South Dakota Farmland Leasing 2003

Nearly 40% of South Dakota agricultural land is operated under a leasing agreement. Presented in this report are recent and longer term trends in land tenure, ownership, and leasing, based on Census of Agriculture data and related materials. Also presented are findings from the 1996 farmland leasing survey completed by 513 South Dakota farm operators: (1) characteristics of rental market participants and of the farmland leasing market, (2) detailed provisions of cash leases and share leases for cropland, and (3) economic evaluation of farmland leasing arrangements. Information from the 1996 survey provides a comprehensive and statistically valid benchmark of agricultural land leasing in South Dakota, with primary emphasis on cropland leasing arrangements. This is the most comprehensive statewide study of South Dakota farmland rental markets since 1986. In many respects this publication updates and extends findings from the 1986 study reported in B 704, Farmland leasing in South Dakota, published by the South Dakota Agricultural Experiment Station (Peterson and Janssen, 1988). This report should be of particular interest to renters and landlords, loan officers, realtors and appraisers, agricultural researchers, and others interested in farmland rental market developments.Agricultural Land,farmland, Cropland, Land Ownership, Land leasing, Land Use, Landownership, Tenancy

Study on Technology Innovation and Human Resources Reallocation

Economic globalization promotes rapid development of scientific technology. The emergence of technological innovation makes that optimal allocation of human resources has been on the agenda gradually.Relying on technological innovation, enhancing the production efficiency of the enterprises constantly, implementing cost-effective or differentiation are the key of devepment.Human resources in the process of devepment plays an important role in technology innovation.Technology innovation guides the enterprises to implement human resources reallocation for reducing the cost of human resources and strengthening the flow of human resource and the usage efficiency.Human resources as the most basic element of enterprise, is the subject of technological innovation.From the points of technical innovation and human resources,this paper analyzes the relationship between them,reveals the principle of the interaction effects, discusses the strategy of human resource reallocation under the technological innovation

Video and Image Super-Resolution via Deep Learning with Attention Mechanism

Image demosaicing, image super-resolution and video super-resolution are three important tasks in color imaging pipeline. Demosaicing deals with the recovery of missing color information and generation of full-resolution color images from so-called Color filter Array (CFA) such as Bayer pattern. Image super-resolution aims at increasing the spatial resolution and enhance important structures (e.g., edges and textures) in super-resolved images. Both spatial and temporal dependency are important to the task of video super-resolution, which has received increasingly more attention in recent years. Traditional solutions to these three low-level vision tasks lack generalization capability especially for real-world data. Recently, deep learning methods have achieved great success in vision problems including image demosaicing and image/video super-resolution. Conceptually similar to adaptation in model-based approaches, attention has received increasing more usage in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism which includes channel attention, spatial attention, non-local attention, etc. has found successful applications in both highlevel and low-level vision tasks. However, to the best of our knowledge, 1) most approaches independently studied super-resolution and demosaicing; little is known about the potential benefit of formulating a joint demosaicing and super-resolution (JDSR) problem; 2) attention mechanism has not been studied for spectral channels of color images in the open literature; 3) current approaches for video super-resolution implement deformable convolution based frame alignment methods and naive spatial attention mechanism. How to exploit attention mechanism in spectral and temporal domains sets up the stage for the research in this dissertation. In this dissertation, we conduct a systematic study about those two issues and make the following contributions: 1) we propose a spatial color attention network (SCAN) designed to jointly exploit the spatial and spectral dependency within color images for single image super-resolution (SISR) problem. We present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. Experimental results have shown that SCAN has achieved superior performance in terms of both subjective and objective qualities on the NTIRE2019 dataset; 2) we propose two competing end-to-end joint optimization solutions to the JDSR problem: Densely-Connected Squeeze-and-Excitation Residual Network (DSERN) vs. Residual-Dense Squeeze-and-Excitation Network (RDSEN). Experimental results have shown that an enhanced design RDSEN can significantly improve both subjective and objective performance over DSERN; 3) we propose a novel deep learning based framework, Deformable Kernel Spatial Attention Network (DKSAN) to super-resolve videos with a scale factor as large as 16 (the extreme SR situation). Thanks to newly designed Deformable Kernel Convolution Alignment (DKC Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can get both better subjective and objective results when compared with the existing state-of-the-art approach enhanced deformable convolutional network (EDVR)