Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.02.26.708401

Dynamic yield responses of chickpea (Cicer arietinum) to terminal drought are accompanied by changes in grain composition

Abstract

Chickpea is predominantly grown under rainfed conditions in regions where terminal drought limits yield, yet little is known about how this stress influences both vegetative allocation and reproductive dynamics leading to altered grain composition. We imposed a controlled terminal drought, with a rewatered treatment group, on three Desi cultivars (ICC4958, ICC1882 and CBA Captain) reported to have contrasting drought tolerance, quantifying vegetative biomass, reproductive node productivity across developmental regions and grain macronutrient composition. Under drought, vegetative responses reflected genotype-specific resource partitioning strategies particularly evident in severe root degradation and increase stem dry matter content that was only partially alleviated in rewatered plants. Reproductive outcomes were strongly influenced by developmental stage at the time of stress, with increased pod abortion observed particularly at nodes initiating seed development under drought treatment. Grain composition of seeds filled under drought was significantly altered by stress, with increased protein concentration and decreased starch content under both Drought and Recovery treatments independent of cultivar, likely due to water limitation at crucial filling stages. These findings demonstrate that the developmental timing of terminal drought interacts with cultivar growth strategy to influence pod production and grain nutritional quality in chickpea. HighlightThe developmental timing of terminal drought interacts with cultivar-dependent growth strategies to influence pod productivity and grain nutritional quality in chickpea.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hopgood, P., Buck, S., Bain, M.. 2026-03-02. Dynamic yield responses of chickpea (Cicer arietinum) to terminal drought are accompanied by changes in grain composition. https://doi.org/10.64898/2026.02.26.708401

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

MpILR1 Hydrolyzes Jasmonate-Amino Acid Conjugates to Activate dn-iso-OPDA Signaling in Marchantia polymorpha.

Jasmonates are essential phytohormones that coordinate defense responses and developmental programs across land plants. In angiosperms, the active jasmonate ligand jasmonoyl-L-isoleucine (JA-Ile), is produced through GH3-mediated conjugation of jasmonic acid to isoleucine and JA-Ile homeostasis is further shaped by ILR1/ILL-family amidohydrolases. In contrast, the primary bioactive jasmonate ligand in bryophytes, dinor-12-oxo-phytodienoic acid (dn-iso-OPDA), is inactivated through conjugation with amino acids, raising the question of whether these conjugates constitute a reversible hormone reservoir or an irreversible catabolic end point. Although the ILR1-like family has been characterized extensively for its role in auxin and jasmonate homeostasis in angiosperms, its function in bryophytes remains basically unexplored. Here we show that MpILR1, the sole Marchantia ortholog of the ILR1/ILL family, hydrolyzes a specific subset of dn-iso-OPDA-amino acid conjugates in vivo. Loss-of-function Mpilr1 mutants exhibit enhanced accumulation of dn-iso-OPDA conjugated to hydrophobic amino acids (Val, Leu and Ile) but not to hydrophilic residues (His, Glu and Gln), demonstrating substrate-selective hydrolysis. MpILR1 hydrolytic activity is required for full dn-iso-OPDA-mediated responses, including transcriptional activation and defense against gastropod herbivory. These findings establish MpILR1 as a key positive regulator of jasmonate signaling in Marchantia polymorpha and suggest that hormone conjugation/deconjugation is an ancient regulatory mechanism evolved during plant terrestrialization.

plant biology↗

Drought-Spec-Net: Early Tomato Drought Detection and Potential Yield-Impact Assessment Using Vis NIR Data

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

plant biology↗

BSA101: Unlocking Historical Mutant Collections with BSA-Seq

Forward genetics is a powerful approach for gene discovery, but identifying causal mutations becomes difficult when mutants are maintained in heterogeneous populations with uncertain pedigrees. This is exemplified by classical tasselseed (ts) mutants, which have long served as a genetic model for studying sex determination and carpel suppression. Decades of repeated outcrossing to diverse inbred lines have created substantial genetic heterogeneity, limiting the effectiveness of conventional bulked-segregant analysis sequencing (BSA-Seq). To address this, we developed a BSA-Seq framework that integrates flexible experimental designs, multiple reference genomes, and complementary statistical methods tailored for genetically heterogeneous populations. Applying this framework revealed that reference genome selection is critical for mapping success and that Euclidean distance raised to the fourth power (ED4) outperformed homozygosity mapping (HM). Furthermore, the framework enables simultaneous mapping of multiple mutations within a single population, eliminating the need for additional mapping populations. Applying this framework to 26 ts mutant stocks from the Maize Genetics Cooperation Stock Center, we successfully mapped 24 mutants to genomic intervals containing known ts genes, while the remaining mutants mapped to distinct genomic intervals, defining novel candidate regions underlying carpel suppression. Together, these results demonstrate that historical mutant collections represent an underutilized resource for gene discovery and establish a generalizable mapping strategy for unlocking their genetic potential across diverse species.

plant biology↗