Agricultural Economics Research Articles

Regulation of civil legal relations according to the Salic truth

The presented article is devoted to the analysis of the norms of the Salic Law which regulated civil legal relations of the Frankish society. The article notes that the Salic Law is a collection that was primarily devoted to the rules of criminal law and criminal procedure. Less attention was paid to the norms of civil law. This was due to the fact that the agricultural economy was not organized on a contractual basis. However, despite this, it contained rules that regulated property rights, obligations, marriage and family law, and inheritance law. The author has established that the Salic Law does not have a clear definition of property. The Franks retained communal ownership of land for a long time, as the rural neighborhood community, the marka, continued to exist despite the formation of the state. It is noted that at the end of the sixth century, the Franks formed a «full allod» – freely alienable land property, which was allowed to be freely given, transferred and bequeathed, including to women. A movable property was owned by families or individuals and could also be freely alienated and inherited. Based on the analysis of the norms of the Salic Law, the author found that the law of obligations was the least developed. There were such types of contracts as sale and purchase, exchange, gift, and loan. For a contract to be valid, in addition to the consent of the parties, it was necessary to observe various rites. As for marriage and family law, the article states that a legal marriage could be concluded between an adult free man and an adult free woman (no minimum age was set), subject to the consent of their relatives and provided that both parties were not bound by prohibited ties of kinship. Marriage was concluded through the purchase of a wife, i.e. power over her, from the person who had previously exercised this power over her. The grounds for invalidating a marriage were also defined. The author also analyzes the order of inheritance according to the Salic Law. It is established that the heirs in descending order were: children (and their descendants); mother and father; brothers and sisters; father’s sisters; mother’s sisters; father’s relatives (up to the sixth generation). Allodial land was а family land, it could not be inherited by females.

Interactive Effect of Nitrogen, Zinc (Zn) and Iron (Fe) on the Growth and yield Characteristics of Gobhi Sarson (Brassica napus L.)

Brassica napus L., commonly known as Gobhi Sarson in India, is a vital oilseed crop contributing significantly to the agricultural economy. India stands as the fourth-largest producer of oilseeds, with mustard-rapeseed, including Brassica napus, constituting about 28.6% of the total oilseed production [1]. The experiment titled "Study on Foliar Application of Nitrogen, Zinc (Zn), and Iron (Fe) on the Growth and Yield Characteristics of Gobhi Sarson (Brassica napus L.)" was conducted at the research farm of Lovely Professional University, Punjab, during the Rabi season of 2023-24. A total of nine treatments with three replications were evaluated using a Randomized Block Design. The findings indicate that Treatment 9 (75%RDF + 1% Urea + 0.5% FeSO4 + 0.5% ZnSO4) demonstrated the highest plant height, fresh weight, dry weight, leaf area, primary and secondary branches per plant, number of siliquae per plant, seeds per siliquae, 1000-seed weight, seed yield, biological yield, and oil content. Treatment 5 (50%RDF + 1% Urea + 0.5% FeSO4 + 0.5% ZnSO4) also performed well across these parameters, albeit to a slightly lesser extent. (T9) recorded the highest seed yield (19.7 q ha-1), significantly outperforming all other treatments, with Treatment 5 being statistically comparable. Moreover, Treatment 9 exhibited the maximum oil content (41.2%), followed closely by Treatment 5 with oil content of 40.6%. Conversely, the control treatment (T1) yielded the lowest oil content. These results underscore the efficacy of foliar application of 75% RDF with 1% Urea, 0.5% FeSO4, and 0.5% ZnSO4 in enhancing the growth and yield characteristics of Gobhi Sarson, with Treatment 5 showing similar trends.

JAE 2023: Report of the Editor‐in‐Chief

AbstractSubmissions fell back slightly in 2023 to 474 manuscripts but remain above pre‐Covid levels. Despite the high number of submissions, there has been little change in the number of papers accepted for publication, with the acceptance rate in 2023 standing at 8%. The acceptance rate is considerably higher for papers originating from Europe and North America. Accepted papers are now routinely made available online as ‘Early View’. The Journal's Impact Factor fell back slightly for 2022, but still compares favourably with other journals in the field of agricultural economics. Operational changes include revisions to the journal's webpage and authors' guidelines and a newly configured editorial team.

