Stakeholder collaboration in climate-smart agricultural production innovations: insights from the Cocoa industry in Ghana

Although collaboration is vital in addressing global environmental sustainability challenges, research understanding on stakeholder engagement in climate-smart production innovation adoption and implementation, remains limited. In this paper, we advance knowledge about stakeholder collaboration by examining the roles played by stakeholders in scaling up ecological sustainability innovations. Using the illustrative context and case of green cocoa industry in Ghana, the analysis identified three distinctive phases of stakeholder engagement in ecological sustainability innovations implemented from 1960-2017. We highlight defining periods of ecological challenges encompassing the production recovery sustainability initiative phase solely driven by the Ghana Cocoa Board (COCOBOD)–a governmental body responsible for production, processing and marketing of cocoa, coffee and sheanut. During the period, major initiatives were driven by non-governmental organisations in collaboration with COCOBOD to implement the Climate-Smart agriculture scheme in the cocoa sector. The findings have implications for cocoa production research and stakeholder collaboration in environmental innovations adoption

BUSHFIRES IN THE KRACHI DISTRICT: THE SOCIO-ECONOMIC AND ENVIRONMENTAL IMPLICATIONS

Bushfires are becoming one of the environmental challenges confronting Ghana and increasingly it has become difficult for the Government to control it because this activity is deeply rooted in the socio-cultural and economic systems of the people. The effects of bushfire on rural livelihoods and on the ecosystem in Ghana are extensive and damaging. Bushfires have accelerated environmental degradation especially in the fragile savannah ecosystem, yet there is very little in the form of public education, published data and information concerning the frequency, intensity, duration and effects of bushfire on the environment and human welfare in Ghana. The study did a change detection of biomass cover using pre and post fire normalized burnt ratio of Landsat TM+ imageries of 2002 and 2003 to determine fire severity on vegetative cover. The socio-economic impact of this disaster was collected using social survey approaches such as interviews and focus group meetings. Some of the consequences of the bushfire include the burning of food stuffs, houses as well as domestic animals. The environmental impacts of these bushfires have been very devastating and these involve the lost of biodiversity (plants and animals) and the depletion of organic matter of the soil thus impoverishing the soils. The research found out that, the continuous prevalence of this activity was due to the laxity in the implementation of bye-laws regulating bushfire burning due to the lack of personnel and logistics to state agencies in the District to combat the problem

Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very-high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very-high-resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique