Context Of Agriculture Research Articles

Optimizing crop yield forecasting with ensemble machine learning techniques

Accurate crop yield prediction is critical for ensuring food security and efficient agricultural management, particu- larly in the face of climate change and rising global populations. Current predictive models often fall short in generalizing across diverse agricultural contexts due to their inability to capture complex interactions between various climatic and soil variables effectively. This study addresses these gaps by proposing a com- prehensive machine-learning framework that integrates ensemble methods to enhance crop yield prediction accuracy. Using a dataset enriched with climatic and agricultural features, we evaluated multiple models, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Bagging Regressor, and K-nearest neighbors. The Random Forest model emerged as the top performer, achieving an accuracy of 0.985 and a Mean Squared Error (MSE) of 1.08e+08. At the same time, the Bagging Regressor closely followed with an accuracy of 0.984 and comparable MSE. Gradient Boosting and XGBoost models also demonstrated robust performance, with accuracies ranging from 0.865 to 0.974 and MSE values between 9.60e+08 and 1.89e+08. Our approach includes extensive hyperparameter tuning and k-fold cross-validation to ensure model generalizability and robustness across agricultural scenarios. These findings highlight the effectiveness of ensemble methods in capturing complex data relationships and their superiority over traditional models in predicting crop yields. Our work sets the stage for future research into integrating real-time data and advanced hybrid models, aiming to refine predictive accuracy further and support sustainable agricultural practices.

Analysis of Spatial Dependence Using the Wave Covariance Structure in Soybean Productivity Associated With Soil Attributes

Objective: The aim of this study is to investigate the interaction between soybean yield and the physical and chemical attributes of the soil, with the goal of developing techniques used in precision agriculture to increase productivity, reduce costs, and minimize environmental impacts. Theoretical Framework: This work is based on the principles of geostatistics, particularly the Wave spatial dependence structure, which is used to model the semivariance function when it exhibits the "hole effect." Method: The research involves a study of soybean yield conducted in a commercial area of 172.04 hectares during the 2022/2023 growing season. Calcium (Ca), copper (Cu), acidity (pH), potassium (K), phosphorus (P), and soil penetration resistance (SPR) levels were used as covariates to explain soybean yield (Prod) through a Gaussian linear spatial model (GLSM). The Thin Plate Spline (TPS) interpolation method was applied to interpolate the physical and chemical soil attributes, considered as fixed covariates, while soybean yield was interpolated using External Drift Kriging (EDK) based on the GLSM. Additionally, techniques for local influence diagnostics were developed and applied to identify observations impacting the results, utilizing the Wave geostatistical model. Results and Discussion: The results revealed that the generated soybean yield map provides important information for defining management zones, optimizing input use, and promoting greater profitability. Furthermore, the removal of locally influential observations alters parameter estimation, the significance of parameters associated with the covariates in the GLSM, and the construction of the interpolated soybean yield map. In the discussion, the results were contextualized in light of the theoretical framework, emphasizing the relevance of the Wave structure and the integration of interpolation techniques in the study of spatial variability. Research Implications: The practical and theoretical implications of this research include improvements in agricultural management by providing support for the delineation of management zones that balance productivity and sustainability. Theoretically, the study contributes to advancing the use of geostatistical models, such as the Wave model, in the analysis of spatial data in precision agriculture. Originality/Value: This study contributes to the literature by exploring the application of the Wave model in an agricultural context, combined with the use of interpolation techniques and local influence diagnostics. Its originality lies in the methodological combination of spatial interpolators and the development of local influence techniques.

On-farm evaluation of swede midge (Diptera: Cecidomyiidae) susceptibility among popular kale varieties.

