Combining modeling and experimental approaches for developing rice-oil palm agroforestry systems.

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.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. RESULTS: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). CONCLUSION: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

AraDiv: a dataset of functional traits and leaf hyperspectral reflectance of Arabidopsis thaliana.

Data from functional trait databases have been increasingly used to address questions related to plant diversity and trait-environment relationships. However, such databases provide intraspecific data that combine individual records obtained from distinct populations at different sites and, hence, environmental conditions. This prevents distinguishing sources of variation (e.g., genetic-based variation vs. phenotypic plasticity), a necessary condition to test for adaptive processes and other determinants of plant phenotypic diversity. Consequently, individual traits measured under common growing conditions and encompassing within-species variation across the occupied geographic range have the potential to leverage trait databases with valuable data for functional and evolutionary ecology. Here, we recorded 16 functional traits and leaf hyperspectral reflectance (NIRS) data for 721 widely distributed Arabidopsis thaliana natural accessions grown in a common garden experiment. These data records, together with meteorological variables obtained during the experiment, were assembled to create the AraDiv dataset. AraDiv is a comprehensive dataset of A. thaliana's intraspecific variability that can be explored to address questions at the interface of genetics and ecology.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Into the range: a latitudinal gradient or a center-margins differentiation of ecological strategies in Arabidopsis thaliana?

BACKGROUND AND AIMS: Determining within-species large-scale variation in phenotypic traits is central to elucidate the drivers of species' ranges. Intraspecific comparisons offer the opportunity to understand how trade-offs and biogeographical history constrain adaptation to contrasted environmental conditions. Here we test whether functional traits, ecological strategies from the CSR scheme and phenotypic plasticity in response to abiotic stress vary along a latitudinal or a center- margins gradient within the native range of Arabidopsis thaliana. METHODS: We experimentally examined the phenotypic outcomes of plant adaptation at the center and margins of its geographic range using 30 accessions from southern, central and northern Europe. We characterized the variation of traits related to stress tolerance, resource use, colonization ability, CSR strategy scores, survival and fecundity in response to high temperature (34 °C) or frost (- 6 °C), combined with a water deficit treatment. KEY RESULTS: We found evidence for both a latitudinal and a center-margins differentiation for the traits under scrutiny. Age at maturity, leaf dry matter content, specific leaf area and leaf nitrogen content varied along a latitudinal gradient. Northern accessions presented a greater survival to stress than central and southern accessions. Leaf area, C-scores, R-scores and fruit number followed a center-margins differentiation. Central accessions displayed a higher phenotypic plasticity than northern and southern accessions for most studied traits. CONCLUSIONS: Traits related to an acquisitive/conservative resource-use trade-off followed a latitudinal gradient. Traits associated with a competition/colonization trade-off differentiated along the historic colonization of the distribution range and then followed a center-margins differentiation. Our findings pinpoint the need to consider the joint effect of evolutionary history and environmental factors when examining phenotypic variation across the distribution range of a species.

Is triose phosphate utilization involved in the feedback inhibition of photosynthesis in rice under conditions of sink limitation?

This study aimed to understand the physiological basis of rice photosynthetic response to C source-sink imbalances, focusing on the dynamics of the photosynthetic parameter triose phosphate utilization (TPU). Here, rice (Oriza sativa L.) indica cultivar IR64 were grown in controlled environment chambers under current ambient CO2 concentration until heading, and thereafter two CO2 treatments (400 and 800 µmol mol-1) were compared in the presence and absence of a panicle-pruning treatment modifying the C sink. At 2 weeks after heading, photosynthetic parameters derived from CO2 response curves, and non-structural carbohydrate content of flag leaf and internodes were measured three to four times of day. Spikelet number per panicle and flag leaf area on the main culm were recorded. Net C assimilation and TPU decreased progressively after midday in panicle-pruned plants, especially under 800 µmol mol-1 CO2. This TPU reduction was explained by sucrose accumulation in the flag leaf resulting from the sink limitation. Taking together, our findings suggest that TPU is involved in the regulation of photosynthesis in rice under elevated CO2 conditions, and that sink limitation effects should be considered in crop models.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping.

BACKGROUND AND AIMS: Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction. METHODS: Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre). KEY RESULTS: The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes. CONCLUSIONS: By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.

Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses.

Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance-covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two-step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how-to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture-recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances.

Plasticity of Sorghum Stem Biomass Accumulation in Response to Water Deficit: A Multiscale Analysis from Internode Tissue to Plant Level.

Sorghum is increasingly used as a biomass crop worldwide. Its genetic diversity provides a large range of stem biochemical composition suitable for various end-uses as bioenergy or forage. Its drought tolerance enables it to reasonably sustain biomass production under water limited conditions. However, drought effect on the accumulation of sorghum stem biomass remains poorly understood which limits progress in crop improvement and management. This study aimed at identifying the morphological, biochemical and histological traits underlying biomass accumulation in the sorghum stem and its plasticity in response to water deficit. Two hybrids (G1, G4) different in stem biochemical composition (G4, more lignified, less sweet) were evaluated during 2 years in the field in Southern France, under two water treatments differentiated during stem elongation (irrigated; 1 month dry-down until an average soil water deficit of -8.85 bars). Plant phenology was observed weekly. At the end of the water treatment and at final harvest, plant height, stem and leaf dry-weight and the size, biochemical composition and tissue histology of internodes at 2-4 positions along the stem were measured. Stem biomass accumulation was significantly reduced by drought (in average 42% at the end of the dry-down). This was due to the reduction of the length, but not diameter, of the internodes expanded during water deficit. These internodes had more soluble sugar but lower lignin and cellulose contents. This was associated with a decrease of the areal proportion of lignified cell wall in internode outer zone whereas the areal proportion of this zone was not affected. All internodes for a given genotype and environment followed a common histochemical dynamics. Hemicellulose content and the areal proportion of inner vs. outer internode tissues were set up early during internode growth and were not drought responsive. G4 exhibited a higher drought sensitivity than G1 for plant height only. At final harvest, the stem dry weight was only 18% lower in water deficit (re-watered) compared to well-watered treatment and internodes growing during re-watering were similar to those on the well-watered plants. These results are being valorized to refine the phenotyping of sorghum diversity panels and breeding populations.

Genome-wide association mapping of root traits in a japonica rice panel.

Rice is a crop prone to drought stress in upland and rainfed lowland ecosystems. A deep root system is recognized as the best drought avoidance mechanism. Genome-wide association mapping offers higher resolution for locating quantitative trait loci (QTLs) than QTL mapping in biparental populations. We performed an association mapping study for root traits using a panel of 167 japonica accessions, mostly of tropical origin. The panel was genotyped at an average density of one marker per 22.5 kb using genotyping by sequencing technology. The linkage disequilibrium in the panel was high (r(2)>0.6, on average, for 20 kb mean distances between markers). The plants were grown in transparent 50 cm × 20 cm × 2 cm Plexiglas nailboard sandwiches filled with 1.5 mm glass beads through which a nutrient solution was circulated. Root system architecture and biomass traits were measured in 30-day-old plants. The panel showed a moderate to high diversity in the various traits, particularly for deep (below 30 cm depth) root mass and the number of deep roots. Association analyses were conducted using a mixed model involving both population structure and kinship to control for false positives. Nineteen associations were significant at P<1e-05, and 78 were significant at P<1e-04. The greatest numbers of significant associations were detected for deep root mass and the number of deep roots, whereas no significant associations were found for total root biomass or deep root proportion. Because several QTLs for different traits were co-localized, 51 unique loci were detected; several co-localized with meta-QTLs for root traits, but none co-localized with rice genes known to be involved in root growth. Several likely candidate genes were found in close proximity to these loci. Additional work is necessary to assess whether these markers are relevant in other backgrounds and whether the genes identified are robust candidates.

Effect of source/sink ratios on yield components, growth dynamics and structural characteristics of oil palm (Elaeis guineensis) bunches.

Source/sink ratios are known to be one of the main determinants of oil palm growth and development. A long-term experiment (9 years) was conducted in Indonesia on mature oil palms subjected to continuous bunch ablation and partial defoliation treatments to artificially modify source/sink ratios. During the experiment, all harvested bunches were dissected and phenological measurements were carried out to analyse the effect of source/sink ratios on yield components explaining variations in bunch number, the number of fruits per bunch and oil dry weight per fruit. An integrative variable (supply/demand ratio) describing the ratio between the assimilate supply from sources and the growing organ demand for carbohydrate was computed for each plant on a daily basis from observations of the number of developing organs and their sink strength, and of climate variables. Defoliation and bunch ablation affected the bunch number and the fruit number per bunch. Variations in bunch number per month were mainly due to variations in the fraction of aborted inflorescence and in the ratio between female and male inflorescences. Under fluctuating trophic conditions, variations in fruit number per bunch resulted both from changes in fruit-set and in the number of branches (rachillae) per inflorescence. For defoliated plants, the decrease in the number of developing reproductive sinks appeared to be sufficient to maintain fruit weight and oil concentration at the control level, without any major decrease in the concentration of non-structural carbohydrate reserves. Computation of the supply/demand ratio revealed that each yield component had a specific phase of sensitivity to supply/demand ratios during inflorescence development. Establishing quantitative relationships between supply/demand ratios, competition and yield components is the first step towards a functional model for oil palm.

Simulation of inflorescence dynamics in oil palm and estimation of environment-sensitive phenological phases: a model based analysis.

