Natural Variation Research Articles

The Role of Epigenetic Switches in Polyphenism Control: Implications from a Nematode Model for the Developmental Regulation of Alternative Phenotypes

Polyphenisms, the capability of organisms to form two or more alternative phenotypes in response to environmental variation, are prevalent in nature. However, associated molecular mechanisms and potential general principles of polyphenisms among major organismal groups remain currently unknown. This review focuses on an emerging model system for developmental plasticity and polyphenism research, the nematode Pristionchus pacificus and explores mechanistic insight obtained through unbiased genetic, experimental and natural variation studies. Resulting findings identify a central role for epigenetic switches in the environmental control of alternative phenotypes and their micro–and macroevolution. Several features observed in P. pacificus are shared with insects and plants and might become general principles for the control of polyphenisms during development.

Heterogeneous effects of climate change on displacement-inducing disasters

With an estimated 357.7 million internal displacements caused since 2008, weather-related disasters are a major driver of human mobility worldwide. As climate change is projected to increase the frequency and intensity of extreme weather events in many parts of the world, it is important to better understand how trends in weather patterns related to global warming have affected the intensity of disasters that have caused displacements. Here we combined observational and counterfactual climate data with global internal displacement records to estimate how climate change has affected precipitation and wind speeds at the time and location of floods and storms that led to internal displacements. We estimate that, on average, climate change increased precipitation and decreased wind speeds during such events by +3.7% and − 1.4%, respectively. However, the variability across events is considerable (±28.6 and ± 6.6%, respectively), highlighting the large signal of natural variability of the weather system as compared to the global warming signal. Our results caution against overstating the role of climate change in displacement-inducing disasters in the past, especially compared to socio-economic and development factors of vulnerability and adaptive capacity that determine whether weather-related hazards turn into disasters.’

Effectiveness of electroacupuncture and acupuncture in alleviating cold hypersensitivity in the hands and feet: A randomized controlled trial

Objective Cold hypersensitivity in the hands and feet(CHHF) is a common condition that reduces the quality of life and causes daily discomfort. The current treatments are primarily pharmacological. This study aimed to expand treatment options by comparing the efficacy of electroacupuncture (EA) and acupuncture (AC) with that of no treatment (control). Methods A three-group randomized controlled trial was conducted with 72 women diagnosed with cold hypersensitivity in the hands and feet, as confirmed by subjective symptoms and objective temperature differences. Participants were randomly assigned to the EA, AC, or control groups. Outcome measures included hand and feet visual analog scale (VAS) scores, temperature changes measured using a non-contact thermometer, and World Health Organization Quality of Life-BREF (WHOQOL-BREF) scores assessed at pretreatment (T0), posttreatment (T1), and follow-up (T2). Repeated measures ANOVA and 2-way mixed-model ANOVA were used to evaluate group, time, and interaction effects. Results Both the EA and AC groups showed significant improvements in hand and feet VAS and WHOQOL-BREF scores compared with those of the control group posttreatment (T1). Notably, the EA group demonstrated sustained benefits at follow-up (T2), with significant reductions in feet VAS scores and positive changes in several WHOQOL-BREF domains. Interaction effects between group and time were observed, indicating that the changes in the EA and AC groups were meaningfully different form those in the control group. The control group also exhibited a statistically significant reduction in the VAS scores at follow-up (T2), likely due to the natural variability of cold extremity symptoms and psychological factors. Conclusion This study demonstrated that EA and AC are effective in alleviating the symptoms of CHHF and enhancing the quality of life compared to no treatment. EA showed long-lasting effects than those of AC, suggesting its potential to regulate the autonomic nervous system. These findings provide a foundation for expanding non-pharmacological treatment options for CHHF and offer clinical guidance on the use of EA and AC.

Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation

Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks.