The swede midge, Contarinia nasturtii Kieffer (Diptera:Cecidomyiidae), is a gall-forming insect pest that targets cruciferous plants. Since its introduction to North America in the early 2000s, the midge has expanded its range to include farming regions in Quebec and the Northeastern USA. In this study, we evaluated the susceptibility of 4 popular kale varieties-Green Curly-(Vates), Red Russian, Red Curly (RedBor), and Lacinato/Tuscan-to swede midge infestation in on-farm trials conducted over consecutive years. Our results indicate substantial variability in susceptibility among varieties, with Red Russian kale consistently exhibiting the highest damage incidence across both years. These findings provide valuable insights for developing effective integrated pest management (IPM) strategies tailored to organic and small-scale vegetable farming contexts, emphasizing the role of crop genetic resources in pest mitigation and sustainable agriculture. In addition, this study highlights the potential of non-chemical approaches for managing swede midge, leading to a more diverse and adaptable IPM toolbox for growers.

Broad-spectrum applications of plant growth-promoting rhizobacteria (PGPR) across diverse crops and intricate planting systems.

Multifunctional plant growth-promoting rhizobacteria (PGPR) have garnered significant attention in agricultural applications; however, a few have applied them in crop rotation or intercropping fields. To identify PGPR with strong colonization ability and broad spectrum benefit, we screened strains from the local tobacco rhizosphere and evaluated their growth-promoting effects across various crops and farming systems. In this study, strain L8, identified as Bacillus thuringiensis, was selected as a multifunctional PGPR capable of producing indole-3-acetic acid (IAA), solubilizing potassium, and mobilizing both organic and inorganic phosphorus. Compared with the control group, the soil treated with strain L8 in tobacco pot experiments showed significantly higher levels of IAA, available potassium, and available phosphorus. Furthermore, tobacco plants inoculated with L8 exhibited significantly improved growth parameters compared with the uninoculated control. The results indicated that compared with the control, strain L8 increased tobacco fresh weight (by 83.18%), plant height (by 29.32%), relative chlorophyll content (by 14.33%), as well as plant phosphorus (by 23.78%) and potassium (by 30.81%). Interestingly, L8 not only enhanced tobacco growth but also improved tobacco root morphology, significantly increasing root length (1.55-fold), root surface area (1.78-fold), and root volume (2.05-fold) compared with the control. These findings provide valuable insights into utilizing plant growth-promoting bacteria for tobacco production. Moreover, the inoculation strain L8 enriched soil organic matter and nutrient content in both wheat (Triticum aestivum)-maize (Zea mays) rotation and peanut (Arachis hypogaea)-maize intercropping systems. Furthermore, within the intricate tillage systems of wheat-corn rotation and peanut-corn intercropping, multifunctional PGPR strain L8 can play a crucial role in crop growth promotion, yield increase, and higher soil nutrient availability.IMPORTANCEThese findings aim to study the application of plant growth-promoting rhizobacteria (PGPR) in diverse crops and complex planting systems, showing their adaptability and effectiveness in various agricultural contexts. The study suggests that this strain improves nutrient utilization in the soil, enhances soil health, and plays a significant role in plant growth through the secretion of substances like auxin (IAA). In parallel, we observed that applying this strain improved the production efficiency of wheat, corn, and peanuts within complex farming systems, indicating that this method holds the potential for enhancing both the yield and quality of various crops.

Soil microbiome bacteria protect plants against filamentous fungal infections via intercellular contacts

Bacterial-fungal interaction (BFI) has significant implications for the health of host plants. While the diffusible antibiotic metabolite-mediated competition in BFI has been extensively characterized, the impact of intercellular contact remains largely elusive. Here, we demonstrate that the intercellular contact is a prevalent mode of interaction between beneficial soil bacteria and pathogenic filamentous fungi. By generating antibiotics-deficient mutants in two common soil bacteria, Lysobacter enzymogenes and Pseudomonas fluorescens, we show that antibiotics-independent BFI effectively inhibits pathogenic fungi. Furthermore, transcriptional and genetic evidence revealed that this antibiotics-independent BFI relies on intercellular contact mediated by the type VI secretion system (T6SS), which may facilitate the translocation of bacterial toxic effectors into fungal cells. Finally, by using a "conidia enrichment" platform, we found that T6SS-mediated fungal inhibition resulting from intercellular contact naturally occurs within the soil microbiome, particularly represented by Pseudomonas fulva. Overall, these results demonstrate that bacteria from the soil microbiome can protect host plants from fungal infection through antibiotics-independent intercellular contacts, thus revealing a naturally occurring and ecologically important mode of BFI in agricultural contexts.

Fungicide resistance in Fusarium species: exploring environmental impacts and sustainable management strategies.

The agricultural productivity and world-wide food security is affected by different phytopathogens, in which Fusarium is more destructive affecting more than 150 crops, now got resistance against many fungicides that possess harmful effects on environment such as soil health, air pollution, and human health. Fusarium fungicide resistance is an increasing concern in agricultural and environmental contexts, requiring a thorough understanding of its causes, implications, and management approaches. The mechanisms of fungicide resistance in Fusarium spp., are reviewed in this article, including increased efflux pump activity, target-site mutations, and metabolic detoxification pathways. Fusarium is naturally resistant to some of the fungicides, on the other hand; it speedily develops resistance against the other fungicides groups. Most of the important plant pathogenic Fusarium species including F. oxysporum, F. psedogramanium, F. graminearium and Fusarium solani, which have shown resistance to major groups of fungicides including triazoles, phenylpyrole and benzimedazoles in various regions of the world. The review also covers a range of management techniques, including fungicide rotation, resistant cultivars, cultural methods, and biological control agents, to lessen fungicide resistance. By shedding light on the current state of knowledge concerning fungicide resistance in Fusarium spp., this review provides valuable information to researchers, policymakers, and practitioners to design long-term effective disease management approaches, as well as fungal menace control to preserve fungicides' effectiveness in agriculture and conservancy activities.

In between the Sites: Understanding Late Holocene Manteño Agricultural Contexts in the Chongón-Colonche Mountains of Coastal Ecuador through UAV-Lidar and Excavation

ABSTRACT Mapping the terrain surrounding concentrations of documented Manteño (ca. a.d. 650–1700) stone architecture within 1.2 km² of the cloud forests of Bola de Oro in southern Manabí, Ecuador using an Uncrewed Aerial Vehicle (UAV)-based lidar Mobile Mapping System (MMS) revealed a modified landscape of agricultural systems. The resulting Digital Terrain Models (DTMs), subsequent ground-truthing, and excavation show a modified Manteño landscape of agricultural terraces, drainage channels, and water retention ponds tailored to the storage and distribution of water brought by seasonal rains and by marine layers precipitating at these higher elevations. Such a massive human investment in this challenging landscape was most likely a result of the Chongón-Colonche Mountains being the most resistant environments to the extreme climate changes brought by El Niño-Southern Oscillation (ENSO) during the climatic shifts of the Medieval Climate Anomaly (MCA, ca. a.d. 950–1250) and the Little Ice Age (LIA, ca. a.d. 1400–1700).

Could Biofloc Technology (BFT) Pave the Way Toward a More Sustainable Aquaculture in Line With the Circular Economy?

Aquaculture is a growing industry, but current practices and raw material utilization must be reviewed to ensure a resilient and sustainable development. In this sense, the transition from a linear economy (take, make, dispose) to a circular one (renew, remake) is accelerating. The biofloc technology (BFT) is a relatively new cultivation system that can be adopted to accomplish more sustainable aquaculture and circularity goals. This document discusses BFT and its association with the circular economy (CE), the current aquaculture challenges, and the role of BFT in overcoming those challenges. This manuscript adopts Cramer’s 10 R’s and Muscat et al.’s five P’s frameworks to understand whether a functioning BFT and its key compartments (i.e., feed, environment, water, system, and microbials) align with CE’s core principles. In addition, the present work provides and discusses relevant insights regarding the further (industry and academia) application of CE approaches, especially in a biofloc‐based farming context. According to the findings and connections with Cramer’s 10 R’s and Muscat et al.’s five P’s frameworks, BFT encompasses several transitioning steps into circularity and could play a crucial role toward a more sustainable aquaculture in line with the CE.

Plant-derived miR166a-3p packaged into exosomes to cross-kingdom inhibit mammary cell proliferation and promote apoptosis by targeting APLNR gene.

Plant-derived microRNAs (miRNAs) have attracted significant attention for their potential in cross-kingdom gene regulation, but the mechanisms of their entry, stability, and function in animal bodies need further investigation. We provided an in-depth analysis of tissue-specific miRNA expression in dairy cows, identifying 347 miRNAs, including 16 novel candidates, across 21 normal tissues. Our findings revealed that specific miRNAs, such as miR-192, miR-143, miR-148a, miR-486, and miR-21-5p, showed distinct tissue enrichment. In addition, a total of 167 maize-derived miRNAs were identified in dairy cow tissues, particularly in the rumen, mammary glands, serum, and exosomes. These exogenous miRNAs, which are abundant and conserved among plants, may be absorbed by the SLC46A2 transporter in the rumen epithelium during feeding and distributed to other tissues via exosomal encapsulation. The maize-derived miR166a-3p was highly abundant. Transfection experiments confirmed that miR166a-3p reduces the expression of proliferation markers (PCNA, Cyclin D, and Cyclin E) and the anti-apoptotic gene Bcl2, while upregulating the pro-apoptotic gene Bax. Moreover, exosomes derived from bovine serum were found to mediate these effects, as miR166a-3p-enriched exosomes inhibited cell proliferation and promoted apoptosis, further supporting the cross-kingdom role of plant-derived miRNAs in regulating biological processes. This study enhances the understanding of miRNA regulatory mechanisms, particularly the absorption and systemic transport of plant-derived miRNAs in dairy cows. The findings underscore the potential for using exogenous miRNAs, like miR166a-3p, in agricultural and medical contexts, warranting further investigation into their functions and cross-species interactions.

The role of social-psychological factors in the adoption of push-pull technology by small-scale farmers in East Africa: Application of the theory of planned behavior.

Push-pull technology (PPT) continues to gain relevance among smallholder farmers across the East African region in managing the constraints affecting cereal crop yields including stemborers, fall armyworm, striga weed, and low soil fertility. While previous research has emphasized the significance of socioeconomic factors in explaining farmers' decisions to adopt PPT, the social-psychological factors that influence farmers' adoption intentions have not been extensively studied. Therefore, this study investigated the influence of social-psychological factors on the intention to adopt or increase the land area under PPT based on the theory of planned behavior (TPB). Moreover, this study extends the applicability of the TPB by incorporating an additional construct that addresses practical limitations within adoption decision-making, and providing evidence of how TPB constructs function differently across distinct agricultural contexts. Nine hundred and seventy-one (971) cereal growers comprising PPT users and non-users were interviewed in Kenya, Uganda, Tanzania, and Rwanda using a structured questionnaire. Utilizing the Partial Least Square-Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN), this study revealed a significant positive influence of TPB conceptual elements attitudes, subjective norms, and perceived behavioral control on farmers' intentions to adopt or expand the land area under PPT. Additionally, the study found that the added construct "perceived limitations" negatively impacts these intentions. The results indicated significant variations in the impact of TPB constructs on the intentions to adopt and expand land under PPT across the studied countries. These insights underscore the need for tailored, context-sensitive interventions that integrate behavioral, social, and practical considerations. Additionally, understanding the country-specific determinants allows for more targeted policy measures, extension services, and support programs that can enhance the adoption and effectiveness of PPT, ultimately improving agricultural outcomes and livelihoods of farmers in this region.

Development of a Concept Inventory of Inheritance for High School Agricultural Education

This study developed three versions of a concept inventory of inheritance tailored for high school agricultural education. These inventories can be used as formative assessments to inform teachers about students’ understanding of inheritance within the context of agriculture or as summative assessments to evaluate learning outcomes. Existing concept inventories in genetics related concepts lack real-world agricultural applications. Our inventories incorporate areas within agriculture, such as animal systems and plant systems, providing students with a deeper understanding of the concept of inheritance. Developed primarily by high school agriculture teachers, these inventories reflect real-world experiences and observations, offer valuable insights into students’ misconceptions, gauge preparation, measure instructional effectiveness, and provide a comprehensive view of learning progressions. We utilized confirmatory factor analysis, Rasch model, and distractor analysis to examine the psychometric properties of the concept inventories.

Farmers' preference of organic farming in Coimbatore district, India

Agriculture plays a pivotal role in the economies of developing countries like India, supporting livelihoods and food security. Organic farming avoids using synthetic inputs, such as fertilizers and pesticides. Instead, it focuses on organic matter (such as crop leftovers, animal residues, legumes and biopesticides) to maintain soil productivity and fertility while managing pests to support sustainable natural resources and a healthy environment. Using cross-sectional methodology, data were obtained from 100 respondents via structured interviews and analyzed using conjoint analysis. According to the findings, economic rewards and market demand strongly motivate the adoption of organic farming, while the complexity and costs of the certification process pose significant hurdles. The findings underscore the importance of targeted training to enhance farmers' knowledge and simplify the certification process. The studys' findings offer valuable insights for policymakers promoting organic agriculture. Key strategies include emphasizing economic incentives, raising awareness and streamlining certification procedures to support sustainable organic farming in the region. This research provides insights into the factors influencing organic farming adoption among farmers in developing agricultural contexts.

Detecting Plant Diseases Using Machine Learning Models

Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the environmental impacts. This paper presents an innovative approach to sustainable development by leveraging machine learning models to detect plant diseases, focusing on tomato crops—a vital and globally significant agricultural product. Advanced object detection models including YOLOv8 (minor and nano variants), Roboflow 3.0 (Fast), EfficientDetV2 (with EfficientNetB0 backbone), and Faster R-CNN (with ResNet50 backbone) were evaluated for their precision, efficiency, and suitability for mobile and field applications. YOLOv8 nano emerged as the optimal choice, offering a mean average precision (MAP) of 98.6% with minimal computational requirements, facilitating its integration into mobile applications for real-time support to farmers. This research underscores the potential of machine learning in advancing sustainable agriculture and highlights future opportunities to integrate these models with drone technology, Internet of Things (IoT)-based irrigation, and disease management systems. Expanding datasets and exploring alternative models could enhance this technology’s efficacy and adaptability to diverse agricultural contexts.

Solar-Powered Irrigation Systems: Sustainability, Advancements and Future Prospects

Green Revolution in India fulfilled food demands through high-yielding crops. However, extensive groundwater irrigation using diesel/electric pumps resulted in environmental issues like air pollution and carbon emissions, among others. In order to address such issues through sustainable solutions, the country is currently turning to solar-powered irrigation systems (SPIS), which is an eco-friendly alternative. With 60% of irrigation relying on groundwater and 26 million pumps are powered with coal-fired electricity or diesel, the SPIS offers the potential to reduce fossil fuel reliance and greenhouse gas emissions. Solar drip irrigation, in particular, can conserve 50%-60% water compared to conventional irrigation systems. This study comprehensively reviews technological advancements in irrigation, focusing on photovoltaic and solar thermal systems. Further, it highlights the advantages and limitations of these technologies, particularly in managing water and energy, extending its scope beyond small-farm areas. The study further explores the techno-economic feasibility of SPIS, considering efficiency, water conservation, costeffectiveness, and sustainability. By synthesizing relevant literature, this review paper provides insights into implementing SPIS in agriculture, emphasizing on both technical and economic viability. This review aims to guide sustainable and efficient irrigation practices across diverse farming contexts.

Implementation of Nginx Server with RTMP Module for Tea Leaf Maturity Monitoring

This research focuses on the application of Nginx Server to monitor the ripeness level of tea leaves in real-time using RTMP module and IP camera. In the tea industry, monitoring the ripeness of tea leaves is essential to ensure the quality of the final product. To achieve this goal, a system was built using Nginx as the main server that manages the real-time video transmission of IP cameras installed in tea plantations. The RTMP module is used to transmit streaming video efficiently and reliably. The implementation of this system involves several stages, including the installation and configuration of the Nginx server, the integration of the RTMP module, and the installation of the IP cameras in strategic locations. The video data collected was analyzed to determine the maturity level of tea leaves based on certain visual indicators. The results of this study show that the use of Nginx server with RTMP module can provide an effective and efficient solution for real-time monitoring of tea leaf ripeness, which can help farmers and tea producers in improving their quality and productivity. These findings make important contributions in the field of smart agriculture and IoT-based monitoring technologies, and open up opportunities for the development of similar systems in other agricultural contexts.

The cow GUTBIOME CY study: investigating the composition of the cattle gut microbiome in health and infectious disease transmission in cyprus

BackgroundRecent evidence suggests that the lower gut microbiome of ruminants presents roles in their health and environment, including the development of the mucosal immune system, milk production efficiency and quality and subsequent methane emissions. However, there are proportionately fewer studies on this complex microbial community in cattle and region-focus studies are non- existent.MethodsHerein, we present the research protocol of the GUTBIOME CY project pertaining to determine the composition of the lower gut microbiome in dairy cows situated in 37 farms across five districts of the island of Cyprus. Detailed questionnaires on animal husbandry and farming practices will be gathered from each farm. Faecal, milk (individual and bulk) and water samples will also be collected from cows and their offspring. Samples will be analysed using a combination of molecular biology and bioinformatics pipelines to define microbiome profiles and antimicrobial resistance (AMR). Information collected from the questionnaires will be used to test for associations between animal husbandry or farming practices and microbiome components and AMR.DiscussionCollected samples will establish the first dairy cattle biobank in the country for contributing substantially towards scientific advancements in microbiome research and providing insights to all stakeholders, tailored to the unique agricultural context of Cyprus.

Adoption of Circular Economy in Sustainable Agriculture Practices in Magelang District: Influence on Productivity and Farmer Welfare

Objective: The objective of this study is to investigate the adoption of circular economy principles in sustainable agriculture practices in Magelang district, focusing on their impact on agricultural productivity and farmer welfare, with the aim of assessing how circular economy practices contribute to sustainable agriculture by improving resource efficiency, enhancing productivity, and promoting farmer welfare in Magelang district. Theoretical Framework: The theoretical framework of this study is grounded in the priciples of the circular economy, which emphasize maximizing resource efficiency, minimizing waste, and fostering environmental regeneration. In agriculture, these principles align with sustainable practices by promoting the reuse of organic materials, efficient resource management, and the reduction of chemical inputs to maintain soil fertility and ecosystem balance. By integrating circular economy principles with sustainable agricultural practices, this framework aims to enhance productivity, reduce costs, and improve farmers’ welfare while ensuring long-term environmental resilience. Method: Using a quantitative approach that includes surveys of 100 farmers and in-depth interviews, this research evaluates the implementation of CE principles, such as composting organic waste, rainwater harvesting, and crop rotation. Results and Discussion: The findings indicate that approximately 60% of farmers have adopted at least one CE practice, leading to a productivity increase of up to 25% compared to conventional methods. Additionally, CE adoption has proven to improve income stability and farmer welfare. However, barriers such as limited access to technology and funding reduce broader adoption of sustainable practices. Research Implications: This study recommends government and institutional support through training, technology access, and financial incentives to accelerate CE adoption. It provides actionable reccomendations for policymakers and stakeholders to support the broader adoption of circular economy practices in agriculture. Originality/Value: The originality of this research lies in its focus on the adoption of circular economy principles within the specific context of sustainable agriculture in Magelang District, an area with unique agricultural challenges and potential. This research combines empirical data on productivity and welfare impacts with qualitative insights into local adoption barriers and drivers. By integrating these aspects, the study provides a novel perspective on implementing sustainable agricultural practices tailored to the region’s socioeconomic and environmental conditions.

