Agricultural Efficiency Research Articles

Insect-based livestock feeds are unlikely to become economically viable in the near future

Insect meal has been proposed as one way for the food system to reduce its reliance on fishmeal and soy meal, given the environmental and human rights problems associated with those inputs. However, insect meal is currently too expensive to compete with fishmeal and soy meal, hindering its adoption in large-scale agriculture. There is little indication that insect meal will become cheaper in the near future, especially as the insect industry encounters barriers such as a lack of large-scale production technologies, limited utilisation of food waste, and a lack of standardisation in insect meal quality. One solution may be to set up production facilities in tropical countries, where energy needs are lower and labour costs are cheaper, but this would mean sacrificing food self-sufficiency and potentially causing job losses. Another solution involves charging a premium for insect-fed meat, but studies reveal that consumers would be unlikely to pay such a premium. Overall, it is unlikely that insect meal will displace fishmeal and soy meal in the near future; it may be more realistic to address the challenges associated with forage fish depletion and soy production via increased agricultural efficiency and targeted domestic and transnational policy instruments.

Pengembangan Inovasi Smart Aeroponik sebagai Upaya Efisiensi Urban Farming Produktif Ramah Energi

This article explores the development and application of smart aeroponic innovation as an effort to improve efficiency in energy-friendly urban farming. Smart aeroponics, which utilizes sensor technology and automatic control systems, is designed to maximize plant growth while minimizing energy and water use. In this community service project, we implemented a smart aeroponic system in several urban locations and trained local communities in its operation and maintenance. Evaluation of the results showed that this system can increase energy efficiency by up to 70% when compared to hydroponics with the Deep Flow Technique (DFT) method or each can save 29.02 kWh for mustard greens cultivation. And 41.00 kWh for lettuce cultivation. The potential of this technology is an effort to improve the efficiency of urban agriculture, but also provides significant ecological and economic benefits for urban communities, so that it can be a sustainable model for future agricultural development.

Technological Support for Increasing Soil Fertility in Organic Farming

Methods for increasing soil fertility in organic farming are presented. Examples of promising technologies and preparations developed by educational, industrial and scientific organizations are shown. It is established that in order to increase the efficiency of organic farming and preserve the environment it is necessary to use crop rotations with green manure crops, gentle soil cultivation, and biopreparations to restore and increase soil fertility.

Do peers and agglomeration affect farm efficiency?

Do peers and agglomeration affect farm efficiency?

DNIM: Deep-sea netting intelligent enhancement and exposure monitoring using bio-vision

Intelligent monitoring of deep-sea nets is affected by light attenuation, light scattering, and limited dynamic range factors of the camera, which can cause color shift, low visibility, and over/underexposure in monitoring, resulting in reduced farming efficiency, and misdetection of biological behavior. Therefore, we propose a method for underwater net tank scene enhancement and exposure using multicolor space and bio-vision, called DNIM. Specifically, we first propose an intelligent enhancement and exposure monitoring method for deep-sea nets using bio-vision, which eliminates the effect of exposure on underwater imaging by introducing a bio-vision system that uses a brightness transformation function to generate a series of exposure images. Inspired by the image fusion method, we designed a color correction and visibility recovery method using bio-vision. In the Lab color model, color shifts in bio-vision are corrected by guiding feature transformations with contrast-constrained adaptive histogram equalization; in the RGB color model, depth information is recovered by creating a linear model for the depth of field of underwater blurred images based on the characteristics of the bio-vision system. To obtain more qualified results, we also propose an efficient perceptual fusion module to mix the output of visibility recovery and color correction. Finally, we propose two deep-sea net tank culture datasets: salmon bait-eating behavior dataset (SBBD) and salmon non-bait-eating behavior dataset (SNBD). By comparing the experimental analysis in four real scenario datasets, our DNIM achieved superior results compared to 16 advanced underwater enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/DNIM.

Early anomaly detection of wind turbine gearbox based on SLFormer neural network

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability.

Farmers’ adaptation to climate change in Southern Issyk-Kul

Agriculture is a key sector for people’s livelihoods in Kyrgyzstan, which highlights the role of local people’s perceptions of agricultural practices and optimisation of land use management. The purpose of this study was to assess the understanding of land use practices by local residents in four districts of the Southern Issyk-Kul. The level of awareness of sectoral issues was investigated by randomly selected farmers. The study found that modern farmers are aware of the existing problems and their contribution to the decline in agricultural productivity and increased vulnerability to environmental change. It was generalised that the solution of the problem under study requires practical interaction between government agencies and farmers aimed at providing the necessary information and resources for adaptation to climate-oriented agricultural practices. The potential of innovative precision farming technologies in the context of the studied issues was analysed. An important problem of the lack of a mechanism for the exchange of agricultural knowledge between farmers was identified, as it is difficult for them to determine where and from whom to gain experience. It was substantiated that the integration of agricultural consulting elements into the environment of farm operation has the potential to significantly stimulate the positive dynamics of agricultural development. Such an approach would allow for the most accurate selection of optimisation measures, which would affect the economic efficiency and environmental performance of farming. The practical value of the results of the study is seen in the possibility of using them to mitigate the process of adaptation of agricultural production in developing countries to inevitable climate change

Development of a cloud-based IoT system for livestock health monitoring using AWS and python

The agriculture industry is currently facing significant challenges in effectively monitoring the health of livestock. Traditional methods of health monitoring are often labor-intensive, inefficient, and insufficiently responsive to the needs of modern farming. As the number of IoT devices in agriculture proliferates, issues of scalability and computational load have become prominent, necessitating efficient and scalable solutions. This research introduces a cloud-based architecture aimed at enhancing livestock health monitoring. This system is designed to track critical health indicators such as movement patterns, body temperature, and heart rate, utilizing AWS for robust data handling and Python for data processing and real-time analytics. The proposed system incorporates Narrow Band IoT (Nb IoT) technology, which is optimized for low-bandwidth, long-range communication, making it suitable for rural and remote farming locations. The architecture's scalability allows for the effective management of varying numbers of IoT devices, which is essential for adapting to changing herd sizes and farm scales. Preliminary experiments conducted to assess the system's performance have demonstrated its durability and effectiveness, indicating a successful integration of AWS IoT Cloud services with the deployed IoT devices. Furthermore, the study explores the implementation of predictive analytics to facilitate proactive health management in livestock. By predicting potential health issues before they become apparent, the system can offer significant improvements in animal welfare and farm efficiency. The integration of cloud computing and IoT not only meets the growing technological needs of modern agriculture but also sets a new benchmark in the development of sustainable farming practices. The findings from this research could have broad implications for the future of livestock management, potentially leading to widespread adoption of technology-driven health monitoring systems in agriculture. This would help in optimizing the health management of livestock globally, thereby enhancing productivity and sustainability in the agricultural sector.

Assessing the environmental efficiency of smart farming via life cycle assessment: a case study in Greece

Assessing the environmental efficiency of smart farming via life cycle assessment: a case study in Greece

Study of Plasma Biochemistry and Plasma Metabolomics Differences in Montbéliard and Holstein Backcross and Holstein Heifers.

