Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production.

This study takes stock of over 10-years of experience and increasing coverage of index-based livestock insurance (IBLI) programs to protect pastoralists from drought shocks in the Intergovernmental Authority on Development (IGAD) region. The study reviews the status of national-level drought/climate risk financing solutions in the region and discusses the operational and technical feasibility of a regional IBLI program, summarizing critical lesson learned and providing a set of recommendations and options for regional implementation.

The Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project is administered by the Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT). AICCRA is a project that helps deliver a climate-smart African future driven by science and innovation in agriculture. AICCRA builds on 50 years of CGIAR innovation, AICCRA works to
scale climate-smart agriculture (CSA) and climate information services (CIS) that reach millions of smallholder farmers in Africa.

Chickpea (Cicer arietinum L.) is the second largest pulse crop grown worldwide and ascochyta blight caused by Ascochyta rabiei (Pass.) Labr. is the most devastating disease of the crop in all chickpea growing areas across the continents. The pathogen A. rabiei is highly variable. The resistant sources available are not sufficient and new sources needs to be identified from time to time as resistance breakdown in existing chickpea varieties is very frequent due to fast evolution of new pathotypes of the pathogen.

Triadic comparison of technology options (tricot) is a research methodology that helps farmers to identify the most suitable technologies for the local conditions of their farm. Tricot (read: ‘try-cot’) engages farmers as ‘farmer researchers’ in the testing or validation of new crop varieties and other promising technologies. Tricot is supported by the ClimMob digital platform (https://climmob.net). This guide provides an introduction to tricot and each of the steps in the experimental cycle.

The Enviro-Champs initiative was developed as a community driven, citizen science initiative in Mpophomeni township in Kwa-Zulu Natal (KZN), South Africa. Over time, the scope of work done and data collected by the Enviro-Champs has expanded. There is now recognition both locally and globally that the Enviro-Champs initiative shows great promise for national and global upscaling. However, several areas within the initiative remain where it could be improved, especially technologically.

After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa.

The quest to transform and hasten the role of smallholder farms in agricultural development and food security through farmer firm linkages has dominated development interventions in low-income countries for several decades. This has mostly been pursued through single- or multi-contract schemes implemented in isolation. Several studies have analyzed the efects of these schemes on smallholder farms with mixed results. A new paradigm is to use Agricultural Public Private Partnership (Ag-PPP) to achieve wider and sustainable impacts.

WP3 intends to address the question: How can cross-food value chain and market services function better to increase employment and boost income of smallholders and SMEs? The WP focuses on two types of cross-value chain services, logistics and financial services. (1) logistics services – supply chain management, transportation, traceability, digital platforms for e-com merce, and (cold) storage. (2) value-chain financial services – mainly focusing on digital financial services (DFS) that facilitate trans actions, savings, access to and use of credit, and insurance.

Finding out how to scale innovations successfully is high on the agendas of researchers, practitioners and policy makers involved in agricultural development. New approaches and methodologies seek to better address related complexities, but none of them include a systematic perspective on the role of capacity in (partnerships for) scaling innovations. We posit that this has left an important topic insufficiently addressed in relation to partnerships for scaling innovations.

The cassava system in Nigeria is developing, with increasing attention to its potential positive outcomes. However, credit access is a major problem in expanding productive activities of the different actors across the value chains of cassava products. This study investigates the extent of access to credit by cassava actors with respect to the different financial institutions in the country using data obtained from a sample of 168 actors, including producers, processors, marketers, fabricators and end users.

In this research, we explored rainfall variability in the Sogamoso River Basin (SRB), its relationship with multiple scales of variability associated with El Niño–Southern Oscillation (ENSO), and the implications for rainfall prolongation during multiyear La Niña events. First, we examined time-frequency rainfall variations in the SRB based on the standardized precipitation index (SPI) from 1982 to 2019, using wavelet transform and principal component analysis (PCA).