Millet Cultivation in North-east Hill Region of India: A Review

The millets are climate resilient, most efficient in use of water, adopted to grown in marginal and sub marginal area, suitably grown in hilly and rainfed area, nutrient dense and having low carbon and water footprint. Considering their significance in present context of agriculture, it is important to know about their region-specific cultivation practices and intervention in it. The North East Hill (NEH) region is known for its diversity in terms of soil and climatic condition as well as crop and plant diversity. The marginal and sub-marginal area with different slope, topography and soil related constraints for production, the millets will be one of the important options for arable crop cultivation. The production practices followed by the farmers and recommended practices were highly diverse due to different agro-climatic condition. At the same time, in several locations production practices were even not developed due to cultivation on very less area with less economic impact at regional or state agriculture. Hence it is informative and knowledge impacting exercise to review the production practices followed and interventions made at institutional and stakeholders level for millets cultivation in NEH region. The present review summarizes the interventions in production practices such as seed and sowing requirement, nutrient, water and weed management as well as insect-pest and disease management in context of NEH region. Besides that, interventions from NEH region at post-harvest and value addition were also discussed in this review. The cost effective and high enumerative interventions in production processes such as substitution of low yielding upland rice cultivation partly or completely with millets, transplanting, fertilization, micronutrient foliar feeding, use of herbicide for weed management and promotion/ up gradation of millets cultivation in new areas (North-East Hill region) are important at production level. The interventions at policy level such as promotion for incorporation of millets in public distribution system, inclusion of millets in school age children diet and different schemes related with supply of nutritious food to children and adolescence are important for NEH region. Increase in diversity of product prepared from millets, initiation of organic seed production of millets, exploring export potential of traditional products prepared from millets to different domestic and international market are important interventions at policy level. At research level, the standardization of production practices for different states in NEH region was initiated and need to be further emphasized rather than using recommendation as it is from other part of India.

Optimizing stewardship of the land?

This research considers the ways in which digital agriculture (DA) technologies (like robotic machinery, big data applications, farm management software platforms and drones) fit into discourses of sustainable agriculture in the Canadian political and media landscape. To undertake this research, I conducted a discourse analysis of relevant government and media materials published between 2016 and 2022. What became evident was an ideology of optimization, which works to communicate that environmental sustainability needs to and will be optimized using DA technologies. I then consider how these findings are related to the federal fertilizer emissions reduction target, aiming to reduce emissions arising from fertilizer application in agricultural contexts by 30% below 2020 levels by 2030. I argue that discourse regarding this target deploys the ideology of optimization to keep current systems of fertilizer use in place, solidifying further the industrial and productivist paradigm of agriculture in Canada.

How multi-species pollination boosts strawberry yield, quality, and nutritional value

A diverse assemblage of insect visitors can provide functional complementarity within plant pollination due to differences in characteristics such as their physical traits, visitation rate and foraging time of day or year. In a horticultural context, greater functional complementarity may play a crucial role in enhancing fruit yield and quality by improving pollination. We tested whether the identity of the crop pollinators (bumblebee Bombus terrestris and hoverfly Eupeodes corollae) independently and additively influenced commercial strawberry yield, quality, and nutritional parameters such as vitamin C and sugar concentration. Fragaria x ananassa “Malling Champion” plants received pollination treatments of either a) “control”: self-pollination where pollinators were excluded, b) “bee”: bumblebee Bombus terrestris, c) “hoverfly”: Eupeodes corollae, d) “combined”: both B. terrestris and E. corollae. Hoverflies and bumblebees exhibited distinct visitation patterns throughout the day, establishing a functional complementary relationship that enhances pollination success and crop output as well as vitamin C concentrations. Strawberries from plants receiving pollination by bumblebees, or bumblebees and hoverflies combined, had higher yields of higher marketable quality. They also had measurably higher vitamin C content than strawberries from plants pollinated by hoverflies alone, or the control (self-pollinating) plants. This study advances our understanding of niche complementarity and its impact on fruit yield and quality. By elucidating the behavioural and temporal dynamics of pollinators, we provide valuable insights for optimizing pollination strategies in agricultural contexts. Our findings highlight the significance of behavioural factors, such as handling time and number of visits, in determining fruit quality.