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Agarwood is an expensive resinous compound, produced by Gyrinops walla as defence response to external damages. The tissue culture technique is the best alternative for both rapid multiplication and product synthesis. This study demonstrated an efficient method of rapid multiplication using liquid cultures for G. walla. MS medium supplemented with 1.0 mg/L BAP and 0.1 mg/L IBA, 100 mg/L Myoinositol and 30.0 g/L of sucrose, and 3.0 g/L Phytagel (Sigma-Aldrich) was used as the control (T0). The effect of shoot growth on liquid culture on the same medium was investigated without inert support (T1) and kept on a shaker, with the support of filter paper bridges (T2), rigid support (T3), wet foam (T4), and a double layer of liquid and semi-solid medium (T5). At the end of the sixth week, growth rates and cell doubling times were calculated. Results showed significant differences between treatments at α=0.05 probability level (P< 0.0001). All liquid cultures except T3 stimulate the biomass. T1 had the highest growth rate (0.38 g/week) and the shortest cell doubling time (1.82 weeks) of G. walla (11-13 shoots from one shoot), but it also had hyperhydricity, which was overcome by combining liquid cultures with a flexible matrix or using a double-layer system. The nature of the matrix was found to be a vital factor in achieving the desired benefits of the liquid cultures. The use of a matrix is unnecessary if T5 is used to achieve the desired results. [ABSTRACT FROM AUTHOR]

Ecosystem services, like water provision or pollination, may increase both agricultural productivity (that is, the capability of the sector to increase the output in volume and value) and alleviate poverty (for instance, through food provision). In addition, increased agricultural productivity can help alleviate poverty by increasing the profitability of the sector, the income of the farmers, and the rates of return on (natural and other) capital investments. However, those beneficial effects come at the cost of (possible) deterioration of existing ESs, that, prima facie, represented the main driver for the generation of benefits. This paper, therefore, identifies and discusses the implications (and possible remedies) of a critical issue that, to our knowledge, is under-studied in an integrated context and methodological approach. [ABSTRACT FROM AUTHOR]

Despite relative richness of the existing literature, it remains a challenge to consistently estimate the impacts of transport connectivity on agricultural production. The paper constructs pseudo-panel data with transport infrastructure defined at high resolution in two periods of time and examines spatially heterogeneous impacts of improved transport connectivity. The paper takes advantage of the unique circumstances in Mozambique where the Government invested intensively in road infrastructure during a relatively short period of time in the 2010s. Combining the highly disaggregated location-specific fixed effects with the instrumental variable method, the paper controls for the endogeneity to show that the improved road connectivity increased agricultural production significantly. Rural connectivity and domestic market accessibility are found to be of particular importance, but substantial heterogeneity exists across regions. The northern provinces, where transport connectivity is limited, have the potential for agricultural growth, exhibiting increasing returns to scale. [ABSTRACT FROM AUTHOR]

India is an agricultural country; wherein about 70% of the population depends on agriculture. Agriculture has changed more in the past century than it has since farming began many millennia ago. Research in agriculture is aimed towards increase of productivity and food quality at reduced expenditure and with increased profit. Current crop production practices, often called Precision Agriculture (PA), benefited from all earlier revolutions in crop production. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. Image processing techniques in agriculture are mainly used for image categorization based on shape, size, color and texture for decision-making. This leads to agricultural input rationalization and environmental damage reduction by adjusting the agricultural practices like fertilizer and pesticide application to the site that demands and profit maximization. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. This paper presents a survey on some of the existing image processing techniques in agriculture. [ABSTRACT FROM AUTHOR]

Estimations of tree annual biomass increments are used by a variety of studies related to forest productivity or carbon fluxes. Biomass increment estimations can be easily obtained from diameter surveys or historical diameter reconstructions based on tree rings records. However, the biomass models rely on the assumption of a constant wood density. Converting volume increment into biomass also requires assumptions on the wood density. Wood density has been largely reported to vary both in time and between trees. In Norway spruce, wood density is known to increase with decreasing ring width. This could lead to underestimating the biomass or carbon deposition in bad years. The variations between trees of wood density has never been discussed but could also contribute to deviations. A modelling approach could attenuate these effects but will also generate errors. Here were developed a model of wood density variations in Norway spruce, and an allometric model of volume growth. We accounted for variations in wood density both between years and between trees, based on specific measurements. We compared the effects of neglecting each variation source on the estimations of annual biomass increment. We also assessed the errors of the biomass increment predictions at tree level, and of the annual productivity at plot level. Our results showed a partial compensation of the decrease in ring width in bad years by the increase in wood density. The underestimation of the biomass increment in those years reached 15%. The errors related to the use of an allometric model of volume growth were modest, around ±15%. The errors related to variations in wood density were much larger, the biggest component being the inter-tree variability. The errors in plot-level annual biomass productivity reached up to 40%, with a full account of all the error sources. [ABSTRACT FROM AUTHOR]

The paper aims, as the main purpose, at assessing the limiting factors of the soil quality in the perimeter of the Commune of Secaş, Timiş County, Romania. To achieve the aim pursued, we identified the main limitative factors of agricultural productivity and soil degradation processes. These were introduced in the database using modern methods, namely the GIS technique, for each soil unit, with "space" representations thereof. The soil surfaces on which each factor and/or degradation process is manifested and which is of particular practical importance in assessing soil quality were calculated automatically. The method used allows one to highlight the soils identified by various maps and cartographic representations. Most of the results were obtained from field work and calculations in the office and laboratory, supplemented by those from O.S.P.A. Timisoara and from literature. The importance of this study materialized in a complex database comprising all soil units in the studied perimeter, limiting factors specific to this area and degradation processes, as well as the establishment of fertility classes, i.e., the quality of existing soils in the studied area. The study highlights that the soils of the Commune of Secaş falls into grade III and IV fertility (quality). [ABSTRACT FROM AUTHOR]

The paper studied the impact of the level of production intensity on productivity and profitability of crop production in agricultural enterprises of the regions of Ukraine. In order to answer the question of how to increase crop yields and financial returns of lands in the face of climate change, we built graphic-analytical models of dependence of the yield (productivity) of basic agricultural crops from the size of operating (production) expenses per 1 ha of harvested area in agricultural enterprises of the Ukrainian regions. The results of the analysis showed that the real increase in the yield of analyzed crops is directly related to an increase in the level of intensity, expressed by the value of expenses per 1 ha of harvested area. The paper employed the correlation and regression analysis for quantifying tightness and mathematical description of the relationship, in particular, the dependence of the yield of winter wheat grain, corn for grain, sunflower, soy, rape and potatoes from the size of production expenses per 1 ha harvested area. The predicted optimal level of intensity was determined, taking into account the action of the economic law of diminishing returns. [ABSTRACT FROM AUTHOR]

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Efficient agricultural advisory and extension service (AES) is poised to lead to improved agricultural productivity as farmers utilize information and knowledge to optimize their use of limited resources. However, the role of AES has progressively evolved due to the changing technological, economic and social context. This paper uses primary and secondary data to study the AES delivery process in Kenya. This process is interrogated from an inclusivity perspective based on the suitability of related AES approaches in reaching out to rural communities in the acquisition of services for innovation purposes. The study finds differences in AES approaches that can effectively stimulate institutional innovation in an equitable way. In addition, approaches for delivery of AES are hampered by several factors that either constrain or perpetuate inclusivity more generally. The paper enhances discussion around the importance of agricultural AES as a key function of the agricultural innovation system, especially in a rural setting. [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]

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Under the background of tighter resource and environmental constraints, whether and how land transfer can promote the green development of agriculture has become a realistic question that needs to be answered urgently. This paper analyzes the internal mechanism between land transfer and agricultural green production by using property rights theory and sustainable development theory. Taking the data of the "investigation on household energy consumption and green agricultural development" in Sichuan Province in 2022 as a sample, it empirically analyzes the impact of land transfer on agricultural green production by using OLS and 2SLS models. The results show that: (1) land inflow significantly improves the level of agricultural green production, with a unit impact of 22.3%; (2) whereas land outflow will inhibit agricultural green production, with a unit impact of 5.46%; (3) the family's long-term agricultural labor, social capital, migrant experience, non-agricultural income, and household clean energy use have a promoting effect on agricultural green production; (4) age, education level, health level and agricultural subsidies inhibit agricultural green production; (5) the heterogeneity analysis found that the inflow of land would significantly promote the level of green agricultural production of farmers who have environmental awareness, have been village cadres, have purchased agricultural insurance, and have not suffered from agricultural disasters; (6) agricultural training, farmers' digital literacy, and agricultural related loans have a positive and strengthened regulatory role in the impact of land transfer on agricultural green production. Based on this, this paper gets policy enlightenment from the government, market, and farmers. [ABSTRACT FROM AUTHOR]

The most effective way to enrich the food resources necessary for humanity is to increase agricultural production per hectare, which can be achieved by introducing new cultivars and high-performance hybrids, by improving the cultivation technology, by calculating the need for nutrients in each crop, and by taking into account the crop, the expected productions and the natural fertility of the soil. This paper aims at evaluating the fertilization plans made on a farm in the town of Curtici, Arad County, Romania, in the main agricultural crops: wheat, barley, maize, and rape. During an annual vegetation cycle, there are several pheno-phases that are characterized by a differentiated consumption of nutrients, which determines the application of different types and doses of fertilizers. The farm is equipped with a series of tractors and agricultural machines with which they work the 600 ha of land and provides various mechanization services to different physical and legal people in the area. The main activity of the farm is the cultivation of cereals. The identified soils were chernozem, preluvosol and alluviosol, i.e., soils that have good and very good natural fertility. Following the calculations made, it turned out that the nutrient requirements were 57.08 t/ha of nitrogen, 78.33 t/ha of phosphorus and 29.80 t/ha of potassium. In wheat, the largest quantity in nutrients is in nitrogen, which plays an essential role in the growth phase, after which the requirement of nitrogen decreases. The productions obtained were different depending on the year, the climatic conditions and the doses of fertilizers applied: in wheat, 7.5 t/ha in 2020 and 8.0 t/ha in 2021; in grain maize, 8.0 t/ha in 2020 and 8.2 t/ha in 2021, with an average of 8.1 t/ha; in barley, between 8.1 t/ha in 2020 and 8.1 t/ha in 2021; and in rapeseed, 3.5 t/ha in 2020 and 3.6 t/ha in 2021. It was found that the application of fertilizers in the recommended doses depending on the crop, on plant growth phase, and on the plant, needs increases the production in all four crops in the study. In conclusion, the correct establishment of fertilization plans is the most useful tool in establishing the recommended fertilizers, taking into account both the natural fertility of the soils on the farm, of their plants, of their nutritional needs, of the expected productions and of the expenses necessary to purchase the fertilizers (whether organic or mineral fertilizers). Thus, making early economic decisions related to what we need to cultivate, on what surfaces, to the works involve in each crop, to the quantities of necessary fertilizers and treatments helps purchase the necessary products in due time and at better prices. This is how one saves considerable price differences, resulting in lower production costs and higher profits. [ABSTRACT FROM AUTHOR]

Purpose: Agricultural carbon productivity considers the dual goals of "agricultural economic growth" and "carbon emission reduction". Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality. Design/methodology/approach: Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs. Findings: The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement. Originality/value: This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration. [ABSTRACT FROM AUTHOR]

There are not many studies conducted related to land fragmentation in the entire country and in particular at different farm levels. Most of the studies in Kosovo are focused on Agriculture and other related issues, which ignores the land fragmentation issues and structure even if it is crucial for productivity. This paper is focused on the study of the relationship between land fragmentations, structure, and impact on farm productivity in Kosovo. The study was based on data collected through a paper questionnaire, which was conducted through face-to-face discussion with representatives of 444 households, which were randomly selected from the Dukagjini region covering the municipalities of Peja, Gjakova, and Prizren. Regarding the theoretical review, we have analyzed studies related to the: influence of various factors such as biophysical factors (climate, soil fertility etc.) and socio-economic (preferences, prices, production objectives, etc.) related to the treatment of farms and labor investments, structure, fragmentation, infrastructure and markets, and management practices. The collected data were processed using the software program SPSS 17. To calculate the productivity of farms we have used the clustering model. As the researchers studied, also in our research results show strong correlations between fragmentation and productivity. Land fragmentation seems to be correlated to farm productivity. In order to measure the correlation between the two variables researched at the beginning we have that farms as we have grouped farms into 6 different clusters such as field crops, permanent crops, grazing livestock, mixed crops, mixed livestock, and mixed crops-livestock. Analysis of six different clusters has shown that farm fragmentation and other factors have affected their productivity. Other key factors that were found to significantly affect farm productivity in combination with farm fragmentation are the agricultural education of the farmers, management, and the farm structure. Policymakers should issue administrative instructions for the protection of land areas from further fragmentation as in many countries around the world, reducing the level of fragmentation of agricultural farms, developing and implementing policies for land consolidation, land consolidation, and protection against change of destination is an immediate need. Increasing cooperation between farmers would affect several aspects: increasing net income per unit of employment, reducing costs per unit of production (the effect of economies of scale), and also investing in the profiling of agricultural farms. [ABSTRACT FROM AUTHOR]