ICARDA, with the support of the African Development Bank (AfDB), is implementing the Technologies for African Agricultural Transformation II (TAATII) Wheat project in Sudan in collaboration with the Agricultural Research Corporation (ARC).
Beyond biodiversity: does “Farming with Alternative Pollinators” also boost farmers’ income in wheat (Triticum aestivum L.) fields? a case study in Morocco
Assessing technical efficiency of crop–livestock systems under conservation agriculture: exploring the potential for sustainable system transformation in Tunisia
Purpose This study was conducted in four semi-arid regions in Tunisia – Kef, Siliana, Zaghouan and Kairouan – which have a similar agroecological system based on crop–livestock integration and experience serious soil erosion. The study objective...
Pyramiding Stripe Rust Resistant Genes Yr5, Yr10 and Yr15 in Sids 12 and Gemmeiza 11 Wheat Derived Lines
A total of 173 lines with to stripe rust (Yr) were created by crossing the highly susceptible cultivars Sids 12 and Gemmeiza 11 with the three monogenic lines carrying Yr5, Yr10 and Yr15 genes from 2016 through 2020. These lines were then...
Genome-wide association analysis of Septoria tritici blotch for adult plant resistance in elite bread wheat (Triticum aestivum L) genotypes
Septoria tritici blotch (STB) is a predominant foliar disease of wheat, caused by the pathogen Zymoseptoria tritici. This disease can lead to substantial yield losses warranting control by using expensive fungicides. One effective method of STB...
Leveraging ML to predict climate change impact on rice crop disease in Eastern India
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical...
Leveraging ML to predict climate change impact on rice crop disease in Eastern India
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical...
