Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) analysis was conducted to assess changes in the economic results of agricultural producers that invest using this support. The comparison of the economic results achieved by supported investors with both non-investing agricultural producers and unsupported investors is a distinguishing element of this study. The relatively rarely used Competitivness Index (CI), which measures the ratio of earned income to the sum of the alternative use of the owned means of production, was used. The positive change in the CI during the analyzed period was 0.14 higher for supported investors than non-investors. No statistically significant change was found were compared to unsupported investors. A clear increase in income, total fixed assets, liabilities, and the level of production in the population of producers using support in relation to non-investors and investing without CAP support was also observed. However, in relationships with investors using their own funds, these differences were mainly due to the difference in the level of investments and were not statistically significant when introducing a correction regarding the scale of the investment. The obtained results remain in line with the results of research shown by a significant part of economists undertaking a similar issue.
The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aiming to develop a scalable, efficient, and secure solution for crop disease detection tailored to the environmental and operational conditions of Minnesota farms. By maintaining sensitive farm data locally and enabling collaborative model updates, our proposed framework seeks to achieve high accuracy in crop disease classification without compromising data privacy. We outline a methodology involving data collection from Minnesota farms, application of local deep learning algorithms, transfer learning, and a central aggregation server for model refinement, aiming to achieve improved accuracy in disease detection, good generalization across agricultural scenarios, lower costs in communication and training time, and earlier identification and intervention against diseases in future implementations. We outline a methodology and anticipated outcomes, setting the stage for empirical validation in subsequent studies. This work comes in a context where more and more demand for data-driven interpretations in agriculture has to be weighed with concerns about privacy from farms that are hesitant to share their operational data. This will be important to provide a secure and efficient disease detection method that can finally revolutionize smart farming systems and solve local agricultural problems with data confidentiality. In doing so, this paper bridges the gap between advanced machine learning techniques and the practical, privacy-sensitive needs of farmers in Minnesota and beyond, leveraging the benefits of federated learning.
Efficient crop-weed segmentation is critical for site-specific weed control in precision agriculture. Conventional CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions. To address these challenges, we propose a lightweight transformer-CNN hybrid. It processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration. Evaluated on the WeedsGalore dataset, the model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points. With only 8.7 million parameters, the model offers high accuracy, computational efficiency, and potential for real-time deployment on Unmanned Aerial Vehicles (UAVs) and edge devices, advancing precision weed management.
Leonardo Saraceni, Ionut Marian Motoi, Daniele Nardi
et al.
In precision agriculture, the scarcity of labeled data and significant covariate shifts pose unique challenges for training machine learning models. This scarcity is particularly problematic due to the dynamic nature of the environment and the evolving appearance of agricultural subjects as living things. We propose a novel system for generating realistic synthetic data to address these challenges. Utilizing a vineyard simulator based on the Unity engine, our system employs a cut-and-paste technique with geometrical consistency considerations to produce accurate photo-realistic images and labels from synthetic environments to train detection algorithms. This approach generates diverse data samples across various viewpoints and lighting conditions. We demonstrate considerable performance improvements in training a state-of-the-art detector by applying our method to table grapes cultivation. The combination of techniques can be easily automated, an increasingly important consideration for adoption in agricultural practice.
Mohamed Shabeer Mohamed Rafi, Mehran Behjati, Ahmad Sahban Rafsanjani
The integration of the Internet of Things (IoT) in smart agriculture has transformed farming practices by enabling real time monitoring, data-driven decision making, and automation. However, ensuring reliable connectivity in diverse agricultural environments remains a critical challenge. This paper analyzes the performance trade offs between Low Power Wide Area Networks (LPWAN), specifically LoRaWAN, NBIoT, and Sigfox and cellular networks (4G and 5G) in agricultural applications. Beyond a comprehensive literature review, this study evaluates hybrid LPWAN and 5G architectures that integrate the strengths of both network types to enhance cost-efficiency and connectivity reliability. Using real-world case studies, the findings demonstrate that hybrid LPWAN and 5G models can reduce connectivity costs by up to 30% while significantly improving network reliability in remote agricultural settings. This work provides actionable recommendations for selecting optimal IoT connectivity solutions based on agricultural requirements and proposes future research directions to further optimize IoT infrastructure in smart farming.
Widiyanto Widiyanto, Lestari Eny, Rusdiyana Eksa
et al.