For oil palm, yield variation is in large part due to variation in the number of harvested bunches. Each successively-produced phytomer carries a female (productive), male or aborted inflorescence. Since phytomer development takes 3-4 years and nearly two phytomers are produced per month, many inflorescences develop in parallel but have different phenological stages. Environment-dependent developmental rate, sex and abortion probability determine bunch productivity, which, in turn, affects other phytomers via source-sink relationships. Water deficit, solar radiation, temperature and day length are considered key external factors driving variation. Their impact is difficult to predict because of system complexity. To address this question we built a simple model (ECOPALM) to simulate the variation in number of harvested bunches. In this model, trophic competition among organs, expressed through a plant-scale index (Ic), drives sex determination and inflorescence abortion during specific sensitive phases at phytomer level. As a supplemental hypothesis, we propose that flowering is affected by photoperiod at phytomer level during a sensitive phase, thus, contributing to seasonal production peaks. The model was used to determine by parameter optimisation the influence of Ic and day length on inflorescence development and the stages at which inflorescences are sensitive to these signals. Parameters were estimated against observation of number of harvested bunches in Ivory Coast using a genetic algorithm. The model was then validated with field observations in Benin and Indonesia. The sensitive phases determined by parameter optimisation agreed with independent experimental evidence, and variation of Ic explained both sex and abortion patterns. Sex determination seemed to coincide with floret meristem individualisation and occurred 29-32 months before bunch harvest. The main abortion stage occurred 10 months before harvest - at the beginning of rapid growth of the inflorescence. Simulation results suggest involvement of photoperiod in the determination of bunch growth dynamics. This study demonstrates that simple modelling approaches can help extracting ecophysiological information from simple field observations on complex systems.

Can early vigour occur in combination with drought tolerance and efficient water use in rice genotypes?

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.

Phenomics of rice early vigour and drought response: Are sugar related and morphogenetic traits relevant?

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.

Studying the reproductive skipping behavior in long-lived birds by adding nest inspection to individual-based data.

The frequency at which individuals breed is an important parameter in population, as well as in evolutionary, studies. However, when nonbreeding individuals are absent from the study area, the reproductive skipping is usually confounded with a recapture failure and cannot be estimated directly. Yet, there are situations in which external information may help to estimate reproductive skipping. Such a situation is found with nest-tenacious birds: the fact that an individual is not encountered in its previous nest is a good indication that it must be skipping reproduction. We illustrate here a general probabilistic framework in which we merged the classical individual capture-recapture information with nest-based information to obtain the simultaneous estimate of recapture, survival, reproductive skipping, and within-colony breeding dispersal probabilities using multi-event models. We applied this approach to Cory's Shearwater (Calonectris diomedea), a long-lived burrow-nesting seabird. By comparing results with those obtained from the analysis of the capture-recapture information alone, we showed that the model separates successfully the probabilities of recapture from those of temporal emigration. We found that the probabilities of future reproduction and breeding-site fidelity were lower for individuals temporarily absent from the colony, suggesting a lower intrinsic quality of intermittent breeders. The new probabilistic framework presented here allowed us to refine the estimates of demographic parameters by simply adding nest-based data, a type of information usually collected in the field but never included in the analysis of individual-based data. Our approach also provides a new and flexible way to test hypotheses on temporal emigration and breeding dispersal in longitudinal data.

Experience-dependent natal philopatry of breeding greater flamingos.

1. Contrary to the generally high level of natal philopatry (i.e. likelihood that individuals breed at their natal colony) found in first-breeding colonial birds, little is known of natal philopatry later in life. Most hypotheses advanced to explain natal philopatry are valid at all ages. However, for young and inexperienced birds, the benefits of natal philopatry may be counterbalanced by the costs of intraspecific competition at the natal colony making dispersal temporarily advantageous. In turn, experience may increase competitive ability and make natal philopatry advantageous again. 2. We evaluated this hypothesis on the large-scale dispersal of greater flamingos Phoenicopterus roseus breeding among three colonies comprising >85% of the Western Mediterranean metapopulation. The Camargue (France) and Fuente de Piedra (Spain) are large and saturated colonies while Molentargius (Sardinia) is a recent and growing colony. 3. We used a 20-year capture-mark-resighting dataset of 4900 flamingos ringed as chicks in Camargue and Fuente de Piedra and breeding at the three colonies. We assessed the effects of natal colony and breeding experience (first-time observed breeders versus confirmed experienced breeders) on dispersal using multistate capture-recapture models. Dispersal to an unobservable state accounted for temporary emigration. 4. Fidelity was higher at the natal colony (>84%) than elsewhere. Fidelity increased with experience in the two large colonies (Camargue and Fuente de Piedra) suggesting a large-scale experience-related despotic distribution. Breeding dispersal was significant (up to 61% and 52% for first-time breeders and experienced breeders, respectively) so that colony dynamics is affected by exchanges with other colonies. Except for Fuente-born breeders leaving Molentargius, dispersal to the natal colony was higher than to any other colonies. 5. Survival was not higher at the natal colony. Inexperienced birds likely had lower breeding success at the Camargue and skipped reproduction after having emigrated to the other large colony but not to Molentargius. Breeding at Molentargius could allow avoiding queuing (and non-breeding) at the large colonies while gaining experience and competitive ability for future attempts. 6 Natal philopatry appears as an important driver of large-scale breeding dispersal in the Greater flamingo. The fitness advantage of natal philopatry is likely experience-dependent and mediated by the variations of intraspecific competition.