Agricultural Food Security Research Articles

Factors influencing women farmers’ participation in agricultural cooperatives in Ondo-State, Nigeria

Cooperative societies provide channels for sharing of input where women farmers can pool their limited resources together to maximize agricultural output and food security in rural areas. This study, therefore, analyzed factors influencing women farmers participation in agricultural cooperatives in Ondo State, Nigeria. The specific objectives of the research were to describe socio-economic characteristics of women farmer’s participation in agricultural cooperative societies, determine the level of participation of women farmers in agricultural cooperatives and examine factors that motivate participation. Data were analyzed using descriptive statistics, Likert type scale, and Pearson Product Moment Correlation analysis. The results revealed that women farmers in the study area were average aged (42.9 years) and mostly possessed tertiary education (38.3%), mean annual income (₦777,846) with farm size (0.8 hectares). The level of participation in agricultural cooperative is high (60.4%). Accessibility to loan (M. S= 3.50), getting information (M. S= 3.42) and input facility (M. S= 3.40) were top most factors that influenced participation in cooperatives among the women. At P< 0.05, Age, marital status and income realized per annum were positively significant to the level of participation in cooperatives. The study recommends the timely provision of loan to enable women farmers expand the scope of their farming activities, increased access to training programs for women farmer leaders in cooperatives as well as extension education should also be intensified.

Climate smart agriculture practices by crop farmers: Evidence from south east Nigeria

Climate change has severely impacted African countries, particularly Nigeria, affecting farmers' productivity due to adverse weather conditions. Climate Smart Agriculture (CSA) has emerged as a crucial approach to ensure sustainable agricultural growth and food security. This study aims to identify the sources of awareness, prevalent innovative agricultural practices, determinants of CSA adoption, and the intensity of CSA practices among crop farmers in Southeast Nigeria. A two-stage multi-random sampling technique was employed to collect data from 270 farmers across various locations in Southeast Nigeria through questionnaires. The analysis used descriptive statistics and Heckman's two-stage selection model to evaluate the sources of awareness, the extent of CSA practice, and factors influencing CSA adoption and its intensity. Results indicate that cooperative societies and extension workers are the primary sources of CSA awareness in the region. Key determinants of CSA adoption identified in the study include gender, household size, farming experience, education level, labor force size, income, extension exposure, credit access, risk orientation, and cooperative membership. The Heckman model revealed that cooperative membership significantly increases the probability of CSA adoption, while risk-averse farmers are less likely to adopt CSA practices. The study concludes that while CSA practices are recognized and adopted to some extent, their implementation is insufficient to comprehensively combat the effects of climate change. Enhanced efforts to promote and integrate CSA practices more widely are critically needed. To improve CSA adoption and effectiveness, it is recommended to provide increased support for cooperative societies and extension services, better access to credit, and targeted education and training programs for farmers. Additionally, policies should focus on reducing risk aversion among farmers by providing insurance schemes and other risk management tools.

Assessing the Impact of Climate Change on Global Crop Yields and Farming Practices

Climate change is recognized as one of the most pressing challenges of our time, with profound implications for global food security. This review article delves into the multifaceted impacts of climate change on crop yields and farming practices on a global scale. By synthesizing recent research findings, identifying key challenges and opportunities, and exploring adaptation strategies, this review provides a comprehensive analysis of the complex interplay between climate change and agriculture. The effects of climate change on agriculture are diverse and far reaching. Alterations in weather patterns, including shifts in temperature, precipitation, and frequency of extreme events, pose significant challenges to agricultural productivity and food security. These changes disrupt traditional farming practices, exacerbate water stress, soil erosion, and crop loss, and threaten the livelihoods of millions of farmers worldwide. Additionally, changes in pest and disease dynamics have further compounded the challenges faced by agricultural systems under pressure from climate variability. These challenges, there are opportunities for innovation and adaptation in the agricultural systems. This review highlights the potential of climate-smart agricultural practices such as crop diversification, conservation agriculture, and precision farming to enhance resilience and mitigate the impacts of climate change on crop yields. investments in climate-resilient crop varieties, improved water management techniques, and sustainable land management practices offer pathways for building adaptive capacities and ensuring food security in a changing climate. addressing the complex challenges of climate change adaptation in agriculture requires concerted action at multiple levels. This review emphasizes the importance of coordinated efforts among policymakers, researchers, farmers, and other stakeholders in developing and implementing effective adaptation strategies. Such strategies should be context-specific, socially equitable, and environmentally sustainable, considering the diverse needs and priorities of different regions and communities. This review underscores the urgent need for coordinated action to build resilient and sustainable food systems in the face of a changing climate. By synthesizing the latest research findings, identifying key challenges and opportunities, and exploring adaptation strategies, this review aims to inform policymakers, researchers, and practitioners about the imperative of addressing climate change’s impacts on agriculture. Only through collaborative and transformative action can we ensure food security for the present and future generations in a rapidly changing world.

Policy analysis for informing climate adaptation, environmental resilience, and irrigation demands in the Rio Grande Basin

Water policymakers internationally face the challenge of adapting to climate, supporting environmental resource needs, and meeting irrigation demands for food security in the world's arid and semi-arid regions. Much recent work has assessed the economic performance of environmental river flows to support endangered species habitat protection. However, little published work to date has systematically formulated and applied methods to identify the economic performance of various policy measures that adapt to climate, support endangered species, and meet irrigation demands for water. This work's original contribution with international relevance is to address those gaps by identifying the economic performance of various water shortage sharing methods for handling climate water stress while supporting protection of critical habitat to protect the endangered Southwestern willow flycatcher (Empidonax traillii extimus) along with Willow tree (Salix bonplandiana) habitat in the middle part of the Rio Grande Basin in North America, a region facing a number of conflicts between commercial uses of water and protection of key ecological assets. It develops a hydroeconomic optimization model containing information on crop water use and endangered species requirements in that region to identify the economic performance of three climate adaptation policy scenarios for handling water shortages while respecting endangered species habitat protection requirements. Results show how water shortages as well as policy responses for handing those shortages affect the economic value of water in agriculture for food security both with and without critical habitat to support the endangered flycatcher. This work's international relevance comes from its capacity to inform policy debates on the costs of protecting endangered species habitat under various climate scenarios and climate policy adaptation measures. Findings provide a general framework to address existing gaps in understanding and measuring the economic performance of measures to promote environmental resilience.

Research on Coupling Coordination of Agricultural Carbon Emission Efficiency and Food Security in Hebei Province, China

The delineation and measurement of carbon emissions in agricultural production systems constitute a complex issue involving multiple factors. Previous research in this area has been limited in terms of comprehensive carbon emission assessment throughout the agricultural production process and systematic measurement. This study focuses on both dynamic and static aspects, systematically analyzing the agricultural carbon emissions and emission efficiency in Hebei Province from 2000 to 2020. It comprehensively explores the influencing factors of carbon emissions and delves into the relationship between agricultural carbon emission efficiency and food security. The experimental results revealed the following: (1) From 2000 to 2020, the agricultural carbon emissions in Hebei Province exhibited a fluctuating downward trend, with a spatial distribution pattern where they were high in the south and low in the north. And the carbon emissions caused by chemical fertilizers and plowed land accounted for 42.6% of the total. (2) The efficiency of agricultural carbon emissions in the static dimension fluctuated at a rate of 0.0265, whereas the ML index fluctuated less in the dynamic dimension, and the agricultural industrial structure had the most significant impact. (3) The coupling coordination degree of food security and agricultural carbon emission efficiency increases with time, and “coordination” gradually dominates in spatial change. The conclusions of this study are of great significance in stabilizing grain production and achieving low-carbon production in the Hebei Province.

Agricultural Innovation and Food Security in Sub-Saharan Africa in Nigeria

Purpose: The aim of the study was to analyze the agricultural innovation and food security in Sub-Saharan Africa in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In Nigeria, agricultural innovation is vital for improving food security. Research suggests that adopting innovative farming methods and technologies can boost productivity, resilience to climate change, and livelihoods for smallholder farmers. Overcoming challenges like limited access to finance and inadequate infrastructure is essential to fully harnessing the benefits of agricultural innovation for food security in Nigeria. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, social capital theory & political economy theory may be used to anchor future studies on agricultural innovation and food security in Sub-Saharan Africa in Nigeria. Promote farmer-centered innovation platforms - establishing can facilitate knowledge sharing, collaboration, and co-creation of agricultural innovations. Adequate funding for research institutions, agricultural universities, and extension agencies is essential to conduct applied research, develop appropriate technologies, and provide technical assistance to farmers.

Unravelling the effects of nano SiO2, nano TiO2 and their nanocomposites on Zea mays L. growth and soil health

Amidst the challenges posed by climate change, exploring advanced technologies like nanotechnology is crucial for enhancing agricultural productivity and food security. Consequently, this study investigated the impact of nano SiO2 (nSiO2), nano TiO2 (nTiO2) and SiO2/TiO2 nanocomposites (NCs) on 30-day-old Zea mays L. plants and soil health at concentrations of 100 and 200 ppm. Results showed that nSiO2 and nTiO2 at 100 ppm and SiO2/TiO2 NCs at both concentrations, positively influenced plant growth, with the best stimulation observed at 200 ppm of SiO2/TiO2 NCs. Improved plant growth was associated with higher chlorophyll content, photosynthetic rate, transpiration rate, stomatal conductance, rhizospheric N-fixing and phosphate solubilizing bacterial population and plant nutrient uptake. Additionally, treated plants exhibited increased cellulose and starch levels. Malondialdehyde (MDA) content was lower or similar to that of the control, except at 200 ppm of nTiO2-treated shoots. Antioxidant enzyme activities fluctuated, indicating physiological adjustments. Overall, 100 ppm of nTiO2 as well as nSiO2 and 100 and 200 ppm of SiO2/TiO2 NCs improved soil fertility and Z. mays growth, suggesting potential benefits for sustainable agriculture. The findings lay the foundation for more comprehensive investigations into the long-term fate of nanomaterials in soil and their intricate molecular-level interactions with Z. mays.

How does shrimp farming impact agricultural production and food security in coastal Bangladesh? Evidence from farmer perception and remote sensing approach

Shrimp farming poses a significant challenge to agriculture in coastal Bangladesh, compounded by climate change and natural disasters. Therefore, the study aims to assess the impact of shrimp farming on food security among marginal farmers using Structural Equation Modeling (SEM). Additionally, we investigate scenarios of agriculture land conversion (1990–2022) in the southwestern coastal regions of Bangladesh through remote sensing techniques. To achieve the study objectives, we collected 672 respondents' opinions from the study regions through a self-administered questionnaire survey. The results indicate that a majority (60%) of the participants believe that rice yields have significantly decreased on their farms in the past ten years, with 70% attributing this decline to increased shrimp cultivation. SEM reveals that shrimp farming has a significant negative impact on farmers' income and dietary habits. Land use and land cover analyses indicate that the area dedicated to aquaculture has nearly doubled from 391.37 km2 in 1990 to 739.93 km2 in 2022.In contrast, agricultural land has decreased by 321.83 km2 over more than thirty years. Our analysis demonstrates that shrimp farming negatively impacts traditional agricultural practices and the sustainability of coastal agriculture by increasing soil salinity, thereby impacting marginalized farmers' food security through income reduction. This study provides valuable insights for policymakers worldwide to implement appropriate laws and regulations to control unplanned shrimp aquaculture in different coastal settings, thus contributing to sustainable development goals and food sovereignty.

TinyML for smart agriculture: Comparative analysis of TinyML platforms and practical deployment for maize leaf disease identification

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Pear (Pyrus communis)-based agroecosystem improves soil nutrient dynamics, microbial biomass, enzymatic activity, farm productivity and profitability

North-western Indian farmers are increasingly showing interest in adopting horticulture-based systems to enhance their farm productivity with anticipated profits. So, there is a need for systematic evaluation of different pear-based agroecosystems with respect to its productivity, profitability, soil health, and biodiversity to tackle the problems of modern agriculture (food security, declining soil health, and sustainability). Therefore, an intercropping experiment comprising 11 treatments viz. [T1: Pear (Pyrus communis) + Pea (Pisum sativum), T2: Pear + Radish (Raphanus sativa), T3: Pear + Coriander (Coriandrum sativum), T4: Pear + Kasuri fenugreek (Trigonella corniculata), T5: Pear + fenugreek (Trigonella foenum-graecum), T6: Pear + Spinach (Spinacia oleracea), T7: Pear + Green onion (Allium fistolosum), T8: Pear + Cauliflower (Brassica oleraceae var. botrytis), T9: Pear + Toria (Brassica rapa), T10: Pear + Knol khol (Brassica oleraceae var. gongylodes) and T11: Pear monoculture] were laid out in randomized block design with three replications at farmer's field, Tarn Taran district, Punjab, India during 2021–22 and 2022–23. The results showed that the lesser values of bulk density (1.30 g/cm3) and higher water holding capacity (42.52%) were observed in the Pear + kasuri fenugreek intercropping system. All the legumes-based intercropping systems viz. Pear + kasuri fenugreek, Pear + fenugreek, and Pear + pea were significantly efficient in bringing enhancements to the chemical properties of the orchard's soil. Under various pear-based intercropping systems, different microbial populations (bacteria, fungi, actinomycetes, and diazotrophs) were significantly higher in legume-based intercropping systems of Pear + kasuri fenugreek, Pear + fenugreek, and Pear + pea and resulting in more enzymatic activity in the soil in terms of dehydrogenase (63.02, 61.88, and 60.48 µg TPF/g of soil/hour, respectively), alkaline phosphatase (10.05, 9.92, and 9.75 µg PNP/g of soil/hour, respectively) and urease (17.84, 17.18, 17.00 µg /g of soil/hour, respectively). The intercropping system of Pear + kasuri fenugreek significantly increased microbial biomass carbon (42.20%) and microbial biomass nitrogen (65.02%) over Pear monoculture. The correlation analysis of various soil's physiochemical and biological parameters indicated significant associations among themselves at p = 0.01, 0.05. The intercropping system of Pear + kasuri fenugreek also produced significantly higher system productivity in early (122.20 kg/ha/day in 2021–22 and 123.86 kg/ha/day in 2022–23) and main season (119.08 kg/ha/day in 2021–22 and 121.60 kg/ha/day in 2022–23) due to significantly higher pear yield (340.02 q/ha in 2021–22 and 341.89 q/ha in 2022–23) and higher pear equivalent yield (105.99 in early season, 94.64 q/ha in main season of 2021–22 & 110.20 in early season, 101.94 q/ha in main season of 2022–23) observed by kasuri fenugreek, resulting in higher net returns, BC ratio and economic efficiency in both seasons during both years over other intercropping systems.

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

The Future of Biofertilizer use in Safe Agriculture

An essential part of a nation's economy is agriculture. Chemical fertilizers and pesticides are widely used in intensive agriculture techniques nowadays to boost crop yield and satisfy the growing global population's nutritional needs. Yet, it has been discovered that increasing urbanization, reduced agricultural lands, sharp climate changes, and widespread use of agrochemicals in farming techniques lead to environmental disruptions and public health risks that compromise agricultural sustainability and food security. Agrochemical overuse is also causing agriculture soils to continuously lose their biological and chemical balance, quality, and physical attributes, as well as their biological health. The potential for plant-associated microorganisms to address these issues and play a critical role in plant growth is immense. With their abilities to promote plant growth, plant-associated bacteria hold great promise for resolving these issues and are essential for increasing agricultural production and biomass in both greenhouse and field settings. Improved nutrient availability (i.e., N, P, K, Zn, and S), phytohormone regulation, biocontrol of phytopathogens, and mitigation of biotic and abiotic stresses are some of the advantageous processes of improved plant growth. Microorganisms and plants interact to support sustainable agriculture, and these microbes may be essential ecological engineers that reduce the demand for chemical fertilizers. Among the steps involved in this process are the production of the inoculum, the inclusion of cell protectants such as glycerol, lactose, and starch, an acceptable carrier material, optimal packing, and the most efficient delivery modalities. Bio-fertilizers are some of the best agricultural tools available today. It's a gift from modern agricultural science. On agricultural land, biofertilizers are applied in lieu of traditional fertilizers. Conventional fertilizers contain green manure, compost, and household waste. These are less effective than chemical fertilizers. Farmers so often try to employ chemical fertilizers in the field to encourage crop development. But it's clear that chemical fertilizers are bad for the environment. They can disperse substances that cause cancer and are accountable for soil, air, and water pollution. Furthermore, they can eventually deplete the soil's fertility. Scientists have created biofertilizers in an attempt to reduce pollution and enhance everyone's health organically. The microorganisms in biofertilizer assist the host plants in maintaining physiologic balance, supporting proper development and growth, and obtaining an adequate quantity of nutrients. Living microorganisms are used to create biofertilizers. Only specific microorganisms that aid in the growth and reproduction of plants are used. There are many different kinds of microorganisms used in biofertilizers. being an essential component of farming organically. Since biofertilizer is a fundamental component of organic farming, it is crucial for preserving soil sustainability and fertility over the long term.

Effect of Snapping Roller Parameters on Maize Harvesting

The effectiveness of maize harvesting is significantly impacted by the parameters of snapping rollers, which have a crucial function in separating maize ears from the stalks. The present study examines the influence of several snapping roller parameters, such as rotational speed, roller spacing, and surface properties, on the efficiency of maize harvesting. The paper provides a detailed examination of the advancements in snapping roller technology, focusing on improvements that seek to optimize performance and minimize harm to crops. The research highlights significant obstacles, including the need for wear resistance, careful material selection, and the capacity to adapt to various maize kinds. The suggested solutions to these difficulties include the use of improved materials, optimal roller designs, and adaptive control systems. Addressing these difficulties may lead to a substantial improvement in the efficiency and sustainability of maize harvesting operations, as shown by the data. This study offers vital insights for the development of more efficient and durable maize harvesting machinery, eventually leading to enhanced agricultural output and food security.

The implications of farmer‐to‐farmer learning and local knowledge networks for food security in Solomon Islands and beyond

Across the Pacific, socio‐cultural networks are a key way to share knowledge and skills. However, as peer‐to‐peer learning is contextually and culturally located, it is important to understand the place‐based specifics of such learning. This article draws on the evaluation of a series of household‐based workshops on agricultural development and food security in a remote area of Solomon Islands. It outlines this community's kinship ecology for farmer learning pathways and demonstrates the interdependence of gender and kinship‐based social networks, showing how topics related to ethics of living were shared within clans, while food and agriculture topics were shared within households. The article concludes that understanding local knowledge networks can contribute to the design of more effective peer learning for food security and rural development.

Effects of Heat and Drought Stress on Sustainable Agriculture and Future Food Security in Türkiye

This review investigates the effects of heat and drought stress on future food security of Turkish agriculture. Temperature average is expected to rise to 3.2°C at the end of the current century while annual precipitation will decline more than 10% in the west and south and rise by 20% in the north of Türkiye, implying that climate change will affect ecosystem sustainability. It is therefore crucial to develop strategies to mitigate and adapt to climate change such as adjusting the planting schedule, reduced tillage, fertiliser microdosing, pre-sowing seed treatment, and the application of growth promoting bacteria to improve tolerance to stress by comprehending how plants respond physiologically and biochemically under these stress conditions. Long-term heat stress may hinder photosynthetic electron transport, decreasing the plant's ability to make use of energy for photosynthesis. The immediate response of plants under drought stress involves closing stomatal openings to reduce water loss through stomatal conductance. Combined heat and drought stress have a greater adverse effect on plant development and production than their effects in isolation. Plant phenotyping can play a major role in “climate-proofing” Turkish agriculture through the identification and development of crop varities with improved prouctivity, climate resilience and input requirements. Digital agriculture will also improve the efficiency of Turkish agricultural systems as the adapt to a hotter drier climate. To ensure future food security and the viability of the agro-economic system in Türkiye steps must be taken to make Turkish agriculture more robust in preparation for the impacts of climate change.

Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints

Introduction & BackgroundThe ability of policymakers to positively transform food environments requires robust empirical evidence that can inform decisions. At present, there is limited data on food-insecurity in the UK that can be used to inform interventions by local authorities, due to the prohibitive costs and logistical challenges of administering longitudinal surveys. This study builds on existing research and a key pilot study developed in partnership between Olio - a food-sharing app with 7 million registered users as of 2023, the University of Nottingham and Havering Council in 2020, which resulted in the world’s first map prototype of food-insecurity. Objectives & ApproachOur approach leverages Machine Learning methods applied to unprecedented food-acquisition behavioural data and open area-level deprivation statistics to model and predict individuals' experience of food-insecurity across London. We used Olio’s extensive network of users to distribute 2,849 surveys, asking respondents across London about their experiences of food-insecurity. The survey was distributed online, adapting the US Department of Agriculture Food Security module. Respondents were asked about their experiences, including (1) eating smaller meals or skipping meals, (2) being hungry but being unable to eat, and (3) not eating for a whole day, because they could not afford food or because they could not get access to food. Using the household, rather than the individual-level version of the food insecurity module helped shed light on the experience of vulnerable groups - such as children. Relevance to Digital FootprintsThe survey responses provided a ground truth about users' experiences of destitution. Deprivation metrics and digital footprint data in the form of food-acquisition behavioural data were then used in a Random Forests Machine Learning model to predict whether households were experiencing food-insecurity, achieving high accuracy. Food-sharing data from almost 50,000 London-based users active on Olio’s platform were then used to identify relevant food-seeking behaviours and aggregate recognised instances of food-insecurity at neighbourhood (MSOA) level. Conclusions & ImplicationsTo identify and rank relevant socio-demographics and food-seeking behaviours most informative for describing food-insecurity an extensive variable selection analysis was performed. The resulting SHAP (SHapley Additive exPlanations) values showed that a combination of food solicitation and the general deprivation of an area were important predictors of food-insecurity.

Arahan Pengembangan Infrastruktur dalam Mendukung Ketahanan Pangan Di Kecamatan Bataguh, Kabupaten Kapuas

Bataguh District has great potential for agricultural resources and is the largest rice producing area in Kapuas Regency, based on field conditions, the existing infrastructure is not yet supportive and most of the infrastructure is still not optimal, so efforts are needed to develop infrastructure for the agricultural sector and resilience. food in Bataguh District. This research aims to determine the infrastructure needed to support regional development of the agricultural sector and food security. Starting with the analysis looking for the basic infrastructure needed in the agricultural sector, starting with a descriptive analysis stage to identify the condition of the infrastructure in the field. The next stage involves using the Importance-Performance Analysis method to evaluate the level of importance and performance of the infrastructure. In the final stage, triangulation analysis is used to conclude the results and provide development directions to support the agricultural sector and food security in Bataguh District, Kapuas Regency. Based on the research results, it is known that the priorities for infrastructure groups are: quadrant 1 (Road Network, Agricultural Irrigation, Storage Warehouses and Agricultural Processing Facilities), quadrant 2 (Communication Network and Electricity Network Coverage), quadrant 3 (Clean water network, Availability of Sanitation, Cooperatives , and gas stations) and quadrant 4 (Modes of Transportation, Markets, and Transportation Support Facilities).

An integrated agroforestry-bioenergy system for enhanced energy and food security in rural sub-Saharan Africa

Most people in rural sub-Saharan Africa lack access to electricity and rely on traditional, inefficient, and polluting cooking solutions that have adverse impacts on both human health and the environment. Here, we propose a novel integrated agroforestry-bioenergy system that combines sustainable biomass production in sequential agroforestry systems with biomass-based cleaner cooking solutions and rural electricity production in small-scale combined heat and power plants and estimate the biophysical system outcomes. Despite conservative assumptions, we demonstrate that on-farm biomass production can cover the household’s fuelwood demand for cooking and still generate a surplus of woody biomass for electricity production via gasification. Agroforestry and biochar soil amendments should increase agricultural productivity and food security. In addition to enhanced energy security, the proposed system should also contribute to improving cooking conditions and health, enhancing soil fertility and food security, climate change mitigation, gender equality, and rural poverty reduction.

Fabrication and application of carrier‐free and carrier‐based nanopesticides in pest management

AbstractPesticides are widely used for pest control to promote sustained and stable growth of agricultural production. However, indiscriminate pesticide usage poses a great threat to environmental and human health. In recent years, nanotechnology has shown the ability to increase the performance of conventional pesticides and has great potential for improving adhesion to crop foliage, solubility, stability, targeted delivery, and so forth. This review discusses two types of nanopesticides, namely, carrier‐free nanopesticides and carrier‐based nanopesticides, that can precisely release necessary and sufficient amounts of active ingredients. At first, the basic characterization and preparation methods of these two distinct types of nanopesticides are briefly summarized. Subsequently, current applications and future perspectives on scientific examples and strategies for promoting the usage efficacy and reducing the environmental risks of these nanopesticides were also described. Overall, nanopesticides can promote higher crop yields and lay the foundation for sustainable agriculture and global food security.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.