The potential of antifungal peptides derived from Lactiplantibacillus plantarum WYH for biocontrol of Aspergillus flavus contamination

Aspergillus flavus is a notorious fungus that contaminates food crops with toxic aflatoxins, posing a serious threat to human health and the agricultural economy. To overcome the inadequacy of traditional control methods and meet consumer preferences for natural-sources additives, there is an urgent demand for novel biocontrol agents that are safe and efficient. This study aims to investigate the antifungal properties of a novel antifungal agent derived from the biologically safe Lactiplantibacillus plantarum WYH. Firstly, antifungal peptides (AFPs) with a molecular weight of less than 3kD, exhibiting remarkable temperature stability and effectively retarding fungal growth in a dose-dependent manner specifically against A. flavus, were concentrated from the fermentation supernatant of L. plantarum WYH and were named as AFPs-WYH. Further analysis demonstrated that AFPs-WYH might exert antifungal effects through the induction of oxidative stress, disruption of mitochondrial function, alteration of membrane permeability, and cell apoptosis in A. flavus. To further validate our findings, a transcriptomics analysis was conducted on A. flavus treated with 2 and 5 mg/mL of AFPs-WYH, which elucidated the potential effect of AFPs-WYH administration on the regulation of genes involved in impairing fungal development and preventing aflatoxin biosynthesis pathways. Overall, AFPs-WYH reduced the A. flavus proliferation and affected the AFB1 biosynthesis, exhibiting a promising potential for food industry applications as a biopreservative and biocontrol agent.

Anti-corruption, credit supply, and agricultural economic development

This study analyzes regional panel data from China (2013–2019) to explore how the government's anti-corruption efforts and credit supply affect agricultural economic development. The empirical results show that anti-corruption and credit supply can effectively enhance agricultural economic development, and there is a complementary relationship between anti-corruption and credit supply, meaning that anti-corruption measures and credit supply can jointly promote the development of the agricultural economy. Further analysis indicates that the complementary and synergistic effect of the two on agricultural economic development is more pronounced in sample groups with a lower degree of marketization.

Comparison of Extraction accuracy of Sugarcane from different resolution satellite images using Deep lab V3+ Mode

Abstract. Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting.

How institutions are related to agriculture? Systematic literature review

In this paper, the author reviews the literature on institutions and their relations with agricultural economics. The aims of the article are to clarify the definition of an institution, indicate its relevance from the perspective of agricultural economics and propose a method of institution classification. Using the PRISMA method, 35 articles were selected out of 238 articles from the Web of Science database that met the established requirements (i.e. they were simultaneously related to institutions, economic performance and the agricultural sector). Based on the identified articles as well as the preliminary literature review it can be concluded that there is a lack of research that precisely defines institutions and examines the historical context at the same time. There are not many papers in which authors assess relations between institutions.

Sustainable Sheep and Goat Farming in Arid Regions of Jordan

This study examines the sustainability of livestock farming in Jordan’s arid regions, focusing on smallholder Awassi sheep and Baladi goat farming. It assesses breeders, economic factors, herd productivity, and sustainability, evaluating the vitality of these breeds by examining reproductive success through birth and survival rates. In 2021–2022, a total of 53 traditional Awassi sheep and Baladi goat farms in Northern Jordan’s Mafraq Governorate were surveyed using a comprehensive questionnaire to gather data on breeders’ information, farm economics, and herd indicators. Dry seasons in arid lands are tough on sheep and goats, with limited fodder and water availability harming their health and reproductive capacity. While wet seasons bring relief, they can also introduce diseases. Special care for these animals throughout the year is crucial to maintain healthy herds in arid regions. Linear models were employed to analyze sample data, with variance and correlation tests used to explore relationships among variables. Additionally, chi-squared analysis was utilized to assess the impact of male replacement plans on these herds. The main outcomes of this study reveal that breeder indicators pose challenges to the sustainability and growth of Awassi sheep and Baladi goat farming. Conversely, economic indicators suggest a blend of financial stability alongside obstacles related to profitability and resource ownership. Breeder age emerges as a significant factor in livestock herd management, influenced by elements such as experience, resource availability, and adherence to traditional practices. Experience notably influences breeders’ decisions regarding male insemination replacements, likely due to acquired skills. Crop cultivation is shown to reduce fodder costs, thereby enhancing profitability for each young animal sold and consequently increasing the breeders’ returns. Winter conditions elevate animal mortality rates due to cold stress, while other animals within herds contribute to increased kid mortality by facilitating disease transmission. Breeder experience significantly impacts lamb survival, underscoring its importance in ensuring proper care. Increasing ewe proportions within herds enhances lamb production and lowers lamb mortality rates. Effective ram management and yearling lamb sales further contribute to herd health and productivity. A higher proportion of goats within a herd positively correlates with the number of young yearlings sold but inversely affects breeding males and overall kid numbers, indicating a delicate balance that influences herd productivity and mortality rates. Sheep (92.5%) and goat herds (90.6%) demonstrate reproductive success, indicative of superior long-term vitality. While sheep excel in birth rates, survival, and sales, they exhibit lower ewe and lambing percentages. Goat farming demonstrates high birth rates, sales, and overall success but requires particular attention to ensuring kid survival, especially during the winter months. Improving the characteristics of the Awassi sheep and Baladi goat flocks is crucial for ensuring sustainability, achievable through proper care practices in dry lands. In conclusion, despite the challenges, there are promising opportunities to enhance sheep and goat farming in Jordan. Effective management, informed by the breeders’ experience and economics, is crucial. Empowering small-scale herders and implementing targeted interventions, like winter mortality solutions and selective breeding, is vital for sustainable growth.

