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- Muleke, Albert, et al.
(författare)
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Sustainable intensification with irrigation raises farm profit despite climate emergency
- 2023
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Ingår i: Plants, People, Planet. - : Wiley. - 2572-2611. ; 5:3, s. 368-385
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Tidskriftsartikel (refereegranskat)abstract
- Societal Impact StatementDespite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates.SummaryResearch aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land-use changes in space and time.Here, we demonstrate how a new framework—WaterCan Profit—can be used to elicit such complexity using an irrigated case study farm with four whole-farm adaptation scenarios (Baseline, Diversified, Intensified and Simplified) with four types of irrigated infrastructure (Gravity, Pipe & Riser, Pivot and Drip).Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole-farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long-term economic returns.We conclude with four key insights: (1) When assessing whole-farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity-based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole-farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole-farm intensification was able to raise profitability under future climates.
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- Wu, Hongjie, et al.
(författare)
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AttentionMGT-DTA : A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 623-636
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Tidskriftsartikel (refereegranskat)abstract
- The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA. © 2023 The Author(s)
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