Bamboo as a sustainable crop for land restoration in India: challenges and opportunities

Sawarkar, Ankush D.; Shrimankar, Deepti D.; Manekar, Swati C.; Kumar, Manish; Garlapati, Phani Kumar; Singh, Lal

doi:10.1007/s10668-023-03911-9

Cited by 6 publications

(3 citation statements)

References 124 publications

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“…In the area of land with mixed seasonal crops, although there are no bamboo plants, the position of the land at the bottom of the demonstration plot is likely to receive an accumulation of litter carried by the flow of rainwater, thereby increasing soil fertility. Bamboo plants have been proven to improve degraded land (Sawarkar et al, 2023).…”

Section: The Effect Of Bamboo Agroforestry On Soil Fertilitymentioning

confidence: 99%

rowth of Betung Bamboo (Dendrocalamus asper) and Food Crop Production Under Agroforestry Bamboo Systems

Hani,

Suryanto,

Murniati

et al. 2024

jtfm

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One of the main species in community forests is betung bamboo (Dendrocalamus asper). Optimizing land use under bamboo can be achieved by cultivating food crops. This research aimed to determine the growth of betung bamboo and the production of food crops. The experimental design used a split plot design with four cultivation patterns as the main plots: agroforestry bamboo pattern i.e. agroforestry bamboo + rice (P1), agroforestry bamboo + rice + cassava (P2), agroforestry bamboo + rice + taro (P3), and mixed food crops (rice + cassava + taro) (P4), while the subplots were rice varieties: rindang 2 (V1), protani (V2), and unsoed (V3). The observed parameters were bamboo growth, soil fertility, and food crop production. The results showed that seven-year-old betung bamboo had an average of 6.01 mature stems per clump, an average plant height of 7.23 m, and a stem diameter of 7.12 cm. The numbers of young stems and shoots per clump was 1.45 and 3.71. The highest betung bamboo growth was in the agroforestry pattern (length: 8,49 m, diameter: 7.17 cm, thickness: 1.95, total weight: 21.31 kg). The highest rice yield was observed in the P2V1 treatment (4.17 kg). The highest cassava tuber yield per plant was observed in P4 (2.12 kg). The taro tuber yield was relatively higher in the agroforestry pattern with a distance of >1 m from the bamboo clump (0.52 kg plant-1). The land equivalent ratio (LER) and area time equivalent ratio (ATER) of bamboo and food crop agroforestry have a value above 1, so it is more efficient in land.

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Section: The Effect Of Bamboo Agroforestry On Soil Fertilitymentioning

confidence: 99%

rowth of Betung Bamboo (Dendrocalamus asper) and Food Crop Production Under Agroforestry Bamboo Systems

Hani,

Suryanto,

Murniati

et al. 2024

jtfm

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“…Bamboo forest areas across the globe cover an approximately 36-million-hectare area (MHA), among which India contains 16 MHA, followed by China with 6.5 MHA. These two countries have a vast range of bamboos, with more than 1000 species [10,11]. Bamboo has mainly two species, Arundinarieae and Bambuseae; among the two, most of the world is covered by the Bambuseae species [12,13].…”

Section: Introductionmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

Applied Sciences

Self Cite

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Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

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“…This meticulous approach resu in the identification of 62 documents deemed suitable for systematic review and anal An exploratory search strategy was employed to locate additional pertinent documents beyond those initially identified through advanced searches. Diverse platforms such as Scopus [34], ResearchGate [35], and Google Scholar [36] were utilized for this purpose. The relevant cited studies in the initially selected documents were also included as part of the exploratory search conducted here.…”

Section: Introductionmentioning

confidence: 99%

Advances in Connection Techniques for Raw Bamboo Structures—A Review

Aniñon,

Garciano

2024

Buildings

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While bamboo’s sustainability and impressive mechanical properties make it suitable for structural use, its application is hindered by challenges in connection systems. Bamboo’s hollow, thin-walled nature, dimensional variations, and anisotropic properties complicate connection design. Despite numerous studies and proposed connection types, a consensus on preferred bamboo connections remains elusive. Ideal connections for raw bamboo structures should be robust, economical, practical, simple, and easy to assemble. This paper reviews 62 scientific papers from the Scopus database published between 2003 and 2024, along with additional relevant references. It identifies research gaps, recommending further studies on bamboo connections considering factors like species, harvest age, treatment type, and node location. The analysis of failure modes and long-term behavior is essential to anticipate and mitigate risks associated with bamboo connections, ensuring durability, and minimizing maintenance needs. Lastly, developing universally accepted standards and codes for bamboo and bamboo connections is crucial for enabling their widespread adoption in the construction industry.

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Bamboo as a sustainable crop for land restoration in India: challenges and opportunities

Cited by 6 publications

References 124 publications

rowth of Betung Bamboo (Dendrocalamus asper) and Food Crop Production Under Agroforestry Bamboo Systems

rowth of Betung Bamboo (Dendrocalamus asper) and Food Crop Production Under Agroforestry Bamboo Systems

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Advances in Connection Techniques for Raw Bamboo Structures—A Review

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