Regional Policy Makers

The case study explores a comprehensive video production training program organized by CCARDESA and designed to accomplish some of the Research 2 Extension objectives while responding to some expectations from participants.

Terms of Reference Undertake a Scoping Study for the Ukama Ustawi Learning Alliance

Plano de Gestão Ambiental e Social - Centro Regional de Liderança da Mandioca: Malanje-Angola

Estudo de Impacto Ambiental e Social - Centro Regional de Liderança da Mandioca: Malanje-Angola

Plano de Gestão da Biodiversidade - Centro Regional de Liderança da Mandioca: Malanje-Angola

Bemisia tabaci is an important vector of cassava brown streak viruses and cassava mosaic begomoviruses, the causal agents of cassava brown streak disease and cassava mosaic disease (CMD), respectively. A study was carried out to determine the genetic variability of B. tabaci associated with cassava and the occurrence of CMD in Zambia in 2013 and 2015. Phylogenetic analysis showed the presence of only the sub-Saharan Africa 1 (SSA1) genetic group in Zambia. The SSA1 population had three population subgroups (SGs): SSA1-SG1, SSA1-SG2 and SSA1-SG3. All three SSA1 population subgroups occurred in Western Province. However, only SSA1-SG3 occurred in Eastern Province, while only SSA1-SG1 occurred in North Western and Luapula Provinces. Adult B. tabaci were most abundant in Western Province in 2013 (11.1/plant) and 2015 (10.8/plant), and least abundant (0.2/plant) in Northern Province in both 2013 and 2015. CMD was prevalent in all seven provinces surveyed, with the highest incidence recorded in Lusaka Province in both 2013 (78%) and 2015 (83.6%), and the lowest in Northern Province in both 2013 (26.6%) and 2015 (29.3%). Although SSA1-SG1 occurred at greater abundances than the other subgroups, there was no direct association demonstrated between whitefly subgroup and incidence of CMD. Establishing which B. tabaci genetic groups and populations are associated with CMD and their distribution in the country is a key factor in guiding the development of CMD control strategies for cassava-dependent households.

No âmbito do Projecto Agricultura Adaptativa ao Clima (Projecto AAC), financiado pela Ajuda Popular da Noruega (APN), o Instituto de Investigação Agrária de Moçambique (IIAM), a Associação Moçambicana para o Desenvolvimento Rural (AMDER) e a União Geral dos Camponeses de Nampula (UGCAN) desenvolveram um consórcio para implementação de suas actividades nos distritos de Angoche e Meconta, provincia de Nampula.

The deep knowledge of genetic diversity in access, in the breeding program, and the contribution of genetic factors as well as the phenotypic expression is of extreme importance for the development of new cultivars and maintenance of the program. This research aimed to study the genetic diversity and selection of the best common bean genotypes with high agronomic performance in the Agrarian Station of Sussundenga-Mozambique. Twenty-two common bean genotypes were evaluated in a randomized block design with three repetitions in the 2021/2022 agricultural season. The agronomic characteristics evaluated were: the number of days to flowering, plant height, plant architecture, number of pods per plant, the weight of 100 seeds, and grain yield. The data obtained were submitted to individual variance analysis, followed by the Scott-Knott grouping test. For the effect of genetic divergence between the genotypes, multivariate analysis was used based on the generalized Mahalanobis distance and Tocher optimization method, UPGMA, and canonical variables for the projection of distance in the 2D plane. It was also verified by the Cophenetic Correlation Coefficient. The Singh criterion (1981) was used, indicating the characteristics Number of pods per plant (42.81%), the weight of 100 seeds (19.59%), and Plant height (16.08%) as the most important for the genetic divergence of bean genotypes.

This Climate Smart Agriculture (CSA) handbook has been developed as a resource material for use by CSA practitioners in providing training, policy advocacy, and upscaling CSA technologies and practices for improving sustainable productivity, adapting to the effects of climate change, and mitigating climate change.

This document is about the Guidelines for Capturing Information and Knowledge Products for CCARDESA