Circular Economy in Banana Cultivation

Gehring, Nicole; Dorneanu, Bogdan; Manrique, José; Ipanaqué, William; Arellano‐García, Harvey

doi:10.1016/b978-0-12-823377-1.50262-7

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Banana plants require ideal rainfall for growth between 1520-3800 mm per year. In addition, the optimal temperature for banana plant growth is between 22-32°C, and the required humidity is between 70-85% [8,14]. Salak plants can thrive in areas with an altitude of 100-1000 meters above sea level.…”

Section: Fruit Plantsmentioning

confidence: 99%

Data Analytics on the Effect of Climate Change to the Production of Fruit Plants during 2014-2020 in Malang District

Wibawa¹,

Pamungkas²

2022

Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)

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Horticultural products are agricultural commodities that have strategic value not only for farmers, but also for market players and consumers in Indonesia. In 2019, the GRDP of horticultural products in East Java reached IDR31.13 trillion. Compared to other districts in East Java, Malang District is the most contributor to horticultural products in East Java. However, the development of horticultural agriculture recently embraces various problems and challenges, one of which is the climate change factor. Meanwhile, new knowledge about the peak harvest times for each year, new strategies regarding crop rehabilitation times, and others are still less explored. Based on these problems, this research presents data analytics on the effect of climate change to the productivity of fruit plants during 2014 to 2020 in Malang district using multiple linear regression and correlation analysis. In this study, we can conclude that the peak harvest time for fruit tends to occur in the October-December. In those months, the rainfall (225.78 mm per month) was classified as moderate, and the temperature level (25.24 °C) with humidity (78.87%) were classified as optimal. Meanwhile, the low harvest time occurs in other months with too high or too low level on those three parameters namely rainfall, temperature, and humidity.

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Section: Fruit Plantsmentioning

confidence: 99%

Data Analytics on the Effect of Climate Change to the Production of Fruit Plants during 2014-2020 in Malang District

Wibawa¹,

Pamungkas²

2022

Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)

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“…Recently, Diéguez-Santana et al (2022) proposed perspectives for progress based on the CE for the agri-food chain of the pitahaya sector in Ecuador, focused on reducing and valorizing food losses and waste. Challenges and possible solutions for banana production stages have been addressed by Gehring et al (2020), who examined the most critical strategies that could be applied in order to introduce the circular economy approach into the Piura region (Peru). In addition, the literature has highlighted the need for reliable metrics to validate circular strategies, measuring the degree of circularity (Stillitano et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Prospective of the circular economy in a banana agri-food chain

Silva-Alvarado,

Orozco-Crespo,

Verduga-Alcívar

et al. 2023

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The circular economy (CE) has been adopted as an alternative to the linear economic model, since it transforms the way products are designed and consumed. Agri-food chains are key to ensuring the transition to a sustainable development model in line with the principles of CE. This study sought to implement the concepts of circular economy at the Ecuador's banana production chain, through the design of prospective from the exhaustive diagnosis of the main processes of the chain. The research was conducted from 2020 to 2022. The studied checklist included 91 items grouped into nine dimensions: source or supply of materials, design, manufacturing, economic circle, distribution and sales, consumption and use, 4R, re-manufacturing, and sustainability. A medium circular economy level (CEL) was evaluated (2.69 points out of 5). Two machine learning techniques (MLTs) were also applied support vector machine (SVM), (with three types of kernel: radial, linear and polynomial) and a neural network (NN). In relation to the Spearman correlation coefficients of the application of the techniques to the study cases, they were higher than 0.90. The most reliable was the SVM Regression-Linear Kernel, which had a coefficient close to 1. Finally, various value-adding options are proposed in order to take advantage of the large amount of waste generated in the banana production system. The proposed options include value-augmentation processes, ranging from organic fertilizers, biofuels, materials for wastewater treatment, and the production of bioplastics and nanoparticles, which can be implemented using the CE approach.

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