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Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural development and to explore practical pathways for enhancing agricultural quality through its promotion. Utilizing panel data spanning 2012 to 2021 from 30 provinces and municipalities in mainland China, the entropy method is employed to gauge levels of new quality productivity and high-quality agricultural development. Additionally, employing research methodologies including SYS-GMM and threshold effect models, this study empirically investigates how the advancement of new quality productivity influences high-quality agricultural development. Our research reveals the following key findings: (1) The development of new quality productive forces significantly enhances high-quality agricultural development, exhibiting a heterogeneous distribution pattern favoring the "eastern region > western region > central region" and "northern region > southern region". (2) New quality productive forces can bolster the level of high-quality agricultural development by fostering innovation, coordination, openness, and shared development within its subsystems. However, they may impede progress by inhibiting improvements in green development within the subsystems. (3) The results of the threshold effect test demonstrate that the promotion effect of the development of new quality productive forces on high-quality agricultural development escalates with the level of high-quality agricultural development. Specifically, as the level of high-quality agricultural development exceeds the first threshold value of 0.1502, the promotion effect becomes significant; crossing the second threshold value of 0.2010 further amplifies this effect. This paper's primary marginal contribution involves empirically analyzing the potential nonlinear effects of advancing new quality productivity in enhancing the level of high-quality agricultural development. This enriches empirical research on how new quality productivity fosters the development of high-quality agriculture. [ABSTRACT FROM AUTHOR]

Agricultural scale management is an important means of improving agricultural production efficiency. To answer the controversy over whether different agricultural scale management models can improve production efficiency, this paper obtained 117,627 samples from 68 domestic and foreign literature and used meta-analysis to study the effects of land-scale operation (LSO) and service-scale operation (SSO) on agricultural production efficiency (APE). The moderators that affect the relationship between LSO, SSO, and APE are also examined. The research results show that (1) both LSO and SSO positively impact production efficiency, but LSO has a stronger impact. (2) The relationship between LSO and APE is positively regulated by the agricultural production and operation environment, characteristics of the agricultural location, degree of farmers' participation, and types of APE, and negatively regulated by the research situation. (3) The relationship between SSO and APE is negatively regulated by APE types and data types. Therefore, the government should promote agricultural dual-scale operation according to the local conditions through the complementary advantages of LSO and SSO, so as to fully release the promotion potential of APE. [ABSTRACT FROM AUTHOR]

MPROVING agricultural output worldwide to satisfy the demands of a rapidly expanding I population may be possible, in part, through the creation and use of novel fertilizers made possible by cutting-edge nanotechnology. As a matter of fact, according to the literature assessment, there are designed nanomaterials that, at specific concentrations, can improve plant development. These materials could be utilized as nanofertilizers in agriculture to boost crop yields and reduce pollution. Nanofertilizers that enhance plant development, nanofertilizers that include nutrients, and nanofertilizers that contain micronutrients are the four main types of nanomaterials discussed in this article. Concerning the chemical makeup of the nanomaterials, the size of the particles, the concentrations used, the plant species that benefited, the techniques of plant incubation, and the features and rates of plant growth promotion, each category is addressed separately. Furthermore, we take a close look at each type of nanofertilizer to determine its relevance, potential future paths, and special research needs in the pursuit of sustainable agriculture. Lastly, this paper argues that protecting the environment and increasing food production should be top research priorities, and they propose developing macronutrient nanofertilizers containing N and P. [ABSTRACT FROM AUTHOR]

Agricultural development is crucial to feed the growing population. Most farmers tend to cultivate the crops which will give the more economical benefits besides checking the suitability of the crop according to the soil conditions. Use of technology in the agricultural sector leads the sustainable improvements in the agricultural production. Machine learning approach to suggest the suitable crop based on the soil parameters can help the farmers to cultivate the crops accordingly and can produce more yield. In this paper Random Forest Classifier is used to train the Machine Learning model on soil dataset using Python. Model performance is evaluated using confusion matrix and classification report having precision, recall and F1 score. Model accuracy achieved is 99% without parameter tuning. [ABSTRACT FROM AUTHOR]