Synthesis of a hydrological, water chemistry, and contaminants research program in the Peace-Athabasca Delta (Canada) to inform long-term monitoring of shallow lakes

In a multistressor world, evidence-based stewardship of aquatic ecosystems requires long-term monitoring data to understand the timing and magnitude of environmental change and potential causes. At the Peace-Athabasca Delta (PAD; northeastern Alberta, Canada), concern for aquatic ecosystem degradation has triggered renewed and urgent calls by Indigenous, national, and international governance bodies for implementation of a long-term lake monitoring program capable of tracking changes in hydrological conditions and contaminant deposition attributable to major energy projects located upstream, climate change, and other unnatural and natural processes. Challenges imposed by the delta's size, hydrological complexity, inaccessibility of lakes, and other factors, however, have long impeded implementation of a delta-wide lake monitoring program. To address this pressing need, here we review and synthesize results obtained during 7 years (2015–2021) of intensive, multifaceted research at 60 shallow lakes spanning the delta's broad hydroecological gradients to inform an integrated hydrology, water chemistry, and contaminants monitoring program. The research involved systematic, repeated measurements of water isotope composition, water depth variation, water chemistry and turbidity, and metal(loid) concentrations in lake surface sediment and periphytic biofilm. Results reveal marked spatial and temporal variation of hydrological processes and their affects on lake water balance and depth, strong association between hydrological processes and lake water chemistry, and that concentrations of nickel and vanadium (key oil sands indicators) remain within the range of natural variation. Correspondence of generalized additive model trendlines for isotope-derived lake evaporation-to-inflow ratios and water chemistry with climate indices (Pacific Decadal Oscillation, Oceanic Niño Index) demonstrates the sensitivity, and predictability, of lake ecosystem processes in the delta to large-scale climatic patterns. We provide recommendations for field sampling, sample analysis, data display, and integration of information for ongoing monitoring at the PAD. These approaches are readily transferable to other complex landscapes with abundant shallow waterbodies threatened by multiple stressors that may alter hydrological regimes and contaminant delivery.

Genetics of Wool and Cashmere Fibre: Progress, Challenges, and Future Research

Wool (sheep) and cashmere (goat) fibres have unique biological, physical, and chemical properties and these fibres are becoming more important as the demand for natural products increases. However, these complex protein fibres are at times compromised by natural variability in their properties, and this can impact their use and value. Genetic improvement via selection and breeding can partly overcome this problem, enabling the farming of sheep and goats that produce more desirable fibre. This review explores the challenges in improving wool and cashmere fibre characteristics using genetics, with a focus on improving our understanding of the key protein components of fibres, wool keratins and keratin-associated proteins (KAPs). Despite progress in our knowledge of these proteins, gaining a better understanding of them and how they affect these fibres remains an ongoing challenge. This is not straight-forward, given the large number of similar yet unique genes that produce the proteins and the gaps that remain in their identification and characterisation. More research is required to clarify gene and protein sequence variability and the location and patterns of gene expression, which in turn limits our understanding of fibre growth and variation. Several aspects that currently hinder our progress in this quest include the incomplete identification of all the genes and weaknesses in the approaches used to characterise them, including newer omics technologies. We describe future research directions and challenges, including the need for ongoing gene identification, variation characterisation, and gene expression analysis and association studies to enable further improvement to these valuable natural fibres.

Chromosome-level assembly of the Clinopodium gracile genome

Clinopodium gracile is an important medicinal herb in the Lamiaceae family. This species lacks corresponding genomic resources, which significantly limits the study of its active compound synthesis pathways, breeding practices, and assessment of natural genetic variations. We assembled the chromosomal-level genome of C. gracile using Oxford Nanopore (ONT) technology and Hi-C sequence. The assembled genome is 307.3 Mb in size and consists of 9 chromosomes. The scaffold N50 was 36.3 Mb. The BUSCO completeness (Embryophyta_db10) of the genome was 97.2%. The genome annotates 40,083 protein coding genes. C. gracile and S. miltiorrhiza diverged approximately 30.615 million years ago. C. gracile has not undergone recent species-specific WGD events. A high proportion of young LTRs indicates a recent transposable element (TE) transposition burst in C. gracile.

