Agricultural Production Practices Research Articles

Research Hotspots and Trends of Nitrification Inhibitors: A Bibliometric Review from 2004–2023

Nitrification inhibitors are essential in agricultural and environmental production practices. They play a crucial role in promoting agricultural and environmental sustainability by enhancing nitrogen use efficiency, boosting crop yields, and mitigating the adverse environmental effects of nitrogen losses. This bibliometric analysis covers the period from 2004 to 2023, offering a detailed examination of the development of nitrification inhibitor research. The study demonstrates a consistent growth in research publications, indicating sustained interest and dedication to advancing the field. It identifies key contributors, such as institutions and researchers, and underscores the significance of their work through citation analysis. Keyword co-occurrence analysis reveals four distinct clusters focusing on enhancing crop yields, understanding microbial community dynamics, exploring grazing pasture applications, and addressing environmental impact mitigation. The cutting-edge area of keyword burst detection research has transitioned from fundamental research to comprehensive nitrogen management practices. This analysis provides insights into the current research landscape of nitrification inhibitors and proposes future research directions, underscoring the critical role of this field in tackling global agricultural and environmental challenges.

Measurement and analysis of regional water-energy-food nexus resilience with an improved hybrid kernel extreme learning machine model based on a dung beetle optimization algorithm

CONTEXTIn traditional water-energy-food nexus (WEFN) system analysis, the impact of resilience is insufficiently considered, and the accuracy of resilience assessment is low, which can easily lead to system imbalance. Modeling measures can yield optimal evaluation results and provide decision makers with a scientific and efficient basis for management. OBJECTIVEThe objective of this study was to provide a new research paradigm for exploring the sustainable development of WEFN systems by quantitatively and accurately assessing WEFN resilience and identifying its key driving factors. METHODSThe connotation of WEFN resilience was established, and a human-centered conceptual framework for WEFN resilience was constructed. A WEFN resilience evaluation system was scientifically and completely constructed using qualitative and quantitative index screening methods. The hybrid kernel extreme learning machine (HKELM) model was improved based on the dung beetle optimization (DBO) algorithm to measure and analyze the WEFN resilience of the China Beidahuang Group. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method was adopted to calculate the evaluation index weight of the WEFN system of the Beidahuang Group. Model performance verification methods, such as application of the theory of serial number summation, were used to evaluate the performance of the DBO-HKELM model. RESULTS AND CONCLUSIONSThe results showed that over time, the Beidahuang Group's WEFN resilience exhibited a fluctuating upward trend. From 2006 to 2008, WEFN resilience declined to level II because of predatory agricultural production and management practices. From 2008 to 2018, the WEFN resilience increased to level IV due to the protection of uncultivated land, the optimization of irrigation methods, and the increasing maturity of the agricultural modernization development model. Likewise, from 2018 to 2020, the WEFN resilience stabilized at level IV because of the optimization of agricultural planting structures and the reduction in coal consumption. Spatially, the WEFN resilience of various branches of the Beidahuang Group exhibited a trend of “gradually decreasing from north to south.” Differences in certain variables, such as the proportion of area irrigated with electromechanical wells, were the most important factors related to changes in WEFN resilience over time. The DBO-HKELM model has notable advantages over the FA-HKELM and NGO-HKELM models in terms of fitting accuracy, optimization rate, reliability, rationality, and robustness. SIGNIFICANCEThese research results could enrich quantitative assessment methods for determining WEFN system recovery strength and provide scientific support for the safe operation and sustainable development of agricultural economies and societies.

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics.

Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

Effects of Protein Hydrolysate Derived from Anchovy By-Product on Plant Growth of Primrose and Root System Architecture Analysis with Machine Learning

Protein hydrolysates (PHs) derived from waste materials are promising for sustainable practices in agricultural production. This study evaluated the effects of PH enzymatically derived from anchovy by-products on the root system architecture (RSA) and aboveground development of potted primrose. The plants were treated with 0.5, 1.0, and 1.5 g/L concentrations of PH by drenching with 100 mL/pot at two-week intervals and irrigated once a week with 100 mL/pot during winter and twice weekly during spring. The results revealed that the 1.5 g/L treatment statistically significantly improved dry weight and leaf area, while the highest leaf chlorophyll content was observed with the 1.0 g/L treatment. The treatments did not influence leaf and flower numbers. Treatment with 1.0 g/L produced the most substantial improvement in root surface area, projected area, volume, length, tips, and forks. Additionally, the study employed machine learning (ML) algorithms, including GP, RF, XGBoost, and an ANN-based MLP. The input variables (root surface area, projected area, volume, length, tips, and forks) were assessed to model and predict the root traits. The ML and ANN algorithms’ R-squared rates were noted in the following order: MLP > GP > RF > XGBoost. These outcomes hold significant implications for enhancing primrose growth.

The Ameliorationist Trap: Reformist Capture and the Long Eighteenth Century

This essay advances ameliorationism as a keyword and diagnostic concept for reappraising eighteenth-century anti-slavery writing and political thought; it also considers the implications of eighteenth-century abolitionism for contemporary abolitionist theory and practice. I argue that Anglophone Atlantic writing on the transatlantic slave trade and colonial plantation slavery is overwhelmingly oriented by a reformist perspective that aims to soften, economize, and refine the practice of slave-based agricultural production in order to stave off demographic and environmental catastrophe. By way of a brief consideration of merchant and philosopher Thomas Tryon, I meditate on how this reformist line of thinking puts forward potent critiques of slavery that nonetheless reinscribe the plantation as a necessary form of social organization. This reformist trap confronts contemporary organizers and theorists of abolition, and scholars of the long eighteenth century might contribute to the struggle to abolish police forces and prisons by critically dismantling fictions about the recuperability of carceral systems.

Reconstructing Late Neolithic animal management practices at Kangjia, North China, using microfossil analysis of dental calculus

Examination of plant microfossils (phytoliths and starch granules) preserved in dental calculus allows for the direct identification of some components of prehistoric diets. In this article, the authors present the results of microfossil analysis of dental calculus from wild and domestic animals at the Late Neolithic site of Kangjia in the Central Plains, an area critical in the emergence of early Chinese states. Consumption of cooked plant foods by domestic pigs and dogs, and of domestic crops by wild animals, at this site hints, the authors argue, at an interdependent relationship between animal management, agricultural production and ritual practices that contributed to the political transformations of Late Neolithic China.

Reducing energy and carbon footprint in semi-arid irrigated cropping systems through crop diversification

Energy and carbon (C) footprints of agricultural production practices have garnered high attention due to rising energy costs and increasing global warming. However, the contribution of conservation and regenerative farming practices, including cover cropping, on energy and C footprints have not yet been documented for cropping systems in arid and semi-arid regions. This study evaluated the energy and C footprint of cover crop integrated silage maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) production systems in the semi-arid region of the southwestern US. The treatments were mixtures of winter cover crops: i) grasses and legumes (GL), ii) grasses, brassicas, and legumes (GBL), iii) grasses and brassicas (GB), and iv) no cover crops (NCC) control for each crop production system. Results showed cover crops had 24.1–24.5% greater energy input than NCC. In silage maize rotation, energy output was 17–22% greater in GBL and GL than in NCC. In silage sorghum rotation, the energy output was 15–24% greater in all cover crops than in NCC. The resulting net energy was 16–21% greater in GBL and GL than in NCC under silage maize, while it was 18–24% greater in GBL and GB than in NCC under silage sorghum. In the silage maize system, the C-footprint per kg yield was not different among treatments, whereas in silage sorghum, it was 58% greater in GBL than in NCC. The benefit-to-cost ratio was greater than one for all treatments, but the additional revenue through C credit programs could make cover cropping a more feasible and beneficial approach, improving economic and environmental sustainability while producing silage crops. While the C footprint was crop rotation specific, cover cropping should be encouraged over crop-fallow systems to producers in semi-arid environments to reduce energy usage and increase C-credit benefits. Clear national and state policy on the C credit program will also enhance economic and environmental benefits by adopting cover cropping and other regenerative farming practices.

