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International audience; The rice industry plays an important role in the agricultural economy of Colombia and its success dependents largely on weather conditions. Rice farmers, policymakers and other stakeholders thus need to understand and manage the risks associated with climate variability, including those related to El Nino Southern Oscillation (ENSO) - the most important source of variability affecting Colombian climates. The objectives of this study were to (1) assess the ENSO influence on the spatio-temporal variability of agro-climatic conditions (crop water requirements, dry and wet spells, and heatwaves) and rice yield across the central producing region of Colombia; and (2) identify the main agro-climatic factors driving crop yield variability. Results showed that rice irrigation water requirements under positive ENSO phases (El Nino) increased by up to 14% compared to the long-term average. These increases were associated with less total precipitation, more dry days and longer dry spells, together with a greater number of day-and-night heatwave episodes. During negative phases (La Nina), on the other hand, irrigation requirements decreased by 16% with respect to the long-term average due to longer and more frequent wet spells, and more total precipitation. Analyses of simulated yields indicated that El Nino years reduce crop yield in about 86% of the study region, while La Nina affects 62% of the region positively. The number of heat nights (i.e. nights with minimum temperature > 23 degrees C) during the growing season was the most important agro-climatic factor causing yield losses during ENSO events. Our results represent an important step towards understanding the interaction between climate variability and rice production in Colombia, which is useful for improving climate risk management at local levels.

Bacterial panicle blight (BPB) caused by Burkholderia glumae is one of the main concerns for rice production in the Americas since bacterial infection can interfere with the grain-filling process and under severe conditions can result in high sterility. B. glumae has been detected in several rice-growing areas of Colombia and other countries of Central and Andean regions in Latin America, although evidence of its involvement in decreasing yield under these conditions is lacking. Analysis of different parameters in trials established in three rice-growing areas showed that, despite BPB presence, severity did not explain the sterility observed in fields. PCR tests for B. glumae confirmed low infection in all sites and genotypes, only 21.4% of the analyzed samples were positive for B. glumae. Climate parameters showed that Montería and Saldaña registered maximum temperature above 34°C, minimum temperature above 23°C, and Relative Humidity above 80%, conditions that favor the invasion model described for this pathogen in Asia. Our study found that in Colombia, minimum temperature above 23°C during 10 days after flowering is the condition that correlates with disease incidence. Therefore, this correlation, and the fact that Montería and Saldaña had a higher level of infected samples according to PCR tests, high minimum temperature, but not maximum temperature, seems to be determinant for B. glumae colonization under studied field conditions. This knowledge is a solid base line to design strategies for disease control, and is also a key element for breeders to develop strategies aimed to decrease the effect of B. glumae and high night-temperature on rice yield under tropical conditions.

International audience; Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R 2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections.

International audience; The upland rice (UR) crop system located in the Brazilian savannas (states of Mato Grosso, Rondonia, Tocantins and Goias) is the main upland rice growing area of Brazil and the largest rainfed rice growing area in Latin America. It plays an important social and economic role in central Brazil. UR cropped area, however, has decreased by 70% in the last two decades, partly due to high drought risk. Here, we hypothesize that the UR breeding program's focus on direct grain selection for wide adaptation has led to an increase in yield potential but a reduction in the capacity of modem cultivars to maintain yield under drought stress. Using the ORYZAv3 rice crop model, we analyzed changes in relative and absolute drought impact for cultivars representative of three decades of breeding: 1980s (1986-1992), 1990s (1993-2002), and 2000s (2003-2013). We found a mean increase in relative drought impact of 12% (0.35% per year) between 1980s and 2000s. Most of the increase occurred during 1980s and 1990s (7%), where a major shift from landraces (i.e. Douradao) to modem cultivars (i.e. cv. BRS Primavera) occurred in the breeding program. Drought adaptation traits such as greater root length density, shorter cycle, and lower stomatal sensitivity to drought have been systematically bred out of the UR material in the period 1980-2013. We recommend the UR breeding program to be adjusted by better targeting to specific drought environments, and by taking into account drought stress profiles and environmental co-variables in statistical analyses for genotype selection. Moreover, physiological studies on tradeoffs between yield potential and drought tolerance should be performed for each target environment to dissect plant traits that confer both high yield potential and low drought sensitivity. These strategies will ultimately ensure that newly released cultivars are adapted to the environmental conditions of the study region, therefore reducing agroclimatic risk for UR farmers.

