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Dr. Sarah Chen
Stanford · NLP & interpretability
91
fit
Fairness + NLP projects overlap with her recent work.
Strong methods fit: model evaluation and interpretability.
Clear outreach angle for current trustworthy AI research.
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Ask how her lab is extending interpretability methods into fairness audits for real-world AI systems.
Ramu Govindasamy
· ProfessorVerified
Rutgers University · Environmental and Resource Economics
EPRA International Journal of Economic and Business Review · 2025-07-30
articleOpen access1st authorCorresponding
This study analyzes growth trends in sugarcane cultivation across Indian states, focusing on area, production and productivity of sugarcane through Compound Growth Rate (CGR), Compound Annual Growth Rate (CAGR) from 2006 to 2022 and trend analysis for export and import of sugarcane. The paper examines inter-state variations using secondary data from various sources such as Cooperative Sugar, Sugar Statistics and Indiastat. Findings reveal that states like Bihar and Punjab exhibit consistent positive growth especially in productivity, while Tamil Nadu, Odisha and Andhra Pradesh show declines across all parameters. Trend analyses suggest that productivity remains relatively stable, while area and production reflect greater fluctuations. Keywords: Sugarcane, Area, Production, Productivity, Import, Export, CGR
EPRA International Journal of Economic and Business Review · 2025-05-08
articleOpen access1st authorCorresponding
This study investigates the macro-nutrient dynamics in India with a comprehensive analysis of fertilizer production, consumption, sales, imports, subsidies and capacity utilization from 2010-11 to 2022-23. Given the country’s heavy dependence on agriculture, fertilizers—particularly NPK nutrients such as Urea, DAP and MOP play a pivotal role in ensuring food security. The research utilizes secondary data sourced from reputed organizations such as the Fertilizer Association of India, Indiastat, etc. Tools like Compound Growth Rate (CGR), Compound Annual Growth Rate (CAGR) were utilized. Findings reveal that while private agencies consistently dominate fertilizer sales, government subsidies, especially for Urea and nutrient-based fertilizers (NPK), have expanded substantially in recent years. Bio-fertilizer production and usage also witnessed a marked increase, with a significant shift towards liquid-based variants. Nutrient consumption trends indicate that Nitrogen dominates both Kharif and Rabi seasons, while Phosphorous and Potassium show variable patterns. Capacity utilization for Nitrogen has remained relatively high, whereas Phosphorous lags behind. The study highlights the growing demand, evolving subsidy structure, and a positive trajectory in nutrient management, providing policy insights for strengthening India’s agricultural sustainability. Keywords: Fertilizer Consumption, Fertilizer Production, Subsidy, Capacity Utilization, Import
International Journal For Multidisciplinary Research · 2025-05-10
articleOpen access1st authorCorresponding
As a cornerstone of agricultural productivity, fertilizers significantly contribute to India’s food security and rural economy. The essential macronutrients—nitrogen (N), phosphorus (P), and Potassium (K)—are critical for enhancing crop yields and maintaining soil health. This study examines the trends in production, consumption, and imports of these nutrients from 2010–11 to 2022–23, along with fertilizer policy developments up to 2024–25. Using secondary data and applying CGR, CAGR and trend analysis, the findings reveal rising nitrogen and urea consumption surpassing production, fluctuating phosphorus and DAP usage, and a steady decline in MOP imports. These dynamics point to gradual progress in domestic self-sufficiency, shaped by evolving policy measures and changing agricultural practices. The study highlight the need for balanced nutrient use and sustainable fertilizer strategies to ensure long-term agricultural resilience.
Purpose This study examines consumer interest in paying farmers and market staff to deliver fresh fruits and vegetables to their homes. It also identifies the profiles of likely users based on observable predictor variables. Paying a fee for the service was explored rather than calculating precise monetary willingness to pay (WTP) estimates, offering a foundation for future research. Design/methodology/approach Responses were obtained from 1,054 Mid-Atlantic US consumers responsible for at least half of the household grocery purchases, participating in agritourism or purchasing food from direct marketers or local sources. This study applies the exhaustive chi-square automatic interaction detector (ECHAID), a decision tree-based classification method that extends the traditional automatic interaction detector (AID) technique. ECHAID systematically segments consumers into interpretable and actionable groups by identifying significant interactions among predictor variables, making it beneficial for market segmentation and consumer behavior analysis without requiring predefined assumptions about data distributions. Findings Seven segments had index scores above 100%, indicating a higher-than-average likelihood of being willing to pay for the service. Respondents in five segments used online order and delivery systems to acquire fresh fruits and vegetables. Notable differences included past purchases from on-farm markets, curbside grocery pickup usage, changes in fresh fruit or vegetable consumption and the presence of children in the household. Originality/value Despite the rise in third-party services for producers to manage and ship produce to local consumers, to date, no studies have yet provided insight into the socioeconomic and behaviors of likely users. Access to these data allows farmers to focus on potential customers rather than the mass market.
Price fluctuations significantly impact supply and demand mechanisms, particularly in agriculture and food production. These effects are often persistent and challenging to adapt directly, making it crucial for agrarian countries to understand the factors driving these changes. This research focuses on calculating a specific food price index related to Turkish food exports, with the goal of evaluating the factors contributing to volatility in this index. Using data from 1991-2022, the analysis employed selected machine learning methodologies to project potential policy interventions. The support vector regression (SVR) predictions revealed that rising prices of exportable products are driven by various factors, including cost items, food price inflation, unemployment levels (as an indicator of income), and exchange rates. The predictions closely aligned with the actual calculated variables, suggesting that variations in aggregate price levels, exchange rates, and technology-related and import-dependent costs are critical for observation and evaluation. These factors appear to play a more significant role in determining price inflation for Turkish agricultural and food products.
Annie’s Project, is a nationally recognized educational program for women farmers focused on five areas of risk management: marketing, production, financial, human, and legal. Some challenges are common to all farms, while some are unique to urban locations. We added urban-focused topics including short-term leases, contaminated soils, water availability, indoor production, and access to capital and resources to develop Annie’s Project: Farming in Cities and the Urban Fringe. As urbanization spreads and a greater percentage of farmers across the United States are women, our curriculum can be used to address needs that are unique to farmers in urban environments.
Tomato, a vital subtropical vegetable crop, is in demand globally but is produced in limited regions. Recently, its supply has become increasingly influenced by internal and external production factors. This study analyzed the impact of price fluctuations and evolving agricultural support schemes on tomato production in three key producers: Mexico, Türkiye, and the United States, which play significant roles in the global market with specialized production and trade. Using time-series price response data from 1991 to 2022, the research examined market prices, government support policies, and international trade agreements. Long-term price effects were similar in Türkiye and the USA but negligible in Mexico. Short-term price differences were positive across all countries, with the strongest impact in the USA. Financial support programs increased supplies in alignment with time-based effects. Deviations from long-term equilibrium were corrected in all countries, with Türkiye showing the fastest recovery. The results suggest that decoupled supports positively influence supply and merit further promotion.
Agritourism as a niche tourism market has become an alternative income-generating sector for conventional farmers that focus on both production and marketing. Agritourist activities involve joining production and harvest, receiving education and on site-training. However, unless individuals endeavor and they consistently demand these activities, agritourism cannot be promoted among farm operators. In other words, agritourism is a demand-driven sector and requires the loyalty of participants which was measured with respect to more than a single take up of agritourism in the past two years. With this research, the loyalty of agritourists and the factors affecting their persistent participation were estimated based on a sample from the Mid-Atlantic states of the USA. The findings infer that agritourism loyalty is stimulated by the rising level of agritourist education and increasing income. Married people with children also prefer rural participation. Agritourists, who have the potential to become loyal, focus above all on buying fresh and high-value products. Following this, they demonstrate a rising tendency to learn about agricultural production, and to spend quality time with family/friends. These results suggest that with proper marketing strategies, supportive actions designed for farmers that seek alternative income, and the involvement of regional/local authorities in decision making and promotional processes, may contribute to the development of agritourism and expand its market through assuring customer loyalty.
Citrus diseases are caused due to bacterial infections. Prolonged infection leads to the degradation of overall productivity. This can be avoided through early detection and identification. This is implemented using Convolutional Neural Networks (CNNs), which provide a novel solution to overcome the constraints. CNNs are better suited for image recognition tasks, which helps in accurate identification of the spread of the bacteria. This is implemented using the multi-stage process. The images are pre-processed and help to provide optimum results. The CNN architecture is trained from the hierarchical demonstration of the input images through adopting convolutional layers. The detection of citrus diseases is done using the training and testing phase. This helps in the particular disease detection task. After the training process, the CNN are used for the representation of real-time disease detection. The input images from the agriculture are captured by drones or various automated imaging devices. Various iterative processes are done to enhance the robustness of the system. Thus, the CNN helps in early identification of diseases which helps to adopt various preventive measures and remedies for infectious fruits and leaves. CNN can able to make automatic decisions about disease control strategies which helps to improve the crop yield. They also provide positive approach to disease management