Влияние предпосевной обработки семян растворами микробиологических препаратов на посевные качества семян и морфофизиологические показатели проростков ярового ячменя (Hordeum vulgare L.)

The paper presents the results of laboratory experience in studying the sowing qualities and morphometric parameters of seedlings of spring barley seeds of the Yaromir variety using seed treatments before sowing with microbiological preparations. The production of spring barley in modern conditions remains a priority in agricultural production. At the same time, the need for obtaining high yields with a minimum amount of chemicals increases. One of the ways to reduce the chemical load is to use seed treatments with microbiological preparations. As a result of the study, it was found that a microbiological preparation based on the strain KL-10 (Pseudomonas sp.) isolated from the barley rhizosphere had a positive effect on germination energy and laboratory germination of seeds, as well as on an increase in the mass of 100 sprouts and roots, and their length. This drug is the most promising for studying in the practice of agricultural production. Keywords: SPRING BARLEY, MICROBIOLOGICAL PREPARATIONS, GERMINATION ENERGY, LABORATORY GERMINATION, SEEDLINGS, ROOTS, SEEDS

5-ALA, DTA-6, and Nitrogen Mitigate NaCl Stress by Promoting Photosynthesis and Carbon Metabolism in Rice Seedlings.

A large number of dead seedlings can occur in saline soils, which seriously affects the large-scale cultivation of rice. This study investigated the effects of plant growth regulators (PGRs) and nitrogen application on seedling growth and salt tolerance (Oryza sativa L.), which is of great significance for agricultural production practices. A conventional rice variety, "Huang Huazhan", was selected for this study. Non-salt stress treatments included 0% NaCl (CK treatment), CK + 0.05 g N/pot (N treatment), CK + 40 mg·L-1 5-aminolevulinic acid (5-ALA) (A treatment), and CK + 30 mg·L-1 diethylaminoethyl acetate (DTA-6) (D treatment). Salt stress treatments included 0.3% NaCl (S treatment), N + 0.3% NaCl (NS treatment), A + 0.3% NaCl (AS treatment), and D + 0.3% NaCl (DS treatment). When 3 leaves and 1 heart emerged from the soil, plants were sprayed with DTA-6 and 5-ALA, followed by the application of 0.3% NaCl (w/w) to the soil after 24 h. Seedling morphology and photosynthetic indices, as well as carbohydrate metabolism and key enzyme activities, were determined for each treatment. Our results showed that N, A, and D treatments promoted seedling growth, photosynthesis, carbohydrate levels, and the activities of key enzymes involved in carbon metabolism when compared to the CK treatment. The A treatment had the most significant effect, with increases in aboveground dry weight and net photosynthetic rates (Pn) ranging from 17.74% to 41.02% and 3.61% to 32.60%, respectively. Stomatal limiting values (Ls) significantly decreased from 19.17% to 43.02%. Salt stress significantly inhibited seedling growth. NS, AS, and DS treatments alleviated the morphological and physiological damage of salt stress on seedlings when compared to the S treatment. The AS treatment was the most effective in improving seedling morphology, promoting photosynthesis, increasing carbohydrate levels, and key enzyme activities. After AS treatment, increases in aboveground dry weight, net photosynthetic rate, soluble sugar content, total sucrose synthase, and amylase activities were 17.50% to 50.79%, 11.39% to 98.10%, 20.20% to 80.85%, 21.21% to 33.53%, and 22.17% to 34.19%, respectively, when compared to the S treatment. In summary, foliar sprays of 5-ALA, DTA-6, and additional nitrogen fertilizer enhanced rice seedling growth, increased photosynthesis, lowered Ls values, and improved seedling salt tolerance. Spraying two regulators, 5-ALA and DTA-6, quantitatively increased the effect of nitrogen fertilizer, with comparable effects on NaCl stress regulation. This study provides the basis for efficient agricultural production.

Perceived effects of COVID-19 on smallholder farmers’ agricultural production practices in Ethiopia

This study analyzed the perceived effects of COVID-19 on smallholder farmers’ agricultural production practices in the South Wollo and Oromo Administrative Zones of Ethiopia. Data were collected from 270 randomly selected respondents from September 5 to October 10, 2020. Data were analyzed qualitatively and quantitatively. The severity of problems is analyzed using a four-point Likert scale. The quantitative data were analyzed using mean and one sample t-test, while qualitative data were analyzed through thematic analysis. The one sample t-test result revealed that the respondents' level of agreement is statistically and significantly different from neutral in all cases at the 95 and 99% levels of confidence. These immediate problems caused the smallholder farmers to develop concerns about the effects of the COVID-19 pandemic. Smallholder farmers in Jige faced challenges like a lack of markets, collaboration issues, delayed agricultural activities, spoilage of produce, labour issues, and increased input prices. The government and development practitioners should prioritize timely agricultural input delivery through cooperative unions, transfer of information and advisory services, and the marketing of agricultural products using alternative strategies. This research contributes evidence-based information for decision-makers to devise appropriate and locally adaptable policy documents for the given policy recommendations.

Weed detection and removal using robotic system

Weeding is one of the most significant practices in agricultural production. Weeds are unwanted plants that grow along with the crops and compete with the crops for space, light, water, and soil nutrients. Weeds propagate themselves either through seeding or creeping rootstalk and decrease yields, increase production costs, interfere with the harvest, and lower the product quality. The use of herbicides reduces labor requirements for weed control by up to 60 percent but affects environmental quality and can be toxic to a wide range of organisms. Hence it is necessary to develop an automated system to identify and remove weeds from the vegetable fields. The objective of the proposed work is to develop a mobility level tracked bot that identifies the weeds and removes them with the help of a robotic end effector and to develop a machine learning model to identify the weeds. This functional module will be processed in a Raspberry Pi processor and by using a Raspberry Pi camera module the bot will detect the weeds in vegetable fields. We performed weed detection with different machine learning models like Haar cascade, YOLOv5, and CNN. To evaluate the performance of the machine learning models used, the performance metrics accuracy, precision, recall, and F-measure are estimated and it has been found that CNN has better accuracy, precision, and recall as compared to YOLOv5 and Haar cascade. CNN has the highest F-measure among the three algorithms at 98%. The weed removal is done using a robotic end-effector which is controlled by the Arduino UNO based on the signal from Raspberry Pi.

AAUConvNeXt: Enhancing Crop Lodging Segmentation with Optimized Deep Learning Architectures

Rice lodging, a phenomenon precipitated by environmental factors or crop characteristics, presents a substantial challenge in agricultural production, notably impacting yield prediction and disaster assessment. Despite that the application of conventional methodologies like visual assessment, mathematical models, and satellite remote sensing technologies has been employed in the segmentation of crop lodging, these approaches are still constrained in precision, immediacy, and capacity for large-scale evaluation. This study introduces an innovative convolutional neural network architecture, AFOA + APOM + UConvNeXt, that integrates intelligent optimization algorithms for automatic selection of optimal network parameters, thereby enhancing the accuracy and efficiency of crop lodging segmentation. The proposed model, empirically validated, outperforms recent state-of-the-art models in crop lodging segmentation, demonstrating higher accuracy, lower computational resource requirements, and greater efficiency, thereby markedly reducing the cost of segmentation. In addition, we investigated the segmentation on half lodging rice, and the results indicate that the model exhibits commendable performance on the half lodging dataset. This outcome holds significant implications for the prediction of rice lodging trends. The fusion of deep learning with intelligent optimization algorithms in this study offers a new effective tool for crop lodging monitoring in agricultural production, providing strong technical support for accurate crop phenotypic information extraction, and is expected to play a significant role in agricultural production practices.

Effect of Salinity Stress on Tomato (Lycopersicon Esculentum L.) and Mitigation Strategies

Tomato (Lycopersicon esculentum L.) belongs to the Solanaceae family. It is a crop of immense economic importance worldwide and salinity is one of the major abiotic factors limiting it's production and productivity globally. The amount of irrigation water and their evapotranspiration is the main reason that causes salinization. Salinity is an abiotic stress that affects agriculture by severely impacting crop growth and, consequently, final yield. Considering that sea levels rise at an alarming rate over year, it is clear that salt stress constitutes a top-ranking threat to agriculture. Among the economically important crops that are sensitive to high salinity is tomato one that is more affected by salt stress. Si plays the beneficial role of the quasi-essential metalloid silicon (Si), which increases the vigor and protects plants against a biotic stresses. The use of silicon fertilization can be used as sustainable practices in agricultural production to increase yield and quality of plants. Silicon fertilization also plays role in plant protection against various range of exogenous stresses especially, under changing environment. The use of appropriate irrigation method, amount and water quality to minimize the risk of salt accumulation around root zone of plants. Different plant growth regulators and amino acids could also play a great role in increasing yield and growth of tomato under salt stress.

Enhancing employment conditions for migrant seasonal workers—Assessing consumer preferences for ‘fair-labour’ apples

Abstract Criticisms over labour practices in agricultural production often target the living and working conditions of migrant seasonal workers. This article assesses consumer preferences for apples produced under social conditions exceeding current legal standards. The analysis is based on a discrete choice experiment (DCE) with a sample of 204 German consumers, who were asked to choose among domestic ‘fair-labour’ apples and standard apples. The former differed in six attributes describing the social conditions facing migrant seasonal workers on German farms. A mixed logit and latent class model were used to analyse the data. The predicted probability of choosing a ‘fair-labour’ apple was 85 per cent. Consumers valued higher minimum wages, the inclusion of migrant workers in Germany's social security system, and bonus payments for work on Sundays and public holidays. Improved accommodation and limits on the maximum permissible weekly working hours were considered less important. The low-price elasticity suggests that farmers could recoup a large share of the extra costs involved in providing enhanced living and working conditions for their workers.

Modelling future climate effects on N2O emission and soil carbon storage in maize fields under controlled-release urea and straw incorporation

Controlled-release urea application and straw incorporation have been conducted in recent years as environmental-friendly and sustainable farming strategies, but the long-term effects of controlled-release urea application and combination with straw on the dryland maize yield, soil fertility and the environment under future climate scenarios remain unclear. Hence, based on a six-year field experiment, four treatments were used to calibrate and validate the DeNitrification-DeComposition (DNDC) model, including non-nitrogen (CK), split applications of conventional urea (UR), single basal application of conventional urea and controlled-release urea at a ratio of 2:1 (CU), and CU combined with straw (CUS). Subsequently, coupled the well-validated model with future climate to evaluate suitable agricultural production practices under two shared socioeconomic pathways (SSP)-SSP245 and SSP585. The validation results indicated a good fit between the simulated and observed data of greenhouse gas emissions, soil organic carbon (SOC) contents and maize yields. With the anticipation of warmer temperatures and increased precipitation in the future, the yields of UR, CU, and CUS treatment significantly rose. Under SSP585 scenario, the positive impacts of CU treatment on maize yields reduced after the 2050s, exhibiting an average decline of 12.03%. Compared with the UR treatment, the CU treatment markedly reduced cumulative N2O emissions, and both treatments maintained the original state of SOC storages in the 2030s, furthermore, the CUS treatment reduced N2O emissions by 47.10%, 35.07%, 23.80% and 10.04% in the 2030s, 2050s, 2070s and 2090s, respectively. SOC storages for the CUS treatment gradually increased with an average of 464.58, 350.22, 250.87 and 177.75 kg C ha−1 y−1 for two SSP scenarios in the 2030s, 2050s, 2070s and 2090s, which excellently offset the CO2 equivalent of emissions caused by N2O emissions. Therefore, in dryland maize production, combined controlled-release urea with straw incorporation could achieve the best comprehensive effect among increase of yield, improvement of SOC storages and alleviation of greenhouse gas emissions under future climate scenario.

The effects of land direct payment program on farm income and production practices

AbstractMany countries of the world have implemented subsidy programs to maintain a minimum amount of farm production especially for rice. Agricultural support programs usually cause significant fiscal burdens on government budgets. These programs can be generally categorized into decoupled and coupled programs, and one longstanding research interest of previous literature is whether these programs are decoupled or coupled with farm production practices. In Taiwan, a unique land direct payment (LDP) program to rice farms was implemented to ease severe financial burden of the price support program with government purchase. In contrast to production control programs imposing quantity restrictions on farm production, this program is voluntary for rice farms, and participants are paid at a fixed rate per hectare of farmland. This paper examines the impacts of this program on farm income and farm production practices in Taiwan. The causal effect of the program is identified based on the exogenous rollout of the policy across multiple times and areas. We construct a dataset linking nationally representative rice farm surveys over several years and administrative records on program recipients to estimate the local average treatment effect of the program. We find that the effect is 4% of farm income. In addition, program participants rely less on price support programs and produce less rice and use less fertilizer, pesticide, and machinery. Although the LDP reduces rice production, we find that it causes overall government spending on the price support and direct payment programs to increase by 8.7% after the LDP.

Dynamics of carbon budget and meteorological factors of a typical maize ecosystem in Songnen Plain, China

Aim of study: Understanding the carbon budget and meteorological factor impacts of farmland ecosystems is helpful for scientific assessment of carbon budget and low-carbon agricultural production practices.
 Area of study: The Songnen Plain, NE China, in 2019.
 Material and methods: Based on eddy-related flux and soil heterotrophic respiration observations from a typical maize farmland ecosystem, using mathematical statistics and carbon balance equation methods, were analyzed.
 Main results: Soil respiration rate (Rs) and composition were influenced and controlled by the synergistic effect of surface soil temperature (Ts) and water content (Wcs). Ts played a leading role, while Wcs played an important role. Ts and Wcs had the greatest influence on the heterotrophic respiration rate (Rh), followed by Rs and autotrophic respiration rate (Ra). Daily variations of net ecosystem productivity were correlated with daily mean air temperature, latent heat flux, and sensible heat flux. Annual carbon revenue was 1139.67 g C m-2, annual carbon expenditure was 456.14 g C m-2, and annual carbon budget was -683.53 g C m-2 in 2019. While considering that maize grain yield (-353.44 g C m-2) was moved out of the field at harvest, the net ecosystem carbon balance was -330.09 g C m-2; then it was carbon sink in 2019. By fully utilizing climate resources and improving agricultural managements, carbon sink is increased in farmland ecosystems.
 Research highlights: Soil respiration rate and composition were influenced and controlled by the synergistic effect of soil temperature and water content; the maize farmland ecosystem is carbon sink.