Sustainable Food Value Chains: Approaches to Transaction Costs in Agro-Alimentary Systems of Developing Countries—A Chile Case Study

This study delves into the dynamics of price linkages and transaction costs in agricultural markets, emphasizing the sustainability of food supply chains. By exploring vertical and horizontal price linkages in agro-farming value chains of a developing country, it addresses the efficiency of market information transmission and the capacity for arbitrage among chain participants. The methodological core of the research involves analyzing price linkages in Chilean horticultural wholesale markets, focusing on key vegetables such as, onions, lettuce, maize, and tomatoes. This analysis is underpinned by a novel approach that models and estimates time-dependent, conditional threshold bands, extending the traditional cointegration models. This method allows a more nuanced understanding of how agricultural market linkages evolve over time, enhancing our comprehension of price transmission behavior and market integration. The results reveal significant non-linear relationships between fuel prices and vegetable prices, particularly in central Chilean regions. This finding challenges the traditional linear perspective, suggesting that factors such as storage capacity and arbitrage behavior can influence price signal transmission. Such insights are crucial for stakeholders in the agribusiness value chain, offering a deeper understanding of market dynamics and aiding in the development of more sustainable and efficient market strategies. This research contributes significantly to the field of agricultural economics by providing a more robust framework for analyzing market behaviors and transaction costs in the context of sustainability and value chains. Its findings have profound implications for both theory and practice, informing policy-making and strategic decision-making in the agribusiness sector.

Spectral–Spatial transformer-based semantic segmentation for large-scale mapping of individual date palm trees using very high-resolution satellite data

Date palm plantations in the United Arab Emirates (UAE) are under threat from soil salinity, drought, and date palm weevils. Accordingly, monitoring and conserving date palms are crucial to preserving a vital component of the country’s agricultural heritage, economy, food security, and ecological balance. Previous studies have effectively identified date palm trees using RGB-based aerial and UAV imagery utilizing diverse deep learning methods. However, the utilization of very high-resolution satellite data for delineating individual date palm crowns remains unexplored due to the limited spatial resolution capabilities of existing satellite systems. This study primarily aimed to achieve precise and comprehensive mapping of date palm trees using WorldView-3 (WV-3) satellite data by leveraging the high representational power of the state-of-the-art vision transformers (ViT) in capturing global information from the input data. First, an in-depth analysis assessment of the various transformer-based semantic segmentation architectures, including UperNet with vision transformer and Swin transformer, SegFormer, Mask2Former, and UniFormer, was conducted. Second, the integration of spectral data on the performance of ViTs was evaluated. Moreover, the models’ generalizability and complexity effect on the segmentation effectiveness were assessed. Accordingly, a postprocessing strategy was developed to aid in delineating and counting date palm trees from semantic segmentation outputs. Results demonstrated that integration of WV-3 spectral data into the analysis resulted in a marked improvement in segmentation quality. The UniFormer, UperNet-Swin, and Mask2Former models demonstrated considerable improvements in multispectral data analysis, with increases in mean intersection over union (mIoU) of 2.17% (77.88% mIoU, 86.01% mean F-score [mF-score]), 2% (78.10% mIoU, 86.18% mF-score), and 1.15% (77.36% mIoU, 85.59% mF-score), respectively, compared with their RGB-based results. Evaluations of model transferability also indicated that Mask2Former, UniFormer, and UperNet-Swin transformers efficiently adapted to multispectral data in the Dibba region. These models achieved mIoU scores of 84.36%, 84.25%, and 83.17% and mF-scores of 90.95%, 90.87%, and 90.13%, highlighting their effectiveness and potential for broader regional application. This research highlights the efficacy and feasibility of using ViTs with WV-3 multispectral data for accurate and comprehensive surveying of date palm plantations, enabling the development of palm tree inventories and continuously updating geospatial databases.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

A comparative metabolomic investigation of different sections of Sicilian Citrus x limon (L.) Osbeck, characterization of bioactive metabolites, and evaluation of in vivo toxicity on zebrafish embryo.

Citrus fruits are a diverse and economically important group of fruit crops known for their distinctive flavors and high nutritional value. Their cultivation and consumption contribute significantly to the global agricultural economy and offer a wide range of health benefits. Among the genetic diversity of citrus species, Citrus x limon (L.) Osbeck is particularly relevant due to its chemical composition and potential health benefits. Two cultivars from the Sicily region (southern Italy) were compared for their phenolic content and preliminary antioxidant activity to select the distinctive extract with potential biological activity. A detailed characterization revealed the occurrence of phenolics, coumarins, and flavonoids. The quantification of metabolites contained in the selected extract was performed by an ultrahigh-performance liquid chromatographic method coupled with an ultraviolet detector. Different concentrations were tested in vivo through the fish embryo acute toxicity test, and the 50% lethal dose of 107,833µgmL-1 was calculated. Finally, the effect of the extract on hatching was evaluated, and a dose-dependent relationship with the accelerated hatching rate was reported, suggesting a Femminello Zagara Bianca green peel upregulating effect on the hatching enzymes. PRACTICAL APPLICATION: Citrus fruits and their products continue to be one of the natural food sources with the highest waste output. In this study, we demonstrate how food industry waste, particularly lemon peel, is rich in bioactive compounds with anti-inflammatory and antioxidant properties that may be used in the nutraceuticals industry.

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

Crop Price Prediction Using Machine Learning

Crop price prediction is a pivotal aspect of agricultural economics, impacting stakeholders across the industry, from farmers to policymakers to consumers. Traditional methodologies often struggle to provide accurate and efficient predictions, primarily due to the intricate and ever-changing nature of agricultural markets. However, in recent years, the emergence of machine learning techniques has offered promising solutions to enhance crop price prediction. This paper conducts an extensive review of various machine learning approaches utilized for this purpose, covering regression-based methods, time series forecasting techniques, ensemble methods, deep learning strategies, and hybrid models. We delve into the unique strengths, limitations, and practical applications of each technique. Moreover, we address the prevalent challenges associated with employing machine learning in crop price prediction, such as data accessibility, feature selection, model interpretability, scalability, and generalization. Additionally, we look ahead to future research avenues and opportunities aimed at refining the accuracy and utility of machine learning models in predicting crop prices. Through this comprehensive review, we aim to provide valuable insights for researchers, practitioners, and policymakers, facilitating informed decision-making in agricultural contexts through the utilization of machine learning methodologies.

An intelligent decision support system for agriculture economy based on fuzzy c-means

Abstract Agricultural informatization construction has developed rapidly in recent years, but its data has not been well utilized to support decision-making. Cluster analysis is an important branch of data mining, which is applied to the agricultural industry and enhances the competitiveness of the industry. In response to the problem of insufficient information space services for rural agricultural economy, the application of clustering analysis algorithm in the field of agricultural feed industry was studied. Based on fuzzy C-means technology, clustering analysis was conducted, aiming to provide a universal decision support platform for agricultural production management departments and achieve value co-creation among farmers, enterprises, and investors. In this paper, a method based on Xie-Ben validity is adopted, and the method is modified. In addition, the steps of removing empty clusters are added after the cycle of FCM algorithm to improve the efficiency of the algorithm. In the experimental stage, different subdivision results are obtained by selecting different parameters, and the results are analyzed to verify the effectiveness of the FCM algorithm. Finally, the improved FCM algorithm is applied to the agricultural marketing decision support system to provide important decision-making for domestic and foreign feed raw material producers and traders, animal husbandry feed production enterprises, agricultural investment institutions and so on.

Betel Vine Marketing in Tamil Nadu, India: Price Spread Analysis

Tamil Nadu is one the leading betel leaf cultivating states in the country. Besides, it faces significant challenges in producing betel vines due to risk of pests and diseases. These risks have a negative impact on the farm economy. Disease outbreaks can be incredibly concerning and have an impact on a company's profitability. The higher labour costs, pest and disease and natural calamities have made it difficult for farmers to produce their crops. Therefore, it was essential to study the marketing cost, price spread and marketing efficiency of betel vine in Tamil Nadu. The use of a well-structured interview schedule allowed for the collection of information from farmers regarding the cost and return of producing betel vines. From the local marketplaces in Thanjavur District, village traders, commission agents, wholesalers and retailers were chosen. Price spread and marketing efficiency were used to know about the marketing cost and the price received by the farmer. The efficient marketing channel was channel III (Producer → Retailer → Consumer) because it has the lowest price spread of 18.9 per cent which be due to a smaller number of intermediaries. The marketing efficiency in channel III (16.1) was higher than in channel I (10.6) and channel II (6.3) respectively. Thus, marketing of betel leaf in channel III was more efficient for farmers.

Identification of seasonal drought and its impacts on cropping season in Bangladesh

PurposeThis study aims to identify seasonal drought using standardized precipitation index (SPI). The following specific objectives are to generate result and identify seasonal drought and determine different scale of seasonal drought and its impacts on cropping season.Design/methodology/approachSeasonal SPI was calculated using long-term rainfall data for three seasons. The SPI was calculated using the formula and it is effective for the determinants. This study showed the functional relationship between drought duration, frequency and drought time scale using the SPI. SPI=X−X¯σ.FindingsSeasonal drought occurs more frequently in Bangladesh that affects crops and the agricultural economy every year. More severe drought was recorded during the Kharif-1 and Kharif-2 seasons and most crops were affected in these two seasons. No severe or moderate drought was recorded during the Rabi season. The results showed that monsoon crops were severely affected severely by extreme and severe droughts during the Kharif-2 season. Eventually, the people remain jobless during the monsoon, and they experience food shortages like monga. Several obstacles were recorded during the season, including delayed preparation of land, sowing, transplanting and other farming activities because of monsoon droughts. This study revealed that very frequently, mild dryness occurs in winter, but crop loss is minimal. The scale and occurrence of extreme droughts are more frequent during monsoons and reduce crop yields, affecting livelihoods in the study area. Seasonal drought affects cropping patterns as well as reduce crop yields.Originality/valueThe outcome of this study derived from the secondary data and field data.

An Economic Analysis of Marketing Efficiency of Maize in Tamil Nadu, India

Maize plays a significant role in the agricultural economy. Since, growers of maize have numerous marketing-related issues. The present study is made with an objective to identify the major maize marketing channels and analyze its cost incurred, price spread and efficiency. A well-structured interview schedule was used to collect primary data. Price spread analysis and marketing efficiency of maize marketing channels is estimated using Acharya and Shepard approach. The lowest marketing spread were observed in Channel III which was about 13.56 percent (Rs. 200 per quintal) of the consumer price, due to less intervention of intermediaries. Therefore, Channel III is the most efficient one among the identified marketing channels. Besides, Channel III has the highest marketing efficiency and the farmers should choose their marketing channel with the less number of intermediaries where it helps them attain better income.

An overview of the history, role, and struggles of agricultural economics and business programs at 1890 Land‐Grant Historically Black Colleges and Universities (HBCU)

AbstractBlacks have been systematically under‐represented in the Agricultural Economics profession, representing only 7% of agricultural economists in academia and between 5% and 10% in the private and public sectors, respectively. Although different policies and funding strategies have been implemented to increase the representation of minorities in the profession, these numbers have not shown significant improvements over time. This paper provides an overview of the history, role, and struggles of 1890 Land‐Grant Historically Black Colleges and Universities in general, and in particular, their agricultural economics and business (AEB) Programs. Recommendations and thoughts on how to increase diversity in AEB programs are also discussed.

Quantifying cost of disease in livestock: a new metric for the Global Burden of Animal Diseases

Increasing awareness of the environmental and public health impacts of expanding and intensifying animal-based food and farming systems creates discord, with the reliance of much of the world's population on animals for livelihoods and essential nutrition. Increasing the efficiency of food production through improved animal health has been identified as a step towards minimising these negative effects without compromising global food security. The Global Burden of Animal Diseases (GBADs) programme aims to provide data and analytical methods to support positive change in animal health across all livestock and aquaculture animal populations. In this study, we present a metric that begins the process of disease burden estimation by converting the physical consequences of disease on animal performance to farm-level costs of disease, and calculates a metric termed the Animal Health Loss Envelope (AHLE) via comparison between the status quo and a disease-free ideal. An example calculation of the AHLE metric for meat production from broiler chickens is provided. The AHLE presents the direct financial costs of disease at farm-level for all causes by estimating losses and expenditure in a given farming system. The general specification of the model measures productivity change at farm-level and provides an upper bound on productivity change in the absence of disease. On its own, it gives an indication of the scale of total disease cost at farm-level. The AHLE is an essential stepping stone within the GBADs programme because it connects the physical performance of animals in farming systems under different environmental and management conditions and different health states to farm economics. Moving forward, AHLE results will be an important step in calculating the wider monetary consequences of changes in animal health as part of the GBADs programme. Bill & Melinda Gates Foundation, the UK Foreign, Commonwealth and Development Office, EU Horizon 2020 Research and Innovation Programme.