National Consultant for AI for Climate-Smart Smallholder Agriculture Capacity Building

Expected outputs By the end of the assignment, the following outputs are expected to be achieved: • A needs-driven, localized online training programme on AI for Climate-Smart Agriculture, tailored to participants’ learning needs and LDC contexts, is designed, approved and ready for delivery. • At least 100 agri-tech entrepreneurs, researchers and developers from the participating LDCs are selected and trained through the Online Innovation Lab, with a completion rate of at least 80%. • Following the Online Innovation Lab, up to six (6) validated, adoption-ready AI solution concepts, two (2) per country, are identified, and the top 20 participants are selected to advance to the residency. • TThe technical content of the one-week in-person residency is designed and delivered, strengthening teams’ adaptation, commercialization and partnership strategies. • Three (3) pilot-ready AI solutions, one per country, with finalized pilot plans, field-testing protocols and commercialization materials, are selected to advance to in-country piloting.

Turkey

7 months

Duties and responsibilities Agriculture underpins the economies of most Least Developed Countries (LDCs), and its largely rain-fed nature leaves smallholder farmers highly exposed to climate variability, droughts and floods. Technology Needs Assessments (TNAs) conducted by the UN Technology Bank for the Least Developed Countries (UNTB) consistently identify agriculture as a priority sector for technological and climate-resilience interventions, including climate information services, early warning systems and more efficient water and land management. Affordable AI-enabled tools adapted to local conditions offer a practical way to bring these solutions to smallholders. In response, UNTB is implementing the project “Artificial Intelligence (AI) Solutions for Climate-Smart, Smallholder Agriculture in 3 African LDCs”. The project aims to enable agri-entrepreneurs and innovators in three sub-Saharan African LDCs to adopt, adapt and localize proven AI-powered, climate-smart agricultural solutions, resulting in one field-tested, pilot-ready Minimum Viable Product (MVP) per country. The project focuses on the adaptation and implementation of existing, validated AI technologies rather than the development of new solutions or large-scale AI infrastructure. Given limited and intermittent connectivity and varying levels of digital literacy in the target countries, it prioritizes low-cost, user-friendly solutions that can function in low-bandwidth environments and on basic mobile devices. Application areas may include climate and weather advisory services, crop monitoring and yield estimation, pest and disease detection, and farm-level decision support. The project also promotes South-South cooperation and knowledge exchange to strengthen the technical quality and local relevance of the adapted solutions. The project is structured in three phases: (i) an Online Innovation Lab, delivering needs-driven training to at least 100 agri-tech entrepreneurs, researchers and developers and identifying up to 20 top participants with six (6) validated, adoption-ready AI solution concepts; (ii) a one-week In-person Residency for the selected participants, resulting in the selection of three final (3) pilot-ready AI solutions; and (iii) Mentorship and In-Country Pilots, resulting in one (1) AI pilot implemented per country and open knowledge resources to support replication. Within this framework, this consultancy will focus on the design and delivery of the learning and capacity-building components under Phase 1 and Phase 2. Purpose and Scope of Assignment The Consultant will be responsible for the design and delivery of the Online Innovation Lab (Phase 1) and for the technical content of the In-person Residency (Phase 2) of the AI for Climate-Smart Agriculture programme. This includes designing the training curriculum and materials, providing participant selection support, delivering online workshops and ideation sprints, assessing solution concepts, informing the residency curriculum design, and providing recommendation on the three (3) pilot-ready solutions to advance to the pilot phase. The assignment calls for a combination of technical understanding of AI, data and geospatial applications in agriculture and climate adaptation in low-resources setting like developing and least developed countries; experience in designing and facilitating learning programmes for international audiences; innovation and entrepreneurship support; and a practical understanding of LDC contexts. Under the overall supervision of UNTB, the Consultant will carry out the key tasks of the assignment with all content, methodologies and selection processes subject to UNTB review and approval. Tasks The following provides an overview of the key tasks to be conducted by the Consultant. The deliverables through which these tasks will be documented and verified are listed further below. • Prepare the Inception Report, informed by a focused desk review of existing AI solutions, Climate-Smart Agriculture practices and agri-entrepreneurship support approaches relevant to LDC contexts, confirming the programme design, curriculum development approach, selection approach, workplan, timeline and coordination arrangements with UNTB. • Maintain regular coordination with UNTB regarding implementation progress, agreed timelines, deliverables and reporting requirements, and coordinate with the regional host institution on the preparation of the residency. • Develop and localize, informed by an entry survey of participants’ learning needs, a structured online curriculum and learning materials for the Online Innovation Lab with modules, toolkits and case studies on AI/ML fundamentals for solution adoption, Climate-Smart Agriculture and entrepreneurship, innovation and business administration, relevant to LDC contexts. • Provide technical support to the global and local mobilization of participants (agri-tech entrepreneurs, researchers and developers), including preparing outreach materials, assisting in the application process, defining participant eligibility and selection criteria, and evaluating and selecting applicants with UNTB. • Deliver the two-month live and self-paced online training programme, monitor participation and completion, prepare a solution proposal template, and guide participants in developing their solution proposals. • Develop, in consultation with UNTB, the selection criteria for solution proposals, assess submitted proposals and participants’ performance, and recommend the top 20 participants and up to six (6) solution concepts to advance to the in-person residency. • Support participant engagement and learning, including tracking attendance, assignments and completion, facilitating peer-learning groups and community channels, and adapting training content based on participant progress and feedback. • Develop the residency agenda and curriculum building on Phase 1 results, and provide technical inputs to UNTB and the regional host institution on resource persons, field or site visits, partnership-matching sessions and participant pre-work. • Support teams in finalizing implementation plans, field-testing protocols and investor-ready commercialization materials using templates prepared by the Consultant, assess final submissions, and recommend three (3) pilot solutions, one per country.

Advanced university degree (Master’s degree or equivalent) in Agriculture or Agricultural Sciences, Computer or Data Science, Engineering, Environmental or Climate Sciences, Business Administration, Innovation Management, Development Studies or a related field is required. A first-level university degree in combination with two (2) additional years of qualifying experience may be accepted in lieu of the advanced university degree. A minimum of seven (7) years of professional experience in digital agriculture, AI and data applications, climate adaptation, innovation and entrepreneurship support, capacity development, or related areas is required. Demonstrated experience in designing and delivering capacity-building, acceleration or incubation programmes (online and/or in-person) for entrepreneurs, innovators or researchers is required. Demonstrated knowledge of AI/ML, data and geospatial applications in agriculture or climate adaptation, including their use in low-resource and low-connectivity settings, is required. Demonstrated analytical, coordination and report-writing skills, evidenced through previous assignments, reports, publications or similar outputs, are required. Experience supporting early-stage ventures through mentoring, acceleration or incubation is desirable.

Fluency in English is required.

Not available.