arXiv subjects

Kartik Sagar

Publications and source records attributed to Kartik Sagar.

3 recordsLinked to original sources

arXiv2026

Carbon Farming: An Expository, Inter-Disciplinary Survey

Carbon farming is the collection of agricultural best practices specifically designed to maximize the capture and long-term storage of atmospheric carbon dioxide in soils and plant biomass, while simultaneously reducing greenhouse gas emissions from cultivation practices. Carbon farming can be viewed as a promising pathway to simultaneously address climate change mitigation, soil degradation, and farmer welfare. For example, if the entire agricultural cropland in India practices carbon farming, this will spectacularly offset about 50% of emissions from the country's annual transport-sector emissions. However, practical deployment of carbon farming is constrained by scientific challenges, inherent complexity, and fragmented understanding across disciplines. This inter-disciplinary, expository survey offers the first unified treatment of carbon farming for practitioners, policymakers, and researchers. The survey integrates insights from agronomy, soil science, climate science, measurement, reporting, and verification (MRV), economics, carbon markets, and policy design. We begin by establishing the conceptual foundations of soil organic carbon dynamics and agricultural carbon sequestration, and compare carbon farming with the paradigms of sustainable, regenerative, and organic agriculture. We then present a comprehensive landscape analysis of carbon-farming best practices, including both generic and crop-specific interventions, and systematically examine their co-benefits and trade-offs. The paper offers a rigorous review of MRV frameworks, emerging digital MRV technologies, and the carbon-credit project life cycle, followed by a structured analysis of voluntary and compliance carbon markets...

econ.GN↗

arXiv2025

Deep Learning Based Auction Design for Selling Agricultural Produce through Farmer Collectives to Maximize Nash Social Welfare

This paper is motivated by the need to design a robust market mechanism to benefit farmers (producers of agricultural produce) as well as buyers of agricultural produce (consumers). Our proposal is a volume discount auction with a Farmer Collective (FC) as the selling agent and high volume or retail consumers as buying agents. An FC is a cooperative of farmers coming together to harness the power of aggregation and economies of scale. Our auction mechanism seeks to satisfy fundamental properties such as incentive compatibility and individual rationality, and an extremely relevant property for the agriculture setting, namely, Nash social welfare maximization. Besides satisfying these properties, our proposed auction mechanism also ensures that certain practical business constraints are met. Since an auction satisfying all of these properties exactly is a theoretical impossibility, we invoke the idea of designing deep learning networks that learn such an auction with minimal violation of the desired properties. The proposed auction, which we call VDA-SAP (Volume Discount Auction for Selling Agricultural Produce), is superior in many ways to the classical VCG (Vickrey-Clarke-Groves) mechanism in terms of richness of properties satisfied and further outperforms other baseline auctions as well. We demonstrate our results for a realistic setting of an FC selling perishable vegetables to potential buyers.

cs.GT↗

arXiv2023

An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction

Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets.

cs.LG↗