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Weeds are a major threat in tropical regions where climate conditions favor their growth and development. This is particularly true in low-input rice-based cropping systems in the Malagasy highlands, where weed management is mainly done by manual removal. Crop rotation is often promoted as an efficient way to control weed infestations, while the role of fertilization is more controversial. In this study, we compared rice monoculture to three rainfed rice-based two-year rotations: rice followed by groundnut, rice followed by sorghum-cowpea mixture, and rice followed by a velvet-bean crotalaria mixture. Each rotation was tested with two levels of fertilization (5 t DM ha−1 organic manure, sole or in combination with mineral fertilizer - 400 kg ha−1 NPK + 200 kg ha−1 urea). We assessed the effect of rotation and fertilization on weed composition, diversity, biomass and rice yield. Additionally, the farmers' perception of weed harmfulness and the relation between their assessment of weed harmfulness and rice production was tested. Our results showed that weed biomass significantly decreased rice yield but only under the low fertilization level. The rotation of rice with the velvet bean-crotalaria mixture was efficient in reducing weed biomass, modified weed community composition and allowed to achieve the highest rice yield. A significant negative relationship was found between weed community harmfulness index and weed species richness. Yet, the lowest rice yield was observed under rice monoculture despite a higher species richness over years and under high fertilization level. The lack of significant correlation between the harmfulness index and the actual rice yield is probably because our index is partly based on farmer's perception, and only on major weeds. More studies on tropical weed harmfulness are needed to support the design of ecologically intensified cropping systems.

Rice is of critical significance regarding food security worldwide, including in Africa. Only two viruses impacting rice production in Africa have been deeply investigated for decades: the rice yellow mottle virus (Solemoviridae) and the rice stripe necrosis virus (Benyviridae). Using viral metagenomics, we aimed at broadening knowledge on interacting communities associated with plants in rice landscapes and exploring the diversity and epidemiological status of viruses circulating in rice fields from Burkina Faso. We performed an epidemiological survey in this country between 2016 and 2019 involving 57 small farmers' rice fields under two production systems: rainfed lowlands and irrigated areas. More than 2,700 rice samples were collected without regard to disease symptoms following a regular scheme. Wild and cultivated Poaceae (maize and sugarcane) growing in nearby rice fields were also collected. Unexpectedly, metagenomics detected maize streak virus (MSV) (Geminiviridae) in analyzed rice samples. Further molecular analyses using rolling circle amplification-polymerase chain reaction showed that MSV was widely distributed and highly prevalent in both rainfed lowlands and irrigated rice areas. MSV-A and MSV-G strains were identified. MSV-G, exclusively identified thus far in wild grasses, was the most prevalent strain, whereas MSV-A, known to cause severe symptoms in maize, was sporadically identified. Using infectious clones in experimental conditions, we confirmed the pathogenicity of both MSV strains in rice. Thus, in addition to contributing to the epidemiological surveillance of rice production in Africa, our results illuminate new epidemiological and pathogenic aspects of one of the most studied plant viruses with significant economic consequences in Africa.

CONTEXT: Over the past two decades, the Mekong region has experienced significant transformation of its agricultural sector from subsistence farming to export crops driven by the expansion of agricultural land and of irrigation, plus intensification thanks to mechanization and the use of chemical inputs. In the context of agrarian transition, maintaining “organic by default” rice farming systems that do not rely on chemical inputs, is challenging. OBJECTIVE: Based on a case study in Preah Vihear, the organic province of Cambodia, this paper examines whether organic rice certification can maintain “organic by default” practices in a context of unprecedented agricultural intensification. METHODS: Using agrarian diagnosis, we investigated the impacts of an organic rice certification scheme on the future of organic rice-based farming systems. We built a functional typology of five farming systems that co-exist in the study area to understand the roles of organic rice in each system. RESULTS AND CONCLUSIONS: From an economic perspective, organic rice is less profitable than recently introduced cash crops, e.g. cassava, cashews. The positive impact of certification of organic rice is threatened by lock-in factors, e.g. labor diversification in both farming and non-farming activities, cash flow management, the certification process, leading farmers to abandon organic rice for cash crops for export. We discuss the key role of agricultural cooperatives in the certification process of organic rice. SIGNIFICANCE: The paper questions the capacity of a certification scheme to keep up with the process of intensification that undermines the profitability of the remaining “organic by default” rice systems. Beyond the significance of the results in the study area, one of the last strongholds of traditional rice systems in Cambodia, the paper illustrates the disappearance of “organic by default” rice systems in the whole country as well as in the Mekong region.

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation ef

Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments.

Despite the centrality of staple grains for human well-being, both as a source of nutrients and of toxic ions, there is little understanding of where and how elements vary, and if there are particular elements that correlate. Here, for shop bought polished (white) rice, we comprehensively characterized trace (arsenic species, cadmium, copper, iron, manganese, molybdenum, rubidium and zinc) and macro-nutrients (calcium, chlorine, potassium, phosphorus and sulphur) for grain purchased in 18 countries, across four continents, a total of 1045 samples. This was to investigate if there were any major differences between geographic location and elemental content, and to observe if there were any patterns in elemental distribution. Greatest variation in the median was observed for the non-essential rubidium (15-fold) and arsenic species (fivefold). Rubidium was the highest in the Americas, lowest in Europe, while inorganic arsenic (iAs) and dimethylarsonic acid (DMA) were low for Africa and high in the South American and European continents. The highest concentrations of cadmium were found in Asian samples, and lowest in South America, with variation within these regions. At the extremes of individual counties, China had fivefold higher concentrations than the global median, while Tanzania was fourfold lower than this value. Calcium, potassium, molybdenum and phosphorus were the highest in European and lowest in African grain, though the fold-differences were relatively low, ~ 0.2, while iron was the highest in African grain and lowest in European, Asian and South American grain, with a ~ twofold difference. Selenium was also higher in Africa versus other regions, and copper, manganese and zinc were the highest in American grain. Factor analysis showed that copper, cadmium, molybdenum, rubidium and selenium were strongly associated together, and these element's factor loadings were diametrically opposed to less tightly associated calcium, chlorine, manganese, potassium, pho

Description du sujet. Le débat sur la relation entre la taille des exploitations agricoles et les résultats de production est ancien, mais il n'est pas encore tranché. Au Sénégal, ce débat est alimenté par le fait que les gouvernements récents tendent à favoriser des exploitations irriguées de grande taille, alors que les premiers périmètres distribués étaient de très petites parcelles. Objectifs. Cet article contribue à identifier, dans une zone où il y a des grandes et des petites exploitations, le type d'exploitation qui produit le plus et qui a les meilleurs rendements. Méthode. Une enquête a été réalisée auprès de 326 ménages de riziculteurs dans les communes de Diama et de Ross Béthio, sises dans le delta du fleuve Sénégal. Résultats. Les petites exploitations obtiennent de meilleurs rendements mais leurs revenus globaux sont trop faibles. Les plus grandes exploitations rizicoles réalisent des rendements moins importants mais permettent à leurs exploitants d'obtenir des revenus suffisants pour vivre et faire fonctionner l'exploitation. Selon nos enquêtes, les couts de production sont les principaux facteurs explicatifs des rendements parmi les riziculteurs enquêtés. Les petits producteurs ont des couts de production relativement élevés pour maximiser leur profit là où les grands exploitants limitent les couts de production pour obtenir des rendements qui restent acceptables pour un revenu d'exploitation décent. Conclusions. Pour mieux lutter contre la pauvreté et atteindre l'autosuffisance en riz, les politiques agricoles devraient permettre aux petits cultivateurs de capter de nouveaux investissements et encourager les grands producteurs à investir plus dans la riziculture afin d'augmenter leurs rendements et leurs profits.

La présente thèse explore le lien entre le modèle de gouvernance de la chaîne de valeur et la circulation de l'information en vue de mettre en évidence les conditions d'utilisabilité des services numériques qui permettent de faciliter la circulation des informations utiles et utilisables dans la chaîne de valeur du riz dans la Plaine de la Ruzizi. En effet, le numérique étant un moyen pour véhiculer l'information, son utilisation par les acteurs dépend aussi de la nature et de la qualité des informations transmises par ce canal. Il faut donc s'interroger sur l'utilité et l'utilisabilité de l'information, avant de questionner l'utilisabilité du numérique dans cette chaîne de valeur. Pour mieux approfondir l'objet de la recherche, deux approches se sont avérées appropriées : l'approche sur la gouvernance dans les chaînes de valeur globale (Global Value Chains), et l'approche sur l'acceptation des technologies dont le modèle TAM. La gouvernance dans les chaînes de valeurs a été ici abordée selon la conception du courant de pensée de l'économie institutionnelle, suite aux rapprochements du contexte de notre cas d'étude avec la problématique que cherche à expliquer le modèle des coûts des transactions issus des défaillances des institutions, dont les marchés. Pour ce modèle, la pertinence d'un système d'information (numérique ou non) dépend du niveau de conséquence qu'entraine le manque d'information et de la possibilité pour un acteur d'utiliser l'information obtenue. Lorsqu'il est possible pour un acteur d'utiliser une information (information utilisable) et que le manque de cette information entraine des conséquences élevées (information utile), il est donc pertinent de mettre en place un système d'information. Dans le cas contraire, la mise en place d'un système d'information devient moins pertinente. L'approche sur la gouvernance dans les chaînes de valeur, a permis de réaliser l'étude sur le fonctionnement des marchés, à travers les transactions qui s'opèrent au se

La filière riz dans la plaine de la Ruzizi à l'est de la République démocratique du Congo (RDC) occupe une place importante au regard des habitudes alimentaires des populations de la région et des revenus des paysans. Cet article est issu d'une enquête auprès d'acteurs de la filière et illustre (i) l'organisation de la filière, en précisant le rôle des intermédiaires commerciaux ; (ii) les caractéristiques transactionnelles (modes de coordination) ; et (iii) les facteurs de mise à profit par les producteurs, des informations sur le prix et sur la qualité. Les transactions dans cette filière s'organisent autour de trois circuits, avec toutefois une large domination du circuit de la brasserie Bralima qui capte près de 80% de la production rizicole locale. C'est un débouché sûr pour les producteurs, mais son poids laisse peu de place au développement de la filière de riz de table qui souffre de la concurrence du riz importé.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all a

Introduction: Rice plays a critical role in human livelihoods and food security. However, its cultivation requires inputs that are not accessible to all farming communities and can have negative effects on ecosystems. simultaneously, ecological research demonstrates that biodiversity management within fields contributes to ecosystem functioning. Methods: This study aims to evaluate the mixture effect of four functionally distinct rice varieties in terms of characteristics and agronomic performance and their spatial arrangement on the upland rice performance in the highlands of Madagascar. The study was conducted during the 2021-2022 rainfall season at two close sites in Madagascar. Both site differ from each other's in soil properties and soil fertility management. The experimental design at each site included three modalities: i) plot composition, i.e., pure stand or binary mixture; ii) the balance between the varieties within a mixture; iii) and for the balanced mixture (50% of each variety), the spatial arrangement, i.e., row or checkerboard patterns. Data were collected on yields (grain and biomass), and resistance to Striga asiatica infestation, Pyricularia oryzea and bacterial leaf blight (BLB) caused by Xanthomonas oryzae-pv from each plot. Results and discussion: Varietal mixtures produced significantly higher grain and biomass yields, and significantly lower incidence of Pyricularia oryzea compared to pure stands. No significant differences were observed for BLB and striga infestation. These effects were influenced by site fertility, the less fertilized site showed stronger mixture effects with greater gains in grain yield (60%) and biomass yield (42%). The most unbalanced repartition (75% and 25% of each variety) showed the greatest mixture effect for grain yield at both sites, with a strong impact of the varietal identity within the plot. The mixture was most effective when EARLY_MUTANT_IAC_165 constituted 75% of the density associated with other varietie