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Sustaining life on the Earth with its ever-growing population is forcing changes in people’s way of life, industrial and agricultural production, exploitation of energy resources, and approaches to ecology. We face continual growth in the world’s population, and the demand for materials is growing even more rapidly. Every manufacturing and consumer sector is looking for ways to save energy and materials, attempting to minimize their negative impacts on the environment. In the pulp and paper industry, one of the segments in which progress can be made and help protect the planet is to reduce the brightness of paper. Such a reduction would lead to a lowering in the energy and material costs associated with paper production. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic limits many physical activities of the society in public areas. The agricultural production sector is one of the important sectors in supporting the country’s nutrition demand. Research is a way to maintain the productivity of the agricultural sector. This study has a purpose to analyze if pandemic COVID-19 has an impact on the number of papers about agriculture recorded by Scopus. This study uses a quantitative with secondary data analysis approach in collecting data. The samples are paper on the Scopus website contains keywords “agriculture& meat”, and “agriculture&egg”. the period of samples are during 2019-2020. The data are analyzed using Vosviewer software to show the institution with the most related topic paper. The result shows that some papers are decreasing while others are increase based on the document type. For paper related to “agriculture” meat”, article as full type research paper increases along with review type paper. For a paper about “agriculture & egg”, conference type paper and review increase, while other decreases. It can be concluded that the type of paper along with the topic affects the institutions’ productivity in publishing papers. [ABSTRACT FROM AUTHOR]

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change's impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R 2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R 2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. [ABSTRACT FROM AUTHOR]

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health. [ABSTRACT FROM AUTHOR]

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research. [ABSTRACT FROM AUTHOR]

The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial and agricultural production. This is representative of glaciers in Middle Asia and represents one of the world's longest observed time series of glaciers, beginning in 1959. The Urumqi Headwater Glacier No. 1 (UHG-1) has a dominant presence in the World Glacier Monitoring Service (WGMS). This paper supplies a comprehensive analysis of past studies and future modeling of glacier changes in the Urumqi Valley. It has received insufficient attention in the past, and the mass balance of UHG-1 was used to verify that the geodetic results and the OGGM model simulation results are convincing. The main conclusions are: The area of 48.68 ± 4.59 km2 delineated by 150 glaciers in 1958 decreased to 21.61 ± 0.27 km2 delineated by 108 glaciers in 2022, with a reduction of 0.47 ± 0.04 km2·a−1 (0.96% a−1 in 1958–2022). The glacier mass balance by geodesy is −0.69 ± 0.11 m w.e.a−1 in 2000–2022, which is just deviating from the measured result (−0.66 m w.e.a−1), but the geodetic result in this paper can be enough to reflect the glacier changes (−0.65 ± 0.11 m w.e.a−1) of the URB in 2000–2022. The future loss rate of area and volume will undergo a rapid and then decelerating process, with the fastest and slowest inflection points occurring around 2035 and 2070, respectively. High temperatures and large precipitation in summer accelerate glacier loss, and the corresponding lag period of glacier change to climate is about 2–3 years. [ABSTRACT FROM AUTHOR]

The agricultural sector of the Republic of Moldova is strongly relying on the crop production sector, which has been holding an average share of about 70% of the total agricultural output, being followed by livestock sector and services. Crop sector's importance is determined through its contribution to ensuring food security, provision of raw material for the food processing sector and also support to the livestock's sector development. Therefore, the paper aims to evaluate the current development trends of the sector, by emphasizing its achievements on the foreign markets through assessment of its competitiveness. The used methodology is based on the scientific research methods like comparative analysis, synthesis, sectoral SWOT analysis and calculation of several competitiveness indices like Revealed Comparative Advantage, Revealed Symmetric Comparative Advantage, Trade Specialization Index and Grubel-Lloyd Index. The results present a continuous development of the sector with the need for production diversification and a competitive advantage on external markets for mostly raw material products like oil seeds, edible fruits and nuts, cereals, etc. [ABSTRACT FROM AUTHOR]

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions. Author summary: Genome editing using the CRISPR-Cas9 system, particularly base substitutions directed by guide RNA, holds immense potential for applications in gene therapy and agricultural productivity. However, the risk of unintended off-target effects poses a challenge, as misdirection of the Cas9 endonuclease can lead to unintended genome alterations. While computational methods exist for predicting off-target effects, there remains a need for encoding methods with more representation space and deep learning models with generalization capability and the adaptability. This paper introduces CRISPR-M, an innovative approach addressing the limitations of existing methods in predicting off-target effects, especially for target sites with indels and mismatches. CRISPR-M employs a novel encoding scheme and a multi-view deep learning model, combining convolutional neural networks and bidirectional long short-term memory recurrent neural networks. The three-branch network structure enhances the prediction accuracy by considering multiple perspectives. Compared with previous representative methods, CRISPR-M exhibits remarkable performance advantages when applied to real-world datasets. The experimental evaluation of CRISPR-M, assessed by various metrics such as ROC, PRC, GC content and melting temperature, demonstrates its ability to extract meaningful features and establishes its superiority in predicting off-target effects of sgRNA. [ABSTRACT FROM AUTHOR]

This paper aims to present an updated evolution of vegetable agricultural production in Vrancea county, during the period 2017-2021, with focus on the county's contributions in total at the country level, using the National Institute of Statistics (NIS) as principal data source. In order to meet the objectives, the methodology of research was based on indicators frequently used in papers about vegetable agricultural production, represented as values and/or percentage units. In 2021, Vrancea county detained 108 civil employment population (with 17% less than in 2017), through 22 employers in agriculture (52% less than in 2017). Among the main findings from the analyse are the following: the areas cultivated with corn grains, oat, sunflower, vegetables (all), dried onion, tomatoes, total fruits trees, plum, apples, pears, cherries and burgundy decreased among the county between 2017 and 2021 period and the areas cultivated with wheat, barley and vineyards in bearing increased. About the production, at the county level, the production of wheat, oat, barley, total fruit trees, plum, apple, pears, cherries, burgundy and grapes increased and the productions like: corn grains, sunflowers, vegetables (all), dried onion, tomatoes, decreased in the analysed period (2017-2021). From the conclusions of this paper, we found out that besides the good image in terms of wine fields, is observed that total fruit production increased by 50%, which can mean a new growth area of the county, regarding of vegetable agricultural production. [ABSTRACT FROM AUTHOR]

Cotton is a crucial cash crop in the tribal belt of Odisha. It has a significant effect on the livelihood of the tribal people. The volatile socio-economic background of the tribal farmers is a major hindrance to the proper development of the agriculture sector. In this regard, it is important to study the possible factors affecting cotton productivity in the tribal region to boost the policy effectiveness for the development of this cash crop in the area. More precisely, how is their socio-economic condition reflected in their crop productivity? So, the development of the tribal farmers can develop this crop in the region. This study analyses the detrimental effect of socio-economic factors on cotton productivity in the tribal area of Odisha. The data are collected using a structured questionnaire and a multiple-sampling technique. To understand the effect of socioeconomic factors on productivity, the ordinary least square (OLS) method has been employed in this paper. The result shows that most of the farmers are illiterate for which productivity is degrading. The age and gender of the head of the household are significant contributors to the cotton productivity of the study area. Male-headed households are more productive than female-headed households. Per capita monthly income and proportion of irrigated land are also important factors to affect cotton productivity positively. However, the social group of the family has no significant effect on the cotton productivity in the study area. Policymakers and agriculturists should emphasize farmer literacy, farmer income and land irrigation facilities to boost the productivity of this crop in the studied region. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems. [ABSTRACT FROM AUTHOR]

A review of the article "Agricultural biotechnology and crop productivity: macro-level evidences on contribution of Bt cotton in India" by S.K. Srivastava and D. Kolady, is presented.

Food security, a vital determinant of overall health and well-being, hinges on the simultaneous achievement of four dimensions: physical availability of food, economic and physical access to food, utilization of nutrients, and stability over time. This paper explores the complex and multi-faceted food security situations in Kenya and Zambia, emphasizing key factors such as agricultural production, government policies, and socio-economic conditions. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to assess the impact of climate changes on agricultural development and food security in Ukraine and Slovakia using statistical data from Statistical Office of the Slovak Republic, State Statistics Service of Ukraine, Eurostat, Food and Agriculture Organization and other sources. The comparison method was used to evaluate changes in agricultural development indicators, as well as climate changes in 1998-2021 in Ukraine and Slovakia. The main indicators of agricultural development used in the research were agricultural raw materials exports(%), agricultural raw materials imports (%), agriculture value added (% of GDP), agriculture value added (annual % growth), agriculture value added (current US$), cereal production (metric tons), cereal yield (kg per hectare), crop production index, food production index. Correlation and regression equations were used to establish the relationship between climatic factors and agricultural development indicators. The multiple correlations between air temperature and indicators of agricultural development was r=0.75 for Ukraine, r=0.69 for Slovakia. Using neuromodeling tools, the indicators of cereal production volumes and cereals yields in Ukraine and Slovakia in 2024-2033 were predicted. Tables were used to summarize the results. In conclusion, it should be noted that climatic conditions should be analyzed in each sector of crop production, and measures should be taken to adapt technologies for the gradual expansion of obligations for the production and export of agricultural products to world markets. [ABSTRACT FROM AUTHOR]

The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

Alongside wheat, maize is one of the most important species of cereals used for food and feed as well as in the bioethanol industry. As a result of this fact, maize is today the spotlight of many researchers, constantly trying to increase the productivity of this species, particularly important i from the economic point of view. The main aim of this article is to investigate the efficiency of the Blidar type filter-paper bridges (BFPB) in initiating the maize in vitro cultures for liquid culture media, in comparison with the conventional agarized culture media - solid culture media. In these experiments a modified Murashige-Skoog culture media (1962) (free of AIA and amino acids) supplemented or not with agar, were used. The inocula consisted in caryopsis of Zea mays L. (hybrid Kiskun 4255) and based on the results of these experiments it can be underlined that growth increases for the cultivated vitroplants on liquid culture media provided with filter-paper bridges compared with those conventianlly cultivated on an agarized culture media, as following 5.34% for dry weight and 356.09% for leafs length. [ABSTRACT FROM AUTHOR]

Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by 'Carbon Peak' target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future. [ABSTRACT FROM AUTHOR]

Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China's farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs." [ABSTRACT FROM AUTHOR]

The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes. The method uses feature fusion to integrate feature information from images, near-infrared spectra, and haptic modalities into a unified feature set and then classifies the maturity of tomatoes through deep learning. Each modality independently extracts features, capturing the tomatoes' exterior color from color images, internal and surface spectral features linked to chemical compositions in the visible and near-infrared spectra (350 nm to 1100 nm), and physical firmness using haptic sensing. By combining preprocessed and extracted features from multiple modalities, data fusion creates a comprehensive representation of information from all three modalities using an eigenvector in an eigenspace suitable for tomato maturity assessment. Then, a fully connected neural network is constructed to process these fused data. This neural network model achieves 99.4% accuracy in tomato maturity classification, surpassing single-modal methods (color imaging: 94.2%; spectroscopy: 87.8%; haptics: 87.2%). For internal and external maturity unevenness, the classification accuracy reaches 94.4%, demonstrating effective results. A comparative analysis of performance between multimodal fusion and single-modal methods validates the stability and applicability of the multimodal fusion technique. These findings demonstrate the key benefits of multimodal fusion in terms of improving the accuracy of tomato ripening classification and provide a strong theoretical and practical basis for applying multimodal fusion technology to classify the quality and maturity of other fruits and vegetables. Utilizing deep learning (a fully connected neural network) for processing multimodal data provides a new and efficient non-destructive approach for the massive classification of agricultural and food products. [ABSTRACT FROM AUTHOR]

Agriculture plays a vital role in driving economic output and employment in SubSaharan African (SSA) countries. Despite some progress, agricultural productivity in SSA still lags behind other regions. Limited access to finance remains a significant impediment to improving agricultural productivity in Sub-Saharan Africa. This paper investigates the effect of financial proximity on agricultural productivity in Sub-Saharan African countries. The study primarily employs the system GMM estimator, chosen to address the endogeneity issue typical in macro panel data. This method is also wellsuited to our study because it accommodates a high number of cross-sections relative to the number of periods within each cross-section. Additionally, the IV-2SLS method is employed to check the robustness of the findings. The study uses an unbalanced panel for the 36 African countries with data for the period 2004-2019. Our findings show that increasing the financial proximity favors agricultural productivity in SSA countries. Specifically, the number of commercial bank branches (CBB) and the number of automated teller machines (ATM) per 100 000 adults, as indicators of financial proximity, have positive and significant effects on agricultural value added per worker in SSA. Additionally, our findings show that rainfall, arable land, and institutional quality are robust determinants of agricultural productivity. The positive effects of the number of CBB and the number of ATM are even more pronounced on agricultural productivity when coupled with strong institutional quality. Our findings suggest that improving financial services' accessibility in rural areas, coupled with enhanced institutional quality, regular rainfall, ample arable land, and increased access to water and land, are pivotal steps for achieving sustainable enhancements in labor productivity. This, in turn, leads to increased returns through value-added growth and expanded employment in the agricultural sector. [ABSTRACT FROM AUTHOR]

The use of biostimulants in plant production is an agricultural practice friendly to environment and one of the tools employed in the sustainable agriculture. The present paper was to determine the influence that the application of products based in oligosaccharines exerted on plant growth and development of garlic (Allium ampeloprasum L.) cv. 'Fredy'. The present work was performed, under field conditions, during the period October 2020 - February 2021 with the objective of determining the influence of the application of products based in oligosaccharines on the growth and development of elephant garlic plants (Allium ampeloprasum L.) cv. 'Fredy'. The effect of determined doses of QuitoMax® and PectiMorf® products, in different ways of application ("seed" imbibition, foliar spray at 80 DAP and the combination of both with the particularity was evaluated of also, combining the products) on the behavior of emergence percentage at 7 and 14 DAP. Besides, plant height (cm) and leaf number at 70, 90 and 110 DAP; as well as, the bulb characteristics and the survival percentage at harvest time was evaluated too. In addition, plant yield was estimated. Results shown that, in general, the application of QuitoMax® was more efficient than that of PectiMorf® in stimulating all the indicators evaluated, although the best results were obtained when both products were used in combination, that is, the "seed" imbibition with QuitoMax® and the foliar spray of the plants with PectiMorf® or viceversa. [ABSTRACT FROM AUTHOR]

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming. [ABSTRACT FROM AUTHOR]

Abiotic stresses occur more often in combination than alone under regular field conditions limiting in more severe way crop production. Stress recognition in plants primarily occurs in the plasma membrane, modification of which is necessary to maintain homeostasis in response to it. It is known that lipid transport proteins (ns-LTPs) participate in modification of the lipidome of cell membranes. Representative of this group, ns-LTP2.8, may be involved in the reaction to abiotic stress of germinating barley plants by mediating the intracellular transport of hydrophobic particles, such as lipids, helping to maintain homeostasis. The ns-LTP2.8 protein was selected for analysis due to its ability to transport not only linear hydrophobic molecules but also compounds with a more complex spatial structure. Moreover, ns-LTP2.8 has been qualified as a member of pathogenesis-related proteins, which makes it particularly important in relation to its high allergenic potential. This paper demonstrates for the first time the influence of various abiotic stresses acting separately as well as in their combinations on the change in the ns-LTP2.8 transcript, ns-LTP2.8 protein and total soluble protein content in the embryonal axes of germinating spring barley genotypes with different ns-LTP2.8 allelic forms and stress tolerance. Tissue localization of ns-LTP2.8 transcript as well as ns-LTP2.8 protein were also examined. Although the impact of abiotic stresses on the regulation of gene transcription and translation processes remains not fully recognized, in this work we managed to demonstrate different impact on applied stresses on the fundamental cellular processes in very little studied tissue of the embryonal axis of barley. [ABSTRACT FROM AUTHOR]

Human capital development is considered the primary source of knowledge and skills in the innovation process. Despite this, there is evidence of a lack of technical and managerial knowledge among emerging sugarcane farmers who are beneficiaries of South Africa's land reform programme, thus, limiting their full potential in terms of innovation and productivity which is detrimental to their competitiveness. This paper employs the Crépon, Duguet, and Mairesse (CDM) approach, correcting for endogeneity problems, to estimate the causal impact of on-the-job training expenditure, used as a proxy for human capital development, on innovation, and innovation on farm productivity. It is based on a case study of 35 emerging sugarcane farmers in the KwaZulu-Natal north coast, South Africa. The results from the CDM model confirm the causal relationships between human capital (on-the-job training) and the innovation behaviour of the farmers, which positively impact the farm's productivity. This result underscores the relevance of human capital development in boosting innovation and productivity in the agricultural sector. [ABSTRACT FROM AUTHOR]

Cuba is struggling with a growing environmental problem: the uncontrolled spread of the allochthonous weed species marabou (Dichrostachys cinerea) throughout the country. Over the last 70 years, marabou has become a formidable invasive species that poses a threat to Cuban biodiversity and agricultural productivity. In this paper, we present a free and affordable method for regularly mapping the spatial distribution of the marabou based on the Google Earth Engine platform and ecological surveys. To test its accuracy, we develop an 18-year remote sensing analysis (2000–2018) of marabou dynamics using the Valle de los Ingenios, a Cuban UNESCO World Heritage Site, as an experimental model. Our spatial analysis reveals clear patterns of marabou distribution and highlights areas of concentrated growth. Temporal trends illustrate the aggressive nature of the species, identifying periods of expansion and decline. In addition, our system is able to detect specific, large-scale human interventions against the marabou plague in the area. The results highlight the urgent need for remedial strategies to maintain the fragile ecological balance in the region. [ABSTRACT FROM AUTHOR]

The availability of coherent time series of crop statistics is essential for better analyzing the past, understanding the present, and predicting future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and that are sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. However, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the EUROSTAT REGIO database – subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower, and sugar beet. All data are harmonized towards the hierarchical structure of the EUROSTAT legend and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344 282 records is presented here (115 974 for wheat, 122 705 for barley, 35 274 for grain maize, 34 916 for sugar beet, and 35 413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time-span range of 21 years. A flagging system details, for each data record, information on data sources, processing steps, and quality-checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning. We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at the EU level are briefly discussed. The dataset is accessible with European Commission Authentication Service (ECAS) login at 10.2905/685949ff-56de-4646-a8df-844b5bb5f835. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to analyse the role of public agricultural spending as a panacea for agricultural subsector production in Cameroon. The data were taken from World Bank development database and processed using growth model, and error correction model. Despite the agricultural sector's significant contributions to the country's economy throughout the years, the country has made little headway in reducing it. Recent interest in the relationship between government spending and economic output has not focused on the central question of whether government spending, particularly agricultural spending increases agricultural subsector output or otherwise. With instantaneous and compound growth rates of 6.7 percent and 6.93 percent, 3.03 percent and 3.08 percent, 13.66 percent and 14.64 percent, respectively, the results demonstrated stagnation in agricultural and livestock subsector production and labor. In contrast, public agriculture spending increased at a faster rate, with instantaneous and compound growth rates of 6.09 percent and 6.27 percent, respectively. In the short run, agricultural land, labor, and public agricultural spending all have a substantial impact on animal productivity. Cutting public agricultural spending by 25% is the most cost-effective way to maintain crop and livestock production in the short term. Incentives such as tax reductions and infrastructure development should be established to attract more private investors, knowledge transfer, and significant capital inflows to ensure the agricultural sub-long-term sector's viability. In the short term, privatization of the government's agricultural development projects will be critical for efficient use of public resources. [ABSTRACT FROM AUTHOR]

The agricultural sector in Moldova is highly exposed to natural factors and hazards, resulting in uncertain incomes and high production volatility. Crop insurance plans against different risks plays a crucial role in supporting farmers' yield and revenues. The aim of this paper is to assess the development of subsidizing production risk insurance policy as a method to enhance agricultural resilience and alleviate the effects of climate change in Moldova. The analyzed data is based on the analysis of regulatory documents for policies regarding subsidizing productions risks insurance. For data analysis, secondary data provided by the National Bureau of Statistics (NBS), and Agency of Interventions and Payments in Agriculture (AIPA) was used. The data refer to the amount of subsidies applications for risks insurance, insured areas cultivated with different crops and its harvests, main risks insured by farmers, distribution of subsidies for risks insurance by development regions and the insurance companies. The subsidization of crop production risk insurance in Moldova has played a vital role in bolstering agricultural resilience and minimizing the adverse effects of climate change. By offering assistance to farmers and promoting the implementation of risk management practices, the government's objective is to stabilize income levels and promote sustainable growth in the agricultural sector. Nevertheless, it is crucial to continuously monitor and evaluate these policies to ensure their efficacy and fair distribution of subsidies across various regions and insurance providers. [ABSTRACT FROM AUTHOR]

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop's nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model's prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. [ABSTRACT FROM AUTHOR]

The present paper addresses a topical issue for the agricultural production sector and it is based on an economic-financial indicators analysis for a series of 17 main agricultural crops. The practical research was carried out with information and data for 10 agricultural holdings in Iaşi County. They operate in the vegetable production sector and have a solid commercial representation on the national market. The main indicators used in the study are: crops and shared areas for each of the 10 analyzed units, considering the relevance of crops for the national economy and economic-financial indicators relevant for the design of the production activity: turnover, profit, employes number. The reference year for which the research was carried out is 2021. The work methodology mainly uses quantitative methods and descriptive analysis, with the processing and interpretation of data that have been organized in a design configured according to the specifics of the paper. In addition, qualitative information collected through interviews is used. The results of the paper indicate the relevance of basic agricultural crops for the performance of large enterprises. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Climate change, global warming are phenomena that have changed the face of the world and that have a direct impact on agricultural production and food security, given that the world's population is growing from one year to the next, without the possibility of growth and natural resources. Another worrying phenomenon is the production of greenhouse gases and their destructive effect on the planet. That's why the technology that is being perfected every day tries to find solutions that simplify both the efforts made to obtain food, but also the possibility of increasing the efficiency of production processes. In this way, in the agricultural field, the idea of developing dual systems appeared, which would protect both agricultural crops, with the aim of increasing productivity, but which would also contribute to obtaining solar energy, necessary not only for own consumption, but also to be included in consumer networks, the proposed concept being that of the AgriVoltaic system. The idea that appeared in 1981 began to be developed, currently reaching the development of a technology that is starting to be adopted in many countries of the world, including Romania. In the current paper, we propose to analyze the opportunity of using AgriVoltaic technology, its advantages and disadvantages, but also the way in which it could contribute to increasing the profitability of agricultural farms. At the same time, increasing the degree of awareness regarding the advantages of introducing agricultural systems and the method of implementing the systems is another objective of the current work. Starting from the analysis of the specialized literature, we outlined the term Agrivoltaic technology and continued the study with an analysis of the possibilities of implementing this technology in Romania, due to the solar energy potential that it benefits from. The research methodology assumed on the one hand the bibliographic analysis, and on the other hand the analysis of the development potential of the system, taking into account the existing agricultural areas and crops suitable for this system. As a result of the research carried out, it turned out that although there are confirmed advantages of using AgriVoltaic technology, it does not yet benefit from sufficient visibility or high accessibility among farmers. Also, the existing legislation at the level of Romania does not yet make possible the development of the dual system. Among the disadvantages of using agrivoltaic systems are the still high costs, but the measures to protect the rural landscape, which occupies an important place in sustainable development, even if the energy produced is renewable. [ABSTRACT FROM AUTHOR]

The paper aims to present models of agricultural production optimization as tools for managers to increase yield, crop structure, crop rotation, increased efficiency and profitability. The two optimization models are: Case 1: Optimization of yield depending on wheat price and subsidy for increasing net return and profitability in wheat culture; Case 2: Optimal model for optimizing crop rotation in cereal culture with maximum income, having two solutions: (a) Basic optimal primal solution and (b) Basic optimal dual solution. The methodology included the calculations regarding the specific indicators reflecting the economic efficiency in wheat cropping for Case 1 and determination of the optimal primal and dual solutions assisted by SOLVER application from MS Excel for Case 2. The two examples come from vegetal farming but, other models of optimization could be developed in animal production for improving livestock structure by species and category and also in animal feeding setting up optimized feed ratios to sustain production. [ABSTRACT FROM AUTHOR]

Currently, the major thrust of plant physiology research is to identify and understand the regulation of genes that might be relevant in plant development and growth. The dominance of a genocentric view of plant behaviour has, unfortunately, resulted in the development of major disconnects in the classical view of plant physiology as a partnership between fundamental and practical research contributing to improved plant production. One disconnect is that much of the genocentric research appears to be organized and executed without regard to the practical needs of enhancing plant performance under applied conditions. Although practical benefits from genocentric research are often claimed, basic assumptions guiding much research and the experimental protocols used are commonly not relevant for real-world plant production. A second disconnect is a failure fully to appreciate the lessons learned in 40 years of classical plant physiology research concerning the role of physiological processes in altering whole plant performance. Regulation of plant systems has proved to be complex and redundant. Alteration of a single physiological process is compensated or dampened so that commonly very little change in plant growth and yield results from modification of a single physiological process. Based on a few successful projects employing classical plant physiology to achieve crop yield increase, key characteristics for research projects that truly seek to increase plant performance in production systems are identified. Basically, the partnership between the fundamental and practical research long espoused for plant physiology needs to be re-established in an intimate and meaningful way. [ABSTRACT FROM PUBLISHER]

This paper addresses the challenge of meeting increasing energy needs by assessing the potential of bioenergy as a sustainable resource option in South Central Texas. Available agricultural crop residues suitable for bioenergy production are evaluated from the 21 counties in South Central Texas Regional Water Planning Area (Region L). The residues produced and available for bioenergy are quantified according to the production areas for each field crop and tree area. Residue-to-product ratios of field crops are determined according to crop type and production quantity. Biomass potential of trees is calculated based on tree density and biomass production per tree. The results demonstrate that the potential productions of utilizable agricultural wastes are in the range of 898.7 t kt–1421.39 kt for Region L. The average annual energy potential is estimated at 19.27 PJ, and ranges between 14.36 and 24.18 PJ. The average potential biomass-based electricity production could compensate significant amount of coal-based electricity generated in the Texas and when agricultural wastes are available. [ABSTRACT FROM AUTHOR]

Context and background African countries have undertaken vast and increasingly ambitious initiatives in recent years to promote land registration in order to secure land tenure and ownership rights. In theory, recognition of property rights should boost agricultural productivity and food security. However, empirical evidence of the link between land security, agricultural productivity and the food security of households remains ambiguous Goal and Objectives: It is against this backdrop that this research aims to study the effect of land tenure security on agricultural productivity and food security of farm households, based on evidence from Togo, and then by extension to ECOWAS countries. More specifically, it aims to (i) identify the determinants of land tenure security of farm households, (ii) analyze the effect of land tenure security on agricultural productivity and food security of farm household by gender. Methodology: The paper relies on Oaxaca and Blinder's decomposition technique and its nonlinear extension with the Propensity Score Matching Method (PSM) for robustness with data mainly from the Harmonized Households Living Conditions Survey (EHCVM) of Togo and selected ECOWAS countries realized in 2018-2019. Results: Empirical results reveal that being a woman reduces the probability of obtaining a document securing one's plot of land. However, the human capital of the head of household increases the probability of holding a land title. They reveal gender-based inequalities in agricultural productivity and food insecurity among farming households. However, holding a title deed to secure one's plot of land is expected to reduce this inequality by improving agricultural productivity and food security among farming households. The results confirm the need to develop land markets to facilitate the transfer of land, as part of measures to improve agricultural productivity and household food security in ECOWAS countries. [ABSTRACT FROM AUTHOR]

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm. [ABSTRACT FROM AUTHOR]

The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production. [ABSTRACT FROM AUTHOR]

Attempts to attain flawless agricultural production are still hampered by a range of restrictive factors including water scarcity, climate change, plant pests and diseases. Worries about the negative and unpleasant repercussions of pesticides on health are evolving in developing countries including Ghana. Cocoa farmers' knowledge awareness, attitude, practices and skills in handling pesticides and the potential hazards it poses to them are vital in preventing pesticide exposure and hazards. This study examines the knowledge, attitudes, practices and aspirations of cocoa farmers in Ghana regarding pesticide use and the potential hazards it poses to their health. The paper highlights the importance of understanding the diverse roles and perspectives of male and female farmers in agricultural production. The study uses a quantitative approach and a cross-sectional descriptive approach to analyze data from 364 cocoa farmers in the Asamankese Cocoa District. The study found that both male and female cocoa farmers have a positive attitude towards pesticide usage, and their knowledge of pesticide handling practices is adequate. The study also found that male and female farmers have different levels of knowledge, attitudes, and practices regarding pesticide use. The findings suggest that there is a need for increased awareness and education on safe pesticide handling practices to prevent pesticide exposure and its negative health effects on farmers. [ABSTRACT FROM AUTHOR]

To achieve inclusive growth, and poverty and inequality reduction, African countries should enhance labour-intensive agricultural production due to their abundance of natural resources and labour. In this paper, we examine the impact of agriculture on the economic growth of Zimbabwe using the Autoregressive Distributed Lag (ardl) model employing data covering the period 1970 to 2019. The results show that agricultural production has a significant positive impact on economic growth in the short runwhile showing no impact on economic growth in the long run. Additionally, the study confirms that inflation, government expenditure and gross fixed capital formation have a positive impact on economic growth in both the long run and short run. Although the agricultural sector plays a salient role in the early stages of economic development, it is, however, not able tomaintain sustainable economic growth over a long period in Zimbabwe. Additional macro-economic policy levers are required to compliment agricultural production and promote sustainable economic growth. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of statistical and remote sensing techniques to analyze the spectral response, grain yield, and infestation of fall armyworm (Spodoptera frugiperda) in corn (Zea mays) based on the application of edaphic and foliar treatments with silicon, comparing the results with those reported in the literature where it has been demonstrated that the incorporation of this nutrient in different crops improves the activity of the enzyme nitrate reductase and dry matter weight gain. The results show that the foliar application of silicon tends to increase grain production in the crop, while the soil treatment does not improve yield. Similarly, foliar silicon application improves the Normalized Difference Vegetation Index, which improves plant health and could be correlated with higher grain yield of the crop. An inverse correlation was detected between the use of foliar silicon and the Normalized Difference Water Index and a direct relationship in the case of direct field application. As for the analysis of the data to verify the influence of the use of silicon on fall armyworm infestation, no statistically significant evidence was found that would lead to the conclusion that the application of this element, whether in soil or foliar form, could lead to a decrease in crop infestation. [ABSTRACT FROM AUTHOR]

Due to their unique characteristics, nanoparticles are increasingly used in agricultural production through foliage spraying and soil application. The use of nanoparticles can improve the efficiency of agricultural chemicals and reduce the pollution caused by the use of agricultural chemicals. However, introducing nanoparticles into agricultural production may pose risks to the environment, food and even human health. Therefore, it is crucial to pay attention to the absorption migration, and transformation in crops, and to the interaction with higher plants and plant toxicity of nanoparticles in agriculture. Research shows that nanoparticles can be absorbed by plants and have an impact on plant physiological activities, but the absorption and transport mechanism of nanoparticles is still unclear. This paper summarizes the research progress of the absorption and transportation of nanoparticles in plants, especially the effect of size, surface charge and chemical composition of nanoparticle on the absorption and transportation in leaf and root through different ways. This paper also reviews the impact of nanoparticles on plant physiological activity. The content of the paper is helpful to guide the rational application of nanoparticles in agricultural production and ensure the sustainability of nanoparticles in agricultural production. [ABSTRACT FROM AUTHOR]

The paper analyzed the evolution animal sector regarding livestock and production in Romania in the period 2007-2020. Two sub-periods: P1- 2007-2013 and P2 2014-2020 were compared for assessing if the achievements in P2 were superior. The data from the National Institute of statistics regarding livestock and production by species were processed using fixed basis index, structural index, regression equations, coefficient of determination, multi annual mean per decade, absolute and relative differences between P2 and P1, in order to emphasize the trends and changes during the studied period. The results emphasized the following: the number of bovines, pigs and poultry decreased, while the number of sheep and goats and bee families increased both in P2 and P1. The total animal live weight at slaughter diminished due to the lower and lower live weight of bovines and pigs. However, the increased live weight at slaughter of sheep, goats and poultry had a positive impact. Milk and egg production declined and continued in P2, while wool and honey production increased. Despite that agricultural production value in animal sector increased by +20,420 trillion Lei in 2020 versus 2007, it was by 68.61% smaller than the agricultural production value achieved in the vegetal sector. In consequence, the contribution of animal sector to agricultural output was just 31.39% in the year 2020. The gaps in domestic production have to be covered by imports to meet consumers' requirements and this will deepen the deficit in agro-food trade balance. The reduction of CO2 emissions in agriculture, where 50% come from animal sector, obliges farmers to set up strategies destined to promote animal products obtained from more environment friendly technologies. [ABSTRACT FROM AUTHOR]

The goal of the study is to reorganize agricultural business management's owning small, fragmented, and dispersed lands according to modern agricultural business management thus ensures less usage of time, labour force and fund, take advantages of production factors and increase in productivity of agricultural business managements, increase agricultural production, and ameliorate life standards of inhabitants in the rural area. The paper is based on a large literature in the field and information from TVK about land consolidation. The results reflected that Turkey has implemented Land Consolidation activities for 1 million hectares per year to correct the unfavorable situation caused by inheritance traditions and other reasons. In the first years of the program, inheritance regulations were not perfectly organized, so applications encountered issues. In 2014, new regulations were implemented to prevent land division but did not have a beneficial effect on the development of enterprises. To ensure compensation for land value when they move away, Land Banking applications are needed. The Ministry of Food, Agriculture and Livestock developed a GIS based web portal named "TVK" to track and monitor all the activities performed during the management of Land Consolidation Projects. TVK ensures following content to be executed: Contract Management, Project Management and Monitoring, Schedule Planning and Monitoring, Operation Management and Monitoring, Data Management (Textual and Geographical), Estimated Cost Calculation for a LCP by using unit price pool, Progress Payment Management and Monitoring, Final Account Calculation, Interim and Final Acceptance Procedures, Official Letters, Official Reports, Project Statistics, Geographical Data Marking, Correction, Comparison, Redlining, Mobile applications and E-government integration. TVK has instant and online access to governments Land Registry and Cadastre Information System, Central Population Information System, Address Registration System, Agricultural Information System and Agricultural Parcel Information System. TVK is developed with open-source technologies and compatible with OGC (Open Geospatial Consortium) and ISO standards. [ABSTRACT FROM AUTHOR]

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information. The paper identifies the challenges and opportunities of crop monitoring with remote sensing. In-situ data accessibility and the negative effects of knowledge-based analysis are essential to reduce the applicability of crop monitoring. [ABSTRACT FROM AUTHOR]

The optimized system of forest strips in Ukraine had been developed on the landscape and ecological bases in order to ensure the formation of a sustainable agroforestry landscape; stabilize the ecological state, preserve the natural resource potential of a studied area, and create optimal conditions for the growth, development, and productivity of agricultural crops. Over nearly a hundred years, there have been developed the principles of combining tree and shrubby species, optimal designs of forest strips, their width parameters, species composition and placement of planting groupings, and their location in the fields. Also, agrotechnical and forestry measures for their care were designed in different soil and climatic conditions of Ukraine. All accounted factors would provide the maximum ecological and economic effects with a minimum planting. The paper presents the results of phytodiversity study and initiated studies of the syntaxonomy of field-protective forest strips in the regions of Ukraine. The issues of the global practice of field-protective afforestation, which is part of the newest direction -- agroforestry, were highlighted. They combine the simultaneous cultivation of tree and shrubby species and agricultural crops in the same areas for environmental, economic, and social benefits. The authors analyzed the current situation related to forest strips in Ukraine and described the negative changes that have occurred over the past thirty years. Mitigation of natural and anthropogenic threats in agricultural lands is a chalange due to the lack of comprehensive land-reclamation measures in agricultural landscapes and the strengthening of degradation processes caused by the global climate change. We conducted analysis and assessment of the state of forest strips and changes they have underwent because of military operations, concluding that optimizing the windbreak system in Ukraine as a particularly effective environment-forming factor is now especially relevant and requires research, updated using modern methods. For this purpose, one should take into account their intended purpose, spatial orientation, placement, optimal composition, considering typological characteristics, design parameters, and other forest-reclamation characteristics, identification of their syntaxonomic diversity, and identifying regularities in formation and differentiation and development of scientific bases of optimization. It would ensure the maximum efficiency of developing conditions for ecological rehabilitation of forest strips and increase their ecological capacity, and, in general, would promote balanced development of agroecosystems. Therefore, the objective of this paper was to analyze the current state of forest strips in Ukraine and substantiate the need to conduct priority measures to restore their functioning as forest-reclamation objects in connection with the latest circumstances in the conditions of military operations. [ABSTRACT FROM AUTHOR]

Due to the massive surge in the world population, the agriculture cycle expansion is necessary to accommodate the anticipated demand. However, this expansion is challenged by weed invasion, a detrimental factor for agricultural production and quality. Therefore, an accurate, automatic, low-cost, environment-friendly, and real-time weed detection technique is required to control weeds on fields. Furthermore, automating the weed classification process according to growth stages is crucial for using appropriate weed controlling techniques, which represents a gap of research. The main focus of the undertaken research described in this paper is on providing a feasibility study for the agriculture community using recent deep-learning models to address this gap of research on classification of weed growth stages. For this paper we used a drone to collect a dataset of four weed (Consolida regalis) growth stages. In addition, we developed and trained one-stage and two-stage models YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones) and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones), respectively. The results show that the generated Yolov5-small model succeeds in detecting weeds and classifying weed growth stages in real time with the highest recall of 0.794. RetinaNet with ResNet-101-FPN backbone shows accurate results in the testing phase (average precision of 87.457). Although Yolov5-large showed the highest precision in classifying almost all weed growth stages, Yolov5-large could not detect all objects in tested images. Overall, RetinaNet with ResNet-101-FPN backbones shows accurate and high precision, whereas Yolov5-small shows the shortest inference time in real time for detecting a weed and classifying its growth stages. [ABSTRACT FROM AUTHOR]

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community. [ABSTRACT FROM AUTHOR]