Ecologically and biophysically optimal allocation of expanded soy production in Bavaria, Germany

A debate about cultivation and trading of soy has emerged among scientists, policymakers, and the public in recent years. Export-orientated soy production in regions of South America is associated with large-scale ecosystem destruction. Since soy is an important source of animal fodder, policymakers are developing schemes to support and enhance sustainable domestic soy cultivation, especially in the EU. Expanded soy cultivation should ideally provide high yields and at the same time promote environmental benefits. For this purpose, we applied a multi-objective optimization algorithm that selects areas with maximum soy suitability, minimum erosion risk, need for low fertilizer input due to water quality issues, and need for diversification of monotonous crop rotations. We use the state of Bavaria in Germany as a case study, modeling full self-sufficiency of soy. The results of the optimization indicate synergies between plantation suitability with need for low fertilization input and crop variation, which implies that the environmental benefit of nitrogen fixation and rotation diversification from soy plants can easily be reconciled with food productivity. However, slight trade-offs occur between erosion risk and the three other objectives, i.e., locations with better soy production might be more prone toward erosion risk. As a potential consequence of expanded soy cultivation in Bavaria, we identified winter wheat, grain maize, potatoes, and sugar beet as those crops that have the highest share of displaced cultivation area. To reduce such land use conflicts and ensure self-sufficiency in relevant crops, we recommend to limit the use of soy as animal feed. Nevertheless, we propose to explicitly incorporate the local need for the environmental benefits of soy cultivation in the planning for soy expansion. In doing so, domestic soy can turn into a real sustainable alternative to imported plant protein

Agricultural intensity interacts with landscape arrangement in driving ecosystem services

Agricultural intensification has enhanced productivity but also led to enormous ecosystem service and biodiversity losses. Strategic spatial landscape design could counteract this trend, but, the scientific understanding of how ecosystem services respond to agricultural practices on one hand and land use composition and configuration on the other is not complete. This study aims to methodically explore how the effect of landscape layout settings on ecosystem services depends on the intensity of agricultural practices in their surroundings. Using the Netherlands as a case study, we used spatial regression models to analyze how agricultural management intensity affects the relationship between spatial composition and configuration metrics and ecosystem service indicators. We found that the effect of large shares of agricultural land use on species richness, pollination and landscape appreciation was increasingly negative with amplified intensity of agricultural practices. With higher agricultural intensity in the surroundings, the positive effects of well-connected natural vegetation on species richness were impaired. In contrast, the negative effects of high-intensity agriculture on pollination service were be buffered well through high shares of natural grassland vegetation. Water-quality related indicators were less affected by variation in spatial metrics and agricultural intensity. The main interactions between agricultural intensity and the spatial metrics were robust at varying scales. Our analysis suggests that both low- and high-intensity agriculture can have a place in future sustainable agricultural systems, provided they are integrated in the appropriate spatial layout. Explicitly addressing farming practices in connection to local spatial settings can improve both landscape planning and ecosystem service modelling

Ecologically and biophysically optimal allocation of expanded soy production in Bavaria, Germany

A debate about cultivation and trading of soy has emerged among scientists, policymakers, and the public in recent years. Export-orientated soy production in regions of South America is associated with large-scale ecosystem destruction. Since soy is an important source of animal fodder, policymakers are developing schemes to support and enhance sustainable domestic soy cultivation, especially in the EU. Expanded soy cultivation should ideally provide high yields and at the same time promote environmental benefits. For this purpose, we applied a multi-objective optimization algorithm that selects areas with maximum soy suitability, minimum erosion risk, need for low fertilizer input due to water quality issues, and need for diversification of monotonous crop rotations. We use the state of Bavaria in Germany as a case study, modeling full self-sufficiency of soy. The results of the optimization indicate synergies between plantation suitability with need for low fertilization input and crop variation, which implies that the environmental benefit of nitrogen fixation and rotation diversification from soy plants can easily be reconciled with food productivity. However, slight trade-offs occur between erosion risk and the three other objectives, i.e., locations with better soy production might be more prone toward erosion risk. As a potential consequence of expanded soy cultivation in Bavaria, we identified winter wheat, grain maize, potatoes, and sugar beet as those crops that have the highest share of displaced cultivation area. To reduce such land use conflicts and ensure self-sufficiency in relevant crops, we recommend to limit the use of soy as animal feed. Nevertheless, we propose to explicitly incorporate the local need for the environmental benefits of soy cultivation in the planning for soy expansion. In doing so, domestic soy can turn into a real sustainable alternative to imported plant protein

Ecologically and biophysically optimal allocation of expanded soy production in Bavaria, Germany

A debate about cultivation and trading of soy has emerged among scientists, policymakers, and the public in recent years. Export-orientated soy production in regions of South America is associated with large-scale ecosystem destruction. Since soy is an important source of animal fodder, policymakers are developing schemes to support and enhance sustainable domestic soy cultivation, especially in the EU. Expanded soy cultivation should ideally provide high yields and at the same time promote environmental benefits. For this purpose, we applied a multi-objective optimization algorithm that selects areas with maximum soy suitability, minimum erosion risk, need for low fertilizer input due to water quality issues, and need for diversification of monotonous crop rotations. We use the state of Bavaria in Germany as a case study, modeling full self-sufficiency of soy. The results of the optimization indicate synergies between plantation suitability with need for low fertilization input and crop variation, which implies that the environmental benefit of nitrogen fixation and rotation diversification from soy plants can easily be reconciled with food productivity. However, slight trade-offs occur between erosion risk and the three other objectives, i.e., locations with better soy production might be more prone toward erosion risk. As a potential consequence of expanded soy cultivation in Bavaria, we identified winter wheat, grain maize, potatoes, and sugar beet as those crops that have the highest share of displaced cultivation area. To reduce such land use conflicts and ensure self-sufficiency in relevant crops, we recommend to limit the use of soy as animal feed. Nevertheless, we propose to explicitly incorporate the local need for the environmental benefits of soy cultivation in the planning for soy expansion. In doing so, domestic soy can turn into a real sustainable alternative to imported plant protein

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd