Farm Profitability Research Articles

Incidence and recurrence of bovine abortion in dairy cattle from Costa Rica

The reproductive efficiency and milk yield of cows are crucial factors in a dairy farm's profitability. However, abortions can have a negative impact on these factors. While the morbidity of abortion has been estimated in many countries, information on the burden on dairy cattle in tropical conditions is limited, and Costa Rica is a good example. This study aims to assess the incidence and recurrence of bovine abortion in dairy cattle from Costa Rica. The study analysed the morbidity of abortion in Costa Rican dairy herds between 2010 and 2022. The incidence rate (IR) and the recurrence rate (ReR) were calculated per 100 cow-months at risk using data from the Veterinary Automated Management and Production Control Programme (VAMPP). The dataset comprised 1032,457 lactations from 330,265 cows in 1134 specialized dairy herds. Abortions were classified either as early foetal mortality (EFM) or late foetal mortality (LFM). Rates were estimated based on cow breed, lactation number, and ecological zone to which the farm belongs. The IR of general abortion, EFM, and LFM cases were 0.98, 0.41, and 0.57 per 100 cow-months at risk, respectively. No statistically significant differences were found in the IR between cow breed, lactation number, and ecological zone, nor for the trend of abortions over calving years. The first ReR (for cows that had one previous abortion during the lactation) was 0.95, and the second ReR (for cows that had two previous abortions during the lactation) was 1.41 per 100 cow-months at risk. These results suggest that bovine abortions are an important ongoing problem in dairy farms in Costa Rica with potentially detrimental effects on the reproductive and productive performance of cows and may be representative of other specialized tropical dairy systems in Latin America.

Integrating ecosystem services, stakeholders’ perspective, and land-use scenarios to safeguard sustainability of the Mulberry-Dyke and Fish-Pond System

ContextOptimizing landscape patterns by considering ecosystem services and human well-being is crucial for landscape sustainability research. Such optimization process shall also consider multiple stakeholders’ perspectives, interests and benefits, which remain less well investigated.ObjectivesThe study aims to integrate ecosystem services and land-use scenario analysis to design sustainable landscapes. The overall goal of spatial optimization is to explore pathways towards sustainable development.MethodsThis study uses the Mulberry-Dyke and Fish-Pond System as a representative agricultural landscape. It analyzes the spatiotemporal pattern of the landscape from 1975 to 2019. The market value method, InVEST model, and MaxEnt model are used to quantify the ecosystem services of the landscape. The analytic hierarchy process is used to set the weight of ecosystem services from the perspective of three stakeholder groups. We compare the outcomes of different scenarios and assessed through emergy yield ratio, environmental loading ratio, and environmental sustainability index.ResultsOur results indicated that: (1) The scale of the Mulberry-Dyke and Fish-Pond system decreased significantly from 1975 to 2019. (2) Among the six major ecosystem services, the average value of water purification service was the highest, while average value of mulberry supply service was the lowest. (3) Spatial optimization of the three design objectives, ‘sustainably intensify’, ‘increase landscape multifunctionality’, and ‘restore ecological integrity’, were primarily distributed in the study area’s eastern, central, and western regions, respectively. (4) All three stakeholder-oriented scenarios showed improvements in ecological quality or agricultural output.ConclusionsOur study incorporates viewpoints of critical, diverse stakeholders in the assessment of ecosystem services and scenario analysis to establish sustainable and culturally important agricultural landscape. The framework and methods can help minimize environmental stress on the system, balance agricultural productivity and profitability, and enhance the sustainability of agricultural landscapes.

Updates and Current Challenges in Reproductive Microbiome: A Comparative Analysis between Cows and Women.

The microbiota plays an important role in numerous physiological processes, pathogenesis, development, and metabolism in different animal species. In humans, several studies have demonstrated an association between the vaginal microbiota and fertility rates, and even success in assisted reproduction techniques. In the context of cattle reproduction, although few studies have addressed the microbiota in a healthy state (which is not associated with diseases that affect the reproductive tract of cows), changes in its composition also seem to influence fertility. This review aims to explain the importance of the reproductive microbiota in female bovines and what is available in the literature regarding its possible role in increasing fertility. What are the challenges involved in this process? Future perspectives on its use and manipulation as a selection or intervention tool. Will it be possible to one day extrapolate the findings to reality and apply them in the field? In short, understanding the role of the reproductive microbiota of female bovines can signal the prospect of increasing production, whether of milk or meat, from the same number of animals, as it can optimize reproductive efficiency and perhaps become an allied tool for the economic profitability and sustainability of livestock farming.

Examination of rams for breeding

Key annual events in the sheep calendar can have a significant impact on production, efficiency and, therefore, farm profits. Veterinarians can play an important and proactive role in these key events, and examination of rams for breeding is an opportunity for veterinary input into a sheep flock. Testing before mating can identify subfertile and infertile rams.

Changes in blood serum parameters in farmed rainbow trout (Oncorhynchus mykiss) during a piscine lactococcosis outbreak.

The aquaculture sector plays a vital role in global food security, yet it grapples with significant challenges posed by infectious diseases. Piscine lactococcosis is one of the significant threats in rainbow trout aquaculture due to its potential to cause severe economic losses through mortalities, reduced growth rates, and increased susceptibility to other pathogens. It poses challenges in disease management strategies, impacting the sustainability and profitability of rainbow trout farming. The current study focuses on the variations in serum blood parameters of farmed rainbow trout Oncorhynchus mykiss during a lactococcosis outbreak caused by Lactococcus garvieae. Blood samples were collected for biochemical analysis, fish were examined for parasites and bacteria, and DNA from bacterial colonies was PCR-amplified and sequenced for identification. Overall, 13 biochemical parameters, including proteins, enzymes, lipids, chemicals, and minerals, were measured in serum blood samples from both diseased and healthy fish. The results indicate significant alterations in the levels of these parameters during the outbreak, highlighting the impact of infections on the blood profile of farmed rainbow trout. Urea levels were significantly higher in diseased fish compared to controls, and creatinine, phosphorus, and magnesium also showed similar trends. Alanine aminotransferase and total protein levels were higher in control fish. Chloride levels differed significantly between groups. Iron levels were higher in controls and lower in diseased fish. No significant differences were found in other parameters. This study reveals significant changes in serum blood parameters of rainbow trout during a lactococcosis outbreak caused by L. garvieae. These changes highlight the potential of these parameters as tools for monitoring health status, stress, and aquaculture management. Continuous monitoring can provide valuable insights into disease severity and overall fish health, aiding in the development of improved management practices. The presented data contribute to understanding the pathophysiology of piscine lactococcosis and developing effective mitigation strategies for farmed rainbow trout.

Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review

Precision agriculture (PA) technologies have spearheaded a revolution in contemporary farming practices, ushering in an era of sustainability, efficiency, and data-driven agricultural production. This comprehensive examination aims to delve deeply into the intricate dynamics surrounding the adoption of precision agriculture technologies among farmers, synthesizing existing literature, identifying key determinants, barriers, and implications. The adoption of precision agriculture technologies is influenced by a myriad of factors spanning socio-economic, institutional, technological, psychological, and attitudinal dimensions. Socio-economic factors such as farm size, income level, access to credit, and demographic characteristics are pivotal in shaping farmers' decisions to adopt PA technologies. Institutional factors, including government policies, extension services, market structures, and support networks, also wield significant influence over adoption patterns. Technological considerations, such as the complexity, compatibility, and perceived benefits of precision agriculture technologies, impact farmers' adoption decisions. Moreover, psychological factors such as risk perception, innovativeness, and attitudes towards change contribute to shaping farmers' willingness to embrace new technologies. The adoption of precision agriculture technologies holds immense promise for enhancing farm productivity, sustainability, and profitability. By enabling precise management of inputs such as water, fertilizers, pesticides, and seeds, PA technologies help optimize resource use, minimize environmental impact, and maximize yield potential. Furthermore, the integration of advanced data analytics, remote sensing, and IoT (Internet of Things) devices facilitates real-time monitoring, decision-making, and predictive analytics, empowering farmers with actionable insights for improved farm management. However, despite the compelling benefits, the adoption of precision agriculture technologies faces several barriers and challenges. High initial investment costs, limited access to capital, inadequate infrastructure, and technical knowledge gaps pose significant hurdles for farmers, particularly those operating in resource-constrained settings. Concerns regarding data privacy, security, and ownership also contribute to farmers' apprehensions about adopting PA technologies, raising questions about data governance and regulatory frameworks. To overcome these barriers and promote the widespread adoption of precision agriculture technologies, concerted efforts are needed from various stakeholders. Farmer education and training programs play a crucial role in enhancing digital literacy, technical skills, and awareness about the potential benefits of PA technologies. Government policies and incentives, including subsidies, grants, and tax incentives, can help alleviate financial barriers and incentivize adoption. Moreover, collaboration between researchers, industry stakeholders, and policymakers is essential to drive technological innovation, develop user-friendly solutions, and address emerging challenges. Investments in infrastructure, such as rural broadband connectivity and sensor networks, are also imperative to ensure the seamless integration and scalability of precision agriculture technologies across diverse farming landscapes.

Deciphering the fiber quality of Gossypium barbadense L. var. brasiliensis in La Convención, Cusco, Perú

BackgroundThe quality of cotton fiber determines its value in the textile market, influencing agricultural profitability and the efficiency of textile processing. The selection of genotypes with superior fibers is a key factor for genetic improvement programs seeking adaptability and sustainability in the face of climate change. This demonstrates the strategic importance of this plant for sustainable agriculture and the global textile industry. The objective of this research was to decipher the fiber quality of Gossypium barbadense var. brasiliensis in the native Amazonian communities of La Convención, Cusco-Perú, and to evaluate other critical aspects of native cotton that have not yet been identified. The methodology included non-probability sampling for accessibility, qualitative and quantitative analyses, and multivariate analyses. The fiber length (mm), micronaire index (maturity/fineness), fiber strength (gf/tex), length uniformity index (%), fiber elongation (%), maturation index (%), and short fiber index (%) were the fiber characteristics evaluated using the HVI method in cotton genotypes.ResultsCotton accessions collected from Koribeni (Gossypium spp.) and Shivankoreni (Gossypium barbadense var. brasiliensis) stood out for their fiber quality properties, especially length, strength, and uniformity, which highlights their relevance for advanced textile applications and potential for use in plant genetic improvement programs.ConclusionThese findings reinforce the need to conserve and study these native cotton accessions from the Peruvian Amazon region, which can offer promising perspectives for the textile industry and agricultural biodiversity.

Effects of unbalanced fertilizer use on system productivity and profitability under rice‐based cropping systems: Evidence from Eastern Gangetic Plain

AbstractUnbalanced nutrient supply is one of the key causes of yield gaps and low farming profitability which impact food insecurity globally. However, the effects of an unbalanced nutrient supply on rice yields and farming profitability have not been quantified for diverse rice‐based cropping systems. To explore the effects, 412 respondents (mostly smallholder farmers) were surveyed in 2019 for the rice crops in three fully rice‐based cropping systems in four agro‐ecological zones of Bangladesh. Two robust estimation approaches namely propensity score matching and endogenous switching regression approaches were used to quantify the effects of unbalanced fertilizer use. The results show that about 68% of farmers in the study areas used unbalanced (over‐doses or under‐doses) nutrient rates relative to government‐endorsed recommendations. Adoption of recommended fertilizer rates was influenced by education, amount of organic manure used, off‐farm income, crop farming‐related training, organizational membership and lack of soil testing tendency. The findings also indicate that adopters of recommended nutrient rates received almost 16% and 46% higher system rice yield and system net return, respectively, relative to non‐adopters. However, based on our research findings, formulation and implementation of effective agricultural policies like effective extension services, strengthening soil testing facilities, increasing organizational membership and targeted awareness programmes could motivate smallholder rice farmers towards adoption of recommended nutrient rates. Adoption of recommended fertilizer rates can significantly enhance crop yields and farming profitability which can boost food security and rural livelihoods in the Eastern Gangetic Plain.

Effect of four different types of vermicomposts on okra productivity and farm income

Organic based fertilizers is crucial for maintaining soil fertility, increasing crop production, and mitigating climate change. Among various organic fertilizers, vermicompost (VC) has been found to be one of the promising options for enhancing soil fertility and productivity. However, there are limited studies on the agronomic effects of vermicomposts in Nepal. In this study, a pot culture experiment was conducted inside a greenhouse to assess the agronomic and economic effects of four different commercially available vermicomposts, available in both reduced and original moisture forms, on okra. Nine treatments with three replications were arranged in a completely randomized design (CRD). These treatments included Divya vermicompost with both reduced (DvRM: 28.82 %) and original moisture (DvOM: 62.17 %); Praramva vermicompost with reduced (PvRM: 29.63 %) and original moisture (PvOM: 54.65 %), Bio-Comp vermicompost with reduced (BvRM: 26.75 %) and original moisture (BvOM: 49.77 %), Sathi vermicompost with reduced (SvRM: 28.52 %) and original moisture (SvOM: 65.18 %), and a control (CK: non-fertilized treatment). All VC treatments, whether in reduced or original moisture forms, showed better performance on plant growth parameters and fruit yield compared to the control. There was no significant difference between the four types of VCs, in either moisture form. The average number of fruits per pot in VC treatments (ranging from 5.5 to 8.0 fruits per pot) increased by 94 % compared to CK (3.5 fruits). Similarly, total fruit yield increased by 126 % in VC treatments (ranging from 16213 kg ha−1 to 23193 kg ha−1) compared to the CK (8260 kg ha−1). The benefit to cost ratio (B:C) of all the VCs was above 1 (B:C > 1), ranging from 1.00 to 1.59, illustrating the economic benefit of using vermicompost in okra cultivation. The findings suggest that farmers should consider the widespread use of vermicomposts to increase both crop productivity and farm profitability, thereby sustaining their livelihoods.

Effects of Local Economy and Seasonal Cleaning Cycle on Yield and Profit of Soiled Solar Farms

Effects of Local Economy and Seasonal Cleaning Cycle on Yield and Profit of Soiled Solar Farms

Optimal Resource Combinations that Maximize the Profits of Date Farmers in Al-Husseiniya District in the Holy Karbala Governorate for the Productive Season (2021)

Optimal Resource Combinations that Maximize the Profits of Date Farmers in Al-Husseiniya District in the Holy Karbala Governorate for the Productive Season (2021)

POTENSI LAHAN SAWAH UNTUK PENGEMBANGAN TANAMAN TEMBAKAU DI KABUPATEN BONDOWOSO

The plantation sector, especially tobacco plants, is the biggest contributor to the country's foreign exchange value non-mining through tobacco excise and export. With the increasing number of active smokers in Indonesia, the need for tobacco continues to increase, emphasizing the role of this crop in the economy and providing large profits for farmers. However, along with the growing issue of land use change, recently many tobacco plants have been planted on unsuitable land. The research was conducted on all rice fields in Bondowoso Regency from October 2022 to March 2023. The land suitability evaluation method was used to assess land characteristics that affect tobacco plant growth. The parameters used were soil pH, soil texture, C-organic, electrical conductivity, cations bases (K, Ca, Na, Mg), cation exchange capacity (CEC), base saturation, total N, available and exchangeable K, slope, temperature, and rainfall. Data analysis was carried out by matching techniques, namely matching land characteristics with the growing conditions of tobacco plants to get some suitable land suitability classes for tobacco plants. The results of the analysis showed that the area of ​​land currently planted with tobacco reaches about 12,155.06 hectares, with a suitable class (S2) reaching 897.82 hectares, marginal (S3) reaching 1,614.44 hectares, and not suitable (N) reaching 9,642.79 hectares. Land that has the potential to grow tobacco is S2 (quite suitable) and S3 (marginal).

Exploring strategies for agricultural sustainability in super hybrid rice using the food-carbon-nitrogen-water-energy-profit nexus framework

The breakthrough in super hybrid rice yield has significantly contributed to China's and global food security. However, the inherent conflict between high productivity and environmentally sustainable agriculture poses challenges. Issues like water scarcity, energy crises, escalating greenhouse gas emissions, and diminishing farm profitability all threaten agricultural sustainability. In response to these challenges, we applied a holistic food-carbon-nitrogen-water-energy-profit (FCNWEP) nexus framework to comprehensively evaluate sustainability of distinct crop management strategies across three sub-sites in central China. Field experiments were conducted in Hubei and Hunan Provinces from 2017 to 2021, with a widely adopted elite super hybrid rice (Y-liangyou900). Four crop management treatments were implemented: a control (CK, 0 kg N ha-1), conventional crop management (CCM, 210-250 kg N ha-1, 7:3 basal: mid-tiller fertilizer ratio), and two integrated crop management treatments (ICM1, 180-210 kg N ha-1, 5:2:3 basal: mid-tiller: panicle initiation fertilizer ratio; ICM2, 240-270 kg N ha-1, 5:2:2:1 basal: mid-tiller: panicle initiation: flowering fertilizer ratio). Grain yield, carbon footprint, nitrogen footprint, energy footprint, nitrogen use efficiency and economic benefits were among the assessed variables. Our results showed that significant yield variations were observed, with ICM2 consistently outperforming CCM and ICM1 across the three sites. In Jingzhou, Suizhou, and Changsha, ICM2’s grain yield was 30.2, 24.7, and 13.3% higher than CCM, respectively. Additionally, net profits for ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2% in Jingzhou, 32.2 and 109.9% in Suizhou, and 15.4 and 34.0% in Changsha. Integrated crop managements, specifically ICM2, demonstrated improved nitrogen and energy use efficiency, resulting in reduced carbon, nitrogen, water, and energy footprints. Overall, composite sustainability scores, calculated using the FCNWEP framework, indicated that both ICM2 and ICM1 generally exhibited higher sustainability levels compared to CCM. This study offers valuable insights into practical management methodologies and provides recommendations for enhancing agricultural sustainability.

Effect of Bio-fortified Vitamin A Cassava on Poverty Status of Farming Households in Nigeria, Evidence from Oyo State.

ABSTRACT Profitable farming is a prime target of all farmers regardless of the scale of production adopted and the veritable opportunity to attain the threshold of bumper harvest is not unconnected with the adoption of new technology. Bio-fortified vitamin A cassava remains the best option for farmers to experience improvement in cassava performance in both the quality and quantity terms and they are encouraged to adopt it in order to improve their households’ welfare. The survey was launched to investigate the effect of bio-fortified vitamin A cassava on the poverty status of the farming households in Nigeria using Oyo State as a case study. A Multistage sampling technique was used to sample a total of 186 cassava farmers comprising 92 adopters and 94 non-adopters of bio-fortified vitamin A cassava variety using copies of well-structured questionnaires. Parametric tools used were descriptive statistics and probit regression for socioeconomic characteristics and adoption status analysis. The socioeconomic results showed that both the adopters and non-adopters were in the productive ages of 42 years and 59 years respectively. The majorities of farmers in the two categories were married and had formal education. Years of experience possessed by them were 11 years and 18 years while both groups were members of associations. Poverty classification result showed 41.3% of adopters being poor while 83.0% of non-adopters were poor. Marital status (2.1271<p=0.05), education (1.1510; p<0.1), seasonal income (0.0757; p=0.05) and poverty status (I.6019; p<0.1) had influence on the farmers’ adoption status while lack of credit was voted highest among the factors militating against adoption of bio-fortified vitamin A cassava. It is therefore recommended that stakeholders in the crop enterprise should make frantic efforts in providing credit and other basic inputs that will enhance improved vitamin A adoption.

Productivity and technical inefficiency: A case study of small dryland mungbean farming in East Nusa Tenggara, Indonesia

Mungbeans are one of the important agricultural products for food security in East Nusa Tenggara (NTT) Province, which is one of the driest regions in Indonesia. The low productivity and profitability of mungbean farming in this region are caused by factors that influence agricultural inefficiency. This study was conducted to measure the productivity and technical efficiency levels, as well as investigate the factors affecting farm inefficiency, using the Technical Efficiency Effect Model (TEEM) applied to the frontier logarithmic transcendental production function. The method applied in this research is descriptive qualitative based on survey and interview results through primary and secondary data. Firstly, the results showed that mungbean farming was in the increasing return to scale stage of production and productivity only reached 57% of its production potential, with a technical efficiency level of 66%. Secondly, the factors that positively influence the low productivity and technical efficiency level of mungbean farming are land, seeds, labor, and the interaction between land and labor. Meanwhile, farmer experience and farm training significantly reduce the inefficiency of mungbean farming. It is suggested that increasing land productivity with appropriate and intensive modern technology and improving labor skills are the main strategies for reducing inefficiencies and increasing the income of smallholder farmers in drylands.

Assessing the impact of flood risk and associated factors on profitability of fish farming operations: evidence from South- South Nigeria

Fish farming plays a crucial role in food security and economic development in Nigeria. However, the country’s susceptibility to floods due to its geographical location and climate change has raised concerns about the profitability and sustainability of fish farming operations. This study aims to assess the impact of flood risk and associated factors on the profitability and sustainability of fish farming operations in South-South, Nigeria. The research employs a purposive random sampling techniques to generate data on flood frequency and severity, associated factor affecting profitability fish farms locations in the three (3) states, and other associated factors affecting profitability of fish farmers, their production figures are collected and analyzed to identify areas prone to flooding and the extent of damage caused to fish farms. Additionally, two hundred and one (180) fish farmers were sampled for the study (Delta 96, Bayelsa45 and Rivers 60 fish farmers) to gather information on flood risk effects, perception and profitability during flood occurrence. Preliminary findings indicate that flood severity is statistically significant at a 5% level (t-ratio: -2.4806) with a negative coefficient (-0.4876). Flood frequency is statistically significant at a 1% level (t-ratio: -4.5934). with a negative coefficient of -3.8035. This suggests that more severe flood events are associated with lower profitability among fish farmers. The result also revealed that at a probability of 0.1%, fish farmers in Delta, Bayelsa and Rivers States were susceptible to flooding with about 92.9% experiencing flood events although at a probability of 0.2%, it was noticed that only 7.1% of fish farmers were facing flood events. The study recommends that adoption of climate- smart farming practices, such as choosing flood resistant fish species and diversifying income sources, can enhance resilience, prioritize conducting thorough risk assessments specific to each fish farming location. These assessments should consider historical flood data, local climate conditions, topography, and the vulnerability of infrastructure. These findings will inform farmers and policy makers about the level of risk and guide mitigation efforts.