Holstein cattle are the main breed of dairy cattle in China. However, given the high degree of purebred selection of Holstein cattle, Chinese dairy cattle are increasingly being characterized by poor disease resistance, poor quality, and declining fertility. In this study, using Montbéliard × Holstein cattle as females and Montbéliard bulls as males for backcross breeding, we sought to provide a reference for improving the quality and performance of Holstein cattle and enhancing the efficiency of dairy farming. On the basis of similar physiological status and age, we selected 24 Montbéliard and Holstein backcross heifers and 11 Holstein heifers fed the same formula for comparative analyses. Plasma samples collected for plasma biochemical index analyses revealed that the content of ALB and BUN in the Montbéliard and Holstein backcross heifers was 20.83% (31.62 g/L to 26.17 g/L) and 42.36% (6.89 mmol/L to 4.84 mmol/L) higher than in the Holsteins (p < 0.01). The ALB/GLB (0.90 to 0.60, p < 0.05) was significantly higher in Montbéliard and Holstein backcross heifers than in Holstein heifers. Similarly, the activity of CAT in the backcross heifers was 61.28% (4.29 U/mL to 2.66 U/mL) higher than that in the Holstein heifers (p < 0.05). Although the activity of GSH-Px in the backcross heifers also showed an increasing trend, the difference did not reach the level of statistical significance (p = 0.052). Compared with Holstein heifers, the concentrations of IgA, IgG, and IL-4 were elevated by 32.52% (24.90 μg/mL to 18.79 μg/mL, p < 0.01), 13.46% (234.32 μg/mL to 206.53 μg/mL, p < 0.01), and 14.59% (306.27 pg/mL to 267.28 pg/mL, p < 0.05), and the contents of IL-6 and TNF-α were decreased by 15.92% (215.71 pg/mL to 256.55 pg/mL, p < 0.01) and 32.17% (7.17 ng/mL to 10.57 ng/mL, p < 0.01) in the plasma of Montbéliard and Holstein backcross heifers. Among the experimental heifers, five animals from each of the two groups were selected for plasma metabolomic analysis based on untargeted liquid chromatography-mass spectrometry. A comparison of the differential metabolites between the two heifer breeds revealed an up-regulation of d-glucuronic acid, s-glutathionyl-l-cysteine, and oleic acid levels in the backcross cattle compared with those in the Holstein heifers. We speculate that changes in the levels of these metabolites may be associated with an enhancement of the anti-inflammatory, antioxidant, and immune systems in these backcross heifers. Collectively, our findings in this study indicate that compared with 12-month-old purebred Holstein heifers, Montbéliard and Holstein backcross heifers of the same age are characterized by higher antioxidant capacity and immunity.

КЛАСТЕРНЫЙ ПОДХОД К ОПРЕДЕЛЕНИЮ ФАКТОРОВ ВЛИЯНИЯ НА МОЛОЧНОПРОДУКТОВЫЙ ПОДКОМПЛЕКС АПК

The paper analyzes the dairy subcomplex of the agroindustrial complex using k-means; stakeholders can identify individual periods with similar characteristics in the dairy industry, which helps develop targeted strategies to increase production efficiency, market distribution and sustainability. Using Jamovi software and its snowCluster and factoextra modules, two clusters were identified. Cluster 1 (2004-2021) has lower milk production and fewer cows but higher imports, indicating dependence on imports due to smaller herd sizes or less efficient farming. Cluster 2 (1995-2003) shows higher production and livestock of cows but lower imports and exports, which indicates self-sufficiency or export orientation. Cluster 1's dependence on imports may lead to economic vulnerability and changes in consumer preferences, which will require policies to stimulate local production. On the contrary, cluster 2, characterized by domestic production and market stability, could focus on sustainable practices, developing the export market and adapting to consumer trends. Understanding these clusters is crucial for the development of individual strategies in the field of agricultural policy, market development and sustainable development.

Controlling Output Power to Enhance the Investment Efficiency of Wind Farms by Maximizing the Capacity of Transmission Transformers and Integrating Energy Storage Systems

Controlling Output Power to Enhance the Investment Efficiency of Wind Farms by Maximizing the Capacity of Transmission Transformers and Integrating Energy Storage Systems

Evaluating environmental and economic efficiency of a super-intensive Vannamei shrimp farm for nutrient flow circulation

Abstract Shrimp farming has provided several significant economic benefits to the residents of the Mekong Delta region. However, it posed numerous environmental concerns due to high nutrient content in wastes. Therefore, the purpose of this study is to assess the nutrient recovery efficiency from a super-intensive shrimp pond system for nutrient circulation. The material flow analysis tool was used to analyze the effectiveness of allocating nutrients (nitrogen, phosphorus, and carbon) in the research area’s super-intensive Vannamei shrimp farming system, and then construct a material flow circulating system. The findings revealed that in the current system, the amount of nutrients that shrimp absorb is 26.3% TN, 12.2% TP and 17.5% TOC. This study designed a system using circulating fluxes of nutrients. This system combines settling ponds and biological sedimentation ponds as well as composting and biogas digester solutions. The proposed system also reduces input nutrient costs by 53.6% TN, 62.8% TP and 40% TOC. On the other hand, the proposed system will meet the amount of heat for cooking for the 3 households and generate 1 ton of compost/crop for the garden.

