The Correlation between Rainfall, Temperature, Relative Humidity, and Rice Field Productivity in Aceh Besar

Author:

Chairani S

Abstract

AbstractVarious factors could affect rice field productivity, such as climate, management practices, and soil properties. Aceh Besar had experienced long drought, higher temperature, shifted seasons, and the decrease yield of rice productivity due to climate change. This research aimed to analyze the correlation between climate variables and rice field productivity, such as rainfall and mean, minimum, and maximum temperatures, relative humidity in Aceh Besar District. The monthly climate data and the rice field productivity data were employed for 10 (2011-2020) and 8 (2011-2018) consecutive years, respectively. The correlation between the climate variables were calculated using Pearson coefficient correlation. The results showed that rainfall and maximum temperature were positively correlated, as well as rainfall and relative humidity. In contrary, rainfall and mean temperature, rainfall and minimum temperature, rainfall and rice field productivity were negatively correlated. The latest indicating that rainfall did not impact the rice field productivity in Aceh Besar. It was quite contradictive to the reality in the field that significantly experiencing the long drought, higher temperature, shifted seasons and the decrease yield of rice field productivity. This was due to the lack of climate data employed that required longer period preferably 30 to 50 years which was not available.

Publisher

IOP Publishing

Subject

General Engineering

Reference11 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Exploring the patterns of dry and wet spells: a case study of Ethekwini District Municipality, KwaZulu Natal, South Africa;International Journal of Business Ecosystem & Strategy (2687-2293);2024-08-23

2. Rice Yield Prediction in Sumatra Indonesia Using Machine Learning and Climate Data;2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA);2023-12-13

3. Utilization of Machine Learning Approaches for Rainfall Data Imputation: A Systematic Literature Review;2023 International Conference on Computer, Control, Informatics and its Applications (IC3INA);2023-10-04

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