| 1 | No tillage outperforms conventional tillage under arid conditions and following fertilization显示文摘Reduced tillage practices present a tool that could sustainably intensify agriculture.The existing literature,however,lacks a consensus on how and when reduced tillage practices should get implemented.We reanalyzed here an extensive dataset comparing how regular tillage practices(i.e.,conventional tillage)impacted yield of eight crops compared to stopping tillage altogether(i.e.,no-tillage practice).We observed that aridity and fertilization favored no tillage over conventional tillage whereas conventional tillage performed better under high fertility settings.We further show that the responses are consistent across the crops.Our reanalysis complements the original and fills a gap in the literature questioning the conditions under which reducing tillage presents a viable alternative to common tillage practices. | Stavros D.Veresoglou Junjiang Chen Xuheng Du Qi Fu QingLiu Geng Chenyan Huang Xilin Huang Nan Hu Yiming Hun Guolin C.Li Zhiman Lin Zhiyu Ma Yuyi Ou Shuo Qi Haitian Qin Yingbo Qiu Xibin Sun Ye Tao YiLing Tian Jie Wang Lingxiao Wu Ziwei Wu Siqi Xie Ao Yang Dan Yang Chen Zeng Ying Zeng RuJie Zhang | 2023 | Soil Ecology Letters2023，5，1： | 1 |
| 2 | Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer显示文摘To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data. | Zhongyao Du Xiaoying Chen Hao Wang Xuheng Wang Yu Deng Liying Sun | 2022 | Energy Engineering2022，119，4： | 0 |
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