Technical Efficiency of Smallholder Oil Palm Farms in Batanghari District Jambi Province, Indonesia

Smallholder oil palm farming is the main livelihood in Batanghari District, although its productivity has not shown maximum results. Maximum productivity achieved, it is necessary for farmers to do their business efficiently. This study aims to analyze technical efficiency and socio-economic factors that affect the technical efficiency of smallholder oil palm farming in Batanghari District. The research sample was determined using the multistage sampling method, which was used to sample farmers using simple random sampling. The number of respondents was 100 farmers. The stochastic frontier production function was used for data analysis. The results showed that the average technical efficiency of oil palm farming was 0,89 and had reached efficiency. However, this level can still be increased by 0,11 to achieve the highest level of technical efficiency. The technical efficiency of farming is affected by socio-economic factors, namely farming experience, distance from home to the farm, and the existence of partnerships. Based on the study's results, the technical efficiency of oil palm cultivation can be improved by utilizing production parameters precisely and accurately and increasing farmers' resources to achieve maximum production. For sustainable agricultural development, further research is needed to analyze the economic efficiency of oil palm farming so that the overall picture of both technical and economic efficiency can be seen.

Development of ISDAAN, an integrated system for data acquisition and automation network for aquaculture

The aquaculture industry has seen tremendous growth in recent years, with the increasing demand for seafood products. However, managing and monitoring aquaculture farms can be a challenging task. This study aimed to design and to develop an integrated system for data acquisition and automation network powered by solar energy which can help improve the efficiency of aquaculture farms and to test its effectiveness and its significant differences in parameters such as water quality and compared to the traditional way of aquafarming. Result showed that the generation of solar energy took five hours to charge and discharge the battery powering up the prototype. Response time from the actuators such as the aerator, filter, and feeder were evaluated using mean and provided reliable results. Turbidity testing was also assessed in the form of spectrophotometry and equipped optimal outcome. T-test analysis showed that there is a significant difference on the traditional way for maintaining fish farms and the integration of the device. The end-user evaluation verified the device’s functional suitability, usability, maintainability, performance efficiency, and reliability which proved that the system’s real-time monitoring capability

Technical Efficiency of Indonesian Potato Farming during Rainy and Dry Seasons: Evidence from Merangin District, Indonesia

Potatoes are among the vegetables highly susceptible to climate change, and its consequences can significantly affect technical efficiency.However, the production of potato relatively fluctuated due to some factors influencing it. The study aims to analyze the factors affecting potato production, measure the level of technical efficiency in potato farming, and identify the socio-economic factors that influence technical inefficiency in potato farming, both in the rainy and dry seasons. The study was conducted in Merangin Regency, Jambi Province. A purposive sampling method was used to select the research area and collect data from 102 potato farmers. The data were analyzed using the Stochastic Frontier Cobb-Douglas production function model with a maximum likelihood estimation (MLE) approach. The research results show that land and seed variables significantly influence potato production, both in the rainy and dry seasons. Meanwhile, fertilizer P and labor only significantly influence the dry season. The average level of technical efficiency in the dry season (0.89) is higher than in the rainy season (0.73). The results of the average difference test (t-test) show that in the dry season, the level of technical efficiency has a higher value than in the rainy season, with a significance of 1%. The significant socio-economic variables influencing technical efficiency are membership in farmer groups.

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions

AbstractThermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.

A circular economy integration approach into vertical farming with computer-based simulation model for resource optimization and waste reduction

