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Sustainably feeding the next generation is often described as one of the most pressing \"grand challenges\" facing the 21st century. Generally, scholars propose addressing this problem by increasing agricultural production, investing in technology to boost yields, changing diets, or reducing food waste. In this paper, we explore whether global food production is nutritionally balanced by comparing the diet that nutritionists recommend versus global agricultural production statistics. Results show that the global agricultural system currently overproduces grains, fats, and sugars while production of fruits and vegetables and protein is not sufficient to meet the nutritional needs of the current population. Correcting this imbalance could reduce the amount of arable land used by agriculture by 51 million ha globally but would increase total land used for agriculture by 407 million ha and increase greenhouse gas emissions. For a growing population, our calculations suggest that the only way to eat a nutritionally balanced diet, save land and reduce greenhouse gas emissions is to consume and produce more fruits and vegetables as well as transition to diets higher in plant-based protein. Such a move will help protect habitats and help meet the Sustainable Development Goals.

Growing conditions for crops such as coffee and wine grapes are shifting to track climate change. Research on these crop responses has focused principally on impacts to food production impacts, but evidence is emerging that they may have serious environmental consequences as well. Recent research has documented potential environmental impacts of shifting cropping patterns, including impacts on water, wildlife, pollinator interaction, carbon storage and nature conservation, on national to global scales. Multiple crops will be moving in response to shifting climatic suitability, and the cumulative environmental effects of these multi-crop shifts at global scales is not known. Here we model for the first time multiple major global commodity crop suitability changes due to climate change, to estimate the impacts of new crop suitability on water, biodiversity and carbon storage. Areas that become newly suitable for one or more crops are Climate-driven Agricultural Frontiers. These frontiers cover an area equivalent to over 30% of the current agricultural land on the planet and have major potential impacts on biodiversity in tropical mountains, on water resources downstream and on carbon storage in high latitude lands. Frontier soils contain up to 177 Gt of C, which might be subject to release, which is the equivalent of over a century of current United States CO2 emissions. Watersheds serving over 1.8 billion people would be impacted by the cultivation of the climate-driven frontiers. Frontiers intersect 19 global biodiversity hotspots and the habitat of 20% of all global restricted range birds. Sound planning and management of climate-driven agricultural frontiers can therefore help reduce globally significant impacts on people, ecosystems and the climate system.

This paper builds on national- and regional-level vulnerability assessments by developing and applying a livelihood vulnerability index at the community and household scales to explore the nature of climate vulnerability. It provides innovative methodological steps in relation to livelihood assessment to identify the vulnerability of households and communities to drought. This will help to improve drought vulnerability assessments in Ghana and more widely as it shows extra information can be obtained from local-level vulnerability assessment that may be lacking in national- and regional-level analysis. The research employs quantitative and qualitative data collected through participatory methods, key informant interviews and a questionnaire survey with 270 households across 6 communities in two regions in Ghana. Results show that within the same agroecological zone, households and communities experience different degrees of climate vulnerability. These differences can be largely explained by socioeconomic characteristics such as wealth and gender, as well as access to capital assets. Results identify vulnerable households within resilient communities as well as more resilient households within vulnerable communities. These outliers are studied in detail. It is found that outlier households in vulnerable communities have an array of alternative livelihood options and tend to be socially well connected, enabling them to take advantage of opportunities associated with environmental and economic changes. To sustain and enhance the livelihoods of vulnerable households and communities, policymakers need to identify and facilitate appropriate interventions that foster asset building, improve institutional capacity as well as build social capital.

Smart farming aims to improve farming using modern technologies and smart devices. Smart devices help farmers to collect and analyze data regarding different aspects of their business. These data are utilized by various stakeholders, including farmers, technology providers, supply chain investigators, and agricultural service providers. These data sources can be considered big data due to their volume, velocity, and variety. The wide use of data collection and communication technologies has increased concerns about the privacy of farmers and their data. Although some previous studies have reviewed the security aspects of smart farming, the privacy challenges and solutions are not sufficiently explored in the literature. In this paper, we present a holistic review of big data privacy in smart farming. The paper utilizes a data lifecycle schema and describes privacy concerns and requirements in smart farming in each of the phases of this data lifecycle. Moreover, it provides a comprehensive review of the existing solutions and the state-of-the-art technologies that can enhance data privacy in smart farming.

To increase donations of nutritious food, Ontario introduced a tax credit for farmers who donate agricultural products to food banks in 2013. This research seeks to investigate the role of Ontario’s Food Donation Tax Credit for Farmers in addressing both food loss and waste (FLW) and food insecurity through a case study of fresh produce rescue in Windsor-Essex, Ontario. This research also documents the challenges associated with rescuing fresh produce from farms, as well as alternatives to donating. Interviews with food banks, producers and key informants revealed that perceptions of the tax credit, and the credit’s ability to address FLW and food insecurity, contrasted greatly with the initial perceptions of the policymakers who created the tax credit. In particular, the legislators did not anticipate the logistical challenges associated with incentivizing this type of donation, nor the limitations of a donation-based intervention to provide food insecure Ontarians with access to fresh, nutritious food.

Agriculture stands on the cusp of a digital revolution, and the same technologies that created the Internet and are transforming medicine are now being applied in our farms and on our fields. Overall, this digital agricultural revolution is being driven by the low cost of collecting data on everything from soil conditions to animal health and crop development along with weather station data and data collected by drones and satellites. The promise of these technologies is more food, produced on less land, with fewer inputs and a smaller environmental footprint. At present, however, barriers to realizing this potential include a lack of ability to aggregate and interpret data in such a way that it results in useful decision support tools for farmers and the need to train farmers in how to use new tools. This article reviews the state of the literature on the promise and barriers to realizing the potential for Big Data to revolutionize agriculture.

This paper builds on existing theory and proposes a framework to identify vulnerability to climate change in food systems by examining historic cases where common environmental problems caused famine. Cases presented are (1) Ireland's Potato Famine, (2) El Niño induced famines during the Colonial period, and (3) Ethiopia between 1965 and 1997. Three factors stand out as common in each. Prior to each famine: (1) there were very few ways that people could obtain a living in the worst affected regions; (2) livelihoods in famine stricken communities came to depend on highly specialized agro-ecosystems that were sensitive to environmental change; (3) institutions failed to provide adequate safety nets to protect livelihoods from failure. This analysis suggests that vulnerability to climate change in food systems can be assessed by looking at agro-ecosystems, livelihoods and institutions. Local conditions, however, mean that ways of measuring these three factors will vary from place to place. As a result, direct comparisons are difficult. By conceptualizing these three variables as the axes of a three dimensional “vulnerability” space, it is possible to compare regions and look at trends over time by studying the paths through this “space” as traced by changes at the agro-ecosystem, livelihood, and institutional scale.

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched.