Cassava breeding and agronomy in Asia: 50 years of history and future directions

Abstract

In Asia, cassava (Manihot esculenta) is cultivated by more than 8 million farmers, driving the rural economy of many countries. The International Center for Tropical Agriculture (CIAT), in partnership with national agricultural research institutes (NARIs), instigated breeding and agronomic research in Asia, 1983. The breeding program has successfully released high-yielding cultivars resulting in an average yield increase from 13.0 t ha^-1 in 1996 to 21.3 t ha^-1 in 2016, with significant economic benefits. Following the success in increasing yields, cassava breeding has turned its focus to higher-value traits, such as waxy cassava, to reach new market niches. More recently, building resistance to invasive pests and diseases has become a top priority due to the emergent threat of cassava mosaic disease (CMD). The agronomic research involves driving profitability with advanced technologies focusing on better agronomic management practices thereby maintaining sustainable production systems. Remote sensing technologies are being tested for trait discovery and large-scale field evaluation of cassava. In summary, cassava breeding in Asia is driven by a combination of food and market demand with technological innovations to increase the productivity. Further, exploration in the potential of data-driven agriculture is needed to empower researchers and producers for sustainable advancement.

Keywords: Asia; CMD; agronomy; cassava; conventional breeding; data-driven agriculture; new breeding techniques.

Fig. 1.

Schematic diagram of data-driven cassava development. Aerial part of cassava plants monitored and phenotyped for traits of interest by UAV and satellite imagery under field conditions. Ground-penetrating radar (GPR) used for belowground traits. Analyzed digital data used by breeders to identify new source of genes in germplasm or select better performing breeding lines in a cassava breeding scheme. In coming years, application of disrupting technologies such as AI with collected data in farmer’s field realized to improve crop productivity and sustainability.