Integrating circular economy into vertical farming holds promises to transform the current economic landscape and bolster environmental sustainability. However, knowledge gaps persist regarding the efficiency of vertical farming methods within the circular economy context, particularly in the development of a simulation model aimed at resource optimization and waste reduction. This study aimed to analyze the vertical farming methods in the circular economy framework and introduce a zero-waste circular economy model both experimentally and through simulation. In the study, a circular economy model was proposed and implemented, utilizing waste generated from hydroponics and aquaponics, alongside vermicompost tea and aerobic compost tea/quick compost, as nutrients for six distinct halophyte plants in the secondary farming setup. The model serves as a valuable tool for identifying and assessing critical factors influencing the circular economy principle, as well as for optimizing resource allocation and management. Over six weeks, plant growth was monitored and compared across the vertical farming methods. Results show that both hydroponic and aquaponic waste proved superior growth compared to the alternatives, highlighting vertical farming's potential for the circular economy. Using the results from the experimental observation, a simulation model was developed. The simulation model's accuracy was validated by comparing outcomes with actual data. Furthermore, the model was used to optimize for balancing resources and capacities in each stage of vertical farming. The vertical farming circular economy model proposed in this study can serve as a blueprint for agriculturalists and decision-makers to adopt more sustainable and efficient farming practices that not only reduce waste but also generate profits.

How can rural digitalization improve agricultural green total factor productivity: Empirical evidence from counties in China

Against the backdrop of escalating environmental pollution and resource depletion induced by traditional agricultural industry development, the imperative for agricultural efficiency and ecologically friendly practices has become pivotal for global agricultural sustainability. In the digital era, rural digitalization has substantially amplified agricultural production efficiency while notably reducing environmental pollution. This study is based on panel data from 1602 counties in China spanning from 2011 to 2020, measuring the levels of rural digitalization and agricultural green total factor productivity (AGTFP) across these counties, empirically examining their dynamic correlation. The research findings reveal: (1) A significant enhancement of AGTFP with the elevation of rural digitalization; for every 1 % increase in rural digitalization, AGTFP increases by 1.78 %. Robustness checks, including instrumental variables and quasi-natural experiments, affirm the validity of these conclusions. Furthermore, by comparing regional differences in China, it can be observed that the impact of rural digitalization on AGTFP varies across different areas: in the eastern, central, and western regions, for every 1 % increase in rural digitalization, the AGTFP increases by 2.65 %, 1.53 %, and 0.82 %, respectively. (2) Rural digitalization fosters an increase in AGTFP by stimulating rural entrepreneurial activity, with the mediation effect of rural entrepreneurial activity accounting for 3.34 % of the total effect. (3) Utilizing a threshold model, observations indicate that when rural digitalization surpasses the first threshold of 0.3200, the AGTFP coefficient escalates from 0.0462 to 0.7519. However, when rural digitalization exceeds the second threshold of 0.7118, this coefficient declines to 0.2429, signifying a nonlinear trend in the marginal effect of rural digitalization on AGTFP—initial enhancement followed by attenuation. (4) In the spatial model analysis, the study confirms spatial spillover effects of rural digitalization on AGTFP, indicating variations in spatial effects among different matrices. However, the indirect effects of rural digitalization on AGTFP, mainly influenced by the neighboring counties' digitalization levels, contribute more significantly than the direct effects. Based on these conclusions, this paper proposes augmenting investments in rural digitalization, implementing diversified rural digitalization schemes, invigorating rural entrepreneurial activity, and harmonizing regional digital development as novel approaches to enhance AGTFP, thereby fostering rural economic development.

A Comparative Analysis of Agriculture in Tropical and Temperate Climates

T he largest agricultural areas in the world are in North America, Euro-Asia, and Central South America. However, agriculture in tropical and temperate climates is very different, mainly because of the environmental conditions. Unlike in temperate climates, there are not four seasons in the tropics. The tropics have only two seasons, rainy and dry, with plants f lowering all year round because air temperatures are not too low in the winter, and crops can grow during this season; thus, some crops may have three cycles per year in the tropics. In most temperate climates, there is only one crop cycle per year due to low winter temperatures, but the yield can be the highest. The low temperatures of a temperate climate, below 0°C, allow soils to be shallow, with high organic matter content and fertility because soil microbial activity and lixiviation are arrested at such temperatures, and organic matter is less degraded than in tropical soils where soil microorganisms and lixiviation continue throughout the year. As a result, yields of major crops in temperate climates are the highest in the world, confirming the efficiency of temperate agriculture, even with only one crop cycle per year. On the other hand, in tropical climates, yields are lower, but crops can be grown several times a year, thus increasing the annual production.