Initial modelled outputs at field scale

This report comprises Deliverable 6.16 in the project, which contributes to the third objective as it presents field-scale evaluation of innovations, in order to adapt and evaluate agroforestry designs and practices for locations where agroforestry is currently not-widely practised or declining. The modelling of outputs at field scale to support best agroforestry practices is an ongoing activity during the AGFORWARD project. This report highlights some of the outputs which has been produced in the form of three papers (either submitted or about to be submitted to a peer-reviewed journal) or in four presentations at the Third European Agroforestry Conference in May 2016N/

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $0.92 \pm 0.04$ and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Climate change and food security are two of humanity’s greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture—agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, site-specific combinations of both demand- and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92 0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support