Abstract

In an era of global environmental challenges, understanding the dynamics of food production is crucial, particularly in regions prone to food insecurity and susceptible to climatic variations. Despite extensive research on agriculture in sub-Saharan Africa (SSA), a thorough examination of the combined effects of various determinants, including food prices and institutional quality, on food security remains limited. Using panel data from 2002 to 2020, this study explores the effects of agricultural land, population growth, environmental degradation, income per capita, food prices, capital formation, and institutional quality on food security in 32 SSA countries. Based on the Pedroni and Kao cointegration test outcomes, a long-run correlation between food security and its influencing factors is evident. The findings from the pooled mean group (PMG) models reveal that extended agricultural land leads to enhanced food security both in the short- and long-run. Likewise, population expansion, rising per capita income, and capital formation drive higher food demand, contributing positively to food security outcomes. Conversely, environmental degradation poses a significant threat, impairing food security in the SSA. Mixed results are observed with food prices, where higher prices can both enhance and reduce food security. The poor institutional quality in SSA correlates with food insecurity. Importantly, the Dumitrescu–Hurlin causality test results reveal bidirectional causality between food security and most variables, except for food inflation and institutional quality. The method of moments quantile regression (MMQR) strengthens the robustness of the study findings. Building on these insights, the study recommends focusing on sustainable land use practices, effective environmental management strategies, increased agricultural investment, governance reforms, and implementing balanced pricing mechanisms.

Introduction

Food security remains a prominent global concern, as asserted in Sustainable Development Goal 2 of the 2030 Agenda. The goal of ending hunger, food insecurity, and undernutrition has been the focus of recent studies [ 1 , 2 ]. Ensuring access to food and its availability is vital for enhancing human development and potential, as it plays a critical role in strengthening human capabilities [ 3 ]. Numerous interrelated variables, including population expansion, climatic issues, conflicts, and land degradation, have threatened global food security [ 4 , 5 ]. Since the beginning of the industrial age, greenhouse gas (GHG) emissions have increased, resulting in greater radiative force that affects the atmosphere, leading to the warming of the earth’s surface and climatic changes [ 6 ]. Consequently, elevated air temperatures, heavy rainfall, and prolonged droughts affected water availability and agricultural yield, ultimately leading to a reduction in food availability and compromising food security [ 7 ]. Environmental shifts greatly affect the lives of rural residents, particularly in sub-Saharan Africa (SSA), who rely predominantly on agriculture for sustenance [ 8 ]. Hence, recognizing and adopting sustainable development strategies is essential to ensuring food security and self-reliance within the agricultural sector while also meeting the growing need for food in the face of land degradation [ 9 ].

Evaluating food security is crucial for aid, famine risk monitoring, nutrition assessment, and policy shaping, requiring global attention from professionals, policymakers, and researchers [ 10 ]. As FAO et al. [ 11 ] highlight, food security is a binary state—a person can be either food-secure or insecure. Food security has continuously acquired prominence and economic relevance since the 1974 World Food Conference, which focused heavily on issues related to hunger, famine, and the food crisis [ 12 , 13 ]. Although the description of food security has developed over time, it continues to denote a condition where all individuals consistently possess physical, social, and economic access to sufficient, nutritionally adequate food that meets their dietary preferences and requirements, enabling them to maintain active and healthy lifestyles. The four main pillars of this comprehensive definition are utilization, affordability, stability, and availability [ 14 , 15 ]. The declining production of major food crops due to environmental degradation has food security implications in low-income countries [ 2 ]. Besides, Pérez-Escamilla [ 16 ] points out that inadequate food has harmful consequences for physical, emotional, and cognitive well-being, disrupting social and environmental balance with far-reaching implications. Additionally, Burchi and De Muro [ 17 ] suggested that inadequate education, health, and essential life skills can contribute to food insecurity.

Conclusion

In the context of mounting global environmental trials, it becomes imperative to comprehend the complexities of food security dynamics, especially in areas grappling with food shortages, vulnerable to climatic fluctuations, and undergoing rapid demographic transformations. Although there is a substantial body of research addressing agriculture in sub-Saharan Africa, a comprehensive analysis of the collective impact of diverse factors, such as food pricing dynamics and the quality of institutional frameworks, on food security is still notably scarce. Therefore, this study utilizes panel data from 2002 to 2020 to examine the effects of agricultural land, population growth, environmental degradation, food prices, gross capital formation, and institutional quality on food security in 32 SSA countries. The study identified cross-sectional dependence to apply panel cointegration methods such as PMG and MG and refuted the null hypothesis that the slope coefficients are homogeneous. Regarding this, we employed second-generation unit root tests, specifically CADF and CIPS, to ascertain the integration order of the variables, revealing a mixed stationarity of I (0) and I (1). Furthermore, the long-run cointegration relationship between the scrutinized variables and food security was established through Pedroni and Kao cointegration tests. The MMQR analysis confirmed the robustness of the long-run findings obtained through the PMG method. Additionally, the study utilized the Dumitrescu-Hurlin test to ascertain the causality pathways among these variables.

Abstract

Food security is pivotal for national sustainable development. This study utilizes panel data from 31 Chinese provinces spanning from 1990 to 2021 to construct distinct indicator systems for urban–rural integration and food security. The entropy method is employed to assess levels of urban–rural integration and food security, with their inter-relationship examined through a fixed-effects model. Additionally, this study conducts rigorous robustness and endogeneity tests, alongside comprehensive heterogeneity analyses across various dimensions and regions. The findings underscore the significant role of urban–rural integration in enhancing food security, particularly within spatial, social, and economic dimensions, albeit encountering challenges in ecological integration. Moreover, the impact of urban–rural integration on food security manifests differently across diverse food-producing regions, exhibiting notable advantages in primary production and distribution hubs while being negligible in balanced regions. These results accentuate the critical necessity for refining urban–rural integration strategies to bolster food security. Consequently, in formulating agricultural regional policies, careful consideration of resource allocation and the developmental stages of each region is imperative to ensure food security and promote sustainable agricultural practices.

Introduction

Food security is fundamentally linked to human well-being, acting as a crucial safeguard for national economic development and playing a direct role in ensuring peace and stability [1]. As we look to the future, the imperative of maintaining food security becomes a global priority, essential for achieving various Millennium Development Goals [2,3]. According to the “2018 World Urbanization Prospects” report, by the mid-21st century, approximately two-thirds (68%) of the world’s population will reside in urban areas, with a projected increase of 2.5 billion in the global urban population by 2050, nearly 90% of which will occur in the developing regions of Asia and Africa [4]. This urban concentration, along with ongoing expansion, poses significant challenges, including a shrinking rural workforce, diminishing arable land, environmental degradation, and compromised food security. The rapid pace of urbanization challenges the development of sustainable production and existing consumption models, necessitating targeted initiatives to manage the increased food consumption of urban populations; enhance urban dietary patterns in response to shifts in food demand, primarily through the efficient reallocation of agricultural resources; and maintain essential interactions between urban and rural areas in developing countries.

The realm of food security, marked by inherent complexity and dynamism, is influenced by a confluence of factors, including global pandemics, geopolitical strife, and climatic volatility, which have introduced challenges on an unprecedented scale [5,6]. In China, significant advances have been achieved in food production, supply chains, and agricultural economic development. Notably, China currently supports 22% of the global populace whilst possessing only 7% of the planet’s arable land [7]. For fourteen consecutive years, China’s per capita food availability has exceeded the globally recognized safety threshold of 400 kg per year, marking a crucial milestone in achieving grain self-sufficiency and the absolute security of staple foods. Despite these advances, a substantial demographic base continues to drive significant food demand, further intensified through the processes of urbanization and rapid economic growth, thereby increasing food requirements across both urban and rural sectors. Enhancements in urban–rural dynamics have facilitated resource distribution; however, disparities in resource allocation continue to pose risks to food security. The promotion of urban–rural integration strategies aims to optimize resource allocation efficiency, thereby reinforcing food security paradigms. The advances in agricultural technology have significantly increased food production levels; however, challenges such as land scarcity, workforce attrition, and an aging demographic persist [8,9]. Ensuring a stable and sufficient food supply remains critical [10]. Addressing the basic nutritional needs of the population will underpin food security. The historical data from the years 1949 to 2021 demonstrate the effective maintenance of per capita food consumption for urban and rural residents, with the food supply–demand ratio rising from 1.05 in 1949 to 3.68. As shown in Figure 1, as the trajectory of per capita food production approaches its peak, showing only a marginal decline, and with the diversification of the food supply leading to a reduction in per capita food demand, China is compelled to recalibrate its food security strategy to address the emerging challenges and shifts in paradigms.

Results

4.1. Analysis of Urban–Rural Integration and Food Security Measurement Results
4.1.1. Analysis of Food Security Measurement Results
Figure 3 illustrates the levels of food security across China’s 31 provinces (excluding Hong Kong, Macau, and Taiwan) from 1990 to 2021, highlighting a consistent enhancement in food security across the provinces. Key grain-producing regions such as Heilongjiang, Henan, and Shandong remain pivotal in ensuring food security despite approaching saturation in their capacity. Leveraging their abundant resources, these primary production areas have efficiently supplied food to major consumption centers, fostering a synergy of regional strengths. Concurrently with economic progress, the focal point of food security is gradually shifting northward, with Xinjiang, Shanxi, and Qinghai emerging as critical hubs for food reserve capabilities, demonstrating significant potential. Although primary consumption areas display low self-sufficiency, their economic influence and robust infrastructure support reliance on primary production regions and trade to ensure food security. Advancements in transportation, technology, and digitalization are steadily enhancing food supply capabilities, underscoring the importance of maintaining regional food security equilibrium as a fundamental aspect of revising the national food security strategy.

Abstract

1. Background

2. Research Methodology

3. Results

4. Discussion

5. Conclusion

Abbreviations

Data Availability

Authors’ Contributions

Acknowledgments

References

Abstract

     Introduction. Analyzing the effect of COVID-19 is an important issue in agricultural sectors. However, such analysis requires a complex hierarchical statistical model. Rapid spread of the COVID-19 pandemic has disrupted the world’s production and productivity in many sectors. Among those sectors, the agricultural sector is highly affected. The Bale zone in the larger extent and Sinana district, in particular, is one of the potential agricultural areas in the Oromia regional state, Ethiopia where agriculture is the major sector in supporting the livelihood of thousands of subsistence farmers in the area as well as the country at large. Research Methodology. This study involved primary data collected from the farmers in the Sinana district during the period 2020–2021. A total of 991 farmers were selected from the entire 22 kebeles in the district. The data were analyzed using multilevel binary logistic random intercept regression models with maximum-likelihood parameter estimation. Results. Of the 991 farmers, 549 (55.4%) responded that COVID-19 has brought only challenges in their agricultural production and 311 (31.4%) responded both challenges and opportunities. About 632 (63.8%) of the farmers said that there was wastage of products such as milk, dairy, fruits, and vegetables. Three hundred twenty-eight (33.1%) of the participants obtained modernization in their agricultural production system like use of tractors and irrigation systems. According to the model results, farmer’s sex, age, educational level, family size, farmland size, types of effect, aggravation in food insecurity, input delay, lack of workers, slowdown of service, falling in income, modernization in the system of production, wastage of product, and types of wasted products were identified as significant factors. About 8% of the total variability in the effect of COVID-19 is due to differences across kebeles (ICC = 0.08,  value ≤0.05), and the remaining is due to individual differences. Conclusion. This study further demonstrated the potential of a hierarchical model for the study of COVID-19 effect variation within and between the kebeles. The majority, about 92% variation in the effect, is due to the disparity of individuals (farmers). The farmers with large family sizes and high capacity to produce and who were females were negatively related to the effect of COVID-19 in agricultural production.

Background

     The rapid spread of COVID-19 pandemic has disrupted the world’s production and productivity in many sectors and, as a consequence, slowed down the world’s economic growth to a larger extent. The agricultural sector is among the highly affected especially in the Third-World countries. Even during prepandemic times, agricultural production was subjected to high risk, as compared to other sectors, because of its dependence on environmental conditions, which are largely unpredictable nowadays. The pandemic halted the rate of economic growth in many countries during 2019 and is expected to result in severe recession in the upcoming years, especially in the Third-World countries where agriculture is a key sector [1]. bib3

     Ethiopia is among the highly affected countries and faced a dramatic decrease in agricultural production and productivity, hence a minimum economic growth (GDP) as compared to the last ten years. The pandemic and related partial social restrictions have posed a negative impact on the smallholder farmers such as market loss and cause significant income losses in specific sectors such as livestock and horticulture [2].

Conclusion

     The main purpose of this study was to estimate the challenges and opportunities of COVID-19 on agricultural production among farmers in Sinana district, Bale zone, Ethiopia. The study revealed a huge negative impact on the agricultural production and productivity of the farmers and the country at large. Of the 991 farmers, 549 said COVID-19 had only challenges in their agricultural production, and 311 had both challenges and opportunities. About 632 (63.8%) of the farmers said that there was wastage of products such as milk, dairy, fruits, and vegetables. Three hundred twenty-eight (33.1%) of the participants obtained modernization in their agricultural production system like use of tractors and irrigation systems. This study further demonstrated the potential of a multilevel random intercept model for the study of COVID-19 effect variation within and between the kebeles. The majority, about 92% variation of the effect, is due to the disparity of individuals (farmers). Farmers with a large family size and high capacity to produce, and whose sex is female were negatively related to the effect of COVID-19 in agricultural production. The odds of farmers who did not face both challenges and opportunities due to the pandemic was 1.308 (adjusted OR = 1.308, 95% CI = (−1.232, 1.769)) times more likely than those farmers who did face only challenges. This study also contributes to the literature by statistically examining challenges and opportunities for agricultural producers in Sinana district in reflection to COVID-19 and its countermeasures based on the variation within individuals and between kebeles that have been explained, which can provide more targeted implications for policymakers.

Abstract

Keywords

1. Introduction

2. Conceptual background

3. Methods

4. Results

5. Discussion

6. Conclusion

Declaration of Competing Interest

Acknowledgement

Appendix.

References

Abstract

Many companies have innovated their business models in their attempts to transition towards a circular economy. However, the label ‘circular’ does not necessarily mean better for the environment. How do companies measure the environmental performance of their business models? And as they alter them for a circular economy, how do they forecast the potential environmental impacts? These questions are important to better understand the impacts of circular business models. This study sets out to answer these questions through 29 semi-structured interviews and 39 survey responses, with business developers, managers, product designers and consultants from more than 10 industries. The results reveal that while most participants measure the impact of their current business models, they do not forecast the future impacts of their circular business ideas before implementation. The most popular measurement method was rules of thumb, followed by life-cycle assessment (LCA) or LCA-based tools. A lack of data, increased uncertainty during experimentation and a lack of knowledge are the common barriers that keep the participants from measuring environmental impacts.

1. Introduction

The need for a more circular economy is increasingly being recognised by both governments and the private sector (European Commission, 2020; Government of Canada, 2019; Government of the Netherlands, 2016; Lewandowski, 2016). Businesses are trying to become more sustainable by trialling circular business models (CBMs) that aim at longer product lifetimes and production of less waste (Bocken and Antikainen, 2018; Geissdoerfer et al., 2020). Companies often do this through business model experimentation (Bocken and Snihur, 2020), which tends to follow the – ‘build, measure, learn’ – Lean Startup approach (Blank, 2013; Ries, 2011). This is an iterative approach, where different business strategies are repeatedly trialled to find the best product-market fit (Chesbrough, 2010). In the circular economy context, business experimentation also focuses on addressing pressing sustainability issues, in particular to narrow, slow, close, and regenerate resource loops (Bocken et al., 2016a,b; Konietzko et al., 2020a).

Abstract

Context

     Crop-livestock integration is a theoretical ideal for sustainable agriculture. However, the number of European crop-livestock farms has decreased due to multiple factors (e.g. agricultural policies and work constraints). Crop-livestock integration beyond the farm level (e.g. through grain-manure exchanges) is a relevant option to address these limiting factors. However, this integration within farmer groups is challenging because it requires collective redesign to address organizational issues.

Objective

     We developed a participatory approach that includes the serious game Dynamix to support the co-design of scenarios of crop-livestock integration among farms.

Methods

     The approach consists of six steps: (1) initial group meeting to define the problem; (2) farmer interviews to identify motivations and collect technical and economic farm data; (3) scenario co-design meeting using the serious game Dynamix, including a spatially explicit board game and a simulation model; (4) multicriteria evaluation of these scenarios at the individual farm and group levels using the simulation model included in Dynamix; (5) group meeting to discuss the results; and (6) monitoring of selected scenario implementation. We applied this methodology with two groups of farmers in southwestern France.

Results and conclusions

     In the two groups, crop farmers wanted to diversify their cropping systems and use manure to improve soil quality. Livestock farmers were interested in local and non-GMO feed for their animals. The scenario they selected included i) inserting cereal-legume mixtures into crop rotations on crop farms to be sold to livestock farms and ii) transferring manure from livestock farms to crop farms. In this scenario, the predicted overall gross margin increased more for livestock farmers (median = 29.90 €/ha) than for crop farmers (median = 6.60 €/ha). Nitrogen balance management was improved: crop farmers decreased their use of mineral fertilizer by 2.8–17.4 kg/ha/year; livestock farmers decreased their feed inputs improving local feed self-sufficiency. However, farmers’ workload and management complexity increased, with 22–54 h of additional work per farmer per year. Compared to other scenarios, trade-offs between individual farm and group benefits resulted in greater local autonomy in inputs but lower autonomy in decision-making. In the two groups, discussions improved trust, which is a key ingredient for transitioning to integration beyond the farm level.

Significance

     Our study is the first to use a standardized participatory approach based on a serious game to support the complex issue of crop-livestock integration beyond the farm level. Applying the approach to a case-study revealed its strong potential. It can easily be scaled-out to other agricultural contexts.

Introduction

     Farms that include crops and livestock are widely perceived as an ideal option to maintain agricultural production levels while limiting environmental impacts on soil and biodiversity (Franzluebbers et al., 2014; Hendrickson et al., 2008; Lemaire et al., 2014). However, globalized markets associated with policy incentives contributed to their decrease in number (Garrett et al., 2017), especially in Europe, due to limited availability of workforce and loss of skills (Ryschawy et al., 2013). Integration beyond the farm level could thus be a relevant alternative to address these limiting factors (Martin et al., 2016). It consists of reconnecting neighboring specialized farms through exchanges of grain, fodder, crop by-products and manure, or even by sharing land and other resources (e.g. workers, equipment).

     Only a few crop-livestock integration initiatives beyond the farm level have been documented (de Wit et al., 2006; Regan et al., 2017; Ryschawy et al., 2019). Asai et al. (2018) compared worldwide case studies and identified a variety of barriers that restrict implementation of crop-livestock integration among farms. Operational barriers related to the availability of on-farm storage capacity and transportation, distance and legal aspects related to contracts and billing. Social barriers related to establishing trust and shared goals, and to the complexity of governance. Frequent meetings and communication among participants and a third party were deemed necessary to develop and maintain effective mediation (Cofr´e-Bravo et al., 2019). Thus, organizing highly coordinated groups of crop farmers and livestock farmers remains a challenge due to the high cost of coordination (Asai et al., 2018).

Conclusion

     This study was the first to use a standardized participatory approach based on a serious game to support crop-livestock integration beyond the farm level, including implementation. The case-study application showed its potential for addressing this complex issue. The method can be easily scaled-out to other contexts and is already planned to be adapted to other cases of crop-livestock integration beyond farm level, including sheep-viticulture systems in France and California, cattle grazing cover crops in Scottland and biogas production in Denmark. Further developments will be needed to include permanent crops and biogas plants in the Dynamix serious game.

Abstract

The sense of belonging to a community, or social cohesion, is established in food spaces enabled by three separate and unique mechanisms: proximity, frequency, and interactions. This study looks specifically at the Fulton Street Farmers Market in Grand Rapids and the effects the COVID-19 pandemic had on the social benefits derived from the mechanisms of social cohesion, in an attempt to identify and discover new ways to leverage social cohesion in food spaces. Three populations were identified at the farmers market: customers, vendors, and market staff. Surveys and informal interviews provided evidence that proximity to and within the market, higher frequencies of attendance, and greater instances of interactions indicate a trend towards stronger feelings of belonging and connectedness (social cohesion) at the farmers market. Data on COVID-19 shows the market became a space where social cohesion was possible and may have even increased in part due to higher attendance, supported by higher food assistance transactions.

Abstract

     A farm scale life cycle assessment (LCA) was conducted to estimate environmental indicators of organic dairy systems in the U.S. and evaluate alternative management practices and methodological decisions. Fourteen farm layouts (including Amish and grass intensive) are evaluated over four U.S. regions. Carbon (C) sequestration from pasture and cropping systems is estimated based on C added to the soil and the crop and grassland management practices. Greenhouse gas (GHG) emissions range from 0.76 to 1.08 kg CO2-eq/kg fat and protein corrected milk (FPCM) after C sequestration. Methane (CH4) from enteric fermentation and liquid-slurry manure storage are major sources of GHGs, with the first related to cow feed to milk conversion efficiency. The production and consumption of fossil energy contribute to GHGs depending on the mix of fuels of regional electricity production. NH3 emissions range from 7.7 to 20.0 g/kg FPCM with differences between regions explained by environmental factors, management practices, and dairy diet composition. Eutrophication potential ranges from 3.4 to 6.6 g PO4/kg FPCM from phosphorus and nitrogen losses after manure application from on-farm and imported feeds. Electricity and diesel are the major contributors to fossil energy depletion (2.1 to3.7 MJ/kg FPCM), with the embedded energy from imported feeds also contributing significantly.

Introduction

     Agricultural systems are challenged to balance feeding a growing population while addressing environmental concerns and providing a high quality of life for farmers and rural communities. Agricultural emissions to air and water contribute to climate change, ecosystem deterioration, and human health issues (Poore and Nemecek, 2018). In the U.S., agriculture is responsible for 10% of total greenhouse gas (GHG) emissions, with soil management, enteric fermentation, and manure accounting for 55%, 28% and 10%, respectively (U.S. EPA, 2021a). Up to 70% of the excreted nitrogen (N) in manure can be emitted as ammonia (NH3) in livestock operations (Hristov et al., 2002). These losses represent 80–90% of the global anthropogenic NH3 emissions (Xu et al., 2019) that can redeposit and lead to impaired waterways (U.S. EPA, 2004), or further transform to particulate matter or N2O. Nutrient losses from manure have the potential to reach ground and surface water contributing to eutrophication.

Conclusions

     GHG emissions (including C sequestration), NH3 emissions, resource depletion (energy, land, and water use), and EP at organic dairy farms in the U.S. were evaluated. Despite that milk production levels are generally lower than conventional systems, environmental impacts are comparable. Carbon sequestration is largely absent from LCA studies given its complexity but can be important for dairies that rely on pasture and forages. This study presents a procedure that can be implemented in other dairy or agricultural related LCA studies and promote discussion around this topic. Enteric CH4 remains the most prominent source of GHGs from cradle-to-farm gate, but results show that farms can still improve feeding efficiencies. Impacts from energy consumption are directly related to the regional electricity mix, with the implementation of renewables having the greatest impact on Amish farms that use diesel generators. As NH3 emissions continue to be an important concern for animal agriculture, baseline estimations will help establish mitigation targets and strategies. Manure management remains an important source of environmental impacts, especially during storage and after land application. Water use is significantly higher on farms that use irrigation and import feed. Alternative management practices can reduce GHGs and can guide on-farm implementation. Variations in results by different methodological decisions show these choices remain an unresolved topic in LCA worthy of discussion.

Abstract

CONTEXT

     Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task.

OBJECTIVE

     Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring.

METHODS

     It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms.

RESULTS AND CONCLUSION

     The result showed that 33 variables measured by various sensors were demonstrated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled environment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation.

SIGNIFICANCE

     Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems.

Introduction

     Crops are essential for human life because they provide food, animal feed, fuel, and raw materials for clothing and shelter. Crop yield has to be doubled in 2050 compared to 2009 in order to meet the demand of a growing population while increasing the food quality and reducing production inputs (Fukase and Martin, 2020). Potential solutions to enhance global food security include closing crop yield gaps, reducing food waste, changing dietary habits, and reducing inefficiencies in resource use (Foley et al., 2011). Reducing inefficiencies in input resources (such as water and nitrogen) can be achieved by continuously monitoring crops, soil, and microclimate, and then properly controlling inputs without sacrificing the yield and quality of the crop. Internet of Things (IoT) becomes a key technology that enables continuous monitoring and control in this scenario. The ability to generate (near) real-time quantitative data with high spatiotemporal resolution is a major advantage of IoT systems (Liao et al., 2017). IoTs are considered big data systems due to volumes, velocities, and varieties of data they generate. These data are mined and modeled to elucidate the relationships between inputs and outputs (Tsai et al., 2014). Correlation, trend analysis, classification, and numerical prediction are implemented on the data to reach meaningful control decisions. Compared with the conventional wireless sensor networks, the holistic approach of IoT technology allows users to incorporate data analytics on the big data collected by IoT sensor devices. Generally, connected actuators are enabled to control the inputs to achieve desired application rates. For example, an internet-connected soil water content (SWC) sensor network measures the plant water deficit and uploads data to a cloud-based data analysis platform. The analysis will find the trend of soil water deficit to determine the best time and quantity to apply irrigation water.

Conclusions and future directions

Conclusions

     Ag-IoT is a promising technology that would increase resource use efficiency in agricultural systems, and is an essential tool for digital agriculture transformation. In this paper, we have overviewed impactful research related to Ag-IoT in the past decade. The data collected from these papers were categorized and analyzed under six main Ag-IoT system design parameters namely sensors, sensing platforms and main control board, communication technology and IoT protocols, cloud platforms, power and energy management, and actuators. According to the analyzed data, it is revealed that there is an increased global attention towards the Ag-IoT system-related research in the recent years. However, there are certain research gaps found in the literature. One of them is that the implementations of the sensors and the actuators seem to be limited to soil and environmental parameter monitoring and irrigation controlling. Furthermore, crop macro and micronutrient demand analyses are still at the infant stage due to the non-availability of sensors that can measure nutrients in real-time. Therefore, it is essential to improve the sensor and actuator applications in crop monitoring and controlling. In addition, heterogeneity of the system parameters (such as data, platforms, required power) is a major challenge to the Ag-IoT systems implementation, to which the improvement of the context-awareness could be a solution. Power harness options for Ag-IoT nodes need more exploration as there are limited options available and it would be a big advantage for the perennial crop monitoring. The perception and the network layers of Ag-IoT systems require more improvements to meet the sensor implementation ergonomics and long-range high-throughput data transmission, respectively. Edge computing can be a replacement of the high throughput long-range communication, but to the best of our knowledge, only a limited number of practical applications have been developed based on edge computing to date. Mobile Ag-IoT platforms such as unmanned aerial and ground vehicles have a huge potential to increase the spatiotemporal resolution in Ag-IoT-based monitoring and controlling.

Abstract

1. Introduction

2. Material and methods

3. Results and discussion

4. Conclusion

Author contribution statement

Acknowledgements

References

Abstract

     The use of digital technologies in agriculture offers various benefits, such as site-specific application, better monitoring, and physical relief. The handling of these technologies requires a specific skill set. Therefore, the question arises of when and how farm managers learn about digital technologies. Aiming to analyse the current situation, the present research investigated the role that digital technologies play in vocational training for future farm managers. Taking the example of farm management information systems (FMIS), the present study also analysed various predictors of adoption, including the effect of training. To investigate these research questions, an online survey among teachers and students of the farm management vocational programme across Switzerland was conducted in the spring of 2021. In total, 150 individuals participated, 41 of whom were teachers. Participants answered questions about the learning content in the farm management programme and their perception of digital technologies in general. Students further reported whether they already had a farm they would be managing in the future and how they perceived FMIS. The results indicate that both teachers and students are convinced that digital technologies play an important role in agriculture and will gain more importance in the future. A substantial part of 43% of the students who participated indicated that they had learned neither about digital technologies during their basic agricultural training nor the subsequent farm management programme. In terms of FMIS, 51% of the student sample indicated that they had never heard about FMIS during their agricultural training.

Introduction
Digital technologies and FMIS in agriculture

     The use of digital technologies in agriculture can facilitate the recording of data, support the sustainable use of resources, and make everyday work easier for farmers (Groher, Heitkamper, et al., 2020a, b). Digital technologies include a wide range of applications such as robots (e.g. for milking or hoeing), GPS applications (e.g. driver assistance or precision farming), sensors (e.g. measuring ammonia levels in barns or soil moisture), and smart phone applications.

     One digital technology that helps farmers collect, manage, and interpret their data is the farm management information system (FMIS). In essence, FMIS are farm record systems, which provide information that supports decision making related to the farm business (Lewis, 1998). They can further assist inexperienced farmers in their decision making by compensating for a relative lack of farming experience (Lewis, 1998). FMIS are a heterogeneous group of systems that cover a range of different functions. Fountas et al. (2015) identified in their review four clusters of FMIS: (1) basic systems, (2) sales-oriented systems, (3) site-specific systems, and (4) complete systems. Basic systems offer a limited set of functions that mainly support traditional farm management. Sales-oriented systems focus on sales and marketing, inventory management, and finance functions. The third group, site-specific systems, focuses on precision agriculture. The final group of complete systems offers a wide range of functions, some of which are also covered by the other three clusters. In this group, the percentage of web-based and mobile functions is the highest (Fountas et al., 2015).

Conclusion

     This work is among the first to investigate the role that digital technologies and FMIS play in agricultural training in Switzerland and what the predictors are for their adoption. The current study finds that only about 60% of the students of the farm management programme learned about digital technologies, and around 50% learned about FMIS during their agricultural training. Whether students had learned about FMIS in their agricultural training, however, had no significant effect on their adoption of FMIS. In line with previous research, which investigated the adoption of other technologies as measured with the TTMA, the current research also identified gender, perceived ease of use, and intention to use more digital technologies in the future as important predictors for the adoption of FMIS. These findings are relevant for both research and practice. For researchers, a better understanding of the predictors of the adoption of FMIS can help build prognoses for their future development. Further, an analysis of the situation in Switzerland complements the numerous studies conducted in Germany and helps reflect the situation in Europe. In terms of practical relevance, this knowledge can be useful to adapt the educational and advisory offers to fit the needs and skill levels of future farm managers. Policymakers can use the information to develop measures supporting the improvement of teaching materials and learning content.

Abstract

     Recent advances in communication technologies with the emergence of connected objects have changed the agricultural area. In this new digital age, the development of artificial intelligence, particularly deep learning, has allowed for acceleration and improvement in the processing of collected data. To highlight the evolution and advances observed in deep learning in agriculture, we conducted a bibliometric study on more than 400 recent research studies. The analyses carried out on recent research works suggest that deep learning is widely involved in the digitization of agriculture areas with high accuracy exceeding the standard image processing techniques. Most of the works focus on crop classification problems, weed, and pest identification. Their methods are mainly based on convolutional neural network architecture. From the cases study, we have identified three key challenges that are essential in the deep learning methods applied in agriculture: (i) the need to consider the perception of the domain actors, their appropriation or interaction with the existing tools; (ii) the requirement to perform statistical tests to analyze the performance of the classifiers resulting from the learning process; and (iii) the need to perform statistical cross-validations with the training data. In the end, we summarized the agricultural data processing process consisting of several parts, for a better consideration of the expectations resulting from the challenges addressed. We consider that this study can serve as a guideline of research for the scientist and practician in the application of deep learning methodology in agriculture.

Introduction

     Agriculture is the practice of growing food. Given the increase in population, this sector must meet many requests for food while considering societal (such as labor), environmental (water scarcity, loss of biodiversity, land degradation, etc.), and economic issues. The constraints of its development have become numerous given the seasonal variability and extreme climate. It is more necessary than ever to find innovative practices for the development of the agricultural sector.

     Digital integration has significantly changed farmers’ knowledge of field management with innovative technologies like intelligent computing, robotics, drones, or sensors onboard farm machinery. Using these technologies is encouraging data scientists and agronomists to design analytical tools and techniques to accurately organize field management and address the new challenges at hand (fungal attack detection, crop yield prediction, advanced spraying, etc.). These novel practices require farmers’ technical assistance to support their needs and help them maximize their crop yields based on data and task automation.

Conclusion

     Precision agriculture is a recent science that is certainly still in its young phase. Deep learning can make a decisive contribution to the analysis of agricultural data, in this case by using computer vision to enable a machine to automatically analyze and understand the visual world. This offers the opportunity to develop intelligent systems, which appear to be one of the many possible ways to tackle the economic, environmental, and social challenges in agriculture. This literature review shows a richness of work in recent years that describe the development of various systems using deep learning to solve various problems in precision agriculture. The problems addressed are mainly yielded losses caused by factors affecting crop growth. In bibliometric methodology, we explored only documents from the Web of Science database. We analyzed the thematic structures and new directions of agricultural research based on deep neural networks were proposed.

     Bibliometric analyses show that the most active countries are China, the United States, India, and Brazil. The hot topics covered include plant disease, feature extraction, transfer learning, disease detection, weed detection, insect pests, convolutional neural networks, image processing, remote sensing, and IoT. Convolutional neural networks (CNNs) are one of the most used architectures thanks to their successful application in deep learning. However, the use of deep learning algorithms in precision agriculture raises many challenges whose resolution is useful to enhance this field and facilitate its appropriation by the stakeholders: