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Climatic hazards affecting the main rice producing regions of Indonesia increase the risk of annual production loss and encourage the development of innovative strategies to maintain stable production. Conversion of oil palm monocultures to rice-based intercropping systems is a strategy to be considered, but relies on the existence of suitable planting management that optimizes both palm productivity while providing enough light for undergrowth rice varieties tolerant to shady conditions. This paper proposes to couple a model of light interception on virtual canopies with indoor experiments to evaluate the feasibility of developing rice-oil palm agroforestry systems. We first selected a planting design that optimized the transmitted light available for rice using a functional-structural plant model (FSPM) of oil palm. Secondly, we reproduced the light regime simulated with specific changes in the intensity and the daily fluctuation of light in controlled conditions. Three light treatments were designed to test independently the effect of daily light quantity and the effect of diurnal fluctuation on contrasted rice subpopulations.Light quantity was the main factor driving changes in plant morphology and architecture, while light fluctuation only appeared to explain variations in yield components and phenology. This study highlighted the importance of light fluctuation in the grain filling process and resource reallocation. The conservation of relative change among varieties between treatments suggests that varietal responses to low light are likely to be heritable, and that varietal screening under full light can provide clue on varietal behavior under low light. However, the identification of specific traits such as a limited expansion of leaf area and a conservation of leaf senescence under shade and high light fluctuation paves the way for selecting varieties dedicated to agroforestry systems. Further investigations including light quality and larger genotypic population to screen are discussed.

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.

Low radiation during grain filling is one of the main climatic factors limiting rice grain yields. This study aims at identifying genetic regions associated with morphophysiological traits conferring tolerance to low radiation during grain filling. Phenotype-genotype evaluation was carried out on a rice diversity panel using whole genome association studies (GWAS). An Indica panel (300 accessions) was evaluated in the field, on two consecutive years. Grain yield, fertility, 1000 Grain weight, stem-leaf ratio, source:sink relation (SSR)and the number of filled grains per panicle were significantly reduced by low radiation and significantly different across genotypes. A total of 108 QTLs (Quantitative trait loci) with a log 10-4 significance, were associated with 20 variables evaluated in the high and low radiation treatments. For low radiation conditions, two common QTLs were found across years. The haplotype associated to the SSR under low radiation conditions showed an impact on grain yield, percentage of fertility, and number of filled grains per panicle. The OSGRAS19 gene associated in previous studies with grain size (Sink size) and light interception (Source activity) was identified in the LD region of the QTL associated with SSR. Future studies are needed to validate the role of OsGRAS19 in grain yield, SSR and fertility under low radiation conditions.

Low radiation is a determining factor in the development and growth of rice crop and causes yield losses in many regions of the world. We evaluate the diversity of response of rice varieties to a reduction of radiation during grain filling stage yield and yield components were measured in field experiments (two consecutive years). Shade during grain filling significantly reduced grain yield, spikelet fertility and 1000-grain weight in both experiments (dry and wet season). Phenotypic plasticity (GxE) was only observed in both experiments for spikelet filling and 1000-grain weight, suggesting differential genotypic response for these traits under low radiation. Two tolerant genotypes VANDANA and NORUNKAN and three susceptible genotypes SWARNA, ZALCHA and RAY NABJA were selected from the field experiments and studied under controlled growth chamber conditions for low radiation tolerance during the vegetative stage. Shade increased the maximum photosynthesis (Amax) for VANDANA, suggesting a shade adapted behavior maximizing carbon gain under low radiation. However; NORUNKAN showed high Amax and the lowest light compensation point (LCP) and respiration under control and shade conditions suggesting a double strategy to maintain carbon (maximize gain and reduce carbon loss under low radiation conditions). This study demonstrates that targeting photosynthetic traits to maximize carbon gain and reduce carbon loss under low radiation conditions is a strategy that should be explored to increase rice tolerance to low radiation conditions during grain filling stage.

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.

This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

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.

Improving the genetic yield potential (YP) of tropical, irrigated rice varieties is a priority objective of rice breeding programs worldwide in the interest of achieving food security and maintaining political stability. But YP has stagnated at about 10 Mg ha−1 since the Green Revolution. We present a survey of researchers' current top yields across different environments and countries, experimentally investigate YP-related traits and radiation use efficiency (RUE) of 12 elite materials, and use a simple model to explore traits that would raise the yield ceiling. The survey indicated that maximal grain yield is between 5 and 12 Mg ha−1 depending on radiation during flowering and grain filling. The experiments conducted in several environments in the Philippines indicated that (1) different morphologies in terms of panicle number and size and leaf size lead to similar YP due to trait–trait compensation, and (2) differences in RUE are partly attributable to variation in terminal senescence which is strongly environment dependent. Simulations thus focused on post-floral physiological processes, namely dynamics of light interception, carbon assimilation and maintenance burden. Scenarios of different degree of stay-green indicated that terminal senescence is essential to limit N requirements and maintenance burden, but partial stay-green would strongly benefit RUE and YP, particularly if accompanied with increased leaf photosynthetic capacity. The need to increase pre-floral C and N reserves for grain filling is discussed, resulting in a concept to refine current ideotypes such as IRRI's New Plant Type and China's Super Hybrid Rice. In conclusion, current best tropical breeding products do not have higher YP than some varieties dating 30–40 years, and new concepts are needed in rice breeding. Breeding for such plants should be done under high N inputs.

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.