Genetic analysis of grain yield and its components in a rice diversity panel identified genetic loci that regulate water deficit tolerance during the reproductive stage., A diversity panel comprising of 296 indica rice genotypes was phenotyped under non-stress and water-deficit stress conditions during the reproductive stage in the 2013 and 2014 dry seasons (DSs) at IRRI, Philippines. We investigated the genotypic variability for grain yield, yield components, and related traits, and conducted genome-wide association studies (GWAS) using high-density 45K single nucleotide polymorphisms. We detected 38 loci in 2013 and 64 loci in 2014 for non-stress conditions and 69 loci in 2013 and 55 loci in 2014 for water-deficit stress. Desynchronized flowering time confounded grain yield and its components under water-deficit stress in the 2013 experiment. Statistically corrected grain yield and yield component values using days to flowering helped to detect 31 additional genetic loci for grain yield, its components, and the harvest index in 2013. There were few overlaps in the detected loci between years and treatments, and when compared with previous studies using the same panel, indicating the complexity of yield formation under stress. Nevertheless, our analyses provided important insights into the potential links between grain yield with seed set and assimilate partitioning. Our findings demonstrate the complex genetic architecture of yield formation and we propose exploring the genetic basis of less complex component traits as an alternative route for further yield enhancement.

With the aim to find new sources of resistance to rice hoja blanca (white leaf) disease, transmitted by the insect Tagosodes orizicolus, 660 genotypes were evaluated under greenhouse and field conditions. Seven resistant genotypes were identified, and genomic studies were performed to demonstrate that the resistance in these sources is genetically different from that of Fedearroz 2000, which is currently the variety with the most resistance to hoja blanca. These new resistance sources constitute a resource that can be used to sustainably extend hoja blanca disease management throughout all of the rice-growing regions of tropical America. This is the first report of hoja blanca resistance in indica rice and different from that of Fedearroz 2000. Con el objetivo de encontrar nuevas fuentes de resistencia a la enfermedad de la hoja blanca del arroz, transmitida por el insecto Tagosodes orizicolus, se evaluaron 660 genotipos en condiciones de invernadero y campo. Se identificaron siete genotipos con resistencia a la enfermedad y se realizaron estudios del genoma para evidenciar que eran genéticamente diferentes a Fedearroz 2000, la variedad de mejor comportamiento ante el virus, en el momento. Estas nuevas fuentes de resistencia constituyen un recurso que puede utilizarse para extender un manejo sostenible de la enfermedad, en todas las regiones productoras de arroz en América tropical. Este es el primer reporte de fuentes de resistencia, tipo indica, diferentes a Fedearroz 2000.

Number of spikelets per panicle (NSP) is a key trait to increase yield potential in rice (O. sativa). The architecture of the rice inflorescence which is mainly determined by the length and number of primary (PBL and PBN) and secondary (SBL and SBN) branches can influence NSP. Although several genes controlling panicle architecture and NSP in rice have been identified, there is little evidence of (i) the genetic control of panicle architecture and NSP in different environments and (ii) the presence of stable genetic associations with panicle architecture across environments. This study combines image phenotyping of 225 accessions belonging to a genetic diversity array of indica rice grown under irrigated field condition in two different environments and Genome Wide Association Studies (GWAS) based on the genotyping of the diversity panel, providing 83,374 SNPs. Accessions sown under direct seeding in one environement had reduced Panicle Length (PL), NSP, PBN, PBL, SBN and SBL compared to those established under transplanting in the second environment. Across environments, NSP was significantly and positively correlated with PBN, SBN and PBL. However, the length of branches (PBL and SBL) was not significantly correlated with variables related to number of branches (PBN and SBN ), suggesting independent genetic control.Twenty- three GWAS sites were detected with P-values ≤ 1.0E-04 and 27 GWAS sites with p ≤ 5.9E-04. We found 17 GWAS sites related to NSP, 10 for PBN and 11 for SBN, 7 for PBL and 11 for SBL. This study revealed new regions related to NSP, but only three associations were related to both branching number (PBN and SBN) and NSP. Two GWAS sites associated with SBL and SBN were stable across contrasting environments and were not related to genes previously reported. The new regions reported in this study can help improving NSP in rice for both direct seeded and transplanted conditions. The integrated approach of high-throughput phenotyping, multi-environment field trials and GWAS has the potential to dissect complex traits, such as NSP, into less complex traits and to match single nucleotide polymorphisms with relevant function under different environments, offering a potential use for molecular breeding.

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.

Publis013-agap-074; The ability to assimilate C and allocate non-structural carbohydrates (NSCs) to the most appropriate organs is crucial to maximize plant ecological or agronomic performance. Such C source and sink activities are differentially affected by environmental constraints. Under drought, plant growth is generally more sink than source limited as organ expansion or appearance rate is earlier and stronger affected than C assimilation. This favors plant survival and recovery but not always agronomic performance as NSC are stored rather than used for growth due to a modified metabolism in source and sink leaves. Such interactions between plant C and water balance are complex and plant modeling can help analyzing their impact on plant phenotype. This paper addresses the impact of trade-offs between C sink and source activities and plant production under drought, combining experimental and modeling approaches. Two contrasted monocotyledonous species (rice, oil palm) were studied. Experimentally, the sink limitation of plant growth under moderate drought was confirmed as well as the modifications in NSC metabolism in source and sink organs. Under severe stress, when C source became limiting, plant NSC concentration decreased. Two plant models dedicated to oil palm and rice morphogenesis were used to perform a sensitivity analysis and further explore how to optimize C sink and source drought sensitivity to maximize plant growth. Modeling results highlighted that optimal drought sensitivity depends both on drought type and species and that modeling is a great opportunity to analyze such complex processes. Further modeling needs and more generally the challenge of using models to support complex trait breeding are discussed.

International audience; The potential for tree components of coffee agroforestry systems to provide ecosystem services is widely recognized. Management practices are a key factor in the amount and quality of ecosystem services pro- vided. There is relatively abundant information on ecosystem services provision within agroforestry sys- tems, but comparatively scant information regarding how coffee farmers manage their plantations, the factors influencing their farming practices and the extent to which farmers’ local knowledge – as opposed to global scientific understanding – underpins management decisions. Policymakers and scientists too frequently design development programs and projects in the coffee sector. On occasion technicians are included in the design process, but farmers and their knowledge are always excluded. This research explores farmers’ knowledge regarding how trees affect coffee productivity and ecosystem services in Costa Rica. Farmers’ knowledge on the effects of trees on coffee productivity was compared with that of other knowledge sources: coffee processors, technicians and scientists. Farmers were shown to have detailed knowledge regarding ecosystem services that their coffee agroforestry systems provide as well as on the interactions between trees and coffee productivity. When asked on the services that trees pro- vide, farmers classified trees according to water protection, soil formation, or contribution to biodiversity conservation. These classifications were related to tree attributes such as leaf size, biomass production or root abundance. Comparison of coffee productivity knowledge from different knowledge sources revealed considerable complementarity and little contradiction. The effects of shade trees on biophysical conditions and their interactions with coffee productivity were well understood by farmers. They recorded and classified shade trees as ‘fresh’ (suitable for integra- tion with coffee) or ‘hot’ (unsuitable) based on their leaf texture and size, foliage density, crown shape, and root system attributes. The fresh/hot classification significantly related to positive/negative provision of services. This classification was widely used by farmers, and unknown by coffee technicians. Detailed local knowledge included several different topics, such as the role of trees in soil formation and in abundance of pollinators. Farmers were also aware of the influence of these ecosystem services on crop productivity. Generally, management decisions were made to maintain coffee productivity rather than ecosystem services. Based on these results, it is suggested that technical interventions addressing the improvement of coffee plantations are more likely to be successful if they take into account not only the scientific information on agroforestry interactions but also the knowledge possessed by farmers. Lack of comprehension of local coffee knowledge could be expected to reduce the success of development pro- grams and projects aimed at improving productivity or other ecosystem services.