Digital Livestock Farming
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
Ecosystem accounting to support the Common Agricultural Policy
حسابداری اکوسیستم برای حمایت از سیاست های کشاورزی مشترک-2021
The System of Environmental-Economic Accounting - Ecosystem Accounting (SEEA EA) provides an integrated statistical framework which organizes spatially explicit data on environmental quality, natural capital and ecosystem services and links this information to economic activities such as agriculture. In this paper we assess how the SEEA EA can support the monitoring and evaluation of environmental objectives of the Common Agricultural Policy (CAP). We focus on the Netherlands, for which an elaborate set of SEEA EA accounts has been published, and the themes of nitrogen pollution and farmland biodiversity. We studied the completeness of in- dicators included in the accounts, their quality and analysed how the accounts could support agri-environmental reporting, agri-environmental measures effectiveness assessments, and results-based payments to farmers. As a reference we used the Driving forces – Pressures – State – Impacts - Responses (DPSIR) framework. The Dutch SEEA EA accounts only include half of the indicators which we considered essential to assess the effects of farming on natural capital and ecosystem services for the two studied environmental themes. However, most gaps in the accounts could be filled with other publicly available environmental monitoring data. Regarding N pollution, the availability and reliability of indicators at landscape and farm scales are not sufficient to support the assessment of agri-environmental measures effectiveness and results-based payments to decrease N pollution. The accounts have a higher potential to support the assessment of measures to conserve farmland biodiversity, in particular due to high resolution maps of ecosystem extent and ecosystem services flows. The potential of the SEEA EA accounts may be more limited in other countries where ecosystem accounting has only recently started. However, the SEEA EA is also implemented at the European Union scale, so that SEEA EA indicators will gradually become available for all European countries. To enhance the relevance of the SEEA EA in the agri- environmental policy area, we recommend to integrate information on farming emissions (externalities) recor- ded in the SEEA Central Framework with SEEA EA accounts and evaluate the applicability of SEEA EA accounts for case studies at landscape and farm scales. Our research shows that the Dutch SEEA EA accounts, com- plemented with other data sources, have potential to strongly enhance the CAP monitoring and evaluation framework but further steps need to be taken to fill data gaps.
keywords: اقدامات زیست محیطی | کلاه لبه دار | رادیو | پایتخت طبیعی | خدمات محیط زیستی | زمینه های کشاورزی | Agri-environment measures | CAP | SEEA EA | Natural capital | Ecosystem services | Farming externalities
Cultural consensus knowledge of rice farmers for climate risk management in the Philippines
دانش اجماع فرهنگی کشاورزان برنج برای مدیریت ریسک آب و هوایی در فیلیپین-2021
Despite efforts and investments to integrate weather and climate knowledges, often dichotomized into the scientific and the local, a top-down practice of science communication that tends to ignore cultural consensus knowledge still prevails. This paper presents an empirical application of cultural consensus analysis for climate risk management. It uses mixed methods such as focus groups, freelisting, pilesorting, and rapid ethnographic assessment to understand farmers’ knowledge of weather and climate conditions in Barangay Biga, Oriental Mindoro, Philippines. Multi-dimensional scaling and aggregate proximity matrix of items are generated to assess the similarity among the different locally perceived weather and climate conditions. Farmers’ knowledge is then qualitatively compared with the technical classification from the government’s weather bureau. There is cultural agreement among farmers that the weather and climate con- ditions can be generally grouped into wet, dry, and unpredictable weather (Maria Loka). Damaging hazards belong into two subgroups on the opposite ends of the wet and dry scale, that is, tropical cyclone is grouped together with La Ni˜na, rainy season, and flooding season, while farmers perceive no significant difference between El Ni˜no, drought, and dry spells. Ethnographic information reveals that compared to the technocrats’ reductive knowledge, farmers imagine weather and climate conditions (panahon) as an event or a phenomenon they are actively experiencing by observing bioindicators, making sense of the interactions between the sky and the landscape, and the agroecology of pest and diseases, while being subjected to agricultural regulations on irrigation, price volatility, and control of power on subsidies and technologies. This situated local knowledge is also being informed by forecasts and advisories from the weather bureau illustrating a hybrid of technical science, both from the technocrats and the farmers, and personal experiences amidst agricultural precarities. Speaking about the hybridity of knowledge rather than localizing the scientific obliges technocrats and scientists to productively engage with different ways of knowing and the tensions that mediate farmers’ knowledge as a societal experience.
keywords: دانش اجماع | پیش بینی آب و هوا | کشاورزی | خطر ابتلا به آب و هوا | Consensus knowledge | Weather forecasting | Agriculture | Climate risk
Capacity building in participatory approaches for hydro-climatic Disaster Risk Management in the Caribbean
ایجاد ظرفیت در رویکردهای مشارکتی برای مدیریت ریسک بلایای آبی-اقلیمی در کارائیب-2021
The participatory approach to Disaster Risk Management (DRM) considers socio-economic factors and facilitates the incorporation of local and indigenous knowledge into management plans while offering an opportunity to all resource users to have an input. Caribbean WaterNet/Cap-Net UNDP, Global Water Partnership-Caribbean (GWP-C), and the Faculty of Food and Agriculture, The University of the West Indies (FFA, UWI) conducted a series of regional training of trainers’ workshops in Integrated Urban Flood Risk Management and Drought Risk Management to build regional capacity in this approach. The trainings took place over two years in six (6) Caribbean Small Island Developing States (SIDS). Over 150 persons from a range of sectors relevant to water resource management participated and contributed. The workshop gathered information on sectoral impacts, potential mitigation measures and challenges of hydro-climatic hazards. Capacity building and knowledge transfer was evaluated at two stages; at the end of the last day of training and 6 months after, as part of a monitoring and evaluation assessment. Both the initial and 6-month evaluations revealed significant knowledge transfer and subsequent institutional and policy impacts. Initial evaluation indicated 99% participant satisfaction with both training content and structure. In the six-month evaluation, 85% of participants indicated that the knowledge gained was used to improve their work performance and, in some cases, contributed to changes in institutional policy and frameworks.
