Smallholder cocoa agroforestry systems; is increased yield worth the labour and capital inputs?

Scudder, Micah G.; Wampe, Nathan; Waviki, Zephaniah; Applegate, Grahame; Herbohn, John

doi:10.1016/j.agsy.2021.103350

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…Furthermore, smallholder farmers who have low incomes find it difficult to adopt the agroforestry system. According to Scudder et al (2022), According to the findings of the study, cocoa agroforestry had a negative net present value for farmers in Papua New Guinea, and their mean hourly income was lower than the minimum wage. Likewise, relevant issues such as excessive input costs, management, and labor cost, and inadequate financial support equally hinder the adoption of the cocoa agroforestry system (Alemagi et al 2015;Boeckx et al 2020;Oduro et al 2018;Scudder et al 2022).…”

Section: The Low-income Earnings Of Smallholder Farmersmentioning

confidence: 92%

“…Among smallholder cocoa agroforestry families in Ghana, only 15% are able to gain enough income to reach the living income level stipulated by the World Bank (Boeckx et al 2020). Cocoa agroforestry is a system that requires additional labor and capital investment in order to improve cocoa yield (Scudder et al 2022). Furthermore, smallholder farmers who have low incomes find it difficult to adopt the agroforestry system.…”

Section: The Low-income Earnings Of Smallholder Farmersmentioning

confidence: 99%

“…Receiving technical support and financial assistance in this direction remains a challenge at the moment (Alemagi et al 2015). Secondly, farmers might find it economically inviable to invest in cocoa agroforestry since it doesn't have a significant positive impact on their revenue (Scudder et al 2022). The lack of technical support for tree planting, the limited access to credit facilities to support tree planting, and the lowincome earnings of smallholder farmers are the limitations that fall under this theme.…”

Section: Economic Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Limitations of Sustainable Cocoa Agroforestry: A Literature Review

Grant¹,

Zhang²,

Brenya³

et al. 2022

J. Agrofor. Environ.

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Agroforestry is a climate-smart strategy adopted in cocoa farms to help cocoa crops adapt to climate change, maintain biodiversity, and improve cocoa yield. Meanwhile, its sustainability is of major concern to farmers. This study brings to the fore the common and persistent factors that pose a threat to the sustainability of cocoa agroforestry, presented through a systematic literature review approach, and further discussed using the United Nations’ Sustainable Development Goals (SDGs) model as the focal point. A number of serious limitations have been identified in the study as limiting the sustainability of cocoa agroforestry, including a lack of technical support for planting trees, an increase in pests and diseases, the intense competition for nutrients between shade trees and cocoa trees, weak land tenure policies, and numerous other factors. The eleven identified limitations were further categorized and analyzed under Environmental, Economic, and Social Limitations, and policy directions were drawn and discussed. Based on the findings of the study, Cocoa Agroforestry must not only support both cocoa production and forest conservation simultaneously but also satisfy all three Sustainable Development Goals. For Cocoa Agroforestry to become an agricultural practice that is sustainable, it must address the environmental limitations, economic limitations, and social limitations simultaneously.

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Section: The Low-income Earnings Of Smallholder Farmersmentioning

confidence: 92%

Section: The Low-income Earnings Of Smallholder Farmersmentioning

confidence: 99%

Section: Economic Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Limitations of Sustainable Cocoa Agroforestry: A Literature Review

Grant¹,

Zhang²,

Brenya³

et al. 2022

J. Agrofor. Environ.

View full text Add to dashboard Cite

show abstract

“…Diseases, insects, and nutrient deficiencies are the most common threats to the growth of crops. Disease diagnosis and treatment, pest management, and fertiliser application are performing an important role in decreasing yield loss [ 2 ]. The traditional process for disease detection is not feasible for all crop fields and farmers.…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

Pandian

Kanchanadevi

Rajalakshmi

et al. 2022

Computational Intelligence and Neuroscience

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In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.

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“…These threats reduce yield and cause economic loss to farmers. Disease diagnosis and treatment, pest management, and fertilizer application all help decrease yield loss 2 . Most plant diseases produce visible symptoms on leaf areas with distinct patterns, but identifying a specific disease from these symptoms requires expertise about the crop and its diseases.…”

Section: Introductionmentioning

confidence: 99%

An improved deep convolutional neural network for detecting plant leaf diseases

Pandian

Kanchanadevi

2022

Concurrency and Computation

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This research proposes a novel dense convolutional neural network with five dense blocks (5DB-DenseConvNet) for the detection of plant leaf diseases. Five dense blocks and four transition layers were used to develop the 5DB-DenseConvNet. The 5DB-DenseConvNet was trained and tested using a plant leaf disease image dataset. Such advanced data augmentation techniques as color augmentations, geometric transformations, neural style transfer, principal component analysis, color augmentation, random erasing, and super-resolution generative adversarial network were used to increase the dataset size. The dataset contains 97 diseased and healthy image classes of 21 plants and comprises 145,500 images of healthy and diseased plant leaves. The Bayesian searching technique was used to optimize the hyperparameter values of 5DB-DenseConvNet. Compression factor was introduced in the transition layer to improve the compactness of the 5DB-DenseConvNet. The 5DB-DenseConvNet was trained on a graphics processing unit environment for 1000 epochs. The trained 5DB-DenseConvNet performed at an average classification accuracy of 99.36% and at an average precision of 98.83% on the test dataset. The experimental results demonstrate that the performance and reliability of the proposed 5DB-DenseConvNet on plant disease detection are superior to prior transfer learning techniques.

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Smallholder cocoa agroforestry systems; is increased yield worth the labour and capital inputs?

Cited by 17 publications

References 17 publications

Limitations of Sustainable Cocoa Agroforestry: A Literature Review

Limitations of Sustainable Cocoa Agroforestry: A Literature Review

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

An improved deep convolutional neural network for detecting plant leaf diseases

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