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Monoculture systems in South East Asia are facing challenges due to climate change-induced extreme weather conditions, leading to significant annual production losses in rice and oil palm. To ensure the stability of these crops, innovative strategies like resilient agroforestry systems need to be explored. Converting oil palm (Elaeis guineensis) monocultures to rice (Oryza sativa)-based intercropping systems shows promise, but achieving optimal yields requires adjusting palm density and identifying rice varieties adapted to changes in light quantity and diurnal fluctuation. This paper proposes a methodology that combines a model of light interception with indoor experiments to assess the feasibility of rice-oil palm agroforestry systems. Using a functional-structural plant model of oil palm, the planting design was optimized to maximize transmitted light for rice. Simulation results estimated the potential impact on oil palm carbon assimilation and transpiration. In growth chambers, simulated light conditions were replicated with adjustments to intensity and daily fluctuation. Three light treatments independently evaluated the effects of light intensity and fluctuation on different rice accessions. The simulation study revealed intercropping designs that significantly increased light transmission for rice cultivation with minimal decrease in oil palm densities compared with conventional designs. The results estimated a loss in oil palm productivity of less than 10%, attributed to improved carbon assimilation and water use efficiency. Changes in rice plant architecture were primarily influenced by light quantity, while variations in yield components were attributed to light fluctuations. Different rice accessions exhibited diverse responses to light fluctuations, indicating the potential for selecting genotypes suitable for agroforestry systems., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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.

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jaramillo-Botero, Colorado, Quimbaya, Rebolledo, Lorieux, Ghneim-Herrera, Arango, Tobón, Finke, Rocha, Muñoz, Riascos, Silva, Chirinda, Caccamo, Vandepoele and Goddard.)

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., Competing Interests: The authors have no competing interests.

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 R2 = 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., Competing Interests: The authors have declared that no competing interests exist.

Soil drying causes leaf rolling in rice, but the relationship between leaf rolling and drought tolerance has historically confounded selection of drought-tolerant genotypes. In this study on tropical japonica and aus diversity panels (170-220 genotypes), the degree of leaf rolling under drought was more affected by leaf morphology than by stomatal conductance, leaf water status, or maintenance of shoot biomass and grain yield. A range of canopy temperature and leaf rolling (measured as change in normalized difference vegetation index [ΔNDVI]) combinations were observed among aus genotypes, indicating that some genotypes continued transpiration while rolled. Association mapping indicated colocation of genomic regions for leaf rolling score and ΔNDVI under drought with previously reported leaf rolling genes and gene networks related to leaf anatomy. The relatively subtle variation across these large diversity panels may explain the lack of agreement of this study with earlier reports that used small numbers of genotypes that were highly divergent in hydraulic traits driving leaf rolling differences. This study highlights the large range of physiological responses to drought among rice genotypes and emphasizes that drought response processes should be understood in detail before incorporating them into a varietal selection programme., (© 2019 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.)

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.

Rice is the most important food crop in the developing world. For rice production systems to address the challenges of increasing demand and climate change, potential and on-farm yield increases must be increased. Breeding is one of the main strategies toward such aim. Here, we hypothesize that climatic and atmospheric changes for the upland rice growing period in central Brazil are likely to alter environment groupings and drought stress patterns by 2050, leading to changing breeding targets during the 21st century. As a result of changes in drought stress frequency and intensity, we found reductions in productivity in the range of 200-600 kg/ha (up to 20%) and reductions in yield stability throughout virtually the entire upland rice growing area (except for the southeast). In the face of these changes, our crop simulation analysis suggests that the current strategy of the breeding program, which aims at achieving wide adaptation, should be adjusted. Based on the results for current and future climates, a weighted selection strategy for the three environmental groups that characterize the region is suggested. For the highly favorable environment (HFE, 36%-41% growing area, depending on RCP), selection should be done under both stress-free and terminal stress conditions; for the favorable environment (FE, 27%-40%), selection should aim at testing under reproductive and terminal stress, and for the least favorable environment (LFE, 23%-27%), selection should be conducted for response to reproductive stress only and for the joint occurrence of reproductive and terminal stress. Even though there are differences in timing, it is noteworthy that stress levels are similar across environments, with 40%-60% of crop water demand unsatisfied. Efficient crop improvement targeted toward adaptive traits for drought tolerance will enhance upland rice crop system resilience under climate change., (© 2018 John Wiley & Sons Ltd.)

We evaluated the yields of Oryza sativa L. 'Nipponbare' rice lines expressing a gene encoding an A20/AN1 domain stress-associated protein, AlSAP, from the halophyte grass Aeluropus littoralis under the control of different promoters. Three independent field trials were conducted, with drought imposed at the reproductive stage. In all trials, the two transgenic lines, RN5 and RN6, consistently out-performed non-transgenic (NT) and wild-type (WT) controls, providing 50-90% increases in grain yield (GY). Enhancement of tillering and panicle fertility contributed to this improved GY under drought. In contrast with physiological records collected during previous greenhouse dry-down experiments, where drought was imposed at the early tillering stage, we did not observe significant differences in photosynthetic parameters, leaf water potential, or accumulation of antioxidants in flag leaves of AlSAP-lines subjected to drought at flowering. However, AlSAP expression alleviated leaf rolling and leaf drying induced by drought, resulting in increased accumulation of green biomass. Therefore, the observed enhanced performance of the AlSAP-lines subjected to drought at the reproductive stage can be tentatively ascribed to a primed status of the transgenic plants, resulting from a higher accumulation of biomass during vegetative growth, allowing reserve remobilization and maintenance of productive tillering and grain filling. Under irrigated conditions, the overall performance of AlSAP-lines was comparable with, or even significantly better than, the NT and WT controls. Thus, AlSAP expression inflicted no penalty on rice yields under optimal growth conditions. Our results support the use of AlSAP transgenics to reduce rice GY losses under drought conditions.

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 ≤ 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., Competing Interests: The authors have declared that no competing interests exist.

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 <1×10(-4) and 64 at P <5×10(-4). Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour. Plant model parameters were used to characterize physiological and genetic trade-offs among component traits. Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number. The potential use of these associations as markers for breeding is discussed., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

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.