The Climate Village Program (ProKlim) aims to strengthen community participation in local climate mitigation and adaptation. Although several areas in Surakarta have reached the Main Class category, sustaining community motivation remains a challenge. This study analyzes the sustainability of ProKlim by examining social, economic, and environmental factors influencing long-term engagement. Using a qualitative case study approach, data were collected through in-depth interviews, FGDs, observations, and documentation. The findings show that strong social capital and local leadership support sustainability, while fluctuating participation, dependence on external incentives, and limited cadre regeneration hinder continuity. Economic activities exist but remain small-scale. The results imply that continuous mentoring, strengthened community-based economic initiatives, and structured monitoring are essential to ensure sustained ProKlim implementation beyond assessment cycle. The result imply that continuous mentoring, strengthened community-base economic initiatives, and structured monitoring to maintain consistent ProKlim implementation beyond competition cycles. Strengthening these aspects is essential for ensuring the ling-term sustainability of Main Class ProKlim as a community-driven climate resilience initiative.
Elvis Frimpong Manso, Alfred Arthur, Joseph Osafo Eduah
et al.
Despite the differences in soil and fertilizer properties affecting fertilizer effectiveness, farmers often use nationwide blanket formulations, which may not optimize cocoa yield. Previous trials have shown that fertilizer application outcomes vary by soil type, prompting recommendations for site-specific fertilizer formulations. Nonetheless, the complexity of creating these models leaves farmers relying on available blanket fertilizers instead. To enable farmers to select fertilizer types that will best suit their soils, the effects of soil properties and fertilizer types on the solubility, availability of macronutrients, and pH in two cocoa soils were investigated. Five kilograms of ferralsol and acrisol were prepared in nursery bags, with five different fertilizers (A, B, C, D, and E) applied at rates of 375, 500, and 625 kg·ha−1 were set in factorial experiment laid in completely randomized design with four replicates each. Following a 3-week incubation, nutrient analysis was conducted weekly. Water solubility was assessed by weighing 1, 2, and 3 g of each fertilizer in 200 mL of distilled water and shaken for 3 hours. Results indicate that lower solute-to-solvent ratios decreased NPK, Ca, and Mg solubility. Fertilizer A increased soil pH from 6.81 to 7.45 in ferralsol and from 5.78 to 7.50 in acrisol. The different soils showed different release trends though the same fertilizers were applied. Available phosphorus rose from 4.76 to 166.69 mg·kg−1 in ferralsol and from 4.32 to 170.00 mg·kg−1 in acrisol, while total nitrogen rose from 0.22% to 0.30% in ferralsol and from 0.16% to 0.20% in acrisol. The findings highlight that soil properties influence fertilizer solubility and nutrient availability in cocoa soils.
Abstract Background Grain number (GN) is one of the key yield contributing factors in modern wheat (Triticum aestivum) varieties. Fruiting efficiency (FE) is a key trait for increasing GN by making more spike assimilates available to reproductive structures. Thousand grain weight (TGW) is also an important component of grain yield. To understand the genetic architecture of FE and TGW, we performed a genome-wide association study (GWAS) in a panel of 236 US soft facultative wheats that were phenotyped in three experiments at two locations in Florida and genotyped with 20,706 single nucleotide polymorphisms (SNPs) generated from genotyping-by-sequencing (GBS). Results FE showed significant positive associations with GN, grain yield (GY), and harvest index (HI). Likewise, TGW mostly had a positive correlation with GY and HI, but a negative correlation with GN. Eighteen marker-trait associations (MTAs) for FE and TGW were identified on 11 chromosomes, with nine MTAs within genes. Several MTAs associated with other traits were found within genes with different biological and metabolic functions including nuclear pore complex protein, F-box protein, oligopeptide transporter, and glycoside vacuolar protein. Two KASP markers showed significant mean differences for FE and TGW traits in a validation population. Conclusions KASP marker development and validation demonstrated the utility of these markers for improving FE and TGW in breeding programs. The results suggest that optimizing intra-spike partitioning and utilizing marker-assisted selection (MAS) can enhance GY and HI.
This study investigates the impacts of agriculture and renewable energy consumption on CO2 emissions in South Korea from 1980 to 2023, highlighting both challenges and opportunities for environmental sustainability. Utilizing bootstrap ARDL, FMOLS, and CCR methodologies, the analysis reveals that traditional agricultural practices significantly increase CO2 emissions, underscoring the urgent need for sustainable agricultural reforms. Conversely, renewable energy consumption effectively reduces CO2 emissions, thereby supporting the nation’s transition towards sustainable energy sources. Additionally, control variables such as industrial activity, urbanization, energy prices, and government environmental policies exhibit significant effects on CO2 emissions. Specifically, industrial activity and urbanization contribute to increased emissions, whereas higher energy prices and stringent environmental policies are associated with reduced emissions. These findings highlight the necessity for targeted agricultural and energy sector reforms to achieve a balance between economic growth and environmental preservation. Robustness tests confirm the stability of these relationships, providing a reliable foundation for policymakers to develop effective strategies for a sustainable future in South Korea.
Michele Carlo La Greca, Mirko Usuelli, Matteo Matteucci
Agriculture, fundamental for human sustenance, faces unprecedented challenges. The need for efficient, human-cooperative, and sustainable farming methods has never been greater. The core contributions of this work involve leveraging Active Vision (AV) techniques and Zero-Shot Learning (ZSL) to improve the robot's ability to perceive and interact with agricultural environment in the context of fruit harvesting. The AV Pipeline implemented within ROS 2 integrates the Next-Best View (NBV) Planning for 3D environment reconstruction through a dynamic 3D Occupancy Map. Our system allows the robotics arm to dynamically plan and move to the most informative viewpoints and explore the environment, updating the 3D reconstruction using semantic information produced through ZSL models. Simulation and real-world experimental results demonstrate our system's effectiveness in complex visibility conditions, outperforming traditional and static predefined planning methods. ZSL segmentation models employed, such as YOLO World + EfficientViT SAM, exhibit high-speed performance and accurate segmentation, allowing flexibility when dealing with semantic information in unknown agricultural contexts without requiring any fine-tuning process.
Laurence Daigle, Hattaw Khalid, Carl A. Gagnon
et al.
Abstract Background The primary objective of this cross-sectional study, conducted in Québec and Bristish Columbia (Canada) between February 2021 and January 2022, was to measure the prevalence of viral RNA in oronasal and rectal swabs and serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amongst cats living in households with at least one confirmed human case. Secondary objectives included a description of potential risk factors for the presence of SARS-CoV-2 antibodies and an estimation of the association between the presence of viral RNA in swabs as well as SARS-CoV-2 antibodies and clinical signs. Oronasal and rectal swabs and sera were collected from 55 cats from 40 households at most 15 days after a human case confirmation, and at up to two follow-up visits. A RT-qPCR assay and an ELISA were used to detect SARS-CoV-2 RNA in swabs and serum SARS-CoV-2 IgG antibodies, respectively. Prevalence and 95% Bayesian credibility intervals (BCI) were calculated, and associations were evaluated using prevalence ratio and 95% BCI obtained from Bayesian mixed log-binomial models. Results Nine (0.16; 95% BCI = 0.08–0.28) and 38 (0.69; 95% BCI = 0.56–0.80) cats had at least one positive RT-qPCR and at least one positive serological test result, respectively. No risk factor was associated with the prevalence of SARS-CoV-2 serum antibodies. The prevalence of clinical signs suggestive of COVID-19 in cats, mainly sneezing, was 2.12 (95% BCI = 1.03–3.98) times higher amongst cats with detectable viral RNA compared to those without. Conclusions We showed that cats develop antibodies to SARS-CoV-2 when exposed to recent human cases, but detection of viral RNA on swabs is rare, even when sampling occurs soon after confirmation of a human case. Moreover, cats with detectable levels of virus showed clinical signs more often than cats without signs, which can be useful for the management of such cases.
Gabriela de Assis dos Santos, João Manoel Magalhães Almeida Bezerra, Ana Beatriz Santana Silva
et al.
O megaesôfago é uma enfermidade ocasionada pela hipomotilidade e dilatação esofágica parcial ou total, podendo ser congênito ou adquirido e subdivididos em primário, secundário, ou idiopático. Na forma congênita a patogenia não está totalmente esclarecida, mas, a sintomatologia inicia-se após o desmame e o filhote da ninhada apresenta subdesenvolvimento. O principal sinal clínico desta doença é a regurgitação após a ingestão de alimento e de água. O diagnóstico definitivo baseia-se no histórico, exame clínico e exames complementares de imagem. O tratamento é conservador, sendo necessário mudar o manejo alimentar e tratar as complicações que podem ser ocasionadas. Além disso, os cães mais acometidos são os médio a grande porte. Sendo assim, o objetivo deste trabalho é relatar um caso de um megaesôfago congênito em cão da raça Pinscher, filhote, com histórico de regurgitação desde o desmame, apetite voraz e retardo no desenvolvimento. Após o diagnóstico através de radiografia simples e contrastada foi iniciado o manejo alimentar e o paciente demonstrou melhora clínica.
Kyalo Gerald, Rajendran Srinivasulu, Alibu Simon
et al.
A crop rotation study was conducted in the Agoro Rice scheme from mid-2015 to 2017 to determine the effect of sweetpotato–rice rotation in the lowlands on financial returns and sweetpotato root, sweetpotato vine, and rice yields compared to monocropping. Treatments included crop rotations of sweetpotato–rice–sweetpotato, rice–sweetpotato–rice, rice–rice–rice (control), and sweetpotato–sweetpotato–sweetpotato (control). The study used the sweetpotato varieties NASPOT 11 (cream-fleshed), NASPOT 10 O, and Ejumula (both orange-fleshed) and the rice varieties Wita 9, Agoro, and Komboka. The results showed that mean sweetpotato root yields in the rotation treatment were significantly higher (28 t ha−1) than the control (19.8 t ha−1), representing a 47% gain in yield. Generally, the percentage gain in yield across years due to rotation ranged from 3 to 132%, depending on the variety. The total number of vine cuttings was significantly different between treatments and seasons (P < 0.001). Mean rice paddy yields in rotation were 8–35% higher than the control. The higher yields of sweetpotato in the rotation can be attributed to the rotation crop benefitting from residual fertilizers applied in rice in the previous season, while rice in the rotation crop could have benefited from the land preparation and establishment of the sweetpotato fields. The benefit of rotation for both crops varied by variety while the revenue-to-cost ratio varied by season and crop variety. Revenue-to-cost ratios for rotation and control treatments were greater than 1, indicating net profits were positive for both. The rotation generated 0.43 times more revenue than rice monocropping. Both rotation and monocropping systems generated profits, but rotation was 43% more profitable. In other words, if monocropping generates 1 dollar, rotation generates 1.43 dollars. The study concludes that rotation of sweetpotato with rice led to (1) increased yields of both rice and sweetpotatoes, (2) more profitable utilization of land, (3) enhanced availability of sweetpotato planting material at the beginning of the upland growing season, and (4) reduced the cost of land preparation for the main rice crop. Findings from this study show that there is great potential for diversification of rice-based cropping systems in Uganda, which will contribute to building sustainable food systems.
Bell peppers (<i>Capsicum annuum</i>) were grown in a greenhouse to evaluate organic fertilizer and foliar seaweed application effects on plant architecture, yield, and fruit quality. Many organic fertilizers contain phytohormones intrinsically. Hydrolyzed and non-hydrolyzed fish fertilizer and cyano-fertilizer treatments were applied in split applications every 7 days over a 135-day growing period. Control plants received no supplemental N. Each fertilizer treatment received applications of one of two different foliar seaweeds or no foliar seaweed in a 4 × 3 factorial design with three replications. Both hydrolyzed and non-hydrolyzed fish fertilizers and cyano-fertilizer increased the number of branches per plant compared to the N-deficient control. The plants receiving cyano-fertilizer or non-hydrolyzed fish fertilizer yielded more than the N-deficient control, and those treatments received 2–3 times the auxin application as the hydrolyzed fish fertilizer. In addition, the leaves from the plants treated with non-hydrolyzed fish fertilizer contained substantially higher levels of abscisic acid, although no abscisic acid was detected in the fertilizers. Both seaweed products decreased the number of fruits that were “bell”-shaped and increased the number of “long”-shaped fruits. Organic fertilizers are complex matrices of nutrients, phytohormones, and other metabolites, making it very challenging to determine the mechanisms behind the observations.
Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge management strategies, such as precision agriculture, empower farmers and decision-makers with rich and actionable information to increase the efficiency and sustainability of their farming practices. Crop-type maps are key information for decision-support tools but are challenging and costly to generate. We investigate the capabilities of Meta AI's Segment Anything Model (SAM) for crop-map prediction task, acknowledging its recent successes at zero-shot image segmentation. However, SAM being limited to up-to 3 channel inputs and its zero-shot usage being class-agnostic in nature pose unique challenges in using it directly for crop-type mapping. We propose using clustering consensus metrics to assess SAM's zero-shot performance in segmenting satellite imagery and producing crop-type maps. Although direct crop-type mapping is challenging using SAM in zero-shot setting, experiments reveal SAM's potential for swiftly and accurately outlining fields in satellite images, serving as a foundation for subsequent crop classification. This paper attempts to highlight a use-case of state-of-the-art image segmentation models like SAM for crop-type mapping and related specific needs of the agriculture industry, offering a potential avenue for automatic, efficient, and cost-effective data products for precision agriculture practices.
The Ministry of Economy has an interest and demand in exploring how to increase the set of [legally registered] small family farmers in Ukraine and to examine more in details measures that could reduce the scale of the shadow agricultural market in Ukraine. Building upon the above political economy background and demand, we will be undertaking the analysis along the two separate but not totally independents streams of analysis, i.e. sustainable small scale (family) farming development and exploring the scale and measures for reducing the shadow agricultural market in Ukraine
Hannah Kerner, Catherine Nakalembe, Adam Yang
et al.
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.