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Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world. [ABSTRACT FROM PUBLISHER]

Sustainable intensification is a process by which agricultural productivity is enhanced whilst also creating environmental and social benefits. We aimed to identify practices likely to deliver sustainable intensification, currently available for UK farms but not yet widely adopted. We compiled a list of 18 farm management practices with the greatest potential to deliver sustainable intensification in the UK, following a well‐developed stepwise methodology for identifying priority solutions, using a group decision‐making technique with key agricultural experts. The list of priority management practices can provide the focal point of efforts to achieve sustainable intensification of agriculture, as the UK develops post‐Brexit agricultural policy, and pursues the second Sustainable Development Goal, which aims to end hunger and promote sustainable agriculture. The practices largely reflect a technological, production‐focused view of sustainable intensification, including for example, precision farming and animal health diagnostics, with less emphasis on the social and environmental aspects of sustainability. However, they do reflect an integrated approach to farming, covering many different aspects, from business organization and planning, to soil and crop management, to landscape and nature conservation. For a subset of 10 of the priority practices, we gathered data on the level of existing uptake in English and Welsh farms through a stratified survey in seven focal regions. We find substantial existing uptake of most of the priority practices, indicating that UK farming is an innovative sector. The data identify two specific practices for which uptake is relatively low, but which some UK farmers find appealing and would consider adopting. These practices are: prediction of pest and disease outbreaks, especially for livestock farms; staff training on environmental issues, especially on arable farms. We identify 18 practices with the greatest potential to deliver "sustainable intensification" in UK agriculture. The practices reflect an integrated approach to sustainable intensification, with a strong focus on technology. Data on uptake of 10 of the practices, from a survey of 243 English and Welsh farms, show that many are more widely adopted than expected, reflecting an innovative UK farming industry. We recommend two practices—predicting pests and diseases in livestock farms, and environmental training for arable farm staff—become the focus of efforts to enable sustainable intensification, as these are appealing to farmers, but not so widely practiced already. [ABSTRACT FROM AUTHOR]

Increasing nitrogen fertilizer applications have sustained a growing world population in the 20th century. However, to avoid any further associated environmental damage, new sustainable agronomic practices together with new cultivars must be developed. To date the concept of nitrogen use efficiency (NUE) has been useful in quantifying the processes of nitrogen uptake and utilization, but we propose a shift in focus to consider nitrogen responsiveness as a more appropriate trait to select varieties with lower nitrogen requirements. We provide a roadmap to integrate the regulation of nitrogen uptake and assimilation into varietal selection and crop breeding programs. The overall goal is to reduce nitrogen inputs by farmers growing crops in contrasting cropping systems around the world, while sustaining yields and reducing greenhouse gas (GHG) emissions. Current practice for managing nitrogen (N) use for cereal production are not environmentally sustainable. Overuse of N fertilizers is a global problem for millions of farmers who must decide on N applications whether, when and how much. A combination of improved advice on N management for specific cropping regimes is required, together with a breeding target of new commercial crop varieties with sustainable yields and a low N requirement. While N use efficiency (NUE) has been a useful concept for quantifying the genetic differences in N uptake and utilization, the concept of an economic N optimum derived from N yield dose–response curves may provide new insights for lowering the N requirement. [ABSTRACT FROM AUTHOR]