Ecosystem degradation and the spread of Covid-19.

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

A review on post-COVID-19 impacts and opportunities of agri-food supply chain in Malaysia.

Background: Malaysia is strongly supported by the agriculture sector as the backbone to drive the economy. However, COVID-19 has significantly affected agriculture across the production, supply, and marketing chains. It also disturbs the balance of food supply and demand in Malaysia. COVID-19 was an unexpected pandemic that resulted in shock and panic and caused a huge global impact. However, the impacts of this pandemic on the agriculture sector in Malaysia, particularly in the production and supply chains, are still unclear and scarce. This review offers insights into the challenges, particularly in sustaining agri-food production and supply chains. It also highlights the opportunity and relevant measures towards sustainability in agriculture to avoid agri-food disasters in the future. Methods: This study was carried out through a desk review of the secondary source of information covering the impact of COVID-19 in Malaysia particularly in the agri-food aspect, and a wide range of strategies and initiatives as the effective measures to overcome the crisis of this pandemic. Online desk research of the government published data and customer desk research were utilized to complete this study. Search engines such as Google Scholar and the statistical data from the official websites including the Department of Statistics Malaysia (DOSM) and the Food and Fertilizer Technology Center for the Asian and Pacific Region (FFTC-AP), were utilized. Keywords such as impact of COVID-19, pandemic, and agri-food supply chain were used to conduct the searches. The articles identified to be related to the study's objective were then downloaded and included in the study. Descriptive methods were used as the primary analysis technique following the descriptive analysis and visual data analysis in performing the sources obtained. Results: This devastating impact damages the lives by causing 4.3 million confirmed infections and more than 290,000 deaths. This disease presents an unprecedented challenge to the public health. The lockdown restriction under the movement control order (MCO), for more than of the world's population in the year 2020 to control the virus from spreading, has disrupted most of the economic sectors. The agriculture industry was seen as one of the essential industries and allowed to operate under strict standard operating procedures (SOP). Working under strict regulations came with a huge price paid for almost all industries. Conclusion: This pandemic has affected the national agri-food availability and accessibility in Malaysia. This outbreak created a reflection of opportunity for sharing a more flexible approaches in handling emergencies on agricultural food production and supply chains. Therefore, the government should be ready with the roadmap and enforce the measures to control the pandemic without disrupting the agri-food supply chain in the near future.

Light-Start CRISPR-Cas12a Reaction with Caged crRNA Enables Rapid and Sensitive Nucleic Acid Detection.

The clustered regularly interspaced short palindromic repeats (CRISPR) system is a promising platform for nucleic acid detection. Regulating the CRISPR reaction would be extremely useful to improve the detection efficiency and speed of CRISPR diagnostic applications. Here, we have developed a light-start CRISPR-Cas12a reaction by employing caged CRISPR RNA (crRNA). When combined with recombinase polymerase amplification, a robust photocontrolled one-pot assay is achieved. The photocontrolled one-pot assay is simpler and is 50-fold more sensitive than the conventional assay. This improved detection efficiency also facilitates the development of a faster CRISPR diagnostic method. The detection of clinical samples demonstrated that 10-20 min is sufficient for effective detection, which is much faster than the current gold-standard technique PCR. We expect this advance in CRISPR diagnostics to promote its widespread detection applications in biomedicine, agriculture, and food safety.

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review.

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security.

As the COVID-19 pandemic continues to unfold around the world, the per unit area yield of the world's three major crops (i.e. maize, rice and wheat) decreased simultaneously for the first time in 20 years, and nearly 2.37 billion people faced food insecurity in 2020. Around 119-124 million people were pushed back into extreme poverty. Drought is one of the natural hazards that mostly affect agricultural production, and 2020 is one of the three warmest years on record. When the pandemic, economic recession and extreme climate change occur simultaneously, food crisis will often be exacerbated. Due to the limited research on the geographic modelling of crops and food security at the country level, we investigated the effects of the COVID-19 pandemic (COVID-19 incidence and mortality rate), economic (GDP and per capita GDP), climate (temperature change and drought), and their compound effects on three crop yields and food security in the world. On the basis of verifying the spatial autocorrelation, we used the global ordinary least squares model to select the explanatory variables. Then, geographically weighted regression (GWR) and multi-scale GWR (MGWR), were utilised to explore spatial non-stationary relationships. Results indicated that the MGWR was more efficient than the traditional GWR. On the whole, per capita GDP was the most important explanatory variable for most countries. However, the direct threats of COVID-19, temperature change and drought on crops and food security were small and localised. This study is the first to utilise advanced spatial methods to analyse the impacts of natural and human disasters on agriculture and food security in various countries, which can serve as a geographical guide for the World Food Organization, other relief agencies and policymakers to conduct food aid, health and medical assistance, financial support, climate change policy formulation, and anti-epidemic policy formulation.