Early vigour is an important objective in rice breeding. A previous study reported strong positive effects of development rate (DR, 1/phyllochron) on early growth vigour in two rice panels. This study provided a model-based analysis of DR effects on rice early vigour and underlying source-sink processes during exponential growth, using Ecomeristem model. Relevant model parameters were fitted to panel observations, and their effect on early vigour was quantified. A sensitivity analysis was performed to quantify the impact of model parameters on simulation outputs. The simulated behaviour of a population of virtual genotypes defined by the combination of model parameter values was compared with that of diversity panel. Finally, a simulation experiment was conducted to analyse source-sink adjustments constituting early vigour across a range of DR. Parameters governing structural development, particularly DR, had greater impact on vigour than parameters for resource acquisition. High DR was associated with rapid dry weight accumulation and low transitory carbohydrate reserves in both real and virtual populations. Genotypic DR is thus a major driver of early vigour in rice under stress-free conditions. To evaluate traits contributing to vigour, the capacity of crop models to simulate interactions between structural development and resource acquisition must be improved. (Resume d'auteur)

Achieving early vigour, that is, rapid dry matter accumulation, is a priority in rice crop improvement, but this trait is complex and not well understood genetically and physiologically. This study tested the hypothesis that the development rate (DR) contributes to early vigour. Two diversity panels were phenotyped during vegetative stage for traits constituting early vigour (shoot dry weight, SDW; relative growth rate, RGR) or contributing to early vigour (tillering, leaf size, DR). The first panel comprised 169 genotypes from all major genetic groups of Oryza sativa and was phenotyped under irrigated upland conditions in the field (Philippines, Exp1). The second panel with 190 genotypes representing the diversity of the tropical japonica group was phenotyped in pots in a greenhouse (Montpellier, France, Exp2). Results from field and pot experiment pointed out that DR, tillering and leaf size were positively correlated with RGR and SDW, although the contribution of leaf size was small. DR was positively correlated with tillering but both were negatively correlated with leaf size. DR vs. RGR correlation was conserved in subsets of genotypes with similar leaf size and tillering, suggesting an effect of DR on RGR independent of the other traits. DR is a promising, still underexploited trait contributing to rice early vigour, requiring further genetic and physiological characterization.

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.

Highlight Plant-model-assisted phenotyping and metabolomics enable phenotypic and genetic dissection of early vigour traits in a rice diversity panel., Early vigour of rice, defined as seedling capacity to accumulate shoot dry weight (SDW) rapidly, is a complex trait. It depends on a genotype propensity to assimilate, store, and/or use non-structural carbohydrates (NSC) for producing large and/or numerous leaves, involving physiological trade-offs in the expression of component traits and, possibly, physiological and genetic linkages. This study explores a plant-model-assisted phenotyping approach to dissect the genetic architecture of rice early vigour, applying the Genome Wide Association Study (GWAS) to morphological and NSC measurements, as well as fitted parameters for the functional–structural plant model, Ecomeristem. Leaf size, number, SDW, and source-leaf NSC concentration were measured on a panel of 123 japonica accessions. The data were used to estimate Ecomeristem genotypic parameters driving organ appearance rate, size, and carbon dynamics. GWAS was performed based on 12 221 single-nucleotide polymorphisms (SNP). Twenty-three associations were detected at P

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.

Selection for early vigour can improve rice (Oryza sativa L.) seedlings’ access to resources, weed competitiveness and yield. Little is known about the relationships between early vigour and drought tolerance. This study explored a panel of 176 rice genotypes in a controlled environment regarding a diversity of traits and trait combinations related to early vigour and water use under drought. The design excluded genotypic differences for root depth. We hypothesised that early vigour (as determined by biomass accumulation under well-watered conditions) was not independent from drought tolerance (determined by biomass accumulation maintenance under drought). Leaf size, developmental rate (DR) and tiller number contributed positively to shoot DW and leaf area, and thus vigour. Early vigour was negatively correlated with growth maintenance and water use efficiency under drought, suggesting tradeoffs. Three clusters of genotypes were identified based on the constitutive traits DR, specific leaf area, tiller number and leaf size. The less drought-tolerant cluster, mainly with lowland O. sativa indica rices, showed a sensitive transpiration response to the fraction of transpirable soil water; however, under well-watered conditions these genotypes were vigorous, with small leaves, high DR and high tillering. This experiment showed that the tradeoff between early vigour and drought tolerance was physiological and not a matter of access to water. The results are discussed with a view to identify drought adaptation strategies for crop improvement. Further improvement of multitrait phenotyping approaches is proposed.

Background Early vigour (biomass accumulation) is a useful but complex trait in rainfed rice (Oryza sativa L). Little is known on trade-offs with drought tolerance. This study explored the relevance of (sugar) metabolic and morphogenetic traits to describe the genetic diversity of rice early vigour and its phenotypic plasticity under drought conditions. A greenhouse experiment was conducted to characterize on a panel of 43 rice genotypes plant morphogenesis and sugar concentration in expanded (source) and expanding (sink) leaves. Results Across genotypes in control treatment, leaf starch concentration was negatively correlated with organogenetic development rate (DR, defined as leaf appearance rate on main stem). Genotypes with small leaves had high DR and tiller number but low leaf starch concentration. Under drought, vigorous genotypes showed stronger growth reduction. Starch concentration decreased in source leaves, by contrast with soluble sugars and with that observed in sink leaves. Accordingly, genotypes were grouped in three clusters differing in constitutive vigour, starch storage and growth maintenance under drought showing a trade off between constitutive vigour and drought tolerance. Conclusions It was therefore suggested that non structural carbohydrates, particularly starch, were relevant markers of early vigour. Their relevance as markers of growth maintenance under drought needs to be further explored. Results are discussed regarding novel process based traits to be introduced in the GRiSP (Global Rice Science Partnership) phenotyping network.

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.