Stay social, stay young: a bioanthropological outlook on the processes linking sociality and ageing

AbstractIn modern human societies, social interactions and pro-social behaviours are associated with better individual and collective health, reduced mortality, and increased longevity. Conversely, social isolation is a predictor of shorter lifespan. The biological processes through which sociality affects the ageing process, as well as healthspan and lifespan, are still poorly understood. Unveiling the physiological, neurological, genomic, epigenomic, and evolutionary mechanisms underlying the association between sociality and longevity may open new perspectives to understand how lifespan is determined in a broader socio/evolutionary outlook. Here we summarize evidence showing how social dynamics can shape the evolution of life history traits through physiological and genetic processes directly or indirectly related to ageing and lifespan. We start by reviewing theories of ageing that incorporate social interactions into their model. Then, we address the link between sociality and lifespan from two separate points of view: (i) considering evidences from comparative evolutionary biology and bioanthropology that demonstrates how sociality contributes to natural variation in lifespan over the course of human evolution and among different human groups in both pre-industrial and post-industrial society, and (ii) discussing the main physiological, neurological, genetic, and epigenetic molecular processes at the interface between sociality and ageing. We highlight that the exposure to chronic social stressors deregulates neurophysiological and immunological pathways and promotes accelerated ageing and thereby reducing lifespan. In conclusion, we describe how sociality and social dynamics are intimately embedded in human biology, influencing healthy ageing and lifespan, and we highlight the need to foster interdisciplinary approaches including social sciences, biological anthropology, human ecology, physiology, and genetics.

Optimal Planning of Standalone Rural Microgrid With Effective Dispatch Strategies and Battery Technology

ABSTRACTMicrogrids are a viable substitute for traditional power systems because they may deliver cleaner, more dependable, affordable power with fewer losses. However, the microgrid's performance is impacted by the variable nature of renewable energy sources. Battery storage is a crucial component of microgrid planning since it defines the system's techno‐economic feasibility. A standalone rural microgrid is designed in the current study, employing three distinct battery types: lithium‐ion, lead acid, and zinc‐bromine flow. The suggested microgrid's techno‐economic analysis employs three distinct dispatch mechanisms, that is, cycle charging, load flow, and complete dispatch. The case study of the suggested framework is carried out in Lucknow (India). The system comprises PV/battery/wind energy system/diesel and battery. The simulation results suggest that the optimal system with the least electrification cost is 0.113 $/kWh using complete dispatch strategies and a zinc‐bromine battery. It has 206 kWh zinc‐bromine flow batteries, a 10 kW converter, a 20 kW PV, a 13‐kW diesel generator, and a Combined dispatch strategy. The system's net present cost per unit cost of energy is $40 275 and 0.113 $/kWh for the chosen region, respectively. Compared to the other two battery technologies for hybrid systems, the zinc‐bromine flow battery technology is also shown to be the most environmentally friendly. Among the three battery technologies available, zinc‐bromine flow is best used in a particular location's hybridized operation.

Comparison of magnification corrected optic disc size by slit-lamp biomicroscopy, fundus photography, and optical coherence tomography

Abstract PURPOSE: Optic disc size measurement is essential for determining landmarks, lesions, and distances of the retina. Due to the size variability in nature and when measured with different instruments, newer methods to compare sizes between different instruments are needed. We aimed to determine the comparability of slit lamp, fundus photography, and optical coherence tomography (OCT) measurements of optic disc size after correcting for both ocular and instrument magnification. MATERIALS AND METHODS: In this prospective, observational study, optic disc size was measured by slit-lamp biomicroscopy, fundus photography, and OCT. Instrument and ocular magnification was calculated, and the measured size was corrected for both using the Littman formula. The corrected values were analyzed using Bland–Altman plots and intra-class correlation (ICC) for agreeability and correlation, respectively. Linear regression analysis was conducted to estimate the systematic errors and interconvertibility in cases of high correlation. RESULTS: Fifty-six eyes were included for the analysis. Uncorrected and corrected values for each method had excellent correlation and agreeability (ICC absolute agreement >0.75, P < 0.01). Among magnification corrected values, slit lamp and OCT had excellent correlation and agreement (ICC consistency = 0.846, confidence interval 0.75–0.91, P < 0.01). This pair had R 2 = 0.73 on linear regression (P < 0.01). Linear regression formulas for interconverting uncorrected and corrected values for the three methods had variable accuracy (R 2 from 0.47 to 0.66). CONCLUSION: Uncorrected values of different methods for disc size measurement cannot be compared directly; they need ocular and instrument magnification correction. Among the three methods compared, the best interconvertible pairs of measurement were magnification corrected slit lamp and OCT values.

Influence of Physiographic Region on Pathogen Concentrations Between Stream Types

Predicting public health risk associated with exposure to and recreational use of surface waters is often challenging due to substantial variability in concentrations of pathogenic bacteria, even among seemingly similar streams. In this study, we document significant differences in the surface water concentrations of the common bacteria indicators Escherichia coli and fecal coliform between two major stream types—blackwater and redwater streams (p < 0.001). We propose and present evidence that these findings result from natural biogeochemical variation between physiographic regions (p < 0.001). These findings suggest that physiographic stream type may influence the degree of exposure to waterborne pathogens and risk of waterborne disease. Future research is needed to assess whether the inclusion of stream type in risk assessments can improve public health modeling and mapping.

Determination of Nanoparticles and Elements in Blue Mussels (Mytilus edulis) along the Norwegian Coastline.

Our work aimed to examine nanoparticle levels in 69 distinct pooled mussel samples along the Norwegian coastline, considering samples from different environmental contexts, including natural locations, potentially polluted hotspots, and mussel farms. Single-particle ICP-MS was utilized to determine particle mass and number concentrations at environmentally relevant levels in addition to the total content of 11 elements: aluminum, barium, cerium, copper, iron, manganese, lead, silicon, silver, titanium, and zirconium. Results showed nanoparticle mass concentrations of few ng/g up to tens of μg/g and number concentrations of 106 to 109 particles/g (wet weight). Certain urban and industrially impacted locations were linked to increased levels of, e.g., silver, lead, cerium, zirconium, and titanium NPs. Farmed mussels exhibited lower concentrations. However, natural variations were considerable, and impacted locations mostly did not differ from the highest levels in pristine areas. The study presents the first extensive survey of NPs of 11 different elements in marine biota and provides evidence of increased levels of NPs in areas with anthropogenic activities.

Algorithmic Persuasion with Evidence

In a game of persuasion with evidence, a sender has private information. By presenting evidence on the information, the sender wishes to persuade a receiver to take a single action (e.g., hire a job candidate, or convict a defendant). The sender’s utility depends solely on whether the receiver takes the action. The receiver’s utility depends on both the action and the sender’s private information. We study three natural variations. First, we consider the problem of computing an equilibrium of the game without commitment power. Second, we consider a persuasion variant, where the sender commits to a signaling scheme and the receiver, after seeing the evidence, takes the action or not. Third, we study a delegation variant, where the receiver first commits to taking the action if being presented certain evidence, and the sender presents evidence to maximize the probability the action is taken. We study these variants through the computational lens, and give hardness results, optimal approximation algorithms, and polynomial-time algorithms for special cases. Among our results is an approximation algorithm that rounds a semidefinite program that might be of independent interest, since, to the best of our knowledge, it is the first such approximation algorithm in algorithmic economics.

