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Rice production in sub-Saharan Africa (SSA) has increaed ten-fold since 1961, whereas its consumption has exceeded the production and the regional self-sufficiency rate is only 48% in 2020. Increase in rice production has come mainly from increased harvested area. Yield increase has been limited and the current average yield in SSA is around 2 t ha−1. This paper aims to provide the status quo of (i) current rice production and its challenges, (ii) selected achievements in rice agronomy research mainly by the Africa Rice Center and its partners, and (iii) perspectives for future research on rice agronomy in SSA. The major problems confronting rice production include low yield in rainfed environments, accounting for 70% of the total rice harvested area. Rainfed rice yields are strongly affected by climate extremes such as water stresses, soil-related constraints, and sub-optimum natural resource management and crop management practices by smallholder farmers including poor water management, and suboptimal use of fertilizers, herbicides, and machineries. For alleviating these constraints, a wide range of technologies have been developed and introduced over the last three decades. These include water conservation technologies in rainfed and irrigated lowland rice, site-specific nutrient management practices, decision support tools such as crop growth simulation models, and labor-saving technologies. We conclude that further research efforts are needed to develop locally adapted agronomic solutions for sustainable intensification, especially in rainfed rice to enhance the resilience to climate change and increase land and labor productivity and sustainability of rice cultivation in SSA. [ABSTRACT FROM AUTHOR]

Increasing fertilizer access and use is an essential component for improving crop production and food security in sub-Saharan Africa (SSA). However, given the heterogeneous nature of smallholder farms, fertilizer application needs to be tailored to specific farming conditions to increase yield, profitability, and nutrient use efficiency. The site-specific nutrient management (SSNM) approach initially developed in the 1990 s for generating field-specific fertilizer recommendations for rice in Asia, has also been introduced to rice, maize and cassava cropping systems in SSA. The SSNM approach has been shown to increase yield, profitability, and nutrient use efficiency. Yield gains of rice and maize with SSNM in SSA were on average 24% and 69% when compared to the farmer practice, respectively, or 11% and 4% when compared to local blanket fertilizer recommendations. However, there is need for more extensive field evaluation to quantify the broader benefits of the SSNM approach in diverse farming systems and environments. Especially for rice, the SSNM approach should be expanded to rainfed systems, which are dominant in SSA and further developed to take into account soil texture and soil water availability. Digital decision support tools such as RiceAdvice and Nutrient Expert can enable wider dissemination of locally relevant SSNM recommendations to reach large numbers of farmers at scale. One of the major limitations of the currently available SSNM decision support tools is the requirement of acquiring a significant amount of farm-specific information needed to formulate SSNM recommendations. The scaling potential of SSNM will be greatly enhanced by integration with other agronomic advisory platforms and seamless integration of digital soil, climate and crop information to improve predictions of SSNM recommendations with reduced need for on-farm data collection. Uncertainty should also be included in future solutions, primarily to also better account for varying prices and economic outcomes. • The SSNM approach was adapted for rice, maize and cassava production systems in SSA. • The SSNM approach increased yield, profitability, and nutrient use efficiency. • SSNM had 24% and 69% higher yield than the farmer practice for rice and maize. • Rice and maize yields were 11% and 4% higher in SSNM than local recommendations. • Integration with other digital platforms and databases can improve SSNM tools. [ABSTRACT FROM AUTHOR]