31 research outputs found

    Genetic variation and host-parasite specificity of Striga resistance and tolerance in rice: the need for predictive breeding

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    The parasitic weeds Striga asiatica and Striga hermonthica cause devastating yield losses to upland rice in Africa. Little is known about genetic variation in host resistance and tolerance across rice genotypes, in relation to virulence differences across Striga species and ecotypes. Diverse rice genotypes were phenotyped for the above traits in S. asiatica- (Tanzania) and S. hermonthica-infested fields (Kenya and Uganda) and under controlled conditions. New rice genotypes with either ecotype-specific or broad-spectrum resistance were identified. Resistance identified in the field was confirmed under controlled conditions, providing evidence that resistance was largely genetically determined. Striga-resistant genotypes contributed to yield security under Striga-infested conditions, although grain yield was also determined by the genotype-specific yield potential and tolerance. Tolerance, the physiological mechanism mitigating Striga effects on host growth and physiology, was unrelated to resistance, implying that any combination of high, medium or low levels of these traits can be found across rice genotypes. Striga virulence varies across species and ecotypes. The extent of Striga-induced host damage results from the interaction between parasite virulence and genetically determined levels of host-plant resistance and tolerance. These novel findings support the need for predictive breeding strategies based on knowledge of host resistance and parasite virulence

    Putting Plant Genetic Diversity and Variability at Work for Breeding: Hybrid Rice Suitability in West Africa

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    Rice is a staple food in West Africa, where its demand keeps increasing due to population growth. Hence, there is an urgent need to identify high yielding rice cultivars that fulfill this demand locally. Rice hybrids are already known to significantly increase productivity. This study evaluated the potential of Asian hybrids with good adaptability to irrigated and rainfed lowland rice areas in Mali, Nigeria, and Senegal. There were 169 hybrids from China included in trials at target sites during 2009 and 2010. The genotype × environment interaction was highly significant (p < 0.0001)for grain yield indicating that the hybrids’ and their respective cultivar checks’ performance differed across locations. Two hybrids had the highest grain yield during 2010 in Mali, while in Nigeria, four hybrids in 2009 and one hybrid in 2010 had higher grain yield and matured earlier than the best local cultivar. The milling recovery, grain shape and cooking features of most hybrids had the quality preferred by West African consumers. Most of the hybrids were, however, susceptible to African rice gall midge (AfRGM) and Rice Yellow Mottle Virus (RMYV) isolate Ng40. About 60% of these hybrids were resistant to blast. Hybrids need to incorporate host plant resistant for AfRGM and RYMV to be grown in West Africa

    Farmers' perceptions on mechanical weeders for rice production in sub-Saharan Africa

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    Competition from weeds is one of the major biophysical constraints to rice (Oryza spp.) production in sub-Saharan Africa. Smallholder rice farmers require efficient, affordable and labour-saving weed management technologies. Mechanical weeders have shown to fit this profile. Several mechanical weeder types exist but little is known about locally specific differences in performance and farmer preference between these types. Three to six different weeder types were evaluated at 10 different sites across seven countries – i.e., Benin, Burkina Faso, Cîte d'Ivoire, Ghana, Nigeria, Rwanda and Togo. A total of 310 farmers (173 male, 137 female) tested the weeders, scored them for their preference, and compared them with their own weed management practices. In a follow-up study, 186 farmers from Benin and Nigeria received the ring hoe, which was the most preferred in these two countries, to use it during the entire crop growing season. Farmers were surveyed on their experiences. The probability of the ring hoe having the highest score among the tested weeders was 71%. The probability of farmers’ preference of the ring hoe over their usual practices – i.e., herbicide, traditional hoe and hand weeding – was 52, 95 and 91%, respectively. The preference of this weeder was not related to gender, years of experience with rice cultivation, rice field size, weed infestation level, water status or soil texture. In the follow-up study, 80% of farmers who used the ring hoe indicated that weeding time was reduced by at least 31%. Of the farmers testing the ring hoe in the follow-up study, 35% used it also for other crops such as vegetables, maize, sorghum, cassava and millet. These results suggest that the ring hoe offers a gender-neutral solution for reducing labour for weeding in rice as well as other crops and that it is compatible with a wide range of environments. The implications of our findings and challenges for out-scaling of mechanical weeders are discussed

    Quantifying rice yield gaps and their causes in Eastern and Southern Africa

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    The demand for rice in Eastern and Southern Africa is rapidly increasing because of changes in consumer preferences and urbanization. However, local rice production lags behind consumption, mainly due to low yield levels. In order to set priorities for research and development aimed at improving rice productivity, there is a need to characterize the rice production environments, to quantify rice yield gaps —i.e. the difference between average on-farm yield and the best farmers’ yield— and to identify causes of yield gaps. Such information will help identifying and targeting technologies to alleviate the main constraints, and consequently to reduce existing yield gaps. Yield gap surveys were conducted on 357 rice farms at eight sites (19-50 farmers per site) across five rice-producing countries in Eastern and Southern Africa —i.e. Ethiopia, Madagascar, Rwanda, Tanzania and Uganda— for one or two years (2012-13) to collect both quantitative and qualitative data at field and farm level. Average farm yields measured at the eight sites ranged from 1.8 to 4.3 t ha–1 and the average yield gap ranged from 0.8 to 3.4 t ha–1. Across rice growing environments, major causes for yield variability were straw management, weeding frequency, growth duration of the variety, weed cover, fertilizer (mineral and organic) application frequency, levelling and iron toxicity. Land levelling increased the yield by 0.74 t ha–1, bird control increased the yield by 1.44 t ha–1, and sub-optimal management of weeds reduced the yield by 3.6 to 4.4 t ha–1. There is great potential to reduce the current rice yield gap in ESA, by focusing on improvements of those crop management practices that address the main site-specific causes for suboptimal yields

    Do NERICA rice cultivars express resistance to Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze under field conditions?

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    The parasitic weeds Striga asiatica and Striga hermonthica cause high yield losses in rain-fed upland rice in Africa. Two resistance classes (pre- and post-attachment) and several resistant genotypes have been identified among NERICA (New Rice for Africa) cultivars under laboratory conditions (in vitro) previously. However, little is known about expression of this resistance under field conditions. Here we investigated (1) whether resistance exhibited under controlled conditions would express under representative Striga-infested field conditions, and (2) whether NERICA cultivars would achieve relatively good grain yields under Striga-infested conditions. Twenty-five rice cultivars, including all 18 upland NERICA cultivars, were screened in S. asiatica-infested (in Tanzania) and S. hermonthica-infested (in Kenya) fields during two seasons. Additionally, a selection of cultivars was tested in vitro, in mini-rhizotron systems. For the first time, resistance observed under controlled conditions was confirmed in the field for NERICA-2, -5, -10 and -17 (against S. asiatica) and NERICA-1 to -5, -10, -12, -13 and -17 (against S. hermonthica). Despite high Striga-infestation levels, yields of around 1.8 t ha−1 were obtained with NERICA-1, -9 and -10 (in the S. asiatica-infested field) and around 1.4 t ha−1 with NERICA-3, -4, -8, -12 and -13 (in the S. hermonthica-infested field). In addition, potential levels of tolerance were identified in vitro, in NERICA-1, -17 and -9 (S. asiatica) and in NERICA-1, -17 and -10 (S. hermonthica). These findings are highly relevant to rice agronomists and breeders and molecular geneticists working on Striga resistance. In addition, cultivars combining broad-spectrum resistance with good grain yields in Striga-infested fields can be recommended to rice farmers in Striga-prone areas

    The ontologies community of practice: a CGIAR initiative for Big Data in agrifood systems

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    Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams

    How to create dataset within AfricaRice Dataverse

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    This document provides information to the user when creating dataset within AfricaRice Dataverse
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