Latent Growth Model with Categorical Indicators: Comparison of the Different Estimators

Despite its potential usage in social and behavioral sciences, there has been limited research on Latent Growth Models (LGM) with categorical indicators. The categorical nature of binary or ordinal variables violates the multivariate normality assumption of the traditional maximum likelihood (ML) estimator. Alternative estimators can be employed, such as marginal maximum likelihood (MML), categorical weighted least squares (c-WLS), and categorical diagonally weighted least squares (c-DWLS). A Monte Carlo simulation study was conducted to compare the performance of the three estimators in the context of categorical LGM. The results revealed that the MML estimator performed the best for the correctly specified linear growth model, whereas the robust c-DWLS or c-WLS estimator excelled in correctly specified quadratic growth model. The robust c-DWLS and c-WLS estimators were more sensitive to model misspecifications. The findings of the present study provide important implications and practical recommendations for empirical researchers using categorical LGM.

Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure.

The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool. We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note-derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians. Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing-driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions. This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF. This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.

Bioelastic state recovery for haptic sensory substitution.

The rich set of mechanoreceptors found in human skin1,2 offers a versatile engineering interface for transmitting information and eliciting perceptions3,4, potentially serving a broad range of applications in patient care5 and other important industries6,7. Targeted multisensory engagement of these afferent units, however, faces persistent challenges, especially for wearable, programmable systems that need to operate adaptively across the body8-11. Here we present a miniaturized electromechanical structure that, when combined with skin as an elastic, energy-storing element, supports bistable, self-sensing modes of deformation. Targeting specific classes of mechanoreceptors as the basis for distinct, programmed sensory responses, this haptic unit can deliver both dynamic and static stimuli, directed as either normal or shear forces. Systematic experimental and theoretical studies establish foundational principles and practical criteria for low-energy operation across natural anatomical variations in the mechanical properties of human skin. A wireless, skin-conformable haptic interface, integrating an array of these bistable transducers, serves as a high-density channel capable of rendering input from smartphone-based 3D scanning and inertial sensors. Demonstrations of this system include sensory substitution designed to improve the quality of life for patients with visual and proprioceptive impairments.

The shape of the corpus callosum is different in bipolar disorder.

Bipolar disorder (BD) is a recurrent chronic disorder characterised by fluctuations in mood and energy disposition. Diseases could lead to degenerative alterations in brain structures such as corpus callosum (CC). Studies demonstrated that abnormalities in CC are associated with BD symptoms. The present study aims to analyse the CC of the patients with statistical shape analysis (SSA) and compare the findings with healthy controls. Forty-one BD patients and 41 healthy individuals in similar age groups, which included 23 female and 18 male subjects, participated in the study. CC was marked with landmarks on the mid-sagittal images of each individual. The mean 'Procrustes' point was calculated, and shape deformations were analysed with thin-plate spline analysis. Significant differences were observed in the shape of CC between the two groups, where maximum CC deformation was observed in posterior region marks in BD patients. There was no significant difference between the CC area of the BD patients and controls. CC analysis conducted with SSA revealed significant differences between patients and healthy controls. The study findings emphasised the abnormal distribution of white matter in CC and the variable subregional nature of CC in BD patients. This study may enable the development of more targeted and effective treatment strategies by taking into account biological factors and understanding the differences in the brain regions of individuals with BD.

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters

The goal of debiasing in classification tasks is to train models to be less sensitive to correlations between a sample’s target attribution and periodically occurring contextual attributes to achieve accurate classification. A prevalent method involves applying re-weighing techniques to lower the weight of bias-aligned samples that contribute to bias, thereby focusing the training on bias-conflicting samples that deviate from the bias patterns. Our empirical analysis indicates that this approach is effective in datasets where bias-conflicting samples constitute a minority compared to bias-aligned samples, yet its effectiveness diminishes in datasets with similar proportions of both. This ineffectiveness in varied dataset compositions suggests that the traditional method cannot be practical in diverse environments as it overlooks the dynamic nature of dataset-induced biases. To address this issue, we introduce a contrastive approach named “AmbiBias Contrast”, which is robust across various dataset compositions. This method accounts for “ambiguity bias”— the variable nature of bias elements across datasets, which cannot be clearly defined. Given the challenge of defining bias due to the fluctuating compositions of datasets, we designed a method of representation learning that accommodates this ambiguity. Our experiments across a range of and dataset configurations verify the robustness of our method, delivering state-of-the-art performance.

