Most people in Niger still rely heavily on agriculture as a source of income. However, low productivity, climate change, soil infertility, pests, and diseases are challenges faced by this sector. As a result, the nation suffers from a severe problem of food insecurity. Many investigations indicate that adopting improved crop varieties(ICVs) increases agricultural productivity. Using information gathered from 1784 farmers, this study assesses the effects of adopting improved crop varieties (ICVs) on household welfare.

The SIMFS initiative in Ethiopia is implemented under the scheme of sustainable intensification of crop-livestock mixed farming systems. Its purposes are to encourage the adoption and innovation of sustainable intensification technologies that improve crop, livestock, and water productivity, and conserve natural resources for smallholder farmers in Ethiopia.

Sub-Sahara Africa (SSA) has degraded soils, epitomized by decades of loss of valuable topsoil caused by continuous cultivation, monocropping, excessive run-off, and the absence of effective conservation-based farming practices. Conservation Agriculture (CA) offers several opportunities to restore land and arrest soil degradation in smallholder farming systems of SSA. This study assessed labor productivity and farm returns associated with CA in Malawi and Zambia using household survey data collected from 500 and 616 farmers in Zambia and Malawi, respectively.

The One CGIAR initiative on Mixed Farming Systems (MFS) entered its 1st year after its official inception in Malawi. Various partners that included the National Agricultural Research and Extension Systems (NARS), and the CGIAR researchers converged in Lilongwe, Malawi, to among other things; share, learn, assess progress, achievements, challenges, gaps, and opportunities, and to develop a strategy on system integration, gender, and social equity across work packages, research sites, and disciplines to support MFS outcomes and deliverables.

Conducted a baseline of farming households that are members of 07 Diet Health Clubs and 02 Vegetable Value Chain Groups. The household survey assessed the current situation at farm level including crop diversity and practices related to production, soil, water and pest management, and food consumption. Furthermore, a qualitative study was conducted with the management boards of the value chain groups to establish current group activities, production practices, products, and revenue.

This research note explains the results of social experiment designed with three primary objectives. These include (1) to mitigate the digital divide concerning the accessibility of forecasted weather information and crop advisories for women farmers in Bangladesh and (2) to assess the potential impact of a digital climate advisory tool on the agricultural practices of women farmers.

Over the past centuries, the mid-hills of Nepal have been inhabited and cultivated by nomads, settlers and in the recent decades intensified farming systems. The system is constantly under systemic change due to farmers own actions and policies within the confines of biophysical resource envelope. Without proper design, the farming system has been characterised by high levels of degradation, expansion to marginal fragile landscapes, outmigration and high cost of production. These challenges pose higher productivity, financial and social risks.

Knowledge management and sharing is essential for agricultural development, fostering the exchange of valuable information and innovative practices among researchers, farmers, and extension officers. Recognising this, regional initiatives such as Ukama Ustawi (UU) play a critical role in ensuring that research findings are not confined to academic journals but are made accessible to those who need them the most.

Accurate crop yield prediction is crucial for optimizing agricultural practices and ensuring food security. The NextGen advisory is a fertilizer recommendation system that can be customized for different farming systems, crop types, and locations. The system uses machine learning models to predict site-specific fertilizer rates using filed trial and covariates (soil, topography, and climate) data.

This paper analyzes integrated crop-livestock farming systems in dryland regions of Senegal using household survey data and whole farm household modeling. It focuses on the Kaffrine and Thies regions, which exhibit differences in cultivated land area, staple crops grown, and livestock holdings. The analysis identifies region-specific opportunities to sustainably enhance productivity, resilience, and food security. The mechanistic model incorporates factors like crop mixes, livestock herd dynamics, climate impacts, economics, and labor to simulate entire farms.