External supply risk of agricultural products trade along the Belt and Road under the background of COVID-19.

Agricultural product trade along the Belt and Road (B&R) is an important part of the international food security system, the vulnerabilities of which have been highlighted by the recent COVID-19 pandemic. Based on the complex network analysis, this study analyzes the characteristics of agricultural products trade network along the B&R. It also combines the effects of COVID-19 with the import trade volume of agricultural products in countries along the B&R to build a risk supply model of agricultural products. The results show that: (1) In 2021, the spatial correlation structure of agricultural products trade along the B&R became increasingly sparse, and the network connectivity and density also decreased. (2) The network showed obvious scale-free distribution characteristics and obvious heterogeneity. Five communities emerged under the influence of the core node countries, but the formation of community in 2021 had obvious geopolitical characteristics. (3) Under the influence of the COVID-19 epidemic, the number of countries with medium-risk and high-risk level along the route facing external dependence risk (REDI), import concentration risk (RHHI) and COVID-19 epidemic risk (RRICI) increased in 2021, and the number of countries with extremely low-risk level decreased. (4) The dominant risk type of external supply of agricultural products along the route changed from compound risk type in 2019 to epidemic risk in 2021. Hence, the results can be expected to prevent external risk impact from reducing excessive concentration of agricultural products trade and excessive dependence on the external market.

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Experiences with Pandemic Food Access Among Clinic-Based Community Supported Agriculture Program Participants.

OBJECTIVES: The COVID-19 pandemic intensified food insecurity (FI) across the country, and families with children were disproportionately affected. This study explores experiences with FI and social resources during the pandemic among families participating in a free, clinic-based community supported agriculture (CSA) program. METHODS: Free weekly boxes of organic produce from local farms were distributed to pediatric caregivers for 12 weeks at two pediatric outpatient centers associated with a children's hospital in a low-income, urban area. Demographics and a two-question FI screen were collected. Caregivers were purposively selected to participate in semi-structured interviews about experiences with FI and community or federal nutrition programs during the pandemic. Interviews were recorded and transcribed. Content analysis with constant comparison was used to code interviews inductively and identify emerging themes. RESULTS: The 31 interviewees were predominantly female; more than half were Black, FI, and SNAP beneficiaries. Study participants were more likely to have repeat participation in the CSA program. Interviews elucidated four major themes of barriers to food access during the pandemic: (1) fluctuations in price, availability, and quality of food; (2) financial strain; (3) faster consumption with all family members home; (4) shopping challenges: infection fears, store closures, childcare. SNAP, WIC, and school meal programs were generally facilitators to food access. Increased SNAP allotments were particularly useful, and delays of mailed WIC benefits were challenging. CONCLUSIONS FOR PRACTICE: This qualitative study describes facilitators and barriers to food access among clinic-based CSA program participants during the pandemic. The findings highlight areas for further exploration and potential policy intervention.

Rural Farmers' Cognition and Climate Change Adaptation Impact on Cash Crop Productivity: Evidence from a Recent Study.

The world faces a once-in-a-century transformation due to the COVID-19 pandemic, adversely affecting farmers' employment, production practices, and livelihood resilience. Meanwhile, climate change (CC) is a crucial issue limiting agricultural production worldwide. Farmers' lives, severely affected by extreme weather conditions, are resulting in the reduced production of major economic crops. The CC has drastically influenced the major agricultural sectors of Pakistan, leading to a significant decline in farmers' living standards and the overall economy. Climate-smart and eco-friendly agricultural practices can mitigate greenhouse gas emissions and ameliorate agricultural productivity under extreme environmental conditions. This paper highlights farmers' autonomous CC adaptation strategies and their influence on cash crop (maize for this study) yield under prevailing circumstances. The current study used a simultaneous equation model to examine the different adaptation impacts on adapters and non-adapters. The survey results of 498 maize farmers in rural Pakistan revealed that growers were aware of the recent CC and had taken adequate adaptive measures to acclimatize to CC. Farmers' arable land area, awareness level, and information accessibility to CC are the most crucial factors that impart a significant role in their adaptation judgments. However, most growers have inadequate adaptation strategies, including improved irrigation and the utilization of extensive fertilizers and pesticides. Using a simultaneous equation model of endogenous switching regression, the study found that farmers not adapted to CC were negatively affecting maize productivity. Therefore, this study suggests that policymakers pay attention to the countermeasures farmers have not taken to mitigate the impact of CC. In addition, policymakers should deliver appropriate adaptation strategies to assist growers in coping with climate-related natural hazards and ensure farmers' livelihood security, rural revitalization, and sustainable agricultural development.

Urban Agriculture as a Means to Food Sovereignty? A Case Study of Baltimore City Residents.

A large body of research suggests that neighborhood disparities in food access persist. Emerging evidence suggests that the global COVID-19 pandemic likely exacerbated disparities in food access. Given the potential role that alternative food networks (AFNs) and local food sources may play during times of extreme scarcity, this study examines urban agriculture (e.g., community farms and gardens) as a sustainable strategy to address food insecurity. In-depth qualitative interviews with fifteen community stakeholders revealed several major themes including food insecurity as a feature of systemic racism, food affordability and distance to food as major barriers to food security, and the role of AFNs in creating community empowerment. Our findings indicate that urban agricultural practices help build social capital, inform and educate community members about healthy eating behaviors, and facilitate the distribution of affordable food. Implications for future research and policy targeting sustainable food distribution in marginalized communities of color are discussed.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China.

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

Does Pandemic Fatigue Prevent Farmers' Participation in the Rural Tourism Industry: A Comparative Study between Two Chinese Villages.

Rural tourism is an important income generation method for farmers post-pandemic. However, few studies have focused on how pandemic fatigue has affected their willingness to participate in rural tourism development. We conducted a quasi-experiment to test these effects using data from two Chinese villages. Shanlian village, which was more severely affected by COVID-19, was the experimental group, while Huashu village was set as the control group. Our results reveal that both physical and mental fatigue hinder farmers' intention to engage in rural tourism. Further, there were significant interaction effects between physical and mental fatigue on the farmers' participation in rural tourism. For farmers with low physical fatigue, the higher their mental fatigue, the less willing they were to participate in rural development. Conversely, for the higher physical fatigue group, farmers with low levels of mental fatigue were still more willing to participate in rural tourism development. These findings reduce the current research gap concerning the relationship between pandemic fatigue and farmers' participation in rural tourism and indicate that practitioners and policymakers should consider farmers' fatigue management as an important factor for the sustainability of rural tourism during the ongoing COVID-19 crisis.

Effect of Farmers' Awareness of Climate Change on Their Willingness to Adopt Low-Carbon Production: Based on the TAM-SOR Model.

The COVID-19 pandemic highlighted the intricate relationships between human health and the social-ecological system in an era of climate and global change. Widespread COVID-19 adversely affected farmers' employment, production practices, and livelihood resilience. At the same time, climate change is a key issue limiting agricultural production worldwide. Emissions of greenhouse gases, such as carbon dioxide, are a major factor leading to global climate change. Greenhouse gas emissions from agricultural production are receiving increasing attention. Therefore, it is particularly important to develop low-carbon agriculture. Based on data from 920 family farms in Jiangsu province and Shaanxi province, this study constructs a structural equation model and empirically tests the relationship between the variables using the bootstrap method. The results show that: (1) climate change awareness did not directly stimulate farmers' willingness to pursue low-carbon production; (2) climate change awareness has an impact on low-carbon production willingness through perceived ease of use and consequence awareness; and (3) anti-risk ability can effectively moderate the impact of climate change awareness on low-carbon production behavior in agriculture. The theoretical model framework proposed in this study provides a reference for research in the field of low-carbon agriculture and also provides some insights and suggestions for environmentalists and governments. In addition, policymakers should effectively raise the sense of responsibility of farmers to address climate change and promote low-carbon agricultural production to achieve healthy and sustainable agricultural development.

Analyzing the barriers for aquaponics adoption using integrated BWM and fuzzy DEMATEL approach in Indian context.

Aquaponic system in greenhouses which can recycle and reuse the water and nutrients is gaining importance across the world to counter the uncertainties due to weather fluctuations. However, there is a slow pace of growth in aquaculture practices around the globe in general and India in particular. There are many barriers to adopt the aquaponic culture. In this study an analysis of the barriers for aquaponics culture in Indian context during the COVID-19 period is presented. Literature review and interactions with various stakeholders help to find out the list of potential factors while gauging the success of their prospective aquaponics project. The "best-worst" methodology (BWM) is employed for ranking of barriers, whereas categorizing of barriers is carried out with the help of fuzzy DEMATEL. Furthermore, the results of this research work are of great value to corporations or start-up companies looking to invest in this technology as well as to farmers who wish to adopt this farming technique.

Fighting Pandemics With Augmented Reality and Smart Sensing-Based Social Distancing.

In a postpandemic world, remaining vigilant and maintaining social distancing are still crucial so societies can contain the virus and the public can avoid disproportionate health impacts. Augmented reality (AR) can visually assist users in understanding the distances in social distancing. However, integrating external sensing and analysis is required for social distancing beyond the users' local environment. We present DistAR, an android-based application for social distancing leveraging AR and smart sensing using on-device analysis of optical images and environment crowdedness from smart campus data. Our prototype is one of the first efforts to combine AR and smart sensing technologies to create a real-time social distancing application.

Agricultural biotechnology for sustainable food security.

Of late, global food security has been under threat by the coronavirus disease 2019 (COVID-19) pandemic and the recent military conflict in Eastern Europe. This article presents the objectives of the Sustainable Development Goals and the European Green Deal related to achieving food security and sustainable development in European Union (EU) agriculture, taking the aforementioned threats into account. In addition, it discusses the future of plant agricultural biotechnology and artificial intelligence (AI) systems, considering their potential for reaching the goal of food security. Paradoxically, the present challenging situation may allow politicians and stakeholders of the EU to realize opportunities and use the potential of the biotechnology sector.

Evaluating the association between in-person work and the risk of SARS-CoV-2 infection through June 2021.

OBJECTIVES: Recent studies have evaluated COVID-19 outbreaks and excess mortality by occupation sectors. Studies on SARS-CoV-2 infection across occupation and occupation-related factors remain lacking. In this study, we estimate the effect of in-person work on SARS-CoV-2 infection risk and describe SARS-CoV-2 seroprevalence among working adults. METHODS: We used Wave 1 data (May to June 2021) from CalScope, a population-based seroprevalence study in California. Occupation data were coded using the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System. Dried blood spot specimens were tested for antibodies to establish evidence of prior infection. We estimated the causal effect of in-person work on SARS-CoV-2 infection risk using the g-formula and describe SARS-CoV-2 seroprevalence across occupation-related factors. RESULTS: Among 4335 working adults, 53% worked in person. In-person work was associated with increased risk of prior SARS-CoV-2 infection (risk difference: 0.03; [95% CI: 0.02-0.04]) compared with working remotely. Workers that reported job loss or who were without medical insurance had higher evidence of prior infection. Amongst in-person workers, evidence of prior infection was highest within farming, fishing, and forestry (55%; [95% CI: 26%-81%]); installation, maintenance, and repair (23%; [12%-39%]); building and grounds cleaning and maintenance (23%; [13%-36%]); food preparation and serving related (22% [13%-35%]); and healthcare support (22%; [13%-34%]) occupations. Workers who identified as Latino, reported a household income of <$25K, or who were without a bachelor's degree also had higher evidence of prior infection. CONCLUSIONS: SARS-CoV-2 infection risk varies by occupation. Future vaccination strategies may consider prioritizing in-person workers.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.