Drosophila Toxicogenomics: genetic variation and sexual dimorphism in susceptibility to 4-Methylimidazole

Background4-methylimidazole is a ubiquitous and potentially carcinogenic environmental toxicant. Genetic factors that contribute to variation in susceptibility to its toxic effects are challenging to assess in human populations. We used the Drosophila melanogaster Genetic Reference Panel (DGRP), a living library of natural genetic variation, to identify genes with human orthologs associated with variation in susceptibility to 4-methylimidazole.ResultsWe screened 204 DGRP lines for survival following 24-hour exposure to 4-methylimidazole. We found extensive genetic variation for survival, with a broad sense heritability of 0.82; as well as genetic variation in sexual dimorphism, with a cross-sex genetic correlation of 0.59. Genome-wide association analyses identified a total of 241 candidate molecular polymorphisms in or near 273 unique genes associated with survival. These polymorphisms had either sex-specific or sex-antagonistic effects, and most had putative regulatory effects. We generated interaction networks using these candidate genes as inputs and computationally recruited genes with known physical or genetic interactions. The network genes were significantly over-represented for gene ontology terms involving all aspects of development (including nervous system development) and cellular and organismal functions as well as canonical signaling pathways, and most had human orthologs.ConclusionsThe genetic basis of variation in sensitivity to acute exposure to 4-methylimidazole in Drosophila is attributable to variation in genes and networks of genes known for their effects on multiple developmental and cellular processes, including possible neurotoxicity. Given evolutionary conservation of the underlying genes and pathways, these insights may be applicable to humans.

Integrating agricultural land suitability and farmers' perception on crop selection in a water-stressed region of eastern India

CONTEXTThe choice of crops for cultivation should be ideally based on the appropriateness of a specific parcel of land for a particular crop. This is a multi-criteria problem associated with a host of natural conditioning variables including topography, climate and pedology. However, in a country like India, where agriculture is often traditional and subsistence-based, the farmers' choice of crop selection is usually influenced by numerous factors and therefore, may not be explained solely by the spatial variation in the agricultural land suitability. OBJECTIVEThis study is an attempt to understand the intricacies involved in the farmers' selection of crops by integrating the dual aspects of agricultural land suitability and the farmers' understanding of the land. The Kumari River Basin in eastern India has been selected for this study. This area is characterized by acute scarcity of water. METHODSThe basic methodology involved extracting systematic information on conditional factors such as topography, climate and soil. After normalizing different parameters by the Fuzzy Membership Function, a reiterative Principal Component Analysis modified Analytical Hierarchy Process was applied to these conditional rasters to derive an index of agricultural land suitability of the studied basin. Furthermore, the crop-specific suitability of paddy and maize was also considered by employing the Boolean Logic. RESULTS AND CONCLUSIONSIt is observed that the lower domain of the Kumari Basin is characterized by greater land suitability as compared to the upper and middle domains and the most important factors behind this distribution include slope, potential evapotranspiration and soil fertility. Crop-specific land suitability suggests that, by and large, the suitability of maize is greater than that of paddy in the basin, except for the lower domain where paddy is more suitable to cultivate. However, a study of the perception of the local farmers reveals that although they are aware of this fact, they prefer paddy over maize and are more inclined towards cultivating paddy. The chief reasons cited by them include the high demand for paddy in the area and the absence of a Minimum Support Price for maize. SIGNIFICANCEThis study is possibly an instance where the agricultural land suitability analysis has been substantiated by a field-based perception survey of the farmers which is expected to help in understanding the complexities of crop selection in this part of India.