PROSPECTS OF GROWING MEDICINAL, ESSENTIAL OIL AND SPICY AROMATIC CULTURES

This work is devoted to the analysis of the most common issues faced by manufacturers of medicinal and essential oil raw materials — the market, the availability and availability of wild raw materials, agro-ecological conditions, the amount of manual labor, its cost, investments in quality seeds and equipment, post-harvest processing and the overall profitability of production. Cultivation of medicinal, essential oil and aromatic plants today is not only a promising alternative to the collection of wild raw materials, which allows preserving the natural biodiversity of plants with medicinal properties, including those species that have a protective status, but is also a powerful branch of the economy that provides high quality raw materials for the pharmaceutical, cosmetic and food industries. As a rule, medicinal and essential oil plants are considered by agricultural producers as secondary crops, as their cultivation is associated with certain problems. These problems are common to both small and powerful agricultural enterprises, both in Ukraine and abroad. They are related to both technological aspects of growing and plant identification, as well as market fluctuations and product prices. Consumers of medicinal and essential oil raw materials increasingly prefer cultivated products, because under such conditions it is easier to predict yield and ensure quality compared to wild-grown raw materials. During cultivation, false identification and unintentional falsification of raw materials are almost excluded, and the use of improved methods of cultivation and the use of modern varieties allows planning the harvest and costs. The profitability of growing medicinal and essential oil crops competes with traditional field crops, which have highly specialized equipment, a significant list of zoned varieties and hybrids, standard procedures for applying fertilizers and protective means, etc. In contrast to the cultivation of traditional crops, in order to ensure the profitability of the production of medicinal and essential oil raw materials, it is necessary to use methods with low production costs in order to be competitive on the international market and with the production of raw materials from natural groups. The work uses generalized material, both from our own many years of scientific and practical experience in the cultivation of medicinal and essential oil raw materials, as well as the experience of scientists and practitioners of foreign countries. On the example of the experience of foreign countries and domestic researchers and producers, the authors sought to prove that the cultivation of agricultural crops for obtaining essential oil and medicinal raw materials is a profitable business and can be considered as a variant of niche crops, be an attractive option for small farms and family farms. Within the scope of this review, it is impossible to consider all the problematic issues of the widespread introduction of medicinal and essential oil crops into the practice of agricultural production, since many more problems need to be solved, which can be the subject of future experiments and research.

Прилагане на системата за потенциална относителна оценка на земеделските земи в производствените практики

The study clearly presents the positive effect of the application of the newly developed “Methodology for the Potential Assessment of Agricultural Lands in Bulgaria” (Radovanova, 2022) and the corresponding software (Radovanova, 2023), simultaneously with the existing “Methodology for the Current Assessment,”, in agricultural production practices. The object of research is agricultural land with alluvial-meadow soil above the floodplain terraces of the Iskar River in the Samokov field. In parallel, Land Evaluation was carried out through two approaches: 1. Actual Evaluation and 2. Potential Evaluation. It has been established that 2 crops are economically profitable to grow - potatoes and wheat.According to the individual ratings (minimum actual and maximum potential) of the characteristics of this land and the corresponding Field Ratings, guidelines are given for optimizing cultivation technologies and recommending triple crop rotation.

How Does Capital Endowment Impact Farmers’ Green Production Behavior? Perspectives on Ecological Cognition and Environmental Regulation

Enhancing rural ecological protection and promoting the adoption of environmentally-friendly agricultural production practices largely depend on the implementation of green production methods by farmers. This research paper focuses on the green production behavior of farmers in Hainan Province, China, drawing insights from survey responses provided by 543 farmers. The study examines how farmers’ capital endowment, ecological cognition, and environmental regulation impact their multiple green production practices, using the ordered probit model, mediation effect model, and moderation effect model for analysis and further applying the Ivoprobit_CMP, PSM, and other methods for endogeneity treatment and a robustness test. The study reveals several key findings. Firstly, only a small proportion of farmers consistently adopt green production practices throughout various stages of agricultural production. Secondly, the research shows that capital endowment, including natural, economic, human, and social capital, has a significant influence on farmers’ green production behavior. Thirdly, the study finds that ecological cognition plays a crucial role in mediating the relationship between capital endowment and its dimensions (human and social capital) and the adoption of green production practices. Additionally, the research discovers that environmental regulation positively moderates the relationship between ecological cognition and farmers’ green production behavior. Finally, the research indicates that factors such as ethnicity, fluctuations in agricultural production, and the location of the village agricultural waste collection site are all significant determinants of farmers’ engagement in green production practices. The study also identifies significant regional disparities in farmers’ adoption of green production practices. To address the issue of farmers’ low adoption of green production practices, the paper recommends promoting the overall green transformation of the entire agricultural production process. This can be achieved by enhancing farmers’ capital endowment in multiple dimensions and improving their ecological cognition through various channels. Additionally, creating a supportive external environment that aligns with green production practices is deemed critical.