCOT-DSSAT棉花模型模拟的物候参数和产量与实际观测数据高度一致。