Climate Resilient Agriculture - Instruction QA

Nearly 100,000 question-and-answer pairs on Indian climate and agriculture: a hybrid dataset combining real farmer and expert conversations with synthetically generated pairs, written as expert-level answers for farmers. It spans climate-resilient farming, water and irrigation, organic and sustainable practices, soil health and carbon sequestration, agroforestry, government schemes, livestock and AI in agriculture. English, instruction-tuning format, from KissanAI.

About Dataset

Climate Resilient Agriculture - Instruction QA is a hybrid instruction-tuning dataset for climate-smart Indian agriculture, combining real farmer and expert conversations with synthetically generated question-answer pairs. It holds tens of thousands of pairs in a single train split, stored as Parquet, with three columns: input (a farmer or practitioner question), instruction (the task directive), and output (an expert-level response that ranges from a short reply to several detailed paragraphs). The language is English. The knowledge is drawn from multiple natural and climate-resilient agriculture sources rather than a single programme. These include community-managed natural-farming systems such as Andhra Pradesh Community-managed Natural Farming (APCNF), alongside agricultural universities, research institutes and field practice, so the answers reflect a range of crops and agro-climatic conditions across India. The pairs are grounded in practical advisory needs across India’s agro-climatic regions, and the topics span climate-resilient farming practices, water conservation and irrigation technologies, organic and sustainable agriculture, soil health and carbon sequestration, agroforestry and biodiversity, government schemes and farmer-support programmes, livestock management and integrated farming systems, and AI and ML applications in agriculture. By blending genuine conversational data with generated examples, the dataset covers a broad, balanced range of climate and agriculture sub-topics that are under-represented in general-purpose corpora, while the input/instruction/output layout makes it ready for supervised fine-tuning (SFT) of instruction-following models. The dataset is published by KissanAI, the team behind the Dhenu agriculture agentic platform, and has already been used to train a small text-generation model, IlaAI-v1. It is suitable for fine-tuning compact, India-aware agricultural advisory assistants and for research on climate-resilient agriculture question answering.

Purpose of Dataset

The Purpose Of Climate Resilient Agriculture - Instruction Qa Is To Give Language Models Grounded, Climate-aware Agricultural Knowledge For The Indian Context. General-purpose Models Rarely Hold Reliable, Region-specific Guidance On Climate-resilient Farming, Water And Soil Management, Schemes Or Sustainable Practices, And High-quality Indian Agricultural Question-answer Data Is Scarce. This Dataset Provides A Large, Ready-to-use Instruction-tuning Corpus To Close That Gap. It Is Designed To Fine-tune Small And Efficient Models That Can Run Cheaply, Even On-device Or At The Edge, So That Climate And Agriculture Advisory Can Reach Farmers At Scale And In Low-connectivity Settings. Beyond Model Training, It Supports Benchmarking And Research On Climate-smart Agriculture Question Answering, And Offers A Reusable Base Of Expert-level Answers Across Water, Soil, Agroforestry, Livestock, Schemes And Ai In Farming. Because It Blends Real Conversations With Generated Examples, It Can Be Extended, Filtered And Recombined To Suit New Tasks And Regions.