Search Results (1,049)

Achieving food security for a growing population under a changing climate is a key concern in Senegal, where agriculture employs 77% of the workforce with a majority of small farmers who rely on the production of crops for their subsistence and for income generation. Moreover, due to the underproductive soils and variable rainfall, Senegal depends on imports to fulfil 70% of its food requirements. In this research, we considered four crops that are crucial for Senegalese agriculture: millet, sorghum, peanuts and rice. We used crop simulation models to explore existing yield gaps and optimal agronomic practices. Improving the N fertilizer management in sorghum and millet resulted in 40–100% increases in grain yields. Improved N symbiotic fixation in peanuts resulted in yield increases of 20–100% with highest impact in wetter locations. Optimizing irrigation management and N fertilizer use resulted in 20–40% gains. The best N fertilizer strategy for sorghum and millet included applying low rates at sowing and in early development stages and adjusting a third application, considering the expected rainfall. Peanut yields of the variety 73-33 were higher than Fleur-11 in all locations, and irrigation showed no clear economic advantage. The best N fertilizer management for rainfed rice included applying 30 kg N/ha at sowing, 25 days after sowing (DAS) and 45 DAS. The best combination of sowing dates for a possible double rice crop depended on irrigation costs, with a first crop planted in January or March and a second crop planted in July. Our work confirmed results obtained in field research experiments and identified management practices for increasing productivity and reducing yield variability. Those crop management practices can be implemented in pilot experiments to further validate the results and to disseminate best management practices for farmers in Senegal. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

Climate change presents urgent and complex challenges to agricultural sustainability and food security, particularly in regions reliant on resource-intensive staple crops. Smart agriculture—through the integration of crop modeling, satellite remote sensing, and artificial intelligence (AI)—offers data-driven strategies to enhance productivity, optimize input use, and mitigate greenhouse gas (GHG) emissions. This study introduces Farmdee-Mesook, a mobile-first smart agriculture platform designed specifically for Thai rice farmers. The platform leverages AquaCrop simulation, open-access satellite data, and localized agronomic models to deliver real-time, field-specific recommendations. Usability-focused design and no-cost access facilitate its widespread adoption, particularly among smallholders. Empirical results show that platform users achieved yield increases of up to 37%, reduced agrochemical costs by 59%, and improved water productivity by 44% under alternate wetting and drying (AWD) irrigation schemes. These outcomes underscore the platform’s role as a scalable, cost-effective solution for operationalizing climate-smart agriculture. Farmdee-Mesook demonstrates that digital technologies, when contextually tailored and institutionally supported, can serve as critical enablers of climate adaptation and sustainable agricultural transformation. Full article

The review summarizes the historical and current research on perennial grain breeding in Russia within the context of growing global interest in perennial crops. N.V. Tsitsin’s pioneering work in the 1930s produced the first wheat–wheatgrass amphiploids, which demonstrated the capacity to regrow after harvest and survive for 2–3 years. Subsequent research at the Main Botanical Garden in Moscow focused on characterizing Tsitsin’s material, selecting superior germplasm, and expanding genetic diversity through new cycles of hybridization and selection. This work led to the development of a new crop species, Trititrigia, and the release of cultivar ‘Pamyati Lyubimovoy’ in 2020, designed for dual-purpose production of high-quality grain and green biomass. Intermediate wheatgrass (Thinopyrum intermedium) is native to Russia, where several forage cultivars have been released and cultivated. Two large-grain cultivars (Sova and Filin) were developed from populations provided by the Land Institute and are now grown by farmers. Perennial rye was developed through interspecific crosses between Secale cereale and S. montanum, demonstrating persistence for 2–3 years with high biomass production and grain yields of 1.5–2.0 t/ha. Hybridization between Sorghum bicolor and S. halepense resulted in two released cultivars of perennial sorghum used primarily for forage production under arid conditions. Russia’s agroclimatic diversity in agricultural production systems provides significant opportunities for perennial crop development. The broader scientific and practical implications of perennial crops in Russia extend to climate-resilient, sustainable agriculture and international cooperation in this emerging field. Full article

Determining the effectiveness of fungicide programs based on cultivar resistance to pathogens, especially late leaf spot (caused by Nothopassalora personata (Berk. & M.A. Curtis) [U. Braun, C. Nakash., Videira & Crous]) is important in establishing recommendations to peanut (Arachis hypogaea L.) farmers. Research was conducted in North Carolina during 2021 and 2022 at three locations to compare the incidence of late leaf spot (e.g., visual estimates of percent of peanut leaflets with lesions), percentage of the peanut canopy defoliated caused by this disease, and yield of the peanut cultivars Bailey II, Emery, and Sullivan when exposed to five fungicide regimens including a non-treated control. Peanut yield was not affected by the interaction of cultivar × fungicide regimens. While differences in leaf spot incidence and canopy defoliation were noted for cultivars, these differences did not translate into differences in peanut yield. All fungicides regimens protected peanut yield from leaf spot disease regardless of the number of sprays during the cropping cycle (e.g., three, four, or five sprays). Peanut yield in the absence of fungicides was 4410 kg/ha compared with a range of 5000 to 5390 kg/ha when fungicides were applied. Peanut yield was greater when fungicides were applied four or five times compared with only three sprays or non-treated peanut. The regimen with five consecutive sprays of chlorothalonil alone for the first and final spray in the regimen and when this fungicide was applied with tebuconazole for the second, third, and fourth sprays was as effective as fungicide regimens including combinations of pydiflumetofen plus azoxystrobin plus benzovindiflupyr, mefentrifluconazole plus pyraclostrobin plus fluxapyroxad, bixafen plus flutriafol, and prothioconazole plus tebuconazole. Full article

Grain production is crucial for national security and stability. Studying the impact of digital literacy on green production by grain farmers is of great significance for ensuring food security and achieving green agricultural development. This article utilizes data from the 2020 China Rural Revitalization Survey (CRRS), selecting a sample of 1811 farming households engaged in grain cultivation. Employing methods such as the ordered Probit model and mediating effect model, it analyzes the impact of digital literacy on green grain production from the perspectives of transformation drivers and pathways. The results show, first, that digital literacy significantly promotes farmers’ green production behaviors, and the findings remain valid after multiple robustness tests. Second, a mechanism analysis reveals that digital literacy drives farmers’ green production by reconstructing their benefit cognition and green cognition and promoting the application of green mechanization technologies. Third, a heterogeneity analysis indicates that the larger the farmers’ operation scale and the stronger their economic capacity, the more significant the promoting effect of digital literacy on their green production. Accordingly, it is necessary to accelerate the improvement of farmers’ digital literacy, reduce green production costs, popularize green mechanization technologies, and promote the green transformation of grain production. Full article

The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability. Full article

Anthelmintic resistance in livestock is a growing concern worldwide, with significant implications for animal health and agricultural productivity. This study explores the perceptions of veterinarians and farmers in Bosnia and Herzegovina regarding the factors contributing to anthelmintic resistance in Haemonchus contortus nematodes. Data were collected through structured questionnaires completed by 106 veterinarians and 188 farmers in 2022 and 2023. The analysis focused on self-reported therapeutic practices, farm management and environmental variables. Logistic regression, including Firth’s penalized approach, was used to assess associations between these perceived factors and the reported occurrence of resistance. Notably, combination anthelmintic treatments were perceived as a significant risk factor (OR > 49.3), while higher altitude was seen as potentially protective (OR = 0.10). Routine prophylactic deworming was associated with an increased likelihood of perceived resistance (OR = 173.7), whereas staying informed about newly registered products was perceived as protective (OR = 0.34). Although the findings are based on the self-reported perceptions and practices of veterinarians and farmers, they align with globally recognized trends and offer the first structured insights into factors perceived to contribute to anthelmintic resistance in Bosnia and Herzegovina. This study underscores the importance of awareness and responsible anthelmintic use and the need for improved diagnostics and ongoing education to combat anthelmintic resistance. Full article

The aim of the study was to assess the diversity of termites used in poultry feed in Burkina Faso. Termite samples were collected in eight of the thirteen regions of the country by poultry farmers, then conserved in 70° alcohol. The criteria used by poultry farmers for identifying the termite were also characterised and discussed with farmers in a village where the use of termites as poultry feed is well developed. Morphological identifications were carried out in the laboratory. Twenty species were identified in two families, six subfamilies, and thirteen genera. In the Heterotermitidae family, a single species belonging to the Coptotermitinae subfamily was identified. The rest belonged to the family Termitidae and the subfamilies Amitermitinae, Microcerotermitinae, Macrotermitinae, Nasutitermitinae, and Cubitermitinae. Three species, Microcerotermes fuscotibialis, Megagnathotermes notandus, and Isognathotermes fungifaber, were found for the first time in Burkina Faso. The largest number of species (eleven) was collected in the Cascades region. Poultry farmers are able to recognise eight genera of termites by the shape, size, and colour of the termites; by the termite mounds; and often by the location of the nest. These results may facilitate the promotion of the use of termites as poultry feed in Burkina Faso and West Africa. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article

In this study, a self-propelled hard hose traveler is developed as a modification of the conventional design. The traveler demonstrated enhanced field applicability and intelligence level in Europe and central–eastern China. A parametric configuration scheme was attained through the irrigator’s computational modeling and experimental validation. This study proposed a uniform water distribution calculation model for single-sprinkler linear-move irrigation. The deviation rate between calculated and experimental values was 7.3%. The average application depth decreased with increased sprinkler motion speed and path spacing. The uniformity of water distribution (CU value) exhibited an oscillating trend as the path spacing changed. As the sprinkler rotation angle increased along a specific path, the CU value first rose from 69.2% to 80.0% and then declined to 68.7%. When irrigation and sprinkler motions were combined, the CU value at 1.5 R initially decreased from 92.1% to 72.9%, then increased to 84.2% as the sprinkler rotation angle increased. The combined sprinkler and irrigation motions showed a significantly better uniformity than the specific path irrigation. The highest CU value was 95.0%, with a nozzle diameter of 16.0 × 6.0 mm, a sprinkler rotation angle of 180°, and a path spacing of 1.6 R. This study introduces a novel approach for water-saving irrigation equipment and offers practical guidance for farmers on operating the self-propelled hard hose traveler. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

A crop yield prediction is critical to increase agricultural sustainability because it allows for the more effective use of natural resources, including water, fertilizers, and soil. Accurate yield estimates enable farmers and governments to more accurately manage resources, decreasing waste and minimizing adverse environmental effects such as the degradation of soil and water quality issues. In addition, predictive models serve to alleviate the consequences of climate change by promoting adaptable farming techniques and improving the availability of food by means of early decision-making. Thus, including a crop yield prediction into farming practices is critical for combining productivity and sustainability. In contrast to conventional machine learning models, which frequently require long feature engineering, deep learning may obtain complicated yield-related characteristics directly from initial or merely preprocessed data from different sources. This research paper aims to demonstrate the strength of deep learning models and their ensembles in agricultural yield prediction in Saudi Arabia, where agriculture faces issues such as scarce water resources and harsh climate conditions. We first define and evaluate a Multilayer Perceptron (MLP), a Gated Recurrent Unit (GRU), and a Convolutional Neural Network (CNN) as baseline deep models for the crop yield prediction. Then, we investigate combining these three models based on stacking, blending, and boosting ensemble methods. Finally, we study the uncertainty quantification for the proposed models, which involves a discussion of many enhancements’ techniques. As a result, this research shows that, by applying the right architectures with strong parametrization and optimization techniques, we obtain models that can explain 96% of the variance in the crop yield with a very low uncertainty rate (reaching an MPIW of 0.60), which proves the reliability and trustworthiness of the prediction. Full article

As Europe is the world’s leading producer of biogas, this article examines how agricultural anaerobic digestion (AD) is organized and governed, and explores the social challenges involved in structuring the sector around a possible “European model”. Following a social science perspective, it presents a systematic review of 64 French- and English-language articles drawn from 16 academic databases. The findings highlight five key dynamics. First, there is a shift from farmer-led to increasingly industrial models of AD. Second, diverse and hybrid business models are emerging, involving new forms of multi-scale coordination. Third, the sector remains structurally dependent on public subsidies and on regulatory frameworks. Fourth, the economic viability of AD for farmers remains uncertain, driving a transition from cogeneration to biomethane injection. Fifth, tensions develop between rural place-based imaginaries and the realities of globalized energy networks. These patterns underscore the complexity of biogas sector-building in Europe and the competing narratives shaping its evolution. We argue that agricultural AD cannot be reduced to a unified trajectory, but reflects ongoing negotiations over energy models, territorial development and socio-technical legitimacy. This paper concludes by discussing the implications of these dynamics for the sustainability and fairness of future biogas trajectories across Europe. Full article

Given the serious economic burden that citrus diseases impose on fruit farmers and related industries, achieving rapid and accurate disease detection is particularly crucial. In response to the challenges posed by resource-limited platforms and complex backgrounds, this paper designs and proposes a lightweight method for the identification and localization of citrus diseases based on the RT-DETR-r18 model—GYS-RT-DETR. This paper proposes an optimization method for target detection that significantly enhances model performance through multi-dimensional technology integration. First, this paper introduces the following innovations in model structure: (1) A Gather-and-Distribute Mechanism is introduced in the Neck section, which effectively enhances the model’s ability to detect medium to large targets through global feature fusion and high-level information injection.(2) Scale Sequence Feature Fusion (SSFF) is used to optimize the Neck structure to improve the detection performance of the model for small targets in complex environments. (3) The Focaler-ShapeIoU loss function is used to solve the problems of unbalanced training samples and inaccurate positioning. Secondly, the model adopts two model optimization strategies: (1) The Group_taylor local pruning algorithm is used to reduce memory occupation and the number of computing parameters of the model. (2) The feature-logic knowledge distillation framework is proposed and adopted to solve the problem of information loss caused by the structural difference between teachers and students, and to ensure a good detection performance, while realizing the lightweight character of the model. The experimental results show that the GYS-RT-DETR model has a precision of 79.1%, a recall of 77.9%, an F1 score of 78.0%, a model size of 23.0 MB, and an mAP value of 77.8%. Compared to the original model, the precision, recall, the F1 score, the mAP value, and the FPS value have improved by 3.5%, 5.3%, 5.0%, 5.3%, and 10.3 f/s, respectively. Additionally, the memory usage of the GYS-RT-DETR model has decreased by 25.5 MB compared to the original model. The GYS-RT-DETR model proposed in this article can effectively detect various citrus diseases in complex backgrounds, addressing the time-consuming nature of manual detection and improving the accuracy of model detection, thereby providing an effective theoretical basis for the automated detection of citrus diseases. Full article