Why do modern states allow parts of their territory to be governed by non‐state actors? We study this question using the Frontier Crimes Regulation (FCR) in Pakistan, a British Colonial law abrogated only in 2018, which left governance to pre‐colonial tribal councils in large parts of modern day Pakistan. In areas where the FCR did not apply, the British and then Pakistani state built modern political and bureaucratic institutions. Using primary legal documents, we build a dataset of when and where the FCR applied between 1901 and 2012. The territorial extent of the formal state is both cleanly demarcated by this law and varies substantially over time, permitting an empirical examination of the determinants of state control. The data reveal that the Green Revolution's potential to transform agriculture played a major role in extending the formal state. The law was repealed first from areas where agricultural productivity benefited the most from the Green Revolution. This is consistent with a model in which technological changes that shift the returns to control influence where states choose to govern. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Michael Callen is an Associate Professor at the LSE. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of land expropriation on the livelihoods of farmers expropriated from the peripheries of Addis Ababa, Ethiopia. A mixed methods approach, including household surveys, key informant interviews, field visits, and document reviews, was employed. A sample of 349 expropriated farmer household heads from four purposively selected sub-cities was drawn using systematic random sampling. The findings reveal significant adverse effects of land expropriation on the livelihoods of expropriated farmers, such as income losses, unemployment, and reduced agricultural production, primarily due to a lack of transparency and consultation during the expropriation process, inadequate compensation, and insufficient resettlement support provided by the city administration. Consequently, the living conditions of most farmers deteriorated in the post-expropriation. To address these effects, the study recommends enhancing procedural clarity, ensuring meaningful participation and consultation with affected farmers, and providing comprehensive resettlement support, including access to housing, employment opportunities, credit, and social services, to help affected farmers transition to new livelihoods. [ABSTRACT FROM AUTHOR]

Based on the panel data of provincial units in China from 2000 to 2020, this paper uses the entropy method to calculate agricultural environmental regulation level in China from the perspectives of afforestation projects, water conservancy projects and environmental protection projects. Then, this paper calculates agricultural non-point source pollutant emissions in China by using the unit survey and evaluation method, and calculates agricultural green productivity growth in China by using the SBM directional distance function and Luenberger productivity index. On this basis, the influence of environmental regulation on agricultural green productivity growth in China and its mechanism are empirically analysed. The results show that:(1) During the sample period, agricultural environmental regulation level and agricultural green productivity growth increased significantly, while the emission density of agricultural non-point source pollution decreased significantly; (2) On the whole, environmental regulation significantly promoted agricultural green productivity growth in China, which was mainly reflected in the promotion of agricultural green technology progress change; (3) Environmental regulation can significantly promote agricultural green productivity growth in China through the channel of agricultural innovation investment. The innovation of this paper lies in the measurement of agricultural environmental regulation, and the analysis of the moderating effect of agricultural innovation investment on the impact of environmental regulation on agricultural green productivity growth in China. The research conclusions of this paper verify the 'Porter Hypothesis' theory in the field of agriculture, and have guiding significance for China to promote agricultural green productivity growth by relying on environmental regulation. [ABSTRACT FROM AUTHOR]

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The Haors in Bangladesh are saucer-shaped, low-lying land depressions that form deep basins; they remain submerged for approximately half of the year, typically from June onwards. This fragile ecosystem spans over 2.0 million hectares in the northeastern region of the country, accounting for roughly 14% of the total areas, where approximately 19.4 million people reside. Factors including floods, flash floods, and low winter temperatures constrain agricultural productivity in the haor areas. It is a great challenge to change the haor areas from less productive to more productive land. This is a comprehensive analysis of the biophysical and socioeconomic characteristics of haors which also highlights the constraints and opportunities in agricultural production. It explores strategies for significantly increasing crop, livestock, and fish production within the haor ecosystem, in alignment with government policies. Some of the proposed agricultural development strategies for the haor areas include the development of short-duration, cold-tolerant crop varieties, such as Boro rice, utilizing relatively flood-free elevated lands and homesteads for vegetable production and promoting agricultural mechanization, livestock rearing, fisheries, and agribusiness development. The recommendations presented in this paper focus on enhancing crop yields, increasing cropping intensity, and boosting livestock and fish production; ultimately, they contribute to food security, poverty reduction, and improved livelihoods for the inhabitants of the haor areas. [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]

Effective spraying is essential for modern agricultural production, to ensure a high-quality and abundant harvest. Simultaneously, it is crucial to minimise the negative impact of crop protection products on the environment. To achieve this, it is necessary to implement the appropriate technical and technological parameters for the treatment and to consider the conditions under which the treatment is carried out. The aim of this study was to determine the relationship between the speed of the sprayer, the pressure of the liquid, and the type of nozzles, as well as air temperature and wind speed, in terms of the degree of coverage of the sprayed surfaces. The degree of coverage was analysed by spraying water-sensitive papers placed on the artificial plant, positioned to obtain horizontal and vertical surfaces. The study found that standard single flat fan nozzles provided greater coverage on upper horizontal surfaces, while standard nozzles were more effective for vertical transverse approach surfaces at lower fluid pressures and travel speeds. Neural networks were used to develop models of the relationships studied. Models with high accuracy for the validation data set were obtained in the case of the coverage of the vertical transverse leaving surface and the upper level surface (R = 0.93 and R = 0.86). These models were used to determine the optimum values of the technical parameters of the spraying process under the selected weather conditions. The maximum spray coverage (41.49%) was predicted for the XR nozzle under the following conditions: pressure = 200 kPa, driving speed = 1.4 m·s−1, temperature = 21.73 °C and wind speed = 0.32 m·s−1. Based on the sensitivity analysis of the neural models it was found that the greatest effect on the coverage of the vertical transverse leaving surface was observed for temperature and the coverage of the upper level surface was mostly influenced by driving speed. [ABSTRACT FROM AUTHOR]

In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse. [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]

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]

Cauliflower cultivation plays a pivotal role in the Indian Subcontinent's winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and handheld devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely 'Bacterial Soft Rot', 'Downey Mildew' and 'Black Rot' are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability [ABSTRACT FROM AUTHOR]

The precise control of a greenhouse environment is vital in production. Currently, environmental control in traditional greenhouse production relies on experience, making it challenging to accurately control it, leading to environmental stress, resource waste, and pollution. Hence, this paper proposes a decision-making greenhouse environment control strategy that employs an existing monitoring system and intelligent algorithms to enhance greenhouse productivity and reduce costs. Specifically, a model library is created based on machine learning algorithms, and an intelligent optimization algorithm is designed based on the Non-Dominated Sorting Genetic Algorithm III (NSGA-3) and an expert experience knowledge base. Then, optimal environmental decision-making solutions under different greenhouse environments are obtained by adjusting the greenhouse environmental parameters. Our method's effectiveness is verified through a simulated fertilization plan that was simulated for a real greenhouse tomato environment. The proposed optimization solution can reduce labor and time costs, enable accurate decision-making in the greenhouse environment, and enhance agricultural production efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to ascertain if practising healthy lifestyles improves the technical efficiency of farms in Kwara state, Nigeria. In theory, all deviations from the optimum level of output are due to random effects and inefficiency of producers in which their health plays a key part and is dependent on the kind of lifestyle practiced whether healthy or unhealthy. Design/methodology/approach: Cross-sectional data were employed through a three-staged sampling technique to pick 320 arable crop farmers across the state using a well-defined questionnaire. Data analysis was carried out using descriptive statistics, healthy lifestyles index (HLI), stochastic production frontier (SPF) and propensity score matching (PSM). Findings: First, the analysis showed that about one-third of the sampled arable crop farmers practised healthy lifestyles. Second, the average technical efficiency of arable crop production for farmers who practised a healthy lifestyle was 0.893, and the level of technical inefficiency of the farms was determined by health-related lifestyle status, number of day's illness and educational level. Third, technical efficiency was improved by 0.00431067 for farms whose farmers practised a healthy lifestyle. Originality/value: Rather than seeing that technical efficiencies of farms are attributed to farm characteristics, inputs used and socioeconomic characteristics alone, the findings suggest that technical inefficiencies of arable crop farmers were also due to the kind of lifestyle practised, which was evidenced in the increased efficiency for farmers who practised healthy lifestyle. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0353 [ABSTRACT FROM AUTHOR]

In the midst of China's ongoing rural–urban integration and development, a pivotal transformation involving the realignment of labour dynamics and land utilisation is underway. This paradigm shift has substantial implications for rural land use and agricultural productivity. Drawing from the field survey conducted in Zhejiang Province in 2019, this study puts non-agricultural employment, land transfer, and land efficiency into one econometric model and establishes a comprehensive framework to explain the mechanisms. Unlike existing research, this paper delves into the impact of different land-transfer behaviours, namely inflow and outflow, on land efficiency. The findings indicate that non-agricultural employment has no significant impact on land efficiency. Rural households acquiring land significantly enhance land efficiency, whereas relinquishing land shows no significance, thus addressing the gap in existing literature regarding the study of different transfer behaviours. Furthermore, to explore the underlying mechanisms, we investigate the mediating effect of land inflows on land efficiency, finding that it operates through plot size. In light of this, we propose that, in guiding land inflows, more emphasis should be placed on the integration and reorganisation of fragmented land rather than simply expanding the total land area, aiming to create large, well-managed areas of arable land by achieving concentrated and contiguous transferable land parcels. [ABSTRACT FROM AUTHOR]

Large-scale management is the key to realizing long-term agricultural growth in smallholder countries. Land-scale management and service-scale management are two forms of agricultural large-scale management. The former is committed to changing the small-scale management pattern, but the latter tends to maintain it. There has been a lack of discussion and controversy about the relationship between the two. From the perspective of market maturity, this paper explores whether the two are complementary or mutually exclusive and how their complementary or mutually exclusive relationship affects agricultural green productivity. The results show the following: Land-scale management and service-scale management are complementary, not superficially contradictory. The benign interaction between the two has a consistent improvement effect on green productivity in both the short and long term, which has spatial spillovers appearing in the long term. The reasons are as follows: The farmland rental market can reverse the inhibitory effect of the current low-maturity outsourcing services market on green productivity. The outsourcing services market can delay the arrival of the inflection point beyond which expansion of farmland rental transactions reduces green productivity, and amplify the positive effect of farmland rental on it. Although the degree of benign interaction between the two forms of large-scale management has gradually increased in recent years, it is still low overall. Agricultural large-scale management in China is still in the stage driven by land-scale management. Smallholder countries such as China need not worry prematurely about which large-scale management path to take, and they should treat both forms of large-scale management with an equal perspective to accelerate the high-level interaction between them. [ABSTRACT FROM AUTHOR]

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management. [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]

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]

The technical bonitation work was carried out based on soil mapping in the reference area, identifying the typical preluvosoil, the typical luvosoil and the gleic alluviosoil as the main soil units. The soils identified and evaluated in these natural conditions are characterized by morphological, physical, hydro physical and chemical properties, specific to the hilly area. Based on the physical-chemical properties, the evaluation of the main identified soils was carried out, in terms of their favorability for different plants grown in the area, by establishing bonitation marks and favorability classes. It was found that the highest bonitation marks (over 50 points) and the lowest favorability classes (III, IV and V) were obtained by the gleic alluviosoil, followed by the typical preluvosoil, and the lowest bonitation marks and the highest favored classes are recorded for the typical luvosoil. The estimation of the productions that can be obtained by the specific crops in this area, are according to the value of the bonitation marks obtained in natural conditions, with the 3 researched soil types. [ABSTRACT FROM AUTHOR]

This study examines the effects of land consolidation works carried out in Cihanbeyli district to provide solutions to important structural problems in Turkey's agricultural enterprise structure. The aim of the research is to evaluate in detail the socioeconomic effects of the consolidation process on farmers' land holdings and number of parcels and the legal dimensions of the process. The study was carried out using multi-layered data sources such as land registry and cadastral records, court cases and field observations, as well as surveys conducted with 26 farmers in villages such as Damlakuyu, Karatepe, Sağlık, Ülerziktepe and Taşpınar in Cihanbeyli district. The research was supported by various statistical tests such as regression analyzes and Wilcoxon Sign Test using the SPSS version 25 statistical package program. Analyzes revealed that there is a significant relationship between factors such as education level, agricultural experience and number of tractors and changes in farmers' land holdings and number of parcels after land consolidation. Satisfaction levels were found to be closely related to the success of the land management and consolidation process, and it was also emphasized that cooperative membership and participation in agricultural training programs were effective in the success of this process. Findings highlight challenges and opportunities associated with land consolidation, including concerns about property rights, legal disputes, and infrastructure development. Despite some dissatisfaction among farmers, the research underscores the potential of consolidation in enhancing agricultural productivity and informing policy decisions for rural development strategies. The study provides valuable insights into the development of agricultural policies and rural development strategies and guide policy makers and practitioners for the effective management of consolidation processes. The research provides a scientific basis for future applications by illuminating the factors that will increase farmer satisfaction and the effectiveness of the consolidation process. [ABSTRACT FROM AUTHOR]

Apple and tomato are important cash crops of Himachal Pradesh. Although, production of apple and tomato is very high but less arrival is coming in sub market yards (SMY). This paper attempts to identify the arrivals of apple and tomato in some markets namely Bandrol, Bhuntar, Kangra, Kullu and Solan of Himachal Pradesh in India. This study is based on secondary data sourced from government data records of agricultural arrival, production and heavy rainfall in selected places for ten years under study (2011-12 to 2020-21). The main motive of this study was to assess the trends in annual, main seasonal arrivals of apple and tomato with the help of tabular data, correlation and forecasting technique. The impact of climate change has been become a major reason in the state which affects the production of apple and tomato that states lesser the production lesser the arrival in the markets. After evaluating the data, it has been revealed that arrival in all the selected markets had showed scattered annual and main seasonal fluctuations during the period of ten years. Also, by using correlation method it was seen that only Bhuntar market was negatively impacted by heavy rainfall in the arrival of tomato and similar arrivals will come in next ten years. [ABSTRACT FROM AUTHOR]

Crop production needs to double by the year 2050 to meet the food requirements of the growing global population. Proper management of fertilization and timely disease control are essential in this endeavor. Machine learning technologies are poised to address these challenges by evaluating various elements, including geographical location, soil acidity, and the presence of essential nutrients such as nitrogen, phosphorus, and potassium. The paper proposes an agro-informatic system that helps farmers in crop prediction, fertilizer recommendation, and disease prediction. The proposed system incorporates critical inputs from farmers, ML algorithms such as RF and thrid-party apps. [ABSTRACT FROM AUTHOR]

For land rent, it is characteristic that it arises as a consequence of capital investment in the purchase of land, which is not a production investment, because capital is not invested for the reason of organizing agricultural production, the main reason of investing capital is to acquire certain ownership of land areas. In this paper, we will present the possibility of solving the problem of rent calculations using the economic application of the definite integral. First, we will show if the integral calcusus is applied in the rent calculation and then in the domain of its calculation. [ABSTRACT FROM AUTHOR]

A Plant Biostimulant is any substance or microorganism applied to plants to enhance nutrition efficiency, abiotic stress tolerance, and/or crop quality traits, regardless of its nutrient content. The application of Plant biostimulants (PBs) in production can reduce the application of traditional pesticides and chemical fertilizers and improve the quality and yield of crops, which is conducive to the sustainable development of agriculture. An in-depth understanding of the mechanism and effect of various PBs is very important for how to apply PBs reasonably and effectively in the practice of crop production. This paper summarizes the main classification of PBs; The growth promotion mechanism of PBs was analyzed from four aspects: improving soil physical and chemical properties, enhancing crop nutrient absorption capacity, photosynthesis capacity, and abiotic stress tolerance; At the same time, the effects of PBs application on seed germination, seedling vigor, crop yield, and quality were summarized; Finally, how to continue to explore and study the use and mechanism of PBs in the future is analyzed and prospected, to better guide the application of PBs in crop production in the future. [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]

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation-maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place. [ABSTRACT FROM AUTHOR]

The proper fallowing of soil maintains or even improves its yield potential. The aim of this research was to compare five methods of soil protection with high production potential on the yield and quality of strategic plants. The tested methods consisted of five variants: bare fallow—BF; natural fallow—NF; fodder galega (Galega orientalis Lam.)—FG; a mixture of fodder galega (Galega orientalis Lam.) with smooth brome (Bromus inermis)—FG+SB; and smooth brome (Bromus inermis)—SB. The soil had been set aside for 9 years, after which time the fallows were terminated and the fields were cropped with winter oilseed rape, winter wheat, and spring wheat in three consecutive years. After the end of fallowing, the content of Nog. and Ctot., pH, and forms of available macro- and microelements in the soil were determined. The influence of each type of fallow on the yield of seeds/grain, straw, total protein, crude fat, and the content of macronutrients in the seeds/grain and straw of the grown crops was determined. Regarding the yields of the crops, the best solution was long-term soil protection via sowing fodder galega or a mixture of fodder galega and smooth brome. A field previously maintained as a fallow with these plants (singly or in combination) could produce over twice-as-high yields of wheat and oilseed rape as those harvested from a field established on bare fallow. The yields of the cereals and oilseed rape obtained in this study prove that food security and environmental protection issues can be reconciled. The methods for protecting farmland temporarily excluded from agricultural production presented in this paper correspond perfectly to the framework of the Green Deal for Europe. Arable land excluded from cultivation can be used to overcome new challenges facing modern agriculture. [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]

Developing green and low-carbon agriculture is an important and effective way to promote farmers' income growth. Given the country's "dual carbon" goal, the study of the impact of green and low-carbon agriculture on the income of farmers in ethnic minority areas is crucial for China to achieve the goals of socialist modernization and common prosperity. Taking Y Town, Zhijin County, Guizhou Province as an example, this paper uses the OLS regression method to empirically study the impact of green and low-carbon agricultural production methods on the income of farmers in ethnic minority mountainous agricultural areas based on the field survey data of 881 farmers. The regression results indicate that there is a positive correlation between green and lowcarbon agricultural production and the household income levels of farmers; adopting green and low-carbon agricultural production technologies can effectively promote the growth of farmers' household income. In addition, education level, health status, and the new rural social pension insurance have all had a significant effect on the income of rural households, however, due to the difficulty in establishing trust relationships, agricultural service outsourcing has reduced the household income level of farmers. As an example, the land transfer behavior in Y Town has no significant effect on increasing farmers' incomes. Finally, it is recommended to increase fiscal and financial support as well as effectively enhancing farmers' policy awareness and perception of green and low-carbon agricultural production technologies by improving farmers' general trust and institutional trust by strengthening farmers' agricultural education and skills training while cultivating technology-based farming. At the same time, it is necessary to break the geographical restrictions on land transfer scale and achieve moderate-scale land management while promoting the use and adoption of green and low-carbon agricultural production technologies, thereby improving agricultural production efficiency and product quality, and increasing the sustainable growth of farmers' income. The main contribution of this study is to expand the research scope of green and low-carbon agriculture to ethnic minorities and mountainous agricultural areas. [ABSTRACT FROM AUTHOR]

Tomato is a most economic vegetable in India. Covers major area in its cultivation during crop production, it is threatened by major viral disease i.e tomato leaf curl disease, caused by begomovirus and transmitted by the vector. It causes yield loss up to 90-100 per cent, application of insecticide for the control of vector causes regular outbreak. Identification of resistant source from wild species and their introgression with susceptible source, development of lines/varieties, resistant/tolerant to Tomato leaf curl disease is the safest and long term alternative for solving this problem. This paper covers the major aspect related to virus, virus causing factors, vectors and sources of disease resistance and also breeding tools employed for its resistance and some of the barriers in breeding programme. [ABSTRACT FROM AUTHOR]

Using the Chinese Agriculture Technology Demonstration Center (ATDC) in Africa as a quasinatural experiment, we examine the causal impacts of China's aid on crop production in Africa. The data pertain to African country-by-crop panel data from 2000 to 2018. Leveraging the triple difference-in-differences method, we find that the ATDC leads to increased crop production and improved trade dependence. The positive effects are more pronounced for free-standing technical support and ATDCs operated by state-owned enterprises. Lastly, the ATDC has profound implications for local agriculture and rural development. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have led to a surge in the use of synthetic chemical compounds in agriculture, elevating environmental toxicity levels. As a response to this concern, there is a growing demand for environmentally friendly solutions. In recent times, the focus has shifted towards the development of cost-effective and ecologically sound organic products known as biostimulants. These innovative products play a pivotal role in enhancing agricultural productivity by fostering comprehensive plant growth and development. Biostimulants encompass a diverse range of natural and synthetic substances, categorized into microbial, non-microbial, and waste-derived sources. When judiciously applied to crops, these substances exhibit the remarkable ability to enhance plant metabolism, bolster productivity, and enhance resilience to adverse environmental conditions. Through modulation of molecular mechanisms and epigenetic alterations, biostimulants achieve this by influencing critical signalling molecules, transcription factors, and hormonal levels, which collectively contribute to stress tolerance. This review paper delves into the burgeoning industrial interest surrounding biostimulants. It sheds light on their intricate modes and mechanisms of action, encompassing both physiochemical and molecular aspects. Furthermore, the paper underscores the captivating potential of biostimulants to induce trans-generational plasticity and metabolite accumulation within plants, a phenomenon warranting deeper exploration through metabolomics. This review paper focusses on valuable insights into the transformative influence of biostimulants on agricultural practices, showcasing their capacity to usher in a new era of sustainable and resilient crop cultivation. [ABSTRACT FROM AUTHOR]

Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed. Core Ideas: Assessment of climate change impacts on agriculture requires a systems approach.Agriculture is weather dependent, and changes in climate can have a drastic impact on food production.Crop models can extend the results of physical experiments to better assess long‐term climate change effects on agriculture.Crop models are critical in assessing climate change impacts and developing adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to the automatic detection of rice leaf diseases using advanced deep learning techniques, specifically focusing on the use of convolutional neural networks (CNNs) augmented with the power of transfer learning. The research compiles a comprehensive dataset of rice leaf images depicting several disease states such as leaf blast, leaf blight, brown spot, as well as healthy leaf conditions. Each disease exhibits distinct visual symptoms: leaf blast is identified by its elongated lesions with darkened perimeters, leaf blight by its irregularly edged lesions, and brown spot by its circular lesions encircled by a brownish hue. The challenge of analyzing images with varying levels of clarity and contrast, particularly in healthy leaf samples, is addressed through the application of transfer learning. This enhances the precision of disease classification within our model. Utilizing well-known algorithms like MobileNet and VGG16, the proposed system efficiently differentiates among various rice leaf ailments, including Bacterial Leaf Blight, Brown Spot, Healthy, Leaf Blast, Leaf Scald, and Narrow Brown Spot. The results demonstrate the model's effectiveness in accurately detecting different rice leaf diseases, highlighting its potential to improve disease management strategies in agriculture and increase crop production efficiency. [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]

With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha-1 to 6703 kg ha-1 while the top yield from 36530 kg ha-1 to 44271 kg ha-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries. [ABSTRACT FROM AUTHOR]

Purpose: Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions. Design/methodology/approach: A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods. Findings: The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method. Originality/value: To the best of the authors' knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section. [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]

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]

Climate change is one of the most significant challenges many rural farmers face in sub-Saharan Africa, as most agricultural practices are rainfed dependent. Many of these rural farmers are small-scale farmers with limited access to financial assets, agricultural equipment, and inputs. With a rapidly changing climate and limited access to agricultural resources, many rural farmers in Zimbabwe have found it extremely difficult to engage in meaningful crop production activities and secure their livelihoods and incomes. This paper employs participatory research methods to examine adaptive strategies adopted by rural farmers. The strategies include optimal water resource utilisation, early maturing seed adoption, soil and water conservation (SWM), and nutrient management techniques (NMT). Cost-effective integration of labour and post-harvest storage facilities is also considered. Rural farmers, despite constraints, actively engage in these adaptive practices. The study assesses the effectiveness of initiatives to enhance crop production and build resilience against climate variability. Discussion centers on the comprehensiveness of these adaptive techniques within the broader framework of sustainable development goals, focusing on goals 1 (No Poverty) and 2 (Zero Hunger). The findings contribute to understanding and promoting resilience among vulnerable households facing climate-related challenges. [ABSTRACT FROM AUTHOR]

In India agricultural and packaging technology practices are constantly evolving to enhance crop production by improving soil quality and creating suitable environments, respectively. Mulching and improved packaging films have emerged as popular methods, but the use of non-biodegradable polyethylene (PE) for both poses disposal challenges. To address this, the focus has shifted to the biodegradable polymer, polylactic acid (PLA), in combination with fruit peel waste powder as filler. This literature review paper was written to describe a detailed survey about PLA with the incorporation of increasing amounts of fruit peel waste to enhance the properties of the PLA film. The primary aim was to explore the potential of PLA mulch film with the incorporation of fruit peel waste as an environmentally friendly alternative for mulching application in agriculture. To date we know of no review article published regarding the incorporation of fruit peel waste as a filler within the PLA matrix to enhance the properties of the films used in agriculture and the packaging industry. Moreover, this review paper also describes recent trends in the application and methods of preparation of PLA mulch films. [ABSTRACT FROM AUTHOR]

Automation and robotics are the key players in modern agriculture. They offer potential solutions for challenges related to the growing global population, demographic shifts, and economic status. This review paper evaluates the challenges and opportunities of using new technologies and the often-missed link between automation technology and agricultural economics. Through a systematic analysis of the literature, this study explores the potential of automation and robotics in farming practices, as well as their socio-economic effects, and provides strategic recommendations for those involved. For this purpose, various types of robots in different fields of agriculture and the technical feasibility and challenges of using automation have been discussed. Other important factors, including demographic shifts, labor market effects, and economic considerations, have been analyzed. Furthermore, this study investigates the social effects of automation, particularly in terms of employment and workforce adaptation. It finds that, while automation boosts productivity and sustainability, it also causes labor displacement and demands considerable technological investment. This thorough investigation fills a crucial gap by assessing economic sustainability, labor market evolution, and the future of precision agriculture. It also charts a course for further research and policy-making at the intersection of agricultural technology and socio-economic fields and outlines a future roadmap for further research and policy. [ABSTRACT FROM AUTHOR]

Based on panel data from 2010 to 2020 of 151 A-share listed food-related enterprises in China, this research uses a fixed-effect model to analyse the impact of digital transformation on total factor productivity (TFP) in food--related enterprises. Our findings indicated that digital transformation has a positive and significant catalytic effect on TFP improvement in food-related enterprises. The mechanism test revealed that both cost-saving and innovation capacity enhancement effects of digital transformation contributed to the promotion of TFP improvement in these enterprises. Moreover, our heterogeneity analysis suggested that digital transformation is more effective in enhancing TFP in state-owned enterprises (SOEs) in the food-related industry, indicating that SOEs play a representative role in promoting advanced productivity in agricultural development. We also found that capital-intensive and technology- intensitve food-related enterprises were experiencing productivity paradox traps. Our results confirmed that digital transformation brings catch-up effects to labor-intensive food-related enterprises and those located in major grain production regions. Overall, this research can provide valuable insights for policymakers to upgrade the digital- -enabled food industry. [ABSTRACT FROM AUTHOR]