Abstract:This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro-food such as ARIMA and SVM methods. However, ordinary least squares (OLS) and neural network (NN) methods have shown better results in modelling time series data. For that reason, the primary objective of this stud… Show more
“…The Center for Agricultural Data and Information Systems shows that watermelon consumption from 2016 to 2020 is 2,242 kg/capita/year in a row; 1,929 kg/capita/year; 1,460 kg/capita/year; 1,727 kg/capita/year; and 1.896 kg/capita/year [2]. Availability of watermelon per capita 2016-2020 consecutively 1,84 kg/capita/year; 1,96 kg/capita/year; 1,80 kg/capita/year; 1,94 kg/capita/year; 1,82 kg/capita/year.…”
This study investigated the impact of electrical conductivity (EC) values and pruning methods on the growth and yield of hydroponic watermelon plants (Citrullus lanatus) at the Wedomartani experimental station, Sleman DIY, from May to August 2022. Using a field experiment in a split plot design, the effects of varying EC values during different plant growth phases and differing pruning practices were evaluated. EC values (in mS/cm) for the vegetative I, vegetative II, and generative phases were manipulated at 1.2;2.0;2.8, 1.5;2.1;3.1, and 1.8;2.6;3.4 respectively, while pruning involved maintaining 2 branches and 1 fruit, 3 branches and 1 fruit, and 3 branches and 2 fruits respectively. Analyses of variance (ANOVA) and Duncan’s Multiple Range Test (DMRT) at a 5% level revealed that EC significantly impacted plant height at 14, 21, and 28 days after planting (DAP), stem diameter at 28 DAP, and leaf area at 14 DAP, but not fruit weight per plant. Pruning had a significant effect on plant height at 14 DAP, with the best results obtained from maintaining 2 branches and 1 fruit. No interaction was found between EC value and pruning on watermelon plant growth and yield. These findings offer potential strategies for improving yield in hydroponic watermelon cultivation.
“…The Center for Agricultural Data and Information Systems shows that watermelon consumption from 2016 to 2020 is 2,242 kg/capita/year in a row; 1,929 kg/capita/year; 1,460 kg/capita/year; 1,727 kg/capita/year; and 1.896 kg/capita/year [2]. Availability of watermelon per capita 2016-2020 consecutively 1,84 kg/capita/year; 1,96 kg/capita/year; 1,80 kg/capita/year; 1,94 kg/capita/year; 1,82 kg/capita/year.…”
This study investigated the impact of electrical conductivity (EC) values and pruning methods on the growth and yield of hydroponic watermelon plants (Citrullus lanatus) at the Wedomartani experimental station, Sleman DIY, from May to August 2022. Using a field experiment in a split plot design, the effects of varying EC values during different plant growth phases and differing pruning practices were evaluated. EC values (in mS/cm) for the vegetative I, vegetative II, and generative phases were manipulated at 1.2;2.0;2.8, 1.5;2.1;3.1, and 1.8;2.6;3.4 respectively, while pruning involved maintaining 2 branches and 1 fruit, 3 branches and 1 fruit, and 3 branches and 2 fruits respectively. Analyses of variance (ANOVA) and Duncan’s Multiple Range Test (DMRT) at a 5% level revealed that EC significantly impacted plant height at 14, 21, and 28 days after planting (DAP), stem diameter at 28 DAP, and leaf area at 14 DAP, but not fruit weight per plant. Pruning had a significant effect on plant height at 14 DAP, with the best results obtained from maintaining 2 branches and 1 fruit. No interaction was found between EC value and pruning on watermelon plant growth and yield. These findings offer potential strategies for improving yield in hydroponic watermelon cultivation.
“…Lin Feng et al proposed a basic method aiming to maximize total profit, presuming the demand curve's dependence on unit price, displayed quantity, and sale date, resulting in an equation for pricing, albeit with a limitation in precision [1] . Subsequently, more sophisticated algorithms, such as machine learning and deep learning methods, were employed by others to predict agricultural product pricing and replenishment [2][3][4] , offering a notable advantage in accuracy. Notably, machine learning's predictive accuracy surpasses statistical models but lags behind deep learning [5][6] .…”
In the realm of fresh produce supermarkets, the expedited turnover of vegetable products necessitates routine replenishment and pricing adjustments by supermarkets. Nonetheless, formulating effective strategies for these aspects presents a substantial challenge to merchants. To address this issue, this study employs the SARIMA prediction model to anticipate future replenishment volumes and pricing for vegetable products in supermarkets. The cost-plus pricing method serves as the foundation for pricing in this predictive analysis. The predictive findings highlight a high accuracy in foreseeing both the upcoming month's replenishment volume and pricing. These forecasts reveal a fluctuating trend, displaying consistent periodicity akin to previous years, indicative of a robust predictive capacity. Consequently, this study culminates in devising a supermarket replenishment and pricing strategy grounded in the SARIMA model. This strategy aims to facilitate improved planning of future restocking and pricing by merchants, thereby fostering enhanced sales of vegetable products within supermarkets.
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