A Roadmap for Lowering Crop Nitrogen Requirement.

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

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

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

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales

Establishing the precision and robustness of farmers’ crop experiments

Precision farming technologies such as global positioning, input placement technologies and on-the-go yield monitoring now provide farmers with the means to conduct their own experiments at scales relevant to their decisions with minimal disruption. However, these experiments are generally incompatible with conventional statistical methods and alternative models of response variables (e.g. yield) must be estimated if the effect of the management decision is to be distinguished from other sources of variation. We explore the precision and robustness of such experiments using four sources of data and experimental designs of different degrees of complexity. We see that there is a trade-off between the precision of the experiment and its complexity and hence implementation cost. In yield experiments with small-grain cereals, standard errors of treatment effects in yield of less than 0.05 t/ha can potentially be achieved when the treatment is varied along the field traffic row and standard errors of less than 0.1 t/ha can potentially be achieved when single treatments are applied in each row but the experiment includes multiple disconnected repetitions of each treatment. Simpler split-field designs are less robust since it can be difficult to distinguish treatment effects from independent spatial trends and discontinuities in the response variable. In some instances, the potential precision is not realised because the data include noise or artefacts that are unrelated to crop performance. Further yield sensor developments are required to minimise these occurrences. The model-based statistical analyses of these experiments require assumptions regarding the variation of the response variable. We see that when these assumptions are inappropriate (e.g. if the correlation between response variable measurements is poorly modelled) then the inferences from the experiments can be unreliable. In particular, we see that the spatial correlation amongst yield measurements tends to be greater along the farm traffic row than perpendicular to it. Standard isotropic models of spatial correlation do not accommodate this feature and led to substantial under-estimation of the standard errors

Cereal yield gaps across Europe

peer-reviewedEurope accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha−1 for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets.We received financial contributions from the strategic investment funds (IPOP) of Wageningen University & Research, Bill & Melinda Gates Foundation, MACSUR under EU FACCE-JPI which was funded through several national contributions, and TempAg (http://tempag.net/)

What are the priority research questions for digital agriculture?

There is a need to identify key existing and emerging issues relevant to digitalisation in agricultural production that would benefit from a stronger evidence base and help steer policy formulation. To address this, a prioritisation exercise was undertaken to identify priority research questions concerning digital agriculture in the UK, but with a view to also informing international contexts. The prioritisation exercise uses an established and effective participatory methodology for capturing and ordering a wide range of views. The method involves identifying a large number of participants and eliciting an initial long list of research questions which is reduced and refined in subsequent voting stages to select the top priorities by theme. Participants were selected using purposive sampling and snowballing to represent a number of sectors, organisations, companies and disciplines across the UK. They were each invited to submit up to 10 questions according to certain criteria, and this resulted in 195 questions from a range of 40 participants (largely from England with some representation from Scotland and Wales). Preliminary analysis and clustering of these questions through iterative analysis identified seven themes as follows: data governance; data management; enabling use of data and technologies; understanding benefits and uptake of data and technologies; optimising data and technologies for performance; impacts of digital agriculture; and new collaborative arrangements. Subsequent stages of voting, using an online ranking exercise and a participant workshop for in-depth discussion, refined the questions to a total of 27 priority research questions categorised into 15 gold, 7 silver and 5 bronze, across the 7 themes. The questions significantly enrich and extend previous clustering and agenda setting using literature sources, and provide a range of new perspectives. The analysis highlights the interconnectedness of themes and questions, and proposes two nexus for future research: the different dimensions of value, and the social and institutional arrangements to support digitalisation in agriculture. These emphasise the importance of interdisciplinarity and transdisciplinarity, and the need to tackle the binary nature of current analytical frames. These new insights are equally relevant to contexts outside the UK. This paper highlights the need for research actions to inform policy, not only instrumentally by strengthening the evidence base, but also conceptually, to prompt new thinking. To our knowledge this methodology has not been previously applied to this topic

Investigating heterosis for yield, breadmaking quality and nitrogen use efficiency in wheat

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Theory Generation for Security Protocols

generation, RV This thesis introduces theory generation, a new general-purpose technique for performing automated verification. Theory generation draws inspiration from, and complements, both automated theorem proving and symbolic model checking, the two approaches that currently dominate mechanical reasoning. At the core of this approach is the notion of producing a finite representation of a theory—all the facts derivable from a set of assumptions. An algorithm is presented for producing compact theory representations for an expressive class of simple logics. Security-sensitive protocols are widely used today, and the growing popularity of electronic commerce is leading to increasing reliance on them. Though simple in structure, these protocols are notoriously difficult to design properly. Since specifications of these protocols typically involve a small number of principals, keys, nonces, and messages, and since many properties of interest can be expressed in “little logics ” such as the Burrows-Abadi-Needham (BAN) logic of authentication