Prédiction de l'interaction génotype x environnement à partir d'indices variétaux de sensibilité à la sécheresse et de bilan hydrique à l'aide d'un modèle de régression factorielle dans des essais d'arachide au Sénégal

Des modèles de régression factorielle sont proposés pour prédire les interactions GxE pour des essais d'arachide (Arachis hypogaea L.) au Sénégal. Au facteur variété est attachée un indice de sensibilité à la sécheresse qui fait intervenir le poids de gousses, le poids de fanes et la durée floraison-récolte des variétés. Au facteur environnement ou année, est associé un taux de satisfaction des besoins calculé sur la première phase de maturité du cycle végétatif de l'arachide. Ce taux a été estimé par simulation à l'aide d'un modèle de bilan hydrique, SarraH, qui utilise en entrée les caractéristiques climatiques du milieu. Sur ces covariables, nous faisons la régression factorielle des observations afin de prédire l'interaction GxE dans de nouveaux environnements. Après avoir replacé la régression factorielle parmi les autres méthodes de modélisation de l 'interaction, le modele présenté par Denis et Vincourt est réécrit afin d'être ajusté sur des données même incomplètes à l'aide d'un logiciel ordinaire de modèle linéaire. Sur l'exemple d'un essai pluriannuel, ce modèle permet de rendre compte de l'interaction sans écrats significatifs. Pour qu'il puisse être appliqué plus largement, la collecte de données climatiques devra être généralisé en essais multilocaux

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

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

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

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, Côte 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

Fertilisers differentially affect facultative and obligate parasitic weeds of rice and only occasionally improve yields in infested fields

Different fertilisers were field tested to investigate whether they (1) suppress the obligate parasitic weed Striga asiatica and the facultative parasitic weed Rhamphicarpa fistulosa, and (2) favour rainfed rice yields under parasitic weed infestation. Four years of experiments were conducted in southwest Tanzania, in a S. asiatica-infested rainfed upland and a R. fistulosa-infested rainfed lowland field. Treatments included sole mineral (NPK or Di-Ammonium Phosphate —DAP— plus urea), organic (cattle manure or rice husk), combinations of mineral and organic fertilisers and a no-fertiliser control. Fertilisers moderately suppressed the obligate parasite S. asiatica, but no correlations between infestation level and soil fertility status were observed. In the lowland field, fertilisers promoted the facultative R. fistulosa, in particular the ones that included organic components. Plant-available phosphorus, exchangeable potassium and soil organic matter content correlated with R. fistulosa infestation levels. Positive fertiliser effects on yields were found in both parasitic weed infested fields, except in years with high S. asiatica infestation levels. Rice husks alone and rice husks or manure combined with DAP and urea increased yields and soil fertility most. We conclude that fertilisation differentially affects the obligate and facultative parasitic weeds of rice systems; obligate parasites may be slightly suppressed, but facultative parasites may even be stimulated. Meanwhile, consistent rice yield increases under parasitic weed infested conditions cannot be obtained with fertiliser application when parasitic weed infestation levels are too high

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

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

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

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

Status quo of chemical weed control in rice in sub-Saharan Africa

If future rice production is to contribute to food security for the increasing population of sub-Saharan Africa (SSA), effective strategies are needed to control weeds, the crop’s fiercest competitors for resources. To gain better insights into farmers’ access to, and use of, herbicides as part of weed control strategies, surveys were conducted in key rice production locations across SSA. Farm surveys were held among 1965 farmers across 20 countries to collect data on rice yields, farmer’s weed management practices, herbicide use, frequencies of interventions and information sources regarding herbicides. Markets were surveyed across 17 countries to collect data on herbicide availability, brand names and local prices (converted to US$ha−1). Herbicides are used by 34$17 ha−1). They are also the most popular herbicides among farmers. For advice on herbicide application methods, farmers primarily rely on their peers, and only a few receive advice from extension services (<23%) or inform themselves by reading the product label (<16%). Herbicide application timings are therefore often (38%) sub-optimal. Herbicide technologies can contribute to reduced production losses in rice in SSA. However, through negative effects on crop, environment and human health, incorrect herbicide use may unintentionally counteract efforts to increase food security. Moving away from this status quo will require strict implementation and monitoring of national pesticide regulations and investment in research and development to innovate and diversify the currently followed weed management strategies, agricultural service provision and communications with farmers

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

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

Prédiction de l'interaction génotype x environnement par linéarisation et régression PLS-mixte

Ce travail porte sur la prédiction de l'interaction entre génotype et environnement (GxE) et est appliqué au contexte sahélien. Après un tour d'horizon des principales méthodes d'analyse de la littérature, nous proposons la méthode APLAT. Le rendement de génotypes prédit à l'aide de covariables d'environnement par un modèle de simulation de cultures est développé en série de Taylor à l'ordre 1 au voisinage du vecteur de paramètres d'un génotype de référence. Nous nous ramenons alors approximativement à un modèle linéaire où la matrice des régresseurs est remplacée par la matrice des dérivées partielles par rapport aux paramètres. Le très grand nombre de paramètres variétaux généralement constaté dans les modèles de simulation de cultures conduit à un nombre important de régresseurs; d'où une estimation par régression Partial Least Squares (PLS). Par la suite, nous proposons APLAT-mixte, une extension de APLAT. Pour ce modèle mixte, nous maintenons le rendement des génotypes linéarisé dans la partie fixe, les interactions GxE résiduelles étant aléatoires, de variances inconnues. Nous introduisons à cet effet la technique PLS-Mixte pour estimer les composantes de variance dans un modèle où il y a plus de régresseurs que d'observations. L'algorithme itératif proposé, qui consiste à imbriquer la régression PLS dans l'algorithme Expectation Maximization (EM), est fondé sur les méthodes de maximisation de la vraisemblance Maximum Likelihood (ML) et Restricted Maximum Likelihood (REML). (Résumé d'auteur