keywords: کاهش خطر بلایا | خشکسالی و سیل | مشاوره با ذینفعان | کشورهای جزیره ای کوچک در حال توسعه | Disaster risk reduction | Drought and floods | Stakeholder consultations | Small island developing states
Management Transformation with a Single Digital Platform as Exemplified by Accounting
تحول مدیریت با یک پلتفرم دیجیتالی واحد به عنوان نمونه با حسابداری-2021
The paper considers the solution to the problem of accounting method transformation under the influence of the accelerating economy digitalization that requires transition to new methods of working with information: in addition to reflecting the history of business activities, accounting statements should allow making decisions for the future at any time and at any production process stage; that is, it should be re-oriented from the control function to the informative one. This, in turn, requires integration based on digital standards of reporting information with information reflecting other aspects of the business and the external environment through developing new indicators, new methods of collecting and processing data. We have shown that a solution to this problem could be creating a single digital economy management platform consisting of two specialized sub-platforms: an aggregator subplatform for collecting and accumulating primary data and an application sub-platform for production management tasks. In this case, a widespread introduction of a single digital platform in any production process allows to migrate to a new type of manufacturing enterprises: from the quality control phase after the production phase towards the principle of current control over all production operation, which should also affect the entire taxation system. This system will become more efficient by increasing tax collection, reducing the cost of maintaining the financial and accounting service and actively including it in the production management system. In addition, such a digital platform will release a significant number of IT specialists, who are sorely lacking in the country, especially in agriculture, from the financial and accounting service with their reorientation to the introduction of new digital technologies in the form of mathematical models, artificial intelligence, big data, neural networks, which will give an additional impetus to the accelerated digitalization of the economy.
Keywords: accounting | digital standards | information resources | digital platform | management.
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions
انقلاب آرام در بینایی ماشین-مقاله ای پیشرفته مروری، شامل مرور تاریخی ، چشم اندازها و جهت های آینده-2021
Over the past few years, what might not unreasonably be described as a true revolution has taken place in the ﬁeld of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efﬁcacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.© 2021 Elsevier B.V. All rights reserved.
Keywords: Machine vision | Machine learning | Deep learning | State-of-the-art
Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
رویکرد بینایی رایانه ای برای توصیف فنوتیپ های اندازه و شکل محصولات باغی با استفاده از تصاویر با توان بالا-2021
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) areroutinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Keywords: Crop phenotyping | Machine learning | Computer vision
Road freight emission in China: From supply chain perspective
انتشار بار جاده ای در چین: از دیدگاه زنجیره تامین-2021
Freight emissions management has entered the deep-water zone. This study evaluated road freight emissions from supply chain perspective using China’s 2007, 2010 and 2012 multiregional input-output table. For the first time, we quantified road freight emission based on sectors in China. Heavy industries, mining, agriculture and light industry contributed 71%,14%, 12% and 3% of total NOx emissions in 2012 from production perspective. Construction was the largest consumption sector (43%) responsible for road freight emission from consumption perspective. Upstream transport and final product transport emitted 3.04 Tg (80%) and 0.77 Tg (20%) NOx in 2012. Huge disparities of road freight emissions flows and allocation patterns were found across provinces in China in terms of resource endowments, geographical position and economic development. The road freight emission increased rapidly from 2007 to 2012, and economic growth effect outpaced emission control effect caused by emission standard upgrade and thus dominated the emission growth. The production structure and consumption pattern changes also promoted the emission growth. It is thus important to mitigate freight emissions with different strategies based on a certain sector’s freight emissions features from the whole supply chain.
Keywords: Road freight emission | Input-output analysis | Driving force | Supply chain
Hybrid governance and performances of environmental accounting
دولت هیبریدی و اجرای حسابداری محیط زیست-2021
Multiple centers of authority in hybrid forms create conditions of radical openness where questions of value and fitness are in flux. Environmental accounting is suggested as a condition for steadying hybrid forms and opening up possibilities for institutional innovations. This paper advances a critical social science analysis of environ- mental accounting to help specify how, when, and in what ways strengthening accounting capacity advances hybrid governance. Social studies of accounting argue that accounting systems are contingent on institutions: rules and social conventions, not only data or science. Our practice-centered analysis of two cases of building environmental accounting tools to advance high profile institutional innovations in US agri-environmental governance finds that the systems of rules that structure and legitimize accounting protocols are not pre- given. The same radical openness that presents opportunities for hybridity also reinforces uncertainties in building accounting standards. We identify two major frictions: a) Conventions for determining technical consensus and b) Rules for determining levels of transaction costs. We conclude by identifying a need to think about hybrid forms critically. Although hybrid forms are an expression of creativity and collaboration, they are also performances of a certain contemporary political covenant that delegitimizes state-centered governance. The challenge ahead is to understand when and where hybrid arrangements add to socio-ecological regulation and where they undermine the possibility of more functional approaches through a performance of seriousness.
keywords: حکومتداری محیط زیست | کشاورزی | حسابداری | معیارهای | تغییرات اقلیمی | مسئوليت | Environmental governance | Agriculture | Accounting | Metrics | Climate change | Accountability