PROFITABILITY AND COMPETITIVENESS OF CORN FARMING IN GOWA REGENCY, SOUTH SULAWESI PROVINCE, INDONESIA

Corn is a multifunctional crop that is useful for food, feed, fertilizer, and biofuel. The increasing demand for corn commodities requires an increase in profitability and competitiveness, so that corn farming will be sustainable. The purpose of this research was to examine the profitability and competitiveness of corn farming in Bontonompo District, Gowa Regency. The survey method was employed in this investigation. Data collection methods include observation and interviews. The sampling technique used was purposive sampling, namely deliberately selecting farmers who were trying to plant corn in paddy fields in the second planting season (MT2), and farmers who were active in farmer group activities, so that a sample of 45 respondents was obtained. There are two types of research data: primary data and secondary data. Profitability analysis and PAM (Policy Analysis Matrix) are two data analysis methodologies. According to the findings of this study, the private profit or financial profit of corn farming in Bontonompo District, Gowa Regency, is Rp 7,731,314.10 per hectare, while the social profit or economic profit is Rp 8,924,460.07 per hectare. Corn farming has a financial feasibility ratio of 2,531, whereas it has an economic feasibility ratio of 2,453. Corn growing in Gowa Regency has a reasonably high level of competitiveness, as evidenced by a private cost ratio (PCR) of 0.333 and a domestic resource cost ratio (DRCR) of 0.293.

A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

Alternative ways of using the mushroom substrate as an additive for feed with the purpose of improving animal productivity in agriculture (review)

In modern agriculture, considerable attention is paid to the search for effective and environmentally safe methods of increasing animal productivity. One of the promising directions is the use of mushroom substrate as feed additives. Residual mushroom substrate is often considered as waste, but its processing and use can help reduce feed costs and increase farm profitability. In addition, the use of mushroom substrate helps to reduce the burden on the environment, promoting a closedproduction cycle. In the article it is considered the alternative ways of using mushroom substrate as a feed additive in order to improve the productivity of animals in agriculture. Mushroom substrate, the residual product after growing mushrooms, contains a rich complex of nutrients, such as proteins, vitamins, minerals and other bioactive components. The use of this substrate as a feed additive can provide economic benefits for farms, reducing the cost of traditional feed and increasing animal productivity. Studies indicate a positive effect of the mushroom substrate on the health of animals, in particular on the improvement of immunity and the general physiological state. In addition to economic benefits, the use of mushroom substrate helps to reduce the amount of waste and improve the environmental sustainability of agriculture, meeting the principles of circular economy and sustainable development. Objective. To analyze alternative ways of using mushroom substrate as animal feed additives. In the article it is reviewed modern research and practical experience of using mushroom substrate, its chemical composition and potential benefits for animal husbandry. Special attention is paid to the effect of mushroom substrate on the health of animals, their productivity and general economic indicators of farms. Revealing the potential of mushroom substrate as a feed additive can be an important step in the development of sustainable agriculture, in particular, in the conditions of growing demand for food and resources for its production. Conclusions. The article emphasizes the needfor further research to optimize the methods of using the mushroom substrate in feed rations of various animal species and to develop new technologies for its processing. The practical implementation of these studies can significantly increase the efficiency and competitiveness of agricultural enterprises. Keywords: mushroom substrate, chemical composition, feed additive, animal productivity, agriculture, bioactive components, feeding.

From leaf to harvest: achieving sustainable agriculture through advanced disease prediction with DBN-EKELM.

In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. The objective of this study is to develop a new deep learning-based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network-based enhanced kernel extreme learning machine (DBN-EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN-EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge-based optimization algorithm (NBGSK). The efficacy of the proposed DBN-EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F-measure. Experimental analyses demonstrate that the DBN-EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry.