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Presents an analysis of the domestic market for field vegetables and comments on the nature of the market changes and the role of market intelligence. Concludes with a discussion of how British producers can use their competitive advantages to exploit current opportunities and to build new markets.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

The purpose of this study is to examine household food gap and food insecurity in Eastern Ethiopia. Differences in food gap and food insecurity were also examined in terms of gender of the household head and location.

A combination of quantitative and qualitative methods such as household survey, key informant interview and focused group discussion were utilized for this study. Households were drawn randomly from the study area.

In terms of food availability, more than half of the households experienced a food gap during the year, especially during the months of July and August. In terms of gender, female-headed households had more months of food shortage compared to their male counterparts. This disparity was also reflected in poorer food access among female-headed households as shown by the higher HFIAS. Differences in food insecurity were obtained in terms of gender of the household head and location. Livestock ownership, cereal crop production, extension contact and household size significantly influenced household food access.

Findings are valid only for low-land agroecologies

This study contributes to the existing literature by examining household food gap and food insecurity using a combination of quantitative and qualitative methods. It adds value in examining disparities between male-headed and female-headed households. Literature related to seasonal household food insecurity is limited in Ethiopia. This study contributes in this regard by examining seasonal food insecurity between post-harvest and pre-harvest seasons.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

The purpose of this paper is to investigate the justification of objective assessment of the agricultural sector energetic potential, and the increasing of the accuracy of assessments results of energy resources of plant by-products.

The study of the problems of bioenergetic potential assessment in the study is carried out in the following order: first, the potential is assessed based upon the 2005-2017 year’s observation data; second, the energetic potential is assessed based upon linear and nonlinear optimization model; and finally, the assessment of the bioenergetic potential predicted values is carried out under the condition of the current pace of development of agricultural business by 2035.

The findings show that the solving of optimization tasks enabled us to make a comparison of the real structure of agricultural production and to justify the optimal structure of the cultivated areas under the conditions of agricultural business profit maximization with due allowance for both main and additional energy products. Using the linear trend model the predicted value of the agricultural sector energetic potential by the year 2035 is obtained. However, it is far more likely that the domestic bioenergetics will take a slower pace of development and to satisfy its own energy demands.

Based on the data of the reference interval of 2005-2018, the predicted values of biomass for 2035 were obtained in the amount of 28 million tons of oil equivalent, which taking into account the indices of generation efficiency, is sufficient to produce 104 billion kW-h.

The use of biomass for energy generation can impact the local environment, for example, by affecting air quality, biodiversity, habitats and ecosystems and water quantity and quality and by changing the local use of land. Social impacts also may arise, notably by affecting local community livelihoods (for example, access to and use of land and resources), food security and economic parameters such as employment and poverty.

The paper presents for the first time the results of the empiric analysis of the Ukrainian sector bioenergetic potential formation that showed that even with respect to the losses during the energy generation, the agricultural production energetic potential will be enough to substitute nuclear national power engineering.

This paper examines credit products, operational performance and business models employed by nontraditional lenders (NTLs) in agricultural credit markets.

Two research methods were employed in this study: (1) an executive interview to collect primary data and (2) a case study approach to analyze the findings and develop insights.

The findings indicate the presence of significant differences among lenders across and within three categories of NTLs (large volume, vendor financing and collateral-based NTLs). For example, collateral-based NTLs employ different strategies focusing on types of loans, funding sources, commodities they support and geographic coverage to further segment the market. NTLs in this study were able to capture market by successfully identifying gaps in the supply side of agricultural credit and developing products that meet the needs of that niche (e.g. heavy renters, large operations, producers seeking fixed interest rates for term loans, financially fragile producers). Most of the interviewed NTLs had credit standards comparable to those of traditional lenders and consider them both competitors and partners since many NTLs partner with traditional lenders on participation loans, loan servicing and/or sourcing funds.

The supply side of a nontraditional lending has not been studied extensively due to the proprietary nature of data. The executive interviews conducted in this study allowed for accumulation of industry data, which is not available otherwise.

Rice is staple food for Bangladeshi people. Although rice markets were liberalized in 1992, the government continued to intervene in this sector. This study examines the farmers’ and private traders’ response in liberalized rice marketing system in two regions. 40 farmers and 20 traders were interviewed by using a structural questionnaire for collecting the necessary information. The liberalization of the rice market, in particular, has been embraced more by the private traders then by the farmers. Although there has been a rapid emergence of private traders, the emergence of a vibrant trading sector that would fill the gap left by the state has been slow. The rice market is segmented with the private traders supplying different market circuits. Farmers on the other have not responded positively due to the lack of capital, lack of storage facilities, lack of market information, dominance of intermediaries and low price during the harvest period. Most of the farmers are unwilling to expand their acreage due to the family requirements of other crops. In rural remote areas where the road infrastructure is poor, private sector marketing activities have not yet emerged. Thus government can foster private participation and market integration by improving the road and storage infrastructure. Furthermore, government needs to take measures that strengthen the agricultural price and marketing information system targeted at both farmers and traders.

This paper aims to test the impact of remittances receipt on agricultural productivity. The paper empirically assesses whether heterogeneity in economic activity of farming households affects the effects of remittances on productivity of tradable and nontradable crop farming households in Ghana.

The authors employ propensity score matching (PSM) methods to address potential endogeneity issues that could arise from the estimation due to selection bias. This paper uses the seventh round of Ghana living standard survey dataset for Ghana.

The authors find that, the involvement of farming households in other economic activities alters the impact of remittances on crop yield. This differential impact also varies according whether the crop is tradeable or not.

Policy can reduce the cost of sending remittances and include financial literacy modules in the farmer training modules to increase farmers' knowledge on investment of remittance in agricultural production.

The authors distinguish the paper from others by controlling for crop types (particularly tradeable or otherwise and gestation period), farming of a second or more crops and engagement of smallholder farmers in nonfarm economic activities.